def axiom_achievers(axiom_instances, state):
axioms_from_pre = defaultdict(list)
for ax in axiom_instances:
for p in ax.preconditions:
assert isinstance(p, Atom)
axioms_from_pre[p].append(ax)
axiom_from_eff = {}
queue = deque()
for head, val in state.items():
if not isinstance(head.function, Predicate) or (val != True):
continue
eval = initialize(head, val)
if isinstance(eval, Atom) and (eval not in axiom_from_eff):
axiom_from_eff[eval] = None
queue.append(eval)
while queue:
pre = queue.popleft()
for ax in axioms_from_pre[pre]:
if (ax.effect not in axiom_from_eff) and all(
p in axiom_from_eff for p in ax.preconditions):
axiom_from_eff[ax.effect] = ax
queue.append(ax.effect)
return axiom_from_eff
def test_start_up(self):
"""
Testing game startup by checking if the username has be sucessfully entered and
the game has been started
"""
username = "******"
question_list = functions.initialize(username)
self.assertEqual(question_list['username'], username)
def evals_from_state(state):
return {initialize(*item) for item in state.items()}
#03/10/17 - Tim - Added integration with functions.py. Now can read temp., pressure, Volt, and Curr
#03/13/17 - Tim - Plots pressure and creates text files. Added headers to Volt and Press
#05/11/17 - Tim - Changed plots to reflect new temp sensors, fixed print to file for temps, set alarm to 0
#TO DO
RX202_lookup = np.loadtxt(
'RX-202A Mean Curve.tbl') #202 ADR sensor look up table
#RX202_lookup = np.loadtxt('RX-102A Mean Curve.tbl') #102 300mK/ 1K sensors
RX202_interp = interpolate.interp1d(
RX202_lookup[:, 1], RX202_lookup[:, 0], fill_value=0., bounds_error=False
) # interpolates the temperature when in between lookup table values
#test = np.float(RX202_interp(4000))
#RX202_temps = RX202_interp(-linear_bridge*1000)
lines, colors, labels, plots = initialize()
# turn on alarm on for certain values
# 4K P.T--4K HTS--50K HTS--Black Body--50K P.T.--50K Plate--ADR Shield--4He Pump--3He Pump--4He Switch--3 He Switch--300 mK Shield--ADK Switch--4-1K Switch--1K Shield--3He Head--4He Head--ADR--
Alarm_on = np.array((0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))
Alarm_value = np.array((0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))
sleep_interval = 10. #seconds change back
Alarm = 0 # 0 for off 1 for on
now = datetime.datetime.now()
date_str = str(now)[0:10]
# we want the file prefix to reflect the date in which the temperature data is taken
file_prefix = "C:/Users/tycho/Desktop/White_Cryo_Code/Temps/" + date_str
file_suffix = ''
file_prefix2 = "C:/Users/tycho/Desktop/White_Cryo_Code/Voltage_Current/" + date_str
def CAS_simulation(input_initial_values_file):
# set simulation parameters
functions.set_parameters()
# create python objects for walkers and balls
if gv.enhanced_sampling_flag == 2:
walker_list = [None] * (gv.num_balls_limit * gv.num_walkers_for_sc * 2)
temp_walker_list = [None] * (gv.num_balls_limit *
gv.num_walkers_for_sc * 2)
else:
walker_list = [None] * (gv.num_balls_limit * gv.num_walkers * 2)
temp_walker_list = [None] * (gv.num_balls_limit * gv.num_walkers * 2)
vacant_walker_indices = []
balls = np.zeros(
(1, gv.num_cvs +
3)) # ball coordinates / ball radius / ball key / # of walkers
ball_to_walkers = {}
key_to_ball = {}
ball_clusters_list = {}
# create walkers and their directories
functions.initialize(input_initial_values_file, walker_list)
for step_num in range(gv.max_num_steps):
# reset ball objects so that balls are newly created at every step
if gv.balls_flag == 0:
balls = np.zeros((1, gv.num_cvs + 3))
ball_to_walkers = {}
key_to_ball = {}
ball_clusters_list = {}
gv.current_num_balls = 0
print 'running ' + str(step_num + 1) + '-th step'
# first, run simulation
t0 = time()
functions.m_simulation(walker_list)
# second, create balls and assign walkers to balls
t1 = time()
if gv.enhanced_sampling_flag == 1:
new_balls = functions.threshold_binning(step_num, walker_list,
temp_walker_list, balls,
ball_to_walkers,
key_to_ball)
else:
new_balls = functions.binning(step_num, walker_list,
temp_walker_list, balls,
ball_to_walkers, key_to_ball)
# third, perform spectral clustering if enhanced_sampling_flag = 2
if gv.enhanced_sampling_flag == 2 and gv.num_balls_for_sc <= gv.num_occupied_balls and gv.sc_performed == 0:
