= self.days_since_pwdlastset or desc == \\\"***TEST***\\\":\\n log.info(\\\"got stale user: %s\\\", user)\\n stale_users[f\\\"{self.days_since_pwdlastset}\\\"].append(user)\\n log.debug(\\\"retrieved the following stale users: %s\\\", stale_users)\\n return stale_users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3d8ed37a3264c08c8e33d630f27f9cc\",\n \"score\": \"0.61370057\",\n \"text\": \"def get_user_list(self):\\n api_call = self._get('user')\\n userlist_dict = self._assert_json_response_stop_on_error(api_call, True)\\n\\n # for item in ('users',):\\n # assert item in userlist_dict.keys(), AssertionError('Unable to find {0}'.format(item))\\n assert 'users' in userlist_dict.keys(), AssertionError('Unable to find users.')\\n\\n user_id_list = list()\\n user_name_list = list()\\n user_group_list = list()\\n userlist_list = userlist_dict['users']\\n for list_item in userlist_list:\\n logger.info('list_item is {0}'.format(list_item))\\n for key in ('user-id',\\n 'user-name',\\n 'user-group',\\n 'user-group-name'):\\n assert key in list_item.keys(), AssertionError('Unable to find {0}'.format(key))\\n user_id_list.append(list_item['user-id'])\\n user_name_list.append(list_item['user-name'])\\n user_group_list.append(list_item['user-group'])\\n\\n for user_id in user_id_list:\\n if user_id not in _UserManagement_Keywords.user_ids.values():\\n _UserManagement_Keywords.user_ids['USER{0}'.format(len(_UserManagement_Keywords.user_ids))] = user_id\\n logger.info(_UserManagement_Keywords.user_ids)\\n\\n for user_name in user_name_list:\\n if user_name not in _UserManagement_Keywords.user_names.values():\\n _UserManagement_Keywords.user_names['USER{0}'.format(len(_UserManagement_Keywords.user_names))] = user_name\\n logger.info(_UserManagement_Keywords.user_names)\\n\\n _UserManagement_Keywords.user_groups.clear()\\n for user_group in user_group_list:\\n _UserManagement_Keywords.user_groups['USER{0}'.format(len(_UserManagement_Keywords.user_groups))] = user_group\\n logger.info(_UserManagement_Keywords.user_groups)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c069e2ec99a38dfaf0a33a3673f46cfc\",\n \"score\": \"0.61314356\",\n \"text\": \"def all_users(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c77a0957d84e92f1be12aedf328d8eec\",\n \"score\": \"0.6123298\",\n \"text\": \"def admin_get_usr_modr_list():\\n stmt = \\\"SELECT * FROM user WHERE user_type=%s OR user_type=%s ORDER BY reg_time DESC\\\"\\n params = (\\\"User\\\", \\\"Moderator\\\")\\n rows = mydb.execute_query(stmt, params)\\n usr_mdrs = []\\n for row in rows:\\n obj = User()\\n obj.set_user(row)\\n usr_mdrs.append(obj)\\n return usr_mdrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c992e90cd03ad5674c0bef5c7a6d3ec7\",\n \"score\": \"0.6121563\",\n \"text\": \"def get_user_list_for(self, me):\\n user_list = self.model.objects.exclude(pk=me.pk).filter(is_staff=False)\\n\\n # Add modified timestamp from thread\\n user_list = user_list.annotate(thread_modified=RawSQL(\\\"\\\"\\\"\\n SELECT ct.modified\\n FROM chat_thread_participants ctp\\n INNER JOIN chat_thread ct ON ctp.thread_id = ct.id\\n WHERE ctp.customuser_id = accounts_customuser.id AND ctp.thread_id IN \\n (SELECT thread_id FROM chat_thread_participants WHERE customuser_id = %s)\\n \\\"\\\"\\\", (me.pk,)))\\n\\n # Add body from last_message\\n user_list = user_list.annotate(last_message_body=RawSQL(\\\"\\\"\\\"\\n SELECT cm.body\\n FROM chat_thread_participants ctp\\n INNER JOIN chat_thread ct ON ctp.thread_id = ct.id\\n INNER JOIN chat_message cm ON ct.last_message_id = cm.id\\n WHERE ctp.customuser_id = accounts_customuser.id AND ctp.thread_id IN \\n (SELECT thread_id FROM chat_thread_participants WHERE customuser_id = %s)\\n \\\"\\\"\\\", (me.pk,)))\\n\\n # Add userid from last_message\\n user_list = user_list.annotate(last_message_sender_id=RawSQL(\\\"\\\"\\\"\\n SELECT cm.sender_id\\n FROM chat_thread_participants ctp\\n INNER JOIN chat_thread ct ON ctp.thread_id = ct.id\\n INNER JOIN chat_message cm ON ct.last_message_id = cm.id\\n WHERE ctp.customuser_id = accounts_customuser.id AND ctp.thread_id IN \\n (SELECT thread_id FROM chat_thread_participants WHERE customuser_id = %s)\\n \\\"\\\"\\\", (me.pk,)))\\n\\n return user_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"561b255c24d9604458117f1bd4d009ef\",\n \"score\": \"0.6120848\",\n \"text\": \"def userList(self):\\n # XXX should we check authenticated?\\n op = SQL('''\\n SELECT sid\\n FROM session\\n ORDER BY sid''')\\n def parseRows(rows):\\n return (x[0] for x in rows)\\n return self.runner.run(op).addCallback(parseRows)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee738b9bdc2c396f6d87208ae54ed3d6\",\n \"score\": \"0.61191773\",\n \"text\": \"def list_users(self):\\n return self._get(route='user')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"271b09c8c98d95eaa02dd12748d87afa\",\n \"score\": \"0.60974765\",\n \"text\": \"def get_active_users(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c093d682870dd9c61706fa74f12c20b\",\n \"score\": \"0.60845804\",\n \"text\": \"def test_get_run_as_users_list(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e26d275f22868d09b70ce2eb2da3556\",\n \"score\": \"0.6074501\",\n \"text\": \"def user_list(self):\\n user_list = list(self.users().itervalues())\\n user_list.sort(key=lambda x: str(x))\\n return user_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e533f741591eed0bd3b96abb924777a0\",\n \"score\": \"0.6071622\",\n \"text\": \"def list_users(ctx, details):\\n with connect(ctx) as nethsm:\\n user_ids = nethsm.list_users()\\n\\n print(f\\\"Users on NetHSM {nethsm.host}:\\\")\\n print()\\n\\n headers = [\\\"User ID\\\"]\\n if details:\\n headers += [\\\"Real name\\\", \\\"Role\\\"]\\n data = []\\n for user_id in user_ids:\\n user = nethsm.get_user(user_id=user_id)\\n data.append([user_id, user.real_name, user.role.value])\\n else:\\n data = [[user_id] for user_id in user_ids]\\n\\n print_table(headers, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fefa76b5abf5dbd82dcd53b1892c46a5\",\n \"score\": \"0.6071184\",\n \"text\": \"def list_users(self,request):\\n\\n\\t\\t# ----------- DEBUG -----------------\\n\\t\\tMODULE.info(\\\"printers/users/query invoked with:\\\")\\n\\t\\tpp = pprint.PrettyPrinter(indent=4)\\n\\t\\tst = pp.pformat(request.options).split(\\\"\\\\n\\\")\\n\\t\\tfor s in st:\\n\\t\\t\\tMODULE.info(\\\" << %s\\\" % s)\\n\\t\\t# -----------------------------------\\n\\n\\t\\tself.lo, self.position = univention.admin.uldap.getMachineConnection(ldap_master=False)\\n\\t\\tobjs = self.lo.search(base=self.position.getDomain(), filter='(&(|(&(objectClass=posixAccount)(objectClass=shadowAccount))(objectClass=univentionMail)(objectClass=sambaSamAccount)(objectClass=simpleSecurityObject)(&(objectClass=person)(objectClass=organizationalPerson)(objectClass=inetOrgPerson)))(!(uidNumber=0))(!(uid=*$)))', attr=['uid'])\\n\\t\\tresult = [ obj[1][\\\"uid\\\"][0] for obj in objs ]\\n\\n\\t\\t# ---------- DEBUG --------------\\n\\t\\tMODULE.info(\\\"printers/users/query returns:\\\")\\n\\t\\tpp = pprint.PrettyPrinter(indent=4)\\n\\t\\tst = ''\\n\\t\\tif len(result) > 5:\\n\\t\\t\\ttmp = result[0:5]\\n\\t\\t\\tMODULE.info(\\\" >> %d entries, first 5 are:\\\" % len(result))\\n\\t\\t\\tst = pp.pformat(tmp).split(\\\"\\\\n\\\")\\n\\t\\telse:\\n\\t\\t\\tst = pp.pformat(result).split(\\\"\\\\n\\\")\\n\\t\\tfor s in st:\\n\\t\\t\\tMODULE.info(\\\" >> %s\\\" % s)\\n\\t\\t# --------------------------------\\n\\n\\t\\tself.finished(request.id,result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c6ffab2e451216ec9152ec228e015fc\",\n \"score\": \"0.60612226\",\n \"text\": \"def list_sessions(request):\\n sessions = Session.objects.filter(expire_date__gte=timezone.now())\\n uid_list = []\\n\\n for session in sessions:\\n data = session.get_decoded()\\n uid_list.append(data.get('_auth_user_id', None))\\n users = []\\n\\n for user in User.objects.filter(id__in=uid_list):\\n try:\\n lastlogin = UserLogin.objects.filter(Subject=user).latest('Timestamp')\\n users.append({'user': user, 'lastlogin': lastlogin})\\n except: # a session without a user (should not happen, only when user is deleted recently)\\n pass\\n\\n return render(request, \\\"godpowers/list_sessions.html\\\", {\\\"users\\\": users})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b58a192aac0231ea2520f5cd01a1fdd1\",\n \"score\": \"0.6057584\",\n \"text\": \"def get_users_info(self):\\n headers.update({'Referer': self.domain + '/standings.phtml'})\\n req = self.session.get(self.urls['team'], headers=headers).content\\n soup = BeautifulSoup(req, \\\"html.parser\\\")\\n\\n money_bids = self.get_money_bids(self.username)\\n\\n info = list()\\n for row in soup.find('table', cellpadding=2).find_all('tr')[1:]:\\n money, max_bid = [0, 0]\\n name = row.a.text\\n user_id = row.find('a')['href'].split('pid=')[1]\\n req = self.session.get(self.urls['player'] % user_id, headers=headers).content\\n html_h1 = BeautifulSoup(req, \\\"html.parser\\\").h1.text\\n username = re.findall('\\\\((.+)\\\\)', html_h1)[0]\\n user_points = int(row.find_all('td')[2].text.replace('.', ''))\\n team_value = int(row.find_all('td')[3].text.replace('.', ''))\\n for user in money_bids['data']['users']:\\n if user['id'] == user_id:\\n money = int(user['dinero'].replace('.', ''))\\n max_bid = int(user['puja'].replace('.', ''))\\n break\\n\\n info.append([name, username, int(user_id), user_points, team_value, money, max_bid])\\n\\n return info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab8db204e33ab7d8eb4255b3f1528c86\",\n \"score\": \"0.60548216\",\n \"text\": \"def list(self):\\n url = '%s/users' % self.region['Python Web Services']\\n r = self.session.get(url)\\n result = response_check(r, 'users')\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"828589521a292dcfd700350e94791245\",\n \"score\": \"0.60537755\",\n \"text\": \"def get_users():\\n\\n users = User.query.all()\\n\\n return render_template('user_list.html', users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14d271ae1fd555e25b3009c39f68aa5c\",\n \"score\": \"0.604675\",\n \"text\": \"def get_users_list():\\n session = start_session()\\n queryset = session.query(User)\\n session.close()\\n return queryset2list(queryset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e6790d98a396fe09b2c26168e093711\",\n \"score\": \"0.60437846\",\n \"text\": \"def get_all_users(self):\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1261904fca8b600c2ca927d3f32bf756\",\n \"score\": \"0.6034384\",\n \"text\": \"def user_list(request):\\n if check_admin_group(request, \\\"Администраторы\\\"):\\n users = User.objects.filter(Q(groups__name=\\\"Менеджеры\\\") | Q(groups__name=\\\"Администраторы\\\")\\n | Q(is_superuser=True)).order_by('-last_login')\\n return render_to_response(\\\"mgmt/admin_user.html\\\", {'users': users},\\n context_instance=RequestContext(request))\\n else:\\n # Return access denied\\n return raise_403(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b27431dd4fb0d5304f13be62109998e7\",\n \"score\": \"0.6028237\",\n \"text\": \"def requestTimeLeft(self, pUserName):\\n # result\\n result = -1\\n message = msg.getTranslation(\\\"TK_MSG_CONFIG_LOADER_USER_NOTFOUND\\\") % (pUserName)\\n\\n # check if we have this user\\n if pUserName in self._timekprUserList:\\n # pass this to actual method\\n self._timekprUserList[pUserName].getTimeLeft(True)\\n\\n # result\\n result = 0\\n message = \\\"\\\"\\n\\n # result\\n return result, message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50f924244cbba2fa530779ce420e3d20\",\n \"score\": \"0.6024228\",\n \"text\": \"def user_list():\\n users = User.query.all()#query all user data from User class\\n return render_template('user_list.html', users=users)#return users.html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51ca36d207547dffe943f6ab0dfc3337\",\n \"score\": \"0.60128903\",\n \"text\": \"def get_all_user_data(self):\\n return [self.url, self.name, self.username, self.about, self.location,\\n self.website, self.joined_on.date(), self.joined_on.time(),\\n self.verified, self.no_of_tweets,\\n self.following, self.followers, self.listed_count]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59692c95d0b172ffa4a867340d8da1b6\",\n \"score\": \"0.60064423\",\n \"text\": \"def get_users(self):\\n\\n return self._read('list/user')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"433a51172c026100e10e297655b4df81\",\n \"score\": \"0.59902114\",\n \"text\": \"def list_users(self, context, limit=None, marker=None,\\n include_marker=False):\\n return guestagent_utils.serialize_list(\\n self._get_listed_users(self.client),\\n limit=limit, marker=marker, include_marker=include_marker)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d8955a103ba48aa7e6bde1ed684e2ba\",\n \"score\": \"0.5987751\",\n \"text\": \"def get_users():\\r\\n connect() # Connect\\r\\n cursor.execute(\\\"SELECT * FROM users\\\") # Select all users\\r\\n item = cursor.fetchall()\\r\\n users = []\\r\\n for user in item:\\r\\n users.append(format_user(user)) # Format the users\\r\\n disconnect()\\r\\n return users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc593bb0e0bfe4c1c1511cff896bd8b\",\n \"score\": \"0.5982436\",\n \"text\": \"def getScheduledUsers(userSource, date=Date()):\\n # type: (String, Optional[Union[Date, int]]) -> List[PyUser]\\n print(userSource, date)\\n return [PyUser()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f38a68511237010667d4db9c6713eb6b\",\n \"score\": \"0.5975413\",\n \"text\": \"def get_user_list():\\n users_list = []\\n for user in Users.query.all():\\n users_list.append(user.name)\\n return jsonify(users_list), 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2840e0d0202037c0a35d742ed4f3ce5\",\n \"score\": \"0.5964898\",\n \"text\": \"def list_users(self, hints):\\n raise exception.NotImplemented() # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b22397a6e42e27107564d7a7a1b2b5\",\n \"score\": \"0.5945334\",\n \"text\": \"def getActiveUsers(self):\\n if self.activeUserCount == 0:\\n logger.info(\\\"Empty room %s\\\" % self.roomName)\\n return list()\\n \\n userList = list()\\n for user in self.activeUsers:\\n userList.append(user.userName)\\n\\n return userList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16e09febd0edb139282376c5fa14558d\",\n \"score\": \"0.594166\",\n \"text\": \"def test_list_user_logins_users(self):\\r\\n user_id = None # Change me!!\\r\\n\\r\\n r = self.client.list_user_logins_users(user_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48db846e89db32b64422ec0ad8857a62\",\n \"score\": \"0.59394574\",\n \"text\": \"def get_users():\\n data = storage.all('User')\\n return list(data) if data is not None else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb254bf30fa7e3eaa8b86194c8da6ece\",\n \"score\": \"0.593879\",\n \"text\": \"def update_timer(self):\\n cooled_down_users = []\\n \\n for user in self.users:\\n if time() - self.users[user] < self.REPEAT_TIME:\\n cooled_down_users.append(user)\\n for user in cooled_down_users:\\n del self.users[user]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"946b7606478e6b305d8c5fa46a8b9d35\",\n \"score\": \"0.59295356\",\n \"text\": \"async def get_all_userData_sorted(self):\\r\\n # get all user stats data from the server (username, wins, loses, games_played)\\r\\n # fetch_all_user_stats = [(\\\"isaac\\\", 12,0,12), (\\\"test1\\\", 0,10,10), (\\\"test2\\\", 4,10,14), (\\\"test3\\\", 10,4,14)]\\r\\n try:\\r\\n self.MSG.set(\\\"\\\")\\r\\n fetch_all_user_stats = await self.controller.SocketConnection.getAllPlayerData()\\r\\n if fetch_all_user_stats != True:\\r\\n self.MSG.set(fetch_all_user_stats)\\r\\n self.userDatas = self.controller.SocketConnection.leaderboard\\r\\n except:\\r\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee2d78fbd925d965e6f8f35572d20fbb\",\n \"score\": \"0.5923908\",\n \"text\": \"def test_all_users_since(self):\\n since = 100000\\n i = self.instance.all_users(since=since)\\n self.get_next(i)\\n\\n self.session.get.assert_called_once_with(\\n url_for(\\\"users\\\"),\\n params={\\\"per_page\\\": 100, \\\"since\\\": since},\\n headers={},\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1dcc036b2aac93c6ac920d6a642a316\",\n \"score\": \"0.59220815\",\n \"text\": \"def get_monitored_users(self):\\n users = []\\n \\n for user in self.shuttledb.cursor().execute('SELECT name FROM Users'):\\n users.append(str(user[0]))\\n\\n return users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea2585e7172124c1240f485492ce1520\",\n \"score\": \"0.59113944\",\n \"text\": \"def list_users():\\n return bbs.dbproxy.DBProxy('userbase').keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"064133f7e652f377fc0fdab64756e15e\",\n \"score\": \"0.5908953\",\n \"text\": \"def user_show(cls):\\n return cls.user_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54e4569524b7400ad37b11b8bbc17acf\",\n \"score\": \"0.589781\",\n \"text\": \"def get_users(self):\\n return self.users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9e0c5330843908b2902a1934a053298\",\n \"score\": \"0.5897741\",\n \"text\": \"def list_users() -> None:\\n app = make_app() # type: ignore\\n with app.app_context():\\n query = User.query.order_by(User.name)\\n for row in query:\\n print(row.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d85bddecf2b6124338a752d8fb2fa1\",\n \"score\": \"0.58975786\",\n \"text\": \"def get_user_list(self):\\n temp_user_list = self.db(self.db.users.id >= 0).select().as_list()\\n user_list = []\\n for row in temp_user_list:\\n user = User( row['username'],\\n row['role'],\\n row['email'],\\n row['password'],\\n row['last_name'],\\n row['first_name'],\\n row['id'] )\\n user_list.append(user)\\n \\n return user_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2afc7b60e573c3218152fe8cd5cb8ead\",\n \"score\": \"0.5891037\",\n \"text\": \"def freeTimeBtw2Users(self, userList1, userList2):\\n if userList1 == False or userList2 == False : \\n return False\\n else :\\n if userList1 and userList2:\\n freeTime = []\\n j = 0\\n i = 0\\n while i < len(userList1) and j<len(userList2) :\\n if userList1[i][1] < userList2[j][0]: \\n i += 1\\n elif userList1[i][0] > userList2[j][1]:\\n j += 1\\n else :\\n start = max(userList1[i][0], userList2[j][0])\\n end = min(userList1[i][1], userList2[j][1])\\n if end == userList1[i][1] :\\n i += 1\\n else:\\n j += 1\\n if end > start :\\n freeTime.append((start, end))\\n return freeTime\\n\\n else : \\n return userList1 if userList1 else userList2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22271af9ec6458161d068954cc671a3c\",\n \"score\": \"0.5880105\",\n \"text\": \"def list_users(limit: int = 10, offset: int = 0):\\n return USERS[offset:offset+limit]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe49d27521236b72ddc84ad60bb2a1b8\",\n \"score\": \"0.5877184\",\n \"text\": \"def list_users(self):\\n return self.user_manager.list_objects()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1502594b5b207fb692528c772bd6f4d\",\n \"score\": \"0.5874288\",\n \"text\": \"def test_list_users(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ee3f5f3023c8fc15e4b394ce5885e99\",\n \"score\": \"0.58718735\",\n \"text\": \"def list_users(args):\\n users_api = UsersApi(get_authenticated_client(args))\\n for user in users_api.list():\\n print user\\n\\n return ExitCodes.OK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c958084ef834d88a42ad466d41189aa\",\n \"score\": \"0.58648956\",\n \"text\": \"def get_users(self):\\n \\n return self.users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4855cf01fcaa031091fd36d62d5320\",\n \"score\": \"0.5861235\",\n \"text\": \"async def read_users(\\n db: Session = Depends(get_db),\\n skip: int = 0,\\n limit: int = 10,\\n current_user: models.User = Depends(get_current_active_user),\\n):\\n if current_user.is_superuser:\\n return crud.user.get_multi(db, skip=skip, limit=limit)\\n else:\\n return [current_user][skip:limit]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9087cd115f68dc012041b5a01248c98b\",\n \"score\": \"0.585103\",\n \"text\": \"def get_users(self):\\n sc = SlackClient(self.slack_token)\\n return [{'name': user['name'],\\n 'id': user['id'],\\n 'first_name': user['profile']['first_name'],\\n 'last_name': user['profile']['last_name']}\\n for user in sc.api_call(\\\"users.list\\\")['members']\\n if 'first_name' in user['profile'].keys() and 'last_name' in user['profile'].keys()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf31305e1f9893f1f97a19ee77114ab\",\n \"score\": \"0.5847081\",\n \"text\": \"def show_users(self):\\n sql = \\\"SELECT username FROM dba_users ORDER BY 1\\\"\\n self.cur.execute(sql)\\n res = self.cur.fetchall()\\n key = ['{#DBUSER}']\\n lst = []\\n for i in res:\\n d = dict(zip(key, i))\\n lst.append(d)\\n print ( json.dumps({'data': lst}) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b47e37a835655713623a72a4c006007\",\n \"score\": \"0.5845344\",\n \"text\": \"def get_all_usernames():\\n return list(map(lambda u: u.username, get_all_users()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b146824bdfbaa1047508928fddd9ed\",\n \"score\": \"0.58410865\",\n \"text\": \"def get(self):\\n return user.get_all_users()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bea1236195d1a8ece56183334f315370\",\n \"score\": \"0.5840651\",\n \"text\": \"def get_users(self):\\n\\t\\tresult = []\\n\\n\\t\\t# get appropriate path from the config\\n\\t\\tpath = self._config['server']['api_paths']['user_list']\\n\\n\\t\\t# send request and return response data\\n\\t\\tresponse = self.send_request(path)\\n\\t\\tif response:\\n\\t\\t\\t# filter user of the current bot execution\\n\\t\\t\\tresult = list(filter(lambda user: user['user']['username'] in self._credentials, response))\\n\\n\\t\\treturn result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf816d3e80ecc5102e04cf69b8a40876\",\n \"score\": \"0.58397985\",\n \"text\": \"def getUsers(self) -> int:\\n return self.QueryResponse_int(\\\"Users?\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bc71231b8938bce5b508f8a1d2ae3ad\",\n \"score\": \"0.58391\",\n \"text\": \"def user_list_view(request):\\n\\n users = User.objects.all().exclude(id=request.user.id).values(\\\"id\\\", \\\"username\\\")\\n\\n context = {\\n 'users': users,\\n }\\n return render(request, 'chats/user_list.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71cc19adaadc31c1ed1f0efb3f8ac1c9\",\n \"score\": \"0.58385795\",\n \"text\": \"def get_user(self):\\n\\n self.navigate_to(self.CONFIGURE, self.CONFIGURE_USERS)\\n\\n user_total = self._get_total_number(self.info['loc_cfg_user_total_number_span'], \\\"Users\\\")\\n user_list = []\\n row = 1\\n i = 0\\n max_row_user = int(self.info['const_cfg_max_row_user'])\\n if user_total == u'0':\\n logging.info(\\\"The Users table is empty\\\")\\n return []\\n\\n while i < int(user_total):\\n username = self.info['loc_cfg_user_name_cell']\\n username = username.replace(\\\"$_$\\\", str(i))\\n get_username = self.s.get_text(username)\\n user_list.append(get_username)\\n if row == max_row_user:\\n row = 0\\n self.s.click_and_wait(self.info['loc_cfg_user_next_image'], 2)\\n\\n row = row + 1\\n i = i + 1\\n\\n return user_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9a07b351a3832b7f3b75c97cdc585aa\",\n \"score\": \"0.5837897\",\n \"text\": \"def show_all_users():\\r\\n conn, c = crd.make_conn_c()\\r\\n user_list = c.execute('SELECT id, name, email, user_group FROM USERS').fetchall()\\r\\n conn.close()\\r\\n\\r\\n return user_list\\r\\n\\r\\n # Get the longest name and email\\r\\n # l_name = 4 # Title name is 4 letters\\r\\n # l_email = 5 # Title email is 5 letters\\r\\n # l_usergroup = 9\\r\\n\\r\\n\\r\\n\\r\\n # for row in user_list:\\r\\n # l_name = len(row[0]) if len(row[0]) > l_name else l_name\\r\\n # l_email = len(row[1]) if len(row[1]) > l_email else l_email\\r\\n # l_usergroup = len(row[2]) if len(row[2]) > l_usergroup else l_usergroup\\r\\n\\r\\n # print row[0] + \\\"*****\\\" + row[1] + \\\"*****\\\" + row[2]\\r\\n\\r\\n # result = \\\"\\\"\\r\\n # result += \\\"Name\\\"+\\\" \\\"*(l_name-4) + \\\" | Email\\\" + \\\" \\\"*(l_email-5) + \\\" | Usergroup\\\"\\r\\n # result += \\\"\\\\n\\\" + \\\"_\\\"*l_name + \\\"_|_\\\" + \\\"_\\\"*l_email + \\\"_|_\\\" + \\\"_\\\" * l_usergroup\\r\\n #\\r\\n # for row in user_list:\\r\\n # result += \\\"\\\\n\\\" + row[0]+ \\\" \\\"*(l_name-len(row[0])) + \\\" | \\\" + row[1] +\\\" \\\"*(l_email-len(row[1])) + \\\" | \\\" + row[2]\\r\\n #\\r\\n # return result\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1032afef3f190e26a6b9a0b76f253eed\",\n \"score\": \"0.58351386\",\n \"text\": \"def list_users():\\n users = User.query.all()\\n return render_template(\\\"index.html\\\", users=users)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994077,"cells":{"query_id":{"kind":"string","value":"640019edd0a391d862b878cb752234ba"},"query":{"kind":"string","value":"Insert new course data"},"positive_passages":{"kind":"list like","value":[{"docid":"9a04d325b5531af61b6049bc93c6890e","score":"0.7448042","text":"def insert_course(self, course):\n largest_id = max([int(i) for i in self.data.keys()])\n new_course_id = str(largest_id + 1)\n self.data[new_course_id] = course\n return self.data","title":""}],"string":"[\n {\n \"docid\": \"9a04d325b5531af61b6049bc93c6890e\",\n \"score\": \"0.7448042\",\n \"text\": \"def insert_course(self, course):\\n largest_id = max([int(i) for i in self.data.keys()])\\n new_course_id = str(largest_id + 1)\\n self.data[new_course_id] = course\\n return self.data\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"026398ace73e1ad5dbfa7bb033766ef4","score":"0.7772229","text":"def add_course(self, info):\n\n\t\twith self.connection.cursor() as cur:\n\t\t\tcur.execute(\"\"\"INSERT INTO courses VALUES (%s, %s, %s, %s)\"\"\", info)\n\n\t\tself.commit()","title":""},{"docid":"123aede24e9b2ea234412a25eabfd090","score":"0.68744904","text":"def add_course():\n conn = mysql.connect\n cur = conn.cursor()\n cur.execute(\"SELECT * FROM department\")\n names = cur.fetchall()\n error = None\n\n # Handle submit\n if request.method == 'POST':\n # Grab user info\n course_title = request.form[\"new_course_title\"]\n course_description = request.form[\"new_course_description\"]\n course_year = request.form[\"new_course_year\"]\n course_department = request.form[\"new_course_dep\"]\n try:\n # Insert new course into DB\n query = f\"INSERT INTO course (`crs_title`, `crs_description`, `crs_year`, `dep_code`)\" \\\n f\" VALUES ('{course_title}', '{course_description}', {course_year}, {course_department})\"\n print(query)\n cur.execute(query)\n conn.commit()\n error = \"Success!\"\n # Handle error\n except Exception as e:\n error = \"Problem creating course: \" + str(e)\n return render_template('addcourse.html', dep_names=names, error=error)","title":""},{"docid":"d486f83852fdc48efb15466446e25a52","score":"0.67363805","text":"def saveCourse_btn_clicked(self):\n mat = self.var_txt_mat.get()\n course_name = self.var_txt_name.get()\n\n try:\n with connection.cursor() as cursor:\n # Create a new record\n sql = \"INSERT INTO Matiere (code_mat, nom_mat) VALUES \\\n (%s, %s)\"\n try:\n cursor.execute(sql, (mat, course_name,))\n except pymysql.err.IntegrityError as e:\n print(\"cannot duplicate Courses : \" + str(e))\n # connection is not autocommit by default.So lets commit to save changes\n connection.commit()\n finally:\n pass\n # connection.close()","title":""},{"docid":"6636b8af4b35d54142786faf9cff6a83","score":"0.6706542","text":"def add_course(name, number, discipline):\n exists = (\n dal.DBSession.query(Course)\n .filter(\n Course.name == name,\n Course.number == number,\n Course.discipline == discipline,\n )\n .first()\n ) is not None\n\n if not exists:\n course = Course(name=name, number=number, discipline=discipline)\n dal.DBSession.add(course)\n dal.DBSession.commit()","title":""},{"docid":"138bea8d6c4c00b476c32d4da24fb85e","score":"0.6697207","text":"def create_course():\n check_course_creation_body(request)\n course = {'name': request.json['name'],\n 'department': request.json['department'],\n 'teacher': request.json['teacher'],\n 'year': request.json['year'],\n 'semester': request.json['semester'],\n 'description': request.json['description'],\n 'schedule': request.json['schedule']}\n\n try:\n added_course = add_course(client, course)\n if added_course is None:\n abort(409) # Conflict\n return jsonify(added_course), 201 # Created\n except CoursesRepositoryException:\n abort(500) # Internal Server Error","title":""},{"docid":"49684582abc49cc4f929bc57544b9391","score":"0.66519773","text":"def add_course(self, data):\n\n course = {'name': data[self.code],\n 'manualTutorialEnrolment': False\n }\n return course","title":""},{"docid":"f99baa8cc3d1eaf47fd9a177a9d224d9","score":"0.6632682","text":"def test_create_new_course(self):\r\n # This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.\r\n pass","title":""},{"docid":"6b380da63dba9e4366a369c112d5bd86","score":"0.65756774","text":"def add_course():\n\n print \"/new-course called\"\n user_id = session[\"user_id\"]\n print user_id\n course = request.form.get(\"course\")\n\n polyline = request.form.get(\"overview-polyline\")\n # print \"polyline is \", polyline\n\n directions_text = request.form.get(\"directions-text\")\n directions_distance = request.form.get(\"directions-distance\")\n\n start_address = request.form.get(\"start-address\")\n end_address = request.form.get(\"end-address\")\n\n new_course = Course(user_id=user_id, course_name=course,\n add_date=datetime.now(),\n start_lat=request.form.get(\"start-lat\"),\n start_long=request.form.get(\"start-long\"),\n end_lat=request.form.get(\"end-lat\"),\n end_long=request.form.get(\"end-long\"),\n course_distance=request.form.get(\"distance\"),\n favorite=request.form.get(\"favorite\"),\n polyline=polyline,\n directions_text=directions_text,\n directions_distance=directions_distance,\n start_address=start_address,\n end_address=end_address\n )\n new_course.add()\n\n return \"Course %s has been saved to your courses\" % course","title":""},{"docid":"d01eb4995874dd506a6b2e644211a0a4","score":"0.652634","text":"def add_department(self, info):\n\n\t\twith self.connection.cursor() as cur:\n\t\t\tcur.execute(\"\"\"INSERT INTO courses VALUES (%s, %s, %s)\"\"\", info)\n\n\t\tself.commit()","title":""},{"docid":"928ac07b53d208db36c9620be1536338","score":"0.64894485","text":"def courses(request):\n user = request.user\n if request.method == 'POST':\n form = OnlineCourseForm(request.POST)\n if form.is_valid():\n # save our data\n data = form.save(commit=False)\n data.created_by = user\n data.updated_by = user\n data.save()\n # insert data into informix\n sql = INSERT_CTC_REC(cid=user.id)\n do_sql(sql, key=DEBUG, earl=EARL)\n return HttpResponseRedirect(reverse_lazy('survey_success'))\n else:\n form = OnlineCourseForm()\n\n return render(request, 'survey/form.html', {'form': form})","title":""},{"docid":"f56de4a944d58a0d1db643e485bfcd84","score":"0.64234525","text":"def add_course(self, title, topic, course_acronym, course_number, section_number, language=\"Python\"): #UPDATE\r\n self._courses.append(Section(title,topic,course_acronym, course_number,section_number,language))","title":""},{"docid":"8d2c55f764822063ca7e9de23aa7757f","score":"0.6401968","text":"def test_add_course(self):\n\n file_path = os.path.join(os.path.dirname(__file__), 'testCourse.json')\n\n with open(file_path) as f:\n json_dict = json.loads(f.read())\n coursera_add_courses(json_dict)\n\n # there is one course in the test file\n all_courses = Course.objects.all()\n self.assertEquals(len(all_courses), 1)\n\n # the course has the right attributes\n the_course = all_courses[0]\n self.assertEquals(the_course.name, 'Machine Learning')\n self.assertEquals(the_course.description, 'Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.')\n self.assertEquals(the_course.instructor, 'Andrew Ng, Associate Professor')\n\n # the course has the right university\n self.assertEquals(the_course.source.name, 'Stanford University')\n\n # the course has the right subjects\n the_course_subjects = the_course.subjects.all()\n self.assertEquals(len(the_course_subjects), 2)\n self.assertEquals(the_course_subjects[0].name, 'stats')\n self.assertEquals(the_course_subjects[1].name, 'cs')","title":""},{"docid":"cb108c2b1b481c2f43a2965915a113dc","score":"0.6361019","text":"def add_course(self, courseID):\n self.current_courses.append(courseID)\n pass","title":""},{"docid":"4f8aa880820d66e575b12d893c5d9920","score":"0.63592136","text":"def create_course(request):\n\n # Like before, get the request's context.\n context = RequestContext(request)\n course_added = False\n\n user = request.user\n profile = get_profile(user)\n\n if 'Instructor' in profile[1]:\n school = UserProfile.objects.get(user=user).school\n if not school:\n return render_permission_denied(context,\n 'create courses. Enrol in a school first.')\n else:\n #return HttpResponse(\"You don't have permission to create courses!\")\n return render_permission_denied(context, 'create courses')\n # If it's a HTTP POST, we're interested in processing form data.\n if request.method == 'POST':\n\n # Attempt to grab information from the raw form information.\n course_form = CourseForm(data=request.POST)\n if course_form.is_valid():\n # Save the event's form data to the database.\n course = course_form.save(commit=False)\n course.school = school\n course.creator = user\n\n # Add the personal calendar for the user\n calendar = Calendar( name = course.code + \" Calendar\")\n calendar.save()\n course.cal = calendar\n\n course.save()\n\n course_added = True\n # Invalid form or forms - mistakes or something else?\n # Print problems to the terminal.\n # They'll also be shown to the user.\n else:\n print course_form.errors\n\n # Not a HTTP POST, so we render our form using the EventForm.\n # These forms will be blank, ready for user input.\n else:\n course_form = CourseForm()\n\n # Render the template depending on the context.\n return render_to_response(\n 'school/create_course.html', {'course_form': course_form, 'user' : user,\n 'course_added': course_added, 'school': school},\n context)","title":""},{"docid":"decfa14d717a3ae7dfdde8acb8f8324a","score":"0.63294107","text":"def CourseDetails(self, mydb, mycursor):\n try:\n sql = '''create table if not exists course_details(COURSE_ID VARCHAR(9) NOT NULL PRIMARY KEY,\n COURSE_NAME VARCHAR(20),\n COURSE_START_DATE DATETIME,\n COURSE_END_DATE DATETIME)'''\n #mycursor.execute(\"drop table course_details\")\n mycursor.execute(sql)\n mydb.commit()\n l = logfile.logger()\n l.info(\"DIMENSION : Course_Details created!\")\n except Exception as e:\n print(\"Error:\", e)","title":""},{"docid":"aa8f6b4e62e6504d935e2299226b810c","score":"0.63152754","text":"def InsertCourseDetails(self,mydb,mycursor):\n try: \n filename = 'C:\\\\Users\\\\mohan\\\\Desktop\\\\csv\\\\dimension\\\\course_dim.csv'\n with open(filename,'r') as fr:\n for line in fr.readlines():\n col_value = line.replace('\\n','').split(',')\n insert_query = (\"insert into course_details values('{}','{}','{}','{}')\"\n .format(col_value[0],col_value[1],datetime.now(),col_value[2]))\n \n mycursor.execute(insert_query)\n mydb.commit()\n l = logfile.logger()\n l.info(\"Values inserted into the course_details table successfully!\")\n except Exception as e:\n l = logfile.logger()\n l.info(e)\n print(\"Error:\",e)","title":""},{"docid":"943409489b95e4776b95ce92b16decd8","score":"0.62980634","text":"def add_course(self, course, grades=[]):\n self.courses[course] = grades","title":""},{"docid":"35a2c60307d6ec25fff0d445e5a778df","score":"0.6285272","text":"def addCourse(self, course: Course):\n\n self.courseList.append(course)","title":""},{"docid":"5ba108cbe77195a44d87ca98b2c22133","score":"0.6275504","text":"def add_course(self, course, comfort=None):\n for cuser in self.courseuser_set.all():\n if cuser.course == course:\n return\n if type(comfort) == type(None):\n CourseUser.objects.create(vsb_user=self, course=course, dirty_comfort=True, course_comfort='')\n else:\n CourseUser.objects.create(vsb_user=self, course=course, dirty_comfort=False, course_comfort=comfort)","title":""},{"docid":"ef69bebe085e537b439668b2612e67c3","score":"0.6264653","text":"def _save(data):\n courses = shelve.open('../src/coursesPages/all_courses.db')\n courses['all_courses'] = data\n courses.close()","title":""},{"docid":"9adf1395b17fe6ee31636eddce359336","score":"0.6244702","text":"def assign_course(self, course_id):\n yield self.modify(\n {\n '$addToSet': {'courses': course_id}\n }\n )","title":""},{"docid":"4f1738a12cedd7cfb6fd8bfb4bb27d1b","score":"0.62353545","text":"def add_course(self, course, grade):\r\n self.courses[course] = grade","title":""},{"docid":"1ff1c3fb5cd9da8be62de31186fc36e0","score":"0.61824876","text":"def courses_add(self, course: str, grade: str) -> None:\r\n\r\n self._courses[course] = grade","title":""},{"docid":"73de286031d4334201d262a9fc6bad9b","score":"0.61489636","text":"def test_add_course(self):\n client = Client()\n client.login(username='testadmin', password='4epape?Huf+V')\n\n #checks that the course is created using the view\n client.post('/settings/courses/add_course', {'course_id': 'TDT4145', 'name': 'databaser', 'nickname': 'data',\n 'term': 'spring', 'description': 'test'})\n course = Course.objects.get(course_id=\"TDT4145\")\n self.assertEqual(course.name,'databaser')\n self.assertEqual(course.nickname, 'data')\n self.assertEqual(course.term, 'spring')\n self.assertEqual(course.description, 'test')\n\n #Checks that you cant overwrite a course that exsists, or make duplicates\n client.post('/settings/courses/add_course', {'course_id': 'TDT4140', 'name': 'programvare utvikling',\n 'nickname': 'pu1', 'term': 'spring 2017', 'description': 'test'})\n course = Course.objects.get(course_id=\"TDT4140\")\n self.assertNotEqual(course.name,'programvare utvikling')\n self.assertNotEqual(course.nickname, 'pu1')\n self.assertNotEqual(course.term, 'spring 2017')\n self.assertNotEqual(course.description, 'test')","title":""},{"docid":"8ac58cfee422c67a9cd7e993d83f3d2a","score":"0.61475503","text":"def create_course(student, mnemonic, number):\n return Course.objects.create(student=student,mnemonic=mnemonic, number=number)","title":""},{"docid":"8ac58cfee422c67a9cd7e993d83f3d2a","score":"0.61475503","text":"def create_course(student, mnemonic, number):\n return Course.objects.create(student=student,mnemonic=mnemonic, number=number)","title":""},{"docid":"147a89bd401140fb086f1d631fbf3085","score":"0.6083887","text":"def add_course(self, course):\n if course.fetch_course_number() in self.courses:\n return False\n else:\n self.courses.update({course.fetch_course_number(): course})\n return True","title":""},{"docid":"1db591493ee91473904e57c7ccba3f81","score":"0.6080842","text":"def handle_course_insert_input(input_value):\n handle_sub_input(input_value, insert_course, 5)\n return input_value","title":""},{"docid":"8832135f03f996c7b72f20357ce51059","score":"0.60423523","text":"def add_course(self, course_id, term_id, program_year_id=None):\n user_course = _user_course.UserCourse.objects(\n user_id=self.id, course_id=course_id).first()\n\n if user_course is None:\n if _course.Course.objects.with_id(course_id) is None:\n # Non-existant course according to our data\n rmclogger.log_event(\n rmclogger.LOG_CATEGORY_DATA_MODEL,\n rmclogger.LOG_EVENT_UNKNOWN_COURSE_ID,\n course_id\n )\n return None\n\n user_course = _user_course.UserCourse(\n user_id=self.id,\n course_id=course_id,\n term_id=term_id,\n program_year_id=program_year_id,\n )\n else:\n # Record only the latest attempt for duplicate/failed courses\n if (term_id > user_course.term_id or\n user_course.term_id == _term.Term.SHORTLIST_TERM_ID):\n user_course.term_id = term_id\n user_course.program_year_id = program_year_id\n\n user_course.save()\n\n if user_course.id not in self.course_history:\n self.course_history.append(user_course.id)\n self.save()\n\n return user_course","title":""},{"docid":"6e894c892ebdb4bab6f986ec7afdcb13","score":"0.60161567","text":"def test_add_to_django(self):\n provider, created = Provider.objects.get_or_create(name='iversity')\n sample_div = \"<article class='courses-list-item'><div class='ribbon-content'>Engineering</div></div><div class='course-body'><header><h2 class='truncate'><a href='https://iversity.org/courses/vehicle-dynamics-i-accelerating-and-braking'>Vehicle Dynamics I: Accelerating and Braking</a></h2><p class='instructors truncate'>Univ.-Prof. Dr.-Ing. Martin Meywerk</p></header><p class='description'>From Bugatti Veyron to Volkswagen Beetle, from racing to passenger car: study about their acceleration and braking and learn from two applications from automotive mechatronics. </p></div></div></div></div></div></div></div></article>\"\n sample_div = BeautifulSoup(sample_div)\n iversity.create_course(sample_div, provider)\n\n # Make sure the Engineering subject was created:\n new_subject = Subject.objects.get(name='engineering')\n self.assertIsNotNone(new_subject)\n\n # Make sure the course itself was created:\n new_course = Course.objects.get(name='Vehicle Dynamics I: Accelerating and Braking', provider=provider,\n subjects=new_subject)\n self.assertIsNotNone(new_course)\n\n # Make sure the course name is set properly:\n self.assertEqual('Vehicle Dynamics I: Accelerating and Braking', new_course.name)\n\n # Make sure the course URL is set properly:\n self.assertEqual('https://iversity.org/courses/vehicle-dynamics-i-accelerating-and-braking', new_course.url)\n\n # Make sure the course instructor is set properly:\n self.assertEqual('Univ.-Prof. Dr.-Ing. Martin Meywerk', new_course.instructor)\n\n # Make sure the course description is set properly:\n self.assertEqual('From Bugatti Veyron to Volkswagen Beetle, from racing to passenger car: study about their acceleration and braking and learn from two applications from automotive mechatronics.', new_course.description)","title":""},{"docid":"4126a3c5783eba72f2aad12fb4354274","score":"0.6015903","text":"def admin_course():\n conn = mysql.connect\n cur = conn.cursor()\n error = None\n if request.method == 'POST':\n # User adds a new course\n if request.args.get(\"add\"):\n cur.execute(\"SELECT * FROM department\")\n course_title = request.form[\"new_course_title\"]\n course_description = request.form[\"new_course_description\"]\n course_year = request.form[\"new_course_year\"]\n course_department = request.form[\"new_course_dep\"]\n try:\n # Insert new record into table course\n query = f\"INSERT INTO course (`crs_title`, `crs_description`, `crs_year`, `dep_code`)\" \\\n f\" VALUES ('{course_title}', '{course_description}', {course_year}, {course_department})\"\n print(query)\n cur.execute(query)\n conn.commit()\n error = \"Success!\"\n except Exception as e:\n # Handle exception\n error = \"Problem creating course: \" + str(e)\n # User deletes the course\n elif request.args.get(\"delete\"):\n data = None\n jsonData = request.get_json()\n course_id = jsonData[\"id\"]\n try:\n # Delete id along with its reference in other tables\n delete_course_query(conn, cur, \"antirequisite\", \"crs_code\", course_id)\n delete_course_query(conn, cur, \"prerequisite\", \"crs_code\", course_id)\n delete_course_query(conn, cur, \"program_requirements\", \"program_crs\", course_id)\n delete_course_query(conn, cur, \"course\", \"crs_code\", course_id)\n data = {\"success\": \"true\"}\n except Exception as e:\n # Handle exception\n traceback.print_exc()\n data = {\"error\": \"Please try again.\"}\n finally:\n # Return json data\n return json.dumps(data)\n\n # User updates the course\n elif request.args.get(\"update\"):\n data = None\n jsonData = request.get_json()\n course_id = jsonData[\"id\"]\n title = jsonData[\"title\"]\n description = jsonData[\"description\"]\n year = jsonData[\"year\"]\n dep_code = jsonData[\"dep_code\"]\n pre_reqs = jsonData[\"pre_reqs\"] # JSON array containing ids of prerequisite\n anti_reqs = jsonData[\"anti_reqs\"] # JSON array containing ids of antirequisite\n\n try:\n # Update the record\n query = f\"UPDATE course \" \\\n f\"SET crs_title= '{title}', crs_description='{description}', crs_year={year}, dep_code={dep_code} \" \\\n f\"WHERE crs_code={course_id}\"\n cur.execute(query)\n conn.commit()\n update_req(conn, cur, \"prerequisite\", course_id, pre_reqs)\n update_req(conn, cur, \"antirequisite\", course_id, anti_reqs)\n data = {\"status\": \"success\"}\n\n except Exception as e:\n # Handle exception\n traceback.print_exc()\n data = {\"error\": 404}\n finally:\n # Return json\n return json.dumps(data)\n\n cur.execute(\"SELECT * FROM department\")\n deps = cur.fetchall()\n\n cur.execute(\"SELECT * FROM course\")\n courses = cur.fetchall()\n data = []\n # Retrieve course's info and add it to data array\n for row in courses:\n course = {}\n course[\"id\"] = row[0]\n course[\"code\"] = str(row[3]) + \"%02d\" % row[0]\n course[\"title\"] = row[1]\n course[\"description\"] = row[2]\n course[\"year\"] = row[3]\n course[\"dep_code\"] = row[4]\n\n query = f'SELECT dep_name FROM department WHERE dep_code={row[4]}'\n cur.execute(query)\n temp = cur.fetchone()[0]\n course[\"dep_name\"] = temp\n\n query = f'SELECT course.*, department.dep_name FROM course, prerequisite, department ' \\\n f'WHERE prerequisite.crs_code = {row[0]}' \\\n f' AND prerequisite.crs_requires = course.crs_code AND course.dep_code = department.dep_code'\n cur.execute(query)\n temp = cur.fetchall()\n pres = get_codes(temp)\n course[\"pre_reqs\"] = pres\n\n query = f'SELECT course.*, department.dep_name FROM course, antirequisite, department ' \\\n f'WHERE antirequisite.crs_code = {row[0]}' \\\n f' AND antirequisite.crs_anti = course.crs_code AND course.dep_code = department.dep_code'\n cur.execute(query)\n temp = cur.fetchall()\n antis = get_codes(temp)\n course[\"anti_reqs\"] = antis\n data.append(course)\n\n return render_template('admin-course.html', data=data, deps=deps, error=error)","title":""},{"docid":"0aa3a712ac00197901fb0e7af5aeebcc","score":"0.59774035","text":"def test_create_course(self):\n parameters = ['config', 'database', 'semester', 'course']\n environment = 'course'\n module_text = 'Migration for a given Submitty course database.'\n parameter_text = \"\"\":param config: Object holding configuration details about Submitty\n :type config: migrator.config.Config\n :param database: Object for interacting with given database for environment\n :type database: migrator.db.Database\n :param semester: Semester of the course being migrated\n :type semester: str\n :param course: Code of course being migrated\n :type course: str\"\"\"\n self.create_test_runner(module_text, parameters, parameter_text, environment)","title":""},{"docid":"2b7f844781d60708a5ec461926c57293","score":"0.5972808","text":"def create_or_update_course(request):\n\n global_stuff = User.objects.filter(is_staff=True).first()\n if global_stuff is not None:\n request.user = global_stuff\n else:\n raise PermissionDenied()\n\n course_key = modulestore().make_course_key(request.json[\"org\"], request.json[\"number\"], request.json[\"run\"])\n with modulestore().bulk_operations(course_key):\n course_key = modulestore().has_course(course_key)\n if course_key is None:\n response = _create_or_rerun_course(request)\n if response.status_code >= 400:\n return response\n course_key_string = json.loads(response.content).get(\"course_key\")\n if course_key_string is not None:\n course_key = CourseKey.from_string(course_key_string)\n else:\n return response\n course_data = request.json.copy()\n if course_data[\"start_date\"] is None:\n course_data[\"start_date\"] = format(DEFAULT_START_DATE, \"%Y-%m-%d\")\n course_data[\"end_date\"] = format(DEFAULT_START_DATE, \"%Y-%m-%d\")\n course_data[\"enrollment_end\"] = format(DEFAULT_START_DATE, \"%Y-%m-%d\")\n CourseDetails.update_from_json(course_key, course_data, global_stuff)\n set_course_cohort_settings(course_key, is_cohorted=True)\n modes = request.json.get(\"course_modes\", [])\n CourseMode.objects.filter(course_id=course_key).exclude(mode_slug__in=[mode[\"mode\"] for mode in modes]).delete()\n for mode in modes:\n mode_params = {\n \"course_id\": course_key,\n \"mode_slug\": mode[\"mode\"]\n }\n if \"price\" in mode:\n mode_params[\"min_price\"] = mode[\"price\"]\n if \"currency\" in mode:\n mode_params[\"currency\"] = mode[\"currency\"]\n if \"title\" in mode:\n mode_params[\"mode_display_name\"] = mode[\"title\"]\n if \"description\" in mode:\n mode_params[\"description\"] = mode[\"description\"]\n if \"upgrade_deadline\" in mode:\n mode_params[\"_expiration_datetime\"] = mode[\"upgrade_deadline\"]\n CourseMode.objects.update_or_create(course_id=course_key, mode_slug=mode[\"mode\"], defaults=mode_params)\n return JsonResponse({\n 'url': reverse_course_url('course_handler', course_key),\n 'course_key': unicode(course_key)\n })","title":""},{"docid":"c7c3835a402bdb22bf899c9e913b1cd5","score":"0.5949433","text":"def add_course(self, course: str, grade: str) -> None:\n self._courses[course] = grade","title":""},{"docid":"c7c3835a402bdb22bf899c9e913b1cd5","score":"0.5949433","text":"def add_course(self, course: str, grade: str) -> None:\n self._courses[course] = grade","title":""},{"docid":"c7c3835a402bdb22bf899c9e913b1cd5","score":"0.5949433","text":"def add_course(self, course: str, grade: str) -> None:\n self._courses[course] = grade","title":""},{"docid":"e26f3af68078dce9891952fb5e6b6be8","score":"0.5937892","text":"def post(self, request, institute_short_name):\n institute = request.user.institute\n form = AddCourseForm(request.POST, institute=institute)\n\n if form.is_valid():\n course = form.cleaned_data['course']\n\n try:\n \"\"\"\n the both field department and course must unique_together.\n if department and course not added before with same values,\n Then execute try block otherwise execute catch block.\n \"\"\"\n object = form.save(commit=False)\n object.institute = institute\n object.save()\n messages.success(request, f'{course} course is successfully added')\n return redirect('institute:administration', institute_short_name=institute_short_name)\n except:\n messages.error(request, f'{course} course with this Institute is already exists.')\n\n return redirect('institute:administration', institute_short_name=institute_short_name)\n\n context = {\n 'institute_short_name': institute_short_name,\n 'form': form,\n }\n\n return render(request, 'institute/administration.html', context)","title":""},{"docid":"55a4bae7d39134776901c060874a2779","score":"0.5934816","text":"def insert_lesson(session, group, subject, teacher, day, start, end, room):\n session.add(Lesson(group, subject, teacher, day, start, end, room))","title":""},{"docid":"5b97bb577662bf638706c4e669bf32b8","score":"0.59191036","text":"def test_copy_course_content(self):\r\n # This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.\r\n pass","title":""},{"docid":"a5af280f94c6cf086c20f0d68622fdb6","score":"0.5916784","text":"def test_corresponding_course_exists(self):\n with self.assertRaises(ValidationError):\n AcademicPlan.objects.create(gradYear=self.year,\n planCode=\"Foobar\",\n courseCode=\"Foobar\",\n mcName=\"Foobar\",\n course_1=\"Foobar\", weight_1=0.5,\n weight_2=0.5)","title":""},{"docid":"c10cb215667de11f3f135f5c206a5005","score":"0.5904627","text":"def save(cls, app_context, course):\n persistent = PersistentCourse13(\n next_id=course.next_id,\n units=course.units, lessons=course.lessons)\n\n fs = app_context.fs.impl\n filename = fs.physical_to_logical(cls.COURSES_FILENAME)\n app_context.fs.put(filename, vfs.FileStreamWrapped(\n None, persistent.serialize()))","title":""},{"docid":"19ebc87c42ea9e86153723a4d55ba292","score":"0.5888098","text":"def copy_to(self, new_course):\n tasks_to_copy = self.course_tasks.all().prefetch_related(\"graded_work\")\n new_tasks = [\n CourseTask(\n course=new_course, description=task.description, duration=task.duration\n )\n for task in tasks_to_copy\n ]\n CourseTask.objects.bulk_create(new_tasks)\n\n # To avoid creating tasks individually, a second pass is needed\n # to add any graded work.\n tasks_to_compare = zip(\n tasks_to_copy, CourseTask.objects.filter(course=new_course)\n )\n graded_work = [\n GradedWork(course_task=copied_task)\n for task_to_copy, copied_task in tasks_to_compare\n if hasattr(task_to_copy, \"graded_work\")\n ]\n GradedWork.objects.bulk_create(graded_work)\n\n resources_to_copy = [\n CourseResource(\n course=new_course, title=resource.title, details=resource.details\n )\n for resource in self.resources.all()\n ]\n CourseResource.objects.bulk_create(resources_to_copy)","title":""},{"docid":"b495111d451119975077934ae8724649","score":"0.5882292","text":"def insert(self):\n self.getDbRecord().insert()\n\n return","title":""},{"docid":"3d3a4c6fd0ab43fadad19fa2d68ca191","score":"0.58681166","text":"def post(self):\n try:\n json_data = request.get_json(force=True)\n if not json_data:\n return {'message': 'No input data provided'}, 400\n\n if 'courses' not in json_data:\n return {'message': 'Invalid input data provided. courses key missing'}, 422\n\n if 'id' not in json_data:\n return {'message': 'Invalid input data provided'}, 422\n\n if json_data[\"courses\"]:\n for course_id in json_data[\"courses\"]:\n if course_id in courses:\n courses[course_id][\"students\"].append(json_data['id'])\n else:\n return {'message': 'Invalid input of course names provided'}, 422\n\n students[json_data['id']] = json_data\n return {'message': 'Student added successfully'}, 200\n\n except Exception as e:\n return {'message': 'Internal Server error', 'error': e}, 500","title":""},{"docid":"73ed42a236d556b1181419bad637b66b","score":"0.58580965","text":"def create_course(self, org, course, run, user_id, fields=None, **kwargs): # lint-amnesty, pylint: disable=arguments-differ\n course_id = CourseKey.from_string('/'.join([org, course, run]))\n\n # Check if a course with this org/course has been defined before (case-insensitive)\n course_search_location = SON([\n ('_id.tag', 'i4x'),\n ('_id.org', re.compile(f'^{course_id.org}$', re.IGNORECASE)),\n ('_id.course', re.compile(f'^{course_id.course}$', re.IGNORECASE)),\n ('_id.category', 'course'),\n ])\n courses = self.collection.find(course_search_location, projection={'_id': True})\n try:\n course = courses.next() # lint-amnesty, pylint: disable=next-method-called\n raise DuplicateCourseError(course_id, course['_id'])\n except StopIteration:\n pass\n\n with self.bulk_operations(course_id):\n xblock = self.create_item(user_id, course_id, 'course', course_id.run, fields=fields, **kwargs)\n\n # create any other necessary things as a side effect\n super().create_course(\n org, course, run, user_id, runtime=xblock.runtime, **kwargs\n )\n\n return xblock","title":""},{"docid":"0808beb259e91ec8233eeb5debddd24f","score":"0.58566546","text":"def insert_data(self):\n Assignment4.cursor.execute('INSERT INTO food (ref_date, geo, dgu_id, food_categories, commodity,uom, uom_id,scalar_factor,scalar_id,vector,coordinate,value,status,symbol,terminated, decimals) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)',\n (self.entryRef_date.get(),self.entryGeo.get(),self.entryDguid.get(),self.entryCategory.get(),self.entryCommodity.get(),self.entryUom.get(),self.entryUom_id.get(),self.entryScalar.get(),\n self.entryScalar_id.get(),self.entryVector.get(),self.entryCoordinate.get(),self.entryValue.get(),self.entryStatus.get(),self.entrySymbol.get(),self.entryTerminated.get(),self.entryDecimal.get()))\n Assignment4.db.commit()\n print(\"New record inserted!!\")\n self.clear_fields()\n Assignment4.read_database(self)","title":""},{"docid":"88da82953f8b57caa55257bd0cac81d4","score":"0.58556044","text":"def get_course(cls, department: Department, course_number: int, course_title: str, credit_hours: int):\n\n course, created = Course.objects.update_or_create(department=department,\n course_number=course_number,\n defaults={'course_title': course_title,\n 'credit_hours': credit_hours})\n\n if created:\n cls.logger.debug(f\"Course Created: {course.__str__()}\")\n\n return course","title":""},{"docid":"b87628d8012ad90b93155110964bc904","score":"0.58519685","text":"def add_course_and_run():\n\n print \"/new-run in server.py called\"\n start_lat = request.form.get(\"start-lat\")\n start_long = request.form.get(\"start-long\")\n end_lat = request.form.get(\"end-lat\")\n end_long = request.form.get(\"end-long\")\n\n add_date = datetime.now()\n course = request.form.get(\"course\")\n distance = request.form.get(\"distance\")\n favorite = request.form.get(\"favorite\")\n date = request.form.get(\"date\")\n user_id = session[\"user_id\"]\n polyline = request.form.get(\"overview-polyline\")\n\n directions_text = request.form.get(\"directions-text\")\n directions_distance = request.form.get(\"directions-distance\")\n\n start_address = request.form.get(\"start-address\")\n end_address = request.form.get(\"end-address\")\n\n # print \"start_lat is %s, start_long is %s, end_lat is %s, end_long is %s, name is %s, distance is %s, favorite is %s\" % (start_lat, start_long, end_lat, end_long, course, distance, favorite)\n\n new_course = Course(user_id=user_id, course_name=course, add_date=add_date,\n start_lat=start_lat, start_long=start_long,\n end_lat=end_lat, end_long=end_long,\n course_distance=distance, favorite=favorite,\n polyline=polyline,\n directions_text=directions_text,\n directions_distance=directions_distance,\n start_address=start_address,\n end_address=end_address\n )\n\n new_course.add()\n # print \"course committed\"\n\n course_id = Course.get_by_course_name(course).course_id\n d = datetime.strptime(date, \"%m/%d/%Y\")\n duration = request.form.get(\"duration\")\n duration = int(duration)\n\n new_run = Run(user_id=user_id, course_id=course_id, run_date=d, duration=duration)\n new_run.add()\n\n return \"Your run was saved\"","title":""},{"docid":"5faa041c1030a536bcac2d7dad2b6463","score":"0.58512056","text":"def course_update_or_create(course):\n defaults = {'name': course['course_name'], 'version_time': timezone.now}\n kwargs = {'courseId': course['courseId'], 'platform': course['platform'], 'defaults': defaults}\n Course.objects.update_or_create(**kwargs)","title":""},{"docid":"c067bd637218deab0bfe0719ba21739f","score":"0.5848478","text":"def register(self, new_course):\n\n # TODO: check timeslot clashes\n\n if new_course.course_code in self.registered_courses:\n # already registered\n print(f\"{new_course.course_name} is already registered\")\n return\n # try to register in course\n if new_course.register_student(self):\n self.registered_courses[new_course.course_code] = new_course\n print(f\"{new_course.course_name} registered for {self}\")\n else:\n print(\"Course capacity full\")","title":""},{"docid":"57a634624195500e03e5cf9b22773cb3","score":"0.5842758","text":"def store_course_student(self, course: str)-> None:\r\n self.courses[course] +=1","title":""},{"docid":"30174b2f11abd041fecaaa2412227c5f","score":"0.5805051","text":"def add_student(self, course_index, name, id):\r\n if not self._courses:\r\n pass\r\n self._courses[course_index].add_student(name, id)","title":""},{"docid":"53e17907975598270154533b2ee6a5c9","score":"0.5786657","text":"def insert(self, title, author, year, isbn):\n self.cur.execute(\"INSERT INTO book VALUES (NULL, ?,?,?,?)\", (title, author, year, isbn))\n self.conn.commit()","title":""},{"docid":"33fc4596a642f302d01924bcf827ea31","score":"0.5769123","text":"def insert(self):\n self.__getDbRecord().insert()\n\n return","title":""},{"docid":"f16a2421ea3c8b4722f3523fabaf27ed","score":"0.576857","text":"def insert_categories(self):\n cat_query = self.get_categories()\n if not cat_query:\n query = \"INSERT INTO Categories (id, name) VALUES (NULL, %s)\"\n self.mycursor.executemany(query, CATEGORIES)\n self.connector.commit()","title":""},{"docid":"37d9e13f0864e4dc711869985f5a3799","score":"0.5763789","text":"def add_student(self, course: str) -> None:\n self._courses[course] += 1","title":""},{"docid":"37d9e13f0864e4dc711869985f5a3799","score":"0.5763789","text":"def add_student(self, course: str) -> None:\n self._courses[course] += 1","title":""},{"docid":"37d9e13f0864e4dc711869985f5a3799","score":"0.5763789","text":"def add_student(self, course: str) -> None:\n self._courses[course] += 1","title":""},{"docid":"ba35b7e6819ab91e36d0b110594a16fd","score":"0.5749186","text":"def insert_data(self, data):\n self.db[self.table_name].insert(data)","title":""},{"docid":"d603c4c601eee832fae70d8a1ab21b84","score":"0.5748691","text":"def add_student(self, course:str) -> None:\r\n \r\n self._students[course] +=1","title":""},{"docid":"d72070bb066aa829c2a452e1bde657d9","score":"0.57170534","text":"def add_inst(self, course):\n self.course[course] += 1","title":""},{"docid":"05310d1b6ca6293c890d69fa5e22ac38","score":"0.5712089","text":"def insert_data_into_db(self):\n data = self.text_fields_into_dict() ## Current item data dict\n parent = self.item_data if not self.new_cls else 'zero' ## set parent data if not new cls\n if self.ids.cls_nome.text.strip() and self.ids.cls_codigo.text.strip() != '':\n ## if required fields are not empty\n try:\n ## Instantiate data manager object \\/\n to_insert = ManageData(item_data=data,parent_data=parent)\n if not self.editing:\n to_insert.insert_into_db() ## inserts into database\n else:\n to_insert.update_db() ## updates database\n except:\n toast('Algo deu errado! Impossível salvar a Classe.')\n else:\n toast('Classe salva com sucesso!',1)\n self.app.root.ids.data_frame.clear_widgets() ## clear data management frame \n self.app.pcd_tree.regen_tree() ## Regenerates pcd treeview\n self.app.pcd_tree.switch_button(reset=True) ## switch toggle nodes button\n self.app.pcd_tree.disabled = False ## Unlocks treeview\n else:\n ## if required fields are empty\n toast('Campos obrigatórios estão em branco!')\n self.ids.cls_codigo.focus = True if self.ids.cls_codigo.text.strip() == '' else False\n self.ids.cls_nome.focus = True if self.ids.cls_nome.text.strip() == '' else False","title":""},{"docid":"95eb9b2e5acb3514763e3a7c4f4769ab","score":"0.5695588","text":"def new_credit (col: pymongo.collection, credit_data: dict):\n try:\n col.insert_one(credit_data)\n except:\n print('Credit could not be saved')","title":""},{"docid":"33bebbe91f643339f68045b8b0255fb3","score":"0.56650835","text":"def test_duplicate_course(self):\n plan = AcademicPlan.objects.create(gradYear=self.year,\n planCode=\"Foobar\",\n courseCode=\"Foobar\",\n mcName=\"Foobar\",\n course_1=\"Foobar\", weight_1=0.5,\n course_2=\"Foobar\", weight_2=0.5)\n with self.assertRaises(ValidationError):\n plan.clean()","title":""},{"docid":"e55766a33c903ecd687eb83da294964b","score":"0.566199","text":"def assign_create(sessions_id, course_id):\n\n session = sessions.get_session(sessions_id)\n course = courses.get_course(course_id)\n\n if g.user['id'] != course['teacher_id']:\n abort(403)\n\n if course['course_num'] != session['course_id']:\n abort(403)\n\n if request.method == 'POST':\n\n name = request.form['name']\n points = request.form['points']\n description = request.form['description']\n due_date = request.form['due_date']\n\n if request.form['type'] == 'upload':\n type = 'upload'\n else:\n # Anything other than upload will default to standard\n type = 'standard'\n\n error = None\n\n try:\n int(points)\n except ValueError:\n error = 'Points are numbers only, check your values.'\n try:\n datetime.datetime.strptime(due_date, '%Y-%m-%dT%H:%M')\n except ValueError:\n error = 'Due Date only allows time data, check your values. Please format the time as such using military time. Year-Month-Day Hour:Minute ex. 2020-06-22 19:10'\n\n\n with db.get_db() as con:\n with con.cursor() as cur:\n\n if not name:\n error = 'Name is required.'\n\n if error is None:\n now = datetime.datetime.utcnow()\n cur.execute(\"\"\"INSERT INTO assignments (sessions_id, assign_name, description, points, due_time, type)\n VALUES (%s, %s, %s, %s, %s, %s)\"\"\",\n (sessions_id, name, description, points, due_date, type,)\n )\n con.commit()\n\n return redirect(url_for(\"assign.assign_manage\", sessions_id=session['id'], course_id=session['course_id'] ))\n\n flash(error)\n\n return render_template('assigns/assign_create.html', session=session)","title":""},{"docid":"827b6bcae4ba073764f04f3c182eb73d","score":"0.5649654","text":"def add_new_question():\n\n # Insert question into database\n db.execute(\"INSERT INTO questions (teacher, question, choice_a, choice_b, choice_c, choice_d, answer) \\\n VALUES (:teacher, :question, :choice_a, :choice_b, :choice_c, :choice_d, :answer)\",\n {\"teacher\":session.get(\"teacher_id\"),\n \"question\":request.form.get(\"question\"),\n \"choice_a\":request.form.get(\"choice_a\"),\n \"choice_b\":request.form.get(\"choice_b\"),\n \"choice_c\":request.form.get(\"choice_c\"),\n \"choice_d\":request.form.get(\"choice_d\"),\n \"answer\":request.form.get(\"answer\")})\n db.commit()\n\n return redirect(\"/teacher\")","title":""},{"docid":"d05c14bc9556c2bd7984172626be93c4","score":"0.56445134","text":"def add_data(self):\n values = []\n \n for entry in self.add_entries: \n if entry.get() == '': #Makes sure dat entries are not black\n msg.showerror('', 'Please fill the data appropriately')\n break\n else: values.append(entry.get()) #Entries are appended to values so dat it can be stored to the database\n \n else:\n if values[2].isdigit():\n\n try:\n execute_cmd(\"\"\"\n INSERT INTO student_data(Name, Class, Phone, DOB, DOA)\n VALUES(%s, %s, %s, %s, %s)\n \"\"\", values, 'school') #Adds valid record\n self.clear_entries(self.add_entries)\n \n for entry in self.add_entries:\n entry.config(state='readonly')\n \n #Threading\n self.create_thread(self.addprogress_thread, [])\n \n except Exception as e: msg.showerror('', 'Please fill dates in the designated')\n else: msg.showerror('', 'Please fill the phone number properly')","title":""},{"docid":"c66ffaf81422c8f29fb290747f003634","score":"0.5639091","text":"def add(self, clist):\n if isinstance(clist, Course):\n # create course listing, rename as clist\n # get instructor and time info\n cnum = clist.getID()\n instr = raw_input(\"Instructor? \")\n time = raw_input(\"Time Block? \")\n clist = CourseListing(cnum, instr, time, 0)\n\n elif not isinstance(clist, CourseListing):\n print \"ERROR: \" + clist + \" is not a Course or CourseListing object\"\n return False\n\n # now we process CourseListing parameter\n cnum = clist.getCourse()\n sect = clist.getSect()\n if cnum in self._courses: # already at least one section\n # check and add section\n cldict = self._courses[cnum]\n if sect == 0 or sect in cldict:\n print \"These sections already exist: \" + str(cldict.keys())\n sect = int(raw_input(\"Enter new section # or existing to overwrite: \"))\n clist.setSect(sect)\n self._courses[cnum][sect] = clist\n else:\n # create empty dictionary of course listings for this course number\n self._courses[cnum] = {} \n self._courses[cnum][1] = clist # assume section 1 for first\n return True","title":""},{"docid":"cd65987953f34ce4d6c3b9e6578e0ac4","score":"0.56317604","text":"def put(self):\n try:\n json_data = request.get_json(force=True)\n if not json_data:\n return {'message': 'No input data provided'}, 400\n\n if 'courses' not in json_data:\n return {'message': 'Invalid input data provided. courses key missing'}, 422\n\n if 'id' not in json_data:\n return {'message': 'Invalid input data provided'}, 422\n\n if json_data[\"courses\"]:\n for course_id in json_data[\"courses\"]:\n if course_id in courses:\n courses[course_id][\"students\"].append(json_data['id'])\n else:\n return {'message': 'Invalid input of course names provided'}, 422\n\n students[json_data['id']]['courses'] += json_data['courses']\n return {'message': 'Student enrolled successfully'}, 200\n\n except Exception as e:\n return {'message': 'Internal Server error', 'error': e}, 500","title":""},{"docid":"c558fc358c87234f3a5c6f7015bb8df5","score":"0.56305623","text":"def insert_product(self, data, category):\n category += 1\n datas = data + (category, )\n query = \"INSERT INTO Products VALUES (NULL, %s, %s, %s, %s, %s)\"\n self.mycursor.execute(query, datas)\n self.connector.commit()","title":""},{"docid":"8e101ad04d6bb1d205136f596c89ef38","score":"0.5610089","text":"def test_update_course(self):\r\n # This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.\r\n pass","title":""},{"docid":"5796f9c4e186f57feb35533eb731e0b2","score":"0.559446","text":"def insert_data(self):\n connection = sqlite3.connect(DATABASE_URI)\n cursor = connection.cursor()\n query_find_by_id = \"SELECT * FROM language WHERE _id=? and language = ?\"\n result = cursor.execute(query_find_by_id, (self._id, self.language))\n rows = result.fetchone()\n\n if rows is not None:\n query = \"UPDATE language \" \\\n \"SET language = ?, understand = ?, speak = ?, read_write = ?\" \\\n \"WHERE _id = ? and language=?\"\n cursor.execute(query,\n (self.language, self.understand, self.speak, self.read_write, self._id,\n self.language,))\n else:\n query = \"INSERT INTO language values(?,?,?,?,?)\"\n cursor.execute(query, (self._id, self.language, self.understand, self.speak, self.read_write,))\n\n connection.commit()\n connection.close()","title":""},{"docid":"6f06b08fe2d201a5775827e36d9af5c7","score":"0.55936116","text":"def insert_data(self):\n for index, row in self.data.iterrows():\n try:\n # SQL declaration\n self.cursor.execute(f\"\"\"INSERT INTO countries(\n id, region, city_name, \n language, time\n ) VALUES (\n {index},\n '{row[\"Region\"]}',\n '{row[\"City Name\"]}',\n '{row[\"Language\"]}',\n {row[\"Time\"]}\n )\"\"\")\n except Exception as e:\n print(str(e))\n self.conn.commit()","title":""},{"docid":"09f7428fbe28e5a7af317974062e7526","score":"0.55889785","text":"def save_course(courses, course, terms):\n for term in terms:\n course_tmp = copy.deepcopy(course)\n course_tmp['academic_period'] = term\n courses.append(course_tmp)\n return courses, {}","title":""},{"docid":"4c76a56c58cc7404d4553390ea333a19","score":"0.5587326","text":"def load_course(course_data, blacklist, duplicates):\n # pylint: disable=too-many-branches,too-many-locals\n\n course_id = course_data.pop(\"course_id\")\n runs_data = course_data.pop(\"runs\", [])\n topics_data = course_data.pop(\"topics\", [])\n offered_bys_data = course_data.pop(\"offered_by\", [])\n\n if course_id in blacklist:\n course_data[\"published\"] = False\n\n duplicates_record = next(\n (\n record\n for record in duplicates\n if course_id in record[\"duplicate_course_ids\"]\n ),\n None,\n )\n\n if duplicates_record:\n course = Course.objects.filter(course_id=duplicates_record[\"course_id\"]).first()\n if not course:\n course_data[\"course_id\"] = duplicates_record[\"course_id\"]\n course = Course.objects.create(**course_data)\n created = True\n else:\n created = False\n\n if course_id != duplicates_record[\"course_id\"]:\n duplicate_course = Course.objects.filter(course_id=course_id).first()\n if duplicate_course:\n duplicate_course.published = False\n duplicate_course.save()\n search_task_helpers.delete_course(duplicate_course)\n else:\n platform = course_data.get(\"platform\")\n course, created = Course.objects.update_or_create(\n platform=platform, course_id=course_id, defaults=course_data\n )\n\n run_ids_to_update_or_create = [run[\"run_id\"] for run in runs_data]\n\n for course_run_data in runs_data:\n load_run(course, course_run_data)\n\n if duplicates_record and not created:\n most_relevent_run = get_most_relevant_run(course.runs.all())\n\n if most_relevent_run.run_id in run_ids_to_update_or_create:\n for attr, val in course_data.items():\n setattr(course, attr, val)\n course.save()\n\n load_topics(course, topics_data)\n load_offered_bys(course, offered_bys_data)\n\n if not created and not course.published:\n search_task_helpers.delete_course(course)\n elif course.published:\n search_task_helpers.upsert_course(course.id)\n\n return course","title":""},{"docid":"06a77387548d110cd8f098bb6f74123d","score":"0.5587003","text":"def test_create_external_tool_courses(self):\r\n # This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.\r\n pass","title":""},{"docid":"ec437d4eb5cf885245d0cda3dda8b529","score":"0.5575287","text":"def courses():\n if authed_user == None or authed_user.role != User.ROLE_ADMIN:\n session.flash = T('You are not authorized to view that page');\n redirect(URL('index'));\n \n form = FORM( TABLE(\n TR(\"Department: \", INPUT(_type='text', _name='dept', requires=IS_NOT_EMPTY())),\n TR(\"Course Number: \", INPUT(_type='text', _name='number', requires=[IS_NOT_EMPTY(),IS_INT_IN_RANGE(0,None)])),\n TR(\"Section: \", INPUT(_type='text', _name='section', requires=IS_NOT_EMPTY())),\n TR(\"Instructor: \", INPUT(_type='text', _name='instructor', requires=IS_NOT_EMPTY())),\n TR(INPUT(_type='submit', _name='submit')))\n )\n \n if form.accepts(request.vars, session, formname='CourseForm', keepvalues=True):\n editCourses.submit_course(form.vars.dept, form.vars.number, form.vars.section, form.vars.instructor);\n elif form.errors:\n response.flash = 'Required course information missing'; \n \n if request.vars.delete:\n deleteId = int(request.vars.delete)\n editCourses.delete_course(deleteId);\n session.flash = 'Course %d deleted' % (deleteId);\n redirect(\"courses\");\n \n courses = searcher.search_courses(CourseSearchParams());\n \n return dict(courses=courses, form=form);","title":""},{"docid":"a7e8ead6d71670a5424fad5ed23975e0","score":"0.55695456","text":"def test_upload_and_import_course(self):\n pass","title":""},{"docid":"79a7fd6cc7e3a335b5ca60503912bc6c","score":"0.55639863","text":"def add_new_booking(cursor, mydb, data):\n full_service = data[\"service\"] + \" \" + data[\"addons\"]\n # the date the comes is 1 day earlier so i'm moving it 1 day forward\n new_date = day_plus_one(data[\"date\"].split(\"T\")[0])\n sql = \"INSERT INTO Customers (Full_Name, Email, Phone, Address, Service, Date, Hour, Price, Comments) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)\"\n val = (data[\"fullName\"], data[\"email\"], data[\"phone\"], data[\"fullAddress\"], full_service, new_date, data[\"hour\"], data[\"price\"], data[\"comments\"], )\n cursor.execute(sql, val)\n add_to_region(cursor, mydb, data[\"cit\"], new_date)\n # mydb.commit()","title":""},{"docid":"45dc39b077ca101388676ababbffcd83","score":"0.55425406","text":"def __init__(self, name, gtid, year = 1):\n self.name = name\n self.gtid = gtid\n self.year = year\n self.courses = {}","title":""},{"docid":"2822c75cfddd77a293e6818f62a1f0bd","score":"0.55265087","text":"def add_session(self, data, course):\n\n session = data[self.session]\n course[session] = {'lectures': [], 'tutorials': []}\n self.add_section(data, course[session], course)\n return session","title":""},{"docid":"7abc90f0143fdda86de30a4983326997","score":"0.5513948","text":"def insertFstudent(self,mydb, mycursor):\n try:\n with open(r\"C:\\Users\\k.a.ramasubramanian\\Desktop\\Training\\git\\DataModel\\prod_data\\fact\\student_fact.csv\") as data:\n for i in reader(data):\n sql = \"INSERT INTO factstudent VALUES(%s,%s,%s,%s,%s,%s)\"\n val = (i[0], i[1], i[2], i[3],datetime.now(), i[5])\n mycursor.execute(sql,val)\n mydb.commit()\n print(\"No. of rows inserted:\", mycursor.rowcount)\n l = logfile.logger()\n l.info(\"FACT_STUDENT-RECORDS inserted\")\n print(\"Rows Inserted\")\n except Exception as e:\n print(\"Error:\", e)","title":""},{"docid":"1b82d3de84c8d2410123f0d5eba3ca5e","score":"0.550831","text":"def add_new_chapter(chap_name, desc, mod_name):\n if get_chapter_id(mod_name, chap_name):\n return False\n\n mod_id = get_module_id(mod_name)\n c.execute(\"INSERT INTO chapters (name, desc, module_id) VALUES (?, ?, ?)\", (chap_name, desc, mod_id))\n conn.commit()\n return True","title":""},{"docid":"8e294c88ec7f630f77c14348cf449181","score":"0.55069983","text":"def add_course(self, flag: str, course: str) -> None:\n if flag == \"R\":\n self._required.append(course)\n elif flag == \"E\":\n self._elective.append(course)","title":""},{"docid":"ec7af8dc1b1ecec764717bad17266646","score":"0.550609","text":"def _create(self, coach, students):\n return","title":""},{"docid":"706b876fbe72403f7f6a3bad78d5fe5d","score":"0.5489819","text":"def _insert(self):\r\n with con:\r\n cur = con.cursor()\r\n fieldnames = \", \".join(self.fields)\r\n SQLPATTERN = \"INSERT INTO {tablename}({fieldnames}) VALUES {values};\"\r\n values = self._field_value_list()\r\n SQL = SQLPATTERN.format(tablename=self.table, fieldnames=fieldnames, values=values)\r\n cur.execute(SQL)\r\n \"\"\" cur.lastrowid = pk that was created in the most recent insert \"\"\"\r\n self.pk = cur.lastrowid","title":""},{"docid":"dfce0fb3de6c5af5012bc9b30b48b0e4","score":"0.5489746","text":"def saveCourses_btn_clicked():\n controller.show_frame(RegisterCourses)","title":""},{"docid":"ad0e18576b19f4aab479b4b5c270c0aa","score":"0.5485913","text":"def run_insert(tokens, description):\n indices = run_location(tokens[1:-1], description)\n inserted_text = run_insert_text(tokens[-1], description)\n new_text = []\n for line, line_indices in zip(description.text, indices):\n new_line = line\n for index in sorted(line_indices, reverse=True):\n new_line = new_line[:index] + inserted_text + new_line[index + 1:]\n new_text.append(new_line)\n return CourseDescription(\n description.dept_code,\n description.number,\n new_text,\n )","title":""},{"docid":"8699c663337d311dd82bd507ec082bab","score":"0.54819226","text":"def store_course_student(self, course: str):\n self._courses[course] += 1","title":""},{"docid":"f4d12d2b267091ff8744b09ec0f84330","score":"0.547943","text":"def add_stud(self, course, grade):\n self.course[course] = grade","title":""},{"docid":"eefb6760a0f222a32724931d47afb6ac","score":"0.5476564","text":"def insert_record(self, attributes, values):\r\n command = \"INSERT INTO \" + self.name + \" \" + attributes + \" VALUES \" + values\r\n\r\n try:\r\n cur = self.db.cursor()\r\n cur.execute(command)\r\n self.db.commit()\r\n except Error as e:\r\n print(e)","title":""},{"docid":"307f42093ef79a697bb8266763dccd24","score":"0.54750353","text":"def register_section_current(request, num_):\n course = Course.objects.get(number__exact = num_)\n section = Section.objects.get(course__id__exact=course.id, year__exact=2018)\n student = Student.objects.get(user__id__exact=request.user.id)\n\n # Create a form instance and populate it with data from the request (binding):\n if student not in section.students.all():\n section.students.add(student)\n section.save()\n\n return HttpResponseRedirect(reverse('index'))","title":""},{"docid":"890fcfedc1f9531729d728b2c5b72a74","score":"0.54736674","text":"def test_insert(self):\n component = Component(self.env)\n component.name = 'component3'\n component.insert()\n component_field = self._get_component_ticket_field()\n\n self.assertEqual(component.name, component._old_name)\n self.assertEqual([('component3', None, None)], self.env.db_query(\"\"\"\n SELECT name, owner, description FROM component\n WHERE name='component3'\"\"\"))\n self.assertIsNotNone(component_field)\n self.assertIn('component3', component_field['options'])","title":""},{"docid":"d8ad4af653acf35d4033765a6f2a7e51","score":"0.54658467","text":"def create_course(self, course_name, course_description):\n\n group_m = self._client.groups\n role_m = self._client.roles\n\n course = self.create(\n name=course_name.replace('|', ''),\n description=course_description\n )\n\n for role in ROLES:\n if role == 'admin':\n group = group_m.find(name='admin')\n else:\n group_name = '{}|{}s'.format(course.name, role)\n group = group_m.create(\n name=group_name,\n description='{}s for course {}'.format(role, course_name),\n domain=course.id\n )\n\n os_role = role_m.find(name=role)\n role_m.grant(role=os_role, group=group.id, domain=course.id)\n\n return course.id","title":""},{"docid":"cbba207cc6fb32f3c5ae5536ed8f78d6","score":"0.5455851","text":"def test_update_courses(self):\r\n # This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.\r\n pass","title":""},{"docid":"3aa16d1009edbda7cf273fdce6f640ba","score":"0.54481393","text":"def generate_course(cls):\n courses = cls._load_courses()\n\n if not courses:\n courses = cls._generate_courses()\n course = CourseDataGenerator._get_random_course_from_courses_list(courses)\n\n course_template['course']['full_name']['course_full_name'] = course.get('course_name')\n course_template['course']['short_name']['short_name'] = course.get('course_short_name')\n course_template['course']['course_id']['id'] = course.get('course_id')\n course_template['course']['summary']['course_summary'] = course.get('course_synopsis')\n return course_template.get('course')","title":""},{"docid":"a836f945b75f912043c5312774650f96","score":"0.5442326","text":"def add_data_Students(dataFileName, cur): \n with open(dataFileName) as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n for row in csv_reader:\n cur.execute(\"\"\"SELECT ID\n FROM Students\n WHERE ID=?\"\"\",(row[0],))\n \n result = cur.fetchone()\n if result:\n print(\"Student \" + row[0] + \" Already in Database\")\n else:\n cur.execute(\"INSERT INTO STUDENTS VALUES (?, ?, ?, ?, ?)\", row)\n \n print(\"Addition Successful\") \n csv_file.close()","title":""},{"docid":"5efd2e90da36b34b8a31775cf1088d56","score":"0.5434153","text":"def add_student(self, first_name, middle_name, last_name, first_major,\n\tsecond_major, first_minor, second_minor, current_gpa, id, graduation_date,\n\tadvisor_id):\n\n\t\twith self.connection.cursor() as cur:\n\t\t\tcur.execute(\"\"\"INSERT INTO students VALUES \n\t\t\t(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s) \"\"\", info)\n\n\t\tself.commit()","title":""},{"docid":"add98144f29a67beae7c23fe4753d4e7","score":"0.54313934","text":"def insert_data(self):\r\n query2 = (\r\n \"INSERT IGNORE INTO Category (id, name) VALUE (%s, %s)\"\r\n )\r\n for name_category in data_categories:\r\n self.cursor.execute(query2, (name_category[0], name_category[1]))\r\n connexion.commit()","title":""},{"docid":"6ccaa88ead1b14777ca8729c43e69602","score":"0.5428111","text":"def on_start(self):\n self.course_data = self.parent.course_data","title":""}],"string":"[\n {\n \"docid\": \"026398ace73e1ad5dbfa7bb033766ef4\",\n \"score\": \"0.7772229\",\n \"text\": \"def add_course(self, info):\\n\\n\\t\\twith self.connection.cursor() as cur:\\n\\t\\t\\tcur.execute(\\\"\\\"\\\"INSERT INTO courses VALUES (%s, %s, %s, %s)\\\"\\\"\\\", info)\\n\\n\\t\\tself.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"123aede24e9b2ea234412a25eabfd090\",\n \"score\": \"0.68744904\",\n \"text\": \"def add_course():\\n conn = mysql.connect\\n cur = conn.cursor()\\n cur.execute(\\\"SELECT * FROM department\\\")\\n names = cur.fetchall()\\n error = None\\n\\n # Handle submit\\n if request.method == 'POST':\\n # Grab user info\\n course_title = request.form[\\\"new_course_title\\\"]\\n course_description = request.form[\\\"new_course_description\\\"]\\n course_year = request.form[\\\"new_course_year\\\"]\\n course_department = request.form[\\\"new_course_dep\\\"]\\n try:\\n # Insert new course into DB\\n query = f\\\"INSERT INTO course (`crs_title`, `crs_description`, `crs_year`, `dep_code`)\\\" \\\\\\n f\\\" VALUES ('{course_title}', '{course_description}', {course_year}, {course_department})\\\"\\n print(query)\\n cur.execute(query)\\n conn.commit()\\n error = \\\"Success!\\\"\\n # Handle error\\n except Exception as e:\\n error = \\\"Problem creating course: \\\" + str(e)\\n return render_template('addcourse.html', dep_names=names, error=error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d486f83852fdc48efb15466446e25a52\",\n \"score\": \"0.67363805\",\n \"text\": \"def saveCourse_btn_clicked(self):\\n mat = self.var_txt_mat.get()\\n course_name = self.var_txt_name.get()\\n\\n try:\\n with connection.cursor() as cursor:\\n # Create a new record\\n sql = \\\"INSERT INTO Matiere (code_mat, nom_mat) VALUES \\\\\\n (%s, %s)\\\"\\n try:\\n cursor.execute(sql, (mat, course_name,))\\n except pymysql.err.IntegrityError as e:\\n print(\\\"cannot duplicate Courses : \\\" + str(e))\\n # connection is not autocommit by default.So lets commit to save changes\\n connection.commit()\\n finally:\\n pass\\n # connection.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6636b8af4b35d54142786faf9cff6a83\",\n \"score\": \"0.6706542\",\n \"text\": \"def add_course(name, number, discipline):\\n exists = (\\n dal.DBSession.query(Course)\\n .filter(\\n Course.name == name,\\n Course.number == number,\\n Course.discipline == discipline,\\n )\\n .first()\\n ) is not None\\n\\n if not exists:\\n course = Course(name=name, number=number, discipline=discipline)\\n dal.DBSession.add(course)\\n dal.DBSession.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"138bea8d6c4c00b476c32d4da24fb85e\",\n \"score\": \"0.6697207\",\n \"text\": \"def create_course():\\n check_course_creation_body(request)\\n course = {'name': request.json['name'],\\n 'department': request.json['department'],\\n 'teacher': request.json['teacher'],\\n 'year': request.json['year'],\\n 'semester': request.json['semester'],\\n 'description': request.json['description'],\\n 'schedule': request.json['schedule']}\\n\\n try:\\n added_course = add_course(client, course)\\n if added_course is None:\\n abort(409) # Conflict\\n return jsonify(added_course), 201 # Created\\n except CoursesRepositoryException:\\n abort(500) # Internal Server Error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49684582abc49cc4f929bc57544b9391\",\n \"score\": \"0.66519773\",\n \"text\": \"def add_course(self, data):\\n\\n course = {'name': data[self.code],\\n 'manualTutorialEnrolment': False\\n }\\n return course\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f99baa8cc3d1eaf47fd9a177a9d224d9\",\n \"score\": \"0.6632682\",\n \"text\": \"def test_create_new_course(self):\\r\\n # This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b380da63dba9e4366a369c112d5bd86\",\n \"score\": \"0.65756774\",\n \"text\": \"def add_course():\\n\\n print \\\"/new-course called\\\"\\n user_id = session[\\\"user_id\\\"]\\n print user_id\\n course = request.form.get(\\\"course\\\")\\n\\n polyline = request.form.get(\\\"overview-polyline\\\")\\n # print \\\"polyline is \\\", polyline\\n\\n directions_text = request.form.get(\\\"directions-text\\\")\\n directions_distance = request.form.get(\\\"directions-distance\\\")\\n\\n start_address = request.form.get(\\\"start-address\\\")\\n end_address = request.form.get(\\\"end-address\\\")\\n\\n new_course = Course(user_id=user_id, course_name=course,\\n add_date=datetime.now(),\\n start_lat=request.form.get(\\\"start-lat\\\"),\\n start_long=request.form.get(\\\"start-long\\\"),\\n end_lat=request.form.get(\\\"end-lat\\\"),\\n end_long=request.form.get(\\\"end-long\\\"),\\n course_distance=request.form.get(\\\"distance\\\"),\\n favorite=request.form.get(\\\"favorite\\\"),\\n polyline=polyline,\\n directions_text=directions_text,\\n directions_distance=directions_distance,\\n start_address=start_address,\\n end_address=end_address\\n )\\n new_course.add()\\n\\n return \\\"Course %s has been saved to your courses\\\" % course\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d01eb4995874dd506a6b2e644211a0a4\",\n \"score\": \"0.652634\",\n \"text\": \"def add_department(self, info):\\n\\n\\t\\twith self.connection.cursor() as cur:\\n\\t\\t\\tcur.execute(\\\"\\\"\\\"INSERT INTO courses VALUES (%s, %s, %s)\\\"\\\"\\\", info)\\n\\n\\t\\tself.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"928ac07b53d208db36c9620be1536338\",\n \"score\": \"0.64894485\",\n \"text\": \"def courses(request):\\n user = request.user\\n if request.method == 'POST':\\n form = OnlineCourseForm(request.POST)\\n if form.is_valid():\\n # save our data\\n data = form.save(commit=False)\\n data.created_by = user\\n data.updated_by = user\\n data.save()\\n # insert data into informix\\n sql = INSERT_CTC_REC(cid=user.id)\\n do_sql(sql, key=DEBUG, earl=EARL)\\n return HttpResponseRedirect(reverse_lazy('survey_success'))\\n else:\\n form = OnlineCourseForm()\\n\\n return render(request, 'survey/form.html', {'form': form})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f56de4a944d58a0d1db643e485bfcd84\",\n \"score\": \"0.64234525\",\n \"text\": \"def add_course(self, title, topic, course_acronym, course_number, section_number, language=\\\"Python\\\"): #UPDATE\\r\\n self._courses.append(Section(title,topic,course_acronym, course_number,section_number,language))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d2c55f764822063ca7e9de23aa7757f\",\n \"score\": \"0.6401968\",\n \"text\": \"def test_add_course(self):\\n\\n file_path = os.path.join(os.path.dirname(__file__), 'testCourse.json')\\n\\n with open(file_path) as f:\\n json_dict = json.loads(f.read())\\n coursera_add_courses(json_dict)\\n\\n # there is one course in the test file\\n all_courses = Course.objects.all()\\n self.assertEquals(len(all_courses), 1)\\n\\n # the course has the right attributes\\n the_course = all_courses[0]\\n self.assertEquals(the_course.name, 'Machine Learning')\\n self.assertEquals(the_course.description, 'Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.')\\n self.assertEquals(the_course.instructor, 'Andrew Ng, Associate Professor')\\n\\n # the course has the right university\\n self.assertEquals(the_course.source.name, 'Stanford University')\\n\\n # the course has the right subjects\\n the_course_subjects = the_course.subjects.all()\\n self.assertEquals(len(the_course_subjects), 2)\\n self.assertEquals(the_course_subjects[0].name, 'stats')\\n self.assertEquals(the_course_subjects[1].name, 'cs')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb108c2b1b481c2f43a2965915a113dc\",\n \"score\": \"0.6361019\",\n \"text\": \"def add_course(self, courseID):\\n self.current_courses.append(courseID)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f8aa880820d66e575b12d893c5d9920\",\n \"score\": \"0.63592136\",\n \"text\": \"def create_course(request):\\n\\n # Like before, get the request's context.\\n context = RequestContext(request)\\n course_added = False\\n\\n user = request.user\\n profile = get_profile(user)\\n\\n if 'Instructor' in profile[1]:\\n school = UserProfile.objects.get(user=user).school\\n if not school:\\n return render_permission_denied(context,\\n 'create courses. Enrol in a school first.')\\n else:\\n #return HttpResponse(\\\"You don't have permission to create courses!\\\")\\n return render_permission_denied(context, 'create courses')\\n # If it's a HTTP POST, we're interested in processing form data.\\n if request.method == 'POST':\\n\\n # Attempt to grab information from the raw form information.\\n course_form = CourseForm(data=request.POST)\\n if course_form.is_valid():\\n # Save the event's form data to the database.\\n course = course_form.save(commit=False)\\n course.school = school\\n course.creator = user\\n\\n # Add the personal calendar for the user\\n calendar = Calendar( name = course.code + \\\" Calendar\\\")\\n calendar.save()\\n course.cal = calendar\\n\\n course.save()\\n\\n course_added = True\\n # Invalid form or forms - mistakes or something else?\\n # Print problems to the terminal.\\n # They'll also be shown to the user.\\n else:\\n print course_form.errors\\n\\n # Not a HTTP POST, so we render our form using the EventForm.\\n # These forms will be blank, ready for user input.\\n else:\\n course_form = CourseForm()\\n\\n # Render the template depending on the context.\\n return render_to_response(\\n 'school/create_course.html', {'course_form': course_form, 'user' : user,\\n 'course_added': course_added, 'school': school},\\n context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"decfa14d717a3ae7dfdde8acb8f8324a\",\n \"score\": \"0.63294107\",\n \"text\": \"def CourseDetails(self, mydb, mycursor):\\n try:\\n sql = '''create table if not exists course_details(COURSE_ID VARCHAR(9) NOT NULL PRIMARY KEY,\\n COURSE_NAME VARCHAR(20),\\n COURSE_START_DATE DATETIME,\\n COURSE_END_DATE DATETIME)'''\\n #mycursor.execute(\\\"drop table course_details\\\")\\n mycursor.execute(sql)\\n mydb.commit()\\n l = logfile.logger()\\n l.info(\\\"DIMENSION : Course_Details created!\\\")\\n except Exception as e:\\n print(\\\"Error:\\\", e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa8f6b4e62e6504d935e2299226b810c\",\n \"score\": \"0.63152754\",\n \"text\": \"def InsertCourseDetails(self,mydb,mycursor):\\n try: \\n filename = 'C:\\\\\\\\Users\\\\\\\\mohan\\\\\\\\Desktop\\\\\\\\csv\\\\\\\\dimension\\\\\\\\course_dim.csv'\\n with open(filename,'r') as fr:\\n for line in fr.readlines():\\n col_value = line.replace('\\\\n','').split(',')\\n insert_query = (\\\"insert into course_details values('{}','{}','{}','{}')\\\"\\n .format(col_value[0],col_value[1],datetime.now(),col_value[2]))\\n \\n mycursor.execute(insert_query)\\n mydb.commit()\\n l = logfile.logger()\\n l.info(\\\"Values inserted into the course_details table successfully!\\\")\\n except Exception as e:\\n l = logfile.logger()\\n l.info(e)\\n print(\\\"Error:\\\",e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"943409489b95e4776b95ce92b16decd8\",\n \"score\": \"0.62980634\",\n \"text\": \"def add_course(self, course, grades=[]):\\n self.courses[course] = grades\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a2c60307d6ec25fff0d445e5a778df\",\n \"score\": \"0.6285272\",\n \"text\": \"def addCourse(self, course: Course):\\n\\n self.courseList.append(course)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ba108cbe77195a44d87ca98b2c22133\",\n \"score\": \"0.6275504\",\n \"text\": \"def add_course(self, course, comfort=None):\\n for cuser in self.courseuser_set.all():\\n if cuser.course == course:\\n return\\n if type(comfort) == type(None):\\n CourseUser.objects.create(vsb_user=self, course=course, dirty_comfort=True, course_comfort='')\\n else:\\n CourseUser.objects.create(vsb_user=self, course=course, dirty_comfort=False, course_comfort=comfort)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef69bebe085e537b439668b2612e67c3\",\n \"score\": \"0.6264653\",\n \"text\": \"def _save(data):\\n courses = shelve.open('../src/coursesPages/all_courses.db')\\n courses['all_courses'] = data\\n courses.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9adf1395b17fe6ee31636eddce359336\",\n \"score\": \"0.6244702\",\n \"text\": \"def assign_course(self, course_id):\\n yield self.modify(\\n {\\n '$addToSet': {'courses': course_id}\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f1738a12cedd7cfb6fd8bfb4bb27d1b\",\n \"score\": \"0.62353545\",\n \"text\": \"def add_course(self, course, grade):\\r\\n self.courses[course] = grade\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ff1c3fb5cd9da8be62de31186fc36e0\",\n \"score\": \"0.61824876\",\n \"text\": \"def courses_add(self, course: str, grade: str) -> None:\\r\\n\\r\\n self._courses[course] = grade\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73de286031d4334201d262a9fc6bad9b\",\n \"score\": \"0.61489636\",\n \"text\": \"def test_add_course(self):\\n client = Client()\\n client.login(username='testadmin', password='4epape?Huf+V')\\n\\n #checks that the course is created using the view\\n client.post('/settings/courses/add_course', {'course_id': 'TDT4145', 'name': 'databaser', 'nickname': 'data',\\n 'term': 'spring', 'description': 'test'})\\n course = Course.objects.get(course_id=\\\"TDT4145\\\")\\n self.assertEqual(course.name,'databaser')\\n self.assertEqual(course.nickname, 'data')\\n self.assertEqual(course.term, 'spring')\\n self.assertEqual(course.description, 'test')\\n\\n #Checks that you cant overwrite a course that exsists, or make duplicates\\n client.post('/settings/courses/add_course', {'course_id': 'TDT4140', 'name': 'programvare utvikling',\\n 'nickname': 'pu1', 'term': 'spring 2017', 'description': 'test'})\\n course = Course.objects.get(course_id=\\\"TDT4140\\\")\\n self.assertNotEqual(course.name,'programvare utvikling')\\n self.assertNotEqual(course.nickname, 'pu1')\\n self.assertNotEqual(course.term, 'spring 2017')\\n self.assertNotEqual(course.description, 'test')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac58cfee422c67a9cd7e993d83f3d2a\",\n \"score\": \"0.61475503\",\n \"text\": \"def create_course(student, mnemonic, number):\\n return Course.objects.create(student=student,mnemonic=mnemonic, number=number)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac58cfee422c67a9cd7e993d83f3d2a\",\n \"score\": \"0.61475503\",\n \"text\": \"def create_course(student, mnemonic, number):\\n return Course.objects.create(student=student,mnemonic=mnemonic, number=number)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147a89bd401140fb086f1d631fbf3085\",\n \"score\": \"0.6083887\",\n \"text\": \"def add_course(self, course):\\n if course.fetch_course_number() in self.courses:\\n return False\\n else:\\n self.courses.update({course.fetch_course_number(): course})\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1db591493ee91473904e57c7ccba3f81\",\n \"score\": \"0.6080842\",\n \"text\": \"def handle_course_insert_input(input_value):\\n handle_sub_input(input_value, insert_course, 5)\\n return input_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8832135f03f996c7b72f20357ce51059\",\n \"score\": \"0.60423523\",\n \"text\": \"def add_course(self, course_id, term_id, program_year_id=None):\\n user_course = _user_course.UserCourse.objects(\\n user_id=self.id, course_id=course_id).first()\\n\\n if user_course is None:\\n if _course.Course.objects.with_id(course_id) is None:\\n # Non-existant course according to our data\\n rmclogger.log_event(\\n rmclogger.LOG_CATEGORY_DATA_MODEL,\\n rmclogger.LOG_EVENT_UNKNOWN_COURSE_ID,\\n course_id\\n )\\n return None\\n\\n user_course = _user_course.UserCourse(\\n user_id=self.id,\\n course_id=course_id,\\n term_id=term_id,\\n program_year_id=program_year_id,\\n )\\n else:\\n # Record only the latest attempt for duplicate/failed courses\\n if (term_id > user_course.term_id or\\n user_course.term_id == _term.Term.SHORTLIST_TERM_ID):\\n user_course.term_id = term_id\\n user_course.program_year_id = program_year_id\\n\\n user_course.save()\\n\\n if user_course.id not in self.course_history:\\n self.course_history.append(user_course.id)\\n self.save()\\n\\n return user_course\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e894c892ebdb4bab6f986ec7afdcb13\",\n \"score\": \"0.60161567\",\n \"text\": \"def test_add_to_django(self):\\n provider, created = Provider.objects.get_or_create(name='iversity')\\n sample_div = \\\"<article class='courses-list-item'><div class='ribbon-content'>Engineering</div></div><div class='course-body'><header><h2 class='truncate'><a href='https://iversity.org/courses/vehicle-dynamics-i-accelerating-and-braking'>Vehicle Dynamics I: Accelerating and Braking</a></h2><p class='instructors truncate'>Univ.-Prof. Dr.-Ing. Martin Meywerk</p></header><p class='description'>From Bugatti Veyron to Volkswagen Beetle, from racing to passenger car: study about their acceleration and braking and learn from two applications from automotive mechatronics. </p></div></div></div></div></div></div></div></article>\\\"\\n sample_div = BeautifulSoup(sample_div)\\n iversity.create_course(sample_div, provider)\\n\\n # Make sure the Engineering subject was created:\\n new_subject = Subject.objects.get(name='engineering')\\n self.assertIsNotNone(new_subject)\\n\\n # Make sure the course itself was created:\\n new_course = Course.objects.get(name='Vehicle Dynamics I: Accelerating and Braking', provider=provider,\\n subjects=new_subject)\\n self.assertIsNotNone(new_course)\\n\\n # Make sure the course name is set properly:\\n self.assertEqual('Vehicle Dynamics I: Accelerating and Braking', new_course.name)\\n\\n # Make sure the course URL is set properly:\\n self.assertEqual('https://iversity.org/courses/vehicle-dynamics-i-accelerating-and-braking', new_course.url)\\n\\n # Make sure the course instructor is set properly:\\n self.assertEqual('Univ.-Prof. Dr.-Ing. Martin Meywerk', new_course.instructor)\\n\\n # Make sure the course description is set properly:\\n self.assertEqual('From Bugatti Veyron to Volkswagen Beetle, from racing to passenger car: study about their acceleration and braking and learn from two applications from automotive mechatronics.', new_course.description)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4126a3c5783eba72f2aad12fb4354274\",\n \"score\": \"0.6015903\",\n \"text\": \"def admin_course():\\n conn = mysql.connect\\n cur = conn.cursor()\\n error = None\\n if request.method == 'POST':\\n # User adds a new course\\n if request.args.get(\\\"add\\\"):\\n cur.execute(\\\"SELECT * FROM department\\\")\\n course_title = request.form[\\\"new_course_title\\\"]\\n course_description = request.form[\\\"new_course_description\\\"]\\n course_year = request.form[\\\"new_course_year\\\"]\\n course_department = request.form[\\\"new_course_dep\\\"]\\n try:\\n # Insert new record into table course\\n query = f\\\"INSERT INTO course (`crs_title`, `crs_description`, `crs_year`, `dep_code`)\\\" \\\\\\n f\\\" VALUES ('{course_title}', '{course_description}', {course_year}, {course_department})\\\"\\n print(query)\\n cur.execute(query)\\n conn.commit()\\n error = \\\"Success!\\\"\\n except Exception as e:\\n # Handle exception\\n error = \\\"Problem creating course: \\\" + str(e)\\n # User deletes the course\\n elif request.args.get(\\\"delete\\\"):\\n data = None\\n jsonData = request.get_json()\\n course_id = jsonData[\\\"id\\\"]\\n try:\\n # Delete id along with its reference in other tables\\n delete_course_query(conn, cur, \\\"antirequisite\\\", \\\"crs_code\\\", course_id)\\n delete_course_query(conn, cur, \\\"prerequisite\\\", \\\"crs_code\\\", course_id)\\n delete_course_query(conn, cur, \\\"program_requirements\\\", \\\"program_crs\\\", course_id)\\n delete_course_query(conn, cur, \\\"course\\\", \\\"crs_code\\\", course_id)\\n data = {\\\"success\\\": \\\"true\\\"}\\n except Exception as e:\\n # Handle exception\\n traceback.print_exc()\\n data = {\\\"error\\\": \\\"Please try again.\\\"}\\n finally:\\n # Return json data\\n return json.dumps(data)\\n\\n # User updates the course\\n elif request.args.get(\\\"update\\\"):\\n data = None\\n jsonData = request.get_json()\\n course_id = jsonData[\\\"id\\\"]\\n title = jsonData[\\\"title\\\"]\\n description = jsonData[\\\"description\\\"]\\n year = jsonData[\\\"year\\\"]\\n dep_code = jsonData[\\\"dep_code\\\"]\\n pre_reqs = jsonData[\\\"pre_reqs\\\"] # JSON array containing ids of prerequisite\\n anti_reqs = jsonData[\\\"anti_reqs\\\"] # JSON array containing ids of antirequisite\\n\\n try:\\n # Update the record\\n query = f\\\"UPDATE course \\\" \\\\\\n f\\\"SET crs_title= '{title}', crs_description='{description}', crs_year={year}, dep_code={dep_code} \\\" \\\\\\n f\\\"WHERE crs_code={course_id}\\\"\\n cur.execute(query)\\n conn.commit()\\n update_req(conn, cur, \\\"prerequisite\\\", course_id, pre_reqs)\\n update_req(conn, cur, \\\"antirequisite\\\", course_id, anti_reqs)\\n data = {\\\"status\\\": \\\"success\\\"}\\n\\n except Exception as e:\\n # Handle exception\\n traceback.print_exc()\\n data = {\\\"error\\\": 404}\\n finally:\\n # Return json\\n return json.dumps(data)\\n\\n cur.execute(\\\"SELECT * FROM department\\\")\\n deps = cur.fetchall()\\n\\n cur.execute(\\\"SELECT * FROM course\\\")\\n courses = cur.fetchall()\\n data = []\\n # Retrieve course's info and add it to data array\\n for row in courses:\\n course = {}\\n course[\\\"id\\\"] = row[0]\\n course[\\\"code\\\"] = str(row[3]) + \\\"%02d\\\" % row[0]\\n course[\\\"title\\\"] = row[1]\\n course[\\\"description\\\"] = row[2]\\n course[\\\"year\\\"] = row[3]\\n course[\\\"dep_code\\\"] = row[4]\\n\\n query = f'SELECT dep_name FROM department WHERE dep_code={row[4]}'\\n cur.execute(query)\\n temp = cur.fetchone()[0]\\n course[\\\"dep_name\\\"] = temp\\n\\n query = f'SELECT course.*, department.dep_name FROM course, prerequisite, department ' \\\\\\n f'WHERE prerequisite.crs_code = {row[0]}' \\\\\\n f' AND prerequisite.crs_requires = course.crs_code AND course.dep_code = department.dep_code'\\n cur.execute(query)\\n temp = cur.fetchall()\\n pres = get_codes(temp)\\n course[\\\"pre_reqs\\\"] = pres\\n\\n query = f'SELECT course.*, department.dep_name FROM course, antirequisite, department ' \\\\\\n f'WHERE antirequisite.crs_code = {row[0]}' \\\\\\n f' AND antirequisite.crs_anti = course.crs_code AND course.dep_code = department.dep_code'\\n cur.execute(query)\\n temp = cur.fetchall()\\n antis = get_codes(temp)\\n course[\\\"anti_reqs\\\"] = antis\\n data.append(course)\\n\\n return render_template('admin-course.html', data=data, deps=deps, error=error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aa3a712ac00197901fb0e7af5aeebcc\",\n \"score\": \"0.59774035\",\n \"text\": \"def test_create_course(self):\\n parameters = ['config', 'database', 'semester', 'course']\\n environment = 'course'\\n module_text = 'Migration for a given Submitty course database.'\\n parameter_text = \\\"\\\"\\\":param config: Object holding configuration details about Submitty\\n :type config: migrator.config.Config\\n :param database: Object for interacting with given database for environment\\n :type database: migrator.db.Database\\n :param semester: Semester of the course being migrated\\n :type semester: str\\n :param course: Code of course being migrated\\n :type course: str\\\"\\\"\\\"\\n self.create_test_runner(module_text, parameters, parameter_text, environment)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b7f844781d60708a5ec461926c57293\",\n \"score\": \"0.5972808\",\n \"text\": \"def create_or_update_course(request):\\n\\n global_stuff = User.objects.filter(is_staff=True).first()\\n if global_stuff is not None:\\n request.user = global_stuff\\n else:\\n raise PermissionDenied()\\n\\n course_key = modulestore().make_course_key(request.json[\\\"org\\\"], request.json[\\\"number\\\"], request.json[\\\"run\\\"])\\n with modulestore().bulk_operations(course_key):\\n course_key = modulestore().has_course(course_key)\\n if course_key is None:\\n response = _create_or_rerun_course(request)\\n if response.status_code >= 400:\\n return response\\n course_key_string = json.loads(response.content).get(\\\"course_key\\\")\\n if course_key_string is not None:\\n course_key = CourseKey.from_string(course_key_string)\\n else:\\n return response\\n course_data = request.json.copy()\\n if course_data[\\\"start_date\\\"] is None:\\n course_data[\\\"start_date\\\"] = format(DEFAULT_START_DATE, \\\"%Y-%m-%d\\\")\\n course_data[\\\"end_date\\\"] = format(DEFAULT_START_DATE, \\\"%Y-%m-%d\\\")\\n course_data[\\\"enrollment_end\\\"] = format(DEFAULT_START_DATE, \\\"%Y-%m-%d\\\")\\n CourseDetails.update_from_json(course_key, course_data, global_stuff)\\n set_course_cohort_settings(course_key, is_cohorted=True)\\n modes = request.json.get(\\\"course_modes\\\", [])\\n CourseMode.objects.filter(course_id=course_key).exclude(mode_slug__in=[mode[\\\"mode\\\"] for mode in modes]).delete()\\n for mode in modes:\\n mode_params = {\\n \\\"course_id\\\": course_key,\\n \\\"mode_slug\\\": mode[\\\"mode\\\"]\\n }\\n if \\\"price\\\" in mode:\\n mode_params[\\\"min_price\\\"] = mode[\\\"price\\\"]\\n if \\\"currency\\\" in mode:\\n mode_params[\\\"currency\\\"] = mode[\\\"currency\\\"]\\n if \\\"title\\\" in mode:\\n mode_params[\\\"mode_display_name\\\"] = mode[\\\"title\\\"]\\n if \\\"description\\\" in mode:\\n mode_params[\\\"description\\\"] = mode[\\\"description\\\"]\\n if \\\"upgrade_deadline\\\" in mode:\\n mode_params[\\\"_expiration_datetime\\\"] = mode[\\\"upgrade_deadline\\\"]\\n CourseMode.objects.update_or_create(course_id=course_key, mode_slug=mode[\\\"mode\\\"], defaults=mode_params)\\n return JsonResponse({\\n 'url': reverse_course_url('course_handler', course_key),\\n 'course_key': unicode(course_key)\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7c3835a402bdb22bf899c9e913b1cd5\",\n \"score\": \"0.5949433\",\n \"text\": \"def add_course(self, course: str, grade: str) -> None:\\n self._courses[course] = grade\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7c3835a402bdb22bf899c9e913b1cd5\",\n \"score\": \"0.5949433\",\n \"text\": \"def add_course(self, course: str, grade: str) -> None:\\n self._courses[course] = grade\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7c3835a402bdb22bf899c9e913b1cd5\",\n \"score\": \"0.5949433\",\n \"text\": \"def add_course(self, course: str, grade: str) -> None:\\n self._courses[course] = grade\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e26f3af68078dce9891952fb5e6b6be8\",\n \"score\": \"0.5937892\",\n \"text\": \"def post(self, request, institute_short_name):\\n institute = request.user.institute\\n form = AddCourseForm(request.POST, institute=institute)\\n\\n if form.is_valid():\\n course = form.cleaned_data['course']\\n\\n try:\\n \\\"\\\"\\\"\\n the both field department and course must unique_together.\\n if department and course not added before with same values,\\n Then execute try block otherwise execute catch block.\\n \\\"\\\"\\\"\\n object = form.save(commit=False)\\n object.institute = institute\\n object.save()\\n messages.success(request, f'{course} course is successfully added')\\n return redirect('institute:administration', institute_short_name=institute_short_name)\\n except:\\n messages.error(request, f'{course} course with this Institute is already exists.')\\n\\n return redirect('institute:administration', institute_short_name=institute_short_name)\\n\\n context = {\\n 'institute_short_name': institute_short_name,\\n 'form': form,\\n }\\n\\n return render(request, 'institute/administration.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55a4bae7d39134776901c060874a2779\",\n \"score\": \"0.5934816\",\n \"text\": \"def insert_lesson(session, group, subject, teacher, day, start, end, room):\\n session.add(Lesson(group, subject, teacher, day, start, end, room))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b97bb577662bf638706c4e669bf32b8\",\n \"score\": \"0.59191036\",\n \"text\": \"def test_copy_course_content(self):\\r\\n # This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5af280f94c6cf086c20f0d68622fdb6\",\n \"score\": \"0.5916784\",\n \"text\": \"def test_corresponding_course_exists(self):\\n with self.assertRaises(ValidationError):\\n AcademicPlan.objects.create(gradYear=self.year,\\n planCode=\\\"Foobar\\\",\\n courseCode=\\\"Foobar\\\",\\n mcName=\\\"Foobar\\\",\\n course_1=\\\"Foobar\\\", weight_1=0.5,\\n weight_2=0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c10cb215667de11f3f135f5c206a5005\",\n \"score\": \"0.5904627\",\n \"text\": \"def save(cls, app_context, course):\\n persistent = PersistentCourse13(\\n next_id=course.next_id,\\n units=course.units, lessons=course.lessons)\\n\\n fs = app_context.fs.impl\\n filename = fs.physical_to_logical(cls.COURSES_FILENAME)\\n app_context.fs.put(filename, vfs.FileStreamWrapped(\\n None, persistent.serialize()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19ebc87c42ea9e86153723a4d55ba292\",\n \"score\": \"0.5888098\",\n \"text\": \"def copy_to(self, new_course):\\n tasks_to_copy = self.course_tasks.all().prefetch_related(\\\"graded_work\\\")\\n new_tasks = [\\n CourseTask(\\n course=new_course, description=task.description, duration=task.duration\\n )\\n for task in tasks_to_copy\\n ]\\n CourseTask.objects.bulk_create(new_tasks)\\n\\n # To avoid creating tasks individually, a second pass is needed\\n # to add any graded work.\\n tasks_to_compare = zip(\\n tasks_to_copy, CourseTask.objects.filter(course=new_course)\\n )\\n graded_work = [\\n GradedWork(course_task=copied_task)\\n for task_to_copy, copied_task in tasks_to_compare\\n if hasattr(task_to_copy, \\\"graded_work\\\")\\n ]\\n GradedWork.objects.bulk_create(graded_work)\\n\\n resources_to_copy = [\\n CourseResource(\\n course=new_course, title=resource.title, details=resource.details\\n )\\n for resource in self.resources.all()\\n ]\\n CourseResource.objects.bulk_create(resources_to_copy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b495111d451119975077934ae8724649\",\n \"score\": \"0.5882292\",\n \"text\": \"def insert(self):\\n self.getDbRecord().insert()\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d3a4c6fd0ab43fadad19fa2d68ca191\",\n \"score\": \"0.58681166\",\n \"text\": \"def post(self):\\n try:\\n json_data = request.get_json(force=True)\\n if not json_data:\\n return {'message': 'No input data provided'}, 400\\n\\n if 'courses' not in json_data:\\n return {'message': 'Invalid input data provided. courses key missing'}, 422\\n\\n if 'id' not in json_data:\\n return {'message': 'Invalid input data provided'}, 422\\n\\n if json_data[\\\"courses\\\"]:\\n for course_id in json_data[\\\"courses\\\"]:\\n if course_id in courses:\\n courses[course_id][\\\"students\\\"].append(json_data['id'])\\n else:\\n return {'message': 'Invalid input of course names provided'}, 422\\n\\n students[json_data['id']] = json_data\\n return {'message': 'Student added successfully'}, 200\\n\\n except Exception as e:\\n return {'message': 'Internal Server error', 'error': e}, 500\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73ed42a236d556b1181419bad637b66b\",\n \"score\": \"0.58580965\",\n \"text\": \"def create_course(self, org, course, run, user_id, fields=None, **kwargs): # lint-amnesty, pylint: disable=arguments-differ\\n course_id = CourseKey.from_string('/'.join([org, course, run]))\\n\\n # Check if a course with this org/course has been defined before (case-insensitive)\\n course_search_location = SON([\\n ('_id.tag', 'i4x'),\\n ('_id.org', re.compile(f'^{course_id.org}$', re.IGNORECASE)),\\n ('_id.course', re.compile(f'^{course_id.course}$', re.IGNORECASE)),\\n ('_id.category', 'course'),\\n ])\\n courses = self.collection.find(course_search_location, projection={'_id': True})\\n try:\\n course = courses.next() # lint-amnesty, pylint: disable=next-method-called\\n raise DuplicateCourseError(course_id, course['_id'])\\n except StopIteration:\\n pass\\n\\n with self.bulk_operations(course_id):\\n xblock = self.create_item(user_id, course_id, 'course', course_id.run, fields=fields, **kwargs)\\n\\n # create any other necessary things as a side effect\\n super().create_course(\\n org, course, run, user_id, runtime=xblock.runtime, **kwargs\\n )\\n\\n return xblock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0808beb259e91ec8233eeb5debddd24f\",\n \"score\": \"0.58566546\",\n \"text\": \"def insert_data(self):\\n Assignment4.cursor.execute('INSERT INTO food (ref_date, geo, dgu_id, food_categories, commodity,uom, uom_id,scalar_factor,scalar_id,vector,coordinate,value,status,symbol,terminated, decimals) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)',\\n (self.entryRef_date.get(),self.entryGeo.get(),self.entryDguid.get(),self.entryCategory.get(),self.entryCommodity.get(),self.entryUom.get(),self.entryUom_id.get(),self.entryScalar.get(),\\n self.entryScalar_id.get(),self.entryVector.get(),self.entryCoordinate.get(),self.entryValue.get(),self.entryStatus.get(),self.entrySymbol.get(),self.entryTerminated.get(),self.entryDecimal.get()))\\n Assignment4.db.commit()\\n print(\\\"New record inserted!!\\\")\\n self.clear_fields()\\n Assignment4.read_database(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88da82953f8b57caa55257bd0cac81d4\",\n \"score\": \"0.58556044\",\n \"text\": \"def get_course(cls, department: Department, course_number: int, course_title: str, credit_hours: int):\\n\\n course, created = Course.objects.update_or_create(department=department,\\n course_number=course_number,\\n defaults={'course_title': course_title,\\n 'credit_hours': credit_hours})\\n\\n if created:\\n cls.logger.debug(f\\\"Course Created: {course.__str__()}\\\")\\n\\n return course\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b87628d8012ad90b93155110964bc904\",\n \"score\": \"0.58519685\",\n \"text\": \"def add_course_and_run():\\n\\n print \\\"/new-run in server.py called\\\"\\n start_lat = request.form.get(\\\"start-lat\\\")\\n start_long = request.form.get(\\\"start-long\\\")\\n end_lat = request.form.get(\\\"end-lat\\\")\\n end_long = request.form.get(\\\"end-long\\\")\\n\\n add_date = datetime.now()\\n course = request.form.get(\\\"course\\\")\\n distance = request.form.get(\\\"distance\\\")\\n favorite = request.form.get(\\\"favorite\\\")\\n date = request.form.get(\\\"date\\\")\\n user_id = session[\\\"user_id\\\"]\\n polyline = request.form.get(\\\"overview-polyline\\\")\\n\\n directions_text = request.form.get(\\\"directions-text\\\")\\n directions_distance = request.form.get(\\\"directions-distance\\\")\\n\\n start_address = request.form.get(\\\"start-address\\\")\\n end_address = request.form.get(\\\"end-address\\\")\\n\\n # print \\\"start_lat is %s, start_long is %s, end_lat is %s, end_long is %s, name is %s, distance is %s, favorite is %s\\\" % (start_lat, start_long, end_lat, end_long, course, distance, favorite)\\n\\n new_course = Course(user_id=user_id, course_name=course, add_date=add_date,\\n start_lat=start_lat, start_long=start_long,\\n end_lat=end_lat, end_long=end_long,\\n course_distance=distance, favorite=favorite,\\n polyline=polyline,\\n directions_text=directions_text,\\n directions_distance=directions_distance,\\n start_address=start_address,\\n end_address=end_address\\n )\\n\\n new_course.add()\\n # print \\\"course committed\\\"\\n\\n course_id = Course.get_by_course_name(course).course_id\\n d = datetime.strptime(date, \\\"%m/%d/%Y\\\")\\n duration = request.form.get(\\\"duration\\\")\\n duration = int(duration)\\n\\n new_run = Run(user_id=user_id, course_id=course_id, run_date=d, duration=duration)\\n new_run.add()\\n\\n return \\\"Your run was saved\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5faa041c1030a536bcac2d7dad2b6463\",\n \"score\": \"0.58512056\",\n \"text\": \"def course_update_or_create(course):\\n defaults = {'name': course['course_name'], 'version_time': timezone.now}\\n kwargs = {'courseId': course['courseId'], 'platform': course['platform'], 'defaults': defaults}\\n Course.objects.update_or_create(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c067bd637218deab0bfe0719ba21739f\",\n \"score\": \"0.5848478\",\n \"text\": \"def register(self, new_course):\\n\\n # TODO: check timeslot clashes\\n\\n if new_course.course_code in self.registered_courses:\\n # already registered\\n print(f\\\"{new_course.course_name} is already registered\\\")\\n return\\n # try to register in course\\n if new_course.register_student(self):\\n self.registered_courses[new_course.course_code] = new_course\\n print(f\\\"{new_course.course_name} registered for {self}\\\")\\n else:\\n print(\\\"Course capacity full\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a634624195500e03e5cf9b22773cb3\",\n \"score\": \"0.5842758\",\n \"text\": \"def store_course_student(self, course: str)-> None:\\r\\n self.courses[course] +=1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30174b2f11abd041fecaaa2412227c5f\",\n \"score\": \"0.5805051\",\n \"text\": \"def add_student(self, course_index, name, id):\\r\\n if not self._courses:\\r\\n pass\\r\\n self._courses[course_index].add_student(name, id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53e17907975598270154533b2ee6a5c9\",\n \"score\": \"0.5786657\",\n \"text\": \"def insert(self, title, author, year, isbn):\\n self.cur.execute(\\\"INSERT INTO book VALUES (NULL, ?,?,?,?)\\\", (title, author, year, isbn))\\n self.conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33fc4596a642f302d01924bcf827ea31\",\n \"score\": \"0.5769123\",\n \"text\": \"def insert(self):\\n self.__getDbRecord().insert()\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f16a2421ea3c8b4722f3523fabaf27ed\",\n \"score\": \"0.576857\",\n \"text\": \"def insert_categories(self):\\n cat_query = self.get_categories()\\n if not cat_query:\\n query = \\\"INSERT INTO Categories (id, name) VALUES (NULL, %s)\\\"\\n self.mycursor.executemany(query, CATEGORIES)\\n self.connector.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37d9e13f0864e4dc711869985f5a3799\",\n \"score\": \"0.5763789\",\n \"text\": \"def add_student(self, course: str) -> None:\\n self._courses[course] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37d9e13f0864e4dc711869985f5a3799\",\n \"score\": \"0.5763789\",\n \"text\": \"def add_student(self, course: str) -> None:\\n self._courses[course] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37d9e13f0864e4dc711869985f5a3799\",\n \"score\": \"0.5763789\",\n \"text\": \"def add_student(self, course: str) -> None:\\n self._courses[course] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba35b7e6819ab91e36d0b110594a16fd\",\n \"score\": \"0.5749186\",\n \"text\": \"def insert_data(self, data):\\n self.db[self.table_name].insert(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d603c4c601eee832fae70d8a1ab21b84\",\n \"score\": \"0.5748691\",\n \"text\": \"def add_student(self, course:str) -> None:\\r\\n \\r\\n self._students[course] +=1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d72070bb066aa829c2a452e1bde657d9\",\n \"score\": \"0.57170534\",\n \"text\": \"def add_inst(self, course):\\n self.course[course] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05310d1b6ca6293c890d69fa5e22ac38\",\n \"score\": \"0.5712089\",\n \"text\": \"def insert_data_into_db(self):\\n data = self.text_fields_into_dict() ## Current item data dict\\n parent = self.item_data if not self.new_cls else 'zero' ## set parent data if not new cls\\n if self.ids.cls_nome.text.strip() and self.ids.cls_codigo.text.strip() != '':\\n ## if required fields are not empty\\n try:\\n ## Instantiate data manager object \\\\/\\n to_insert = ManageData(item_data=data,parent_data=parent)\\n if not self.editing:\\n to_insert.insert_into_db() ## inserts into database\\n else:\\n to_insert.update_db() ## updates database\\n except:\\n toast('Algo deu errado! Impossível salvar a Classe.')\\n else:\\n toast('Classe salva com sucesso!',1)\\n self.app.root.ids.data_frame.clear_widgets() ## clear data management frame \\n self.app.pcd_tree.regen_tree() ## Regenerates pcd treeview\\n self.app.pcd_tree.switch_button(reset=True) ## switch toggle nodes button\\n self.app.pcd_tree.disabled = False ## Unlocks treeview\\n else:\\n ## if required fields are empty\\n toast('Campos obrigatórios estão em branco!')\\n self.ids.cls_codigo.focus = True if self.ids.cls_codigo.text.strip() == '' else False\\n self.ids.cls_nome.focus = True if self.ids.cls_nome.text.strip() == '' else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95eb9b2e5acb3514763e3a7c4f4769ab\",\n \"score\": \"0.5695588\",\n \"text\": \"def new_credit (col: pymongo.collection, credit_data: dict):\\n try:\\n col.insert_one(credit_data)\\n except:\\n print('Credit could not be saved')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33bebbe91f643339f68045b8b0255fb3\",\n \"score\": \"0.56650835\",\n \"text\": \"def test_duplicate_course(self):\\n plan = AcademicPlan.objects.create(gradYear=self.year,\\n planCode=\\\"Foobar\\\",\\n courseCode=\\\"Foobar\\\",\\n mcName=\\\"Foobar\\\",\\n course_1=\\\"Foobar\\\", weight_1=0.5,\\n course_2=\\\"Foobar\\\", weight_2=0.5)\\n with self.assertRaises(ValidationError):\\n plan.clean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e55766a33c903ecd687eb83da294964b\",\n \"score\": \"0.566199\",\n \"text\": \"def assign_create(sessions_id, course_id):\\n\\n session = sessions.get_session(sessions_id)\\n course = courses.get_course(course_id)\\n\\n if g.user['id'] != course['teacher_id']:\\n abort(403)\\n\\n if course['course_num'] != session['course_id']:\\n abort(403)\\n\\n if request.method == 'POST':\\n\\n name = request.form['name']\\n points = request.form['points']\\n description = request.form['description']\\n due_date = request.form['due_date']\\n\\n if request.form['type'] == 'upload':\\n type = 'upload'\\n else:\\n # Anything other than upload will default to standard\\n type = 'standard'\\n\\n error = None\\n\\n try:\\n int(points)\\n except ValueError:\\n error = 'Points are numbers only, check your values.'\\n try:\\n datetime.datetime.strptime(due_date, '%Y-%m-%dT%H:%M')\\n except ValueError:\\n error = 'Due Date only allows time data, check your values. Please format the time as such using military time. Year-Month-Day Hour:Minute ex. 2020-06-22 19:10'\\n\\n\\n with db.get_db() as con:\\n with con.cursor() as cur:\\n\\n if not name:\\n error = 'Name is required.'