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..............."