def opt_fg():
#Check if queue.txt is usable
log, ts, it_n, first_guess, queue_txt = fc.setup(True)
#Get data and sample them
one_queue, shift_queue = fc.sample_data(queue_txt, log)
fc.print_pic_1_2(one_queue, shift_queue, queue_txt, it_n, ts, log)
waiter, cooks = fc.allocation_fg(log, first_guess)
fc.write_to_file(log, waiter, cooks, True)
fc.print_3_fg(ts, log, first_guess)
fc.print_3_im_new_all(ts, it_n, log, waiter, cooks)
#Check for number of values file
fc.finish(it_n, log)
def in_output(self):
"""Concourse resource `in` main """
output = {
"version": self.version,
"metadata": self.metadata,
"original_msg": self.original_msg
}
# Write response as bender.json for further use
write_to_file(json.dumps(output),
'{}/bender.json'.format(self.working_dir))
# Write template if specified
if self.templated_string:
write_to_file(
self.templated_string, '{}/{}'.format(self.working_dir,
self.template_filename))
# Print concourse output string
print(json.dumps(output, indent=4, sort_keys=True))
def opt():
#Set objectives
obj_5ord = 0.7
obj_8ord = 0.99
obj_5kit = 0.7
obj_8kit = 0.82
#Set timestamp and iterationnumber and if queue.txt is usable
log, ts, it_n, exp_txt, queue_txt = fc.setup()
#Analyse previous results
mean_res, std_res = fc.analyse(exp_txt, log)
#Objectives fulfilled?
fc.obj_fulfill(mean_res, log)
#Utilisation rates
fc.ut_rates(mean_res, log)
#Get data and sample them
one_queue, shift_queue = fc.sample_data(queue_txt, log)
# print 1_queue_Iteration and 2_shift_queue_iteration
max_dia = fc.print_pic_1_2(one_queue, shift_queue, queue_txt, it_n, ts,
log)
#Update staff allocation (1. get and allocate, 2. update queues, 3. update obj)
waiter, cooks = fc.allocation(log)
waiter, cooks, co_ql = fc.upd_ql(log, it_n, waiter, cooks, shift_queue)
waiter, cooks, co_obj = fc.upd_obj(log, it_n, waiter, cooks, shift_queue,
mean_res)
waiter, cooks, co_sm = fc.upd_sm(log, it_n, waiter, cooks, co_ql, co_obj)
fc.print_changes(log, waiter, cooks, co_ql, co_obj, co_sm)
print(waiter)
print(cooks)
#Write to files (allocation_staff.xlsx, waiter.txt, cook.txt)
fc.write_to_file(log, waiter, cooks)
#produce staffing plot
fc.print_3_im_new_all(ts, it_n, log, waiter, cooks)
#Clean up, update iterationnumber
fc.finish(it_n, log)
def username(playernum):
# User(s) enter their desired usernames #
instruct_num = str(leaderboard_len("data/leaderboard.json") + 1)
if request.method == "POST":
if not (check_in_file("data/global_users.txt", request.form["username"].title())):
# If not in global leaderboard #
write_to_file("data/users.txt", request.form["username"].title() + "\n")
write_to_file("data/global_users.txt", request.form["username"].title() + "\n")
add_to_leaderboard(request.form["username"].title(), 'data/leaderboard.json')
add_to_leaderboard_global(request.form["username"].title(), 'data/global_leaderboard.json')
new_num = str(int(playernum) - 1)
playernum = new_num
else:
if not (check_in_file("data/users.txt", request.form["username"].title())):
# If in global but not in local leaderboard #
write_to_file("data/users.txt", request.form["username"].title() + "\n")
add_to_leaderboard(request.form["username"].title(), 'data/leaderboard.json')
new_num = str(int(playernum) - 1)
playernum = new_num
else:
# If in global and local leaderboards #
flash("Sorry Your Chosen Username Is Unavailable. Please Try Another")
if int(playernum) == 0:
qnumber = str(
leaderboard_len("data/leaderboard.json") - 1
)
question = random_number_generator()
return redirect(
request.form["username"] +
'/' + '1' + '/' + qnumber +
'/' + question
)
else:
return redirect("setup" + '/' + playernum)
return render_template("username.html", player=instruct_num)
def test_write_to_file():
out = StringIO()
functions.write_to_file(["one", "two", "three", "four"], out)
n.assert_equal(out.getvalue(), "1 one\n2 two\n3 three\n4 four\n")
except err.ProjectFilesError:
txt.print_red(
"ProjectFilesError: you cannot read or modify project files!!"
)
except FileNotFoundError:
txt.print_red("File does not exist!")
except PermissionError:
txt.print_red(
"You do not have permission to read this file. :/")
# if the command is 'write' then try to write to the provided filename
# and if the filename was not provided or the string was not provided,
# write an error message. If permission is denied, also write an error.
elif commands[0].lower() == "write" or commands[0].lower() == "wrt":
try:
if len(commands) >= 3:
fn.write_to_file(commands)
else:
raise err.FieldNotProvidedError
except err.FieldNotProvidedError:
txt.print_red(
"FieldNotProvidedError: Cannot execute: not enough arguments!"
)
except err.ProjectFilesError:
txt.print_red(
"ProjectFilesError: you cannot read or modify project files!!"