\\n\\n if error is None:\\n now = datetime.datetime.utcnow()\\n cur.execute(\\\"\\\"\\\"INSERT INTO assignments (sessions_id, assign_name, description, points, due_time, type)\\n VALUES (%s, %s, %s, %s, %s, %s)\\\"\\\"\\\",\\n (sessions_id, name, description, points, due_date, type,)\\n )\\n con.commit()\\n\\n return redirect(url_for(\\\"assign.assign_manage\\\", sessions_id=session['id'], course_id=session['course_id'] ))\\n\\n flash(error)\\n\\n return render_template('assigns/assign_create.html', session=session)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"827b6bcae4ba073764f04f3c182eb73d\",\n \"score\": \"0.5649654\",\n \"text\": \"def add_new_question():\\n\\n # Insert question into database\\n db.execute(\\\"INSERT INTO questions (teacher, question, choice_a, choice_b, choice_c, choice_d, answer) \\\\\\n VALUES (:teacher, :question, :choice_a, :choice_b, :choice_c, :choice_d, :answer)\\\",\\n {\\\"teacher\\\":session.get(\\\"teacher_id\\\"),\\n \\\"question\\\":request.form.get(\\\"question\\\"),\\n \\\"choice_a\\\":request.form.get(\\\"choice_a\\\"),\\n \\\"choice_b\\\":request.form.get(\\\"choice_b\\\"),\\n \\\"choice_c\\\":request.form.get(\\\"choice_c\\\"),\\n \\\"choice_d\\\":request.form.get(\\\"choice_d\\\"),\\n \\\"answer\\\":request.form.get(\\\"answer\\\")})\\n db.commit()\\n\\n return redirect(\\\"/teacher\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d05c14bc9556c2bd7984172626be93c4\",\n \"score\": \"0.56445134\",\n \"text\": \"def add_data(self):\\n values = []\\n \\n for entry in self.add_entries: \\n if entry.get() == '': #Makes sure dat entries are not black\\n msg.showerror('', 'Please fill the data appropriately')\\n break\\n else: values.append(entry.get()) #Entries are appended to values so dat it can be stored to the database\\n \\n else:\\n if values[2].isdigit():\\n\\n try:\\n execute_cmd(\\\"\\\"\\\"\\n INSERT INTO student_data(Name, Class, Phone, DOB, DOA)\\n VALUES(%s, %s, %s, %s, %s)\\n \\\"\\\"\\\", values, 'school') #Adds valid record\\n self.clear_entries(self.add_entries)\\n \\n for entry in self.add_entries:\\n entry.config(state='readonly')\\n \\n #Threading\\n self.create_thread(self.addprogress_thread, [])\\n \\n except Exception as e: msg.showerror('', 'Please fill dates in the designated')\\n else: msg.showerror('', 'Please fill the phone number properly')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c66ffaf81422c8f29fb290747f003634\",\n \"score\": \"0.5639091\",\n \"text\": \"def add(self, clist):\\n if isinstance(clist, Course):\\n # create course listing, rename as clist\\n # get instructor and time info\\n cnum = clist.getID()\\n instr = raw_input(\\\"Instructor? \\\")\\n time = raw_input(\\\"Time Block? \\\")\\n clist = CourseListing(cnum, instr, time, 0)\\n\\n elif not isinstance(clist, CourseListing):\\n print \\\"ERROR: \\\" + clist + \\\" is not a Course or CourseListing object\\\"\\n return False\\n\\n # now we process CourseListing parameter\\n cnum = clist.getCourse()\\n sect = clist.getSect()\\n if cnum in self._courses: # already at least one section\\n # check and add section\\n cldict = self._courses[cnum]\\n if sect == 0 or sect in cldict:\\n print \\\"These sections already exist: \\\" + str(cldict.keys())\\n sect = int(raw_input(\\\"Enter new section # or existing to overwrite: \\\"))\\n clist.setSect(sect)\\n self._courses[cnum][sect] = clist\\n else:\\n # create empty dictionary of course listings for this course number\\n self._courses[cnum] = {} \\n self._courses[cnum][1] = clist # assume section 1 for first\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd65987953f34ce4d6c3b9e6578e0ac4\",\n \"score\": \"0.56317604\",\n \"text\": \"def put(self):\\n try:\\n json_data = request.get_json(force=True)\\n if not json_data:\\n return {'message': 'No input data provided'}, 400\\n\\n if 'courses' not in json_data:\\n return {'message': 'Invalid input data provided. courses key missing'}, 422\\n\\n if 'id' not in json_data:\\n return {'message': 'Invalid input data provided'}, 422\\n\\n if json_data[\\\"courses\\\"]:\\n for course_id in json_data[\\\"courses\\\"]:\\n if course_id in courses:\\n courses[course_id][\\\"students\\\"].append(json_data['id'])\\n else:\\n return {'message': 'Invalid input of course names provided'}, 422\\n\\n students[json_data['id']]['courses'] += json_data['courses']\\n return {'message': 'Student enrolled successfully'}, 200\\n\\n except Exception as e:\\n return {'message': 'Internal Server error', 'error': e}, 500\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c558fc358c87234f3a5c6f7015bb8df5\",\n \"score\": \"0.56305623\",\n \"text\": \"def insert_product(self, data, category):\\n category += 1\\n datas = data + (category, )\\n query = \\\"INSERT INTO Products VALUES (NULL, %s, %s, %s, %s, %s)\\\"\\n self.mycursor.execute(query, datas)\\n self.connector.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e101ad04d6bb1d205136f596c89ef38\",\n \"score\": \"0.5610089\",\n \"text\": \"def test_update_course(self):\\r\\n # This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5796f9c4e186f57feb35533eb731e0b2\",\n \"score\": \"0.559446\",\n \"text\": \"def insert_data(self):\\n connection = sqlite3.connect(DATABASE_URI)\\n cursor = connection.cursor()\\n query_find_by_id = \\\"SELECT * FROM language WHERE _id=? and language = ?\\\"\\n result = cursor.execute(query_find_by_id, (self._id, self.language))\\n rows = result.fetchone()\\n\\n if rows is not None:\\n query = \\\"UPDATE language \\\" \\\\\\n \\\"SET language = ?, understand = ?, speak = ?, read_write = ?\\\" \\\\\\n \\\"WHERE _id = ? and language=?\\\"\\n cursor.execute(query,\\n (self.language, self.understand, self.speak, self.read_write, self._id,\\n self.language,))\\n else:\\n query = \\\"INSERT INTO language values(?,?,?,?,?)\\\"\\n cursor.execute(query, (self._id, self.language, self.understand, self.speak, self.read_write,))\\n\\n connection.commit()\\n connection.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f06b08fe2d201a5775827e36d9af5c7\",\n \"score\": \"0.55936116\",\n \"text\": \"def insert_data(self):\\n for index, row in self.data.iterrows():\\n try:\\n # SQL declaration\\n self.cursor.execute(f\\\"\\\"\\\"INSERT INTO countries(\\n id, region, city_name, \\n language, time\\n ) VALUES (\\n {index},\\n '{row[\\\"Region\\\"]}',\\n '{row[\\\"City Name\\\"]}',\\n '{row[\\\"Language\\\"]}',\\n {row[\\\"Time\\\"]}\\n )\\\"\\\"\\\")\\n except Exception as e:\\n print(str(e))\\n self.conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09f7428fbe28e5a7af317974062e7526\",\n \"score\": \"0.55889785\",\n \"text\": \"def save_course(courses, course, terms):\\n for term in terms:\\n course_tmp = copy.deepcopy(course)\\n course_tmp['academic_period'] = term\\n courses.append(course_tmp)\\n return courses, {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c76a56c58cc7404d4553390ea333a19\",\n \"score\": \"0.5587326\",\n \"text\": \"def load_course(course_data, blacklist, duplicates):\\n # pylint: disable=too-many-branches,too-many-locals\\n\\n course_id = course_data.pop(\\\"course_id\\\")\\n runs_data = course_data.pop(\\\"runs\\\", [])\\n topics_data = course_data.pop(\\\"topics\\\", [])\\n offered_bys_data = course_data.pop(\\\"offered_by\\\", [])\\n\\n if course_id in blacklist:\\n course_data[\\\"published\\\"] = False\\n\\n duplicates_record = next(\\n (\\n record\\n for record in duplicates\\n if course_id in record[\\\"duplicate_course_ids\\\"]\\n ),\\n None,\\n )\\n\\n if duplicates_record:\\n course = Course.objects.filter(course_id=duplicates_record[\\\"course_id\\\"]).first()\\n if not course:\\n course_data[\\\"course_id\\\"] = duplicates_record[\\\"course_id\\\"]\\n course = Course.objects.create(**course_data)\\n created = True\\n else:\\n created = False\\n\\n if course_id != duplicates_record[\\\"course_id\\\"]:\\n duplicate_course = Course.objects.filter(course_id=course_id).first()\\n if duplicate_course:\\n duplicate_course.published = False\\n duplicate_course.save()\\n search_task_helpers.delete_course(duplicate_course)\\n else:\\n platform = course_data.get(\\\"platform\\\")\\n course, created = Course.objects.update_or_create(\\n platform=platform, course_id=course_id, defaults=course_data\\n )\\n\\n run_ids_to_update_or_create = [run[\\\"run_id\\\"] for run in runs_data]\\n\\n for course_run_data in runs_data:\\n load_run(course, course_run_data)\\n\\n if duplicates_record and not created:\\n most_relevent_run = get_most_relevant_run(course.runs.all())\\n\\n if most_relevent_run.run_id in run_ids_to_update_or_create:\\n for attr, val in course_data.items():\\n setattr(course, attr, val)\\n course.save()\\n\\n load_topics(course, topics_data)\\n load_offered_bys(course, offered_bys_data)\\n\\n if not created and not course.published:\\n search_task_helpers.delete_course(course)\\n elif course.published:\\n search_task_helpers.upsert_course(course.id)\\n\\n return course\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a77387548d110cd8f098bb6f74123d\",\n \"score\": \"0.5587003\",\n \"text\": \"def test_create_external_tool_courses(self):\\r\\n # This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec437d4eb5cf885245d0cda3dda8b529\",\n \"score\": \"0.5575287\",\n \"text\": \"def courses():\\n if authed_user == None or authed_user.role != User.ROLE_ADMIN:\\n session.flash = T('You are not authorized to view that page');\\n redirect(URL('index'));\\n \\n form = FORM( TABLE(\\n TR(\\\"Department: \\\", INPUT(_type='text', _name='dept', requires=IS_NOT_EMPTY())),\\n TR(\\\"Course Number: \\\", INPUT(_type='text', _name='number', requires=[IS_NOT_EMPTY(),IS_INT_IN_RANGE(0,None)])),\\n TR(\\\"Section: \\\", INPUT(_type='text', _name='section', requires=IS_NOT_EMPTY())),\\n TR(\\\"Instructor: \\\", INPUT(_type='text', _name='instructor', requires=IS_NOT_EMPTY())),\\n TR(INPUT(_type='submit', _name='submit')))\\n )\\n \\n if form.accepts(request.vars, session, formname='CourseForm', keepvalues=True):\\n editCourses.submit_course(form.vars.dept, form.vars.number, form.vars.section, form.vars.instructor);\\n elif form.errors:\\n response.flash = 'Required course information missing'; \\n \\n if request.vars.delete:\\n deleteId = int(request.vars.delete)\\n editCourses.delete_course(deleteId);\\n session.flash = 'Course %d deleted' % (deleteId);\\n redirect(\\\"courses\\\");\\n \\n courses = searcher.search_courses(CourseSearchParams());\\n \\n return dict(courses=courses, form=form);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7e8ead6d71670a5424fad5ed23975e0\",\n \"score\": \"0.55695456\",\n \"text\": \"def test_upload_and_import_course(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a7fd6cc7e3a335b5ca60503912bc6c\",\n \"score\": \"0.55639863\",\n \"text\": \"def add_new_booking(cursor, mydb, data):\\n full_service = data[\\\"service\\\"] + \\\" \\\" + data[\\\"addons\\\"]\\n # the date the comes is 1 day earlier so i'm moving it 1 day forward\\n new_date = day_plus_one(data[\\\"date\\\"].split(\\\"T\\\")[0])\\n sql = \\\"INSERT INTO Customers (Full_Name, Email, Phone, Address, Service, Date, Hour, Price, Comments) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)\\\"\\n val = (data[\\\"fullName\\\"], data[\\\"email\\\"], data[\\\"phone\\\"], data[\\\"fullAddress\\\"], full_service, new_date, data[\\\"hour\\\"], data[\\\"price\\\"], data[\\\"comments\\\"], )\\n cursor.execute(sql, val)\\n add_to_region(cursor, mydb, data[\\\"cit\\\"], new_date)\\n # mydb.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45dc39b077ca101388676ababbffcd83\",\n \"score\": \"0.55425406\",\n \"text\": \"def __init__(self, name, gtid, year = 1):\\n self.name = name\\n self.gtid = gtid\\n self.year = year\\n self.courses = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2822c75cfddd77a293e6818f62a1f0bd\",\n \"score\": \"0.55265087\",\n \"text\": \"def add_session(self, data, course):\\n\\n session = data[self.session]\\n course[session] = {'lectures': [], 'tutorials': []}\\n self.add_section(data, course[session], course)\\n return session\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7abc90f0143fdda86de30a4983326997\",\n \"score\": \"0.5513948\",\n \"text\": \"def insertFstudent(self,mydb, mycursor):\\n try:\\n with open(r\\\"C:\\\\Users\\\\k.a.ramasubramanian\\\\Desktop\\\\Training\\\\git\\\\DataModel\\\\prod_data\\\\fact\\\\student_fact.csv\\\") as data:\\n for i in reader(data):\\n sql = \\\"INSERT INTO factstudent VALUES(%s,%s,%s,%s,%s,%s)\\\"\\n val = (i[0], i[1], i[2], i[3],datetime.now(), i[5])\\n mycursor.execute(sql,val)\\n mydb.commit()\\n print(\\\"No. of rows inserted:\\\", mycursor.rowcount)\\n l = logfile.logger()\\n l.info(\\\"FACT_STUDENT-RECORDS inserted\\\")\\n print(\\\"Rows Inserted\\\")\\n except Exception as e:\\n print(\\\"Error:\\\", e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b82d3de84c8d2410123f0d5eba3ca5e\",\n \"score\": \"0.550831\",\n \"text\": \"def add_new_chapter(chap_name, desc, mod_name):\\n if get_chapter_id(mod_name, chap_name):\\n return False\\n\\n mod_id = get_module_id(mod_name)\\n c.execute(\\\"INSERT INTO chapters (name, desc, module_id) VALUES (?, ?, ?)\\\", (chap_name, desc, mod_id))\\n conn.commit()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e294c88ec7f630f77c14348cf449181\",\n \"score\": \"0.55069983\",\n \"text\": \"def add_course(self, flag: str, course: str) -> None:\\n if flag == \\\"R\\\":\\n self._required.append(course)\\n elif flag == \\\"E\\\":\\n self._elective.append(course)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec7af8dc1b1ecec764717bad17266646\",\n \"score\": \"0.550609\",\n \"text\": \"def _create(self, coach, students):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"706b876fbe72403f7f6a3bad78d5fe5d\",\n \"score\": \"0.5489819\",\n \"text\": \"def _insert(self):\\r\\n with con:\\r\\n cur = con.cursor()\\r\\n fieldnames = \\\", \\\".join(self.fields)\\r\\n SQLPATTERN = \\\"INSERT INTO {tablename}({fieldnames}) VALUES {values};\\\"\\r\\n values = self._field_value_list()\\r\\n SQL = SQLPATTERN.format(tablename=self.table, fieldnames=fieldnames, values=values)\\r\\n cur.execute(SQL)\\r\\n \\\"\\\"\\\" cur.lastrowid = pk that was created in the most recent insert \\\"\\\"\\\"\\r\\n self.pk = cur.lastrowid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfce0fb3de6c5af5012bc9b30b48b0e4\",\n \"score\": \"0.5489746\",\n \"text\": \"def saveCourses_btn_clicked():\\n controller.show_frame(RegisterCourses)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad0e18576b19f4aab479b4b5c270c0aa\",\n \"score\": \"0.5485913\",\n \"text\": \"def run_insert(tokens, description):\\n indices = run_location(tokens[1:-1], description)\\n inserted_text = run_insert_text(tokens[-1], description)\\n new_text = []\\n for line, line_indices in zip(description.text, indices):\\n new_line = line\\n for index in sorted(line_indices, reverse=True):\\n new_line = new_line[:index] + inserted_text + new_line[index + 1:]\\n new_text.append(new_line)\\n return CourseDescription(\\n description.dept_code,\\n description.number,\\n new_text,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8699c663337d311dd82bd507ec082bab\",\n \"score\": \"0.54819226\",\n \"text\": \"def store_course_student(self, course: str):\\n self._courses[course] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4d12d2b267091ff8744b09ec0f84330\",\n \"score\": \"0.547943\",\n \"text\": \"def add_stud(self, course, grade):\\n self.course[course] = grade\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eefb6760a0f222a32724931d47afb6ac\",\n \"score\": \"0.5476564\",\n \"text\": \"def insert_record(self, attributes, values):\\r\\n command = \\\"INSERT INTO \\\" + self.name + \\\" \\\" + attributes + \\\" VALUES \\\" + values\\r\\n\\r\\n try:\\r\\n cur = self.db.cursor()\\r\\n cur.execute(command)\\r\\n self.db.commit()\\r\\n except Error as e:\\r\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307f42093ef79a697bb8266763dccd24\",\n \"score\": \"0.54750353\",\n \"text\": \"def register_section_current(request, num_):\\n course = Course.objects.get(number__exact = num_)\\n section = Section.objects.get(course__id__exact=course.id, year__exact=2018)\\n student = Student.objects.get(user__id__exact=request.user.id)\\n\\n # Create a form instance and populate it with data from the request (binding):\\n if student not in section.students.all():\\n section.students.add(student)\\n section.save()\\n\\n return HttpResponseRedirect(reverse('index'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"890fcfedc1f9531729d728b2c5b72a74\",\n \"score\": \"0.54736674\",\n \"text\": \"def test_insert(self):\\n component = Component(self.env)\\n component.name = 'component3'\\n component.insert()\\n component_field = self._get_component_ticket_field()\\n\\n self.assertEqual(component.name, component._old_name)\\n self.assertEqual([('component3', None, None)], self.env.db_query(\\\"\\\"\\\"\\n SELECT name, owner, description FROM component\\n WHERE name='component3'\\\"\\\"\\\"))\\n self.assertIsNotNone(component_field)\\n self.assertIn('component3', component_field['options'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8ad4af653acf35d4033765a6f2a7e51\",\n \"score\": \"0.54658467\",\n \"text\": \"def create_course(self, course_name, course_description):\\n\\n group_m = self._client.groups\\n role_m = self._client.roles\\n\\n course = self.create(\\n name=course_name.replace('|', ''),\\n description=course_description\\n )\\n\\n for role in ROLES:\\n if role == 'admin':\\n group = group_m.find(name='admin')\\n else:\\n group_name = '{}|{}s'.format(course.name, role)\\n group = group_m.create(\\n name=group_name,\\n description='{}s for course {}'.format(role, course_name),\\n domain=course.id\\n )\\n\\n os_role = role_m.find(name=role)\\n role_m.grant(role=os_role, group=group.id, domain=course.id)\\n\\n return course.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbba207cc6fb32f3c5ae5536ed8f78d6\",\n \"score\": \"0.5455851\",\n \"text\": \"def test_update_courses(self):\\r\\n # This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa16d1009edbda7cf273fdce6f640ba\",\n \"score\": \"0.54481393\",\n \"text\": \"def generate_course(cls):\\n courses = cls._load_courses()\\n\\n if not courses:\\n courses = cls._generate_courses()\\n course = CourseDataGenerator._get_random_course_from_courses_list(courses)\\n\\n course_template['course']['full_name']['course_full_name'] = course.get('course_name')\\n course_template['course']['short_name']['short_name'] = course.get('course_short_name')\\n course_template['course']['course_id']['id'] = course.get('course_id')\\n course_template['course']['summary']['course_summary'] = course.get('course_synopsis')\\n return course_template.get('course')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a836f945b75f912043c5312774650f96\",\n \"score\": \"0.5442326\",\n \"text\": \"def add_data_Students(dataFileName, cur): \\n with open(dataFileName) as csv_file:\\n csv_reader = csv.reader(csv_file, delimiter=',')\\n for row in csv_reader:\\n cur.execute(\\\"\\\"\\\"SELECT ID\\n FROM Students\\n WHERE ID=?\\\"\\\"\\\",(row[0],))\\n \\n result = cur.fetchone()\\n if result:\\n print(\\\"Student \\\" + row[0] + \\\" Already in Database\\\")\\n else:\\n cur.execute(\\\"INSERT INTO STUDENTS VALUES (?, ?, ?, ?, ?)\\\", row)\\n \\n print(\\\"Addition Successful\\\") \\n csv_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5efd2e90da36b34b8a31775cf1088d56\",\n \"score\": \"0.5434153\",\n \"text\": \"def add_student(self, first_name, middle_name, last_name, first_major,\\n\\tsecond_major, first_minor, second_minor, current_gpa, id, graduation_date,\\n\\tadvisor_id):\\n\\n\\t\\twith self.connection.cursor() as cur:\\n\\t\\t\\tcur.execute(\\\"\\\"\\\"INSERT INTO students VALUES \\n\\t\\t\\t(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s) \\\"\\\"\\\", info)\\n\\n\\t\\tself.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"add98144f29a67beae7c23fe4753d4e7\",\n \"score\": \"0.54313934\",\n \"text\": \"def insert_data(self):\\r\\n query2 = (\\r\\n \\\"INSERT IGNORE INTO Category (id, name) VALUE (%s, %s)\\\"\\r\\n )\\r\\n for name_category in data_categories:\\r\\n self.cursor.execute(query2, (name_category[0], name_category[1]))\\r\\n connexion.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ccaa88ead1b14777ca8729c43e69602\",\n \"score\": \"0.5428111\",\n \"text\": \"def on_start(self):\\n self.course_data = self.parent.course_data\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994078,"cells":{"query_id":{"kind":"string","value":"565d4025c023804111e8665eb2b2f138"},"query":{"kind":"string","value":"Sets the campaign_id of this Context132."},"positive_passages":{"kind":"list like","value":[{"docid":"eaf13e8218bd8433db01dd35c98a3556","score":"0.8459122","text":"def campaign_id(self, campaign_id):\n\n self._campaign_id = campaign_id","title":""}],"string":"[\n {\n \"docid\": \"eaf13e8218bd8433db01dd35c98a3556\",\n \"score\": \"0.8459122\",\n \"text\": \"def campaign_id(self, campaign_id):\\n\\n self._campaign_id = campaign_id\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a618fa22da5ee9e1f5b3ee60371da942","score":"0.8029553","text":"def campaign_id(self, value):\n\n self._campaign_id.set(value)","title":""},{"docid":"80ed485b0d0c85fc62a94f2903a5d87d","score":"0.7275301","text":"def setCampaign(self, campaign):\n self.data.campaign = campaign\n return","title":""},{"docid":"741a2be4e558e66f7d5ee3271b1391e1","score":"0.7208308","text":"def campaign_id(self):\n\n return self._campaign_id.value","title":""},{"docid":"1e135d87bd26144b6f9810704e1b741a","score":"0.71861076","text":"def campaign_id(self):\n return self._campaign_id","title":""},{"docid":"1e135d87bd26144b6f9810704e1b741a","score":"0.71861076","text":"def campaign_id(self):\n return self._campaign_id","title":""},{"docid":"f373fc641927f976ed7f8937b2b936cf","score":"0.70853436","text":"def campaign_id(self):\n\n return self._identifier.campaign_id","title":""},{"docid":"2b9f95e2d784376b48511079de923ee2","score":"0.7004804","text":"def campaign(self, campaign):\n\n self._campaign = campaign","title":""},{"docid":"2b9f95e2d784376b48511079de923ee2","score":"0.7004804","text":"def campaign(self, campaign):\n\n self._campaign = campaign","title":""},{"docid":"2b9f95e2d784376b48511079de923ee2","score":"0.7004804","text":"def campaign(self, campaign):\n\n self._campaign = campaign","title":""},{"docid":"0fb53a0293118699e0c907164a6eb8b7","score":"0.69668645","text":"def campaign(self, campaign):\n if self.local_vars_configuration.client_side_validation and campaign is None: # noqa: E501\n raise ValueError(\"Invalid value for `campaign`, must not be `None`\") # noqa: E501\n\n self._campaign = campaign","title":""},{"docid":"3dc757a331e36741d82671500c6abcc3","score":"0.61387277","text":"def contact_id(self, contact_id):\n self._contact_id = contact_id","title":""},{"docid":"011bccaa67473256f2152bfcc9b8159c","score":"0.6089699","text":"def getCampaignId(self):\n return self.config['campaign']['id']","title":""},{"docid":"c059083ce10f05f03542abbc1d1e0aa8","score":"0.6061528","text":"def contact_id(self, contact_id):\n\n self._contact_id = contact_id","title":""},{"docid":"c059083ce10f05f03542abbc1d1e0aa8","score":"0.6061528","text":"def contact_id(self, contact_id):\n\n self._contact_id = contact_id","title":""},{"docid":"c059083ce10f05f03542abbc1d1e0aa8","score":"0.6061528","text":"def contact_id(self, contact_id):\n\n self._contact_id = contact_id","title":""},{"docid":"d68a9c11950ac004d715ba2de22d2c81","score":"0.6013564","text":"def campaign_name(self, campaign_name):\n if self.local_vars_configuration.client_side_validation and campaign_name is None: # noqa: E501\n raise ValueError(\"Invalid value for `campaign_name`, must not be `None`\") # noqa: E501\n\n self._campaign_name = campaign_name","title":""},{"docid":"d68a9c11950ac004d715ba2de22d2c81","score":"0.6013564","text":"def campaign_name(self, campaign_name):\n if self.local_vars_configuration.client_side_validation and campaign_name is None: # noqa: E501\n raise ValueError(\"Invalid value for `campaign_name`, must not be `None`\") # noqa: E501\n\n self._campaign_name = campaign_name","title":""},{"docid":"c89c0d0f8c54fbd3df3ce9545ace3426","score":"0.5931133","text":"def activity_id(self, activity_id):\n self._activity_id = activity_id","title":""},{"docid":"c89c0d0f8c54fbd3df3ce9545ace3426","score":"0.5931133","text":"def activity_id(self, activity_id):\n self._activity_id = activity_id","title":""},{"docid":"91f316044a9f123555fcbff106168300","score":"0.58913976","text":"def activity_id(self, activity_id):\n\n self._activity_id = activity_id","title":""},{"docid":"91f316044a9f123555fcbff106168300","score":"0.58913976","text":"def activity_id(self, activity_id):\n\n self._activity_id = activity_id","title":""},{"docid":"446a7c4e8c264e79a7b6b2d5afd38c9e","score":"0.58248484","text":"def get_campaign_id(self, campaign_name):\n return self._client.get_campaign_id(campaign_name)","title":""},{"docid":"92a5bfffabd745ca6d8441e7492f78b8","score":"0.5823947","text":"def contact_entity_id(self, contact_entity_id):\n\n self._contact_entity_id = contact_entity_id","title":""},{"docid":"de96a6f5bb09709e5139e0aab6736e66","score":"0.5787186","text":"def utm_campaign(self, utm_campaign):\n\n self._utm_campaign = utm_campaign","title":""},{"docid":"0628abfeb4671af20d3fdfc870a50463","score":"0.5767397","text":"def campaign_delete(self, campaign_id: ID):\n response = requests.delete(\n self._url(FW_CAMPAIGN, id=campaign_id),\n headers=self._headers()\n )\n response.raise_for_status()\n LOG.info('Deleted campaign %s', campaign_id)","title":""},{"docid":"103cef5cc582e118625aba3be7a648f3","score":"0.5732969","text":"def addToCampaign(contactId: int, campaignId: int) -> bool:\n pass","title":""},{"docid":"5bd3ab71765283a7eae12a61eb3e2d52","score":"0.5703808","text":"def attribute_set_id(self, attribute_set_id):\n\n self._attribute_set_id = attribute_set_id","title":""},{"docid":"721994961924ac44067e6867b58e1245","score":"0.56745857","text":"def email_campaign_id(self, email_campaign_id):\n if self.local_vars_configuration.client_side_validation and email_campaign_id is None: # noqa: E501\n raise ValueError(\"Invalid value for `email_campaign_id`, must not be `None`\") # noqa: E501\n\n self._email_campaign_id = email_campaign_id","title":""},{"docid":"3d3dd8719db3188fcd8fe888b113af73","score":"0.5629351","text":"def set_cid(cid):\n setattr(_thread_locals, 'CID', cid)","title":""},{"docid":"f0c094006adaf28a8a57cba5dc509e26","score":"0.5622132","text":"def competition_id(self, competition_id):\n\n self._competition_id = competition_id","title":""},{"docid":"c78a83ee0a80c33bf38527a8c0c543eb","score":"0.5601656","text":"def set_id(self, ssc_id):\r\n self.ssc_id = ssc_id","title":""},{"docid":"142534183344c33e71c74df1b8457cd3","score":"0.5561093","text":"def set_id(self, a_id):\n self._id = a_id","title":""},{"docid":"b7e0626e30bc722df2524dd1d8646baf","score":"0.55458677","text":"def delete_campaign(self, campaign_id):\n api = self._get_api(update_service.DefaultApi)\n api.update_campaign_destroy(campaign_id)\n return","title":""},{"docid":"f001337f200e86a3bccbedfd64c2154f","score":"0.5538001","text":"def ad_id(self, ad_id):\n\n self._ad_id = ad_id","title":""},{"docid":"e2ac30420aaa0084ac3621578be0372e","score":"0.5534075","text":"def facebook_add_campaign(self, integration_id, campaign_id):\n return super(Integration, self).post(\n 'facebook', integration_id, 'campaign')","title":""},{"docid":"b74411fc0d35b5fd5aebb26f8510474e","score":"0.5532426","text":"def set_google_id(self, id):\n self._google_id = id","title":""},{"docid":"4994e7268f40f0a6e3f558723146b27b","score":"0.55277777","text":"def conversation_id(self, conversation_id):\n self._conversation_id = conversation_id","title":""},{"docid":"2addd684e941dbcdb4bab2deb391bc9c","score":"0.5516335","text":"def start_campaign(self, campaign_uuid, campaign_name, email):","title":""},{"docid":"8d55c61ea9341cbd3398413163f4caf8","score":"0.55095065","text":"def sentence_id(self, sentence_id: int):\n\n self._sentence_id = sentence_id","title":""},{"docid":"422cfe25cb77978a7b7eb3f2a508ca7c","score":"0.5477686","text":"def crf_id(self, crf_id):\n\n self._crf_id = crf_id","title":""},{"docid":"daa199703035575471551d0f2fc17978","score":"0.5474826","text":"def SetId(self, id: int) -> None:\n ...","title":""},{"docid":"c53423742254fc2f039fc471721691cd","score":"0.5462202","text":"def context_entity_id(self, context_entity_id):\n\n self._context_entity_id = context_entity_id","title":""},{"docid":"f5d3c96fcba3e06e2c8c10a7d3f7c7b1","score":"0.54597604","text":"def set_cust_id(self, cid):\n self.cust_id_ent.delete(0, \"end\")\n self.cust_id_ent.insert(0, str(cid))\n\n self.clear_cust_id = False","title":""},{"docid":"75fabfcbc4bd41a2c4e161928d7b871a","score":"0.5452416","text":"def put(self):\n data = flask.request.data\n campaign_json = json.loads(data.decode('utf-8'))\n prepid = campaign_controller.create_campaign(campaign_json)\n return self.output_text({'response': prepid, 'success': True, 'message': ''})","title":""},{"docid":"619f39d1b0d49d61f950534a47910c62","score":"0.54131067","text":"def SetId(self, id):","title":""},{"docid":"619f39d1b0d49d61f950534a47910c62","score":"0.54131067","text":"def SetId(self, id):","title":""},{"docid":"619f39d1b0d49d61f950534a47910c62","score":"0.54127693","text":"def SetId(self, id):","title":""},{"docid":"0fdc7c5430446c68df50efa412283c38","score":"0.54058033","text":"def conversation_id(self, conversation_id):\n\n self._conversation_id = conversation_id","title":""},{"docid":"427e1750b109981cf6af5d474cf57c89","score":"0.5392319","text":"def SetAffirmativeId(self, id):","title":""},{"docid":"9ea9514e692d9351da7405754dba5335","score":"0.5381878","text":"def customer_group_id(self, customer_group_id):\n\n self._customer_group_id = customer_group_id","title":""},{"docid":"dbba99b621e7a6c3afb06877fc831a68","score":"0.5365705","text":"def add_campaign(self, campaign_name, campaign_target):\n\n self.campaigns.add((campaign_name, campaign_target))","title":""},{"docid":"a94696757d455591bdf6ff15ff0e3007","score":"0.5349297","text":"def incident_id(self, incident_id):\n\n self._incident_id = incident_id","title":""},{"docid":"85071f7b01eedda052e3e42465e7c451","score":"0.53443485","text":"def _set_contact_id(cls, data):\n document_id = data.get(\"_id\")\n if document_id:\n data[\"contact_id\"] = document_id\n return data","title":""},{"docid":"c81f56c5f3923ea9ede4bd6d46565e44","score":"0.53192264","text":"def client_acct_id(self, client_acct_id):\n\n self._client_acct_id = client_acct_id","title":""},{"docid":"861ab0b76e09482e1dd7316def5217d3","score":"0.5318342","text":"def id_conta(self, id_conta):\n self._id_conta = id_conta","title":""},{"docid":"861ab0b76e09482e1dd7316def5217d3","score":"0.5318342","text":"def id_conta(self, id_conta):\n self._id_conta = id_conta","title":""},{"docid":"cb3e5bbdbb66c571625cb964fe303d44","score":"0.5313205","text":"def set_id(self, entity):\n self._id = entity","title":""},{"docid":"a809e15447b322b85d2026cea20aa46a","score":"0.53080744","text":"def campaign_create(\n self,\n name: str,\n manifest_id: ID,\n device_filter: str\n ) -> ID:\n try:\n response = requests.post(\n self._url(FW_CAMPAIGNS),\n headers=self._headers(),\n json={\n 'campaign_strategy': 'one-shot',\n 'description': 'Development campaign',\n 'device_filter': device_filter,\n 'name': name,\n 'root_manifest_id': manifest_id\n }\n )\n response.raise_for_status()\n campaign_id = response.json()['id']\n LOG.info('Created Campaign ID: %s', campaign_id)\n return campaign_id\n except requests.HTTPError:\n LOG.error('Failed to create campaign')\n raise","title":""},{"docid":"a123c45ca4e33fa910bf5d02188f50de","score":"0.53037035","text":"def setId(self, id):\n self.__id = id","title":""},{"docid":"a9eecb56e683ba60ca77593bd4d04a1b","score":"0.5300951","text":"def set_id(self, id):\r\n self._id = id","title":""},{"docid":"ee1f68ce7dc94a233e968c37748ab47b","score":"0.5285731","text":"def set_id(self, id):\n self.id = id","title":""},{"docid":"ee1f68ce7dc94a233e968c37748ab47b","score":"0.5285731","text":"def set_id(self, id):\n self.id = id","title":""},{"docid":"d0d1fdaa6e8094557687e88cbb4a8c3c","score":"0.52790743","text":"def set_id( self, id ):\n self.id = id","title":""},{"docid":"b0a90661eb2ffcfbe19fed184bb00c0c","score":"0.5277671","text":"def activity_process_id(self, activity_process_id):\n\n self._activity_process_id = activity_process_id","title":""},{"docid":"d648da41884c3f9e0cd4f001e00a4d90","score":"0.52734834","text":"def campaign_name(self):\n return self._identifier.campaign_name","title":""},{"docid":"478a8a2a37ef4a5bb18749169eaf0892","score":"0.52688164","text":"def campaign_name(self):\n\n return self._identifier.campaign_name","title":""},{"docid":"a918a56f117643c82d9596d93448daa4","score":"0.5264504","text":"def course_id(self, value):\r\n self.logger.warn(\"Setting values on course_id will NOT update the remote Canvas instance.\")\r\n self._course_id = value","title":""},{"docid":"1ab19028a38b8f443e9d2ed0f4c5ed38","score":"0.5247934","text":"def set_id(self, id):\r\n self.id = id","title":""},{"docid":"1ab19028a38b8f443e9d2ed0f4c5ed38","score":"0.5247934","text":"def set_id(self, id):\r\n self.id = id","title":""},{"docid":"6269a0886f4313a771d77c130300a5e4","score":"0.5244841","text":"def SetId(self, id):\n self._id = id","title":""},{"docid":"5b5128e5dc7594984c3547d83fa3197a","score":"0.5243339","text":"def course_id(self, value):\n self.logger.warn(\n \"Setting values on course_id will NOT update the remote Canvas instance.\"\n )\n self._course_id = value","title":""},{"docid":"1c9a642c8a21aaeb7b5a7936e31a061e","score":"0.5241628","text":"def build_set_configuration_id(self, build_set_configuration_id):\n self._build_set_configuration_id = build_set_configuration_id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"61f018b2a549ddfdbb66bd43b1fd29d5","score":"0.5238848","text":"def id(self, id):\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\n raise ValueError(\"Invalid value for `id`, must not be `None`\") # noqa: E501\n\n self._id = id","title":""},{"docid":"55b54cc05feaa243f46ebbb64a3ec12d","score":"0.5237134","text":"def audience_id(self, audience_id):\n if self.local_vars_configuration.client_side_validation and audience_id is None: # noqa: E501\n raise ValueError(\"Invalid value for `audience_id`, must not be `None`\") # noqa: E501\n\n self._audience_id = audience_id","title":""},{"docid":"ac79da4555cc6c635c3bf2f195dcc556","score":"0.5234369","text":"def get_campaign(self, campaign_id):\n api = self._get_api(update_service.DefaultApi)\n return Campaign(api.update_campaign_retrieve(campaign_id))","title":""},{"docid":"bf8a9e049cd358ac4ab970252eae8c2e","score":"0.522874","text":"def set_id(self, key):\n self._id = key","title":""},{"docid":"21234ee1cd54a22bc9f756e427a2e56c","score":"0.5228145","text":"def campaign(self):\n\n return self._campaign","title":""},{"docid":"effb789ae800a290c5dea3f6ff8e7fea","score":"0.5227302","text":"def campaign_ids(self):\n\n return self._campaign_ids","title":""},{"docid":"fe21965c625db913856fb3c445c2d072","score":"0.52220494","text":"def advisory_id(self, advisory_id):\n\n self._advisory_id = advisory_id","title":""},{"docid":"81cb8c9d6725531b71faf40c23919e6d","score":"0.52166605","text":"def dataset_id(self, dataset_id):\n\n self._dataset_id = dataset_id","title":""}],"string":"[\n {\n \"docid\": \"a618fa22da5ee9e1f5b3ee60371da942\",\n \"score\": \"0.8029553\",\n \"text\": \"def campaign_id(self, value):\\n\\n self._campaign_id.set(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ed485b0d0c85fc62a94f2903a5d87d\",\n \"score\": \"0.7275301\",\n \"text\": \"def setCampaign(self, campaign):\\n self.data.campaign = campaign\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"741a2be4e558e66f7d5ee3271b1391e1\",\n \"score\": \"0.7208308\",\n \"text\": \"def campaign_id(self):\\n\\n return self._campaign_id.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e135d87bd26144b6f9810704e1b741a\",\n \"score\": \"0.71861076\",\n \"text\": \"def campaign_id(self):\\n return self._campaign_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e135d87bd26144b6f9810704e1b741a\",\n \"score\": \"0.71861076\",\n \"text\": \"def campaign_id(self):\\n return self._campaign_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f373fc641927f976ed7f8937b2b936cf\",\n \"score\": \"0.70853436\",\n \"text\": \"def campaign_id(self):\\n\\n return self._identifier.campaign_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b9f95e2d784376b48511079de923ee2\",\n \"score\": \"0.7004804\",\n \"text\": \"def campaign(self, campaign):\\n\\n self._campaign = campaign\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b9f95e2d784376b48511079de923ee2\",\n \"score\": \"0.7004804\",\n \"text\": \"def campaign(self, campaign):\\n\\n self._campaign = campaign\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b9f95e2d784376b48511079de923ee2\",\n \"score\": \"0.7004804\",\n \"text\": \"def campaign(self, campaign):\\n\\n self._campaign = campaign\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fb53a0293118699e0c907164a6eb8b7\",\n \"score\": \"0.69668645\",\n \"text\": \"def campaign(self, campaign):\\n if self.local_vars_configuration.client_side_validation and campaign is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `campaign`, must not be `None`\\\") # noqa: E501\\n\\n self._campaign = campaign\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dc757a331e36741d82671500c6abcc3\",\n \"score\": \"0.61387277\",\n \"text\": \"def contact_id(self, contact_id):\\n self._contact_id = contact_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"011bccaa67473256f2152bfcc9b8159c\",\n \"score\": \"0.6089699\",\n \"text\": \"def getCampaignId(self):\\n return self.config['campaign']['id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c059083ce10f05f03542abbc1d1e0aa8\",\n \"score\": \"0.6061528\",\n \"text\": \"def contact_id(self, contact_id):\\n\\n self._contact_id = contact_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c059083ce10f05f03542abbc1d1e0aa8\",\n \"score\": \"0.6061528\",\n \"text\": \"def contact_id(self, contact_id):\\n\\n self._contact_id = contact_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c059083ce10f05f03542abbc1d1e0aa8\",\n \"score\": \"0.6061528\",\n \"text\": \"def contact_id(self, contact_id):\\n\\n self._contact_id = contact_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d68a9c11950ac004d715ba2de22d2c81\",\n \"score\": \"0.6013564\",\n \"text\": \"def campaign_name(self, campaign_name):\\n if self.local_vars_configuration.client_side_validation and campaign_name is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `campaign_name`, must not be `None`\\\") # noqa: E501\\n\\n self._campaign_name = campaign_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d68a9c11950ac004d715ba2de22d2c81\",\n \"score\": \"0.6013564\",\n \"text\": \"def campaign_name(self, campaign_name):\\n if self.local_vars_configuration.client_side_validation and campaign_name is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `campaign_name`, must not be `None`\\\") # noqa: E501\\n\\n self._campaign_name = campaign_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c89c0d0f8c54fbd3df3ce9545ace3426\",\n \"score\": \"0.5931133\",\n \"text\": \"def activity_id(self, activity_id):\\n self._activity_id = activity_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c89c0d0f8c54fbd3df3ce9545ace3426\",\n \"score\": \"0.5931133\",\n \"text\": \"def activity_id(self, activity_id):\\n self._activity_id = activity_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91f316044a9f123555fcbff106168300\",\n \"score\": \"0.58913976\",\n \"text\": \"def activity_id(self, activity_id):\\n\\n self._activity_id = activity_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91f316044a9f123555fcbff106168300\",\n \"score\": \"0.58913976\",\n \"text\": \"def activity_id(self, activity_id):\\n\\n self._activity_id = activity_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"446a7c4e8c264e79a7b6b2d5afd38c9e\",\n \"score\": \"0.58248484\",\n \"text\": \"def get_campaign_id(self, campaign_name):\\n return self._client.get_campaign_id(campaign_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92a5bfffabd745ca6d8441e7492f78b8\",\n \"score\": \"0.5823947\",\n \"text\": \"def contact_entity_id(self, contact_entity_id):\\n\\n self._contact_entity_id = contact_entity_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de96a6f5bb09709e5139e0aab6736e66\",\n \"score\": \"0.5787186\",\n \"text\": \"def utm_campaign(self, utm_campaign):\\n\\n self._utm_campaign = utm_campaign\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0628abfeb4671af20d3fdfc870a50463\",\n \"score\": \"0.5767397\",\n \"text\": \"def campaign_delete(self, campaign_id: ID):\\n response = requests.delete(\\n self._url(FW_CAMPAIGN, id=campaign_id),\\n headers=self._headers()\\n )\\n response.raise_for_status()\\n LOG.info('Deleted campaign %s', campaign_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"103cef5cc582e118625aba3be7a648f3\",\n \"score\": \"0.5732969\",\n \"text\": \"def addToCampaign(contactId: int, campaignId: int) -> bool:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bd3ab71765283a7eae12a61eb3e2d52\",\n \"score\": \"0.5703808\",\n \"text\": \"def attribute_set_id(self, attribute_set_id):\\n\\n self._attribute_set_id = attribute_set_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"721994961924ac44067e6867b58e1245\",\n \"score\": \"0.56745857\",\n \"text\": \"def email_campaign_id(self, email_campaign_id):\\n if self.local_vars_configuration.client_side_validation and email_campaign_id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `email_campaign_id`, must not be `None`\\\") # noqa: E501\\n\\n self._email_campaign_id = email_campaign_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d3dd8719db3188fcd8fe888b113af73\",\n \"score\": \"0.5629351\",\n \"text\": \"def set_cid(cid):\\n setattr(_thread_locals, 'CID', cid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0c094006adaf28a8a57cba5dc509e26\",\n \"score\": \"0.5622132\",\n \"text\": \"def competition_id(self, competition_id):\\n\\n self._competition_id = competition_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c78a83ee0a80c33bf38527a8c0c543eb\",\n \"score\": \"0.5601656\",\n \"text\": \"def set_id(self, ssc_id):\\r\\n self.ssc_id = ssc_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"142534183344c33e71c74df1b8457cd3\",\n \"score\": \"0.5561093\",\n \"text\": \"def set_id(self, a_id):\\n self._id = a_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7e0626e30bc722df2524dd1d8646baf\",\n \"score\": \"0.55458677\",\n \"text\": \"def delete_campaign(self, campaign_id):\\n api = self._get_api(update_service.DefaultApi)\\n api.update_campaign_destroy(campaign_id)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f001337f200e86a3bccbedfd64c2154f\",\n \"score\": \"0.5538001\",\n \"text\": \"def ad_id(self, ad_id):\\n\\n self._ad_id = ad_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ac30420aaa0084ac3621578be0372e\",\n \"score\": \"0.5534075\",\n \"text\": \"def facebook_add_campaign(self, integration_id, campaign_id):\\n return super(Integration, self).post(\\n 'facebook', integration_id, 'campaign')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b74411fc0d35b5fd5aebb26f8510474e\",\n \"score\": \"0.5532426\",\n \"text\": \"def set_google_id(self, id):\\n self._google_id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4994e7268f40f0a6e3f558723146b27b\",\n \"score\": \"0.55277777\",\n \"text\": \"def conversation_id(self, conversation_id):\\n self._conversation_id = conversation_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2addd684e941dbcdb4bab2deb391bc9c\",\n \"score\": \"0.5516335\",\n \"text\": \"def start_campaign(self, campaign_uuid, campaign_name, email):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d55c61ea9341cbd3398413163f4caf8\",\n \"score\": \"0.55095065\",\n \"text\": \"def sentence_id(self, sentence_id: int):\\n\\n self._sentence_id = sentence_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"422cfe25cb77978a7b7eb3f2a508ca7c\",\n \"score\": \"0.5477686\",\n \"text\": \"def crf_id(self, crf_id):\\n\\n self._crf_id = crf_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daa199703035575471551d0f2fc17978\",\n \"score\": \"0.5474826\",\n \"text\": \"def SetId(self, id: int) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c53423742254fc2f039fc471721691cd\",\n \"score\": \"0.5462202\",\n \"text\": \"def context_entity_id(self, context_entity_id):\\n\\n self._context_entity_id = context_entity_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5d3c96fcba3e06e2c8c10a7d3f7c7b1\",\n \"score\": \"0.54597604\",\n \"text\": \"def set_cust_id(self, cid):\\n self.cust_id_ent.delete(0, \\\"end\\\")\\n self.cust_id_ent.insert(0, str(cid))\\n\\n self.clear_cust_id = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75fabfcbc4bd41a2c4e161928d7b871a\",\n \"score\": \"0.5452416\",\n \"text\": \"def put(self):\\n data = flask.request.data\\n campaign_json = json.loads(data.decode('utf-8'))\\n prepid = campaign_controller.create_campaign(campaign_json)\\n return self.output_text({'response': prepid, 'success': True, 'message': ''})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"619f39d1b0d49d61f950534a47910c62\",\n \"score\": \"0.54131067\",\n \"text\": \"def SetId(self, id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"619f39d1b0d49d61f950534a47910c62\",\n \"score\": \"0.54131067\",\n \"text\": \"def SetId(self, id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"619f39d1b0d49d61f950534a47910c62\",\n \"score\": \"0.54127693\",\n \"text\": \"def SetId(self, id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fdc7c5430446c68df50efa412283c38\",\n \"score\": \"0.54058033\",\n \"text\": \"def conversation_id(self, conversation_id):\\n\\n self._conversation_id = conversation_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"427e1750b109981cf6af5d474cf57c89\",\n \"score\": \"0.5392319\",\n \"text\": \"def SetAffirmativeId(self, id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ea9514e692d9351da7405754dba5335\",\n \"score\": \"0.5381878\",\n \"text\": \"def customer_group_id(self, customer_group_id):\\n\\n self._customer_group_id = customer_group_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbba99b621e7a6c3afb06877fc831a68\",\n \"score\": \"0.5365705\",\n \"text\": \"def add_campaign(self, campaign_name, campaign_target):\\n\\n self.campaigns.add((campaign_name, campaign_target))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a94696757d455591bdf6ff15ff0e3007\",\n \"score\": \"0.5349297\",\n \"text\": \"def incident_id(self, incident_id):\\n\\n self._incident_id = incident_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85071f7b01eedda052e3e42465e7c451\",\n \"score\": \"0.53443485\",\n \"text\": \"def _set_contact_id(cls, data):\\n document_id = data.get(\\\"_id\\\")\\n if document_id:\\n data[\\\"contact_id\\\"] = document_id\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c81f56c5f3923ea9ede4bd6d46565e44\",\n \"score\": \"0.53192264\",\n \"text\": \"def client_acct_id(self, client_acct_id):\\n\\n self._client_acct_id = client_acct_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861ab0b76e09482e1dd7316def5217d3\",\n \"score\": \"0.5318342\",\n \"text\": \"def id_conta(self, id_conta):\\n self._id_conta = id_conta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861ab0b76e09482e1dd7316def5217d3\",\n \"score\": \"0.5318342\",\n \"text\": \"def id_conta(self, id_conta):\\n self._id_conta = id_conta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb3e5bbdbb66c571625cb964fe303d44\",\n \"score\": \"0.5313205\",\n \"text\": \"def set_id(self, entity):\\n self._id = entity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a809e15447b322b85d2026cea20aa46a\",\n \"score\": \"0.53080744\",\n \"text\": \"def campaign_create(\\n self,\\n name: str,\\n manifest_id: ID,\\n device_filter: str\\n ) -> ID:\\n try:\\n response = requests.post(\\n self._url(FW_CAMPAIGNS),\\n headers=self._headers(),\\n json={\\n 'campaign_strategy': 'one-shot',\\n 'description': 'Development campaign',\\n 'device_filter': device_filter,\\n 'name': name,\\n 'root_manifest_id': manifest_id\\n }\\n )\\n response.raise_for_status()\\n campaign_id = response.json()['id']\\n LOG.info('Created Campaign ID: %s', campaign_id)\\n return campaign_id\\n except requests.HTTPError:\\n LOG.error('Failed to create campaign')\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a123c45ca4e33fa910bf5d02188f50de\",\n \"score\": \"0.53037035\",\n \"text\": \"def setId(self, id):\\n self.__id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9eecb56e683ba60ca77593bd4d04a1b\",\n \"score\": \"0.5300951\",\n \"text\": \"def set_id(self, id):\\r\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee1f68ce7dc94a233e968c37748ab47b\",\n \"score\": \"0.5285731\",\n \"text\": \"def set_id(self, id):\\n self.id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee1f68ce7dc94a233e968c37748ab47b\",\n \"score\": \"0.5285731\",\n \"text\": \"def set_id(self, id):\\n self.id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0d1fdaa6e8094557687e88cbb4a8c3c\",\n \"score\": \"0.52790743\",\n \"text\": \"def set_id( self, id ):\\n self.id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0a90661eb2ffcfbe19fed184bb00c0c\",\n \"score\": \"0.5277671\",\n \"text\": \"def activity_process_id(self, activity_process_id):\\n\\n self._activity_process_id = activity_process_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d648da41884c3f9e0cd4f001e00a4d90\",\n \"score\": \"0.52734834\",\n \"text\": \"def campaign_name(self):\\n return self._identifier.campaign_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"478a8a2a37ef4a5bb18749169eaf0892\",\n \"score\": \"0.52688164\",\n \"text\": \"def campaign_name(self):\\n\\n return self._identifier.campaign_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a918a56f117643c82d9596d93448daa4\",\n \"score\": \"0.5264504\",\n \"text\": \"def course_id(self, value):\\r\\n self.logger.warn(\\\"Setting values on course_id will NOT update the remote Canvas instance.\\\")\\r\\n self._course_id = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ab19028a38b8f443e9d2ed0f4c5ed38\",\n \"score\": \"0.5247934\",\n \"text\": \"def set_id(self, id):\\r\\n self.id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ab19028a38b8f443e9d2ed0f4c5ed38\",\n \"score\": \"0.5247934\",\n \"text\": \"def set_id(self, id):\\r\\n self.id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6269a0886f4313a771d77c130300a5e4\",\n \"score\": \"0.5244841\",\n \"text\": \"def SetId(self, id):\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b5128e5dc7594984c3547d83fa3197a\",\n \"score\": \"0.5243339\",\n \"text\": \"def course_id(self, value):\\n self.logger.warn(\\n \\\"Setting values on course_id will NOT update the remote Canvas instance.\\\"\\n )\\n self._course_id = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c9a642c8a21aaeb7b5a7936e31a061e\",\n \"score\": \"0.5241628\",\n \"text\": \"def build_set_configuration_id(self, build_set_configuration_id):\\n self._build_set_configuration_id = build_set_configuration_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f018b2a549ddfdbb66bd43b1fd29d5\",\n \"score\": \"0.5238848\",\n \"text\": \"def id(self, id):\\n if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `id`, must not be `None`\\\") # noqa: E501\\n\\n self._id = id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55b54cc05feaa243f46ebbb64a3ec12d\",\n \"score\": \"0.5237134\",\n \"text\": \"def audience_id(self, audience_id):\\n if self.local_vars_configuration.client_side_validation and audience_id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `audience_id`, must not be `None`\\\") # noqa: E501\\n\\n self._audience_id = audience_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac79da4555cc6c635c3bf2f195dcc556\",\n \"score\": \"0.5234369\",\n \"text\": \"def get_campaign(self, campaign_id):\\n api = self._get_api(update_service.DefaultApi)\\n return Campaign(api.update_campaign_retrieve(campaign_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8a9e049cd358ac4ab970252eae8c2e\",\n \"score\": \"0.522874\",\n \"text\": \"def set_id(self, key):\\n self._id = key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21234ee1cd54a22bc9f756e427a2e56c\",\n \"score\": \"0.5228145\",\n \"text\": \"def campaign(self):\\n\\n return self._campaign\",\n \"title\": \"\"\n },\n {\n \"docid\": \"effb789ae800a290c5dea3f6ff8e7fea\",\n \"score\": \"0.5227302\",\n \"text\": \"def campaign_ids(self):\\n\\n return self._campaign_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe21965c625db913856fb3c445c2d072\",\n \"score\": \"0.52220494\",\n \"text\": \"def advisory_id(self, advisory_id):\\n\\n self._advisory_id = advisory_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81cb8c9d6725531b71faf40c23919e6d\",\n \"score\": \"0.52166605\",\n \"text\": \"def dataset_id(self, dataset_id):\\n\\n self._dataset_id = dataset_id\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994079,"cells":{"query_id":{"kind":"string","value":"48738414c52251aace9e1552c271cc99"},"query":{"kind":"string","value":"Finds the nearest region to given coordinates."},"positive_passages":{"kind":"list like","value":[{"docid":"c8906b8ca44ffb522de320e48c3f128e","score":"0.7113429","text":"def FindNearestRegion(self, lat, lon):\n self._Refresh()\n return self._cities.FindNearestNeighbor(lat, lon).parent","title":""}],"string":"[\n {\n \"docid\": \"c8906b8ca44ffb522de320e48c3f128e\",\n \"score\": \"0.7113429\",\n \"text\": \"def FindNearestRegion(self, lat, lon):\\n self._Refresh()\\n return self._cities.FindNearestNeighbor(lat, lon).parent\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"cc5224e323d654e0d4732c87fd7ff737","score":"0.6737705","text":"def nearest(coordinate, coordinate_list, limit):","title":""},{"docid":"11396b53fc89921c28fd992f59f2363d","score":"0.6671556","text":"def get_nearest_valid_coordinates(self, coordinates):\n pass","title":""},{"docid":"e348d8ecb20c1ceba88dd9f2efe2a611","score":"0.660837","text":"def locate(self, lat, lon):\n for key, region in self.regions.iteritems():\n if region.contains(lat, lon):\n return key\n return None","title":""},{"docid":"eb5f877eea7163bc2e96be144c6b64c7","score":"0.6579592","text":"def nearest_boundaries_location(self,x,y,xi,yi):\n mdgrd = np.array(zip( x.ravel(),y.ravel() ))\n kdt = kd.KDTree(mdgrd)\n self.mdi_dist, self.mdi = kdt.query(np.array(zip( xi, yi) ))","title":""},{"docid":"cf233f76183b0e1d9c61461acc74d290","score":"0.6503124","text":"def nearest_grid_point(distances):\n return distances.index(min(distances))","title":""},{"docid":"5adfd91824ae7915f5603993294d9411","score":"0.6436937","text":"def getClosest(self, coordinates: tuple, searchTolerance: str = \"\"):\n pass","title":""},{"docid":"04803e163799e7eb1f0e470bfcc01801","score":"0.63568676","text":"def closest_to(cls, coordinates):\n # I should be using the db's geo stuff for this, but I'm keeping it\n # database-agnostic\n cities = cls.objects.all()\n cities = list(cities)\n\n cities.sort(key=lambda x: x.distance_from_coordinates(coordinates))\n return cities[0] if cities else None","title":""},{"docid":"cb304253516b3ff2c27e50ffd59628ee","score":"0.62382346","text":"def nearestPoint( pos ):\n ( current_row, current_col ) = pos\n\n grid_row = int( current_row + 0.5 )\n grid_col = int( current_col + 0.5 )\n return ( grid_row, grid_col )","title":""},{"docid":"75c1caf8d0aa9507b97abb12caeda00e","score":"0.6215297","text":"def find_node(self, coordinates):\n node = self.origin\n if node == None:\n return None\n z = 0\n while node.up and z < coordinates[2]:\n node = node.up\n z += 1\n y = 0\n while node.north and y < coordinates[1]:\n node = node.north\n y += 1\n x = 0\n while node.east and x < coordinates[0]:\n node = node.east\n x += 1\n if node.coordinates != coordinates:\n return None\n else:\n return node","title":""},{"docid":"003552daf7ae1c21048ffad336e9d359","score":"0.6199199","text":"def find_closest_intersection(instructions_a, instructions_b, origin = (0, 0)):\n a = instructions_to_locations(instructions_a)\n b = instructions_to_locations(instructions_b)\n #print(a)\n #print(b)\n crossing_points = list(a.intersection(b))\n #print(crossing_points)\n winner = find_nearest_location(crossing_points)\n return winner","title":""},{"docid":"46d0d1e97a5f63ed5a25dc28228c685f","score":"0.61891586","text":"def find_nearest_center(x, y):\n xs = np.array([j.x for j in self.current_scan.joints])\n ys = np.array([j.y for j in self.current_scan.joints])\n distance_partial = (ys - y) ** 2 + (xs - x) ** 2\n loc = np.where(distance_partial == distance_partial.min())[0][0]\n return loc","title":""},{"docid":"9820e07d9f1673cb07cc0d472b858a34","score":"0.61860967","text":"def Nearest(*args):\n return _AIS.ais_Nearest(*args)","title":""},{"docid":"13b7b6f1fae7e949e6d6d01b589220b5","score":"0.61570233","text":"def getClosestRegion(self, ids=None):\n if ids is None:\n ids = self.ids\n\n if ids is None:\n res = None\n elif len(ids) == 0:\n res = numpy.array([])\n else:\n res = numpy.zeros(max(ids)+1) -1\n res[ids] = self._closestRegion\n\n return res","title":""},{"docid":"641fcfcef8604c7f379f01819355cc91","score":"0.6150702","text":"def get_nearest(points, coord):\n dists = [(pow(point[0] - coord[0], 2) + pow(point[1] - coord[1], 2), point)\n for point in points] # list of (dist, point) tuples\n nearest = min(dists)\n return nearest[1]","title":""},{"docid":"a4a9d7af195790da3fe334f7abd2c496","score":"0.61413366","text":"def find_nearest(shapes, x_pos, y_pos):\n for shape in shapes:\n for x2, y2 in shape:\n if distance([x_pos, y_pos], [x2, y2]) < 1 / 100:\n return [x2, y2]\n return [x_pos, y_pos]","title":""},{"docid":"27b2dd6751eeb9d2d13ec9eea442330c","score":"0.6138079","text":"def nearest(x,y,z,refGrid,N):\r\n pass\r\n \"\"\" NOT NEEDED!!!\"\"\"","title":""},{"docid":"dad98dd0f74b37d6526365798a3977d6","score":"0.6120982","text":"def in_grid(coordinates):","title":""},{"docid":"7c523dc2290b12d877d6290466b2032b","score":"0.60085577","text":"def get_nearest(self, lon, lat, shape):\n\t\tnearest = \"\"\n\t\tnearest_dist = 1000000 #arbitary huge number\n\t\tfor i in range(len(shape)):\n\t\t\tzone_lon, zone_lat = self.get_midpoint(shape[i].shape.points)\n\t\t\tdist = self.dist((lon, lat), (zone_lon, zone_lat))\n\t\t\tif dist < nearest_dist:\n\t\t\t\tnearest_dist = dist\n\t\t\t\tnearest = shape[i].record[1]\n\t\treturn nearest","title":""},{"docid":"4e1ff8cf2a65de3cc45dc7016a68feb6","score":"0.60053796","text":"def near_geo(ddsx, ddsy, XA):\n\n d = {'x': ddsx, 'y': ddsy, 'myx': XA[0], 'myy': XA[1]}\n df = pd.DataFrame(d)\n df['dis'] = df.apply(_pd_haversine, axis=1)\n ind = df['dis'].idxmin()\n dis = df.iloc[ind]['dis']\n return dis, ind","title":""},{"docid":"6087a8a634e11f47a4fbeb54dc24678c","score":"0.5978894","text":"def closest_aqhi(self, lat, lon):\n region_list = self.get_aqhi_regions()\n\n def site_distance(site):\n \"\"\"Calculate distance to a region.\"\"\"\n return distance.distance(\n (lat, lon), (site[\"latitude\"], site[\"longitude\"])\n )\n closest = min(region_list, key=site_distance)\n\n return closest['abbreviation'], closest['cgndb']","title":""},{"docid":"ad7be3724656e9aa69eb7bc1cbdd2bdb","score":"0.59621394","text":"def get_nearest_node(self, coords, n=1):\n\n if not coords or len(coords) < 1:\n return\n elif n < 1:\n # TODO: print warning or throw an exception \n # TODO: search the KD tree for the coordinates\n return","title":""},{"docid":"967ffce5cad7a5a57e4109b2d34cfa67","score":"0.59406275","text":"def _find_nearest_from_rectilinear_centroids(lon_centroids, lat_centroids,\n lon_point, lat_point,\n maximum_distance=None):\n\n # First calculate the difference in longitudes and warp overflowing\n # values back to [0, 360]\n distance_lon = np.mod(np.abs(lon_centroids-lon_point), 360)\n # For distances over 180, there's a shorter distance going the other\n # way around the globe.\n warp_indices = np.where(distance_lon > 180)\n distance_lon[warp_indices] = np.abs(distance_lon[warp_indices]-360.0)\n indices = np.where(distance_lon.min() == distance_lon)\n if len(indices[0]) > 1:\n msg = \"More than one nearest meridian, returning first index.\"\n logging.warning(msg)\n i = indices[0][0]\n distance_lat = np.abs(lat_centroids-lat_point)\n indices = np.where(distance_lat.min() == distance_lat)\n if len(indices[0]) > 1:\n msg = \"More than one nearest parallel, returning first index.\"\n logging.warning(msg)\n j = indices[0][0]\n minimum_distance = distance_lon_lat(lon_centroids[i], lat_centroids[j],\n lon_point, lat_point)\n if maximum_distance is not None:\n if minimum_distance > maximum_distance:\n raise GeogridError(\"No points within provided maximum distance.\")\n return (i, j)","title":""},{"docid":"66e07eb2189a8cde0a23d88aaa69df63","score":"0.59346557","text":"def nearest(coord, url_config=RequestConfig):\n\thost = check_host(url_config.host)\n\turl = '/'.join(\n\t\t[host, 'nearest', url_config.version, url_config.profile,\n\t\t str(coord).replace('(', '').replace(')', '').replace(' ', '')]\n\t)\n\trep = requests.get(url)\n\tparsed_json = json.loads(rep.text.decode('utf-8'))\n\treturn parsed_json","title":""},{"docid":"c69c40ba2e72a1eedaa30077c730cde8","score":"0.59068733","text":"def find_node_by_coords(coords, nodes):\n (x_value, y_value) = coords\n node = [node for node in nodes if ((node.x_coord == x_value) and\n (node.y_coord == y_value))]\n if len(node):\n return node[0]\n else:\n return None","title":""},{"docid":"3a2fc2c5c3cdff6545416ff12c4bb988","score":"0.5888296","text":"def nearest_point(lat_var, lon_var, lats, lons):\r\n # this part is to handle if lons are givn 0-360 or -180-180\r\n if any(lons > 180.0) and (lon_var < 0.0):\r\n lon_var = lon_var + 360.0\r\n else:\r\n lon_var = lon_var\r\n \r\n lat = lats\r\n lon = lons\r\n\r\n if lat.ndim == 2:\r\n lat = lat[:, 0]\r\n else:\r\n pass\r\n if lon.ndim == 2:\r\n lon = lon[0, :]\r\n else:\r\n pass\r\n\r\n index_a = np.where(lat >= lat_var)[0][-1]\r\n index_b = np.where(lat <= lat_var)[0][-1]\r\n\r\n if abs(lat[index_a] - lat_var) >= abs(lat[index_b] - lat_var):\r\n index_lat = index_b\r\n else:\r\n index_lat = index_a\r\n\r\n index_a = np.where(lon >= lon_var)[0][0]\r\n index_b = np.where(lon <= lon_var)[0][0]\r\n if abs(lon[index_a] - lon_var) >= abs(lon[index_b] - lon_var):\r\n index_lon = index_b\r\n else:\r\n index_lon = index_a\r\n\r\n return index_lat, index_lon","title":""},{"docid":"524960b4f799fd8efa8ba23560e634a2","score":"0.58849317","text":"def idx_of_nearest(coords, val):\n X = np.abs(coords.flatten()-val)\n idx = np.where(X == X.min())\n idx = idx[0][0]\n return coords.flatten()[idx]","title":""},{"docid":"d593075f229e7283f1c473d80d053f87","score":"0.58619833","text":"def nearest_loc(query_location, reference_grid, tolerance=0.5):\n\n distances = np.abs(reference_grid - query_location)\n ind = distances.argmin()\n if distances[ind] >= tolerance:\n return np.nan\n else:\n return ind","title":""},{"docid":"2773c32f94f7ca7ed1ac866d9ada5791","score":"0.58555204","text":"def getclosest_ij(lats, lons, latpt, lonpt):\n # find squared distance of every point on grid\n lats_dist = (lats - latpt)**2\n lons_dist = (lons - lonpt)**2\n return lats_dist.argmin(), lons_dist.argmin()","title":""},{"docid":"dddde2a127500d05870f601bc55995b5","score":"0.58491766","text":"def get_closest_point(point, coordinates):\n line = geom.LineString(coordinates)\n return line.interpolate(line.project(geom.Point(point))).coords[0]","title":""},{"docid":"c5995ec63c283ab222953455240feb46","score":"0.58040106","text":"def find_closest(location, centroids):\n # BEGIN Question 3\n return min(centroids, key=lambda x: distance(location, x))\n # END Question 3","title":""},{"docid":"8a59b7afef3d7249813f76ff1b7ecd55","score":"0.5791258","text":"def get_region_of_interest(self):\n cv.namedWindow(\"Source Image\")\n cv.setMouseCallback(\"Source Image\", self.__click_mouse_callback)\n\n print(\"Getting the region of interest...\")\n while (True):\n cv.imshow(\"Source Image\", self.__image)\n if self.print_flag:\n print(f\"Location: ({self.coordenates})\")\n\n #! Fix the problem with negative-coordinates\n for j in iter(range(self.coordenates.shape[1])):\n self.coordenates[:,j] = np.sort(self.coordenates[:,j], 0)\n\n self.roiImage = self.__image[\n self.coordenates[0,0]:self.coordenates[1,0],\n self.coordenates[0,1]:self.coordenates[1,1]\n ]\n\n cv.imshow(\"Source Image - ROI\",self.roiImage)\n self.print_flag = False\n if (cv.waitKey(1) & 0xFF == ord('q')):\n break","title":""},{"docid":"7bb2f88773d2a0a8532eb2a034515f25","score":"0.57878023","text":"def surrounding_pixels(array, coords, mode=\"direct neighborhood\"):\n shape = array.shape\n new_positions = []\n first, second = coords[0], coords[1]\n if mode == \"full neighborhood\":\n for ypos in range(first - 1, first + 2):\n for xpos in range(second - 1, second + 2):\n # print(xpos, ypos)\n # if xpos == first and ypos == second:\n # continue\n if ypos >= shape[0] or xpos >= shape[1] or xpos < 0 or ypos < 0:\n continue\n new_positions.append((ypos, xpos))\n elif mode == \"direct neighborhood\":\n positions = ((first - 1, second), (first + 1, second), (first, second),\n (first, second - 1), (first, second + 1))\n for xpos, ypos in positions:\n if xpos >= shape[0] or ypos >= shape[1] or xpos < 0 or ypos < 0:\n continue\n new_positions.append((xpos, ypos))\n return new_positions","title":""},{"docid":"51ab6fde2af7b5e8988fe4871b387183","score":"0.5765581","text":"def closest_point(self, xy, r=float('inf')):\n dist,i = self.kdtree.query(xy, distance_upper_bound=r)\n return i,dist","title":""},{"docid":"3a543c35d288c17ce8641dad981853a5","score":"0.57636493","text":"def FindNearestCity(self, lat, lon):\n self._Refresh()\n return self._cities.FindNearestNeighbor(lat, lon)","title":""},{"docid":"b80541dda12456bae03254413e4d6f5a","score":"0.5722385","text":"def nearest(cls, lat, long, **kwargs):\n if kwargs.get('buses', False):\n buses = kwargs['buses']\n elif kwargs.get('route', False):\n buses = kwargs['route'].get_buses()\n else:\n buses = cls.all()\n\n dists = map(lambda bus: distance(\n # Avoid using cached location, to avoid more requests.\n (bus.__lat__, bus.__long__),\n (lat, long)\n ), buses)\n\n return min(zip(dists, buses))[::-1]","title":""},{"docid":"1e436d2968f540e0620113f220016de8","score":"0.5721261","text":"def FindNearestCountry(self, lat, lon):\n self._Refresh()\n return self._cities.FindNearestNeighbor(lat, lon).parent.parent","title":""},{"docid":"1c1d118146c51e1a7388af04c2c12d00","score":"0.56799155","text":"def __find_neighbor_at(self, x, y, list_of_nodes):\n for node in list_of_nodes:\n if node.location == (x, y):\n return node","title":""},{"docid":"f8f0a2ed6ec77f521c7afc727861d76f","score":"0.5674876","text":"def find_node_location(coordinates):\n latitude = coordinates[0];\n longitude = coordinates[1];\n \n if((latitude <= 79 and latitude >= 54.548) and(longitude >= -180 and longitude <= -60.021)):\n return \"Node_1\";\n elif((latitude <= 79 and latitude >= 54.548) and(longitude >=-60 and longitude <= 59.989)):\n return \"Node_2\";\n elif((latitude <= 79 and latitude >= 54.548) and(longitude >=60 and longitude <= 180)):\n return \"Node_3\";\n elif((latitude <=54.52 and latitude >= -2.155) and(longitude >= -180 and longitude <= -60.021)):\n return \"Node_4\";\n elif((latitude <=54.52 and latitude >= -2.155) and(longitude >= -60 and longitude <= 59.989)):\n return \"Node_5\";\n elif((latitude <=54.52 and latitude >= -2.155) and(longitude >= 60 and longitude <= 180)):\n return \"Node_6\";\n elif((latitude <=-2.187 and latitude >= -56.97) and(longitude >= -180 and longitude <= -60.021)):\n return \"Node_7\";\n elif((latitude <=-2.187 and latitude >= -56.97) and(longitude >= -60 and longitude <= 59.989)):\n return \"Node_8\";\n elif((latitude <=-2.187 and latitude >= -56.97) and(longitude >= 60 and longitude <= 180)):\n return \"Node_9\";","title":""},{"docid":"93872353d084d89ddd73ec86d8323b8c","score":"0.5665834","text":"def neighbourhoodsearch(self, center, iselite):\n result = center\n neighbourfabric = NeighbourCoordinateFabric(center,\n self.settings)\n\n if (iselite): itnum = self.settings.RECRUITEDELITE\n else: itnum = self.settings.RECRUITEDNONELITE\n\n for i in range(itnum):\n current = neighbourfabric.getcoordinate()\n if (current.z < result.z): result = current\n return result","title":""},{"docid":"3d09415059f6485df6b0023aba155fe0","score":"0.5664631","text":"def find_closest2d(A):","title":""},{"docid":"8c6370426cfc9e2e55d3a2327935b9f7","score":"0.56597936","text":"def region(allcoordinates, coordinates):\n p = False #auxilary variable to check if the region was found or not\n n = 0\n while n <= len(allcoordinates)-1 and not p:\n name = allcoordinates[n]['name']\n a = 0\n while a <= len(allcoordinates[n]['coordinates'])-1:\n p = polyregions(verts1, coordinates)\n a += 1\n n += 1\n if p:\n return name","title":""},{"docid":"5c09582c05815ee02e53117dc32bb866","score":"0.56463283","text":"def nearxy(x,y,xi,yi):\n ind=ones(len(xi),dtype=int)\n dd=ones(len(xi),dtype='float')\n for i in arange(len(xi)):\n dist=sqrt((x-xi[i])**2+(y-yi[i])**2)\n ind[i]=dist.argmin()\n dd[i]=dist[ind[i]]\n return ind,dd","title":""},{"docid":"7152b94eec4e5c542164ff68cbef577b","score":"0.56310844","text":"def nearest_neighbor(nodes, origin):\n distances = [distance(origin, node, 100) for node in nodes]\n if origin in nodes:\n distances[nodes.index(origin)] = float(\"Inf\")\n result = min(distances)\n return (distances.index(result), result)","title":""},{"docid":"11033e415150acd05c1bb3250bcd89cf","score":"0.5630308","text":"def getXY(lats,lons,ptlat,ptlon):\n # Find closest lat/lon in array\n minlat = abs(lats-ptlat).min()\n minlon = abs(lons-ptlon).min()\n # Find where these are in the grid\n wherelat = N.where(abs(lats-ptlat) == minlat)\n wherelon = N.where(abs(lons-ptlon) == minlon)\n # pdb.set_trace()\n lat_idx = N.where(lats==lats[wherelat])[0][0]\n lon_idx = N.where(lons==lons[wherelon])[0][0]\n exactlat = lats[wherelat]\n exactlon = lons[wherelon]\n return lat_idx,lon_idx, exactlat, exactlon","title":""},{"docid":"2117f3c9795dc5c2f64faafc3ef0476c","score":"0.56120026","text":"def get_nearest_neighbors(self):\n current_x, current_y = self.get_current_pos()\n\n # North\n try:\n tile_type = self.fountain.get_tile(\n current_x, current_y + TILE_LENGTH).get_tile_type()\n north = state_to_int[tile_type]\n except KeyError as e:\n north = 2\n\n # East\n try:\n tile_type = self.fountain.get_tile(\n current_x + TILE_LENGTH, current_y).get_tile_type()\n east = state_to_int[tile_type]\n except KeyError as e:\n east = 2\n\n # South\n try:\n tile_type = self.fountain.get_tile(\n current_x, current_y - TILE_LENGTH).get_tile_type()\n south = state_to_int[tile_type]\n except KeyError as e:\n south = 2\n\n # West\n try:\n tile_type = self.fountain.get_tile(\n current_x - TILE_LENGTH, current_y).get_tile_type()\n west = state_to_int[tile_type]\n except KeyError as e:\n west = 2\n\n # Below\n try:\n tile_type = self.fountain.get_tile(\n current_x, current_y).get_tile_type()\n center = state_to_int[tile_type]\n except KeyError as e:\n center = 2\n\n temp = north * (3**4)\n temp += east * (3**3)\n temp += south * (3**2)\n temp += west * 3\n temp += center\n\n return self.moveset[temp]","title":""},{"docid":"290f28eaaa9721f1628746b4683b9b75","score":"0.5609461","text":"def _propose_nearby_loc(self,pos):\n pos = (pos[0] + random.uniform(-self.jitter,self.jitter), pos[1] + random.uniform(-self.jitter,self.jitter), pos[2])\n return self.mesh.nearest_point(pos=pos, radius=self.snap_radius)","title":""},{"docid":"eee54e83f6f532091cd65612cd85b92a","score":"0.5603823","text":"def _findClosest(self, objects):\n closest = None\n obj = None\n relativePos = None\n robotPos = self.robot.position + \\\n (self.robot.orientation * ogre.Vector3(0.5, 0, 0))\n\n for o in objects:\n toObj = o.position - robotPos\n \n # None yet found, default to this one\n if closest is None:\n closest = (o, toObj)\n else:\n obj, realtivePos = closest\n if toObj.squaredLength() < realtivePos.squaredLength():\n # Found a better one switch to it\n closest = (o, toObj)\n\n return closest","title":""},{"docid":"d951914b3601f5741466523814872000","score":"0.5602097","text":"def nearest_interp(xi, x, y):\r\n idx = abs(x - xi[:,None])\r\n return y[idx.argmin(axis=1)]","title":""},{"docid":"34b3353d5f2fd0b3dac6867a4492a71c","score":"0.55902964","text":"def find_closest(self, x, y, halo=None, start=None):\r\n cx,cy = self._o2c(x,y)\r\n return self.find('closest', cx, cy, halo, start)","title":""},{"docid":"3e6c7af74376f050780de921f8cdbff6","score":"0.5590099","text":"def cityreader_stretch(lat1, lon1, lat2, lon2, cities=[]):\n # within will hold the cities that fall within the specified region\n within = []\n\n # TODO Ensure that the lat and lon valuse are all floats\n coord_list = [lat1, lon1, lat2, lon2]\n\n # separate and sort lat and lon coordinates \n lat_coords = [coord_list[0], coord_list[2]]\n lon_coords = [coord_list[1], coord_list[3]]\n lat_coords = sorted(lat_coords)\n lon_coords = sorted(lon_coords)\n\n # Loop through each city and check to see if it falls within \n # the specified coordinates.\n for city in cities:\n if (city.lat > lat_coords[0] and city.lat < lat_coords[1]) and (\n city.lon > lon_coords[0] and city.lon < lon_coords[1]):\n within.append(city)\n\n return within","title":""},{"docid":"a0c7b5117542191038df843b2dfec0af","score":"0.55851114","text":"def FindNearestNeighbors(self, lat, lon):\n return {'country': self.FindNearestCountry(lat, lon),\n 'region': self.FindNearestRegion(lat, lon),\n 'city': self.FindNearestCity(lat, lon)}","title":""},{"docid":"178bfb3ff391f1d750cb74bc48e697b8","score":"0.55836624","text":"def closest(c, coords):\n best = 0\n best_distance = distance(c, coords[0])\n for i in range(1, len(coords)):\n c2 = coords[i]\n d = distance(c, c2)\n\n if d < best_distance:\n best = i\n best_distance = d\n return best","title":""},{"docid":"fbd5a115ee44277f8b0decc5151ebf1d","score":"0.5573302","text":"def get_area_of_interest(image, search_area):\n top = search_area[0][1]\n bottom = search_area[1][1]\n left = search_area[0][0]\n right = search_area[1][0]\n\n return image[top:bottom, left:right, :]","title":""},{"docid":"8c95c0a7408895c6269ea6c1c14226cf","score":"0.55606455","text":"def getlocation(indices, labels, Ni, Nj):\n zc = indices[labels == 1]\n if len(zc) == 0:\n return 0, None\n else:\n xmin = int(min(zc[:,1]))\n ymin = int(min(zc[:,0]))\n xmax = int(xmin + Nj)\n ymax = int(max(zc[:,0]) + Ni)\n return 1, ((xmin, ymin), (xmax, ymax))","title":""},{"docid":"fbc2651e39806aed12199fe44b54109b","score":"0.55543536","text":"def _getSearchBoundaries(self, target_pixel) :\n n, m = target_pixel\n\n if self.local_radius :\n upper_i = min(n + self.local_radius, self.image.shape[0] - self.offset - 1)\n lower_i = max(n - self.local_radius, self.offset)\n upper_j = min(m + self.local_radius, self.image.shape[1] - self.offset - 1)\n lower_j = max(m - self.local_radius, self.offset)\n else :\n upper_i = self.image.shape[0] - self.offset - 1\n lower_i = self.offset\n upper_j = self.image.shape[1] - self.offset - 1\n lower_j = self.offset\n \n return upper_i, lower_i, upper_j, lower_j","title":""},{"docid":"ac11c3f6bb2faf404c6b419d816195e9","score":"0.5552873","text":"def FindNearestNeighbor(self, lat, lon):\n cart = self._LatLonToCartesian(lat, lon)\n _, locale, _ = self._tree.nearest_neighbor(cart)\n return locale","title":""},{"docid":"a6736bace561b3f2208b155fa6a605c6","score":"0.5551941","text":"def FindClosestPointWithinRadius(self, p_float, , p_float_4):\n ...","title":""},{"docid":"e7e89de8e8759695f20a5b149cdc8766","score":"0.55451214","text":"def get_nearest(src_points, candidates, k_neighbors=1, distance_threshold=None):\n\n # Create tree from the candidate points\n coordinates = np.vstack(candidates.geometry.centroid.apply(lambda geom: (geom.x,geom.y)))\n tree = BallTree(coordinates, leaf_size=15, metric='haversine')\n\n # Find closest points and distances\n #src_points = src_points.reset_index() \n src_x = src_points.geometry.centroid.x\n src_y = src_points.geometry.centroid.y\n \n src_points = np.array([src_x, src_y]).reshape(-1,2)\n \n #If there are not enough neighbors, reduce K, then pad to original\n if k_neighbors > candidates.shape[0]:\n effective_neighbors = candidates.shape[0]\n else:\n effective_neighbors = k_neighbors\n \n distances, indices = tree.query(src_points, k=effective_neighbors)\n \n neighbor_geoms = candidates[candidates.index.isin(indices[0])]\n neighbor_geoms = neighbor_geoms.loc[indices[0]]\n \n #order by the indices\n neighbor_geoms[\"distance\"] = distances[0]\n\n if distance_threshold:\n neighbor_geoms = neighbor_geoms[neighbor_geoms.distance > distance_threshold]\n \n # Return indices and distances\n return neighbor_geoms","title":""},{"docid":"76bd88fe9b7da42e6fe9ba2bebf0d8fc","score":"0.5535252","text":"def nearest(pos, board):\n\n a, b = pos\n i = 0 # Number of cells away from target\n possible = [] # Possibilities for the nearest cell\n while True:\n added = False\n if a + i <= 27 and board[a+i][b]: # Right\n possible.append((a+i, b))\n added = True\n if a - i > 0 and board[a-i][b]: # Left\n possible.append((a-i, b))\n added = True\n if b + i <= 35 and board[a][b+i]: # Top\n possible.append((a, b+i))\n added = True\n if b - i > 0 and board[a][b-i]: # Bottom\n possible.append((a, b-i))\n added = True\n if added:\n break\n else: # If not valid tile is found increment i\n i += 1\n\n return random.choice(possible) # Choose a random cell based on all of the possible nearest ones","title":""},{"docid":"cd69b910a9e25ed1f782728f2b396169","score":"0.5529872","text":"def find_closest_room(self, position):\n position = self.map_from_global(position)\n self.update_room_positions()\n new_room = None\n min_dist = -1\n x_pos = position.x()\n y_pos = position.y()\n # O(n^2) solution, but too fast to need optimizations\n for room, loc in self.room_locations.items():\n dist = np.sqrt(\n np.square(x_pos - loc[0]) + np.square(y_pos - loc[1]))\n if min_dist < 0:\n min_dist = dist\n new_room = room\n elif dist < min_dist:\n min_dist = dist\n new_room = room\n return new_room","title":""},{"docid":"9049129fc607a4ac1819a238708ab215","score":"0.55296147","text":"def nearest_node(point, nodes):\n return nearest(point, nodes)","title":""},{"docid":"9049129fc607a4ac1819a238708ab215","score":"0.55296147","text":"def nearest_node(point, nodes):\n return nearest(point, nodes)","title":""},{"docid":"0f0f70c736cb344abb97633c6570ff75","score":"0.5518578","text":"def _find_nearest_from_irregular_centroids(lon_centroids, lat_centroids,\n lon_point, lat_point,\n maximum_distance=None):\n\n d = distance_lon_lat(lon_centroids, lat_centroids, lon_point, lat_point)\n minimum_distance = d.min()\n if maximum_distance is not None:\n if minimum_distance > maximum_distance:\n raise GeogridError(\"No points within provided maximum distance.\")\n indices = np.where(d == d.min())\n if len(indices[0]) > 1:\n logging.warning(\"More than one nearest point, returning first index.\")\n return (indices[0][0], indices[1][0])","title":""},{"docid":"77c459c86bdf9eb1c02e3614d75f604f","score":"0.5518351","text":"def locate_object(self, name, x0=None, y0=None):\n # TODO: Find out what this does and optimize it.\n if x0 is None:\n x0 = len(self.vision[0])\n if y0 is None:\n y0 = len(self.vision)\n number_of_coordinates = 0\n x_sum = 0\n y_sum = 0\n for y, row in enumerate(self.vision[:y0]):\n for x, object_name in enumerate(row[:x0]):\n if object_name == name:\n x_sum += x\n y_sum += y\n number_of_coordinates += 1\n if number_of_coordinates == 0:\n return None\n # Why is this writen this way?\n x_coordinate = x_sum / number_of_coordinates\n y_coordinate = y_sum / number_of_coordinates\n return x_coordinate, y_coordinate","title":""},{"docid":"e0a8c8e86327a57d005e9db11dc2118f","score":"0.55168664","text":"def _find_region(self, base: Geometry, region: RegionLocator):\n if not isinstance(base, Undefined):\n self.logger.warning(\"Using absolute region coordinates\")\n\n position = Region(region.left, region.top, region.right, region.bottom)\n return [position]","title":""},{"docid":"b95d523053f2132e3e5561a855e62a50","score":"0.5512935","text":"def find_location(location: Tuple[float, float]) -> Optional[SensorPoint]:\n\n # Get all rows\n rows = SensorPoint.objects.all()\n # Case the database is empty\n if len(rows) == 0:\n return None\n smallest_distance = None\n closest_point = None\n for i in range(0, len(rows)):\n d_distance = haversine_formula(location, (float(rows[i].latitude), float(rows[i].longitude)))\n if d_distance < 500.0:\n if smallest_distance is None or d_distance < smallest_distance:\n smallest_distance = d_distance\n closest_point = rows[i]\n return closest_point","title":""},{"docid":"5e4d0d1a8b38a12a394d9b9a0088ed82","score":"0.55100685","text":"def find_location(coordinates):\n\n locator = Nominatim(user_agent='Name')\n location = locator.reverse(coordinates, language='en')\n\n return (location.raw['address']['country'],\\\n location.raw['address']['city'],\\\n location.raw['address']['state'])","title":""},{"docid":"bc35d484f7caf786a23b46b9abc66bf7","score":"0.5509586","text":"def find_nearest(longitudes, latitudes, longitude, latitude,\n maximum_distance=None, grid_type=None,\n lon_dimensions=None, lat_dimensions=None):\n\n if grid_type is None:\n grid_type = detect_grid(longitudes, latitudes, lon_dimensions,\n lat_dimensions)\n if grid_type == 'list_of_2d_points':\n f = _find_nearest_from_list_of_points\n return f(longitudes, latitudes, longitude, latitude, maximum_distance)\n elif grid_type == 'rectilinear_2d_centroids':\n f = _find_nearest_from_rectilinear_centroids\n return f(longitudes, latitudes, longitude, latitude, maximum_distance)\n elif grid_type == 'rectilinear_2d_bounds':\n f = rectilinear_2d_bounds_to_vertices\n (lon_vertices, lat_vertices) = f(longitudes, latitudes)\n f = _find_nearest_from_rectilinear_vertices\n return f(lon_vertices, lat_vertices, longitude, latitude,\n maximum_distance)\n elif grid_type == 'rectilinear_2d_vertices':\n f = _find_nearest_from_rectilinear_vertices\n return f(longitudes, latitudes, longitude, latitude, maximum_distance)\n elif grid_type == 'irregular_2d_centroids':\n f = _find_nearest_from_irregular_centroids\n return f(longitudes, latitudes, longitude, latitude, maximum_distance)\n elif grid_type == 'irregular_2d_vertices':\n f = _find_nearest_from_irregular_vertices\n return f(longitudes, latitudes, longitude, latitude, maximum_distance)","title":""},{"docid":"b578caa94ddfc78ca5365195ecb5f568","score":"0.55021286","text":"def find_closest_neighbor(point, neighbors):\n uv_point = ll2uv(point[0], point[1])\n id_neighbor = None\n angle_min = float(\"inf\")\n for i, n in enumerate(neighbors):\n angle = angular_distance(uv_point, ll2uv(n[0], n[1]))\n if angle < angle_min:\n id_neighbor = i\n angle_min = angle\n\n return id_neighbor","title":""},{"docid":"58768599a8c020c8ab3c9e447ef2620d","score":"0.55007684","text":"def _find_region(self, point: Tuple[int, int]) -> str:\n if point[0] <= self._centre[0] and point[1] <= self._centre[1]: # NW\n return 'NW'\n elif point[0] <= self._centre[0]: # SW\n return 'SW'\n elif point[1] <= self._centre[1]: # NE\n return 'NE'\n else: # SE\n return 'SE'","title":""},{"docid":"e3752a0e50cb0779cee68e879efba6d6","score":"0.5499344","text":"def get_river_src(coords):\r\n # Get elevation information from .tif file\r\n gdal.UseExceptions()\r\n try:\r\n src_ds = gdal.Open( 'H:\\spatial entity\\data\\shpdata\\\\chinasrtm24000_16000.tif' )\r\n except RuntimeError, e:\r\n print 'Unable to open INPUT.tif'\r\n print e\r\n sys.exit(1) \r\n try:\r\n srcband = src_ds.GetRasterBand(1)\r\n except RuntimeError, e:\r\n print 'Band ( %i ) not found' # band_num\r\n print e\r\n sys.exit(1) \r\n elevation = srcband.ReadAsArray()\r\n # Transform coordinates to pixel \r\n gt = src_ds.GetGeoTransform() \r\n coords_elevation = [] \r\n for i in range(0,len(coords)):\r\n px = int((coords[i][0] - gt[0]) / gt[1]) # y pixel\r\n py = int((coords[i][1] - gt[3]) / gt[5]) # x pixel\r\n coords_elevation += [elevation[py][px]]\r\n # Get the index of maximum elevation\r\n return coords_elevation.index(max(coords_elevation))","title":""},{"docid":"53c9f99cce164ea8b3c00e42e71e81b5","score":"0.549926","text":"def ais_Nearest(*args):\n return _AIS.ais_Nearest(*args)","title":""},{"docid":"79b252fa7089edd47c0662afa14699d6","score":"0.5499103","text":"def bestMatch(images, region = SCREEN, minOverlap = 0.9):\n if len(images) == 0:\n return None\n matches = getAllMatches(images, **{ 'region' : region, 'timeout' : 0 })\n if matches[0] is not None:\n best_match = 0\n best_score = matches[0].getScore()\n else:\n best_match = None\n best_score = 0\n for m in range(1, len(matches)):\n match = matches[m]\n if match is None:\n continue\n score = match.getScore()\n if best_match is None:\n best_match = m\n best_score = score\n else:\n if not sameRegion(matches[best_match], match, minOverlap):\n raise Exception('images %d, %d found in different regions' %\n (best_match, m))\n if score > best_score:\n best_match = m\n best_score = score\n if best_match is None:\n if region.getThrowException():\n raise FindFailed('none of the images was found')\n else:\n return None\n return best_match, matches[best_match]","title":""},{"docid":"43aa4ea3c1ea8596124264226c36a81f","score":"0.54961836","text":"def NearestApex(*args):\n return _AIS.ais_NearestApex(*args)","title":""},{"docid":"5bf75e2b9048c33cca8fc8d013b19436","score":"0.54923683","text":"def closest(self, coords=[], **kwargs):\n if kwargs and coords:\n raise ValueError(\"Specify coordinate using as either a list \"\n \"keyword arguments not both\")\n if kwargs:\n coords = []\n getter = []\n for k, v in kwargs.items():\n idx = self.get_dimension_index(k)\n if np.isscalar(v):\n coords.append((0, v) if idx else (v, 0))\n else:\n if isinstance(v, list):\n coords = [(0, c) if idx else (c, 0) for c in v]\n if len(coords) not in [0, len(v)]:\n raise ValueError(\"Length of samples must match\")\n elif len(coords):\n coords = [(t[abs(idx-1)], c) if idx else (c, t[abs(idx-1)])\n for c, t in zip(v, coords)]\n getter.append(idx)\n else:\n getter = [0, 1]\n getter = itemgetter(*sorted(getter))\n if len(coords) == 1:\n coords = coords[0]\n if isinstance(coords, tuple):\n return getter(self.closest_cell_center(*coords))\n else:\n return [getter(self.closest_cell_center(*el)) for el in coords]","title":""},{"docid":"be71a08c4892610430148064741e02f0","score":"0.5483117","text":"def get_nearest(src_points, candidates, k_neighbors=1, distance_threshold=None):\n\n # Create tree from the candidate points\n coordinates = np.vstack(candidates.geometry.centroid.apply(lambda geom: (geom.x,geom.y)))\n tree = BallTree(coordinates, leaf_size=15, metric='haversine')\n\n # Find closest points and distances\n #src_points = src_points.reset_index() \n src_x = src_points.geometry.centroid.x\n src_y = src_points.geometry.centroid.y\n \n src_points = np.array([src_x, src_y]).reshape(-1,2)\n \n #If there are not enough neighbors, reduce K, then pad to original\n if k_neighbors > candidates.shape[0]:\n effective_neighbors = candidates.shape[0]\n else:\n effective_neighbors = k_neighbors\n \n distances, indices = tree.query(src_points, k=effective_neighbors)\n \n neighbor_geoms = candidates[candidates.index.isin(indices[0])]\n neighbor_geoms[\"distance\"] = distances[0]\n\n if distance_threshold:\n neighbor_geoms = neighbor_geoms[neighbor_geoms.distance > distance_threshold]\n \n # Return indices and distances\n return neighbor_geoms","title":""},{"docid":"1546d6e2909192819d711d30d4d2715d","score":"0.54806334","text":"def get_closest_obstacle_distance(self,x,y):\n x, y = int(x), int(y)\n\n # check if we are in bounds\n if x >= self.width or x < 0 or y >= self.height or y < 0:\n return float('nan')\n\n return self.closest_occ[y,x]","title":""},{"docid":"f6f16c3e9603699ef3c6cf2af03a2746","score":"0.5479858","text":"def match_to_closest_location(self, locations):\r\n # Convert the list of locations to arrays.\r\n end_lats = []\r\n end_lons = []\r\n for location in locations:\r\n end_lats.append(location[0])\r\n end_lons.append(location[1])\r\n end_lats = np.array(end_lats)\r\n end_lons = np.array(end_lons)\r\n\r\n distances = self.get_distances_to_lat_lons(end_lats, end_lons)\r\n closest_locations = np.argmin(distances, axis=0)\r\n return closest_locations","title":""},{"docid":"bdf8d14c75e758483fa886e367084bdf","score":"0.5461208","text":"def get_distance_binary_search(coordinates: Tuple[float, float]) -> int:\n # Find four equidistant points on the MKAD with which to compare.\n index_list = [(len(mkad_coords) // 4) * i for i in range(4)]\n distance_list = []\n\n # Obtain the distances from the given coords to the four points\n for index in index_list:\n new_distance = distance(coordinates, (mkad_coords[index][2],\n mkad_coords[index][1])).km\n distance_list.append([index, new_distance])\n\n # Sort list from shorter to longer distances, keeping corresponding index\n distance_list.sort(key=lambda x: x[1])\n\n index_list = []\n\n # If the second shortest point is the same distance as the third shortest\n # point, then the given coordinates are in such a position that we cannot\n # take the first and second shortest point from the four indexes, instead\n # we take the second and third shortest.\n if (distance_list[1][1] == distance_list[2][1]):\n low_point = distance_list[1][0]\n high_point = distance_list[2][0]\n # Otherwise we take the first and second shortest.\n else:\n low_point = distance_list[0][0]\n high_point = distance_list[1][0]\n\n # We now proceed to eliminate points that are in between the two points\n # we have selected. All other points are discarded.\n\n while high_point - low_point > 1:\n low_distance = distance(\n coordinates,\n (mkad_coords[low_point][2], mkad_coords[low_point][1])\n ).km\n high_distance = distance(\n coordinates,\n (mkad_coords[high_point][2], mkad_coords[high_point][1])\n ).km\n\n if low_distance == high_distance:\n low_point += 1\n elif low_distance < high_distance:\n mid_point = (low_point + high_point) // 2\n high_point = mid_point\n elif low_distance > high_distance:\n mid_point = (low_point + high_point) // 2\n low_point = mid_point\n\n # After the while loop we have reduced the possibilities to two points\n # on the MKAD, indexed as low_point and high_point. We simply measure\n # the distances to these and return the shortest.\n\n low_distance = distance(\n coordinates,\n (mkad_coords[low_point][2], mkad_coords[low_point][1])\n ).km\n high_distance = distance(\n coordinates,\n (mkad_coords[high_point][2], mkad_coords[high_point][1])\n ).km\n\n if low_distance < high_distance:\n return int(low_distance)\n else:\n return int(high_distance)","title":""},{"docid":"e7d7900777c579f740adcc5a7e431871","score":"0.5457809","text":"def _find_nearest_from_rectilinear_vertices(lon_vertices, lat_vertices,\n lon_point, lat_point,\n maximum_distance=None):\n\n f = rectilinear_vertices_to_centroids\n (lon_centroids, lat_centroids) = f(lon_vertices, lat_vertices)\n return _find_nearest_from_rectilinear_centroids(lon_centroids,\n lat_centroids)","title":""},{"docid":"7d162b990429663beaf951aa029aa61c","score":"0.54533446","text":"def find_nearest(self, position, at_sublattice=None):\n at_sublattice = self.lattice[at_sublattice] if at_sublattice is not None else -1\n if hasattr(self.impl, 'find_nearest'):\n # use cpp implementation\n return self.impl.find_nearest(position, int(at_sublattice))\n else:\n # fallback numpy implementation\n sites = Sites(self.positions, self.sublattices)\n return sites.find_nearest(position, at_sublattice)","title":""},{"docid":"81e55c9393f488c89f3d79bf9b5d894d","score":"0.54512286","text":"def match_coordinates(longitude, latitude):\n\n lon = float(longitude)\n lat = float(latitude)\n pnt = Point(lon, lat)\n result = Biology.objects.filter(geom__distance_lte=(pnt, Distance(m=1)))\n match_result = (False, None)\n if (len(result)) == 1:\n match_result = (True, result)\n elif len(result) >= 1:\n match_result = (False, result)\n elif len(result) == 0:\n match_result = (False, None)\n return match_result","title":""},{"docid":"7ec2f2607315ec5116b444c2c3babf8c","score":"0.5449289","text":"def _find_new_inside(self):\n npoints = len(self._points)\n if npoints > 4:\n points = np.vstack((self._points, self._points[1]))\n A = points[:-2]\n B = points[1:-1]\n C = points[2:]\n orient = great_circle_arc.triple_product(A-B, C-B, B)\n if np.sum(orient) < 0.0:\n orient = -1.0 * orient\n midpoint = great_circle_arc.midpoint(A, C)\n candidate = max(zip(orient, midpoint), key=lambda x: x[0])\n inside = candidate[1]\n else:\n # Fall back on computing the mean point\n inside = self._points.mean(axis=0)\n vector.normalize_vector(inside, output=inside)\n return inside","title":""},{"docid":"8ae51fe0c7982db53c7040dc60b8021c","score":"0.5442965","text":"def star_closest_to_world_coords(self, ra, dec):\n\n if not len(self):\n raise ValueError(\"database is empty\")\n\n self._execute(\"SELECT id, ra, dec FROM stars\")\n\n closest_id = None\n closest_distance = float('inf')\n coordinates = astromatic.Coordinates(ra, dec)\n for star_id, star_ra, star_dec in self._rows:\n star_coords = astromatic.Coordinates(star_ra, star_dec)\n star_distance = coordinates.distance(star_coords)\n if star_distance < closest_distance:\n closest_id = star_id\n closest_distance = star_distance\n\n return closest_id, closest_distance","title":""},{"docid":"e36f3085d79859f311dd23d1594063b9","score":"0.54392976","text":"def _get_nearest(self, pose, window=2):\r\n min_idx = self._idx\r\n max_idx = np.minimum(self._pos.shape[0], self._idx + window)\r\n idx = range(min_idx, max_idx)\r\n nearest = idx[np.argmin(distance(pose[:2], self._pos[idx]))]\r\n self._idx = nearest\r\n\r\n # Check if goal has been reached\r\n if distance(pose[:2], self._pos[-1]) <= self._goal_threshold:\r\n self._goal_reached = True\r\n\r\n return nearest","title":""},{"docid":"e3300b5093810499a478443a0c1343f2","score":"0.54318255","text":"def find_neighborhood(shapes, lat, lng):\n point = Point(lng, lat)\n\n # sorts the shapes by distance from shape centroid the point\n sorted_shapes = sort_shapes(shapes, point)\n\n # now that shapes are sorted, we should find the\n # boundary shape pretty quick\n for shape in sorted_shapes:\n if shape.polygon.contains(point):\n return shape.name\n\n return None","title":""},{"docid":"741adc48acb2c4eba9ca299514f1c08f","score":"0.5428988","text":"def getLocation( min_point: tuple, max_point: tuple ) -> tuple:\n # Unpack the tuples into min/max values\n xmin, ymin = min_point\n xmax, ymax = max_point\n # Take midpoint of x-coordinate and ymax for bottom middle of box\n x_result = ( xmin + xmax ) // 2\n y_result = ymax\n # Return location\n return ( x_result, y_result )","title":""},{"docid":"03b0be6306604db546410e7b94452660","score":"0.54253185","text":"def findRoute(self, x1, y1, x2, y2):\r\n pass","title":""},{"docid":"1a740a9f694277f8e6fb36c9495a3e40","score":"0.5423184","text":"def get_closest_obstacle_distance(self, x, y):\n x_coord = (x - self.map.info.origin.position.x)/self.map.info.resolution\n y_coord = (y - self.map.info.origin.position.y)/self.map.info.resolution\n if type(x) is np.ndarray:\n x_coord = x_coord.astype(np.int)\n y_coord = y_coord.astype(np.int)\n else:\n x_coord = int(x_coord)\n y_coord = int(y_coord)\n\n is_valid = (x_coord >= 0) & (y_coord >= 0) & (x_coord < self.map.info.width) & (y_coord < self.map.info.height)\n if type(x) is np.ndarray:\n distances = np.float('nan')*np.ones(x_coord.shape)\n distances[is_valid] = self.closest_occ[x_coord[is_valid], y_coord[is_valid]]\n return distances\n else:\n return self.closest_occ[x_coord, y_coord] if is_valid else float('nan')","title":""},{"docid":"3456e7a84b76647762c0113c07daef64","score":"0.5418462","text":"def region_index(lats0, lons0): \n Bi = np.zeros(lons0.shape[0]) #The grid cell index where a particle is released at the bottom\n\n for i in range(lons0.shape[0]):\n lo = find_down(Lons,lons0[i]);lon = Lons[lo];\n la = find_down(Lats,lats0[i]);lat = Lats[la];\n Bi[i] = np.where(np.logical_and(vLons==lon,vLats==lat))[0] \n return Bi","title":""},{"docid":"651b63ee59610a63edd359da7fb72936","score":"0.5415594","text":"def nearest_neighbour(src_lon, src_lat, dst_lon, dst_lat, src_grid=\"regular\", dst_grid=\"unstructured\", npoints=1, method=\"mean\"):\n # create an array containing all coordinates\n if src_grid == \"regular\":\n if src_lon.ndim == 1:\n lon_2d, lat_2d = np.meshgrid(src_lon, src_lat)\n elif src_lon.ndim == 2:\n if src_lon.shape != src_lat.shape:\n raise ValueError(\"for 2d-coordinates, the shapes have to match!\")\n lon_2d, lat_2d = src_lon, src_lat\n else:\n raise ValueError(\"only 1d- and 2d-coordinates are supported for regular grids\")\n coords = np.stack((lon_2d.flatten(), lat_2d.flatten()), axis=1)\n input_dims = lon_2d.shape\n elif src_grid == \"unstructured\":\n if src_lon.ndim == 1:\n coords = np.stack((src_lon, src_lat), axis=1)\n input_dims = src_lon.shape\n else:\n raise ValueError(\"an unstructured grid is supposed to have 1d-coordinate arrays!\")\n\n # convert point coordinates if given as scalar\n if not hasattr(dst_lon, \"__len__\"):\n dst_lon = np.array((dst_lon,))\n dst_lat = np.array((dst_lat,))\n\n # keep the target coordinates\n target_lon = dst_lon\n target_lat = dst_lat\n\n # create coordinates for regular output grid\n if dst_grid == \"regular\" and dst_lon.ndim == 1 and dst_lat.ndim == 1:\n point_lon_2d, point_lat_2d = np.meshgrid(dst_lon, dst_lat)\n dst_lon = point_lon_2d.flatten()\n dst_lat = point_lat_2d.flatten()\n target_shape = point_lon_2d.shape\n else:\n target_shape = (len(dst_lon),)\n\n # create the kd-tree and calculate the indices and the weights for the indices\n kdtree = scipy.spatial.cKDTree(coords)\n\n # estimate the distance between grid points by calculating the distance to the next neighbour for three points\n grid_point_distance, _ = kdtree.query(coords[[0, len(coords) // 2, len(coords)-1]], k=2)\n mean_grid_point_distance = grid_point_distance[:, 1].mean()\n\n # calculate indices and distances for the target points\n if len(dst_lon) == 1:\n distances, indices_flat = kdtree.query((dst_lon[0], dst_lat[0]), k=npoints)\n else:\n distances, indices_flat = kdtree.query(np.stack((dst_lon, dst_lat), axis=1), k=npoints)\n if npoints > 1 and len(distances.shape) == 1:\n distances = np.expand_dims(distances, 0)\n indices_flat = np.expand_dims(indices_flat, 0)\n if npoints > 1:\n if method == \"mean\":\n weights = np.empty(distances.shape)\n weights[:] = 1.0 / npoints\n elif method == \"d-mean\":\n weights = 1.0 / np.maximum(distances, mean_grid_point_distance / 2.0)**2\n weights = weights / np.tile(np.expand_dims(np.sum(weights, 1), 1), npoints)\n else:\n raise ValueError(\"unsupported method: %s\" % method)\n else:\n weights = 1\n if len(input_dims) > 1:\n indices = np.unravel_index(indices_flat, input_dims)\n else:\n indices = indices_flat\n if type(indices) == int:\n indices = np.asarray([indices])\n\n # construct and return the interpolator object\n return NearestNeighbourInterpolator(indices, weights, distances, input_dims, len(dst_lon), npoints, target_shape, target_lon, target_lat)","title":""},{"docid":"4b1d0eb9b1e9408b0119b6b5781d02d6","score":"0.5405547","text":"def find_coordinates(numpoints, refcoords, defcoords, defatomcoords):\n refcenter, fitcenter, rotation = qfit(numpoints, refcoords, defcoords)\n newcoords = qtransform(1, defatomcoords, refcenter, fitcenter, rotation)\n # Only return the first coordinates\n return newcoords[0]","title":""},{"docid":"1ed839944b066a0f4bf559cd0a7d2589","score":"0.54023427","text":"def FindClosestPoint(self, ):\n ...","title":""},{"docid":"cf310dc4796a27ff572a99502dcb2789","score":"0.5400604","text":"def query_coordinates(self, coordinates):\r\n try:\r\n distances, indices = self.tree.query(coordinates, k=1) #, distance_upper_bound=0.1\r\n except ValueError as e:\r\n logger.erro('Unable to parse coordinates:', coordinates)\r\n raise e\r\n else:\r\n results = []\r\n for distance, index in zip(distances, indices):\r\n if not isinf(distance):\r\n result = self.locations[index]\r\n result['distance'] = distance\r\n\r\n results.append(result)\r\n\r\n return results","title":""},{"docid":"c960465a5c8453728418b7c7c8f12741","score":"0.53992957","text":"def findAt(self, coordinates: tuple, printWarning: Boolean = True):\n pass","title":""},{"docid":"c960465a5c8453728418b7c7c8f12741","score":"0.53992957","text":"def findAt(self, coordinates: tuple, printWarning: Boolean = True):\n pass","title":""},{"docid":"8f96a121329590f95ea92a94091931f0","score":"0.5396925","text":"def th_nearest_interp2d(input, coords):\n # take clamp of coords so they're in the image bounds\n x = th.clamp(coords[:, :, 0], 0, input.size(1) - 1).round()\n y = th.clamp(coords[:, :, 1], 0, input.size(2) - 1).round()\n\n stride = th.LongTensor(input.stride())\n x_ix = x.mul(stride[1]).long()\n y_ix = y.mul(stride[2]).long()\n\n input_flat = input.view(input.size(0), -1)\n\n mapped_vals = input_flat.gather(1, x_ix.add(y_ix))\n\n return mapped_vals.view_as(input)","title":""},{"docid":"77db11cf9ca2b313312a460149fbd7ce","score":"0.5393471","text":"def nearest(self, pt):\n tidx = super(FlatCAMRTreeStorage, self).nearest(pt)\n return (tidx.bbox[0], tidx.bbox[1]), self.objects[tidx.object]","title":""},{"docid":"f1e97c3d2cecb342529408dd6e9e4a19","score":"0.5390935","text":"def getIntersectionPoint(non_street_point,street_point,coordinates):\n (x1,y1) = coordinates[non_street_point[0]]\n (x2,y2) = coordinates[non_street_point[1]]\n (x3,y3) = coordinates[street_point[0]]\n (x4,y4) = coordinates[street_point[1]]\n den = (x1-x2)*(y3-y4) - (y1-y2)*(x3-x4)\n n1 = (x1*y2 - y1*x2)\n n2 = (x3*y4 - y3*x4)\n px = (n1*(x3 - x4) - n2*(x1 - x2)) / den\n py = (n1*(y3 - y4) - n2*(y1 - y2)) / den\n return (px,py)","title":""},{"docid":"44ddab67f26a3d04c067d32fe7738816","score":"0.5386557","text":"def test_get_near_point():\n sh = SpatialHash(cell_size=10)\n sprite_1 = arcade.SpriteSolidColor(10, 10, center_x=0)\n sprite_2 = arcade.SpriteSolidColor(10, 10, center_x=5)\n sh.add(sprite_1)\n sh.add(sprite_2)\n\n nearby_sprites = sh.get_sprites_near_point((0, 0))\n assert isinstance(nearby_sprites, set)\n assert len(nearby_sprites) == 2\n assert nearby_sprites == set([sprite_1, sprite_2])\n\n nearby_sprites = sh.get_sprites_near_point((20, 0))\n assert isinstance(nearby_sprites, set)\n assert len(nearby_sprites) == 0","title":""}],"string":"[\n {\n \"docid\": \"cc5224e323d654e0d4732c87fd7ff737\",\n \"score\": \"0.6737705\",\n \"text\": \"def nearest(coordinate, coordinate_list, limit):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11396b53fc89921c28fd992f59f2363d\",\n \"score\": \"0.6671556\",\n \"text\": \"def get_nearest_valid_coordinates(self, coordinates):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e348d8ecb20c1ceba88dd9f2efe2a611\",\n \"score\": \"0.660837\",\n \"text\": \"def locate(self, lat, lon):\\n for key, region in self.regions.iteritems():\\n if region.contains(lat, lon):\\n return key\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb5f877eea7163bc2e96be144c6b64c7\",\n \"score\": \"0.6579592\",\n \"text\": \"def nearest_boundaries_location(self,x,y,xi,yi):\\n mdgrd = np.array(zip( x.ravel(),y.ravel() ))\\n kdt = kd.KDTree(mdgrd)\\n self.mdi_dist, self.mdi = kdt.query(np.array(zip( xi, yi) ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf233f76183b0e1d9c61461acc74d290\",\n \"score\": \"0.6503124\",\n \"text\": \"def nearest_grid_point(distances):\\n return distances.index(min(distances))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5adfd91824ae7915f5603993294d9411\",\n \"score\": \"0.6436937\",\n \"text\": \"def getClosest(self, coordinates: tuple, searchTolerance: str = \\\"\\\"):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04803e163799e7eb1f0e470bfcc01801\",\n \"score\": \"0.63568676\",\n \"text\": \"def closest_to(cls, coordinates):\\n # I should be using the db's geo stuff for this, but I'm keeping it\\n # database-agnostic\\n cities = cls.objects.all()\\n cities = list(cities)\\n\\n cities.sort(key=lambda x: x.distance_from_coordinates(coordinates))\\n return cities[0] if cities else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb304253516b3ff2c27e50ffd59628ee\",\n \"score\": \"0.62382346\",\n \"text\": \"def nearestPoint( pos ):\\n ( current_row, current_col ) = pos\\n\\n grid_row = int( current_row + 0.5 )\\n grid_col = int( current_col + 0.5 )\\n return ( grid_row, grid_col )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75c1caf8d0aa9507b97abb12caeda00e\",\n \"score\": \"0.6215297\",\n \"text\": \"def find_node(self, coordinates):\\n node = self.origin\\n if node == None:\\n return None\\n z = 0\\n while node.up and z < coordinates[2]:\\n node = node.up\\n z += 1\\n y = 0\\n while node.north and y < coordinates[1]:\\n node = node.north\\n y += 1\\n x = 0\\n while node.east and x < coordinates[0]:\\n node = node.east\\n x += 1\\n if node.coordinates != coordinates:\\n return None\\n else:\\n return node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"003552daf7ae1c21048ffad336e9d359\",\n \"score\": \"0.6199199\",\n \"text\": \"def find_closest_intersection(instructions_a, instructions_b, origin = (0, 0)):\\n a = instructions_to_locations(instructions_a)\\n b = instructions_to_locations(instructions_b)\\n #print(a)\\n #print(b)\\n crossing_points = list(a.intersection(b))\\n #print(crossing_points)\\n winner = find_nearest_location(crossing_points)\\n return winner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46d0d1e97a5f63ed5a25dc28228c685f\",\n \"score\": \"0.61891586\",\n \"text\": \"def find_nearest_center(x, y):\\n xs = np.array([j.x for j in self.current_scan.joints])\\n ys = np.array([j.y for j in self.current_scan.joints])\\n distance_partial = (ys - y) ** 2 + (xs - x) ** 2\\n loc = np.where(distance_partial == distance_partial.min())[0][0]\\n return loc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9820e07d9f1673cb07cc0d472b858a34\",\n \"score\": \"0.61860967\",\n \"text\": \"def Nearest(*args):\\n return _AIS.ais_Nearest(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13b7b6f1fae7e949e6d6d01b589220b5\",\n \"score\": \"0.61570233\",\n \"text\": \"def getClosestRegion(self, ids=None):\\n if ids is None:\\n ids = self.ids\\n\\n if ids is None:\\n res = None\\n elif len(ids) == 0:\\n res = numpy.array([])\\n else:\\n res = numpy.zeros(max(ids)+1) -1\\n res[ids] = self._closestRegion\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"641fcfcef8604c7f379f01819355cc91\",\n \"score\": \"0.6150702\",\n \"text\": \"def get_nearest(points, coord):\\n dists = [(pow(point[0] - coord[0], 2) + pow(point[1] - coord[1], 2), point)\\n for point in points] # list of (dist, point) tuples\\n nearest = min(dists)\\n return nearest[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4a9d7af195790da3fe334f7abd2c496\",\n \"score\": \"0.61413366\",\n \"text\": \"def find_nearest(shapes, x_pos, y_pos):\\n for shape in shapes:\\n for x2, y2 in shape:\\n if distance([x_pos, y_pos], [x2, y2]) < 1 / 100:\\n return [x2, y2]\\n return [x_pos, y_pos]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27b2dd6751eeb9d2d13ec9eea442330c\",\n \"score\": \"0.6138079\",\n \"text\": \"def nearest(x,y,z,refGrid,N):\\r\\n pass\\r\\n \\\"\\\"\\\" NOT NEEDED!!!\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dad98dd0f74b37d6526365798a3977d6\",\n \"score\": \"0.6120982\",\n \"text\": \"def in_grid(coordinates):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c523dc2290b12d877d6290466b2032b\",\n \"score\": \"0.60085577\",\n \"text\": \"def get_nearest(self, lon, lat, shape):\\n\\t\\tnearest = \\\"\\\"\\n\\t\\tnearest_dist = 1000000 #arbitary huge number\\n\\t\\tfor i in range(len(shape)):\\n\\t\\t\\tzone_lon, zone_lat = self.get_midpoint(shape[i].shape.points)\\n\\t\\t\\tdist = self.dist((lon, lat), (zone_lon, zone_lat))\\n\\t\\t\\tif dist < nearest_dist:\\n\\t\\t\\t\\tnearest_dist = dist\\n\\t\\t\\t\\tnearest = shape[i].record[1]\\n\\t\\treturn nearest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e1ff8cf2a65de3cc45dc7016a68feb6\",\n \"score\": \"0.60053796\",\n \"text\": \"def near_geo(ddsx, ddsy, XA):\\n\\n d = {'x': ddsx, 'y': ddsy, 'myx': XA[0], 'myy': XA[1]}\\n df = pd.DataFrame(d)\\n df['dis'] = df.apply(_pd_haversine, axis=1)\\n ind = df['dis'].idxmin()\\n dis = df.iloc[ind]['dis']\\n return dis, ind\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6087a8a634e11f47a4fbeb54dc24678c\",\n \"score\": \"0.5978894\",\n \"text\": \"def closest_aqhi(self, lat, lon):\\n region_list = self.get_aqhi_regions()\\n\\n def site_distance(site):\\n \\\"\\\"\\\"Calculate distance to a region.\\\"\\\"\\\"\\n return distance.distance(\\n (lat, lon), (site[\\\"latitude\\\"], site[\\\"longitude\\\"])\\n )\\n closest = min(region_list, key=site_distance)\\n\\n return closest['abbreviation'], closest['cgndb']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad7be3724656e9aa69eb7bc1cbdd2bdb\",\n \"score\": \"0.59621394\",\n \"text\": \"def get_nearest_node(self, coords, n=1):\\n\\n if not coords or len(coords) < 1:\\n return\\n elif n < 1:\\n # TODO: print warning or throw an exception \\n # TODO: search the KD tree for the coordinates\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"967ffce5cad7a5a57e4109b2d34cfa67\",\n \"score\": \"0.59406275\",\n \"text\": \"def _find_nearest_from_rectilinear_centroids(lon_centroids, lat_centroids,\\n lon_point, lat_point,\\n maximum_distance=None):\\n\\n # First calculate the difference in longitudes and warp overflowing\\n # values back to [0, 360]\\n distance_lon = np.mod(np.abs(lon_centroids-lon_point), 360)\\n # For distances over 180, there's a shorter distance going the other\\n # way around the globe.\\n warp_indices = np.where(distance_lon > 180)\\n distance_lon[warp_indices] = np.abs(distance_lon[warp_indices]-360.0)\\n indices = np.where(distance_lon.min() == distance_lon)\\n if len(indices[0]) > 1:\\n msg = \\\"More than one nearest meridian, returning first index.\\\"\\n logging.warning(msg)\\n i = indices[0][0]\\n distance_lat = np.abs(lat_centroids-lat_point)\\n indices = np.where(distance_lat.min() == distance_lat)\\n if len(indices[0]) > 1:\\n msg = \\\"More than one nearest parallel, returning first index.\\\"\\n logging.warning(msg)\\n j = indices[0][0]\\n minimum_distance = distance_lon_lat(lon_centroids[i], lat_centroids[j],\\n lon_point, lat_point)\\n if maximum_distance is not None:\\n if minimum_distance > maximum_distance:\\n raise GeogridError(\\\"No points within provided maximum distance.\\\")\\n return (i, j)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66e07eb2189a8cde0a23d88aaa69df63\",\n \"score\": \"0.59346557\",\n \"text\": \"def nearest(coord, url_config=RequestConfig):\\n\\thost = check_host(url_config.host)\\n\\turl = '/'.join(\\n\\t\\t[host, 'nearest', url_config.version, url_config.profile,\\n\\t\\t str(coord).replace('(', '').replace(')', '').replace(' ', '')]\\n\\t)\\n\\trep = requests.get(url)\\n\\tparsed_json = json.loads(rep.text.decode('utf-8'))\\n\\treturn parsed_json\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c69c40ba2e72a1eedaa30077c730cde8\",\n \"score\": \"0.59068733\",\n \"text\": \"def find_node_by_coords(coords, nodes):\\n (x_value, y_value) = coords\\n node = [node for node in nodes if ((node.x_coord == x_value) and\\n (node.y_coord == y_value))]\\n if len(node):\\n return node[0]\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2fc2c5c3cdff6545416ff12c4bb988\",\n \"score\": \"0.5888296\",\n \"text\": \"def nearest_point(lat_var, lon_var, lats, lons):\\r\\n # this part is to handle if lons are givn 0-360 or -180-180\\r\\n if any(lons > 180.0) and (lon_var < 0.0):\\r\\n lon_var = lon_var + 360.0\\r\\n else:\\r\\n lon_var = lon_var\\r\\n \\r\\n lat = lats\\r\\n lon = lons\\r\\n\\r\\n if lat.ndim == 2:\\r\\n lat = lat[:, 0]\\r\\n else:\\r\\n pass\\r\\n if lon.ndim == 2:\\r\\n lon = lon[0, :]\\r\\n else:\\r\\n pass\\r\\n\\r\\n index_a = np.where(lat >= lat_var)[0][-1]\\r\\n index_b = np.where(lat <= lat_var)[0][-1]\\r\\n\\r\\n if abs(lat[index_a] - lat_var) >= abs(lat[index_b] - lat_var):\\r\\n index_lat = index_b\\r\\n else:\\r\\n index_lat = index_a\\r\\n\\r\\n index_a = np.where(lon >= lon_var)[0][0]\\r\\n index_b = np.where(lon <= lon_var)[0][0]\\r\\n if abs(lon[index_a] - lon_var) >= abs(lon[index_b] - lon_var):\\r\\n index_lon = index_b\\r\\n else:\\r\\n index_lon = index_a\\r\\n\\r\\n return index_lat, index_lon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"524960b4f799fd8efa8ba23560e634a2\",\n \"score\": \"0.58849317\",\n \"text\": \"def idx_of_nearest(coords, val):\\n X = np.abs(coords.flatten()-val)\\n idx = np.where(X == X.min())\\n idx = idx[0][0]\\n return coords.flatten()[idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d593075f229e7283f1c473d80d053f87\",\n \"score\": \"0.58619833\",\n \"text\": \"def nearest_loc(query_location, reference_grid, tolerance=0.5):\\n\\n distances = np.abs(reference_grid - query_location)\\n ind = distances.argmin()\\n if distances[ind] >= tolerance:\\n return np.nan\\n else:\\n return ind\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2773c32f94f7ca7ed1ac866d9ada5791\",\n \"score\": \"0.58555204\",\n \"text\": \"def getclosest_ij(lats, lons, latpt, lonpt):\\n # find squared distance of every point on grid\\n lats_dist = (lats - latpt)**2\\n lons_dist = (lons - lonpt)**2\\n return lats_dist.argmin(), lons_dist.argmin()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dddde2a127500d05870f601bc55995b5\",\n \"score\": \"0.58491766\",\n \"text\": \"def get_closest_point(point, coordinates):\\n line = geom.LineString(coordinates)\\n return line.interpolate(line.project(geom.Point(point))).coords[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5995ec63c283ab222953455240feb46\",\n \"score\": \"0.58040106\",\n \"text\": \"def find_closest(location, centroids):\\n # BEGIN Question 3\\n return min(centroids, key=lambda x: distance(location, x))\\n # END Question 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a59b7afef3d7249813f76ff1b7ecd55\",\n \"score\": \"0.5791258\",\n \"text\": \"def get_region_of_interest(self):\\n cv.namedWindow(\\\"Source Image\\\")\\n cv.setMouseCallback(\\\"Source Image\\\", self.__click_mouse_callback)\\n\\n print(\\\"Getting the region of interest...\\\")\\n while (True):\\n cv.imshow(\\\"Source Image\\\", self.__image)\\n if self.print_flag:\\n print(f\\\"Location: ({self.coordenates})\\\")\\n\\n #! Fix the problem with negative-coordinates\\n for j in iter(range(self.coordenates.shape[1])):\\n self.coordenates[:,j] = np.sort(self.coordenates[:,j], 0)\\n\\n self.roiImage = self.__image[\\n self.coordenates[0,0]:self.coordenates[1,0],\\n self.coordenates[0,1]:self.coordenates[1,1]\\n ]\\n\\n cv.imshow(\\\"Source Image - ROI\\\",self.roiImage)\\n self.print_flag = False\\n if (cv.waitKey(1) & 0xFF == ord('q')):\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bb2f88773d2a0a8532eb2a034515f25\",\n \"score\": \"0.57878023\",\n \"text\": \"def surrounding_pixels(array, coords, mode=\\\"direct neighborhood\\\"):\\n shape = array.shape\\n new_positions = []\\n first, second = coords[0], coords[1]\\n if mode == \\\"full neighborhood\\\":\\n for ypos in range(first - 1, first + 2):\\n for xpos in range(second - 1, second + 2):\\n # print(xpos, ypos)\\n # if xpos == first and ypos == second:\\n # continue\\n if ypos >= shape[0] or xpos >= shape[1] or xpos < 0 or ypos < 0:\\n continue\\n new_positions.append((ypos, xpos))\\n elif mode == \\\"direct neighborhood\\\":\\n positions = ((first - 1, second), (first + 1, second), (first, second),\\n (first, second - 1), (first, second + 1))\\n for xpos, ypos in positions:\\n if xpos >= shape[0] or ypos >= shape[1] or xpos < 0 or ypos < 0:\\n continue\\n new_positions.append((xpos, ypos))\\n return new_positions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51ab6fde2af7b5e8988fe4871b387183\",\n \"score\": \"0.5765581\",\n \"text\": \"def closest_point(self, xy, r=float('inf')):\\n dist,i = self.kdtree.query(xy, distance_upper_bound=r)\\n return i,dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a543c35d288c17ce8641dad981853a5\",\n \"score\": \"0.57636493\",\n \"text\": \"def FindNearestCity(self, lat, lon):\\n self._Refresh()\\n return self._cities.FindNearestNeighbor(lat, lon)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b80541dda12456bae03254413e4d6f5a\",\n \"score\": \"0.5722385\",\n \"text\": \"def nearest(cls, lat, long, **kwargs):\\n if kwargs.get('buses', False):\\n buses = kwargs['buses']\\n elif kwargs.get('route', False):\\n buses = kwargs['route'].get_buses()\\n else:\\n buses = cls.all()\\n\\n dists = map(lambda bus: distance(\\n # Avoid using cached location, to avoid more requests.\\n (bus.__lat__, bus.__long__),\\n (lat, long)\\n ), buses)\\n\\n return min(zip(dists, buses))[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e436d2968f540e0620113f220016de8\",\n \"score\": \"0.5721261\",\n \"text\": \"def FindNearestCountry(self, lat, lon):\\n self._Refresh()\\n return self._cities.FindNearestNeighbor(lat, lon).parent.parent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c1d118146c51e1a7388af04c2c12d00\",\n \"score\": \"0.56799155\",\n \"text\": \"def __find_neighbor_at(self, x, y, list_of_nodes):\\n for node in list_of_nodes:\\n if node.location == (x, y):\\n return node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8f0a2ed6ec77f521c7afc727861d76f\",\n \"score\": \"0.5674876\",\n \"text\": \"def find_node_location(coordinates):\\n latitude = coordinates[0];\\n longitude = coordinates[1];\\n \\n if((latitude <= 79 and latitude >= 54.548) and(longitude >= -180 and longitude <= -60.021)):\\n return \\\"Node_1\\\";\\n elif((latitude <= 79 and latitude >= 54.548) and(longitude >=-60 and longitude <= 59.989)):\\n return \\\"Node_2\\\";\\n elif((latitude <= 79 and latitude >= 54.548) and(longitude >=60 and longitude <= 180)):\\n return \\\"Node_3\\\";\\n elif((latitude <=54.52 and latitude >= -2.155) and(longitude >= -180 and longitude <= -60.021)):\\n return \\\"Node_4\\\";\\n elif((latitude <=54.52 and latitude >= -2.155) and(longitude >= -60 and longitude <= 59.989)):\\n return \\\"Node_5\\\";\\n elif((latitude <=54.52 and latitude >= -2.155) and(longitude >= 60 and longitude <= 180)):\\n return \\\"Node_6\\\";\\n elif((latitude <=-2.187 and latitude >= -56.97) and(longitude >= -180 and longitude <= -60.021)):\\n return \\\"Node_7\\\";\\n elif((latitude <=-2.187 and latitude >= -56.97) and(longitude >= -60 and longitude <= 59.989)):\\n return \\\"Node_8\\\";\\n elif((latitude <=-2.187 and latitude >= -56.97) and(longitude >= 60 and longitude <= 180)):\\n return \\\"Node_9\\\";\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93872353d084d89ddd73ec86d8323b8c\",\n \"score\": \"0.5665834\",\n \"text\": \"def neighbourhoodsearch(self, center, iselite):\\n result = center\\n neighbourfabric = NeighbourCoordinateFabric(center,\\n self.settings)\\n\\n if (iselite): itnum = self.settings.RECRUITEDELITE\\n else: itnum = self.settings.RECRUITEDNONELITE\\n\\n for i in range(itnum):\\n current = neighbourfabric.getcoordinate()\\n if (current.z < result.z): result = current\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d09415059f6485df6b0023aba155fe0\",\n \"score\": \"0.5664631\",\n \"text\": \"def find_closest2d(A):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c6370426cfc9e2e55d3a2327935b9f7\",\n \"score\": \"0.56597936\",\n \"text\": \"def region(allcoordinates, coordinates):\\n p = False #auxilary variable to check if the region was found or not\\n n = 0\\n while n <= len(allcoordinates)-1 and not p:\\n name = allcoordinates[n]['name']\\n a = 0\\n while a <= len(allcoordinates[n]['coordinates'])-1:\\n p = polyregions(verts1, coordinates)\\n a += 1\\n n += 1\\n if p:\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c09582c05815ee02e53117dc32bb866\",\n \"score\": \"0.56463283\",\n \"text\": \"def nearxy(x,y,xi,yi):\\n ind=ones(len(xi),dtype=int)\\n dd=ones(len(xi),dtype='float')\\n for i in arange(len(xi)):\\n dist=sqrt((x-xi[i])**2+(y-yi[i])**2)\\n ind[i]=dist.argmin()\\n dd[i]=dist[ind[i]]\\n return ind,dd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7152b94eec4e5c542164ff68cbef577b\",\n \"score\": \"0.56310844\",\n \"text\": \"def nearest_neighbor(nodes, origin):\\n distances = [distance(origin, node, 100) for node in nodes]\\n if origin in nodes:\\n distances[nodes.index(origin)] = float(\\\"Inf\\\")\\n result = min(distances)\\n return (distances.index(result), result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11033e415150acd05c1bb3250bcd89cf\",\n \"score\": \"0.5630308\",\n \"text\": \"def getXY(lats,lons,ptlat,ptlon):\\n # Find closest lat/lon in array\\n minlat = abs(lats-ptlat).min()\\n minlon = abs(lons-ptlon).min()\\n # Find where these are in the grid\\n wherelat = N.where(abs(lats-ptlat) == minlat)\\n wherelon = N.where(abs(lons-ptlon) == minlon)\\n # pdb.set_trace()\\n lat_idx = N.where(lats==lats[wherelat])[0][0]\\n lon_idx = N.where(lons==lons[wherelon])[0][0]\\n exactlat = lats[wherelat]\\n exactlon = lons[wherelon]\\n return lat_idx,lon_idx, exactlat, exactlon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2117f3c9795dc5c2f64faafc3ef0476c\",\n \"score\": \"0.56120026\",\n \"text\": \"def get_nearest_neighbors(self):\\n current_x, current_y = self.get_current_pos()\\n\\n # North\\n try:\\n tile_type = self.fountain.get_tile(\\n current_x, current_y + TILE_LENGTH).get_tile_type()\\n north = state_to_int[tile_type]\\n except KeyError as e:\\n north = 2\\n\\n # East\\n try:\\n tile_type = self.fountain.get_tile(\\n current_x + TILE_LENGTH, current_y).get_tile_type()\\n east = state_to_int[tile_type]\\n except KeyError as e:\\n east = 2\\n\\n # South\\n try:\\n tile_type = self.fountain.get_tile(\\n current_x, current_y - TILE_LENGTH).get_tile_type()\\n south = state_to_int[tile_type]\\n except KeyError as e:\\n south = 2\\n\\n # West\\n try:\\n tile_type = self.fountain.get_tile(\\n current_x - TILE_LENGTH, current_y).get_tile_type()\\n west = state_to_int[tile_type]\\n except KeyError as e:\\n west = 2\\n\\n # Below\\n try:\\n tile_type = self.fountain.get_tile(\\n current_x, current_y).get_tile_type()\\n center = state_to_int[tile_type]\\n except KeyError as e:\\n center = 2\\n\\n temp = north * (3**4)\\n temp += east * (3**3)\\n temp += south * (3**2)\\n temp += west * 3\\n temp += center\\n\\n return self.moveset[temp]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"290f28eaaa9721f1628746b4683b9b75\",\n \"score\": \"0.5609461\",\n \"text\": \"def _propose_nearby_loc(self,pos):\\n pos = (pos[0] + random.uniform(-self.jitter,self.jitter), pos[1] + random.uniform(-self.jitter,self.jitter), pos[2])\\n return self.mesh.nearest_point(pos=pos, radius=self.snap_radius)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eee54e83f6f532091cd65612cd85b92a\",\n \"score\": \"0.5603823\",\n \"text\": \"def _findClosest(self, objects):\\n closest = None\\n obj = None\\n relativePos = None\\n robotPos = self.robot.position + \\\\\\n (self.robot.orientation * ogre.Vector3(0.5, 0, 0))\\n\\n for o in objects:\\n toObj = o.position - robotPos\\n \\n # None yet found, default to this one\\n if closest is None:\\n closest = (o, toObj)\\n else:\\n obj, realtivePos = closest\\n if toObj.squaredLength() < realtivePos.squaredLength():\\n # Found a better one switch to it\\n closest = (o, toObj)\\n\\n return closest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d951914b3601f5741466523814872000\",\n \"score\": \"0.5602097\",\n \"text\": \"def nearest_interp(xi, x, y):\\r\\n idx = abs(x - xi[:,None])\\r\\n return y[idx.argmin(axis=1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34b3353d5f2fd0b3dac6867a4492a71c\",\n \"score\": \"0.55902964\",\n \"text\": \"def find_closest(self, x, y, halo=None, start=None):\\r\\n cx,cy = self._o2c(x,y)\\r\\n return self.find('closest', cx, cy, halo, start)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e6c7af74376f050780de921f8cdbff6\",\n \"score\": \"0.5590099\",\n \"text\": \"def cityreader_stretch(lat1, lon1, lat2, lon2, cities=[]):\\n # within will hold the cities that fall within the specified region\\n within = []\\n\\n # TODO Ensure that the lat and lon valuse are all floats\\n coord_list = [lat1, lon1, lat2, lon2]\\n\\n # separate and sort lat and lon coordinates \\n lat_coords = [coord_list[0], coord_list[2]]\\n lon_coords = [coord_list[1], coord_list[3]]\\n lat_coords = sorted(lat_coords)\\n lon_coords = sorted(lon_coords)\\n\\n # Loop through each city and check to see if it falls within \\n # the specified coordinates.\\n for city in cities:\\n if (city.lat > lat_coords[0] and city.lat < lat_coords[1]) and (\\n city.lon > lon_coords[0] and city.lon < lon_coords[1]):\\n within.append(city)\\n\\n return within\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0c7b5117542191038df843b2dfec0af\",\n \"score\": \"0.55851114\",\n \"text\": \"def FindNearestNeighbors(self, lat, lon):\\n return {'country': self.FindNearestCountry(lat, lon),\\n 'region': self.FindNearestRegion(lat, lon),\\n 'city': self.FindNearestCity(lat, lon)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"178bfb3ff391f1d750cb74bc48e697b8\",\n \"score\": \"0.55836624\",\n \"text\": \"def closest(c, coords):\\n best = 0\\n best_distance = distance(c, coords[0])\\n for i in range(1, len(coords)):\\n c2 = coords[i]\\n d = distance(c, c2)\\n\\n if d < best_distance:\\n best = i\\n best_distance = d\\n return best\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbd5a115ee44277f8b0decc5151ebf1d\",\n \"score\": \"0.5573302\",\n \"text\": \"def get_area_of_interest(image, search_area):\\n top = search_area[0][1]\\n bottom = search_area[1][1]\\n left = search_area[0][0]\\n right = search_area[1][0]\\n\\n return image[top:bottom, left:right, :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c95c0a7408895c6269ea6c1c14226cf\",\n \"score\": \"0.55606455\",\n \"text\": \"def getlocation(indices, labels, Ni, Nj):\\n zc = indices[labels == 1]\\n if len(zc) == 0:\\n return 0, None\\n else:\\n xmin = int(min(zc[:,1]))\\n ymin = int(min(zc[:,0]))\\n xmax = int(xmin + Nj)\\n ymax = int(max(zc[:,0]) + Ni)\\n return 1, ((xmin, ymin), (xmax, ymax))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbc2651e39806aed12199fe44b54109b\",\n \"score\": \"0.55543536\",\n \"text\": \"def _getSearchBoundaries(self, target_pixel) :\\n n, m = target_pixel\\n\\n if self.local_radius :\\n upper_i = min(n + self.local_radius, self.image.shape[0] - self.offset - 1)\\n lower_i = max(n - self.local_radius, self.offset)\\n upper_j = min(m + self.local_radius, self.image.shape[1] - self.offset - 1)\\n lower_j = max(m - self.local_radius, self.offset)\\n else :\\n upper_i = self.image.shape[0] - self.offset - 1\\n lower_i = self.offset\\n upper_j = self.image.shape[1] - self.offset - 1\\n lower_j = self.offset\\n \\n return upper_i, lower_i, upper_j, lower_j\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac11c3f6bb2faf404c6b419d816195e9\",\n \"score\": \"0.5552873\",\n \"text\": \"def FindNearestNeighbor(self, lat, lon):\\n cart = self._LatLonToCartesian(lat, lon)\\n _, locale, _ = self._tree.nearest_neighbor(cart)\\n return locale\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6736bace561b3f2208b155fa6a605c6\",\n \"score\": \"0.5551941\",\n \"text\": \"def FindClosestPointWithinRadius(self, p_float, , p_float_4):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7e89de8e8759695f20a5b149cdc8766\",\n \"score\": \"0.55451214\",\n \"text\": \"def get_nearest(src_points, candidates, k_neighbors=1, distance_threshold=None):\\n\\n # Create tree from the candidate points\\n coordinates = np.vstack(candidates.geometry.centroid.apply(lambda geom: (geom.x,geom.y)))\\n tree = BallTree(coordinates, leaf_size=15, metric='haversine')\\n\\n # Find closest points and distances\\n #src_points = src_points.reset_index() \\n src_x = src_points.geometry.centroid.x\\n src_y = src_points.geometry.centroid.y\\n \\n src_points = np.array([src_x, src_y]).reshape(-1,2)\\n \\n #If there are not enough neighbors, reduce K, then pad to original\\n if k_neighbors > candidates.shape[0]:\\n effective_neighbors = candidates.shape[0]\\n else:\\n effective_neighbors = k_neighbors\\n \\n distances, indices = tree.query(src_points, k=effective_neighbors)\\n \\n neighbor_geoms = candidates[candidates.index.isin(indices[0])]\\n neighbor_geoms = neighbor_geoms.loc[indices[0]]\\n \\n #order by the indices\\n neighbor_geoms[\\\"distance\\\"] = distances[0]\\n\\n if distance_threshold:\\n neighbor_geoms = neighbor_geoms[neighbor_geoms.distance > distance_threshold]\\n \\n # Return indices and distances\\n return neighbor_geoms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76bd88fe9b7da42e6fe9ba2bebf0d8fc\",\n \"score\": \"0.5535252\",\n \"text\": \"def nearest(pos, board):\\n\\n a, b = pos\\n i = 0 # Number of cells away from target\\n possible = [] # Possibilities for the nearest cell\\n while True:\\n added = False\\n if a + i <= 27 and board[a+i][b]: # Right\\n possible.append((a+i, b))\\n added = True\\n if a - i > 0 and board[a-i][b]: # Left\\n possible.append((a-i, b))\\n added = True\\n if b + i <= 35 and board[a][b+i]: # Top\\n possible.append((a, b+i))\\n added = True\\n if b - i > 0 and board[a][b-i]: # Bottom\\n possible.append((a, b-i))\\n added = True\\n if added:\\n break\\n else: # If not valid tile is found increment i\\n i += 1\\n\\n return random.choice(possible) # Choose a random cell based on all of the possible nearest ones\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd69b910a9e25ed1f782728f2b396169\",\n \"score\": \"0.5529872\",\n \"text\": \"def find_closest_room(self, position):\\n position = self.map_from_global(position)\\n self.update_room_positions()\\n new_room = None\\n min_dist = -1\\n x_pos = position.x()\\n y_pos = position.y()\\n # O(n^2) solution, but too fast to need optimizations\\n for room, loc in self.room_locations.items():\\n dist = np.sqrt(\\n np.square(x_pos - loc[0]) + np.square(y_pos - loc[1]))\\n if min_dist < 0:\\n min_dist = dist\\n new_room = room\\n elif dist < min_dist:\\n min_dist = dist\\n new_room = room\\n return new_room\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9049129fc607a4ac1819a238708ab215\",\n \"score\": \"0.55296147\",\n \"text\": \"def nearest_node(point, nodes):\\n return nearest(point, nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9049129fc607a4ac1819a238708ab215\",\n \"score\": \"0.55296147\",\n \"text\": \"def nearest_node(point, nodes):\\n return nearest(point, nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f0f70c736cb344abb97633c6570ff75\",\n \"score\": \"0.5518578\",\n \"text\": \"def _find_nearest_from_irregular_centroids(lon_centroids, lat_centroids,\\n lon_point, lat_point,\\n maximum_distance=None):\\n\\n d = distance_lon_lat(lon_centroids, lat_centroids, lon_point, lat_point)\\n minimum_distance = d.min()\\n if maximum_distance is not None:\\n if minimum_distance > maximum_distance:\\n raise GeogridError(\\\"No points within provided maximum distance.