functions.spectral_clustering(step_num, temp_walker_list,
new_balls, ball_clusters_list)
# fourth, resample walkers for every ball
if gv.sc_performed == 1:
functions.resampling_for_sc(walker_list, temp_walker_list,
new_balls, ball_to_walkers,
ball_clusters_list,
vacant_walker_indices)
else:
functions.resampling(walker_list, temp_walker_list, new_balls,
ball_to_walkers, vacant_walker_indices)
else:
functions.resampling(walker_list, temp_walker_list, new_balls,
ball_to_walkers, vacant_walker_indices)
# finally, output the results in text files
functions.print_status(step_num, walker_list, new_balls,
ball_to_walkers, ball_clusters_list,
key_to_ball)
balls = new_balls
t2 = time()
os.chdir(gv.main_directory + '/CAS')
f = open('time_record.txt', 'a')
f.write(
str(step_num + 1) + '-th step: ' + 'simulation time: ' +
str(t1 - t0) + ' ' + 'post-processing time: ' + str(t2 - t1) +
'\n')
f.close()
def CAS_simulation(input_initial_values_file):
# set simulation parameters
functions.set_parameters()
# create python objects for walkers and balls
if gv.enhanced_sampling_flag == 2:
walker_list = [None]*(gv.num_balls_limit*gv.num_walkers_for_sc*2)
temp_walker_list = [None]*(gv.num_balls_limit*gv.num_walkers_for_sc*2)
else:
walker_list = [None]*(gv.num_balls_limit*gv.num_walkers*2)
temp_walker_list = [None]*(gv.num_balls_limit*gv.num_walkers*2)
vacant_walker_indices = []
balls = np.zeros((1, gv.num_cvs+3)) # ball coordinates / ball radius / ball key / # of walkers
ball_to_walkers = {}
key_to_ball = {}
ball_clusters_list = {}
# create walkers and their directories
new_balls = functions.initialize(input_initial_values_file, walker_list, temp_walker_list, balls, ball_to_walkers,
vacant_walker_indices)
balls = new_balls
for step_num in range(gv.initial_step_num, gv.initial_step_num + gv.max_num_steps):
# reset ball objects so that balls are newly created at every step
if gv.balls_flag == 0 and step_num != gv.initial_step_num:
balls = np.zeros((1, gv.num_cvs+3))
ball_to_walkers = {}
key_to_ball = {}
ball_clusters_list = {}
gv.current_num_balls = 0
if gv.simulation_flag != 0 and step_num == gv.initial_step_num:
pass
else:
gv.first_walker = 0
gv.last_walker = gv.total_num_walkers-1
print 'running ' + str(step_num+1) + '-th step'
os.chdir(gv.main_directory)
f = open('bash_script_input_file.txt', 'w')
f.write(str(gv.first_walker))
f.write(' first_' + str(gv.last_walker) + '_last')
f.close()
# first, run simulation or clean up with bash script
t0 = time()
if (gv.simulation_flag == 3 or gv.simulation_flag == 4) and step_num == gv.initial_step_num:
pass
elif gv.simulation_flag == 2 and step_num == gv.initial_step_num:
os.system('./clean_up.sh')
else:
os.system('./simulations.sh')
# second, create balls and assign walkers to balls
t1 = time()
if gv.enhanced_sampling_flag == 1:
new_balls = functions.threshold_binning(step_num, walker_list, temp_walker_list, balls, ball_to_walkers,
key_to_ball)
else:
new_balls = functions.binning(step_num, walker_list, temp_walker_list, balls, ball_to_walkers, key_to_ball)
# third, perform spectral clustering if enhanced_sampling_flag = 2
if gv.enhanced_sampling_flag == 2 and gv.num_balls_for_sc <= gv.num_occupied_balls \
and step_num != gv.initial_step_num and gv.sc_performed == 0:
functions.spectral_clustering(step_num, temp_walker_list, new_balls, ball_clusters_list)
# fourth, resample walkers for every ball
if gv.sc_performed == 1:
functions.resampling_for_sc(walker_list, temp_walker_list, new_balls, ball_to_walkers,
ball_clusters_list, vacant_walker_indices)
else:
functions.resampling(walker_list, temp_walker_list, new_balls, ball_to_walkers, vacant_walker_indices)
else:
functions.resampling(walker_list, temp_walker_list, new_balls, ball_to_walkers, vacant_walker_indices)
# finally, output the results in text files
functions.print_status(step_num, walker_list, new_balls, ball_to_walkers, ball_clusters_list, key_to_ball)
balls = new_balls
t2 = time()
os.chdir(gv.main_directory+'/CAS')
f = open('time_record.txt', 'a')
f.write(str(step_num+1) + '-th step: ' + 'simulation time: ' + str(t1-t0) + ' ' + 'post-processing time: ' +
str(t2-t1) + '\n')
f.close()
def questions(username):
#Set up page
title = "Question Game"
description = "Welcome {0}!".format(username.capitalize())