)
except PermissionError:
txt.print_red(
"You do not have permission to write to this file. :/")
elif commands[0].lower() == "append" or commands[0].lower() == "apd":
try:
def test_clear_text_file(self):
# Test if function clears a txt file #
write_to_file("data/users.txt", "data")
self.assertEqual(clear_text_file("data/users.txt", 'data'), False)
def test_write_to_file(self):
# Test if function writes to a txt file #
self.assertEqual(write_to_file("data/users.txt", 'Test'.title()), True)
def test_check_in_file(self):
# Test if functions checks if data is in a txt file #
write_to_file("data/users.txt", 'Test'.title())
self.assertEqual(check_in_file("data/users.txt", 'Test'.title()), True)
self.assertEqual(check_in_file("data/users.txt", '0'), False)
############################################
#
# Jose Marcelo Sandoval-Castaneda (jms1595)
# Artificial Intelligence, Fall 2018
# 01 Nov 2018
#
############################################
import classes
import functions
# Load graph from file input.txt.
graph = classes.Graph('input.txt')
# Write key and clauses onto key.txt and clauses.txt, respectively.
functions.write_to_file('key.txt', graph.make_key())
functions.write_to_file('clauses.txt', graph.make_clauses())
tic = time()
print("Found {} job links in page {}: {}".format(len(extracted_links),
page_num, base_url))
print("Time taken to extract page links: {}".format(tic - toc))
print("Starting scape and writing to csv...\n")
i = 0
toc = time()
for extracted_link in extracted_links:
scraped_html = None
scraped_html = page_html(extracted_link)
if scraped_html != None:
job_num = job_num + 1
jobpage_info = jobpage_scrape(extracted_link, scraped_html)
write_to_file(jobpage_info)
i = i + 1
if (job_num >= target_num):
run = False
print('\n')
print("Job done! Exiting program...")
break
else:
stdout.write("\rPage scrape progress: %d/ %d" %
(i, len(extracted_links)))
stdout.flush()
# stdout.write("\n") # move the cursor to the next line
else:
# print("Error detected in one link")
continue
tic = time()
def test_write_to_file():
out = StringIO()
functions.write_to_file(["one", "two", "three", "four"], out)
n.assert_equal(out.getvalue(), "1 one\n2 two\n3 three\n4 four\n")
############################################
import functions
# Load the Davis-Putnam output and the key.
dp = functions.load_dp_output('dp-output.txt')
key = functions.load_key('key.txt')
# Assign truth values to the names in the key.
output = []
for d in dp:
for k in key:
if d[0] == k[0]:
output.append([k[1], d[1]])
break
# Make a list only of the truth values to establish a path and sort it.
ans = []
for i in range(len(output)):
if output[i][1]:
ans.append(output[i])
ans.sort(key=lambda x: int(x[0][-1]))
# Write the path onto a string.
output_str = ''
for val in ans:
output_str += val[0] + '\n'
# Write the string onto a file.
functions.write_to_file('output.txt', output_str)
import sys
reload(sys)
sys.setdefaultencoding('utf8')
from path import add_parent_to_path
add_parent_to_path()
from functions import read_corpus, replace_compounds, reddy_ncs, pre_process, print_every, read_ncs, \
write_to_file, get_preprocess_args
import logging
from config import logging_config
import nltk
nltk.download('punkt')
nltk.download('wordnet')
if __name__ == '__main__':
logging.info('Reading train and evaluation ncs')
args = get_preprocess_args()
r_ncs, _ = reddy_ncs(args.p2ec)
ncs = read_ncs(args.p2tc)
ncs.extend(r_ncs)
logging.info('Reading corpus')
sentences = read_corpus(args.p2corp)
lemmatizer = nltk.stem.WordNetLemmatizer()
output = []
logging.info('Replacing ncs in corpus')
for i in range(0, len(sentences)):
s = sentences[i]
print_every(s, i + 1, 10000)
output.append(replace_compounds(pre_process(s, lemmatizer), ncs))
logging.info('Writing results in ' + args.p2out)
write_to_file(output, args.p2out)
############################################
#
# Jose Marcelo Sandoval-Castaneda (jms1595)
# Artificial Intelligence, Fall 2018
# 01 Nov 2018
#
############################################
import functions
# Load clauses.
clauses = functions.load_clauses('clauses.txt')
# Execute Davis-Putnam.
result = functions.davis_putnam(clauses)
# Write results of Davis-Putnam onto a file.
functions.write_to_file('dp-output.txt', result)
data_directory = "datasets/" file_name = "DatafinitiElectronicsProductsPricingData.csv" data = pd.read_csv(data_directory + file_name) # limit to 1st 500 items, amountMin <= 150, sorted by brand df = data df = df[['prices.amountMin', 'brand']] df = df[(data['prices.amountMin'] > 100)] df = df.sort_values(by='brand') df = df.reset_index() df = df.head(500) count = len(df['brand'].value_counts()) print(len(df)) func.write_to_file(df, 'def', 'csv') # plot graph fig = plt.figure() plot = fig.add_subplot(111) """ Single """ # x_points = [] # y_points = [] # # for index, row in df.iterrows(): # x_points.append(index)