\\\")\\n indices = np.where(d == d.min())\\n if len(indices[0]) > 1:\\n logging.warning(\\\"More than one nearest point, returning first index.\\\")\\n return (indices[0][0], indices[1][0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c459c86bdf9eb1c02e3614d75f604f\",\n \"score\": \"0.5518351\",\n \"text\": \"def locate_object(self, name, x0=None, y0=None):\\n # TODO: Find out what this does and optimize it.\\n if x0 is None:\\n x0 = len(self.vision[0])\\n if y0 is None:\\n y0 = len(self.vision)\\n number_of_coordinates = 0\\n x_sum = 0\\n y_sum = 0\\n for y, row in enumerate(self.vision[:y0]):\\n for x, object_name in enumerate(row[:x0]):\\n if object_name == name:\\n x_sum += x\\n y_sum += y\\n number_of_coordinates += 1\\n if number_of_coordinates == 0:\\n return None\\n # Why is this writen this way?\\n x_coordinate = x_sum / number_of_coordinates\\n y_coordinate = y_sum / number_of_coordinates\\n return x_coordinate, y_coordinate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0a8c8e86327a57d005e9db11dc2118f\",\n \"score\": \"0.55168664\",\n \"text\": \"def _find_region(self, base: Geometry, region: RegionLocator):\\n if not isinstance(base, Undefined):\\n self.logger.warning(\\\"Using absolute region coordinates\\\")\\n\\n position = Region(region.left, region.top, region.right, region.bottom)\\n return [position]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b95d523053f2132e3e5561a855e62a50\",\n \"score\": \"0.5512935\",\n \"text\": \"def find_location(location: Tuple[float, float]) -> Optional[SensorPoint]:\\n\\n # Get all rows\\n rows = SensorPoint.objects.all()\\n # Case the database is empty\\n if len(rows) == 0:\\n return None\\n smallest_distance = None\\n closest_point = None\\n for i in range(0, len(rows)):\\n d_distance = haversine_formula(location, (float(rows[i].latitude), float(rows[i].longitude)))\\n if d_distance < 500.0:\\n if smallest_distance is None or d_distance < smallest_distance:\\n smallest_distance = d_distance\\n closest_point = rows[i]\\n return closest_point\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e4d0d1a8b38a12a394d9b9a0088ed82\",\n \"score\": \"0.55100685\",\n \"text\": \"def find_location(coordinates):\\n\\n locator = Nominatim(user_agent='Name')\\n location = locator.reverse(coordinates, language='en')\\n\\n return (location.raw['address']['country'],\\\\\\n location.raw['address']['city'],\\\\\\n location.raw['address']['state'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc35d484f7caf786a23b46b9abc66bf7\",\n \"score\": \"0.5509586\",\n \"text\": \"def find_nearest(longitudes, latitudes, longitude, latitude,\\n maximum_distance=None, grid_type=None,\\n lon_dimensions=None, lat_dimensions=None):\\n\\n if grid_type is None:\\n grid_type = detect_grid(longitudes, latitudes, lon_dimensions,\\n lat_dimensions)\\n if grid_type == 'list_of_2d_points':\\n f = _find_nearest_from_list_of_points\\n return f(longitudes, latitudes, longitude, latitude, maximum_distance)\\n elif grid_type == 'rectilinear_2d_centroids':\\n f = _find_nearest_from_rectilinear_centroids\\n return f(longitudes, latitudes, longitude, latitude, maximum_distance)\\n elif grid_type == 'rectilinear_2d_bounds':\\n f = rectilinear_2d_bounds_to_vertices\\n (lon_vertices, lat_vertices) = f(longitudes, latitudes)\\n f = _find_nearest_from_rectilinear_vertices\\n return f(lon_vertices, lat_vertices, longitude, latitude,\\n maximum_distance)\\n elif grid_type == 'rectilinear_2d_vertices':\\n f = _find_nearest_from_rectilinear_vertices\\n return f(longitudes, latitudes, longitude, latitude, maximum_distance)\\n elif grid_type == 'irregular_2d_centroids':\\n f = _find_nearest_from_irregular_centroids\\n return f(longitudes, latitudes, longitude, latitude, maximum_distance)\\n elif grid_type == 'irregular_2d_vertices':\\n f = _find_nearest_from_irregular_vertices\\n return f(longitudes, latitudes, longitude, latitude, maximum_distance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b578caa94ddfc78ca5365195ecb5f568\",\n \"score\": \"0.55021286\",\n \"text\": \"def find_closest_neighbor(point, neighbors):\\n uv_point = ll2uv(point[0], point[1])\\n id_neighbor = None\\n angle_min = float(\\\"inf\\\")\\n for i, n in enumerate(neighbors):\\n angle = angular_distance(uv_point, ll2uv(n[0], n[1]))\\n if angle < angle_min:\\n id_neighbor = i\\n angle_min = angle\\n\\n return id_neighbor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58768599a8c020c8ab3c9e447ef2620d\",\n \"score\": \"0.55007684\",\n \"text\": \"def _find_region(self, point: Tuple[int, int]) -> str:\\n if point[0] <= self._centre[0] and point[1] <= self._centre[1]: # NW\\n return 'NW'\\n elif point[0] <= self._centre[0]: # SW\\n return 'SW'\\n elif point[1] <= self._centre[1]: # NE\\n return 'NE'\\n else: # SE\\n return 'SE'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3752a0e50cb0779cee68e879efba6d6\",\n \"score\": \"0.5499344\",\n \"text\": \"def get_river_src(coords):\\r\\n # Get elevation information from .tif file\\r\\n gdal.UseExceptions()\\r\\n try:\\r\\n src_ds = gdal.Open( 'H:\\\\spatial entity\\\\data\\\\shpdata\\\\\\\\chinasrtm24000_16000.tif' )\\r\\n except RuntimeError, e:\\r\\n print 'Unable to open INPUT.tif'\\r\\n print e\\r\\n sys.exit(1) \\r\\n try:\\r\\n srcband = src_ds.GetRasterBand(1)\\r\\n except RuntimeError, e:\\r\\n print 'Band ( %i ) not found' # band_num\\r\\n print e\\r\\n sys.exit(1) \\r\\n elevation = srcband.ReadAsArray()\\r\\n # Transform coordinates to pixel \\r\\n gt = src_ds.GetGeoTransform() \\r\\n coords_elevation = [] \\r\\n for i in range(0,len(coords)):\\r\\n px = int((coords[i][0] - gt[0]) / gt[1]) # y pixel\\r\\n py = int((coords[i][1] - gt[3]) / gt[5]) # x pixel\\r\\n coords_elevation += [elevation[py][px]]\\r\\n # Get the index of maximum elevation\\r\\n return coords_elevation.index(max(coords_elevation))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53c9f99cce164ea8b3c00e42e71e81b5\",\n \"score\": \"0.549926\",\n \"text\": \"def ais_Nearest(*args):\\n return _AIS.ais_Nearest(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79b252fa7089edd47c0662afa14699d6\",\n \"score\": \"0.5499103\",\n \"text\": \"def bestMatch(images, region = SCREEN, minOverlap = 0.9):\\n if len(images) == 0:\\n return None\\n matches = getAllMatches(images, **{ 'region' : region, 'timeout' : 0 })\\n if matches[0] is not None:\\n best_match = 0\\n best_score = matches[0].getScore()\\n else:\\n best_match = None\\n best_score = 0\\n for m in range(1, len(matches)):\\n match = matches[m]\\n if match is None:\\n continue\\n score = match.getScore()\\n if best_match is None:\\n best_match = m\\n best_score = score\\n else:\\n if not sameRegion(matches[best_match], match, minOverlap):\\n raise Exception('images %d, %d found in different regions' %\\n (best_match, m))\\n if score > best_score:\\n best_match = m\\n best_score = score\\n if best_match is None:\\n if region.getThrowException():\\n raise FindFailed('none of the images was found')\\n else:\\n return None\\n return best_match, matches[best_match]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43aa4ea3c1ea8596124264226c36a81f\",\n \"score\": \"0.54961836\",\n \"text\": \"def NearestApex(*args):\\n return _AIS.ais_NearestApex(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf75e2b9048c33cca8fc8d013b19436\",\n \"score\": \"0.54923683\",\n \"text\": \"def closest(self, coords=[], **kwargs):\\n if kwargs and coords:\\n raise ValueError(\\\"Specify coordinate using as either a list \\\"\\n \\\"keyword arguments not both\\\")\\n if kwargs:\\n coords = []\\n getter = []\\n for k, v in kwargs.items():\\n idx = self.get_dimension_index(k)\\n if np.isscalar(v):\\n coords.append((0, v) if idx else (v, 0))\\n else:\\n if isinstance(v, list):\\n coords = [(0, c) if idx else (c, 0) for c in v]\\n if len(coords) not in [0, len(v)]:\\n raise ValueError(\\\"Length of samples must match\\\")\\n elif len(coords):\\n coords = [(t[abs(idx-1)], c) if idx else (c, t[abs(idx-1)])\\n for c, t in zip(v, coords)]\\n getter.append(idx)\\n else:\\n getter = [0, 1]\\n getter = itemgetter(*sorted(getter))\\n if len(coords) == 1:\\n coords = coords[0]\\n if isinstance(coords, tuple):\\n return getter(self.closest_cell_center(*coords))\\n else:\\n return [getter(self.closest_cell_center(*el)) for el in coords]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be71a08c4892610430148064741e02f0\",\n \"score\": \"0.5483117\",\n \"text\": \"def get_nearest(src_points, candidates, k_neighbors=1, distance_threshold=None):\\n\\n # Create tree from the candidate points\\n coordinates = np.vstack(candidates.geometry.centroid.apply(lambda geom: (geom.x,geom.y)))\\n tree = BallTree(coordinates, leaf_size=15, metric='haversine')\\n\\n # Find closest points and distances\\n #src_points = src_points.reset_index() \\n src_x = src_points.geometry.centroid.x\\n src_y = src_points.geometry.centroid.y\\n \\n src_points = np.array([src_x, src_y]).reshape(-1,2)\\n \\n #If there are not enough neighbors, reduce K, then pad to original\\n if k_neighbors > candidates.shape[0]:\\n effective_neighbors = candidates.shape[0]\\n else:\\n effective_neighbors = k_neighbors\\n \\n distances, indices = tree.query(src_points, k=effective_neighbors)\\n \\n neighbor_geoms = candidates[candidates.index.isin(indices[0])]\\n neighbor_geoms[\\\"distance\\\"] = distances[0]\\n\\n if distance_threshold:\\n neighbor_geoms = neighbor_geoms[neighbor_geoms.distance > distance_threshold]\\n \\n # Return indices and distances\\n return neighbor_geoms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1546d6e2909192819d711d30d4d2715d\",\n \"score\": \"0.54806334\",\n \"text\": \"def get_closest_obstacle_distance(self,x,y):\\n x, y = int(x), int(y)\\n\\n # check if we are in bounds\\n if x >= self.width or x < 0 or y >= self.height or y < 0:\\n return float('nan')\\n\\n return self.closest_occ[y,x]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6f16c3e9603699ef3c6cf2af03a2746\",\n \"score\": \"0.5479858\",\n \"text\": \"def match_to_closest_location(self, locations):\\r\\n # Convert the list of locations to arrays.\\r\\n end_lats = []\\r\\n end_lons = []\\r\\n for location in locations:\\r\\n end_lats.append(location[0])\\r\\n end_lons.append(location[1])\\r\\n end_lats = np.array(end_lats)\\r\\n end_lons = np.array(end_lons)\\r\\n\\r\\n distances = self.get_distances_to_lat_lons(end_lats, end_lons)\\r\\n closest_locations = np.argmin(distances, axis=0)\\r\\n return closest_locations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdf8d14c75e758483fa886e367084bdf\",\n \"score\": \"0.5461208\",\n \"text\": \"def get_distance_binary_search(coordinates: Tuple[float, float]) -> int:\\n # Find four equidistant points on the MKAD with which to compare.\\n index_list = [(len(mkad_coords) // 4) * i for i in range(4)]\\n distance_list = []\\n\\n # Obtain the distances from the given coords to the four points\\n for index in index_list:\\n new_distance = distance(coordinates, (mkad_coords[index][2],\\n mkad_coords[index][1])).km\\n distance_list.append([index, new_distance])\\n\\n # Sort list from shorter to longer distances, keeping corresponding index\\n distance_list.sort(key=lambda x: x[1])\\n\\n index_list = []\\n\\n # If the second shortest point is the same distance as the third shortest\\n # point, then the given coordinates are in such a position that we cannot\\n # take the first and second shortest point from the four indexes, instead\\n # we take the second and third shortest.\\n if (distance_list[1][1] == distance_list[2][1]):\\n low_point = distance_list[1][0]\\n high_point = distance_list[2][0]\\n # Otherwise we take the first and second shortest.\\n else:\\n low_point = distance_list[0][0]\\n high_point = distance_list[1][0]\\n\\n # We now proceed to eliminate points that are in between the two points\\n # we have selected. All other points are discarded.\\n\\n while high_point - low_point > 1:\\n low_distance = distance(\\n coordinates,\\n (mkad_coords[low_point][2], mkad_coords[low_point][1])\\n ).km\\n high_distance = distance(\\n coordinates,\\n (mkad_coords[high_point][2], mkad_coords[high_point][1])\\n ).km\\n\\n if low_distance == high_distance:\\n low_point += 1\\n elif low_distance < high_distance:\\n mid_point = (low_point + high_point) // 2\\n high_point = mid_point\\n elif low_distance > high_distance:\\n mid_point = (low_point + high_point) // 2\\n low_point = mid_point\\n\\n # After the while loop we have reduced the possibilities to two points\\n # on the MKAD, indexed as low_point and high_point. We simply measure\\n # the distances to these and return the shortest.\\n\\n low_distance = distance(\\n coordinates,\\n (mkad_coords[low_point][2], mkad_coords[low_point][1])\\n ).km\\n high_distance = distance(\\n coordinates,\\n (mkad_coords[high_point][2], mkad_coords[high_point][1])\\n ).km\\n\\n if low_distance < high_distance:\\n return int(low_distance)\\n else:\\n return int(high_distance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7d7900777c579f740adcc5a7e431871\",\n \"score\": \"0.5457809\",\n \"text\": \"def _find_nearest_from_rectilinear_vertices(lon_vertices, lat_vertices,\\n lon_point, lat_point,\\n maximum_distance=None):\\n\\n f = rectilinear_vertices_to_centroids\\n (lon_centroids, lat_centroids) = f(lon_vertices, lat_vertices)\\n return _find_nearest_from_rectilinear_centroids(lon_centroids,\\n lat_centroids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d162b990429663beaf951aa029aa61c\",\n \"score\": \"0.54533446\",\n \"text\": \"def find_nearest(self, position, at_sublattice=None):\\n at_sublattice = self.lattice[at_sublattice] if at_sublattice is not None else -1\\n if hasattr(self.impl, 'find_nearest'):\\n # use cpp implementation\\n return self.impl.find_nearest(position, int(at_sublattice))\\n else:\\n # fallback numpy implementation\\n sites = Sites(self.positions, self.sublattices)\\n return sites.find_nearest(position, at_sublattice)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e55c9393f488c89f3d79bf9b5d894d\",\n \"score\": \"0.54512286\",\n \"text\": \"def match_coordinates(longitude, latitude):\\n\\n lon = float(longitude)\\n lat = float(latitude)\\n pnt = Point(lon, lat)\\n result = Biology.objects.filter(geom__distance_lte=(pnt, Distance(m=1)))\\n match_result = (False, None)\\n if (len(result)) == 1:\\n match_result = (True, result)\\n elif len(result) >= 1:\\n match_result = (False, result)\\n elif len(result) == 0:\\n match_result = (False, None)\\n return match_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ec2f2607315ec5116b444c2c3babf8c\",\n \"score\": \"0.5449289\",\n \"text\": \"def _find_new_inside(self):\\n npoints = len(self._points)\\n if npoints > 4:\\n points = np.vstack((self._points, self._points[1]))\\n A = points[:-2]\\n B = points[1:-1]\\n C = points[2:]\\n orient = great_circle_arc.triple_product(A-B, C-B, B)\\n if np.sum(orient) < 0.0:\\n orient = -1.0 * orient\\n midpoint = great_circle_arc.midpoint(A, C)\\n candidate = max(zip(orient, midpoint), key=lambda x: x[0])\\n inside = candidate[1]\\n else:\\n # Fall back on computing the mean point\\n inside = self._points.mean(axis=0)\\n vector.normalize_vector(inside, output=inside)\\n return inside\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae51fe0c7982db53c7040dc60b8021c\",\n \"score\": \"0.5442965\",\n \"text\": \"def star_closest_to_world_coords(self, ra, dec):\\n\\n if not len(self):\\n raise ValueError(\\\"database is empty\\\")\\n\\n self._execute(\\\"SELECT id, ra, dec FROM stars\\\")\\n\\n closest_id = None\\n closest_distance = float('inf')\\n coordinates = astromatic.Coordinates(ra, dec)\\n for star_id, star_ra, star_dec in self._rows:\\n star_coords = astromatic.Coordinates(star_ra, star_dec)\\n star_distance = coordinates.distance(star_coords)\\n if star_distance < closest_distance:\\n closest_id = star_id\\n closest_distance = star_distance\\n\\n return closest_id, closest_distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e36f3085d79859f311dd23d1594063b9\",\n \"score\": \"0.54392976\",\n \"text\": \"def _get_nearest(self, pose, window=2):\\r\\n min_idx = self._idx\\r\\n max_idx = np.minimum(self._pos.shape[0], self._idx + window)\\r\\n idx = range(min_idx, max_idx)\\r\\n nearest = idx[np.argmin(distance(pose[:2], self._pos[idx]))]\\r\\n self._idx = nearest\\r\\n\\r\\n # Check if goal has been reached\\r\\n if distance(pose[:2], self._pos[-1]) <= self._goal_threshold:\\r\\n self._goal_reached = True\\r\\n\\r\\n return nearest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3300b5093810499a478443a0c1343f2\",\n \"score\": \"0.54318255\",\n \"text\": \"def find_neighborhood(shapes, lat, lng):\\n point = Point(lng, lat)\\n\\n # sorts the shapes by distance from shape centroid the point\\n sorted_shapes = sort_shapes(shapes, point)\\n\\n # now that shapes are sorted, we should find the\\n # boundary shape pretty quick\\n for shape in sorted_shapes:\\n if shape.polygon.contains(point):\\n return shape.name\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"741adc48acb2c4eba9ca299514f1c08f\",\n \"score\": \"0.5428988\",\n \"text\": \"def getLocation( min_point: tuple, max_point: tuple ) -> tuple:\\n # Unpack the tuples into min/max values\\n xmin, ymin = min_point\\n xmax, ymax = max_point\\n # Take midpoint of x-coordinate and ymax for bottom middle of box\\n x_result = ( xmin + xmax ) // 2\\n y_result = ymax\\n # Return location\\n return ( x_result, y_result )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b0be6306604db546410e7b94452660\",\n \"score\": \"0.54253185\",\n \"text\": \"def findRoute(self, x1, y1, x2, y2):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a740a9f694277f8e6fb36c9495a3e40\",\n \"score\": \"0.5423184\",\n \"text\": \"def get_closest_obstacle_distance(self, x, y):\\n x_coord = (x - self.map.info.origin.position.x)/self.map.info.resolution\\n y_coord = (y - self.map.info.origin.position.y)/self.map.info.resolution\\n if type(x) is np.ndarray:\\n x_coord = x_coord.astype(np.int)\\n y_coord = y_coord.astype(np.int)\\n else:\\n x_coord = int(x_coord)\\n y_coord = int(y_coord)\\n\\n is_valid = (x_coord >= 0) & (y_coord >= 0) & (x_coord < self.map.info.width) & (y_coord < self.map.info.height)\\n if type(x) is np.ndarray:\\n distances = np.float('nan')*np.ones(x_coord.shape)\\n distances[is_valid] = self.closest_occ[x_coord[is_valid], y_coord[is_valid]]\\n return distances\\n else:\\n return self.closest_occ[x_coord, y_coord] if is_valid else float('nan')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3456e7a84b76647762c0113c07daef64\",\n \"score\": \"0.5418462\",\n \"text\": \"def region_index(lats0, lons0): \\n Bi = np.zeros(lons0.shape[0]) #The grid cell index where a particle is released at the bottom\\n\\n for i in range(lons0.shape[0]):\\n lo = find_down(Lons,lons0[i]);lon = Lons[lo];\\n la = find_down(Lats,lats0[i]);lat = Lats[la];\\n Bi[i] = np.where(np.logical_and(vLons==lon,vLats==lat))[0] \\n return Bi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"651b63ee59610a63edd359da7fb72936\",\n \"score\": \"0.5415594\",\n \"text\": \"def nearest_neighbour(src_lon, src_lat, dst_lon, dst_lat, src_grid=\\\"regular\\\", dst_grid=\\\"unstructured\\\", npoints=1, method=\\\"mean\\\"):\\n # create an array containing all coordinates\\n if src_grid == \\\"regular\\\":\\n if src_lon.ndim == 1:\\n lon_2d, lat_2d = np.meshgrid(src_lon, src_lat)\\n elif src_lon.ndim == 2:\\n if src_lon.shape != src_lat.shape:\\n raise ValueError(\\\"for 2d-coordinates, the shapes have to match!\\\")\\n lon_2d, lat_2d = src_lon, src_lat\\n else:\\n raise ValueError(\\\"only 1d- and 2d-coordinates are supported for regular grids\\\")\\n coords = np.stack((lon_2d.flatten(), lat_2d.flatten()), axis=1)\\n input_dims = lon_2d.shape\\n elif src_grid == \\\"unstructured\\\":\\n if src_lon.ndim == 1:\\n coords = np.stack((src_lon, src_lat), axis=1)\\n input_dims = src_lon.shape\\n else:\\n raise ValueError(\\\"an unstructured grid is supposed to have 1d-coordinate arrays!\\\")\\n\\n # convert point coordinates if given as scalar\\n if not hasattr(dst_lon, \\\"__len__\\\"):\\n dst_lon = np.array((dst_lon,))\\n dst_lat = np.array((dst_lat,))\\n\\n # keep the target coordinates\\n target_lon = dst_lon\\n target_lat = dst_lat\\n\\n # create coordinates for regular output grid\\n if dst_grid == \\\"regular\\\" and dst_lon.ndim == 1 and dst_lat.ndim == 1:\\n point_lon_2d, point_lat_2d = np.meshgrid(dst_lon, dst_lat)\\n dst_lon = point_lon_2d.flatten()\\n dst_lat = point_lat_2d.flatten()\\n target_shape = point_lon_2d.shape\\n else:\\n target_shape = (len(dst_lon),)\\n\\n # create the kd-tree and calculate the indices and the weights for the indices\\n kdtree = scipy.spatial.cKDTree(coords)\\n\\n # estimate the distance between grid points by calculating the distance to the next neighbour for three points\\n grid_point_distance, _ = kdtree.query(coords[[0, len(coords) // 2, len(coords)-1]], k=2)\\n mean_grid_point_distance = grid_point_distance[:, 1].mean()\\n\\n # calculate indices and distances for the target points\\n if len(dst_lon) == 1:\\n distances, indices_flat = kdtree.query((dst_lon[0], dst_lat[0]), k=npoints)\\n else:\\n distances, indices_flat = kdtree.query(np.stack((dst_lon, dst_lat), axis=1), k=npoints)\\n if npoints > 1 and len(distances.shape) == 1:\\n distances = np.expand_dims(distances, 0)\\n indices_flat = np.expand_dims(indices_flat, 0)\\n if npoints > 1:\\n if method == \\\"mean\\\":\\n weights = np.empty(distances.shape)\\n weights[:] = 1.0 / npoints\\n elif method == \\\"d-mean\\\":\\n weights = 1.0 / np.maximum(distances, mean_grid_point_distance / 2.0)**2\\n weights = weights / np.tile(np.expand_dims(np.sum(weights, 1), 1), npoints)\\n else:\\n raise ValueError(\\\"unsupported method: %s\\\" % method)\\n else:\\n weights = 1\\n if len(input_dims) > 1:\\n indices = np.unravel_index(indices_flat, input_dims)\\n else:\\n indices = indices_flat\\n if type(indices) == int:\\n indices = np.asarray([indices])\\n\\n # construct and return the interpolator object\\n return NearestNeighbourInterpolator(indices, weights, distances, input_dims, len(dst_lon), npoints, target_shape, target_lon, target_lat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b1d0eb9b1e9408b0119b6b5781d02d6\",\n \"score\": \"0.5405547\",\n \"text\": \"def find_coordinates(numpoints, refcoords, defcoords, defatomcoords):\\n refcenter, fitcenter, rotation = qfit(numpoints, refcoords, defcoords)\\n newcoords = qtransform(1, defatomcoords, refcenter, fitcenter, rotation)\\n # Only return the first coordinates\\n return newcoords[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ed839944b066a0f4bf559cd0a7d2589\",\n \"score\": \"0.54023427\",\n \"text\": \"def FindClosestPoint(self, ):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf310dc4796a27ff572a99502dcb2789\",\n \"score\": \"0.5400604\",\n \"text\": \"def query_coordinates(self, coordinates):\\r\\n try:\\r\\n distances, indices = self.tree.query(coordinates, k=1) #, distance_upper_bound=0.1\\r\\n except ValueError as e:\\r\\n logger.erro('Unable to parse coordinates:', coordinates)\\r\\n raise e\\r\\n else:\\r\\n results = []\\r\\n for distance, index in zip(distances, indices):\\r\\n if not isinf(distance):\\r\\n result = self.locations[index]\\r\\n result['distance'] = distance\\r\\n\\r\\n results.append(result)\\r\\n\\r\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c960465a5c8453728418b7c7c8f12741\",\n \"score\": \"0.53992957\",\n \"text\": \"def findAt(self, coordinates: tuple, printWarning: Boolean = True):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c960465a5c8453728418b7c7c8f12741\",\n \"score\": \"0.53992957\",\n \"text\": \"def findAt(self, coordinates: tuple, printWarning: Boolean = True):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f96a121329590f95ea92a94091931f0\",\n \"score\": \"0.5396925\",\n \"text\": \"def th_nearest_interp2d(input, coords):\\n # take clamp of coords so they're in the image bounds\\n x = th.clamp(coords[:, :, 0], 0, input.size(1) - 1).round()\\n y = th.clamp(coords[:, :, 1], 0, input.size(2) - 1).round()\\n\\n stride = th.LongTensor(input.stride())\\n x_ix = x.mul(stride[1]).long()\\n y_ix = y.mul(stride[2]).long()\\n\\n input_flat = input.view(input.size(0), -1)\\n\\n mapped_vals = input_flat.gather(1, x_ix.add(y_ix))\\n\\n return mapped_vals.view_as(input)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77db11cf9ca2b313312a460149fbd7ce\",\n \"score\": \"0.5393471\",\n \"text\": \"def nearest(self, pt):\\n tidx = super(FlatCAMRTreeStorage, self).nearest(pt)\\n return (tidx.bbox[0], tidx.bbox[1]), self.objects[tidx.object]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1e97c3d2cecb342529408dd6e9e4a19\",\n \"score\": \"0.5390935\",\n \"text\": \"def getIntersectionPoint(non_street_point,street_point,coordinates):\\n (x1,y1) = coordinates[non_street_point[0]]\\n (x2,y2) = coordinates[non_street_point[1]]\\n (x3,y3) = coordinates[street_point[0]]\\n (x4,y4) = coordinates[street_point[1]]\\n den = (x1-x2)*(y3-y4) - (y1-y2)*(x3-x4)\\n n1 = (x1*y2 - y1*x2)\\n n2 = (x3*y4 - y3*x4)\\n px = (n1*(x3 - x4) - n2*(x1 - x2)) / den\\n py = (n1*(y3 - y4) - n2*(y1 - y2)) / den\\n return (px,py)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44ddab67f26a3d04c067d32fe7738816\",\n \"score\": \"0.5386557\",\n \"text\": \"def test_get_near_point():\\n sh = SpatialHash(cell_size=10)\\n sprite_1 = arcade.SpriteSolidColor(10, 10, center_x=0)\\n sprite_2 = arcade.SpriteSolidColor(10, 10, center_x=5)\\n sh.add(sprite_1)\\n sh.add(sprite_2)\\n\\n nearby_sprites = sh.get_sprites_near_point((0, 0))\\n assert isinstance(nearby_sprites, set)\\n assert len(nearby_sprites) == 2\\n assert nearby_sprites == set([sprite_1, sprite_2])\\n\\n nearby_sprites = sh.get_sprites_near_point((20, 0))\\n assert isinstance(nearby_sprites, set)\\n assert len(nearby_sprites) == 0\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994080,"cells":{"query_id":{"kind":"string","value":"63dca1f63f881be5338907f45eeddff6"},"query":{"kind":"string","value":"Creates a SQL context"},"positive_passages":{"kind":"list like","value":[{"docid":"cf9d4d144a78107634dc376e8e35f83b","score":"0.6197181","text":"def __init__(self, url=None, connection=None, schema=None):\r\n\r\n if not url and not connection:\r\n raise AttributeError(\"Either url or connection should be provided\" \\\r\n \" for SQL data source\")\r\n\r\n super(SQLContext, self).__init__()\r\n\r\n if connection:\r\n self.connection = connection\r\n self.should_close = False\r\n else:\r\n engine = sqlalchemy.create_engine(url)\r\n self.connection = engine.connect()\r\n self.should_close = True\r\n\r\n self.metadata = sqlalchemy.MetaData()\r\n self.metadata.bind = self.connection.engine\r\n self.schema = schema","title":""}],"string":"[\n {\n \"docid\": \"cf9d4d144a78107634dc376e8e35f83b\",\n \"score\": \"0.6197181\",\n \"text\": \"def __init__(self, url=None, connection=None, schema=None):\\r\\n\\r\\n if not url and not connection:\\r\\n raise AttributeError(\\\"Either url or connection should be provided\\\" \\\\\\r\\n \\\" for SQL data source\\\")\\r\\n\\r\\n super(SQLContext, self).__init__()\\r\\n\\r\\n if connection:\\r\\n self.connection = connection\\r\\n self.should_close = False\\r\\n else:\\r\\n engine = sqlalchemy.create_engine(url)\\r\\n self.connection = engine.connect()\\r\\n self.should_close = True\\r\\n\\r\\n self.metadata = sqlalchemy.MetaData()\\r\\n self.metadata.bind = self.connection.engine\\r\\n self.schema = schema\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"22361e6b7fd296a7df81b6110d25b91b","score":"0.68279225","text":"def _make_context():\n return {'app': app, 'db': db}","title":""},{"docid":"4c9b643c0b896ecd18fb03e4b3fd641b","score":"0.6694832","text":"def sql_context(self):\n if not self._sql_context:\n import pyspark.sql\n self._sql_context = pyspark.sql.SQLContext(self.context())\n return self._sql_context","title":""},{"docid":"78512e5377e9ce4ef25c1f733886afb2","score":"0.667269","text":"def create_context(cls):\n pass","title":""},{"docid":"d4b71f5e84a0ea98d66c4f057d26e875","score":"0.6592965","text":"def _make_context():\n return {'app': app, 'db': db, 'User': User}","title":""},{"docid":"1401ef394cb69f1c31de173d34e1320e","score":"0.6579943","text":"def _make_context():\n return {'app': app, 'db': db, 'User': User}","title":""},{"docid":"b8d86042691083f634b2bee3382ffcae","score":"0.6545147","text":"def _make_context():\n\n return {\n 'app': app,\n 'db': db,\n 'User': User\n }","title":""},{"docid":"5214707e030aec7c546d18e1fa94fe03","score":"0.65256286","text":"def _make_context():\n return {'app': app,\n 'db': db,\n 'User': User\n }","title":""},{"docid":"60d04494aa38c7b632691e33d5f9bf73","score":"0.6358311","text":"def build_from_environment():\n\n pg_username = os.environ['POSTGRES_USERNAME']\n pg_password = os.environ['POSTGRES_PASSWORD']\n\n try:\n conn = psycopg2.connect(\"user=\" + pg_username + \" host='localhost' password=\" + pg_password)\n # program ends if the connection fails.\n except psycopg2.OperationalError, oe:\n raise oe\n sys.exit(1)\n\n return PgDbContext(conn)","title":""},{"docid":"3e6477a1c1cf47b95e5bf15126746aca","score":"0.62872374","text":"def _make_context():\n return {'User': User, 'CreditCard': CreditCard, 'Transaction': Transaction, 'db': db, 'jsonify':jsonify}","title":""},{"docid":"d910fe18fbef4319354befcc35d03628","score":"0.61972845","text":"def create_db(self):\n return None","title":""},{"docid":"3bd7d69a3dfe3dd8a3d938923ea3db8d","score":"0.61969036","text":"def create_db(self):","title":""},{"docid":"b0ecc694dfd3ed2931ba205d16a5ce25","score":"0.6186409","text":"def create_db():\n init_postgres(current_app.config['SQLALCHEMY_DATABASE_URI'])","title":""},{"docid":"20f5026a936d29e11c080ff400ee5b64","score":"0.61260194","text":"def make_shell_context():\n return {'db': db, 'User': User, 'Post': Post}","title":""},{"docid":"45993aef131bdde8469803413d2faacd","score":"0.60089785","text":"def prepare_database(config):\n global Session\n engine = sqlalchemy.create_engine(config.db_string)\n session_factory = sqlalchemy.orm.sessionmaker(bind=engine)\n Session = sqlalchemy.orm.scoped_session(session_factory)","title":""},{"docid":"fedddbc9c1637fc554a9ee398b3f7b17","score":"0.5995722","text":"def init_db(self):\n\n # The user can provide a custom string\n if self.database is None:\n self.logger.error(\"You must provide a database url, exiting.\")\n sys.exit(1)\n\n self.engine = create_engine(self.database, convert_unicode=True)\n self.session = scoped_session(\n sessionmaker(autocommit=False, autoflush=False, bind=self.engine)\n )\n\n # Database Setup\n Base.query = self.session.query_property()\n\n # import all modules here that might define models so that\n # they will be registered properly on the metadata. Otherwise\n # you will have to import them first before calling init_db()\n import expfactory.database.models\n\n self.Base = Base\n self.Base.metadata.create_all(bind=self.engine)","title":""},{"docid":"8cfb194104acbd857427d389d5d1ff7e","score":"0.59844375","text":"def generate_context(self) -> Context:\n self._transient_context = Context()\n return self._transient_context","title":""},{"docid":"512dd1266a0b346338850a76b4c521fa","score":"0.5969605","text":"def make_context(self, engine, args):\n args = self.normalize_args(args)\n _, ctx = self._make_argkey_and_context(engine, args)\n return ctx","title":""},{"docid":"6a5ab511e6551482b2a0dabf9b2acedc","score":"0.5920828","text":"def shell_context():\n from .models import db, Politico, Reembolso, Feedback\n return dict(db=db, Politico=Politico,\n Reembolso=Reembolso, Feedback=Feedback)","title":""},{"docid":"bc792ec70f31d198a4b10b2af6b35849","score":"0.5920192","text":"def __init__(self):\n engine = db_connect()\n create_tables(engine)\n self.Session = sessionmaker(bind=engine)","title":""},{"docid":"6373708b7fd02359f3bb1b349b87bcb6","score":"0.59160876","text":"def make_shell_context():\n return dict(app=app,\n db=models.db,\n User=models.User,\n Post=models.Post,\n Comment=models.Comment,\n Tag=models.Tag,\n Server=Server)","title":""},{"docid":"7664c5e9b3875976116dd44e487725fd","score":"0.590667","text":"def session_context(func):\r\n def wrapper(*args, **kwargs):\r\n self = args[0]\r\n with self._create_db_session() as db:\r\n self.db = db\r\n return func(*args, **kwargs)\r\n return wrapper","title":""},{"docid":"99e3181df349f4040581c96e82492919","score":"0.5887355","text":"def init_database(cls):\n conn = config.db_connection_string(Settings)\n cls.Engine = create_engine(conn, echo=Settings.get('DEBUG'))\n cls.Session = sessionmaker(bind=cls.Engine)\n return cls","title":""},{"docid":"f40b1d77807d96063903410f8f9b0a5a","score":"0.58802176","text":"def __init__(self, path=None, echo=False):\r\n super(SqlAlchemyDB, self).__init__()\r\n if path is None:\r\n path = _SQL_ALCHEMY_MEMORY\r\n else:\r\n if path != _SQL_ALCHEMY_MEMORY:\r\n dirname = os.path.dirname(path)\r\n if dirname and dirname != \"\":\r\n os.makedirs(dirname, exist_ok=True)\r\n path = \"sqlite:///\" + path\r\n self._engine = create_engine(path, echo=echo)\r\n Base.metadata.create_all(self._engine)\r\n self._Session = sessionmaker(bind=self._engine)","title":""},{"docid":"e91ab6ab7af3d2303df2d758e664ee48","score":"0.5877039","text":"def init_db():\n # We are setting the module variables here for the first time, so disable the warning\n global DB_USER_TABLE # pylint: disable=global-variable-undefined\n global DB_CUSTOMER_TABLE # pylint: disable=global-variable-undefined\n global DB_USER_CUSTOMER_RELS_TABLE # pylint: disable=global-variable-undefined\n global DB_TICKET_TABLE # pylint: disable=global-variable-undefined\n global DB_COMMENT_TABLE # pylint: disable=global-variable-undefined\n\n db = TinyDB(app.config['DB_NAME'])\n\n DB_USER_TABLE = db.table('users')\n DB_CUSTOMER_TABLE = db.table('customers')\n DB_USER_CUSTOMER_RELS_TABLE = db.table('user_customer_rels')\n DB_TICKET_TABLE = db.table('tickets')\n DB_COMMENT_TABLE = db.table('comments')","title":""},{"docid":"2e3f1f32101d4306b8b932b88f41c4a1","score":"0.5877013","text":"def __init__(self, *args, **kwargs):\n self.context = kwargs.get(\"config\")\n if hasattr(self.context, 'database'):\n # XXX this should be replaced with connection_context instead\n self.context.database['database_host'] = \\\n self.context.database.database_hostname\n self.context.database['database_port'] = \\\n self.context.database.database_port\n self.context.database['database_name'] = \\\n self.context.database.database_name\n self.context.database['database_username'] = \\\n self.context.database.database_username\n self.context.database['database_password'] = \\\n self.context.database.database_password\n self.database = db.Database(self.context.database)\n else:\n # the old middleware\n self.database = db.Database(self.context)","title":""},{"docid":"e928fa48f22ea0bcb4157c6581181d4f","score":"0.58652604","text":"def make_shell_context():\n\n return dict(app=app, db=db, User=User)","title":""},{"docid":"e928fa48f22ea0bcb4157c6581181d4f","score":"0.58652604","text":"def make_shell_context():\n\n return dict(app=app, db=db, User=User)","title":""},{"docid":"c657b038e3358f2fc4f4ba1d83a4beca","score":"0.58486253","text":"def create():\n\tcreate_db()","title":""},{"docid":"954520a01001cb314c73bbf161c84eaa","score":"0.58432645","text":"def __init__(\n self,\n sql_username,\n sql_password,\n sql_host,\n sql_port,\n sql_db,\n sql_url_template=(\"mysql+mysqldb://{username}:{password}@\"\n \"{host}:{port}/{db}?charset=utf8mb4\"),\n **kwargs\n ):\n\n # Internalize arguments.\n self.sql_username = sql_username\n self.sql_password = sql_password\n self.sql_host = sql_host\n self.sql_port = sql_port\n self.sql_db = sql_db\n self.sql_url_template = sql_url_template\n\n # Inspecting the presence of keyword arguments and (should they not be\n # defined) setting defaults.\n self.sql_engine_pool_size = kwargs.get(\"sql_engine_pool_size\", 1)\n self.sql_engine_pool_recycle = kwargs.get(\n \"sql_engine_pool_recycle\", 3600\n )\n self.sql_engine_echo = kwargs.get(\"sql_engine_echo\", False)\n self.mysqldb_sscursor = kwargs.get(\"mysqldb_sscursor\", False)\n self.expire_on_commit = kwargs.get(\"expire_on_commit\", False)\n\n # create DB engine.\n self.engine = self.connect()\n\n # create new session.\n self.session_factory = sqlalchemy.orm.sessionmaker(\n bind=self.engine,\n expire_on_commit=self.expire_on_commit\n )","title":""},{"docid":"d731712486f72ab154bcacf33f884f62","score":"0.582744","text":"def init_database(self):\n engine = create_engine('sqlite:///todo.db?check_same_thread=False')\n self.Base.metadata.create_all(engine)\n self.session = sessionmaker(bind=engine)()","title":""},{"docid":"e82aa9a0d85f00aefbb11c18077005e3","score":"0.5823347","text":"def make_context(source, frmat='table'):\n return Context.fromstring(source, frmat=frmat)","title":""},{"docid":"34d719710f9a8478ca30d1a3c704876b","score":"0.5819967","text":"def __init__(self):\n engine = db_connect()\n create_table(engine)\n self.session = sessionmaker(bind=engine)","title":""},{"docid":"0f256e4d6ecd9594b4bc894645fa9f35","score":"0.58028","text":"def _session_maker(self) -> ContextManager[Session]:\r\n session = self._Session()\r\n try:\r\n yield session\r\n session.commit()\r\n except DBAPIError:\r\n session.rollback()\r\n raise\r\n finally:\r\n session.close()","title":""},{"docid":"8866cfd358f5b03b4c13d3794cb52783","score":"0.5800551","text":"async def database(self, *_):\n config = {'dsn': os.getenv('DATABASE_URL')}\n self.db = await aiopg.sa.create_engine(**config)\n yield\n self.db.close()\n await self.db.wait_closed()","title":""},{"docid":"372e7ae0b7c210e988d2526375a9a85f","score":"0.5800246","text":"def __init__(self):\r\n self.conn = create_connection(DATABASE_PATH)","title":""},{"docid":"19571ab4a7054106e5000b1f245500ce","score":"0.57969123","text":"def initialize_database():\n\n global DatabaseSession\n from config import config\n\n engine = create_engine(\n config[saq.CONFIG['global']['instance_type']].SQLALCHEMY_DATABASE_URI, \n **config[saq.CONFIG['global']['instance_type']].SQLALCHEMY_DATABASE_OPTIONS)\n\n DatabaseSession = sessionmaker(bind=engine)\n saq.db = scoped_session(DatabaseSession)","title":""},{"docid":"b4b39dcc6bd9c89b0956e0d369da9deb","score":"0.5791556","text":"def _initTestingDB(): \n from sqlalchemy import create_engine\n engine = create_engine('sqlite://')\n from .models import (\n Base,\n TodoUser,\n )\n DBSession.configure(bind=engine)\n Base.metadata.create_all(engine)\n \n return DBSession","title":""},{"docid":"0c549d12998dd88468d960202c7a0aaa","score":"0.57832694","text":"def mkcontext(self,\n context= [],\n contextobj=None):\n if contextobj == None:\n raise ValueError, \"mkcontext: contextobj is None\"\n return jsoncall.do_call(\"mkcontext\", {'modelname':self.modelname,\\\n 'user':self.user,\\\n 'password':self.password,\\\n 'context':context,\\\n 'contextobj':contextobj.__dict__},\n self.connection)","title":""},{"docid":"d225a91ad16014e80a89c3b1afb7f90a","score":"0.57599616","text":"def make_shell_context():\n return dict(server=server,\n db=db,\n User=User,\n Article=Article,\n Topic=Topic,\n Comment=Comment,\n Subscription=Subscription)","title":""},{"docid":"60284ebce2ca97eb6768c58fa2df711f","score":"0.57571405","text":"def __init__(self):\n engine = db_connect()\n create_table(engine)\n self.Session = sessionmaker(bind=engine)","title":""},{"docid":"60284ebce2ca97eb6768c58fa2df711f","score":"0.57571405","text":"def __init__(self):\n engine = db_connect()\n create_table(engine)\n self.Session = sessionmaker(bind=engine)","title":""},{"docid":"7b3b999ddc7dd29dcd305eb277bbcd16","score":"0.5750913","text":"def init_db():\n return SQLAlchemy(app, session_options={\n 'expire_on_commit': False,\n })","title":""},{"docid":"2fde45b60b00c93e74b97456fd80ec79","score":"0.5750327","text":"def make_shell_context():\n\n context = dict(app=app, db=db)\n for class_ in [FormData, CaseData, CaseIndex, Synclog, OwnershipCleanlinessFlag]:\n context[class_.__name__] = class_\n return context","title":""},{"docid":"3b457a0c0061049dcaa30aae04dbe1d7","score":"0.57479906","text":"def _db():\n db_fd, db_path = tempfile.mkstemp()\n app = create_app()\n app.config['TESTING'] = True\n app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n app.config['DATABASE'] = db_path\n app.config['SQLALCHEMY_DATABASE_URI'] = \"sqlite://\"\n\n with app.app_context():\n db.init_app(app)\n db.create_all()\n\n yield db\n\n os.close(db_fd)\n os.unlink(db_path)","title":""},{"docid":"28583a5c3fa06235c2e8dab4b0d88f9e","score":"0.5745354","text":"def make_shell_context():\n\n return dict(app=app, db=db, Posts = Posts, Comments=Comments)","title":""},{"docid":"4ebbae0cad9ef8def93995393582a79d","score":"0.57446367","text":"def _initDb(self):\n CREATE_TOKEN_TABLE = '''create table token\n (token text, id int primary key)\n '''\n CREATE_DOCS_TABLE = '''create table docs\n (local_path text, resource_id text primary key, etag text, title text)\n '''\n \n try:\n self.db.execute(CREATE_TOKEN_TABLE)\n self.db.execute(CREATE_DOCS_TABLE)\n except sqlite3.OperationalError, error:\n pass","title":""},{"docid":"f3f110e2e4bcbd7a581b4cdce1d1c83a","score":"0.5742504","text":"def dbinit( *args, **kwargs ):","title":""},{"docid":"e7d7bf46ca0da667d19f9ff4cd67223f","score":"0.574025","text":"def get_sql_context_instance(spark_configuration):\n if 'sparkSessionSingletonInstance' not in globals():\n globals()['sparkSessionSingletonInstance'] = SparkSession.builder.config(conf=spark_configuration).getOrCreate()\n return globals()['sparkSessionSingletonInstance']","title":""},{"docid":"b2c62a3dd4afec88118519fd468ad628","score":"0.5739886","text":"def db_connect():\n return create_engine(URL(**product_crawlers.settings.DATABASE))","title":""},{"docid":"00a9888a639feb1a52665f6c5a7b0ab4","score":"0.5734747","text":"def construct_global_ctx(self):\n super().construct_global_ctx()\n gtx = self.gtx\n rc = self.rc\n if \"groups\" in self.needed_dbs:\n rc.pi_id = get_pi_id(rc)\n rc.coll = f\"{TARGET_COLL}\"\n try:\n if not rc.database:\n rc.database = rc.databases[0][\"name\"]\n except:\n pass\n colls = [\n sorted(\n all_docs_from_collection(rc.client, collname), key=_id_key\n )\n for collname in self.needed_dbs\n ]\n for db, coll in zip(self.needed_dbs, colls):\n gtx[db] = coll\n gtx[\"all_docs_from_collection\"] = all_docs_from_collection\n gtx[\"float\"] = float\n gtx[\"str\"] = str\n gtx[\"zip\"] = zip","title":""},{"docid":"192cf231f1d4576cad44e57e69213fd6","score":"0.57337713","text":"def create_db_statement(self):\n return Engine.create_db_statement(self).replace(\"DATABASE\", \"SCHEMA\")","title":""},{"docid":"d348e563e20bcad9aa2d1d8708ce814d","score":"0.57224196","text":"def __init__(self, connection_url, echo=False):\n if not connection_url:\n raise ValueError('No database connection URL provided.')\n engine = create_engine(connection_url, echo=echo)\n PipelineRun.metadata.create_all(engine)\n self.session_factory = sessionmaker(bind=engine)","title":""},{"docid":"fd2a6fd00d1fe9979eed36be7f190638","score":"0.57155734","text":"def init_with_context(self, context):\n pass","title":""},{"docid":"5553f3498bd2c6bed14afdb824210e97","score":"0.57128376","text":"def db_session_context(db_name):\n try:\n db = psycopg2.connect(database=db_name)\n yield db\n finally:\n db.close()","title":""},{"docid":"4794c5f681478a05bfdbc767b1e61ac2","score":"0.5697702","text":"def setup_db():\n\n engine = config['tg.app_globals'].sa_engine\n # model.init_model(engine)\n # model.metadata.create_all(engine)","title":""},{"docid":"5e3d4bddff67a3bc1a4ec20ccaf8c29b","score":"0.5693486","text":"def _create_db(self):\n self.db = easydms.dbcore.Database(\":memory:\")\n self.db.create_db()","title":""},{"docid":"519c3acbe9341d4d5642815536e0fb71","score":"0.56910944","text":"def init_database(self):\n # init_database(self.engine)","title":""},{"docid":"ea3b1ea3d4565089f8aad10f2c647b1d","score":"0.56887364","text":"def init_db(connection, echo):\r\n\r\n # create the database tables as defined\r\n engine = create_engine(connection, echo=echo)\r\n Base.metadata.create_all(engine)\r\n\r\n # create a session\r\n Base.metadata.bind = engine\r\n BaseSession = sessionmaker(bind=engine)\r\n session = BaseSession()\r\n\r\n # set the shared Model session\r\n Model.use_session(session)\r\n\r\n return (engine, session)","title":""},{"docid":"aab77d341977da7da8bde92082e1194d","score":"0.5685786","text":"def sql_engine(tmpdir: Path) -> Generator[SQLEngine, None, None]:\n db_file_path = tmpdir / \"test.db\"\n sql_engine = create_sql_engine(db_file_path)\n yield sql_engine\n sql_engine.dispose()","title":""},{"docid":"24c2f9a03f641ca31795b69b41185432","score":"0.5685584","text":"def create_gt_database_template(self):\n pass\n with self.connection as cursor:\n fn = os.path.join(os.path.dirname(__file__), 'gtlog.sql')\n self.cursor.execute(open(fn, \"r\").read())","title":""},{"docid":"33276f8ae22ca4dc106cf877316baa01","score":"0.5678844","text":"def __init__(self, sql_config = {}, verbose = True):\n\n self.sql_config = sql_config\n self.verbose = verbose\n self._setup()","title":""},{"docid":"996f2d647af300387d2170a56979ba93","score":"0.56667346","text":"def database_setup():\n Base.metadata.create_all(bind=engine)\n db = LocalSession()\n try:\n populate_from_json(db, Vector, str(VECTORS))\n populate_from_json(db, Gender, str(GENDERS))\n populate_from_json(db, Tag, str(TAGS))\n finally:\n db.close()","title":""},{"docid":"13887516abec59b7a50b684b708b8618","score":"0.5660363","text":"def init_db(self):\n self.db_config = databaseutils.process_db_config(self.config['db'])\n\n from sqlalchemy import create_engine\n from sqlalchemy.orm import sessionmaker, scoped_session\n self.engine = create_engine(self.db_config.constr, pool_recycle=3600)\n self.session = scoped_session(sessionmaker(bind=self.engine))\n\n # Make sure tables are created\n DB_Base.metadata.create_all(self.engine)\n\n if self.sqlite_file is not None:\n dbname = 'sqlite:///%s' % self.sqlite_file\n self.sqlite_engine = create_engine(dbname, echo=False)\n self.sqlite_session = scoped_session(sessionmaker(bind=self.sqlite_engine))\n DB_Base.metadata.create_all(self.sqlite_engine)\n logger.info('Using SQLite %s' % self.sqlite_engine)","title":""},{"docid":"c8cbb1fc0adf1f17ea7355a562797994","score":"0.56507915","text":"def generate_database_object(**kwargs):\n return app.database.Database(\"test.db\", **kwargs)","title":""},{"docid":"39cc6c47e34de172016a324ad010e821","score":"0.5650551","text":"def create_connection(self, context, *, engine):\n try:\n connection = engine.connect()\n except OperationalError as exc:\n raise UnrecoverableError('Could not create SQLAlchemy connection: {}.'.format(str(exc).replace('\\n', ''))\n ) from exc\n\n with connection:\n yield connection","title":""},{"docid":"20852cc79ae276c170ed99e88f0a5cf9","score":"0.56468374","text":"def _create_sqlite_file_engine( conn=next( file_path_generator ), echo=True ):\n print( \"creating connection: %s \" % conn )\n return create_engine( conn, echo=echo )","title":""},{"docid":"bbe3abd6806bbb4df2cd9defaf0af95e","score":"0.56466085","text":"def create_engine(self):\n return create_engine('sqlite:///' + self.database_name, echo=True)","title":""},{"docid":"07de65ef0e9c1dac254701f6f539b0d5","score":"0.56367636","text":"def create_sqlite_engine( echo=False ):\n conn = 'sqlite:///:memory:'\n print( \"creating connection: %s \" % conn )\n return create_engine( conn, echo=False )","title":""},{"docid":"55795f4779f4c418d600fa5b252ccd7d","score":"0.56306756","text":"def create_database():\n with connection:\n connection.execute(CREATE_MOVIE_TABLE)\n connection.execute(CREATE_USER_TABLE)\n connection.execute(CREATE_WATCHED_TABLE)","title":""},{"docid":"a529a94312627b14eb74b7678bd98687","score":"0.5626167","text":"def get_sqls(self):\n return {\n \"prepare_check\": \"select id from dbo.sysobjects where id = object_id('{0}')\".format(self.table_name),\n \"prepare_create\": \"create table {0} (channel NVARCHAR(20), channel_user_id NVARCHAR(100), timestamp DATETIME2, topic_name NVARCHAR(100), topic_status NVARCHAR(100), topic_previous NVARCHAR(4000), topic_priority INT, data NVARCHAR(MAX), primary key(channel, channel_user_id))\".format(self.table_name),\n \"get_context\": \"select top 1 * from {0} where channel=? and channel_user_id=?\".format(self.table_name),\n \"save_context\": \"\"\"\n merge into {0} as A\n using (select ? as channel, ? as channel_user_id, ? as timestamp, ? as topic_name, ? as topic_status, ? as topic_previous, ? as topic_priority, ? as data) as B\n on (A.channel = B.channel and A.channel_user_id = B.channel_user_id)\n when matched then\n update set timestamp=B.timestamp, topic_name=B.topic_name, topic_status=B.topic_status, topic_previous=B.topic_previous, topic_priority=B.topic_priority, data=B.data\n when not matched then\n insert (channel, channel_user_id, timestamp, topic_name, topic_status, topic_previous, topic_priority, data) values (B.channel, B.channel_user_id, B.timestamp, B.topic_name, B.topic_status, B.topic_previous, B.topic_priority, B.data);\n \"\"\".format(self.table_name)\n }","title":""},{"docid":"04f8eac0f9fbab51d117b39064c06d77","score":"0.5624793","text":"def init_database():\n\n # The current dir should be the script home\n homedir = os.path.normpath(\n os.path.dirname(\n sys.executable if getattr(sys, 'frozen', False) else\n __file__)) # cx_Freeze compatibility\n os.chdir(homedir)\n\n engine = create_engine(\"sqlite:///data.db\")\n BASE.metadata.bind = engine\n BASE.metadata.create_all()\n\n return engine","title":""},{"docid":"5699fa8fd217e24b0877e1e82519e960","score":"0.5613715","text":"def session_context(pytestconfig, request, tmp_env):\n ctx = Context.only()\n\n # Temporary, empty local directory for local data\n session_tmp_dir = Path(request.config._tmp_path_factory.mktemp(\"data\"))\n\n # Set the cache path according to whether pytest --local-cache was given. If True,\n # pick up the existing setting from the user environment. If False, use a pytest-\n # managed cache directory that persists across test sessions.\n ctx.cache_path = (\n ctx.local_data.joinpath(\"cache\")\n if request.config.option.local_cache\n # TODO use pytestconfig.cache.mkdir() when pytest >= 6.3 is available\n else Path(pytestconfig.cache.makedir(\"cache\"))\n )\n\n # Other local data in the temporary directory for this session only\n ctx.local_data = session_tmp_dir\n\n platform_name = \"message-ix-models\"\n\n # Add a platform connected to an in-memory database\n # NB cannot call Config.add_platform() here because it does not support supplying a\n # URL for a HyperSQL database.\n # TODO add that feature upstream.\n ixmp_config.values[\"platform\"][platform_name] = {\n \"class\": \"jdbc\",\n \"driver\": \"hsqldb\",\n \"url\": f\"jdbc:hsqldb:mem://{platform_name}\",\n }\n\n # Launch Platform and connect to testdb (reconnect if closed)\n mp = Platform(name=platform_name)\n mp.open_db()\n\n ctx.platform_info[\"name\"] = platform_name\n\n yield ctx\n\n ctx.close_db()\n ixmp_config.remove_platform(platform_name)","title":""},{"docid":"de43df056ad075876853bc882b943f6a","score":"0.5610763","text":"def init_db():\n with LoggerApi.app_context():\n db = get_db()\n with LoggerApi.open_resource('schema.sql', mode='r') as f:\n db.cursor().executescript(f.read())\n db.commit()","title":""},{"docid":"79fe40448334a47535d6216cd550a9fd","score":"0.56061286","text":"def context(self) -> CONTEXT:","title":""},{"docid":"49bc95c1c1caf1564178c54a906156d9","score":"0.55998296","text":"def __init__(self):\n engine = db_connect()\n create_table(engine)\n self.Session = sessionmaker(bind=engine)\n logging.info(\"****DuplicatesPipeline: database connected****\")","title":""},{"docid":"569aed92cd6b3dbc0d12e81fc8780059","score":"0.55969864","text":"def __init__(self):\n engine = db_connect()\n self.Session = sessionmaker(bind=engine)","title":""},{"docid":"4e2473f46ea6b831e377a1497d3a192e","score":"0.5595775","text":"def session_context(self):\n session = self.Session()\n try:\n yield session\n session.commit()\n except: # noqa E722\n session.rollback()\n raise\n finally:\n session.close()","title":""},{"docid":"4aed93ec66a3914852af452593b745dd","score":"0.55913657","text":"def session_scope( engine=None ):\n try:\n if engine is None:\n engine = initialize_engine( environment.ENGINE )\n # DataTools's handle to database at global level\n # SessionFactory = make_scoped_session_factory(engine)\n SessionFactory = make_session_factory( engine )\n\n if environment.ENGINE == 'sqlite' or environment.ENGINE == 'sqlite-file':\n # We need to get the db into memory when start up\n # environmental variables will determine details of the\n # db\n create_db_tables( engine )\n\n session = SessionFactory()\n yield session\n session.commit()\n\n except:\n session.rollback()\n raise\n\n finally:\n if session:\n session.close()","title":""},{"docid":"9acf907fd3230a868c35813b62d84a30","score":"0.5588803","text":"def init():\n\n # delete existing file\n if os.path.exists(DBFILE):\n os.remove(DBFILE)\n\n db = sqlite3.connect(DBFILE)\n # create tables\n create(db, PARAGRAPH, \"paragraph\")\n create(db, QUESTION, \"question\")\n create(db, ANSWER, \"answer\")\n\n return db","title":""},{"docid":"0da328eecf2a7a047cbc5c450aed8410","score":"0.5585826","text":"def __init__(self, db_sql_path, mode='r', verbose=1):\n \n self._exec_date = datetime.now()\n if not os.path.exists(db_sql_path):\n raise Exception('path %s does not exist'%db_sql_path)\n if not os.path.isdir(db_sql_path):\n raise Exception('path %s is not a directory'%db_sql_path)\n db_sql_path = os.path.abspath(db_sql_path)\n self._path = os.path.join(db_sql_path, 'database.sql')\n if mode not in ['r', 'w']:\n raise Exception('Mode must be either r: read only or w: read and write')\n self._mode = mode\n self._verbose = verbose\n self.__within_context = False\n self.__special_exit = None\n self.__initialized = False\n if self._mode == 'r':\n self.__initialization__()","title":""},{"docid":"58ba988fa59c6c64c5c0874596e05198","score":"0.55807924","text":"def _context():\n global _trident_context\n if _trident_context is None:\n _trident_context = _Context()\n return _trident_context","title":""},{"docid":"8ed913bfec489c7d0a3fb53665a4ff27","score":"0.5580359","text":"def init_db(self):\n self.db_config = databaseutils.process_db_config(self.config['db'])\n\n from sqlalchemy import create_engine\n from sqlalchemy.orm import sessionmaker, scoped_session\n self.engine = create_engine(self.db_config.constr, pool_recycle=3600)\n self.session = scoped_session(sessionmaker(bind=self.engine))\n\n # Make sure tables are created\n DB_Base.metadata.create_all(self.engine)","title":""},{"docid":"6977fcfed11e3cce4391ba1343ba89d8","score":"0.5579277","text":"def _context_new(self):\n assert self._pa_mainloop is not None\n app_name = self._get_app_name()\n context = pa.pa_context_new(self._pa_mainloop,\n app_name.encode('ASCII')\n )\n return context","title":""},{"docid":"86f4f54fcea59b51df4534630b411709","score":"0.5578862","text":"def __init__(self):\n engine = connect_to_db()\n create_lyrics_table(engine) #declarative base stuff\n self.Session = sessionmaker(bind=engine)\n\n # self.create_connection()\n # self.create_table()","title":""},{"docid":"a3eb57211c08ee9b7eb0f801656f3676","score":"0.5578381","text":"def setup_db():\n engine = create_engine(settings.DATABASE)\n ModelBase.metadata.bind = engine\n ModelBase.metadata.create_all(engine)\n\n return scoped_session(sessionmaker(bind=engine))","title":""},{"docid":"a79a7efbb8a73688769289c54b63f4e5","score":"0.557326","text":"def create_database():\n create_db(app)","title":""},{"docid":"70c8418c7da4556e3f9c626b6781540a","score":"0.5572984","text":"def __init__(self):\n engine = db_connect()\n create_table(engine)\n self.Session = sessionmaker(bind=engine)\n logging.info(\"****SaveRestaurantsPipeline: database connected****\")","title":""},{"docid":"c0d535c6c4943d5eaa8e53098caf59f1","score":"0.5565452","text":"def get_template_context(self):\n context = super(Spamassassin, self).get_template_context()\n if self.dbengine == \"postgres\":\n store_module = \"Mail::SpamAssassin::BayesStore::PgSQL\"\n dsn = \"DBI:Pg:dbname={};host={};port={}\".format(\n self.dbname, self.dbhost, self.dbport)\n else:\n store_module = \"Mail::SpamAssassin::BayesStore::MySQL\"\n dsn = \"DBI:mysql:{}:{}:{}\".format(\n self.dbname, self.dbhost, self.dbport)\n context.update({\n \"store_module\": store_module, \"dsn\": dsn, \"dcc_enabled\": \"#\"})\n return context","title":""},{"docid":"83bd8b12c77cde96e2493c3709df894d","score":"0.55614823","text":"def db_connect():\n if 'db' not in g:\n g.db = sql.connect(current_app.config[\"DATABASE\"], detect_types=sql.PARSE_DECLTYPES)\n g.db.row_factory = sql.Row\n return g.db","title":""},{"docid":"3d0e8a68e10c671dbacccbfff7c8a38c","score":"0.5556699","text":"def init_context(self) -> None:\n self.context = AKSManagedClusterContext(\n self.cmd, AKSManagedClusterParamDict(self.__raw_parameters), self.models, DecoratorMode.CREATE\n )","title":""},{"docid":"c728dfa0817337b4b45ef95022b84cbf","score":"0.55560327","text":"def logic_db_engine(self):\n try:\n boto_session = boto3.Session(profile_name='loidsig')\n except:\n boto_session = boto3.Session()\n sm_client = boto_session.client(\n service_name='secretsmanager',\n region_name='us-east-1',\n endpoint_url='https://secretsmanager.us-east-1.amazonaws.com'\n )\n get_secret_value_response = sm_client.get_secret_value(SecretId='Loidsig_DB')\n cred_dict = ast.literal_eval(get_secret_value_response['SecretString'])\n db_user, db_pass = cred_dict['username'], cred_dict['password']\n db_host, db_port, db_name = cred_dict['host'], cred_dict['port'], cred_dict['dbname']\n try:\n postgres_engine = create_engine(f'postgresql://{db_user}:{db_pass}@{db_host}:{db_port}/{db_name}')\n except Exception as e:\n print(\"Unable to connect to postgres! Error: {}\".format(e))\n raise\n return postgres_engine","title":""},{"docid":"e821c05bc98bd1105a4a30258244d9a7","score":"0.5545746","text":"def create(self) -> SQLAlchemy:\n self.db.create_all()\n self.db.session.commit()\n return self.db","title":""},{"docid":"737ff63c6084fac9d5d6b8952505d61d","score":"0.55449027","text":"def _init_db(self):\n cursor = self._main_connection.cursor()\n cursor.execute(self.sql[\"create_table\"])\n self._main_connection.commit()","title":""},{"docid":"b9c50738f72c88bc6fd9de3b3f6fe293","score":"0.5543919","text":"def make_data_connection():\n\n global _engine # pylint: disable=W0603\n\n db_url = 'postgres://{0}:{1}@{2}:{3}/{4}'.format(DataBase.USER,\n DataBase.PASSWORD,\n DataBase.HOST,\n DataBase.PORT,\n DataBase.DB_NAME)\n _engine = create_engine(db_url, echo=DataBase.ECHO)","title":""},{"docid":"7810d7680fea6ef69953e371a29f2795","score":"0.5539203","text":"def __init__(self):\n engine = db_connect()\n create_reals_table(engine)\n self.Session = sessionmaker(bind=engine)","title":""},{"docid":"496a18896d6d8a741dbc034811867c05","score":"0.5536988","text":"def create_student_db(connection):\r\n with connection:\r\n connection.execute(CREATE_TABLE_STUDENTS_DATA)","title":""},{"docid":"c40966d70f9366d76af8dc418610d536","score":"0.5535386","text":"def sqlite_setup(driver=':memory:', extensions=None):\n conn = __Handler(driver, extensions)\n return conn.conn","title":""},{"docid":"0a08683a5e1cfd691dc70b2270151524","score":"0.5531912","text":"def initialize_sqlite_memory():\n global engine\n if engine is None:\n engine = create_engine(\"sqlite://\")\n Session.configure(bind=engine)\n _populate()","title":""},{"docid":"e635ebbb0a26390cc87cb0bb29f5e263","score":"0.55299956","text":"def init():\n print(\"Executing initialization\")\n print(db.dsn)\n cursor = yield momoko.Op(\n db.execute,\n \"\"\"\n DROP SCHEMA public CASCADE;\n CREATE SCHEMA public;\n CREATE TABLE game\n (\n game_id text PRIMARY KEY,\n players integer,\n state bytea,\n timestamp timestamp\n );\n CREATE UNIQUE INDEX ix_game_id\n ON game\n (game_id);\n CREATE INDEX ix_timestamp\n ON game\n (timestamp);\n \"\"\")\n try:\n print(cursor.fetchall())\n except psycopg2.ProgrammingError:\n pass\n io = ioloop.IOLoop.instance()\n io.stop()","title":""},{"docid":"3dee2a9ce3e3c0b886dcc3c834ea8765","score":"0.5516978","text":"def __init__(self, filename):\n # this boilerplate is turning me off from sqlalchemy. Why can't I have\n # one object that I interact with, instead of four levels of crap?\n self.sql_engine = create_engine(\n 'sqlite:///'+filename,\n isolation_level='READ UNCOMMITTED'\n )\n self.sql_session_maker = sessionmaker(bind=self.sql_engine)\n self.sql_session = self.sql_session_maker()\n Base.metadata.create_all(bind=self.sql_engine)\n self.term_cache = {}","title":""}],"string":"[\n {\n \"docid\": \"22361e6b7fd296a7df81b6110d25b91b\",\n \"score\": \"0.68279225\",\n \"text\": \"def _make_context():\\n return {'app': app, 'db': db}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c9b643c0b896ecd18fb03e4b3fd641b\",\n \"score\": \"0.6694832\",\n \"text\": \"def sql_context(self):\\n if not self._sql_context:\\n import pyspark.sql\\n self._sql_context = pyspark.sql.SQLContext(self.context())\\n return self._sql_context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78512e5377e9ce4ef25c1f733886afb2\",\n \"score\": \"0.667269\",\n \"text\": \"def create_context(cls):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4b71f5e84a0ea98d66c4f057d26e875\",\n \"score\": \"0.6592965\",\n \"text\": \"def _make_context():\\n return {'app': app, 'db': db, 'User': User}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1401ef394cb69f1c31de173d34e1320e\",\n \"score\": \"0.6579943\",\n \"text\": \"def _make_context():\\n return {'app': app, 'db': db, 'User': User}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8d86042691083f634b2bee3382ffcae\",\n \"score\": \"0.6545147\",\n \"text\": \"def _make_context():\\n\\n return {\\n 'app': app,\\n 'db': db,\\n 'User': User\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5214707e030aec7c546d18e1fa94fe03\",\n \"score\": \"0.65256286\",\n \"text\": \"def _make_context():\\n return {'app': app,\\n 'db': db,\\n 'User': User\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60d04494aa38c7b632691e33d5f9bf73\",\n \"score\": \"0.6358311\",\n \"text\": \"def build_from_environment():\\n\\n pg_username = os.environ['POSTGRES_USERNAME']\\n pg_password = os.environ['POSTGRES_PASSWORD']\\n\\n try:\\n conn = psycopg2.connect(\\\"user=\\\" + pg_username + \\\" host='localhost' password=\\\" + pg_password)\\n # program ends if the connection fails.\\n except psycopg2.OperationalError, oe:\\n raise oe\\n sys.exit(1)\\n\\n return PgDbContext(conn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e6477a1c1cf47b95e5bf15126746aca\",\n \"score\": \"0.62872374\",\n \"text\": \"def _make_context():\\n return {'User': User, 'CreditCard': CreditCard, 'Transaction': Transaction, 'db': db, 'jsonify':jsonify}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d910fe18fbef4319354befcc35d03628\",\n \"score\": \"0.61972845\",\n \"text\": \"def create_db(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bd7d69a3dfe3dd8a3d938923ea3db8d\",\n \"score\": \"0.61969036\",\n \"text\": \"def create_db(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0ecc694dfd3ed2931ba205d16a5ce25\",\n \"score\": \"0.6186409\",\n \"text\": \"def create_db():\\n init_postgres(current_app.config['SQLALCHEMY_DATABASE_URI'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f5026a936d29e11c080ff400ee5b64\",\n \"score\": \"0.61260194\",\n \"text\": \"def make_shell_context():\\n return {'db': db, 'User': User, 'Post': Post}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45993aef131bdde8469803413d2faacd\",\n \"score\": \"0.60089785\",\n \"text\": \"def prepare_database(config):\\n global Session\\n engine = sqlalchemy.create_engine(config.db_string)\\n session_factory = sqlalchemy.orm.sessionmaker(bind=engine)\\n Session = sqlalchemy.orm.scoped_session(session_factory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fedddbc9c1637fc554a9ee398b3f7b17\",\n \"score\": \"0.5995722\",\n \"text\": \"def init_db(self):\\n\\n # The user can provide a custom string\\n if self.database is None:\\n self.logger.error(\\\"You must provide a database url, exiting.\\\")\\n sys.exit(1)\\n\\n self.engine = create_engine(self.database, convert_unicode=True)\\n self.session = scoped_session(\\n sessionmaker(autocommit=False, autoflush=False, bind=self.engine)\\n )\\n\\n # Database Setup\\n Base.query = self.session.query_property()\\n\\n # import all modules here that might define models so that\\n # they will be registered properly on the metadata. Otherwise\\n # you will have to import them first before calling init_db()\\n import expfactory.database.models\\n\\n self.Base = Base\\n self.Base.metadata.create_all(bind=self.engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cfb194104acbd857427d389d5d1ff7e\",\n \"score\": \"0.59844375\",\n \"text\": \"def generate_context(self) -> Context:\\n self._transient_context = Context()\\n return self._transient_context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"512dd1266a0b346338850a76b4c521fa\",\n \"score\": \"0.5969605\",\n \"text\": \"def make_context(self, engine, args):\\n args = self.normalize_args(args)\\n _, ctx = self._make_argkey_and_context(engine, args)\\n return ctx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5ab511e6551482b2a0dabf9b2acedc\",\n \"score\": \"0.5920828\",\n \"text\": \"def shell_context():\\n from .models import db, Politico, Reembolso, Feedback\\n return dict(db=db, Politico=Politico,\\n Reembolso=Reembolso, Feedback=Feedback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc792ec70f31d198a4b10b2af6b35849\",\n \"score\": \"0.5920192\",\n \"text\": \"def __init__(self):\\n engine = db_connect()\\n create_tables(engine)\\n self.Session = sessionmaker(bind=engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6373708b7fd02359f3bb1b349b87bcb6\",\n \"score\": \"0.59160876\",\n \"text\": \"def make_shell_context():\\n return dict(app=app,\\n db=models.db,\\n User=models.User,\\n Post=models.Post,\\n Comment=models.Comment,\\n Tag=models.Tag,\\n Server=Server)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7664c5e9b3875976116dd44e487725fd\",\n \"score\": \"0.590667\",\n \"text\": \"def session_context(func):\\r\\n def wrapper(*args, **kwargs):\\r\\n self = args[0]\\r\\n with self._create_db_session() as db:\\r\\n self.db = db\\r\\n return func(*args, **kwargs)\\r\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99e3181df349f4040581c96e82492919\",\n \"score\": \"0.5887355\",\n \"text\": \"def init_database(cls):\\n conn = config.db_connection_string(Settings)\\n cls.Engine = create_engine(conn, echo=Settings.get('DEBUG'))\\n cls.Session = sessionmaker(bind=cls.Engine)\\n return cls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f40b1d77807d96063903410f8f9b0a5a\",\n \"score\": \"0.58802176\",\n \"text\": \"def __init__(self, path=None, echo=False):\\r\\n super(SqlAlchemyDB, self).__init__()\\r\\n if path is None:\\r\\n path = _SQL_ALCHEMY_MEMORY\\r\\n else:\\r\\n if path != _SQL_ALCHEMY_MEMORY:\\r\\n dirname = os.path.dirname(path)\\r\\n if dirname and dirname != \\\"\\\":\\r\\n os.makedirs(dirname, exist_ok=True)\\r\\n path = \\\"sqlite:///\\\" + path\\r\\n self._engine = create_engine(path, echo=echo)\\r\\n Base.metadata.create_all(self._engine)\\r\\n self._Session = sessionmaker(bind=self._engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e91ab6ab7af3d2303df2d758e664ee48\",\n \"score\": \"0.5877039\",\n \"text\": \"def init_db():\\n # We are setting the module variables here for the first time, so disable the warning\\n global DB_USER_TABLE # pylint: disable=global-variable-undefined\\n global DB_CUSTOMER_TABLE # pylint: disable=global-variable-undefined\\n global DB_USER_CUSTOMER_RELS_TABLE # pylint: disable=global-variable-undefined\\n global DB_TICKET_TABLE # pylint: disable=global-variable-undefined\\n global DB_COMMENT_TABLE # pylint: disable=global-variable-undefined\\n\\n db = TinyDB(app.config['DB_NAME'])\\n\\n DB_USER_TABLE = db.table('users')\\n DB_CUSTOMER_TABLE = db.table('customers')\\n DB_USER_CUSTOMER_RELS_TABLE = db.table('user_customer_rels')\\n DB_TICKET_TABLE = db.table('tickets')\\n DB_COMMENT_TABLE = db.table('comments')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e3f1f32101d4306b8b932b88f41c4a1\",\n \"score\": \"0.5877013\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n self.context = kwargs.get(\\\"config\\\")\\n if hasattr(self.context, 'database'):\\n # XXX this should be replaced with connection_context instead\\n self.context.database['database_host'] = \\\\\\n self.context.database.database_hostname\\n self.context.database['database_port'] = \\\\\\n self.context.database.database_port\\n self.context.database['database_name'] = \\\\\\n self.context.database.database_name\\n self.context.database['database_username'] = \\\\\\n self.context.database.database_username\\n self.context.database['database_password'] = \\\\\\n self.context.database.database_password\\n self.database = db.Database(self.context.database)\\n else:\\n # the old middleware\\n self.database = db.Database(self.context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e928fa48f22ea0bcb4157c6581181d4f\",\n \"score\": \"0.58652604\",\n \"text\": \"def make_shell_context():\\n\\n return dict(app=app, db=db, User=User)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e928fa48f22ea0bcb4157c6581181d4f\",\n \"score\": \"0.58652604\",\n \"text\": \"def make_shell_context():\\n\\n return dict(app=app, db=db, User=User)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c657b038e3358f2fc4f4ba1d83a4beca\",\n \"score\": \"0.58486253\",\n \"text\": \"def create():\\n\\tcreate_db()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"954520a01001cb314c73bbf161c84eaa\",\n \"score\": \"0.58432645\",\n \"text\": \"def __init__(\\n self,\\n sql_username,\\n sql_password,\\n sql_host,\\n sql_port,\\n sql_db,\\n sql_url_template=(\\\"mysql+mysqldb://{username}:{password}@\\\"\\n \\\"{host}:{port}/{db}?charset=utf8mb4\\\"),\\n **kwargs\\n ):\\n\\n # Internalize arguments.\\n self.sql_username = sql_username\\n self.sql_password = sql_password\\n self.sql_host = sql_host\\n self.sql_port = sql_port\\n self.sql_db = sql_db\\n self.sql_url_template = sql_url_template\\n\\n # Inspecting the presence of keyword arguments and (should they not be\\n # defined) setting defaults.\\n self.sql_engine_pool_size = kwargs.get(\\\"sql_engine_pool_size\\\", 1)\\n self.sql_engine_pool_recycle = kwargs.get(\\n \\\"sql_engine_pool_recycle\\\", 3600\\n )\\n self.sql_engine_echo = kwargs.get(\\\"sql_engine_echo\\\", False)\\n self.mysqldb_sscursor = kwargs.get(\\\"mysqldb_sscursor\\\", False)\\n self.expire_on_commit = kwargs.get(\\\"expire_on_commit\\\", False)\\n\\n # create DB engine.\\n self.engine = self.connect()\\n\\n # create new session.\\n self.session_factory = sqlalchemy.orm.sessionmaker(\\n bind=self.engine,\\n expire_on_commit=self.expire_on_commit\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d731712486f72ab154bcacf33f884f62\",\n \"score\": \"0.582744\",\n \"text\": \"def init_database(self):\\n engine = create_engine('sqlite:///todo.db?check_same_thread=False')\\n self.Base.metadata.create_all(engine)\\n self.session = sessionmaker(bind=engine)()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e82aa9a0d85f00aefbb11c18077005e3\",\n \"score\": \"0.5823347\",\n \"text\": \"def make_context(source, frmat='table'):\\n return Context.fromstring(source, frmat=frmat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34d719710f9a8478ca30d1a3c704876b\",\n \"score\": \"0.5819967\",\n \"text\": \"def __init__(self):\\n engine = db_connect()\\n create_table(engine)\\n self.session = sessionmaker(bind=engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f256e4d6ecd9594b4bc894645fa9f35\",\n \"score\": \"0.58028\",\n \"text\": \"def _session_maker(self) -> ContextManager[Session]:\\r\\n session = self._Session()\\r\\n try:\\r\\n yield session\\r\\n session.commit()\\r\\n except DBAPIError:\\r\\n session.rollback()\\r\\n raise\\r\\n finally:\\r\\n session.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8866cfd358f5b03b4c13d3794cb52783\",\n \"score\": \"0.5800551\",\n \"text\": \"async def database(self, *_):\\n config = {'dsn': os.getenv('DATABASE_URL')}\\n self.db = await aiopg.sa.create_engine(**config)\\n yield\\n self.db.close()\\n await self.db.wait_closed()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"372e7ae0b7c210e988d2526375a9a85f\",\n \"score\": \"0.5800246\",\n \"text\": \"def __init__(self):\\r\\n self.conn = create_connection(DATABASE_PATH)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19571ab4a7054106e5000b1f245500ce\",\n \"score\": \"0.57969123\",\n \"text\": \"def initialize_database():\\n\\n global DatabaseSession\\n from config import config\\n\\n engine = create_engine(\\n config[saq.CONFIG['global']['instance_type']].SQLALCHEMY_DATABASE_URI, \\n **config[saq.CONFIG['global']['instance_type']].SQLALCHEMY_DATABASE_OPTIONS)\\n\\n DatabaseSession = sessionmaker(bind=engine)\\n saq.db = scoped_session(DatabaseSession)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4b39dcc6bd9c89b0956e0d369da9deb\",\n \"score\": \"0.5791556\",\n \"text\": \"def _initTestingDB(): \\n from sqlalchemy import create_engine\\n engine = create_engine('sqlite://')\\n from .models import (\\n Base,\\n TodoUser,\\n )\\n DBSession.configure(bind=engine)\\n Base.metadata.create_all(engine)\\n \\n return DBSession\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c549d12998dd88468d960202c7a0aaa\",\n \"score\": \"0.57832694\",\n \"text\": \"def mkcontext(self,\\n context= [],\\n contextobj=None):\\n if contextobj == None:\\n raise ValueError, \\\"mkcontext: contextobj is None\\\"\\n return jsoncall.do_call(\\\"mkcontext\\\", {'modelname':self.modelname,\\\\\\n 'user':self.user,\\\\\\n 'password':self.password,\\\\\\n 'context':context,\\\\\\n 'contextobj':contextobj.__dict__},\\n self.connection)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d225a91ad16014e80a89c3b1afb7f90a\",\n \"score\": \"0.57599616\",\n \"text\": \"def make_shell_context():\\n return dict(server=server,\\n db=db,\\n User=User,\\n Article=Article,\\n Topic=Topic,\\n Comment=Comment,\\n Subscription=Subscription)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60284ebce2ca97eb6768c58fa2df711f\",\n \"score\": \"0.57571405\",\n \"text\": \"def __init__(self):\\n engine = db_connect()\\n create_table(engine)\\n self.Session = sessionmaker(bind=engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60284ebce2ca97eb6768c58fa2df711f\",\n \"score\": \"0.57571405\",\n \"text\": \"def __init__(self):\\n engine = db_connect()\\n create_table(engine)\\n self.Session = sessionmaker(bind=engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3b999ddc7dd29dcd305eb277bbcd16\",\n \"score\": \"0.5750913\",\n \"text\": \"def init_db():\\n return SQLAlchemy(app, session_options={\\n 'expire_on_commit': False,\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fde45b60b00c93e74b97456fd80ec79\",\n \"score\": \"0.5750327\",\n \"text\": \"def make_shell_context():\\n\\n context = dict(app=app, db=db)\\n for class_ in [FormData, CaseData, CaseIndex, Synclog, OwnershipCleanlinessFlag]:\\n context[class_.__name__] = class_\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b457a0c0061049dcaa30aae04dbe1d7\",\n \"score\": \"0.57479906\",\n \"text\": \"def _db():\\n db_fd, db_path = tempfile.mkstemp()\\n app = create_app()\\n app.config['TESTING'] = True\\n app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\\n app.config['DATABASE'] = db_path\\n app.config['SQLALCHEMY_DATABASE_URI'] = \\\"sqlite://\\\"\\n\\n with app.app_context():\\n db.init_app(app)\\n db.create_all()\\n\\n yield db\\n\\n os.close(db_fd)\\n os.unlink(db_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28583a5c3fa06235c2e8dab4b0d88f9e\",\n \"score\": \"0.5745354\",\n \"text\": \"def make_shell_context():\\n\\n return dict(app=app, db=db, Posts = Posts, Comments=Comments)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ebbae0cad9ef8def93995393582a79d\",\n \"score\": \"0.57446367\",\n \"text\": \"def _initDb(self):\\n CREATE_TOKEN_TABLE = '''create table token\\n (token text, id int primary key)\\n '''\\n CREATE_DOCS_TABLE = '''create table docs\\n (local_path text, resource_id text primary key, etag text, title text)\\n '''\\n \\n try:\\n self.db.execute(CREATE_TOKEN_TABLE)\\n self.db.execute(CREATE_DOCS_TABLE)\\n except sqlite3.OperationalError, error:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3f110e2e4bcbd7a581b4cdce1d1c83a\",\n \"score\": \"0.5742504\",\n \"text\": \"def dbinit( *args, **kwargs ):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7d7bf46ca0da667d19f9ff4cd67223f\",\n \"score\": \"0.574025\",\n \"text\": \"def get_sql_context_instance(spark_configuration):\\n if 'sparkSessionSingletonInstance' not in globals():\\n globals()['sparkSessionSingletonInstance'] = SparkSession.builder.config(conf=spark_configuration).getOrCreate()\\n return globals()['sparkSessionSingletonInstance']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2c62a3dd4afec88118519fd468ad628\",\n \"score\": \"0.5739886\",\n \"text\": \"def db_connect():\\n return create_engine(URL(**product_crawlers.settings.DATABASE))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00a9888a639feb1a52665f6c5a7b0ab4\",\n \"score\": \"0.5734747\",\n \"text\": \"def construct_global_ctx(self):\\n super().construct_global_ctx()\\n gtx = self.gtx\\n rc = self.rc\\n if \\\"groups\\\" in self.needed_dbs:\\n rc.pi_id = get_pi_id(rc)\\n rc.coll = f\\\"{TARGET_COLL}\\\"\\n try:\\n if not rc.database:\\n rc.database = rc.databases[0][\\\"name\\\"]\\n except:\\n pass\\n colls = [\\n sorted(\\n all_docs_from_collection(rc.client, collname), key=_id_key\\n )\\n for collname in self.needed_dbs\\n ]\\n for db, coll in zip(self.needed_dbs, colls):\\n gtx[db] = coll\\n gtx[\\\"all_docs_from_collection\\\"] = all_docs_from_collection\\n gtx[\\\"float\\\"] = float\\n gtx[\\\"str\\\"] = str\\n gtx[\\\"zip\\\"] = zip\",\n \"title\": \"\"\n },\n {\n \"docid\": \"192cf231f1d4576cad44e57e69213fd6\",\n \"score\": \"0.57337713\",\n \"text\": \"def create_db_statement(self):\\n return Engine.create_db_statement(self).replace(\\\"DATABASE\\\", \\\"SCHEMA\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d348e563e20bcad9aa2d1d8708ce814d\",\n \"score\": \"0.57224196\",\n \"text\": \"def __init__(self, connection_url, echo=False):\\n if not connection_url:\\n raise ValueError('No database connection URL provided.')\\n engine = create_engine(connection_url, echo=echo)\\n PipelineRun.metadata.create_all(engine)\\n self.session_factory = sessionmaker(bind=engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd2a6fd00d1fe9979eed36be7f190638\",\n \"score\": \"0.57155734\",\n \"text\": \"def init_with_context(self, context):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5553f3498bd2c6bed14afdb824210e97\",\n \"score\": \"0.57128376\",\n \"text\": \"def db_session_context(db_name):\\n try:\\n db = psycopg2.connect(database=db_name)\\n yield db\\n finally:\\n db.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4794c5f681478a05bfdbc767b1e61ac2\",\n \"score\": \"0.5697702\",\n \"text\": \"def setup_db():\\n\\n engine = config['tg.app_globals'].sa_engine\\n # model.init_model(engine)\\n # model.metadata.create_all(engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e3d4bddff67a3bc1a4ec20ccaf8c29b\",\n \"score\": \"0.5693486\",\n \"text\": \"def _create_db(self):\\n self.db = easydms.dbcore.Database(\\\":memory:\\\")\\n self.db.create_db()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"519c3acbe9341d4d5642815536e0fb71\",\n \"score\": \"0.56910944\",\n \"text\": \"def init_database(self):\\n # init_database(self.engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea3b1ea3d4565089f8aad10f2c647b1d\",\n \"score\": \"0.56887364\",\n \"text\": \"def init_db(connection, echo):\\r\\n\\r\\n # create the database tables as defined\\r\\n engine = create_engine(connection, echo=echo)\\r\\n Base.metadata.create_all(engine)\\r\\n\\r\\n # create a session\\r\\n Base.metadata.bind = engine\\r\\n BaseSession = sessionmaker(bind=engine)\\r\\n session = BaseSession()\\r\\n\\r\\n # set the shared Model session\\r\\n Model.use_session(session)\\r\\n\\r\\n return (engine, session)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab77d341977da7da8bde92082e1194d\",\n \"score\": \"0.5685786\",\n \"text\": \"def sql_engine(tmpdir: Path) -> Generator[SQLEngine, None, None]:\\n db_file_path = tmpdir / \\\"test.db\\\"\\n sql_engine = create_sql_engine(db_file_path)\\n yield sql_engine\\n sql_engine.dispose()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24c2f9a03f641ca31795b69b41185432\",\n \"score\": \"0.5685584\",\n \"text\": \"def create_gt_database_template(self):\\n pass\\n with self.connection as cursor:\\n fn = os.path.join(os.path.dirname(__file__), 'gtlog.sql')\\n self.cursor.execute(open(fn, \\\"r\\\").read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33276f8ae22ca4dc106cf877316baa01\",\n \"score\": \"0.5678844\",\n \"text\": \"def __init__(self, sql_config = {}, verbose = True):\\n\\n self.sql_config = sql_config\\n self.verbose = verbose\\n self._setup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"996f2d647af300387d2170a56979ba93\",\n \"score\": \"0.56667346\",\n \"text\": \"def database_setup():\\n Base.metadata.create_all(bind=engine)\\n db = LocalSession()\\n try:\\n populate_from_json(db, Vector, str(VECTORS))\\n populate_from_json(db, Gender, str(GENDERS))\\n populate_from_json(db, Tag, str(TAGS))\\n finally:\\n db.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13887516abec59b7a50b684b708b8618\",\n \"score\": \"0.5660363\",\n \"text\": \"def init_db(self):\\n self.db_config = databaseutils.process_db_config(self.config['db'])\\n\\n from sqlalchemy import create_engine\\n from sqlalchemy.orm import sessionmaker, scoped_session\\n self.engine = create_engine(self.db_config.constr, pool_recycle=3600)\\n self.session = scoped_session(sessionmaker(bind=self.engine))\\n\\n # Make sure tables are created\\n DB_Base.metadata.create_all(self.engine)\\n\\n if self.sqlite_file is not None:\\n dbname = 'sqlite:///%s' % self.sqlite_file\\n self.sqlite_engine = create_engine(dbname, echo=False)\\n self.sqlite_session = scoped_session(sessionmaker(bind=self.sqlite_engine))\\n DB_Base.metadata.create_all(self.sqlite_engine)\\n logger.info('Using SQLite %s' % self.sqlite_engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8cbb1fc0adf1f17ea7355a562797994\",\n \"score\": \"0.56507915\",\n \"text\": \"def generate_database_object(**kwargs):\\n return app.database.Database(\\\"test.db\\\", **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39cc6c47e34de172016a324ad010e821\",\n \"score\": \"0.5650551\",\n \"text\": \"def create_connection(self, context, *, engine):\\n try:\\n connection = engine.connect()\\n except OperationalError as exc:\\n raise UnrecoverableError('Could not create SQLAlchemy connection: {}.'.format(str(exc).replace('\\\\n', ''))\\n ) from exc\\n\\n with connection:\\n yield connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20852cc79ae276c170ed99e88f0a5cf9\",\n \"score\": \"0.56468374\",\n \"text\": \"def _create_sqlite_file_engine( conn=next( file_path_generator ), echo=True ):\\n print( \\\"creating connection: %s \\\" % conn )\\n return create_engine( conn, echo=echo )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe3abd6806bbb4df2cd9defaf0af95e\",\n \"score\": \"0.56466085\",\n \"text\": \"def create_engine(self):\\n return create_engine('sqlite:///' + self.database_name, echo=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07de65ef0e9c1dac254701f6f539b0d5\",\n \"score\": \"0.56367636\",\n \"text\": \"def create_sqlite_engine( echo=False ):\\n conn = 'sqlite:///:memory:'\\n print( \\\"creating connection: %s \\\" % conn )\\n return create_engine( conn, echo=False )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55795f4779f4c418d600fa5b252ccd7d\",\n \"score\": \"0.56306756\",\n \"text\": \"def create_database():\\n with connection:\\n connection.execute(CREATE_MOVIE_TABLE)\\n connection.execute(CREATE_USER_TABLE)\\n connection.execute(CREATE_WATCHED_TABLE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a529a94312627b14eb74b7678bd98687\",\n \"score\": \"0.5626167\",\n \"text\": \"def get_sqls(self):\\n return {\\n \\\"prepare_check\\\": \\\"select id from dbo.sysobjects where id = object_id('{0}')\\\".format(self.table_name),\\n \\\"prepare_create\\\": \\\"create table {0} (channel NVARCHAR(20), channel_user_id NVARCHAR(100), timestamp DATETIME2, topic_name NVARCHAR(100), topic_status NVARCHAR(100), topic_previous NVARCHAR(4000), topic_priority INT, data NVARCHAR(MAX), primary key(channel, channel_user_id))\\\".format(self.table_name),\\n \\\"get_context\\\": \\\"select top 1 * from {0} where channel=? and channel_user_id=?\\\".format(self.table_name),\\n \\\"save_context\\\": \\\"\\\"\\\"\\n merge into {0} as A\\n using (select ? as channel, ? as channel_user_id, ? as timestamp, ? as topic_name, ? as topic_status, ? as topic_previous, ? as topic_priority, ? as data) as B\\n on (A.channel = B.channel and A.channel_user_id = B.channel_user_id)\\n when matched then\\n update set timestamp=B.timestamp, topic_name=B.topic_name, topic_status=B.topic_status, topic_previous=B.topic_previous, topic_priority=B.topic_priority, data=B.data\\n when not matched then\\n insert (channel, channel_user_id, timestamp, topic_name, topic_status, topic_previous, topic_priority, data) values (B.channel, B.channel_user_id, B.timestamp, B.topic_name, B.topic_status, B.topic_previous, B.topic_priority, B.data);\\n \\\"\\\"\\\".format(self.table_name)\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04f8eac0f9fbab51d117b39064c06d77\",\n \"score\": \"0.5624793\",\n \"text\": \"def init_database():\\n\\n # The current dir should be the script home\\n homedir = os.path.normpath(\\n os.path.dirname(\\n sys.executable if getattr(sys, 'frozen', False) else\\n __file__)) # cx_Freeze compatibility\\n os.chdir(homedir)\\n\\n engine = create_engine(\\\"sqlite:///data.db\\\")\\n BASE.metadata.bind = engine\\n BASE.metadata.create_all()\\n\\n return engine\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5699fa8fd217e24b0877e1e82519e960\",\n \"score\": \"0.5613715\",\n \"text\": \"def session_context(pytestconfig, request, tmp_env):\\n ctx = Context.only()\\n\\n # Temporary, empty local directory for local data\\n session_tmp_dir = Path(request.config._tmp_path_factory.mktemp(\\\"data\\\"))\\n\\n # Set the cache path according to whether pytest --local-cache was given. If True,\\n # pick up the existing setting from the user environment. If False, use a pytest-\\n # managed cache directory that persists across test sessions.\\n ctx.cache_path = (\\n ctx.local_data.joinpath(\\\"cache\\\")\\n if request.config.option.local_cache\\n # TODO use pytestconfig.cache.mkdir() when pytest >= 6.3 is available\\n else Path(pytestconfig.cache.makedir(\\\"cache\\\"))\\n )\\n\\n # Other local data in the temporary directory for this session only\\n ctx.local_data = session_tmp_dir\\n\\n platform_name = \\\"message-ix-models\\\"\\n\\n # Add a platform connected to an in-memory database\\n # NB cannot call Config.add_platform() here because it does not support supplying a\\n # URL for a HyperSQL database.\\n # TODO add that feature upstream.\\n ixmp_config.values[\\\"platform\\\"][platform_name] = {\\n \\\"class\\\": \\\"jdbc\\\",\\n \\\"driver\\\": \\\"hsqldb\\\",\\n \\\"url\\\": f\\\"jdbc:hsqldb:mem://{platform_name}\\\",\\n }\\n\\n # Launch Platform and connect to testdb (reconnect if closed)\\n mp = Platform(name=platform_name)\\n mp.open_db()\\n\\n ctx.platform_info[\\\"name\\\"] = platform_name\\n\\n yield ctx\\n\\n ctx.close_db()\\n ixmp_config.remove_platform(platform_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de43df056ad075876853bc882b943f6a\",\n \"score\": \"0.5610763\",\n \"text\": \"def init_db():\\n with LoggerApi.app_context():\\n db = get_db()\\n with LoggerApi.open_resource('schema.sql', mode='r') as f:\\n db.cursor().executescript(f.read())\\n db.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79fe40448334a47535d6216cd550a9fd\",\n \"score\": \"0.56061286\",\n \"text\": \"def context(self) -> CONTEXT:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49bc95c1c1caf1564178c54a906156d9\",\n \"score\": \"0.55998296\",\n \"text\": \"def __init__(self):\\n engine = db_connect()\\n create_table(engine)\\n self.Session = sessionmaker(bind=engine)\\n logging.info(\\\"****DuplicatesPipeline: database connected****\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"569aed92cd6b3dbc0d12e81fc8780059\",\n \"score\": \"0.55969864\",\n \"text\": \"def __init__(self):\\n engine = db_connect()\\n self.Session = sessionmaker(bind=engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e2473f46ea6b831e377a1497d3a192e\",\n \"score\": \"0.5595775\",\n \"text\": \"def session_context(self):\\n session = self.Session()\\n try:\\n yield session\\n session.commit()\\n except: # noqa E722\\n session.rollback()\\n raise\\n finally:\\n session.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aed93ec66a3914852af452593b745dd\",\n \"score\": \"0.55913657\",\n \"text\": \"def session_scope( engine=None ):\\n try:\\n if engine is None:\\n engine = initialize_engine( environment.ENGINE )\\n # DataTools's handle to database at global level\\n # SessionFactory = make_scoped_session_factory(engine)\\n SessionFactory = make_session_factory( engine )\\n\\n if environment.ENGINE == 'sqlite' or environment.ENGINE == 'sqlite-file':\\n # We need to get the db into memory when start up\\n # environmental variables will determine details of the\\n # db\\n create_db_tables( engine )\\n\\n session = SessionFactory()\\n yield session\\n session.commit()\\n\\n except:\\n session.rollback()\\n raise\\n\\n finally:\\n if session:\\n session.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9acf907fd3230a868c35813b62d84a30\",\n \"score\": \"0.5588803\",\n \"text\": \"def init():\\n\\n # delete existing file\\n if os.path.exists(DBFILE):\\n os.remove(DBFILE)\\n\\n db = sqlite3.connect(DBFILE)\\n # create tables\\n create(db, PARAGRAPH, \\\"paragraph\\\")\\n create(db, QUESTION, \\\"question\\\")\\n create(db, ANSWER, \\\"answer\\\")\\n\\n return db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0da328eecf2a7a047cbc5c450aed8410\",\n \"score\": \"0.5585826\",\n \"text\": \"def __init__(self, db_sql_path, mode='r', verbose=1):\\n \\n self._exec_date = datetime.now()\\n if not os.path.exists(db_sql_path):\\n raise Exception('path %s does not exist'%db_sql_path)\\n if not os.path.isdir(db_sql_path):\\n raise Exception('path %s is not a directory'%db_sql_path)\\n db_sql_path = os.path.abspath(db_sql_path)\\n self._path = os.path.join(db_sql_path, 'database.sql')\\n if mode not in ['r', 'w']:\\n raise Exception('Mode must be either r: read only or w: read and write')\\n self._mode = mode\\n self._verbose = verbose\\n self.__within_context = False\\n self.__special_exit = None\\n self.__initialized = False\\n if self._mode == 'r':\\n self.__initialization__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58ba988fa59c6c64c5c0874596e05198\",\n \"score\": \"0.55807924\",\n \"text\": \"def _context():\\n global _trident_context\\n if _trident_context is None:\\n _trident_context = _Context()\\n return _trident_context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ed913bfec489c7d0a3fb53665a4ff27\",\n \"score\": \"0.5580359\",\n \"text\": \"def init_db(self):\\n self.db_config = databaseutils.process_db_config(self.config['db'])\\n\\n from sqlalchemy import create_engine\\n from sqlalchemy.orm import sessionmaker, scoped_session\\n self.engine = create_engine(self.db_config.constr, pool_recycle=3600)\\n self.session = scoped_session(sessionmaker(bind=self.engine))\\n\\n # Make sure tables are created\\n DB_Base.metadata.create_all(self.engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6977fcfed11e3cce4391ba1343ba89d8\",\n \"score\": \"0.5579277\",\n \"text\": \"def _context_new(self):\\n assert self._pa_mainloop is not None\\n app_name = self._get_app_name()\\n context = pa.pa_context_new(self._pa_mainloop,\\n app_name.encode('ASCII')\\n )\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86f4f54fcea59b51df4534630b411709\",\n \"score\": \"0.5578862\",\n \"text\": \"def __init__(self):\\n engine = connect_to_db()\\n create_lyrics_table(engine) #declarative base stuff\\n self.Session = sessionmaker(bind=engine)\\n\\n # self.create_connection()\\n # self.create_table()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3eb57211c08ee9b7eb0f801656f3676\",\n \"score\": \"0.5578381\",\n \"text\": \"def setup_db():\\n engine = create_engine(settings.DATABASE)\\n ModelBase.metadata.bind = engine\\n ModelBase.metadata.create_all(engine)\\n\\n return scoped_session(sessionmaker(bind=engine))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a79a7efbb8a73688769289c54b63f4e5\",\n \"score\": \"0.557326\",\n \"text\": \"def create_database():\\n create_db(app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70c8418c7da4556e3f9c626b6781540a\",\n \"score\": \"0.5572984\",\n \"text\": \"def __init__(self):\\n engine = db_connect()\\n create_table(engine)\\n self.Session = sessionmaker(bind=engine)\\n logging.info(\\\"****SaveRestaurantsPipeline: database connected****\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d535c6c4943d5eaa8e53098caf59f1\",\n \"score\": \"0.5565452\",\n \"text\": \"def get_template_context(self):\\n context = super(Spamassassin, self).get_template_context()\\n if self.dbengine == \\\"postgres\\\":\\n store_module = \\\"Mail::SpamAssassin::BayesStore::PgSQL\\\"\\n dsn = \\\"DBI:Pg:dbname={};host={};port={}\\\".format(\\n self.dbname, self.dbhost, self.dbport)\\n else:\\n store_module = \\\"Mail::SpamAssassin::BayesStore::MySQL\\\"\\n dsn = \\\"DBI:mysql:{}:{}:{}\\\".format(\\n self.dbname, self.dbhost, self.dbport)\\n context.update({\\n \\\"store_module\\\": store_module, \\\"dsn\\\": dsn, \\\"dcc_enabled\\\": \\\"#\\\"})\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83bd8b12c77cde96e2493c3709df894d\",\n \"score\": \"0.55614823\",\n \"text\": \"def db_connect():\\n if 'db' not in g:\\n g.db = sql.connect(current_app.config[\\\"DATABASE\\\"], detect_types=sql.PARSE_DECLTYPES)\\n g.db.row_factory = sql.Row\\n return g.db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d0e8a68e10c671dbacccbfff7c8a38c\",\n \"score\": \"0.5556699\",\n \"text\": \"def init_context(self) -> None:\\n self.context = AKSManagedClusterContext(\\n self.cmd, AKSManagedClusterParamDict(self.__raw_parameters), self.models, DecoratorMode.CREATE\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c728dfa0817337b4b45ef95022b84cbf\",\n \"score\": \"0.55560327\",\n \"text\": \"def logic_db_engine(self):\\n try:\\n boto_session = boto3.Session(profile_name='loidsig')\\n except:\\n boto_session = boto3.Session()\\n sm_client = boto_session.client(\\n service_name='secretsmanager',\\n region_name='us-east-1',\\n endpoint_url='https://secretsmanager.us-east-1.amazonaws.com'\\n )\\n get_secret_value_response = sm_client.get_secret_value(SecretId='Loidsig_DB')\\n cred_dict = ast.literal_eval(get_secret_value_response['SecretString'])\\n db_user, db_pass = cred_dict['username'], cred_dict['password']\\n db_host, db_port, db_name = cred_dict['host'], cred_dict['port'], cred_dict['dbname']\\n try:\\n postgres_engine = create_engine(f'postgresql://{db_user}:{db_pass}@{db_host}:{db_port}/{db_name}')\\n except Exception as e:\\n print(\\\"Unable to connect to postgres! Error: {}\\\".format(e))\\n raise\\n return postgres_engine\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e821c05bc98bd1105a4a30258244d9a7\",\n \"score\": \"0.5545746\",\n \"text\": \"def create(self) -> SQLAlchemy:\\n self.db.create_all()\\n self.db.session.commit()\\n return self.db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"737ff63c6084fac9d5d6b8952505d61d\",\n \"score\": \"0.55449027\",\n \"text\": \"def _init_db(self):\\n cursor = self._main_connection.cursor()\\n cursor.execute(self.sql[\\\"create_table\\\"])\\n self._main_connection.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9c50738f72c88bc6fd9de3b3f6fe293\",\n \"score\": \"0.5543919\",\n \"text\": \"def make_data_connection():\\n\\n global _engine # pylint: disable=W0603\\n\\n db_url = 'postgres://{0}:{1}@{2}:{3}/{4}'.format(DataBase.USER,\\n DataBase.PASSWORD,\\n DataBase.HOST,\\n DataBase.PORT,\\n DataBase.DB_NAME)\\n _engine = create_engine(db_url, echo=DataBase.ECHO)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7810d7680fea6ef69953e371a29f2795\",\n \"score\": \"0.5539203\",\n \"text\": \"def __init__(self):\\n engine = db_connect()\\n create_reals_table(engine)\\n self.Session = sessionmaker(bind=engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"496a18896d6d8a741dbc034811867c05\",\n \"score\": \"0.5536988\",\n \"text\": \"def create_student_db(connection):\\r\\n with connection:\\r\\n connection.execute(CREATE_TABLE_STUDENTS_DATA)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c40966d70f9366d76af8dc418610d536\",\n \"score\": \"0.5535386\",\n \"text\": \"def sqlite_setup(driver=':memory:', extensions=None):\\n conn = __Handler(driver, extensions)\\n return conn.conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a08683a5e1cfd691dc70b2270151524\",\n \"score\": \"0.5531912\",\n \"text\": \"def initialize_sqlite_memory():\\n global engine\\n if engine is None:\\n engine = create_engine(\\\"sqlite://\\\")\\n Session.configure(bind=engine)\\n _populate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e635ebbb0a26390cc87cb0bb29f5e263\",\n \"score\": \"0.55299956\",\n \"text\": \"def init():\\n print(\\\"Executing initialization\\\")\\n print(db.dsn)\\n cursor = yield momoko.Op(\\n db.execute,\\n \\\"\\\"\\\"\\n DROP SCHEMA public CASCADE;\\n CREATE SCHEMA public;\\n CREATE TABLE game\\n (\\n game_id text PRIMARY KEY,\\n players integer,\\n state bytea,\\n timestamp timestamp\\n );\\n CREATE UNIQUE INDEX ix_game_id\\n ON game\\n (game_id);\\n CREATE INDEX ix_timestamp\\n ON game\\n (timestamp);\\n \\\"\\\"\\\")\\n try:\\n print(cursor.fetchall())\\n except psycopg2.ProgrammingError:\\n pass\\n io = ioloop.IOLoop.instance()\\n io.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dee2a9ce3e3c0b886dcc3c834ea8765\",\n \"score\": \"0.5516978\",\n \"text\": \"def __init__(self, filename):\\n # this boilerplate is turning me off from sqlalchemy. Why can't I have\\n # one object that I interact with, instead of four levels of crap?\\n self.sql_engine = create_engine(\\n 'sqlite:///'+filename,\\n isolation_level='READ UNCOMMITTED'\\n )\\n self.sql_session_maker = sessionmaker(bind=self.sql_engine)\\n self.sql_session = self.sql_session_maker()\\n Base.metadata.create_all(bind=self.sql_engine)\\n self.term_cache = {}\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994081,"cells":{"query_id":{"kind":"string","value":"09a4bd3f156469c3b8e7375097f7f947"},"query":{"kind":"string","value":"Gets is_grade attribute of instance Returns"},"positive_passages":{"kind":"list like","value":[{"docid":"56b57c1cb0e5c0ace8d84a7e09e28fdb","score":"0.7083966","text":"def get_is_graded(self):\n return self.is_graded","title":""}],"string":"[\n {\n \"docid\": \"56b57c1cb0e5c0ace8d84a7e09e28fdb\",\n \"score\": \"0.7083966\",\n \"text\": \"def get_is_graded(self):\\n return self.is_graded\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ebc51373a9a05f5f317713d900e8baf8","score":"0.743497","text":"def get_grade(self):\n return self.grade","title":""},{"docid":"dfb54dfff723a785b613028952041e0a","score":"0.6901927","text":"def grade(self):\n return self._grade","title":""},{"docid":"2a5d7ef1ebc409c567dbe6e12fba2bef","score":"0.65599567","text":"def getGrValue(self):\n return self.grade_value","title":""},{"docid":"1aa05500c7bdd0cb87a6f59d2735bd49","score":"0.6482489","text":"def grade(self):\n return self._response['grade']","title":""},{"docid":"a5fe758fde8f3bda2996f53addb2c27a","score":"0.6173418","text":"def passed(grade):\n grades = {\n 'E': True,\n 'Exceptional': True,\n 'G': True,\n 'Good': True,\n 'A': True,\n 'Acceptable': True,\n 'P': False,\n 'Poor': False,\n 'H': False,\n 'Horrible': False,\n }\n\n return grades.get(grade, False)","title":""},{"docid":"7714522ac571d5de7cf1d4767ae0ffa1","score":"0.6038436","text":"def get_grader(self):\n return self.to_dict().get(GRADER_KEY)","title":""},{"docid":"31dd21dc3f59110e89e8dd00e741b2fd","score":"0.6010256","text":"def __str__(self):\n return self.grade","title":""},{"docid":"b91402ca672aad76a420bf4421f192cc","score":"0.5985245","text":"def get_course_grade(self):\n return CourseGradeFactory().read(self.student_user, self.course)","title":""},{"docid":"3abec9684f9e65d83213a6b739d22af8","score":"0.595143","text":"def can_assign_grade_system(self):\n return # boolean","title":""},{"docid":"013696b64c22fa3919567870088174f5","score":"0.57968026","text":"def can_overridecalculated_grade_entries(self):\n return # boolean","title":""},{"docid":"5fd3fdcbe39490364fab3c1bc03f9e51","score":"0.57591295","text":"def DSG(self):\n return self.has_property(\"featureType\")","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"533d669b64048f80beff8c7a74035f71","score":"0.57512814","text":"def get_gradebook(self):\n return # osid.grading.Gradebook","title":""},{"docid":"c206f07fc977fd877f085a581db27bf2","score":"0.57271826","text":"def is_graded(self):\n return hasattr(self, \"graded_work\")","title":""},{"docid":"069f33e975ec0d964df88d39f5519fe5","score":"0.5716374","text":"def can_lookup_grade_entries(self):\n return # boolean","title":""},{"docid":"26769f46bdbc6cbc1257456900f5c453","score":"0.56508","text":"def badges_enabled(self):\n if \"badgesEnabled\" in self._prop_dict:\n return self._prop_dict[\"badgesEnabled\"]\n else:\n return None","title":""},{"docid":"e3ff5fa8b05c4ee3cf54cd3f76aa8488","score":"0.55828094","text":"def by_grade(cls, grade):\n if grade == None or grade > 100:\n return cls.NOT_STARTED\n if grade == 100:\n return cls.SUCCEEDED\n if grade <= 99 and grade >= 1:\n return cls.PART_SUCC\n if grade == 0:\n return cls.FAILED\n if grade > -1:\n return cls.STARTED\n if grade <= -2 and grade >= -100:\n return cls.TEACHER_EXC\n return cls.SANDBOX_EXC","title":""},{"docid":"0a439fe5d8e65d619c992fead33ff338","score":"0.5567315","text":"def review_grades(self):\r\n return self._review_grades","title":""},{"docid":"49661d6ad193e96d3748f2aae057d755","score":"0.5564851","text":"def clean_grade(self):\n\n grade = self.cleaned_data['grade']\n # map to bool\n grade_vals = {'pass': True, 'fail': False, '': ''}\n\n return grade_vals.get(grade, None)","title":""},{"docid":"1bae47008fded4dd53034e668f922e96","score":"0.5563756","text":"def get_boolean(self):","title":""},{"docid":"12e34874826e552a0e44d0920592efb8","score":"0.5553184","text":"def fetch_grades(self):\n return self.enrolled_students","title":""},{"docid":"cb8c13bb13c8cbb65df7214c71862ead","score":"0.55406314","text":"def _get_isG2(self) -> \"bool\" :\n return _fusion.FilletFeature__get_isG2(self)","title":""},{"docid":"5fa72acbb4f2a081487d66b4c3a53da0","score":"0.55338603","text":"def is_on(self) -> bool:\n val = False\n if self._attr in self._data:\n val = self._data[self._attr]\n\n return val","title":""},{"docid":"46036a8387b6760c6f83c210991c2f15","score":"0.55083734","text":"def can_lookup_grade_systems(self):\n return # boolean","title":""},{"docid":"c13a5c7f6601f346033adf0bba2229ad","score":"0.5495199","text":"def requires_grad(self):\n return self._requires_grad","title":""},{"docid":"c13a5c7f6601f346033adf0bba2229ad","score":"0.5495199","text":"def requires_grad(self):\n return self._requires_grad","title":""},{"docid":"4eae4a236ad2e5dd5d83ac9d187e284f","score":"0.549249","text":"def Enabled(self):\r\n\t\treturn self._get_attribute('enabled')","title":""},{"docid":"e3d274cb14e2e16579df66b7bcb820e0","score":"0.54852563","text":"def get_grade_system(self, grade_system_id):\n return # osid.grading.GradeSystem","title":""},{"docid":"39efe85f48adf5151ea0b34414adfe40","score":"0.5473113","text":"def get_grade_entry_query(self):\n return # osid.grading.GradeEntryQuery","title":""},{"docid":"134fe98541fa7b039c43aeb203a66db9","score":"0.54713374","text":"def get_student(self):\n return self.student","title":""},{"docid":"dde5f91a741f95f95a09fc86b17de1ab","score":"0.54577273","text":"def __bool__(self):\n\n return bool(self.attribute)","title":""},{"docid":"dde5f91a741f95f95a09fc86b17de1ab","score":"0.54577273","text":"def __bool__(self):\n\n return bool(self.attribute)","title":""},{"docid":"ab5dcbb52dd3e421c70969899a3bb753","score":"0.54488","text":"def maybe_enable_grading(self):\n\n if not self._lti_user.is_instructor:\n # Only instructors can grade assignments.\n return\n\n if \"lis_outcome_service_url\" not in self._request.params:\n # Only \"gradeable\" assignments can be graded.\n # Assignments that don't have the lis_outcome_service_url param\n # aren't set as gradeable in the LMS.\n return\n\n if self._context.is_canvas:\n # Don't show our built-in grader in Canvas because it has its own\n # \"SpeedGrader\" and we support that instead.\n return\n\n self._config[\"grading\"] = {\n \"enabled\": True,\n \"courseName\": self._request.params.get(\"context_title\"),\n \"assignmentName\": self._request.params.get(\"resource_link_title\"),\n \"students\": list(self._get_students()),\n }","title":""},{"docid":"a18ab8b3a195891be76070e35887af9a","score":"0.544498","text":"def getFields(self, *args):\n\n # try to fetch from survey_record\n if hasattr(self.survey_record, 'grade'):\n grade = self.survey_record.grade\n\n # remap bool to string values as the ChoiceField validates on 'choices'.\n vals_grade = {True: 'pass', False: 'fail'}\n\n self.data['grade'] = vals_grade.get(grade, None) or grade\n\n return super(GradeSurveyRecordForm, self).getFields(*args)","title":""},{"docid":"ca8cb2c658c26f61cabbcae9522f2368","score":"0.5440322","text":"def is_graded(self, code: str, name: Optional[str] = None) -> bool:\n if not name:\n name = self.submitter\n # TODO: yikes, 4 dictionary accesses xD. is there a better way?\n return (self._grades[name][\"scores\"][code][\"award\"]\n is not None)","title":""},{"docid":"6b932a98f5e1ad276391ce4355730276","score":"0.5438885","text":"def __get_education__(self):\n return self.education","title":""},{"docid":"7394ac41882520331f888575c63311d7","score":"0.5435297","text":"def testClassPropertyGrades(self):\n\n # data for the unit class grad setter\n unitGradesPass = [None, 5, 51.5, 0, '1.5']\n unitGradesTypeError = [[1, 5]]\n unitGradesValueError = ['testString', -2, 456]\n\n # appropriate values\n for testGrade in unitGradesPass:\n self.testModule.grade = testGrade\n if testGrade is None:\n self.assertEqual(self.testModule.grade, testGrade)\n else:\n testGrade = float(testGrade)\n if testGrade < 1:\n self.assertEqual(self.testModule.grade, 1)\n elif testGrade > 4:\n self.assertEqual(self.testModule.grade, 5)\n else:\n self.assertEqual(self.testModule.grade, testGrade)\n\n # inappropriate values\n with self.assertRaises(TypeError):\n for testGrade in unitGradesTypeError:\n self.testModule.grade = testGrade\n with self.assertRaises(ValueError):\n for testGrade in unitGradesValueError:\n self.testModule.grade = testGrade\n\n # data for the course class grad setter\n courseGrades = [0, 3, None, 78]\n\n # appropriate values\n for testGrade in courseGrades:\n self.testCourse.grade = testGrade\n\n if testGrade is None:\n self.assertFalse(self.testCourse.passed)\n elif testGrade > 4:\n self.assertFalse(self.testCourse.passed)\n else:\n self.assertTrue(self.testCourse.passed)","title":""},{"docid":"486a0b80b3c68bfbe6afc69111c30a2d","score":"0.5417543","text":"def _get_isActive(self) -> \"bool\" :\n return _fusion.Occurrence__get_isActive(self)","title":""},{"docid":"963c5b80b0453621e4572b502ea4b1ff","score":"0.5396134","text":"def can_create_grade_entries(self):\n return # boolean","title":""},{"docid":"669de770250abf018713444db74d4fb5","score":"0.53954","text":"def _get_isUniform(self) -> \"bool\" :\n return _fusion.ScaleFeature__get_isUniform(self)","title":""},{"docid":"ed76b871326cc3e4bddadfb79f33dedc","score":"0.53860086","text":"def has_many_grade_levels(self):\n return self.grade_levels.count() > 1","title":""},{"docid":"b0c2104f44b104d93c88a0deea364dfc","score":"0.5373989","text":"def get_grade_entry(self, grade_entry_id):\n return # osid.grading.GradeEntry","title":""},{"docid":"7753749ce34c637ede923ad064118e06","score":"0.53579813","text":"def can_search_grade_entries(self):\n return # boolean","title":""},{"docid":"7967c1e9ab40557cc943f6c50e10548c","score":"0.53566635","text":"def gender(self):\n return self.member.gender","title":""},{"docid":"57f442e835222c6a0ecde946e36042e7","score":"0.5355678","text":"def _get_isG2(self) -> \"bool\" :\n return _fusion.FilletFeatureInput__get_isG2(self)","title":""},{"docid":"381b3ad44e4becb44f95c814d67b70e2","score":"0.53447855","text":"def gender(self):\n return self._gender","title":""},{"docid":"381b3ad44e4becb44f95c814d67b70e2","score":"0.53447855","text":"def gender(self):\n return self._gender","title":""},{"docid":"381b3ad44e4becb44f95c814d67b70e2","score":"0.53447855","text":"def gender(self):\n return self._gender","title":""},{"docid":"a8f57247b7e5167cda45537a0d7e6301","score":"0.5338988","text":"def get_grade_entry_search(self):\n return # osid.grading.GradeEntrySearch","title":""},{"docid":"34ce66b0f7d38fe54867d81745fa44da","score":"0.5334409","text":"def checked(self):\n # !!! this should be replaced by another logic that will get the real stat\n # from the DOM\n return self._checked","title":""},{"docid":"69069e8afc1ae49373d093786458fea9","score":"0.5323901","text":"def contains_attr(self, gi):\n if gi is None:\n return False\n for gi_obj in self.gradual_items:\n if gi.attribute_col == gi_obj.attribute_col:\n return True\n return False","title":""},{"docid":"501b46aeaded12751c5cbdb546560f7e","score":"0.53137267","text":"def can_lookup_gradebooks(self):\n return # boolean","title":""},{"docid":"797026e094fc0d3c5d20c6e6c0f563e9","score":"0.5297622","text":"def getRating(self):\n\t\treturn self._rating","title":""},{"docid":"32b90e312e8763287a5dd0bafb9daf91","score":"0.5296641","text":"def can_update_grade_systems(self):\n return # boolean","title":""},{"docid":"4d0bf06783ca1edda099c8a5fb829759","score":"0.5295841","text":"def state(self):\n return self.attribute_value","title":""},{"docid":"4d0bf06783ca1edda099c8a5fb829759","score":"0.5295841","text":"def state(self):\n return self.attribute_value","title":""},{"docid":"1addd6bb6b9711972333a8e7ec389901","score":"0.5293318","text":"def _get_isActive(self) -> \"bool\" :\n return _fusion.FusionDocument__get_isActive(self)","title":""},{"docid":"3d712c43e34c5fc77c108914b93e52d4","score":"0.52853703","text":"def student_call():\n return face_analyzer.student_attentiveness()","title":""},{"docid":"140d0e23b5e5ca6130fbfac0791de0b4","score":"0.5284669","text":"def get_attribs(self):\n return self.attribs","title":""},{"docid":"140d0e23b5e5ca6130fbfac0791de0b4","score":"0.5284669","text":"def get_attribs(self):\n return self.attribs","title":""},{"docid":"c644dfcb6faeac973fd770f068dd55ba","score":"0.52812827","text":"def can_search_grade_systems(self):\n return # boolean","title":""},{"docid":"c769007f6cc6c4d238b406d2bee69676","score":"0.52785134","text":"def can_create_grade_systems(self):\n return # boolean","title":""},{"docid":"0b23e8570ba2cbbfbd66944b0fb0656f","score":"0.5269091","text":"def value(self) -> bool:\n ...","title":""},{"docid":"0b23e8570ba2cbbfbd66944b0fb0656f","score":"0.5269091","text":"def value(self) -> bool:\n ...","title":""},{"docid":"6e67af65f2341d31869a29bf36b4ba2c","score":"0.5259277","text":"def _inSample(self):\n #if hasattr(self._userModel(), \"_d_inSample\"):\n return self._d_inSample\n #else:\n # return False","title":""},{"docid":"26673f7aea374d912e89c2b03c339d5f","score":"0.52510893","text":"def can_update_grade_entries(self):\n return # boolean","title":""},{"docid":"c4d4f51f269ac04b1bb9848f7779f8e9","score":"0.5250907","text":"def get_gradebook(self, gradebook_id):\n return # osid.grading.Gradebook","title":""},{"docid":"92fd067364305b2141a3c3bc62c543a7","score":"0.52462745","text":"def get_prop(self):\n return self.prop","title":""},{"docid":"92fd067364305b2141a3c3bc62c543a7","score":"0.52462745","text":"def get_prop(self):\n return self.prop","title":""},{"docid":"f6bdba43265e6c20c6dcfcd7a8e783bb","score":"0.52400917","text":"def review():\n context.grader.manual_grade()\n # raise NotImplemented(\"This feature is not currently implemented.\")","title":""},{"docid":"7197c307090e4c811be6d11cadda4354","score":"0.5238365","text":"def get_gradebooks(self):\n return # osid.grading.GradebookList","title":""},{"docid":"ed07c02854e0f184fb3ca19395b540e6","score":"0.52303505","text":"def is_active(self):\n return self.__user['object']['enabled']","title":""},{"docid":"c1c65a6fe41d17f66f29a2c1bac42bfe","score":"0.5229278","text":"def get_score(self):\n\n\treturn self.__score","title":""},{"docid":"1dec89c629e9527f502f50db18858774","score":"0.52241856","text":"def IsSexuallyActive( self ):\n # assert self.__gender == MALE\n return self.__age >= 16","title":""},{"docid":"5b3fe1fb6fb49a71d7fdaa6a2d0467fa","score":"0.52211696","text":"def get_grade_system_query(self):\n return # osid.grading.GradeSystemQuery","title":""},{"docid":"5b3fe1fb6fb49a71d7fdaa6a2d0467fa","score":"0.52211696","text":"def get_grade_system_query(self):\n return # osid.grading.GradeSystemQuery","title":""},{"docid":"ce4e2fde558bac42a7538485c22927b5","score":"0.5216244","text":"def genders(self):\n\n return ('male', 'female')","title":""},{"docid":"b949910c108d4bcfa102d69ed8cb9d8a","score":"0.52103674","text":"def inspect_grade_system_query(self):\n return # osid.grading.GradeSystemQueryInspector","title":""},{"docid":"e88a081963c15b2a2d5084d2a9b8ea8d","score":"0.5208438","text":"def __getitem__(self, rubric_subitem) -> Dict[str, Union[bool, str]]:\n return self._grades[self.submitter][\"scores\"][rubric_subitem]","title":""},{"docid":"58d907b6f7815d2824fc11d2d9c55b72","score":"0.5201994","text":"def get(self, grading_id):\n adm = ProjectAdministration()\n grad = adm.get_grading_by_id(grading_id)\n print(grad)\n return grad","title":""},{"docid":"59356c14a35fe2683a442717efa6aeea","score":"0.52018815","text":"def grade(self):\r\n if self.is_royal_flush():\r\n return self.ROYAL_FLUSH\r\n if self.is_straight_flush():\r\n return self.STRAIGHT_FLUSH\r\n if self.is_flush():\r\n return self.FLUSH\r\n if self.is_four_of_a_kind():\r\n return self.FOUR_OF_A_KIND\r\n if self.is_full_house():\r\n return self.FULL_HOUSE\r\n if self.is_three_of_a_kind():\r\n return self.THREE_OF_A_KIND\r\n if self.is_two_pair():\r\n return self.TWO_PAIR\r\n if self.is_pair():\r\n return self.PAIR\r\n return self.LOW","title":""},{"docid":"caa6b94bd23a5af0a5f4fb83a894ae89","score":"0.520172","text":"def get_color(self)->bool:\n state = self.get_state()\n return state.color","title":""},{"docid":"5f683f127053f92bd3aa7da8dc853391","score":"0.5186911","text":"def classification(self):\n return self.Classification","title":""},{"docid":"4d4dc49708fc4bfdf807f0d6a6b8fb32","score":"0.5171784","text":"def get_grade_entries(self):\n return # osid.grading.GradeEntryList","title":""},{"docid":"42d7f1f37961b22a17fb5bcea776708d","score":"0.5168761","text":"def __repr__(self):\n return f'Grade(abbrev={self.abbreviation}, course_number={self.course_number}, ' \\\n f'section_number={self.section_number}, semester={self.semester}, ' \\\n f'year={self.year}, average={self.average})'","title":""},{"docid":"d0ee07d28ecabe5258c42ebb6acddc3b","score":"0.5161924","text":"def can_create_gradebooks(self):\n return # boolean","title":""},{"docid":"d25738771d9d3f137223e8b8ab971fe0","score":"0.5161804","text":"def available(self):\n return self.attributes is not None","title":""},{"docid":"1a37089cb3259e90c60758b63e5d9933","score":"0.51573265","text":"def enrolled(self):\n return self._student_enrolled","title":""}],"string":"[\n {\n \"docid\": \"ebc51373a9a05f5f317713d900e8baf8\",\n \"score\": \"0.743497\",\n \"text\": \"def get_grade(self):\\n return self.grade\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb54dfff723a785b613028952041e0a\",\n \"score\": \"0.6901927\",\n \"text\": \"def grade(self):\\n return self._grade\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a5d7ef1ebc409c567dbe6e12fba2bef\",\n \"score\": \"0.65599567\",\n \"text\": \"def getGrValue(self):\\n return self.grade_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aa05500c7bdd0cb87a6f59d2735bd49\",\n \"score\": \"0.6482489\",\n \"text\": \"def grade(self):\\n return self._response['grade']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5fe758fde8f3bda2996f53addb2c27a\",\n \"score\": \"0.6173418\",\n \"text\": \"def passed(grade):\\n grades = {\\n 'E': True,\\n 'Exceptional': True,\\n 'G': True,\\n 'Good': True,\\n 'A': True,\\n 'Acceptable': True,\\n 'P': False,\\n 'Poor': False,\\n 'H': False,\\n 'Horrible': False,\\n }\\n\\n return grades.get(grade, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7714522ac571d5de7cf1d4767ae0ffa1\",\n \"score\": \"0.6038436\",\n \"text\": \"def get_grader(self):\\n return self.to_dict().get(GRADER_KEY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31dd21dc3f59110e89e8dd00e741b2fd\",\n \"score\": \"0.6010256\",\n \"text\": \"def __str__(self):\\n return self.grade\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b91402ca672aad76a420bf4421f192cc\",\n \"score\": \"0.5985245\",\n \"text\": \"def get_course_grade(self):\\n return CourseGradeFactory().read(self.student_user, self.course)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3abec9684f9e65d83213a6b739d22af8\",\n \"score\": \"0.595143\",\n \"text\": \"def can_assign_grade_system(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"013696b64c22fa3919567870088174f5\",\n \"score\": \"0.57968026\",\n \"text\": \"def can_overridecalculated_grade_entries(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fd3fdcbe39490364fab3c1bc03f9e51\",\n \"score\": \"0.57591295\",\n \"text\": \"def DSG(self):\\n return self.has_property(\\\"featureType\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d669b64048f80beff8c7a74035f71\",\n \"score\": \"0.57512814\",\n \"text\": \"def get_gradebook(self):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c206f07fc977fd877f085a581db27bf2\",\n \"score\": \"0.57271826\",\n \"text\": \"def is_graded(self):\\n return hasattr(self, \\\"graded_work\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"069f33e975ec0d964df88d39f5519fe5\",\n \"score\": \"0.5716374\",\n \"text\": \"def can_lookup_grade_entries(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26769f46bdbc6cbc1257456900f5c453\",\n \"score\": \"0.56508\",\n \"text\": \"def badges_enabled(self):\\n if \\\"badgesEnabled\\\" in self._prop_dict:\\n return self._prop_dict[\\\"badgesEnabled\\\"]\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3ff5fa8b05c4ee3cf54cd3f76aa8488\",\n \"score\": \"0.55828094\",\n \"text\": \"def by_grade(cls, grade):\\n if grade == None or grade > 100:\\n return cls.NOT_STARTED\\n if grade == 100:\\n return cls.SUCCEEDED\\n if grade <= 99 and grade >= 1:\\n return cls.PART_SUCC\\n if grade == 0:\\n return cls.FAILED\\n if grade > -1:\\n return cls.STARTED\\n if grade <= -2 and grade >= -100:\\n return cls.TEACHER_EXC\\n return cls.SANDBOX_EXC\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a439fe5d8e65d619c992fead33ff338\",\n \"score\": \"0.5567315\",\n \"text\": \"def review_grades(self):\\r\\n return self._review_grades\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49661d6ad193e96d3748f2aae057d755\",\n \"score\": \"0.5564851\",\n \"text\": \"def clean_grade(self):\\n\\n grade = self.cleaned_data['grade']\\n # map to bool\\n grade_vals = {'pass': True, 'fail': False, '': ''}\\n\\n return grade_vals.get(grade, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bae47008fded4dd53034e668f922e96\",\n \"score\": \"0.5563756\",\n \"text\": \"def get_boolean(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e34874826e552a0e44d0920592efb8\",\n \"score\": \"0.5553184\",\n \"text\": \"def fetch_grades(self):\\n return self.enrolled_students\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb8c13bb13c8cbb65df7214c71862ead\",\n \"score\": \"0.55406314\",\n \"text\": \"def _get_isG2(self) -> \\\"bool\\\" :\\n return _fusion.FilletFeature__get_isG2(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fa72acbb4f2a081487d66b4c3a53da0\",\n \"score\": \"0.55338603\",\n \"text\": \"def is_on(self) -> bool:\\n val = False\\n if self._attr in self._data:\\n val = self._data[self._attr]\\n\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46036a8387b6760c6f83c210991c2f15\",\n \"score\": \"0.55083734\",\n \"text\": \"def can_lookup_grade_systems(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c13a5c7f6601f346033adf0bba2229ad\",\n \"score\": \"0.5495199\",\n \"text\": \"def requires_grad(self):\\n return self._requires_grad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c13a5c7f6601f346033adf0bba2229ad\",\n \"score\": \"0.5495199\",\n \"text\": \"def requires_grad(self):\\n return self._requires_grad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eae4a236ad2e5dd5d83ac9d187e284f\",\n \"score\": \"0.549249\",\n \"text\": \"def Enabled(self):\\r\\n\\t\\treturn self._get_attribute('enabled')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3d274cb14e2e16579df66b7bcb820e0\",\n \"score\": \"0.54852563\",\n \"text\": \"def get_grade_system(self, grade_system_id):\\n return # osid.grading.GradeSystem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39efe85f48adf5151ea0b34414adfe40\",\n \"score\": \"0.5473113\",\n \"text\": \"def get_grade_entry_query(self):\\n return # osid.grading.GradeEntryQuery\",\n \"title\": \"\"\n },\n {\n \"docid\": \"134fe98541fa7b039c43aeb203a66db9\",\n \"score\": \"0.54713374\",\n \"text\": \"def get_student(self):\\n return self.student\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dde5f91a741f95f95a09fc86b17de1ab\",\n \"score\": \"0.54577273\",\n \"text\": \"def __bool__(self):\\n\\n return bool(self.attribute)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dde5f91a741f95f95a09fc86b17de1ab\",\n \"score\": \"0.54577273\",\n \"text\": \"def __bool__(self):\\n\\n return bool(self.attribute)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab5dcbb52dd3e421c70969899a3bb753\",\n \"score\": \"0.54488\",\n \"text\": \"def maybe_enable_grading(self):\\n\\n if not self._lti_user.is_instructor:\\n # Only instructors can grade assignments.\\n return\\n\\n if \\\"lis_outcome_service_url\\\" not in self._request.params:\\n # Only \\\"gradeable\\\" assignments can be graded.\\n # Assignments that don't have the lis_outcome_service_url param\\n # aren't set as gradeable in the LMS.\\n return\\n\\n if self._context.is_canvas:\\n # Don't show our built-in grader in Canvas because it has its own\\n # \\\"SpeedGrader\\\" and we support that instead.\\n return\\n\\n self._config[\\\"grading\\\"] = {\\n \\\"enabled\\\": True,\\n \\\"courseName\\\": self._request.params.get(\\\"context_title\\\"),\\n \\\"assignmentName\\\": self._request.params.get(\\\"resource_link_title\\\"),\\n \\\"students\\\": list(self._get_students()),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a18ab8b3a195891be76070e35887af9a\",\n \"score\": \"0.544498\",\n \"text\": \"def getFields(self, *args):\\n\\n # try to fetch from survey_record\\n if hasattr(self.survey_record, 'grade'):\\n grade = self.survey_record.grade\\n\\n # remap bool to string values as the ChoiceField validates on 'choices'.