#Find out the length of the game
game_length = functions.get_file_length()
#Logic for every time the check button is pressed
if request.method == 'POST':
form = request.form
"""
Starts the game with default values when you access play the game
from the start game page
"""
if form.get('start-game') == 'true':
data = functions.initialize(username)
return render_template('questions.html',
data=data,
title=title,
description=description)
else:
try:
question_index = int(request.form.get('question_index'))
score = int(request.form.get('current_score'))
question = functions.get_question(question_index)
# Check whether the answer is correct
user_answer = request.form.get('user_answer').lower().strip()
real_answer = question['english'].lower()
real_question = question['spanish'].lower()
correct = user_answer == real_answer
"""
Main Game Logic
"""
while question_index < game_length:
#Correct Questions
if correct:
#increment score and question index
question_index += 1
score += 1
#Displays message to my html when I get a question correct
flash(
'The translation of {0} is {1}'.format(
real_question, real_answer), 'success')
#Load Next Question
next_question = functions.get_question(question_index)
#Incorrect Questions
else:
#Increment question index
question_index += 1
#Displays message to my html when I get a question wrong
flash(
'The translation of {0} is {1}. You said {2}'.
format(real_question, real_answer,
user_answer), 'error')
#Load Next Question
next_question = functions.get_question(question_index)
#Setting up question information to be rendered to html
if next_question is not None:
data = {
'question_index': question_index,
'english': next_question['english'],
'spanish': next_question['spanish'],
'username': username,
'current_score': score,
'length': game_length
}
return render_template('questions.html',
data=data,
title=title,
description=description)
else:
#Clears the messages
session.pop('_flashes', None)
#Set the score
functions.set_high_score(username, score)
return render_template(
'scores.html',
scores=functions.get_high_score(),
title="Game Over",
description="{0} your score is: {1}".format(
username.capitalize(), score))
#Game restart error handling
except Exception as e:
print("Error : {}".format(e))
# Redirect to the homepage with an error if using GET
return redirect('/')
if '20190703' in gps_csv:
print("Completed 20190703")
continue
if '20190704' in gps_csv:
print("Completed 20190704")
continue
if '20190706' in gps_csv:
print("Completed 20190706")
continue
print(csv_name, 'START ', str( datetime.now() ))
# 1. remove old data and create necessary directories
initialize()
# 2. ananymize ap_id column to int value , clip points within boundary
#gdf_probe_clipped, gdf_target = get_points_within_target_region (gps_csv, anonymize=True, display_plot = False)
gdf_probe_clipped, gdf_target = get_points_within_target_region (gps_csv, anonymize=False, display_plot = False)
#print('----2 done----')
# 3. Preprocess: cleaning data & applying sampling
df_sample = preprocess_data()
#print('----3 done----')
# 4. map matching with osm roads using graphhopper
df_mapped_route = map_match_csv2gpx_multithread(df_sample) # multithreaded
send_url = 'http://freegeoip.net/json'
#Application API Keys from Internet of Things Service from IBM Bluemix
username = "******"
password = "******"
temp = username.split("-")
organization = temp[1]
#any string for type e.g. "JavaDevice"
deviceType = "Pi"
deviceId = str(hex(int(get_mac())))[2:]
deviceId = 'gateway_'+deviceId[:-1]
deviceCli = functions.initialize(username, password, organization, deviceType, deviceId)
deviceCli.connect()
#r = requests.get(send_url)
#j = json.loads(r.text)
#lat = j['latitude']
#lon = j['longitude']
#Manyata Tech Park IBM
lat = 13.048291
lon = 77.620382
#EGL IBM
#lat = 12.951432
#lon = 77.643296
import dataTypes
# stub: add code to ask user for
# - life table
# - initial population size
# - initial demographics
# -model run time (in breeding cycles)
lifeTable = [[0, 1, 0], [1, 0.75, 4], [2, 0.4, 5]]
lifeTable_wrightFisher = [[0, 1, 1]]
pop_size = 100
initialDemographics = [0.25, 0.5, 0.25]
maxYears = 10
# a history of every organism that has ever lived (and then died)
organismRecordList = []
