def autoPopRoomsByPropKey(request, prop_key=None): import json config = Config() propSvc = PropertyService(config) result = [] if prop_key: temp = propSvc.fetchRoomsByPropertyKey(prop_key) if temp: # reformat for jQuery datatables for rooms in temp: # process beds beds = '' for val in rooms['beds']: beds += val + ', ' beds = beds[0:-2] room_config = config.getConfig('rooms') link = '<a target="_blank" href="' + room_config[ 'details_url'] + rooms['roomTypeKey'] + '/">Details</a>' result.append({ 'type': rooms['type'], 'view': rooms['view'], 'beds': beds, 'available': rooms['numAvailable'], 'link': link }) return HttpResponse(json.dumps(result), content_type='application/json')
def index(request): config = Config() conn = Connection(config.getConfig('db')) db = conn.getDatabase('biglittle') main = render_to_string('home.html') home = render_to_string('layout.html', {'contents': main}) return HttpResponse(home)
def write_cache(self, key, value): info = self.read_cache() info.update({key: value}) config = Config() fn = config.getConfig('cache_file') fh = open(fn, 'w') result = fh.write(json.dumps(info)) fh.close() return result
def del_cache(self, key): info = self.read_cache() if key in info: del info[key] config = Config() fn = config.getConfig('cache_file') fh = open(fn, 'w') result = fh.write(json.dumps(info)) fh.close() return result
def var_export(self, obj, ret_val=False): if ret_val == False: pprint.pprint(obj) return None config = Config() fn = config.getConfig('temp_file') temp = sys.stdout # store original stdout object for later sys.stdout = open(fn, 'w') # redirect all prints to this log file pprint.pprint(obj) sys.stdout.close() # ordinary file object sys.stdout = temp # restore print commands to interactive prompt return open(fn, 'r').read()
def index(request): config = Config() conn = Connection(config.getConfig('db')) db = conn.getDatabase() html = '<h1>Welcome to Book Someplace!</h1>' html += '<hr>' html += '<ul>' import pprint for item in db.common.find() : html += '<li>' + pprint.saferepr(item) + '</li>' html += '</ul>' main = render_to_string('home.html') home = render_to_string('layout.html', {'contents' : main}) return HttpResponse(home)
def read_cache(self, key=None, del_after_read=False): config = Config() fn = config.getConfig('cache_file') info = {} val = None if os.path.exists(fn): raw_info = open(fn, 'r').read().strip() if len(raw_info) > 1: info = json.loads(raw_info) if key: if key in info: val = info[key] if del_after_read: self.del_cache(key) else: val = info return val
def __init__(self): stemmer = PorterStemmer() stop_words = get_stop_words() conf = Config("config/system.config") lstm_model_path = conf.getConfig("PATHS", "tf_model") word2_vec_model = conf.getConfig("PATHS", "word_vec_model") self.max_seq = conf.getConfig("MODEL_PARAMS", "sentence_length") self.vec_dim = conf.getConfig("MODEL_PARAMS", "word_vec_dim") self.input_dim = conf.getConfig("MODEL_PARAMS", "feature_vector_dim") num_layers = conf.getConfig("MODEL_PARAMS", "num_layers") class_size = conf.getConfig("MODEL_PARAMS", "class_size") rnn_size = conf.getConfig("MODEL_PARAMS", "rnn_size") word_model = gensim.models.Word2Vec.load(word2_vec_model) self.model = Model(self.max_seq, self.input_dim, num_layers, class_size, rnn_size) self.sess = tf.Session() saver = tf.train.Saver() saver.restore(self.sess, lstm_model_path)
t = line.split()[2] #print line.split() if t.endswith('B-L'): tag.append(np.array([1, 0, 0])) elif t.endswith('I-L'): tag.append(np.array([0, 1, 0])) elif t.endswith('O'): tag.append(np.array([0, 0, 1])) assert (len(sentence) == len(sentence_tag)) pkl.dump(sentence, open(output_embed, 'wb')) pkl.dump(sentence_tag, open(output_tag, 'wb')) print("Done................................") word_vec_dim = conf.getConfig("MODEL_PARAMS", "word_vec_dim") sen_len = conf.getConfig("MODEL_PARAMS", "sentence_length") word_vec_model = conf.getConfig("PATHS", "word_vec_model") train_file = conf.getConfig("PATHS", "train_csv_path") test_file_one = conf.getConfig("PATHS", "test_csv_path") train_embedding = conf.getConfig("PATHS", "train_embedding") train_tag = conf.getConfig("PATHS", "train_tag") test_embedding = conf.getConfig("PATHS", "test_embedding") test_tag = conf.getConfig("PATHS", "test_tag") word2vec_model = gensim.models.Word2Vec.load(word_vec_model) get_input(word2vec_model, word_vec_dim, train_file, train_embedding,