\\n vals_grade = {True: 'pass', False: 'fail'}\\n\\n self.data['grade'] = vals_grade.get(grade, None) or grade\\n\\n return super(GradeSurveyRecordForm, self).getFields(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca8cb2c658c26f61cabbcae9522f2368\",\n \"score\": \"0.5440322\",\n \"text\": \"def is_graded(self, code: str, name: Optional[str] = None) -> bool:\\n if not name:\\n name = self.submitter\\n # TODO: yikes, 4 dictionary accesses xD. is there a better way?\\n return (self._grades[name][\\\"scores\\\"][code][\\\"award\\\"]\\n is not None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b932a98f5e1ad276391ce4355730276\",\n \"score\": \"0.5438885\",\n \"text\": \"def __get_education__(self):\\n return self.education\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7394ac41882520331f888575c63311d7\",\n \"score\": \"0.5435297\",\n \"text\": \"def testClassPropertyGrades(self):\\n\\n # data for the unit class grad setter\\n unitGradesPass = [None, 5, 51.5, 0, '1.5']\\n unitGradesTypeError = [[1, 5]]\\n unitGradesValueError = ['testString', -2, 456]\\n\\n # appropriate values\\n for testGrade in unitGradesPass:\\n self.testModule.grade = testGrade\\n if testGrade is None:\\n self.assertEqual(self.testModule.grade, testGrade)\\n else:\\n testGrade = float(testGrade)\\n if testGrade < 1:\\n self.assertEqual(self.testModule.grade, 1)\\n elif testGrade > 4:\\n self.assertEqual(self.testModule.grade, 5)\\n else:\\n self.assertEqual(self.testModule.grade, testGrade)\\n\\n # inappropriate values\\n with self.assertRaises(TypeError):\\n for testGrade in unitGradesTypeError:\\n self.testModule.grade = testGrade\\n with self.assertRaises(ValueError):\\n for testGrade in unitGradesValueError:\\n self.testModule.grade = testGrade\\n\\n # data for the course class grad setter\\n courseGrades = [0, 3, None, 78]\\n\\n # appropriate values\\n for testGrade in courseGrades:\\n self.testCourse.grade = testGrade\\n\\n if testGrade is None:\\n self.assertFalse(self.testCourse.passed)\\n elif testGrade > 4:\\n self.assertFalse(self.testCourse.passed)\\n else:\\n self.assertTrue(self.testCourse.passed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"486a0b80b3c68bfbe6afc69111c30a2d\",\n \"score\": \"0.5417543\",\n \"text\": \"def _get_isActive(self) -> \\\"bool\\\" :\\n return _fusion.Occurrence__get_isActive(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"963c5b80b0453621e4572b502ea4b1ff\",\n \"score\": \"0.5396134\",\n \"text\": \"def can_create_grade_entries(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"669de770250abf018713444db74d4fb5\",\n \"score\": \"0.53954\",\n \"text\": \"def _get_isUniform(self) -> \\\"bool\\\" :\\n return _fusion.ScaleFeature__get_isUniform(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed76b871326cc3e4bddadfb79f33dedc\",\n \"score\": \"0.53860086\",\n \"text\": \"def has_many_grade_levels(self):\\n return self.grade_levels.count() > 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0c2104f44b104d93c88a0deea364dfc\",\n \"score\": \"0.5373989\",\n \"text\": \"def get_grade_entry(self, grade_entry_id):\\n return # osid.grading.GradeEntry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7753749ce34c637ede923ad064118e06\",\n \"score\": \"0.53579813\",\n \"text\": \"def can_search_grade_entries(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7967c1e9ab40557cc943f6c50e10548c\",\n \"score\": \"0.53566635\",\n \"text\": \"def gender(self):\\n return self.member.gender\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57f442e835222c6a0ecde946e36042e7\",\n \"score\": \"0.5355678\",\n \"text\": \"def _get_isG2(self) -> \\\"bool\\\" :\\n return _fusion.FilletFeatureInput__get_isG2(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"381b3ad44e4becb44f95c814d67b70e2\",\n \"score\": \"0.53447855\",\n \"text\": \"def gender(self):\\n return self._gender\",\n \"title\": \"\"\n },\n {\n \"docid\": \"381b3ad44e4becb44f95c814d67b70e2\",\n \"score\": \"0.53447855\",\n \"text\": \"def gender(self):\\n return self._gender\",\n \"title\": \"\"\n },\n {\n \"docid\": \"381b3ad44e4becb44f95c814d67b70e2\",\n \"score\": \"0.53447855\",\n \"text\": \"def gender(self):\\n return self._gender\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8f57247b7e5167cda45537a0d7e6301\",\n \"score\": \"0.5338988\",\n \"text\": \"def get_grade_entry_search(self):\\n return # osid.grading.GradeEntrySearch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34ce66b0f7d38fe54867d81745fa44da\",\n \"score\": \"0.5334409\",\n \"text\": \"def checked(self):\\n # !!! this should be replaced by another logic that will get the real stat\\n # from the DOM\\n return self._checked\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69069e8afc1ae49373d093786458fea9\",\n \"score\": \"0.5323901\",\n \"text\": \"def contains_attr(self, gi):\\n if gi is None:\\n return False\\n for gi_obj in self.gradual_items:\\n if gi.attribute_col == gi_obj.attribute_col:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"501b46aeaded12751c5cbdb546560f7e\",\n \"score\": \"0.53137267\",\n \"text\": \"def can_lookup_gradebooks(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"797026e094fc0d3c5d20c6e6c0f563e9\",\n \"score\": \"0.5297622\",\n \"text\": \"def getRating(self):\\n\\t\\treturn self._rating\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32b90e312e8763287a5dd0bafb9daf91\",\n \"score\": \"0.5296641\",\n \"text\": \"def can_update_grade_systems(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d0bf06783ca1edda099c8a5fb829759\",\n \"score\": \"0.5295841\",\n \"text\": \"def state(self):\\n return self.attribute_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d0bf06783ca1edda099c8a5fb829759\",\n \"score\": \"0.5295841\",\n \"text\": \"def state(self):\\n return self.attribute_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1addd6bb6b9711972333a8e7ec389901\",\n \"score\": \"0.5293318\",\n \"text\": \"def _get_isActive(self) -> \\\"bool\\\" :\\n return _fusion.FusionDocument__get_isActive(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d712c43e34c5fc77c108914b93e52d4\",\n \"score\": \"0.52853703\",\n \"text\": \"def student_call():\\n return face_analyzer.student_attentiveness()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"140d0e23b5e5ca6130fbfac0791de0b4\",\n \"score\": \"0.5284669\",\n \"text\": \"def get_attribs(self):\\n return self.attribs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"140d0e23b5e5ca6130fbfac0791de0b4\",\n \"score\": \"0.5284669\",\n \"text\": \"def get_attribs(self):\\n return self.attribs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c644dfcb6faeac973fd770f068dd55ba\",\n \"score\": \"0.52812827\",\n \"text\": \"def can_search_grade_systems(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c769007f6cc6c4d238b406d2bee69676\",\n \"score\": \"0.52785134\",\n \"text\": \"def can_create_grade_systems(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b23e8570ba2cbbfbd66944b0fb0656f\",\n \"score\": \"0.5269091\",\n \"text\": \"def value(self) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b23e8570ba2cbbfbd66944b0fb0656f\",\n \"score\": \"0.5269091\",\n \"text\": \"def value(self) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e67af65f2341d31869a29bf36b4ba2c\",\n \"score\": \"0.5259277\",\n \"text\": \"def _inSample(self):\\n #if hasattr(self._userModel(), \\\"_d_inSample\\\"):\\n return self._d_inSample\\n #else:\\n # return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26673f7aea374d912e89c2b03c339d5f\",\n \"score\": \"0.52510893\",\n \"text\": \"def can_update_grade_entries(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4d4f51f269ac04b1bb9848f7779f8e9\",\n \"score\": \"0.5250907\",\n \"text\": \"def get_gradebook(self, gradebook_id):\\n return # osid.grading.Gradebook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92fd067364305b2141a3c3bc62c543a7\",\n \"score\": \"0.52462745\",\n \"text\": \"def get_prop(self):\\n return self.prop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92fd067364305b2141a3c3bc62c543a7\",\n \"score\": \"0.52462745\",\n \"text\": \"def get_prop(self):\\n return self.prop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6bdba43265e6c20c6dcfcd7a8e783bb\",\n \"score\": \"0.52400917\",\n \"text\": \"def review():\\n context.grader.manual_grade()\\n # raise NotImplemented(\\\"This feature is not currently implemented.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7197c307090e4c811be6d11cadda4354\",\n \"score\": \"0.5238365\",\n \"text\": \"def get_gradebooks(self):\\n return # osid.grading.GradebookList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed07c02854e0f184fb3ca19395b540e6\",\n \"score\": \"0.52303505\",\n \"text\": \"def is_active(self):\\n return self.__user['object']['enabled']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1c65a6fe41d17f66f29a2c1bac42bfe\",\n \"score\": \"0.5229278\",\n \"text\": \"def get_score(self):\\n\\n\\treturn self.__score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dec89c629e9527f502f50db18858774\",\n \"score\": \"0.52241856\",\n \"text\": \"def IsSexuallyActive( self ):\\n # assert self.__gender == MALE\\n return self.__age >= 16\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b3fe1fb6fb49a71d7fdaa6a2d0467fa\",\n \"score\": \"0.52211696\",\n \"text\": \"def get_grade_system_query(self):\\n return # osid.grading.GradeSystemQuery\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b3fe1fb6fb49a71d7fdaa6a2d0467fa\",\n \"score\": \"0.52211696\",\n \"text\": \"def get_grade_system_query(self):\\n return # osid.grading.GradeSystemQuery\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce4e2fde558bac42a7538485c22927b5\",\n \"score\": \"0.5216244\",\n \"text\": \"def genders(self):\\n\\n return ('male', 'female')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b949910c108d4bcfa102d69ed8cb9d8a\",\n \"score\": \"0.52103674\",\n \"text\": \"def inspect_grade_system_query(self):\\n return # osid.grading.GradeSystemQueryInspector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e88a081963c15b2a2d5084d2a9b8ea8d\",\n \"score\": \"0.5208438\",\n \"text\": \"def __getitem__(self, rubric_subitem) -> Dict[str, Union[bool, str]]:\\n return self._grades[self.submitter][\\\"scores\\\"][rubric_subitem]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58d907b6f7815d2824fc11d2d9c55b72\",\n \"score\": \"0.5201994\",\n \"text\": \"def get(self, grading_id):\\n adm = ProjectAdministration()\\n grad = adm.get_grading_by_id(grading_id)\\n print(grad)\\n return grad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59356c14a35fe2683a442717efa6aeea\",\n \"score\": \"0.52018815\",\n \"text\": \"def grade(self):\\r\\n if self.is_royal_flush():\\r\\n return self.ROYAL_FLUSH\\r\\n if self.is_straight_flush():\\r\\n return self.STRAIGHT_FLUSH\\r\\n if self.is_flush():\\r\\n return self.FLUSH\\r\\n if self.is_four_of_a_kind():\\r\\n return self.FOUR_OF_A_KIND\\r\\n if self.is_full_house():\\r\\n return self.FULL_HOUSE\\r\\n if self.is_three_of_a_kind():\\r\\n return self.THREE_OF_A_KIND\\r\\n if self.is_two_pair():\\r\\n return self.TWO_PAIR\\r\\n if self.is_pair():\\r\\n return self.PAIR\\r\\n return self.LOW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caa6b94bd23a5af0a5f4fb83a894ae89\",\n \"score\": \"0.520172\",\n \"text\": \"def get_color(self)->bool:\\n state = self.get_state()\\n return state.color\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f683f127053f92bd3aa7da8dc853391\",\n \"score\": \"0.5186911\",\n \"text\": \"def classification(self):\\n return self.Classification\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d4dc49708fc4bfdf807f0d6a6b8fb32\",\n \"score\": \"0.5171784\",\n \"text\": \"def get_grade_entries(self):\\n return # osid.grading.GradeEntryList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42d7f1f37961b22a17fb5bcea776708d\",\n \"score\": \"0.5168761\",\n \"text\": \"def __repr__(self):\\n return f'Grade(abbrev={self.abbreviation}, course_number={self.course_number}, ' \\\\\\n f'section_number={self.section_number}, semester={self.semester}, ' \\\\\\n f'year={self.year}, average={self.average})'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0ee07d28ecabe5258c42ebb6acddc3b\",\n \"score\": \"0.5161924\",\n \"text\": \"def can_create_gradebooks(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d25738771d9d3f137223e8b8ab971fe0\",\n \"score\": \"0.5161804\",\n \"text\": \"def available(self):\\n return self.attributes is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a37089cb3259e90c60758b63e5d9933\",\n \"score\": \"0.51573265\",\n \"text\": \"def enrolled(self):\\n return self._student_enrolled\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994082,"cells":{"query_id":{"kind":"string","value":"f293cb81966d4d558331b83cb872dc75"},"query":{"kind":"string","value":"Ensure that all entries are returned."},"positive_passages":{"kind":"list like","value":[{"docid":"57a0df6352d166edd9eb96351fc2fc59","score":"0.6007018","text":"def test_view_everything_returns_all_records(self):\n data = self.create_test_dates()['test_log_entry_data']\n\n records = self.dbm.view_everything()\n records = [dict(entry) for entry in records]\n\n self.assertEqual(len(data), len(records))\n for datum in data:\n self.assertIn(datum, records)","title":""}],"string":"[\n {\n \"docid\": \"57a0df6352d166edd9eb96351fc2fc59\",\n \"score\": \"0.6007018\",\n \"text\": \"def test_view_everything_returns_all_records(self):\\n data = self.create_test_dates()['test_log_entry_data']\\n\\n records = self.dbm.view_everything()\\n records = [dict(entry) for entry in records]\\n\\n self.assertEqual(len(data), len(records))\\n for datum in data:\\n self.assertIn(datum, records)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b5acbfbedb9e1524417d1e5e14283e4f","score":"0.7266865","text":"def all_entries(self) -> requests.models.Response:","title":""},{"docid":"151603cea417ba3dabcb63e9d0ba8d72","score":"0.7057368","text":"def getAllEntries(self):\n try:\n self.data.entryList = self.APP.getAllEntries()\n if not self.__sendAppOperationResult(lambda: self.data.entryList is not None,\n ControllerLogicException('Load entries failed.')):\n return\n except Exception, e:\n self.__sendError(e)","title":""},{"docid":"1140cee3127b7a665943779f6ac9713b","score":"0.68558085","text":"def all(self):\n self.scan()\n return self.entries","title":""},{"docid":"2fa33d70eb0c45040ec4be65d0f962f8","score":"0.6569235","text":"def get(self):\n return get_all_entries()","title":""},{"docid":"e38b26abe2f00ae481e46756657afb0d","score":"0.6526549","text":"def get_entries(self) -> Generator[tuple, None, None]:\n for entry in self.entries_batch:\n yield entry\n\n self.entries_batch = []","title":""},{"docid":"cfc93173820ff5d608d5508be434da0c","score":"0.6519066","text":"def get_all_entries(self):\n\n self.refresh()\n while True:\n for entry in self.entries:\n yield entry\n\n # Navigate to next page - HTTP call\n if self.next() is None:\n break","title":""},{"docid":"508efee73a1728fda5a7d7bbc053e1e9","score":"0.6487228","text":"def get_all_entries():\n entries = Entry.objects.all()\n return entries","title":""},{"docid":"b7c2253ebdf3305ee8361aed2414aab8","score":"0.6355247","text":"def all(self):\n self.scan()\n return list(self.entries)","title":""},{"docid":"10d3aa0e5219be23c38d70d2faa7a9e9","score":"0.6343515","text":"def get_all(self):\n pass","title":""},{"docid":"e144f30655b64b59c5dd1815d6186ccf","score":"0.63129425","text":"def get_all_items(self):","title":""},{"docid":"a09470d44b62e478a3fa35fc04d187fb","score":"0.62576747","text":"def get_all(self):\n self.get_new_entries()\n self.get_changed_entries()\n self.get_removed_entries()","title":""},{"docid":"2e12436e4249ed88b5e4636ace39fbd1","score":"0.6249732","text":"def get_all(self):\n raise NotImplemented()","title":""},{"docid":"92b8360a16f7ca49f57c2bb2ad5a6581","score":"0.6216486","text":"def test_get_all_entries(self):\n\n response = self.client.get('/api/v1/entries',\n content_type='application/json',\n data=json.dumps(entry1))\n self.assertEqual(response.status_code, 200)\n self.assertIn('Technical Leader', str(response.data))","title":""},{"docid":"acefbb95634855a960aa2b06fe50733a","score":"0.6166555","text":"def get_all_records(self):\n pass","title":""},{"docid":"26d2f055d02402577a612db3cbc6caec","score":"0.61484027","text":"def test_get_all_entries(self):\n\n testing_user = app.test_client(self)\n\n # Add first entry\n res = testing_user.post('/api/v1/entries',\n data=json.dumps(self.my_entries[0]),\n content_type='application/json')\n self.assertEqual(res.status_code, 201)\n self.assertIn('Your memory entitled ' + self.my_entries[0]['title'] +\n ' has been saved', str(res.data))\n\n # Add second entry\n response = testing_user.post('/api/v1/entries',\n data=json.dumps(self.my_entries[1]),\n content_type='application/json')\n self.assertEqual(res.status_code, 201)\n self.assertIn('Your memory entitled ' + self.my_entries[1]['title'] +\n ' has been saved', str(response.data))\n\n # Get all entries\n final_response = testing_user.get('/api/v1/entries')\n self.assertEqual(final_response.status_code, 200)\n self.assertIn('Learning Flask', str(final_response.data))\n self.assertIn('My first Flask API', str(final_response.data))","title":""},{"docid":"1c88e6c9bffa2edf8ce8ddc6b4d2e7e1","score":"0.6137212","text":"def all(self):\n return self._perform_query()","title":""},{"docid":"f3477b6370bd0255f3ad55937aba438c","score":"0.6130469","text":"def all(self):\n return self.many()","title":""},{"docid":"0c0a297d51ee3bdd7a06ecf8b68d7923","score":"0.612449","text":"def _fetch_all(self):\n if self._result_cache is None:\n self._result_cache = list(self.iterator())\n if not isinstance(self, ValuesListQuerySet):\n for x in self._result_cache:\n self._set_query_time(x)\n if self._prefetch_related_lookups and not self._prefetch_done:\n self._prefetch_related_objects()","title":""},{"docid":"cd7e9c236d0c0319a066e981e180ed47","score":"0.61147547","text":"def GetAll(self):\n return self.ok + self.GetAllBroken()","title":""},{"docid":"cd926c005ff344fbee36cfbf72cb4cd0","score":"0.6110133","text":"def _fetch_all(self):\n if self._result_cache is None:\n self._result_cache, self._pagination_item = self.model.get(self.request_options)","title":""},{"docid":"55dec3a91ce2bbebac8afe451f850693","score":"0.6080038","text":"def test_read_all(self):\n with patch.object(LogEntries, \"read\",\n side_effect=self._get_entries):\n # assert returning all sorted entries\n entries = LogEntries.read_all(list(self._get_entries_dict().keys()),\n None, None, None)\n self.assertEqual(len(entries), 9)\n for i in range(len(entries) - 1):\n self.assertLessEqual(entries[i][\"time\"],\n entries[i + 1][\"time\"])\n\n # assert returning last 3 sorted entries\n entries = LogEntries.read_all(list(self._get_entries_dict().keys()),\n 3, None, None)\n self.assertEqual(len(entries), 3)\n expected_entries = [200, 600, 1000]\n for i in range(len(entries) - 1):\n # assert ordering\n self.assertLessEqual(entries[i][\"time\"],\n entries[i + 1][\"time\"])\n # assert values\n self.assertEqual(entries[i][\"time\"], expected_entries[i])\n self.assertEqual(entries[i+1][\"time\"], expected_entries[i+1])","title":""},{"docid":"70d00effe8da0d6965aa9b0aee15b104","score":"0.60755086","text":"def get_entries():\n data = Diary.all_entries()\n return data","title":""},{"docid":"3adc7b3b70576031e93ca35999f393da","score":"0.6066714","text":"def get_all(self):\n raise NotImplementedError","title":""},{"docid":"3adc7b3b70576031e93ca35999f393da","score":"0.6066714","text":"def get_all(self):\n raise NotImplementedError","title":""},{"docid":"3adc7b3b70576031e93ca35999f393da","score":"0.6066714","text":"def get_all(self):\n raise NotImplementedError","title":""},{"docid":"b1ff18e18ca7e02635504b6d58f2295f","score":"0.60474014","text":"def all(self):\n with self._lock:\n self.update()\n\n return list(self.entries)","title":""},{"docid":"cf62aef74ebace865132164fc86a403a","score":"0.6045557","text":"def has_all_entries(self):\n for entry in self.entries:\n self.related_entry_ids.update(entry.get_related_entries())\n self.related_entry_ids.add(entry.id)\n if set(self.related_entry_ids) == set([entry.id for entry in self.entries]):\n return True\n else:\n print 'Study', id, 'does not have all entries', set(self.related_entry_ids), set([entry.id for entry in self.entries])\n return False","title":""},{"docid":"e0d951e59a0e7292b0e921618ed26f62","score":"0.601289","text":"def fetch(self) -> list[Entry]:","title":""},{"docid":"8f5653134c7333ac74551e33c9740853","score":"0.5974942","text":"def all() -> dict:\n ...","title":""},{"docid":"8ef3c2ecbbb8cb7eb306396200d95024","score":"0.5950022","text":"def get_entries(self) -> Generator[str, None, None]:\n return (entry for entry in self.entries)","title":""},{"docid":"54973ce35943e3c38300e700dad39c91","score":"0.5945292","text":"def fetchall(self):\n raise NotImplementedError('Method not implemented.')","title":""},{"docid":"9c3b2eca379418af3e13aa23352b4a94","score":"0.59449697","text":"def _get_results(self):\n return []","title":""},{"docid":"65d39b400093177d6242184752bd95f1","score":"0.59397185","text":"def test_all_empty(self):\n\n nutrition = MemoryNutritionStore()\n self.assertEqual({ }, nutrition.all())","title":""},{"docid":"c025e6597f6840dce3a0a5fa39737957","score":"0.59388345","text":"def all(self) -> dict:\n ...","title":""},{"docid":"4ac57927482ae5ff1d0a99476e2f865a","score":"0.5933919","text":"def get_entries(self):\n return self.entries","title":""},{"docid":"37c3254db7dfc91a82e6fe9467b0dcdb","score":"0.5930503","text":"def fetchall(self):\n raise NotImplementedError","title":""},{"docid":"52f07dd693782254758243910bf44548","score":"0.5916642","text":"def test_entries(self):\n self.assertEqual(len(self.entries), 25)","title":""},{"docid":"8db8e7df3a91186728da2daa09586a5d","score":"0.5894018","text":"def get_all_library_entries():\n key = 'library_entries'\n data = cache.get(key)\n if data is None:\n try:\n data = models.ApplicationLibraryEntry.objects.filter(listing__is_deleted=False).all()\n cache.set(key, data)\n return data\n except ObjectDoesNotExist:\n return None\n else:\n return data","title":""},{"docid":"2653d04dc013f02a87b9a94af4dcf7e2","score":"0.5882078","text":"def entries(self):\n return self._entries","title":""},{"docid":"635397129a843d61780e474acebd5a07","score":"0.5880765","text":"def get_all_data(self):\n pass","title":""},{"docid":"21ec344036edd163caaf2fa6862ccf84","score":"0.58756435","text":"def test_getall(self):\n pass","title":""},{"docid":"1b1103d01b49838bd90680bfa2fa702e","score":"0.5836943","text":"def get_all(self):\n\n raise NotImplementedError()","title":""},{"docid":"1b1103d01b49838bd90680bfa2fa702e","score":"0.5836943","text":"def get_all(self):\n\n raise NotImplementedError()","title":""},{"docid":"0d55610edf56149e4f189fb9a0420ce6","score":"0.5823098","text":"def test_find_all_return_empty_list():\n all_entries = rldb.find_all({ 'env-title': 'There is no env with this title.' })\n\n assert all_entries == []","title":""},{"docid":"6c953ca53d9e516e498bbd168b9f7285","score":"0.58151126","text":"def get_all_for_all(self):\n\n for name, rs in self.result_sets.items():\n print(\"Retrieving {}s for result set {}...\".format(self.item, name))\n self.get_all_items(name)","title":""},{"docid":"9c08bc29b8130efab99fe58edf1975d4","score":"0.5813334","text":"def _get_entries(self, query):\n # @see http://www.solrtutorial.com/solr-query-syntax.html\n return self._kibana.get_rows(match={\"@exception.class\": \"Wikia\\\\Util\\\\AssertionException\"}, limit=self.LIMIT)","title":""},{"docid":"f81be40393f2c4d6f97b57f2def8730f","score":"0.58030194","text":"def fetchall(self):\n out = self._results[:]\n self._results = []\n return out","title":""},{"docid":"2c15db1d5b5d20654669e10701db451f","score":"0.57954216","text":"def all(self) -> Iterable[\"Row\"]:","title":""},{"docid":"147940dd0eb713d6564cc769d0fea4ea","score":"0.57885486","text":"def all(self):\n return self.multi()","title":""},{"docid":"22fed8c1386721bb5acd9c28b742684b","score":"0.5781892","text":"def get_all(self):\n return imap(self.get_one, ifilter(None, self.list()))","title":""},{"docid":"71c2424c45edc9bef93928b9bd78e287","score":"0.57688826","text":"def all(self):\n return [record for record in iter(self)]","title":""},{"docid":"e4dd194f2629aeb1d13ab85de93a4443","score":"0.57681453","text":"def get_all_entries(self, slug: str, batch_size: int = 10) -> Iterable[Entry]:\n offset = 0\n entries_batch = self.get_entries(slug, limit=batch_size, offset=offset)\n while entries_batch:\n yield from entries_batch\n offset += batch_size\n entries_batch = self.get_entries(slug, limit=batch_size, offset=offset)","title":""},{"docid":"8a6c99a887b406c94c789281f8170476","score":"0.5756834","text":"def get_entries(self):\n\n\t\treturn self.__entries","title":""},{"docid":"fc32de4e88ee73fcde536b94daf09ea4","score":"0.57488054","text":"def validateAllDataItems(self):\n return self.dataStore.validateAllDataItems()","title":""},{"docid":"50b2a92a3568ccc67ec1db535189e61d","score":"0.5737981","text":"def iterentries(self):\n raise NotImplementedError(self.iterentries)","title":""},{"docid":"65e723eb7b341aa7f70d3fe77fd3e9e7","score":"0.57354146","text":"def all_entries():\n s = Session()\n\n try:\n entries_list = s.query(Entry).all()\n s.close()\n return render_template(\"entries/entries_list.html\", entries=entries_list)\n except Exception as e:\n return str(e)","title":""},{"docid":"1932615a364c14bf335b501c2f8f50a8","score":"0.5729247","text":"def getAllOrNone(self):\n return self.__allOrNone","title":""},{"docid":"1375a1c6a839613dfa00215f808fe747","score":"0.5728215","text":"def get_entries(self):\n return self.__entries","title":""},{"docid":"1375a1c6a839613dfa00215f808fe747","score":"0.5728215","text":"def get_entries(self):\n return self.__entries","title":""},{"docid":"c280454ea6baedd522a64a308cf32a91","score":"0.5722788","text":"def get_all():\n res = get_aps()\n if res is None:\n return flask.Response('Organization not found', 404)\n return res","title":""},{"docid":"e69d8f59b3aeefeffbeb49475cc395ad","score":"0.5720823","text":"def test_all(self):\n self.objs = self.storage.all()\n self.assertIsNotNone(self.objs)\n self.assertEqual(type(self.objs), dict)","title":""},{"docid":"e69d8f59b3aeefeffbeb49475cc395ad","score":"0.5720823","text":"def test_all(self):\n self.objs = self.storage.all()\n self.assertIsNotNone(self.objs)\n self.assertEqual(type(self.objs), dict)","title":""},{"docid":"cd36a8943e7c0bfb9389eeb133cd7d78","score":"0.5720548","text":"def all(self):\n self.query()\n return self.list","title":""},{"docid":"b5ee4eab50f885f6bf909fc5e1c9692b","score":"0.57047886","text":"def _all(self):\n pass","title":""},{"docid":"c09f395ca324c4b1a107106e058810ae","score":"0.57030624","text":"def all():\n return list()","title":""},{"docid":"80758f8a539f3723cb7d10823a5ba77b","score":"0.56985116","text":"def all(self) -> dict[str, _t.Any]: # pragma: no cover\n return self.get_all()","title":""},{"docid":"40ad81d997f1200865f8b5b6b8bfc98f","score":"0.56823474","text":"def fetchall(self):\n return []","title":""},{"docid":"40536cef1a989823dd85581f29555f9b","score":"0.56790763","text":"def get_all_records(self) -> Tuple[Optional[List[Records]], Optional[Exception]]:\n return self.get_all_objects(Records)","title":""},{"docid":"de76411e01d9d399573017b03d6dff2e","score":"0.56751245","text":"def _validate_market_data_entries(entries):\n if entries is None:\n entries = [entry for entry in MarketDataEntry]\n else:\n for entry in entries:\n if not isinstance(entry, MarketDataEntry):\n print(\"WARNING: Market Data Entry not defined: \" + str(entry))\n\n return entries","title":""},{"docid":"bdd72b8bdf466ba1b4c1db1ac658bb1c","score":"0.5673406","text":"def _collect_results(self, items):\r\n for item in items:\r\n if not utils.is_url(item[\"link\"]):\r\n continue\r\n if item in self.results:\r\n continue\r\n if self.ignore_duplicate_urls and item[\"link\"] in self.results.links():\r\n continue\r\n if self.ignore_duplicate_domains and item[\"host\"] in self.results.hosts():\r\n continue\r\n self.results.append(item)","title":""},{"docid":"48f8674942c023df087b892ef6cfcc2a","score":"0.5658856","text":"def get_all_ids(self):\n while True:\n results = self.get_ids()\n ids = results[\"id_set\"]\n if not ids - self.article_ids:\n break\n self.article_ids = self.article_ids | ids\n if len(self.article_ids) >= results[\"result_count\"]:\n break","title":""},{"docid":"d23483c5246ae5acba008f0d1938dba1","score":"0.5650488","text":"def retrieve_all(self, key):\n raise NotImplementedError","title":""},{"docid":"84e288d3572c28a5b2bc4622b49218c9","score":"0.5641535","text":"def get_all_items(self) -> Iterable[\"Item_Type\"]:\n if self.assert_conforms_to(ConformanceClasses.ITEM_SEARCH):\n yield from self.get_items()\n else:\n yield from super().get_items()","title":""},{"docid":"f344706ceae6dda9e6b5e17112715621","score":"0.5628937","text":"def get_entries(self):\n return filter(\n lambda e: not e.name.endswith(self.empty_index),\n self.entries\n )","title":""},{"docid":"a1dbcc52b323a4375656f71e5af724f1","score":"0.56237346","text":"def All(self) -> List[dict]:\r\n # Default return if nothing matches the query\r\n if len(self.results) < 1:\r\n return []\r\n\r\n return self.__get_unindexed_all_results()","title":""},{"docid":"acb8aca500c6135ce12baf6b6c5c8665","score":"0.5622763","text":"def get_all(self) -> dict[str, _t.Any]:","title":""},{"docid":"e028b389d835bc29da31f863604d9cdd","score":"0.5619233","text":"def entries():","title":""},{"docid":"e028b389d835bc29da31f863604d9cdd","score":"0.5619233","text":"def entries():","title":""},{"docid":"e30e1a2ba33f9419a8e383333e8538cd","score":"0.56087714","text":"def test_all_items(self):\n logging.info(\"Start test case: get all items\")\n get_items = get_all_items()\n assert get_items.status_code == 200, f\"The status_code is {get_items.status_code}\"","title":""},{"docid":"919a2bb51208826d11baeef10a327bff","score":"0.5602807","text":"def query_all(self) -> List[Any]:\n return [v for v in self.batch_query(self.query_keys()) if v is not None]","title":""},{"docid":"6611cbc29a55f641d06fdb7fbff46c4f","score":"0.559653","text":"def __iter__(self):\n return self.all()","title":""},{"docid":"f3cd3ae4bc4102406c84db58261e60f1","score":"0.5594151","text":"def all(self):\n return [(key, self[key]) for key in self.map.keys() \\\n if self[key] is not None and len(self[key])]","title":""},{"docid":"cd229cfe9c7a9e21557899d22e256c87","score":"0.5584198","text":"def getAll(self):","title":""},{"docid":"580c1d7e78593508cb7a235f0243173c","score":"0.55834055","text":"def get_all_data(self) -> Optional[list[dict]]:\n data = []\n\n try:\n entries = self.db.collection(constants.COLLECTION).get()\n\n for entry in entries:\n if entry.exists:\n data.append(entry.to_dict())\n\n except FirebaseError:\n raise DataDidNotLoadError\n\n return data","title":""},{"docid":"0d5bf396d3a4d29da148574892acdead","score":"0.55720246","text":"def test_get_entries():\n entries = get_entries()\n assert len(entries) == 1\n assert entries[0] == {\n 'accelerated_justification': 'Closing Time',\n 'additional_text': 'Additional Text',\n 'attachments': 'Attachments',\n 'awarded_date': 'Awarded Value',\n 'awarded_value': 'Supplier [Name|Address|Ref type|Ref Number|Is SME|Is VCSE]',\n 'closing_date': 'Closing Date',\n 'closing_time': 'Is sub-contract',\n 'contact_address_1': 'Contact Address 1',\n 'contact_address_2': 'Contact Address 2',\n 'contact_country': 'Contact Country',\n 'contact_email': 'Contact Email',\n 'contact_name': 'Contact Name',\n 'contact_postcode': 'Contact Postcode',\n 'contact_telephone': 'Contact Telephone',\n 'contact_town': 'Contact Town',\n 'contact_website': 'Contact Website',\n 'contract_end_date': 'OJEU Procedure Type',\n 'contract_start_date': 'Contract end date',\n 'cpv_codes': 'Cpv Codes',\n 'description': 'Description',\n 'end_date': 'End Date',\n 'is_sub_contract': 'Parent Reference',\n 'links': 'Links',\n 'nationwide': 'Nationwide',\n 'notice_identifier': 'Notice Identifier',\n 'notice_type': 'Notice Type',\n 'ojeu_contract_type': 'Value Low',\n 'ojeu_procedure_type': 'Accelerated Justification',\n 'organisation_name': 'Organisation Name',\n 'parent_reference': 'Suitable for SME',\n 'postcode': 'Postcode',\n 'published_date': 'Published Date',\n 'region': 'Region',\n 'start_date': 'Start Date',\n 'status': 'Status',\n 'suitable_for_sme': 'Suitable for VCO',\n 'suitable_for_vco': 'Supply Chain',\n 'supplier_contact_name': 'Contract start date',\n 'supplier_info': \"Supplier's contact name\",\n 'supply_chain': 'OJEU Contract Type',\n 'title': 'Title',\n 'value_high': 'Awarded Date',\n 'value_low': 'Value High',\n }","title":""},{"docid":"36d9e826a8effd4f93a35f7386b37371","score":"0.5565586","text":"def getall(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"36d9e826a8effd4f93a35f7386b37371","score":"0.5565586","text":"def getall(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"26a710f8c06e4f042143d2650ee4890d","score":"0.55651474","text":"def _validate_results(self):\n pass","title":""},{"docid":"c556656a02ebe5f64396ac2ca36dafe1","score":"0.5555562","text":"def CheckAll(self):\r\n for ii in xrange(self.GetItemCount()):\r\n self.CheckItem(ii,True)","title":""},{"docid":"102d37a700710c79040530ac57e320ae","score":"0.55473876","text":"def requestAll():\n pass","title":""},{"docid":"bbd4ff7dd6b17bc3a5341aca549565ac","score":"0.55456877","text":"def _emptyRawResults(self):\n self._rawresults = {}","title":""},{"docid":"21b9e57d8e6dd3c1751f469703756d4e","score":"0.553741","text":"def get_allresults(self):\r\n where = \"\"\r\n values = ()\r\n return Caremanagerzip.select_many(where, values)","title":""},{"docid":"ebe79b1f6ec5b8b583c12aec2227bb8c","score":"0.5533506","text":"def check_results(self):\n pass","title":""},{"docid":"ebe79b1f6ec5b8b583c12aec2227bb8c","score":"0.5533506","text":"def check_results(self):\n pass","title":""},{"docid":"92e0d60b27be875ef157edb649f13846","score":"0.55325264","text":"def test_all(self):\n all = storage.all()\n self.assertIsInstance(all, dict)","title":""},{"docid":"0428238d7b608a976582b176a47a9f87","score":"0.5532113","text":"def _get_missing_data(self, c, db_data, entries_required):\n parameters = self._requests_parameters(entries_required,\n settings.ROWS_LIMIT)\n reject_values = [to_string(day) for day in\n list_values(db_data, self.__column)]\n url = self.__api_url.safe_substitute(coin=self.__currency)\n worker = ApiWorker(\n self.__db, url, parameters, self.__modifications, self.__table,\n {'currency': c}, self.__column, reject_values\n )\n new_data, error = worker.data_one_to_many_or_no_relationship()\n return new_data, error","title":""},{"docid":"6ae61d3df7046f36f3d7ec52255684a7","score":"0.5526723","text":"def all(self):\n return self._items","title":""},{"docid":"fd585de38f8722a2dce2cda02eee3298","score":"0.55102766","text":"def on_get(self, req, resp):\n rv = self._service.all()\n resp.body = self._json_or_404(rv)","title":""},{"docid":"0cf25196892c1aa51e025356409a0615","score":"0.55056417","text":"def test_set_many_returns_empty_list_on_success(self):\n failing_keys = cache.set_many({\"key1\": \"spam\", \"key2\": \"eggs\"})\n self.assertEqual(failing_keys, [])","title":""},{"docid":"293975a783fbdf17d56480e30dcfee56","score":"0.55036354","text":"def test_fetch_all_items(self):\n event = {\"body\": None}\n result = todo_handler.fetch_items(event, None)\n self.assertEqual(result[\"statusCode\"], 200)","title":""}],"string":"[\n {\n \"docid\": \"b5acbfbedb9e1524417d1e5e14283e4f\",\n \"score\": \"0.7266865\",\n \"text\": \"def all_entries(self) -> requests.models.Response:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"151603cea417ba3dabcb63e9d0ba8d72\",\n \"score\": \"0.7057368\",\n \"text\": \"def getAllEntries(self):\\n try:\\n self.data.entryList = self.APP.getAllEntries()\\n if not self.__sendAppOperationResult(lambda: self.data.entryList is not None,\\n ControllerLogicException('Load entries failed.')):\\n return\\n except Exception, e:\\n self.__sendError(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1140cee3127b7a665943779f6ac9713b\",\n \"score\": \"0.68558085\",\n \"text\": \"def all(self):\\n self.scan()\\n return self.entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fa33d70eb0c45040ec4be65d0f962f8\",\n \"score\": \"0.6569235\",\n \"text\": \"def get(self):\\n return get_all_entries()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e38b26abe2f00ae481e46756657afb0d\",\n \"score\": \"0.6526549\",\n \"text\": \"def get_entries(self) -> Generator[tuple, None, None]:\\n for entry in self.entries_batch:\\n yield entry\\n\\n self.entries_batch = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfc93173820ff5d608d5508be434da0c\",\n \"score\": \"0.6519066\",\n \"text\": \"def get_all_entries(self):\\n\\n self.refresh()\\n while True:\\n for entry in self.entries:\\n yield entry\\n\\n # Navigate to next page - HTTP call\\n if self.next() is None:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"508efee73a1728fda5a7d7bbc053e1e9\",\n \"score\": \"0.6487228\",\n \"text\": \"def get_all_entries():\\n entries = Entry.objects.all()\\n return entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7c2253ebdf3305ee8361aed2414aab8\",\n \"score\": \"0.6355247\",\n \"text\": \"def all(self):\\n self.scan()\\n return list(self.entries)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10d3aa0e5219be23c38d70d2faa7a9e9\",\n \"score\": \"0.6343515\",\n \"text\": \"def get_all(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e144f30655b64b59c5dd1815d6186ccf\",\n \"score\": \"0.63129425\",\n \"text\": \"def get_all_items(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a09470d44b62e478a3fa35fc04d187fb\",\n \"score\": \"0.62576747\",\n \"text\": \"def get_all(self):\\n self.get_new_entries()\\n self.get_changed_entries()\\n self.get_removed_entries()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e12436e4249ed88b5e4636ace39fbd1\",\n \"score\": \"0.6249732\",\n \"text\": \"def get_all(self):\\n raise NotImplemented()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b8360a16f7ca49f57c2bb2ad5a6581\",\n \"score\": \"0.6216486\",\n \"text\": \"def test_get_all_entries(self):\\n\\n response = self.client.get('/api/v1/entries',\\n content_type='application/json',\\n data=json.dumps(entry1))\\n self.assertEqual(response.status_code, 200)\\n self.assertIn('Technical Leader', str(response.data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acefbb95634855a960aa2b06fe50733a\",\n \"score\": \"0.6166555\",\n \"text\": \"def get_all_records(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26d2f055d02402577a612db3cbc6caec\",\n \"score\": \"0.61484027\",\n \"text\": \"def test_get_all_entries(self):\\n\\n testing_user = app.test_client(self)\\n\\n # Add first entry\\n res = testing_user.post('/api/v1/entries',\\n data=json.dumps(self.my_entries[0]),\\n content_type='application/json')\\n self.assertEqual(res.status_code, 201)\\n self.assertIn('Your memory entitled ' + self.my_entries[0]['title'] +\\n ' has been saved', str(res.data))\\n\\n # Add second entry\\n response = testing_user.post('/api/v1/entries',\\n data=json.dumps(self.my_entries[1]),\\n content_type='application/json')\\n self.assertEqual(res.status_code, 201)\\n self.assertIn('Your memory entitled ' + self.my_entries[1]['title'] +\\n ' has been saved', str(response.data))\\n\\n # Get all entries\\n final_response = testing_user.get('/api/v1/entries')\\n self.assertEqual(final_response.status_code, 200)\\n self.assertIn('Learning Flask', str(final_response.data))\\n self.assertIn('My first Flask API', str(final_response.data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c88e6c9bffa2edf8ce8ddc6b4d2e7e1\",\n \"score\": \"0.6137212\",\n \"text\": \"def all(self):\\n return self._perform_query()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3477b6370bd0255f3ad55937aba438c\",\n \"score\": \"0.6130469\",\n \"text\": \"def all(self):\\n return self.many()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c0a297d51ee3bdd7a06ecf8b68d7923\",\n \"score\": \"0.612449\",\n \"text\": \"def _fetch_all(self):\\n if self._result_cache is None:\\n self._result_cache = list(self.iterator())\\n if not isinstance(self, ValuesListQuerySet):\\n for x in self._result_cache:\\n self._set_query_time(x)\\n if self._prefetch_related_lookups and not self._prefetch_done:\\n self._prefetch_related_objects()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd7e9c236d0c0319a066e981e180ed47\",\n \"score\": \"0.61147547\",\n \"text\": \"def GetAll(self):\\n return self.ok + self.GetAllBroken()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd926c005ff344fbee36cfbf72cb4cd0\",\n \"score\": \"0.6110133\",\n \"text\": \"def _fetch_all(self):\\n if self._result_cache is None:\\n self._result_cache, self._pagination_item = self.model.get(self.request_options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55dec3a91ce2bbebac8afe451f850693\",\n \"score\": \"0.6080038\",\n \"text\": \"def test_read_all(self):\\n with patch.object(LogEntries, \\\"read\\\",\\n side_effect=self._get_entries):\\n # assert returning all sorted entries\\n entries = LogEntries.read_all(list(self._get_entries_dict().keys()),\\n None, None, None)\\n self.assertEqual(len(entries), 9)\\n for i in range(len(entries) - 1):\\n self.assertLessEqual(entries[i][\\\"time\\\"],\\n entries[i + 1][\\\"time\\\"])\\n\\n # assert returning last 3 sorted entries\\n entries = LogEntries.read_all(list(self._get_entries_dict().keys()),\\n 3, None, None)\\n self.assertEqual(len(entries), 3)\\n expected_entries = [200, 600, 1000]\\n for i in range(len(entries) - 1):\\n # assert ordering\\n self.assertLessEqual(entries[i][\\\"time\\\"],\\n entries[i + 1][\\\"time\\\"])\\n # assert values\\n self.assertEqual(entries[i][\\\"time\\\"], expected_entries[i])\\n self.assertEqual(entries[i+1][\\\"time\\\"], expected_entries[i+1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d00effe8da0d6965aa9b0aee15b104\",\n \"score\": \"0.60755086\",\n \"text\": \"def get_entries():\\n data = Diary.all_entries()\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3adc7b3b70576031e93ca35999f393da\",\n \"score\": \"0.6066714\",\n \"text\": \"def get_all(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3adc7b3b70576031e93ca35999f393da\",\n \"score\": \"0.6066714\",\n \"text\": \"def get_all(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3adc7b3b70576031e93ca35999f393da\",\n \"score\": \"0.6066714\",\n \"text\": \"def get_all(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1ff18e18ca7e02635504b6d58f2295f\",\n \"score\": \"0.60474014\",\n \"text\": \"def all(self):\\n with self._lock:\\n self.update()\\n\\n return list(self.entries)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf62aef74ebace865132164fc86a403a\",\n \"score\": \"0.6045557\",\n \"text\": \"def has_all_entries(self):\\n for entry in self.entries:\\n self.related_entry_ids.update(entry.get_related_entries())\\n self.related_entry_ids.add(entry.id)\\n if set(self.related_entry_ids) == set([entry.id for entry in self.entries]):\\n return True\\n else:\\n print 'Study', id, 'does not have all entries', set(self.related_entry_ids), set([entry.id for entry in self.entries])\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0d951e59a0e7292b0e921618ed26f62\",\n \"score\": \"0.601289\",\n \"text\": \"def fetch(self) -> list[Entry]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f5653134c7333ac74551e33c9740853\",\n \"score\": \"0.5974942\",\n \"text\": \"def all() -> dict:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ef3c2ecbbb8cb7eb306396200d95024\",\n \"score\": \"0.5950022\",\n \"text\": \"def get_entries(self) -> Generator[str, None, None]:\\n return (entry for entry in self.entries)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54973ce35943e3c38300e700dad39c91\",\n \"score\": \"0.5945292\",\n \"text\": \"def fetchall(self):\\n raise NotImplementedError('Method not implemented.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c3b2eca379418af3e13aa23352b4a94\",\n \"score\": \"0.59449697\",\n \"text\": \"def _get_results(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d39b400093177d6242184752bd95f1\",\n \"score\": \"0.59397185\",\n \"text\": \"def test_all_empty(self):\\n\\n nutrition = MemoryNutritionStore()\\n self.assertEqual({ }, nutrition.all())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c025e6597f6840dce3a0a5fa39737957\",\n \"score\": \"0.59388345\",\n \"text\": \"def all(self) -> dict:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ac57927482ae5ff1d0a99476e2f865a\",\n \"score\": \"0.5933919\",\n \"text\": \"def get_entries(self):\\n return self.entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37c3254db7dfc91a82e6fe9467b0dcdb\",\n \"score\": \"0.5930503\",\n \"text\": \"def fetchall(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52f07dd693782254758243910bf44548\",\n \"score\": \"0.5916642\",\n \"text\": \"def test_entries(self):\\n self.assertEqual(len(self.entries), 25)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8db8e7df3a91186728da2daa09586a5d\",\n \"score\": \"0.5894018\",\n \"text\": \"def get_all_library_entries():\\n key = 'library_entries'\\n data = cache.get(key)\\n if data is None:\\n try:\\n data = models.ApplicationLibraryEntry.objects.filter(listing__is_deleted=False).all()\\n cache.set(key, data)\\n return data\\n except ObjectDoesNotExist:\\n return None\\n else:\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2653d04dc013f02a87b9a94af4dcf7e2\",\n \"score\": \"0.5882078\",\n \"text\": \"def entries(self):\\n return self._entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"635397129a843d61780e474acebd5a07\",\n \"score\": \"0.5880765\",\n \"text\": \"def get_all_data(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21ec344036edd163caaf2fa6862ccf84\",\n \"score\": \"0.58756435\",\n \"text\": \"def test_getall(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b1103d01b49838bd90680bfa2fa702e\",\n \"score\": \"0.5836943\",\n \"text\": \"def get_all(self):\\n\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b1103d01b49838bd90680bfa2fa702e\",\n \"score\": \"0.5836943\",\n \"text\": \"def get_all(self):\\n\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d55610edf56149e4f189fb9a0420ce6\",\n \"score\": \"0.5823098\",\n \"text\": \"def test_find_all_return_empty_list():\\n all_entries = rldb.find_all({ 'env-title': 'There is no env with this title.' })\\n\\n assert all_entries == []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c953ca53d9e516e498bbd168b9f7285\",\n \"score\": \"0.58151126\",\n \"text\": \"def get_all_for_all(self):\\n\\n for name, rs in self.result_sets.items():\\n print(\\\"Retrieving {}s for result set {}...\\\".format(self.item, name))\\n self.get_all_items(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c08bc29b8130efab99fe58edf1975d4\",\n \"score\": \"0.5813334\",\n \"text\": \"def _get_entries(self, query):\\n # @see http://www.solrtutorial.com/solr-query-syntax.html\\n return self._kibana.get_rows(match={\\\"@exception.class\\\": \\\"Wikia\\\\\\\\Util\\\\\\\\AssertionException\\\"}, limit=self.LIMIT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f81be40393f2c4d6f97b57f2def8730f\",\n \"score\": \"0.58030194\",\n \"text\": \"def fetchall(self):\\n out = self._results[:]\\n self._results = []\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c15db1d5b5d20654669e10701db451f\",\n \"score\": \"0.57954216\",\n \"text\": \"def all(self) -> Iterable[\\\"Row\\\"]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147940dd0eb713d6564cc769d0fea4ea\",\n \"score\": \"0.57885486\",\n \"text\": \"def all(self):\\n return self.multi()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22fed8c1386721bb5acd9c28b742684b\",\n \"score\": \"0.5781892\",\n \"text\": \"def get_all(self):\\n return imap(self.get_one, ifilter(None, self.list()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71c2424c45edc9bef93928b9bd78e287\",\n \"score\": \"0.57688826\",\n \"text\": \"def all(self):\\n return [record for record in iter(self)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4dd194f2629aeb1d13ab85de93a4443\",\n \"score\": \"0.57681453\",\n \"text\": \"def get_all_entries(self, slug: str, batch_size: int = 10) -> Iterable[Entry]:\\n offset = 0\\n entries_batch = self.get_entries(slug, limit=batch_size, offset=offset)\\n while entries_batch:\\n yield from entries_batch\\n offset += batch_size\\n entries_batch = self.get_entries(slug, limit=batch_size, offset=offset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a6c99a887b406c94c789281f8170476\",\n \"score\": \"0.5756834\",\n \"text\": \"def get_entries(self):\\n\\n\\t\\treturn self.__entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc32de4e88ee73fcde536b94daf09ea4\",\n \"score\": \"0.57488054\",\n \"text\": \"def validateAllDataItems(self):\\n return self.dataStore.validateAllDataItems()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50b2a92a3568ccc67ec1db535189e61d\",\n \"score\": \"0.5737981\",\n \"text\": \"def iterentries(self):\\n raise NotImplementedError(self.iterentries)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65e723eb7b341aa7f70d3fe77fd3e9e7\",\n \"score\": \"0.57354146\",\n \"text\": \"def all_entries():\\n s = Session()\\n\\n try:\\n entries_list = s.query(Entry).all()\\n s.close()\\n return render_template(\\\"entries/entries_list.html\\\", entries=entries_list)\\n except Exception as e:\\n return str(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1932615a364c14bf335b501c2f8f50a8\",\n \"score\": \"0.5729247\",\n \"text\": \"def getAllOrNone(self):\\n return self.__allOrNone\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1375a1c6a839613dfa00215f808fe747\",\n \"score\": \"0.5728215\",\n \"text\": \"def get_entries(self):\\n return self.__entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1375a1c6a839613dfa00215f808fe747\",\n \"score\": \"0.5728215\",\n \"text\": \"def get_entries(self):\\n return self.__entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c280454ea6baedd522a64a308cf32a91\",\n \"score\": \"0.5722788\",\n \"text\": \"def get_all():\\n res = get_aps()\\n if res is None:\\n return flask.Response('Organization not found', 404)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e69d8f59b3aeefeffbeb49475cc395ad\",\n \"score\": \"0.5720823\",\n \"text\": \"def test_all(self):\\n self.objs = self.storage.all()\\n self.assertIsNotNone(self.objs)\\n self.assertEqual(type(self.objs), dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e69d8f59b3aeefeffbeb49475cc395ad\",\n \"score\": \"0.5720823\",\n \"text\": \"def test_all(self):\\n self.objs = self.storage.all()\\n self.assertIsNotNone(self.objs)\\n self.assertEqual(type(self.objs), dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd36a8943e7c0bfb9389eeb133cd7d78\",\n \"score\": \"0.5720548\",\n \"text\": \"def all(self):\\n self.query()\\n return self.list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5ee4eab50f885f6bf909fc5e1c9692b\",\n \"score\": \"0.57047886\",\n \"text\": \"def _all(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c09f395ca324c4b1a107106e058810ae\",\n \"score\": \"0.57030624\",\n \"text\": \"def all():\\n return list()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80758f8a539f3723cb7d10823a5ba77b\",\n \"score\": \"0.56985116\",\n \"text\": \"def all(self) -> dict[str, _t.Any]: # pragma: no cover\\n return self.get_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40ad81d997f1200865f8b5b6b8bfc98f\",\n \"score\": \"0.56823474\",\n \"text\": \"def fetchall(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40536cef1a989823dd85581f29555f9b\",\n \"score\": \"0.56790763\",\n \"text\": \"def get_all_records(self) -> Tuple[Optional[List[Records]], Optional[Exception]]:\\n return self.get_all_objects(Records)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de76411e01d9d399573017b03d6dff2e\",\n \"score\": \"0.56751245\",\n \"text\": \"def _validate_market_data_entries(entries):\\n if entries is None:\\n entries = [entry for entry in MarketDataEntry]\\n else:\\n for entry in entries:\\n if not isinstance(entry, MarketDataEntry):\\n print(\\\"WARNING: Market Data Entry not defined: \\\" + str(entry))\\n\\n return entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdd72b8bdf466ba1b4c1db1ac658bb1c\",\n \"score\": \"0.5673406\",\n \"text\": \"def _collect_results(self, items):\\r\\n for item in items:\\r\\n if not utils.is_url(item[\\\"link\\\"]):\\r\\n continue\\r\\n if item in self.results:\\r\\n continue\\r\\n if self.ignore_duplicate_urls and item[\\\"link\\\"] in self.results.links():\\r\\n continue\\r\\n if self.ignore_duplicate_domains and item[\\\"host\\\"] in self.results.hosts():\\r\\n continue\\r\\n self.results.append(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48f8674942c023df087b892ef6cfcc2a\",\n \"score\": \"0.5658856\",\n \"text\": \"def get_all_ids(self):\\n while True:\\n results = self.get_ids()\\n ids = results[\\\"id_set\\\"]\\n if not ids - self.article_ids:\\n break\\n self.article_ids = self.article_ids | ids\\n if len(self.article_ids) >= results[\\\"result_count\\\"]:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d23483c5246ae5acba008f0d1938dba1\",\n \"score\": \"0.5650488\",\n \"text\": \"def retrieve_all(self, key):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84e288d3572c28a5b2bc4622b49218c9\",\n \"score\": \"0.5641535\",\n \"text\": \"def get_all_items(self) -> Iterable[\\\"Item_Type\\\"]:\\n if self.assert_conforms_to(ConformanceClasses.ITEM_SEARCH):\\n yield from self.get_items()\\n else:\\n yield from super().get_items()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f344706ceae6dda9e6b5e17112715621\",\n \"score\": \"0.5628937\",\n \"text\": \"def get_entries(self):\\n return filter(\\n lambda e: not e.name.endswith(self.empty_index),\\n self.entries\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1dbcc52b323a4375656f71e5af724f1\",\n \"score\": \"0.56237346\",\n \"text\": \"def All(self) -> List[dict]:\\r\\n # Default return if nothing matches the query\\r\\n if len(self.results) < 1:\\r\\n return []\\r\\n\\r\\n return self.__get_unindexed_all_results()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb8aca500c6135ce12baf6b6c5c8665\",\n \"score\": \"0.5622763\",\n \"text\": \"def get_all(self) -> dict[str, _t.Any]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e028b389d835bc29da31f863604d9cdd\",\n \"score\": \"0.5619233\",\n \"text\": \"def entries():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e028b389d835bc29da31f863604d9cdd\",\n \"score\": \"0.5619233\",\n \"text\": \"def entries():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e30e1a2ba33f9419a8e383333e8538cd\",\n \"score\": \"0.56087714\",\n \"text\": \"def test_all_items(self):\\n logging.info(\\\"Start test case: get all items\\\")\\n get_items = get_all_items()\\n assert get_items.status_code == 200, f\\\"The status_code is {get_items.status_code}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"919a2bb51208826d11baeef10a327bff\",\n \"score\": \"0.5602807\",\n \"text\": \"def query_all(self) -> List[Any]:\\n return [v for v in self.batch_query(self.query_keys()) if v is not None]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6611cbc29a55f641d06fdb7fbff46c4f\",\n \"score\": \"0.559653\",\n \"text\": \"def __iter__(self):\\n return self.all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3cd3ae4bc4102406c84db58261e60f1\",\n \"score\": \"0.5594151\",\n \"text\": \"def all(self):\\n return [(key, self[key]) for key in self.map.keys() \\\\\\n if self[key] is not None and len(self[key])]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd229cfe9c7a9e21557899d22e256c87\",\n \"score\": \"0.5584198\",\n \"text\": \"def getAll(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"580c1d7e78593508cb7a235f0243173c\",\n \"score\": \"0.55834055\",\n \"text\": \"def get_all_data(self) -> Optional[list[dict]]:\\n data = []\\n\\n try:\\n entries = self.db.collection(constants.COLLECTION).get()\\n\\n for entry in entries:\\n if entry.exists:\\n data.append(entry.to_dict())\\n\\n except FirebaseError:\\n raise DataDidNotLoadError\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d5bf396d3a4d29da148574892acdead\",\n \"score\": \"0.55720246\",\n \"text\": \"def test_get_entries():\\n entries = get_entries()\\n assert len(entries) == 1\\n assert entries[0] == {\\n 'accelerated_justification': 'Closing Time',\\n 'additional_text': 'Additional Text',\\n 'attachments': 'Attachments',\\n 'awarded_date': 'Awarded Value',\\n 'awarded_value': 'Supplier [Name|Address|Ref type|Ref Number|Is SME|Is VCSE]',\\n 'closing_date': 'Closing Date',\\n 'closing_time': 'Is sub-contract',\\n 'contact_address_1': 'Contact Address 1',\\n 'contact_address_2': 'Contact Address 2',\\n 'contact_country': 'Contact Country',\\n 'contact_email': 'Contact Email',\\n 'contact_name': 'Contact Name',\\n 'contact_postcode': 'Contact Postcode',\\n 'contact_telephone': 'Contact Telephone',\\n 'contact_town': 'Contact Town',\\n 'contact_website': 'Contact Website',\\n 'contract_end_date': 'OJEU Procedure Type',\\n 'contract_start_date': 'Contract end date',\\n 'cpv_codes': 'Cpv Codes',\\n 'description': 'Description',\\n 'end_date': 'End Date',\\n 'is_sub_contract': 'Parent Reference',\\n 'links': 'Links',\\n 'nationwide': 'Nationwide',\\n 'notice_identifier': 'Notice Identifier',\\n 'notice_type': 'Notice Type',\\n 'ojeu_contract_type': 'Value Low',\\n 'ojeu_procedure_type': 'Accelerated Justification',\\n 'organisation_name': 'Organisation Name',\\n 'parent_reference': 'Suitable for SME',\\n 'postcode': 'Postcode',\\n 'published_date': 'Published Date',\\n 'region': 'Region',\\n 'start_date': 'Start Date',\\n 'status': 'Status',\\n 'suitable_for_sme': 'Suitable for VCO',\\n 'suitable_for_vco': 'Supply Chain',\\n 'supplier_contact_name': 'Contract start date',\\n 'supplier_info': \\\"Supplier's contact name\\\",\\n 'supply_chain': 'OJEU Contract Type',\\n 'title': 'Title',\\n 'value_high': 'Awarded Date',\\n 'value_low': 'Value High',\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d9e826a8effd4f93a35f7386b37371\",\n \"score\": \"0.5565586\",\n \"text\": \"def getall(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d9e826a8effd4f93a35f7386b37371\",\n \"score\": \"0.5565586\",\n \"text\": \"def getall(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26a710f8c06e4f042143d2650ee4890d\",\n \"score\": \"0.55651474\",\n \"text\": \"def _validate_results(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c556656a02ebe5f64396ac2ca36dafe1\",\n \"score\": \"0.5555562\",\n \"text\": \"def CheckAll(self):\\r\\n for ii in xrange(self.GetItemCount()):\\r\\n self.CheckItem(ii,True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"102d37a700710c79040530ac57e320ae\",\n \"score\": \"0.55473876\",\n \"text\": \"def requestAll():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbd4ff7dd6b17bc3a5341aca549565ac\",\n \"score\": \"0.55456877\",\n \"text\": \"def _emptyRawResults(self):\\n self._rawresults = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21b9e57d8e6dd3c1751f469703756d4e\",\n \"score\": \"0.553741\",\n \"text\": \"def get_allresults(self):\\r\\n where = \\\"\\\"\\r\\n values = ()\\r\\n return Caremanagerzip.select_many(where, values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebe79b1f6ec5b8b583c12aec2227bb8c\",\n \"score\": \"0.5533506\",\n \"text\": \"def check_results(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebe79b1f6ec5b8b583c12aec2227bb8c\",\n \"score\": \"0.5533506\",\n \"text\": \"def check_results(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92e0d60b27be875ef157edb649f13846\",\n \"score\": \"0.55325264\",\n \"text\": \"def test_all(self):\\n all = storage.all()\\n self.assertIsInstance(all, dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0428238d7b608a976582b176a47a9f87\",\n \"score\": \"0.5532113\",\n \"text\": \"def _get_missing_data(self, c, db_data, entries_required):\\n parameters = self._requests_parameters(entries_required,\\n settings.ROWS_LIMIT)\\n reject_values = [to_string(day) for day in\\n list_values(db_data, self.__column)]\\n url = self.__api_url.safe_substitute(coin=self.__currency)\\n worker = ApiWorker(\\n self.__db, url, parameters, self.__modifications, self.__table,\\n {'currency': c}, self.__column, reject_values\\n )\\n new_data, error = worker.data_one_to_many_or_no_relationship()\\n return new_data, error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ae61d3df7046f36f3d7ec52255684a7\",\n \"score\": \"0.5526723\",\n \"text\": \"def all(self):\\n return self._items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd585de38f8722a2dce2cda02eee3298\",\n \"score\": \"0.55102766\",\n \"text\": \"def on_get(self, req, resp):\\n rv = self._service.all()\\n resp.body = self._json_or_404(rv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cf25196892c1aa51e025356409a0615\",\n \"score\": \"0.55056417\",\n \"text\": \"def test_set_many_returns_empty_list_on_success(self):\\n failing_keys = cache.set_many({\\\"key1\\\": \\\"spam\\\", \\\"key2\\\": \\\"eggs\\\"})\\n self.assertEqual(failing_keys, [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"293975a783fbdf17d56480e30dcfee56\",\n \"score\": \"0.55036354\",\n \"text\": \"def test_fetch_all_items(self):\\n event = {\\\"body\\\": None}\\n result = todo_handler.fetch_items(event, None)\\n self.assertEqual(result[\\\"statusCode\\\"], 200)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994083,"cells":{"query_id":{"kind":"string","value":"d5d1ba5f2a340e997fd179d7fbbeb007"},"query":{"kind":"string","value":"The rsquared value (a.k.a. coefficient of determination)"},"positive_passages":{"kind":"list like","value":[{"docid":"ac093f765728612363ed6781c28d505b","score":"0.0","text":"def r_squared(A, b):\n ssr = sum_of_squared_residuals(A, b)\n sst = total_sum_of_squares(A, b)\n return 1.0 - ssr / sst","title":""}],"string":"[\n {\n \"docid\": \"ac093f765728612363ed6781c28d505b\",\n \"score\": \"0.0\",\n \"text\": \"def r_squared(A, b):\\n ssr = sum_of_squared_residuals(A, b)\\n sst = total_sum_of_squares(A, b)\\n return 1.0 - ssr / sst\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"72fac2c5ab96a955bb8597aa092c64df","score":"0.7222706","text":"def rsq(self):\n return metrics.rsq(self.model, self.x, self.y)","title":""},{"docid":"47d63c0cfd2b0557ff484c91495f6744","score":"0.70095164","text":"def get_res_rsquare(self, res_type: str) -> float:\n return self.config['resistor']['info'][res_type]['rsq']","title":""},{"docid":"21855c2964f2efb00acc5b223af76ac0","score":"0.68036026","text":"def rsquared(predictions, actual):\n ybar = 1/len(actual)*actual.sum()\n\n var_yhat = predictions.var()\n var_y = actual.var()\n return var_yhat/var_y","title":""},{"docid":"329213ae4abfed64e7373564dc738bb8","score":"0.66765714","text":"def residual(self):\n if self.expr.value is None:\n return None\n return np.abs(self.expr.value)","title":""},{"docid":"329213ae4abfed64e7373564dc738bb8","score":"0.66765714","text":"def residual(self):\n if self.expr.value is None:\n return None\n return np.abs(self.expr.value)","title":""},{"docid":"797fa1000f6c7b0c76177c2d73b70241","score":"0.6674307","text":"def dQR(self):\n return math.sqrt(self.d2(self.q, self.r))","title":""},{"docid":"c123fd3a2e65923d1fedaa96d35215e4","score":"0.6543117","text":"def getResidual(self):\n\n return self.eval()","title":""},{"docid":"767ae89be29b482fe10ca0c0f10d1a32","score":"0.6506872","text":"def compute_Rsquared(y1,y2):\n sum_var = np.sum( (y1-np.mean(y1))**2 )\n sum_res = np.sum( (y1-y2)**2 ) \n return float(1)-float(sum_res)/sum_var","title":""},{"docid":"adc204316eb500a0c936f855aaa53635","score":"0.65018874","text":"def calculate_r(self):\n\n return np.exp(-(self.mc.x-self.centers_x)**2/(self.sigmax**2) - (self.mc.x_d-self.centers_xd)**2/(self.sigmay**2))","title":""},{"docid":"36ff3d005be0c85ac09ce385ee238bc2","score":"0.6469571","text":"def compute_rsquared(self):\n # First read the total sum of squares for each run\n total_sumsquares = self.sum_squares(\"sstot\")\n\n for part in [\"full\", \"main\"]:\n\n # Read in the residual sum of squares and take grand sum\n res_sumsquares = self.sum_squares(\"ssres\", part)\n\n # Calculate the full model R2\n r2 = 1 - res_sumsquares / total_sumsquares\n\n # Save an image with these data\n self.save_image(r2, \"r2_\" + part)","title":""},{"docid":"b6a62136e06437f44ca2627da1a02126","score":"0.64525515","text":"def rmse(self):\n return math.sqrt(self.mse())","title":""},{"docid":"91bed44d2e771daa15a03eaa3ca6e77b","score":"0.6398252","text":"def _Rr(self, r):\r\n return np.sqrt(r / (self.M * self.N *\r\n (self.Tmn_meas * self._weights).max()**2))","title":""},{"docid":"bbfbee56f58828a92a3382f34a1a28f4","score":"0.6378755","text":"def residual(self):\n if self.expr.value is None:\n return None\n return np.maximum(self.expr.value, 0)","title":""},{"docid":"bbfbee56f58828a92a3382f34a1a28f4","score":"0.6378755","text":"def residual(self):\n if self.expr.value is None:\n return None\n return np.maximum(self.expr.value, 0)","title":""},{"docid":"2a7008e723a7d6e8d2251a2a98d94121","score":"0.6356604","text":"def rmse(self):\n return self.metrics.rootMeanSquaredError","title":""},{"docid":"117ce2876a1613428b7e089b9bb985dc","score":"0.6264112","text":"def calc_ri(self):\n\n # Function call.\n chi2 = self.func_mf(self.params)\n\n # Return the single value.\n return self.data[0].ri[0]","title":""},{"docid":"a388f5fc351b9557d2df2d321d90cda5","score":"0.62585545","text":"def get_rsquared(x, y):\n results = {}\n\n coeffs = np.polyfit(x, y, 1)\n\n # Polynomial Coefficients\n results['polynomial'] = coeffs.tolist()\n\n # r-squared\n p = np.poly1d(coeffs)\n # fit values, and mean\n yhat = p(x) # or [p(z) for z in x]\n ybar = np.sum(y)/len(y) # or sum(y)/len(y)\n ssreg = np.sum((yhat-ybar)**2) # or sum([ (yihat - ybar)**2 for yihat in yhat])\n sstot = np.sum((y - ybar)**2) # or sum([ (yi - ybar)**2 for yi in y])\n results['determination'] = ssreg / sstot\n rsquared=(ssreg/sstot)\n\n return rsquared","title":""},{"docid":"fe84229489d82dd645678eade051702b","score":"0.625804","text":"def rsq(model, x, y):\n yhat = model.predict(x)\n mse = keras.metrics.MeanSquaredError()\n mse(y, np.mean(y))\n sst = mse.result().numpy()\n mse = keras.metrics.MeanSquaredError()\n mse(y, yhat)\n sse = mse.result().numpy()\n return 1 - sse/sst","title":""},{"docid":"0eb22a51d22073aad9eb8b03840c307a","score":"0.6234662","text":"def r_squared(x, y):\n return (covariance(x,y))**2 / float(variance(x)*variance(y))","title":""},{"docid":"24c5ad95c02666f6c709161d954cef92","score":"0.62283397","text":"def get_rmse(self):\n try:\n rmse = ((2 * self.get_tsse()) / \n (len(self.training_vectors) * len(self.outputs)))\n except TypeError:\n # tsse returned None...\n return None\n return math.sqrt(rmse)","title":""},{"docid":"c2232b5e48f8c11e336a131c49080f1d","score":"0.6228258","text":"def r_squared(y, estimated):\n assert isinstance(y, pylab.ndarray)\n assert isinstance(estimated, pylab.ndarray)\n assert len(y) == len(estimated)\n\n mean_y = y.mean()\n res_ss = ((y - estimated) ** 2).sum()\n tot_ss = ((y - mean_y) ** 2).sum()\n r2 = 1 - (res_ss / tot_ss)\n\n return r2","title":""},{"docid":"b456a2ea4c88c25b77d1d66a84d7c356","score":"0.62168825","text":"def value(self) -> float:\n if self.norm:\n return math.sqrt(self.mean_square_error / self.norm)\n else:\n return None","title":""},{"docid":"b426b0de9ff6aae7a207ec2f6c8122f0","score":"0.62139463","text":"def residual(self):\n if self.expr.value is None:\n return None\n return np.abs(np.minimum(self.expr.value, 0))","title":""},{"docid":"524bef68be283e14e46262f2b3b37e6c","score":"0.62038237","text":"def rsqrt(self):\n return self.no_params_func(\"rsqrt\", return_response=True)","title":""},{"docid":"524bef68be283e14e46262f2b3b37e6c","score":"0.62038237","text":"def rsqrt(self):\n return self.no_params_func(\"rsqrt\", return_response=True)","title":""},{"docid":"3cd276773f9ef1afa8f35d3cb32d4cae","score":"0.61621875","text":"def rsq_adj(self):\n return metrics.rsq_adj(self.model, self.x, self.y)","title":""},{"docid":"7a551d4a4b728b3d36361cc2f5d799ef","score":"0.615304","text":"def r2_calculation(y_true, y_predicted):\n y_true = y_true.reshape(y_true.shape[0], 1)\n y_predicted = y_predicted.reshape(y_predicted.shape[0], 1)\n input_y_mean = np.mean(y_true, axis=0)\n ss_total = np.sum((y_true - input_y_mean) ** 2)\n ss_residual = np.sum((y_predicted - y_true) ** 2)\n r_square = 1 - (ss_residual / ss_total)\n return r_square","title":""},{"docid":"494a68bd2dc58895ae88b9ec718cfdb6","score":"0.6138141","text":"def crl(self, **kwargs):\n try:\n out = np.corrcoef(self.n, self.n+self.residual)[0,1]\n except:\n out = 0.\n\n if np.isfinite(out) is False or self.success is False:\n out = 0.\n return out","title":""},{"docid":"626ac8650d4001a2ac8e2096e81915de","score":"0.6123889","text":"def dPR(self):\n return math.sqrt(self.d2(self.p, self.r))","title":""},{"docid":"ec9c9d7bc2370b533feec7e66d875cb0","score":"0.61001736","text":"def rsq_cv(self, folds = 10):\n return metrics.rsq_cv(self.model, self.x, self.y, folds)","title":""},{"docid":"109128bb07992740166c1265c1ff8a62","score":"0.6097402","text":"def r_squared(output, trait, p, dataset):\n\n R2 = 1 - output.redchi / np.var(trait) # Convert reduced chi to R2\n n = len(dataset)\n\n adj_R2 = 1 - (1 - R2) * ((n - 1) / (n - p - 1))\n\n return adj_R2","title":""},{"docid":"02e52af532a14198b532483d61d11439","score":"0.6087016","text":"def mse(self):\n\t\txs, ys = self.R.nonzero()\n\t\tpredicted = self.full_matrix()\n\t\terror = 0\n\t\tfor x, y in zip(xs, ys):\n\t\t\terror += pow(self.R[x, y] - predicted[x, y], 2)\n\t\treturn np.sqrt(error)","title":""},{"docid":"f00001f748fff63970a43737489cf7a4","score":"0.6086425","text":"def __abs__(self) -> float:\r\n square_sum = sum([val**2.0 for val in self.values])\r\n return sqrt(square_sum)","title":""},{"docid":"3f828529d52da2be95b5a929a524822b","score":"0.60673237","text":"def _sklR2(self) :\n if self.yHat is None :\n self._sklPredict()\n self._R2 = metrics.r2_score(self.y, self.yHat)\n return self._R2","title":""},{"docid":"a72f513618d6c908cf2ad3c4a3de537f","score":"0.6065664","text":"def get_coefficient(self):\n self.get_header()\n return self.coefficient","title":""},{"docid":"9bb44bddb01dca3b9bd08ad26743d925","score":"0.6064083","text":"def get_rsquared_acv(cov, nsample_ratios, get_discrepancy_covariances):\n CF, cf = get_discrepancy_covariances(cov, nsample_ratios)\n if type(cov) == np.ndarray:\n try:\n rsquared = np.dot(cf, np.linalg.solve(CF, cf))/cov[0, 0]\n except:\n return np.array([0.0])*nsample_ratios[0]\n else:\n try:\n rsquared = torch.dot(cf, torch.mv(torch.inverse(CF), cf))/cov[0, 0]\n except:\n #print(\"Error computing inverse of CF\")\n return torch.tensor([0.0], dtype=torch.double)*nsample_ratios[0]\n return rsquared","title":""},{"docid":"826f0db44d70495078d80f6990691465","score":"0.60559535","text":"def r_squared(y, estimated):\r\n # TODO\r\n y, estimated = np.array(y), np.array(estimated)\r\n SEE = ((estimated - y)**2).sum()\r\n mMean = y.sum()/float(len(y))\r\n MV = ((mMean - y)**2).sum()\r\n return 1 - SEE/MV","title":""},{"docid":"b905af14728f9741979d0a6981efd778","score":"0.60468847","text":"def R(self) -> float:\n\n return self._R","title":""},{"docid":"06d7df0bd13c6b6a80ec8fab1a2fd334","score":"0.6036403","text":"def res(self):\n return self._read_cached_property('RES', float)","title":""},{"docid":"7c923b1bae75e2c93451a734c27dc28e","score":"0.6034592","text":"def r_squared(data: NDArray, data_p: NDArray) -> Number:\n\n return 1 - residual_sum_of_squares(data, data_p) / total_sum_of_squares(data)","title":""},{"docid":"719a1e9651275db0a6d3eb97e5e93674","score":"0.6023582","text":"def calc_r2(trueVal,predVal):\n if len(trueVal) == len(predVal):\n if np.nansum(trueVal) > 0:\n #trueVal_a = np.array(trueVal).ravel()\n #predVal_a = np.array(predVal).ravel()\n #matVal = np.array([trueVal_a,predVal_a]).transpose()\n #matVal = matVal[~np.isnan(matVal).any(1)]\n #matVal = matVal[~np.isnan(matVal).any(1)]\n #trueVal_f = matVal[:,0]\n #predVal_f = matVal[:,1]\n #r2=r2_score(trueVal_f, predVal_f)\n SSR = np.nansum((np.array(trueVal) - np.array(predVal))**2)\n #ybar = np.nanmean(np.array(trueVal))\n #SST = np.nansum((np.array(trueVal)-ybar)**2)\n SST = np.nansum(np.array(trueVal)**2)\n r2 = 1-(SSR/SST)\n else:\n return np.NaN\n else:\n print(\"ERROR: number of predicted and measured values must be equal; \\n n(predicted):\"+str(len(predVal))+\"\\n n(measured):\"+str(len(trueVal)))\n sys.exit(-1)\n return r2","title":""},{"docid":"777c882f8bf68b4d694727a27228b07b","score":"0.6015555","text":"def get_RMSE(x, y):\n return np.sqrt(get_MSE(x = x, y = y))","title":""},{"docid":"adcc3adc89dfb1c497a15e7d91801e27","score":"0.5998813","text":"def Rsquared(y1,y2):\n sum_var = np.sum( (y1-np.mean(y1))**2 )\n sum_res = np.sum( (y1-y2)**2 ) \n return float(1)-float(sum_res)/sum_var","title":""},{"docid":"3cd79e4dec4cecae7783b43aa3a54911","score":"0.5991861","text":"def calculate_score_1(self):\n chi2 = 0.\n residual_dicts = self.extrapolate.residual_dicts\n for detector in [self.residuals_source]:\n for axis in self.residuals_variables:\n residual = residual_dicts[detector][axis]\n for value in residual:\n chi2 += value**2/len(residual)\n self.best_guess[\"score\"] = {\"score\":chi2}\n return chi2","title":""},{"docid":"9d3ee0b72c6bc36a3a7deeec3d2731d4","score":"0.5989096","text":"def rmse(self):\r\n return np.round(self.mse() ** 0.5, 2)","title":""},{"docid":"9dc17e0f647a491cfc1a5bf5fe025b53","score":"0.59805596","text":"def get_regressor_r_score(self):\n return self.regressor_r_score","title":""},{"docid":"ee98ab7ef2bca3fc20e178b0a3672581","score":"0.59724665","text":"def get_r_squared(t, dof):\r\n\r\n\tt_squared = t * t\r\n\treturn t_squared / (t_squared + dof)","title":""},{"docid":"ecef1a22303389bc2d5ddf08f61bf471","score":"0.5969","text":"def R(self):\n return 287.058","title":""},{"docid":"7c159535025cd33f823e276f2668fec5","score":"0.5957302","text":"def sqrt(self):\n return self.__pow__(0.5)","title":""},{"docid":"61dcd90bf97a18e055ae189701f78fce","score":"0.595717","text":"def calculateRmse(self, actualData, predictedData):\n\n mse = mean_squared_error(actualData, predictedData)\n return sqrt(mse)","title":""},{"docid":"a7c08e4e4e3fbb815b6e55a4341b31dd","score":"0.59528166","text":"def mse(self):\n xs, ys = self.R.nonzero()\n predicted = self.full_matrix()\n error = 0\n for x, y in zip(xs, ys):\n error += pow(self.R[x, y] - predicted[x, y], 2)\n return np.sqrt(error)","title":""},{"docid":"a7c08e4e4e3fbb815b6e55a4341b31dd","score":"0.59528166","text":"def mse(self):\n xs, ys = self.R.nonzero()\n predicted = self.full_matrix()\n error = 0\n for x, y in zip(xs, ys):\n error += pow(self.R[x, y] - predicted[x, y], 2)\n return np.sqrt(error)","title":""},{"docid":"3cac7c33e810869c3e8d9c21c394497e","score":"0.5952339","text":"def fric_coeff(self):\n rn = self.reynolds_no()\n cf = 0.075/((math.log10(rn) - 2)**2)\n print(\"rn : %0.3f, cf : %0.5f\"%(rn, cf))\n return cf","title":""},{"docid":"36081732ecdca0f90e14dd0be20999d4","score":"0.59481925","text":"def value(self):\n nd1 = self.nd1()\n nd2 = self.nd2()\n f1 = nd1 * self.s \n f2 = nd2 * self.k * math.e ** (-self.r * self.t)\n return f1 - f2","title":""},{"docid":"1d56f0170a72d65338c32f2b09b1628d","score":"0.5946827","text":"def celly(self) -> float:\n return self._obj.res[1]","title":""},{"docid":"313f271c189ca148cdfdb5059fd68a6b","score":"0.59432983","text":"def score(self, x_test, y_test):\n\n #######################################################################\n # ** START OF YOUR CODE **\n #######################################################################\n\n return self.mse_r2_var(x_test, y_test)[0]\n\n #######################################################################\n # ** END OF YOUR CODE **\n #######################################################################","title":""},{"docid":"17fd11e49da5b65aa23b45d974467663","score":"0.5939473","text":"def _eval_rmse(self, mse: float) -> float:\n return np.sqrt(mse)","title":""},{"docid":"9db9a6b0886cb9df3f9323a1380755b4","score":"0.5928769","text":"def compute(self):\n return self.matthews_corr_coef / self.total","title":""},{"docid":"26f3d876fede6c4846209d5b05b45898","score":"0.5911921","text":"def resnik(self) -> float:\n return self._resnik_mica[0]","title":""},{"docid":"aa9ed7c890e9eb76ddb9493050cdc89f","score":"0.58806205","text":"def pseudo_r2(self):\n y_reg = self.yvals['model'][self.start_ind:]\n y_dat = self.y[self.start_ind:]\n \n SSres = ((y_dat - y_reg)**2).sum()\n SStot = ((y_dat - y_dat.mean())**2).sum()\n return 1 - SSres/SStot","title":""},{"docid":"ca485874773a91f1db3a768cd4bcb8c5","score":"0.5876993","text":"def getvalue(self):\n ce = self.solver.getValue(self.cvc_expr)\n rational = ce.getConstRational()\n numerator = int(rational.getNumerator().toString())\n denominator = int(rational.getDenominator().toString())\n if rational.isIntegral():\n return numerator // denominator\n else:\n return numerator / denominator","title":""},{"docid":"0488a2f3e858d856f51ce63eaef6318f","score":"0.5868227","text":"def residual(self):\n return self.product.length - self.sumCutLength()","title":""},{"docid":"3c00867b058eddd432fbdfd807738401","score":"0.5863317","text":"def get_length_sqrd(self) -> float:\r\n return self.x ** 2 + self.y ** 2","title":""},{"docid":"6c69fa90b57b74aa35702e4e37e5355b","score":"0.5854565","text":"def var(self):\n return self._sd ** 2","title":""},{"docid":"f3b70c55c0923728b745c9897a3dcbd5","score":"0.58493006","text":"def determinant(self):\n return float()","title":""},{"docid":"f3b70c55c0923728b745c9897a3dcbd5","score":"0.58493006","text":"def determinant(self):\n return float()","title":""},{"docid":"f63e51efcc05eae56a956c43d59dfb3b","score":"0.5837071","text":"def value(self) -> float:\n if self.random_mean_square:\n return (self.data_mean - self.random_mean) / math.sqrt(self.random_mean_square)\n else:\n return None","title":""},{"docid":"cae342d34afaa6e36b104e7176b71ee8","score":"0.5828752","text":"def evaluate_predicated(test, predict):\n rmse = math.sqrt(((test - predict) ** 2).sum()/test.shape[0])\n corr = np.corrcoef(predict, y=test)\n return rmse, corr[0, 1]","title":""},{"docid":"b36ce0f0d0cd1cb57a95c9d74ae6cfd5","score":"0.5828699","text":"def residual_risk(self, benchmark, rf_tick=\"$TNX\", tick=None):\r\n return np.std(self.residual_return(benchmark=benchmark, rf_tick=rf_tick, tick=tick))","title":""},{"docid":"ef2af29a8344c05730117cd04063d73b","score":"0.58236355","text":"def r2score(self, X, y):\n y_predict = self.predict(X)\n r2score = 1 - ((np.sum((y - y_predict)**2))/(np.sum((y - np.mean(y))**2)))\n return r2score","title":""},{"docid":"e3cdade95861a35c93c36e79715dadad","score":"0.58210474","text":"def reciprocal(self):\n return self.no_params_func(\"reciprocal\", return_response=True)","title":""},{"docid":"e3cdade95861a35c93c36e79715dadad","score":"0.58210474","text":"def reciprocal(self):\n return self.no_params_func(\"reciprocal\", return_response=True)","title":""},{"docid":"bdb8f34920b7d6fc4502657c8a74dc56","score":"0.58178693","text":"def evaluate(self) -> float:\n # TODO: finish\n return self.evaluate_by_material()","title":""},{"docid":"8cdc7bedf7ab13e5e1b9a5253eec9f3a","score":"0.5817181","text":"def squared_error(actual: np.array, predicted: np.array) -> np.array:\n\n return (actual - predicted)**2","title":""},{"docid":"716dd69a16337cf6a6a1312f095a1860","score":"0.5813695","text":"def p_R(q):\n return float(q[0] * q[4]**(-1))","title":""},{"docid":"716dd69a16337cf6a6a1312f095a1860","score":"0.5813695","text":"def p_R(q):\n return float(q[0] * q[4]**(-1))","title":""},{"docid":"b3d1b811a585c6a6e55d5b65e05d86d9","score":"0.5789468","text":"def residuals(self,Y): \n\n return (Y-np.dot(self._create_B(Y),self._create_Z(Y)))","title":""},{"docid":"1bd17615ca11e54e77f554cf652f1124","score":"0.5788133","text":"def result(self):\r\n return self.best.get() + (self.counteval, int(self.counteval/self.lam), self.xmean)","title":""},{"docid":"261f8adc854ac8b58567f44b4671a1ed","score":"0.57801414","text":"def rrse(actual: np.ndarray, predicted: np.ndarray):\n return np.sqrt(np.sum(np.square(actual - predicted)) / np.sum(np.square(actual - np.mean(actual))))","title":""},{"docid":"6209fb18bf8bc817b044392b8f96df57","score":"0.57765824","text":"def rmse(self,output):\r\n y_pred, y = output\r\n squared_errors = torch.pow(torch.abs(y_pred - y.view_as(y_pred)), 2)\r\n self._sum_of_errors_rmse += torch.sum(squared_errors).item()\r\n self._num_examples_rmse += y.shape[0]\r\n\r\n if self._num_examples_rmse == 0:\r\n raise NotComputableError(\"RootMeanSquaredError must have at least one example before it can be computed.\")\r\n return round(math.sqrt(self._sum_of_errors_rmse / self._num_examples_rmse), 3)","title":""},{"docid":"847ad5512ca77c7387cafad39c12e77f","score":"0.57648116","text":"def getResidual(self):\n res = self.u - self.u_Dt\n return res","title":""},{"docid":"8e1d4a2121f9a02e22521424adf03839","score":"0.5759966","text":"def norm_squared(self):\n absolutes = Array(self.length, self.value_type.value_type)\n\n if self.value_type.value_type == sbitfix:\n @for_range(absolutes.length)\n def _(i):\n absolutes[i] = self[i].square_abs()\n else:\n @for_range_parallel(absolutes.length, absolutes.length)\n def _(i):\n # absolutes[regint(i)] = self[regint(i)].square_abs()\n absolutes[i] = self[i].square_abs()\n norm_squared = sum(absolutes)\n return norm_squared","title":""},{"docid":"60629db67c0b2148d63acfd33d9b3eda","score":"0.57557845","text":"def rsqrt_(self):\n return self.no_params_func(\"rsqrt_\")","title":""},{"docid":"917a33ed003ead3eba237bb949d75f32","score":"0.5755451","text":"def get_regressor_r2_score(self):\n return self.regressor_r2_score","title":""},{"docid":"aff01260d5ef413bc9f751ed7f636f06","score":"0.57535493","text":"def var(self):\n if self.n <= 1:\n return np.zeros(self.shape)\n\n val = np.array((self._Ex2 - (self._Ex*self._Ex)/np.float64(self.n)) / np.float64(self.n-1.))\n\n return val","title":""},{"docid":"56b0ae522b33dd52921987f8ba7c8fe5","score":"0.57487357","text":"def c(self):\n return self._scalar","title":""},{"docid":"83147ffa4db817baf16f1d1ce43cbfbc","score":"0.5747919","text":"def result(self):\n return self.best.get() + (self.counteval,\n int(self.counteval / self.lam),\n self.xmean)","title":""},{"docid":"bf486d9522d6b294b4e44de6ab89f51e","score":"0.5743605","text":"def __call__(self):\n x, _ = self.y()\n error = self.r() - x\n return (error,)","title":""},{"docid":"7d927b3b8c6f4bebfae631184b58aa36","score":"0.57410794","text":"def _manualR2(self) :\n if self.yHat is None :\n self._manualPredict()\n yMean = (1.0 / self.y.size) * np.sum(self.y)\n self._R2 = 1.0 - np.sum((self.yHat - self.y)**2) / np.sum((self.y - yMean)**2)\n return self._R2","title":""},{"docid":"2d901eee5034fe3823a869ad6ac3099a","score":"0.5732048","text":"def r2(self):\n return norm(self.p2 - self.C)","title":""},{"docid":"5181a7366d2e9dbd030365a2fcfa671f","score":"0.5729349","text":"def vc(self,r):\n return numpy.sqrt(self.vc2(r))","title":""},{"docid":"bd05aa7058e5f4f0e55db99947635e10","score":"0.5723851","text":"def frobenius_norm(self):\n # check if A S and Y exist\n if hasattr(self,'A') and hasattr(self,'S') and hasattr(self,'Y'):\n if scipy.sparse.issparse(self.data):\n tmp = self.data[:,:] - ((self.A * self.S) * self.Y)\n tmp = tmp.multiply(tmp).sum()\n #err = tmp\n err = np.sqrt(tmp)\n else:\n #err = np.sum((self.data[:,:] - np.dot(np.dot(self.A, self.S), self.Y))**2 )\n err = np.sqrt( np.sum((self.data[:,:] - np.dot(np.dot(self.A, self.S), self.Y))**2 ))\n else:\n err = None\n\n return err","title":""},{"docid":"5d4196a0a931ae5e46f7eb5a1714dce7","score":"0.5720659","text":"def evaluate(self, X_test, y_test):\n y_pred = self.run().predict(X_test)\n rmse = compute_rmse(y_pred, y_test)\n # self.mlflow_log_param('model', \"LinearRegression\")\n # self.mlflow_log_param('rmse', rmse)\n return rmse","title":""},{"docid":"eaed03f90f4b886d3282fe514d521ce0","score":"0.57184035","text":"def risk(self):\n return 1 - self.accuracy()","title":""},{"docid":"a41d5423ccc1a20ec70e824f5a9d78f1","score":"0.57113546","text":"def calc_R(xc, yc):\n return sqrt((x-xc)**2 + (y-yc)**2)","title":""},{"docid":"95de0220335f98921c8fe0faec44a817","score":"0.56980366","text":"def r1(self):\n return norm(self.p1 - self.C)","title":""},{"docid":"df2f598d1654e392a8aa43f1c68952c7","score":"0.5687673","text":"def rmse(y_true, y_pred):\n res_sq = (y_pred - y_true) ** 2\n score = np.sqrt(sum(res_sq/len(y_true)))\n return score","title":""},{"docid":"ec641346c92c4108ec1e747da8264b6e","score":"0.56868243","text":"def __abs__(self):\n return math.sqrt(self*self)","title":""},{"docid":"d49c8293ce8ff24a5b5583f8b5b79b40","score":"0.56856936","text":"def CoefDetermination(ys, res):\n ybar, vary = thinkstats.MeanVar(ys)\n resbar, varres = thinkstats.MeanVar(res)\n return 1 - varres / vary","title":""},{"docid":"5d83586749c3c8b4ed56ccfe9033d895","score":"0.56839424","text":"def dGm_correction(self):\n return settings.RT * self._GetSumCoeff() * log(1e-3)","title":""},{"docid":"0b7408c9c70b90166ab20cd99212040d","score":"0.5680366","text":"def residuals(params,f_model,x_values,y_values):\n\n d_values = y_values - f_model(x_values,params)\n return d_values","title":""}],"string":"[\n {\n \"docid\": \"72fac2c5ab96a955bb8597aa092c64df\",\n \"score\": \"0.7222706\",\n \"text\": \"def rsq(self):\\n return metrics.rsq(self.model, self.x, self.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47d63c0cfd2b0557ff484c91495f6744\",\n \"score\": \"0.70095164\",\n \"text\": \"def get_res_rsquare(self, res_type: str) -> float:\\n return self.config['resistor']['info'][res_type]['rsq']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21855c2964f2efb00acc5b223af76ac0\",\n \"score\": \"0.68036026\",\n \"text\": \"def rsquared(predictions, actual):\\n ybar = 1/len(actual)*actual.sum()\\n\\n var_yhat = predictions.var()\\n var_y = actual.var()\\n return var_yhat/var_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"329213ae4abfed64e7373564dc738bb8\",\n \"score\": \"0.66765714\",\n \"text\": \"def residual(self):\\n if self.expr.value is None:\\n return None\\n return np.abs(self.expr.value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"329213ae4abfed64e7373564dc738bb8\",\n \"score\": \"0.66765714\",\n \"text\": \"def residual(self):\\n if self.expr.value is None:\\n return None\\n return np.abs(self.expr.value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"797fa1000f6c7b0c76177c2d73b70241\",\n \"score\": \"0.6674307\",\n \"text\": \"def dQR(self):\\n return math.sqrt(self.d2(self.q, self.r))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c123fd3a2e65923d1fedaa96d35215e4\",\n \"score\": \"0.6543117\",\n \"text\": \"def getResidual(self):\\n\\n return self.eval()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"767ae89be29b482fe10ca0c0f10d1a32\",\n \"score\": \"0.6506872\",\n \"text\": \"def compute_Rsquared(y1,y2):\\n sum_var = np.sum( (y1-np.mean(y1))**2 )\\n sum_res = np.sum( (y1-y2)**2 ) \\n return float(1)-float(sum_res)/sum_var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adc204316eb500a0c936f855aaa53635\",\n \"score\": \"0.65018874\",\n \"text\": \"def calculate_r(self):\\n\\n return np.exp(-(self.mc.x-self.centers_x)**2/(self.sigmax**2) - (self.mc.x_d-self.centers_xd)**2/(self.sigmay**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36ff3d005be0c85ac09ce385ee238bc2\",\n \"score\": \"0.6469571\",\n \"text\": \"def compute_rsquared(self):\\n # First read the total sum of squares for each run\\n total_sumsquares = self.sum_squares(\\\"sstot\\\")\\n\\n for part in [\\\"full\\\", \\\"main\\\"]:\\n\\n # Read in the residual sum of squares and take grand sum\\n res_sumsquares = self.sum_squares(\\\"ssres\\\", part)\\n\\n # Calculate the full model R2\\n r2 = 1 - res_sumsquares / total_sumsquares\\n\\n # Save an image with these data\\n self.save_image(r2, \\\"r2_\\\" + part)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6a62136e06437f44ca2627da1a02126\",\n \"score\": \"0.64525515\",\n \"text\": \"def rmse(self):\\n return math.sqrt(self.mse())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91bed44d2e771daa15a03eaa3ca6e77b\",\n \"score\": \"0.6398252\",\n \"text\": \"def _Rr(self, r):\\r\\n return np.sqrt(r / (self.M * self.N *\\r\\n (self.Tmn_meas * self._weights).max()**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbfbee56f58828a92a3382f34a1a28f4\",\n \"score\": \"0.6378755\",\n \"text\": \"def residual(self):\\n if self.expr.value is None:\\n return None\\n return np.maximum(self.expr.value, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbfbee56f58828a92a3382f34a1a28f4\",\n \"score\": \"0.6378755\",\n \"text\": \"def residual(self):\\n if self.expr.value is None:\\n return None\\n return np.maximum(self.expr.value, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a7008e723a7d6e8d2251a2a98d94121\",\n \"score\": \"0.6356604\",\n \"text\": \"def rmse(self):\\n return self.metrics.rootMeanSquaredError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"117ce2876a1613428b7e089b9bb985dc\",\n \"score\": \"0.6264112\",\n \"text\": \"def calc_ri(self):\\n\\n # Function call.\\n chi2 = self.func_mf(self.params)\\n\\n # Return the single value.\\n return self.data[0].ri[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a388f5fc351b9557d2df2d321d90cda5\",\n \"score\": \"0.62585545\",\n \"text\": \"def get_rsquared(x, y):\\n results = {}\\n\\n coeffs = np.polyfit(x, y, 1)\\n\\n # Polynomial Coefficients\\n results['polynomial'] = coeffs.tolist()\\n\\n # r-squared\\n p = np.poly1d(coeffs)\\n # fit values, and mean\\n yhat = p(x) # or [p(z) for z in x]\\n ybar = np.sum(y)/len(y) # or sum(y)/len(y)\\n ssreg = np.sum((yhat-ybar)**2) # or sum([ (yihat - ybar)**2 for yihat in yhat])\\n sstot = np.sum((y - ybar)**2) # or sum([ (yi - ybar)**2 for yi in y])\\n results['determination'] = ssreg / sstot\\n rsquared=(ssreg/sstot)\\n\\n return rsquared\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe84229489d82dd645678eade051702b\",\n \"score\": \"0.625804\",\n \"text\": \"def rsq(model, x, y):\\n yhat = model.predict(x)\\n mse = keras.metrics.MeanSquaredError()\\n mse(y, np.mean(y))\\n sst = mse.result().numpy()\\n mse = keras.metrics.MeanSquaredError()\\n mse(y, yhat)\\n sse = mse.result().numpy()\\n return 1 - sse/sst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eb22a51d22073aad9eb8b03840c307a\",\n \"score\": \"0.6234662\",\n \"text\": \"def r_squared(x, y):\\n return (covariance(x,y))**2 / float(variance(x)*variance(y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24c5ad95c02666f6c709161d954cef92\",\n \"score\": \"0.62283397\",\n \"text\": \"def get_rmse(self):\\n try:\\n rmse = ((2 * self.get_tsse()) / \\n (len(self.training_vectors) * len(self.outputs)))\\n except TypeError:\\n # tsse returned None...\\n return None\\n return math.sqrt(rmse)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2232b5e48f8c11e336a131c49080f1d\",\n \"score\": \"0.6228258\",\n \"text\": \"def r_squared(y, estimated):\\n assert isinstance(y, pylab.ndarray)\\n assert isinstance(estimated, pylab.ndarray)\\n assert len(y) == len(estimated)\\n\\n mean_y = y.mean()\\n res_ss = ((y - estimated) ** 2).sum()\\n tot_ss = ((y - mean_y) ** 2).sum()\\n r2 = 1 - (res_ss / tot_ss)\\n\\n return r2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b456a2ea4c88c25b77d1d66a84d7c356\",\n \"score\": \"0.62168825\",\n \"text\": \"def value(self) -> float:\\n if self.norm:\\n return math.sqrt(self.mean_square_error / self.norm)\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b426b0de9ff6aae7a207ec2f6c8122f0\",\n \"score\": \"0.62139463\",\n \"text\": \"def residual(self):\\n if self.expr.value is None:\\n return None\\n return np.abs(np.minimum(self.expr.value, 0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"524bef68be283e14e46262f2b3b37e6c\",\n \"score\": \"0.62038237\",\n \"text\": \"def rsqrt(self):\\n return self.no_params_func(\\\"rsqrt\\\", return_response=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"524bef68be283e14e46262f2b3b37e6c\",\n \"score\": \"0.62038237\",\n \"text\": \"def rsqrt(self):\\n return self.no_params_func(\\\"rsqrt\\\", return_response=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cd276773f9ef1afa8f35d3cb32d4cae\",\n \"score\": \"0.61621875\",\n \"text\": \"def rsq_adj(self):\\n return metrics.rsq_adj(self.model, self.x, self.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a551d4a4b728b3d36361cc2f5d799ef\",\n \"score\": \"0.615304\",\n \"text\": \"def r2_calculation(y_true, y_predicted):\\n y_true = y_true.reshape(y_true.shape[0], 1)\\n y_predicted = y_predicted.reshape(y_predicted.shape[0], 1)\\n input_y_mean = np.mean(y_true, axis=0)\\n ss_total = np.sum((y_true - input_y_mean) ** 2)\\n ss_residual = np.sum((y_predicted - y_true) ** 2)\\n r_square = 1 - (ss_residual / ss_total)\\n return r_square\",\n \"title\": \"\"\n },\n {\n \"docid\": \"494a68bd2dc58895ae88b9ec718cfdb6\",\n \"score\": \"0.6138141\",\n \"text\": \"def crl(self, **kwargs):\\n try:\\n out = np.corrcoef(self.n, self.n+self.residual)[0,1]\\n except:\\n out = 0.\\n\\n if np.isfinite(out) is False or self.success is False:\\n out = 0.\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"626ac8650d4001a2ac8e2096e81915de\",\n \"score\": \"0.6123889\",\n \"text\": \"def dPR(self):\\n return math.sqrt(self.d2(self.p, self.r))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec9c9d7bc2370b533feec7e66d875cb0\",\n \"score\": \"0.61001736\",\n \"text\": \"def rsq_cv(self, folds = 10):\\n return metrics.rsq_cv(self.model, self.x, self.y, folds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"109128bb07992740166c1265c1ff8a62\",\n \"score\": \"0.6097402\",\n \"text\": \"def r_squared(output, trait, p, dataset):\\n\\n R2 = 1 - output.redchi / np.var(trait) # Convert reduced chi to R2\\n n = len(dataset)\\n\\n adj_R2 = 1 - (1 - R2) * ((n - 1) / (n - p - 1))\\n\\n return adj_R2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02e52af532a14198b532483d61d11439\",\n \"score\": \"0.6087016\",\n \"text\": \"def mse(self):\\n\\t\\txs, ys = self.R.nonzero()\\n\\t\\tpredicted = self.full_matrix()\\n\\t\\terror = 0\\n\\t\\tfor x, y in zip(xs, ys):\\n\\t\\t\\terror += pow(self.R[x, y] - predicted[x, y], 2)\\n\\t\\treturn np.sqrt(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f00001f748fff63970a43737489cf7a4\",\n \"score\": \"0.6086425\",\n \"text\": \"def __abs__(self) -> float:\\r\\n square_sum = sum([val**2.0 for val in self.values])\\r\\n return sqrt(square_sum)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f828529d52da2be95b5a929a524822b\",\n \"score\": \"0.60673237\",\n \"text\": \"def _sklR2(self) :\\n if self.yHat is None :\\n self._sklPredict()\\n self._R2 = metrics.r2_score(self.y, self.yHat)\\n return self._R2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a72f513618d6c908cf2ad3c4a3de537f\",\n \"score\": \"0.6065664\",\n \"text\": \"def get_coefficient(self):\\n self.get_header()\\n return self.coefficient\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb44bddb01dca3b9bd08ad26743d925\",\n \"score\": \"0.6064083\",\n \"text\": \"def get_rsquared_acv(cov, nsample_ratios, get_discrepancy_covariances):\\n CF, cf = get_discrepancy_covariances(cov, nsample_ratios)\\n if type(cov) == np.ndarray:\\n try:\\n rsquared = np.dot(cf, np.linalg.solve(CF, cf))/cov[0, 0]\\n except:\\n return np.array([0.0])*nsample_ratios[0]\\n else:\\n try:\\n rsquared = torch.dot(cf, torch.mv(torch.inverse(CF), cf))/cov[0, 0]\\n except:\\n #print(\\\"Error computing inverse of CF\\\")\\n return torch.tensor([0.0], dtype=torch.double)*nsample_ratios[0]\\n return rsquared\",\n \"title\": \"\"\n },\n {\n \"docid\": \"826f0db44d70495078d80f6990691465\",\n \"score\": \"0.60559535\",\n \"text\": \"def r_squared(y, estimated):\\r\\n # TODO\\r\\n y, estimated = np.array(y), np.array(estimated)\\r\\n SEE = ((estimated - y)**2).sum()\\r\\n mMean = y.sum()/float(len(y))\\r\\n MV = ((mMean - y)**2).sum()\\r\\n return 1 - SEE/MV\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b905af14728f9741979d0a6981efd778\",\n \"score\": \"0.60468847\",\n \"text\": \"def R(self) -> float:\\n\\n return self._R\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06d7df0bd13c6b6a80ec8fab1a2fd334\",\n \"score\": \"0.6036403\",\n \"text\": \"def res(self):\\n return self._read_cached_property('RES', float)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c923b1bae75e2c93451a734c27dc28e\",\n \"score\": \"0.6034592\",\n \"text\": \"def r_squared(data: NDArray, data_p: NDArray) -> Number:\\n\\n return 1 - residual_sum_of_squares(data, data_p) / total_sum_of_squares(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"719a1e9651275db0a6d3eb97e5e93674\",\n \"score\": \"0.6023582\",\n \"text\": \"def calc_r2(trueVal,predVal):\\n if len(trueVal) == len(predVal):\\n if np.nansum(trueVal) > 0:\\n #trueVal_a = np.array(trueVal).ravel()\\n #predVal_a = np.array(predVal).ravel()\\n #matVal = np.array([trueVal_a,predVal_a]).transpose()\\n #matVal = matVal[~np.isnan(matVal).any(1)]\\n #matVal = matVal[~np.isnan(matVal).any(1)]\\n #trueVal_f = matVal[:,0]\\n #predVal_f = matVal[:,1]\\n #r2=r2_score(trueVal_f, predVal_f)\\n SSR = np.nansum((np.array(trueVal) - np.array(predVal))**2)\\n #ybar = np.nanmean(np.array(trueVal))\\n #SST = np.nansum((np.array(trueVal)-ybar)**2)\\n SST = np.nansum(np.array(trueVal)**2)\\n r2 = 1-(SSR/SST)\\n else:\\n return np.NaN\\n else:\\n print(\\\"ERROR: number of predicted and measured values must be equal; \\\\n n(predicted):\\\"+str(len(predVal))+\\\"\\\\n n(measured):\\\"+str(len(trueVal)))\\n sys.exit(-1)\\n return r2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"777c882f8bf68b4d694727a27228b07b\",\n \"score\": \"0.6015555\",\n \"text\": \"def get_RMSE(x, y):\\n return np.sqrt(get_MSE(x = x, y = y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adcc3adc89dfb1c497a15e7d91801e27\",\n \"score\": \"0.5998813\",\n \"text\": \"def Rsquared(y1,y2):\\n sum_var = np.sum( (y1-np.mean(y1))**2 )\\n sum_res = np.sum( (y1-y2)**2 ) \\n return float(1)-float(sum_res)/sum_var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cd79e4dec4cecae7783b43aa3a54911\",\n \"score\": \"0.5991861\",\n \"text\": \"def calculate_score_1(self):\\n chi2 = 0.\\n residual_dicts = self.extrapolate.residual_dicts\\n for detector in [self.residuals_source]:\\n for axis in self.residuals_variables:\\n residual = residual_dicts[detector][axis]\\n for value in residual:\\n chi2 += value**2/len(residual)\\n self.best_guess[\\\"score\\\"] = {\\\"score\\\":chi2}\\n return chi2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d3ee0b72c6bc36a3a7deeec3d2731d4\",\n \"score\": \"0.5989096\",\n \"text\": \"def rmse(self):\\r\\n return np.round(self.mse() ** 0.5, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dc17e0f647a491cfc1a5bf5fe025b53\",\n \"score\": \"0.59805596\",\n \"text\": \"def get_regressor_r_score(self):\\n return self.regressor_r_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee98ab7ef2bca3fc20e178b0a3672581\",\n \"score\": \"0.59724665\",\n \"text\": \"def get_r_squared(t, dof):\\r\\n\\r\\n\\tt_squared = t * t\\r\\n\\treturn t_squared / (t_squared + dof)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecef1a22303389bc2d5ddf08f61bf471\",\n \"score\": \"0.5969\",\n \"text\": \"def R(self):\\n return 287.058\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c159535025cd33f823e276f2668fec5\",\n \"score\": \"0.5957302\",\n \"text\": \"def sqrt(self):\\n return self.__pow__(0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61dcd90bf97a18e055ae189701f78fce\",\n \"score\": \"0.595717\",\n \"text\": \"def calculateRmse(self, actualData, predictedData):\\n\\n mse = mean_squared_error(actualData, predictedData)\\n return sqrt(mse)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7c08e4e4e3fbb815b6e55a4341b31dd\",\n \"score\": \"0.59528166\",\n \"text\": \"def mse(self):\\n xs, ys = self.R.nonzero()\\n predicted = self.full_matrix()\\n error = 0\\n for x, y in zip(xs, ys):\\n error += pow(self.R[x, y] - predicted[x, y], 2)\\n return np.sqrt(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7c08e4e4e3fbb815b6e55a4341b31dd\",\n \"score\": \"0.59528166\",\n \"text\": \"def mse(self):\\n xs, ys = self.R.nonzero()\\n predicted = self.full_matrix()\\n error = 0\\n for x, y in zip(xs, ys):\\n error += pow(self.R[x, y] - predicted[x, y], 2)\\n return np.sqrt(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cac7c33e810869c3e8d9c21c394497e\",\n \"score\": \"0.5952339\",\n \"text\": \"def fric_coeff(self):\\n rn = self.reynolds_no()\\n cf = 0.075/((math.log10(rn) - 2)**2)\\n print(\\\"rn : %0.3f, cf : %0.5f\\\"%(rn, cf))\\n return cf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36081732ecdca0f90e14dd0be20999d4\",\n \"score\": \"0.59481925\",\n \"text\": \"def value(self):\\n nd1 = self.nd1()\\n nd2 = self.nd2()\\n f1 = nd1 * self.s \\n f2 = nd2 * self.k * math.e ** (-self.r * self.t)\\n return f1 - f2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d56f0170a72d65338c32f2b09b1628d\",\n \"score\": \"0.5946827\",\n \"text\": \"def celly(self) -> float:\\n return self._obj.res[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"313f271c189ca148cdfdb5059fd68a6b\",\n \"score\": \"0.59432983\",\n \"text\": \"def score(self, x_test, y_test):\\n\\n #######################################################################\\n # ** START OF YOUR CODE **\\n #######################################################################\\n\\n return self.mse_r2_var(x_test, y_test)[0]\\n\\n #######################################################################\\n # ** END OF YOUR CODE **\\n #######################################################################\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17fd11e49da5b65aa23b45d974467663\",\n \"score\": \"0.5939473\",\n \"text\": \"def _eval_rmse(self, mse: float) -> float:\\n return np.sqrt(mse)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9db9a6b0886cb9df3f9323a1380755b4\",\n \"score\": \"0.5928769\",\n \"text\": \"def compute(self):\\n return self.matthews_corr_coef / self.total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26f3d876fede6c4846209d5b05b45898\",\n \"score\": \"0.5911921\",\n \"text\": \"def resnik(self) -> float:\\n return self._resnik_mica[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa9ed7c890e9eb76ddb9493050cdc89f\",\n \"score\": \"0.58806205\",\n \"text\": \"def pseudo_r2(self):\\n y_reg = self.yvals['model'][self.start_ind:]\\n y_dat = self.y[self.start_ind:]\\n \\n SSres = ((y_dat - y_reg)**2).sum()\\n SStot = ((y_dat - y_dat.mean())**2).sum()\\n return 1 - SSres/SStot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca485874773a91f1db3a768cd4bcb8c5\",\n \"score\": \"0.5876993\",\n \"text\": \"def getvalue(self):\\n ce = self.solver.getValue(self.cvc_expr)\\n rational = ce.getConstRational()\\n numerator = int(rational.getNumerator().toString())\\n denominator = int(rational.getDenominator().toString())\\n if rational.isIntegral():\\n return numerator // denominator\\n else:\\n return numerator / denominator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0488a2f3e858d856f51ce63eaef6318f\",\n \"score\": \"0.5868227\",\n \"text\": \"def residual(self):\\n return self.product.length - self.sumCutLength()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c00867b058eddd432fbdfd807738401\",\n \"score\": \"0.5863317\",\n \"text\": \"def get_length_sqrd(self) -> float:\\r\\n return self.x ** 2 + self.y ** 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c69fa90b57b74aa35702e4e37e5355b\",\n \"score\": \"0.5854565\",\n \"text\": \"def var(self):\\n return self._sd ** 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b70c55c0923728b745c9897a3dcbd5\",\n \"score\": \"0.58493006\",\n \"text\": \"def determinant(self):\\n return float()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b70c55c0923728b745c9897a3dcbd5\",\n \"score\": \"0.58493006\",\n \"text\": \"def determinant(self):\\n return float()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f63e51efcc05eae56a956c43d59dfb3b\",\n \"score\": \"0.5837071\",\n \"text\": \"def value(self) -> float:\\n if self.random_mean_square:\\n return (self.data_mean - self.random_mean) / math.sqrt(self.random_mean_square)\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cae342d34afaa6e36b104e7176b71ee8\",\n \"score\": \"0.5828752\",\n \"text\": \"def evaluate_predicated(test, predict):\\n rmse = math.sqrt(((test - predict) ** 2).sum()/test.shape[0])\\n corr = np.corrcoef(predict, y=test)\\n return rmse, corr[0, 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b36ce0f0d0cd1cb57a95c9d74ae6cfd5\",\n \"score\": \"0.5828699\",\n \"text\": \"def residual_risk(self, benchmark, rf_tick=\\\"$TNX\\\", tick=None):\\r\\n return np.std(self.residual_return(benchmark=benchmark, rf_tick=rf_tick, tick=tick))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef2af29a8344c05730117cd04063d73b\",\n \"score\": \"0.58236355\",\n \"text\": \"def r2score(self, X, y):\\n y_predict = self.predict(X)\\n r2score = 1 - ((np.sum((y - y_predict)**2))/(np.sum((y - np.mean(y))**2)))\\n return r2score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3cdade95861a35c93c36e79715dadad\",\n \"score\": \"0.58210474\",\n \"text\": \"def reciprocal(self):\\n return self.no_params_func(\\\"reciprocal\\\", return_response=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3cdade95861a35c93c36e79715dadad\",\n \"score\": \"0.58210474\",\n \"text\": \"def reciprocal(self):\\n return self.no_params_func(\\\"reciprocal\\\", return_response=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdb8f34920b7d6fc4502657c8a74dc56\",\n \"score\": \"0.58178693\",\n \"text\": \"def evaluate(self) -> float:\\n # TODO: finish\\n return self.evaluate_by_material()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cdc7bedf7ab13e5e1b9a5253eec9f3a\",\n \"score\": \"0.5817181\",\n \"text\": \"def squared_error(actual: np.array, predicted: np.array) -> np.array:\\n\\n return (actual - predicted)**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"716dd69a16337cf6a6a1312f095a1860\",\n \"score\": \"0.5813695\",\n \"text\": \"def p_R(q):\\n return float(q[0] * q[4]**(-1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"716dd69a16337cf6a6a1312f095a1860\",\n \"score\": \"0.5813695\",\n \"text\": \"def p_R(q):\\n return float(q[0] * q[4]**(-1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3d1b811a585c6a6e55d5b65e05d86d9\",\n \"score\": \"0.5789468\",\n \"text\": \"def residuals(self,Y): \\n\\n return (Y-np.dot(self._create_B(Y),self._create_Z(Y)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bd17615ca11e54e77f554cf652f1124\",\n \"score\": \"0.5788133\",\n \"text\": \"def result(self):\\r\\n return self.best.get() + (self.counteval, int(self.counteval/self.lam), self.xmean)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261f8adc854ac8b58567f44b4671a1ed\",\n \"score\": \"0.57801414\",\n \"text\": \"def rrse(actual: np.ndarray, predicted: np.ndarray):\\n return np.sqrt(np.sum(np.square(actual - predicted)) / np.sum(np.square(actual - np.mean(actual))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6209fb18bf8bc817b044392b8f96df57\",\n \"score\": \"0.57765824\",\n \"text\": \"def rmse(self,output):\\r\\n y_pred, y = output\\r\\n squared_errors = torch.pow(torch.abs(y_pred - y.view_as(y_pred)), 2)\\r\\n self._sum_of_errors_rmse += torch.sum(squared_errors).item()\\r\\n self._num_examples_rmse += y.shape[0]\\r\\n\\r\\n if self._num_examples_rmse == 0:\\r\\n raise NotComputableError(\\\"RootMeanSquaredError must have at least one example before it can be computed.\\\")\\r\\n return round(math.sqrt(self._sum_of_errors_rmse / self._num_examples_rmse), 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"847ad5512ca77c7387cafad39c12e77f\",\n \"score\": \"0.57648116\",\n \"text\": \"def getResidual(self):\\n res = self.u - self.u_Dt\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e1d4a2121f9a02e22521424adf03839\",\n \"score\": \"0.5759966\",\n \"text\": \"def norm_squared(self):\\n absolutes = Array(self.length, self.value_type.value_type)\\n\\n if self.value_type.value_type == sbitfix:\\n @for_range(absolutes.length)\\n def _(i):\\n absolutes[i] = self[i].square_abs()\\n else:\\n @for_range_parallel(absolutes.length, absolutes.length)\\n def _(i):\\n # absolutes[regint(i)] = self[regint(i)].square_abs()\\n absolutes[i] = self[i].square_abs()\\n norm_squared = sum(absolutes)\\n return norm_squared\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60629db67c0b2148d63acfd33d9b3eda\",\n \"score\": \"0.57557845\",\n \"text\": \"def rsqrt_(self):\\n return self.no_params_func(\\\"rsqrt_\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"917a33ed003ead3eba237bb949d75f32\",\n \"score\": \"0.5755451\",\n \"text\": \"def get_regressor_r2_score(self):\\n return self.regressor_r2_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aff01260d5ef413bc9f751ed7f636f06\",\n \"score\": \"0.57535493\",\n \"text\": \"def var(self):\\n if self.n <= 1:\\n return np.zeros(self.shape)\\n\\n val = np.array((self._Ex2 - (self._Ex*self._Ex)/np.float64(self.n)) / np.float64(self.n-1.))\\n\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56b0ae522b33dd52921987f8ba7c8fe5\",\n \"score\": \"0.57487357\",\n \"text\": \"def c(self):\\n return self._scalar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83147ffa4db817baf16f1d1ce43cbfbc\",\n \"score\": \"0.5747919\",\n \"text\": \"def result(self):\\n return self.best.get() + (self.counteval,\\n int(self.counteval / self.lam),\\n self.xmean)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf486d9522d6b294b4e44de6ab89f51e\",\n \"score\": \"0.5743605\",\n \"text\": \"def __call__(self):\\n x, _ = self.y()\\n error = self.r() - x\\n return (error,)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d927b3b8c6f4bebfae631184b58aa36\",\n \"score\": \"0.57410794\",\n \"text\": \"def _manualR2(self) :\\n if self.yHat is None :\\n self._manualPredict()\\n yMean = (1.0 / self.y.size) * np.sum(self.y)\\n self._R2 = 1.0 - np.sum((self.yHat - self.y)**2) / np.sum((self.y - yMean)**2)\\n return self._R2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d901eee5034fe3823a869ad6ac3099a\",\n \"score\": \"0.5732048\",\n \"text\": \"def r2(self):\\n return norm(self.p2 - self.C)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5181a7366d2e9dbd030365a2fcfa671f\",\n \"score\": \"0.5729349\",\n \"text\": \"def vc(self,r):\\n return numpy.sqrt(self.vc2(r))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd05aa7058e5f4f0e55db99947635e10\",\n \"score\": \"0.5723851\",\n \"text\": \"def frobenius_norm(self):\\n # check if A S and Y exist\\n if hasattr(self,'A') and hasattr(self,'S') and hasattr(self,'Y'):\\n if scipy.sparse.issparse(self.data):\\n tmp = self.data[:,:] - ((self.A * self.S) * self.Y)\\n tmp = tmp.multiply(tmp).sum()\\n #err = tmp\\n err = np.sqrt(tmp)\\n else:\\n #err = np.sum((self.data[:,:] - np.dot(np.dot(self.A, self.S), self.Y))**2 )\\n err = np.sqrt( np.sum((self.data[:,:] - np.dot(np.dot(self.A, self.S), self.Y))**2 ))\\n else:\\n err = None\\n\\n return err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d4196a0a931ae5e46f7eb5a1714dce7\",\n \"score\": \"0.5720659\",\n \"text\": \"def evaluate(self, X_test, y_test):\\n y_pred = self.run().predict(X_test)\\n rmse = compute_rmse(y_pred, y_test)\\n # self.mlflow_log_param('model', \\\"LinearRegression\\\")\\n # self.mlflow_log_param('rmse', rmse)\\n return rmse\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaed03f90f4b886d3282fe514d521ce0\",\n \"score\": \"0.57184035\",\n \"text\": \"def risk(self):\\n return 1 - self.accuracy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a41d5423ccc1a20ec70e824f5a9d78f1\",\n \"score\": \"0.57113546\",\n \"text\": \"def calc_R(xc, yc):\\n return sqrt((x-xc)**2 + (y-yc)**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95de0220335f98921c8fe0faec44a817\",\n \"score\": \"0.56980366\",\n \"text\": \"def r1(self):\\n return norm(self.p1 - self.C)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df2f598d1654e392a8aa43f1c68952c7\",\n \"score\": \"0.5687673\",\n \"text\": \"def rmse(y_true, y_pred):\\n res_sq = (y_pred - y_true) ** 2\\n score = np.sqrt(sum(res_sq/len(y_true)))\\n return score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec641346c92c4108ec1e747da8264b6e\",\n \"score\": \"0.56868243\",\n \"text\": \"def __abs__(self):\\n return math.sqrt(self*self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d49c8293ce8ff24a5b5583f8b5b79b40\",\n \"score\": \"0.56856936\",\n \"text\": \"def CoefDetermination(ys, res):\\n ybar, vary = thinkstats.MeanVar(ys)\\n resbar, varres = thinkstats.MeanVar(res)\\n return 1 - varres / vary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d83586749c3c8b4ed56ccfe9033d895\",\n \"score\": \"0.56839424\",\n \"text\": \"def dGm_correction(self):\\n return settings.RT * self._GetSumCoeff() * log(1e-3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7408c9c70b90166ab20cd99212040d\",\n \"score\": \"0.5680366\",\n \"text\": \"def residuals(params,f_model,x_values,y_values):\\n\\n d_values = y_values - f_model(x_values,params)\\n return d_values\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994084,"cells":{"query_id":{"kind":"string","value":"2e27c39c5690eadd8bfa2d8d5fe35f2e"},"query":{"kind":"string","value":"Given a username, sign in the user."},"positive_passages":{"kind":"list like","value":[{"docid":"adaa9822983a2910836001f830cdc2cc","score":"0.77780646","text":"def sign_in(self, username):\n if self.url == 'about:blank':\n # We need a page loaded in order to set an authentication cookie.\n self.visit('/')\n # This is duplicated from User.sign_in to work with Splinter's cookie API.\n token = user.uuid.uuid4().hex\n expires = user.utcnow() + user.SESSION_TIMEOUT\n P(username).update_session(token, expires)\n self.cookies.add({user.SESSION: token})","title":""}],"string":"[\n {\n \"docid\": \"adaa9822983a2910836001f830cdc2cc\",\n \"score\": \"0.77780646\",\n \"text\": \"def sign_in(self, username):\\n if self.url == 'about:blank':\\n # We need a page loaded in order to set an authentication cookie.\\n self.visit('/')\\n # This is duplicated from User.sign_in to work with Splinter's cookie API.\\n token = user.uuid.uuid4().hex\\n expires = user.utcnow() + user.SESSION_TIMEOUT\\n P(username).update_session(token, expires)\\n self.cookies.add({user.SESSION: token})\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"386a8dc8087d28e7989fe62d2c220f31","score":"0.7601162","text":"def login(self, username, password):\n self._set_username(username=username)\n self._set_password(password=password)\n self._sign_in()","title":""},{"docid":"c5fae1aed819e10ecaea99aa83104ab4","score":"0.7356293","text":"def login_as(self, username):\n self.assert_(self.client.login(username=username, password=username),\n \"Login as \" + username + \" failed\")","title":""},{"docid":"b0238089df44d328aea5b9b98980a495","score":"0.7245241","text":"def login(username, password):\n auth.login(username, password)","title":""},{"docid":"bdbe0c04ee258efaf1d762daf6ca6807","score":"0.70926285","text":"def signin():\r\n if current_user.is_authenticated:\r\n return \"You need to logout first\"\r\n args = request.args\r\n password = args.get(\"password\")\r\n username = args.get(\"username\")\r\n try:\r\n hashed_password = bcrypt.generate_password_hash(password).decode('utf8')\r\n user = User(username=username, password=hashed_password)\r\n user_check = User.query.filter_by(username=username).first()\r\n if user_check:\r\n return \"Existing username, please choose a different one.\"\r\n else:\r\n db.session.add(user)\r\n db.session.commit()\r\n return \"Success sign in!\"\r\n except:\r\n return \"Please sign in\"","title":""},{"docid":"ec950f2e8cf5f5bf7a70c99b882d0db3","score":"0.6877121","text":"def do_login(username, password):\n user = get_user(username=username)\n if user is None: raise ValueError\n if not check_password_hash(user[\"password\"], password):\n raise ValueError\n if user[\"status\"] != constants.ENABLED:\n raise ValueError\n flask.session[\"username\"] = user[\"username\"]\n flask.session.permanent = True\n utils.get_logger().info(f\"logged in {user['username']}\")","title":""},{"docid":"27029794cbd6cdb8e52e96f6b30e3b46","score":"0.68768674","text":"def login_the_user(self, username='testusername', password='testpassword'):\n self.go_to_url_name('authentication:login')\n self.write_in_id('id_username', username)\n self.write_in_id('id_password', password)\n self.click_on_id('login_form_btn')","title":""},{"docid":"ba53b49763a312fabcaf8eae3e17bc20","score":"0.68750507","text":"def sign_in(self, username, password, exit_on_failure=True):\n sel = self.selenium\n login_url = urljoin(self.home_url, 'user')\n sel.go_to(login_url)\n sel.input_text('id=edit-name', username)\n sel.input_password('id=edit-pass', password)\n sel.click_button('id=edit-submit')\n\n # Checking...\n with FailureManager(exit_on_failure, \"Authentication failed\"):\n sel.page_should_contain_link(\"xpath=//a[contains(@href, '/user/logout')]\")","title":""},{"docid":"e2c6f0ba18fe2fc71575a2e874d4b392","score":"0.68492544","text":"def loginUser(self, username, password):\n self.driver.get(self.base_url + '/example3/login/')\n self.find(id='username').send_keys(username)\n self.find(id='password').send_keys(password)\n self.find(id='submit').click()","title":""},{"docid":"8e75286fae8caedc540eb0ade2d4d17e","score":"0.6846441","text":"def login(self, username, password, optional=None):\n self.http.authenticate(username, password, optional)","title":""},{"docid":"8e75286fae8caedc540eb0ade2d4d17e","score":"0.6846441","text":"def login(self, username, password, optional=None):\n self.http.authenticate(username, password, optional)","title":""},{"docid":"0bfae1194156ddd139471064260ee26f","score":"0.6827666","text":"def login(self, username: str, password: str):\n self.service.login(username=username, password=password)","title":""},{"docid":"108fba5d87094d89eaa30f558f0c31d9","score":"0.6827072","text":"def login_user(username, password): # noqa: E501\n return 'do some magic!'","title":""},{"docid":"03b8ead030bc62a96764a6375bd563d3","score":"0.6822801","text":"def login_user(self, username, password):\n login = {\n \"username\": username,\n \"password\": password\n }\n return self.client.post(\n '/api/v1/auth/login',\n content_type=\"application/json\",\n data=json.dumps(login)\n )","title":""},{"docid":"48c180f75858027e2448e2e4a458a145","score":"0.6773298","text":"async def login_user(self, request):\n data = await request.post()\n username = data.get('username')\n password = data.get('password')\n if self.ldap_config:\n verified = await self._ldap_login(username, password)\n else:\n verified = await self._check_credentials(request.app.user_map, username, password)\n\n if verified:\n self.log.debug('%s logging in:' % username)\n response = web.HTTPFound('/')\n await remember(request, response, username)\n raise response\n self.log.debug('%s failed login attempt: ' % username)\n raise web.HTTPFound('/login')","title":""},{"docid":"198b210d53109878f5acad8c2f66ab8c","score":"0.6741499","text":"def submit_login():\n username_test = request.form.get('username', '').lower()\n password_test = request.form.get('password', '')\n user_to_login = User(username=username_test)\n if user_to_login.authenticate(username_test, password_test):\n session['username'] = user_to_login.username\n return redirect(url_for('home'))\n flash(\"Incorrect username or password\")\n return redirect(url_for('login'))","title":""},{"docid":"2617cf4ab6a1aec5737e98320f16ca9a","score":"0.6728161","text":"def username(self, username: str):\n self._invalid_credentials = False\n self.__credentials[\"username\"] = username","title":""},{"docid":"48bc3c530e1370d055112e395d25eafc","score":"0.6708446","text":"def signin():\n error = None\n form_data = None\n # get request.form data\n try:\n required_keys = ('email', 'password')\n form_data = controller.process_form_data(dict(request.form), *required_keys)\n except (AttributeError, ValueError):\n error = \"Invalid form input\"\n \n if form_data:\n try:\n user = db.get_user_by_email(form_data['email'])\n if user is None:\n raise KeyError('User non-existent')\n except KeyError:\n error = \"User does not exist\"\n else:\n # if user exists, check against the saved password\n if user.password == form_data['password']:\n # if it is the same, save username to session\n controller.add_user_to_session(user.key)\n flash('Login successful')\n return redirect(url_for('orders_list',\n user_key=user.key))\n else:\n error = \"Invalid password or username\"\n if error:\n flash(error)\n return redirect(url_for('index'))","title":""},{"docid":"b0638b4fe91404b89ace2722b05c9eb4","score":"0.6697413","text":"def login(self):\r\n if not hasattr(self, 'username'):\r\n self.username = 'fred'\r\n if not hasattr(self, 'password'):\r\n self.password = 'bob'\r\n if not User.objects.filter(username=self.username).exists():\r\n User.objects.create_user(self.username, password=self.password)\r\n self.user = User.objects.get(username=self.username)\r\n logged_in = self.client.login(username=self.username,\r\n password=self.password)\r\n if not logged_in:\r\n self.fail(\"LOGIN failed\")","title":""},{"docid":"3501d6c88fd16bea5daaac24292389fe","score":"0.6681337","text":"def log_in(self, username, password):\n username_field = self.driver.find_element_by_id(self.USERNAME_FIELD_ID)\n password_field = self.driver.find_element_by_id(self.PASSWORD_FIELD_ID)\n log_in_button = self.driver.find_element_by_id(self.LOG_IN_BUTTON_ID)\n\n username_field.click()\n sleep(self.KEYBOARD_ANIMATION_DELAY)\n username_field.send_keys(username)\n\n password_field.click()\n sleep(self.KEYBOARD_ANIMATION_DELAY)\n password_field.send_keys(password)\n\n log_in_button.click()","title":""},{"docid":"f4ea3f0398aa88c1edb22ebbb9329b38","score":"0.6673321","text":"def login(self, username, password):\n return self.app.post(\"/login\", data={\"username\": username, \"password\": password})","title":""},{"docid":"f4ea3f0398aa88c1edb22ebbb9329b38","score":"0.6673321","text":"def login(self, username, password):\n return self.app.post(\"/login\", data={\"username\": username, \"password\": password})","title":""},{"docid":"8de78ba8d6b9207edf6fdc3058b52780","score":"0.667124","text":"def login(self, username):\n if not self.browser:\n assert RuntimeError(\"Cant use browser methods \"\n \"without specifying a site type\")\n return self.automation.login(username, self.keys[username].key)","title":""},{"docid":"e9d513884850610ccabf17a7fe33c99b","score":"0.66253185","text":"def login(self, username, password):\n return self.app.post('/login', data=dict(\n username=username,\n password=password\n ), follow_redirects=True)","title":""},{"docid":"3740dd5b77c24810fe6aaf86465c74ce","score":"0.66232705","text":"def login(self, username, password):\n resp = self.client.post('/auth/login/?',\n {'username': username, 'password': password})\n if 'error' in resp['authentication']:\n raise AuthenticationFailed('username or password is incorrect')\n # the API returns what headers should be set\n self.client.add_headers(resp['authentication']['parameters']['header'])\n self.logged_in = True","title":""},{"docid":"0cd2a8160a146ad9a2f14bd952716937","score":"0.659948","text":"def login(self, username=None, password=None):\n if username is None:\n username = self.read_username_from_user_input()\n if password is None:\n password = self.read_password_from_user_input()\n\n # TODO: implement timeout\n self._browser.open(self._url_login)\n\n # Select the signup form\n self._browser.select_form('form[action=\"/' + self._name + '/profil/loginaction\"]')\n\n # Fill it out and submit\n self._browser['kennung'] = username\n self._browser['passwort'] = password\n self._browser.submit_selected()\n\n if self._browser.get_url() == self._url: # redirection to group page successful?\n return True\n else:\n return False","title":""},{"docid":"3daf82221f1f85e92234750bc4162447","score":"0.6591645","text":"def sign_in_standard(self, username, password):\n body = self.SIGNIN_STANDARD_BODY % (username, password)\n response = requests.post(self.signin_url, data=body)\n return self._process_sign_in_result(response)","title":""},{"docid":"3ffa57fde91017387aa48dbd85fd46b7","score":"0.65669054","text":"def login(self, user, password):\n pass","title":""},{"docid":"c8a237349e17f370c9be05f8193b732a","score":"0.6522416","text":"def login(self, username, password):\n if self.authenticate_user(username, password):\n return \"Login successful\"\n else:\n return \"Login unsuccessful\"","title":""},{"docid":"083ff04cb12d5b8df872bf63919ce70b","score":"0.65044355","text":"def user_sign_in(connection: ServerConnection, user_name: str) -> None:\n _write_line(connection, \"I32CFSP_HELLO\" + \" \" + user_name)\n _outcome_(connection, \"WELCOME\" + \" \" + user_name)","title":""},{"docid":"4c252c54001ec6a5d013b028e37d2beb","score":"0.6500778","text":"def enter_username(self, username):\n self.se2lib.input_text(self.locator.username, username)","title":""},{"docid":"85880a8c38618d1c9d94852a7077daca","score":"0.6483652","text":"def login(self, username, password):\n self._unset_auth()\n r = self._request('login', {\n 'username': username,\n 'password': password\n })\n result = r.json()\n if 'auth_token' in result:\n self.auth_token = result['auth_token']\n if 'username' in result:\n self.username = username\n return result","title":""},{"docid":"0cf82748c3a2aca9d00b66ba37891d99","score":"0.64766437","text":"def login_user(self):\n self.client.logout()\n self.client.login(username='testuser', password='12345')","title":""},{"docid":"101c43a30bcf86ab4c82bd6a24a663c6","score":"0.64733917","text":"def sign_in(username, password):\n # If already logged in, don't log in again\n global global_session\n if global_session is not None:\n return True\n # Create Non-JS browser\n browser = RoboBrowser(parser='html.parser')\n # Open login page\n browser.open('https://doh.arcabc.ca/user/login')\n # Get the login form\n form = browser.get_form(id='user-login')\n # Set the username & password\n form['name'].value = username\n form['pass'].value = password\n # Submit the form\n browser.submit_form(form)\n # If successfully signed in\n h1 = browser.find(class_='page__title')\n if h1.text == username:\n # Set the global session\n global_session = browser.session\n return True\n else:\n return False","title":""},{"docid":"26921c3f46860e86c3606bcb1011f33c","score":"0.6456115","text":"def login(self):\n get_username = self.username_entry_var.get()\n get_password = self.password_entry_var.get()\n\n self.login_for_use(get_username, get_password)","title":""},{"docid":"8c92e5d75568f88e33abe4aba943847d","score":"0.6443906","text":"def login_user(username, password):\n\n check_user = Attributes.authenticate_user(username, password)\n return check_user","title":""},{"docid":"1b8afe97d6a50dbf7d1d39879c25de32","score":"0.64202285","text":"def login():\n user = User.get_by_email_or_username(r.json['username'].lower())\n if user is not None and user.check_password(r.json['password']):\n login_user(user)\n return jsonify(marshal_current_user(user))\n return form_error('Invalid username/password.')","title":""},{"docid":"40c8f0111e3de13308e5ec4810ea2e3d","score":"0.64131665","text":"def login_account():\n username = request.form.get('name')\n password = request.form.get('password')\n\n if username and password:\n user = db.execute('SELECT * FROM users WHERE username = :username AND password = :password', {'username': username, 'password': password}).fetchone()\n if user:\n session['user_id'] = user[0]\n return render_template('success.html', username=username)\n return render_template('error_login.html', message='username or password is incorrect.')