# list of organism that are alive.
currentlyAliveList = []
# DEBUGGING OUTPUT
for ageCohort in lifeTable:
print("age : ", ageCohort[0], ", survival probability : ", ageCohort[1],
", mean fertility : ", ageCohort[2])
# step 1: create generation 1.
initialize(organismRecordList, currentlyAliveList, pop_size,
initialDemographics)
for i in currentlyAliveList:
print(i)
def main():
global per
initialize()
input("\nPress enter to generate your population: ")
print("\n")
per = {x: Susceptible() for x in range(1, config.initPop + 1)}
for x in per:
print(str(x) + ": " + str(per[x]))
for x in range(1, config.initPop + 1):
globals()["per" + str(x)] = Susceptible()
totalID.append(globals()["per" + str(x)].getID())
susceptID.append(globals()["per" + str(x)].getID())
for y in range(1, config.zombiePop + 1):
globals()["per" + str(y)].changeStatus("Zombie")
endVal = len(totalID) + 1
for x in range(1, endVal):
print(str(x) + ": " + str(globals()["per" + str(x)]))
input("\nPress enter to run simulation: ")
print("\n")
config.susceptPop = len(susceptID)
runSim = True
while runSim:
#while config.time < 672:
#print("\nTime {}".format(time))
for x in range(1, endVal):
globals()["per" + str(x)].walk()
if globals()["per" + str(x)].getStatus() == "Susceptible":
globals()["per" + str(x)].buildDefense()
if globals()["per" + str(x)].getStatus() == "Infected" or globals(
)["per" + str(x)].getStatus() == "Recovered":
globals()["per" + str(x)].determineFate()
if globals()["per" + str(x)].getStatus() == "Removed":
globals()["per" + str(x)].setCOD("Died of infection")
if globals()["per" + str(x)].getStatus() == "Immune":
globals()["per" + str(x)].developCure()
for x in range(1, endVal):
for y in range(1, endVal):
if globals()["per" + str(x)] == globals()["per" + str(y)]:
if globals()["per" +
str(x)].getStatus() == "Zombie" and globals()[
"per" +
str(y)].getStatus() == "Susceptible":
globals()["per" + str(x)].bite(globals()["per" +
str(y)])
if globals()["per" +
str(x)].getStatus() == "Zombie" and globals()[
"per" + str(y)].getStatus() == "Immune":
globals()["per" + str(x)].bite(globals()["per" +
str(y)])
for x in range(1, endVal):
for y in range(1, endVal):
if globals()["per" + str(x)] == globals()["per" + str(y)]:
if globals()["per" +
str(x)].getStatus() == "Immune" and globals()[
"per" + str(y)].getStatus() == "Zombie":
globals()["per" + str(x)].heal(globals()["per" +
str(y)])
if globals()["per" +
str(x)].getStatus() == "Immune" and globals()[
"per" + str(y)].getStatus() == "Infected":
globals()["per" + str(x)].heal(globals()["per" +
str(y)])
susceptList.append(len(susceptID))
zombieList.append(len(zombieID))
removeList.append(len(removeID))
infectList.append(len(infectID))
recoverList.append(len(recoverID))
immuneList.append(len(immuneID))
config.time += 1
if len(zombieID) == 0 and len(infectID) == 0:
config.endScene = 1
runSim = False
if len(susceptID) == 0 and len(immuneID) == 0 and len(recoverID) == 0:
config.endScene = 2
runSim = False
if config.time >= 1500:
config.endScene = 3
runSim = False
for x in range(1, endVal):
print(str(x) + ": " + str(globals()["per" + str(x)]))
