def import_perpetual_access_files():
results = []
my_files = glob.glob("/Users/hpiwowar/Downloads/wvu_perpetual_access.csv")
my_files.reverse()
for my_file in my_files:
print my_file
if False:
xlsx_file = open(my_file, "rb")
workbook = openpyxl.load_workbook(xlsx_file, read_only=True)
sheetnames = list(workbook.sheetnames)
for sheetname in sheetnames:
sheet = workbook[sheetname]
column_names = {}
for i, column in enumerate(
list(sheet.iter_rows(min_row=1, max_row=1))[0]):
column_names[column.value] = i
for row_cells in sheet.iter_rows(min_row=1):
username = row_cells[column_names["Account Name"]].value
issn = row_cells[column_names["ISSN (FS split)"]].value
start_date = row_cells[
column_names["Content Start Date"]].value
end_date = row_cells[
column_names["Content End Date"]].value
if is_issn(issn):
new_dict = {
"username": username,
"issn": issn,
"start_date": start_date,
"end_date": end_date
}
results.append(new_dict)
# print new_dict
print ".",
else:
rows = read_csv_file(my_file)
for row in rows:
print row
new_dict = {
"username": "******",
"issn": row["issn"],
"start_date": row["start_date"],
"end_date": row["end_date"]
}
results.append(new_dict)
# print new_dict
print ".",
with open("/Users/hpiwowar/Downloads/perpetual_access_cleaned.csv",
"w") as csv_file:
csv_writer = csv.writer(csv_file, encoding="utf-8")
header = ["username", "issn", "start_date", "end_date"]
csv_writer.writerow(header)
for my_dict in results:
csv_writer.writerow([my_dict[k] for k in header])
print "/Users/hpiwowar/Downloads/perpetual_access_cleaned.csv"
def main_csv_reader(args):
path_to_coffee = args.path_to_coffee
path_to_matched = args.matched_json
all_people_list = flat_list(list(read_csv_file(path_to_coffee)))
matched_in_this_session = []
error = False
if path_to_matched:
try:
matched_people_json = read_json_file(path_to_matched)
tuple_list = create_tuple_list(all_people_list,
matched_people_json)
sorted_people_list = sort_tuple_list(tuple_list)
except:
raise ('Only use the program generated matched_people.json file')
else:
write_json_file()
matched_people_json = read_json_file('matched_people.json')
sorted_people_list = all_people_list
unmatched_people = []
for person in sorted_people_list:
if person not in matched_in_this_session:
individual_match_list = invidual_preproc(person, all_people_list,
matched_people_json,
matched_in_this_session)
if individual_match_list:
matched_pair = coffee_roulette(person, individual_match_list)
if matched_pair is not None:
for person in matched_pair:
matched_in_this_session.append(person)
else:
error = True
break
else:
unmatched_people.append(person)
else:
pass
if error is False:
create_today_matched(matched_in_this_session)
if unmatched_people:
create_today_unmatched(unmatched_people)
updated_json = update_current_json(matched_people_json,
matched_in_this_session)
summary = "\n{} Matches".format(date.today())
summary = create_matched_people_string(matched_in_this_session,
summary)
summary_messsage, alone = make_summary(matched_in_this_session,
unmatched_people, summary, "")
summary += alone
write_json_file(updated_json)
write_txt_file(summary)
print(summary_messsage)
def log_file():
analysis = request.args.get('analysis')
start_date = request.args.get('start-date')
end_date = request.args.get('end-date')
# Parameter
try:
start_date = parse_date(start_date)
except:
return ("ERROR: Start date can't be parsed by YYYY-MM-DD format.", 400)
try:
end_date = parse_date(end_date)
except:
return ("ERROR: End date can't be parsed by YYYY-MM-DD format.", 400)
# Validate
if start_date > end_date:
return ("ERROR: Start date can't be ahead of the end date.", 400)
# Logic
log_file = cli.generate_log_file(start_date, end_date)
if analysis is None or analysis == 'summary':
return jsonify(read_csv_file(cli.generate_summary_file(log_file)))
elif analysis == 'revision':
return jsonify(read_csv_file(cli.generate_revision_file(log_file)))
elif analysis == 'coupling':
return jsonify(read_csv_file(cli.generate_coupling_file(log_file)))
elif analysis == 'age':
return jsonify(read_csv_file(cli.generate_age_file(log_file)))
elif analysis == 'abs-churn':
return jsonify(
read_csv_file(cli.generate_absolute_churn_file(log_file)))
elif analysis == 'author-churn':
return jsonify(read_csv_file(cli.generate_author_churn_file(log_file)))
elif analysis == 'entity-churn':
return jsonify(read_csv_file(cli.generate_entity_churn_file(log_file)))
elif analysis == 'entity-ownership':
return jsonify(
read_csv_file(cli.generate_entity_ownership_file(log_file)))
elif analysis == 'entity-effort':
return jsonify(read_csv_file(
cli.generate_entity_effort_file(log_file)))
else:
return ("ERROR: Analysis type not in selection.", 400)
def main():
if not util.is_there_csvfile():
print("A CSV of Alexa list does not exist!")
if not util.fetch_alexa_list():
print("It could not fetch the file!")
exit(1)
website_dict = util.read_csv_file(10)
print(website_dict['de'])
util.write_dict_to_json(website_dict)
util.dict_to_bookmark(website_dict)
#preprocess #train dfTrain['clickTime_day_gap'] = dfTrain['clickTime'].apply(util.get_train_time_day) dfTrain['clickTime_hour'] = dfTrain['clickTime'].apply(util.get_time_hour) #test dfTest['clickTime_day_gap'] = dfTest['clickTime'].apply(util.get_test_time_day) dfTest['clickTime_hour'] = dfTest['clickTime'].apply(util.get_time_hour) #ad ad = util.read_csv_file(data_root+'/ad.csv',logging=True) #app app_categories = util.read_csv_file(data_root+'/app_categories.csv',logging=True) app_categories["app_categories_first_class"] = app_categories['appCategory'].apply(util.categories_process_first_class) app_categories["app_categories_second_class"] = app_categories['appCategory'].apply(util.categories_process_second_class) #user user = util.read_csv_file(data_root+'/user.csv',logging=True) user['age_process'] = user['age'].apply(util.age_process) user["hometown_province"] = user['hometown'].apply(util.hometown_process_province) user["hometown_city"] = user['hometown'].apply(util.hometown_process_city)
p_feature_map = get_feature_map(p_features)
q_feature_map = get_feature_map(q_features)
for feature_p, feature_q in zip(p_features, q_features):
p_x = float(p_feature_map[tuple(feature_p)]) / max_sample
q_x = float(q_feature_map[tuple(feature_q)]) / max_sample
s += (log(float(p_x / q_x)))
s = s / max_sample
return s
if __name__ == "__main__":
# Extract all the features from the csv file
total_feat_printing_1st_to_2nd_1st, total_feat_printing_1st_to_2nd_2nd, total_feat_printing_2nd_to_3rd_2nd, total_feat_cursive_2nd_to_3rd_3rd, total_feat_printing_2nd_to_3rd_3rd, total_feat_cursive_3rd_to_4th_3rd, total_feat_printing_3rd_to_4th_3rd, total_feat_cursive_3rd_to_4th_4th, total_feat_printing_3rd_to_4th_4th, total_feat_cursive_4th_to_5th_4th, total_feat_printing_4th_to_5th_4th, total_feat_cursive_4th_to_5th_5th, total_feat_printing_4th_to_5th_5th = read_csv_file(
SETTINGS.file_path_temporal_data)
#Grade 1st to 2nd
total_feat_printing_1st_to_2nd_1st = get_parsed_data(
total_feat_printing_1st_to_2nd_1st)
total_feat_printing_1st_to_2nd_2nd = get_parsed_data(
total_feat_printing_1st_to_2nd_2nd)
#Grade 2nd to 3rd
total_feat_printing_2nd_to_3rd_2nd = get_parsed_data(
total_feat_printing_2nd_to_3rd_2nd)
total_feat_cursive_2nd_to_3rd_3rd = get_parsed_data(
total_feat_cursive_2nd_to_3rd_3rd)
total_feat_printing_2nd_to_3rd_3rd = get_parsed_data(
total_feat_printing_2nd_to_3rd_3rd)
#coding=utf-8
import pandas as pd
import sys
from util import read_csv_file,get_time_day,get_time_hour,categories_process_first_class,categories_process_second_class,\
age_process,hometown_process_city,hometown_process_province
ad = read_csv_file("../data/ad.csv")
app_categories = read_csv_file("../data/app_categories.csv").head(100)
position = read_csv_file("../data/position.csv").head(100)
test = read_csv_file("../data/test.csv").head(100)
train = read_csv_file("../data/train.csv")
# test.to_csv("result.csv")
# sys.exit()
user = read_csv_file("../data/user.csv").head(100)
user_app_actions = read_csv_file("../data/user_app_actions.csv").head(100)
user_installedapps = read_csv_file("../data/user_installedapps.csv").head(100)
'''
ad.csv preprocess
['creativeID' 'adID' 'camgaignID' 'advertiserID' 'appID' 'appPlatform']
['creativeID' 'adID' 'camgaignID' 'advertiserID' 'appID' 'appPlatform_1'
'appPlatform_2']
'''
ad_columns = ad.columns.values
print ad_columns
type=str,
default=None,
help="input file to parse")
parser.add_argument("--username",
type=str,
default=None,
help="username to input")
parsed_args = parser.parse_args()
parsed_vars = vars(parsed_args)
# create_accounts(parsed_vars["filename"])
# build_counter_import_file(filename=parsed_vars["filename"], username=parsed_vars["username"])
crkn_ids = read_csv_file(
u"/Users/hpiwowar/Documents/Projects/tiv2/jump-api/data/crkn_lookup.csv"
)
institution_for_all_these_packages = "institution-fVnPvXK9iBYA"
# report_name = "trj2"
# all_in_one_data_rows = read_csv_file(u"/Users/hpiwowar/Documents/Projects/tiv2/jump-api/data/counter5_crkn/TR_J2 SUSHI Harvester CRKN_Wiley-2019_incl-non-participants.csv")
# report_name = "trj3"
# all_in_one_data_rows = read_csv_file(u"/Users/hpiwowar/Documents/Projects/tiv2/jump-api/data/counter5_crkn/TR_J3 SUSHI Harvester CRKN_Wiley-2019.csv")
# report_name = "trj4"
# all_in_one_data_rows = read_csv_file(u"/Users/hpiwowar/Documents/Projects/tiv2/jump-api/data/counter5_crkn/1 TR_J4 SUSHI Harvester CRKN_Wiley-2019.csv")
# all_in_one_data_rows = read_csv_file(u"/Users/hpiwowar/Documents/Projects/tiv2/jump-api/data/counter5_crkn/2 TR_J4 SUSHI Harvester CRKN_Wiley-2019.csv")
report_name = "trj4"
all_in_one_data_rows = read_csv_file(
import simplejson as json
import re
from util import read_csv_file
error_rows = read_csv_file(
"/Users/hpiwowar/Downloads/jump_file_import_error_rows.csv")
rows = [d for d in error_rows if d["file"] == "price"]
each_error = []
i = 0
for row in rows:
hits = re.findall('{"ri(.*?)wrong_publisher(.*?)}', row["errors"])
if hits:
print hits[0]
print len(hits)
print i
i = i + 1
# unknown_issn
# wrong_publisher
def __init__(self):
self.results = []
rows, first_header = read_csv_file(
'data/LeaderBoardData/TADPOLE_Submission_Leaderboard_TeamName.csv')
self.results.append(first_header)