\n else:\n return render_template('error_login.html', message='Enter a username and password.')","title":""},{"docid":"8133ff8757a2a83a3888e3a178da78f2","score":"0.641015","text":"def login(client, username, password):\n data = {\n 'username': username,\n 'password': password\n }\n return client.post('/login', data=data, follow_redirects=True)","title":""},{"docid":"7894d83f330e1bb8ec6f38623d14763a","score":"0.64075035","text":"def process_sign_in(request):\n uname = request.POST['username']\n passwd = request.POST['password']\n user = auth.authenticate(username=uname, password=passwd)\n if user is not None and user.is_active:\n auth.login(request, user)\n s = Session()\n s.user = user\n s.login_timestamp = datetime.datetime.now()\n s.save() \n next = \"/account/\"\n log.info(\"%s logged in succesfully.\"%(user)) \n return HttpResponseRedirect(next) \n else:\n messages.error(request, \"Incorrect username or password!!!\")\n log.error(\"Login failed for user: %s.\"%(uname)) \n return HttpResponseRedirect(reverse('home'))","title":""},{"docid":"7aac08a8dff4c8fafe09cff3eeece814","score":"0.64017224","text":"def signin():\n # request form data\n form = SignInForm(request.form)\n # validate form\n if request.method == \"POST\" and form.validate():\n # check if username exists in database\n user_name = request.form.get(\"username\").lower()\n existing_user = user_get(user_name)\n if existing_user:\n entered_password = form.password.data\n if user_password_check(user_name, entered_password, existing_user):\n return redirect(url_for(\"home\"))\n else:\n return redirect(url_for(\"userauth.signin\"))\n else:\n return redirect(url_for(\"userauth.signin\"))\n\n return render_template(\"signin.html\", form=form)","title":""},{"docid":"a26e6e7922b882db3d984a353dd6ae72","score":"0.6393919","text":"def log_in(self, user=None):\n return self.client.login(\n username=(user or self.student).username,\n password=self.password,\n )","title":""},{"docid":"bff1e032dcd4dc841197a89799571533","score":"0.63717514","text":"def login():\n\n if \"username\" in session:\n return redirect(f'/users/{session[\"username\"]}')\n\n form = LoginUserForm()\n if form.validate_on_submit():\n data = {k: v for k, v in form.data.items() if k != \"csrf_token\"}\n user = User.authenticate(**data)\n\n if user:\n flash(f\"Welcome back {user.username}!\", \"info\")\n session[\"username\"] = user.username\n return redirect(f\"/users/{user.username}\")\n else:\n form.username.errors = [\"Invalid username or password\"]\n\n return render_template(\"login.html\", form=form)","title":""},{"docid":"47d69fc0876a0c48b4f6761e4099a2cd","score":"0.63702494","text":"def login(self):\n self._username = input(\"Username:\")\n self._password = getpass.getpass(\"Password:\")","title":""},{"docid":"a03008e5b9665f3b55235d1ba516affb","score":"0.6369439","text":"def ncsa_login():\n import getpass\n output(\"Using %s for username.\" % username)\n username = getpass.getuser()\n if password is None:\n\tpassword = getpass.getpass()\n ncsa_wireless_login(username, password)","title":""},{"docid":"5bb7284462d49e1a428ef0fadea3030e","score":"0.63649523","text":"def login(args):\n _check_credentials(args)\n _save_credentials(args.edi, args.token)\n\n LOG.info('Success! Credentials were saved.')","title":""},{"docid":"0b408ecd709600939914c2a5909afa83","score":"0.6359406","text":"def login():\n\tform = LoginForm()\n\tif form.validate_on_submit():\n\t\tuser = User.query.filter_by(username=form.username.data).first()\n\t\t\n\t\tlogin_user(user, remember=form.remember.data)\n\t\treturn redirect(url_for('user.profile', some_name=user.username))\n\t\t\n\treturn render_template('user/login.html', form = form)","title":""},{"docid":"fa71cf235d48adf3623db2cc60e161f9","score":"0.6352569","text":"def login_user(client, create_user):\n user = create_user\n\n response = client.post(\n reverse(\"log_in\"), data={\"email\": user.email, \"password\": \"pAssw0rd!\"}\n )\n\n client.login(email=user.email, password=\"pAssw0rd!\")\n\n return response","title":""},{"docid":"780f8de7501b3c6cf32b91b1b2b80903","score":"0.6346898","text":"def logging_in():\n print('Logging in...')\n while True:\n username = input('username: ')\n password = getpass.getpass('password: ')\n try:\n authenticator.login(username, password)\n except (InvalidUsername, InvalidPassword) as er:\n print(er)\n login = input('maybe you don\\'t have an account\\n'\n 'Do you want to sign up now?\\n'\n '(enter yes if you want, leave field empty '\n 'otherwise)\\n')\n if login == 'yes':\n create_user()\n continue\n else:\n break","title":""},{"docid":"79f30c2b345af327df64441c8d02b9ea","score":"0.63243645","text":"def login():\n if g.user:\n return redirect(url_for('timeline'))\n error = None\n if request.method == 'POST':\n user = query_db('''select * from user where\n username = ?''', [request.form['username']], one=True)\n if user is None:\n error = 'Invalid username'\n elif not check_password_hash(user['pw_hash'],\n request.form['password']):\n error = 'Invalid password'\n else:\n flash('You were logged in')\n session['user_id'] = user['user_id']\n return redirect(url_for('timeline'))\n return render_template('login.html', error=error)","title":""},{"docid":"8e5368fe3065d146f1a057f0d1cbb31c","score":"0.63217384","text":"def login():\n if \"username\" in session:\n return redirect(f\"/users/{session['username']}\")\n\n form = LoginForm()\n\n if form.validate_on_submit():\n username = form.username.data\n password = form.password.data\n\n user = User.authenticate(username, password)\n if user:\n session['username'] = user.username\n return redirect(f\"/users/{user.username}\")\n form.username.errors = [\"Invalid Input. Try Again\"]\n return render_template(\"users/login.html\", form=form)\n\n return render_template(\"users/login.html\", form=form)","title":""},{"docid":"ad786ca25e50228037212ced42084b7f","score":"0.6318838","text":"def login(username, password, cmd_args=None):\n r = Result(\"login\")\n r.add_action(oc_action(cur_context(), \"login\", cmd_args=['-u', username, '-p', password, cmd_args]))\n r.fail_if('Error when trying to login')\n return True","title":""},{"docid":"8d2fe9abb727d6c215bd5ac37dc37ae6","score":"0.6314192","text":"def test_login_with_username(client, user_A):\n data = {\"username\": user_A.username, \"password\": user_A.password}\n\n response = client.post(\"/account/login/\", data)\n\n assert response.status_code == 200","title":""},{"docid":"0a3e898ece2297d95d1d3ae3a5bbe130","score":"0.631078","text":"def login(self, user):\n token = os.urandom(16).encode('hex')\n self.redis.set('session:%s' % token, json.dumps({\n 'id': user[0],\n 'first_name': user[1],\n 'last_name': user[2],\n 'email': user[3]\n }), Account.SESSION_EXPIRE)\n self.set_secure_cookie('session', token)","title":""},{"docid":"71f0a408ae2be37b7e53758a25afe97e","score":"0.6308988","text":"def login(self, username, password):\n\n self.session = requests.Session()\n\n if not username or not password:\n raise Exception(\"Cannot log in, need both username and password\")\n\n # Get CSRF token for login\n # [todo] Find this properly with beautifulsoup or something\n resp = self.session.get(self.base_url + '/signin', headers=self.headers)\n match = re.search(r'input value=\"(.+?)\" name=\"_csrf\"', resp.text)\n if match:\n csrf_token = match.group(1)\n else:\n raise Exception(\"Couldn't get csrf token\")\n\n auth = {\n 'username': username,\n 'password': password,\n '_csrf': csrf_token,\n }\n\n # log in\n resp = self.session.post('https://bank.simple.com/signin',\n headers=self.headers,\n data=auth)\n\n if resp.status_code not in range(200, 400) or 'form id=\"login\"' in resp.text:\n self.session = None # log in failed, clobber session\n\n return self.session is not None","title":""},{"docid":"99bee85d616cac9b08da888b556d01ec","score":"0.6300775","text":"def login(username, password, registry):\n click.echo(login_client_func(username, password, registry))","title":""},{"docid":"6c0cc48b77c80fca3aac2e558c735dca","score":"0.6297087","text":"def login():\n form = LoginForm()\n\n if form.validate_on_submit():\n user = User.authenticate(form.username.data,\n form.password.data)\n\n if user:\n do_login(user)\n flash(f\"Hello, {user.username}!\", \"success\")\n return redirect(\"/q\")\n\n flash(\"Invalid credentials.\", 'danger')\n\n return render_template('userLoginSignupForm/login.html', form=form)","title":""},{"docid":"cdda07abeeb4e17c84c1a65a011e70f7","score":"0.62922907","text":"def login(self, username, password):\n return self._command_and_check('login', username, password, unpack=True)","title":""},{"docid":"0e63c137801504eb31426df460c29a58","score":"0.6283022","text":"def login(self, username, password):\n with FlaskClient(self.app) as client:\n return client.post('/login', data=dict(\n username=username,\n password=password\n ), follow_redirects=True)","title":""},{"docid":"ed1974735a364e12f81e19fd375d93d7","score":"0.62815565","text":"def login(self, username, password, session):\n response = SHARED_SERVER.get_validate_client(username, password)\n if response.status_code == 200:\n session['username'] = username\n return response","title":""},{"docid":"43d940a1bee85da65300816843589701","score":"0.6281021","text":"def _loginAsUser(self, username):\n uf = self.getPortal().acl_users\n user = uf.getUserById(username)\n self.assertNotEquals(user, None, 'No user %s' % username)\n newSecurityManager(None, user.__of__(uf))","title":""},{"docid":"22d7e2e9ef5538e97d17b299cc70981c","score":"0.62752986","text":"def user(username):\n env.user = username","title":""},{"docid":"9733884fdb73a367568875288fe72c35","score":"0.6263563","text":"def login(username, password):\n error = list()\n if len(username) == 0 or len(password) == 0:\n error.append('Please fill up the login form')\n return error\n username = escape_string(username)\n password = sha256_crypt.encrypt(escape_string(password))\n conn, c = connect()\n query = c.execute(\"SELECT * FROM users WHERE username=(%s) AND password=(%s)\",\n (username, password))\n c.close()\n conn.close()\n if int(query) > 0:\n session['logged_in'] = True\n session['username'] = username\n gc.collect()\n return SUCCESS\n error.append('Invalid login credentials')\n return error","title":""},{"docid":"ecaaeaf81a2faef0623d68d3c6d5b324","score":"0.62588984","text":"def login(session, username, password):\n auth = dict(username=username, password=password)\n return session.post(path + \"/users/login\", auth).json()","title":""},{"docid":"03c31d0426e5190f7067fd31ddf144a9","score":"0.6253474","text":"def login():\r\n if current_user.is_authenticated:\r\n return \"You need to logout first\"\r\n args = request.args\r\n password = args.get(\"password\")\r\n username = args.get(\"username\")\r\n\r\n user = User.query.filter_by(username=username).first()\r\n if user and bcrypt.check_password_hash(user.password, password):\r\n login_user(user)\r\n return \"Success log in!\"\r\n else:\r\n return \"Wrong username or password, try again.\"","title":""},{"docid":"4c94acdd766f282dda5631e83bd7682a","score":"0.6250542","text":"def login(self, username, password):\n try:\n self.__logged_in = True\n response = self.server.user.login(username, password)\n self.trans.add_csrf(response[\"token\"])\n return response\n except xmlrpc.client.Fault as e:\n raise BoaException(e).with_traceback(e.__traceback__)","title":""},{"docid":"3ab8426c0810367cb0225754898b791a","score":"0.6247292","text":"def login(self, username, password):\n self.session.post(\n '{base_url}/rest_login'.format(base_url=self.base_url),\n data={'username': username, 'password': password},\n )","title":""},{"docid":"50739c40685ef224474680a1292e8cc6","score":"0.6239849","text":"def login_user(self, request, user, user_status):\n login(\n request,\n user,\n backend=\"tdpservice.users.authentication.CustomAuthentication\",\n )\n logger.info(\"%s: %s on %s\", user_status, user.username, timezone.now)","title":""},{"docid":"6f7fe50ca24074a55ed5513212aec214","score":"0.6236192","text":"def login():\n return _authn('login-action')","title":""},{"docid":"949ef0f68aae049d8f127973336dfea0","score":"0.62256086","text":"def _signIn(self):\n login_url='https://www.glassdoor.com/profile/login_input.htm'\n self.getURL(login_url)\n \n if self._isLoginRequired():\n try:\n self.driver.find_element_by_name('username').send_keys(self.email)\n self.driver.find_element_by_name('password').send_keys(self.password)\n self.driver.find_element_by_xpath('//button[@type=\"submit\"]').click()\n print('Login was successful.')\n except:\n print('Login was NOT successful.')\n else:\n pass","title":""},{"docid":"c9ac967a39a65f44524bf493d04b4dea","score":"0.62111574","text":"def post(self):\n\n\t\tusername = self.request.get('username')\n\t\tpassword = self.request.get('password')\n\n\t\ttry:\n\t\t\tuser_id = User.authenticate(username, password)\n\t\t\tself.set_secure_cookie('user', str(user_id))\n\t\t\tself.redirect('/')\n\t\t\n\t\texcept Exception, e:\n\t\t\tself.render(\"login.html\", user=self.user, error = e)","title":""},{"docid":"cc5b2e76f2ffea81aed21925a9acc405","score":"0.62042177","text":"def _sign_in(self):\n\n sign_button = self.find_locator_by_element(locator=LoginPageLocators.LOGIN_BUTTON)\n sign_button.click()","title":""},{"docid":"2fb0060432308b724db02664fd389a1e","score":"0.61974937","text":"def _login(self, username: str, password: str) -> requests.models.Response:\n url = \"v1/auth/login/\"\n auth = {\"username\": username, \"password\": password}\n response = self.post(url, auth)\n self._set_csrf_header()\n return response","title":""},{"docid":"cbd5e486ca675cf352a7956f7eaa0291","score":"0.6195577","text":"def login(self, username, userid, remember):\n session = web.ctx.session\n session.username = username\n session.userid = userid\n if remember:\n rememberme = self.rememberme()\n web.setcookie('remember', rememberme , 300000000)\n\n models.user.update_login_info_by_username(username)","title":""},{"docid":"203badebdd5fb77a9f366eeb735fe917","score":"0.6193408","text":"def login(self):\n # collect username via CLI\n username = input('Username: ')\n # use getpass to not expose password\n password = getpass.getpass('Password: ')\n\n self.__login(username, password)\n self.__bypass_wall()","title":""},{"docid":"55f75c7212613a5752a840df4f1ce21d","score":"0.6192581","text":"def login(self, username, password):\n form = \"username=\" + username + \"&password=\" + password\n try:\n self.curl.get(Crawler.LOG_IN_PAGE)\n except:\n raise Exception(\"Cannot access log in page\")\n try:\n csrf_token = self.curl.get_cookie('csrftoken')\n except:\n raise Exception(\"No csrf_token in the header of response from login page\")\n form += (\"&csrfmiddlewaretoken=\" + csrf_token)\n headers = {}\n try:\n loginResponse = self.curl.post(Crawler.LOG_IN_PAGE, headers, str(form))\n except:\n raise Exception(\"Cannot make post to log in page\")\n if loginResponse.status_code != HTTP_STATUS.FOUND:\n raise Exception(\"fail to login\")\n self.response_processor(Crawler.LOG_IN_PAGE, loginResponse)","title":""},{"docid":"ce829ac22ee62df0c56ccdb6ec297be2","score":"0.61877537","text":"def login(self, username, password):\n\n # send the username to the server\n self.send(\"AUTHINFO USER \" + username)\n\n # get code 381 if a password is required\n if self.code != '381':\n return False\n\n # send the password to the server\n self.send(\"AUTHINFO PASS \" + password)\n\n # get code 281 if successfully logged in\n if self.code != '281':\n return False\n\n # all went well, return true\n return True","title":""},{"docid":"f07d6b6e91b0759b17c7c974d19be7f5","score":"0.6171236","text":"def login(self, username, password):\n token = username + password\n data = dict(username=username, password=password, token=token)\n\n result = self.app.post('/login', data=data)\n assert result.status_code == 200\n\n return token","title":""},{"docid":"3bcb516c8f6a11738f95ca685df8ca8c","score":"0.61581534","text":"def login(self, username, password):\n\n # check username and password\n if self.users.has_key(username):\n if self.users[username] == password:\n # generate session id and save it\n session_id = self._generate_session_id(username)\n self.sessions[session_id] = {\"username\" : username,\n \"session_id\": session_id,\n \"password\" : password,\n \"last_visit\": get_timestamp()}\n\n return session_id\n\n raise Fault(\"unknown username or password\", \"Please check your username and password\")","title":""},{"docid":"478d1d3752178c6cfd75f59aa6cf0632","score":"0.6156539","text":"def login(self, user):\n self.set_secure_cookie('user_id', str(user.key().id()))","title":""},{"docid":"478d1d3752178c6cfd75f59aa6cf0632","score":"0.6156539","text":"def login(self, user):\n self.set_secure_cookie('user_id', str(user.key().id()))","title":""},{"docid":"478d1d3752178c6cfd75f59aa6cf0632","score":"0.6156539","text":"def login(self, user):\n self.set_secure_cookie('user_id', str(user.key().id()))","title":""},{"docid":"487a755a09d8b8af40ff227ab00f7f8e","score":"0.6156364","text":"def login(self, user, password):\n self._query.login(user, password)","title":""},{"docid":"ba1d3ab64a144c59986f919c6ab77dd3","score":"0.6152977","text":"def login():\n try:\n return user.login()\n except errors.ServerError as e:\n abort(e.status)","title":""},{"docid":"7a71fb61d912cd63a481618fb062b838","score":"0.6151953","text":"def login():\n if g.user:\n return redirect(url_for('index'))\n error = None\n if request.method == 'POST':\n rv = mongo.db.users.find({\"username\": request.form['username']})\n user = rv[0] if rv.count() != 0 else None\n if user is None:\n error = 'Invalid username'\n elif not check_password_hash(user['pw_hash'],\n request.form['password']):\n error = 'Invalid password'\n else:\n flash('You were logged in')\n session['user_id'] = user['user_id']\n return redirect(url_for('index'))\n return render_template('login.html', error=error)","title":""},{"docid":"91d454cb161039587491fdd393c446af","score":"0.6149912","text":"def enter_username(self, username):\n WebDriverWait(self.driver, 25).until(EC.visibility_of_element_located((By.CSS_SELECTOR, 'button.login-to-checkout-btn')))\n # WebDriverWait(self.driver, 3).until(EC.invisibility_of_element_located((By.CSS_SELECTOR, self.CSS_LOADER)))\n time.sleep(2)\n login_to_checkout=self.driver.find_element_by_css_selector(\"button.login-to-checkout-btn\")\n action = TouchActions(self.driver)\n action.tap(login_to_checkout).perform()\n WebDriverWait(self.driver, 10).until(EC.visibility_of_element_located((By.CSS_SELECTOR, self.CSS_LOGINVIAPASSWORD)))\n self.driver.find_element_by_css_selector(self.CSS_LOGINVIAPASSWORD).click()\n self.driver.implicitly_wait(2)\n element = self.driver.find_element_by_css_selector(self.CSS_USERNAME)\n element.send_keys(username)","title":""},{"docid":"e00367771f84dcbdc866c1e41165f707","score":"0.61398","text":"def login(self, username=None, password=None):\n if not username:\n username = self._username\n\n if not password:\n password = self._password\n\n body_node, doc = generate_envelope('Login')\n append_text_node_to('USERNAME', username, body_node)\n append_text_node_to('PASSWORD', password, body_node)\n\n (success, tree, self.error) = self.get(doc, False)\n if success:\n session_id = tree.find(\"Body/RESULT/SESSIONID\").text\n self._session = Session(session_id)\n\n return success","title":""},{"docid":"4117b5c4b50be8a3599cc500306a3731","score":"0.61172205","text":"def login(self, username=None, password=None):\n if password and not username:\n raise Exception('Username must be provided when password is.')\n\n user = username or self.config.user or raw_input('Username: ')\n if username and username == self.config.user:\n pswd = password or self.config.pswd\n elif not username and self.config.user:\n pswd = self.config.pswd\n else:\n import getpass\n pswd = password or getpass.getpass('Password for %s: ' % user)\n\n params = {'api_type': 'json',\n 'passwd': pswd,\n 'user': user}\n response = self.request_json(self.config['login'] % user, params)\n self.modhash = response['data']['modhash']\n self.user = self.get_redditor(user)\n self.user.__class__ = reddit.objects.LoggedInRedditor","title":""},{"docid":"c735d467136416fce74fe728fdf35aa8","score":"0.61100256","text":"def signin(request):\n form = handle_signin(request)\n if request.user.is_authenticated():\n redirect_url = get_next_url(request) or reverse('innovate_splash')\n return HttpResponseRedirect(redirect_url)\n return jingo.render(request, 'users/signin.html', {\n 'form': form,\n })","title":""},{"docid":"b1b60457a40889cb77523b76270b7dd8","score":"0.61078954","text":"def login(ctx, email, password):\n save_session(\n email=email,\n session_id=get_client().user.login(\n email=email,\n password=password\n )\n )\n click.echo('Successfully logged in!')","title":""},{"docid":"2338ee8620391330caa6b1a08a9856fc","score":"0.60997355","text":"def user_login(user):\n session['username'] = user.username\n session[\"Passes\"] = {}","title":""},{"docid":"93969058159cb5e86d883373395a80d1","score":"0.6094007","text":"def login():\n if hasattr(g, 'user') and g.user.is_authenticated():\n return redirect(url_for('snaps.listing'))\n\n form = LoginForm()\n\n if form.validate_on_submit():\n\n user = User.query.filter_by(username=form.username.data).first()\n\n if not user or not flask_crypt.check_password_hash(user.password, form.password.data):\n flash(\"No such user exists.\")\n return render_template('users/login.html', form=form)\n login_user(user, remember=True)\n return redirect(url_for('snaps.listing'))\n return render_template('users/login.html', form=form)","title":""},{"docid":"85f157a2e3ce56373db3fa3d75a163ce","score":"0.60905105","text":"def login():\n if request.method == 'POST':\n # check if username exists in db\n existing_user = mongo.db.users.find_one(\n {'username': request.form.get('username').lower()})\n\n if existing_user:\n # ensure hashed password matches user input\n if check_password_hash(\n existing_user['password'], request.form.get('password')):\n session['user'] = request.form.get('username').lower()\n return redirect(url_for('profile', username=session['user']))\n else:\n # invalid password match\n flash('Incorrect Username and/or Password', 'login')\n return redirect(url_for('login'))\n\n else:\n # username doesn't exist\n flash('Incorrect Username and/or Password', 'login')\n return redirect(url_for('login'))\n\n return render_template('login.html')","title":""},{"docid":"c1c7af1be28a1e27a8837b34c0c0a8eb","score":"0.6085751","text":"def _log_in(self):\n # Pass cookies.\n self.driver.find_element_by_xpath(\n '/html/body/div[2]/div/div/div/div[2]/button[1]').click()\n # Insert login and password.\n self.driver.find_element_by_xpath(\n '//input[@name=\\\"username\\\"]').send_keys(self.username)\n self.driver.find_element_by_xpath(\n '//input[@name=\\\"password\\\"]').send_keys(self.password)\n # Click login.\n self.driver.find_element_by_xpath(\n '//*[@id=\"loginForm\"]/div/div[3]').click()\n # Pass remember login popoup.","title":""},{"docid":"4e938c9f48516edda8971b2cb5bc4b29","score":"0.6084318","text":"def user_auth():\n form = request.form.to_dict()\n existing_user = user_collection.find_one({\"username\": form[\"username\"]})\n # Checks if the user is in db\n if existing_user:\n # If passwords match (hashed / real)\n if check_password_hash(\n (existing_user[\"password\"]),\n form[\"user_password\"]):\n # Log in the user\n session[\"user\"] = form[\"username\"]\n # If user is admin redirect to admin page\n if session[\"user\"] == \"admin\":\n return redirect(url_for(\"admin\"))\n\n flash(\"You are now logged in!\")\n return redirect(url_for(\n \"profile\", user=existing_user[\"username\"]))\n\n flash(\"Wrong username and/or password\")\n return redirect(url_for(\"login\"))\n\n flash(\"You must be registered first before you can log in.\")\n return redirect(url_for(\"pages/signup\"))","title":""},{"docid":"408fdd4d2de07806b026da8e50e75805","score":"0.6081615","text":"def login(self):\n username = self.cleaned_data.get('username')\n password = self.cleaned_data.get('password')\n user = authenticate(username=username, password=password)\n\n return user","title":""},{"docid":"f41ae63048ef9f10f44465607c95a873","score":"0.6080548","text":"def login(self, username, password):\n driver = self.driver\n driver.find_element_by_id(\"topbar-login\").click()\n driver.find_element_by_id(\"username\").clear()\n driver.find_element_by_id(\"username\").send_keys(username)\n driver.find_element_by_id(\"password\").clear()\n driver.find_element_by_id(\"password\").send_keys(password)\n driver.find_element_by_id(\"login\").click()\n time.sleep(1)","title":""},{"docid":"fd84b219c040708407f670203c44bdc3","score":"0.6076396","text":"def login(self, controller):\n \n username = self.entries[self.fieldnames[0]].get()\n \n if not username:\n showinfo(title=\"Pop-up\", message=\"Please enter a username.\")\n return\n\n password = self.entries[self.fieldnames[1]].get()\n \n if not password:\n showinfo(title=\"Pop-up\", message=\"Please enter the password.\")\n return\n\n if not self.verifyUser(username, password):\n showinfo(title=\"Pop-up\", message=\"Invalid username or password.\")\n return\n\n controller.showFrame(PageOne)","title":""},{"docid":"b3d50a31e394df5c0aba0a60e3f2e4f1","score":"0.6071949","text":"def login(self, username, password):\r\n\t\tself.username = username\r\n\t\tself.password = password\r\n\t\t\r\n\t\tcreds = {\"username\":username,\"password\":password}\r\n\t\t\r\n\t\tr = requests.post(self.url+\"/session\", data=creds, verify=False)\r\n\t\tif r.status_code != 200: \r\n\t\t\traise ValueError(\"Login failed with error code: \"+str(r.status_code)+\"\\n\"+r.text)\r\n\t\t\t\r\n\t\tself.token = json.loads(r.text)[\"token\"]\r\n\t\tself.getKeys()","title":""},{"docid":"db58246792872edea06234cfa1d5acb1","score":"0.6068966","text":"def logon(self, username, password):\n if self._token:\n self.logoff()\n try:\n response = self.__makerequest('logon', email=username, password=password)\n except FogBugzAPIError, e:\n raise FogBugzLogonError(e)\n \n self._token = response.token.string\n if type(self._token) == CData:\n self._token = self._token.encode('utf-8')","title":""},{"docid":"a14a7bca5d6c1a785a6d6c7855f60eef","score":"0.6068324","text":"def login():\n login_form = classes.LogInForm()\n if login_form.validate_on_submit():\n username = login_form.username.data\n password = login_form.password.data\n # Look for it in the database.\n user = classes.User.query.filter_by(username=username).first()\n\n # Login and validate the user.\n if user is not None and user.check_password(password):\n login_user(user)\n return redirect(url_for('upload'))\n # return(\"<h1> Welcome {}!</h1>\".format(username))\n\n return render_template('login.html', form=login_form)","title":""}],"string":"[\n {\n \"docid\": \"386a8dc8087d28e7989fe62d2c220f31\",\n \"score\": \"0.7601162\",\n \"text\": \"def login(self, username, password):\\n self._set_username(username=username)\\n self._set_password(password=password)\\n self._sign_in()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5fae1aed819e10ecaea99aa83104ab4\",\n \"score\": \"0.7356293\",\n \"text\": \"def login_as(self, username):\\n self.assert_(self.client.login(username=username, password=username),\\n \\\"Login as \\\" + username + \\\" failed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0238089df44d328aea5b9b98980a495\",\n \"score\": \"0.7245241\",\n \"text\": \"def login(username, password):\\n auth.login(username, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdbe0c04ee258efaf1d762daf6ca6807\",\n \"score\": \"0.70926285\",\n \"text\": \"def signin():\\r\\n if current_user.is_authenticated:\\r\\n return \\\"You need to logout first\\\"\\r\\n args = request.args\\r\\n password = args.get(\\\"password\\\")\\r\\n username = args.get(\\\"username\\\")\\r\\n try:\\r\\n hashed_password = bcrypt.generate_password_hash(password).decode('utf8')\\r\\n user = User(username=username, password=hashed_password)\\r\\n user_check = User.query.filter_by(username=username).first()\\r\\n if user_check:\\r\\n return \\\"Existing username, please choose a different one.\\\"\\r\\n else:\\r\\n db.session.add(user)\\r\\n db.session.commit()\\r\\n return \\\"Success sign in!\\\"\\r\\n except:\\r\\n return \\\"Please sign in\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec950f2e8cf5f5bf7a70c99b882d0db3\",\n \"score\": \"0.6877121\",\n \"text\": \"def do_login(username, password):\\n user = get_user(username=username)\\n if user is None: raise ValueError\\n if not check_password_hash(user[\\\"password\\\"], password):\\n raise ValueError\\n if user[\\\"status\\\"] != constants.ENABLED:\\n raise ValueError\\n flask.session[\\\"username\\\"] = user[\\\"username\\\"]\\n flask.session.permanent = True\\n utils.get_logger().info(f\\\"logged in {user['username']}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27029794cbd6cdb8e52e96f6b30e3b46\",\n \"score\": \"0.68768674\",\n \"text\": \"def login_the_user(self, username='testusername', password='testpassword'):\\n self.go_to_url_name('authentication:login')\\n self.write_in_id('id_username', username)\\n self.write_in_id('id_password', password)\\n self.click_on_id('login_form_btn')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba53b49763a312fabcaf8eae3e17bc20\",\n \"score\": \"0.68750507\",\n \"text\": \"def sign_in(self, username, password, exit_on_failure=True):\\n sel = self.selenium\\n login_url = urljoin(self.home_url, 'user')\\n sel.go_to(login_url)\\n sel.input_text('id=edit-name', username)\\n sel.input_password('id=edit-pass', password)\\n sel.click_button('id=edit-submit')\\n\\n # Checking...\\n with FailureManager(exit_on_failure, \\\"Authentication failed\\\"):\\n sel.page_should_contain_link(\\\"xpath=//a[contains(@href, '/user/logout')]\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c6f0ba18fe2fc71575a2e874d4b392\",\n \"score\": \"0.68492544\",\n \"text\": \"def loginUser(self, username, password):\\n self.driver.get(self.base_url + '/example3/login/')\\n self.find(id='username').send_keys(username)\\n self.find(id='password').send_keys(password)\\n self.find(id='submit').click()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e75286fae8caedc540eb0ade2d4d17e\",\n \"score\": \"0.6846441\",\n \"text\": \"def login(self, username, password, optional=None):\\n self.http.authenticate(username, password, optional)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e75286fae8caedc540eb0ade2d4d17e\",\n \"score\": \"0.6846441\",\n \"text\": \"def login(self, username, password, optional=None):\\n self.http.authenticate(username, password, optional)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bfae1194156ddd139471064260ee26f\",\n \"score\": \"0.6827666\",\n \"text\": \"def login(self, username: str, password: str):\\n self.service.login(username=username, password=password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"108fba5d87094d89eaa30f558f0c31d9\",\n \"score\": \"0.6827072\",\n \"text\": \"def login_user(username, password): # noqa: E501\\n return 'do some magic!'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b8ead030bc62a96764a6375bd563d3\",\n \"score\": \"0.6822801\",\n \"text\": \"def login_user(self, username, password):\\n login = {\\n \\\"username\\\": username,\\n \\\"password\\\": password\\n }\\n return self.client.post(\\n '/api/v1/auth/login',\\n content_type=\\\"application/json\\\",\\n data=json.dumps(login)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48c180f75858027e2448e2e4a458a145\",\n \"score\": \"0.6773298\",\n \"text\": \"async def login_user(self, request):\\n data = await request.post()\\n username = data.get('username')\\n password = data.get('password')\\n if self.ldap_config:\\n verified = await self._ldap_login(username, password)\\n else:\\n verified = await self._check_credentials(request.app.user_map, username, password)\\n\\n if verified:\\n self.log.debug('%s logging in:' % username)\\n response = web.HTTPFound('/')\\n await remember(request, response, username)\\n raise response\\n self.log.debug('%s failed login attempt: ' % username)\\n raise web.HTTPFound('/login')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"198b210d53109878f5acad8c2f66ab8c\",\n \"score\": \"0.6741499\",\n \"text\": \"def submit_login():\\n username_test = request.form.get('username', '').lower()\\n password_test = request.form.get('password', '')\\n user_to_login = User(username=username_test)\\n if user_to_login.authenticate(username_test, password_test):\\n session['username'] = user_to_login.username\\n return redirect(url_for('home'))\\n flash(\\\"Incorrect username or password\\\")\\n return redirect(url_for('login'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2617cf4ab6a1aec5737e98320f16ca9a\",\n \"score\": \"0.6728161\",\n \"text\": \"def username(self, username: str):\\n self._invalid_credentials = False\\n self.__credentials[\\\"username\\\"] = username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48bc3c530e1370d055112e395d25eafc\",\n \"score\": \"0.6708446\",\n \"text\": \"def signin():\\n error = None\\n form_data = None\\n # get request.form data\\n try:\\n required_keys = ('email', 'password')\\n form_data = controller.process_form_data(dict(request.form), *required_keys)\\n except (AttributeError, ValueError):\\n error = \\\"Invalid form input\\\"\\n \\n if form_data:\\n try:\\n user = db.get_user_by_email(form_data['email'])\\n if user is None:\\n raise KeyError('User non-existent')\\n except KeyError:\\n error = \\\"User does not exist\\\"\\n else:\\n # if user exists, check against the saved password\\n if user.password == form_data['password']:\\n # if it is the same, save username to session\\n controller.add_user_to_session(user.key)\\n flash('Login successful')\\n return redirect(url_for('orders_list',\\n user_key=user.key))\\n else:\\n error = \\\"Invalid password or username\\\"\\n if error:\\n flash(error)\\n return redirect(url_for('index'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0638b4fe91404b89ace2722b05c9eb4\",\n \"score\": \"0.6697413\",\n \"text\": \"def login(self):\\r\\n if not hasattr(self, 'username'):\\r\\n self.username = 'fred'\\r\\n if not hasattr(self, 'password'):\\r\\n self.password = 'bob'\\r\\n if not User.objects.filter(username=self.username).exists():\\r\\n User.objects.create_user(self.username, password=self.password)\\r\\n self.user = User.objects.get(username=self.username)\\r\\n logged_in = self.client.login(username=self.username,\\r\\n password=self.password)\\r\\n if not logged_in:\\r\\n self.fail(\\\"LOGIN failed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3501d6c88fd16bea5daaac24292389fe\",\n \"score\": \"0.6681337\",\n \"text\": \"def log_in(self, username, password):\\n username_field = self.driver.find_element_by_id(self.USERNAME_FIELD_ID)\\n password_field = self.driver.find_element_by_id(self.PASSWORD_FIELD_ID)\\n log_in_button = self.driver.find_element_by_id(self.LOG_IN_BUTTON_ID)\\n\\n username_field.click()\\n sleep(self.KEYBOARD_ANIMATION_DELAY)\\n username_field.send_keys(username)\\n\\n password_field.click()\\n sleep(self.KEYBOARD_ANIMATION_DELAY)\\n password_field.send_keys(password)\\n\\n log_in_button.click()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ea3f0398aa88c1edb22ebbb9329b38\",\n \"score\": \"0.6673321\",\n \"text\": \"def login(self, username, password):\\n return self.app.post(\\\"/login\\\", data={\\\"username\\\": username, \\\"password\\\": password})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ea3f0398aa88c1edb22ebbb9329b38\",\n \"score\": \"0.6673321\",\n \"text\": \"def login(self, username, password):\\n return self.app.post(\\\"/login\\\", data={\\\"username\\\": username, \\\"password\\\": password})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8de78ba8d6b9207edf6fdc3058b52780\",\n \"score\": \"0.667124\",\n \"text\": \"def login(self, username):\\n if not self.browser:\\n assert RuntimeError(\\\"Cant use browser methods \\\"\\n \\\"without specifying a site type\\\")\\n return self.automation.login(username, self.keys[username].key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9d513884850610ccabf17a7fe33c99b\",\n \"score\": \"0.66253185\",\n \"text\": \"def login(self, username, password):\\n return self.app.post('/login', data=dict(\\n username=username,\\n password=password\\n ), follow_redirects=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3740dd5b77c24810fe6aaf86465c74ce\",\n \"score\": \"0.66232705\",\n \"text\": \"def login(self, username, password):\\n resp = self.client.post('/auth/login/?',\\n {'username': username, 'password': password})\\n if 'error' in resp['authentication']:\\n raise AuthenticationFailed('username or password is incorrect')\\n # the API returns what headers should be set\\n self.client.add_headers(resp['authentication']['parameters']['header'])\\n self.logged_in = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd2a8160a146ad9a2f14bd952716937\",\n \"score\": \"0.659948\",\n \"text\": \"def login(self, username=None, password=None):\\n if username is None:\\n username = self.read_username_from_user_input()\\n if password is None:\\n password = self.read_password_from_user_input()\\n\\n # TODO: implement timeout\\n self._browser.open(self._url_login)\\n\\n # Select the signup form\\n self._browser.select_form('form[action=\\\"/' + self._name + '/profil/loginaction\\\"]')\\n\\n # Fill it out and submit\\n self._browser['kennung'] = username\\n self._browser['passwort'] = password\\n self._browser.submit_selected()\\n\\n if self._browser.get_url() == self._url: # redirection to group page successful?\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3daf82221f1f85e92234750bc4162447\",\n \"score\": \"0.6591645\",\n \"text\": \"def sign_in_standard(self, username, password):\\n body = self.SIGNIN_STANDARD_BODY % (username, password)\\n response = requests.post(self.signin_url, data=body)\\n return self._process_sign_in_result(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ffa57fde91017387aa48dbd85fd46b7\",\n \"score\": \"0.65669054\",\n \"text\": \"def login(self, user, password):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8a237349e17f370c9be05f8193b732a\",\n \"score\": \"0.6522416\",\n \"text\": \"def login(self, username, password):\\n if self.authenticate_user(username, password):\\n return \\\"Login successful\\\"\\n else:\\n return \\\"Login unsuccessful\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"083ff04cb12d5b8df872bf63919ce70b\",\n \"score\": \"0.65044355\",\n \"text\": \"def user_sign_in(connection: ServerConnection, user_name: str) -> None:\\n _write_line(connection, \\\"I32CFSP_HELLO\\\" + \\\" \\\" + user_name)\\n _outcome_(connection, \\\"WELCOME\\\" + \\\" \\\" + user_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c252c54001ec6a5d013b028e37d2beb\",\n \"score\": \"0.6500778\",\n \"text\": \"def enter_username(self, username):\\n self.se2lib.input_text(self.locator.username, username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85880a8c38618d1c9d94852a7077daca\",\n \"score\": \"0.6483652\",\n \"text\": \"def login(self, username, password):\\n self._unset_auth()\\n r = self._request('login', {\\n 'username': username,\\n 'password': password\\n })\\n result = r.json()\\n if 'auth_token' in result:\\n self.auth_token = result['auth_token']\\n if 'username' in result:\\n self.username = username\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cf82748c3a2aca9d00b66ba37891d99\",\n \"score\": \"0.64766437\",\n \"text\": \"def login_user(self):\\n self.client.logout()\\n self.client.login(username='testuser', password='12345')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"101c43a30bcf86ab4c82bd6a24a663c6\",\n \"score\": \"0.64733917\",\n \"text\": \"def sign_in(username, password):\\n # If already logged in, don't log in again\\n global global_session\\n if global_session is not None:\\n return True\\n # Create Non-JS browser\\n browser = RoboBrowser(parser='html.parser')\\n # Open login page\\n browser.open('https://doh.arcabc.ca/user/login')\\n # Get the login form\\n form = browser.get_form(id='user-login')\\n # Set the username & password\\n form['name'].value = username\\n form['pass'].value = password\\n # Submit the form\\n browser.submit_form(form)\\n # If successfully signed in\\n h1 = browser.find(class_='page__title')\\n if h1.text == username:\\n # Set the global session\\n global_session = browser.session\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26921c3f46860e86c3606bcb1011f33c\",\n \"score\": \"0.6456115\",\n \"text\": \"def login(self):\\n get_username = self.username_entry_var.get()\\n get_password = self.password_entry_var.get()\\n\\n self.login_for_use(get_username, get_password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c92e5d75568f88e33abe4aba943847d\",\n \"score\": \"0.6443906\",\n \"text\": \"def login_user(username, password):\\n\\n check_user = Attributes.authenticate_user(username, password)\\n return check_user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b8afe97d6a50dbf7d1d39879c25de32\",\n \"score\": \"0.64202285\",\n \"text\": \"def login():\\n user = User.get_by_email_or_username(r.json['username'].lower())\\n if user is not None and user.check_password(r.json['password']):\\n login_user(user)\\n return jsonify(marshal_current_user(user))\\n return form_error('Invalid username/password.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40c8f0111e3de13308e5ec4810ea2e3d\",\n \"score\": \"0.64131665\",\n \"text\": \"def login_account():\\n username = request.form.get('name')\\n password = request.form.get('password')\\n\\n if username and password:\\n user = db.execute('SELECT * FROM users WHERE username = :username AND password = :password', {'username': username, 'password': password}).fetchone()\\n if user:\\n session['user_id'] = user[0]\\n return render_template('success.html', username=username)\\n return render_template('error_login.html', message='username or password is incorrect.')\\n else:\\n return render_template('error_login.html', message='Enter a username and password.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8133ff8757a2a83a3888e3a178da78f2\",\n \"score\": \"0.641015\",\n \"text\": \"def login(client, username, password):\\n data = {\\n 'username': username,\\n 'password': password\\n }\\n return client.post('/login', data=data, follow_redirects=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7894d83f330e1bb8ec6f38623d14763a\",\n \"score\": \"0.64075035\",\n \"text\": \"def process_sign_in(request):\\n uname = request.POST['username']\\n passwd = request.POST['password']\\n user = auth.authenticate(username=uname, password=passwd)\\n if user is not None and user.is_active:\\n auth.login(request, user)\\n s = Session()\\n s.user = user\\n s.login_timestamp = datetime.datetime.now()\\n s.save() \\n next = \\\"/account/\\\"\\n log.info(\\\"%s logged in succesfully.\\\"%(user)) \\n return HttpResponseRedirect(next) \\n else:\\n messages.error(request, \\\"Incorrect username or password!!!\\\")\\n log.error(\\\"Login failed for user: %s.\\\"%(uname)) \\n return HttpResponseRedirect(reverse('home'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aac08a8dff4c8fafe09cff3eeece814\",\n \"score\": \"0.64017224\",\n \"text\": \"def signin():\\n # request form data\\n form = SignInForm(request.form)\\n # validate form\\n if request.method == \\\"POST\\\" and form.validate():\\n # check if username exists in database\\n user_name = request.form.get(\\\"username\\\").lower()\\n existing_user = user_get(user_name)\\n if existing_user:\\n entered_password = form.password.data\\n if user_password_check(user_name, entered_password, existing_user):\\n return redirect(url_for(\\\"home\\\"))\\n else:\\n return redirect(url_for(\\\"userauth.signin\\\"))\\n else:\\n return redirect(url_for(\\\"userauth.signin\\\"))\\n\\n return render_template(\\\"signin.html\\\", form=form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a26e6e7922b882db3d984a353dd6ae72\",\n \"score\": \"0.6393919\",\n \"text\": \"def log_in(self, user=None):\\n return self.client.login(\\n username=(user or self.student).username,\\n password=self.password,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff1e032dcd4dc841197a89799571533\",\n \"score\": \"0.63717514\",\n \"text\": \"def login():\\n\\n if \\\"username\\\" in session:\\n return redirect(f'/users/{session[\\\"username\\\"]}')\\n\\n form = LoginUserForm()\\n if form.validate_on_submit():\\n data = {k: v for k, v in form.data.items() if k != \\\"csrf_token\\\"}\\n user = User.authenticate(**data)\\n\\n if user:\\n flash(f\\\"Welcome back {user.username}!\\\", \\\"info\\\")\\n session[\\\"username\\\"] = user.username\\n return redirect(f\\\"/users/{user.username}\\\")\\n else:\\n form.username.errors = [\\\"Invalid username or password\\\"]\\n\\n return render_template(\\\"login.html\\\", form=form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47d69fc0876a0c48b4f6761e4099a2cd\",\n \"score\": \"0.63702494\",\n \"text\": \"def login(self):\\n self._username = input(\\\"Username:\\\")\\n self._password = getpass.getpass(\\\"Password:\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a03008e5b9665f3b55235d1ba516affb\",\n \"score\": \"0.6369439\",\n \"text\": \"def ncsa_login():\\n import getpass\\n output(\\\"Using %s for username.\\\" % username)\\n username = getpass.getuser()\\n if password is None:\\n\\tpassword = getpass.getpass()\\n ncsa_wireless_login(username, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb7284462d49e1a428ef0fadea3030e\",\n \"score\": \"0.63649523\",\n \"text\": \"def login(args):\\n _check_credentials(args)\\n _save_credentials(args.edi, args.token)\\n\\n LOG.info('Success! Credentials were saved.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b408ecd709600939914c2a5909afa83\",\n \"score\": \"0.6359406\",\n \"text\": \"def login():\\n\\tform = LoginForm()\\n\\tif form.validate_on_submit():\\n\\t\\tuser = User.query.filter_by(username=form.username.data).first()\\n\\t\\t\\n\\t\\tlogin_user(user, remember=form.remember.data)\\n\\t\\treturn redirect(url_for('user.profile', some_name=user.username))\\n\\t\\t\\n\\treturn render_template('user/login.html', form = form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa71cf235d48adf3623db2cc60e161f9\",\n \"score\": \"0.6352569\",\n \"text\": \"def login_user(client, create_user):\\n user = create_user\\n\\n response = client.post(\\n reverse(\\\"log_in\\\"), data={\\\"email\\\": user.email, \\\"password\\\": \\\"pAssw0rd!\\\"}\\n )\\n\\n client.login(email=user.email, password=\\\"pAssw0rd!\\\")\\n\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"780f8de7501b3c6cf32b91b1b2b80903\",\n \"score\": \"0.6346898\",\n \"text\": \"def logging_in():\\n print('Logging in...')\\n while True:\\n username = input('username: ')\\n password = getpass.getpass('password: ')\\n try:\\n authenticator.login(username, password)\\n except (InvalidUsername, InvalidPassword) as er:\\n print(er)\\n login = input('maybe you don\\\\'t have an account\\\\n'\\n 'Do you want to sign up now?\\\\n'\\n '(enter yes if you want, leave field empty '\\n 'otherwise)\\\\n')\\n if login == 'yes':\\n create_user()\\n continue\\n else:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f30c2b345af327df64441c8d02b9ea\",\n \"score\": \"0.63243645\",\n \"text\": \"def login():\\n if g.user:\\n return redirect(url_for('timeline'))\\n error = None\\n if request.method == 'POST':\\n user = query_db('''select * from user where\\n username = ?''', [request.form['username']], one=True)\\n if user is None:\\n error = 'Invalid username'\\n elif not check_password_hash(user['pw_hash'],\\n request.form['password']):\\n error = 'Invalid password'\\n else:\\n flash('You were logged in')\\n session['user_id'] = user['user_id']\\n return redirect(url_for('timeline'))\\n return render_template('login.html', error=error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e5368fe3065d146f1a057f0d1cbb31c\",\n \"score\": \"0.63217384\",\n \"text\": \"def login():\\n if \\\"username\\\" in session:\\n return redirect(f\\\"/users/{session['username']}\\\")\\n\\n form = LoginForm()\\n\\n if form.validate_on_submit():\\n username = form.username.data\\n password = form.password.data\\n\\n user = User.authenticate(username, password)\\n if user:\\n session['username'] = user.username\\n return redirect(f\\\"/users/{user.username}\\\")\\n form.username.errors = [\\\"Invalid Input. Try Again\\\"]\\n return render_template(\\\"users/login.html\\\", form=form)\\n\\n return render_template(\\\"users/login.html\\\", form=form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad786ca25e50228037212ced42084b7f\",\n \"score\": \"0.6318838\",\n \"text\": \"def login(username, password, cmd_args=None):\\n r = Result(\\\"login\\\")\\n r.add_action(oc_action(cur_context(), \\\"login\\\", cmd_args=['-u', username, '-p', password, cmd_args]))\\n r.fail_if('Error when trying to login')\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d2fe9abb727d6c215bd5ac37dc37ae6\",\n \"score\": \"0.6314192\",\n \"text\": \"def test_login_with_username(client, user_A):\\n data = {\\\"username\\\": user_A.username, \\\"password\\\": user_A.password}\\n\\n response = client.post(\\\"/account/login/\\\", data)\\n\\n assert response.status_code == 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a3e898ece2297d95d1d3ae3a5bbe130\",\n \"score\": \"0.631078\",\n \"text\": \"def login(self, user):\\n token = os.urandom(16).encode('hex')\\n self.redis.set('session:%s' % token, json.dumps({\\n 'id': user[0],\\n 'first_name': user[1],\\n 'last_name': user[2],\\n 'email': user[3]\\n }), Account.SESSION_EXPIRE)\\n self.set_secure_cookie('session', token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71f0a408ae2be37b7e53758a25afe97e\",\n \"score\": \"0.6308988\",\n \"text\": \"def login(self, username, password):\\n\\n self.session = requests.Session()\\n\\n if not username or not password:\\n raise Exception(\\\"Cannot log in, need both username and password\\\")\\n\\n # Get CSRF token for login\\n # [todo] Find this properly with beautifulsoup or something\\n resp = self.session.get(self.base_url + '/signin', headers=self.headers)\\n match = re.search(r'input value=\\\"(.+?)\\\" name=\\\"_csrf\\\"', resp.text)\\n if match:\\n csrf_token = match.group(1)\\n else:\\n raise Exception(\\\"Couldn't get csrf token\\\")\\n\\n auth = {\\n 'username': username,\\n 'password': password,\\n '_csrf': csrf_token,\\n }\\n\\n # log in\\n resp = self.session.post('https://bank.simple.com/signin',\\n headers=self.headers,\\n data=auth)\\n\\n if resp.status_code not in range(200, 400) or 'form id=\\\"login\\\"' in resp.text:\\n self.session = None # log in failed, clobber session\\n\\n return self.session is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99bee85d616cac9b08da888b556d01ec\",\n \"score\": \"0.6300775\",\n \"text\": \"def login(username, password, registry):\\n click.echo(login_client_func(username, password, registry))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c0cc48b77c80fca3aac2e558c735dca\",\n \"score\": \"0.6297087\",\n \"text\": \"def login():\\n form = LoginForm()\\n\\n if form.validate_on_submit():\\n user = User.authenticate(form.username.data,\\n form.password.data)\\n\\n if user:\\n do_login(user)\\n flash(f\\\"Hello, {user.username}!\\\", \\\"success\\\")\\n return redirect(\\\"/q\\\")\\n\\n flash(\\\"Invalid credentials.\\\", 'danger')\\n\\n return render_template('userLoginSignupForm/login.html', form=form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdda07abeeb4e17c84c1a65a011e70f7\",\n \"score\": \"0.62922907\",\n \"text\": \"def login(self, username, password):\\n return self._command_and_check('login', username, password, unpack=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e63c137801504eb31426df460c29a58\",\n \"score\": \"0.6283022\",\n \"text\": \"def login(self, username, password):\\n with FlaskClient(self.app) as client:\\n return client.post('/login', data=dict(\\n username=username,\\n password=password\\n ), follow_redirects=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed1974735a364e12f81e19fd375d93d7\",\n \"score\": \"0.62815565\",\n \"text\": \"def login(self, username, password, session):\\n response = SHARED_SERVER.get_validate_client(username, password)\\n if response.status_code == 200:\\n session['username'] = username\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d940a1bee85da65300816843589701\",\n \"score\": \"0.6281021\",\n \"text\": \"def _loginAsUser(self, username):\\n uf = self.getPortal().acl_users\\n user = uf.getUserById(username)\\n self.assertNotEquals(user, None, 'No user %s' % username)\\n newSecurityManager(None, user.__of__(uf))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22d7e2e9ef5538e97d17b299cc70981c\",\n \"score\": \"0.62752986\",\n \"text\": \"def user(username):\\n env.user = username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9733884fdb73a367568875288fe72c35\",\n \"score\": \"0.6263563\",\n \"text\": \"def login(username, password):\\n error = list()\\n if len(username) == 0 or len(password) == 0:\\n error.append('Please fill up the login form')\\n return error\\n username = escape_string(username)\\n password = sha256_crypt.encrypt(escape_string(password))\\n conn, c = connect()\\n query = c.execute(\\\"SELECT * FROM users WHERE username=(%s) AND password=(%s)\\\",\\n (username, password))\\n c.close()\\n conn.close()\\n if int(query) > 0:\\n session['logged_in'] = True\\n session['username'] = username\\n gc.collect()\\n return SUCCESS\\n error.append('Invalid login credentials')\\n return error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecaaeaf81a2faef0623d68d3c6d5b324\",\n \"score\": \"0.62588984\",\n \"text\": \"def login(session, username, password):\\n auth = dict(username=username, password=password)\\n return session.post(path + \\\"/users/login\\\", auth).json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03c31d0426e5190f7067fd31ddf144a9\",\n \"score\": \"0.6253474\",\n \"text\": \"def login():\\r\\n if current_user.is_authenticated:\\r\\n return \\\"You need to logout first\\\"\\r\\n args = request.args\\r\\n password = args.get(\\\"password\\\")\\r\\n username = args.get(\\\"username\\\")\\r\\n\\r\\n user = User.query.filter_by(username=username).first()\\r\\n if user and bcrypt.check_password_hash(user.password, password):\\r\\n login_user(user)\\r\\n return \\\"Success log in!\\\"\\r\\n else:\\r\\n return \\\"Wrong username or password, try again.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c94acdd766f282dda5631e83bd7682a\",\n \"score\": \"0.6250542\",\n \"text\": \"def login(self, username, password):\\n try:\\n self.__logged_in = True\\n response = self.server.user.login(username, password)\\n self.trans.add_csrf(response[\\\"token\\\"])\\n return response\\n except xmlrpc.client.Fault as e:\\n raise BoaException(e).with_traceback(e.__traceback__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ab8426c0810367cb0225754898b791a\",\n \"score\": \"0.6247292\",\n \"text\": \"def login(self, username, password):\\n self.session.post(\\n '{base_url}/rest_login'.format(base_url=self.base_url),\\n data={'username': username, 'password': password},\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50739c40685ef224474680a1292e8cc6\",\n \"score\": \"0.6239849\",\n \"text\": \"def login_user(self, request, user, user_status):\\n login(\\n request,\\n user,\\n backend=\\\"tdpservice.users.authentication.CustomAuthentication\\\",\\n )\\n logger.info(\\\"%s: %s on %s\\\", user_status, user.username, timezone.now)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f7fe50ca24074a55ed5513212aec214\",\n \"score\": \"0.6236192\",\n \"text\": \"def login():\\n return _authn('login-action')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"949ef0f68aae049d8f127973336dfea0\",\n \"score\": \"0.62256086\",\n \"text\": \"def _signIn(self):\\n login_url='https://www.glassdoor.com/profile/login_input.htm'\\n self.getURL(login_url)\\n \\n if self._isLoginRequired():\\n try:\\n self.driver.find_element_by_name('username').send_keys(self.email)\\n self.driver.find_element_by_name('password').send_keys(self.password)\\n self.driver.find_element_by_xpath('//button[@type=\\\"submit\\\"]').click()\\n print('Login was successful.')\\n except:\\n print('Login was NOT successful.')\\n else:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ac967a39a65f44524bf493d04b4dea\",\n \"score\": \"0.62111574\",\n \"text\": \"def post(self):\\n\\n\\t\\tusername = self.request.get('username')\\n\\t\\tpassword = self.request.get('password')\\n\\n\\t\\ttry:\\n\\t\\t\\tuser_id = User.authenticate(username, password)\\n\\t\\t\\tself.set_secure_cookie('user', str(user_id))\\n\\t\\t\\tself.redirect('/')\\n\\t\\t\\n\\t\\texcept Exception, e:\\n\\t\\t\\tself.render(\\\"login.html\\\", user=self.user, error = e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc5b2e76f2ffea81aed21925a9acc405\",\n \"score\": \"0.62042177\",\n \"text\": \"def _sign_in(self):\\n\\n sign_button = self.find_locator_by_element(locator=LoginPageLocators.LOGIN_BUTTON)\\n sign_button.click()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fb0060432308b724db02664fd389a1e\",\n \"score\": \"0.61974937\",\n \"text\": \"def _login(self, username: str, password: str) -> requests.models.Response:\\n url = \\\"v1/auth/login/\\\"\\n auth = {\\\"username\\\": username, \\\"password\\\": password}\\n response = self.post(url, auth)\\n self._set_csrf_header()\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbd5e486ca675cf352a7956f7eaa0291\",\n \"score\": \"0.6195577\",\n \"text\": \"def login(self, username, userid, remember):\\n session = web.ctx.session\\n session.username = username\\n session.userid = userid\\n if remember:\\n rememberme = self.rememberme()\\n web.setcookie('remember', rememberme , 300000000)\\n\\n models.user.update_login_info_by_username(username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"203badebdd5fb77a9f366eeb735fe917\",\n \"score\": \"0.6193408\",\n \"text\": \"def login(self):\\n # collect username via CLI\\n username = input('Username: ')\\n # use getpass to not expose password\\n password = getpass.getpass('Password: ')\\n\\n self.__login(username, password)\\n self.__bypass_wall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55f75c7212613a5752a840df4f1ce21d\",\n \"score\": \"0.6192581\",\n \"text\": \"def login(self, username, password):\\n form = \\\"username=\\\" + username + \\\"&password=\\\" + password\\n try:\\n self.curl.get(Crawler.LOG_IN_PAGE)\\n except:\\n raise Exception(\\\"Cannot access log in page\\\")\\n try:\\n csrf_token = self.curl.get_cookie('csrftoken')\\n except:\\n raise Exception(\\\"No csrf_token in the header of response from login page\\\")\\n form += (\\\"&csrfmiddlewaretoken=\\\" + csrf_token)\\n headers = {}\\n try:\\n loginResponse = self.curl.post(Crawler.LOG_IN_PAGE, headers, str(form))\\n except:\\n raise Exception(\\\"Cannot make post to log in page\\\")\\n if loginResponse.status_code != HTTP_STATUS.FOUND:\\n raise Exception(\\\"fail to login\\\")\\n self.response_processor(Crawler.LOG_IN_PAGE, loginResponse)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce829ac22ee62df0c56ccdb6ec297be2\",\n \"score\": \"0.61877537\",\n \"text\": \"def login(self, username, password):\\n\\n # send the username to the server\\n self.send(\\\"AUTHINFO USER \\\" + username)\\n\\n # get code 381 if a password is required\\n if self.code != '381':\\n return False\\n\\n # send the password to the server\\n self.send(\\\"AUTHINFO PASS \\\" + password)\\n\\n # get code 281 if successfully logged in\\n if self.code != '281':\\n return False\\n\\n # all went well, return true\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f07d6b6e91b0759b17c7c974d19be7f5\",\n \"score\": \"0.6171236\",\n \"text\": \"def login(self, username, password):\\n token = username + password\\n data = dict(username=username, password=password, token=token)\\n\\n result = self.app.post('/login', data=data)\\n assert result.status_code == 200\\n\\n return token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bcb516c8f6a11738f95ca685df8ca8c\",\n \"score\": \"0.61581534\",\n \"text\": \"def login(self, username, password):\\n\\n # check username and password\\n if self.users.has_key(username):\\n if self.users[username] == password:\\n # generate session id and save it\\n session_id = self._generate_session_id(username)\\n self.sessions[session_id] = {\\\"username\\\" : username,\\n \\\"session_id\\\": session_id,\\n \\\"password\\\" : password,\\n \\\"last_visit\\\": get_timestamp()}\\n\\n return session_id\\n\\n raise Fault(\\\"unknown username or password\\\", \\\"Please check your username and password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"478d1d3752178c6cfd75f59aa6cf0632\",\n \"score\": \"0.6156539\",\n \"text\": \"def login(self, user):\\n self.set_secure_cookie('user_id', str(user.key().id()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"478d1d3752178c6cfd75f59aa6cf0632\",\n \"score\": \"0.6156539\",\n \"text\": \"def login(self, user):\\n self.set_secure_cookie('user_id', str(user.key().id()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"478d1d3752178c6cfd75f59aa6cf0632\",\n \"score\": \"0.6156539\",\n \"text\": \"def login(self, user):\\n self.set_secure_cookie('user_id', str(user.key().id()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"487a755a09d8b8af40ff227ab00f7f8e\",\n \"score\": \"0.6156364\",\n \"text\": \"def login(self, user, password):\\n self._query.login(user, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba1d3ab64a144c59986f919c6ab77dd3\",\n \"score\": \"0.6152977\",\n \"text\": \"def login():\\n try:\\n return user.login()\\n except errors.ServerError as e:\\n abort(e.status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a71fb61d912cd63a481618fb062b838\",\n \"score\": \"0.6151953\",\n \"text\": \"def login():\\n if g.user:\\n return redirect(url_for('index'))\\n error = None\\n if request.method == 'POST':\\n rv = mongo.db.users.find({\\\"username\\\": request.form['username']})\\n user = rv[0] if rv.count() != 0 else None\\n if user is None:\\n error = 'Invalid username'\\n elif not check_password_hash(user['pw_hash'],\\n request.form['password']):\\n error = 'Invalid password'\\n else:\\n flash('You were logged in')\\n session['user_id'] = user['user_id']\\n return redirect(url_for('index'))\\n return render_template('login.html', error=error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91d454cb161039587491fdd393c446af\",\n \"score\": \"0.6149912\",\n \"text\": \"def enter_username(self, username):\\n WebDriverWait(self.driver, 25).until(EC.visibility_of_element_located((By.CSS_SELECTOR, 'button.login-to-checkout-btn')))\\n # WebDriverWait(self.driver, 3).until(EC.invisibility_of_element_located((By.CSS_SELECTOR, self.CSS_LOADER)))\\n time.sleep(2)\\n login_to_checkout=self.driver.find_element_by_css_selector(\\\"button.login-to-checkout-btn\\\")\\n action = TouchActions(self.driver)\\n action.tap(login_to_checkout).perform()\\n WebDriverWait(self.driver, 10).until(EC.visibility_of_element_located((By.CSS_SELECTOR, self.CSS_LOGINVIAPASSWORD)))\\n self.driver.find_element_by_css_selector(self.CSS_LOGINVIAPASSWORD).click()\\n self.driver.implicitly_wait(2)\\n element = self.driver.find_element_by_css_selector(self.CSS_USERNAME)\\n element.send_keys(username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e00367771f84dcbdc866c1e41165f707\",\n \"score\": \"0.61398\",\n \"text\": \"def login(self, username=None, password=None):\\n if not username:\\n username = self._username\\n\\n if not password:\\n password = self._password\\n\\n body_node, doc = generate_envelope('Login')\\n append_text_node_to('USERNAME', username, body_node)\\n append_text_node_to('PASSWORD', password, body_node)\\n\\n (success, tree, self.error) = self.get(doc, False)\\n if success:\\n session_id = tree.find(\\\"Body/RESULT/SESSIONID\\\").text\\n self._session = Session(session_id)\\n\\n return success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4117b5c4b50be8a3599cc500306a3731\",\n \"score\": \"0.61172205\",\n \"text\": \"def login(self, username=None, password=None):\\n if password and not username:\\n raise Exception('Username must be provided when password is.')\\n\\n user = username or self.config.user or raw_input('Username: ')\\n if username and username == self.config.user:\\n pswd = password or self.config.pswd\\n elif not username and self.config.user:\\n pswd = self.config.pswd\\n else:\\n import getpass\\n pswd = password or getpass.getpass('Password for %s: ' % user)\\n\\n params = {'api_type': 'json',\\n 'passwd': pswd,\\n 'user': user}\\n response = self.request_json(self.config['login'] % user, params)\\n self.modhash = response['data']['modhash']\\n self.user = self.get_redditor(user)\\n self.user.__class__ = reddit.objects.LoggedInRedditor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c735d467136416fce74fe728fdf35aa8\",\n \"score\": \"0.61100256\",\n \"text\": \"def signin(request):\\n form = handle_signin(request)\\n if request.user.is_authenticated():\\n redirect_url = get_next_url(request) or reverse('innovate_splash')\\n return HttpResponseRedirect(redirect_url)\\n return jingo.render(request, 'users/signin.html', {\\n 'form': form,\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1b60457a40889cb77523b76270b7dd8\",\n \"score\": \"0.61078954\",\n \"text\": \"def login(ctx, email, password):\\n save_session(\\n email=email,\\n session_id=get_client().user.login(\\n email=email,\\n password=password\\n )\\n )\\n click.echo('Successfully logged in!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2338ee8620391330caa6b1a08a9856fc\",\n \"score\": \"0.60997355\",\n \"text\": \"def user_login(user):\\n session['username'] = user.username\\n session[\\\"Passes\\\"] = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93969058159cb5e86d883373395a80d1\",\n \"score\": \"0.6094007\",\n \"text\": \"def login():\\n if hasattr(g, 'user') and g.user.is_authenticated():\\n return redirect(url_for('snaps.listing'))\\n\\n form = LoginForm()\\n\\n if form.validate_on_submit():\\n\\n user = User.query.filter_by(username=form.username.data).first()\\n\\n if not user or not flask_crypt.check_password_hash(user.password, form.password.data):\\n flash(\\\"No such user exists.\\\")\\n return render_template('users/login.html', form=form)\\n login_user(user, remember=True)\\n return redirect(url_for('snaps.listing'))\\n return render_template('users/login.html', form=form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85f157a2e3ce56373db3fa3d75a163ce\",\n \"score\": \"0.60905105\",\n \"text\": \"def login():\\n if request.method == 'POST':\\n # check if username exists in db\\n existing_user = mongo.db.users.find_one(\\n {'username': request.form.get('username').lower()})\\n\\n if existing_user:\\n # ensure hashed password matches user input\\n if check_password_hash(\\n existing_user['password'], request.form.get('password')):\\n session['user'] = request.form.get('username').lower()\\n return redirect(url_for('profile', username=session['user']))\\n else:\\n # invalid password match\\n flash('Incorrect Username and/or Password', 'login')\\n return redirect(url_for('login'))\\n\\n else:\\n # username doesn't exist\\n flash('Incorrect Username and/or Password', 'login')\\n return redirect(url_for('login'))\\n\\n return render_template('login.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1c7af1be28a1e27a8837b34c0c0a8eb\",\n \"score\": \"0.6085751\",\n \"text\": \"def _log_in(self):\\n # Pass cookies.\\n self.driver.find_element_by_xpath(\\n '/html/body/div[2]/div/div/div/div[2]/button[1]').click()\\n # Insert login and password.\\n self.driver.find_element_by_xpath(\\n '//input[@name=\\\\\\\"username\\\\\\\"]').send_keys(self.username)\\n self.driver.find_element_by_xpath(\\n '//input[@name=\\\\\\\"password\\\\\\\"]').send_keys(self.password)\\n # Click login.\\n self.driver.find_element_by_xpath(\\n '//*[@id=\\\"loginForm\\\"]/div/div[3]').click()\\n # Pass remember login popoup.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e938c9f48516edda8971b2cb5bc4b29\",\n \"score\": \"0.6084318\",\n \"text\": \"def user_auth():\\n form = request.form.to_dict()\\n existing_user = user_collection.find_one({\\\"username\\\": form[\\\"username\\\"]})\\n # Checks if the user is in db\\n if existing_user:\\n # If passwords match (hashed / real)\\n if check_password_hash(\\n (existing_user[\\\"password\\\"]),\\n form[\\\"user_password\\\"]):\\n # Log in the user\\n session[\\\"user\\\"] = form[\\\"username\\\"]\\n # If user is admin redirect to admin page\\n if session[\\\"user\\\"] == \\\"admin\\\":\\n return redirect(url_for(\\\"admin\\\"))\\n\\n flash(\\\"You are now logged in!\\\")\\n return redirect(url_for(\\n \\\"profile\\\", user=existing_user[\\\"username\\\"]))\\n\\n flash(\\\"Wrong username and/or password\\\")\\n return redirect(url_for(\\\"login\\\"))\\n\\n flash(\\\"You must be registered first before you can log in.\\\")\\n return redirect(url_for(\\\"pages/signup\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"408fdd4d2de07806b026da8e50e75805\",\n \"score\": \"0.6081615\",\n \"text\": \"def login(self):\\n username = self.cleaned_data.get('username')\\n password = self.cleaned_data.get('password')\\n user = authenticate(username=username, password=password)\\n\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f41ae63048ef9f10f44465607c95a873\",\n \"score\": \"0.6080548\",\n \"text\": \"def login(self, username, password):\\n driver = self.driver\\n driver.find_element_by_id(\\\"topbar-login\\\").click()\\n driver.find_element_by_id(\\\"username\\\").clear()\\n driver.find_element_by_id(\\\"username\\\").send_keys(username)\\n driver.find_element_by_id(\\\"password\\\").clear()\\n driver.find_element_by_id(\\\"password\\\").send_keys(password)\\n driver.find_element_by_id(\\\"login\\\").click()\\n time.sleep(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd84b219c040708407f670203c44bdc3\",\n \"score\": \"0.6076396\",\n \"text\": \"def login(self, controller):\\n \\n username = self.entries[self.fieldnames[0]].get()\\n \\n if not username:\\n showinfo(title=\\\"Pop-up\\\", message=\\\"Please enter a username.\\\")\\n return\\n\\n password = self.entries[self.fieldnames[1]].get()\\n \\n if not password:\\n showinfo(title=\\\"Pop-up\\\", message=\\\"Please enter the password.\\\")\\n return\\n\\n if not self.verifyUser(username, password):\\n showinfo(title=\\\"Pop-up\\\", message=\\\"Invalid username or password.\\\")\\n return\\n\\n controller.showFrame(PageOne)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3d50a31e394df5c0aba0a60e3f2e4f1\",\n \"score\": \"0.6071949\",\n \"text\": \"def login(self, username, password):\\r\\n\\t\\tself.username = username\\r\\n\\t\\tself.password = password\\r\\n\\t\\t\\r\\n\\t\\tcreds = {\\\"username\\\":username,\\\"password\\\":password}\\r\\n\\t\\t\\r\\n\\t\\tr = requests.post(self.url+\\\"/session\\\", data=creds, verify=False)\\r\\n\\t\\tif r.status_code != 200: \\r\\n\\t\\t\\traise ValueError(\\\"Login failed with error code: \\\"+str(r.status_code)+\\\"\\\\n\\\"+r.text)\\r\\n\\t\\t\\t\\r\\n\\t\\tself.token = json.loads(r.text)[\\\"token\\\"]\\r\\n\\t\\tself.getKeys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db58246792872edea06234cfa1d5acb1\",\n \"score\": \"0.6068966\",\n \"text\": \"def logon(self, username, password):\\n if self._token:\\n self.logoff()\\n try:\\n response = self.__makerequest('logon', email=username, password=password)\\n except FogBugzAPIError, e:\\n raise FogBugzLogonError(e)\\n \\n self._token = response.token.string\\n if type(self._token) == CData:\\n self._token = self._token.encode('utf-8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a14a7bca5d6c1a785a6d6c7855f60eef\",\n \"score\": \"0.6068324\",\n \"text\": \"def login():\\n login_form = classes.LogInForm()\\n if login_form.validate_on_submit():\\n username = login_form.username.data\\n password = login_form.password.data\\n # Look for it in the database.\\n user = classes.User.query.filter_by(username=username).first()\\n\\n # Login and validate the user.\\n if user is not None and user.check_password(password):\\n login_user(user)\\n return redirect(url_for('upload'))\\n # return(\\\"<h1> Welcome {}!</h1>\\\".format(username))\\n\\n return render_template('login.html', form=login_form)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994085,"cells":{"query_id":{"kind":"string","value":"404671f607e8e6fefe302d3dceab2092"},"query":{"kind":"string","value":"Sets the access control policy on the specified resource. Replaces any existing policy."},"positive_passages":{"kind":"list like","value":[{"docid":"9cd543d8f92bc3bf059f6ebd6f1d2a6b","score":"0.50599295","text":"def SetIamPolicy(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","title":""}],"string":"[\n {\n \"docid\": \"9cd543d8f92bc3bf059f6ebd6f1d2a6b\",\n \"score\": \"0.50599295\",\n \"text\": \"def SetIamPolicy(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"43c26021a7a50ef9c9a7379a2cefa46d","score":"0.7376781","text":"def set_resource_policy(self, resource, policy):\n\n @lockutils.synchronized(self.lock_name)\n def _set_resource_policy():\n policy_id = self.resource_2_qos_policies.get(resource)\n old_policy = self.known_policies.get(policy_id)\n self.known_policies[policy.id] = policy\n self.resource_2_qos_policies[resource] = policy.id\n self.qos_policy_2_resources[policy.id].add(resource)\n if old_policy and old_policy.id != policy.id:\n self.qos_policy_2_resources[old_policy.id].remove(resource)\n\n _set_resource_policy()","title":""},{"docid":"db34fd03f6a13178c904eb26f447d294","score":"0.67079204","text":"def set_iam_policy(\n self,\n resource,\n policy,\n retry=google.api_core.gapic_v1.method.DEFAULT,\n timeout=google.api_core.gapic_v1.method.DEFAULT,\n metadata=None,\n ):\n # Wrap the transport method to add retry and timeout logic.\n if \"set_iam_policy\" not in self._inner_api_calls:\n self._inner_api_calls[\n \"set_iam_policy\"\n ] = google.api_core.gapic_v1.method.wrap_method(\n self.transport.set_iam_policy,\n default_retry=self._method_configs[\"SetIamPolicy\"].retry,\n default_timeout=self._method_configs[\"SetIamPolicy\"].timeout,\n client_info=self._client_info,\n )\n\n request = iam_policy_pb2.SetIamPolicyRequest(resource=resource, policy=policy)\n if metadata is None:\n metadata = []\n metadata = list(metadata)\n try:\n routing_header = [(\"resource\", resource)]\n except AttributeError:\n pass\n else:\n routing_metadata = google.api_core.gapic_v1.routing_header.to_grpc_metadata(\n routing_header\n )\n metadata.append(routing_metadata)\n\n return self._inner_api_calls[\"set_iam_policy\"](\n request, retry=retry, timeout=timeout, metadata=metadata\n )","title":""},{"docid":"db34fd03f6a13178c904eb26f447d294","score":"0.67079204","text":"def set_iam_policy(\n self,\n resource,\n policy,\n retry=google.api_core.gapic_v1.method.DEFAULT,\n timeout=google.api_core.gapic_v1.method.DEFAULT,\n metadata=None,\n ):\n # Wrap the transport method to add retry and timeout logic.\n if \"set_iam_policy\" not in self._inner_api_calls:\n self._inner_api_calls[\n \"set_iam_policy\"\n ] = google.api_core.gapic_v1.method.wrap_method(\n self.transport.set_iam_policy,\n default_retry=self._method_configs[\"SetIamPolicy\"].retry,\n default_timeout=self._method_configs[\"SetIamPolicy\"].timeout,\n client_info=self._client_info,\n )\n\n request = iam_policy_pb2.SetIamPolicyRequest(resource=resource, policy=policy)\n if metadata is None:\n metadata = []\n metadata = list(metadata)\n try:\n routing_header = [(\"resource\", resource)]\n except AttributeError:\n pass\n else:\n routing_metadata = google.api_core.gapic_v1.routing_header.to_grpc_metadata(\n routing_header\n )\n metadata.append(routing_metadata)\n\n return self._inner_api_calls[\"set_iam_policy\"](\n request, retry=retry, timeout=timeout, metadata=metadata\n )","title":""},{"docid":"3ba4d69be746325e853980f9a6f4ee3d","score":"0.6511385","text":"def policy(self, policy):\n\n self._policy = policy","title":""},{"docid":"e6c5bfefe93820638a51a3a234b42790","score":"0.64612085","text":"def set_iam_policy(self, policy):\n table_client = self._instance._client.table_admin_client\n resp = table_client.set_iam_policy(\n request={\"resource\": self.name, \"policy\": policy.to_pb()}\n )\n return Policy.from_pb(resp)","title":""},{"docid":"3678a0df4233c2bcbda47d811913151c","score":"0.63890755","text":"def set_iam_policy(self, policy):\n instance_admin_client = self._client.instance_admin_client\n resp = instance_admin_client.set_iam_policy(\n request={\"resource\": self.name, \"policy\": policy.to_pb()}\n )\n return Policy.from_pb(resp)","title":""},{"docid":"4a89ec0988bff1b9e978a9fea14f991c","score":"0.6303304","text":"def put_policy(self, policy):\n try:\n self.bucket.Policy().put(Policy=json.dumps(policy))\n logger.info(\"Put policy %s for bucket '%s'.\", policy, self.bucket.name)\n except ClientError:\n logger.exception(\"Couldn't apply policy to bucket '%s'.\", self.bucket.name)\n raise","title":""},{"docid":"6cb567bb19d9e41e66d792e8a8539ccf","score":"0.61875004","text":"def set_policy(cmd, client, resource_group_name, vault_name,\n object_id=None, application_id=None, spn=None, upn=None, key_permissions=None, secret_permissions=None,\n certificate_permissions=None, storage_permissions=None, no_wait=False):\n\n VaultCreateOrUpdateParameters = cmd.get_models('VaultCreateOrUpdateParameters',\n resource_type=ResourceType.MGMT_KEYVAULT)\n AccessPolicyEntry = cmd.get_models('AccessPolicyEntry', resource_type=ResourceType.MGMT_KEYVAULT)\n Permissions = cmd.get_models('Permissions', resource_type=ResourceType.MGMT_KEYVAULT)\n object_id = _object_id_args_helper(cmd.cli_ctx, object_id, spn, upn)\n vault = client.get(resource_group_name=resource_group_name,\n vault_name=vault_name)\n\n key_permissions = _permissions_distinct(key_permissions)\n secret_permissions = _permissions_distinct(secret_permissions)\n certificate_permissions = _permissions_distinct(certificate_permissions)\n storage_permissions = _permissions_distinct(storage_permissions)\n\n try:\n enable_rbac_authorization = getattr(vault.properties, 'enable_rbac_authorization')\n except: # pylint: disable=bare-except\n pass\n else:\n if enable_rbac_authorization:\n raise CLIError('Cannot set policies to a vault with \\'--enable-rbac-authorization\\' specified')\n\n # Find the existing policy to set\n policy = next((p for p in vault.properties.access_policies\n if object_id.lower() == p.object_id.lower() and\n (application_id or '').lower() == (p.application_id or '').lower() and\n vault.properties.tenant_id.lower() == p.tenant_id.lower()), None)\n if not policy:\n # Add new policy as none found\n vault.properties.access_policies.append(AccessPolicyEntry(\n tenant_id=vault.properties.tenant_id,\n object_id=object_id,\n application_id=application_id,\n permissions=Permissions(keys=key_permissions,\n secrets=secret_permissions,\n certificates=certificate_permissions,\n storage=storage_permissions)))\n else:\n # Modify existing policy.\n # If key_permissions is not set, use prev. value (similarly with secret_permissions).\n keys = policy.permissions.keys if key_permissions is None else key_permissions\n secrets = policy.permissions.secrets if secret_permissions is None else secret_permissions\n certs = policy.permissions.certificates \\\n if certificate_permissions is None else certificate_permissions\n storage = policy.permissions.storage if storage_permissions is None else storage_permissions\n policy.permissions = Permissions(keys=keys, secrets=secrets, certificates=certs, storage=storage)\n\n return _azure_stack_wrapper(cmd, client, 'create_or_update',\n resource_type=ResourceType.MGMT_KEYVAULT,\n min_api_version='2018-02-14',\n resource_group_name=resource_group_name,\n vault_name=vault_name,\n parameters=VaultCreateOrUpdateParameters(\n location=vault.location,\n tags=vault.tags,\n properties=vault.properties),\n no_wait=no_wait)","title":""},{"docid":"44d0aa117a57e5df63889bbcd878b942","score":"0.6023322","text":"def set_policy(self, policy_name):\n assert (type(policy_name) == str)\n\n params = bytes(policy_name, 'ascii')\n if len(params) > 8:\n raise RuntimeError(\"Policy name is too long\")\n params += bytes(8 - len(params))\n\n self._send_and_check(api.Request(api.MKE_REQUEST_SET_POLICY,\n params=params),\n [api.MKE_REPLY_OK])","title":""},{"docid":"ecce04a3d65d59dac5bbe208c5448537","score":"0.59802914","text":"def set_bucket_policy(self, bucket_name, policy):\n check_bucket_name(bucket_name)\n is_valid_policy_type(policy)\n self._execute(\n \"PUT\",\n bucket_name,\n body=policy,\n headers={\"Content-MD5\": md5sum_hash(policy)},\n query_params={\"policy\": \"\"},\n )","title":""},{"docid":"3c197c17468bf8a78cf54e44db635b85","score":"0.596857","text":"def rewritepolicy(self, rewritepolicy) :\n\t\ttry :\n\t\t\tself._rewritepolicy = rewritepolicy\n\t\texcept Exception as e:\n\t\t\traise e","title":""},{"docid":"4ac3fa6bf03d6ca53082112e6634c986","score":"0.5948425","text":"def _update_policy(self):\n pass","title":""},{"docid":"d07035cb31e53823209b7b5e64cbc728","score":"0.59340125","text":"def set_policy(self, name, rules):\n if isinstance(rules, dict):\n rules = json.dumps(rules)\n params = {\n 'rules': rules,\n }\n api_path = '/v1/sys/policy/{name}'.format(\n name=name,\n )\n self._adapter.put(api_path, json=params)","title":""},{"docid":"beea47a3a1adf91defe3bc1bd359b10f","score":"0.5890776","text":"def _set_iam_policies(self, file_name, role, policy_name):\n\n with open(file_name, 'r') as policy_file:\n policy_string = policy_file.read()\n\n deploy_template = jinja2.Template(policy_string)\n policy_string = deploy_template.render(cluster_name=self.cluster_name,\n aws_account=self.cluster_config['aws_account'],\n s3_bucket=self.cluster_config['s3_bucket'],\n dns_zone=self.cluster_config['dns_zone_id'])\n\n # Loop through inline policies for this role and update the one with the right name\n for policy in role.policies.all():\n if policy.policy_name == policy_name:\n logging.info(\"Updating policy %s\", policy_name)\n policy.put(PolicyDocument=policy_string)\n return\n\n raise SystemError('Policy not found')","title":""},{"docid":"79c9ea07102eeaa93625b53cb9dab8e7","score":"0.5769831","text":"def set_project_policy(service, project, policy):\n policy = service.projects().setIamPolicy(\n resource=project, body={\n 'policy': policy\n }).execute()\n return policy","title":""},{"docid":"a02e347c845aca3289ebbc10a87b053e","score":"0.5739763","text":"def enablePolicy(self, policy):\n self.log.debug(\"Enabling policy %s\", str(policy))\n policyInfo = self.policyDatabase.getPolicyInfo(policy.id)\n if policyInfo is None:\n self.log.error(\"Unable to get policy from the database: %s\", str(policy))\n return\n # enable the policy in memory and in the database\n if not policy.isEnabled():\n policy.state = States.ENABLED\n self.policyDatabase.enablePolicy(policy.id)\n else:\n self.log.info(\"Policy is already enabled %s\", str(policy))","title":""},{"docid":"1035aa37685e605ba17fc5a77e6d7291","score":"0.5714539","text":"def update_policy(self,\n id,\n name,\n datacenters=None,\n description=None,\n rules=None):\n return self._put_response_body([\"policy\", id], {},\n dict(\n model.ACLPolicy(\n name=name,\n datacenters=datacenters,\n description=description,\n rules=rules)))","title":""},{"docid":"ae527c3afb0fa7dd40eea5f74298504a","score":"0.5702328","text":"def policy_uri(self, policy_uri):\n\n self._policy_uri = policy_uri","title":""},{"docid":"6f36d1a285b1fc0ddfba8b08ff333ea8","score":"0.563909","text":"def update_policy():\n self.policy = self.inner + (parallel(rps) & self.query)","title":""},{"docid":"403c697b840a3186db5363ed67e9c797","score":"0.56348604","text":"def policies(self, policies):\n\n self._policies = policies","title":""},{"docid":"73a3a726c7ae1098e33fd299db16e628","score":"0.5606487","text":"def set_forecast_permissions(self, name, data_bucket_name_resource: CfnResource):\n function = self.functions[name]\n function.role.attach_inline_policy(\n self.policies.forecast_read_write_policy(name)\n )\n function.role.attach_inline_policy(\n self.policies.s3_bucket_read_policy(name, data_bucket_name_resource)\n )\n function.role.attach_inline_policy(self.policies.forecast_kms_read_policy)","title":""},{"docid":"da8fe4e55be1c74cfd9345477cd4ca4b","score":"0.55380553","text":"def policy_rules(self, policy_rules):\n\n self._policy_rules = policy_rules","title":""},{"docid":"ba052a5cc26f1cbda6be5dc65bc5cae2","score":"0.5526146","text":"def dns_policy(self, dns_policy):\n\n self._dns_policy = dns_policy","title":""},{"docid":"cd5b79afbf6ceaa1dd55b156e95bf4ff","score":"0.550821","text":"def __attach_inline_policy(self, username, policy_document):\n client = self.client\n\n response = client.put_user_policy(\n UserName=username,\n PolicyName=\"threatresponse-temporal-key-revocation\",\n PolicyDocument=policy_document\n )\n return response","title":""},{"docid":"dbb8102bae8047e236675c6b8a29d725","score":"0.5502422","text":"def cachepolicy(self, cachepolicy) :\n\t\ttry :\n\t\t\tself._cachepolicy = cachepolicy\n\t\texcept Exception as e:\n\t\t\traise e","title":""},{"docid":"b94b11b9fdee7f235df0368f83ce8668","score":"0.54893875","text":"def AddPolicy (self, policy):\n self.policies.append(policy)","title":""},{"docid":"e27165074db779b7f4642f89bf61da32","score":"0.54777825","text":"def responderpolicy(self, responderpolicy) :\n\t\ttry :\n\t\t\tself._responderpolicy = responderpolicy\n\t\texcept Exception as e:\n\t\t\traise e","title":""},{"docid":"8f5e04253171804d0194a67ecdc9ed6e","score":"0.5467731","text":"def update_policy():\n self.policy = self.forward + self.query","title":""},{"docid":"956f02f04a7094a429188784fbbbb65e","score":"0.5461217","text":"def full_policy(self, resource: str) -> dict:\n return dict(Version=\"2012-10-17\", Statement=[dict(Effect=\"Allow\", Resource=\"*\", Action=f\"{resource}:*\")])","title":""},{"docid":"65682b03d6ce1132e316f3c1a4549862","score":"0.5456855","text":"def block_policy(self, block_policy):\n\n self._block_policy = block_policy","title":""},{"docid":"6630aa59baa2291d15952368224f9941","score":"0.5426399","text":"def SetIamPolicy(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","title":""},{"docid":"f086e1ddaeb8d88f0d539bd28394a5ab","score":"0.5418963","text":"def set_acl(self, acl_or_str, key_name='', headers=None, version_id=None,\n generation=None, if_generation=None, if_metageneration=None):\n if isinstance(acl_or_str, Policy):\n raise InvalidAclError('Attempt to set S3 Policy on GS ACL')\n elif isinstance(acl_or_str, ACL):\n self.set_xml_acl(acl_or_str.to_xml(), key_name, headers=headers,\n generation=generation,\n if_generation=if_generation,\n if_metageneration=if_metageneration)\n else:\n self.set_canned_acl(acl_or_str, key_name, headers=headers,\n generation=generation,\n if_generation=if_generation,\n if_metageneration=if_metageneration)","title":""},{"docid":"aa5e264e01e6d5771bddf7dbe842151e","score":"0.54021513","text":"def set_access_policy( # pylint: disable=inconsistent-return-statements\n self,\n timeout: Optional[int] = None,\n request_id_parameter: Optional[str] = None,\n queue_acl: Optional[List[_models.SignedIdentifier]] = None,\n **kwargs: Any\n ) -> None:\n error_map = {\n 401: ClientAuthenticationError,\n 404: ResourceNotFoundError,\n 409: ResourceExistsError,\n 304: ResourceNotModifiedError,\n }\n error_map.update(kwargs.pop(\"error_map\", {}) or {})\n\n _headers = case_insensitive_dict(kwargs.pop(\"headers\", {}) or {})\n _params = case_insensitive_dict(kwargs.pop(\"params\", {}) or {})\n\n comp: Literal[\"acl\"] = kwargs.pop(\"comp\", _params.pop(\"comp\", \"acl\"))\n content_type: str = kwargs.pop(\"content_type\", _headers.pop(\"Content-Type\", \"application/xml\"))\n cls: ClsType[None] = kwargs.pop(\"cls\", None)\n\n serialization_ctxt = {\"xml\": {\"name\": \"SignedIdentifiers\", \"wrapped\": True}}\n if queue_acl is not None:\n _content = self._serialize.body(\n queue_acl, \"[SignedIdentifier]\", is_xml=True, serialization_ctxt=serialization_ctxt\n )\n else:\n _content = None\n\n request = build_set_access_policy_request(\n url=self._config.url,\n timeout=timeout,\n request_id_parameter=request_id_parameter,\n comp=comp,\n content_type=content_type,\n version=self._config.version,\n content=_content,\n template_url=self.set_access_policy.metadata[\"url\"],\n headers=_headers,\n params=_params,\n )\n request = _convert_request(request)\n request.url = self._client.format_url(request.url)\n\n _stream = False\n pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access\n request, stream=_stream, **kwargs\n )\n\n response = pipeline_response.http_response\n\n if response.status_code not in [204]:\n map_error(status_code=response.status_code, response=response, error_map=error_map)\n error = self._deserialize.failsafe_deserialize(_models.StorageError, pipeline_response)\n raise HttpResponseError(response=response, model=error)\n\n response_headers = {}\n response_headers[\"x-ms-request-id\"] = self._deserialize(\"str\", response.headers.get(\"x-ms-request-id\"))\n response_headers[\"x-ms-version\"] = self._deserialize(\"str\", response.headers.get(\"x-ms-version\"))\n response_headers[\"Date\"] = self._deserialize(\"rfc-1123\", response.headers.get(\"Date\"))\n\n if cls:\n return cls(pipeline_response, None, response_headers)","title":""},{"docid":"1ef50ce64babd6c77da321acf1c7b947","score":"0.53903216","text":"def billing_policy(self, billing_policy):\n\n\n self._billing_policy = billing_policy","title":""},{"docid":"64a0deeba01bbab8e768b9a32576ebf2","score":"0.53304446","text":"def set_policy_state(self, policy_state: dict) -> None:\n self._policy.set_state(policy_state)","title":""},{"docid":"24034104d470a702a5afa6117f78f7ca","score":"0.53252965","text":"def SetIamPolicy(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","title":""},{"docid":"25fd10c66eab40c25b387a4db764d2dc","score":"0.5317774","text":"def authenticationpolicy(self, authenticationpolicy) :\n\t\ttry :\n\t\t\tself._authenticationpolicy = authenticationpolicy\n\t\texcept Exception as e:\n\t\t\traise e","title":""},{"docid":"752d0d2962249fc3c4561aa78d3fc7c2","score":"0.52665293","text":"async def set_iam_policy(\n self,\n request: Optional[Union[iam_policy_pb2.SetIamPolicyRequest, dict]] = None,\n *,\n resource: Optional[str] = None,\n retry: OptionalRetry = gapic_v1.method.DEFAULT,\n timeout: Union[float, object] = gapic_v1.method.DEFAULT,\n metadata: Sequence[Tuple[str, str]] = (),\n ) -> policy_pb2.Policy:\n # Create or coerce a protobuf request object.\n # Quick check: If we got a request object, we should *not* have\n # gotten any keyword arguments that map to the request.\n has_flattened_params = any([resource])\n if request is not None and has_flattened_params:\n raise ValueError(\n \"If the `request` argument is set, then none of \"\n \"the individual field arguments should be set.\"\n )\n\n # The request isn't a proto-plus wrapped type,\n # so it must be constructed via keyword expansion.\n if isinstance(request, dict):\n request = iam_policy_pb2.SetIamPolicyRequest(**request)\n elif not request:\n request = iam_policy_pb2.SetIamPolicyRequest(\n resource=resource,\n )\n\n # Wrap the RPC method; this adds retry and timeout information,\n # and friendly error handling.\n rpc = gapic_v1.method_async.wrap_method(\n self._client._transport.set_iam_policy,\n default_timeout=60.0,\n client_info=DEFAULT_CLIENT_INFO,\n )\n\n # Certain fields should be provided within the metadata header;\n # add these here.\n metadata = tuple(metadata) + (\n gapic_v1.routing_header.to_grpc_metadata(((\"resource\", request.resource),)),\n )\n\n # Send the request.\n response = await rpc(\n request,\n retry=retry,\n timeout=timeout,\n metadata=metadata,\n )\n\n # Done; return the response.\n return response","title":""},{"docid":"ec5cd7d508e01fca5f6bf530e476fa58","score":"0.52648395","text":"def set_bucket_policy(self,bucket):\n policy = \"\"\"\n {\n \"Version\":\"2012-10-17\",\n \"Statement\":[\n {\n \"Sid\":\"PublicRead\",\n \"Effect\":\"Allow\",\n \"Principal\": \"*\",\n \"Action\":[\"s3:GetObject\"],\n \"Resource\":[\"arn:aws:s3:::%s/*\"]\n }\n ]\n }\n \"\"\" % bucket.name\n #remove any space\n policy = policy.strip()\n pol = bucket.Policy()\n pol.put(Policy=policy)","title":""},{"docid":"1fa922c1920f39af328c7e0e3de71f97","score":"0.5261521","text":"def set_acl(self, path, version, acl):\n pc = utils.StatePipeCondition()\n ok = zookeeper.aset_acl(self._zhandle, path, version, acl,\n functools.partial(_generic_completion,\n pc))\n assert ok == zookeeper.OK\n results = pc.wait_and_get()\n pc.close()\n #unpack result as void_completion\n handle, rc = results\n assert handle == self._zhandle\n if rc == zookeeper.OK:\n return rc\n self._raise_exception(rc)","title":""},{"docid":"1e4f1be8c19ba518c85957cc311aa6fd","score":"0.52539706","text":"def SetIamPolicy(self, request, global_params=None):\n config = self.GetMethodConfig('SetIamPolicy')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"1e4f1be8c19ba518c85957cc311aa6fd","score":"0.52539706","text":"def SetIamPolicy(self, request, global_params=None):\n config = self.GetMethodConfig('SetIamPolicy')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"1e4f1be8c19ba518c85957cc311aa6fd","score":"0.52539706","text":"def SetIamPolicy(self, request, global_params=None):\n config = self.GetMethodConfig('SetIamPolicy')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"1e4f1be8c19ba518c85957cc311aa6fd","score":"0.52539706","text":"def SetIamPolicy(self, request, global_params=None):\n config = self.GetMethodConfig('SetIamPolicy')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"1e4f1be8c19ba518c85957cc311aa6fd","score":"0.52539706","text":"def SetIamPolicy(self, request, global_params=None):\n config = self.GetMethodConfig('SetIamPolicy')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"1e4f1be8c19ba518c85957cc311aa6fd","score":"0.52539706","text":"def SetIamPolicy(self, request, global_params=None):\n config = self.GetMethodConfig('SetIamPolicy')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"1e4f1be8c19ba518c85957cc311aa6fd","score":"0.52539706","text":"def SetIamPolicy(self, request, global_params=None):\n config = self.GetMethodConfig('SetIamPolicy')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"1e4f1be8c19ba518c85957cc311aa6fd","score":"0.52539706","text":"def SetIamPolicy(self, request, global_params=None):\n config = self.GetMethodConfig('SetIamPolicy')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"1e4f1be8c19ba518c85957cc311aa6fd","score":"0.52539706","text":"def SetIamPolicy(self, request, global_params=None):\n config = self.GetMethodConfig('SetIamPolicy')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"1e4f1be8c19ba518c85957cc311aa6fd","score":"0.52539706","text":"def SetIamPolicy(self, request, global_params=None):\n config = self.GetMethodConfig('SetIamPolicy')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"8271b1ed755415fd8ba7b1f9f79a7945","score":"0.5248899","text":"def enable_fpolicy_policy(self, share_name, policy_name, sequence_number):\n volume = self._get_volume_by_args(vol_name=share_name)\n svm_uuid = volume['svm']['uuid']\n body = {\n 'priority': sequence_number,\n }\n\n self.send_request(\n f'/protocols/fpolicy/{svm_uuid}/policies/{policy_name}', 'patch',\n body=body)","title":""},{"docid":"335e516e2b1058f7980eace0556dbc89","score":"0.52447236","text":"def SetIamPolicy(self, request, global_params=None):\n config = self.GetMethodConfig('SetIamPolicy')\n return self._RunMethod(config, request, global_params=global_params)","title":""},{"docid":"335e516e2b1058f7980eace0556dbc89","score":"0.52447236","text":"def SetIamPolicy(self, request, global_params=None):\n config = self.GetMethodConfig('SetIamPolicy')\n return self._RunMethod(config, request, global_params=global_params)","title":""},{"docid":"16e49b25fc9f6034915d647bc4d9a9c9","score":"0.52340275","text":"def _confirm_and_lock_retention_policy(self, api_client, bucket_resource,\n request_config):\n lock_prompt = (\n 'This will permanently set the retention policy on \"{}\" to the'\n ' following:\\n\\n{}\\n\\nThis setting cannot be reverted. Continue? '\n ).format(self._bucket_resource, bucket_resource.retention_policy)\n\n if not bucket_resource.retention_policy:\n raise command_errors.Error(\n 'Bucket \"{}\" does not have a retention policy.'.format(\n self._bucket_resource))\n elif bucket_resource.retention_policy_is_locked:\n log.error('Retention policy on \"{}\" is already locked.'.format(\n self._bucket_resource))\n elif console_io.PromptContinue(message=lock_prompt, default=False):\n log.status.Print('Locking retention policy on {}...'.format(\n self._bucket_resource))\n api_client.lock_bucket_retention_policy(bucket_resource, request_config)\n else:\n # Gsutil does not update the exit code here, so we cannot use\n # cancel_or_no with PromptContinue.\n log.error('Abort locking retention policy on \"{}\".'.format(\n self._bucket_resource))","title":""},{"docid":"8517a81a6411b783dfc84575658ab955","score":"0.5215342","text":"def device_policy(policy):\n ctx = context()\n old_policy = ctx.device_policy\n try:\n ctx.device_policy = policy\n yield\n finally:\n ctx.device_policy = old_policy","title":""},{"docid":"cefb29a09235947182c721b3dd09d28e","score":"0.5215194","text":"def set_enforcement(self, result, match_code='550',\n match_message='5.7.1 Access denied'):\n if result.lower() not in ['pass', 'permerror', 'fail', 'temperror',\n 'softfail', 'none', 'neutral']:\n raise ValueError(result)\n self.policies[result.lower()] = (match_code, match_message)","title":""},{"docid":"f9c88c672c5fd709f096b8c54b12f111","score":"0.521288","text":"def set_def_acl(self, acl_or_str, headers=None):\n if isinstance(acl_or_str, Policy):\n raise InvalidAclError('Attempt to set S3 Policy on GS ACL')\n elif isinstance(acl_or_str, ACL):\n self.set_def_xml_acl(acl_or_str.to_xml(), headers=headers)\n else:\n self.set_def_canned_acl(acl_or_str, headers=headers)","title":""},{"docid":"2e7ff2c4eb05c9a7534d8245854c0fd8","score":"0.5203805","text":"def icapolicy(self, icapolicy) :\n\t\ttry :\n\t\t\tself._icapolicy = icapolicy\n\t\texcept Exception as e:\n\t\t\traise e","title":""},{"docid":"8bc7fe5176cd4d2f2eff8f2652979e61","score":"0.51957875","text":"def put(self, PolicyDocument: str):\n pass","title":""},{"docid":"8bc7fe5176cd4d2f2eff8f2652979e61","score":"0.51957875","text":"def put(self, PolicyDocument: str):\n pass","title":""},{"docid":"8bc7fe5176cd4d2f2eff8f2652979e61","score":"0.51957875","text":"def put(self, PolicyDocument: str):\n pass","title":""},{"docid":"48d3f6b4a700a1f4de34904b41004711","score":"0.5192792","text":"def policy_scope(policy):\n old_policy = _global_policy\n try:\n set_global_policy(policy)\n yield\n finally:\n set_global_policy(old_policy)","title":""},{"docid":"d3092c317db587e5d3c713f3a250b8e9","score":"0.5181993","text":"def set_access_control(self, timeout=None, owner=None, group=None, posix_permissions=None, posix_acl=None, request_id=None, lease_access_conditions=None, modified_access_conditions=None, cls=None, **kwargs):\n error_map = kwargs.pop('error_map', None)\n lease_id = None\n if lease_access_conditions is not None:\n lease_id = lease_access_conditions.lease_id\n if_match = None\n if modified_access_conditions is not None:\n if_match = modified_access_conditions.if_match\n if_none_match = None\n if modified_access_conditions is not None:\n if_none_match = modified_access_conditions.if_none_match\n if_modified_since = None\n if modified_access_conditions is not None:\n if_modified_since = modified_access_conditions.if_modified_since\n if_unmodified_since = None\n if modified_access_conditions is not None:\n if_unmodified_since = modified_access_conditions.if_unmodified_since\n\n action = \"setAccessControl\"\n\n # Construct URL\n url = self.set_access_control.metadata['url']\n path_format_arguments = {\n 'url': self._serialize.url(\"self._config.url\", self._config.url, 'str', skip_quote=True)\n }\n url = self._client.format_url(url, **path_format_arguments)\n\n # Construct parameters\n query_parameters = {}\n if timeout is not None:\n query_parameters['timeout'] = self._serialize.query(\"timeout\", timeout, 'int', minimum=0)\n query_parameters['action'] = self._serialize.query(\"action\", action, 'str')\n\n # Construct headers\n header_parameters = {}\n if owner is not None:\n header_parameters['x-ms-owner'] = self._serialize.header(\"owner\", owner, 'str')\n if group is not None:\n header_parameters['x-ms-group'] = self._serialize.header(\"group\", group, 'str')\n if posix_permissions is not None:\n header_parameters['x-ms-permissions'] = self._serialize.header(\"posix_permissions\", posix_permissions, 'str')\n if posix_acl is not None:\n header_parameters['x-ms-acl'] = self._serialize.header(\"posix_acl\", posix_acl, 'str')\n if request_id is not None:\n header_parameters['x-ms-client-request-id'] = self._serialize.header(\"request_id\", request_id, 'str')\n header_parameters['x-ms-version'] = self._serialize.header(\"self._config.version\", self._config.version, 'str')\n if lease_id is not None:\n header_parameters['x-ms-lease-id'] = self._serialize.header(\"lease_id\", lease_id, 'str')\n if if_match is not None:\n header_parameters['If-Match'] = self._serialize.header(\"if_match\", if_match, 'str')\n if if_none_match is not None:\n header_parameters['If-None-Match'] = self._serialize.header(\"if_none_match\", if_none_match, 'str')\n if if_modified_since is not None:\n header_parameters['If-Modified-Since'] = self._serialize.header(\"if_modified_since\", if_modified_since, 'rfc-1123')\n if if_unmodified_since is not None:\n header_parameters['If-Unmodified-Since'] = self._serialize.header(\"if_unmodified_since\", if_unmodified_since, 'rfc-1123')\n\n # Construct and send request\n request = self._client.patch(url, query_parameters, header_parameters)\n pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)\n response = pipeline_response.http_response\n\n if response.status_code not in [200]:\n map_error(status_code=response.status_code, response=response, error_map=error_map)\n raise models.DataLakeStorageErrorException(response, self._deserialize)\n\n if cls:\n response_headers = {\n 'Date': self._deserialize('rfc-1123', response.headers.get('Date')),\n 'ETag': self._deserialize('str', response.headers.get('ETag')),\n 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')),\n 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')),\n 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')),\n 'x-ms-client-request-id': self._deserialize('str', response.headers.get('x-ms-client-request-id')),\n }\n return cls(response, None, response_headers)","title":""},{"docid":"dfc77def82982f7cdf06d605f562ee76","score":"0.51793885","text":"def policyname(self, policyname) :\n\t\ttry :\n\t\t\tself._policyname = policyname\n\t\texcept Exception as e:\n\t\t\traise e","title":""},{"docid":"65f08fc466375e3cbc2e2dc0d8bd1624","score":"0.51673204","text":"def set_locking_policy(self, locking_policy):\n\n self._notnull()\n error = error_details_t()\n result = lib.set_locking_policy(self, locking_policy, byref(error))\n error.raise_on_error()\n return result","title":""},{"docid":"7c395d5d6fd2c351cc9ef48785401dd0","score":"0.515749","text":"def _add_s3_access_policies_to_role(\n self, node: Union[HeadNode, BaseQueue, LoginNodesPool], role_ref: str, name: str\n ):\n read_only_s3_resources = []\n read_write_s3_resources = []\n for s3_access in node.iam.s3_access:\n for resource in s3_access.resource_regex:\n arn = self._format_arn(service=\"s3\", resource=resource, region=\"\", account=\"\")\n if s3_access.enable_write_access:\n read_write_s3_resources.append(arn)\n else:\n read_only_s3_resources.append(arn)\n _, policy_name = add_cluster_iam_resource_prefix(\n self._config.cluster_name, self._config, \"S3Access\", iam_type=\"AWS::IAM::Policy\"\n )\n\n s3_access_policy = iam.CfnPolicy(\n Stack.of(self),\n name,\n policy_document=iam.PolicyDocument(statements=[]),\n roles=[role_ref],\n policy_name=policy_name or \"S3Access\",\n )\n\n if read_only_s3_resources:\n s3_access_policy.policy_document.add_statements(\n iam.PolicyStatement(\n sid=\"S3Read\",\n effect=iam.Effect.ALLOW,\n actions=[\"s3:Get*\", \"s3:List*\"],\n resources=read_only_s3_resources,\n )\n )\n\n if read_write_s3_resources:\n s3_access_policy.policy_document.add_statements(\n iam.PolicyStatement(\n sid=\"S3ReadWrite\", effect=iam.Effect.ALLOW, actions=[\"s3:*\"], resources=read_write_s3_resources\n )\n )","title":""},{"docid":"8dd5ddb75cac9857f880b02a066efe93","score":"0.5149983","text":"def update_policy(self,policy):\n grant = self.cleaned_data['grant']\n if grant != 'no':\n policy.default_grant = True\n policy.minimum_grade = grant\n else:\n policy.default_grant = False\n policy.minimum_grade = None\n policy.save()","title":""},{"docid":"3f54e743097f8a34ebc704bf25493668","score":"0.51313686","text":"def sync_policy(self, sync_policy):\n\n self._sync_policy = sync_policy","title":""},{"docid":"70f97c4e37956c1d1b3cc01d6a33f4c9","score":"0.51287806","text":"async def set_policy_aad_secured(self):\n\n write_banner(\"set_policy_aad_secured\")\n print(\"Set Secured Policy on an AAD mode attestation instance.\")\n async with DefaultAzureCredential() as credential, AttestationAdministrationClient(\n os.environ.get(\"ATTESTATION_AAD_URL\"), credential\n ) as admin_client:\n\n # [START set_secured_policy]\n # Create an RSA Key and wrap an X.509 certificate around\n # the public key for that certificate.\n rsa_key = create_rsa_key()\n cert = create_x509_certificate(rsa_key, u\"TestCertificate\")\n\n # Set a minimal policy.\n set_result, _ = await admin_client.set_policy(\n AttestationType.SGX_ENCLAVE,\n \"\"\"version= 1.0;authorizationrules{=> permit();};issuancerules {};\"\"\",\n signing_key=rsa_key,\n signing_certificate=cert,\n )\n print(\"Policy Set Resolution: \", set_result.policy_resolution)\n print(\"Resulting policy signer should match the input certificate:\")\n print(\"Policy Signer: \", set_result.policy_signer.certificates[0])\n print(\"Certificate: \", cert)\n # [END set_secured_policy]\n\n # Reset the policy now that we're done.\n await admin_client.reset_policy(AttestationType.SGX_ENCLAVE)","title":""},{"docid":"f9c3888a648acf8a6b3be088250f8678","score":"0.5115968","text":"def _enforce(self, req, action, target=None):\n if target is None:\n target = {}\n try:\n self.policy.enforce(req.context, action, target)\n except exception.Forbidden:\n raise HTTPForbidden()","title":""},{"docid":"328e0272a235290e256ce45b1f2819fa","score":"0.5099834","text":"def update_policy():\n self.policy = self.forward + (wp & self.query)","title":""},{"docid":"f34a5087c07f0417df6451fde75a138f","score":"0.50747025","text":"def test_set_bucket_policy_readonly(log_entry):\n\n # Get a unique bucket_name\n bucket_name = _gen_bucket_name()\n log_entry[\"args\"] = {\n \"bucket_name\": bucket_name,\n }\n\n _CLIENT.make_bucket(bucket_name)\n try:\n # read-only policy\n policy = {\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Sid\": \"\",\n \"Effect\": \"Allow\",\n \"Principal\": {\"AWS\": \"*\"},\n \"Action\": \"s3:GetBucketLocation\",\n \"Resource\": \"arn:aws:s3:::\" + bucket_name,\n },\n {\n \"Sid\": \"\",\n \"Effect\": \"Allow\",\n \"Principal\": {\"AWS\": \"*\"},\n \"Action\": \"s3:ListBucket\",\n \"Resource\": \"arn:aws:s3:::\" + bucket_name,\n },\n {\n \"Sid\": \"\",\n \"Effect\": \"Allow\",\n \"Principal\": {\"AWS\": \"*\"},\n \"Action\": \"s3:GetObject\",\n \"Resource\": f\"arn:aws:s3:::{bucket_name}/*\",\n },\n ],\n }\n # Set read-only policy\n _CLIENT.set_bucket_policy(bucket_name, json.dumps(policy))\n # Validate if the policy is set correctly\n if not _validate_policy(bucket_name, policy):\n raise ValueError(\"Failed to set ReadOnly bucket policy\")\n finally:\n _CLIENT.remove_bucket(bucket_name)","title":""},{"docid":"6ad3ba9f00da842160108d2ffa80291b","score":"0.50653607","text":"def UpdateConsumerPolicy(self, request, global_params=None):\n config = self.GetMethodConfig('UpdateConsumerPolicy')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"7b0c779d600eb5a79609c0f19fe950fe","score":"0.50534487","text":"def update(self, id, data):\n\t\tassert isinstance(id, str), 'The ID must be a string'\n\t\tassert id, 'The ID must not be an empty string'\n\t\tassert isinstance(data, dict), 'The data type must be a dictionary'\n\t\tassert data, 'Policy data must not be an empty dictionary'\n\n\t\turl = f'{self.root.url}/api/v1/policies/{id}'\n\t\tdata = json.dumps(data)\n\t\treturn self.root.r('PUT', url, data, headers=None, verify=self.root.verify)","title":""},{"docid":"a042131a7d15399ddc48652b362f1071","score":"0.5046923","text":"def load_policy(self, policy):\n pass","title":""},{"docid":"aaeee4f6f929a226380cbe405d533a97","score":"0.50342745","text":"def set_iam_policy(\n self,\n request: iam_policy_pb2.SetIamPolicyRequest = None,\n *,\n retry: retries.Retry = gapic_v1.method.DEFAULT,\n timeout: float = None,\n metadata: Sequence[Tuple[str, str]] = (),\n ) -> policy_pb2.Policy:\n # Create or coerce a protobuf request object.\n if isinstance(request, dict):\n # The request isn't a proto-plus wrapped type,\n # so it must be constructed via keyword expansion.\n request = iam_policy_pb2.SetIamPolicyRequest(**request)\n elif not request:\n # Null request, just make one.\n request = iam_policy_pb2.SetIamPolicyRequest()\n\n # Wrap the RPC method; this adds retry and timeout information,\n # and friendly error handling.\n rpc = self._transport._wrapped_methods[self._transport.set_iam_policy]\n\n # Certain fields should be provided within the metadata header;\n # add these here.\n metadata = tuple(metadata) + (\n gapic_v1.routing_header.to_grpc_metadata(((\"resource\", request.resource),)),\n )\n\n # Send the request.\n response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)\n\n # Done; return the response.\n return response","title":""},{"docid":"2bda2eaa957476ec7f8a50ac78eb1019","score":"0.50315076","text":"def grant_put_permission_to(self, role: aws.iam.Role) -> None:\n aws.iam.RolePolicy(\n f\"{role._name}-writes-objects-to-{self._name}\",\n role=role.name,\n policy=self.arn.apply(\n lambda bucket_arn: json.dumps(\n {\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Effect\": \"Allow\",\n \"Action\": \"s3:PutObject\",\n \"Resource\": f\"{bucket_arn}/*\",\n }\n ],\n }\n )\n ),\n opts=pulumi.ResourceOptions(parent=role),\n )","title":""},{"docid":"254d847f3a7cda610c0dc88fda1f14eb","score":"0.50277835","text":"def failure_policy(self, failure_policy):\n\n self._failure_policy = failure_policy","title":""},{"docid":"1c37234e2076d91b456c2d277365494d","score":"0.49952072","text":"def create_policy(name, description, actions, resource_arn):\n policy_doc = {\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Effect\": \"Allow\",\n \"Action\": actions,\n \"Resource\": resource_arn\n }\n ]\n }\n try:\n policy = iam.create_policy(\n PolicyName=name, Description=description,\n PolicyDocument=json.dumps(policy_doc))\n logger.info(\"Created policy %s.\", policy.arn)\n except ClientError:\n logger.exception(\"Couldn't create policy %s.\", name)\n raise\n else:\n return policy","title":""},{"docid":"ad937215f521d3b92e43c7a2e7b82440","score":"0.49939954","text":"def set_iam_policy(\n self,\n request: Optional[iam_policy_pb2.SetIamPolicyRequest] = None,\n *,\n retry: OptionalRetry = gapic_v1.method.DEFAULT,\n timeout: Union[float, object] = gapic_v1.method.DEFAULT,\n metadata: Sequence[Tuple[str, str]] = (),\n ) -> policy_pb2.Policy:\n # Create or coerce a protobuf request object.\n\n # The request isn't a proto-plus wrapped type,\n # so it must be constructed via keyword expansion.\n if isinstance(request, dict):\n request = iam_policy_pb2.SetIamPolicyRequest(**request)\n\n # Wrap the RPC method; this adds retry and timeout information,\n # and friendly error handling.\n rpc = gapic_v1.method.wrap_method(\n self._transport.set_iam_policy,\n default_timeout=None,\n client_info=DEFAULT_CLIENT_INFO,\n )\n\n # Certain fields should be provided within the metadata header;\n # add these here.\n metadata = tuple(metadata) + (\n gapic_v1.routing_header.to_grpc_metadata(((\"resource\", request.resource),)),\n )\n\n # Send the request.\n response = rpc(\n request,\n retry=retry,\n timeout=timeout,\n metadata=metadata,\n )\n\n # Done; return the response.\n return response","title":""},{"docid":"1e7970143f88e6ec22ca04846093a850","score":"0.49908513","text":"def set_device_policy(device_policy):\n if device_policy == 'silent':\n context.context().device_policy = context.DEVICE_PLACEMENT_SILENT\n elif device_policy == 'silent_for_int32':\n context.context().device_policy = context.DEVICE_PLACEMENT_SILENT_FOR_INT32\n elif device_policy == 'warn':\n context.context().device_policy = context.DEVICE_PLACEMENT_WARN\n elif device_policy == 'explicit':\n context.context().device_policy = context.DEVICE_PLACEMENT_EXPLICIT\n elif device_policy is None:\n context.context().device_policy = None\n else:\n raise ValueError('Not a valid device policy: %r' % device_policy)","title":""},{"docid":"3813ad93b73de96e9f5dd4874ab293af","score":"0.4988412","text":"def _enforce(self, req, action):\n try:\n self.policy.enforce(req.context, action)\n except heat_exception.Forbidden:\n msg = _(\"Action %s not allowed for user\") % action\n raise exception.HeatAccessDeniedError(msg)\n except Exception:\n # We expect policy.enforce to either pass or raise Forbidden\n # however, if anything else happens, we want to raise\n # HeatInternalFailureError, failure to do this results in\n # the user getting a big stacktrace spew as an API response\n msg = _(\"Error authorizing action %s\") % action\n raise exception.HeatInternalFailureError(msg)","title":""},{"docid":"e07e9e7fc3a972d5188cde37223380e9","score":"0.49798617","text":"def object_change_policy(self, account, container, obj, policy, **kwargs):\n meta, stream = self.object_fetch(account, container, obj, **kwargs)\n # Before we started generating predictable chunk IDs, it was possible\n # to change to the same policy: it just renewed all chunks and updated\n # the modification time.\n # Now that we generate predictable chunk IDs, we must change something\n # in the object description in order to get a different set of chunks\n # (we don't want to change the object version).\n if meta[\"policy\"] == policy:\n del stream\n raise exc.Conflict(\n \"The object is already using the %s storage policy\" % policy\n )\n kwargs[\"version\"] = meta[\"version\"]\n return self.object_create_ext(\n account,\n container,\n obj_name=meta[\"name\"],\n data=stream,\n policy=policy,\n change_policy=True,\n **kwargs\n )","title":""},{"docid":"28c8a95d2ffa623c7321a82f5a6f56a2","score":"0.49760813","text":"def auto_scaling_policy(self, auto_scaling_policy):\n self._auto_scaling_policy = auto_scaling_policy","title":""},{"docid":"a7028222209980f7233b6e397b41d8a0","score":"0.49747008","text":"def add_grading_policy(self, grading_policy):\n\n self.course.grading_policy = grading_policy\n self.update_course(self.course, self.student_user.id)\n self.refresh_course()","title":""},{"docid":"2bb7dc80fdb4824c9890ccb31c154e88","score":"0.4970173","text":"def scoring_policy(self, value):\r\n self.logger.warn(\"Setting values on scoring_policy will NOT update the remote Canvas instance.\")\r\n self._scoring_policy = value","title":""},{"docid":"945e3850adfd2891919e63ca98e443c4","score":"0.4963867","text":"def SetIamPolicy(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details(\"Method not implemented!\")\n raise NotImplementedError(\"Method not implemented!\")","title":""},{"docid":"945e3850adfd2891919e63ca98e443c4","score":"0.4963867","text":"def SetIamPolicy(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details(\"Method not implemented!\")\n raise NotImplementedError(\"Method not implemented!\")","title":""},{"docid":"99a3adcdbb764c25e9e0196c45837347","score":"0.49574706","text":"async def set_iam_policy(\n self,\n request: Optional[iam_policy_pb2.SetIamPolicyRequest] = None,\n *,\n retry: OptionalRetry = gapic_v1.method.DEFAULT,\n timeout: Union[float, object] = gapic_v1.method.DEFAULT,\n metadata: Sequence[Tuple[str, str]] = (),\n ) -> policy_pb2.Policy:\n # Create or coerce a protobuf request object.\n\n # The request isn't a proto-plus wrapped type,\n # so it must be constructed via keyword expansion.\n if isinstance(request, dict):\n request = iam_policy_pb2.SetIamPolicyRequest(**request)\n\n # Wrap the RPC method; this adds retry and timeout information,\n # and friendly error handling.\n rpc = gapic_v1.method.wrap_method(\n self._client._transport.set_iam_policy,\n default_timeout=None,\n client_info=DEFAULT_CLIENT_INFO,\n )\n\n # Certain fields should be provided within the metadata header;\n # add these here.\n metadata = tuple(metadata) + (\n gapic_v1.routing_header.to_grpc_metadata(((\"resource\", request.resource),)),\n )\n\n # Send the request.\n response = await rpc(\n request,\n retry=retry,\n timeout=timeout,\n metadata=metadata,\n )\n\n # Done; return the response.\n return response","title":""},{"docid":"3cda5d6c89eda901cbfa7b37c0667f53","score":"0.49320412","text":"def SetIamPolicy(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\n raise NotImplementedError()","title":""},{"docid":"45d78d1891e4c45a16941dadc0a0ccb0","score":"0.49171555","text":"async def set_policy_aad_unsecured(self):\n\n write_banner(\"set_policy_aad_unsecured\")\n print(\"Set an unsecured Policy on an AAD mode attestation instance.\")\n # [BEGIN set_policy_unsecured]\n async with DefaultAzureCredential() as credential, AttestationAdministrationClient(\n os.environ.get(\"ATTESTATION_AAD_URL\"), credential\n ) as admin_client:\n new_policy = \"\"\"\nversion= 1.0;\nauthorizationrules\n{\n [ type==\"x-ms-sgx-is-debuggable\", value==false ] &&\n [ type==\"x-ms-sgx-product-id\", value==1 ] &&\n [ type==\"x-ms-sgx-svn\", value>= 0 ] &&\n [ type==\"x-ms-sgx-mrsigner\", value==\"2c1a44952ae8207135c6c29b75b8c029372ee94b677e15c20bd42340f10d41aa\"]\n => permit();\n};\nissuancerules {\n c:[type==\"x-ms-sgx-mrsigner\"] => issue(type=\"My-MrSigner\", value=c.value);\n};\n \"\"\"\n\n set_result, _ = await admin_client.set_policy(\n AttestationType.OPEN_ENCLAVE, new_policy\n )\n print(\"Policy Set result: \", set_result.policy_resolution)\n # [END set_policy_unsecured]\n\n get_policy, _ = await admin_client.get_policy(AttestationType.OPEN_ENCLAVE)\n if new_policy != get_policy:\n print(\"Policy does not match set policy.\")\n # Attest an OpenEnclave using the new policy.\n await self._attest_open_enclave(os.environ.get(\"ATTESTATION_AAD_URL\"))","title":""},{"docid":"2a83314bb8063f8505e4f49ca8ab1a98","score":"0.49021062","text":"def SetIamPolicy(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\n raise NotImplementedError()","title":""},{"docid":"0128bc9b4702a144822bc2ac77aca7dd","score":"0.49009973","text":"def set_interactive_policy(*, locals=None, banner=None, serve=None,\n prompt_control=None):\n policy = InteractiveEventLoopPolicy(\n locals=locals,\n banner=banner,\n serve=serve,\n prompt_control=prompt_control)\n asyncio.set_event_loop_policy(policy)","title":""},{"docid":"a9866e188a7924529fc03cc3fa1de42f","score":"0.48997706","text":"def SetIamPolicy(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","title":""},{"docid":"f2121e22c850a7d268c5cc40db9134ea","score":"0.4899416","text":"def istio_authorization_policies(self, istio_authorization_policies):\n\n self._istio_authorization_policies = istio_authorization_policies","title":""},{"docid":"3cca192a7d2b3e83c1093c89dba3c496","score":"0.48831755","text":"def restart_policy(self, restart_policy):\n\n self._restart_policy = restart_policy","title":""},{"docid":"50de3157bcfcb639b479f8ce6278f09a","score":"0.48806158","text":"def update_policy(name, short_term_storage, long_term_storage, versioning, backup_duration):\n if not backup_duration and versioning:\n backup_duration = click.prompt(\n \"How many days backups of the datastorage will be stored? (Empty means deletion of the current rule)\",\n default=\"\")\n DataStorageOperations.policy(name, short_term_storage, long_term_storage, backup_duration, versioning)","title":""},{"docid":"35fae65c3bec60e40e6f8bea107dd669","score":"0.48776403","text":"def external_traffic_policy(self, external_traffic_policy):\n\n self._external_traffic_policy = external_traffic_policy","title":""},{"docid":"17865c02f427f4fe755fc7f9121ad54e","score":"0.48763523","text":"def write_authorize(cls, user, obj):\n if not obj.period.can_save(user):\n raise PermissionDenied()","title":""},{"docid":"f5ec5c42c5a13498297e0ca9224103da","score":"0.4874774","text":"def setCheckingPolicy(self, checkingPolicy: cern.rbac.common.authorization.CheckingPolicy) -> 'AccessCheckerRequestBuilder':\n ...","title":""}],"string":"[\n {\n \"docid\": \"43c26021a7a50ef9c9a7379a2cefa46d\",\n \"score\": \"0.7376781\",\n \"text\": \"def set_resource_policy(self, resource, policy):\\n\\n @lockutils.synchronized(self.lock_name)\\n def _set_resource_policy():\\n policy_id = self.resource_2_qos_policies.get(resource)\\n old_policy = self.known_policies.get(policy_id)\\n self.known_policies[policy.id] = policy\\n self.resource_2_qos_policies[resource] = policy.id\\n self.qos_policy_2_resources[policy.id].add(resource)\\n if old_policy and old_policy.id != policy.id:\\n self.qos_policy_2_resources[old_policy.id].remove(resource)\\n\\n _set_resource_policy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db34fd03f6a13178c904eb26f447d294\",\n \"score\": \"0.67079204\",\n \"text\": \"def set_iam_policy(\\n self,\\n resource,\\n policy,\\n retry=google.api_core.gapic_v1.method.DEFAULT,\\n timeout=google.api_core.gapic_v1.method.DEFAULT,\\n metadata=None,\\n ):\\n # Wrap the transport method to add retry and timeout logic.\\n if \\\"set_iam_policy\\\" not in self._inner_api_calls:\\n self._inner_api_calls[\\n \\\"set_iam_policy\\\"\\n ] = google.api_core.gapic_v1.method.wrap_method(\\n self.transport.set_iam_policy,\\n default_retry=self._method_configs[\\\"SetIamPolicy\\\"].retry,\\n default_timeout=self._method_configs[\\\"SetIamPolicy\\\"].timeout,\\n client_info=self._client_info,\\n )\\n\\n request = iam_policy_pb2.SetIamPolicyRequest(resource=resource, policy=policy)\\n if metadata is None:\\n metadata = []\\n metadata = list(metadata)\\n try:\\n routing_header = [(\\\"resource\\\", resource)]\\n except AttributeError:\\n pass\\n else:\\n routing_metadata = google.api_core.gapic_v1.routing_header.to_grpc_metadata(\\n routing_header\\n )\\n metadata.append(routing_metadata)\\n\\n return self._inner_api_calls[\\\"set_iam_policy\\\"](\\n request, retry=retry, timeout=timeout, metadata=metadata\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db34fd03f6a13178c904eb26f447d294\",\n \"score\": \"0.67079204\",\n \"text\": \"def set_iam_policy(\\n self,\\n resource,\\n policy,\\n retry=google.api_core.gapic_v1.method.DEFAULT,\\n timeout=google.api_core.gapic_v1.method.DEFAULT,\\n metadata=None,\\n ):\\n # Wrap the transport method to add retry and timeout logic.\\n if \\\"set_iam_policy\\\" not in self._inner_api_calls:\\n self._inner_api_calls[\\n \\\"set_iam_policy\\\"\\n ] = google.api_core.gapic_v1.method.wrap_method(\\n self.transport.set_iam_policy,\\n default_retry=self._method_configs[\\\"SetIamPolicy\\\"].retry,\\n default_timeout=self._method_configs[\\\"SetIamPolicy\\\"].timeout,\\n client_info=self._client_info,\\n )\\n\\n request = iam_policy_pb2.SetIamPolicyRequest(resource=resource, policy=policy)\\n if metadata is None:\\n metadata = []\\n metadata = list(metadata)\\n try:\\n routing_header = [(\\\"resource\\\", resource)]\\n except AttributeError:\\n pass\\n else:\\n routing_metadata = google.api_core.gapic_v1.routing_header.to_grpc_metadata(\\n routing_header\\n )\\n metadata.append(routing_metadata)\\n\\n return self._inner_api_calls[\\\"set_iam_policy\\\"](\\n request, retry=retry, timeout=timeout, metadata=metadata\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ba4d69be746325e853980f9a6f4ee3d\",\n \"score\": \"0.6511385\",\n \"text\": \"def policy(self, policy):\\n\\n self._policy = policy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6c5bfefe93820638a51a3a234b42790\",\n \"score\": \"0.64612085\",\n \"text\": \"def set_iam_policy(self, policy):\\n table_client = self._instance._client.table_admin_client\\n resp = table_client.set_iam_policy(\\n request={\\\"resource\\\": self.name, \\\"policy\\\": policy.to_pb()}\\n )\\n return Policy.from_pb(resp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3678a0df4233c2bcbda47d811913151c\",\n \"score\": \"0.63890755\",\n \"text\": \"def set_iam_policy(self, policy):\\n instance_admin_client = self._client.instance_admin_client\\n resp = instance_admin_client.set_iam_policy(\\n request={\\\"resource\\\": self.name, \\\"policy\\\": policy.to_pb()}\\n )\\n return Policy.from_pb(resp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a89ec0988bff1b9e978a9fea14f991c\",\n \"score\": \"0.6303304\",\n \"text\": \"def put_policy(self, policy):\\n try:\\n self.bucket.Policy().put(Policy=json.dumps(policy))\\n logger.info(\\\"Put policy %s for bucket '%s'.\\\", policy, self.bucket.name)\\n except ClientError:\\n logger.exception(\\\"Couldn't apply policy to bucket '%s'.\\\", self.bucket.name)\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cb567bb19d9e41e66d792e8a8539ccf\",\n \"score\": \"0.61875004\",\n \"text\": \"def set_policy(cmd, client, resource_group_name, vault_name,\\n object_id=None, application_id=None, spn=None, upn=None, key_permissions=None, secret_permissions=None,\\n certificate_permissions=None, storage_permissions=None, no_wait=False):\\n\\n VaultCreateOrUpdateParameters = cmd.get_models('VaultCreateOrUpdateParameters',\\n resource_type=ResourceType.MGMT_KEYVAULT)\\n AccessPolicyEntry = cmd.get_models('AccessPolicyEntry', resource_type=ResourceType.MGMT_KEYVAULT)\\n Permissions = cmd.get_models('Permissions', resource_type=ResourceType.MGMT_KEYVAULT)\\n object_id = _object_id_args_helper(cmd.cli_ctx, object_id, spn, upn)\\n vault = client.get(resource_group_name=resource_group_name,\\n vault_name=vault_name)\\n\\n key_permissions = _permissions_distinct(key_permissions)\\n secret_permissions = _permissions_distinct(secret_permissions)\\n certificate_permissions = _permissions_distinct(certificate_permissions)\\n storage_permissions = _permissions_distinct(storage_permissions)\\n\\n try:\\n enable_rbac_authorization = getattr(vault.properties, 'enable_rbac_authorization')\\n except: # pylint: disable=bare-except\\n pass\\n else:\\n if enable_rbac_authorization:\\n raise CLIError('Cannot set policies to a vault with \\\\'--enable-rbac-authorization\\\\' specified')\\n\\n # Find the existing policy to set\\n policy = next((p for p in vault.properties.access_policies\\n if object_id.lower() == p.object_id.lower() and\\n (application_id or '').lower() == (p.application_id or '').lower() and\\n vault.properties.tenant_id.lower() == p.tenant_id.lower()), None)\\n if not policy:\\n # Add new policy as none found\\n vault.properties.access_policies.append(AccessPolicyEntry(\\n tenant_id=vault.properties.tenant_id,\\n object_id=object_id,\\n application_id=application_id,\\n permissions=Permissions(keys=key_permissions,\\n secrets=secret_permissions,\\n certificates=certificate_permissions,\\n storage=storage_permissions)))\\n else:\\n # Modify existing policy.\\n # If key_permissions is not set, use prev. value (similarly with secret_permissions).\\n keys = policy.permissions.keys if key_permissions is None else key_permissions\\n secrets = policy.permissions.secrets if secret_permissions is None else secret_permissions\\n certs = policy.permissions.certificates \\\\\\n if certificate_permissions is None else certificate_permissions\\n storage = policy.permissions.storage if storage_permissions is None else storage_permissions\\n policy.permissions = Permissions(keys=keys, secrets=secrets, certificates=certs, storage=storage)\\n\\n return _azure_stack_wrapper(cmd, client, 'create_or_update',\\n resource_type=ResourceType.MGMT_KEYVAULT,\\n min_api_version='2018-02-14',\\n resource_group_name=resource_group_name,\\n vault_name=vault_name,\\n parameters=VaultCreateOrUpdateParameters(\\n location=vault.location,\\n tags=vault.tags,\\n properties=vault.properties),\\n no_wait=no_wait)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44d0aa117a57e5df63889bbcd878b942\",\n \"score\": \"0.6023322\",\n \"text\": \"def set_policy(self, policy_name):\\n assert (type(policy_name) == str)\\n\\n params = bytes(policy_name, 'ascii')\\n if len(params) > 8:\\n raise RuntimeError(\\\"Policy name is too long\\\")\\n params += bytes(8 - len(params))\\n\\n self._send_and_check(api.Request(api.MKE_REQUEST_SET_POLICY,\\n params=params),\\n [api.MKE_REPLY_OK])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecce04a3d65d59dac5bbe208c5448537\",\n \"score\": \"0.59802914\",\n \"text\": \"def set_bucket_policy(self, bucket_name, policy):\\n check_bucket_name(bucket_name)\\n is_valid_policy_type(policy)\\n self._execute(\\n \\\"PUT\\\",\\n bucket_name,\\n body=policy,\\n headers={\\\"Content-MD5\\\": md5sum_hash(policy)},\\n query_params={\\\"policy\\\": \\\"\\\"},\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c197c17468bf8a78cf54e44db635b85\",\n \"score\": \"0.596857\",\n \"text\": \"def rewritepolicy(self, rewritepolicy) :\\n\\t\\ttry :\\n\\t\\t\\tself._rewritepolicy = rewritepolicy\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ac3fa6bf03d6ca53082112e6634c986\",\n \"score\": \"0.5948425\",\n \"text\": \"def _update_policy(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d07035cb31e53823209b7b5e64cbc728\",\n \"score\": \"0.59340125\",\n \"text\": \"def set_policy(self, name, rules):\\n if isinstance(rules, dict):\\n rules = json.dumps(rules)\\n params = {\\n 'rules': rules,\\n }\\n api_path = '/v1/sys/policy/{name}'.format(\\n name=name,\\n )\\n self._adapter.put(api_path, json=params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beea47a3a1adf91defe3bc1bd359b10f\",\n \"score\": \"0.5890776\",\n \"text\": \"def _set_iam_policies(self, file_name, role, policy_name):\\n\\n with open(file_name, 'r') as policy_file:\\n policy_string = policy_file.read()\\n\\n deploy_template = jinja2.Template(policy_string)\\n policy_string = deploy_template.render(cluster_name=self.cluster_name,\\n aws_account=self.cluster_config['aws_account'],\\n s3_bucket=self.cluster_config['s3_bucket'],\\n dns_zone=self.cluster_config['dns_zone_id'])\\n\\n # Loop through inline policies for this role and update the one with the right name\\n for policy in role.policies.all():\\n if policy.policy_name == policy_name:\\n logging.info(\\\"Updating policy %s\\\", policy_name)\\n policy.put(PolicyDocument=policy_string)\\n return\\n\\n raise SystemError('Policy not found')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c9ea07102eeaa93625b53cb9dab8e7\",\n \"score\": \"0.5769831\",\n \"text\": \"def set_project_policy(service, project, policy):\\n policy = service.projects().setIamPolicy(\\n resource=project, body={\\n 'policy': policy\\n }).execute()\\n return policy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a02e347c845aca3289ebbc10a87b053e\",\n \"score\": \"0.5739763\",\n \"text\": \"def enablePolicy(self, policy):\\n self.log.debug(\\\"Enabling policy %s\\\", str(policy))\\n policyInfo = self.policyDatabase.getPolicyInfo(policy.id)\\n if policyInfo is None:\\n self.log.error(\\\"Unable to get policy from the database: %s\\\", str(policy))\\n return\\n # enable the policy in memory and in the database\\n if not policy.isEnabled():\\n policy.state = States.ENABLED\\n self.policyDatabase.enablePolicy(policy.id)\\n else:\\n self.log.info(\\\"Policy is already enabled %s\\\", str(policy))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1035aa37685e605ba17fc5a77e6d7291\",\n \"score\": \"0.5714539\",\n \"text\": \"def update_policy(self,\\n id,\\n name,\\n datacenters=None,\\n description=None,\\n rules=None):\\n return self._put_response_body([\\\"policy\\\", id], {},\\n dict(\\n model.ACLPolicy(\\n name=name,\\n datacenters=datacenters,\\n description=description,\\n rules=rules)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae527c3afb0fa7dd40eea5f74298504a\",\n \"score\": \"0.5702328\",\n \"text\": \"def policy_uri(self, policy_uri):\\n\\n self._policy_uri = policy_uri\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f36d1a285b1fc0ddfba8b08ff333ea8\",\n \"score\": \"0.563909\",\n \"text\": \"def update_policy():\\n self.policy = self.inner + (parallel(rps) & self.query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403c697b840a3186db5363ed67e9c797\",\n \"score\": \"0.56348604\",\n \"text\": \"def policies(self, policies):\\n\\n self._policies = policies\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73a3a726c7ae1098e33fd299db16e628\",\n \"score\": \"0.5606487\",\n \"text\": \"def set_forecast_permissions(self, name, data_bucket_name_resource: CfnResource):\\n function = self.functions[name]\\n function.role.attach_inline_policy(\\n self.policies.forecast_read_write_policy(name)\\n )\\n function.role.attach_inline_policy(\\n self.policies.s3_bucket_read_policy(name, data_bucket_name_resource)\\n )\\n function.role.attach_inline_policy(self.policies.forecast_kms_read_policy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da8fe4e55be1c74cfd9345477cd4ca4b\",\n \"score\": \"0.55380553\",\n \"text\": \"def policy_rules(self, policy_rules):\\n\\n self._policy_rules = policy_rules\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba052a5cc26f1cbda6be5dc65bc5cae2\",\n \"score\": \"0.5526146\",\n \"text\": \"def dns_policy(self, dns_policy):\\n\\n self._dns_policy = dns_policy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd5b79afbf6ceaa1dd55b156e95bf4ff\",\n \"score\": \"0.550821\",\n \"text\": \"def __attach_inline_policy(self, username, policy_document):\\n client = self.client\\n\\n response = client.put_user_policy(\\n UserName=username,\\n PolicyName=\\\"threatresponse-temporal-key-revocation\\\",\\n PolicyDocument=policy_document\\n )\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbb8102bae8047e236675c6b8a29d725\",\n \"score\": \"0.5502422\",\n \"text\": \"def cachepolicy(self, cachepolicy) :\\n\\t\\ttry :\\n\\t\\t\\tself._cachepolicy = cachepolicy\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b94b11b9fdee7f235df0368f83ce8668\",\n \"score\": \"0.54893875\",\n \"text\": \"def AddPolicy (self, policy):\\n self.policies.append(policy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e27165074db779b7f4642f89bf61da32\",\n \"score\": \"0.54777825\",\n \"text\": \"def responderpolicy(self, responderpolicy) :\\n\\t\\ttry :\\n\\t\\t\\tself._responderpolicy = responderpolicy\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f5e04253171804d0194a67ecdc9ed6e\",\n \"score\": \"0.5467731\",\n \"text\": \"def update_policy():\\n self.policy = self.forward + self.query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"956f02f04a7094a429188784fbbbb65e\",\n \"score\": \"0.5461217\",\n \"text\": \"def full_policy(self, resource: str) -> dict:\\n return dict(Version=\\\"2012-10-17\\\", Statement=[dict(Effect=\\\"Allow\\\", Resource=\\\"*\\\", Action=f\\\"{resource}:*\\\")])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65682b03d6ce1132e316f3c1a4549862\",\n \"score\": \"0.5456855\",\n \"text\": \"def block_policy(self, block_policy):\\n\\n self._block_policy = block_policy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6630aa59baa2291d15952368224f9941\",\n \"score\": \"0.5426399\",\n \"text\": \"def SetIamPolicy(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f086e1ddaeb8d88f0d539bd28394a5ab\",\n \"score\": \"0.5418963\",\n \"text\": \"def set_acl(self, acl_or_str, key_name='', headers=None, version_id=None,\\n generation=None, if_generation=None, if_metageneration=None):\\n if isinstance(acl_or_str, Policy):\\n raise InvalidAclError('Attempt to set S3 Policy on GS ACL')\\n elif isinstance(acl_or_str, ACL):\\n self.set_xml_acl(acl_or_str.to_xml(), key_name, headers=headers,\\n generation=generation,\\n if_generation=if_generation,\\n if_metageneration=if_metageneration)\\n else:\\n self.set_canned_acl(acl_or_str, key_name, headers=headers,\\n generation=generation,\\n if_generation=if_generation,\\n if_metageneration=if_metageneration)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5e264e01e6d5771bddf7dbe842151e\",\n \"score\": \"0.54021513\",\n \"text\": \"def set_access_policy( # pylint: disable=inconsistent-return-statements\\n self,\\n timeout: Optional[int] = None,\\n request_id_parameter: Optional[str] = None,\\n queue_acl: Optional[List[_models.SignedIdentifier]] = None,\\n **kwargs: Any\\n ) -> None:\\n error_map = {\\n 401: ClientAuthenticationError,\\n 404: ResourceNotFoundError,\\n 409: ResourceExistsError,\\n 304: ResourceNotModifiedError,\\n }\\n error_map.update(kwargs.pop(\\\"error_map\\\", {}) or {})\\n\\n _headers = case_insensitive_dict(kwargs.pop(\\\"headers\\\", {}) or {})\\n _params = case_insensitive_dict(kwargs.pop(\\\"params\\\", {}) or {})\\n\\n comp: Literal[\\\"acl\\\"] = kwargs.pop(\\\"comp\\\", _params.pop(\\\"comp\\\", \\\"acl\\\"))\\n content_type: str = kwargs.pop(\\\"content_type\\\", _headers.pop(\\\"Content-Type\\\", \\\"application/xml\\\"))\\n cls: ClsType[None] = kwargs.pop(\\\"cls\\\", None)\\n\\n serialization_ctxt = {\\\"xml\\\": {\\\"name\\\": \\\"SignedIdentifiers\\\", \\\"wrapped\\\": True}}\\n if queue_acl is not None:\\n _content = self._serialize.body(\\n queue_acl, \\\"[SignedIdentifier]\\\", is_xml=True, serialization_ctxt=serialization_ctxt\\n )\\n else:\\n _content = None\\n\\n request = build_set_access_policy_request(\\n url=self._config.url,\\n timeout=timeout,\\n request_id_parameter=request_id_parameter,\\n comp=comp,\\n content_type=content_type,\\n version=self._config.version,\\n content=_content,\\n template_url=self.set_access_policy.metadata[\\\"url\\\"],\\n headers=_headers,\\n params=_params,\\n )\\n request = _convert_request(request)\\n request.url = self._client.format_url(request.url)\\n\\n _stream = False\\n pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access\\n request, stream=_stream, **kwargs\\n )\\n\\n response = pipeline_response.http_response\\n\\n if response.status_code not in [204]:\\n map_error(status_code=response.status_code, response=response, error_map=error_map)\\n error = self._deserialize.failsafe_deserialize(_models.StorageError, pipeline_response)\\n raise HttpResponseError(response=response, model=error)\\n\\n response_headers = {}\\n response_headers[\\\"x-ms-request-id\\\"] = self._deserialize(\\\"str\\\", response.headers.get(\\\"x-ms-request-id\\\"))\\n response_headers[\\\"x-ms-version\\\"] = self._deserialize(\\\"str\\\", response.headers.get(\\\"x-ms-version\\\"))\\n response_headers[\\\"Date\\\"] = self._deserialize(\\\"rfc-1123\\\", response.headers.get(\\\"Date\\\"))\\n\\n if cls:\\n return cls(pipeline_response, None, response_headers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ef50ce64babd6c77da321acf1c7b947\",\n \"score\": \"0.53903216\",\n \"text\": \"def billing_policy(self, billing_policy):\\n\\n\\n self._billing_policy = billing_policy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64a0deeba01bbab8e768b9a32576ebf2\",\n \"score\": \"0.53304446\",\n \"text\": \"def set_policy_state(self, policy_state: dict) -> None:\\n self._policy.set_state(policy_state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24034104d470a702a5afa6117f78f7ca\",\n \"score\": \"0.53252965\",\n \"text\": \"def SetIamPolicy(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25fd10c66eab40c25b387a4db764d2dc\",\n \"score\": \"0.5317774\",\n \"text\": \"def authenticationpolicy(self, authenticationpolicy) :\\n\\t\\ttry :\\n\\t\\t\\tself._authenticationpolicy = authenticationpolicy\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"752d0d2962249fc3c4561aa78d3fc7c2\",\n \"score\": \"0.52665293\",\n \"text\": \"async def set_iam_policy(\\n self,\\n request: Optional[Union[iam_policy_pb2.SetIamPolicyRequest, dict]] = None,\\n *,\\n resource: Optional[str] = None,\\n retry: OptionalRetry = gapic_v1.method.DEFAULT,\\n timeout: Union[float, object] = gapic_v1.method.DEFAULT,\\n metadata: Sequence[Tuple[str, str]] = (),\\n ) -> policy_pb2.Policy:\\n # Create or coerce a protobuf request object.\\n # Quick check: If we got a request object, we should *not* have\\n # gotten any keyword arguments that map to the request.\\n has_flattened_params = any([resource])\\n if request is not None and has_flattened_params:\\n raise ValueError(\\n \\\"If the `request` argument is set, then none of \\\"\\n \\\"the individual field arguments should be set.\\\"\\n )\\n\\n # The request isn't a proto-plus wrapped type,\\n # so it must be constructed via keyword expansion.\\n if isinstance(request, dict):\\n request = iam_policy_pb2.SetIamPolicyRequest(**request)\\n elif not request:\\n request = iam_policy_pb2.SetIamPolicyRequest(\\n resource=resource,\\n )\\n\\n # Wrap the RPC method; this adds retry and timeout information,\\n # and friendly error handling.\\n rpc = gapic_v1.method_async.wrap_method(\\n self._client._transport.set_iam_policy,\\n default_timeout=60.0,\\n client_info=DEFAULT_CLIENT_INFO,\\n )\\n\\n # Certain fields should be provided within the metadata header;\\n # add these here.\\n metadata = tuple(metadata) + (\\n gapic_v1.routing_header.to_grpc_metadata(((\\\"resource\\\", request.resource),)),\\n )\\n\\n # Send the request.\\n response = await rpc(\\n request,\\n retry=retry,\\n timeout=timeout,\\n metadata=metadata,\\n )\\n\\n # Done; return the response.\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec5cd7d508e01fca5f6bf530e476fa58\",\n \"score\": \"0.52648395\",\n \"text\": \"def set_bucket_policy(self,bucket):\\n policy = \\\"\\\"\\\"\\n {\\n \\\"Version\\\":\\\"2012-10-17\\\",\\n \\\"Statement\\\":[\\n {\\n \\\"Sid\\\":\\\"PublicRead\\\",\\n \\\"Effect\\\":\\\"Allow\\\",\\n \\\"Principal\\\": \\\"*\\\",\\n \\\"Action\\\":[\\\"s3:GetObject\\\"],\\n \\\"Resource\\\":[\\\"arn:aws:s3:::%s/*\\\"]\\n }\\n ]\\n }\\n \\\"\\\"\\\" % bucket.name\\n #remove any space\\n policy = policy.strip()\\n pol = bucket.Policy()\\n pol.put(Policy=policy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fa922c1920f39af328c7e0e3de71f97\",\n \"score\": \"0.5261521\",\n \"text\": \"def set_acl(self, path, version, acl):\\n pc = utils.StatePipeCondition()\\n ok = zookeeper.aset_acl(self._zhandle, path, version, acl,\\n functools.partial(_generic_completion,\\n pc))\\n assert ok == zookeeper.OK\\n results = pc.wait_and_get()\\n pc.close()\\n #unpack result as void_completion\\n handle, rc = results\\n assert handle == self._zhandle\\n if rc == zookeeper.OK:\\n return rc\\n self._raise_exception(rc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4f1be8c19ba518c85957cc311aa6fd\",\n \"score\": \"0.52539706\",\n \"text\": \"def SetIamPolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('SetIamPolicy')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4f1be8c19ba518c85957cc311aa6fd\",\n \"score\": \"0.52539706\",\n \"text\": \"def SetIamPolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('SetIamPolicy')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4f1be8c19ba518c85957cc311aa6fd\",\n \"score\": \"0.52539706\",\n \"text\": \"def SetIamPolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('SetIamPolicy')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4f1be8c19ba518c85957cc311aa6fd\",\n \"score\": \"0.52539706\",\n \"text\": \"def SetIamPolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('SetIamPolicy')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4f1be8c19ba518c85957cc311aa6fd\",\n \"score\": \"0.52539706\",\n \"text\": \"def SetIamPolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('SetIamPolicy')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4f1be8c19ba518c85957cc311aa6fd\",\n \"score\": \"0.52539706\",\n \"text\": \"def SetIamPolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('SetIamPolicy')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4f1be8c19ba518c85957cc311aa6fd\",\n \"score\": \"0.52539706\",\n \"text\": \"def SetIamPolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('SetIamPolicy')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4f1be8c19ba518c85957cc311aa6fd\",\n \"score\": \"0.52539706\",\n \"text\": \"def SetIamPolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('SetIamPolicy')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4f1be8c19ba518c85957cc311aa6fd\",\n \"score\": \"0.52539706\",\n \"text\": \"def SetIamPolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('SetIamPolicy')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4f1be8c19ba518c85957cc311aa6fd\",\n \"score\": \"0.52539706\",\n \"text\": \"def SetIamPolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('SetIamPolicy')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8271b1ed755415fd8ba7b1f9f79a7945\",\n \"score\": \"0.5248899\",\n \"text\": \"def enable_fpolicy_policy(self, share_name, policy_name, sequence_number):\\n volume = self._get_volume_by_args(vol_name=share_name)\\n svm_uuid = volume['svm']['uuid']\\n body = {\\n 'priority': sequence_number,\\n }\\n\\n self.send_request(\\n f'/protocols/fpolicy/{svm_uuid}/policies/{policy_name}', 'patch',\\n body=body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"335e516e2b1058f7980eace0556dbc89\",\n \"score\": \"0.52447236\",\n \"text\": \"def SetIamPolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('SetIamPolicy')\\n return self._RunMethod(config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"335e516e2b1058f7980eace0556dbc89\",\n \"score\": \"0.52447236\",\n \"text\": \"def SetIamPolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('SetIamPolicy')\\n return self._RunMethod(config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16e49b25fc9f6034915d647bc4d9a9c9\",\n \"score\": \"0.52340275\",\n \"text\": \"def _confirm_and_lock_retention_policy(self, api_client, bucket_resource,\\n request_config):\\n lock_prompt = (\\n 'This will permanently set the retention policy on \\\"{}\\\" to the'\\n ' following:\\\\n\\\\n{}\\\\n\\\\nThis setting cannot be reverted. Continue? '\\n ).format(self._bucket_resource, bucket_resource.retention_policy)\\n\\n if not bucket_resource.retention_policy:\\n raise command_errors.Error(\\n 'Bucket \\\"{}\\\" does not have a retention policy.'.format(\\n self._bucket_resource))\\n elif bucket_resource.retention_policy_is_locked:\\n log.error('Retention policy on \\\"{}\\\" is already locked.'.format(\\n self._bucket_resource))\\n elif console_io.PromptContinue(message=lock_prompt, default=False):\\n log.status.Print('Locking retention policy on {}...'.format(\\n self._bucket_resource))\\n api_client.lock_bucket_retention_policy(bucket_resource, request_config)\\n else:\\n # Gsutil does not update the exit code here, so we cannot use\\n # cancel_or_no with PromptContinue.\\n log.error('Abort locking retention policy on \\\"{}\\\".'.format(\\n self._bucket_resource))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8517a81a6411b783dfc84575658ab955\",\n \"score\": \"0.5215342\",\n \"text\": \"def device_policy(policy):\\n ctx = context()\\n old_policy = ctx.device_policy\\n try:\\n ctx.device_policy = policy\\n yield\\n finally:\\n ctx.device_policy = old_policy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cefb29a09235947182c721b3dd09d28e\",\n \"score\": \"0.5215194\",\n \"text\": \"def set_enforcement(self, result, match_code='550',\\n match_message='5.7.1 Access denied'):\\n if result.lower() not in ['pass', 'permerror', 'fail', 'temperror',\\n 'softfail', 'none', 'neutral']:\\n raise ValueError(result)\\n self.policies[result.lower()] = (match_code, match_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c88c672c5fd709f096b8c54b12f111\",\n \"score\": \"0.521288\",\n \"text\": \"def set_def_acl(self, acl_or_str, headers=None):\\n if isinstance(acl_or_str, Policy):\\n raise InvalidAclError('Attempt to set S3 Policy on GS ACL')\\n elif isinstance(acl_or_str, ACL):\\n self.set_def_xml_acl(acl_or_str.to_xml(), headers=headers)\\n else:\\n self.set_def_canned_acl(acl_or_str, headers=headers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e7ff2c4eb05c9a7534d8245854c0fd8\",\n \"score\": \"0.5203805\",\n \"text\": \"def icapolicy(self, icapolicy) :\\n\\t\\ttry :\\n\\t\\t\\tself._icapolicy = icapolicy\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc7fe5176cd4d2f2eff8f2652979e61\",\n \"score\": \"0.51957875\",\n \"text\": \"def put(self, PolicyDocument: str):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc7fe5176cd4d2f2eff8f2652979e61\",\n \"score\": \"0.51957875\",\n \"text\": \"def put(self, PolicyDocument: str):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc7fe5176cd4d2f2eff8f2652979e61\",\n \"score\": \"0.51957875\",\n \"text\": \"def put(self, PolicyDocument: str):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48d3f6b4a700a1f4de34904b41004711\",\n \"score\": \"0.5192792\",\n \"text\": \"def policy_scope(policy):\\n old_policy = _global_policy\\n try:\\n set_global_policy(policy)\\n yield\\n finally:\\n set_global_policy(old_policy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3092c317db587e5d3c713f3a250b8e9\",\n \"score\": \"0.5181993\",\n \"text\": \"def set_access_control(self, timeout=None, owner=None, group=None, posix_permissions=None, posix_acl=None, request_id=None, lease_access_conditions=None, modified_access_conditions=None, cls=None, **kwargs):\\n error_map = kwargs.pop('error_map', None)\\n lease_id = None\\n if lease_access_conditions is not None:\\n lease_id = lease_access_conditions.lease_id\\n if_match = None\\n if modified_access_conditions is not None:\\n if_match = modified_access_conditions.if_match\\n if_none_match = None\\n if modified_access_conditions is not None:\\n if_none_match = modified_access_conditions.if_none_match\\n if_modified_since = None\\n if modified_access_conditions is not None:\\n if_modified_since = modified_access_conditions.if_modified_since\\n if_unmodified_since = None\\n if modified_access_conditions is not None:\\n if_unmodified_since = modified_access_conditions.if_unmodified_since\\n\\n action = \\\"setAccessControl\\\"\\n\\n # Construct URL\\n url = self.set_access_control.metadata['url']\\n path_format_arguments = {\\n 'url': self._serialize.url(\\\"self._config.url\\\", self._config.url, 'str', skip_quote=True)\\n }\\n url = self._client.format_url(url, **path_format_arguments)\\n\\n # Construct parameters\\n query_parameters = {}\\n if timeout is not None:\\n query_parameters['timeout'] = self._serialize.query(\\\"timeout\\\", timeout, 'int', minimum=0)\\n query_parameters['action'] = self._serialize.query(\\\"action\\\", action, 'str')\\n\\n # Construct headers\\n header_parameters = {}\\n if owner is not None:\\n header_parameters['x-ms-owner'] = self._serialize.header(\\\"owner\\\", owner, 'str')\\n if group is not None:\\n header_parameters['x-ms-group'] = self._serialize.header(\\\"group\\\", group, 'str')\\n if posix_permissions is not None:\\n header_parameters['x-ms-permissions'] = self._serialize.header(\\\"posix_permissions\\\", posix_permissions, 'str')\\n if posix_acl is not None:\\n header_parameters['x-ms-acl'] = self._serialize.header(\\\"posix_acl\\\", posix_acl, 'str')\\n if request_id is not None:\\n header_parameters['x-ms-client-request-id'] = self._serialize.header(\\\"request_id\\\", request_id, 'str')\\n header_parameters['x-ms-version'] = self._serialize.header(\\\"self._config.version\\\", self._config.version, 'str')\\n if lease_id is not None:\\n header_parameters['x-ms-lease-id'] = self._serialize.header(\\\"lease_id\\\", lease_id, 'str')\\n if if_match is not None:\\n header_parameters['If-Match'] = self._serialize.header(\\\"if_match\\\", if_match, 'str')\\n if if_none_match is not None:\\n header_parameters['If-None-Match'] = self._serialize.header(\\\"if_none_match\\\", if_none_match, 'str')\\n if if_modified_since is not None:\\n header_parameters['If-Modified-Since'] = self._serialize.header(\\\"if_modified_since\\\", if_modified_since, 'rfc-1123')\\n if if_unmodified_since is not None:\\n header_parameters['If-Unmodified-Since'] = self._serialize.header(\\\"if_unmodified_since\\\", if_unmodified_since, 'rfc-1123')\\n\\n # Construct and send request\\n request = self._client.patch(url, query_parameters, header_parameters)\\n pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)\\n response = pipeline_response.http_response\\n\\n if response.status_code not in [200]:\\n map_error(status_code=response.status_code, response=response, error_map=error_map)\\n raise models.DataLakeStorageErrorException(response, self._deserialize)\\n\\n if cls:\\n response_headers = {\\n 'Date': self._deserialize('rfc-1123', response.headers.get('Date')),\\n 'ETag': self._deserialize('str', response.headers.get('ETag')),\\n 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')),\\n 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')),\\n 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')),\\n 'x-ms-client-request-id': self._deserialize('str', response.headers.get('x-ms-client-request-id')),\\n }\\n return cls(response, None, response_headers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfc77def82982f7cdf06d605f562ee76\",\n \"score\": \"0.51793885\",\n \"text\": \"def policyname(self, policyname) :\\n\\t\\ttry :\\n\\t\\t\\tself._policyname = policyname\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65f08fc466375e3cbc2e2dc0d8bd1624\",\n \"score\": \"0.51673204\",\n \"text\": \"def set_locking_policy(self, locking_policy):\\n\\n self._notnull()\\n error = error_details_t()\\n result = lib.set_locking_policy(self, locking_policy, byref(error))\\n error.raise_on_error()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c395d5d6fd2c351cc9ef48785401dd0\",\n \"score\": \"0.515749\",\n \"text\": \"def _add_s3_access_policies_to_role(\\n self, node: Union[HeadNode, BaseQueue, LoginNodesPool], role_ref: str, name: str\\n ):\\n read_only_s3_resources = []\\n read_write_s3_resources = []\\n for s3_access in node.iam.s3_access:\\n for resource in s3_access.resource_regex:\\n arn = self._format_arn(service=\\\"s3\\\", resource=resource, region=\\\"\\\", account=\\\"\\\")\\n if s3_access.enable_write_access:\\n read_write_s3_resources.append(arn)\\n else:\\n read_only_s3_resources.append(arn)\\n _, policy_name = add_cluster_iam_resource_prefix(\\n self._config.cluster_name, self._config, \\\"S3Access\\\", iam_type=\\\"AWS::IAM::Policy\\\"\\n )\\n\\n s3_access_policy = iam.CfnPolicy(\\n Stack.of(self),\\n name,\\n policy_document=iam.PolicyDocument(statements=[]),\\n roles=[role_ref],\\n policy_name=policy_name or \\\"S3Access\\\",\\n )\\n\\n if read_only_s3_resources:\\n s3_access_policy.policy_document.add_statements(\\n iam.PolicyStatement(\\n sid=\\\"S3Read\\\",\\n effect=iam.Effect.ALLOW,\\n actions=[\\\"s3:Get*\\\", \\\"s3:List*\\\"],\\n resources=read_only_s3_resources,\\n )\\n )\\n\\n if read_write_s3_resources:\\n s3_access_policy.policy_document.add_statements(\\n iam.PolicyStatement(\\n sid=\\\"S3ReadWrite\\\", effect=iam.Effect.ALLOW, actions=[\\\"s3:*\\\"], resources=read_write_s3_resources\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dd5ddb75cac9857f880b02a066efe93\",\n \"score\": \"0.5149983\",\n \"text\": \"def update_policy(self,policy):\\n grant = self.cleaned_data['grant']\\n if grant != 'no':\\n policy.default_grant = True\\n policy.minimum_grade = grant\\n else:\\n policy.default_grant = False\\n policy.minimum_grade = None\\n policy.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f54e743097f8a34ebc704bf25493668\",\n \"score\": \"0.51313686\",\n \"text\": \"def sync_policy(self, sync_policy):\\n\\n self._sync_policy = sync_policy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70f97c4e37956c1d1b3cc01d6a33f4c9\",\n \"score\": \"0.51287806\",\n \"text\": \"async def set_policy_aad_secured(self):\\n\\n write_banner(\\\"set_policy_aad_secured\\\")\\n print(\\\"Set Secured Policy on an AAD mode attestation instance.\\\")\\n async with DefaultAzureCredential() as credential, AttestationAdministrationClient(\\n os.environ.get(\\\"ATTESTATION_AAD_URL\\\"), credential\\n ) as admin_client:\\n\\n # [START set_secured_policy]\\n # Create an RSA Key and wrap an X.509 certificate around\\n # the public key for that certificate.\\n rsa_key = create_rsa_key()\\n cert = create_x509_certificate(rsa_key, u\\\"TestCertificate\\\")\\n\\n # Set a minimal policy.\\n set_result, _ = await admin_client.set_policy(\\n AttestationType.SGX_ENCLAVE,\\n \\\"\\\"\\\"version= 1.0;authorizationrules{=> permit();};issuancerules {};\\\"\\\"\\\",\\n signing_key=rsa_key,\\n signing_certificate=cert,\\n )\\n print(\\\"Policy Set Resolution: \\\", set_result.policy_resolution)\\n print(\\\"Resulting policy signer should match the input certificate:\\\")\\n print(\\\"Policy Signer: \\\", set_result.policy_signer.certificates[0])\\n print(\\\"Certificate: \\\", cert)\\n # [END set_secured_policy]\\n\\n # Reset the policy now that we're done.\\n await admin_client.reset_policy(AttestationType.SGX_ENCLAVE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c3888a648acf8a6b3be088250f8678\",\n \"score\": \"0.5115968\",\n \"text\": \"def _enforce(self, req, action, target=None):\\n if target is None:\\n target = {}\\n try:\\n self.policy.enforce(req.context, action, target)\\n except exception.Forbidden:\\n raise HTTPForbidden()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"328e0272a235290e256ce45b1f2819fa\",\n \"score\": \"0.5099834\",\n \"text\": \"def update_policy():\\n self.policy = self.forward + (wp & self.query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f34a5087c07f0417df6451fde75a138f\",\n \"score\": \"0.50747025\",\n \"text\": \"def test_set_bucket_policy_readonly(log_entry):\\n\\n # Get a unique bucket_name\\n bucket_name = _gen_bucket_name()\\n log_entry[\\\"args\\\"] = {\\n \\\"bucket_name\\\": bucket_name,\\n }\\n\\n _CLIENT.make_bucket(bucket_name)\\n try:\\n # read-only policy\\n policy = {\\n \\\"Version\\\": \\\"2012-10-17\\\",\\n \\\"Statement\\\": [\\n {\\n \\\"Sid\\\": \\\"\\\",\\n \\\"Effect\\\": \\\"Allow\\\",\\n \\\"Principal\\\": {\\\"AWS\\\": \\\"*\\\"},\\n \\\"Action\\\": \\\"s3:GetBucketLocation\\\",\\n \\\"Resource\\\": \\\"arn:aws:s3:::\\\" + bucket_name,\\n },\\n {\\n \\\"Sid\\\": \\\"\\\",\\n \\\"Effect\\\": \\\"Allow\\\",\\n \\\"Principal\\\": {\\\"AWS\\\": \\\"*\\\"},\\n \\\"Action\\\": \\\"s3:ListBucket\\\",\\n \\\"Resource\\\": \\\"arn:aws:s3:::\\\" + bucket_name,\\n },\\n {\\n \\\"Sid\\\": \\\"\\\",\\n \\\"Effect\\\": \\\"Allow\\\",\\n \\\"Principal\\\": {\\\"AWS\\\": \\\"*\\\"},\\n \\\"Action\\\": \\\"s3:GetObject\\\",\\n \\\"Resource\\\": f\\\"arn:aws:s3:::{bucket_name}/*\\\",\\n },\\n ],\\n }\\n # Set read-only policy\\n _CLIENT.set_bucket_policy(bucket_name, json.dumps(policy))\\n # Validate if the policy is set correctly\\n if not _validate_policy(bucket_name, policy):\\n raise ValueError(\\\"Failed to set ReadOnly bucket policy\\\")\\n finally:\\n _CLIENT.remove_bucket(bucket_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ad3ba9f00da842160108d2ffa80291b\",\n \"score\": \"0.50653607\",\n \"text\": \"def UpdateConsumerPolicy(self, request, global_params=None):\\n config = self.GetMethodConfig('UpdateConsumerPolicy')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b0c779d600eb5a79609c0f19fe950fe\",\n \"score\": \"0.50534487\",\n \"text\": \"def update(self, id, data):\\n\\t\\tassert isinstance(id, str), 'The ID must be a string'\\n\\t\\tassert id, 'The ID must not be an empty string'\\n\\t\\tassert isinstance(data, dict), 'The data type must be a dictionary'\\n\\t\\tassert data, 'Policy data must not be an empty dictionary'\\n\\n\\t\\turl = f'{self.root.url}/api/v1/policies/{id}'\\n\\t\\tdata = json.dumps(data)\\n\\t\\treturn self.root.r('PUT', url, data, headers=None, verify=self.root.verify)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a042131a7d15399ddc48652b362f1071\",\n \"score\": \"0.5046923\",\n \"text\": \"def load_policy(self, policy):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaeee4f6f929a226380cbe405d533a97\",\n \"score\": \"0.50342745\",\n \"text\": \"def set_iam_policy(\\n self,\\n request: iam_policy_pb2.SetIamPolicyRequest = None,\\n *,\\n retry: retries.Retry = gapic_v1.method.DEFAULT,\\n timeout: float = None,\\n metadata: Sequence[Tuple[str, str]] = (),\\n ) -> policy_pb2.Policy:\\n # Create or coerce a protobuf request object.\\n if isinstance(request, dict):\\n # The request isn't a proto-plus wrapped type,\\n # so it must be constructed via keyword expansion.\\n request = iam_policy_pb2.SetIamPolicyRequest(**request)\\n elif not request:\\n # Null request, just make one.\\n request = iam_policy_pb2.SetIamPolicyRequest()\\n\\n # Wrap the RPC method; this adds retry and timeout information,\\n # and friendly error handling.\\n rpc = self._transport._wrapped_methods[self._transport.set_iam_policy]\\n\\n # Certain fields should be provided within the metadata header;\\n # add these here.\\n metadata = tuple(metadata) + (\\n gapic_v1.routing_header.to_grpc_metadata(((\\\"resource\\\", request.resource),)),\\n )\\n\\n # Send the request.\\n response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)\\n\\n # Done; return the response.\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bda2eaa957476ec7f8a50ac78eb1019\",\n \"score\": \"0.50315076\",\n \"text\": \"def grant_put_permission_to(self, role: aws.iam.Role) -> None:\\n aws.iam.RolePolicy(\\n f\\\"{role._name}-writes-objects-to-{self._name}\\\",\\n role=role.name,\\n policy=self.arn.apply(\\n lambda bucket_arn: json.dumps(\\n {\\n \\\"Version\\\": \\\"2012-10-17\\\",\\n \\\"Statement\\\": [\\n {\\n \\\"Effect\\\": \\\"Allow\\\",\\n \\\"Action\\\": \\\"s3:PutObject\\\",\\n \\\"Resource\\\": f\\\"{bucket_arn}/*\\\",\\n }\\n ],\\n }\\n )\\n ),\\n opts=pulumi.ResourceOptions(parent=role),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"254d847f3a7cda610c0dc88fda1f14eb\",\n \"score\": \"0.50277835\",\n \"text\": \"def failure_policy(self, failure_policy):\\n\\n self._failure_policy = failure_policy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c37234e2076d91b456c2d277365494d\",\n \"score\": \"0.49952072\",\n \"text\": \"def create_policy(name, description, actions, resource_arn):\\n policy_doc = {\\n \\\"Version\\\": \\\"2012-10-17\\\",\\n \\\"Statement\\\": [\\n {\\n \\\"Effect\\\": \\\"Allow\\\",\\n \\\"Action\\\": actions,\\n \\\"Resource\\\": resource_arn\\n }\\n ]\\n }\\n try:\\n policy = iam.create_policy(\\n PolicyName=name, Description=description,\\n PolicyDocument=json.dumps(policy_doc))\\n logger.info(\\\"Created policy %s.\\\", policy.arn)\\n except ClientError:\\n logger.exception(\\\"Couldn't create policy %s.\\\", name)\\n raise\\n else:\\n return policy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad937215f521d3b92e43c7a2e7b82440\",\n \"score\": \"0.49939954\",\n \"text\": \"def set_iam_policy(\\n self,\\n request: Optional[iam_policy_pb2.SetIamPolicyRequest] = None,\\n *,\\n retry: OptionalRetry = gapic_v1.method.DEFAULT,\\n timeout: Union[float, object] = gapic_v1.method.DEFAULT,\\n metadata: Sequence[Tuple[str, str]] = (),\\n ) -> policy_pb2.Policy:\\n # Create or coerce a protobuf request object.\\n\\n # The request isn't a proto-plus wrapped type,\\n # so it must be constructed via keyword expansion.\\n if isinstance(request, dict):\\n request = iam_policy_pb2.SetIamPolicyRequest(**request)\\n\\n # Wrap the RPC method; this adds retry and timeout information,\\n # and friendly error handling.\\n rpc = gapic_v1.method.wrap_method(\\n self._transport.set_iam_policy,\\n default_timeout=None,\\n client_info=DEFAULT_CLIENT_INFO,\\n )\\n\\n # Certain fields should be provided within the metadata header;\\n # add these here.\\n metadata = tuple(metadata) + (\\n gapic_v1.routing_header.to_grpc_metadata(((\\\"resource\\\", request.resource),)),\\n )\\n\\n # Send the request.\\n response = rpc(\\n request,\\n retry=retry,\\n timeout=timeout,\\n metadata=metadata,\\n )\\n\\n # Done; return the response.\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e7970143f88e6ec22ca04846093a850\",\n \"score\": \"0.49908513\",\n \"text\": \"def set_device_policy(device_policy):\\n if device_policy == 'silent':\\n context.context().device_policy = context.DEVICE_PLACEMENT_SILENT\\n elif device_policy == 'silent_for_int32':\\n context.context().device_policy = context.DEVICE_PLACEMENT_SILENT_FOR_INT32\\n elif device_policy == 'warn':\\n context.context().device_policy = context.DEVICE_PLACEMENT_WARN\\n elif device_policy == 'explicit':\\n context.context().device_policy = context.DEVICE_PLACEMENT_EXPLICIT\\n elif device_policy is None:\\n context.context().device_policy = None\\n else:\\n raise ValueError('Not a valid device policy: %r' % device_policy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3813ad93b73de96e9f5dd4874ab293af\",\n \"score\": \"0.4988412\",\n \"text\": \"def _enforce(self, req, action):\\n try:\\n self.policy.enforce(req.context, action)\\n except heat_exception.Forbidden:\\n msg = _(\\\"Action %s not allowed for user\\\") % action\\n raise exception.HeatAccessDeniedError(msg)\\n except Exception:\\n # We expect policy.enforce to either pass or raise Forbidden\\n # however, if anything else happens, we want to raise\\n # HeatInternalFailureError, failure to do this results in\\n # the user getting a big stacktrace spew as an API response\\n msg = _(\\\"Error authorizing action %s\\\") % action\\n raise exception.HeatInternalFailureError(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e07e9e7fc3a972d5188cde37223380e9\",\n \"score\": \"0.49798617\",\n \"text\": \"def object_change_policy(self, account, container, obj, policy, **kwargs):\\n meta, stream = self.object_fetch(account, container, obj, **kwargs)\\n # Before we started generating predictable chunk IDs, it was possible\\n # to change to the same policy: it just renewed all chunks and updated\\n # the modification time.\\n # Now that we generate predictable chunk IDs, we must change something\\n # in the object description in order to get a different set of chunks\\n # (we don't want to change the object version).\\n if meta[\\\"policy\\\"] == policy:\\n del stream\\n raise exc.Conflict(\\n \\\"The object is already using the %s storage policy\\\" % policy\\n )\\n kwargs[\\\"version\\\"] = meta[\\\"version\\\"]\\n return self.object_create_ext(\\n account,\\n container,\\n obj_name=meta[\\\"name\\\"],\\n data=stream,\\n policy=policy,\\n change_policy=True,\\n **kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28c8a95d2ffa623c7321a82f5a6f56a2\",\n \"score\": \"0.49760813\",\n \"text\": \"def auto_scaling_policy(self, auto_scaling_policy):\\n self._auto_scaling_policy = auto_scaling_policy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7028222209980f7233b6e397b41d8a0\",\n \"score\": \"0.49747008\",\n \"text\": \"def add_grading_policy(self, grading_policy):\\n\\n self.course.grading_policy = grading_policy\\n self.update_course(self.course, self.student_user.id)\\n self.refresh_course()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bb7dc80fdb4824c9890ccb31c154e88\",\n \"score\": \"0.4970173\",\n \"text\": \"def scoring_policy(self, value):\\r\\n self.logger.warn(\\\"Setting values on scoring_policy will NOT update the remote Canvas instance.\\\")\\r\\n self._scoring_policy = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"945e3850adfd2891919e63ca98e443c4\",\n \"score\": \"0.4963867\",\n \"text\": \"def SetIamPolicy(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details(\\\"Method not implemented!\\\")\\n raise NotImplementedError(\\\"Method not implemented!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"945e3850adfd2891919e63ca98e443c4\",\n \"score\": \"0.4963867\",\n \"text\": \"def SetIamPolicy(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details(\\\"Method not implemented!\\\")\\n raise NotImplementedError(\\\"Method not implemented!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99a3adcdbb764c25e9e0196c45837347\",\n \"score\": \"0.49574706\",\n \"text\": \"async def set_iam_policy(\\n self,\\n request: Optional[iam_policy_pb2.SetIamPolicyRequest] = None,\\n *,\\n retry: OptionalRetry = gapic_v1.method.DEFAULT,\\n timeout: Union[float, object] = gapic_v1.method.DEFAULT,\\n metadata: Sequence[Tuple[str, str]] = (),\\n ) -> policy_pb2.Policy:\\n # Create or coerce a protobuf request object.\\n\\n # The request isn't a proto-plus wrapped type,\\n # so it must be constructed via keyword expansion.\\n if isinstance(request, dict):\\n request = iam_policy_pb2.SetIamPolicyRequest(**request)\\n\\n # Wrap the RPC method; this adds retry and timeout information,\\n # and friendly error handling.\\n rpc = gapic_v1.method.wrap_method(\\n self._client._transport.set_iam_policy,\\n default_timeout=None,\\n client_info=DEFAULT_CLIENT_INFO,\\n )\\n\\n # Certain fields should be provided within the metadata header;\\n # add these here.\\n metadata = tuple(metadata) + (\\n gapic_v1.routing_header.to_grpc_metadata(((\\\"resource\\\", request.resource),)),\\n )\\n\\n # Send the request.\\n response = await rpc(\\n request,\\n retry=retry,\\n timeout=timeout,\\n metadata=metadata,\\n )\\n\\n # Done; return the response.\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cda5d6c89eda901cbfa7b37c0667f53\",\n \"score\": \"0.49320412\",\n \"text\": \"def SetIamPolicy(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45d78d1891e4c45a16941dadc0a0ccb0\",\n \"score\": \"0.49171555\",\n \"text\": \"async def set_policy_aad_unsecured(self):\\n\\n write_banner(\\\"set_policy_aad_unsecured\\\")\\n print(\\\"Set an unsecured Policy on an AAD mode attestation instance.\\\")\\n # [BEGIN set_policy_unsecured]\\n async with DefaultAzureCredential() as credential, AttestationAdministrationClient(\\n os.environ.get(\\\"ATTESTATION_AAD_URL\\\"), credential\\n ) as admin_client:\\n new_policy = \\\"\\\"\\\"\\nversion= 1.0;\\nauthorizationrules\\n{\\n [ type==\\\"x-ms-sgx-is-debuggable\\\", value==false ] &&\\n [ type==\\\"x-ms-sgx-product-id\\\", value==1 ] &&\\n [ type==\\\"x-ms-sgx-svn\\\", value>= 0 ] &&\\n [ type==\\\"x-ms-sgx-mrsigner\\\", value==\\\"2c1a44952ae8207135c6c29b75b8c029372ee94b677e15c20bd42340f10d41aa\\\"]\\n => permit();\\n};\\nissuancerules {\\n c:[type==\\\"x-ms-sgx-mrsigner\\\"] => issue(type=\\\"My-MrSigner\\\", value=c.value);\\n};\\n \\\"\\\"\\\"\\n\\n set_result, _ = await admin_client.set_policy(\\n AttestationType.OPEN_ENCLAVE, new_policy\\n )\\n print(\\\"Policy Set result: \\\", set_result.policy_resolution)\\n # [END set_policy_unsecured]\\n\\n get_policy, _ = await admin_client.get_policy(AttestationType.OPEN_ENCLAVE)\\n if new_policy != get_policy:\\n print(\\\"Policy does not match set policy.\\\")\\n # Attest an OpenEnclave using the new policy.\\n await self._attest_open_enclave(os.environ.get(\\\"ATTESTATION_AAD_URL\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a83314bb8063f8505e4f49ca8ab1a98\",\n \"score\": \"0.49021062\",\n \"text\": \"def SetIamPolicy(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0128bc9b4702a144822bc2ac77aca7dd\",\n \"score\": \"0.49009973\",\n \"text\": \"def set_interactive_policy(*, locals=None, banner=None, serve=None,\\n prompt_control=None):\\n policy = InteractiveEventLoopPolicy(\\n locals=locals,\\n banner=banner,\\n serve=serve,\\n prompt_control=prompt_control)\\n asyncio.set_event_loop_policy(policy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9866e188a7924529fc03cc3fa1de42f\",\n \"score\": \"0.48997706\",\n \"text\": \"def SetIamPolicy(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2121e22c850a7d268c5cc40db9134ea\",\n \"score\": \"0.4899416\",\n \"text\": \"def istio_authorization_policies(self, istio_authorization_policies):\\n\\n self._istio_authorization_policies = istio_authorization_policies\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cca192a7d2b3e83c1093c89dba3c496\",\n \"score\": \"0.48831755\",\n \"text\": \"def restart_policy(self, restart_policy):\\n\\n self._restart_policy = restart_policy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50de3157bcfcb639b479f8ce6278f09a\",\n \"score\": \"0.48806158\",\n \"text\": \"def update_policy(name, short_term_storage, long_term_storage, versioning, backup_duration):\\n if not backup_duration and versioning:\\n backup_duration = click.prompt(\\n \\\"How many days backups of the datastorage will be stored? (Empty means deletion of the current rule)\\\",\\n default=\\\"\\\")\\n DataStorageOperations.policy(name, short_term_storage, long_term_storage, backup_duration, versioning)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35fae65c3bec60e40e6f8bea107dd669\",\n \"score\": \"0.48776403\",\n \"text\": \"def external_traffic_policy(self, external_traffic_policy):\\n\\n self._external_traffic_policy = external_traffic_policy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17865c02f427f4fe755fc7f9121ad54e\",\n \"score\": \"0.48763523\",\n \"text\": \"def write_authorize(cls, user, obj):\\n if not obj.period.can_save(user):\\n raise PermissionDenied()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5ec5c42c5a13498297e0ca9224103da\",\n \"score\": \"0.4874774\",\n \"text\": \"def setCheckingPolicy(self, checkingPolicy: cern.rbac.common.authorization.CheckingPolicy) -> 'AccessCheckerRequestBuilder':\\n ...\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994086,"cells":{"query_id":{"kind":"string","value":"3ac87c776c8aadc9761f4a9bee3738c6"},"query":{"kind":"string","value":"Generates a link allowing the data in a given panda dataframe to be downloaded"},"positive_passages":{"kind":"list like","value":[{"docid":"5f40d00bea1eb279ad6117ffc5b495d6","score":"0.7543758","text":"def get_table_download_link(df):\n csv = df.to_csv(index=False)\n b64 = base64.b64encode(\n csv.encode()\n ).decode() # some strings <-> bytes conversions necessary here\n return f'<a href=\"data:file/csv;base64,{b64}\" download=\"lineup_template.csv\">Download Template as csv file</a>'","title":""}],"string":"[\n {\n \"docid\": \"5f40d00bea1eb279ad6117ffc5b495d6\",\n \"score\": \"0.7543758\",\n \"text\": \"def get_table_download_link(df):\\n csv = df.to_csv(index=False)\\n b64 = base64.b64encode(\\n csv.encode()\\n ).decode() # some strings <-> bytes conversions necessary here\\n return f'<a href=\\\"data:file/csv;base64,{b64}\\\" download=\\\"lineup_template.csv\\\">Download Template as csv file</a>'\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"652badd8b29f86288a249339bc14ca84","score":"0.7853004","text":"def get_table_download_link(dataframe: pd.DataFrame):\n val = to_excel(dataframe)\n b64 = base64.b64encode(val)\n # TODO: Create a random number as filename, check generate_xlsx\n return ('<p style=\"text-align:center;\">'\n f'<a href=\"data:application/octet-stream;base64,{b64.decode()}\" '\n 'download=\"mann_kendall.xlsx\">Download Excel file</a></p>')\n # decode b'abc' => abc)","title":""},{"docid":"b7ccfae5069af110c88e109ae4e718f8","score":"0.7717478","text":"def get_table_download_link(df):\r\n csv = df.to_csv(index=False)\r\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\r\n href = f'<a href=\"data:file/csv;base64,{b64}\">Download csv file</a>'\r\n return href","title":""},{"docid":"fd1e52f4bfee19580b16aabb680bca81","score":"0.77031136","text":"def get_table_download_link(df):\r\n csv = df.to_csv(index=False)\r\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\r\n href = f'<a href=\"data:file/csv;base64,{b64}\">Download csv</a>'\r\n return href","title":""},{"docid":"39fdbbc659b5b85ef19ee65c812a3d1e","score":"0.76984584","text":"def get_table_download_link(df):\n csv = df.to_csv(index=False)\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\n href = f'<a href=\"data:file/csv;base64,{b64}\">Download csv file</a>'\n return href","title":""},{"docid":"39fdbbc659b5b85ef19ee65c812a3d1e","score":"0.76984584","text":"def get_table_download_link(df):\n csv = df.to_csv(index=False)\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\n href = f'<a href=\"data:file/csv;base64,{b64}\">Download csv file</a>'\n return href","title":""},{"docid":"0b1bd2ccce3a783132c2ded38d50fffd","score":"0.76605654","text":"def get_table_download_link(df):\n csv = df.to_csv(index=False)\n b64 = base64.b64encode(\n csv.encode()\n ).decode() # some strings <-> bytes conversions necessary here\n return f'<a href=\"data:file/csv;base64,{b64}\" download=\"dataset.csv\">Download CSV file</a>'","title":""},{"docid":"f3f9e9dc4d320916717e52208944911c","score":"0.7629094","text":"def get_table_download_link(df):\r\n csv = df.to_csv(index=False)\r\n b64 = pybase64.b64encode(\r\n csv.encode()\r\n ).decode() # some strings <-> bytes conversions necessary here\r\n return f'<a href=\"data:file/csv;base64,{b64}\" download=\"Prediction.csv\">⬇️ Download output CSV File</a>'","title":""},{"docid":"30861d6c1da187b69b2626884f8cb8e9","score":"0.756977","text":"def get_table_download_link(df, out_file, label):\n csv = df.to_csv(index=False)\n b64 = base64.b64encode(\n csv.encode()\n ).decode() # some strings <-> bytes conversions necessary here\n out = f'<a href=\"data:file/csv;base64,{b64}\" download=\"%s\">%s</a>' % (out_file, label)\n return out","title":""},{"docid":"ab186da2d19282b7f4186eecc2c8811e","score":"0.7558905","text":"def get_table_download_link(df):\r\n val = to_excel(df)\r\n b64 = base64.b64encode(val) # val looks like b'...'\r\n return f'<a href=\"data:application/octet-stream;base64,{b64.decode()}\" download=\"classified_data.xlsx\">Download file</a>' # decode b'abc' => abc\r","title":""},{"docid":"59f5c6bd8e95d7a9f0efec004885c1b0","score":"0.7536353","text":"def get_table_download_link(df):\n csv = df.to_csv(index=False)\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\n href = f'<a href=\"data:file/csv;base64,{b64}\">Download: Right click here and save as CSV</a>'\n return href","title":""},{"docid":"13cd219f2f8562ed90eb577cddeff28d","score":"0.7458118","text":"def df_download_link(df: pd.DataFrame, text: str, filename: str):\n csv_bytes = df.to_csv(index=False).encode()\n b64_str = base64.b64encode(csv_bytes).decode()\n html_str = f'<a download=\"{filename}.csv\" href=\"data:file/csv;name={filename}.csv;base64,{b64_str}\">{text}</a>'\n st.markdown(html_str, unsafe_allow_html=True)","title":""},{"docid":"338175ccbf76d0baae50a6d67562b6ec","score":"0.7433467","text":"def get_table_download_link(df):\n val = to_excel(df)\n b64 = base64.b64encode(val) # val looks like b'...'\n return f'<a href=\"data:application/octet-stream;base64,{b64.decode()}\" download=\"DataNews.xlsx\">Download Excel file</a>' # decode b'abc' => abc","title":""},{"docid":"c722d25f2061bdb349f8131ba8eb8306","score":"0.7415955","text":"def get_table_download_link(df):\r\n val = to_excel(df)\r\n b64 = base64.b64encode(val) # val looks like b'...'\r\n return f'<a href=\"data:application/octet-stream;base64,{b64.decode()}\" download=\"Summary.xlsx\">Export to Excel</a>' # decode b'abc' => abc\r","title":""},{"docid":"44c6033575b1e89a0c662e9a3936a73d","score":"0.73187155","text":"def get_table_download_link(df, text='Click here to download table'):\r\n csv = df.to_csv(index=False)\r\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\r\n # href = f'<a href=\"data:file/csv;base64,{b64}\">Download csv file</a>'\r\n href = f'<a href=\"data:file/csv;base64,{b64}\" download=\"table.csv\">{text}</a>'\r\n return href","title":""},{"docid":"0286fb87ce7f72036e960f4d95ae8053","score":"0.7140357","text":"def get_table_download_link_csv(df):\r\n csv = df.to_csv(index=False)\r\n b64 = base64.b64encode(\r\n csv.encode()\r\n ).decode() # some strings <-> bytes conversions necessary here\r\n return f'<a href=\"data:file/csv;base64,{b64}\" download=\"download.csv\">Download csv file</a>'","title":""},{"docid":"97d8bc5f47d5a4d820f67967ca8a3fde","score":"0.70307845","text":"def get_table_download_link_xlsx(df):\r\n val = to_excel(df)\r\n b64 = base64.b64encode(val) # val looks like b'...'\r\n return f'<a href=\"data:application/octet-stream;base64,{b64.decode()}\" download=\"download.xlsx\">Download xlsx file</a>' # decode b'abc' => abc\r","title":""},{"docid":"92bd98737850ed07411b4ab075a84eeb","score":"0.6968001","text":"def get_table_download_link(dafr):\r\n csv = dafr.to_csv(index=False)\r\n b64 = base64.b64encode(\r\n csv.encode()\r\n ).decode() # some strings <-> bytes conversions necessary here\r\n return f'<a href=\"data:file/csv;base64,{b64}\" download=\"Results.csv\">Download Result (CSV)</a>'","title":""},{"docid":"b6bf54f464b06c3407b9706382237a5e","score":"0.65727204","text":"def download_link(object_to_download, download_filename, download_link_text):\n if isinstance(object_to_download,pd.DataFrame):\n object_to_download = object_to_download.to_csv(index=False)\n\n # some strings <-> bytes conversions necessary here\n b64 = base64.b64encode(object_to_download.encode()).decode()\n\n return f'<a href=\"data:file/txt;base64,{b64}\" download=\"{download_filename}\">{download_link_text}</a>'","title":""},{"docid":"b6bf54f464b06c3407b9706382237a5e","score":"0.65727204","text":"def download_link(object_to_download, download_filename, download_link_text):\n if isinstance(object_to_download,pd.DataFrame):\n object_to_download = object_to_download.to_csv(index=False)\n\n # some strings <-> bytes conversions necessary here\n b64 = base64.b64encode(object_to_download.encode()).decode()\n\n return f'<a href=\"data:file/txt;base64,{b64}\" download=\"{download_filename}\">{download_link_text}</a>'","title":""},{"docid":"2a1ab01ed05305edeab81026c97c94c3","score":"0.65723854","text":"def csv_downloader(data):\n time_stamp = time.strftime(\"%Y%m%d_%H%M%S\")\n \n csvfile = data.to_csv()\n \n b64 = base64.b64encode(csvfile.encode()).decode()\n \n new_filename = f\"fit_results{time_stamp}.csv\"\n href = f'** Download DataFrame as .csv: ** \\\n <a href=\"data:file/csv;base64,{b64}\" \\\n download=\"{new_filename}\">Click Here</a>'\n st.markdown(href, unsafe_allow_html=True)","title":""},{"docid":"9285f24031cf527126dd828c71629438","score":"0.6354674","text":"def download_dataframe_as_csv(dataframe, filename):\n dataframe.to_csv(filename)\n files.download(filename)","title":""},{"docid":"3185fb6527d6f3466514db45089a7fee","score":"0.6178509","text":"def url_string(it):\n \n b_link =\"https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni-\"\n \n return b_link + it + \".csv\"","title":""},{"docid":"c07d89cb8a13d195b0fed1cfb2b1122d","score":"0.61095786","text":"def format_url(df):\n\n ix_col = df.index.name\n df = df.reset_index()\n df[ix_col] = df.apply(\n lambda x: f'<a href=\"{x[\"URL\"]}\">{x[ix_col]}</a>', axis=1\n )\n df = df.drop(\"URL\", axis=1)\n\n df = df.set_index(ix_col)\n\n return df","title":""},{"docid":"c3bb5283123cd397d820dc67364a0dba","score":"0.6088772","text":"def download_on_dataframe(self, **kwargs):\n\n if self.blob is None:\n raise ValueError(\"No file selected. Set it with select_file method first.\")\n\n logger.debug(f\"gs path: {self.uri}\")\n return pd.read_csv(self.uri, **kwargs)","title":""},{"docid":"cea8b8fc72a932b2bf26a80d88bf3e0c","score":"0.6074464","text":"def download():\r\n return response.download(request, db2)","title":""},{"docid":"ce8da0ce41714656c57e002c29a67771","score":"0.6072765","text":"def download_csv(data):\n csv = data.to_csv().encode()\n b64 = base64.b64encode(csv).decode()\n href = f'<a href=\"data:file/csv;base64,{b64}\" download=\"result.csv\" target=\"_blank\">Download CSV</a>'\n\n return href","title":""},{"docid":"775f4d5c7409fbeefe4e2d1d300dd7fa","score":"0.60203224","text":"def download_data_from_table(self, mydb, db_name, table_name, selection_col, file_name_for_download,file_download_location):\r\n try:\r\n query_download = f\"\"\"select * from {db_name}.{table_name} {selection_col};\"\"\"\r\n data = pd.read_sql(query_download, mydb)\r\n\r\n data.to_csv(f\"\"\"{file_download_location}\\\\{file_name_for_download}.csv\"\"\", index=False)\r\n logging.debug(f\"\"\" All records successfully downloaded at {file_download_location}\\\\{file_name_for_download}.csv\"\"\")\r\n\r\n return f\"\"\"All records successfully downloaded at {file_download_location}\\\\{file_name_for_download}.csv\"\"\"\r\n\r\n except Exception as e:\r\n logging.error(f\"\"\"ERROR IN : download_data_from_table(), ERROR : {str(e)}\"\"\")\r\n return f\"\"\"ERROR IN : download_data_from_table(), ERROR : {str(e)}\"\"\"","title":""},{"docid":"681ec8a506d95ab2ebbb21031867317a","score":"0.59827954","text":"def toggle_download_data_link(_, dl_data_choice, stored_data):\n fname = \"%s_data.json\" % dl_data_choice\n fpath = \"%s%s\" % (stored_data['info']['dl_data_path'], fname)\n return fpath","title":""},{"docid":"53c5d10f2d16687398ba6e396b736924","score":"0.5969805","text":"def export_dataframe_html(dataframe):\n global variables, result, resultMetadata\n import pandas as pd\n\n limit_output_view = variables.get(\"LIMIT_OUTPUT_VIEW\")\n limit_output_view = 5 if limit_output_view is None else int(limit_output_view)\n if limit_output_view > 0:\n info = \"The task preview is limited to \" + str(limit_output_view) + \" rows\"\n dataframe = dataframe.head(limit_output_view).copy()\n else:\n nrows = len(dataframe.index)\n info = \"Total of \" + str(nrows) + \" rows\"\n print(info)\n with pd.option_context('display.max_colwidth', -1):\n result = dataframe.to_html(escape=False, classes='table table-bordered table-striped', justify='center')\n result = \"\"\"\n <!DOCTYPE html>\n <html>\n <head>\n <meta charset=\"UTF-8\">\n <title>Machine Learning Preview</title>\n <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css\" \n integrity=\"sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T\" crossorigin=\"anonymous\">\n </head>\n <body>\n <h1 class=\"text-center my-4\" style=\"color:#003050;\">Data Preview</h1>\n <p align=\"center\">{0}</p>\n {1}\n </body></html>\"\"\".format(info, result)\n result = result.encode('utf-8')\n resultMetadata.put(\"file.extension\", \".html\")\n resultMetadata.put(\"file.name\", \"output.html\")\n resultMetadata.put(\"content.type\", \"text/html\")","title":""},{"docid":"cb697b6a3a1b84b2066f131d094c3b9f","score":"0.5959588","text":"def link_to_pub(records):\n if 'metaknowledge' in str(type(records)).lower():\n recs = records.forNLP(extraColumns=[\"AU\", \"SO\", \"DE\", 'DOI'], lower=False, removeNonWords=False)\n df = pd.DataFrame(recs)\n elif 'dataframe' in str(type(records)).lower():\n df = records.copy()\n # End if\n\n df.loc[df['DOI'] != '', 'DOI link'] = \"https://dx.doi.org/\" + df.loc[df['DOI'] != '', 'DOI'].astype(str)\n\n return df","title":""},{"docid":"56fb28cfd437b853155f7add3e263fe7","score":"0.59426004","text":"def get_download_link(self):\n raise NotImplementedError()","title":""},{"docid":"fe5d9bf254dbc00d739a521a4dd728fb","score":"0.591364","text":"def download_link(object_to_download, download_filename, download_link_text):\n\n\n # some strings <-> bytes conversions necessary here\n b64 = base64.b64encode(object_to_download.encode()).decode()\n\n return f'<a href=\"data:file/txt;base64,{b64}\" download=\"{download_filename}\">{download_link_text}</a>'","title":""},{"docid":"dd54626d8d88f05bcb411c40146284a3","score":"0.58500165","text":"def download():\n return response.download(request,db)","title":""},{"docid":"dd54626d8d88f05bcb411c40146284a3","score":"0.58500165","text":"def download():\n return response.download(request,db)","title":""},{"docid":"dd54626d8d88f05bcb411c40146284a3","score":"0.58500165","text":"def download():\n return response.download(request,db)","title":""},{"docid":"dd54626d8d88f05bcb411c40146284a3","score":"0.58500165","text":"def download():\n return response.download(request,db)","title":""},{"docid":"c2e0c541bf13a84c2d4711914076eeae","score":"0.5832997","text":"def download(self):","title":""},{"docid":"9a42985872da94830e9c189c7cd1f035","score":"0.5823003","text":"def getData(startdt, savepath):\n \n #create filename url list (summer 2015 start date is 15,7,4)\n startdt = date.date(startdt[0],startdt[1],startdt[2])\n uri = \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_\"\n urllist = [re.sub(\"-\",\"\",uri+str(startdt + date.timedelta(days=i))[2:]+str('.txt')) for i in np.arange(0,90,7)]\n \n #urllist = [\"http://web.mta.info/developers/data/nyct/turnstile/turnstile_150704.txt\",\n # \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_150711.txt\",\n # \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_150718.txt\"]\n \n \n #download from first url with header\n response = ulib.urlopen(urllist[0])\n df = pd.read_csv(response, \n header=0)\n #dfbig = pd.DataFrame({})\n response.close()\n \n #download from remaining urls and concatenate without header to dataframe\n for url in urllist[1:]:\n response = ulib.urlopen(url)\n df = pd.concat([df,pd.read_csv(response, \n skip_blank_lines=True)])\n response.close()\n \n df.drop(df.columns[[0,1,2,3,4,5,6,7,8,9,10]],axis=1)\n\n #df = pd.concat([df,dfbig], axis=0)\n #'/Users/ash/ds/projects/data/mta/mta-turnstile-summer2015data.csv'\n pd.DataFrame.to_csv(df, savepath)","title":""},{"docid":"b6f3542c637ad94eb9d886dfb88eb380","score":"0.5820612","text":"def generate_urls():\n yield base_url.format(\"ProductionAndCurtailmentsData-May1_2014-May31_2017.xlsx\")\n yield base_url.format(\"ProductionAndCurtailmentsData-Jun1_2017-Dec31_2017.xlsx\")\n for year in range(2018, 2021):\n yield base_url.format(f\"ProductionAndCurtailmentsData_{year}.xlsx\")","title":""},{"docid":"f5c0762107632a7cd8b72fd3f41afa96","score":"0.5785434","text":"def return_csv(new_df, name):\n redirect('http://localhost:5000')\n new_df.to_csv('./' + name + '.csv')\n return send_file('./' + name + '.csv',\n attachment_filename='./' + name + '.csv',\n as_attachment=True)","title":""},{"docid":"db55dffdff914f0995e3eb4d9d381ece","score":"0.57851064","text":"def download_urls(self, df, start = 0, end = 10, headers = headers):\n all_download_urls = []\n urls = list(df['Album hrefs'])\n tracks = list(df['Track'])\n\n for i in range(start, end):\n #Print the current time for every 50 urls obtained\n if i%50 ==0:\n now = datetime.now()\n current_time = str(now.strftime(\"%H:%M:%S\"))\n print(\"{} Current Time = {}\".format(i, current_time))\n payload = {}\n headers = headers\n url = urls[i]\n track = tracks[i]\n\n \n try:\n response = requests.request(\"GET\", url, headers=headers, data=payload)\n if response.status_code == 200:\n all_download_urls.append(self.get_url_download_(response, str(track)))\n else:\n all_download_urls.append('None')\n except:\n all_download_urls.append('None')\n\n return all_download_urls","title":""},{"docid":"a40b56a37f1e2d4218eaaf96d567ad8e","score":"0.5767065","text":"def download_dataset(url: str) -> pd.DataFrame:\n print(f'downloading training data from {DATA_URL}')\n data_file = urlopen(url)\n # app_train = pd.read_excel('final data sales details.xlsx', index_col = 'Client Account No.')\n return pd.read_excel(data_file, index_col = 'Client Account No.')","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"cd30c2e3a4426e0af0135a664167e528","score":"0.57450867","text":"def download():\n return response.download(request, db)","title":""},{"docid":"7c66ccaa44b5c533fc58ec1e61bbfd83","score":"0.5743465","text":"def download():\n\treturn response.download(request, db)","title":""},{"docid":"340de45ce944ffe89fa16bdcc70aaa8c","score":"0.57317215","text":"def file_download_link(filename):\n location = \"./download/{}\".format(urlquote(filename))\n return html.A(filename, href=location)","title":""},{"docid":"629f5567f6f66a8977ca26bf08e3c332","score":"0.573103","text":"def data_frame(list_of_dicts):\n df = pd.DataFrame(list_of_dicts)\n index_df = df.set_index('url')\n index_df.to_csv('result.csv')","title":""},{"docid":"653480b3869fa8a43b8475173d5c2516","score":"0.5708914","text":"def append_download_urls_save_df(self, df, download_urls, filename):\n df['Download URLs'] = download_urls\n df.to_csv(filename, index=False)\n return","title":""},{"docid":"5ab7d9f24c9712346d9a8eff654b65b1","score":"0.5701151","text":"def format_metadata_url(api_key, table_name):\n query_params = [('api_key', api_key), ('qopts.export', 'true')]\n\n return (\n QUANDL_DATA_URL + table_name + \".csv?\" + urlencode(query_params)\n )","title":""},{"docid":"7c8fc4399952b787d6afb3b3ec916311","score":"0.5682233","text":"def file_download_link(filename):\n location = \"/download/{}\".format(urlquote(filename))\n return html.A(filename, href=location)","title":""},{"docid":"4a7ee823b2ec283d9c83f8a33c5d9f22","score":"0.5651486","text":"def generate_link(df_drugs: pd.DataFrame, df_reference: pd.DataFrame) -> {}:\n\n drug_dict = dict()\n # define a list of drugs. Faster to loop on a list than on a pandas df.\n drugs = df_drugs['drug'].to_list()\n # print(\"drugs\" ,+ drugs )\n\n for drug in drugs:\n drug_dict[drug] = dict()\n\n # pubMed\n drug_dict[drug][\"pubMed\"] = finding_mention(drug, df_reference, \"title\")[\"title\"].to_list()\n drug_dict[drug][\"pubMedJournal\"] = finding_mention(drug, df_reference, \"title\")[\"journal\"].to_list()\n drug_dict[drug][\"pubMedDate\"] = finding_mention(drug, df_reference, \"title\")[\"date\"].to_list()\n\n # clinical_trials\n drug_dict[drug][\"clinicalTrial\"] = finding_mention(drug, df_reference, \"title\")[\"title\"].to_list()\n drug_dict[drug][\"clinicalTrialJournal\"] = finding_mention(drug, df_reference, \"title\")[\"journal\"].to_list()\n drug_dict[drug][\"clinicalTrialDate\"] = finding_mention(drug, df_reference, \"title\")[\"date\"].to_list()\n return drug_dict","title":""},{"docid":"6494098b252ffd478e8c0fb4f2d53d70","score":"0.56379694","text":"def download_csv(readings, filename_base=\"results\"):\n df = pd.DataFrame(readings)\n output_buffer = io.BytesIO()\n df.to_csv(output_buffer)\n output_buffer.seek(0)\n filename = (\n filename_base + \"_\" + datetime.now().strftime(\"%d-%m-%Y_%H-%M-%S\") + \".csv\"\n )\n return send_file(\n output_buffer, download_name=filename, mimetype=\"text/csv\", as_attachment=True\n )","title":""},{"docid":"fffafd15f6e6ef701e17d79348e9bde3","score":"0.56352186","text":"def downloadSubmissions(challenge, showMissing):\n\n # generator depends on challenge type and sometimes mode (2019 vs 2020) active\n if challenge == 'cea':\n if config.OUTPUT_2019_FORMAT:\n def generate():\n \"\"\"2019 format: col_id before row_id\"\"\"\n for row in db.getAllTargets('cea', not showMissing):\n yield '{},{},{},{}{}\\n'.format(\n row['table_name'],\n row['col_id'],\n row['row_id'],\n config.URL_PREFIX if config.OUTPUT_ADD_PREFIX else '',\n row['mapped']\n )\n else:\n def generate():\n \"\"\"2020 format: row_id before col_id\"\"\"\n for row in db.getAllTargets('cea', not showMissing):\n yield '{},{},{},{}{}\\n'.format(\n row['table_name'],\n row['row_id'],\n row['col_id'],\n config.URL_PREFIX if config.OUTPUT_ADD_PREFIX else '',\n row['mapped']\n )\n elif challenge == 'cpa':\n def generate():\n for row in db.getAllTargets('cpa', not showMissing):\n yield '{},{},{},{}{}\\n'.format(\n row['table_name'],\n row['sub_id'],\n row['obj_id'],\n config.URL_PREFIX if config.OUTPUT_ADD_PREFIX else '',\n row['mapped']\n )\n elif challenge == 'cta':\n def generate():\n for row in db.getAllTargets('cta', not showMissing):\n yield '{},{},{}{}\\n'.format(\n row['table_name'],\n row['col_id'],\n config.URL_PREFIX if config.OUTPUT_ADD_PREFIX else '',\n row['mapped']\n )\n else:\n raise Exception('Unknown challenge: {}'.format(challenge))\n\n # stream response\n filename = '{}{}.csv'.format(challenge, 'Missing' if showMissing else '')\n return flask.Response(\n generate(),\n mimetype='text/csv',\n headers={\n \"Content-disposition\": \"attachment; filename={}\".format(filename)\n }\n )","title":""},{"docid":"96a50c95e27e648dee3cc8794bddfff5","score":"0.56314445","text":"def _get_data_url(self, **form_data):\n # For old data links of Google Spreadsheets\n if 'ccc?key' in form_data['googledocs_url']:\n return ''.join([form_data['googledocs_url'], '&output=csv'])\n # New data format for Google Drive import is like this:\n # https://docs.google.com/spreadsheets/d/key/edit?usp=sharing\n else:\n return ''.join([form_data['googledocs_url'].split('edit')[0],\n 'export?format=csv'])","title":""},{"docid":"c422055b33969fb5a2700303d5edc175","score":"0.5620762","text":"def Download_Reg(self, StDt, EndDt, Days, db):\r\n t0 = datetime.now()\r\n print('Start time: ', t0)\r\n self.StDt = datetime.strptime(str(StDt), '%d.%m.%Y').date()\r\n\r\n if EndDt != 0:\r\n self.EndDt = datetime.strptime(str(EndDt), '%d.%m.%Y').date()\r\n self.Days_between = self.StDt - self.EndDt\r\n self.Days_to_Down = self.Days_between.days\r\n elif Days != 0:\r\n self.Days_to_Down = int(Days)\r\n self.EndDt = self.StDt - timedelta(days = Days)\r\n self.conn = sqlite3.connect(db)\r\n self.cur = self.conn.cursor()\r\n self.cur.execute(\"SELECT * from links_reg_match\")\r\n Dates = [tup[1] for tup in self.cur.fetchall()]\r\n self.cur.execute(\"SELECT * from links_reg_match\")\r\n links = [tup[0] for tup in self.cur.fetchall()] \r\n df = pd.DataFrame({'Dates':Dates, 'Link':links})\r\n df['Dates'] = pd.to_datetime(df['Dates'], format='%d.%m.%Y')\r\n l = []\r\n for i in range(0, len(df['Dates'])):\r\n l.append(df.at[i, 'Dates'].date())\r\n df['Dates'] = l\r\n df.sort_values('Dates', inplace=True, axis=0)\r\n df.set_index('Dates', inplace = True)\r\n df = df.loc[self.EndDt:self.StDt]\r\n df.reset_index(inplace = True)\r\n time_to_download = 0.30\r\n print('Downloading from ',self.EndDt,' to ',self.StDt)\r\n print('Matches to download: ',len(df['Link']))\r\n print('Estimated finish time: ',(t0 + timedelta(seconds=(time_to_download*len(df['Link'])))))\r\n for f in range(0,len(df['Link'])): \r\n if f == 0:\r\n print('Initiating the reg download...')\r\n #Progress\r\n if f%1000 == 0:\r\n print('Progress: ', round(f/len(df['Link']) * 100, 2), '%')\r\n page = requests.get(df.at[f, 'Link'])\r\n html_soup = soup(page.text,'html.parser')\r\n if html_soup.find('tr', class_='tour_head unpair') is not None:\r\n df.at[f, 'Date'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[0].text\r\n df.at[f, 'Date'] = df.at[i, 'Date'][:5] + '.20' + df.at[i, 'Date'][6:8]\r\n df.at[f, 'Round'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[1].text\r\n df.at[f, 'Player1'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[2].a.get('title')\r\n df.at[f, 'Player2'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[3].a.get('title')\r\n df.at[f, 'Score'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[4].select_one(\"span[id=score]\").text\r\n df.at[f, 'Location'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[5].a.get('title')\r\n df.at[f, 'Surface'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[6].text\r\n if html_soup.find('table', class_='table_stats_match') is not None:\r\n for r in range(1,len(html_soup.find('table', class_='table_stats_match').find_all('tr'))):\r\n if html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == '1st SERVE %':\r\n df.at[f, '1st Serve % P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\r\n df.at[f, '1st Serve % P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\r\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == '1st SERVE POINTS WON':\r\n df.at[f, '1st Serve Points Won P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\r\n df.at[f, '1st Serve Points Won P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\r\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == '2nd SERVE POINTS WON':\r\n df.at[f, '2nd Serve Points Won P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\r\n df.at[f, '2nd Serve Points Won P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\r\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == 'BREAK POINTS WON':\r\n df.at[f, 'BREAK POINTS WON P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\r\n df.at[f, 'BREAK POINTS WON P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\r\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == 'TOTAL RETURN POINTS WON':\r\n df.at[f, 'TOTAL RETURN POINTS WON P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\r\n df.at[f, 'TOTAL RETURN POINTS WON P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\r\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == 'TOTAL POINTS WON':\r\n df.at[f, 'TOTAL POINTS WON P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\r\n df.at[f, 'TOTAL POINTS WON P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\r\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == 'DOUBLE FAULTS':\r\n df.at[f, 'DOUBLE FAULTS P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\r\n df.at[f, 'DOUBLE FAULTS P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\r\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == 'ACES':\r\n df.at[f, 'ACES P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\r\n df.at[f, 'ACES P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\r\n \r\n #exclude doubles \r\n df = df[~df[\"Player1\"].str.contains(\"/\")]\r\n \r\n for i in range(0,len(df['Link'])):\r\n try:\r\n self.cur.execute(\"INSERT INTO reg_master VALUES (?, ?, NULL, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\", \r\n (df.at[i, 'Link'], df.at[i, 'Date'], df.at[i, 'Player1'], df.at[i, 'Player2'], df.at[i, 'Round'], df.at[i, 'Score'], \r\n df.at[i, 'Location'], df.at[i, 'Surface'], df.at[i, '1st Serve % P1'], df.at[i, '1st Serve Points Won P1'], \r\n df.at[i, '2nd Serve Points Won P1'], df.at[i, 'BREAK POINTS WON P1'], df.at[i, 'TOTAL RETURN POINTS WON P1'], \r\n df.at[i, 'TOTAL POINTS WON P1'], df.at[i, 'DOUBLE FAULTS P1'], df.at[i, 'ACES P1'], df.at[i, '1st Serve % P2'], \r\n df.at[i, '1st Serve Points Won P2'], df.at[i, '2nd Serve Points Won P2'], df.at[i, 'BREAK POINTS WON P2'], \r\n df.at[i, 'TOTAL RETURN POINTS WON P2'], df.at[i, 'TOTAL POINTS WON P2'], df.at[i, 'DOUBLE FAULTS P2'], df.at[i, 'ACES P2']))\r\n self.conn.commit()\r\n except:\r\n pass\r\n\r\n t1 = datetime.now()\r\n print(t1)\r\n print(\"It took \",(t1-t0),\" seconds.\")","title":""},{"docid":"50e18eeb206cde9cf7002b24c5ec6eaa","score":"0.56139016","text":"def export_dataframe_tableau(dataframe, filename=\"dataframe.hyper\"):\n global result\n import pantab\n # TODO: Fix ModuleNotFoundError: No module named 'tableauhyperapi'\n result = dataframe.to_json(orient='split')\n pantab.frame_to_hyper(dataframe, filename)\n with open(filename, \"rb\") as binary_file:\n file_bin = binary_file.read()\n assert file_bin is not None\n result = file_bin\n resultMetadata.put(\"file.extension\", \".hyper\")\n resultMetadata.put(\"file.name\", filename)\n resultMetadata.put(\"content.type\", \"application/octet-stream\")","title":""},{"docid":"dfb43b88dac405007d8a2b700c53b453","score":"0.5594382","text":"def get_link(df_list, files):\n pats = [re.compile(s) for s in ['http://','https://','www.']]\n link_df = []\n for i in range(len(df_list)):\n df = df_list[i]\n link = df[df.iloc[:,2].str.contains('link')].iloc[:,[1,3,7]]\n link.columns = ['time','origin','content']\n idx = pd.isnull(link.origin) != True\n link = link.loc[idx,['time','content']]\n for ii in range(len(link.content)):\n s = link.content.iloc[ii]\n for pat in pats:\n s = pat.sub('',s)\n link.content.iloc[ii] = s\n filenumber = int(re.findall(r'\\d+', files[i])[0])\n link['id'] = pd.Series((np.repeat(filenumber,np.shape(link)[0])), index = link.index)\n cols = link.columns.tolist()\n cols = cols[-1:] + cols[:-1]\n link_df.append(link[cols])\n link_df = pd.concat(link_df)\n link_df = link_df.reset_index()\n return link_df.drop('index',axis = 1)","title":""},{"docid":"b2f53471845cbf3169c1bb51965fe49d","score":"0.5583656","text":"def download_url(self):\n return","title":""},{"docid":"234cac62679fe769c31516daf3e9d5dc","score":"0.5575599","text":"def download_csv(data, filename):\n\theader = data[0].keys() #Data must have at least one record.\n\twith open('downloads/' + filename, \"w+\") as f:\n\t\twriter = csv.writer(f, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n\t\twriter.writerow(header)\n\t\tfor row in data:\n\t\t\twriter.writerow(list(row.values()))\n\n\t#Push the file to the view\n\treturn send_file('downloads/' + filename,\n\t\t\t\t mimetype='text/csv',\n\t\t\t\t attachment_filename=filename,\n\t\t\t\t as_attachment=True)","title":""},{"docid":"2fbb5e324590101c410ae1f35cc03c31","score":"0.5571424","text":"def csv_dl(links, start):\n for link in links:\n if link.string[:3] == 'Jan':\n start -= 1\n path = base + link['href']\n #2 types of links: a short one that leads to a page with the dl (else statemtent), and direct links to the file\n if len(link['href']) > 11:\n pass\n else:\n s2 = BeautifulSoup(requests.get(path).content)\n path = s2.find('a', {'href': re.compile(r'/files*')})['href']\n\n resp = requests.get(path)\n with open('data/' + str(start) + link.string[:3] + '.xlsx', 'wb') as file:\n file.write(resp.content)\n return start","title":""},{"docid":"2a4dc1c63efe5bdd5cdb0ba77297e118","score":"0.55572283","text":"def export_data() -> None:\n connection = connect()\n curr = connection.cursor()\n curr.execute('''\n SELECT keyword, query_timestamp, title, price, rating, reviews_count, item_url, image_url\n FROM keywords\n INNER JOIN keywords_data ON keywords_data.keyword_id = keywords.id;''')\n rows = curr.fetchall()\n df = pd.DataFrame(rows, columns=[x[0] for x in curr.description])\n df.to_csv('data_export.csv', index=False)\n print('Data was exported successfully to data_export.csv')","title":""},{"docid":"457a78255302d30511514d8f69d0f1e2","score":"0.5556854","text":"def downloads(self, result_loc_, date_):\n end_date = date_ + timedelta(days=1)\n query = Template(content_downloads.init())\n query = query.substitute(app=self.config['context']['pdata']['id']['app'],\n start_date=datetime.strftime(date_, '%Y-%m-%dT00:00:00+00:00'),\n end_date=datetime.strftime(end_date, '%Y-%m-%dT00:00:00+00:00'))\n\n headers = {\n 'Content-Type': \"application/json\"\n }\n url = \"{}druid/v2/\".format(self.druid_hostname)\n response = requests.request(\"POST\", url, data=query, headers=headers)\n records = [events['event'] for events in response.json()]\n data = pd.DataFrame(records)\n content = pd.read_csv(str(result_loc_.parent.joinpath('tb_metadata', date_.strftime('%Y-%m-%d'), 'textbook_snapshot.csv')))\n content = content[content['contentType']=='Resource']\n content = content[['identifier', 'channel']]\n content.drop_duplicates(inplace=True)\n content.rename(columns={'identifier': 'object_id'}, inplace=True)\n data = data.merge(content, on=\"object_id\", how=\"left\")\n data = data[data['channel'].notnull()]\n download_counts = data.groupby('channel').sum()\n download_counts.reset_index(inplace=True)\n\n result_loc_.joinpath(date_.strftime('%Y-%m-%d')).mkdir(exist_ok=True)\n download_counts.to_csv(result_loc_.joinpath(date_.strftime('%Y-%m-%d'), 'downloads.csv'), index=False)\n post_data_to_blob(result_loc_.joinpath(date_.strftime('%Y-%m-%d'), 'downloads.csv'), backup=True)","title":""},{"docid":"675994d5ffa91308b8eea838a9d1afcb","score":"0.5554471","text":"def result_download_button(app, df):\n df_ser = encode_df(df)\n return html.Form([\n dcc.Input(\n value=df_ser,\n name='result',\n type='text',\n style={'display': 'none'}),\n html.Button([\n html.Img(src=app.get_asset_url('download.svg')),\n html.Span('Download results')\n ],\n type='submit',\n className='div-with-image small-image'\n )\n ],\n method='POST',\n action='/download_df/'\n )","title":""},{"docid":"b8a30e726636ded875fbe4f249cc62d9","score":"0.55536354","text":"def file_download_link(filename):\n location = \"/downloads/{}\".format(urlquote(filename))\n return html.A(filename, href=location)","title":""},{"docid":"021b6619ef24e0e66b8fabbb2787b8eb","score":"0.555286","text":"def download_dat_files():\r\n url = 'https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat'\r\n wget.download(url, 'C:\\sqlite3\\Python Database files')\r\n\r\n url2 = 'https://raw.githubusercontent.com/jpatokal/openflights/master/data/airlines.dat'\r\n wget.download(url2, 'C:\\sqlite3\\Python Database files')\r\n\r\n url3 = 'https://raw.githubusercontent.com/jpatokal/openflights/master/data/routes.dat'\r\n wget.download(url3, 'C:\\sqlite3\\Python Database files')","title":""},{"docid":"05897914db6b7477d2432b34d9cd3fa6","score":"0.55320585","text":"def report_href(**kwargs):\n params = args.copy()\n if sort_col:\n params['sort'] = sort_col\n params['page'] = page\n if max:\n params['max'] = max\n params.update(kwargs)\n params['asc'] = '1' if params.get('asc', asc) else '0'\n return req.href.report(id, params)","title":""},{"docid":"d3b4bd7fbb11900f01b0567c24099a08","score":"0.5530999","text":"def download(self):\n pass","title":""},{"docid":"d3b4bd7fbb11900f01b0567c24099a08","score":"0.5530999","text":"def download(self):\n pass","title":""},{"docid":"dd7c00a5d8646e39615e71a76a52dc61","score":"0.5512493","text":"def get_data(year,league=\"E0\",base_link=base_link):\n\n if not os.path.isdir(\"../Data/\"):\n os.mkdir(\"../Data/\")\n\n final_link = base_link + year + \"/\" + league\n filename = \"../Data/\" + year + \"_\" + league + \".csv\"\n\n datafile = urllib2.urlopen(final_link)\n output = open(filename,'wb')\n output.write(datafile.read())\n output.close()\n return pd.read_csv(filename)","title":""},{"docid":"f6d73064848724cc1a96a46127c34fdc","score":"0.55080026","text":"def download(self, destination: str) -> str:\n data = self.materialize()\n try:\n data.to_csv(destination, index=False)\n return \"Success\"\n except Exception as e:\n return \"Failed: %s\" % str(e)","title":""},{"docid":"f48eb01602359981bdcf48cd97ffa7db","score":"0.5486758","text":"def enhanced_generate_link(df_drugs: pd.DataFrame, df_reference: pd.DataFrame) -> {}:\n\n drug_dict = dict()\n # define a list of drugs. Faster to loop on a list than on a pandas df.\n drugs = df_drugs['drug'].to_list()\n\n for drug in drugs:\n drug_dict[drug] = dict()\n\n # pubMed\n drug_dict[drug][\"pubMed\"] = finding_pubmed_mention(drug, df_reference, \"title\")[\"title\"].to_list()\n drug_dict[drug][\"pubMedJournal\"] = finding_pubmed_mention(drug, df_reference, \"title\")[\"journal\"].to_list()\n drug_dict[drug][\"pubMedDate\"] = finding_pubmed_mention(drug, df_reference, \"title\")[\"date\"].to_list()\n\n # clinical_trials\n drug_dict[drug][\"clinicalTrial\"] = finding_clinical_mention(drug, df_reference, \"title\")[\"title\"].to_list()\n drug_dict[drug][\"clinicalTrialJournal\"] = finding_clinical_mention(drug, df_reference, \"title\")[\n \"journal\"].to_list()\n drug_dict[drug][\"clinicalTrialDate\"] = finding_clinical_mention(drug, df_reference, \"title\")[\"date\"].to_list()\n return drug_dict","title":""},{"docid":"f5a01135a95d6d8def4f8a86867b1bff","score":"0.54821986","text":"def download_data(model_dir, season):\n file_name = model_dir + \"/\" + season + \".csv\"\n if (not skip_download): \n # urllib.request.urlretrieve(\n # \"http://217.160.223.109/mmz4281/\"+season+\"/D1.csv\",\n # file_name) # pylint: disable=line-too-long\n \n url = \"http://www.football-data.co.uk/mmz4281/\"+season+\"/D1.csv\"\n print(\"Downloading %s\" % url)\n urllib.request.urlretrieve(\n url,\n file_name) # pylint: disable=line-too-long\n print(\"Data is downloaded to %s\" % file_name)\n data = pd.read_csv(\n tf.gfile.Open(file_name),\n skipinitialspace=True,\n engine=\"python\",\n skiprows=0)\n data[\"Season\"]= season\n return data","title":""},{"docid":"29fbabc6fc60102a26c4472381f6e06f","score":"0.547121","text":"def make_download_url(pano_id: str, zoom: int, x: int, y: int) -> str:\n return (\n \"https://cbk0.google.com/cbk\"\n f\"?output=tile&panoid={pano_id}&zoom={zoom}&x={x}&y={y}\"\n )","title":""},{"docid":"56d1103bd7d2988abfcef6e378b6c655","score":"0.54653656","text":"def full_data_frame_extract(webdriver_path,base_url,numbers,folder_path):\n\n driver = webdriver.Chrome(webdriver_path)\n wait = WebDriverWait(driver,15)\n driver.get(base_url)\n \n\n Df = pd.DataFrame()\n Df['date_time'] = np.NaN\n Df['player'] = np.NaN\n Df['position_and_team'] = np.NaN\n Df['title'] = np.NaN\n Df['summary'] = np.NaN\n Df['social_headline'] = np.NaN\n Df['players_related'] = np.NaN\n Df['source'] = np.NaN\n \n \n \n for num in range(1,numbers+1):\n wait.until(EC.visibility_of_element_located((By.XPATH,\"//*[@id='player-news-page-wrapper']/div/div/div[3]/ul/li[1]/article/div[1]\")))\n content = driver.page_source\n full_df = source_code_to_df(content)\n Df = Df.append(full_df)\n \n click_next = wait.until(EC.element_to_be_clickable((By.XPATH,\"//*[@id='player-news-page-wrapper']/div/div/div[2]/a[2]\")))\n click_next.click()\n \n if(num%1000 == 0):\n path = folder_path + 'injuries' + str(num/1000) +'.csv'\n Df.to_csv(path, index = False)\n print(\"number of reports is:\", num)\n \n driver.quit()\n return Df","title":""}],"string":"[\n {\n \"docid\": \"652badd8b29f86288a249339bc14ca84\",\n \"score\": \"0.7853004\",\n \"text\": \"def get_table_download_link(dataframe: pd.DataFrame):\\n val = to_excel(dataframe)\\n b64 = base64.b64encode(val)\\n # TODO: Create a random number as filename, check generate_xlsx\\n return ('<p style=\\\"text-align:center;\\\">'\\n f'<a href=\\\"data:application/octet-stream;base64,{b64.decode()}\\\" '\\n 'download=\\\"mann_kendall.xlsx\\\">Download Excel file</a></p>')\\n # decode b'abc' => abc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7ccfae5069af110c88e109ae4e718f8\",\n \"score\": \"0.7717478\",\n \"text\": \"def get_table_download_link(df):\\r\\n csv = df.to_csv(index=False)\\r\\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\\r\\n href = f'<a href=\\\"data:file/csv;base64,{b64}\\\">Download csv file</a>'\\r\\n return href\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd1e52f4bfee19580b16aabb680bca81\",\n \"score\": \"0.77031136\",\n \"text\": \"def get_table_download_link(df):\\r\\n csv = df.to_csv(index=False)\\r\\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\\r\\n href = f'<a href=\\\"data:file/csv;base64,{b64}\\\">Download csv</a>'\\r\\n return href\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39fdbbc659b5b85ef19ee65c812a3d1e\",\n \"score\": \"0.76984584\",\n \"text\": \"def get_table_download_link(df):\\n csv = df.to_csv(index=False)\\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\\n href = f'<a href=\\\"data:file/csv;base64,{b64}\\\">Download csv file</a>'\\n return href\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39fdbbc659b5b85ef19ee65c812a3d1e\",\n \"score\": \"0.76984584\",\n \"text\": \"def get_table_download_link(df):\\n csv = df.to_csv(index=False)\\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\\n href = f'<a href=\\\"data:file/csv;base64,{b64}\\\">Download csv file</a>'\\n return href\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b1bd2ccce3a783132c2ded38d50fffd\",\n \"score\": \"0.76605654\",\n \"text\": \"def get_table_download_link(df):\\n csv = df.to_csv(index=False)\\n b64 = base64.b64encode(\\n csv.encode()\\n ).decode() # some strings <-> bytes conversions necessary here\\n return f'<a href=\\\"data:file/csv;base64,{b64}\\\" download=\\\"dataset.csv\\\">Download CSV file</a>'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3f9e9dc4d320916717e52208944911c\",\n \"score\": \"0.7629094\",\n \"text\": \"def get_table_download_link(df):\\r\\n csv = df.to_csv(index=False)\\r\\n b64 = pybase64.b64encode(\\r\\n csv.encode()\\r\\n ).decode() # some strings <-> bytes conversions necessary here\\r\\n return f'<a href=\\\"data:file/csv;base64,{b64}\\\" download=\\\"Prediction.csv\\\">⬇️ Download output CSV File</a>'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30861d6c1da187b69b2626884f8cb8e9\",\n \"score\": \"0.756977\",\n \"text\": \"def get_table_download_link(df, out_file, label):\\n csv = df.to_csv(index=False)\\n b64 = base64.b64encode(\\n csv.encode()\\n ).decode() # some strings <-> bytes conversions necessary here\\n out = f'<a href=\\\"data:file/csv;base64,{b64}\\\" download=\\\"%s\\\">%s</a>' % (out_file, label)\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab186da2d19282b7f4186eecc2c8811e\",\n \"score\": \"0.7558905\",\n \"text\": \"def get_table_download_link(df):\\r\\n val = to_excel(df)\\r\\n b64 = base64.b64encode(val) # val looks like b'...'\\r\\n return f'<a href=\\\"data:application/octet-stream;base64,{b64.decode()}\\\" download=\\\"classified_data.xlsx\\\">Download file</a>' # decode b'abc' => abc\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59f5c6bd8e95d7a9f0efec004885c1b0\",\n \"score\": \"0.7536353\",\n \"text\": \"def get_table_download_link(df):\\n csv = df.to_csv(index=False)\\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\\n href = f'<a href=\\\"data:file/csv;base64,{b64}\\\">Download: Right click here and save as CSV</a>'\\n return href\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13cd219f2f8562ed90eb577cddeff28d\",\n \"score\": \"0.7458118\",\n \"text\": \"def df_download_link(df: pd.DataFrame, text: str, filename: str):\\n csv_bytes = df.to_csv(index=False).encode()\\n b64_str = base64.b64encode(csv_bytes).decode()\\n html_str = f'<a download=\\\"{filename}.csv\\\" href=\\\"data:file/csv;name={filename}.csv;base64,{b64_str}\\\">{text}</a>'\\n st.markdown(html_str, unsafe_allow_html=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"338175ccbf76d0baae50a6d67562b6ec\",\n \"score\": \"0.7433467\",\n \"text\": \"def get_table_download_link(df):\\n val = to_excel(df)\\n b64 = base64.b64encode(val) # val looks like b'...'\\n return f'<a href=\\\"data:application/octet-stream;base64,{b64.decode()}\\\" download=\\\"DataNews.xlsx\\\">Download Excel file</a>' # decode b'abc' => abc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c722d25f2061bdb349f8131ba8eb8306\",\n \"score\": \"0.7415955\",\n \"text\": \"def get_table_download_link(df):\\r\\n val = to_excel(df)\\r\\n b64 = base64.b64encode(val) # val looks like b'...'\\r\\n return f'<a href=\\\"data:application/octet-stream;base64,{b64.decode()}\\\" download=\\\"Summary.xlsx\\\">Export to Excel</a>' # decode b'abc' => abc\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c6033575b1e89a0c662e9a3936a73d\",\n \"score\": \"0.73187155\",\n \"text\": \"def get_table_download_link(df, text='Click here to download table'):\\r\\n csv = df.to_csv(index=False)\\r\\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\\r\\n # href = f'<a href=\\\"data:file/csv;base64,{b64}\\\">Download csv file</a>'\\r\\n href = f'<a href=\\\"data:file/csv;base64,{b64}\\\" download=\\\"table.csv\\\">{text}</a>'\\r\\n return href\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0286fb87ce7f72036e960f4d95ae8053\",\n \"score\": \"0.7140357\",\n \"text\": \"def get_table_download_link_csv(df):\\r\\n csv = df.to_csv(index=False)\\r\\n b64 = base64.b64encode(\\r\\n csv.encode()\\r\\n ).decode() # some strings <-> bytes conversions necessary here\\r\\n return f'<a href=\\\"data:file/csv;base64,{b64}\\\" download=\\\"download.csv\\\">Download csv file</a>'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97d8bc5f47d5a4d820f67967ca8a3fde\",\n \"score\": \"0.70307845\",\n \"text\": \"def get_table_download_link_xlsx(df):\\r\\n val = to_excel(df)\\r\\n b64 = base64.b64encode(val) # val looks like b'...'\\r\\n return f'<a href=\\\"data:application/octet-stream;base64,{b64.decode()}\\\" download=\\\"download.xlsx\\\">Download xlsx file</a>' # decode b'abc' => abc\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92bd98737850ed07411b4ab075a84eeb\",\n \"score\": \"0.6968001\",\n \"text\": \"def get_table_download_link(dafr):\\r\\n csv = dafr.to_csv(index=False)\\r\\n b64 = base64.b64encode(\\r\\n csv.encode()\\r\\n ).decode() # some strings <-> bytes conversions necessary here\\r\\n return f'<a href=\\\"data:file/csv;base64,{b64}\\\" download=\\\"Results.csv\\\">Download Result (CSV)</a>'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6bf54f464b06c3407b9706382237a5e\",\n \"score\": \"0.65727204\",\n \"text\": \"def download_link(object_to_download, download_filename, download_link_text):\\n if isinstance(object_to_download,pd.DataFrame):\\n object_to_download = object_to_download.to_csv(index=False)\\n\\n # some strings <-> bytes conversions necessary here\\n b64 = base64.b64encode(object_to_download.encode()).decode()\\n\\n return f'<a href=\\\"data:file/txt;base64,{b64}\\\" download=\\\"{download_filename}\\\">{download_link_text}</a>'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6bf54f464b06c3407b9706382237a5e\",\n \"score\": \"0.65727204\",\n \"text\": \"def download_link(object_to_download, download_filename, download_link_text):\\n if isinstance(object_to_download,pd.DataFrame):\\n object_to_download = object_to_download.to_csv(index=False)\\n\\n # some strings <-> bytes conversions necessary here\\n b64 = base64.b64encode(object_to_download.encode()).decode()\\n\\n return f'<a href=\\\"data:file/txt;base64,{b64}\\\" download=\\\"{download_filename}\\\">{download_link_text}</a>'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a1ab01ed05305edeab81026c97c94c3\",\n \"score\": \"0.65723854\",\n \"text\": \"def csv_downloader(data):\\n time_stamp = time.strftime(\\\"%Y%m%d_%H%M%S\\\")\\n \\n csvfile = data.to_csv()\\n \\n b64 = base64.b64encode(csvfile.encode()).decode()\\n \\n new_filename = f\\\"fit_results{time_stamp}.csv\\\"\\n href = f'** Download DataFrame as .csv: ** \\\\\\n <a href=\\\"data:file/csv;base64,{b64}\\\" \\\\\\n download=\\\"{new_filename}\\\">Click Here</a>'\\n st.markdown(href, unsafe_allow_html=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9285f24031cf527126dd828c71629438\",\n \"score\": \"0.6354674\",\n \"text\": \"def download_dataframe_as_csv(dataframe, filename):\\n dataframe.to_csv(filename)\\n files.download(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3185fb6527d6f3466514db45089a7fee\",\n \"score\": \"0.6178509\",\n \"text\": \"def url_string(it):\\n \\n b_link =\\\"https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni-\\\"\\n \\n return b_link + it + \\\".csv\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c07d89cb8a13d195b0fed1cfb2b1122d\",\n \"score\": \"0.61095786\",\n \"text\": \"def format_url(df):\\n\\n ix_col = df.index.name\\n df = df.reset_index()\\n df[ix_col] = df.apply(\\n lambda x: f'<a href=\\\"{x[\\\"URL\\\"]}\\\">{x[ix_col]}</a>', axis=1\\n )\\n df = df.drop(\\\"URL\\\", axis=1)\\n\\n df = df.set_index(ix_col)\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3bb5283123cd397d820dc67364a0dba\",\n \"score\": \"0.6088772\",\n \"text\": \"def download_on_dataframe(self, **kwargs):\\n\\n if self.blob is None:\\n raise ValueError(\\\"No file selected. Set it with select_file method first.\\\")\\n\\n logger.debug(f\\\"gs path: {self.uri}\\\")\\n return pd.read_csv(self.uri, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cea8b8fc72a932b2bf26a80d88bf3e0c\",\n \"score\": \"0.6074464\",\n \"text\": \"def download():\\r\\n return response.download(request, db2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce8da0ce41714656c57e002c29a67771\",\n \"score\": \"0.6072765\",\n \"text\": \"def download_csv(data):\\n csv = data.to_csv().encode()\\n b64 = base64.b64encode(csv).decode()\\n href = f'<a href=\\\"data:file/csv;base64,{b64}\\\" download=\\\"result.csv\\\" target=\\\"_blank\\\">Download CSV</a>'\\n\\n return href\",\n \"title\": \"\"\n },\n {\n \"docid\": \"775f4d5c7409fbeefe4e2d1d300dd7fa\",\n \"score\": \"0.60203224\",\n \"text\": \"def download_data_from_table(self, mydb, db_name, table_name, selection_col, file_name_for_download,file_download_location):\\r\\n try:\\r\\n query_download = f\\\"\\\"\\\"select * from {db_name}.{table_name} {selection_col};\\\"\\\"\\\"\\r\\n data = pd.read_sql(query_download, mydb)\\r\\n\\r\\n data.to_csv(f\\\"\\\"\\\"{file_download_location}\\\\\\\\{file_name_for_download}.csv\\\"\\\"\\\", index=False)\\r\\n logging.debug(f\\\"\\\"\\\" All records successfully downloaded at {file_download_location}\\\\\\\\{file_name_for_download}.csv\\\"\\\"\\\")\\r\\n\\r\\n return f\\\"\\\"\\\"All records successfully downloaded at {file_download_location}\\\\\\\\{file_name_for_download}.csv\\\"\\\"\\\"\\r\\n\\r\\n except Exception as e:\\r\\n logging.error(f\\\"\\\"\\\"ERROR IN : download_data_from_table(), ERROR : {str(e)}\\\"\\\"\\\")\\r\\n return f\\\"\\\"\\\"ERROR IN : download_data_from_table(), ERROR : {str(e)}\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"681ec8a506d95ab2ebbb21031867317a\",\n \"score\": \"0.59827954\",\n \"text\": \"def toggle_download_data_link(_, dl_data_choice, stored_data):\\n fname = \\\"%s_data.json\\\" % dl_data_choice\\n fpath = \\\"%s%s\\\" % (stored_data['info']['dl_data_path'], fname)\\n return fpath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53c5d10f2d16687398ba6e396b736924\",\n \"score\": \"0.5969805\",\n \"text\": \"def export_dataframe_html(dataframe):\\n global variables, result, resultMetadata\\n import pandas as pd\\n\\n limit_output_view = variables.get(\\\"LIMIT_OUTPUT_VIEW\\\")\\n limit_output_view = 5 if limit_output_view is None else int(limit_output_view)\\n if limit_output_view > 0:\\n info = \\\"The task preview is limited to \\\" + str(limit_output_view) + \\\" rows\\\"\\n dataframe = dataframe.head(limit_output_view).copy()\\n else:\\n nrows = len(dataframe.index)\\n info = \\\"Total of \\\" + str(nrows) + \\\" rows\\\"\\n print(info)\\n with pd.option_context('display.max_colwidth', -1):\\n result = dataframe.to_html(escape=False, classes='table table-bordered table-striped', justify='center')\\n result = \\\"\\\"\\\"\\n <!DOCTYPE html>\\n <html>\\n <head>\\n <meta charset=\\\"UTF-8\\\">\\n <title>Machine Learning Preview</title>\\n <link rel=\\\"stylesheet\\\" href=\\\"https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css\\\" \\n integrity=\\\"sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T\\\" crossorigin=\\\"anonymous\\\">\\n </head>\\n <body>\\n <h1 class=\\\"text-center my-4\\\" style=\\\"color:#003050;\\\">Data Preview</h1>\\n <p align=\\\"center\\\">{0}</p>\\n {1}\\n </body></html>\\\"\\\"\\\".format(info, result)\\n result = result.encode('utf-8')\\n resultMetadata.put(\\\"file.extension\\\", \\\".html\\\")\\n resultMetadata.put(\\\"file.name\\\", \\\"output.html\\\")\\n resultMetadata.put(\\\"content.type\\\", \\\"text/html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb697b6a3a1b84b2066f131d094c3b9f\",\n \"score\": \"0.5959588\",\n \"text\": \"def link_to_pub(records):\\n if 'metaknowledge' in str(type(records)).lower():\\n recs = records.forNLP(extraColumns=[\\\"AU\\\", \\\"SO\\\", \\\"DE\\\", 'DOI'], lower=False, removeNonWords=False)\\n df = pd.DataFrame(recs)\\n elif 'dataframe' in str(type(records)).lower():\\n df = records.copy()\\n # End if\\n\\n df.loc[df['DOI'] != '', 'DOI link'] = \\\"https://dx.doi.org/\\\" + df.loc[df['DOI'] != '', 'DOI'].astype(str)\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56fb28cfd437b853155f7add3e263fe7\",\n \"score\": \"0.59426004\",\n \"text\": \"def get_download_link(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe5d9bf254dbc00d739a521a4dd728fb\",\n \"score\": \"0.591364\",\n \"text\": \"def download_link(object_to_download, download_filename, download_link_text):\\n\\n\\n # some strings <-> bytes conversions necessary here\\n b64 = base64.b64encode(object_to_download.encode()).decode()\\n\\n return f'<a href=\\\"data:file/txt;base64,{b64}\\\" download=\\\"{download_filename}\\\">{download_link_text}</a>'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd54626d8d88f05bcb411c40146284a3\",\n \"score\": \"0.58500165\",\n \"text\": \"def download():\\n return response.download(request,db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd54626d8d88f05bcb411c40146284a3\",\n \"score\": \"0.58500165\",\n \"text\": \"def download():\\n return response.download(request,db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd54626d8d88f05bcb411c40146284a3\",\n \"score\": \"0.58500165\",\n \"text\": \"def download():\\n return response.download(request,db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd54626d8d88f05bcb411c40146284a3\",\n \"score\": \"0.58500165\",\n \"text\": \"def download():\\n return response.download(request,db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2e0c541bf13a84c2d4711914076eeae\",\n \"score\": \"0.5832997\",\n \"text\": \"def download(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a42985872da94830e9c189c7cd1f035\",\n \"score\": \"0.5823003\",\n \"text\": \"def getData(startdt, savepath):\\n \\n #create filename url list (summer 2015 start date is 15,7,4)\\n startdt = date.date(startdt[0],startdt[1],startdt[2])\\n uri = \\\"http://web.mta.info/developers/data/nyct/turnstile/turnstile_\\\"\\n urllist = [re.sub(\\\"-\\\",\\\"\\\",uri+str(startdt + date.timedelta(days=i))[2:]+str('.txt')) for i in np.arange(0,90,7)]\\n \\n #urllist = [\\\"http://web.mta.info/developers/data/nyct/turnstile/turnstile_150704.txt\\\",\\n # \\\"http://web.mta.info/developers/data/nyct/turnstile/turnstile_150711.txt\\\",\\n # \\\"http://web.mta.info/developers/data/nyct/turnstile/turnstile_150718.txt\\\"]\\n \\n \\n #download from first url with header\\n response = ulib.urlopen(urllist[0])\\n df = pd.read_csv(response, \\n header=0)\\n #dfbig = pd.DataFrame({})\\n response.close()\\n \\n #download from remaining urls and concatenate without header to dataframe\\n for url in urllist[1:]:\\n response = ulib.urlopen(url)\\n df = pd.concat([df,pd.read_csv(response, \\n skip_blank_lines=True)])\\n response.close()\\n \\n df.drop(df.columns[[0,1,2,3,4,5,6,7,8,9,10]],axis=1)\\n\\n #df = pd.concat([df,dfbig], axis=0)\\n #'/Users/ash/ds/projects/data/mta/mta-turnstile-summer2015data.csv'\\n pd.DataFrame.to_csv(df, savepath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6f3542c637ad94eb9d886dfb88eb380\",\n \"score\": \"0.5820612\",\n \"text\": \"def generate_urls():\\n yield base_url.format(\\\"ProductionAndCurtailmentsData-May1_2014-May31_2017.xlsx\\\")\\n yield base_url.format(\\\"ProductionAndCurtailmentsData-Jun1_2017-Dec31_2017.xlsx\\\")\\n for year in range(2018, 2021):\\n yield base_url.format(f\\\"ProductionAndCurtailmentsData_{year}.xlsx\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c0762107632a7cd8b72fd3f41afa96\",\n \"score\": \"0.5785434\",\n \"text\": \"def return_csv(new_df, name):\\n redirect('http://localhost:5000')\\n new_df.to_csv('./' + name + '.csv')\\n return send_file('./' + name + '.csv',\\n attachment_filename='./' + name + '.csv',\\n as_attachment=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db55dffdff914f0995e3eb4d9d381ece\",\n \"score\": \"0.57851064\",\n \"text\": \"def download_urls(self, df, start = 0, end = 10, headers = headers):\\n all_download_urls = []\\n urls = list(df['Album hrefs'])\\n tracks = list(df['Track'])\\n\\n for i in range(start, end):\\n #Print the current time for every 50 urls obtained\\n if i%50 ==0:\\n now = datetime.now()\\n current_time = str(now.strftime(\\\"%H:%M:%S\\\"))\\n print(\\\"{} Current Time = {}\\\".format(i, current_time))\\n payload = {}\\n headers = headers\\n url = urls[i]\\n track = tracks[i]\\n\\n \\n try:\\n response = requests.request(\\\"GET\\\", url, headers=headers, data=payload)\\n if response.status_code == 200:\\n all_download_urls.append(self.get_url_download_(response, str(track)))\\n else:\\n all_download_urls.append('None')\\n except:\\n all_download_urls.append('None')\\n\\n return all_download_urls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a40b56a37f1e2d4218eaaf96d567ad8e\",\n \"score\": \"0.5767065\",\n \"text\": \"def download_dataset(url: str) -> pd.DataFrame:\\n print(f'downloading training data from {DATA_URL}')\\n data_file = urlopen(url)\\n # app_train = pd.read_excel('final data sales details.xlsx', index_col = 'Client Account No.')\\n return pd.read_excel(data_file, index_col = 'Client Account No.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd30c2e3a4426e0af0135a664167e528\",\n \"score\": \"0.57450867\",\n \"text\": \"def download():\\n return response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c66ccaa44b5c533fc58ec1e61bbfd83\",\n \"score\": \"0.5743465\",\n \"text\": \"def download():\\n\\treturn response.download(request, db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"340de45ce944ffe89fa16bdcc70aaa8c\",\n \"score\": \"0.57317215\",\n \"text\": \"def file_download_link(filename):\\n location = \\\"./download/{}\\\".format(urlquote(filename))\\n return html.A(filename, href=location)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"629f5567f6f66a8977ca26bf08e3c332\",\n \"score\": \"0.573103\",\n \"text\": \"def data_frame(list_of_dicts):\\n df = pd.DataFrame(list_of_dicts)\\n index_df = df.set_index('url')\\n index_df.to_csv('result.csv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"653480b3869fa8a43b8475173d5c2516\",\n \"score\": \"0.5708914\",\n \"text\": \"def append_download_urls_save_df(self, df, download_urls, filename):\\n df['Download URLs'] = download_urls\\n df.to_csv(filename, index=False)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ab7d9f24c9712346d9a8eff654b65b1\",\n \"score\": \"0.5701151\",\n \"text\": \"def format_metadata_url(api_key, table_name):\\n query_params = [('api_key', api_key), ('qopts.export', 'true')]\\n\\n return (\\n QUANDL_DATA_URL + table_name + \\\".csv?\\\" + urlencode(query_params)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c8fc4399952b787d6afb3b3ec916311\",\n \"score\": \"0.5682233\",\n \"text\": \"def file_download_link(filename):\\n location = \\\"/download/{}\\\".format(urlquote(filename))\\n return html.A(filename, href=location)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a7ee823b2ec283d9c83f8a33c5d9f22\",\n \"score\": \"0.5651486\",\n \"text\": \"def generate_link(df_drugs: pd.DataFrame, df_reference: pd.DataFrame) -> {}:\\n\\n drug_dict = dict()\\n # define a list of drugs. Faster to loop on a list than on a pandas df.\\n drugs = df_drugs['drug'].to_list()\\n # print(\\\"drugs\\\" ,+ drugs )\\n\\n for drug in drugs:\\n drug_dict[drug] = dict()\\n\\n # pubMed\\n drug_dict[drug][\\\"pubMed\\\"] = finding_mention(drug, df_reference, \\\"title\\\")[\\\"title\\\"].to_list()\\n drug_dict[drug][\\\"pubMedJournal\\\"] = finding_mention(drug, df_reference, \\\"title\\\")[\\\"journal\\\"].to_list()\\n drug_dict[drug][\\\"pubMedDate\\\"] = finding_mention(drug, df_reference, \\\"title\\\")[\\\"date\\\"].to_list()\\n\\n # clinical_trials\\n drug_dict[drug][\\\"clinicalTrial\\\"] = finding_mention(drug, df_reference, \\\"title\\\")[\\\"title\\\"].to_list()\\n drug_dict[drug][\\\"clinicalTrialJournal\\\"] = finding_mention(drug, df_reference, \\\"title\\\")[\\\"journal\\\"].to_list()\\n drug_dict[drug][\\\"clinicalTrialDate\\\"] = finding_mention(drug, df_reference, \\\"title\\\")[\\\"date\\\"].to_list()\\n return drug_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6494098b252ffd478e8c0fb4f2d53d70\",\n \"score\": \"0.56379694\",\n \"text\": \"def download_csv(readings, filename_base=\\\"results\\\"):\\n df = pd.DataFrame(readings)\\n output_buffer = io.BytesIO()\\n df.to_csv(output_buffer)\\n output_buffer.seek(0)\\n filename = (\\n filename_base + \\\"_\\\" + datetime.now().strftime(\\\"%d-%m-%Y_%H-%M-%S\\\") + \\\".csv\\\"\\n )\\n return send_file(\\n output_buffer, download_name=filename, mimetype=\\\"text/csv\\\", as_attachment=True\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fffafd15f6e6ef701e17d79348e9bde3\",\n \"score\": \"0.56352186\",\n \"text\": \"def downloadSubmissions(challenge, showMissing):\\n\\n # generator depends on challenge type and sometimes mode (2019 vs 2020) active\\n if challenge == 'cea':\\n if config.OUTPUT_2019_FORMAT:\\n def generate():\\n \\\"\\\"\\\"2019 format: col_id before row_id\\\"\\\"\\\"\\n for row in db.getAllTargets('cea', not showMissing):\\n yield '{},{},{},{}{}\\\\n'.format(\\n row['table_name'],\\n row['col_id'],\\n row['row_id'],\\n config.URL_PREFIX if config.OUTPUT_ADD_PREFIX else '',\\n row['mapped']\\n )\\n else:\\n def generate():\\n \\\"\\\"\\\"2020 format: row_id before col_id\\\"\\\"\\\"\\n for row in db.getAllTargets('cea', not showMissing):\\n yield '{},{},{},{}{}\\\\n'.format(\\n row['table_name'],\\n row['row_id'],\\n row['col_id'],\\n config.URL_PREFIX if config.OUTPUT_ADD_PREFIX else '',\\n row['mapped']\\n )\\n elif challenge == 'cpa':\\n def generate():\\n for row in db.getAllTargets('cpa', not showMissing):\\n yield '{},{},{},{}{}\\\\n'.format(\\n row['table_name'],\\n row['sub_id'],\\n row['obj_id'],\\n config.URL_PREFIX if config.OUTPUT_ADD_PREFIX else '',\\n row['mapped']\\n )\\n elif challenge == 'cta':\\n def generate():\\n for row in db.getAllTargets('cta', not showMissing):\\n yield '{},{},{}{}\\\\n'.format(\\n row['table_name'],\\n row['col_id'],\\n config.URL_PREFIX if config.OUTPUT_ADD_PREFIX else '',\\n row['mapped']\\n )\\n else:\\n raise Exception('Unknown challenge: {}'.format(challenge))\\n\\n # stream response\\n filename = '{}{}.csv'.format(challenge, 'Missing' if showMissing else '')\\n return flask.Response(\\n generate(),\\n mimetype='text/csv',\\n headers={\\n \\\"Content-disposition\\\": \\\"attachment; filename={}\\\".format(filename)\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96a50c95e27e648dee3cc8794bddfff5\",\n \"score\": \"0.56314445\",\n \"text\": \"def _get_data_url(self, **form_data):\\n # For old data links of Google Spreadsheets\\n if 'ccc?key' in form_data['googledocs_url']:\\n return ''.join([form_data['googledocs_url'], '&output=csv'])\\n # New data format for Google Drive import is like this:\\n # https://docs.google.com/spreadsheets/d/key/edit?usp=sharing\\n else:\\n return ''.join([form_data['googledocs_url'].split('edit')[0],\\n 'export?format=csv'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c422055b33969fb5a2700303d5edc175\",\n \"score\": \"0.5620762\",\n \"text\": \"def Download_Reg(self, StDt, EndDt, Days, db):\\r\\n t0 = datetime.now()\\r\\n print('Start time: ', t0)\\r\\n self.StDt = datetime.strptime(str(StDt), '%d.%m.%Y').date()\\r\\n\\r\\n if EndDt != 0:\\r\\n self.EndDt = datetime.strptime(str(EndDt), '%d.%m.%Y').date()\\r\\n self.Days_between = self.StDt - self.EndDt\\r\\n self.Days_to_Down = self.Days_between.days\\r\\n elif Days != 0:\\r\\n self.Days_to_Down = int(Days)\\r\\n self.EndDt = self.StDt - timedelta(days = Days)\\r\\n self.conn = sqlite3.connect(db)\\r\\n self.cur = self.conn.cursor()\\r\\n self.cur.execute(\\\"SELECT * from links_reg_match\\\")\\r\\n Dates = [tup[1] for tup in self.cur.fetchall()]\\r\\n self.cur.execute(\\\"SELECT * from links_reg_match\\\")\\r\\n links = [tup[0] for tup in self.cur.fetchall()] \\r\\n df = pd.DataFrame({'Dates':Dates, 'Link':links})\\r\\n df['Dates'] = pd.to_datetime(df['Dates'], format='%d.%m.%Y')\\r\\n l = []\\r\\n for i in range(0, len(df['Dates'])):\\r\\n l.append(df.at[i, 'Dates'].date())\\r\\n df['Dates'] = l\\r\\n df.sort_values('Dates', inplace=True, axis=0)\\r\\n df.set_index('Dates', inplace = True)\\r\\n df = df.loc[self.EndDt:self.StDt]\\r\\n df.reset_index(inplace = True)\\r\\n time_to_download = 0.30\\r\\n print('Downloading from ',self.EndDt,' to ',self.StDt)\\r\\n print('Matches to download: ',len(df['Link']))\\r\\n print('Estimated finish time: ',(t0 + timedelta(seconds=(time_to_download*len(df['Link'])))))\\r\\n for f in range(0,len(df['Link'])): \\r\\n if f == 0:\\r\\n print('Initiating the reg download...')\\r\\n #Progress\\r\\n if f%1000 == 0:\\r\\n print('Progress: ', round(f/len(df['Link']) * 100, 2), '%')\\r\\n page = requests.get(df.at[f, 'Link'])\\r\\n html_soup = soup(page.text,'html.parser')\\r\\n if html_soup.find('tr', class_='tour_head unpair') is not None:\\r\\n df.at[f, 'Date'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[0].text\\r\\n df.at[f, 'Date'] = df.at[i, 'Date'][:5] + '.20' + df.at[i, 'Date'][6:8]\\r\\n df.at[f, 'Round'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[1].text\\r\\n df.at[f, 'Player1'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[2].a.get('title')\\r\\n df.at[f, 'Player2'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[3].a.get('title')\\r\\n df.at[f, 'Score'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[4].select_one(\\\"span[id=score]\\\").text\\r\\n df.at[f, 'Location'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[5].a.get('title')\\r\\n df.at[f, 'Surface'] = html_soup.find('tr', class_='tour_head unpair').find_all('td')[6].text\\r\\n if html_soup.find('table', class_='table_stats_match') is not None:\\r\\n for r in range(1,len(html_soup.find('table', class_='table_stats_match').find_all('tr'))):\\r\\n if html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == '1st SERVE %':\\r\\n df.at[f, '1st Serve % P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\\r\\n df.at[f, '1st Serve % P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\\r\\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == '1st SERVE POINTS WON':\\r\\n df.at[f, '1st Serve Points Won P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\\r\\n df.at[f, '1st Serve Points Won P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\\r\\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == '2nd SERVE POINTS WON':\\r\\n df.at[f, '2nd Serve Points Won P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\\r\\n df.at[f, '2nd Serve Points Won P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\\r\\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == 'BREAK POINTS WON':\\r\\n df.at[f, 'BREAK POINTS WON P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\\r\\n df.at[f, 'BREAK POINTS WON P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\\r\\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == 'TOTAL RETURN POINTS WON':\\r\\n df.at[f, 'TOTAL RETURN POINTS WON P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\\r\\n df.at[f, 'TOTAL RETURN POINTS WON P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\\r\\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == 'TOTAL POINTS WON':\\r\\n df.at[f, 'TOTAL POINTS WON P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\\r\\n df.at[f, 'TOTAL POINTS WON P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\\r\\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == 'DOUBLE FAULTS':\\r\\n df.at[f, 'DOUBLE FAULTS P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\\r\\n df.at[f, 'DOUBLE FAULTS P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\\r\\n elif html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[0].text == 'ACES':\\r\\n df.at[f, 'ACES P1'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[1].text\\r\\n df.at[f, 'ACES P2'] = html_soup.find('table', class_='table_stats_match').find_all('tr')[r].find_all('td')[2].text\\r\\n \\r\\n #exclude doubles \\r\\n df = df[~df[\\\"Player1\\\"].str.contains(\\\"/\\\")]\\r\\n \\r\\n for i in range(0,len(df['Link'])):\\r\\n try:\\r\\n self.cur.execute(\\\"INSERT INTO reg_master VALUES (?, ?, NULL, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\\\", \\r\\n (df.at[i, 'Link'], df.at[i, 'Date'], df.at[i, 'Player1'], df.at[i, 'Player2'], df.at[i, 'Round'], df.at[i, 'Score'], \\r\\n df.at[i, 'Location'], df.at[i, 'Surface'], df.at[i, '1st Serve % P1'], df.at[i, '1st Serve Points Won P1'], \\r\\n df.at[i, '2nd Serve Points Won P1'], df.at[i, 'BREAK POINTS WON P1'], df.at[i, 'TOTAL RETURN POINTS WON P1'], \\r\\n df.at[i, 'TOTAL POINTS WON P1'], df.at[i, 'DOUBLE FAULTS P1'], df.at[i, 'ACES P1'], df.at[i, '1st Serve % P2'], \\r\\n df.at[i, '1st Serve Points Won P2'], df.at[i, '2nd Serve Points Won P2'], df.at[i, 'BREAK POINTS WON P2'], \\r\\n df.at[i, 'TOTAL RETURN POINTS WON P2'], df.at[i, 'TOTAL POINTS WON P2'], df.at[i, 'DOUBLE FAULTS P2'], df.at[i, 'ACES P2']))\\r\\n self.conn.commit()\\r\\n except:\\r\\n pass\\r\\n\\r\\n t1 = datetime.now()\\r\\n print(t1)\\r\\n print(\\\"It took \\\",(t1-t0),\\\" seconds.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50e18eeb206cde9cf7002b24c5ec6eaa\",\n \"score\": \"0.56139016\",\n \"text\": \"def export_dataframe_tableau(dataframe, filename=\\\"dataframe.hyper\\\"):\\n global result\\n import pantab\\n # TODO: Fix ModuleNotFoundError: No module named 'tableauhyperapi'\\n result = dataframe.to_json(orient='split')\\n pantab.frame_to_hyper(dataframe, filename)\\n with open(filename, \\\"rb\\\") as binary_file:\\n file_bin = binary_file.read()\\n assert file_bin is not None\\n result = file_bin\\n resultMetadata.put(\\\"file.extension\\\", \\\".hyper\\\")\\n resultMetadata.put(\\\"file.name\\\", filename)\\n resultMetadata.put(\\\"content.type\\\", \\\"application/octet-stream\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb43b88dac405007d8a2b700c53b453\",\n \"score\": \"0.5594382\",\n \"text\": \"def get_link(df_list, files):\\n pats = [re.compile(s) for s in ['http://','https://','www.']]\\n link_df = []\\n for i in range(len(df_list)):\\n df = df_list[i]\\n link = df[df.iloc[:,2].str.contains('link')].iloc[:,[1,3,7]]\\n link.columns = ['time','origin','content']\\n idx = pd.isnull(link.origin) != True\\n link = link.loc[idx,['time','content']]\\n for ii in range(len(link.content)):\\n s = link.content.iloc[ii]\\n for pat in pats:\\n s = pat.sub('',s)\\n link.content.iloc[ii] = s\\n filenumber = int(re.findall(r'\\\\d+', files[i])[0])\\n link['id'] = pd.Series((np.repeat(filenumber,np.shape(link)[0])), index = link.index)\\n cols = link.columns.tolist()\\n cols = cols[-1:] + cols[:-1]\\n link_df.append(link[cols])\\n link_df = pd.concat(link_df)\\n link_df = link_df.reset_index()\\n return link_df.drop('index',axis = 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2f53471845cbf3169c1bb51965fe49d\",\n \"score\": \"0.5583656\",\n \"text\": \"def download_url(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"234cac62679fe769c31516daf3e9d5dc\",\n \"score\": \"0.5575599\",\n \"text\": \"def download_csv(data, filename):\\n\\theader = data[0].keys() #Data must have at least one record.\\n\\twith open('downloads/' + filename, \\\"w+\\\") as f:\\n\\t\\twriter = csv.writer(f, delimiter=',', quotechar='\\\"', quoting=csv.QUOTE_MINIMAL)\\n\\t\\twriter.writerow(header)\\n\\t\\tfor row in data:\\n\\t\\t\\twriter.writerow(list(row.values()))\\n\\n\\t#Push the file to the view\\n\\treturn send_file('downloads/' + filename,\\n\\t\\t\\t\\t mimetype='text/csv',\\n\\t\\t\\t\\t attachment_filename=filename,\\n\\t\\t\\t\\t as_attachment=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fbb5e324590101c410ae1f35cc03c31\",\n \"score\": \"0.5571424\",\n \"text\": \"def csv_dl(links, start):\\n for link in links:\\n if link.string[:3] == 'Jan':\\n start -= 1\\n path = base + link['href']\\n #2 types of links: a short one that leads to a page with the dl (else statemtent), and direct links to the file\\n if len(link['href']) > 11:\\n pass\\n else:\\n s2 = BeautifulSoup(requests.get(path).content)\\n path = s2.find('a', {'href': re.compile(r'/files*')})['href']\\n\\n resp = requests.get(path)\\n with open('data/' + str(start) + link.string[:3] + '.xlsx', 'wb') as file:\\n file.write(resp.content)\\n return start\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a4dc1c63efe5bdd5cdb0ba77297e118\",\n \"score\": \"0.55572283\",\n \"text\": \"def export_data() -> None:\\n connection = connect()\\n curr = connection.cursor()\\n curr.execute('''\\n SELECT keyword, query_timestamp, title, price, rating, reviews_count, item_url, image_url\\n FROM keywords\\n INNER JOIN keywords_data ON keywords_data.keyword_id = keywords.id;''')\\n rows = curr.fetchall()\\n df = pd.DataFrame(rows, columns=[x[0] for x in curr.description])\\n df.to_csv('data_export.csv', index=False)\\n print('Data was exported successfully to data_export.csv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"457a78255302d30511514d8f69d0f1e2\",\n \"score\": \"0.5556854\",\n \"text\": \"def downloads(self, result_loc_, date_):\\n end_date = date_ + timedelta(days=1)\\n query = Template(content_downloads.init())\\n query = query.substitute(app=self.config['context']['pdata']['id']['app'],\\n start_date=datetime.strftime(date_, '%Y-%m-%dT00:00:00+00:00'),\\n end_date=datetime.strftime(end_date, '%Y-%m-%dT00:00:00+00:00'))\\n\\n headers = {\\n 'Content-Type': \\\"application/json\\\"\\n }\\n url = \\\"{}druid/v2/\\\".format(self.druid_hostname)\\n response = requests.request(\\\"POST\\\", url, data=query, headers=headers)\\n records = [events['event'] for events in response.json()]\\n data = pd.DataFrame(records)\\n content = pd.read_csv(str(result_loc_.parent.joinpath('tb_metadata', date_.strftime('%Y-%m-%d'), 'textbook_snapshot.csv')))\\n content = content[content['contentType']=='Resource']\\n content = content[['identifier', 'channel']]\\n content.drop_duplicates(inplace=True)\\n content.rename(columns={'identifier': 'object_id'}, inplace=True)\\n data = data.merge(content, on=\\\"object_id\\\", how=\\\"left\\\")\\n data = data[data['channel'].notnull()]\\n download_counts = data.groupby('channel').sum()\\n download_counts.reset_index(inplace=True)\\n\\n result_loc_.joinpath(date_.strftime('%Y-%m-%d')).mkdir(exist_ok=True)\\n download_counts.to_csv(result_loc_.joinpath(date_.strftime('%Y-%m-%d'), 'downloads.csv'), index=False)\\n post_data_to_blob(result_loc_.joinpath(date_.strftime('%Y-%m-%d'), 'downloads.csv'), backup=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"675994d5ffa91308b8eea838a9d1afcb\",\n \"score\": \"0.5554471\",\n \"text\": \"def result_download_button(app, df):\\n df_ser = encode_df(df)\\n return html.Form([\\n dcc.Input(\\n value=df_ser,\\n name='result',\\n type='text',\\n style={'display': 'none'}),\\n html.Button([\\n html.Img(src=app.get_asset_url('download.svg')),\\n html.Span('Download results')\\n ],\\n type='submit',\\n className='div-with-image small-image'\\n )\\n ],\\n method='POST',\\n action='/download_df/'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8a30e726636ded875fbe4f249cc62d9\",\n \"score\": \"0.55536354\",\n \"text\": \"def file_download_link(filename):\\n location = \\\"/downloads/{}\\\".format(urlquote(filename))\\n return html.A(filename, href=location)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"021b6619ef24e0e66b8fabbb2787b8eb\",\n \"score\": \"0.555286\",\n \"text\": \"def download_dat_files():\\r\\n url = 'https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat'\\r\\n wget.download(url, 'C:\\\\sqlite3\\\\Python Database files')\\r\\n\\r\\n url2 = 'https://raw.githubusercontent.com/jpatokal/openflights/master/data/airlines.dat'\\r\\n wget.download(url2, 'C:\\\\sqlite3\\\\Python Database files')\\r\\n\\r\\n url3 = 'https://raw.githubusercontent.com/jpatokal/openflights/master/data/routes.dat'\\r\\n wget.download(url3, 'C:\\\\sqlite3\\\\Python Database files')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05897914db6b7477d2432b34d9cd3fa6\",\n \"score\": \"0.55320585\",\n \"text\": \"def report_href(**kwargs):\\n params = args.copy()\\n if sort_col:\\n params['sort'] = sort_col\\n params['page'] = page\\n if max:\\n params['max'] = max\\n params.update(kwargs)\\n params['asc'] = '1' if params.get('asc', asc) else '0'\\n return req.href.report(id, params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b4bd7fbb11900f01b0567c24099a08\",\n \"score\": \"0.5530999\",\n \"text\": \"def download(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b4bd7fbb11900f01b0567c24099a08\",\n \"score\": \"0.5530999\",\n \"text\": \"def download(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd7c00a5d8646e39615e71a76a52dc61\",\n \"score\": \"0.5512493\",\n \"text\": \"def get_data(year,league=\\\"E0\\\",base_link=base_link):\\n\\n if not os.path.isdir(\\\"../Data/\\\"):\\n os.mkdir(\\\"../Data/\\\")\\n\\n final_link = base_link + year + \\\"/\\\" + league\\n filename = \\\"../Data/\\\" + year + \\\"_\\\" + league + \\\".csv\\\"\\n\\n datafile = urllib2.urlopen(final_link)\\n output = open(filename,'wb')\\n output.write(datafile.read())\\n output.close()\\n return pd.read_csv(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6d73064848724cc1a96a46127c34fdc\",\n \"score\": \"0.55080026\",\n \"text\": \"def download(self, destination: str) -> str:\\n data = self.materialize()\\n try:\\n data.to_csv(destination, index=False)\\n return \\\"Success\\\"\\n except Exception as e:\\n return \\\"Failed: %s\\\" % str(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f48eb01602359981bdcf48cd97ffa7db\",\n \"score\": \"0.5486758\",\n \"text\": \"def enhanced_generate_link(df_drugs: pd.DataFrame, df_reference: pd.DataFrame) -> {}:\\n\\n drug_dict = dict()\\n # define a list of drugs. Faster to loop on a list than on a pandas df.\\n drugs = df_drugs['drug'].to_list()\\n\\n for drug in drugs:\\n drug_dict[drug] = dict()\\n\\n # pubMed\\n drug_dict[drug][\\\"pubMed\\\"] = finding_pubmed_mention(drug, df_reference, \\\"title\\\")[\\\"title\\\"].to_list()\\n drug_dict[drug][\\\"pubMedJournal\\\"] = finding_pubmed_mention(drug, df_reference, \\\"title\\\")[\\\"journal\\\"].to_list()\\n drug_dict[drug][\\\"pubMedDate\\\"] = finding_pubmed_mention(drug, df_reference, \\\"title\\\")[\\\"date\\\"].to_list()\\n\\n # clinical_trials\\n drug_dict[drug][\\\"clinicalTrial\\\"] = finding_clinical_mention(drug, df_reference, \\\"title\\\")[\\\"title\\\"].to_list()\\n drug_dict[drug][\\\"clinicalTrialJournal\\\"] = finding_clinical_mention(drug, df_reference, \\\"title\\\")[\\n \\\"journal\\\"].to_list()\\n drug_dict[drug][\\\"clinicalTrialDate\\\"] = finding_clinical_mention(drug, df_reference, \\\"title\\\")[\\\"date\\\"].to_list()\\n return drug_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5a01135a95d6d8def4f8a86867b1bff\",\n \"score\": \"0.54821986\",\n \"text\": \"def download_data(model_dir, season):\\n file_name = model_dir + \\\"/\\\" + season + \\\".csv\\\"\\n if (not skip_download): \\n # urllib.request.urlretrieve(\\n # \\\"http://217.160.223.109/mmz4281/\\\"+season+\\\"/D1.csv\\\",\\n # file_name) # pylint: disable=line-too-long\\n \\n url = \\\"http://www.football-data.co.uk/mmz4281/\\\"+season+\\\"/D1.csv\\\"\\n print(\\\"Downloading %s\\\" % url)\\n urllib.request.urlretrieve(\\n url,\\n file_name) # pylint: disable=line-too-long\\n print(\\\"Data is downloaded to %s\\\" % file_name)\\n data = pd.read_csv(\\n tf.gfile.Open(file_name),\\n skipinitialspace=True,\\n engine=\\\"python\\\",\\n skiprows=0)\\n data[\\\"Season\\\"]= season\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29fbabc6fc60102a26c4472381f6e06f\",\n \"score\": \"0.547121\",\n \"text\": \"def make_download_url(pano_id: str, zoom: int, x: int, y: int) -> str:\\n return (\\n \\\"https://cbk0.google.com/cbk\\\"\\n f\\\"?output=tile&panoid={pano_id}&zoom={zoom}&x={x}&y={y}\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56d1103bd7d2988abfcef6e378b6c655\",\n \"score\": \"0.54653656\",\n \"text\": \"def full_data_frame_extract(webdriver_path,base_url,numbers,folder_path):\\n\\n driver = webdriver.Chrome(webdriver_path)\\n wait = WebDriverWait(driver,15)\\n driver.get(base_url)\\n \\n\\n Df = pd.DataFrame()\\n Df['date_time'] = np.NaN\\n Df['player'] = np.NaN\\n Df['position_and_team'] = np.NaN\\n Df['title'] = np.NaN\\n Df['summary'] = np.NaN\\n Df['social_headline'] = np.NaN\\n Df['players_related'] = np.NaN\\n Df['source'] = np.NaN\\n \\n \\n \\n for num in range(1,numbers+1):\\n wait.until(EC.visibility_of_element_located((By.XPATH,\\\"//*[@id='player-news-page-wrapper']/div/div/div[3]/ul/li[1]/article/div[1]\\\")))\\n content = driver.page_source\\n full_df = source_code_to_df(content)\\n Df = Df.append(full_df)\\n \\n click_next = wait.until(EC.element_to_be_clickable((By.XPATH,\\\"//*[@id='player-news-page-wrapper']/div/div/div[2]/a[2]\\\")))\\n click_next.click()\\n \\n if(num%1000 == 0):\\n path = folder_path + 'injuries' + str(num/1000) +'.csv'\\n Df.to_csv(path, index = False)\\n print(\\\"number of reports is:\\\", num)\\n \\n driver.quit()\\n return Df\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994087,"cells":{"query_id":{"kind":"string","value":"13bc9786d72c182ae5556b7203767daf"},"query":{"kind":"string","value":"Convert topology object into an ns2 Tcl script that deploys that topology into ns2."},"positive_passages":{"kind":"list like","value":[{"docid":"602ccfd7732aa6a1f9642f34693d5188","score":"0.55723596","text":"def to_ns2(topology, path, stacks=True):\n try:\n from mako.template import Template\n except ImportError:\n raise ImportError('Cannot import mako.template module. '\n 'Make sure mako is installed on this machine.')\n set_buffers = True\n set_delays = True\n # if all links are annotated with weights, then set weights\n set_weights = all('weight' in topology.adj[u][v]\n for u, v in topology.edges())\n\n if not 'capacity_unit' in topology.graph:\n raise ValueError('The given topology does not have capacity data.')\n if not topology.graph['capacity_unit'] in capacity_units:\n raise ValueError('The given topology does not have a valid capacity unit')\n if not 'buffer_unit' in topology.graph:\n warn('Missing buffer size unit attribute in the topology. '\\\n 'Output file will be generated without buffer assignments.')\n set_buffers = False\n elif not topology.graph['buffer_unit'] == 'packets':\n warn('The buffer size unit of the topology is %s. The only supported '\n 'unit is packets. Output file will be generated without buffer '\n 'assignments' % topology.graph['buffer_unit'])\n set_buffers = False\n if not 'delay_unit' in topology.graph or not topology.graph['delay_unit'] in time_units:\n warn('Missing or invalid delay unit attribute in the topology. The '\n 'output file will be generated with all link delays set to 0.')\n set_delays = False\n if stacks:\n if not validate_ns2_stacks(topology):\n warn('Some application stacks cannot be parsed correctly. The '\n 'output file will be generated without stack assignments.')\n stacks = False\n if not any('stack' in topology.node[v] for v in topology.nodes()):\n stacks = False\n template = Template(__TEMPLATE)\n variables = {\n 'topology': topology,\n 'deploy_stacks': stacks,\n 'set_delays': set_delays,\n 'set_buffers': set_buffers,\n 'set_weights': set_weights\n }\n with open(path, \"w\") as out:\n out.write(template.render(**variables))","title":""}],"string":"[\n {\n \"docid\": \"602ccfd7732aa6a1f9642f34693d5188\",\n \"score\": \"0.55723596\",\n \"text\": \"def to_ns2(topology, path, stacks=True):\\n try:\\n from mako.template import Template\\n except ImportError:\\n raise ImportError('Cannot import mako.template module. '\\n 'Make sure mako is installed on this machine.')\\n set_buffers = True\\n set_delays = True\\n # if all links are annotated with weights, then set weights\\n set_weights = all('weight' in topology.adj[u][v]\\n for u, v in topology.edges())\\n\\n if not 'capacity_unit' in topology.graph:\\n raise ValueError('The given topology does not have capacity data.')\\n if not topology.graph['capacity_unit'] in capacity_units:\\n raise ValueError('The given topology does not have a valid capacity unit')\\n if not 'buffer_unit' in topology.graph:\\n warn('Missing buffer size unit attribute in the topology. '\\\\\\n 'Output file will be generated without buffer assignments.')\\n set_buffers = False\\n elif not topology.graph['buffer_unit'] == 'packets':\\n warn('The buffer size unit of the topology is %s. The only supported '\\n 'unit is packets. Output file will be generated without buffer '\\n 'assignments' % topology.graph['buffer_unit'])\\n set_buffers = False\\n if not 'delay_unit' in topology.graph or not topology.graph['delay_unit'] in time_units:\\n warn('Missing or invalid delay unit attribute in the topology. The '\\n 'output file will be generated with all link delays set to 0.')\\n set_delays = False\\n if stacks:\\n if not validate_ns2_stacks(topology):\\n warn('Some application stacks cannot be parsed correctly. The '\\n 'output file will be generated without stack assignments.')\\n stacks = False\\n if not any('stack' in topology.node[v] for v in topology.nodes()):\\n stacks = False\\n template = Template(__TEMPLATE)\\n variables = {\\n 'topology': topology,\\n 'deploy_stacks': stacks,\\n 'set_delays': set_delays,\\n 'set_buffers': set_buffers,\\n 'set_weights': set_weights\\n }\\n with open(path, \\\"w\\\") as out:\\n out.write(template.render(**variables))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2b5d43ca9342feb75ca534ffe1a11c9d","score":"0.54831195","text":"def topology(args):\n info(\"*** Creating network\\n\")\n topology_data = load_topology() \n \n add_hmds()\n add_base_stations(topology_data)\n sdn_controller = get_sdn_controller()\n\n info(\"*** Configuring propagation model\\n\")\n NET.setPropagationModel(model=\"logDistance\", exp=5)\n\n info(\"*** Configuring wifi nodes\\n\")\n NET.configureWifiNodes()\n\n create_bs_links(topology_data)\n \n info(\"*** Configuring mobility model\\n\")\n NET.setMobilityModel(time=0, model='RandomDirection', seed=20, AC='ssf')\n\n #info(\"\"\"*** Starting the graph interface\\n\"\"\")\n #NET.plotGraph(max_x=NET_GRAPH_DIMENSION, max_y=NET_GRAPH_DIMENSION) \n \n info(\"*** Starting network\\n\")\n NET.build()\n sdn_controller.start()\n \n config_ovs_switches(sdn_controller)\n\n info(\"*** Running CLI\\n\")\n CLI_THREAD = threading.Thread(target=lambda: CLI(NET))\n CLI_THREAD.daemon = True\n \n set_hmd_range_color(HMDS_SET)\n ping_nodes()\n\n #CLI(net)","title":""},{"docid":"80bbf92dab4f27d633154a3b8cbaec89","score":"0.5285671","text":"def build_topology(self):\n self.dend.connect(self.soma(1))","title":""},{"docid":"87102fc05b76af0cfd2afadcb4671032","score":"0.5043602","text":"def do_topo(self, args):\n\n self.spp_topo.run(args, self.get_sec_ids('nfv'))","title":""},{"docid":"800183f4cb488876c0061e704366228e","score":"0.5004334","text":"def gen_tcl(self):\n s = ''\n s += '\\n'.join(self.bd_tcl_cmds['place_bd_ip'])\n s += '\\n'\n s += '\\n'.join(self.bd_tcl_cmds['build_bd'])\n s += '\\n'\n s += '\\n'.join(self.bd_tcl_cmds['assign_bd_addrs'])\n return s","title":""},{"docid":"cde366ecb10bf14e7b028bb5eeb70a26","score":"0.49921468","text":"def produce_layout(eoi, graph):\n\n subprocess.run((\"/usr/bin/dot\", \"{}\".format(graph.filename), \"-o\", \n \"{}/layout.dot\".format(eoi)))","title":""},{"docid":"f060bd93777451d23b0870552bfeda9d","score":"0.49778432","text":"def get_pipeline_script_cmdcall_snli_converter():\n # io snli files into jack format\n cmd = \"python3 jack/io/SNLI2jtr.py\"\n return cmd","title":""},{"docid":"1366f6b2c834128797b0f73ad1884dda","score":"0.4976361","text":"def generate_ot2_script(ot2_script_path, template_path, **kwargs):\n with open(ot2_script_path, 'w') as wf:\n with open(template_path, 'r') as rf:\n for index, line in enumerate(rf):\n if line[:3] == 'def':\n function_start = index\n break\n else:\n wf.write(line)\n for key, value in kwargs.items():\n wf.write('{}='.format(key))\n if type(value) == dict:\n wf.write(json.dumps(value))\n elif type(value) == str:\n wf.write(\"'{}'\".format(value))\n else:\n wf.write(str(value))\n wf.write('\\n')\n wf.write('\\n')\n with open(template_path, 'r') as rf:\n for index, line in enumerate(rf):\n if index >= function_start - 1:\n wf.write(line)","title":""},{"docid":"61ed9f3cab2b6c40ef8ec198e1044736","score":"0.49728763","text":"def py2tcl(pystr):\n # new = '\\n'.join(pystr.split()[1:]) # removes the import line\n new = pystr.replace('(', ' ')\n new = new.replace(')', ' ')\n new = new.replace('opy.', '')\n new = new.replace('ops.', '')\n new = new.replace(',', '')\n new = new.replace(\"'\", '')\n new = new.replace('\"', '')\n # lines = new.splitlines()\n # for i in range(len(lines)):\n # if 'for i in range(' in lines[i]:\n # line = lines.replace('for i in range(', 'for {set i 1} {$i <= num} {incr i 1} {')\n return new","title":""},{"docid":"d3429b3bb8b494948e78f7a129fd8f81","score":"0.49418485","text":"def create_scionlab_as_local_gen(args, tp):\n new_ia = TopoID(args.joining_ia)\n core_ia = ISD_AS(args.core_ia)\n local_gen_path = os.path.join(args.package_path, args.user_id)\n try:\n os.makedirs(local_gen_path)\n except:\n pass\n os.chdir(local_gen_path) # functions from $SC/python/topology use relative paths\n local_gen_path = os.path.join(local_gen_path, 'gen')\n try:\n shutil.rmtree(local_gen_path)\n except:\n pass\n as_obj = generate_certificate(\n new_ia, core_ia, args.core_sign_priv_key_file, args.core_cert_file, args.trc_file)\n write_dispatcher_config(local_gen_path)\n write_toml_files(tp, new_ia)\n for service_type, type_key in TYPES_TO_KEYS.items():\n executable_name = TYPES_TO_EXECUTABLES[service_type]\n instances = tp[type_key].keys()\n for instance_name in instances:\n config = prep_supervisord_conf(tp[type_key][instance_name], executable_name,\n service_type, instance_name, new_ia)\n instance_path = get_elem_dir(local_gen_path, new_ia, instance_name)\n write_certs_trc_keys(new_ia, as_obj, instance_path)\n write_as_conf_and_path_policy(new_ia, as_obj, instance_path)\n write_supervisord_config(config, instance_path)\n write_topology_file(tp, type_key, instance_path)\n write_zlog_file(service_type, instance_name, instance_path)\n generate_sciond_config(TopoID(args.joining_ia), as_obj, tp, local_gen_path)\n write_overlay_config(local_gen_path)\n if not args.no_prometheus:\n generate_prom_config(new_ia, tp, local_gen_path)","title":""},{"docid":"a9bc3bce81c646c858b8bedd15488fb3","score":"0.49216047","text":"def main(args):\n ic = lanenet_processer()\n rospy.init_node('template_node', anonymous=True)\n try:\n rospy.spin()\n except KeyboardInterrupt:\n print \"Shutting down ROS node\"","title":""},{"docid":"64d75f535c716eed9d8ddc7d3e360263","score":"0.4887493","text":"def define_imine_topology(bb1, bb2, topology, property):\n # Define the topologies.\n topo_func = None\n bb_vect = None\n if property == 'stk_func':\n if topology == '2p3':\n topo_func = stk.cage.TwoPlusThree()\n bb_vect = None\n elif topology == '4p6':\n topo_func = stk.cage.FourPlusSix()\n bb_vect = None\n elif topology == '4p62':\n topo_func = stk.cage.FourPlusSix2()\n bb_vect = None\n elif topology == '6p9':\n topo_func = stk.cage.SixPlusNine()\n bb_vect = None\n elif topology == '1p1':\n topo_func = stk.cage.OnePlusOne()\n bb_vect = {\n bb1: [topo_func.vertices[0]],\n bb2: [topo_func.vertices[1]],\n }\n elif topology == '4p4':\n topo_func = stk.cage.FourPlusFour()\n bb_vect = {\n bb1: topo_func.vertices[0, 2, 4, 6],\n bb2: topo_func.vertices[1, 3, 5, 7],\n }\n\n return topo_func, bb_vect","title":""},{"docid":"4acc506885c104ee134d68980939825e","score":"0.4881019","text":"def maketopo():\n nxpoints=200\n nypoints=200\n xupper=2.e0\n yupper=2.e0\n xlower = -2.e0\n ylower = -2.e0\n outfile= \"bowl.topotype2\"\n topo2writer(outfile,topo,xlower,xupper,ylower,yupper,nxpoints,nypoints)\n\n # convert to netcdf form:\n topo4 = topotools.Topography()\n topo4.read(outfile,2)\n topo4.write(\"bowl.nc\",topo_type=4)","title":""},{"docid":"6598b14f3db27127fa4b0eb7492095d3","score":"0.4849923","text":"def insert_topologytemplate(description, rtable, readme, user, template_name, visibility=db.TopologyTemplate.PROTECTED):\n\n # Check we don't already have a template by this name\n # TODO: change so that django doesn't require a unique name, so we don't\n # have to do this check which allows any user who can create a template to\n # see the names already in use\n duplicate = True\n try:\n _ = db.TopologyTemplate.objects.get(name = template_name)\n except db.TopologyTemplate.DoesNotExist:\n duplicate = False\n \n if duplicate:\n raise TopologyNameError(\"This topology name is already in use\")\n \n # Dictionaries for the nodes, ports and links so we don't start putting\n # stuff in the DB and then come across an error\n\n # nodes is a dictionary mapping a node name to a type\n nodes = {}\n\n # paths maps names of webservers to paths\n paths = {}\n\n # ip_offsets maps node names to ip offsets\n ip_offsets = {}\n\n # ports is a dictionary mapping a node name to a list of interfaces\n ports = {}\n\n # links maps (name, iface) pairs together, and should be symmetric\n links = {}\n\n line_no = 0\n\n # List of node types that are grouped in Node\n # WebServer is a class of its own, since it has extra attributes, so is not\n # included here\n node_types = {\"gateway\":db.Node.GATEWAY_ID,\n \"hub\":db.Node.HUB_ID,\n \"blackhole\":db.Node.BLACK_HOLE_ID,\n \"virtual\":db.Node.VIRTUAL_NODE_ID}\n\n \n def check_valid_name(name):\n \"\"\"Check that a potential name is valid, i.e. starts wth a letter and\n contains only letters and numbers\"\"\"\n if name == \"\" or not name[0].isalpha() or not name.isalnum():\n raise TopologyNameError(\"Line %d: invalid name for node or interface; names must begin with a letter and contain only alphanumeric characters\" % line_no)\n\n def create_node(name, node_type, ip_offset):\n check_valid_name(name)\n try:\n ip_offset = int(ip_offset)\n except ValueError:\n raise TopologyArgumentError(\"Line %d: IP offset must be an integer\" % line_no)\n if name in nodes:\n raise TopologyNameError(\"Line %d: Duplicate node name\" % line_no)\n nodes[name] = node_type\n ip_offsets[name] = ip_offset\n\n def create_link(line):\n \n # We expect a line of the form \"name1.iface1=name2.iface2\"\n parts = line.split('=')\n if len(parts) != 2:\n raise TopologySyntaxError(\"Line %d: syntax error\" % line_no)\n \n ni1 = parts[0].split('.')\n if len(ni1) != 2:\n raise TopologySyntaxError(\"Line %d: syntax error\" % line_no)\n\n ni2 = parts[1].split('.')\n if len(ni2) != 2:\n raise TopologySyntaxError(\"Line %d: syntax error\" % line_no)\n\n name1 = ni1[0].strip()\n iface1 = ni1[1].strip()\n name2 = ni2[0].strip()\n iface2 = ni2[1].strip()\n\n check_valid_name (name1);\n check_valid_name (name2);\n check_valid_name (iface1);\n check_valid_name (iface2);\n\n # Check the nodes exist\n if name1 not in nodes:\n raise TopologyNameError(\"Line %d: node name '%s' does not exist\" % (line_no, name1))\n if name2 not in nodes:\n raise TopologyNameError(\"Line %d: node name '%s' does not exist\" % (line_no, name2))\n\n # Check the ports don't already exist\n if (name1, iface1) in links:\n (name3, iface3) = links[(name1, iface1)]\n raise TopologySyntaxError(\"Line %d: %s.%s is already connected to %s.%s\" % (line_no, name1, iface1, name3, iface3))\n if (name2, iface2) in links:\n (name3, iface3) = links[(name1, iface1)]\n raise TopologySyntaxError(\"Line %d: %s.%s is already connected to %s.%s\" % (line_no, name2, iface2, name3, iface3))\n \n # If we've got this far, it's safe to insert the link\n if name1 not in ports:\n ports[name1] = [iface1]\n else:\n if iface1 not in (ports[name1]):\n ports[name1].append(iface1)\n if name2 not in ports:\n ports[name2] = [iface2]\n else:\n if iface2 not in (ports[name2]):\n ports[name2].append(iface2)\n \n links[(name1, iface1)] = (name2, iface2)\n links[(name2, iface2)] = (name1, iface1)\n\n \n lines = description.splitlines()\n for l in lines:\n\n line_no += 1\n \n if l == \"\":\n # Ignore blank lines\n continue\n \n toks = l.split()\n if len(toks) < 1:\n continue\n \n if toks[0] == \"webserver\":\n # Create a new webserver node\n if len(toks) == 3:\n # We don't have to assign it a particular IP\n name = toks[1]\n path = toks[2]\n create_node(name, \"webserver\", -2)\n paths[name] = path\n elif len(toks) == 4:\n # We do have to assign it a particular IP\n create_node(toks[1], \"webserver\", toks[3])\n paths[toks[1]] = toks[2]\n else:\n raise TopologyArgumentError(\"Line %d: Wrong number of arguments - expect webserver <name> <path> [ip_offset]\" % line_no)\n\n elif toks[0] in node_types:\n # Create a new node of a non-webserver type\n if len(toks) == 2:\n # No IP offset\n create_node(toks[1], toks[0], -2)\n elif len(toks) == 3:\n # IP offset is present\n create_node(toks[1], toks[0], toks[2])\n else:\n raise TopologyArgumentError(\"Line %d: Wrong number of erguments - expect %s <name> [ip_offset]\" % (line_no, toks[0]))\n else:\n # It doesn't start with a node type; assume it's inserting a link \n create_link(l)\n\n # Add everything to the DB\n tt = db.TopologyTemplate()\n tt.name = template_name\n tt.owner = user\n tt.org = user.get_profile().org\n tt.visibility = visibility\n tt.readme = readme\n tt.rtable = rtable\n tt.specification = description\n tt.save()\n\n dbnodes = {}\n dbports = {}\n links_inserted = {}\n\n # Insert nodes into the database\n for (node_name, node_type) in nodes.iteritems():\n n = None\n if node_type == \"webserver\":\n # A webserver is a different class, as it has a path to serve\n p = db.WebServerPath()\n p.path = paths[node_name]\n p.save()\n n = db.WebServer()\n n.path_to_serve=p\n n.type=db.Node.WEB_SERVER_ID\n n.name=node_name\n n.template=tt\n else:\n n = db.Node()\n n.type=node_types[node_type]\n n.name=node_name\n n.template=tt\n n.save()\n dbnodes[node_name] = n\n\n # Add the ports to the database\n for (node_name, ifaces) in ports.iteritems():\n offset_2 = 0\n for iface in ifaces:\n\n # If we have an IP offset for the node, assign consecutive\n # IP offsets for each interface\n if ip_offsets[node_name] >= 0:\n ip = ip_offsets[node_name] + offset_2\n offset_2 += 1\n else:\n ip = ip_offsets[node_name]\n p = db.Port()\n p.node=dbnodes[node_name]\n p.name=iface\n p.ip_offset=ip\n dbports[(node_name, iface)] = p\n p.save()\n\n for (i1, i2) in links.iteritems():\n # links contains two entries, one in each direction, for each link, so\n # exclude duplicates\n if i1 not in links_inserted and i2 not in links_inserted:\n l = db.Link()\n l.port1=dbports[i1]\n l.port2=dbports[i2]\n l.lossiness=0.0\n links_inserted[i1] = True\n links_inserted[i2] = True\n l.save()","title":""},{"docid":"7b79f779f4b45740c1a6527ee727b6ff","score":"0.479371","text":"def to_executables(nodes, label, context):\n raise NotImplementedError()","title":""},{"docid":"42820c54e35a35645ac471c2d85cae6e","score":"0.47915757","text":"def convert(topology, backend, device, extra_config={}):\n assert topology is not None, \"Cannot convert a Topology object of type None.\"\n assert backend is not None, \"Cannot convert a Topology object into backend None.\"\n assert device is not None, \"Cannot convert a Topology object into device None.\"\n\n operator_map = {}\n\n for operator in topology.topological_operator_iterator():\n try:\n converter = get_converter(operator.type)\n\n if backend == onnx.__name__:\n # vers = LooseVersion(torch.__version__)\n # allowed_min = LooseVersion(\"1.6.0\")\n # Pytorch <= 1.6.0 has a bug with exporting GEMM into ONNX.\n # For the moment only tree_trav is enabled for pytorch <= 1.6.0\n # if vers < allowed_min:\n extra_config[constants.TREE_IMPLEMENTATION] = \"tree_trav\"\n\n operator_map[operator.full_name] = converter(operator, device, extra_config)\n except ValueError:\n raise MissingConverter(\n \"Unable to find converter for {} type {} with extra config: {}.\".format(\n operator.type, type(getattr(operator, \"raw_model\", None)), extra_config\n )\n )\n except Exception as e:\n raise e\n\n operators = list(topology.topological_operator_iterator())\n torch_model = PyTorchBackendModel(\n topology.raw_model.input_names, topology.raw_model.output_names, operator_map, operators, extra_config\n ).eval()\n\n if backend == onnx.__name__:\n onnx_model_name = output_model_name = None\n target_opset = 11\n\n # Set optional configuration options for ONNX if any.\n if constants.ONNX_OUTPUT_MODEL_NAME in extra_config:\n onnx_model_name = extra_config[constants.ONNX_OUTPUT_MODEL_NAME]\n output_model_name = onnx_model_name + \".onnx\"\n if constants.ONNX_TARGET_OPSET in extra_config:\n target_opset = extra_config[constants.ONNX_TARGET_OPSET]\n if output_model_name is None:\n output_model_name = str(uuid4().hex) + \".onnx\"\n\n # Put the tracing test input into the right format.\n trace_input = _get_trace_input_from_test_input(extra_config[constants.TEST_INPUT])\n\n # Generate the ONNX models\n torch.onnx.export(\n torch_model,\n trace_input,\n output_model_name,\n input_names=topology.raw_model.input_names,\n output_names=topology.raw_model.output_names,\n keep_initializers_as_inputs=False,\n opset_version=target_opset,\n do_constant_folding=True,\n )\n hb_model = onnx.load(output_model_name)\n os.remove(output_model_name)\n\n # Set the ONNX model name if any.\n if onnx_model_name is not None:\n hb_model.graph.name = onnx_model_name\n\n # Fix the model to use arbitrary batch dimensions\n def fix_dim(dim):\n updated = False\n if dim.HasField(\"dim_value\"):\n dim.Clear()\n updated = True\n dim.dim_param = \"sym\"\n\n return updated\n\n def fix_value_info(value):\n num_fixed = 0\n if value.type.HasField(\"tensor_type\"):\n shape = value.type.tensor_type.shape\n if shape:\n dim = shape.dim[0]\n if fix_dim(dim):\n num_fixed += 1\n\n return num_fixed\n\n def fix_graph(graph):\n num_fixed = 0\n for input in graph.input:\n num_fixed += fix_value_info(input)\n\n for output in graph.output:\n num_fixed += fix_value_info(output)\n\n for node in graph.node:\n for attr in node.attribute:\n if attr.HasField(\"g\"):\n num_fixed += fix_graph(attr.g)\n\n return num_fixed\n\n fix_graph(hb_model.graph)\n else:\n # Set the device for the model.\n if device != \"cpu\":\n if backend == torch.__name__ or torch.jit.__name__:\n torch_model = torch_model.to(device)\n\n # If the backend is tochscript, jit the model.\n if backend == torch.jit.__name__:\n trace_input = _get_trace_input_from_test_input(extra_config[constants.TEST_INPUT])\n if device != \"cpu\":\n trace_input.to(device)\n torch_model = torch.jit.trace(torch_model, trace_input).eval()\n torch.jit.optimized_execution(torch_model)\n hb_model = torch_model\n\n # Return if the container is not needed.\n if constants.CONTAINER in extra_config and not extra_config[constants.CONTAINER]:\n return hb_model\n\n # We scan the operators backwards until we find an operator with a defined type\n # This is necessary because ONNX models can have arbitrary operators doing casting, reshaping etc.\n idx = len(operators) - 1\n while (\n idx >= 0\n and not operator_map[operators[idx].full_name].regression\n and not operator_map[operators[idx].full_name].classification\n and not operator_map[operators[idx].full_name].anomaly_detection\n and not operator_map[operators[idx].full_name].transformer\n ):\n idx -= 1\n\n assert idx >= 0, \"Cannot detect container type. Please fill an issue at https://github.com/microsoft/hummingbird.\"\n\n # If is a transformer, we need to check whether there is another operator type before.\n # E.g., normalization after classification.\n tmp_idx = idx\n if operator_map[operators[idx].full_name].transformer:\n while (\n idx >= 0\n and not operator_map[operators[idx].full_name].regression\n and not operator_map[operators[idx].full_name].classification\n and not operator_map[operators[idx].full_name].anomaly_detection\n ):\n idx -= 1\n if idx < 0:\n idx = tmp_idx\n\n if operator_map[operators[idx].full_name].regression:\n # We are doing a regression task.\n if backend == torch.jit.__name__:\n container = TorchScriptSklearnContainerRegression\n elif backend == onnx.__name__:\n container = ONNXSklearnContainerRegression\n else:\n container = PyTorchSklearnContainerRegression\n elif operator_map[operators[idx].full_name].anomaly_detection:\n # We are doing anomaly detection.\n if backend == torch.jit.__name__:\n container = TorchScriptSklearnContainerAnomalyDetection\n elif backend == onnx.__name__:\n container = ONNXSklearnContainerAnomalyDetection\n else:\n container = PyTorchSklearnContainerAnomalyDetection\n elif operator_map[operators[idx].full_name].transformer:\n # We are just transforming the input data.\n if backend == torch.jit.__name__:\n container = TorchScriptSklearnContainerTransformer\n elif backend == onnx.__name__:\n container = ONNXSklearnContainerTransformer\n else:\n container = PyTorchSklearnContainerTransformer\n else:\n # We are doing a classification task.\n if backend == torch.jit.__name__:\n container = TorchScriptSklearnContainerClassification\n elif backend == onnx.__name__:\n container = ONNXSklearnContainerClassification\n else:\n container = PyTorchSklearnContainerClassification\n\n hb_model = container(hb_model, extra_config)\n\n return hb_model","title":""},{"docid":"0ee2732725a729e31ad492835f691992","score":"0.47445887","text":"def topologytemplate_view(request, template):\n tn = 'vns/topologytemplate_view.html'\n\n return direct_to_template(request, tn, {\"t\":template})","title":""},{"docid":"17a6a2297e34a2f5f7746a2d4e55d8b7","score":"0.47296414","text":"def write_cytoscape_script():\n\t\n\tprint \"Writing cytoscape script...\"\n\n\tf = open(file_cytoscape_script, 'w')\n\n\t### Import network\n\tf.write(\"\"\"network import file file=\"{file}\" firstRowAsColumnNames=true startLoadRow=1 indexColumnSourceInteraction=1 indexColumnTargetInteraction=2\"\"\".format(file=file_network_table)+\"\\n\")\n\n\t### Import node attributes\n\t# REF: http://wiki.cytoscape.org/Cytoscape_3/UserManual/Attributes\n\t# *OBS*: tab seperated\n\tf.write(r\"\"\"table import file file=\"{file}\" firstRowAsColumnNames=true startLoadRow=1 keyColumnIndex=1 delimiters=\"\\t\" \"\"\".format(file=file_node_attribute)+\"\\n\")\n\t\t### ^ use raw string to inhibit python from setting a \"\t\" character\n\n\t### Set layout\n\tf.write(\"\"\"layout kamada-kawai EdgeAttribute=Pearson_correlation unweighted=false\"\"\"+\"\\n\")\n\t\t# Kamada and Kawai (1988): same as \"Spring-Embedded Layout\"\n\t\t# Network nodes are treated like physical objects that repel each other, such as electrons. \n\t\t# The layout algorithm sets the positions of the nodes in a way that minimizes the sum of forces in the network\n\t# 1) [yWorks - not available through command line] yFiles Organic Layout: organic layout algorithm is a kind of *spring-embedded algorithm*\n\t# 2) layout force-directed EdgeAttribute=Pearson_correlation unweighted=false\n\t# ---> NOTE: the applying a WEIGHTED layout algorithm may fail if the network is small or has few edges. This is tricky...\n\n\t### Load and apply visual style\n\t# *OBS*: KEEP STYLE NAME UPDATED - must match name in .xml file\n\tf.write(\"\"\"vizmap load file file=\"{file}\" \"\"\".format(file=cytoscape_style)+\"\\n\")\n\tf.write(\"\"\"vizmap apply styles=\"DEPICT-style-v1\" \"\"\"+\"\\n\")\n\t\n\t### Set view to fit display\n\tf.write(\"\"\"view fit content\"\"\"+\"\\n\")\n\n\t### Export\n\tf.write(\"\"\"view export OutputFile=\"{file_out}\" options=PDF\"\"\".format(file_out=file_cytoscape_graphics)+\"\\n\")\n\tf.write(\"\"\"view export OutputFile=\"{file_out}\" options=PNG\"\"\".format(file_out=file_cytoscape_graphics)+\"\\n\")\n\n\tf.write(\"\"\"session save as file=\"{file_out}\" \"\"\".format(file_out=file_cytoscape_session)+\"\\n\")\n\n\t\n\n\t### OPTIONAL: exit script when done.\n\tif not interactive_cytoscape_session:\n\t\tf.write(\"\"\"command quit\"\"\"+\"\\n\")\n\t\n\n\tf.close()","title":""},{"docid":"38d33016f8da8f7b624d3aea39ccb4b1","score":"0.46605805","text":"def main(args):\n create_output = []\n startup_output = []\n stop_output = []\n delete_output = []\n delete_net_output = []\n topo_yaml = openTopo(args.topo)\n create_startup = args.startup\n ceos_image = topo_yaml['images']['ceos']\n host_image = topo_yaml['images']['host']\n _tag = topo_yaml['topology']['name']\n nodes = topo_yaml['nodes']\n hosts = topo_yaml['hosts']\n _ceos_script_location = \"{0}/{1}\".format(CEOS_SCRIPTS, _tag)\n _tfa_version = 1\n BASE_TERMINATTR = \"\"\"\ndaemon TerminAttr\n exec /usr/bin/TerminAttr -cvaddr={0}:9910 -taillogs -cvcompression=gzip -cvauth=key,{1} -smashexcludes=ale,flexCounter,hardware,kni,pulse,strata -ingestexclude=/Sysdb/cell/1/agent,/Sysdb/cell/2/agent\n no shutdown\n!\n \"\"\"\n BASE_MGMT = \"\"\"\ninterface Management0\n description Management\n ip address {0}/24\n!\nip routing\n!\nip route 0.0.0.0/0 {1}\n!\nmanagement api http-commands\n no shutdown\n!\n \"\"\"\n BASE_STARTUP = \"\"\"\nhostname {0}\n!\n \"\"\"\n if create_startup:\n pS(\"INFO\", \"Bare Startup config will be created for all cEOS nodes\")\n # Check for mgmt Bridge\n try:\n mgmt_network = topo_yaml['infra']['bridge']\n if not mgmt_network:\n pS(\"INFO\", \"No mgmt bridge specified. Creating isolated network.\")\n except KeyError:\n pS(\"INFO\", \"No mgmt bridge specified. Creating isolated network.\")\n mgmt_network = False\n # Check for TFA Version\n try:\n _tfa_version = topo_yaml['topology']['vforward']\n if _tfa_version > 1:\n pS(\"INFO\", \"Leveraging ArFA forwarding agent\")\n else:\n pS(\"INFO\", \"Leveraging default dataplane\")\n except:\n pS(\"INFO\", \"Leveraging default dataplane\")\n\n # Load cEOS nodes specific information\n for _node in nodes:\n try:\n _node_ip = nodes[_node]['ipaddress']\n except KeyError:\n _node_ip = \"\"\n CEOS[_node] = CEOS_NODE(_node, _node_ip, nodes[_node]['mac'], nodes[_node]['neighbors'], _tag, ceos_image)\n # Load Host nodes specific information\n if hosts:\n for _host in hosts:\n _tmp_host = hosts[_host]\n HOSTS[_host] = HOST_NODE(_host, _tmp_host['ipaddress'], _tmp_host['mask'], _tmp_host['gateway'], _tmp_host['neighbors'], _tag, host_image)\n # Check for output directory\n if checkDir(_ceos_script_location):\n pS(\"OK\", \"Directory is present now.\")\n else:\n pS(\"iBerg\", \"Error creating directory.\")\n create_output.append(\"#!/bin/bash\\n\")\n create_output.append(NOTIFY_ADJUST)\n # Check for container images are present\n create_output.append(\"if [ \\\"$(docker image ls | grep ceosimage | grep -c {0})\\\" == 0 ]\\n\".format(ceos_image))\n create_output.append(\"then\\n echo \\\"Docker image not found for ceosimage:{0}, please build it first.\\\"\\n exit\\nfi\\n\".format(ceos_image))\n create_output.append(\"if [ \\\"$(docker image ls | grep chost | grep -c {0})\\\" == 0 ]\\n\".format(host_image))\n create_output.append(\"then\\n echo \\\"Docker image not found for chost:{0}, please build it first.\\\"\\n exit\\nfi\\n\".format(host_image))\n create_output.append(\"sudo ip netns add {0}\\n\".format(_tag))\n startup_output.append(\"#!/bin/bash\\n\")\n startup_output.append(NOTIFY_ADJUST)\n stop_output.append(\"#!/bin/bash\\n\")\n delete_output.append(\"#!/bin/bash\\n\")\n startup_output.append(\"sudo ip netns add {0}\\n\".format(_tag))\n delete_net_output.append(\"sudo ip netns delete {0}\\n\".format(_tag))\n # Get the veths created\n create_output.append(\"# Creating veths\\n\")\n for _veth in VETH_PAIRS:\n _v1, _v2 = _veth.split(\"-\")\n create_output.append(\"sudo ip link add {0} type veth peer name {1}\\n\".format(_v1, _v2))\n startup_output.append(\"sudo ip link add {0} type veth peer name {1}\\n\".format(_v1, _v2))\n delete_output.append(\"sudo ip link delete {0} type veth peer name {1}\\n\".format(_v1, _v2))\n create_output.append(\"#\\n#\\n# Creating anchor containers\\n#\\n\")\n # Create initial cEOS anchor containers\n create_output.append(\"# Checking to make sure topo config directory exists\\n\")\n create_output.append('if ! [ -d \"{0}/{1}\" ]; then mkdir {0}/{1}; fi\\n'.format(CONFIGS, _tag))\n for _node in CEOS:\n # Add in code to perform check in configs directory and create a basis for ceos-config\n create_output.append(\"# Checking for configs directory for each cEOS node\\n\")\n create_output.append('if ! [ -d \"{0}/{1}/{2}\" ]; then mkdir {0}/{1}/{2}; fi\\n'.format(CONFIGS, _tag, _node))\n create_output.append(\"# Creating the ceos-config file.\\n\")\n create_output.append('echo \"SERIALNUMBER={0}\" > {1}/{2}/{3}/ceos-config\\n'.format(CEOS[_node].ceos_name, CONFIGS, _tag, _node))\n create_output.append('echo \"SYSTEMMACADDR={0}\" >> {1}/{2}/{3}/ceos-config\\n'.format(CEOS[_node].system_mac, CONFIGS, _tag, _node))\n if _tfa_version > 1:\n create_output.append('echo \"TFA_VERSION={0}\" >> {1}/{2}/{3}/ceos-config\\n'.format(_tfa_version, CONFIGS, _tag, _node))\n # Perform check to see if a bare startup-config needs to be created\n if create_startup:\n create_output.append(\"# Creating a bare startup configuration for {0}\\n\".format(_node))\n _tmp_startup = []\n _tmp_startup.append(BASE_STARTUP.format(CEOS[_node].ceos_name))\n if mgmt_network:\n _tmp_startup.append(BASE_MGMT.format(CEOS[_node].ip, topo_yaml['infra']['gateway']))\n if 'cvpaddress' and 'cvp-key' in topo_yaml['topology']:\n _tmp_startup.append(BASE_TERMINATTR.format(topo_yaml['topology']['cvpaddress'], topo_yaml['topology']['cvp-key']))\n create_output.append('echo \"{0}\" > {1}/{2}/{3}/startup-config\\n'.format(''.join(_tmp_startup), CONFIGS, _tag, _node))\n # Creating anchor containers\n create_output.append(\"# Getting {0} nodes plumbing\\n\".format(_node))\n create_output.append(\"docker run -d --restart=always --log-opt max-size=10k --name={0}-net --net=none busybox /bin/init\\n\".format(CEOS[_node].ceos_name))\n startup_output.append(\"docker start {0}-net\\n\".format(CEOS[_node].ceos_name))\n create_output.append(\"{0}pid=$(docker inspect --format '{{{{.State.Pid}}}}' {0}-net)\\n\".format(CEOS[_node].ceos_name))\n create_output.append(\"sudo ln -sf /proc/${{{0}pid}}/ns/net /var/run/netns/{0}\\n\".format(CEOS[_node].ceos_name))\n # Stop cEOS containers\n startup_output.append(\"docker stop {0}\\n\".format(CEOS[_node].ceos_name))\n stop_output.append(\"docker stop {0}\\n\".format(CEOS[_node].ceos_name))\n stop_output.append(\"docker stop {0}-net\\n\".format(CEOS[_node].ceos_name))\n delete_output.append(\"docker stop {0}\\n\".format(CEOS[_node].ceos_name))\n delete_output.append(\"docker stop {0}-net\\n\".format(CEOS[_node].ceos_name))\n # Remove cEOS containers\n startup_output.append(\"docker rm {0}\\n\".format(CEOS[_node].ceos_name))\n delete_output.append(\"docker rm {0}\\n\".format(CEOS[_node].ceos_name))\n delete_output.append(\"docker rm {0}-net\\n\".format(CEOS[_node].ceos_name))\n delete_net_output.append(\"sudo rm -rf /var/run/netns/{0}\\n\".format(CEOS[_node].ceos_name))\n startup_output.append(\"{0}pid=$(docker inspect --format '{{{{.State.Pid}}}}' {0}-net)\\n\".format(CEOS[_node].ceos_name))\n startup_output.append(\"sudo ln -sf /proc/${{{0}pid}}/ns/net /var/run/netns/{0}\\n\".format(CEOS[_node].ceos_name))\n create_output.append(\"# Connecting cEOS containers together\\n\")\n # Output veth commands\n for _intf in CEOS[_node].intfs:\n _tmp_intf = CEOS[_node].intfs[_intf]\n if _node in _tmp_intf['veth'].split('-')[0]:\n create_output.append(\"sudo ip link set {0} netns {1} name {2} up\\n\".format(_tmp_intf['veth'].split('-')[0], CEOS[_node].ceos_name, _tmp_intf['port']))\n startup_output.append(\"sudo ip link set {0} netns {1} name {2} up\\n\".format(_tmp_intf['veth'].split('-')[0], CEOS[_node].ceos_name, _tmp_intf['port']))\n else:\n create_output.append(\"sudo ip link set {0} netns {1} name {2} up\\n\".format(_tmp_intf['veth'].split('-')[1], CEOS[_node].ceos_name, _tmp_intf['port']))\n startup_output.append(\"sudo ip link set {0} netns {1} name {2} up\\n\".format(_tmp_intf['veth'].split('-')[1], CEOS[_node].ceos_name, _tmp_intf['port']))\n # Perform check if mgmt network is available\n if mgmt_network:\n # Get MGMT VETHS\n create_output.append(\"sudo ip link add {0}-eth0 type veth peer name {0}-mgmt\\n\".format(CEOS[_node].ceos_name))\n create_output.append(\"sudo brctl addif {0} {1}-mgmt\\n\".format(mgmt_network, CEOS[_node].ceos_name))\n create_output.append(\"sudo ip link set {0}-eth0 netns {0} name eth0 up\\n\".format(CEOS[_node].ceos_name))\n create_output.append(\"sudo ip link set {0}-mgmt up\\n\".format(CEOS[_node].ceos_name))\n create_output.append(\"sleep 1\\n\")\n create_output.append(\"docker run -d --name={0} --log-opt max-size=1m --net=container:{0}-net --ip {1} --privileged -v /etc/sysctl.d/99-zceos.conf:/etc/sysctl.d/99-zceos.conf:ro -v {2}/{4}/{5}:/mnt/flash:Z -e INTFTYPE=et -e MGMT_INTF=eth0 -e ETBA=1 -e SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 -e CEOS=1 -e EOS_PLATFORM=ceoslab -e container=docker -i -t ceosimage:{3} /sbin/init systemd.setenv=INTFTYPE=et systemd.setenv=MGMT_INTF=eth0 systemd.setenv=ETBA=1 systemd.setenv=SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 systemd.setenv=CEOS=1 systemd.setenv=EOS_PLATFORM=ceoslab systemd.setenv=container=docker\\n\".format(CEOS[_node].ceos_name, CEOS[_node].ip, CONFIGS, CEOS[_node].image, _tag, _node))\n startup_output.append(\"sudo ip link add {0}-eth0 type veth peer name {0}-mgmt\\n\".format(CEOS[_node].ceos_name))\n startup_output.append(\"sudo brctl addif {0} {1}-mgmt\\n\".format(mgmt_network, CEOS[_node].ceos_name))\n startup_output.append(\"sudo ip link set {0}-eth0 netns {0} name eth0 up\\n\".format(CEOS[_node].ceos_name))\n startup_output.append(\"sudo ip link set {0}-mgmt up\\n\".format(CEOS[_node].ceos_name))\n startup_output.append(\"sleep 1\\n\")\n startup_output.append(\"docker run -d --name={0} --log-opt max-size=1m --net=container:{0}-net --ip {1} --privileged -v /etc/sysctl.d/99-zceos.conf:/etc/sysctl.d/99-zceos.conf:ro -v {2}/{4}/{5}:/mnt/flash:Z -e INTFTYPE=et -e MGMT_INTF=eth0 -e ETBA=1 -e SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 -e CEOS=1 -e EOS_PLATFORM=ceoslab -e container=docker -i -t ceosimage:{3} /sbin/init systemd.setenv=INTFTYPE=et systemd.setenv=MGMT_INTF=eth0 systemd.setenv=ETBA=1 systemd.setenv=SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 systemd.setenv=CEOS=1 systemd.setenv=EOS_PLATFORM=ceoslab systemd.setenv=container=docker\\n\".format(CEOS[_node].ceos_name, CEOS[_node].ip, CONFIGS, CEOS[_node].image, _tag, _node))\n else:\n create_output.append(\"sleep 1\\n\")\n create_output.append(\"docker run -d --name={0} --log-opt max-size=1m --net=container:{0}-net --privileged -v /etc/sysctl.d/99-zceos.conf:/etc/sysctl.d/99-zceos.conf:ro -v {1}/{3}/{4}:/mnt/flash:Z -e INTFTYPE=et -e MGMT_INTF=eth0 -e ETBA=1 -e SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 -e CEOS=1 -e EOS_PLATFORM=ceoslab -e container=docker -i -t ceosimage:{2} /sbin/init systemd.setenv=INTFTYPE=et systemd.setenv=MGMT_INTF=eth0 systemd.setenv=ETBA=1 systemd.setenv=SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 systemd.setenv=CEOS=1 systemd.setenv=EOS_PLATFORM=ceoslab systemd.setenv=container=docker\\n\".format(CEOS[_node].ceos_name, CONFIGS, CEOS[_node].image, _tag, _node))\n startup_output.append(\"sleep 1\\n\")\n startup_output.append(\"docker run -d --name={0} --log-opt max-size=1m --net=container:{0}-net --privileged -v /etc/sysctl.d/99-zceos.conf:/etc/sysctl.d/99-zceos.conf:ro -v {1}/{3}/{4}:/mnt/flash:Z -e INTFTYPE=et -e MGMT_INTF=eth0 -e ETBA=1 -e SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 -e CEOS=1 -e EOS_PLATFORM=ceoslab -e container=docker -i -t ceosimage:{2} /sbin/init systemd.setenv=INTFTYPE=et systemd.setenv=MGMT_INTF=eth0 systemd.setenv=ETBA=1 systemd.setenv=SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 systemd.setenv=CEOS=1 systemd.setenv=EOS_PLATFORM=ceoslab systemd.setenv=container=docker\\n\".format(CEOS[_node].ceos_name, CONFIGS, CEOS[_node].image, _tag, _node))\n # Create initial host anchor containers\n for _host in HOSTS:\n create_output.append(\"# Getting {0} nodes plumbing\\n\".format(_host))\n create_output.append(\"docker run -d --restart=always --log-opt max-size=10k --name={0}-net --net=none busybox /bin/init\\n\".format(HOSTS[_host].c_name))\n startup_output.append(\"docker start {0}-net\\n\".format(HOSTS[_host].c_name))\n create_output.append(\"{0}pid=$(docker inspect --format '{{{{.State.Pid}}}}' {0}-net)\\n\".format(HOSTS[_host].c_name))\n create_output.append(\"sudo ln -sf /proc/${{{0}pid}}/ns/net /var/run/netns/{0}\\n\".format(HOSTS[_host].c_name))\n # Stop host containers\n startup_output.append(\"docker stop {0}\\n\".format(HOSTS[_host].c_name))\n stop_output.append(\"docker stop {0}\\n\".format(HOSTS[_host].c_name))\n stop_output.append(\"docker stop {0}-net\\n\".format(HOSTS[_host].c_name))\n delete_output.append(\"docker stop {0}\\n\".format(HOSTS[_host].c_name))\n delete_output.append(\"docker stop {0}-net\\n\".format(HOSTS[_host].c_name))\n # Remove host containers\n startup_output.append(\"docker rm {0}\\n\".format(HOSTS[_host].c_name))\n delete_output.append(\"docker rm {0}\\n\".format(HOSTS[_host].c_name))\n delete_output.append(\"docker rm {0}-net\\n\".format(HOSTS[_host].c_name))\n startup_output.append(\"{0}pid=$(docker inspect --format '{{{{.State.Pid}}}}' {0}-net)\\n\".format(HOSTS[_host].c_name))\n startup_output.append(\"sudo ln -sf /proc/${{{0}pid}}/ns/net /var/run/netns/{0}\\n\".format(HOSTS[_host].c_name))\n create_output.append(\"# Connecting host containers together\\n\")\n # Output veth commands\n for _intf in HOSTS[_host].intfs:\n _tmp_intf = HOSTS[_host].intfs[_intf]\n if _host in _tmp_intf['veth'].split('-')[0]:\n create_output.append(\"sudo ip link set {0} netns {1} name {2} up\\n\".format(_tmp_intf['veth'].split('-')[0], HOSTS[_host].c_name, _tmp_intf['port']))\n startup_output.append(\"sudo ip link set {0} netns {1} name {2} up\\n\".format(_tmp_intf['veth'].split('-')[0], HOSTS[_host].c_name, _tmp_intf['port']))\n else:\n create_output.append(\"sudo ip link set {0} netns {1} name {2} up\\n\".format(_tmp_intf['veth'].split('-')[1], HOSTS[_host].c_name, _tmp_intf['port']))\n startup_output.append(\"sudo ip link set {0} netns {1} name {2} up\\n\".format(_tmp_intf['veth'].split('-')[1], HOSTS[_host].c_name, _tmp_intf['port']))\n create_output.append(\"sleep 1\\n\")\n create_output.append(\"docker run -d --name={0} --privileged --log-opt max-size=1m --net=container:{0}-net -e HOSTNAME={0} -e HOST_IP={1} -e HOST_MASK={3} -e HOST_GW={4} chost:{2} ipnet\\n\".format(HOSTS[_host].c_name, HOSTS[_host].ip, HOSTS[_host].image, HOSTS[_host].mask, HOSTS[_host].gw))\n startup_output.append(\"sleep 1\\n\")\n startup_output.append(\"docker run -d --name={0} --privileged --log-opt max-size=1m --net=container:{0}-net -e HOSTNAME={0} -e HOST_IP={1} -e HOST_MASK={3} -e HOST_GW={4} chost:{2} ipnet\\n\".format(HOSTS[_host].c_name, HOSTS[_host].ip, HOSTS[_host].image, HOSTS[_host].mask, HOSTS[_host].gw))\n # Check for iPerf3 commands\n if topo_yaml['iperf']:\n _iperf = topo_yaml['iperf']\n _port = _iperf['port']\n _brate = _iperf['brate']\n for _server in _iperf['servers']:\n create_output.append(\"docker exec -d {0} iperf3 -s -p {1}\\n\".format(HOSTS[_server].c_name, _port))\n startup_output.append(\"docker exec -d {0} iperf3 -s -p {1}\\n\".format(HOSTS[_server].c_name, _port))\n for _client in _iperf['clients']:\n _target = topo_yaml['hosts'][_client['target']]['ipaddress']\n create_output.append(\"docker exec -d {0} iperf3client {1} {2} {3}\\n\".format(HOSTS[_client['client']].c_name, _target, _port, _brate))\n startup_output.append(\"docker exec -d {0} iperf3client {1} {2} {3}\\n\".format(HOSTS[_client['client']].c_name, _target, _port, _brate))\n # Create the initial deployment files\n with open(CEOS_SCRIPTS + '/{0}/Create.sh'.format(_tag), 'w') as cout:\n for _create in create_output:\n cout.write(_create)\n with open(CEOS_SCRIPTS + '/{0}/Startup.sh'.format(_tag), 'w') as cout:\n for _start in startup_output:\n cout.write(_start)\n with open(CEOS_SCRIPTS + '/{0}/Stop.sh'.format(_tag), 'w') as cout:\n for _stop in stop_output:\n cout.write(_stop)\n with open(CEOS_SCRIPTS + '/{0}/Delete.sh'.format(_tag), 'w') as cout:\n for _delete in delete_output:\n cout.write(_delete)\n for _delete in delete_net_output:\n cout.write(_delete)","title":""},{"docid":"5e525c13c9e792024a5a418c70244fcc","score":"0.4641946","text":"def topology (self):\n self.log.info(\"Call %s function: topology\" % self.LOGGER_NAME)\n # Forward call to main layer class\n topology = self._proceed_API_call('api_sas_get_topology')\n if topology is None:\n self.send_error(404, message=\"Resource info is missing!\")\n return\n # Setup OK status for HTTP response\n self.send_response(200)\n if topology is False:\n self.log.info(\n \"Requested resource has not changed! Respond with cached topology...\")\n if self.virtualizer_format_enabled:\n data = self.server.last_response.xml()\n else:\n data = self.server.last_response.dump()\n else:\n if self.virtualizer_format_enabled:\n self.log.debug(\"Convert internal NFFG to Virtualizer...\")\n converter = NFFGConverter(domain=None, logger=log)\n # Dump to plain text format\n v_topology = converter.dump_to_Virtualizer(nffg=topology)\n # Cache converted data for edit-config patching\n self.log.debug(\"Cache converted topology...\")\n self.server.last_response = v_topology\n # Dump to plain text format\n data = v_topology.xml()\n # Setup HTTP response format\n else:\n self.log.debug(\"Cache converted topology...\")\n self.server.last_response = topology\n data = topology.dump()\n if self.virtualizer_format_enabled:\n self.send_header('Content-Type', 'application/xml')\n else:\n self.send_header('Content-Type', 'application/json')\n self.log.log(VERBOSE, \"Responded topology for 'get-config':\\n%s\" % data)\n # Setup length for HTTP response\n self.send_header('Content-Length', len(data))\n self.end_headers()\n self.log.info(\"Send back topology description...\")\n self.wfile.write(data)\n self.log.debug(\"%s function: get-config ended!\" % self.LOGGER_NAME)","title":""},{"docid":"a856608789fa792161e2084c7a8d3777","score":"0.46241966","text":"def maketopo():\n nxpoints=200\n nypoints=200\n xupper=2.e0\n yupper=2.e0\n xlower = -2.e0\n ylower = -2.e0\n outfile= \"bowl.topotype2\"\n\n topography = Topography(topo_func=topo)\n topography.x = linspace(xlower,xupper,nxpoints)\n topography.y = linspace(ylower,yupper,nypoints)\n topography.write(outfile, topo_type=2, Z_format=\"%22.15e\")","title":""},{"docid":"a0c30869cfa1d840294e99235270f209","score":"0.4614379","text":"def _populate_bin(self, mount_point):\n for script_filename, tensor_to_tensors in [('inputs', self._create_tensor_to_inputs),\n ('outputs', self._create_tensor_to_outputs)]:\n script = _read_template_script('print_tensor_dependencies.py',\n MOUNT_POINT_PLACEHOLDER=\"'{}/'\".format(mount_point),\n TENSOR_TO_TENSORS_PLACEHOLDER=str(tensor_to_tensors()))\n self._bin_scripts['/bin/' + script_filename] = script\n self._files['/bin/' + script_filename] = _create_script_file(script)","title":""},{"docid":"c1f240d5fc2079d31d48d2bae9e65276","score":"0.46109778","text":"def generate_topology(self, name='topol.top', xlinked=False, restrained=False):\n\n if restrained:\n r = restrain.RestrainedTopology(self.gro_name, self.restraint_residue, self.build_restraints, com=False,\n xlink=xlinked, vparams=None)\n r.add_position_restraints('xyz', self.restraints)\n r.write_topology()\n\n self.topology = SystemTopology(self.gro_name, xlink=xlinked, restraints=[self.restraint_residue])\n self.topology.write_top(name=name)","title":""},{"docid":"0b1a402ee04ff032ed1ac469bfad8db8","score":"0.4560311","text":"def make_object(self, data):\n return OperationScript(**data)","title":""},{"docid":"0b1a402ee04ff032ed1ac469bfad8db8","score":"0.4560311","text":"def make_object(self, data):\n return OperationScript(**data)","title":""},{"docid":"0b1a402ee04ff032ed1ac469bfad8db8","score":"0.4560311","text":"def make_object(self, data):\n return OperationScript(**data)","title":""},{"docid":"918281b18af420ed221fcbab10221621","score":"0.45487487","text":"def configure_junosphere(self):\n LOG.debug(\"Configuring Junosphere\") \n vmm_template = lookup.get_template(\"junos/topology_vmm.mako\")\n topology_data = {}\n # Generator for private0, private1, etc\n collision_to_bridge_mapping = {}\n private_bridges = []\n junosphere_predefined_bridge_count = 124 # have to explicitly create bridges past 124\n\n image_tuple = namedtuple('image', \"alias, basedisk\")\n\n if self.junosphere_olive:\n image = image_tuple(\"MY_DISK\", config.settings['Junosphere']['basedisk'])\n else:\n image = image_tuple(\"VJX1000_LATEST\", None)\n\n\n bridge_id_generator = (i for i in itertools.count(0))\n def next_bridge_id():\n bridge_id = bridge_id_generator.next()\n retval = \"private%s\" % bridge_id\n if bridge_id > junosphere_predefined_bridge_count:\n private_bridges.append(retval)\n return retval\n\n for device in sorted(self.network.devices(), key = lambda x: x.fqdn):\n hostname = device.hostname\n topology_data[hostname] = {\n 'image': image.alias,\n 'config': router_conf_file(self.network, device),\n 'interfaces': [],\n }\n for src, dst, data in sorted(self.network.graph.edges(device, data=True), key = lambda (s,t,d): t.fqdn):\n subnet = data['sn']\n description = 'Interface %s -> %s' % (\n ank.fqdn(self.network, src), \n ank.fqdn(self.network, dst))\n# Bridge information for topology config\n if subnet in collision_to_bridge_mapping:\n# Use bridge allocated for this subnet\n bridge_id = collision_to_bridge_mapping[subnet]\n else:\n# Allocate a bridge for this subnet\n bridge_id = next_bridge_id()\n collision_to_bridge_mapping[subnet] = bridge_id\n\n if not self.junosphere_olive:\n description += \"(%s)\" % self.int_id(data['id']) \n\n topology_data[hostname]['interfaces'].append({\n 'description': description,\n 'id': self.int_id_em(data['id']),\n 'id_ge': self.int_id(data['id']),\n 'bridge_id': bridge_id,\n })\n\n if self.junosphere_olive:\n# em2 is dead on Olive Junosphere platform\n topology_data[hostname]['interfaces'].append({\n 'description': \"dead interface\",\n 'id': \"em2\",\n 'bridge_id': \"dead\",\n })\n \n vmm_file = os.path.join(lab_dir(), \"topology.vmm\")\n with open( vmm_file, 'wb') as f_vmm:\n f_vmm.write( vmm_template.render(\n topology_data = topology_data,\n private_bridges = private_bridges,\n image = image,\n olive_based = self.junosphere_olive,\n ))","title":""},{"docid":"80f381beed0799261fc2b6bc390b1329","score":"0.4512595","text":"def Run(args):\n\n args = args[list(args.keys())[0]]\n ###print args #debug\n \n\n oobj = Syntax([\n Template(\"YVARS\", subc=\"\", ktype=\"existingvarlist\", var=\"yvars\", islist=True),\n Template(\"XVARS\", subc=\"\", ktype=\"existingvarlist\", var=\"xvars\", islist=True),\n Template(\"COLOR\", subc=\"\", ktype=\"existingvarlist\", var=\"color\"),\n Template(\"SIZE\", subc=\"\", ktype=\"existingvarlist\", var=\"size\"),\n Template(\"SHAPE\", subc=\"\", ktype=\"existingvarlist\", var=\"shape\"),\n Template(\"LABEL\", subc=\"\", ktype=\"existingvarlist\", var=\"label\"),\n Template(\"LINEAR\", subc=\"FITLINES\", ktype=\"bool\", var=\"linear\"),\n Template(\"QUADRATIC\", subc=\"FITLINES\", ktype=\"bool\", var=\"quadratic\"),\n Template(\"CUBIC\", subc=\"FITLINES\", ktype=\"bool\", var=\"cubic\"),\n Template(\"LOESS\", subc=\"FITLINES\", ktype=\"bool\", var=\"loess\"),\n Template(\"IGNORE\", subc=\"FITLINES\", ktype=\"str\", var=\"ignore\"),\n Template(\"APPLYTO\", subc=\"FITLINES\", ktype=\"str\", var=\"applyfitto\", vallist=[\"total\", \"group\"]),\n Template(\"CATEGORICAL\", subc=\"OPTIONS\", ktype=\"str\", var=\"categorical\", vallist=[\"bars\", \"lines\", \"boxplot\"]),\n Template(\"GROUP\", subc=\"OPTIONS\", ktype=\"int\", var=\"group\"),\n Template(\"BOXPLOTS\", subc=\"OPTIONS\", ktype=\"bool\", var=\"boxplots\"),\n Template(\"HEXBIN\", subc=\"OPTIONS\", ktype=\"bool\", var=\"hexbin\"),\n Template(\"TITLE\", subc=\"OPTIONS\", ktype=\"literal\", var=\"title\"),\n Template(\"INDENT\", subc=\"OPTIONS\", ktype=\"int\", var=\"indent\", vallist=[0, 50]),\n Template(\"YSCALE\", subc=\"OPTIONS\", ktype=\"int\", var=\"yscale\", vallist=[50, 100]),\n Template(\"PAGEX\", subc=\"OPTIONS\", ktype=\"float\", var=\"pagex\", vallist=[1]),\n Template(\"PAGEY\", subc=\"OPTIONS\", ktype=\"float\", var=\"pagey\", vallist=[1]),\n Template(\"HELP\", subc=\"\", ktype=\"bool\")])\n \n # ensure localization function is defined\n global _\n try:\n _(\"---\")\n except:\n def _(msg):\n return msg\n\n # A HELP subcommand overrides all else\n if \"HELP\" in args:\n #print helptext\n helper()\n else:\n processcmd(oobj, args, plots, vardict=spssaux.VariableDict())","title":""},{"docid":"ca9d9c073a8aa850c9b942946fbe7096","score":"0.4506192","text":"def get_score_tcl(coord_path, struct_path):\n tcl = \"\"\"coordinates %s\nstructure %s\nparaTypeCharmm on\nparameters %s\ntemperature 300.0\ngbis on\nalphaCutoff 15.0\nionConcentration 1.0\nGBISDelta 0.8\nGBISBeta 0.0\nGBISGamma 2.90912\nexclude scaled1-4\n1-4scaling 1.0\ncutoff 16.0\nswitching off\npairlistdist 24\nPME no\nfixedAtoms on\noutputName not_used\nbinaryoutput no\nrun 0\n\"\"\" % (coord_path, struct_path, FORCE_FIELD_PATH)\n return tcl","title":""},{"docid":"26fd0556101ec62f6e3aec42499326f3","score":"0.4505297","text":"def setup_host(bridge='br0', int_port='p0', int_ip=None, ip='', \n contr_addr=None, connections=None):\n print \"Setting up ovs for {}\".format(ip)\n #read the template file\n if contr_addr is None:\n template_name = 'config_ovs_standalone.template.sh'\n else:\n template_name = 'config_ovs.template.sh'\n with open(template_name) as template_file:\n template = Template(template_file.read())\n\n #instantiate the template\n #create ovs-setup script for this host\n script_text = template.render(bridge=bridge, contr_addr=contr_addr, \n int_port=int_port, int_ip=int_ip, connections=connections)\n \n #write the script to an actual file\n script_name = \"config_ovs.sh\"\n with open(script_name, \"w\") as script:\n script.write(script_text) \n\n #call ansible\n results = ansible_wrapper.playbook(playbook='ovs_config_play.yaml', hosts=[ip])\n #print results\n print results","title":""},{"docid":"b59486e672066b07fe47188f590e50d9","score":"0.44782212","text":"def __call__(self, taskObject):\n if taskObject['Type'] != \"StageOut\":\n return\n # //\n # // Lists of pre stage out and post stage out shell commands\n #// for setting up stage out env\n # //Should be bash shell commands as strings\n taskObject['PreStageOutCommands'] = []\n taskObject['PostStageOutCommands'] = []\n\n # //\n # // Determine what is being staged out from the parent node\n #// (This should be a CMSSW node)\n parent = taskObject.parent\n if parent == None:\n # no parent => dont know what to stage out\n return\n\n if parent['Type'] not in (\"CMSSW\", \"SVSuite\"):\n # parent isnt a CMSSW node, dont know what it does...\n return\n stageOutFor = parent['Name']\n taskObject['StageOutFor'] = taskObject['PayloadNode'].configuration\n return","title":""},{"docid":"b080d63eaec2a94da81577af536fd5ad","score":"0.44656765","text":"def _get_master_script_template(self):\n \n commands=\"\"\"#!/bin/bash\n\nfunction go {\n hostname\n\n ls /opt/astro/SL53/bin/setup.astro.sh\n\n gmvers=\"%(version)s\"\n module unload gmix_image && module load gmix_image/work\n module unload psfex && module load psfex/work\n module unload meds && module load meds/work\n module unload gmix_meds && module load gmix_meds/$gmvers\n\n\n confname=\"%(config)s.yaml\"\n conf=\"$GMIX_MEDS_DIR/share/config/$confname\"\n\n python -u $GMIX_MEDS_DIR/bin/gmix-fit-meds \\\\\n --obj-range $start,$end \\\\\n --work-dir $tmpdir \\\\\n $conf $meds_file $out_file\n \n exit_status=$?\n\n}\n\n#nsetup_ess\nsource ~/.bashrc\n\nif [ $# -lt 5 ]; then\n echo \"error: meds_file start end out_file\"\n exit 1\nfi\n\n# this can be a list\nmeds_file=\"$1\"\nstart=\"$2\"\nend=\"$3\"\nout_file=\"$4\"\nlog_file=\"$5\"\n\nif [[ -n $_CONDOR_SCRATCH_DIR ]]; then\n tmpdir=$_CONDOR_SCRATCH_DIR\nelse\n tmpdir=$TMPDIR\nfi\n\noutdir=$(dirname $out_file)\nmkdir -p $outdir\n\nlbase=$(basename $log_file)\ntmplog=\"$tmpdir/$lbase\"\n\ngo &> \"$tmplog\"\ncp \"$tmplog\" \"$log_file\"\n\nexit $exit_status\\n\"\"\"\n\n return commands","title":""},{"docid":"48d76382f1dc72fa140deeb279e9eeea","score":"0.44400838","text":"def cmd_from_template(node, vat_template_file, json_param=True, **vat_args):\n with VatTerminal(node, json_param=json_param) as vat:\n return vat.vat_terminal_exec_cmd_from_template(\n vat_template_file, **vat_args\n )","title":""},{"docid":"387b444e71bb225d1039d41c2da20704","score":"0.4438441","text":"def write_tcl_one(options, A, tag):\n with open(\"POSCAR_A.%s\" % tag, \"w\") as f: pcwrite.poscar(A, f, vasp5=True)\n write_xyz(options, A, \"A.%s\" % tag,options.output_tiles)\n\n fout = file(\"struct.%s.tcl\" % tag, \"w\")\n center = False\n write_struct(fout, A, \"A.%s.xyz\" % tag, 0, center)\n fout.close()","title":""},{"docid":"bbefe8e0b5e2c5b9ab19957df3d5d2e8","score":"0.44263136","text":"def acStartAutoscript():\n\n dctData = ImmutableMultiDict(request.values)\n dctData = ImmutableMultiDict(dctData).to_dict()\n acConProtocol = dctData['protocol'] \n acConIp = dctData['conIp']\n acConPort = dctData['conPort']\n\n try:\n objAutoScriptObject = clsMessageObjects()\n for acKeyName, objName in _Protocol_class.G_dctAutoScriptOnlyZMQ_Objects.items():\n objAutoScriptObject.vUnbindPubSoc(objName, acKeyName)\n\n except Exception as E:\n logging.error(\"can not unbind from this IP, error -> %s\", E)\n\n acAutoScriptingPath = os.path.join(os.path.dirname(__file__), \"./scriptingFiles/\")\n acAutoScriptFiles = os.listdir(acAutoScriptingPath)\n for iIndex, acItem in enumerate(acAutoScriptFiles):\n if acItem[-5:] == \".json\":\n acScriptFileName = acItem[:-5]\n try:\n with open(os.path.join(os.path.dirname(__file__), \"./scriptingFiles/\" + acItem), 'r') as f:\n dctFileData = json.load(f)\n\n \n for messageKey, messageContent in dctFileData.items():\n if acConProtocol == \"MQTT\":\n dctFileData[messageKey][\"Connection\"].update({\"Protocol\": acConProtocol})\n dctFileData[messageKey].update({\"Individual_Message_Protocol\": acConProtocol})\n dctFileData[messageKey][\"Connection\"].update({\"MQTTRemoteIP\": acConIp})\n dctFileData[messageKey][\"Connection\"].update({\"MQTTPort\": acConPort})\n elif acConProtocol == \"ZMQ\":\n dctFileData[messageKey][\"Connection\"].update({\"Protocol\": acConProtocol})\n dctFileData[messageKey].update({\"Individual_Message_Protocol\": acConProtocol})\n dctFileData[messageKey][\"Connection\"].update({\"ZeroMQRemoteIPPub\": acConIp})\n dctFileData[messageKey][\"Connection\"].update({\"ZeroMQPortPub\": acConPort})\n elif acConProtocol == \"previous\":\n pass\n\n _Protocol_class.G_dctJsonAutoScriptFile.update({acScriptFileName: dctFileData})\n f.close()\n\n except Exception as E:\n logging.error(\"unable to write to file, error -> %s\", E)\n return 'error'\n\n try:\n with open(os.path.join(os.path.dirname(__file__),\"./scriptingFiles/\"+acScriptFileName+\".json\"), 'w') as f:\n f.write(json.dumps(_Protocol_class.G_dctJsonAutoScriptFile[acScriptFileName]))\n f.close()\n except Exception as E:\n logging.error(\"unable to write to file, error -> %s\", E)\n return 'error'\n\n objNewAutoScriptObject = clsMessageObjects()\n acConnectionMessage = objNewAutoScriptObject.vMakeConnection()\n\n if acConnectionMessage == \"error\":\n return \"error\"\n return 'Auto Script started'","title":""},{"docid":"aca718bc40db834783a24d1c632e52c5","score":"0.44044036","text":"def test_write_xml_from_topology(self):\n pass","title":""},{"docid":"b93ccd2255198742a3d087a721caa757","score":"0.44002232","text":"def create_simulation_script(case_name, n_tasks, tasks_per_node, threads_per_core, partition, modules, scripts):\r\n\r\n header = create_script_header(n_tasks, tasks_per_node, threads_per_core, partition, \"{0}_sim\".format(case_name))\r\n\r\n module_string = create_script_modules(modules, scripts)\r\n\r\n if n_tasks > 1:\r\n commands = \"\"\"decomposePar\r\nmpirun -np {0} simpleFoam -parallel | tee simpleFoam.log\r\nreconstructPar\r\nrm -rf processor*\\n\"\"\".format(n_tasks)\r\n else:\r\n commands = \"simpleFoam | tee simpleFoam.log\\n\"\r\n\r\n commands += \"foamToVTK -latestTime -ascii\\n\"\r\n\r\n script = open(\"runSimulations.sh\", \"w\")\r\n script.write(header)\r\n script.write(module_string)\r\n script.write(commands)\r\n script.close()","title":""},{"docid":"b81fab7e7f133435c3c3617646de9b7d","score":"0.43991858","text":"def __call__(self, taskObject):\n if taskObject['Type'] != \"StageOut\":\n return\n\n\n # //\n # // Pre and Post Stage out commands\n #//\n precomms = taskObject.get(\"PreStageOutCommands\", [])\n postcomms = taskObject.get(\"PostStageOutCommands\", [])\n\n\n # //\n # // Install the main script\n #//\n srcfile = inspect.getsourcefile(NewRuntimeStageOut)\n if not os.access(srcfile, os.X_OK):\n os.system(\"chmod +x %s\" % srcfile)\n taskObject.attachFile(srcfile)\n\n # //\n # // Failure script\n #//\n fsrcfile = inspect.getsourcefile(RuntimeStageOutFailure)\n if not os.access(fsrcfile, os.X_OK):\n os.system(\"chmod +x %s\" % fsrcfile)\n taskObject.attachFile(fsrcfile)\n\n exeScript = taskObject[taskObject['Executable']]\n\n envScript = taskObject[taskObject[\"BashEnvironment\"]]\n envCommand = \"%s %s\" % (envScript.interpreter, envScript.name)\n exeScript.append(envCommand)\n\n for precomm in precomms:\n exeScript.append(str(precomm))\n exeScript.append(\"chmod +x ./RuntimeStageOut.py\")\n exeScript.append(\"./RuntimeStageOut.py\")\n exeScript.append(_StageOutFailureScript )\n for postcomm in postcomms:\n exeScript.append(str(postcomm))\n\n\n\n # //\n # // Insert End Control Point check on exit status\n #//\n controlP = taskObject['ShREEKTask'].endControlPoint\n exitCheck = CheckExitCode()\n exitCheck.attrs['OnFail'] = \"skipToLog\"\n exitAction = SetNextTask(\"skipToLog\")\n exitAction.content = \"logArchive\"\n controlP.addConditional(exitCheck)\n controlP.addAction(exitAction)\n\n return","title":""},{"docid":"3f254dcb606cff1eff9aeb30cb64062b","score":"0.43946475","text":"def transformation_results_yaml(topology,ignore=('addressing','defaults','nodes_map','includes')):\n for k in ignore:\n del topology[k]\n\n\n \"\"\"\n If we're using a dictionary extension that has to_yaml method use that,\n otherwise use pyyaml (hoping it won't generate extraneous attributes)\n \"\"\"\n if callable(getattr(topology,\"to_yaml\",None)):\n return topology.to_yaml()\n else:\n return yaml.dump(topology)","title":""},{"docid":"ab9e26bce65abfc2b7d5804924974c10","score":"0.4391522","text":"def main():\n # TODO(mlegner): Add option specifying already existing keys and certificates\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--joining_ia\",\n help='ISD-AS for which the configuration is generated.')\n parser.add_argument(\"--core_ia\",\n help='Signing Core ISD-AS',\n default='1-1')\n parser.add_argument(\"--core_sign_priv_key_file\",\n help='Signing private key of the core AS',\n default=DEFAULT_CORE_SIG_KEY)\n parser.add_argument(\"--core_cert_file\",\n help='Certificate file of the signing core AS',\n default=DEFAULT_CORE_CERT_FILE)\n parser.add_argument(\"--trc_file\",\n help='Trusted Root Configuration file',\n default=DEFAULT_TRC_FILE)\n parser.add_argument(\"--topo_file\",\n help='Topology file to be used for config generation.')\n parser.add_argument(\"--package_path\",\n help='Path to generate and store AS configurations.',\n default=DEFAULT_PACKAGE_PATH)\n parser.add_argument(\"--user_id\",\n help='User Identifier (email + IA)')\n parser.add_argument(\"--no-prometheus\",\n help='Don\\'t generate prometheus configuration',\n action='store_true',)\n args = parser.parse_args()\n\n def jsonhook(d):\n ret = {}\n for k,v in d.items():\n try:\n k = int(k)\n except:\n pass\n ret[k] = v\n return ret\n\n with open(args.topo_file) as json_data:\n topo_dict = json.load(json_data, object_hook=jsonhook)\n create_scionlab_as_local_gen(args, topo_dict)","title":""},{"docid":"ee930de7751ccaec4231e7f9b9f96dc9","score":"0.43838167","text":"def __init__(self, n=1, cpu=.1, bw=10, delay=None,\n max_queue_size=None, **params):\n\n # Initialize topo\n Topo.__init__(self, **params)\n\n # Host and link configuration\n hconfig = {'cpu': cpu}\n lconfig = {'bw': bw, 'delay': delay,\n 'max_queue_size': max_queue_size }\n\n # Create the actual topology\n receiver = self.addHost('receiver')\n\n # Switch ports 1:uplink 2:hostlink 3:downlink\n uplink, hostlink, downlink = 1, 2, 3\n\n # The following template code creates a parking lot topology\n # for N = 1\n # TODO: Replace the template code to create a parking lot topology for any arbitrary N (>= 1)\n # Begin: Template code\n s1 = self.addSwitch('s1')\n h1 = self.addHost('h1', mac='00:00:00:00:00:01', **hconfig)\n switches = [s1]\n\n\n # create Linear topology\n for i in range(1, n):\n switch = self.addSwitch('s' + str(i+1))\n host = self.addHost('h' + str(i+1), mac='00:00:00:00:00:0'+str(i+1), **hconfig)\n self.addLink(switch, switches[i-1], port1=uplink, port2=downlink, loss=20, **lconfig)\n self.addLink(host, switch, port1=0, port2=hostlink, loss=20, **lconfig)\n switches.append(switch)\n\n # for i in range(1, n):\n # switch = self.addSwitch('s' + str(i+1))\n # host = self.addHost('h' + str(i+1), mac='00:00:00:00:00:0'+str(i+1), **hconfig)\n # self.addLink(host, switch, port1=0, port2=hostlink, **lconfig)\n # switches.append(switch)\n\n # for i in range(0, len(switches)):\n # for j in range(i+1, len(switches)):\n # self.addLink(switches[i], switches[j])\n \n\n # Wire up receiver\n self.addLink(receiver, s1,\n port1=0, port2=uplink, **lconfig)\n\n # Wire up clients:\n self.addLink(h1, s1,\n port1=0, port2=hostlink, **lconfig)\n\n # add link between the first switch and last switch to create the ring topology\n # self.addLink(s1, switches[-1], port1=4, port2=downlink, **lconfig)\n\n # Uncomment the next 8 lines to create a N = 3 parking lot topology\n #s2 = self.addSwitch('s2')\n #h2 = self.addHost('h2', **hconfig)\n #self.addLink(s1, s2,\n # port1=downlink, port2=uplink, **lconfig)\n #self.addLink(h2, s2,\n # port1=0, port2=hostlink, **lconfig)\n #s3 = self.addSwitch('s3')\n #h3 = self.addHost('h3', **hconfig)\n #self.addLink(s2, s3,\n # port1=downlink, port2=uplink, **lconfig)\n #self.addLink(h3, s3,\n # port1=0, port2=hostlink, **lconfig)\n\n # End: Template code","title":""},{"docid":"03c8bf6b31a70635e1ee7567503c9881","score":"0.43728969","text":"def condor_bash_template():\n bash_template = \"\"\"#!/bin/tcsh\necho \" > Starting CONDOR job at `date` on `hostname`\"\n\n\n## Copy CMSSW tarball from eos, untar it, and remove the tarball\nxrdcp -s root://cmseos.fnal.gov/%(eos_path_to_tarball)s/%(cmsRelease)s.tgz .\ntar -xf %(cmsRelease)s.tgz\nrm %(cmsRelease)s.tgz\n\n## copy transferred data in batch directory (only need 3 files)\nif (! -d %(cmsRelease)s/src/Analysis/CyMiniAna/%(unique_id_batch_path)s/ ) then\n mkdir -p %(cmsRelease)s/src/Analysis/CyMiniAna/%(unique_id_batch_path)s/\nendif\n\nmv cmaConfig.txt %(cmsRelease)s/src/Analysis/CyMiniAna/%(unique_id_batch_path)s/\nmv listOfFiles.txt %(cmsRelease)s/src/Analysis/CyMiniAna/%(unique_id_batch_path)s/\nmv run_condor.sh %(cmsRelease)s/src/Analysis/CyMiniAna/%(unique_id_batch_path)s/\n\n## Setup CMSSW environment\nsetenv SCRAM_ARCH slc6_amd64_gcc530\ncd %(cmsRelease)s/src/\nscramv1 b ProjectRename\neval `scramv1 runtime -csh` # cmsenv is an alias not on the workers\nscram b -j8\n\n\n## Execute CyMiniAna\ncd Analysis/CyMiniAna\n\necho \" > Run the program \"\n%(executable)s %(cfg_filename)s\n\n\n## Move output file to EOS (make new directory if necessary)\necho \" > Finishing executing \"\necho \" > Move output file to EOS\"\n\n# -- Only copy files if they exist\nset outfiles = `ls %(local_output_path)s/`\nif ( \"$outfiles\" != \"\" ) then\n echo \" Output file exists, copy to EOS \"\n xrdcp %(local_output_path)s/*.root %(outputDir)s/%(local_output_path)s\nendif\n\n\n## Cleanup\nrm %(local_output_path)s/*.root # delete output files\ncd ${_CONDOR_SCRATCH_DIR} # delete working directory\nrm -rf %(cmsRelease)s\n\necho \" > Ended at `date` on `hostname`\"\nls -alh\nexit 0\n\"\"\"\n\n return bash_template","title":""},{"docid":"939e928fe224db22921dd4e0c7562b06","score":"0.43652397","text":"def handleScriptTaskObject(self, taskObject):\n exeScript = taskObject[taskObject['Executable']]\n jobSpec = taskObject['JobSpecNode']\n exeCommand = jobSpec.application['Executable']\n exeScript.append(exeCommand)\n return","title":""},{"docid":"ab7411ad677c7f55fc496f0e1baa5957","score":"0.4357492","text":"def condor_script_template():\n condor_template = \"\"\"\nUniverse = vanilla\nExecutable = %(condorExec)s\n\nShould_Transfer_Files = YES\nWhenToTransferOutput = ON_EXIT\nTransfer_Input_Files = %(unique_id_batch_path)s/cmaConfig.txt,%(unique_id_batch_path)s/listOfFiles.txt,%(unique_id_batch_path)s/run_condor.sh\nnotify_user = ${LOGNAME}@FNAL.GOV\nx509userproxy = $ENV(X509_USER_PROXY)\n\nLog = %(unique_id_batch_path)s/condor_$(Cluster)_$(Process).log\nError\t = %(unique_id_batch_path)s/condor_$(Cluster)_$(Process).error\nOutput = %(unique_id_batch_path)s/condor_$(Cluster)_$(Process).out\n\nQueue 1\n\"\"\"\n\n return condor_template","title":""},{"docid":"00521d4ae95de8a5845136da236e5093","score":"0.43561456","text":"def main():\n args = parse_args()\n psyml = PSyml(args.file)\n getattr(psyml, args.command)()","title":""},{"docid":"999147ffcf203b56fc6739d942246b29","score":"0.43453547","text":"def parse(self, data):\n graph = networkx.Graph()\n if 'topology' not in data:\n raise ParserError('Parse error, \"topology\" key not found')\n # loop over topology section and create networkx graph\n for link in data[\"topology\"]:\n try:\n source = link[\"lastHopIP\"]\n dest = link[\"destinationIP\"]\n cost = link[\"tcEdgeCost\"]\n except KeyError as e:\n raise ParserError('Parse error, \"%s\" key not found' % e)\n # original olsrd cost (jsoninfo multiplies by 1024)\n cost = float(cost) / 1024.0\n # add link to Graph\n graph.add_edge(source, dest, weight=cost)\n self.graph = graph\n # determine version and revision\n if 'config' in data:\n version_info = data['config']['olsrdVersion'].replace(' ', '').split('-')\n self.version = version_info[1]\n # try to get only the git hash\n if 'hash_' in version_info[-1]:\n version_info[-1] = version_info[-1].split('hash_')[-1]\n self.revision = version_info[-1]","title":""},{"docid":"026fd333e1bd36c6b52b682cac784105","score":"0.43439808","text":"def convert(self, input, filename=None):\n if python2:\n if self.encoding and isinstance(input, str):\n input = input.decode(self.encoding)\n self._reset(input, filename)\n buf = []\n self.before_convert(buf)\n self.parse_stmts(buf, input)\n self.after_convert(buf)\n script = ''.join(buf)\n self.script = script\n return script","title":""},{"docid":"54dec25a3217aa4d1d73341d52250ce5","score":"0.43172985","text":"def _get_topology_from_file(topology_file):\n topology_dir, mod_name = os.path.split(topology_file)\n # Remove .py extension before trying to import\n mod_name = mod_name[:-3]\n sys.path.append(os.path.join(topology_dir, '..', 'src'))\n sys.path.append(topology_dir)\n mod = importlib.import_module(mod_name)\n for attr in mod.__dict__.values():\n if isinstance(attr, TopologyType) and attr != Topology:\n topology_class = attr\n break\n else:\n raise ValueError('Could not find topology subclass in topology module.')\n return topology_class","title":""},{"docid":"fb14debd4bfe39a60ef458002d3d4f75","score":"0.43159938","text":"def osm_prep_schema_task(self, result={}, task_uid=None, stage_dir=None, job_name=None):\n\n self.update_task_state(result=result, task_uid=task_uid)\n osm = os.path.join(stage_dir, '{0}.pbf'.format(job_name))\n gpkg = os.path.join(stage_dir, '{0}.gpkg'.format(job_name))\n osmconf_ini = os.path.join(stage_dir, '{0}.ini'.format(job_name))\n osmparser = osmparse.OSMParser(osm=osm, gpkg=gpkg, osmconf=osmconf_ini, task_uid=task_uid)\n osmparser.create_geopackage()\n osmparser.create_default_schema_gpkg()\n osmparser.update_zindexes()\n result['result'] = gpkg\n result['geopackage'] = gpkg\n return result","title":""},{"docid":"39f5193f8d782a9cc400e0f88802ec45","score":"0.43153542","text":"def main():\n parser = argparse.ArgumentParser(prog=\"clustergen\")\n parser.add_argument(\"--clustercfg\",\n \"-c\",\n metavar=\"file\",\n type=argparse.FileType('r'),\n required=True,\n help=\"A cluster configuration file\")\n parser.add_argument(\"--outdir\",\n \"-o\",\n type=pathlib.Path,\n required=True,\n help=\"Target directory.\")\n parser.add_argument(\"--wrapper\",\n action=\"store_true\",\n help=\"Generate Snitch cluster wrapper\")\n parser.add_argument(\"--linker\",\n action=\"store_true\",\n help=\"Generate linker script\")\n parser.add_argument(\"--bootdata\",\n action=\"store_true\",\n help=\"Generate bootdata\")\n parser.add_argument(\"--memories\",\n action=\"store_true\",\n help=\"Generate memories\")\n parser.add_argument(\"--template\",\n metavar=\"template\",\n help=\"Name of the template file\")\n\n args = parser.parse_args()\n\n # Read HJSON description\n with args.clustercfg as file:\n try:\n srcfull = file.read()\n obj = hjson.loads(srcfull, use_decimal=True)\n obj = JsonRef.replace_refs(obj)\n except ValueError:\n raise SystemExit(sys.exc_info()[1])\n\n cluster_tb = SnitchClusterTB(obj)\n\n if not args.outdir.is_dir():\n exit(\"Out directory is not a valid path.\")\n\n outdir = args.outdir\n outdir.mkdir(parents=True, exist_ok=True)\n\n ##############\n # Misc files #\n ##############\n\n if args.wrapper:\n with open(outdir / \"snitch_cluster_wrapper.sv\", \"w\") as f:\n f.write(cluster_tb.render_wrapper())\n\n if args.linker:\n with open(outdir / \"link.ld\", \"w\") as f:\n f.write(cluster_tb.render_linker_script())\n\n if args.bootdata:\n with open(outdir / \"bootdata.cc\", \"w\") as f:\n f.write(cluster_tb.render_bootdata())\n\n if args.memories:\n with open(outdir / \"memories.json\", \"w\") as f:\n f.write(cluster_tb.cluster.memory_cfg())\n\n ####################\n # Generic template #\n ####################\n\n kwargs = {\n \"cfg\": cluster_tb.cfg,\n }\n\n if args.template:\n write_template(args.template, outdir, **kwargs)","title":""},{"docid":"fe7bbba84772305469d9124b8945a4d7","score":"0.43111256","text":"def toTopoFile(topo, out, excludeHosts=set()):\n out.write(SECTION_NODES + '\\n')\n for host in topo.nodes(False):\n if host in excludeHosts:\n continue\n out.write('%s: _' % host)\n attributes = topo.nodeInfo(host).get('mnndn', dict())\n for kv in attributes.iteritems():\n out.write(' %s=%s' % kv)\n out.write('\\n')\n\n out.write(SECTION_LINKS + '\\n')\n for (src, dst) in topo.iterLinks():\n if src in excludeHosts or dst in excludeHosts:\n continue\n out.write('%s:%s' % (src, dst))\n attributes = topo.linkInfo(src, dst).get('mnndn', dict())\n for kv in attributes.iteritems():\n out.write(' %s=%s' % kv)\n out.write('\\n')","title":""},{"docid":"f7c0ebbb5a797f27b90fb951a1feb58a","score":"0.4310493","text":"def main():\n\n from argparse import ArgumentParser, RawDescriptionHelpFormatter\n from textwrap import dedent\n parser = ArgumentParser(description=dedent(main.__doc__),\n formatter_class=RawDescriptionHelpFormatter)\n parser.add_argument('--version', action='version', version='%(prog)s 1.1')\n parser.add_argument('-a', '--atomtypes', help='The atom type numbers you want converted.'\n ' These should be written in \"# element\" format.', nargs='+')\n parser.add_argument('-n', '--newfile', help='New file name.', nargs='+')\n parser.add_argument('-c', '--convert', help='Convert atom type numbers to element names.',\n action='store_true', default=False)\n parser.add_argument('-t', '--topology', help='Create an MS-EVB topology file from the final '\n 'coordinates of the trajectory.', action='store_true', default=False)\n parser.add_argument('-e', '--evbin', help='The MS-EVB input file containing atom type information.') \n parser.add_argument('-d', '--datafile', help='The data file containing atom ID number information.') \n parser.add_argument('files', help='The files that you want converted.'\n , nargs='+')\n parser.add_argument('-k', '--cp2k', help='Create coordinate blocks for CP2K.',\n action='store_true', default=False)\n parser.add_argument('-s', '--snapshot', help='Chooses the snapshot of the system for CP2K output.',\n default=-1)\n args = parser.parse_args()\n\n # Store command line arguments as convenient variables\n fh = args.files[0]\n table = args.atomtypes\n conv = args.convert\n topology = args.topology\n evbin = args.evbin\n datafile = args.datafile\n cp2k = args.cp2k\n snapshot = int(args.snapshot)\n\n # Check that the user specified either convert or topology. Die here if not.\n if conv is False and topology is False and cp2k is False:\n error = 'Must specify the -c, -t, or -k flag. Use -h flag for information.'\n sys.exit('\\n' + '%'*len(error) + '\\n' + error + '\\n' + '%'*len(error) + '\\n')\n\n # Check if the user is trying to convert the file. Exit here if -c is specified\n # without giving the atom type numbers.\n if conv is True and table is None:\n error = 'Must use -a flag when converting a trajectory file. Use -h flag for information.'\n sys.exit('\\n' + '%'*len(error) + '\\n' + error + '\\n' + '%'*len(error) + '\\n')\n\n # Check if the user is trying to generate a topology file. Exit here if\n # -t is specified without giving the evbin, par4amber and data files.\n if topology is True and (evbin is None or datafile is None):\n error = 'Must use -e and -d flags when generating a topology file. Use -h flag for information.'\n sys.exit('\\n' + '%'*len(error) + '\\n' + error + '\\n' + '%'*len(error) + '\\n')\n \n # Convert the table to a dictionary\n if conv or cp2k:\n convert = {}\n for item in table:\n item = item.split()\n convert[item[0]] = item[1]\n\n # Data blocks\n data = ['ITEM: TIMESTEP',\n 'ITEM: NUMBER OF ATOMS',\n 'ITEM: BOX BOUNDS pp pp pp',\n 'ITEM: ATOMS id type x y z fx fy fz',\n 'ITEM: ATOMS id type x y z ix iy iz',\n 'ITEM: ATOMS id mol type x y z ix iy iz',\n 'ITEM: ATOMS id type q x y z', \n 'ITEM: ATOMS id mol type q x y z']\n\n # Collect all data into memory for data retention\n with open(fh) as fl:\n f = tuple([line.rstrip() for line in fl])\n\n # Locate data blocks\n # Need to find:\n # 1. Timestep value\n # 2. Number of atoms\n # 3. Box bounds\n # 4. Coordinates/forces for each timestep\n timestep = []\n natoms = 0\n box_bounds = []\n coord_and_force = []\n coord_and_index = []\n coord_and_charge = []\n coord_molid_and_index = []\n coord_and_molid = []\n for i,line in enumerate(f):\n # Timestep value\n if 'TIMESTEP' in line:\n timestep.append(int(f[i+1]))\n # Number of atoms\n if natoms == 0:\n if 'NUMBER OF ATOMS' in line:\n natoms = int(f[i+1])\n # Box bounds\n #if box_bounds == []:\n if ('BOX BOUNDS pp pp pp' in line or\n 'BOX BOUNDS ff ff ff' in line or\n 'BOX BOUNDS fm fm fm' in line or\n 'BOX BOUNDS mm mm mm' in line):\n tmp1 = f[i+1].split()\n tmp2 = f[i+2].split()\n tmp3 = f[i+3].split()\n tmp4 = []\n box_bounds.append([ [float(tmp1[0]),float(tmp1[1])],\n [float(tmp2[0]),float(tmp2[1])],\n [float(tmp3[0]),float(tmp3[1])] ])\n # Coordinates and forces\n if ('ITEM: ATOMS id type x y z fx fy fz' in line or\n 'ITEM: ATOMS id type xu yu zu fx fy fz' in line):\n # Temporary list\n tmp = []\n for ix in range(i+1,i+1+natoms):\n ln = f[ix] # Grab the current line\n ln = ln.split() # Split on whitespace\n if conv or cp2k:\n ln[1] = convert[ln[1]] # Use the conversion table to convert the label to element\n else:\n ln[1] = int(ln[1])\n ln[2] = float(ln[2])\n ln[3] = float(ln[3])\n ln[4] = float(ln[4])\n ln[5] = float(ln[5])\n ln[6] = float(ln[6])\n ln[7] = float(ln[7])\n tmp.append(ln) # Add the line to tmp\n coord_and_force.append(tmp) # Make coord_and_force a list of lists.\n # Coordinates and box indices\n if 'ITEM: ATOMS id type x y z ix iy iz' in line:\n tmp = []\n for ix in range(i+1,i+1+natoms):\n ln = f[ix] # Grab the current line\n ln = ln.split() # Split on whitespace\n if conv or cp2k:\n ln[1] = convert[ln[1]] # Use the conversion table to convert the label to element\n else:\n ln[1] = int(ln[1])\n ln[2] = float(ln[2])\n ln[3] = float(ln[3])\n ln[4] = float(ln[4])\n ln[5] = int(ln[5])\n ln[6] = int(ln[6])\n ln[7] = int(ln[7])\n tmp.append(ln) # Add the line to tmp\n coord_and_index.append(tmp) # Make coord_and_force a list of lists.\n # Coordinates, charges\n if 'ITEM: ATOMS id type q x y z' in line:\n tmp = []\n for ix in range(i+1,i+1+natoms):\n ln = f[ix] # Grab the current line\n ln = ln.split() # Split on whitespace\n if conv or cp2k:\n ln[1] = convert[ln[1]] # Use the conversion table to convert the label to element\n else:\n ln[1] = int(ln[1])\n ln[2] = float(ln[2])\n ln[3] = float(ln[3])\n ln[4] = float(ln[4])\n ln[5] = float(ln[5])\n tmp.append(ln) # Add the line to tmp\n coord_and_charge.append(tmp) # Make coord_and_force a list of lists.\n # Coordinates, mol_id and box indices\n if 'ITEM: ATOMS id mol type x y z ix iy iz' in line:\n tmp = []\n for ix in range(i+1,i+1+natoms):\n ln = f[ix] # Grab the current line\n ln = ln.split() # Split on whitespace\n if conv or cp2k:\n ln[2] = convert[ln[2]] # Use the conversion table to convert the label to element\n else:\n ln[2] = int(ln[2])\n ln[3] = float(ln[3])\n ln[4] = float(ln[4])\n ln[5] = float(ln[5])\n ln[6] = int(ln[6])\n ln[7] = int(ln[7])\n ln[8] = int(ln[8])\n tmp.append(ln) # Add the line to tmp\n coord_molid_and_index.append(tmp) # Make coord_molid_and_force a list of lists.\n # Coordinates and mol_id\n if 'ITEM: ATOMS id mol type q x y z' in line:\n tmp = []\n for ix in range(i+1,i+1+natoms):\n ln = f[ix] # Grab the current line\n ln = ln.split() # Split on whitespace\n if conv or cp2k:\n ln[2] = convert[ln[2]] # Use the conversion table to convert the label to element\n else:\n ln[2] = int(ln[2])\n ln[3] = float(ln[3])\n ln[4] = float(ln[4])\n ln[5] = float(ln[5])\n ln[6] = float(ln[6])\n tmp.append(ln) # Add the line to tmp\n coord_and_molid.append(tmp) # Make coord_molid_and_force a list of lists.\n\n # Close the trajectory file and empty the tuple\n f = () \n\n # Convert atom type numbers to element names.\n if conv:\n # Number of timesteps\n ntstep = len(timestep)\n\n # Print data to a new file.\n nfile = []\n if args.newfile is not None:\n for item in args.newfile:\n nfile.append(args.newfile)\n else:\n nfile.append(fh.split('.')[0] + '_new.lammpstrj')\n\n lstyle1 = '{0:>9.5f} {1:>9.5f}'\n lstyle2 = '{0:>4} {1:>4} {2:>9.5f} {3:>9.5f} {4:>9.5f} {5:>9.5f} {6:>9.5f} {7:>9.5f}'\n lstyle3 = '{0:>4} {1:>4} {2:>9.5f} {3:>9.5f} {4:>9.5f} {5:>4} {6:>4} {7:>4}'\n lstyle4 = '{0:>4} {1:>4} {2:>2} {3:>9.5f} {4:>9.5f} {5:>9.5f} {6:>4} {7:>4} {8:>4}'\n lstyle5 = '{0:>4} {1:>4} {2:>5.2f} {3:>9.5f} {4:>9.5f} {5:>9.5f}'\n lstyle6 = '{0:>4} {1:>4} {2:>2} {3:>5.2f} {4:>9.5f} {5:>9.5f} {6:>9.5f}'\n for item in nfile:\n fl = open(item, 'w')\n for ix in range(ntstep):\n print(data[0], file=fl)\n print(timestep[ix], file=fl)\n print(data[1], file=fl)\n print(natoms, file=fl)\n print(data[2], file=fl)\n print(lstyle1.format(box_bounds[ix][0][0], box_bounds[ix][0][1]), file=fl)\n print(lstyle1.format(box_bounds[ix][1][0], box_bounds[ix][1][1]), file=fl)\n print(lstyle1.format(box_bounds[ix][2][0], box_bounds[ix][2][1]), file=fl)\n if coord_and_force != []:\n print(data[3], file=fl)\n for ln in coord_and_force[ix]:\n print(lstyle2.format(ln[0], ln[1], ln[2], ln[3], ln[4], ln[5], ln[6], ln[7]), file=fl)\n elif coord_and_index != []:\n print(data[4], file=fl)\n for ln in coord_and_index[ix]:\n print(lstyle3.format(ln[0], ln[1], ln[2], ln[3], ln[4], ln[5], ln[6], ln[7]), file=fl)\n elif coord_and_charge != []:\n print(data[6], file=fl)\n for ln in coord_and_charge[ix]:\n print(lstyle5.format(ln[0], ln[1], ln[2], ln[3], ln[4], ln[5]), file=fl)\n elif coord_molid_and_index != []:\n print(data[5], file=fl)\n for ln in coord_molid_and_index[ix]:\n print(lstyle4.format(ln[0], ln[1], ln[2], ln[3], ln[4], ln[5], ln[6], ln[7], ln[8]), file=fl)\n elif coord_and_molid != []:\n print(data[7], file=fl)\n for ln in coord_and_molid[ix]:\n print(lstyle6.format(ln[0], ln[1], ln[2], ln[3], ln[4], ln[5], ln[6]), file=fl)\n # Generate a topology file for RAPTOR.\n elif topology:\n # Trajectory data\n traj_data = coord_and_force[-1]\n\n # /\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\n # Read the input file for the MS-EVB code\n # /\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\n # This tells us the atom types in RAPTOR related to the LAMMPS atom types\n with open(evbin) as data:\n evb = tuple([line.rstrip() for line in data])\n\n # Read the LAMMPS types and molecule numbers from the MS-EVB input\n lammps_type = {}\n reaction_mol_num = {}\n for i,line in enumerate(evb):\n if 'MODEL' in line:\n s1 = i + 3\n if 'Reaction' in line:\n m1 = i + 1\n if ': atom type' in line:\n s2 = i + 2\n if ': bond type' in line:\n e2 = i - 1\n \n # Store the reaction type(s)\n for data in evb[m1:]:\n if '#define' in data:\n tmp = data.split()\n reaction_mol_num[tmp[1]] = {}\n # Stop the loop if a blank line is encountered\n if data == '':\n break\n \n # Store the molecule names and numbers belonging to a reaction type\n # I assume that the naming convention of reactions has correspondence\n # with the naming of the molecular model\n for i,data in enumerate(evb[s1:m1]):\n # This part is pretty convoluted. At least it works.\n if '#define' in data:\n count = s1 + i + 1\n tmp = data.split()\n # Here we loop through the reaction types we stored above\n for reaction in reaction_mol_num.keys():\n if tmp[1] in reaction.split('_'):\n reaction_mol_num[reaction][tmp[1]] = {}\n nloop = 0\n # Here we look through lines greater and stash the molecule\n # number information. \n for line in evb[count:]:\n mol = line.split()\n if '#define' in mol:\n break\n elif mol == []:\n break\n else:\n reaction_mol_num[reaction][tmp[1]][mol[1]] = int(mol[2])\n\n # Store the LAMMPS types\n for data in evb[s2:e2]:\n if '#define' in data:\n tmp = data.split()\n lammps_type[tmp[1]] = int(tmp[2])\n\n # Clear out the tuple (free memory)\n evb = ()\n\n # /\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\n # Read the data file from the LAMMPS output\n # /\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\n # This tells us the atom topology information\n with open(datafile) as data:\n df = tuple([line.rstrip() for line in data])\n\n # Collect the 'Atoms' information from the data file.\n for i,line in enumerate(df):\n if 'Atoms' in line:\n s = i + 2\n elif 'Velocities' in line:\n e = i - 1\n elif 'Bonds' in line:\n s1 = i + 2\n elif 'Angles' in line:\n e1 = i - 1\n\n # Store the atom number and atom type.\n natoms = len(df[s:e])\n atomdata = {}\n for atom in df[s:e]:\n tmp = atom.split()\n atomdata[tmp[0]] = [int(tmp[2]),[]]\n\n # Also store the bond topology. Note that only only the \"central\"\n # atom in the molecule has bond information.\n for atom in df[s1:e1]:\n tmp = atom.split()\n atomdata[tmp[2]][1].append(int(tmp[3])) \n\n # Clear out the tuple (free memory)\n df = ()\n\n # What we have now\n # 1. lammps_type is a dictionary containing the correspondence between \n # RAPTOR atom types and LAMMPS atom ID numbers\n # 2. reaction_mol_num defines the correspondence between reaction names,\n # molecule names, and molecule numbers (\"kernels\").\n # 3. atomdata defines the atom number, its LAMMPS atom ID, and which atoms\n # it is bonded to (if it is a central atom in the molecule).\n # \n # This information can be used to print the bond topology file. The columns\n # in the topology file are:\n # atom number | molecule number | RAPTOR atom type\n\n # Molecule to atom name correspondence\n mol2atom = { 'OW' : 'H2O',\n 'HW' : 'H2O',\n 'OH' : 'H3O', \n 'HO' : 'H3O' }\n\n # Based on the atomtype of the central atom, it is now possible to determine\n # the types of the terminal atoms.\n ltop = [[0,0,0] for row in range(natoms)]\n for key in atomdata.keys():\n ltop[int(key)-1][0] = int(key)\n\n #fi = open('top.tmp', 'w')\n\n # Based on the bonding topology, we can determine the molecule number (\"kernel\").\n for key in atomdata.keys():\n # Identify central atoms. These contain bonding information.\n if atomdata[key][1] != []:\n #print(key, atomdata[key], file=fi)\n # List of bonded partners\n tmp = atomdata[key][1]\n # Type of the central atom\n ctype = atomdata[key][0]\n # Central atom\n # Here, we match ctype with what is contained in lammps_type.\n for key2 in lammps_type.keys():\n if lammps_type[key2] == ctype:\n mol = mol2atom[key2]\n # Subtraction is accounting for Python numbering.\n ltop[int(key)-1][1] = mol\n # Central atom is assumed to always have index 1 for convenience.\n # If this wasn't true, this part would be a lot more complicated.\n ltop[int(key)-1][2] = 1\n # Terminal atoms. Start the counter at 2 since the central atom is 1.\n count = 2\n for item in tmp:\n # Account for Python numbering\n i = item - 1\n # Terminal atom type to be compared with lammps_type\n ttype = atomdata[str(item)][0]\n for key3 in lammps_type.keys():\n if lammps_type[key3] == ttype:\n mol = mol2atom[key3]\n # Temporarily place the molecule name in the list.\n ltop[i][1] = mol\n # Place the counter in the list. This is trivial as\n # long as the terminal atoms are identical. We must\n # be careful in situations where this isn't true.\n ltop[i][2] = count\n count += 1\n\n # Finally we convert molecule strings to molecule numbers\n for i in range(len(ltop)):\n for reaction in reaction_mol_num.keys():\n for key in reaction_mol_num[reaction].keys():\n ltop[i][1] = reaction_mol_num[reaction][key][ltop[i][1]] \n\n # Output data to a file\n stop = 'lammps_msevb.top'\n top = open(stop, 'w')\n \n fmt = '{0:>4} {1:>2} {2:>2}'\n for i in range(len(ltop)):\n print(fmt.format(ltop[i][0], ltop[i][1], ltop[i][2]), file=top)\n elif cp2k:\n # Trajectory data\n traj_data = coord_and_force[snapshot]\n\n # Print out box bounds\n bb = box_bounds[snapshot]\n x = bb[0][1] - bb[0][0]\n y = bb[1][1] - bb[1][0]\n z = bb[2][1] - bb[2][0]\n fmt = ' ABC {0:7.5f} {1:7.5f} {2:7.5f}'\n print('&CELL')\n print(fmt.format(x,y,z))\n print('&END CELL')\n print('#')\n print('&COORD')\n lnstyle = ' {0:2} {1:10.5f} {2:10.5f} {3:10.5f}'\n for ln in traj_data:\n print(lnstyle.format(ln[1],ln[2],ln[3],ln[4]))\n print('&END COORD')","title":""},{"docid":"b8477e9d7f1edbb350c4aa3147a9da9c","score":"0.4306115","text":"def create_subtop(self, label_type, label):\n from gmso.parameterization.molecule_utils import (\n molecule_angles,\n molecule_bonds,\n molecule_dihedrals,\n molecule_impropers,\n )\n\n of_group = True if label_type == \"group\" else False\n sites_dict = {\n site: (idx, site.clone())\n for idx, site in enumerate(self.iter_sites(label_type, label))\n }\n bonds_dict = {\n bond: tuple(\n sites_dict[bond.connection_members[i]][0] for i in range(2)\n )\n for bond in molecule_bonds(self, label, of_group)\n }\n\n angles_dict = {\n angle: tuple(\n sites_dict[angle.connection_members[i]][0] for i in range(3)\n )\n for angle in molecule_angles(self, label, of_group)\n }\n\n dihedrals_dict = {\n dihedral: tuple(\n sites_dict[dihedral.connection_members[i]][0] for i in range(4)\n )\n for dihedral in molecule_dihedrals(self, label, of_group)\n }\n\n impropers_dict = {\n improper: tuple(\n sites_dict[improper.connection_members[i]][0] for i in range(4)\n )\n for improper in molecule_impropers(self, label, of_group)\n }\n\n new_top = gmso.Topology(\n name=label if isinstance(label, str) else label[0]\n )\n\n for ref_site, new_site in sites_dict.items():\n new_top.add_site(new_site[1])\n for ref_conn, conn_idx in bonds_dict.items():\n bond = gmso.Bond(\n connection_members=[\n new_top.sites[conn_idx[i]] for i in range(2)\n ],\n bond_type=None\n if not ref_conn.connection_type\n else ref_conn.connection_type.clone(),\n )\n new_top.add_connection(bond)\n for ref_conn, conn_idx in angles_dict.items():\n angle = gmso.Angle(\n connection_members=[\n new_top.sites[conn_idx[i]] for i in range(3)\n ],\n angle_type=None\n if not ref_conn.connection_type\n else ref_conn.connection_type.clone(),\n )\n new_top.add_connection(angle)\n for ref_conn, conn_idx in dihedrals_dict.items():\n dihedral = gmso.Dihedral(\n connection_members=[\n new_top.sites[conn_idx[i]] for i in range(4)\n ],\n dihedral_type=None\n if not ref_conn.connection_type\n else ref_conn.connection_type.clone(),\n )\n new_top.add_connection(dihedral)\n for ref_conn, conn_idx in impropers_dict.items():\n improper = gmso.Improper(\n connection_members=[\n new_top.sites[conn_idx[i]] for i in range(4)\n ],\n improper_type=None\n if not ref_conn.connection_type\n else ref_conn.connection_type.clone(),\n )\n new_top.add_connection(improper)\n\n new_top.update_topology()\n return new_top","title":""},{"docid":"861c27a581961250817921baa484d8fb","score":"0.4301693","text":"def topologytemplate_spec(request, template):\n return HttpResponse(template.specification, mimetype=\"text/plain\")","title":""},{"docid":"2e5242a8f0213f785b0690639ec44fcf","score":"0.42926726","text":"def inject_label_matchers(self, expression, topology) -> str:\n if not topology:\n return expression\n if not self.path:\n logger.debug(\"`cos-tool` unavailable. Leaving expression unchanged: %s\", expression)\n return expression\n args = [str(self.path), \"transform\"]\n args.extend(\n [\"--label-matcher={}={}\".format(key, value) for key, value in topology.items()]\n )\n\n args.extend([\"{}\".format(expression)])\n # noinspection PyBroadException\n try:\n return self._exec(args)\n except subprocess.CalledProcessError as e:\n logger.debug('Applying the expression failed: \"%s\", falling back to the original', e)\n return expression","title":""},{"docid":"555319af7520c988a93c5100170d8cc5","score":"0.4291545","text":"def _write_loader_script(path, state_name, package, top):\r\n if state_name.startswith(package):\r\n pkg_arg = ''\r\n else:\r\n pkg_arg = \", package='%s'\" % package\r\n\r\n if top:\r\n top_arg = ''\r\n else:\r\n top_arg = ', top_obj=False'\r\n\r\n out = open(path, 'w')\r\n out.write(\"\"\"\\\r\nimport os\r\nimport sys\r\nif not '.' in sys.path:\r\n sys.path.append('.')\r\n\r\ntry:\r\n from openmdao.main.api import Component, SAVE_CPICKLE\r\nexcept ImportError:\r\n print 'No OpenMDAO distribution available.'\r\n if __name__ != '__main__':\r\n print 'You can unzip the egg to access the enclosed files.'\r\n print 'To get OpenMDAO, please visit openmdao.org'\r\n sys.exit(1)\r\n\r\ndef load(**kwargs):\r\n '''Create object(s) from state file.'''\r\n return Component.load('%(name)s', SAVE_CPICKLE%(pkg)s%(top)s, **kwargs)\r\n\r\ndef main():\r\n '''Load state and run.'''\r\n model = load()\r\n model.run()\r\n\r\nif __name__ == '__main__':\r\n main()\r\n\"\"\" % {'name': state_name, 'pkg': pkg_arg, 'top': top_arg})\r\n out.close()","title":""},{"docid":"334ad27ce5271dc9a26655e8f23fce5a","score":"0.4276431","text":"def main(server=None, input=None):\n if not input:\n input = {}\n\n try:\n # CUSTOM_SCRIPT00019\n #\n # Created by Matthew Misenhimer\n #\n def make_timestamp():\n #Makes a Timestamp for postgres\n import datetime\n now = datetime.datetime.now()\n return now.strftime(\"%Y-%m-%d %H:%M:%S\")\n \n def are_no_hackpipes_preceding(sob, ignore_code):\n #Checks to see if there are any manually inserted/non-pipeline Projects or Work Orders that lead in to the sob that are not completed yet\n #If there are incompleted hackpipe work orders or projs that precede the sob, then this will return false.\n #Ignore code is usually the code of the task just completed. We don't care about looking at it's status in determining this. \n boolio = True\n matcher = '' #This is the type of sob (PROJ or WORK_ORDER) that we care about looking at\n if 'PROJ' in sob.get('code'):\n matcher = 'PROJ'\n elif 'WORK_ORDER' in sob.get('code'):\n matcher = 'WORK_ORDER'\n pre_hacks_expr = \"@SOBJECT(twog/hackpipe_out['out_to','%s'])\" % sob.get('code') #See what hackpipes lead in to this sob\n pre_hacks = server.eval(pre_hacks_expr)\n for ph in pre_hacks:\n if matcher in ph.get('lookup_code') and ignore_code not in ph.get('lookup_code'): #If it's the type we care about and it isn't the main sob\n ph_task = server.eval(\"@SOBJECT(sthpw/task['lookup_code','%s'])\" % ph.get('lookup_code'))\n if ph_task:\n ph_task = ph_task[0]\n if ph_task.get('status') != 'Completed': #If it hasn't been completed, then there is an incomplete hackpipe preceding sob, so return false\n boolio = False\n return boolio\n \n def block_manual_status_adjust_for_inactive_hackup(sob, task):\n #This was created because people used to be able to change the status of inactive work orders\n #I think this is prevented in other ways now, but I am keeping it in just in case it is still needed.\n from pyasm.common import TacticException\n good = True\n if sob.get('creation_type') == 'hackup':\n if task.get('active') not in [True,'true','t',1,'1']:\n raise TacticException('This needs to be active in order to change the status')\n return good\n \n from pyasm.common import TacticException, Environment\n # input and server are assumed variables\n # define some contants here\n #print \"\\n\\nIN KICKOFF\"\n COMPLETE = 'Completed'\n READY = 'Ready'\n PENDING = 'Pending'\n sobj = input.get('sobject')\n this_process = sobj.get('process')\n this_lookup = sobj.get('lookup_code')\n sk = input.get('search_key')\n task_code = sobj.get('code')\n update_data = input.get('update_data') #These are the new values\n prev_data = {}\n old_status = ''\n if 'prev_data' in input.keys():\n prev_data = input.get('prev_data') #These are the old values\n if 'status' in prev_data.keys():\n old_status = prev_data.get('status')\n new_status = update_data.get('status')\n login = Environment.get_login()\n user_name = login.get_login()\n assigned_login_group = sobj.get('assigned_login_group')\n parent_obj = None\n title = None\n order = None\n if 'PROJ' in this_lookup:\n parent_obj = server.eval(\"@SOBJECT(twog/proj['code','%s'])\" % this_lookup)[0] #Parent Obj is the Proj attached to the task\n proj = parent_obj\n title = server.eval(\"@SOBJECT(twog/title['code','%s'])\" % parent_obj.get('title_code'))[0]\n order = server.eval(\"@SOBJECT(twog/order['code','%s'])\" % title.get('order_code'))[0] \n elif 'WORK_ORDER' in this_lookup:\n parent_obj = server.eval(\"@SOBJECT(twog/work_order['code','%s'])\" % this_lookup)[0] #Parent Obj is the Work Order attached to the task\n work_order = parent_obj\n proj = server.eval(\"@SOBJECT(twog/proj['code','%s'])\" % work_order.get('proj_code'))[0]\n title = server.eval(\"@SOBJECT(twog/proj['code','%s'].twog/title)\" % parent_obj.get('proj_code'))[0] \n order = server.eval(\"@SOBJECT(twog/order['code','%s'])\" % title.get('order_code'))[0] \n \n if 'PROJ' in this_lookup and title.get('priority_triggers') != 'No':\n #If the new status for this Proj is ready, then grab the priority attached to he proj and give it to the Title\n #This is to control priority order per department\n if new_status == 'Ready':\n server.update(title.get('__search_key__'), {'priority': parent_obj.get('priority')}, triggers=False)\n elif 'WORK_ORDER' in this_lookup:\n t_wo_completed = title.get('wo_completed') #This is for the completion ratio on title\n o_wo_completed = order.get('wo_completed') #This is for the completion ratio on order\n if new_status == COMPLETE:\n title_str = title.get('title') #This is for a potential alert/exception\n if title.get('episode') not in [None,'']:\n title_str = '%s: %s' % (title_str, title.get('episode'))\n #Block QC and Edel from completing their work orders if the TRT or TRT w/Textless are not filled in\n if 'qc' in assigned_login_group or 'edeliveries' in assigned_login_group:\n total_program_runtime = title.get('total_program_runtime')\n total_runtime_w_textless = title.get('total_runtime_w_textless')\n say_str = ''\n say_str2 = ''\n if total_program_runtime in [None,''] or total_runtime_w_textless in [None,'']:\n if total_program_runtime in [None,'']:\n say_str = 'Total Program Runtime has' \n if total_runtime_w_textless in [None,'']:\n if say_str == '': \n say_str = 'Total Runtime With Textless has'\n else:\n say_str = '%s and Total Runtime With Textless have' % (say_str[:-4]) \n say_str2 = \"%s (%s)'s %s not been filled. You must enter this data before trying to complete this work order.\" % (title_str, title.get('code'), say_str)\n if 'qc' in assigned_login_group:\n if total_program_runtime in [None,''] or total_runtime_w_textless in [None,'']:\n raise TacticException(say_str2)\n else:\n #They were filled in, so finish completing the task and send a note\n from pyasm.biz import Note\n from pyasm.search import Search\n title_obj2 = Search.get_by_search_key(title.get('__search_key__')) #This is the type of object required for Note creation\n note_text = '%s (%s) has been Passed and Completed by %s in QC' % (sobj.get('process'), this_lookup, user_name)\n note = Note.create(title_obj2, note_text, context='QC Completed', process='QC Completed')\n elif 'edeliveries' in assigned_login_group and (total_program_runtime in [None,''] or total_runtime_w_textless in [None,'']):\n raise TacticException(say_str2)\n #This section is turned off due to logistical problems with it. \n #It intended to block machine room, edit, and compression from completing a work order unless the pulled_blacks had been filled out.\n #if 'machine_room' in assigned_login_group or 'edit' in assigned_login_group or 'compression' in assigned_login_group:\n # pulled_blacks = title.get('pulled_blacks')\n # if pulled_blacks in [None,'','0']:\n # raise TacticException(\"%s (%s)'s pulled_blacks has not been filled, or is still '0'.\" % (title_str, title.get('code'))) \n t_wo_completed = t_wo_completed + 1\n o_wo_completed = o_wo_completed + 1\n #Update the completion ratios attached, since there were no blocking exceptions \n server.update(title.get('__search_key__'), {'wo_completed': t_wo_completed})\n server.update(order.get('__search_key__'), {'wo_completed': o_wo_completed})\n elif old_status == COMPLETE:\n t_wo_completed = t_wo_completed - 1\n o_wo_completed = o_wo_completed - 1\n #Reduce the completion ratio, since it was completed but has now been taken off that status\n server.update(title.get('__search_key__'), {'wo_completed': t_wo_completed})\n server.update(order.get('__search_key__'), {'wo_completed': o_wo_completed})\n #Still doing this, but don't know if it's neccessary anymore \n mmkay = block_manual_status_adjust_for_inactive_hackup(parent_obj, sobj)\n if new_status == COMPLETE and 'PROJ' not in this_lookup:\n #Make sure they have set the assigned person to the work order.\n if sobj.get('assigned') in [None,'']:\n task_assigned_expr = \"@GET(sthpw/task['code','%s'].assigned)\" % sobj.get('code') #MTM: Do I need to retrieve the task again, or can I just use the sobj's \"assigned\"? \n task_assigned = server.eval(task_assigned_expr)\n if task_assigned:\n task_assigned = task_assigned[0]\n if task_assigned in [None,'']:\n raise TacticException('Before completing a work order, someone must be assigned to it.')\n #Make sure they have added work hours. If not, error out.\n whs_expr = \"@SOBJECT(sthpw/work_hour['task_code','%s'])\" % task_code\n whs = server.eval(whs_expr)\n sum = 0\n for wh in whs:\n straight_time = wh.get('straight_time')\n if straight_time in [None,'']:\n straight_time = 0\n else:\n straight_time = float(straight_time)\n sum = float(sum) + straight_time\n sum = str(sum)\n if sum in ['0','',0,0.0]:\n raise TacticException('You need to save the hours you worked on this before you can set the status to \"Completed\".')\n \n now_timestamp = make_timestamp() \n #Since there have been no blocking exceptions, record the status change\n server.insert('twog/status_log', {'login': user_name, 'timestamp': now_timestamp, 'from_status': old_status, 'status': new_status, 'task_code': task_code, 'lookup_code': this_lookup, 'order_code': sobj.get('order_code'), 'title_code': sobj.get('title_code'), 'process': this_process})\n if new_status == COMPLETE:\n #Record the completion date on the work order, and take it off the BigBoard\n import datetime\n now = datetime.datetime.now()\n timestamp_str = '%s-%s-%s %s:%s:%s' % (now.year, now.month, now.day, now.hour, now.minute, now.second)\n updict = {'actual_end_date': timestamp_str}\n if 'WORK_ORDER' in this_lookup:\n updict['bigboard'] = False\n server.update(sk, updict)\n elif new_status not in ['Pending','Ready','Completed'] and 'WORK_ORDER' in this_lookup:\n server.update(server.build_search_key('sthpw/task', proj.get('task_code')), {'status': new_status}) #NEW\n if 'PROJ' in sobj.get('lookup_code'):\n #MTM: This annoying section is for passing Proj's their task's status.\n #I don't know if this is needed at all anymore. Will have to check other triggers and reports.\n #The \"tripwire\" stuff was just to keep it from infinitely passing statuses from proj to task, task to proj\n do_it = True\n if 'tripwire' in update_data.keys():\n if update_data.get('tripwire') == 'No Send Back': #?\n do_it = False\n server.update(input.get('search_key'), {'tripwire': ''}, triggers=False) #Empty the tripwire and do nothing\n server.update(proj.get('__search_key__'), {'tripwire': '', 'status': sobj.get('status')}, triggers=False) #?\n if do_it:\n if proj:\n server.update(proj.get('__search_key__'), {'status': new_status})\n if title.get('priority_triggers') != 'No':\n #Update Title Priority for On Hold Status, or having that status removed -- BEGIN\n if sobj.get('status') in ['On_Hold','On Hold']:\n title_priority = title.get('priority')\n server.update(title.get('__search_key__'), {'saved_priority': title_priority, 'priority': 200}, triggers=False) \n else:\n if old_status in ['On_Hold','On Hold']:\n saved_priority = title.get('saved_priority')\n server.update(title.get('__search_key__'), {'priority': saved_priority}, triggers=False) \n #Update Title Priority for On Hold Status, or having that status removed -- END\n \n #Update Title Priority for Client Response Status, or having that status removed -- BEGIN\n if sobj.get('status') == 'Client Response':\n title_priority = title.get('priority')\n crc = title.get('client_response_count')\n crc_num = 0\n if crc not in [None,'']:\n crc_num = int(crc)\n crc_num = crc_num + 1 \n server.update(title.get('__search_key__'), {'saved_priority': title_priority, 'priority': 300, 'client_response_count': crc_num}, triggers=False) \n else:\n if old_status == 'Client Response':\n saved_priority = title.get('saved_priority')\n crc = title.get('client_response_count')\n crc_num = 0\n if crc not in [None,'']:\n crc_num = int(crc)\n if crc_num > 0:\n crc_num = crc_num - 1 \n server.update(title.get('__search_key__'), {'priority': saved_priority, 'client_response_count': crc_num}, triggers=False) \n #Update Title Priority for Client Response Status, or having that status removed -- END\n \n if sobj.get('status') in ['In_Progress','In Progress','DR In_Progress','DR In Progress', 'Amberfin01_In_Progress', 'Amberfin01 In Progress', 'Amberfin02_In_Progress', 'Amberfin02 In Progress','BATON In_Progress','BATON In Progress','Export In_Progress','Export In Progress','Buddy Check In_Progress','Buddy Check In Progress','Need Buddy Check','Completed'] and old_status not in ['In_Progress','DR In_Progress','DR In Progress','Amberfin01_In_Progress','Amberfin01 In Progress', 'Amberfin02_In_Progress','Amberfin02 In Progress','BATON In_Progress','BATON In Progress','Export In_Progress','Export In Progress','Buddy Check In_Progress','Buddy Check In Progress','Need Buddy Check','In Progress']:\n #Update the actual start date if they just set the status to 'In Progress'\n if sobj.get('actual_start_date') in ['',None]:\n now_timestamp = make_timestamp() \n server.update(sk, {'actual_start_date': now_timestamp})\n if sobj.get('status') in ['Ready','In_Progress','In Progress'] and 'WORK_ORDER' in sobj.get('lookup_code'):\n if title.get('client_status') != 'In Production':\n server.update(title.get('__search_key__'), {'client_status': 'In Production', 'status': 'In Production'})\n elif sobj.get('status') in ['Rejected','Fix Needed'] and 'WORK_ORDER' in sobj.get('lookup_code'):\n from pyasm.biz import Note\n from pyasm.search import Search\n server.insert('twog/production_error', {'error_type': sobj.get('status'), 'process': sobj.get('process'), 'work_order_code': sobj.get('lookup_code'), 'title': title.get('title'), 'episode': title.get('episode'), 'title_code': title.get('code'), 'order_code': order.get('code'), 'order_name': order.get('name'), 'po_number': order.get('po_number'), 'proj_code': proj.get('code'), 'scheduler_login': sobj.get('creator_login'), 'operator_login': user_name, 'login': user_name}) \n if sobj.get('status') == 'Rejected':\n server.update(title.get('__search_key__'), {'client_status': 'QC Rejected'})\n if title.get('priority_triggers') != 'No':\n server.update(title.get('__search_key__'), {'priority': 90}, triggers=False)\n title_obj2 = Search.get_by_search_key(title.get('__search_key__')) #This is the type of object required for Note creation\n note_text = '%s (%s) has been Rejected, as marked by %s' % (sobj.get('process'), this_lookup, user_name)\n note = Note.create(title_obj2, note_text, context='QC Rejected', process='QC Rejected')\n \n if sobj.get('status') == COMPLETE and title.get('status_triggers') != 'No':\n # Now we need to set the next task(s) statuses to 'Ready'\n parent = server.get_parent(sk)\n # Get all process information from the pipeline regarding processes linked to this process in the normal pipeline\n info = server.get_pipeline_processes_info(parent.get('__search_key__'), related_process=this_process)\n input_processes = info.get('input_processes')\n output_processes = info.get('output_processes')\n # this combines all other input_processes and this process\n # including this_process in case this process has more than 1 task\n ready = False\n if input_processes:\n input_processes.append(this_process)\n ready = True\n input_tasks = server.query('sthpw/task', filters = [('search_type', sobj.get('search_type')), ('search_id', sobj.get('search_id')), ('process', input_processes), ('title_code',sobj.get('title_code'))])\n for task in input_tasks:\n if task.get('status') != COMPLETE:\n ready = False\n else:\n ready = True\n #Now we need to check the manually entered work orders and projs \n #This section may be replaced with the are_no_hackpipes_preceding function, but for the sake of stability, I won't do it until I test it again\n hack_outs_expr = \"@SOBJECT(twog/hackpipe_out['out_to','%s'])\" % sobj.get('lookup_code')\n hack_outs = server.eval(hack_outs_expr)\n stype = ''\n stype_st = ''\n if 'PROJ' in sobj.get('lookup_code'):\n stype = 'PROJ'\n stype_st = 'twog/proj'\n elif 'WORK_ORDER' in sobj.get('lookup_code'):\n stype = 'WORK_ORDER'\n stype_st = 'twog/work_order'\n for ho in hack_outs:\n lookup_code = ho.get('lookup_code')\n if stype in lookup_code:\n hacktasks = server.eval(\"@SOBJECT(sthpw/task['lookup_code','%s'])\" % lookup_code)\n for hacktask in hacktasks:\n if hacktask.get('status') != COMPLETE:\n ready = False\n \n # make the next process ready\n if ready == True:\n output_tasks = server.query('sthpw/task', filters = [('search_type', sobj.get('search_type')), ('search_id', sobj.get('search_id')), ('process', output_processes), ('title_code',sobj.get('title_code'))])\n update_data = {}\n \n for task in output_tasks:\n if task.get('lookup_code') not in [None,'']:\n if task.get('status') == 'Pending':\n #Need to make sure other tasks leading into this one (an output task for the triggered task) are also complete - both ways, with pipeline and hackpipe - before allowing this status update \n \n out_info = server.get_pipeline_processes_info(parent.get('__search_key__'), related_process=task.get('process'))\n input_to_out = out_info.get('input_processes') \n if this_process in input_to_out:\n prc_idx = input_to_out.index(this_process)\n input_to_out.pop(prc_idx)\n ready2 = False\n if input_to_out:\n #input_to_out.append(task.get('process'))\n ts_st = ''\n if 'PROJ' in task.get('lookup_code'):\n ts_st = 'twog/proj'\n elif 'WORK_ORDER' in task.get('lookup_code'):\n ts_st = 'twog/work_order'\n tsob = server.eval(\"@SOBJECT(%s['code','%s'])\" % (ts_st, task.get('lookup_code')))[0] \n ready2 = are_no_hackpipes_preceding(tsob, sobj.get('lookup_code'))\n into_out_tasks = server.query('sthpw/task', filters = [('search_type', task.get('search_type')), ('search_id', task.get('search_id')), ('process', input_to_out)])\n for iotask in into_out_tasks:\n # If preceding tasks have not yet been set to 'Completed', do not change the next task's status\n if iotask.get('status') != COMPLETE:\n ready2 = False\n else:\n ready2 = True\n hacks_expr = \"@SOBJECT(twog/hackpipe_out['out_to','%s'])\" % task.get('lookup_code')\n hacks = server.eval(hacks_expr)\n for hack in hacks:\n lu_code = hack.get('lookup_code') \n hacktasks = server.eval(\"@SOBJECT(sthpw/task['lookup_code','%s'])\" % lu_code)\n for hacktask in hacktasks:\n if hacktask.get('status') != COMPLETE:\n ready2 = False\n if ready2 == True:\n update_data[task.get('__search_key__')] = { 'status': READY }\n if 'PROJ' in task.get('lookup_code'):\n get_proj_expr = \"@SOBJECT(twog/proj['code','%s'])\" % task.get('lookup_code')\n proj = server.eval(get_proj_expr)[0]\n # FIND ALL NON HACK WOS\n wos = server.eval(\"@SOBJECT(twog/work_order['proj_code','%s'])\" % proj.get('code'))\n for wo in wos: \n if wo.get('creation_type') not in ['hackpipe','hackup']:\n this_processer = wo.get('process')\n info2 = server.get_pipeline_processes_info(proj.get('__search_key__'), related_process=this_processer)\n input_processes2 = info2.get('input_processes')\n okayed = are_no_hackpipes_preceding(wo, sobj.get('lookup_code'))\n len_proc = 0\n if input_processes2 not in ['',{},[],None]:\n len_proc = len(input_processes2)\n if len_proc < 1 and okayed:\n task2 = server.eval(\"@SOBJECT(sthpw/task['code','%s'])\" % wo.get('task_code')) \n if task2:\n task2 = task2[0]\n if task2.get('status') == 'Pending':\n server.update(task2.get('__search_key__'), {'status': READY}) #?\n # FIND ALL HACK WOS HERE.........\n hack_dudes = server.eval(\"@SOBJECT(twog/hackpipe_out['lookup_code','%s'])\" % proj.get('code'))\n for ho in hack_dudes:\n ready3 = True\n out_to = ho.get('out_to')\n label = ''\n if 'PROJ' in out_to:\n label = 'twog/proj'\n elif 'WORK_ORDER' in out_to:\n label = 'twog/work_order'\n sob_guy = server.eval(\"@SOBJECT(%s['code','%s'])\" % (label, out_to))\n for sobby in sob_guy:\n ready3 = are_no_hackpipes_preceding(sobby, sobj.get('lookup_code'))\n if ready3:\n htask = server.eval(\"@SOBJECT(sthpw/task['lookup_code','%s'])\" % out_to)\n if htask:\n htask = htask[0]\n if htask.get('status') == 'Pending':\n server.update(htask.get('__search_key__'), {'status': READY})\n \n hackers = server.eval(\"@SOBJECT(twog/hackpipe_out['lookup_code','%s'])\" % sobj.get('lookup_code'))\n for hack in hackers:\n if stype in hack.get('out_to'):\n out_to = hack.get('out_to')\n out_sob = server.eval(\"@SOBJECT(%s['code','%s'])\" % (stype_st, out_to))\n if out_sob:\n out_sob = out_sob[0] \n ready4 = are_no_hackpipes_preceding(out_sob, sobj.get('lookup_code'))\n if ready4:\n tasker = server.eval(\"@SOBJECT(sthpw/task['code','%s'])\" % out_sob.get('task_code'))\n if tasker:\n tasker = tasker[0]\n if tasker.get('status') == 'Pending':\n server.update(tasker.get('__search_key__'), {'status': READY})\n if stype == 'PROJ':\n # Need to do the same thing here, looking at pipeline and hackpipe\n hack_wos = server.eval(\"@SOBJECT(twog/hackpipe_out['lookup_code','%s'])\" % out_to)\n for hos in hack_wos:\n if 'PROJ' not in hos.get('out_to'):\n ho_wo = server.eval(\"@SOBJECT(twog/work_order['code','%s'])\" % hos.get('out_to'))\n if ho_wo:\n ho_wo = ho_wo[0]\n ready5 = are_no_hackpipes_preceding(ho_wo, out_sob.get('code'))\n if ready5:\n ho_wo_task_sk = server.build_search_key('sthpw/task', ho_wo.get('task_code'))\n #7/10/2014 --- WAIT. WTF. THERE IS NO STATUS ON WOS, JUST THE TASK..... MTMMTMMTM!!!\n if ho_wo.get('status') == 'Pending':\n server.update(ho_wo_task_sk, {'status': READY})\n # NEED TO LOOK AT PROJ PIPELINE NOW\n proj_sk = server.build_search_key('twog/proj', out_to)\n pipe_wos = server.eval(\"@SOBJECT(twog/work_order['proj_code','%s'])\" % out_to)\n for pwos in pipe_wos:\n if pwos.get('creation_type') not in ['hackpipe','hackup']:\n info2 = server.get_pipeline_processes_info(proj_sk, related_process=pwos.get('process'))\n if 'input_processes' in info2.keys():\n input_processes2 = info2.get('input_processes')\n whack_says = are_no_hackpipes_preceding(pwos, out_to)\n # If there are no input processes, it must be a work order that should also be set to ready - as long as hackpipe says it's ok\n len_proc2 = 0\n if input_processes2 not in ['',{},[],None]:\n len_proc2 = len(input_processes2)\n if len_proc2 < 1 and whack_says:\n wtask_code = pwos.get('task_code')\n # Get the task sobject associated with this work order\n wtask = server.eval(\"@SOBJECT(sthpw/task['code','%s'])\" % wtask_code)\n if wtask:\n wtask = wtask[0]\n # If the task's status has not been touched yet ('Pending') and active is set to true, update the status with 'Ready'\n if wtask.get('status') == 'Pending':\n wdata = {}\n wdata['status'] = 'Ready'\n if wtask.get('status') == 'Pending':\n server.update(wtask.get('__search_key__'), wdata)\n \n \n \n #\n # HERE NEED TO FIND ANY OTHER TASKS THIS GOES 'OUT_TO' and make sure they don't also depend on another task's status (if so, make sure it is completed)\n #\n \n # this is optional, for simplicity, turn off triggers for these updates\n if update_data != {} and title.get('priority_triggers') != 'No':\n #make title priority the proj priority, if proj is becoming \"Ready\"\n for tkey in update_data.keys():\n record = update_data.get(tkey)\n tkcode = tkey.split('code=')[1]\n if 'status' in record.keys():\n if record.get('status') == 'Ready':\n ttt = server.eval(\"@GET(sthpw/task['code','%s'].lookup_code)\" % tkcode)[0]\n if 'PROJ' in ttt:\n pjj = server.eval(\"@SOBJECT(twog/proj['code','%s'])\" % ttt)[0]\n proj_title_code = pjj.get('title_code')\n proj_prio = pjj.get('priority')\n server.update(server.build_search_key('twog/title',proj_title_code), {'priority': proj_prio}, triggers=False)\n server.update_multiple(update_data, triggers=False) #? Should triggers=False?\n \n \n # Now see if all wos under proj or all projs under title are completed. If so, make their parent's status completed\n all_wos_completed = False\n all_wos_pending = False\n prj = None\n if new_status in [COMPLETE,PENDING]: \n if 'WORK_ORDER' in this_lookup:\n wo = server.eval(\"@SOBJECT(twog/work_order['code','%s'])\" % sobj.get('lookup_code'))\n wo = wo[0]\n other_wotasks_expr = \"@SOBJECT(twog/proj['code','%s'].twog/work_order.WT:sthpw/task)\" % wo.get('proj_code')\n other_wo_tasks = server.eval(other_wotasks_expr)\n all_wos_completed = True\n all_wos_pending = True\n if new_status == PENDING:\n all_wos_completed = False\n else:\n all_wos_pending = False\n for owt in other_wo_tasks:\n if owt.get('lookup_code') != wo.get('code'):\n if owt.get('status') != COMPLETE:\n all_wos_completed = False\n if owt.get('status') != PENDING:\n all_wos_pending = False\n prj = server.eval(\"@SOBJECT(twog/proj['code','%s'])\" % wo.get('proj_code'))\n if len(prj) > 0:\n prj = prj[0]\n else:\n prj = None\n if (all_wos_completed or all_wos_pending) and prj not in [None,'']:\n if title.get('status_triggers') != 'No' or all_wos_pending == True:\n prj_task = server.eval(\"@SOBJECT(sthpw/task['lookup_code','%s'])\" % prj.get('code'))\n if prj_task:\n prj_task = prj_task[0]\n server.update(prj_task.get('__search_key__'), {'status': new_status})\n elif 'PROJ' in this_lookup:\n prj = server.eval(\"@SOBJECT(twog/proj['code','%s'])\" % this_lookup)\n if prj:\n prj = prj[0]\n else:\n prj = None\n \n all_projs_completed = True\n all_projs_pending = True\n all_titles_completed = False\n all_titles_pending = False\n if prj not in [None,'']:\n title_proj_tasks = server.eval(\"@SOBJECT(twog/title['code','%s'].twog/proj.PT:sthpw/task)\" % prj.get('title_code'))\n for tpt in title_proj_tasks:\n if tpt.get('status') != COMPLETE:\n all_projs_completed = False\n if tpt.get('status') != PENDING:\n all_projs_pending = False\n title_updated = False\n if all_projs_completed:\n title_sk = server.build_search_key('twog/title', prj.get('title_code'))\n if title.get('priority_triggers') != 'No' and title.get('status_triggers') != 'No':\n server.update(title_sk, {'status': COMPLETE, 'bigboard': False, 'priority': 5000})\n titles_completed = order.get('titles_completed')\n title_codes_completed = order.get('title_codes_completed')\n if title.get('code') not in title_codes_completed:\n if titles_completed in [None,'']:\n titles_completed = 0\n else:\n titles_completed = int(titles_completed)\n titles_completed = titles_completed + 1\n if title_codes_completed == '':\n title_codes_completed = title.get('code')\n else:\n title_codes_completed = '%s,%s' % (title_codes_completed, title.get('code'))\n server.update(order.get('__search_key__'), {'titles_completed': titles_completed, 'title_codes_completed': title_codes_completed}) \n title_updated = True\n all_titles_completed = True\n title = server.eval(\"@SOBJECT(twog/title['code','%s'])\" % prj.get('title_code'))\n if title and title_updated:\n title = title[0]\n other_titles = server.eval(\"@SOBJECT(twog/order['code','%s'].twog/title)\" % title.get('order_code'))\n for ot in other_titles:\n if title.get('code') != ot.get('code'):\n if ot.get('status') != COMPLETE:\n all_titles_completed = False\n else:\n all_titles_completed = False\n if all_projs_pending:\n title_sk = server.build_search_key('twog/title', prj.get('title_code'))\n if title.get('priority_triggers') != 'No' and title.get('status_triggers') != 'No':\n server.update(title_sk, {'status': '', 'bigboard': False})\n title_codes_completed = order.get('title_codes_completed')\n if title.get('code') in title_codes_completed:\n title_codes_completed = title_codes_completed.replace(',%s' % title.get('code'),'').replace('%s,' % title.get('code'),'').replace('%s' % title.get('code'),'')\n titles_completed = order.get('titles_completed')\n if titles_completed in [None,'']:\n titles_completed = 0\n else:\n titles_completed = int(titles_completed) - 1\n server.update(order.get('__search_key__'), {'titles_completed': titles_completed, 'title_codes_completed': title_codes_completed})\n title_updated = True\n all_titles_pending = True\n title = server.eval(\"@SOBJECT(twog/title['code','%s'])\" % prj.get('title_code'))\n if title and title_updated:\n title = title[0]\n other_titles = server.eval(\"@SOBJECT(twog/order['code','%s'].twog/title)\" % title.get('order_code'))\n for ot in other_titles:\n if title.get('code') != ot.get('code'):\n if ot.get('status') != '':\n all_titles_pending = False\n else:\n all_titles_pending = False\n if all_titles_pending:\n server.update(server.build_search_key('twog/order', title.get('order_code')), {'needs_completion_review': False}) \n if all_titles_completed:\n server.update(server.build_search_key('twog/order', title.get('order_code')), {'needs_completion_review': True}) \n \n #print \"LEAVING KICKOFF\"\n except AttributeError as e:\n traceback.print_exc()\n print str(e) + '\\nMost likely the server object does not exist.'\n raise e\n except KeyError as e:\n traceback.print_exc()\n print str(e) + '\\nMost likely the input dictionary does not exist.'\n raise e\n except Exception as e:\n traceback.print_exc()\n print str(e)\n raise e","title":""},{"docid":"fec6355e967e199b1e27d6e5414498ca","score":"0.42743507","text":"def _substitute(self, tpl):\n p = self.params\n # We do not yet know NFS_SERVER_IP_ADDRESS so leave it a template.