input("\nPress enter to display graphs:")
fileName = "simulation--{}-{}-{}--{}-{}-{}.pdf".format(
now.year, now.month, now.day, now.hour, now.minute, now.second)
pp = PdfPages(fileName)
plt.plot(susceptList, label="Susceptible")
plt.plot(zombieList, label="Zombie")
plt.legend()
plt.xlabel("Time (hours)")
plt.ylabel("Number of People")
pp.savefig()
plt.show()
plt.plot(immuneList, label="Immune")
plt.plot(removeList, label="Removed")
plt.legend()
plt.xlabel("Time (hours)")
plt.ylabel("Number of People")
pp.savefig()
plt.show()
while True:
try:
summary = input(
"\nWould you like to view the simulation summary? (Y/N): ")
if summary not in ['N', 'n', 'Y', 'y']:
raise ValueError
except ValueError:
print("\nPlease enter 'Y' or 'N'\n")
else:
break
if summary in ['N', 'n']:
pp.close()
elif summary in ['Y', 'y'] and config.endScene == 1:
end = "\n\nThe simulation lasted for {} hours. It ended because the Zombie and Infected population both reached zero.\n\
There are {} people of class Immune and {} people of class Susceptible. {} {} Recovering. \n\n".format(
config.time, len(immuneID), len(susceptID), len(recoverID),
config.p1 if (len(recoverID) == 1) else config.p2)
print(end)
elif summary in ['Y', 'y'] and config.endScene == 2:
end = "\n\nThe simulation lasted for {} hours. It ended because the Susceptible, Immune, and Recovering populations all reached zero.\n\
There are {} people of class Zombie and {} people of class Infected.\n\n".format(
config.time, len(zombieID), len(infectID))
print(end)
elif summary in ['Y', 'y'] and config.endScene == 3:
end = "\n\nThe simulation lasted for the maximum alloted time, {} hours.\n\
There are {} Susceptible's, {} Zombie's, {} Immune's, {} Recovered and {} Removed.".format(
config.time, len(susceptID), len(zombieID), len(immuneID),
len(recoverID), len(removeID))
print(end)
firstPage = plt.figure(figsize=(11.69, 8.27))
firstPage.clf()
firstPage.text(0.5,
0.5,
end,
transform=firstPage.transFigure,
size=12,
ha="center")
pp.savefig()
plt.close()
pp.close()
input("Press enter to end the simulation.")
def CAS_simulation(input_initial_values_file):
# set simulation parameters.
functions.set_parameters()
# create python objects for walkers and macrostates.
# walker_list keeps track of previous information whereas temp_walker_list keeps track of current/new information.
if gv.enhanced_sampling_flag == 2:
walker_list = [None]*(gv.num_balls_for_sc*gv.num_walkers*100)
temp_walker_list = [None]*(gv.num_balls_for_sc*gv.num_walkers*100)
else:
walker_list = [None]*(gv.num_balls_limit*gv.num_walkers*2)
temp_walker_list = [None]*(gv.num_balls_limit*gv.num_walkers*2)
# balls is recorded in the following order: ball coordinates / ball radius / ball key / # of walkers
balls = np.zeros((1, gv.num_cvs+3))
balls_array = np.zeros((1, gv.num_cvs))
# maps ball coordinates to walkers
ball_to_walkers = {}
# create walkers and their directories.
balls, balls_array = functions.initialize(input_initial_values_file, walker_list, temp_walker_list, balls,
balls_array, ball_to_walkers)
for step_num in range(gv.initial_step_num, gv.initial_step_num + gv.max_num_steps):
