__author__ = 'ykogan'
import ReadMongoDB
import ReadCustomerUploads
import re
existing_models = ReadMongoDB.read_mongodb_server_models()
#for existing_model in existing_models:
# print(existing_model)
uploaded_models = ReadCustomerUploads.read_server_models()
matching_models = dict()
for uploaded_model in uploaded_models:
umodel_name = str(uploaded_model).upper()
umodel_words = [x for x in re.split('\W+', umodel_name) if x != '']
for existing_model in existing_models:
emodel_name = str(existing_model).upper()
emodel_words = [x for x in re.split('\W+', emodel_name) if x != '']
model_match = list(set(umodel_words) & set(emodel_words))
if len(model_match) == len(umodel_words):
matching_models["_".join(sorted(umodel_words))] = 'Exists'
break
elif len(model_match) > 0:
if ("_".join(sorted(umodel_words)) in matching_models):
temp_set = re.split("\W+", str(matching_models["_".join(sorted(umodel_words))]))
if (len(temp_set) >= len(matching_models)):
continue
matching_models["_".join(sorted(umodel_words))] = "Partial match (" + "_".join(sorted(model_match)) + ")"
__author__ = 'ykogan'
import ReadMongoDB
import re
import numpy as np
import pandas as pd
import numexpr
import ReadCustomerUploads
import ListServerModels
#existing_models = MongoDB.read_mongodb_server_models()
existing_models = ReadCustomerUploads.read_server_models()
known_series = ListServerModels.getKnownSeries()
known_models = ListServerModels.getKnownModels()
known_generations = ListServerModels.getKnownGenerations()
words_to_skip = ListServerModels.getWordsToSkip()
all_words = dict()
rev_all_words = dict()
w2w = np.zeros([2000,2000])
df = pd.DataFrame(columns=("Series", "Model", "Generation", "SKU", "Description"))
for existing_model in existing_models:
emodel_name = str(existing_model).upper()
emodel_words = [x for x in re.split('\W+', emodel_name) if x != '']
for word in emodel_words:
