def report():
report = request.args.get("report")
report = report[-16:]
# try:
try:
analyzed = r.hgetall(report)
boss_list = analyzed["kills"]
rankings = analyzed["details"]
boss_list = ast.literal_eval(boss_list)
rankings = ast.literal_eval(rankings)
# print "I have this report"
return render_template("report.html",
boss_list=boss_list,
rankings=rankings,
report=report)
except:
# print "trying to analyze"
analyzed = model.analyze(report)
boss_list = analyzed["kills"]
rankings = analyzed["details"]
r.hmset(report, analyzed)
return render_template("report.html",
boss_list=boss_list,
rankings=rankings,
report=report)
def tagDownload(tag):
tag = tag+" review"
tag = urllib.quote(tag.encode("utf-8"))
reviews = {'data': []}
response = requests.post("https://www.reddit.com/api/v1/access_token",# accquire the user token
auth=client_auth, data=post_data, headers=headers)
response = response.json()
access_token = response['access_token']
token_type = response['token_type']
new_headers = {"Authorization": str(
token_type+" "+access_token), "User-Agent": "MovieReviewer/0.1 by michaeljavy"}
response = requests.get("https://oauth.reddit.com/r/all/search?q=" + # call the search api from reddit and sort the results by relevance, only fetch the first 10 posts
tag+"&sort=relevance&limit=10", headers=new_headers)
if response.status_code == 200:
redditData = response.json()
redditData = redditData["data"]["children"]
# loop all of the posts and scrape all of the comments with depth 1.
for post in redditData:
post_id = post["data"]["id"]
response = requests.get(
"https://oauth.reddit.com/r/all/comments/"+post_id+"?depth=1", headers=new_headers)
if response.status_code == 200:
d = response.json()[1]["data"]["children"][:-1]
for comment in d:
reviews['data'] += [{"up": comment["data"]["ups"], "review":comment["data"] # store the number of ups and comment url for further use cases.
["body"], "url":"https://www.reddit.com"+comment["data"]["permalink"]}]
else:
return None
return analyze(tag, reviews)
def refresh_report():
try:
report = request.args.get("report")
report = report[-16:]
r.delete(report)
analyzed = model.analyze(report)
boss_list = analyzed["kills"]
rankings = analyzed["details"]
r.hmset(report, analyzed)
return render_template("report.html", boss_list=boss_list, rankings=rankings, report=report)
except:
return render_template("badlog.html")
def refresh_report():
try:
report = request.args.get("report")
report = report[-16:]
r.delete(report)
analyzed = model.analyze(report)
boss_list = analyzed["kills"]
rankings = analyzed["details"]
r.hmset(report, analyzed)
return render_template("report.html",
boss_list=boss_list,
rankings=rankings,
report=report)
except:
return render_template("badlog.html")
def application(environ, start_response):
write = start_response('200 OK', [
('Access-Control-Allow-Origin', '*'),
('Access-Control-Allow-Methods', 'GET, POST, PUT, DELETE, OPTIONS'),
('Content-Type', 'application/json')
])
raw = environ['wsgi.input'].read(int(environ.get('CONTENT_LENGTH', '0')))
if len(raw) > 0:
log.logit(raw)
try:
content = json.loads(raw)
except ValueError:
return [json.dumps({"error": "I need JSON, with your input as the value to the data key"})]
# The challenge makes sure that UUIDs don't get stomped
# by people who want to hijack an existing comment.
#
# Both the publically accessible uuid and the privately
# stored challenge has to match before anything is updated
my_challenge = 0
if 'uid' in content and content['uid'] != 0:
my_guid = content['uid']
if 'c' in content:
my_challenge = content['c']
else:
my_guid = myrand()
my_challenge = myrand()
if 'id' in content and content['id'] == 'fave':
log.faveit(raw)
else:
res = model.analyze(content['data'])
res['uid'] = my_guid
res['c'] = my_challenge
return [json.dumps(res)]
return [json.dumps({"error": "I need some POST input to analyze, dumbfuck."})]
def report():
report = request.args.get("report")
report = report[-16:]
# try:
try:
analyzed = r.hgetall(report)
boss_list = analyzed["kills"]
rankings = analyzed["details"]
boss_list = ast.literal_eval(boss_list)
rankings = ast.literal_eval(rankings)
# print "I have this report"
return render_template("report.html", boss_list=boss_list, rankings=rankings, report=report)
except:
# print "trying to analyze"
analyzed = model.analyze(report)
boss_list = analyzed["kills"]
rankings = analyzed["details"]
r.hmset(report, analyzed)
return render_template("report.html", boss_list=boss_list, rankings=rankings, report=report)
#!/usr/bin/python # -*- coding: UTF-8 -*- import json import sys import log import model content = sys.stdin.read() log.logit(content) print json.dumps(model.analyze(content))
import argparse
import model
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='run experiments of language analysis.')
parser.add_argument('path',
help='path to csv file with dataset')
parser.add_argument('alg', choices=['lasso', 'l1', 'ridge', 'l2', 'svm'], help='learning algorithm')
parser.add_argument('score',
choices=['tr_f1', 'tr_prec', 'tr_rec', 'tr_auc', 'val_f1', 'val_prec', 'val_rec', 'val_auc'],
help='scoring function to validate')
parser.add_argument('-t', '--limit', type=int, default=0, help='maximum number of tweets recovered (0 for all)')
parser.add_argument('-k', '--kfolds', type=int, default=10, help='k for k-fold cross-validation')
parser.add_argument('-s', '--stopwords', action='store_true', help='remove stopwords if active')
parser.add_argument('-r', '--keepwordsrank', type=int,
help='number of features to be used ranked by number of appearances')
parser.add_argument('-a', '--alpha', type=float, nargs='+', default=1.0, help='regularization parameters to try')
args = parser.parse_args()
print('path:', args.path)
print('alg:', args.alg)
print('score:', args.score)
print('limit:', args.limit)
print('kfolds:', args.kfolds)
print('stopwords:', args.stopwords)
print('keepwordsrank:', args.keepwordsrank)
print('alpha:', args.alpha)
model.analyze(args.path, args.alg, args.score, args.limit, args.kfolds, args.stopwords, args.keepwordsrank,
args.alpha)
''' main file which takes argument from encrypt.py and executes further functions as documented in the file ''' import imgpros import sys import utilities import model import results '''Initializes the inputs taken from encrypt.py and stores them for furthur use''' utilities.intializer(sys.argv) '''Displays a preview of initial 50 frames to help cropping of rotating cell by the user''' utilities.preview() '''Allows user to select a free-size rectangular portion of the first frame consisting of a cell for furthur analysis''' imgpros.crop() '''Performs Linear Regression Analysis the cropped portion of all the frames and outputs the centre of mass(COM) data for and a plot of COM for furthur use''' model.analyze() '''Calculates frequency, change in angle per frame, total clockwise/counter-clockwise time interval and no of frames and all else necessary outputs. Also outputs the finally analyzed data in the form of CSV files and graphs''' model.compute() '''Saves the graphs and CSV files obtained after analysis in the required folder''' results.save()
station_index += 1
print('Processing %s - %s (%d/%d)' %
(city, station, station_index, len(stations)))
data = list(zip(*city_data[station]))
for i in range(len(predict_arr)):
# if all data is zero, the model gg
not_zero = 0
for j in data[predict_arr[i]]:
if j > 0:
not_zero += 1
if not_zero < len(data[predict_arr[i]]) / 3:
for index in range(48):
submission_data[station][index][i] = 0
continue
predict = model.analyze(data[predict_arr[i]], data[0], predict_start,
predict_end)
index = 0
for j in range(len(predict.index)):
if predict.index[j] >= submission_start:
# print(predict.index[j], predict[j])
submission_data[station][index][i] = max(predict[j], 0)
index += 1
f = open('submission.csv', 'w', newline='')
writer = csv.writer(f)
writer.writerow(['test_id', 'PM2.5', 'PM10', 'O3'])
for station in submission_data:
for i in range(len(submission_data[station])):
writer.writerow([station + '#' + str(i)] + submission_data[station][i])
