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)