def updateBorrowerListener(self, arg): # imports from decimal import Decimal from bson.decimal128 import Decimal128 # init vars loan = arg['loan'] amtPaid = arg['amtPaid'] amtDueFromLoan = Decimal(0.00) amtPaidFromLoan = Decimal(0.00) amtDueFromUser = Decimal(0.00) amtPaidFromUser = Decimal(0.00) # build discrepancy log file name config = Config() log_fh = open(config.getConfig('discrepancy_log'), 'a') # current date from time import gmtime, strftime now = strftime('%Y-%m-%d', gmtime()) # get amountDue and amountPaid totals from user instance borrowerKey = loan.get('borrowerKey') borrower = self.fetchUserByBorrowerKey(borrowerKey) if not borrower: log_fh.write(now + ' : User entry for ' + borrowerKey + ' not found ' + "\n") else: # convert from NumberDecimal to Python decimal.Decimal amtDueFromUser = borrower.get('amountDue').to_decimal() amtPaidFromUser = borrower.get('amountPaid').to_decimal() # update amounts amtDueFromUser -= amtPaid amtPaidFromUser += amtPaid # perform update filt = {'userKey': borrower.getKey()} updateDoc = { '$set': { 'amountDue': Decimal128(str(amtDueFromUser)), 'amountPaid': Decimal128(str(amtPaidFromUser)) } } self.collection.update_one(filt, updateDoc) # accuracy check: calculate amountDue and amountPaid totals from loan.payments for doc in loan.getPayments(): amtDueFromLoan += doc['amountDue'] amtPaidFromLoan += doc['amountPaid'] # log any discrepancies but do not take any further action if amtDueFromUser != amtDueFromLoan: log_fh.write(now + ' : Amount due discrepancy for : ' + borrower.getFullName() + ' : ' + borrowerKey + "\n") log_fh.write('--data from "users" collection: ' + str(amtDueFromUser) + "\n") log_fh.write('--data from "loans" collection: ' + str(amtDueFromLoan) + "\n") if amtPaidFromUser != amtPaidFromLoan: log_fh.write(now + ' : Amount paid discrepancy for : ' + borrower.getFullName() + ' : ' + borrowerKey + "\n") log_fh.write('--data from "users" collection: ' + str(amtPaidFromUser) + "\n") log_fh.write('--data from "loans" collection: ' + str(amtPaidFromLoan) + "\n") log_fh.close()
sys.path.append(src_path) # enable error display import cgitb cgitb.enable(display=1, logdir='../data') # custom imports from config.config import Config from web.responder.html import HtmlResponder from db.mongodb.connection import Connection from sweetscomplete.domain.product import ProductService from sweetscomplete.entity.product import Product # create CustomerService instance config = Config() db_config = config.getConfig('db') prod_conn = Connection(db_config['host'], db_config['port'], Product) prod_service = ProductService(prod_conn, db_config['database']) # HTML output message = 'SORRY! Unable to find information on this product' response = HtmlResponder('templates/details.html') # check for form posting import cgi form = cgi.FieldStorage() if 'product' in form: # get product key key = form['product'].value
import cgitb cgitb.enable(display=1, logdir='../data') # custom imports from config.config import Config from web.responder.html import HtmlResponder from web.auth import SimpleAuth from db.mongodb.connection import Connection from sweetscomplete.domain.customer import CustomerService from sweetscomplete.entity.customer import Customer from sweetscomplete.domain.product import ProductService from sweetscomplete.entity.product import Product # create CustomerService instance config = Config() db_config = config.getConfig('db') cust_conn = Connection(db_config['host'], db_config['port'], Customer) cust_service = CustomerService(cust_conn, db_config['database']) prod_conn = Connection(db_config['host'], db_config['port'], Product) prod_service = ProductService(prod_conn, db_config['database']) # init vars auth = SimpleAuth(cust_service, config.getConfig('session_storage')) cust = auth.getIdentity() # HTML output response = HtmlResponder('templates/select.html') response.addInsert('%message%', '<br>') response.addInsert('%ajax_url%', config.getConfig('ajax_url')) # output