# reset macrostate objects so that macrostates are newly created at every step.
if gv.balls_flag == 0 and step_num != gv.initial_step_num:
balls = np.zeros((1, gv.num_cvs+3))
balls_array = np.zeros((1, gv.num_cvs))
ball_to_walkers = {}
gv.current_num_balls = 0
if gv.simulation_flag != 0 and step_num == gv.initial_step_num:
pass
else:
gv.first_walker = 0
gv.last_walker = gv.total_num_walkers-1
print 'running ' + str(step_num+1) + '-th step'
os.chdir(gv.main_directory)
f = open('bash_script_input_file.txt', 'w')
f.write(str(gv.first_walker))
f.write(' first_' + str(gv.last_walker) + '_last')
f.close()
# first, run simulation or clean up unfinished processes with bash script.
t0 = time()
if (gv.simulation_flag == 3 or gv.simulation_flag == 4) and step_num == gv.initial_step_num:
pass
elif gv.simulation_flag == 2 and step_num == gv.initial_step_num:
os.system('./clean_up.sh')
else:
os.system('./simulations.sh')
# second, create macrostates and assign or bin walkers to macrostates.
t1 = time()
if gv.enhanced_sampling_flag == 1:
balls, balls_array = functions.threshold_binning(step_num, walker_list, temp_walker_list, balls,
balls_array, ball_to_walkers)
else:
balls, balls_array = functions.binning(step_num, walker_list, temp_walker_list, balls, balls_array,
ball_to_walkers)
t2 = time()
# third, perform spectral clustering if enhanced_sampling_flag = 2.
if gv.enhanced_sampling_flag == 2 and gv.num_balls_for_sc <= gv.num_occupied_balls and gv.sc_performed == 0 \
and gv.sc_start == -1:
# start fixing macrostates from this point on until we finish calculating the transition matrix
gv.balls_flag = 1
gv.sc_start = step_num
if gv.enhanced_sampling_flag == 2 and gv.sc_performed == 0 and gv.sc_start != -1:
functions.calculate_trans_mat_for_sc(step_num, temp_walker_list, balls, balls_array)
if gv.enhanced_sampling_flag == 2 and gv.sc_performed == 1 and gv.sc_start != -1:
ball_clusters_list = functions.spectral_clustering(step_num, balls)
# fourth, resample walkers for every macrostate.
if gv.sc_performed == 1:
balls = functions.resampling_for_sc(walker_list, temp_walker_list, balls, ball_to_walkers,
ball_clusters_list)
else:
balls = functions.resampling(step_num, walker_list, temp_walker_list, balls, ball_to_walkers)
else:
balls = functions.resampling(step_num, walker_list, temp_walker_list, balls, ball_to_walkers)
# finally, output the results as text files.
balls = functions.print_status(step_num, walker_list, balls, ball_to_walkers)
t3 = time()
os.chdir(gv.main_directory+'/CAS')
f = open('time_record.txt', 'a')
f.write(str(step_num+1) + '-th step: simulation time: ' + str(t1-t0) + ' binning time: ' + str(t2-t1) +
' resampling time: ' + str(t3-t2) + '\n')
f.close()
+ Increased Storage in the Text Box within Datalocker
+ Windows are now Transient to each other;You cant interact with the root window until the child window is closed
- This also fixes the issue of having multiple of the same window open
'''
# Change this text if program is modified or updated
version = "Encryptor v.16"
# Import Function.py and other modules
import functions as imp
import tkinter as tk
from tkinter import messagebox
from cryptography.fernet import Fernet
#Creating Missing Files
imp.initialize()
# Finish Button Command for Data Locker
def finish_button():
txt = textbox.get('1.0',tk.END)
imp.encrypt_and_store(txt, username)
messagebox.showinfo('Info',"Data Saved in File! You may close the program safely now")
# The Data Locker Window
def Data_Locker():
window = tk.Toplevel()
window.title("Your Data Locker")
window.iconbitmap(r"iconlocked.ico")
window.resizable(0,0)
tk.Label(window,text = "Your Data Locker",font = ('Consolas',24)).grid(row = 1,column = 0,columnspan = 2)
# Made by: Jose Lorenzo Castro
# Date made: 10 Aug 2019
#!/usr/bin/env python
import os
import sqlite3
from flask import Flask, request, render_template
import functions as fun
app = Flask(__name__)
#fun.cleanSlate()
fun.initialize()
#test = fun.findTwoVar('Student', 'Castro', 'Jose Lorenzo')
#if (test):
# print ('Found Record')
#else:
# print ('Sadness')
@app.route('/')
def index():
return render_template("home.html")
@app.route('/add')
def addDir():
return render_template("add/add.html")