import redis import sys sys.path.append("../../..") from config.config import Config conf = Config() configs = conf.getConfig() class BjjsRedis: def __init__(self): self.conn = redis.Redis(host=configs.REDIS['host'], port=configs.REDIS['port'], db=0,password=configs.REDIS['pass']) def save(self,key,val,ex=None): self.conn.set(key,val,ex) def get(self,key): return self.conn.get(key) if __name__ == '__main__': rr = BjjsRedis() rr.save("test","test1") print rr.get("test") if(rr.get("test")): print "exist" else: print "not exist"
import cgitb cgitb.enable(display=1, logdir=".") # custom imports from config.config import Config from web.responder.html import HtmlResponder from web.auth import SimpleAuth from db.mongodb.connection import Connection from sweetscomplete.domain.customer import CustomerService from sweetscomplete.entity.customer import Customer from sweetscomplete.domain.purchase import PurchaseService from sweetscomplete.entity.purchase import Purchase # create CustomerService instance using configuration class config = Config() db_config = config.getConfig('db') cust_conn = Connection(db_config['host'], db_config['port'], Customer) cust_service = CustomerService(cust_conn, db_config['database']) purch_conn = Connection(db_config['host'], db_config['port'], Purchase) purch_service = PurchaseService(purch_conn, db_config['database']) # init vars auth = SimpleAuth(cust_service, config.getConfig('session_storage')) cust = auth.getIdentity() response = HtmlResponder('templates/history.html') # get page number import cgi info = cgi.FieldStorage() if 'page' in info:
import re from nltk.tokenize import sent_tokenize from nltk.tokenize import TweetTokenizer from nltk.tokenize import word_tokenize from nltk.stem import PorterStemmer from config.config import Config import random from nltk import pos_tag from utils.util import * import csv stemmer = PorterStemmer() conf = Config("config/system.config") training_data = conf.getConfig("PATHS", "training_data_path") test_data = conf.getConfig("PATHS", "test_data") train_csv_path = conf.getConfig("PATHS", "train_csv_path") test_csv_path = conf.getConfig("PATHS", "test_csv_path") max_seq_length = conf.getConfig("MODEL_PARAMS", "sentence_length") def message_to_sentences(message, remove_stopwords=False): raw_sentences = sent_tokenize(clean_str(message)) sentences = [] for raw_sentence in raw_sentences: if "<" in raw_sentence and ">" in raw_sentence: tokens = sentence_to_token_list(raw_sentence) if len(tokens) <= max_seq_length: sentences.append(tokens) """ elif "<" not in raw_sentence and ">" not in raw_sentence: tokens = sentence_to_token_list(raw_sentence)
model.output_data: test_a_out }) print("epoch %d:" % e) print('test_a score:') m = f1(pred, test_a_out, length) if m > maximum: maximum = m save_path = saver.save(sess, model_path) print("max model saved in file: %s" % save_path) pred, length = sess.run([model.prediction, model.length], { model.input_data: test_b_inp, model.output_data: test_b_out }) print("test_b score:") f1(pred, test_b_out, length) conf = Config("config/system.config") model_path = conf.getConfig("PATHS", "tf_model") input_dim = conf.getConfig("MODEL_PARAMS", "feature_vector_dim") sentence_length = conf.getConfig("MODEL_PARAMS", "sentence_length") class_size = conf.getConfig("MODEL_PARAMS", "class_size") rnn_size = conf.getConfig("MODEL_PARAMS", "rnn_size") num_layers = conf.getConfig("MODEL_PARAMS", "num_layers") batch_size = conf.getConfig("MODEL_PARAMS", "batch_size") epoch = conf.getConfig("MODEL_PARAMS", "epoch") train(batch_size, sentence_length, input_dim, num_layers, class_size, rnn_size) print "Training Done..............."