\n # Same with DB_IDENTIFIER\n return tpl.render(CLUSTERNAME=p['kubernetes_cluster_name'],\n OAUTH_PROVIDER=self.encode_value(\n 'oauth_provider'),\n OAUTH_CLIENT_ID=self.encode_value(\n 'oauth_client_id'),\n OAUTH_SECRET=self.encode_value(\n 'oauth_secret'),\n OAUTH_CALLBACK_URL=self.encode_value(\n 'oauth_callback_url'),\n GITHUB_ORGANIZATION_WHITELIST=self.encode_value(\n 'github_organization_whitelist'),\n CILOGON_GROUP_WHITELIST=self.encode_value(\n 'cilogon_group_whitelist'),\n GITHUB_ORGANIZATION_DENYLIST=self.encode_value(\n 'github_organization_denylist'),\n CILOGON_GROUP_DENYLIST=self.encode_value(\n 'cilogon_group_denylist'),\n SESSION_DB_URL=self.encode_value(\n 'session_db_url'),\n JUPYTERHUB_CRYPTO_KEY=self.encode_value(\n 'crypto_key'),\n CONFIGPROXY_AUTH_TOKEN=self.encode_value(\n 'configproxy_auth_token'),\n CLUSTER_IDENTIFIER=p[\n 'kubernetes_cluster_namespace'],\n SHARED_VOLUME_SIZE=p[\n 'nfs_volume_size'],\n PHYSICAL_SHARED_VOLUME_SIZE=p[\n 'volume_size'],\n ROOT_CHAIN_PEM=self.encode_file('tls_root_chain'),\n DHPARAM_PEM=self.encode_value(\"dhparams\"),\n TLS_CRT=self.encode_file('tls_cert'),\n TLS_KEY=self.encode_file('tls_key'),\n HOSTNAME=p['hostname'],\n FQDN=p['hostname'],\n CA_CERTIFICATE=self.encode_file('beats_ca'),\n BEATS_CERTIFICATE=self.encode_file('beats_cert'),\n BEATS_KEY=self.encode_file('beats_key'),\n SHIPPER_NAME=p['log_shipper_name'],\n RABBITMQ_PAN_PASSWORD=self.encode_value(\n 'rabbitmq_pan_password'),\n RABBITMQ_TARGET_HOST=p['rabbitmq_target_host'],\n RABBITMQ_TARGET_VHOST=p['rabbitmq_target_vhost'],\n DEBUG=p['debug'],\n PREPULLER_IMAGE_LIST=p['prepuller_image_list'],\n PREPULLER_NO_SCAN=p['prepuller_no_scan'],\n PREPULLER_REPO=p['prepuller_repo'],\n PREPULLER_OWNER=p['prepuller_owner'],\n PREPULLER_IMAGE_NAME=p['prepuller_image_name'],\n PREPULLER_EXPERIMENTALS=p['prepuller_experimentals'],\n PREPULLER_DAILIES=p['prepuller_dailies'],\n PREPULLER_WEEKLIES=p['prepuller_weeklies'],\n PREPULLER_RELEASES=p['prepuller_releases'],\n PREPULLER_PORT=p['prepuller_port'],\n PREPULLER_SORT_FIELD=p['prepuller_sort_field'],\n PREPULLER_COMMAND=p['prepuller_command'],\n PREPULLER_NAMESPACE=p['prepuller_namespace'],\n PREPULLER_MINUTE=p['prepuller_minute'],\n LAB_REPO_HOST=p['lab_repo_host'],\n LAB_REPO_OWNER=p['lab_repo_owner'],\n LAB_REPO_NAME=p['lab_repo_name'],\n LAB_IMAGE=p['lab_image'],\n LAB_SELECTOR_TITLE=p['lab_selector_title'],\n LAB_IDLE_TIMEOUT=p['lab_idle_timeout'],\n LAB_MEM_LIMIT=p['lab_mem_limit'],\n LAB_CPU_LIMIT=p['lab_cpu_limit'],\n LAB_MEM_GUARANTEE=p['lab_mem_guarantee'],\n LAB_CPU_GUARANTEE=p['lab_cpu_guarantee'],\n LAB_NODEJS_MAX_MEM=p['lab_nodejs_max_mem'],\n TINY_MAX_CPU=p['tiny_max_cpu'],\n MB_PER_CPU=p['mb_per_cpu'],\n LAB_SIZE_RANGE=p['lab_size_range'],\n AUTO_REPO_URLS=p['auto_repo_urls'],\n ALLOW_DASK_SPAWN=p['allow_dask_spawn'],\n MAX_DASK_WORKERS=p['max_dask_workers'],\n SIZE_INDEX=p['size_index'],\n HUB_ROUTE=p['hub_route'],\n FIREFLY_ADMIN_PASSWORD=self.encode_value(\n 'firefly_admin_password'),\n FIREFLY_REPLICAS=p['firefly_replicas'],\n FIREFLY_CONTAINER_MEM_LIMIT=p[\n 'firefly_container_mem_limit'],\n FIREFLY_CONTAINER_CPU_LIMIT=p[\n 'firefly_container_cpu_limit'],\n FIREFLY_MAX_JVM_SIZE=p['firefly_max_jvm_size'],\n FIREFLY_UID=p['firefly_uid'],\n FIREFLY_ROUTE=p['firefly_route'],\n JS9_ROUTE=p['js9_route'],\n API_ROUTE=p['api_route'],\n TAP_ROUTE=p['tap_route'],\n SODA_ROUTE=p['soda_route'],\n EXTERNAL_INSTANCE_URL=p['external_instance_url'],\n EXTERNAL_FIREFLY_URL=p['external_firefly_url'],\n EXTERNAL_JS9_URL=p['external_js9_url'],\n EXTERNAL_API_URL=p['external_api_url'],\n EXTERNAL_TAP_URL=p['external_tap_url'],\n EXTERNAL_SODA_URL=p['external_soda_url'],\n EXTERNAL_URL=p['external_soda_url'],\n RESTRICT_DASK_NODES=p['restrict_dask_nodes'],\n RESTRICT_LAB_NODES=p['restrict_lab_nodes'],\n MAX_HTTP_HEADER_SIZE=p['max_http_header_size'],\n DB_IDENTIFIER='{{DB_IDENTIFIER}}',\n NFS_SERVER_IP_ADDRESS='{{NFS_SERVER_IP_ADDRESS}}',\n )","title":""},{"docid":"6c20f2bb9733fea4788200595a686f03","score":"0.4263419","text":"def tree(input_file, output_file):\n\n\ttree=\"muscle -maketree -in \"+input_file+\" -out \"+output_file+ \\\n\t\t\" -cluster neighborjoining \"+ \" 2>/dev/null \"\n\tos.system(tree)","title":""},{"docid":"f3eae7d3f04468a6936ad7e5af7cbbdf","score":"0.4260095","text":"def Initialise_plan():\n rp = rospkg.RosPack()\n pkg_path = rp.get_path('eyantra_task')\n arg_file_path = pkg_path + '/config/ur5_2_trajectories/'\n\n saved_plans_to_homes = { 'zero': {'home1': 'zero_to_home1.yaml' , \n 'home3':'zero_to_home3.yaml'},\n 'red' : {'home1': 'red_to_home1.yaml' , \n 'home2': 'red_to_home2.yaml' , \n 'home3' : 'red_to_home3.yaml'} ,\n 'yellow': {'home1': 'yellow_to_home1.yaml' , \n 'home2': 'yellow_to_home2.yaml' , \n 'home3' : 'yellow_to_home3.yaml'},\n 'green': { 'home1': 'green_to_home1.yaml' , \n 'home2': 'green_to_home2.yaml' , \n 'home3' : 'green_to_home3.yaml'}}\n\n saved_homes_to_bin = { 'home1' : { 'red' : 'home1_to_red.yaml' , \n 'yellow': 'home1_to_yellow.yaml' } ,\n 'home2' : { 'red' : 'home2_to_red.yaml' , \n 'yellow': 'home2_to_yellow.yaml' } ,\n 'home3' : { 'green' : 'home3_to_green.yaml' } }\n\n\n loaded_home = { 'zero': { 'home1': ' ' , 'home3': ' '} , \n 'red' : { 'home1': ' ' , 'home2': ' ' , 'home3': ' ' } ,\n 'yellow' : { 'home1': ' ' , 'home2': ' ' , 'home3': ' ' } ,\n 'green': { 'home1': ' ' , 'home2': ' ' , 'home3': ' ' } }\n\n loaded_pkg = { 'home1': {'red' : ' ' , 'green': ' ' } , 'home2':{'red':' ' , 'green': ' '} , 'home3': { 'green':' '} }\n\n saved_plans = [ saved_plans_to_homes , saved_homes_to_bin ]\n loaded_plans = [ loaded_home , loaded_pkg ]\n\n for i in range(len(saved_plans)):\n temp_keys = list(saved_plans[i].keys())\n for keys in temp_keys:\n temp_keys2 = list(saved_plans[i][keys].keys())\n for keys2 in temp_keys2:\n file_name = saved_plans[i][keys][keys2]\n file_path = arg_file_path + file_name\n with open(file_path, 'r') as file_open:\n loaded_plan = yaml.load(file_open)\n loaded_plans[i][keys][keys2] = loaded_plan\n\n rospy.loginfo(\"Loaded the plans....\")\n return loaded_plans","title":""},{"docid":"49162f00f04e935c6f5bf0caf8df634f","score":"0.42598808","text":"def generate(c):\n check_main_jsonnet_here()\n res = c.run(\"tk eval .\", hide=True)\n data = json.loads(res.stdout)\n for stack_name, files_cfg in data.items():\n for fname, content in files_cfg.items():\n path = DEPLOY_PATH / stack_name / fname\n path.parent.mkdir(parents=True, exist_ok=True)\n path.write_text(content, encoding=\"utf-8\")\n print(path)","title":""},{"docid":"d8e15e15a5bafb64267fd149eb21c64d","score":"0.42580685","text":"def _generateScripts(simconfig, curcomp):\n from supercomputer import JobConfig\n from datetime import time\n from os.path import join\n from shutil import copy\n #Configure job\n jconfig = JobConfig()\n jconfig.nodes = simconfig.node_count\n jconfig.mpn = simconfig.mpn\n jconfig.omp = simconfig.omp_thds\n jconfig.label = simconfig.run_label\n jconfig.time = time(4,0,0) \n jconfig.exe = simconfig.exe\n jconfig.inputs = simconfig.inputs\n\n #Make strings\n outpath = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Runs',\n simconfig.run_label, 'run')\n tpath = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Templates',\n 'titan.run.template')\n\n #Generate job script\n curcomp.generateJobScript(outpath, jconfig, tpath)\n\n #Copy process script over\n proc_temp = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Templates',\n 'process.titan.template')\n proc_dst = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Runs',\n simconfig.run_label, 'run', 'process.titan')\n copy(proc_temp, proc_dst)","title":""},{"docid":"b3c8eee520b6fd06d2613bbe4cf74624","score":"0.42566642","text":"def convert_from_backup(backup_file, output_file=None):\n if not os.path.exists(backup_file):\n raise ValueError(\"%s does not exists.\" % backup_file)\n\n tree = ET.parse(backup_file)\n root = tree.getroot()\n\n script_output = _generate_query(root)\n imports_str = _generate_imports(import_list)\n boilerplate_start_str = \"\"\"#!/usr/bin/env python\n\nfrom ucsmsdk.ucshandle import UcsHandle\n\nhandle = UcsHandle(ip=\"\", username=\"\", password=\"\")\nhandle.login()\n\n\"\"\"\n\n boilerplate_end_str = \"\"\"handle.logout()\"\"\"\n\n final_str = boilerplate_start_str + imports_str + script_output + boilerplate_end_str\n if output_file:\n _outfile = open(output_file, 'w')\n _outfile.write(final_str)\n _outfile.close()\n else:\n print(final_str)","title":""},{"docid":"24cd93a75cd645c67a51996d385bd54f","score":"0.42557374","text":"def main():\n parser = argparse.ArgumentParser(usage=\"python generate_scene_desc.py --pb=<path> --tmpl=<scene-template>\")\n parser.add_argument('--tmpl', type=str, help='Scene template json file.')\n parser.add_argument('--pb', type=str, help='Path to the protobuf file.')\n parser.add_argument('--out', type=str, required=True, help='Output directory')\n parser.add_argument('--data-root', type=str, default='./', help='Data root.')\n\n args = parser.parse_args()\n print(args)\n\n protobuf_path = os.path.expanduser(args.pb)\n data_root_path = os.path.expanduser(args.data_root)\n out_dir = os.path.expanduser(args.out)\n\n if not os.path.exists(out_dir):\n os.makedirs(out_dir)\n\n trajectories = sn.Trajectories()\n try:\n with open(protobuf_path, 'rb') as f:\n trajectories.ParseFromString(f.read())\n except IOError:\n print('Scenenet protobuf data not found at location:{0}'.format(data_root_path))\n\n print('Number of trajectories:{0}'.format(len(trajectories.trajectories)))\n\n for traj_no, traj in enumerate(trajectories.trajectories):\n layout_type = sn.SceneLayout.LayoutType.Name(traj.layout.layout_type)\n layout_path = traj.layout.model\n\n print('=' * 20)\n print('Render path:{0}'.format(traj.render_path))\n print('Layout type:{0} path:{1}'.format(layout_type, layout_path))\n print('=' * 20)\n obj_filename = 'trajectory_{}.obj'.format(traj_no)\n print(obj_filename)\n scene_desc = {\"file-format\": {\"type\": \"diffrend\", \"version\": \"0.2\"},\n \"glsl\": {\"vertex\": \"../shaders/phong/vs.glsl\", \"fragment\": \"../shaders/phong/fs.glsl\"},\n \"camera\": {\n \"proj_type\": \"perspective\",\n \"viewport\": [0, 0, 640, 480],\n \"fovy\": 1.04,\n \"focal_length\": 1.0,\n \"eye\": [3.5, 2.0, 3.5, 1.0],\n \"up\": [0.0, 1.0, 0.0, 0.0],\n \"at\": [0.0, 1.0, 0.0, 1.0],\n \"near\": 0.1, \"far\": 1000.0\n },\n \"lights\": {\"pos\": [],\n \"color_idx\": [],\n \"attenuation\": [],\n \"ambient\": [0.01, 0.01, 0.01]\n },\n \"colors\": [],\n \"materials\": {\n \"albedo\": [[0.0, 0.0, 0.0], [0.1, 0.1, 0.1], [0.2, 0.2, 0.2], [0.5, 0.5, 0.5],\n [0.8, 0.8, 0.8], [0.44, 0.55, 0.64]],\n \"coeffs\": [[1.0, 0.0, 0.0], [1.0, 0.0, 0.0], [1.0, 0.0, 0.0], [1.0, 0.0, 0.0],\n [1.0, 0.0, 0.0], [1.0, 0.0, 0.0]]\n },\n \"objects\": {\"obj\": [{\"path\": \"../objs/scenenetrgbd/\" + obj_filename, \"material_idx\": 4,\n \"scale\": [1.0, 1.0, 1.0]\n }\n ]\n },\n \"tonemap\": {\"type\": \"gamma\", \"gamma\": [1.0]}\n }\n for instance in traj.instances:\n if instance.instance_type == sn.Instance.LIGHT_OBJECT:\n light = instance.light_info\n light_type = sn.LightInfo.LightType.Name(light.light_type)\n light_intensity = [light.light_output.r, light.light_output.g, light.light_output.b]\n light_pos = [light.position.x, light.position.y, light.position.z]\n print('=' * 20)\n print('Light type:{0}'.format(light_type))\n print(light)\n print('emission: ', light_intensity)\n print('pos: ', light_pos)\n scene_desc[\"colors\"].append(light_intensity)\n scene_desc['lights'][\"color_idx\"].append(len(scene_desc[\"colors\"]) - 1)\n scene_desc['lights'][\"pos\"].append(light_pos)\n scene_desc['lights'][\"attenuation\"].append([0.0, 0.0, 0.4])\n\n print(scene_desc)\n outfilename = 'scenenet_{:04}.json'.format(traj_no)\n print(outfilename)\n with open(os.path.join(out_dir, outfilename), 'w') as fid:\n json.dump(scene_desc, fid)","title":""},{"docid":"b2bad0ed2603a1d075b46b3e09430b00","score":"0.42413738","text":"def write_tcl(options, A, B, pairs, tag=\"\", center=False):\n AA = deepcopy(A)\n BB = deepcopy(B)\n if pairs != None and len(pairs) > 0:\n for p in pairs:\n ia = p[0]\n ib = p[1]\n AA[ia].pos = p[3]\n BB[ib].pos = p[4]\n\n with open(\"POSCAR_A.%s\" % tag, \"w\") as f: pcwrite.poscar(AA, f, vasp5=True)\n with open(\"POSCAR_B.%s\" % tag, \"w\") as f: pcwrite.poscar(BB, f, vasp5=True)\n write_xyz(options, AA, \"A.%s\" % tag,options.output_tiles)\n write_xyz(options, BB, \"B.%s\" % tag,options.output_tiles)\n\n fout = file(\"plotpairs.%s.tcl\" % tag, \"w\")\n write_struct(fout, AA, \"A.%s.xyz\" % tag, 0, center)\n write_struct(fout, BB, \"B.%s.xyz\" %tag, 1, center)\n\n linestr = \"draw color green; graphics top line {%f %f %f} {%f %f %f} width 3 style dashed\\n\"\n# sum = 0\n for p in pairs:\n print p\n ia = p[0]\n ib = p[1]\n p1 = AA.scale * AA[ia].pos\n p2 = BB.scale * BB[ib].pos\n## double check! print \"TCL types: %s %s\" % (A[ia].type, B[ib].type)\n# val = npl.norm(np.array(p1)-np.array(p2))\n# sum += val\n# print \"write_tcl pairing: \", p1, p2, val\n fout.write(linestr % (p1[0], p1[1], p1[2], p2[0], p2[1], p2[2]))\n# print \"write_tcl total pairing dist: \", sum\n\n fout.close()","title":""},{"docid":"61d95e842da2c0d243564c422879ccf9","score":"0.4233163","text":"def create_topology(nc, num):\n rolls = ['Input', 'Output']\n for i in range(num):\n NEAT.GeneticOperation.add_hidden(nc, rolls[np.random.randint(0, 2)])","title":""},{"docid":"187e0437b755b557a9ee96c680de76da","score":"0.42321697","text":"def generate_test_topology():\n config = FBOSSTestTopology(\"rsw1aj.20.snc1\")\n\n \"\"\" This example has two testing hosts \"\"\"\n f154 = TestHost(\"fboss154.20.snc1\")\n f154.add_interface(\"eth0\", \"2401:db00:111:400a::154\")\n config.add_host(f154)\n\n f155 = TestHost(\"fboss155.20.snc1\")\n f155.add_interface(\"eth0\", \"2401:db00:111:400a::155\")\n config.add_host(f155)\n return config","title":""},{"docid":"cd1168a769b7395c168ad1580cb3e005","score":"0.4226332","text":"def build_tree(script):\n return ast.parse(script,mode=\"exec\")","title":""},{"docid":"4c3b837db91a3e356985d1b8d94ed16d","score":"0.4226303","text":"def notify_topology_update(self):\n name = 'kytos/topology.updated'\n event = KytosEvent(name=name, content={'topology': self.topology})\n self.controller.buffers.app.put(event)","title":""},{"docid":"2c4bdaac2dae6e2288734e103617b789","score":"0.422377","text":"def _build_network_definition_using_topology(self):\n log.debug(\"%r: _build_network_definition_using_topology: %s\",\n self._platform_id, self._topology)\n\n def build(platform_id, children):\n \"\"\"\n Returns the root NNode for the given platform_id with its\n children according to the given list.\n \"\"\"\n nnode = NNode(platform_id)\n if self._agent_device_map:\n self._set_attributes_and_ports_from_agent_device_map(nnode)\n\n log.debug('Created NNode for %r', platform_id)\n\n for subplatform_id in children:\n subplatform_children = self._topology.get(subplatform_id, [])\n sub_nnode = build(subplatform_id, subplatform_children)\n nnode.add_subplatform(sub_nnode)\n\n return nnode\n\n children = self._topology.get(self._platform_id, [])\n return build(self._platform_id, children)","title":""},{"docid":"9e8f498922156d0d2c106d55511a4497","score":"0.4215529","text":"def do_topo_subgraph(self, args):\n\n # logger.info(\"Topo initialized with sec IDs %s\" % sec_ids)\n\n # delimiter of node in dot file\n delim_node = '_'\n\n args_cleaned = re.sub(r\"\\s+\", ' ', args).strip()\n # Show subgraphs if given no argments\n if (args_cleaned == ''):\n if len(self.spp_topo.subgraphs) == 0:\n print(\"No subgraph.\")\n else:\n for label, subg in self.spp_topo.subgraphs.items():\n print('label: %s\\tsubgraph: \"%s\"' % (label, subg))\n else: # add or del\n tokens = args_cleaned.split(' ')\n # Add subgraph\n if tokens[0] == 'add':\n if len(tokens) == 3:\n label = tokens[1]\n subg = tokens[2]\n if ',' in subg:\n subg = re.sub(r'%s' % delim_node, ':', subg)\n subg = re.sub(r\",\", \";\", subg)\n\n # TODO(yasufum) add validation for subgraph\n self.spp_topo.subgraphs[label] = subg\n print(\"Add subgraph '%s'\" % label)\n else:\n print(\"Invalid syntax '%s'!\" % args_cleaned)\n # Delete subgraph\n elif ((tokens[0] == 'del') or\n (tokens[0] == 'delete') or\n (tokens[0] == 'remove')):\n del(self.spp_topo.subgraphs[tokens[1]])\n print(\"Delete subgraph '%s'\" % tokens[1])\n\n else:\n print(\"Ivalid subcommand '%s'!\" % tokens[0])","title":""},{"docid":"d4d7ab3893642a4825fc02c383858933","score":"0.4205685","text":"def get_topology(self):\n return json.dumps(self.topology.to_json())","title":""},{"docid":"b139a4ba887e5b907890a7a41d3efc90","score":"0.4204748","text":"def main():\n args = prepare_argparser(allowed_formats)\n inp_format, inp_loc = filter_dict(\"input\", args.__dict__)\n out_format, out_loc = filter_dict(\"output\", args.__dict__)\n output_nodes = args.output_nodes.split(',')\n convert(inp_format, inp_loc, out_format, out_loc, output_nodes,\n args.ngbackend)\n print('Converted the model. Exiting now')","title":""},{"docid":"5ff53d6dca043bfefcbf28508ebded68","score":"0.42006487","text":"def create_vm(self, name, conn, template):\n xml = \"\"\n tree = ElementTree.parse(template)\n root = tree.getroot()\n tree.find('.//name').text = name\n xml = ElementTree.tostring(root, encoding='utf8', method='xml')\n vm = conn.defineXML(xml)\n return vm","title":""},{"docid":"71eb8d4147fea5ad60498aab0fff6c5c","score":"0.4198723","text":"def generate(self, namespace):","title":""},{"docid":"c875bd57eca71458b3ba5dcaa68f9c42","score":"0.4195698","text":"def create_root(parser: ConfigParser):\n slsoptions = parser[\"sls\"]\n\n sls_source = slsoptions[\"sls_source\"]\n mount_point = Path(slsoptions[\"mount_point\"])\n disk_path = slsoptions[\"disk_path\"]\n tarfile = slsoptions[\"tarfile\"]\n newfs = str(Path(sls_source, \"tools\", \"newfs_sls\", \"newfs_sls\"))\n\n bashcmd([newfs, disk_path])\n bashcmd([\"mount\", \"-t\", \"slsfs\", disk_path, str(mount_point)])\n bashcmd([\"tar\", \"-xf\", tarfile, \"-C\", mount_point])\n bashcmd([\"mount\", \"-t\", \"devfs\", \"devfs\", str(mount_point / \"dev\")])","title":""},{"docid":"0d4138f4f892dfe00691614705265d02","score":"0.41942257","text":"def j2template (self, template_file):\n expansion = {}\n for host in self.hosts():\n expansion[host] = ansible.utils.template.template_from_file (\n None,\n template_file,\n self.hostvars(host)\n )\n return expansion","title":""},{"docid":"c9b4f1364a1f9d99f912b951ca1bfc8f","score":"0.41930586","text":"def CASE102( self, main ):\n main.HA.startTopology( main )","title":""},{"docid":"9975a53c433dd8b35f917e1896375275","score":"0.4192809","text":"def __init__(self,itpname,groname):\n Topology.__init__(self)\n fid = open(itpname)\n topology = fid.readlines()\n fid.close()\n fid = open(groname)\n grofile = fid.readlines()\n grodata = grofile[2:(len(grofile)-1)]\n box = grofile[len(grofile)-1].split()\n self.box = [float(box[0]),float(box[1]),float(box[2])]\n fid.close()\n flag = 'notes'\n for line in topology:\n if line.strip() == '[ moleculetype ]':\n flag = 'moltype'\n continue\n if line.strip() == '[ atoms ]':\n flag = 'atoms'\n continue\n if line.strip() == '[ bonds ]':\n flag = 'bonds'\n continue\n if line.strip() == '[ constraints ]':\n flag = 'cons'\n continue\n if line.strip() == '[ angles ]':\n flag = 'angles'\n continue\n if line.strip() == '[ dihedrals ]':\n flag = 'dihs'\n continue\n if flag == 'notes':\n self.title += line\n else:\n if line[0] == ';' or line == '\\n' or line[0] == '#':\n continue\n else:\n spline = line.split()\n if flag == 'moltype':\n self.moltype[0] = spline[0]\n self.moltype[1] = int(spline[1])\n elif flag == 'atoms':\n resNo = int(spline[2])\n resname = spline[3]\n beadname = spline[4]\n beadno = int(spline[0])\n beadtype = spline[1]\n gline = grodata[beadno-1]\n spgline = gline.split()\n pos = np.array([float(spgline[3]),float(spgline[4]),\n float(spgline[5])])\n vel = np.array([0.,0.,0.])\n A = Bead(resNo,resname,beadname,beadno,pos,vel,\n beadtype)\n self.atomlist.append(A)\n elif flag == 'bonds':\n try:\n (beads,params,notes) = self.__bangle__(spline,2)\n except:\n pdb.set_trace()\n B = Bond(beads,params,notes)\n self.bondlist.append(B)\n elif flag == 'cons':\n (beads,params,notes) = self.__bangle__(spline,2)\n C = Bond(beads,params,notes)\n self.conlist.append(C)\n elif flag == 'angles':\n (beads,params,notes) = self.__bangle__(spline,3)\n A = Angle(beads,params,notes)\n self.anglist.append(A)\n elif flag == 'dihs':\n (beads,params,notes) = self.__bangle__(spline,4)\n D = Dihedral(beads,params,notes)\n self.dihlist.append(D)","title":""},{"docid":"61a9f042ff6c709c4f5d9380a9ec8f6a","score":"0.4184373","text":"def transform_object(obj_name, t=None, r=None, s=None):\n t = \"\" if t is None else (\"T=(%f %f %f)\" % (t[0], t[1], t[2]))\n r = \"\" if r is None else (\"R=(%f %f %f)\" % (r[0], r[1], r[2]))\n s = \"\" if s is None else (\"S=(%f %f %f)\" % (s[0], s[1], s[2]))\n msg = (\"TransformObject {name} {t} {r} {s}\"\n .format(name=obj_name, t=t, r=r, s=s))\n connection.send_message(msg)","title":""},{"docid":"e9ef855f1ce211e92cd665286f68e7b3","score":"0.41660273","text":"def main():\n args = prepare_argparser(allowed_formats)\n inp_format, inp_loc = filter_dict(\"input\", args.__dict__)\n out_format, out_loc = filter_dict(\"output\", args.__dict__)\n output_nodes = args.output_nodes.split(',')\n extra_params = parse_extra_params_string(args.extra_params)\n convert(inp_format, inp_loc, out_format, out_loc, output_nodes,\n args.ng_backend, args.device_id, extra_params)\n print('Converted the model. Exiting now')","title":""},{"docid":"c12e7aa9a130cfe410569818f50af000","score":"0.41633734","text":"def t2_mapping(filename):\n\n scan_key = filename.split('-')[0]\n mkdir(scan_key)\n command = 'mv {}-* {};' \\\n 'cd {};' \\\n 'fslsplit {}-5T2s echo -t;' \\\n ''.format(scan_key, scan_key, scan_key, scan_key)\n for i in range(1,5):\n command += f'fslmaths echo{i:04} -add 0 echo{(i-1):04} -odt float;'\n command += 'gunzip echo0*;' \\\n 't2_map_mask_float 4 40 60 80 100;' \\\n 'gzip chi2.nii mask.nii t2vol.nii pdw.nii;' \\\n 'bet echo0000 brain -S;' \\\n 'fslmaths brain_mask -fillh26 brain_mask -odt char;' \\\n 'fslmaths t2vol -mas brain_mask -abs t2vol1;' \\\n 'fslmaths t2vol1 -thr 250 -bin mask250;' \\\n 'fslmaths brain_mask -sub mask250 -ero chi2mask -odt char;' \\\n 'rm echo0* brain.nii.gz brain_skull.nii.gz mask250.nii.gz t2vol1.nii.gz;' \\\n 'mv chi2.nii.gz ../../QA/chi2/{}-chi2.nii.gz;' \\\n 'mv mask.nii.gz ../../t2s/masks/{}-headmask.nii.gz;' \\\n 'mv t2vol.nii.gz ../../t2s/raw/{}-rawT2.nii.gz;' \\\n 'mv brain_mask.nii.gz ../../t2s/masks/{}-brainmask.nii.gz;' \\\n 'mv chi2mask.nii.gz ../../QA/chi2/{}-chi2mask.nii.gz;' \\\n 'mv pdw.nii.gz ../../pdw/raw/{}-rawPDw.nii.gz;' \\\n 'fslmaths ../../pdw/raw/{}-rawPDw -mas ../../t2s/masks/{}-headmask ../../pdw/{}-pdw;' \\\n 'fslmaths ../../t2s/raw/{}-rawT2 -mas ../../t2s/masks/{}-headmask -thr 0 ../../t2s/{}-t2;' \\\n 'mv *-5T2s.nii.gz ..;' \\\n 'cd ..;' \\\n 'rmdir {}' \\\n ''.format(scan_key, scan_key, scan_key, scan_key, scan_key,\n scan_key, scan_key, scan_key, scan_key, scan_key,\n scan_key, scan_key, scan_key)\n\n result = run(command, stdout=PIPE, stderr=STDOUT, check=True, shell=True)\n if result.stdout.decode('utf-8') != '':\n with open(f'{scan_key}.log', 'wb') as logfile:\n logfile.write(result.stdout)\n return","title":""},{"docid":"eda91de728bf59c213eaef2fd349e3d3","score":"0.4162229","text":"def save_topology(name):\n if not request.is_json:\n return json.dumps('{\"error\": \"gt was not a JSON request\"}'), 400\n topology = request.get_json()\n with open(join(settings.TOPOLOGY_DIR, name + '.json'), 'w') as outfile:\n json.dump(topology, outfile)\n return json.dumps({'response': 'Saved'}), 201","title":""},{"docid":"0ab9b4eee4d0582845eaacf04541b042","score":"0.41589352","text":"def write_bd_tcl(tclFileName, force=False):\n params = []\n if force:\n params.append('-force')\n return \"write_bd_tcl %s %s\" % (' '.join(params), tclFileName)","title":""},{"docid":"00e4e791123a8163d4e227767c7283d2","score":"0.41488856","text":"def setrun(claw_pkg='geoclaw'):\n#------------------------------\n\n from clawpack.clawutil import data\n\n assert claw_pkg.lower() == 'geoclaw', \"Expected claw_pkg = 'geoclaw'\"\n\n num_dim = 2\n\n rundata = data.ClawRunData(claw_pkg, num_dim)\n\n topofile = 'topos/TetonLarge.topo'\n\n #------------------------------------------------------------------\n # GeoClaw specific parameters:\n #------------------------------------------------------------------\n rundata = setgeo(rundata)\n\n #------------------------------------------------------------------\n # Standard Clawpack parameters to be written to claw.data:\n # (or to amr2ez.data for AMR)\n #------------------------------------------------------------------\n clawdata = rundata.clawdata # initialized when rundata instantiated\n\n\n # Set single grid parameters first.\n # See below for AMR parameters.\n\n\n # ---------------\n # Spatial domain:\n # ---------------\n\n # Number of space dimensions:\n clawdata.num_dim = num_dim\n\n m_topo,n_topo,xllcorner,yllcorner,cellsize = tools.read_topo_data(topofile)\n\n\n # Topo info (TetonDamLatLong.topo)\n # m_topo = 4180\n # n_topo = 1464\n # xllcorner = -112.390734400000\n # yllcorner = 43.581746970335\n # cellsize = 0.000277729665\n\n # Derived info from the topo map\n mx_topo = m_topo - 1\n my_topo = n_topo - 1\n xurcorner = xllcorner + cellsize*mx_topo\n yurcorner = yllcorner + cellsize*my_topo\n ll_topo = np.array([xllcorner, yllcorner])\n ur_topo = np.array([xurcorner, yurcorner])\n\n print(\"\")\n print(\"Topo domain\")\n print(\"%-12s (%14.8f, %12.8f)\" % (\"Lower left\",ll_topo[0],ll_topo[1]))\n print(\"%-12s (%14.8f, %12.8f)\" % (\"Upper right\",ur_topo[0],ur_topo[1]))\n print(\"\")\n\n dims_topo = ur_topo - ll_topo\n\n # Try to match aspect ratio of topo map\n clawdata.num_cells[0] = 54\n clawdata.num_cells[1] = 54\n\n dim_topo = ur_topo - ll_topo\n mdpt_topo = ll_topo + 0.5*dim_topo\n\n dim_comp = 0.975*dim_topo # Shrink domain inside of given bathymetry.\n\n clawdata.lower[0] = mdpt_topo[0] - dim_comp[0]/2.0\n clawdata.upper[0] = mdpt_topo[0] + dim_comp[0]/2.0\n\n clawdata.lower[1] = mdpt_topo[1] - dim_comp[1]/2.0\n clawdata.upper[1] = mdpt_topo[1] + dim_comp[1]/2.0\n\n print(\"\")\n print(\"Computational domain\")\n print(\"%-12s (%14.8f, %12.8f)\" % (\"Lower left\",clawdata.lower[0],clawdata.lower[1]))\n print(\"%-12s (%14.8f, %12.8f)\" % (\"Upper right\",clawdata.upper[0],clawdata.upper[1]))\n print(\"\")\n\n print(\"Approximate aspect ratio : {0:16.8f}\".format(float(clawdata.num_cells[0])/clawdata.num_cells[1]))\n print(\"Actual aspect ratio : {0:16.8f}\".format(dims_topo[0]/dims_topo[1]))\n\n # print \"[{0:20.12f},{1:20.12f}]\".format(*clawdata.lower)\n # print \"[{0:20.12f},{1:20.12f}]\".format(*clawdata.upper)\n\n dims_computed = np.array([clawdata.upper[0]-clawdata.lower[0], clawdata.upper[1]-clawdata.lower[1]])\n print(\"Computed aspect ratio : {0:20.12f}\".format(dims_computed[0]/dims_computed[1]))\n\n print(\"\")\n print(\"Details in km : \") \n\n lon = np.array([clawdata.lower[0],clawdata.upper[0]])\n lat = np.array([clawdata.lower[1],clawdata.upper[1]])\n d = tools.compute_distances(lon,lat)\n \n # ---------------\n # Size of system:\n # ---------------\n\n # Number of equations in the system:\n clawdata.num_eqn = 3\n\n # Number of auxiliary variables in the aux array (initialized in setaux)\n clawdata.num_aux = 3\n\n # Index of aux array corresponding to capacity function, if there is one:\n clawdata.capa_index = 2\n\n # -------------\n # Initial time:\n # -------------\n\n clawdata.t0 = 0.0\n\n\n # Restart from checkpoint file of a previous run?\n # Note: If restarting, you must also change the Makefile to set:\n # RESTART = True\n # If restarting, t0 above should be from original run, and the\n # restart_file 'fort.chkNNNNN' specified below should be in\n # the OUTDIR indicated in Makefile.\n\n clawdata.restart = False # True to restart from prior results\n clawdata.restart_file = 'fort.chk00006' # File to use for restart data\n\n # -------------\n # Output times:\n #--------------\n\n # Specify at what times the results should be written to fort.q files.\n # Note that the time integration stops after the final output time.\n # The solution at initial time t0 is always written in addition.\n\n clawdata.output_style = 1\n\n if clawdata.output_style == 1:\n # Output nout frames at equally spaced times up to tfinal:\n n_hours = 2.0\n frames_per_minute = 60.0/5.0 # Frames every 5 seconds\n clawdata.num_output_times = int(frames_per_minute*60*n_hours) # Plot every 10 seconds\n clawdata.tfinal = 60*60*n_hours\n clawdata.output_t0 = True # output at initial (or restart) time?\n\n elif clawdata.output_style == 2:\n # Specify a list of output times.\n clawdata.output_times = [0.5, 1.0]\n\n elif clawdata.output_style == 3:\n # Output every iout timesteps with a total of ntot time steps:\n clawdata.output_step_interval = 1\n clawdata.total_steps = 1\n clawdata.output_t0 = True\n\n\n clawdata.output_format = 'ascii' # 'ascii' or 'netcdf'\n\n clawdata.output_q_components = 'all' # could be list such as [True,True]\n clawdata.output_aux_components = 'none' # could be list\n clawdata.output_aux_onlyonce = True # output aux arrays only at t0\n\n\n\n # ---------------------------------------------------\n # Verbosity of messages to screen during integration:\n # ---------------------------------------------------\n\n # The current t, dt, and cfl will be printed every time step\n # at AMR levels <= verbosity. Set verbosity = 0 for no printing.\n # (E.g. verbosity == 2 means print only on levels 1 and 2.)\n clawdata.verbosity = 1\n\n\n\n # --------------\n # Time stepping:\n # --------------\n\n # if dt_variable==1: variable time steps used based on cfl_desired,\n # if dt_variable==0: fixed time steps dt = dt_initial will always be used.\n clawdata.dt_variable = True\n\n # Initial time step for variable dt.\n # If dt_variable==0 then dt=dt_initial for all steps:\n clawdata.dt_initial = 0.0001\n\n # Max time step to be allowed if variable dt used:\n clawdata.dt_max = 1e+99\n\n # Desired Courant number if variable dt used, and max to allow without\n # retaking step with a smaller dt:\n clawdata.cfl_desired = 0.75\n clawdata.cfl_max = 1.0\n\n # Maximum number of time steps to allow between output times:\n clawdata.steps_max = 5000\n\n\n\n\n # ------------------\n # Method to be used:\n # ------------------\n\n # Order of accuracy: 1 => Godunov, 2 => Lax-Wendroff plus limiters\n clawdata.order = 2\n\n # Use dimensional splitting? (not yet available for AMR)\n clawdata.dimensional_split = 'unsplit'\n\n # For unsplit method, transverse_waves can be\n # 0 or 'none' ==> donor cell (only normal solver used)\n # 1 or 'increment' ==> corner transport of waves\n # 2 or 'all' ==> corner transport of 2nd order corrections too\n clawdata.transverse_waves = 2\n\n # Number of waves in the Riemann solution:\n clawdata.num_waves = 3\n\n # List of limiters to use for each wave family:\n # Required: len(limiter) == num_waves\n # Some options:\n # 0 or 'none' ==> no limiter (Lax-Wendroff)\n # 1 or 'minmod' ==> minmod\n # 2 or 'superbee' ==> superbee\n # 3 or 'mc' ==> MC limiter\n # 4 or 'vanleer' ==> van Leer\n clawdata.limiter = ['mc', 'mc', 'mc']\n\n clawdata.use_fwaves = True # True ==> use f-wave version of algorithms\n\n # Source terms splitting:\n # src_split == 0 or 'none' ==> no source term (src routine never called)\n # src_split == 1 or 'godunov' ==> Godunov (1st order) splitting used,\n # src_split == 2 or 'strang' ==> Strang (2nd order) splitting used, not recommended.\n clawdata.source_split = 'godunov'\n\n\n # --------------------\n # Boundary conditions:\n # --------------------\n\n # Number of ghost cells (usually 2)\n clawdata.num_ghost = 2\n\n # Choice of BCs at xlower and xupper:\n # 0 => user specified (must modify bcN.f to use this option)\n # 1 => extrapolation (non-reflecting outflow)\n # 2 => periodic (must specify this at both boundaries)\n # 3 => solid wall for systems where q(2) is normal velocity\n\n clawdata.bc_lower[0] = 'extrap'\n clawdata.bc_upper[0] = 'extrap'\n\n clawdata.bc_lower[1] = 'extrap'\n clawdata.bc_upper[1] = 'extrap'\n\n # Specify when checkpoint files should be created that can be\n # used to restart a computation.\n\n clawdata.checkpt_style = 0\n\n if clawdata.checkpt_style == 0:\n # Do not checkpoint at all\n pass\n\n elif clawdata.checkpt_style == 1:\n # Checkpoint only at tfinal.\n pass\n\n elif clawdata.checkpt_style == 2:\n # Specify a list of checkpoint times.\n clawdata.checkpt_times = [0.1,0.15]\n\n elif clawdata.checkpt_style == 3:\n # Checkpoint every checkpt_interval timesteps (on Level 1)\n # and at the final time.\n clawdata.checkpt_interval = 5\n\n\n # -----------------------------------------------\n # AMR parameters:\n # -----------------------------------------------\n amrdata = rundata.amrdata\n\n maxlevel = 7\n\n amrdata.amr_levels_max = maxlevel # Set to 3 for best results\n amrdata.refinement_ratios_x = [2]*7\n amrdata.refinement_ratios_y = [2]*7\n amrdata.refinement_ratios_t = [2]*7\n # rundata.tol = -1\n # rundata.tolsp = 0.001\n\n # Specify type of each aux variable in amrdata.auxtype.\n # This must be a list of length maux, each element of which is one of:\n # 'center', 'capacity', 'xleft', or 'yleft' (see documentation).\n\n amrdata.aux_type = ['center','capacity','yleft']\n\n\n # Flag using refinement routine flag2refine rather than richardson error\n amrdata.flag_richardson = False # use Richardson?\n amrdata.flag2refine = True\n amrdata.regrid_interval = 3\n amrdata.regrid_buffer_width = 2\n amrdata.clustering_cutoff = 0.700000\n amrdata.verbosity_regrid = 0\n\n # -----------------------------------------------\n # INL Regions\n # Regions to be refined :\n # (1) Refine initial reservoir to level 4\n # (otherwise, we won't resolve valley, and\n # won't fill the reservoir properly)\n # (2) Refine around nuclear power plant (indicated by gauge\n # 100, 101, ..., 115, below)\n # (3) Computational domain, with maxlevel=4\n #\n # To specify regions of refinement append lines of the form\n # regions.append([minlevel,maxlevel,t1,t2,x1,x2,y1,y2])\n\n # -----------------------------------------------\n regions = rundata.regiondata.regions\n\n # Region containing initial reservoir\n regions.append([maxlevel,maxlevel, 0, 1.e10,-111.543,-111.24,43.88, 43.965])\n\n # Box containing gauge location locations\n\n# xll = [-111.64, 43.913661] # From email\n# xur = [-111.60, 43.92] # from email\n# region_lower, region_upper,_ = tools.region_coords(xll,xur,\n# clawdata.num_cells,\n# clawdata.lower,\n# clawdata.upper)\n#\n# regions.append([maxlevel,maxlevel,0, 1e10, region_lower[0],region_upper[0],\n# region_lower[1],region_upper[1]])\n\n # Computational domain. With exception of region above, don't go beyond level 4\n regions.append([0,maxlevel-1,0, 1e10, clawdata.lower[0],clawdata.upper[0],\n clawdata.lower[1],clawdata.upper[1]])\n\n # -------------------------------------------------------\n # INL Gauges\n # -- Set gauges at Teton City and Wilford\n # -- Remaining gauges build border around power plant\n #\n # For gauges append lines of the form [gaugeno, x, y, t1, t2]\n # -------------------------------------------------------\n\n # Wilford\n xc,yc = [-111.672222,43.914444]\n rundata.gaugedata.gauges.append([1,xc,yc,0.,clawdata.tfinal]) # Wilford\n\n # Teton City\n xc,yc = [-111.669167,43.887778]\n rundata.gaugedata.gauges.append([2,xc,yc,0.,clawdata.tfinal]) # Teton City\n\n # Power plant, with border constructed of 4*m gauges\n # Start at SW corner; build gauges in counter-clockwise order in a\n # square around the region [xll,xur].\n\n# m = 2 # Gauge spacing along one edge (m=4 --> edge divided into four sections)\n# gauge_counter = 100\n#\n# # South West corner of power plant\n# xll = [-111.623926, 43.913661] # From email\n#\n# # North East corner of power plant\n# xur = [-111.620150, 43.916382] # from email\n#\n# s = np.linspace(0,1.,m+1)\n# for i in range(0,m):\n# x = xll[0] + (xur[0] - xll[0])*s[i]\n# rundata.gaugedata.gauges.append([gauge_counter,x,xll[1],0.,clawdata.tfinal])\n# gauge_counter = gauge_counter + 1\n#\n# for i in range(0,m):\n# y = xll[1] + (xur[1] - xll[1])*s[i]\n# rundata.gaugedata.gauges.append([gauge_counter,xur[0],y,0.,clawdata.tfinal])\n# gauge_counter = gauge_counter + 1\n#\n# for i in range(0,m):\n# x = xur[0] + (xll[0] - xur[0])*s[i]\n# rundata.gaugedata.gauges.append([gauge_counter,x,xur[1],0.,clawdata.tfinal])\n# gauge_counter = gauge_counter + 1\n#\n# for i in range(0,m):\n# y = xur[1] + (xll[1] - xur[1])*s[i]\n# rundata.gaugedata.gauges.append([gauge_counter,xll[0],y,0.,clawdata.tfinal])\n# gauge_counter = gauge_counter + 1\n\n\n # -------------------------------------------------------\n # For developers\n # -- Toggle debugging print statements:\n # -------------------------------------------------------\n amrdata.dprint = False # print domain flags\n amrdata.eprint = False # print err est flags\n amrdata.edebug = False # even more err est flags\n amrdata.gprint = False # grid bisection/clustering\n amrdata.nprint = False # proper nesting output\n amrdata.pprint = False # proj. of tagged points\n amrdata.rprint = False # print regridding summary\n amrdata.sprint = False # space/memory output\n amrdata.tprint = True # time step reporting each level\n amrdata.uprint = False # update/upbnd reporting\n\n\n return rundata\n # end of function setrun\n # ----------------------","title":""},{"docid":"0522bf7c5e5b77e0cef1cfb67b4a936a","score":"0.4146013","text":"def script(self):\n text = []\n\n # Load waveforms\n if self.waveforms:\n par_string = \" \".join([f\"constants.{wfm.name}={wfm.name}\" for wfm in self.waveforms])\n text.append(\n f\".exec\\n\"\n f\"load {self.wfm_file}\\n\"\n f\"let constants.tm=tm {par_string}\\n\"\n f\".endc\"\n )\n\n # Declare all subcircuits\n for name,subckt in self.subcircuits.items():\n text.append(subckt.script())\n # Declare models\n for name, model in self.models.items():\n line = [\".model \", name, ' ',model[\"type\"],'(']\n pams = []\n for k,v in model[\"params\"].items():\n pams.append(\"%s=%s\" %(str(k),str(v)))\n line.append(','.join(pams))\n line.append(')')\n text.append(''.join(line))\n # Declare components\n for k,comp in self.components.items():\n text.append(comp.script())\n # Extra scripts\n if len(self._extrascript)>0:\n text.append(self._extrascript)\n return \"\\n\".join(text)","title":""},{"docid":"5c5dea2a65f2331c88844a437b30ccfb","score":"0.4136075","text":"def tasks_merge_cluster_and_release(self):\n self.show_step(1) # Revert snapshot\n self.env.revert_snapshot('pre_provision_ubuntu_slaves_3')\n graph_type = 'custom_graph'\n\n self.show_step(2) # Create cluster\n cluster_id = self.fuel_web.create_cluster(name=self.__class__.__name__)\n rel_id = self.fuel_web.get_cluster_release_id(cluster_id)\n self.move_ubuntu_target_image(rel_id, cluster_id)\n\n self.show_step(3) # Upload 'custom_graph' tasks to release\n rel_tasks_filename = 'release_custom_tasks.yaml'\n local_tasks_file = os.path.join(os.path.dirname(fuelweb_test.__file__),\n 'config_templates',\n rel_tasks_filename)\n with open(local_tasks_file, 'r') as yaml_file:\n release_tasks_yaml_data = yaml.load(yaml_file)\n upload_tasks_path = '/tmp/{}'.format(rel_tasks_filename)\n\n self.ssh_manager.upload_to_remote(\n ip=self.ssh_manager.admin_ip,\n source=local_tasks_file,\n target=upload_tasks_path)\n upload_tasks_cmd = 'fuel2 graph upload -e {cluster_id} -t ' \\\n '{graph_type} -f {path}'.format(\n cluster_id=cluster_id,\n graph_type=graph_type,\n path=upload_tasks_path\n )\n self.ssh_manager.execute_on_remote(\n ip=self.ssh_manager.admin_ip,\n cmd=upload_tasks_cmd)\n\n self.show_step(4) # Upload 'custom_graph' tasks to cluster\n c_tasks_filename = 'custom_graph_tasks.yaml'\n local_tasks_file = os.path.join(os.path.dirname(fuelweb_test.__file__),\n 'config_templates',\n c_tasks_filename)\n with open(local_tasks_file, 'r') as yaml_file:\n cluster_tasks_yaml_data = yaml.load(yaml_file)\n upload_tasks_path = '/tmp/{}'.format(rel_tasks_filename)\n\n self.ssh_manager.upload_to_remote(\n ip=self.ssh_manager.admin_ip,\n source=local_tasks_file,\n target=upload_tasks_path)\n upload_tasks_cmd = 'fuel2 graph upload -e {cluster_id} -t ' \\\n '{graph_type} -f {path}'.format(\n cluster_id=cluster_id,\n graph_type=graph_type,\n path=upload_tasks_path\n )\n self.ssh_manager.execute_on_remote(\n ip=self.ssh_manager.admin_ip,\n cmd=upload_tasks_cmd)\n\n self.show_step(5) # Download 'custom_graph' deployment graph\n custom_tasks = \\\n self.fuel_web.client.get_custom_cluster_deployment_tasks(\n cluster_id,\n graph_type)\n\n self.show_step(6) # 'custom_graph' is a merge of release and cluster.\n generated_names = set([t['task_name'] for t in custom_tasks])\n uploaded_names = set(\n [t['id'] for t in release_tasks_yaml_data] +\n [t['id'] for t in cluster_tasks_yaml_data])\n diff = generated_names - uploaded_names\n assert_equal(diff, set([]), 'Tasks are not result of merge!')\n\n self.show_step(7) # Create snapshot 'tasks_diff'\n self.env.make_snapshot('tasks_diff')","title":""},{"docid":"bc5b467462eca59fc358e848492095e1","score":"0.41351083","text":"def config_composer(self, model, hostname, junos_on_box_version):\n if (\"SRX3\" in model) or (\"vsrx\" in model):\n template_filename = \"SRX_template.j2\"\n network_parameter_filename = \"SRX_networkParameters.yaml\"\n elif \"EX\" in model:\n template_filename = \"EX_template.j2\"\n network_parameter_filename = \"EX_networkParameters.yaml\"\n complete_path = os.path.join(os.getcwd(), 'Config')\n ENV = jinja2.Environment(loader=jinja2.FileSystemLoader(complete_path))\n template = ENV.get_template(template_filename)\n with open(complete_path + \"/\" + network_parameter_filename) as yamlfile:\n dict = yaml.load(yamlfile) # yaml file is loaded as a dictionary with key value pairs\n addition = {\"hostname\": hostname, \"version\": junos_on_box_version}\n dict.update(addition)\n # print dict\n content = template.render(dict)\n # print content\n target = open(\"Config_History/\" + hostname + \".set\", 'w')\n target.write(content)\n target.close()","title":""},{"docid":"6ce304f3414997de3316712e68a7af5d","score":"0.41311955","text":"def main():\n\n #Example usage:\n #rosrun tf2seds.py -b <path_to_tf_bag_files> -o <path_to_seds_bagfile>\n\n\n # rospy gets first crack at sys.argv\n rospy.myargv(argv=sys.argv)\n (options,args) = getopt.getopt(sys.argv[1:], 'b:o:s:t:', ['bags=','ouptut=','source=','target='])\n\n rospy.init_node(\"tf2seds\")\n\n # should be in tf2seds namespace\n source_frameid = rospy.get_param(\"/tf2seds/source_frameid\",\"torso_lift_link\")\n target_frameid = rospy.get_param(\"/tf2seds/target_frameid\",\"r_gripper_tool_frame\")\n path = rospy.get_param(\"/tf2seds/source_directory\", None)\n outfile = rospy.get_param(\"/tf2seds/outputfile\", None)\n\n # or specify any of these options on the command line\n for o,a in options:\n if o in ('-b','--bags'):\n path = a\n elif o in ('-o','--output'):\n outfile = a\n elif o in ('-s','--source'):\n source_frameid = a\n elif o in ('-t','--target'):\n target_frameid = a\n\n if not path or not outfile:\n raise Exception, \"You must specify bagfile input and output locations.\"\n\n bagfiles = glob.glob(os.path.normpath(path + \"/*.bag\"))\n bagfiles.sort() # given the default naming these should be sorted by time\n\n rospy.loginfo(\"dir: %s out: %s sf: %s tf: %s\", path, outfile, source_frameid, target_frameid)\n\n\n process_bags(outfile, bagfiles, source_frameid,target_frameid)","title":""},{"docid":"c524b2440cf453469e52ff6b59e4c599","score":"0.41297665","text":"def generate_launch_description():\n\n tensorrt_yolo3_dir = get_package_share_directory('tensorrt_yolo3')\n # Arguments\n prototxt_file = DeclareLaunchArgument(\n 'prototxt_file',\n default_value=os.path.join(tensorrt_yolo3_dir, 'data/yolov3_416_trt.prototxt'),\n description='prototxt file for tensorrt yolo3'\n )\n\n caffemodel_file = DeclareLaunchArgument(\n 'caffemodel_file',\n default_value=os.path.join(tensorrt_yolo3_dir, 'data/yolov3_416.caffemodel'),\n description='caffemodel file for tensorrt yolo3'\n )\n\n input_topic = DeclareLaunchArgument(\n 'input_topic',\n default_value='/image_raw',\n description='input topic name'\n )\n\n output_topic = DeclareLaunchArgument(\n 'output_topic',\n default_value='rois',\n description='output topic name'\n )\n\n # Nodes\n tensorrt_yolo3 = Node(\n package='tensorrt_yolo3',\n node_executable='tensorrt_yolo3_node_exe',\n node_name='tensorrt_yolo3_node',\n node_namespace='',\n output='screen',\n parameters=[{'prototxt_file': LaunchConfiguration('prototxt_file'),\n 'caffemodel_file': LaunchConfiguration('caffemodel_file'), }],\n remappings=[\n (\"in_image_base_topic\", LaunchConfiguration('input_topic')),\n (\"out_image_base_topic\", [LaunchConfiguration('output_topic'), '/debug/image']),\n (\"out_objects_topic\", LaunchConfiguration('output_topic'))\n ]\n )\n\n return LaunchDescription([\n prototxt_file,\n caffemodel_file,\n input_topic,\n output_topic,\n tensorrt_yolo3\n ])","title":""},{"docid":"e1d99c5fbd13bad88498da44b3a83fd1","score":"0.41294044","text":"def populate_from_topology(graph):\n topology = Topology()\n topology.hosts = graph.find(is_a=Host)\n # TODO(cs): broken: can't set attribute\n topology.switches = graph.find(is_a=SoftwareSwitch)\n topology.access_links = [AccessLink(host, switch[0], switch[1][0], switch[1][1])\n for host in topology.hosts\n for switch in filter(\n lambda n: isinstance(n[1][0], SoftwareSwitch),\n graph.ports_for_node(host).iteritems())]\n return topology","title":""},{"docid":"fd9b69f1582e19e6edcf5af4cdf2a3f9","score":"0.41284353","text":"def main(args):\n\n optparser = argparse.ArgumentParser(description=\"Generate OWL from Comments in an ICD-11 file\")\n optparser.add_argument('owlfile', help=\"Input OWL file\")\n optparser.add_argument('-f', '--fullydefined', help=\"Definitions are fully defined\", action=\"store_true\")\n optparser.add_argument('-p', '--port', help=\"SCT Converter gateway port\", type=int)\n optparser.add_argument('-o', '--out', help=\"Output file\", required=True)\n optparser.add_argument('-s', '--shorturi', help=\"Shorten URI's for readability\", action=\"store_true\")\n optparser.add_argument('-r', '--removesctid', help=\"Remove the SCT class declarations\", action=\"store_true\")\n\n opts = optparser.parse_args(args)\n\n gw = SCTConverterGateway(opts.port) if opts.port else SCTConverterGateway()\n\n g = Graph()\n target_graph = add_namespaces(Graph())\n g.parse(opts.owlfile)\n target_graph.parse(opts.owlfile)\n\n # Iterate over the comments with the compositional expressions\n for subj, desc in list(g.subject_objects(icdf_comments)):\n if cgre1.search(desc):\n cgexpr = cgre1.sub(r'\\2', desc)\n elif cgre2.search(desc):\n cgexpr = cgre2.sub(r'\\2', desc)\n else:\n cgexpr = None\n\n if cgexpr:\n if not parse_and_load(gw, subj, not bool(opts.fullydefined), cgexpr, target_graph):\n print(\"Conversion error on %s (%s)\" % (subj, cgexpr), file=sys.stderr)\n else:\n print(\"No conversion available for %s (%s)\" % (subj, desc), file=sys.stderr)\n target = serialize_graph(target_graph, removesctid=opts.removesctid, shorturi=opts.shorturi)\n open(opts.out, 'w').write(target)","title":""},{"docid":"fb387bdc0789d7bb01742e074fd4ce4d","score":"0.41276404","text":"def convert_stp_files_to_neutronics_geometry():\n\n os.system(\"trelis -batch -nographics make_faceteted_neutronics_model.py\")\n\n os.system(\"make_watertight dagmc_notwatertight.h5m -o dagmc.h5m\")","title":""},{"docid":"584509ba00369834101cb5dc6af0c7df","score":"0.41266462","text":"def create_script(self):\n\n # script prep, headers and variable assignments\n self.align_script = self.script_init()\n self.align_script += self.script_set_vars()\n\n # do some actual work\n self.align_script += self.script_results_dir()\n self.align_script += self.script_copy_data()\n if self.uvars.align_centers == 'yes':\n self.align_script += self.script_align_centers()\n self.align_script += self.script_align_datasets()\n\n # add commands ...\n\n if len(self.errors) > 0: return # if any errors so far, give up\n\n return","title":""},{"docid":"6d82916bdbda8b06a443feddd5c899bf","score":"0.41259828","text":"def model_to_python(pipeline: Pipeline) -> str:\n imports = []\n for name, step in pipeline.steps:\n imports.append(f\"from {step.__module__} import {step.__class__.__name__}\")\n\n script = (\"from sklearn.pipeline import Pipeline\\n\" +\n '\\n'.join(imports) + '\\n\\n' +\n 'pipeline = ' + str(pipeline) + '\\n')\n\n return script","title":""},{"docid":"d6af484b5dc21648ba50fa9fc7cb4df6","score":"0.41259146","text":"def create_exec_stmt(self, script):\n return 'exec \"{0}\"'.format(script)","title":""},{"docid":"4bba512a0742004032fcd93b1c514d82","score":"0.41226125","text":"def main(ymlfile):\n\n with ymlfile as f:\n cfg = yaml.load(f, Loader)\n\n kwargs = {}\n cfg['params']['config_file'] = ymlfile.name\n kwargs['params'] = cfg['params']\n root = cfg['params']['data_dir']\n kwargs['data'] = complete_paths(cfg['data'], root)\n kwargs['velocity'] = complete_vel_paths(cfg['velocity'], root)\n kwargs['layers'] = complete_map_paths(cfg['map_layers'], root)\n kwargs['potfields'] = complete_pot_paths(cfg['potfields'], root)\n\n tc = TransectContainer(**kwargs)\n\n tc.plot()","title":""},{"docid":"faab9fa27f38a3099e6625863955b81b","score":"0.41208908","text":"def make_dbt_task(self, node_name, dbt_verb):\n\n model_name = node_name.split('.')[-1]\n if dbt_verb == 'test':\n node_name = node_name.replace('model', 'test') # Just a cosmetic renaming of the task\n task_group = self.dbt_test_group\n else:\n task_group = self.dbt_run_group\n\n dbt_task = BashOperator(\n task_id=node_name,\n task_group=task_group,\n bash_command=f'\\\ndbt {self.dbt_global_cli_flags} {dbt_verb} --target {self.dbt_target} --models {model_name} \\\n--profiles-dir {self.dbt_profiles_dir} --project-dir {self.dbt_project_dir}\\\n ',\n dag=self.dag,\n )\n # Keeping the log output, it's convenient to see when testing the python code outside of Airflow\n logging.info('Created task: %s', node_name)\n return dbt_task","title":""},{"docid":"70386385f58597a916719d311b96c081","score":"0.41149753","text":"def template_to_binary(self, template):\n\t\tleft = Chem.MolFromSmarts(template.split('>>')[0]).ToBinary()\n\t\tright = Chem.MolFromSmarts(template.split('>>')[1]).ToBinary()\n\t\treturn b'>>'.join([left, right])","title":""},{"docid":"223150bc4d587d947c9d3ce6229401c7","score":"0.4113279","text":"def create_tcl_for_talb(base_dir):\n\n binding_residues = ['179', '226', '224', '130', '152', '174', '15', '33', '240', '30', '93']\n base_dir_ligand = ''.join([base_dir, '/TalB/3_MD_post_dock2016/'])\n file_names_list = [''.join([base_dir_ligand, '1_APO/1_S7P_linear/cl101/0_complex_prep/TALB_WT_APO-103_receptor_S7P_linear_docked.pdb']),\n ''.join([base_dir_ligand, '1_APO/1_S7P_linear/cl101_2/0_complex_prep/TALB_WT_APO-515_receptor_S7P_docked.pdb']),\n ''.join([base_dir_ligand, '1_APO/1_S7P_linear/cl102/0_complex_prep/TALB_WT_APO-091_receptor_S7P_linear_docked.pdb']),\n ''.join([base_dir_ligand, '1_APO/1_S7P_linear/cl102_2/0_complex_prep/TALB_WT_APO-535_receptor_S7P_docked.pdb']),\n ''.join([base_dir_ligand, '1_APO/1_S7P_linear/cl212/0_complex_prep/TALB_WT_APO-036_receptor_S7P_linear_docked.pdb']),\n ''.join([base_dir_ligand, '1_APO/1_S7P_linear/cl212_2/0_complex_prep/TALB_WT_APO-777_receptor_S7P_docked.pdb']),\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/1_S7P_linear/cl101/0_complex_prep/TALB_WT_HALO_S7P_remS7P-237_receptor_S7P_linear_docked.pdb']),\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/1_S7P_linear/cl101_2/0_complex_prep/TALB_WT_HALO_S7P_remS7P-260_receptor_S7P_docked.pdb']),\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/1_S7P_linear/cl302/0_complex_prep/TALB_WT_HALO_S7P_remS7P-091_receptor_S7P_linear_docked.pdb']),\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/1_S7P_linear/cl302_2/0_complex_prep/TALB_WT_HALO_S7P_remS7P-492_receptor_S7P_docked.pdb']),\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/1_S7P_linear/cl322/0_complex_prep/TALB_WT_HALO_S7P_remS7P-042_receptor_S7P_linear_docked.pdb']),\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/1_S7P_linear/cl322_2/0_complex_prep/TALB_WT_HALO_S7P_remS7P-184_receptor_S7P_docked.pdb']),\n ''.join([base_dir, '/TalB/0_Crystalline_structures/1UCW_wt_S7P_watbox.pdb'])]\n\n file_out = ''.join([base_dir, '/TalB/3_MD_post_dock2016/load_s7p_frames.tcl'])\n ligand_list = ['S7P']\n create_vmd_file(file_names_list, file_out, ligand_list, binding_residues)\n\n\n base_dir_ligand = ''.join([base_dir, '/TalB/3_MD_post_dock2016/'])\n file_names_list = [''.join([base_dir_ligand, '1_APO/2_F6P/cl202/0_complex_prep/TALB_WT_APO-533_receptor_F6P_docked.pdb']),\n ''.join([base_dir_ligand, '1_APO/2_F6P/cl202_2/0_complex_prep/TALB_WT_APO-314_receptor_F6P_docked.pdb']),\n ''.join([base_dir_ligand, '1_APO/2_F6P/cl212/0_complex_prep/TALB_WT_APO-139_receptor_F6P_docked.pdb']),\n ''.join([base_dir_ligand, '1_APO/2_F6P/cl212_2/0_complex_prep/TALB_WT_APO-464_receptor_F6P_docked.pdb']),\n ''.join([base_dir_ligand, '1_APO/2_F6P/cl321/0_complex_prep/TALB_WT_APO-799_receptor_F6P_docked.pdb']),\n ''.join([base_dir_ligand, '1_APO/2_F6P/cl321_2/0_complex_prep/TALB_WT_APO-003_receptor_F6P_docked.pdb']),\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/2_F6P/cl101/0_complex_prep/TALB_WT_HALO_S7P_remS7P-492_receptor_F6P_docked.pdb']),\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/2_F6P/cl101_2/0_complex_prep/TALB_WT_HALO_S7P_remS7P-056_receptor_F6P_docked.pdb']),\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/2_F6P/cl201/0_complex_prep/TALB_WT_HALO_S7P_remS7P-381_receptor_F6P_docked.pdb']),\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/2_F6P/cl201_2/0_complex_prep/TALB_WT_HALO_S7P_remS7P-117_receptor_F6P_docked.pdb']),\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/2_F6P/cl311/0_complex_prep/TALB_WT_HALO_S7P_remS7P-014_receptor_F6P_docked.pdb']),\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/2_F6P/cl311_2/0_complex_prep/TALB_WT_HALO_S7P_remS7P-222_receptor_F6P_docked.pdb']),\n ''.join([base_dir, '/TalB/0_Crystalline_structures/1UCW_wt_S7P_watbox.pdb'])]\n\n\n file_out = ''.join([base_dir, '/TalB/3_MD_post_dock2016/load_f6p_frames.tcl'])\n ligand_list = ['F6P']\n create_vmd_file(file_names_list, file_out, ligand_list, binding_residues)","title":""},{"docid":"54f3c8d20c155c9fb4f0a77e33a5fe8d","score":"0.4107464","text":"def topology(self, name):\n if request.method == 'POST':\n return self.save_topology(name)\n return self.get_topology(name)","title":""}],"string":"[\n {\n \"docid\": \"2b5d43ca9342feb75ca534ffe1a11c9d\",\n \"score\": \"0.54831195\",\n \"text\": \"def topology(args):\\n info(\\\"*** Creating network\\\\n\\\")\\n topology_data = load_topology() \\n \\n add_hmds()\\n add_base_stations(topology_data)\\n sdn_controller = get_sdn_controller()\\n\\n info(\\\"*** Configuring propagation model\\\\n\\\")\\n NET.setPropagationModel(model=\\\"logDistance\\\", exp=5)\\n\\n info(\\\"*** Configuring wifi nodes\\\\n\\\")\\n NET.configureWifiNodes()\\n\\n create_bs_links(topology_data)\\n \\n info(\\\"*** Configuring mobility model\\\\n\\\")\\n NET.setMobilityModel(time=0, model='RandomDirection', seed=20, AC='ssf')\\n\\n #info(\\\"\\\"\\\"*** Starting the graph interface\\\\n\\\"\\\"\\\")\\n #NET.plotGraph(max_x=NET_GRAPH_DIMENSION, max_y=NET_GRAPH_DIMENSION) \\n \\n info(\\\"*** Starting network\\\\n\\\")\\n NET.build()\\n sdn_controller.start()\\n \\n config_ovs_switches(sdn_controller)\\n\\n info(\\\"*** Running CLI\\\\n\\\")\\n CLI_THREAD = threading.Thread(target=lambda: CLI(NET))\\n CLI_THREAD.daemon = True\\n \\n set_hmd_range_color(HMDS_SET)\\n ping_nodes()\\n\\n #CLI(net)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80bbf92dab4f27d633154a3b8cbaec89\",\n \"score\": \"0.5285671\",\n \"text\": \"def build_topology(self):\\n self.dend.connect(self.soma(1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87102fc05b76af0cfd2afadcb4671032\",\n \"score\": \"0.5043602\",\n \"text\": \"def do_topo(self, args):\\n\\n self.spp_topo.run(args, self.get_sec_ids('nfv'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800183f4cb488876c0061e704366228e\",\n \"score\": \"0.5004334\",\n \"text\": \"def gen_tcl(self):\\n s = ''\\n s += '\\\\n'.join(self.bd_tcl_cmds['place_bd_ip'])\\n s += '\\\\n'\\n s += '\\\\n'.join(self.bd_tcl_cmds['build_bd'])\\n s += '\\\\n'\\n s += '\\\\n'.join(self.bd_tcl_cmds['assign_bd_addrs'])\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cde366ecb10bf14e7b028bb5eeb70a26\",\n \"score\": \"0.49921468\",\n \"text\": \"def produce_layout(eoi, graph):\\n\\n subprocess.run((\\\"/usr/bin/dot\\\", \\\"{}\\\".format(graph.filename), \\\"-o\\\", \\n \\\"{}/layout.dot\\\".format(eoi)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f060bd93777451d23b0870552bfeda9d\",\n \"score\": \"0.49778432\",\n \"text\": \"def get_pipeline_script_cmdcall_snli_converter():\\n # io snli files into jack format\\n cmd = \\\"python3 jack/io/SNLI2jtr.py\\\"\\n return cmd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1366f6b2c834128797b0f73ad1884dda\",\n \"score\": \"0.4976361\",\n \"text\": \"def generate_ot2_script(ot2_script_path, template_path, **kwargs):\\n with open(ot2_script_path, 'w') as wf:\\n with open(template_path, 'r') as rf:\\n for index, line in enumerate(rf):\\n if line[:3] == 'def':\\n function_start = index\\n break\\n else:\\n wf.write(line)\\n for key, value in kwargs.items():\\n wf.write('{}='.format(key))\\n if type(value) == dict:\\n wf.write(json.dumps(value))\\n elif type(value) == str:\\n wf.write(\\\"'{}'\\\".format(value))\\n else:\\n wf.write(str(value))\\n wf.write('\\\\n')\\n wf.write('\\\\n')\\n with open(template_path, 'r') as rf:\\n for index, line in enumerate(rf):\\n if index >= function_start - 1:\\n wf.write(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61ed9f3cab2b6c40ef8ec198e1044736\",\n \"score\": \"0.49728763\",\n \"text\": \"def py2tcl(pystr):\\n # new = '\\\\n'.join(pystr.split()[1:]) # removes the import line\\n new = pystr.replace('(', ' ')\\n new = new.replace(')', ' ')\\n new = new.replace('opy.', '')\\n new = new.replace('ops.', '')\\n new = new.replace(',', '')\\n new = new.replace(\\\"'\\\", '')\\n new = new.replace('\\\"', '')\\n # lines = new.splitlines()\\n # for i in range(len(lines)):\\n # if 'for i in range(' in lines[i]:\\n # line = lines.replace('for i in range(', 'for {set i 1} {$i <= num} {incr i 1} {')\\n return new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3429b3bb8b494948e78f7a129fd8f81\",\n \"score\": \"0.49418485\",\n \"text\": \"def create_scionlab_as_local_gen(args, tp):\\n new_ia = TopoID(args.joining_ia)\\n core_ia = ISD_AS(args.core_ia)\\n local_gen_path = os.path.join(args.package_path, args.user_id)\\n try:\\n os.makedirs(local_gen_path)\\n except:\\n pass\\n os.chdir(local_gen_path) # functions from $SC/python/topology use relative paths\\n local_gen_path = os.path.join(local_gen_path, 'gen')\\n try:\\n shutil.rmtree(local_gen_path)\\n except:\\n pass\\n as_obj = generate_certificate(\\n new_ia, core_ia, args.core_sign_priv_key_file, args.core_cert_file, args.trc_file)\\n write_dispatcher_config(local_gen_path)\\n write_toml_files(tp, new_ia)\\n for service_type, type_key in TYPES_TO_KEYS.items():\\n executable_name = TYPES_TO_EXECUTABLES[service_type]\\n instances = tp[type_key].keys()\\n for instance_name in instances:\\n config = prep_supervisord_conf(tp[type_key][instance_name], executable_name,\\n service_type, instance_name, new_ia)\\n instance_path = get_elem_dir(local_gen_path, new_ia, instance_name)\\n write_certs_trc_keys(new_ia, as_obj, instance_path)\\n write_as_conf_and_path_policy(new_ia, as_obj, instance_path)\\n write_supervisord_config(config, instance_path)\\n write_topology_file(tp, type_key, instance_path)\\n write_zlog_file(service_type, instance_name, instance_path)\\n generate_sciond_config(TopoID(args.joining_ia), as_obj, tp, local_gen_path)\\n write_overlay_config(local_gen_path)\\n if not args.no_prometheus:\\n generate_prom_config(new_ia, tp, local_gen_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9bc3bce81c646c858b8bedd15488fb3\",\n \"score\": \"0.49216047\",\n \"text\": \"def main(args):\\n ic = lanenet_processer()\\n rospy.init_node('template_node', anonymous=True)\\n try:\\n rospy.spin()\\n except KeyboardInterrupt:\\n print \\\"Shutting down ROS node\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64d75f535c716eed9d8ddc7d3e360263\",\n \"score\": \"0.4887493\",\n \"text\": \"def define_imine_topology(bb1, bb2, topology, property):\\n # Define the topologies.\\n topo_func = None\\n bb_vect = None\\n if property == 'stk_func':\\n if topology == '2p3':\\n topo_func = stk.cage.TwoPlusThree()\\n bb_vect = None\\n elif topology == '4p6':\\n topo_func = stk.cage.FourPlusSix()\\n bb_vect = None\\n elif topology == '4p62':\\n topo_func = stk.cage.FourPlusSix2()\\n bb_vect = None\\n elif topology == '6p9':\\n topo_func = stk.cage.SixPlusNine()\\n bb_vect = None\\n elif topology == '1p1':\\n topo_func = stk.cage.OnePlusOne()\\n bb_vect = {\\n bb1: [topo_func.vertices[0]],\\n bb2: [topo_func.vertices[1]],\\n }\\n elif topology == '4p4':\\n topo_func = stk.cage.FourPlusFour()\\n bb_vect = {\\n bb1: topo_func.vertices[0, 2, 4, 6],\\n bb2: topo_func.vertices[1, 3, 5, 7],\\n }\\n\\n return topo_func, bb_vect\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4acc506885c104ee134d68980939825e\",\n \"score\": \"0.4881019\",\n \"text\": \"def maketopo():\\n nxpoints=200\\n nypoints=200\\n xupper=2.e0\\n yupper=2.e0\\n xlower = -2.e0\\n ylower = -2.e0\\n outfile= \\\"bowl.topotype2\\\"\\n topo2writer(outfile,topo,xlower,xupper,ylower,yupper,nxpoints,nypoints)\\n\\n # convert to netcdf form:\\n topo4 = topotools.Topography()\\n topo4.read(outfile,2)\\n topo4.write(\\\"bowl.nc\\\",topo_type=4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6598b14f3db27127fa4b0eb7492095d3\",\n \"score\": \"0.4849923\",\n \"text\": \"def insert_topologytemplate(description, rtable, readme, user, template_name, visibility=db.TopologyTemplate.PROTECTED):\\n\\n # Check we don't already have a template by this name\\n # TODO: change so that django doesn't require a unique name, so we don't\\n # have to do this check which allows any user who can create a template to\\n # see the names already in use\\n duplicate = True\\n try:\\n _ = db.TopologyTemplate.objects.get(name = template_name)\\n except db.TopologyTemplate.DoesNotExist:\\n duplicate = False\\n \\n if duplicate:\\n raise TopologyNameError(\\\"This topology name is already in use\\\")\\n \\n # Dictionaries for the nodes, ports and links so we don't start putting\\n # stuff in the DB and then come across an error\\n\\n # nodes is a dictionary mapping a node name to a type\\n nodes = {}\\n\\n # paths maps names of webservers to paths\\n paths = {}\\n\\n # ip_offsets maps node names to ip offsets\\n ip_offsets = {}\\n\\n # ports is a dictionary mapping a node name to a list of interfaces\\n ports = {}\\n\\n # links maps (name, iface) pairs together, and should be symmetric\\n links = {}\\n\\n line_no = 0\\n\\n # List of node types that are grouped in Node\\n # WebServer is a class of its own, since it has extra attributes, so is not\\n # included here\\n node_types = {\\\"gateway\\\":db.Node.GATEWAY_ID,\\n \\\"hub\\\":db.Node.HUB_ID,\\n \\\"blackhole\\\":db.Node.BLACK_HOLE_ID,\\n \\\"virtual\\\":db.Node.VIRTUAL_NODE_ID}\\n\\n \\n def check_valid_name(name):\\n \\\"\\\"\\\"Check that a potential name is valid, i.e. starts wth a letter and\\n contains only letters and numbers\\\"\\\"\\\"\\n if name == \\\"\\\" or not name[0].isalpha() or not name.isalnum():\\n raise TopologyNameError(\\\"Line %d: invalid name for node or interface; names must begin with a letter and contain only alphanumeric characters\\\" % line_no)\\n\\n def create_node(name, node_type, ip_offset):\\n check_valid_name(name)\\n try:\\n ip_offset = int(ip_offset)\\n except ValueError:\\n raise TopologyArgumentError(\\\"Line %d: IP offset must be an integer\\\" % line_no)\\n if name in nodes:\\n raise TopologyNameError(\\\"Line %d: Duplicate node name\\\" % line_no)\\n nodes[name] = node_type\\n ip_offsets[name] = ip_offset\\n\\n def create_link(line):\\n \\n # We expect a line of the form \\\"name1.iface1=name2.iface2\\\"\\n parts = line.split('=')\\n if len(parts) != 2:\\n raise TopologySyntaxError(\\\"Line %d: syntax error\\\" % line_no)\\n \\n ni1 = parts[0].split('.')\\n if len(ni1) != 2:\\n raise TopologySyntaxError(\\\"Line %d: syntax error\\\" % line_no)\\n\\n ni2 = parts[1].split('.')\\n if len(ni2) != 2:\\n raise TopologySyntaxError(\\\"Line %d: syntax error\\\" % line_no)\\n\\n name1 = ni1[0].strip()\\n iface1 = ni1[1].strip()\\n name2 = ni2[0].strip()\\n iface2 = ni2[1].strip()\\n\\n check_valid_name (name1);\\n check_valid_name (name2);\\n check_valid_name (iface1);\\n check_valid_name (iface2);\\n\\n # Check the nodes exist\\n if name1 not in nodes:\\n raise TopologyNameError(\\\"Line %d: node name '%s' does not exist\\\" % (line_no, name1))\\n if name2 not in nodes:\\n raise TopologyNameError(\\\"Line %d: node name '%s' does not exist\\\" % (line_no, name2))\\n\\n # Check the ports don't already exist\\n if (name1, iface1) in links:\\n (name3, iface3) = links[(name1, iface1)]\\n raise TopologySyntaxError(\\\"Line %d: %s.%s is already connected to %s.%s\\\" % (line_no, name1, iface1, name3, iface3))\\n if (name2, iface2) in links:\\n (name3, iface3) = links[(name1, iface1)]\\n raise TopologySyntaxError(\\\"Line %d: %s.%s is already connected to %s.%s\\\" % (line_no, name2, iface2, name3, iface3))\\n \\n # If we've got this far, it's safe to insert the link\\n if name1 not in ports:\\n ports[name1] = [iface1]\\n else:\\n if iface1 not in (ports[name1]):\\n ports[name1].append(iface1)\\n if name2 not in ports:\\n ports[name2] = [iface2]\\n else:\\n if iface2 not in (ports[name2]):\\n ports[name2].append(iface2)\\n \\n links[(name1, iface1)] = (name2, iface2)\\n links[(name2, iface2)] = (name1, iface1)\\n\\n \\n lines = description.splitlines()\\n for l in lines:\\n\\n line_no += 1\\n \\n if l == \\\"\\\":\\n # Ignore blank lines\\n continue\\n \\n toks = l.split()\\n if len(toks) < 1:\\n continue\\n \\n if toks[0] == \\\"webserver\\\":\\n # Create a new webserver node\\n if len(toks) == 3:\\n # We don't have to assign it a particular IP\\n name = toks[1]\\n path = toks[2]\\n create_node(name, \\\"webserver\\\", -2)\\n paths[name] = path\\n elif len(toks) == 4:\\n # We do have to assign it a particular IP\\n create_node(toks[1], \\\"webserver\\\", toks[3])\\n paths[toks[1]] = toks[2]\\n else:\\n raise TopologyArgumentError(\\\"Line %d: Wrong number of arguments - expect webserver <name> <path> [ip_offset]\\\" % line_no)\\n\\n elif toks[0] in node_types:\\n # Create a new node of a non-webserver type\\n if len(toks) == 2:\\n # No IP offset\\n create_node(toks[1], toks[0], -2)\\n elif len(toks) == 3:\\n # IP offset is present\\n create_node(toks[1], toks[0], toks[2])\\n else:\\n raise TopologyArgumentError(\\\"Line %d: Wrong number of erguments - expect %s <name> [ip_offset]\\\" % (line_no, toks[0]))\\n else:\\n # It doesn't start with a node type; assume it's inserting a link \\n create_link(l)\\n\\n # Add everything to the DB\\n tt = db.TopologyTemplate()\\n tt.name = template_name\\n tt.owner = user\\n tt.org = user.get_profile().org\\n tt.visibility = visibility\\n tt.readme = readme\\n tt.rtable = rtable\\n tt.specification = description\\n tt.save()\\n\\n dbnodes = {}\\n dbports = {}\\n links_inserted = {}\\n\\n # Insert nodes into the database\\n for (node_name, node_type) in nodes.iteritems():\\n n = None\\n if node_type == \\\"webserver\\\":\\n # A webserver is a different class, as it has a path to serve\\n p = db.WebServerPath()\\n p.path = paths[node_name]\\n p.save()\\n n = db.WebServer()\\n n.path_to_serve=p\\n n.type=db.Node.WEB_SERVER_ID\\n n.name=node_name\\n n.template=tt\\n else:\\n n = db.Node()\\n n.type=node_types[node_type]\\n n.name=node_name\\n n.template=tt\\n n.save()\\n dbnodes[node_name] = n\\n\\n # Add the ports to the database\\n for (node_name, ifaces) in ports.iteritems():\\n offset_2 = 0\\n for iface in ifaces:\\n\\n # If we have an IP offset for the node, assign consecutive\\n # IP offsets for each interface\\n if ip_offsets[node_name] >= 0:\\n ip = ip_offsets[node_name] + offset_2\\n offset_2 += 1\\n else:\\n ip = ip_offsets[node_name]\\n p = db.Port()\\n p.node=dbnodes[node_name]\\n p.name=iface\\n p.ip_offset=ip\\n dbports[(node_name, iface)] = p\\n p.save()\\n\\n for (i1, i2) in links.iteritems():\\n # links contains two entries, one in each direction, for each link, so\\n # exclude duplicates\\n if i1 not in links_inserted and i2 not in links_inserted:\\n l = db.Link()\\n l.port1=dbports[i1]\\n l.port2=dbports[i2]\\n l.lossiness=0.0\\n links_inserted[i1] = True\\n links_inserted[i2] = True\\n l.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b79f779f4b45740c1a6527ee727b6ff\",\n \"score\": \"0.479371\",\n \"text\": \"def to_executables(nodes, label, context):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42820c54e35a35645ac471c2d85cae6e\",\n \"score\": \"0.47915757\",\n \"text\": \"def convert(topology, backend, device, extra_config={}):\\n assert topology is not None, \\\"Cannot convert a Topology object of type None.\\\"\\n assert backend is not None, \\\"Cannot convert a Topology object into backend None.\\\"\\n assert device is not None, \\\"Cannot convert a Topology object into device None.\\\"\\n\\n operator_map = {}\\n\\n for operator in topology.topological_operator_iterator():\\n try:\\n converter = get_converter(operator.type)\\n\\n if backend == onnx.__name__:\\n # vers = LooseVersion(torch.__version__)\\n # allowed_min = LooseVersion(\\\"1.6.0\\\")\\n # Pytorch <= 1.6.0 has a bug with exporting GEMM into ONNX.\\n # For the moment only tree_trav is enabled for pytorch <= 1.6.0\\n # if vers < allowed_min:\\n extra_config[constants.TREE_IMPLEMENTATION] = \\\"tree_trav\\\"\\n\\n operator_map[operator.full_name] = converter(operator, device, extra_config)\\n except ValueError:\\n raise MissingConverter(\\n \\\"Unable to find converter for {} type {} with extra config: {}.\\\".format(\\n operator.type, type(getattr(operator, \\\"raw_model\\\", None)), extra_config\\n )\\n )\\n except Exception as e:\\n raise e\\n\\n operators = list(topology.topological_operator_iterator())\\n torch_model = PyTorchBackendModel(\\n topology.raw_model.input_names, topology.raw_model.output_names, operator_map, operators, extra_config\\n ).eval()\\n\\n if backend == onnx.__name__:\\n onnx_model_name = output_model_name = None\\n target_opset = 11\\n\\n # Set optional configuration options for ONNX if any.\\n if constants.ONNX_OUTPUT_MODEL_NAME in extra_config:\\n onnx_model_name = extra_config[constants.ONNX_OUTPUT_MODEL_NAME]\\n output_model_name = onnx_model_name + \\\".onnx\\\"\\n if constants.ONNX_TARGET_OPSET in extra_config:\\n target_opset = extra_config[constants.ONNX_TARGET_OPSET]\\n if output_model_name is None:\\n output_model_name = str(uuid4().hex) + \\\".onnx\\\"\\n\\n # Put the tracing test input into the right format.\\n trace_input = _get_trace_input_from_test_input(extra_config[constants.TEST_INPUT])\\n\\n # Generate the ONNX models\\n torch.onnx.export(\\n torch_model,\\n trace_input,\\n output_model_name,\\n input_names=topology.raw_model.input_names,\\n output_names=topology.raw_model.output_names,\\n keep_initializers_as_inputs=False,\\n opset_version=target_opset,\\n do_constant_folding=True,\\n )\\n hb_model = onnx.load(output_model_name)\\n os.remove(output_model_name)\\n\\n # Set the ONNX model name if any.\\n if onnx_model_name is not None:\\n hb_model.graph.name = onnx_model_name\\n\\n # Fix the model to use arbitrary batch dimensions\\n def fix_dim(dim):\\n updated = False\\n if dim.HasField(\\\"dim_value\\\"):\\n dim.Clear()\\n updated = True\\n dim.dim_param = \\\"sym\\\"\\n\\n return updated\\n\\n def fix_value_info(value):\\n num_fixed = 0\\n if value.type.HasField(\\\"tensor_type\\\"):\\n shape = value.type.tensor_type.shape\\n if shape:\\n dim = shape.dim[0]\\n if fix_dim(dim):\\n num_fixed += 1\\n\\n return num_fixed\\n\\n def fix_graph(graph):\\n num_fixed = 0\\n for input in graph.input:\\n num_fixed += fix_value_info(input)\\n\\n for output in graph.output:\\n num_fixed += fix_value_info(output)\\n\\n for node in graph.node:\\n for attr in node.attribute:\\n if attr.HasField(\\\"g\\\"):\\n num_fixed += fix_graph(attr.g)\\n\\n return num_fixed\\n\\n fix_graph(hb_model.graph)\\n else:\\n # Set the device for the model.\\n if device != \\\"cpu\\\":\\n if backend == torch.__name__ or torch.jit.__name__:\\n torch_model = torch_model.to(device)\\n\\n # If the backend is tochscript, jit the model.\\n if backend == torch.jit.__name__:\\n trace_input = _get_trace_input_from_test_input(extra_config[constants.TEST_INPUT])\\n if device != \\\"cpu\\\":\\n trace_input.to(device)\\n torch_model = torch.jit.trace(torch_model, trace_input).eval()\\n torch.jit.optimized_execution(torch_model)\\n hb_model = torch_model\\n\\n # Return if the container is not needed.\\n if constants.CONTAINER in extra_config and not extra_config[constants.CONTAINER]:\\n return hb_model\\n\\n # We scan the operators backwards until we find an operator with a defined type\\n # This is necessary because ONNX models can have arbitrary operators doing casting, reshaping etc.\\n idx = len(operators) - 1\\n while (\\n idx >= 0\\n and not operator_map[operators[idx].full_name].regression\\n and not operator_map[operators[idx].full_name].classification\\n and not operator_map[operators[idx].full_name].anomaly_detection\\n and not operator_map[operators[idx].full_name].transformer\\n ):\\n idx -= 1\\n\\n assert idx >= 0, \\\"Cannot detect container type. Please fill an issue at https://github.com/microsoft/hummingbird.\\\"\\n\\n # If is a transformer, we need to check whether there is another operator type before.\\n # E.g., normalization after classification.\\n tmp_idx = idx\\n if operator_map[operators[idx].full_name].transformer:\\n while (\\n idx >= 0\\n and not operator_map[operators[idx].full_name].regression\\n and not operator_map[operators[idx].full_name].classification\\n and not operator_map[operators[idx].full_name].anomaly_detection\\n ):\\n idx -= 1\\n if idx < 0:\\n idx = tmp_idx\\n\\n if operator_map[operators[idx].full_name].regression:\\n # We are doing a regression task.\\n if backend == torch.jit.__name__:\\n container = TorchScriptSklearnContainerRegression\\n elif backend == onnx.__name__:\\n container = ONNXSklearnContainerRegression\\n else:\\n container = PyTorchSklearnContainerRegression\\n elif operator_map[operators[idx].full_name].anomaly_detection:\\n # We are doing anomaly detection.\\n if backend == torch.jit.__name__:\\n container = TorchScriptSklearnContainerAnomalyDetection\\n elif backend == onnx.__name__:\\n container = ONNXSklearnContainerAnomalyDetection\\n else:\\n container = PyTorchSklearnContainerAnomalyDetection\\n elif operator_map[operators[idx].full_name].transformer:\\n # We are just transforming the input data.\\n if backend == torch.jit.__name__:\\n container = TorchScriptSklearnContainerTransformer\\n elif backend == onnx.__name__:\\n container = ONNXSklearnContainerTransformer\\n else:\\n container = PyTorchSklearnContainerTransformer\\n else:\\n # We are doing a classification task.\\n if backend == torch.jit.__name__:\\n container = TorchScriptSklearnContainerClassification\\n elif backend == onnx.__name__:\\n container = ONNXSklearnContainerClassification\\n else:\\n container = PyTorchSklearnContainerClassification\\n\\n hb_model = container(hb_model, extra_config)\\n\\n return hb_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ee2732725a729e31ad492835f691992\",\n \"score\": \"0.47445887\",\n \"text\": \"def topologytemplate_view(request, template):\\n tn = 'vns/topologytemplate_view.html'\\n\\n return direct_to_template(request, tn, {\\\"t\\\":template})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17a6a2297e34a2f5f7746a2d4e55d8b7\",\n \"score\": \"0.47296414\",\n \"text\": \"def write_cytoscape_script():\\n\\t\\n\\tprint \\\"Writing cytoscape script...\\\"\\n\\n\\tf = open(file_cytoscape_script, 'w')\\n\\n\\t### Import network\\n\\tf.write(\\\"\\\"\\\"network import file file=\\\"{file}\\\" firstRowAsColumnNames=true startLoadRow=1 indexColumnSourceInteraction=1 indexColumnTargetInteraction=2\\\"\\\"\\\".format(file=file_network_table)+\\\"\\\\n\\\")\\n\\n\\t### Import node attributes\\n\\t# REF: http://wiki.cytoscape.org/Cytoscape_3/UserManual/Attributes\\n\\t# *OBS*: tab seperated\\n\\tf.write(r\\\"\\\"\\\"table import file file=\\\"{file}\\\" firstRowAsColumnNames=true startLoadRow=1 keyColumnIndex=1 delimiters=\\\"\\\\t\\\" \\\"\\\"\\\".format(file=file_node_attribute)+\\\"\\\\n\\\")\\n\\t\\t### ^ use raw string to inhibit python from setting a \\\"\\t\\\" character\\n\\n\\t### Set layout\\n\\tf.write(\\\"\\\"\\\"layout kamada-kawai EdgeAttribute=Pearson_correlation unweighted=false\\\"\\\"\\\"+\\\"\\\\n\\\")\\n\\t\\t# Kamada and Kawai (1988): same as \\\"Spring-Embedded Layout\\\"\\n\\t\\t# Network nodes are treated like physical objects that repel each other, such as electrons. \\n\\t\\t# The layout algorithm sets the positions of the nodes in a way that minimizes the sum of forces in the network\\n\\t# 1) [yWorks - not available through command line] yFiles Organic Layout: organic layout algorithm is a kind of *spring-embedded algorithm*\\n\\t# 2) layout force-directed EdgeAttribute=Pearson_correlation unweighted=false\\n\\t# ---> NOTE: the applying a WEIGHTED layout algorithm may fail if the network is small or has few edges. This is tricky...\\n\\n\\t### Load and apply visual style\\n\\t# *OBS*: KEEP STYLE NAME UPDATED - must match name in .xml file\\n\\tf.write(\\\"\\\"\\\"vizmap load file file=\\\"{file}\\\" \\\"\\\"\\\".format(file=cytoscape_style)+\\\"\\\\n\\\")\\n\\tf.write(\\\"\\\"\\\"vizmap apply styles=\\\"DEPICT-style-v1\\\" \\\"\\\"\\\"+\\\"\\\\n\\\")\\n\\t\\n\\t### Set view to fit display\\n\\tf.write(\\\"\\\"\\\"view fit content\\\"\\\"\\\"+\\\"\\\\n\\\")\\n\\n\\t### Export\\n\\tf.write(\\\"\\\"\\\"view export OutputFile=\\\"{file_out}\\\" options=PDF\\\"\\\"\\\".format(file_out=file_cytoscape_graphics)+\\\"\\\\n\\\")\\n\\tf.write(\\\"\\\"\\\"view export OutputFile=\\\"{file_out}\\\" options=PNG\\\"\\\"\\\".format(file_out=file_cytoscape_graphics)+\\\"\\\\n\\\")\\n\\n\\tf.write(\\\"\\\"\\\"session save as file=\\\"{file_out}\\\" \\\"\\\"\\\".format(file_out=file_cytoscape_session)+\\\"\\\\n\\\")\\n\\n\\t\\n\\n\\t### OPTIONAL: exit script when done.\\n\\tif not interactive_cytoscape_session:\\n\\t\\tf.write(\\\"\\\"\\\"command quit\\\"\\\"\\\"+\\\"\\\\n\\\")\\n\\t\\n\\n\\tf.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38d33016f8da8f7b624d3aea39ccb4b1\",\n \"score\": \"0.46605805\",\n \"text\": \"def main(args):\\n create_output = []\\n startup_output = []\\n stop_output = []\\n delete_output = []\\n delete_net_output = []\\n topo_yaml = openTopo(args.topo)\\n create_startup = args.startup\\n ceos_image = topo_yaml['images']['ceos']\\n host_image = topo_yaml['images']['host']\\n _tag = topo_yaml['topology']['name']\\n nodes = topo_yaml['nodes']\\n hosts = topo_yaml['hosts']\\n _ceos_script_location = \\\"{0}/{1}\\\".format(CEOS_SCRIPTS, _tag)\\n _tfa_version = 1\\n BASE_TERMINATTR = \\\"\\\"\\\"\\ndaemon TerminAttr\\n exec /usr/bin/TerminAttr -cvaddr={0}:9910 -taillogs -cvcompression=gzip -cvauth=key,{1} -smashexcludes=ale,flexCounter,hardware,kni,pulse,strata -ingestexclude=/Sysdb/cell/1/agent,/Sysdb/cell/2/agent\\n no shutdown\\n!\\n \\\"\\\"\\\"\\n BASE_MGMT = \\\"\\\"\\\"\\ninterface Management0\\n description Management\\n ip address {0}/24\\n!\\nip routing\\n!\\nip route 0.0.0.0/0 {1}\\n!\\nmanagement api http-commands\\n no shutdown\\n!\\n \\\"\\\"\\\"\\n BASE_STARTUP = \\\"\\\"\\\"\\nhostname {0}\\n!\\n \\\"\\\"\\\"\\n if create_startup:\\n pS(\\\"INFO\\\", \\\"Bare Startup config will be created for all cEOS nodes\\\")\\n # Check for mgmt Bridge\\n try:\\n mgmt_network = topo_yaml['infra']['bridge']\\n if not mgmt_network:\\n pS(\\\"INFO\\\", \\\"No mgmt bridge specified. Creating isolated network.\\\")\\n except KeyError:\\n pS(\\\"INFO\\\", \\\"No mgmt bridge specified. Creating isolated network.\\\")\\n mgmt_network = False\\n # Check for TFA Version\\n try:\\n _tfa_version = topo_yaml['topology']['vforward']\\n if _tfa_version > 1:\\n pS(\\\"INFO\\\", \\\"Leveraging ArFA forwarding agent\\\")\\n else:\\n pS(\\\"INFO\\\", \\\"Leveraging default dataplane\\\")\\n except:\\n pS(\\\"INFO\\\", \\\"Leveraging default dataplane\\\")\\n\\n # Load cEOS nodes specific information\\n for _node in nodes:\\n try:\\n _node_ip = nodes[_node]['ipaddress']\\n except KeyError:\\n _node_ip = \\\"\\\"\\n CEOS[_node] = CEOS_NODE(_node, _node_ip, nodes[_node]['mac'], nodes[_node]['neighbors'], _tag, ceos_image)\\n # Load Host nodes specific information\\n if hosts:\\n for _host in hosts:\\n _tmp_host = hosts[_host]\\n HOSTS[_host] = HOST_NODE(_host, _tmp_host['ipaddress'], _tmp_host['mask'], _tmp_host['gateway'], _tmp_host['neighbors'], _tag, host_image)\\n # Check for output directory\\n if checkDir(_ceos_script_location):\\n pS(\\\"OK\\\", \\\"Directory is present now.\\\")\\n else:\\n pS(\\\"iBerg\\\", \\\"Error creating directory.\\\")\\n create_output.append(\\\"#!/bin/bash\\\\n\\\")\\n create_output.append(NOTIFY_ADJUST)\\n # Check for container images are present\\n create_output.append(\\\"if [ \\\\\\\"$(docker image ls | grep ceosimage | grep -c {0})\\\\\\\" == 0 ]\\\\n\\\".format(ceos_image))\\n create_output.append(\\\"then\\\\n echo \\\\\\\"Docker image not found for ceosimage:{0}, please build it first.\\\\\\\"\\\\n exit\\\\nfi\\\\n\\\".format(ceos_image))\\n create_output.append(\\\"if [ \\\\\\\"$(docker image ls | grep chost | grep -c {0})\\\\\\\" == 0 ]\\\\n\\\".format(host_image))\\n create_output.append(\\\"then\\\\n echo \\\\\\\"Docker image not found for chost:{0}, please build it first.\\\\\\\"\\\\n exit\\\\nfi\\\\n\\\".format(host_image))\\n create_output.append(\\\"sudo ip netns add {0}\\\\n\\\".format(_tag))\\n startup_output.append(\\\"#!/bin/bash\\\\n\\\")\\n startup_output.append(NOTIFY_ADJUST)\\n stop_output.append(\\\"#!/bin/bash\\\\n\\\")\\n delete_output.append(\\\"#!/bin/bash\\\\n\\\")\\n startup_output.append(\\\"sudo ip netns add {0}\\\\n\\\".format(_tag))\\n delete_net_output.append(\\\"sudo ip netns delete {0}\\\\n\\\".format(_tag))\\n # Get the veths created\\n create_output.append(\\\"# Creating veths\\\\n\\\")\\n for _veth in VETH_PAIRS:\\n _v1, _v2 = _veth.split(\\\"-\\\")\\n create_output.append(\\\"sudo ip link add {0} type veth peer name {1}\\\\n\\\".format(_v1, _v2))\\n startup_output.append(\\\"sudo ip link add {0} type veth peer name {1}\\\\n\\\".format(_v1, _v2))\\n delete_output.append(\\\"sudo ip link delete {0} type veth peer name {1}\\\\n\\\".format(_v1, _v2))\\n create_output.append(\\\"#\\\\n#\\\\n# Creating anchor containers\\\\n#\\\\n\\\")\\n # Create initial cEOS anchor containers\\n create_output.append(\\\"# Checking to make sure topo config directory exists\\\\n\\\")\\n create_output.append('if ! [ -d \\\"{0}/{1}\\\" ]; then mkdir {0}/{1}; fi\\\\n'.format(CONFIGS, _tag))\\n for _node in CEOS:\\n # Add in code to perform check in configs directory and create a basis for ceos-config\\n create_output.append(\\\"# Checking for configs directory for each cEOS node\\\\n\\\")\\n create_output.append('if ! [ -d \\\"{0}/{1}/{2}\\\" ]; then mkdir {0}/{1}/{2}; fi\\\\n'.format(CONFIGS, _tag, _node))\\n create_output.append(\\\"# Creating the ceos-config file.\\\\n\\\")\\n create_output.append('echo \\\"SERIALNUMBER={0}\\\" > {1}/{2}/{3}/ceos-config\\\\n'.format(CEOS[_node].ceos_name, CONFIGS, _tag, _node))\\n create_output.append('echo \\\"SYSTEMMACADDR={0}\\\" >> {1}/{2}/{3}/ceos-config\\\\n'.format(CEOS[_node].system_mac, CONFIGS, _tag, _node))\\n if _tfa_version > 1:\\n create_output.append('echo \\\"TFA_VERSION={0}\\\" >> {1}/{2}/{3}/ceos-config\\\\n'.format(_tfa_version, CONFIGS, _tag, _node))\\n # Perform check to see if a bare startup-config needs to be created\\n if create_startup:\\n create_output.append(\\\"# Creating a bare startup configuration for {0}\\\\n\\\".format(_node))\\n _tmp_startup = []\\n _tmp_startup.append(BASE_STARTUP.format(CEOS[_node].ceos_name))\\n if mgmt_network:\\n _tmp_startup.append(BASE_MGMT.format(CEOS[_node].ip, topo_yaml['infra']['gateway']))\\n if 'cvpaddress' and 'cvp-key' in topo_yaml['topology']:\\n _tmp_startup.append(BASE_TERMINATTR.format(topo_yaml['topology']['cvpaddress'], topo_yaml['topology']['cvp-key']))\\n create_output.append('echo \\\"{0}\\\" > {1}/{2}/{3}/startup-config\\\\n'.format(''.join(_tmp_startup), CONFIGS, _tag, _node))\\n # Creating anchor containers\\n create_output.append(\\\"# Getting {0} nodes plumbing\\\\n\\\".format(_node))\\n create_output.append(\\\"docker run -d --restart=always --log-opt max-size=10k --name={0}-net --net=none busybox /bin/init\\\\n\\\".format(CEOS[_node].ceos_name))\\n startup_output.append(\\\"docker start {0}-net\\\\n\\\".format(CEOS[_node].ceos_name))\\n create_output.append(\\\"{0}pid=$(docker inspect --format '{{{{.State.Pid}}}}' {0}-net)\\\\n\\\".format(CEOS[_node].ceos_name))\\n create_output.append(\\\"sudo ln -sf /proc/${{{0}pid}}/ns/net /var/run/netns/{0}\\\\n\\\".format(CEOS[_node].ceos_name))\\n # Stop cEOS containers\\n startup_output.append(\\\"docker stop {0}\\\\n\\\".format(CEOS[_node].ceos_name))\\n stop_output.append(\\\"docker stop {0}\\\\n\\\".format(CEOS[_node].ceos_name))\\n stop_output.append(\\\"docker stop {0}-net\\\\n\\\".format(CEOS[_node].ceos_name))\\n delete_output.append(\\\"docker stop {0}\\\\n\\\".format(CEOS[_node].ceos_name))\\n delete_output.append(\\\"docker stop {0}-net\\\\n\\\".format(CEOS[_node].ceos_name))\\n # Remove cEOS containers\\n startup_output.append(\\\"docker rm {0}\\\\n\\\".format(CEOS[_node].ceos_name))\\n delete_output.append(\\\"docker rm {0}\\\\n\\\".format(CEOS[_node].ceos_name))\\n delete_output.append(\\\"docker rm {0}-net\\\\n\\\".format(CEOS[_node].ceos_name))\\n delete_net_output.append(\\\"sudo rm -rf /var/run/netns/{0}\\\\n\\\".format(CEOS[_node].ceos_name))\\n startup_output.append(\\\"{0}pid=$(docker inspect --format '{{{{.State.Pid}}}}' {0}-net)\\\\n\\\".format(CEOS[_node].ceos_name))\\n startup_output.append(\\\"sudo ln -sf /proc/${{{0}pid}}/ns/net /var/run/netns/{0}\\\\n\\\".format(CEOS[_node].ceos_name))\\n create_output.append(\\\"# Connecting cEOS containers together\\\\n\\\")\\n # Output veth commands\\n for _intf in CEOS[_node].intfs:\\n _tmp_intf = CEOS[_node].intfs[_intf]\\n if _node in _tmp_intf['veth'].split('-')[0]:\\n create_output.append(\\\"sudo ip link set {0} netns {1} name {2} up\\\\n\\\".format(_tmp_intf['veth'].split('-')[0], CEOS[_node].ceos_name, _tmp_intf['port']))\\n startup_output.append(\\\"sudo ip link set {0} netns {1} name {2} up\\\\n\\\".format(_tmp_intf['veth'].split('-')[0], CEOS[_node].ceos_name, _tmp_intf['port']))\\n else:\\n create_output.append(\\\"sudo ip link set {0} netns {1} name {2} up\\\\n\\\".format(_tmp_intf['veth'].split('-')[1], CEOS[_node].ceos_name, _tmp_intf['port']))\\n startup_output.append(\\\"sudo ip link set {0} netns {1} name {2} up\\\\n\\\".format(_tmp_intf['veth'].split('-')[1], CEOS[_node].ceos_name, _tmp_intf['port']))\\n # Perform check if mgmt network is available\\n if mgmt_network:\\n # Get MGMT VETHS\\n create_output.append(\\\"sudo ip link add {0}-eth0 type veth peer name {0}-mgmt\\\\n\\\".format(CEOS[_node].ceos_name))\\n create_output.append(\\\"sudo brctl addif {0} {1}-mgmt\\\\n\\\".format(mgmt_network, CEOS[_node].ceos_name))\\n create_output.append(\\\"sudo ip link set {0}-eth0 netns {0} name eth0 up\\\\n\\\".format(CEOS[_node].ceos_name))\\n create_output.append(\\\"sudo ip link set {0}-mgmt up\\\\n\\\".format(CEOS[_node].ceos_name))\\n create_output.append(\\\"sleep 1\\\\n\\\")\\n create_output.append(\\\"docker run -d --name={0} --log-opt max-size=1m --net=container:{0}-net --ip {1} --privileged -v /etc/sysctl.d/99-zceos.conf:/etc/sysctl.d/99-zceos.conf:ro -v {2}/{4}/{5}:/mnt/flash:Z -e INTFTYPE=et -e MGMT_INTF=eth0 -e ETBA=1 -e SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 -e CEOS=1 -e EOS_PLATFORM=ceoslab -e container=docker -i -t ceosimage:{3} /sbin/init systemd.setenv=INTFTYPE=et systemd.setenv=MGMT_INTF=eth0 systemd.setenv=ETBA=1 systemd.setenv=SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 systemd.setenv=CEOS=1 systemd.setenv=EOS_PLATFORM=ceoslab systemd.setenv=container=docker\\\\n\\\".format(CEOS[_node].ceos_name, CEOS[_node].ip, CONFIGS, CEOS[_node].image, _tag, _node))\\n startup_output.append(\\\"sudo ip link add {0}-eth0 type veth peer name {0}-mgmt\\\\n\\\".format(CEOS[_node].ceos_name))\\n startup_output.append(\\\"sudo brctl addif {0} {1}-mgmt\\\\n\\\".format(mgmt_network, CEOS[_node].ceos_name))\\n startup_output.append(\\\"sudo ip link set {0}-eth0 netns {0} name eth0 up\\\\n\\\".format(CEOS[_node].ceos_name))\\n startup_output.append(\\\"sudo ip link set {0}-mgmt up\\\\n\\\".format(CEOS[_node].ceos_name))\\n startup_output.append(\\\"sleep 1\\\\n\\\")\\n startup_output.append(\\\"docker run -d --name={0} --log-opt max-size=1m --net=container:{0}-net --ip {1} --privileged -v /etc/sysctl.d/99-zceos.conf:/etc/sysctl.d/99-zceos.conf:ro -v {2}/{4}/{5}:/mnt/flash:Z -e INTFTYPE=et -e MGMT_INTF=eth0 -e ETBA=1 -e SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 -e CEOS=1 -e EOS_PLATFORM=ceoslab -e container=docker -i -t ceosimage:{3} /sbin/init systemd.setenv=INTFTYPE=et systemd.setenv=MGMT_INTF=eth0 systemd.setenv=ETBA=1 systemd.setenv=SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 systemd.setenv=CEOS=1 systemd.setenv=EOS_PLATFORM=ceoslab systemd.setenv=container=docker\\\\n\\\".format(CEOS[_node].ceos_name, CEOS[_node].ip, CONFIGS, CEOS[_node].image, _tag, _node))\\n else:\\n create_output.append(\\\"sleep 1\\\\n\\\")\\n create_output.append(\\\"docker run -d --name={0} --log-opt max-size=1m --net=container:{0}-net --privileged -v /etc/sysctl.d/99-zceos.conf:/etc/sysctl.d/99-zceos.conf:ro -v {1}/{3}/{4}:/mnt/flash:Z -e INTFTYPE=et -e MGMT_INTF=eth0 -e ETBA=1 -e SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 -e CEOS=1 -e EOS_PLATFORM=ceoslab -e container=docker -i -t ceosimage:{2} /sbin/init systemd.setenv=INTFTYPE=et systemd.setenv=MGMT_INTF=eth0 systemd.setenv=ETBA=1 systemd.setenv=SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 systemd.setenv=CEOS=1 systemd.setenv=EOS_PLATFORM=ceoslab systemd.setenv=container=docker\\\\n\\\".format(CEOS[_node].ceos_name, CONFIGS, CEOS[_node].image, _tag, _node))\\n startup_output.append(\\\"sleep 1\\\\n\\\")\\n startup_output.append(\\\"docker run -d --name={0} --log-opt max-size=1m --net=container:{0}-net --privileged -v /etc/sysctl.d/99-zceos.conf:/etc/sysctl.d/99-zceos.conf:ro -v {1}/{3}/{4}:/mnt/flash:Z -e INTFTYPE=et -e MGMT_INTF=eth0 -e ETBA=1 -e SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 -e CEOS=1 -e EOS_PLATFORM=ceoslab -e container=docker -i -t ceosimage:{2} /sbin/init systemd.setenv=INTFTYPE=et systemd.setenv=MGMT_INTF=eth0 systemd.setenv=ETBA=1 systemd.setenv=SKIP_ZEROTOUCH_BARRIER_IN_SYSDBINIT=1 systemd.setenv=CEOS=1 systemd.setenv=EOS_PLATFORM=ceoslab systemd.setenv=container=docker\\\\n\\\".format(CEOS[_node].ceos_name, CONFIGS, CEOS[_node].image, _tag, _node))\\n # Create initial host anchor containers\\n for _host in HOSTS:\\n create_output.append(\\\"# Getting {0} nodes plumbing\\\\n\\\".format(_host))\\n create_output.append(\\\"docker run -d --restart=always --log-opt max-size=10k --name={0}-net --net=none busybox /bin/init\\\\n\\\".format(HOSTS[_host].c_name))\\n startup_output.append(\\\"docker start {0}-net\\\\n\\\".format(HOSTS[_host].c_name))\\n create_output.append(\\\"{0}pid=$(docker inspect --format '{{{{.State.Pid}}}}' {0}-net)\\\\n\\\".format(HOSTS[_host].c_name))\\n create_output.append(\\\"sudo ln -sf /proc/${{{0}pid}}/ns/net /var/run/netns/{0}\\\\n\\\".format(HOSTS[_host].c_name))\\n # Stop host containers\\n startup_output.append(\\\"docker stop {0}\\\\n\\\".format(HOSTS[_host].c_name))\\n stop_output.append(\\\"docker stop {0}\\\\n\\\".format(HOSTS[_host].c_name))\\n stop_output.append(\\\"docker stop {0}-net\\\\n\\\".format(HOSTS[_host].c_name))\\n delete_output.append(\\\"docker stop {0}\\\\n\\\".format(HOSTS[_host].c_name))\\n delete_output.append(\\\"docker stop {0}-net\\\\n\\\".format(HOSTS[_host].c_name))\\n # Remove host containers\\n startup_output.append(\\\"docker rm {0}\\\\n\\\".format(HOSTS[_host].c_name))\\n delete_output.append(\\\"docker rm {0}\\\\n\\\".format(HOSTS[_host].c_name))\\n delete_output.append(\\\"docker rm {0}-net\\\\n\\\".format(HOSTS[_host].c_name))\\n startup_output.append(\\\"{0}pid=$(docker inspect --format '{{{{.State.Pid}}}}' {0}-net)\\\\n\\\".format(HOSTS[_host].c_name))\\n startup_output.append(\\\"sudo ln -sf /proc/${{{0}pid}}/ns/net /var/run/netns/{0}\\\\n\\\".format(HOSTS[_host].c_name))\\n create_output.append(\\\"# Connecting host containers together\\\\n\\\")\\n # Output veth commands\\n for _intf in HOSTS[_host].intfs:\\n _tmp_intf = HOSTS[_host].intfs[_intf]\\n if _host in _tmp_intf['veth'].split('-')[0]:\\n create_output.append(\\\"sudo ip link set {0} netns {1} name {2} up\\\\n\\\".format(_tmp_intf['veth'].split('-')[0], HOSTS[_host].c_name, _tmp_intf['port']))\\n startup_output.append(\\\"sudo ip link set {0} netns {1} name {2} up\\\\n\\\".format(_tmp_intf['veth'].split('-')[0], HOSTS[_host].c_name, _tmp_intf['port']))\\n else:\\n create_output.append(\\\"sudo ip link set {0} netns {1} name {2} up\\\\n\\\".format(_tmp_intf['veth'].split('-')[1], HOSTS[_host].c_name, _tmp_intf['port']))\\n startup_output.append(\\\"sudo ip link set {0} netns {1} name {2} up\\\\n\\\".format(_tmp_intf['veth'].split('-')[1], HOSTS[_host].c_name, _tmp_intf['port']))\\n create_output.append(\\\"sleep 1\\\\n\\\")\\n create_output.append(\\\"docker run -d --name={0} --privileged --log-opt max-size=1m --net=container:{0}-net -e HOSTNAME={0} -e HOST_IP={1} -e HOST_MASK={3} -e HOST_GW={4} chost:{2} ipnet\\\\n\\\".format(HOSTS[_host].c_name, HOSTS[_host].ip, HOSTS[_host].image, HOSTS[_host].mask, HOSTS[_host].gw))\\n startup_output.append(\\\"sleep 1\\\\n\\\")\\n startup_output.append(\\\"docker run -d --name={0} --privileged --log-opt max-size=1m --net=container:{0}-net -e HOSTNAME={0} -e HOST_IP={1} -e HOST_MASK={3} -e HOST_GW={4} chost:{2} ipnet\\\\n\\\".format(HOSTS[_host].c_name, HOSTS[_host].ip, HOSTS[_host].image, HOSTS[_host].mask, HOSTS[_host].gw))\\n # Check for iPerf3 commands\\n if topo_yaml['iperf']:\\n _iperf = topo_yaml['iperf']\\n _port = _iperf['port']\\n _brate = _iperf['brate']\\n for _server in _iperf['servers']:\\n create_output.append(\\\"docker exec -d {0} iperf3 -s -p {1}\\\\n\\\".format(HOSTS[_server].c_name, _port))\\n startup_output.append(\\\"docker exec -d {0} iperf3 -s -p {1}\\\\n\\\".format(HOSTS[_server].c_name, _port))\\n for _client in _iperf['clients']:\\n _target = topo_yaml['hosts'][_client['target']]['ipaddress']\\n create_output.append(\\\"docker exec -d {0} iperf3client {1} {2} {3}\\\\n\\\".format(HOSTS[_client['client']].c_name, _target, _port, _brate))\\n startup_output.append(\\\"docker exec -d {0} iperf3client {1} {2} {3}\\\\n\\\".format(HOSTS[_client['client']].c_name, _target, _port, _brate))\\n # Create the initial deployment files\\n with open(CEOS_SCRIPTS + '/{0}/Create.sh'.format(_tag), 'w') as cout:\\n for _create in create_output:\\n cout.write(_create)\\n with open(CEOS_SCRIPTS + '/{0}/Startup.sh'.format(_tag), 'w') as cout:\\n for _start in startup_output:\\n cout.write(_start)\\n with open(CEOS_SCRIPTS + '/{0}/Stop.sh'.format(_tag), 'w') as cout:\\n for _stop in stop_output:\\n cout.write(_stop)\\n with open(CEOS_SCRIPTS + '/{0}/Delete.sh'.format(_tag), 'w') as cout:\\n for _delete in delete_output:\\n cout.write(_delete)\\n for _delete in delete_net_output:\\n cout.write(_delete)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e525c13c9e792024a5a418c70244fcc\",\n \"score\": \"0.4641946\",\n \"text\": \"def topology (self):\\n self.log.info(\\\"Call %s function: topology\\\" % self.LOGGER_NAME)\\n # Forward call to main layer class\\n topology = self._proceed_API_call('api_sas_get_topology')\\n if topology is None:\\n self.send_error(404, message=\\\"Resource info is missing!\\\")\\n return\\n # Setup OK status for HTTP response\\n self.send_response(200)\\n if topology is False:\\n self.log.info(\\n \\\"Requested resource has not changed! Respond with cached topology...\\\")\\n if self.virtualizer_format_enabled:\\n data = self.server.last_response.xml()\\n else:\\n data = self.server.last_response.dump()\\n else:\\n if self.virtualizer_format_enabled:\\n self.log.debug(\\\"Convert internal NFFG to Virtualizer...\\\")\\n converter = NFFGConverter(domain=None, logger=log)\\n # Dump to plain text format\\n v_topology = converter.dump_to_Virtualizer(nffg=topology)\\n # Cache converted data for edit-config patching\\n self.log.debug(\\\"Cache converted topology...\\\")\\n self.server.last_response = v_topology\\n # Dump to plain text format\\n data = v_topology.xml()\\n # Setup HTTP response format\\n else:\\n self.log.debug(\\\"Cache converted topology...\\\")\\n self.server.last_response = topology\\n data = topology.dump()\\n if self.virtualizer_format_enabled:\\n self.send_header('Content-Type', 'application/xml')\\n else:\\n self.send_header('Content-Type', 'application/json')\\n self.log.log(VERBOSE, \\\"Responded topology for 'get-config':\\\\n%s\\\" % data)\\n # Setup length for HTTP response\\n self.send_header('Content-Length', len(data))\\n self.end_headers()\\n self.log.info(\\\"Send back topology description...\\\")\\n self.wfile.write(data)\\n self.log.debug(\\\"%s function: get-config ended!\\\" % self.LOGGER_NAME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a856608789fa792161e2084c7a8d3777\",\n \"score\": \"0.46241966\",\n \"text\": \"def maketopo():\\n nxpoints=200\\n nypoints=200\\n xupper=2.e0\\n yupper=2.e0\\n xlower = -2.e0\\n ylower = -2.e0\\n outfile= \\\"bowl.topotype2\\\"\\n\\n topography = Topography(topo_func=topo)\\n topography.x = linspace(xlower,xupper,nxpoints)\\n topography.y = linspace(ylower,yupper,nypoints)\\n topography.write(outfile, topo_type=2, Z_format=\\\"%22.15e\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0c30869cfa1d840294e99235270f209\",\n \"score\": \"0.4614379\",\n \"text\": \"def _populate_bin(self, mount_point):\\n for script_filename, tensor_to_tensors in [('inputs', self._create_tensor_to_inputs),\\n ('outputs', self._create_tensor_to_outputs)]:\\n script = _read_template_script('print_tensor_dependencies.py',\\n MOUNT_POINT_PLACEHOLDER=\\\"'{}/'\\\".format(mount_point),\\n TENSOR_TO_TENSORS_PLACEHOLDER=str(tensor_to_tensors()))\\n self._bin_scripts['/bin/' + script_filename] = script\\n self._files['/bin/' + script_filename] = _create_script_file(script)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1f240d5fc2079d31d48d2bae9e65276\",\n \"score\": \"0.46109778\",\n \"text\": \"def generate_topology(self, name='topol.top', xlinked=False, restrained=False):\\n\\n if restrained:\\n r = restrain.RestrainedTopology(self.gro_name, self.restraint_residue, self.build_restraints, com=False,\\n xlink=xlinked, vparams=None)\\n r.add_position_restraints('xyz', self.restraints)\\n r.write_topology()\\n\\n self.topology = SystemTopology(self.gro_name, xlink=xlinked, restraints=[self.restraint_residue])\\n self.topology.write_top(name=name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b1a402ee04ff032ed1ac469bfad8db8\",\n \"score\": \"0.4560311\",\n \"text\": \"def make_object(self, data):\\n return OperationScript(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b1a402ee04ff032ed1ac469bfad8db8\",\n \"score\": \"0.4560311\",\n \"text\": \"def make_object(self, data):\\n return OperationScript(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b1a402ee04ff032ed1ac469bfad8db8\",\n \"score\": \"0.4560311\",\n \"text\": \"def make_object(self, data):\\n return OperationScript(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"918281b18af420ed221fcbab10221621\",\n \"score\": \"0.45487487\",\n \"text\": \"def configure_junosphere(self):\\n LOG.debug(\\\"Configuring Junosphere\\\") \\n vmm_template = lookup.get_template(\\\"junos/topology_vmm.mako\\\")\\n topology_data = {}\\n # Generator for private0, private1, etc\\n collision_to_bridge_mapping = {}\\n private_bridges = []\\n junosphere_predefined_bridge_count = 124 # have to explicitly create bridges past 124\\n\\n image_tuple = namedtuple('image', \\\"alias, basedisk\\\")\\n\\n if self.junosphere_olive:\\n image = image_tuple(\\\"MY_DISK\\\", config.settings['Junosphere']['basedisk'])\\n else:\\n image = image_tuple(\\\"VJX1000_LATEST\\\", None)\\n\\n\\n bridge_id_generator = (i for i in itertools.count(0))\\n def next_bridge_id():\\n bridge_id = bridge_id_generator.next()\\n retval = \\\"private%s\\\" % bridge_id\\n if bridge_id > junosphere_predefined_bridge_count:\\n private_bridges.append(retval)\\n return retval\\n\\n for device in sorted(self.network.devices(), key = lambda x: x.fqdn):\\n hostname = device.hostname\\n topology_data[hostname] = {\\n 'image': image.alias,\\n 'config': router_conf_file(self.network, device),\\n 'interfaces': [],\\n }\\n for src, dst, data in sorted(self.network.graph.edges(device, data=True), key = lambda (s,t,d): t.fqdn):\\n subnet = data['sn']\\n description = 'Interface %s -> %s' % (\\n ank.fqdn(self.network, src), \\n ank.fqdn(self.network, dst))\\n# Bridge information for topology config\\n if subnet in collision_to_bridge_mapping:\\n# Use bridge allocated for this subnet\\n bridge_id = collision_to_bridge_mapping[subnet]\\n else:\\n# Allocate a bridge for this subnet\\n bridge_id = next_bridge_id()\\n collision_to_bridge_mapping[subnet] = bridge_id\\n\\n if not self.junosphere_olive:\\n description += \\\"(%s)\\\" % self.int_id(data['id']) \\n\\n topology_data[hostname]['interfaces'].append({\\n 'description': description,\\n 'id': self.int_id_em(data['id']),\\n 'id_ge': self.int_id(data['id']),\\n 'bridge_id': bridge_id,\\n })\\n\\n if self.junosphere_olive:\\n# em2 is dead on Olive Junosphere platform\\n topology_data[hostname]['interfaces'].append({\\n 'description': \\\"dead interface\\\",\\n 'id': \\\"em2\\\",\\n 'bridge_id': \\\"dead\\\",\\n })\\n \\n vmm_file = os.path.join(lab_dir(), \\\"topology.vmm\\\")\\n with open( vmm_file, 'wb') as f_vmm:\\n f_vmm.write( vmm_template.render(\\n topology_data = topology_data,\\n private_bridges = private_bridges,\\n image = image,\\n olive_based = self.junosphere_olive,\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80f381beed0799261fc2b6bc390b1329\",\n \"score\": \"0.4512595\",\n \"text\": \"def Run(args):\\n\\n args = args[list(args.keys())[0]]\\n ###print args #debug\\n \\n\\n oobj = Syntax([\\n Template(\\\"YVARS\\\", subc=\\\"\\\", ktype=\\\"existingvarlist\\\", var=\\\"yvars\\\", islist=True),\\n Template(\\\"XVARS\\\", subc=\\\"\\\", ktype=\\\"existingvarlist\\\", var=\\\"xvars\\\", islist=True),\\n Template(\\\"COLOR\\\", subc=\\\"\\\", ktype=\\\"existingvarlist\\\", var=\\\"color\\\"),\\n Template(\\\"SIZE\\\", subc=\\\"\\\", ktype=\\\"existingvarlist\\\", var=\\\"size\\\"),\\n Template(\\\"SHAPE\\\", subc=\\\"\\\", ktype=\\\"existingvarlist\\\", var=\\\"shape\\\"),\\n Template(\\\"LABEL\\\", subc=\\\"\\\", ktype=\\\"existingvarlist\\\", var=\\\"label\\\"),\\n Template(\\\"LINEAR\\\", subc=\\\"FITLINES\\\", ktype=\\\"bool\\\", var=\\\"linear\\\"),\\n Template(\\\"QUADRATIC\\\", subc=\\\"FITLINES\\\", ktype=\\\"bool\\\", var=\\\"quadratic\\\"),\\n Template(\\\"CUBIC\\\", subc=\\\"FITLINES\\\", ktype=\\\"bool\\\", var=\\\"cubic\\\"),\\n Template(\\\"LOESS\\\", subc=\\\"FITLINES\\\", ktype=\\\"bool\\\", var=\\\"loess\\\"),\\n Template(\\\"IGNORE\\\", subc=\\\"FITLINES\\\", ktype=\\\"str\\\", var=\\\"ignore\\\"),\\n Template(\\\"APPLYTO\\\", subc=\\\"FITLINES\\\", ktype=\\\"str\\\", var=\\\"applyfitto\\\", vallist=[\\\"total\\\", \\\"group\\\"]),\\n Template(\\\"CATEGORICAL\\\", subc=\\\"OPTIONS\\\", ktype=\\\"str\\\", var=\\\"categorical\\\", vallist=[\\\"bars\\\", \\\"lines\\\", \\\"boxplot\\\"]),\\n Template(\\\"GROUP\\\", subc=\\\"OPTIONS\\\", ktype=\\\"int\\\", var=\\\"group\\\"),\\n Template(\\\"BOXPLOTS\\\", subc=\\\"OPTIONS\\\", ktype=\\\"bool\\\", var=\\\"boxplots\\\"),\\n Template(\\\"HEXBIN\\\", subc=\\\"OPTIONS\\\", ktype=\\\"bool\\\", var=\\\"hexbin\\\"),\\n Template(\\\"TITLE\\\", subc=\\\"OPTIONS\\\", ktype=\\\"literal\\\", var=\\\"title\\\"),\\n Template(\\\"INDENT\\\", subc=\\\"OPTIONS\\\", ktype=\\\"int\\\", var=\\\"indent\\\", vallist=[0, 50]),\\n Template(\\\"YSCALE\\\", subc=\\\"OPTIONS\\\", ktype=\\\"int\\\", var=\\\"yscale\\\", vallist=[50, 100]),\\n Template(\\\"PAGEX\\\", subc=\\\"OPTIONS\\\", ktype=\\\"float\\\", var=\\\"pagex\\\", vallist=[1]),\\n Template(\\\"PAGEY\\\", subc=\\\"OPTIONS\\\", ktype=\\\"float\\\", var=\\\"pagey\\\", vallist=[1]),\\n Template(\\\"HELP\\\", subc=\\\"\\\", ktype=\\\"bool\\\")])\\n \\n # ensure localization function is defined\\n global _\\n try:\\n _(\\\"---\\\")\\n except:\\n def _(msg):\\n return msg\\n\\n # A HELP subcommand overrides all else\\n if \\\"HELP\\\" in args:\\n #print helptext\\n helper()\\n else:\\n processcmd(oobj, args, plots, vardict=spssaux.VariableDict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca9d9c073a8aa850c9b942946fbe7096\",\n \"score\": \"0.4506192\",\n \"text\": \"def get_score_tcl(coord_path, struct_path):\\n tcl = \\\"\\\"\\\"coordinates %s\\nstructure %s\\nparaTypeCharmm on\\nparameters %s\\ntemperature 300.0\\ngbis on\\nalphaCutoff 15.0\\nionConcentration 1.0\\nGBISDelta 0.8\\nGBISBeta 0.0\\nGBISGamma 2.90912\\nexclude scaled1-4\\n1-4scaling 1.0\\ncutoff 16.0\\nswitching off\\npairlistdist 24\\nPME no\\nfixedAtoms on\\noutputName not_used\\nbinaryoutput no\\nrun 0\\n\\\"\\\"\\\" % (coord_path, struct_path, FORCE_FIELD_PATH)\\n return tcl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26fd0556101ec62f6e3aec42499326f3\",\n \"score\": \"0.4505297\",\n \"text\": \"def setup_host(bridge='br0', int_port='p0', int_ip=None, ip='', \\n contr_addr=None, connections=None):\\n print \\\"Setting up ovs for {}\\\".format(ip)\\n #read the template file\\n if contr_addr is None:\\n template_name = 'config_ovs_standalone.template.sh'\\n else:\\n template_name = 'config_ovs.template.sh'\\n with open(template_name) as template_file:\\n template = Template(template_file.read())\\n\\n #instantiate the template\\n #create ovs-setup script for this host\\n script_text = template.render(bridge=bridge, contr_addr=contr_addr, \\n int_port=int_port, int_ip=int_ip, connections=connections)\\n \\n #write the script to an actual file\\n script_name = \\\"config_ovs.sh\\\"\\n with open(script_name, \\\"w\\\") as script:\\n script.write(script_text) \\n\\n #call ansible\\n results = ansible_wrapper.playbook(playbook='ovs_config_play.yaml', hosts=[ip])\\n #print results\\n print results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b59486e672066b07fe47188f590e50d9\",\n \"score\": \"0.44782212\",\n \"text\": \"def __call__(self, taskObject):\\n if taskObject['Type'] != \\\"StageOut\\\":\\n return\\n # //\\n # // Lists of pre stage out and post stage out shell commands\\n #// for setting up stage out env\\n # //Should be bash shell commands as strings\\n taskObject['PreStageOutCommands'] = []\\n taskObject['PostStageOutCommands'] = []\\n\\n # //\\n # // Determine what is being staged out from the parent node\\n #// (This should be a CMSSW node)\\n parent = taskObject.parent\\n if parent == None:\\n # no parent => dont know what to stage out\\n return\\n\\n if parent['Type'] not in (\\\"CMSSW\\\", \\\"SVSuite\\\"):\\n # parent isnt a CMSSW node, dont know what it does...\\n return\\n stageOutFor = parent['Name']\\n taskObject['StageOutFor'] = taskObject['PayloadNode'].configuration\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b080d63eaec2a94da81577af536fd5ad\",\n \"score\": \"0.44656765\",\n \"text\": \"def _get_master_script_template(self):\\n \\n commands=\\\"\\\"\\\"#!/bin/bash\\n\\nfunction go {\\n hostname\\n\\n ls /opt/astro/SL53/bin/setup.astro.sh\\n\\n gmvers=\\\"%(version)s\\\"\\n module unload gmix_image && module load gmix_image/work\\n module unload psfex && module load psfex/work\\n module unload meds && module load meds/work\\n module unload gmix_meds && module load gmix_meds/$gmvers\\n\\n\\n confname=\\\"%(config)s.yaml\\\"\\n conf=\\\"$GMIX_MEDS_DIR/share/config/$confname\\\"\\n\\n python -u $GMIX_MEDS_DIR/bin/gmix-fit-meds \\\\\\\\\\n --obj-range $start,$end \\\\\\\\\\n --work-dir $tmpdir \\\\\\\\\\n $conf $meds_file $out_file\\n \\n exit_status=$?\\n\\n}\\n\\n#nsetup_ess\\nsource ~/.bashrc\\n\\nif [ $# -lt 5 ]; then\\n echo \\\"error: meds_file start end out_file\\\"\\n exit 1\\nfi\\n\\n# this can be a list\\nmeds_file=\\\"$1\\\"\\nstart=\\\"$2\\\"\\nend=\\\"$3\\\"\\nout_file=\\\"$4\\\"\\nlog_file=\\\"$5\\\"\\n\\nif [[ -n $_CONDOR_SCRATCH_DIR ]]; then\\n tmpdir=$_CONDOR_SCRATCH_DIR\\nelse\\n tmpdir=$TMPDIR\\nfi\\n\\noutdir=$(dirname $out_file)\\nmkdir -p $outdir\\n\\nlbase=$(basename $log_file)\\ntmplog=\\\"$tmpdir/$lbase\\\"\\n\\ngo &> \\\"$tmplog\\\"\\ncp \\\"$tmplog\\\" \\\"$log_file\\\"\\n\\nexit $exit_status\\\\n\\\"\\\"\\\"\\n\\n return commands\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48d76382f1dc72fa140deeb279e9eeea\",\n \"score\": \"0.44400838\",\n \"text\": \"def cmd_from_template(node, vat_template_file, json_param=True, **vat_args):\\n with VatTerminal(node, json_param=json_param) as vat:\\n return vat.vat_terminal_exec_cmd_from_template(\\n vat_template_file, **vat_args\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"387b444e71bb225d1039d41c2da20704\",\n \"score\": \"0.4438441\",\n \"text\": \"def write_tcl_one(options, A, tag):\\n with open(\\\"POSCAR_A.%s\\\" % tag, \\\"w\\\") as f: pcwrite.poscar(A, f, vasp5=True)\\n write_xyz(options, A, \\\"A.%s\\\" % tag,options.output_tiles)\\n\\n fout = file(\\\"struct.%s.tcl\\\" % tag, \\\"w\\\")\\n center = False\\n write_struct(fout, A, \\\"A.%s.xyz\\\" % tag, 0, center)\\n fout.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbefe8e0b5e2c5b9ab19957df3d5d2e8\",\n \"score\": \"0.44263136\",\n \"text\": \"def acStartAutoscript():\\n\\n dctData = ImmutableMultiDict(request.values)\\n dctData = ImmutableMultiDict(dctData).to_dict()\\n acConProtocol = dctData['protocol'] \\n acConIp = dctData['conIp']\\n acConPort = dctData['conPort']\\n\\n try:\\n objAutoScriptObject = clsMessageObjects()\\n for acKeyName, objName in _Protocol_class.G_dctAutoScriptOnlyZMQ_Objects.items():\\n objAutoScriptObject.vUnbindPubSoc(objName, acKeyName)\\n\\n except Exception as E:\\n logging.error(\\\"can not unbind from this IP, error -> %s\\\", E)\\n\\n acAutoScriptingPath = os.path.join(os.path.dirname(__file__), \\\"./scriptingFiles/\\\")\\n acAutoScriptFiles = os.listdir(acAutoScriptingPath)\\n for iIndex, acItem in enumerate(acAutoScriptFiles):\\n if acItem[-5:] == \\\".json\\\":\\n acScriptFileName = acItem[:-5]\\n try:\\n with open(os.path.join(os.path.dirname(__file__), \\\"./scriptingFiles/\\\" + acItem), 'r') as f:\\n dctFileData = json.load(f)\\n\\n \\n for messageKey, messageContent in dctFileData.items():\\n if acConProtocol == \\\"MQTT\\\":\\n dctFileData[messageKey][\\\"Connection\\\"].update({\\\"Protocol\\\": acConProtocol})\\n dctFileData[messageKey].update({\\\"Individual_Message_Protocol\\\": acConProtocol})\\n dctFileData[messageKey][\\\"Connection\\\"].update({\\\"MQTTRemoteIP\\\": acConIp})\\n dctFileData[messageKey][\\\"Connection\\\"].update({\\\"MQTTPort\\\": acConPort})\\n elif acConProtocol == \\\"ZMQ\\\":\\n dctFileData[messageKey][\\\"Connection\\\"].update({\\\"Protocol\\\": acConProtocol})\\n dctFileData[messageKey].update({\\\"Individual_Message_Protocol\\\": acConProtocol})\\n dctFileData[messageKey][\\\"Connection\\\"].update({\\\"ZeroMQRemoteIPPub\\\": acConIp})\\n dctFileData[messageKey][\\\"Connection\\\"].update({\\\"ZeroMQPortPub\\\": acConPort})\\n elif acConProtocol == \\\"previous\\\":\\n pass\\n\\n _Protocol_class.G_dctJsonAutoScriptFile.update({acScriptFileName: dctFileData})\\n f.close()\\n\\n except Exception as E:\\n logging.error(\\\"unable to write to file, error -> %s\\\", E)\\n return 'error'\\n\\n try:\\n with open(os.path.join(os.path.dirname(__file__),\\\"./scriptingFiles/\\\"+acScriptFileName+\\\".json\\\"), 'w') as f:\\n f.write(json.dumps(_Protocol_class.G_dctJsonAutoScriptFile[acScriptFileName]))\\n f.close()\\n except Exception as E:\\n logging.error(\\\"unable to write to file, error -> %s\\\", E)\\n return 'error'\\n\\n objNewAutoScriptObject = clsMessageObjects()\\n acConnectionMessage = objNewAutoScriptObject.vMakeConnection()\\n\\n if acConnectionMessage == \\\"error\\\":\\n return \\\"error\\\"\\n return 'Auto Script started'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aca718bc40db834783a24d1c632e52c5\",\n \"score\": \"0.44044036\",\n \"text\": \"def test_write_xml_from_topology(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b93ccd2255198742a3d087a721caa757\",\n \"score\": \"0.44002232\",\n \"text\": \"def create_simulation_script(case_name, n_tasks, tasks_per_node, threads_per_core, partition, modules, scripts):\\r\\n\\r\\n header = create_script_header(n_tasks, tasks_per_node, threads_per_core, partition, \\\"{0}_sim\\\".format(case_name))\\r\\n\\r\\n module_string = create_script_modules(modules, scripts)\\r\\n\\r\\n if n_tasks > 1:\\r\\n commands = \\\"\\\"\\\"decomposePar\\r\\nmpirun -np {0} simpleFoam -parallel | tee simpleFoam.log\\r\\nreconstructPar\\r\\nrm -rf processor*\\\\n\\\"\\\"\\\".format(n_tasks)\\r\\n else:\\r\\n commands = \\\"simpleFoam | tee simpleFoam.log\\\\n\\\"\\r\\n\\r\\n commands += \\\"foamToVTK -latestTime -ascii\\\\n\\\"\\r\\n\\r\\n script = open(\\\"runSimulations.sh\\\", \\\"w\\\")\\r\\n script.write(header)\\r\\n script.write(module_string)\\r\\n script.write(commands)\\r\\n script.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b81fab7e7f133435c3c3617646de9b7d\",\n \"score\": \"0.43991858\",\n \"text\": \"def __call__(self, taskObject):\\n if taskObject['Type'] != \\\"StageOut\\\":\\n return\\n\\n\\n # //\\n # // Pre and Post Stage out commands\\n #//\\n precomms = taskObject.get(\\\"PreStageOutCommands\\\", [])\\n postcomms = taskObject.get(\\\"PostStageOutCommands\\\", [])\\n\\n\\n # //\\n # // Install the main script\\n #//\\n srcfile = inspect.getsourcefile(NewRuntimeStageOut)\\n if not os.access(srcfile, os.X_OK):\\n os.system(\\\"chmod +x %s\\\" % srcfile)\\n taskObject.attachFile(srcfile)\\n\\n # //\\n # // Failure script\\n #//\\n fsrcfile = inspect.getsourcefile(RuntimeStageOutFailure)\\n if not os.access(fsrcfile, os.X_OK):\\n os.system(\\\"chmod +x %s\\\" % fsrcfile)\\n taskObject.attachFile(fsrcfile)\\n\\n exeScript = taskObject[taskObject['Executable']]\\n\\n envScript = taskObject[taskObject[\\\"BashEnvironment\\\"]]\\n envCommand = \\\"%s %s\\\" % (envScript.interpreter, envScript.name)\\n exeScript.append(envCommand)\\n\\n for precomm in precomms:\\n exeScript.append(str(precomm))\\n exeScript.append(\\\"chmod +x ./RuntimeStageOut.py\\\")\\n exeScript.append(\\\"./RuntimeStageOut.py\\\")\\n exeScript.append(_StageOutFailureScript )\\n for postcomm in postcomms:\\n exeScript.append(str(postcomm))\\n\\n\\n\\n # //\\n # // Insert End Control Point check on exit status\\n #//\\n controlP = taskObject['ShREEKTask'].endControlPoint\\n exitCheck = CheckExitCode()\\n exitCheck.attrs['OnFail'] = \\\"skipToLog\\\"\\n exitAction = SetNextTask(\\\"skipToLog\\\")\\n exitAction.content = \\\"logArchive\\\"\\n controlP.addConditional(exitCheck)\\n controlP.addAction(exitAction)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f254dcb606cff1eff9aeb30cb64062b\",\n \"score\": \"0.43946475\",\n \"text\": \"def transformation_results_yaml(topology,ignore=('addressing','defaults','nodes_map','includes')):\\n for k in ignore:\\n del topology[k]\\n\\n\\n \\\"\\\"\\\"\\n If we're using a dictionary extension that has to_yaml method use that,\\n otherwise use pyyaml (hoping it won't generate extraneous attributes)\\n \\\"\\\"\\\"\\n if callable(getattr(topology,\\\"to_yaml\\\",None)):\\n return topology.to_yaml()\\n else:\\n return yaml.dump(topology)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab9e26bce65abfc2b7d5804924974c10\",\n \"score\": \"0.4391522\",\n \"text\": \"def main():\\n # TODO(mlegner): Add option specifying already existing keys and certificates\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"--joining_ia\\\",\\n help='ISD-AS for which the configuration is generated.')\\n parser.add_argument(\\\"--core_ia\\\",\\n help='Signing Core ISD-AS',\\n default='1-1')\\n parser.add_argument(\\\"--core_sign_priv_key_file\\\",\\n help='Signing private key of the core AS',\\n default=DEFAULT_CORE_SIG_KEY)\\n parser.add_argument(\\\"--core_cert_file\\\",\\n help='Certificate file of the signing core AS',\\n default=DEFAULT_CORE_CERT_FILE)\\n parser.add_argument(\\\"--trc_file\\\",\\n help='Trusted Root Configuration file',\\n default=DEFAULT_TRC_FILE)\\n parser.add_argument(\\\"--topo_file\\\",\\n help='Topology file to be used for config generation.')\\n parser.add_argument(\\\"--package_path\\\",\\n help='Path to generate and store AS configurations.',\\n default=DEFAULT_PACKAGE_PATH)\\n parser.add_argument(\\\"--user_id\\\",\\n help='User Identifier (email + IA)')\\n parser.add_argument(\\\"--no-prometheus\\\",\\n help='Don\\\\'t generate prometheus configuration',\\n action='store_true',)\\n args = parser.parse_args()\\n\\n def jsonhook(d):\\n ret = {}\\n for k,v in d.items():\\n try:\\n k = int(k)\\n except:\\n pass\\n ret[k] = v\\n return ret\\n\\n with open(args.topo_file) as json_data:\\n topo_dict = json.load(json_data, object_hook=jsonhook)\\n create_scionlab_as_local_gen(args, topo_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee930de7751ccaec4231e7f9b9f96dc9\",\n \"score\": \"0.43838167\",\n \"text\": \"def __init__(self, n=1, cpu=.1, bw=10, delay=None,\\n max_queue_size=None, **params):\\n\\n # Initialize topo\\n Topo.__init__(self, **params)\\n\\n # Host and link configuration\\n hconfig = {'cpu': cpu}\\n lconfig = {'bw': bw, 'delay': delay,\\n 'max_queue_size': max_queue_size }\\n\\n # Create the actual topology\\n receiver = self.addHost('receiver')\\n\\n # Switch ports 1:uplink 2:hostlink 3:downlink\\n uplink, hostlink, downlink = 1, 2, 3\\n\\n # The following template code creates a parking lot topology\\n # for N = 1\\n # TODO: Replace the template code to create a parking lot topology for any arbitrary N (>= 1)\\n # Begin: Template code\\n s1 = self.addSwitch('s1')\\n h1 = self.addHost('h1', mac='00:00:00:00:00:01', **hconfig)\\n switches = [s1]\\n\\n\\n # create Linear topology\\n for i in range(1, n):\\n switch = self.addSwitch('s' + str(i+1))\\n host = self.addHost('h' + str(i+1), mac='00:00:00:00:00:0'+str(i+1), **hconfig)\\n self.addLink(switch, switches[i-1], port1=uplink, port2=downlink, loss=20, **lconfig)\\n self.addLink(host, switch, port1=0, port2=hostlink, loss=20, **lconfig)\\n switches.append(switch)\\n\\n # for i in range(1, n):\\n # switch = self.addSwitch('s' + str(i+1))\\n # host = self.addHost('h' + str(i+1), mac='00:00:00:00:00:0'+str(i+1), **hconfig)\\n # self.addLink(host, switch, port1=0, port2=hostlink, **lconfig)\\n # switches.append(switch)\\n\\n # for i in range(0, len(switches)):\\n # for j in range(i+1, len(switches)):\\n # self.addLink(switches[i], switches[j])\\n \\n\\n # Wire up receiver\\n self.addLink(receiver, s1,\\n port1=0, port2=uplink, **lconfig)\\n\\n # Wire up clients:\\n self.addLink(h1, s1,\\n port1=0, port2=hostlink, **lconfig)\\n\\n # add link between the first switch and last switch to create the ring topology\\n # self.addLink(s1, switches[-1], port1=4, port2=downlink, **lconfig)\\n\\n # Uncomment the next 8 lines to create a N = 3 parking lot topology\\n #s2 = self.addSwitch('s2')\\n #h2 = self.addHost('h2', **hconfig)\\n #self.addLink(s1, s2,\\n # port1=downlink, port2=uplink, **lconfig)\\n #self.addLink(h2, s2,\\n # port1=0, port2=hostlink, **lconfig)\\n #s3 = self.addSwitch('s3')\\n #h3 = self.addHost('h3', **hconfig)\\n #self.addLink(s2, s3,\\n # port1=downlink, port2=uplink, **lconfig)\\n #self.addLink(h3, s3,\\n # port1=0, port2=hostlink, **lconfig)\\n\\n # End: Template code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03c8bf6b31a70635e1ee7567503c9881\",\n \"score\": \"0.43728969\",\n \"text\": \"def condor_bash_template():\\n bash_template = \\\"\\\"\\\"#!/bin/tcsh\\necho \\\" > Starting CONDOR job at `date` on `hostname`\\\"\\n\\n\\n## Copy CMSSW tarball from eos, untar it, and remove the tarball\\nxrdcp -s root://cmseos.fnal.gov/%(eos_path_to_tarball)s/%(cmsRelease)s.tgz .\\ntar -xf %(cmsRelease)s.tgz\\nrm %(cmsRelease)s.tgz\\n\\n## copy transferred data in batch directory (only need 3 files)\\nif (! -d %(cmsRelease)s/src/Analysis/CyMiniAna/%(unique_id_batch_path)s/ ) then\\n mkdir -p %(cmsRelease)s/src/Analysis/CyMiniAna/%(unique_id_batch_path)s/\\nendif\\n\\nmv cmaConfig.txt %(cmsRelease)s/src/Analysis/CyMiniAna/%(unique_id_batch_path)s/\\nmv listOfFiles.txt %(cmsRelease)s/src/Analysis/CyMiniAna/%(unique_id_batch_path)s/\\nmv run_condor.sh %(cmsRelease)s/src/Analysis/CyMiniAna/%(unique_id_batch_path)s/\\n\\n## Setup CMSSW environment\\nsetenv SCRAM_ARCH slc6_amd64_gcc530\\ncd %(cmsRelease)s/src/\\nscramv1 b ProjectRename\\neval `scramv1 runtime -csh` # cmsenv is an alias not on the workers\\nscram b -j8\\n\\n\\n## Execute CyMiniAna\\ncd Analysis/CyMiniAna\\n\\necho \\\" > Run the program \\\"\\n%(executable)s %(cfg_filename)s\\n\\n\\n## Move output file to EOS (make new directory if necessary)\\necho \\\" > Finishing executing \\\"\\necho \\\" > Move output file to EOS\\\"\\n\\n# -- Only copy files if they exist\\nset outfiles = `ls %(local_output_path)s/`\\nif ( \\\"$outfiles\\\" != \\\"\\\" ) then\\n echo \\\" Output file exists, copy to EOS \\\"\\n xrdcp %(local_output_path)s/*.root %(outputDir)s/%(local_output_path)s\\nendif\\n\\n\\n## Cleanup\\nrm %(local_output_path)s/*.root # delete output files\\ncd ${_CONDOR_SCRATCH_DIR} # delete working directory\\nrm -rf %(cmsRelease)s\\n\\necho \\\" > Ended at `date` on `hostname`\\\"\\nls -alh\\nexit 0\\n\\\"\\\"\\\"\\n\\n return bash_template\",\n \"title\": \"\"\n },\n {\n \"docid\": \"939e928fe224db22921dd4e0c7562b06\",\n \"score\": \"0.43652397\",\n \"text\": \"def handleScriptTaskObject(self, taskObject):\\n exeScript = taskObject[taskObject['Executable']]\\n jobSpec = taskObject['JobSpecNode']\\n exeCommand = jobSpec.application['Executable']\\n exeScript.append(exeCommand)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab7411ad677c7f55fc496f0e1baa5957\",\n \"score\": \"0.4357492\",\n \"text\": \"def condor_script_template():\\n condor_template = \\\"\\\"\\\"\\nUniverse = vanilla\\nExecutable = %(condorExec)s\\n\\nShould_Transfer_Files = YES\\nWhenToTransferOutput = ON_EXIT\\nTransfer_Input_Files = %(unique_id_batch_path)s/cmaConfig.txt,%(unique_id_batch_path)s/listOfFiles.txt,%(unique_id_batch_path)s/run_condor.sh\\nnotify_user = ${LOGNAME}@FNAL.GOV\\nx509userproxy = $ENV(X509_USER_PROXY)\\n\\nLog = %(unique_id_batch_path)s/condor_$(Cluster)_$(Process).log\\nError\\t = %(unique_id_batch_path)s/condor_$(Cluster)_$(Process).error\\nOutput = %(unique_id_batch_path)s/condor_$(Cluster)_$(Process).out\\n\\nQueue 1\\n\\\"\\\"\\\"\\n\\n return condor_template\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00521d4ae95de8a5845136da236e5093\",\n \"score\": \"0.43561456\",\n \"text\": \"def main():\\n args = parse_args()\\n psyml = PSyml(args.file)\\n getattr(psyml, args.command)()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"999147ffcf203b56fc6739d942246b29\",\n \"score\": \"0.43453547\",\n \"text\": \"def parse(self, data):\\n graph = networkx.Graph()\\n if 'topology' not in data:\\n raise ParserError('Parse error, \\\"topology\\\" key not found')\\n # loop over topology section and create networkx graph\\n for link in data[\\\"topology\\\"]:\\n try:\\n source = link[\\\"lastHopIP\\\"]\\n dest = link[\\\"destinationIP\\\"]\\n cost = link[\\\"tcEdgeCost\\\"]\\n except KeyError as e:\\n raise ParserError('Parse error, \\\"%s\\\" key not found' % e)\\n # original olsrd cost (jsoninfo multiplies by 1024)\\n cost = float(cost) / 1024.0\\n # add link to Graph\\n graph.add_edge(source, dest, weight=cost)\\n self.graph = graph\\n # determine version and revision\\n if 'config' in data:\\n version_info = data['config']['olsrdVersion'].replace(' ', '').split('-')\\n self.version = version_info[1]\\n # try to get only the git hash\\n if 'hash_' in version_info[-1]:\\n version_info[-1] = version_info[-1].split('hash_')[-1]\\n self.revision = version_info[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"026fd333e1bd36c6b52b682cac784105\",\n \"score\": \"0.43439808\",\n \"text\": \"def convert(self, input, filename=None):\\n if python2:\\n if self.encoding and isinstance(input, str):\\n input = input.decode(self.encoding)\\n self._reset(input, filename)\\n buf = []\\n self.before_convert(buf)\\n self.parse_stmts(buf, input)\\n self.after_convert(buf)\\n script = ''.join(buf)\\n self.script = script\\n return script\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54dec25a3217aa4d1d73341d52250ce5\",\n \"score\": \"0.43172985\",\n \"text\": \"def _get_topology_from_file(topology_file):\\n topology_dir, mod_name = os.path.split(topology_file)\\n # Remove .py extension before trying to import\\n mod_name = mod_name[:-3]\\n sys.path.append(os.path.join(topology_dir, '..', 'src'))\\n sys.path.append(topology_dir)\\n mod = importlib.import_module(mod_name)\\n for attr in mod.__dict__.values():\\n if isinstance(attr, TopologyType) and attr != Topology:\\n topology_class = attr\\n break\\n else:\\n raise ValueError('Could not find topology subclass in topology module.')\\n return topology_class\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb14debd4bfe39a60ef458002d3d4f75\",\n \"score\": \"0.43159938\",\n \"text\": \"def osm_prep_schema_task(self, result={}, task_uid=None, stage_dir=None, job_name=None):\\n\\n self.update_task_state(result=result, task_uid=task_uid)\\n osm = os.path.join(stage_dir, '{0}.pbf'.format(job_name))\\n gpkg = os.path.join(stage_dir, '{0}.gpkg'.format(job_name))\\n osmconf_ini = os.path.join(stage_dir, '{0}.ini'.format(job_name))\\n osmparser = osmparse.OSMParser(osm=osm, gpkg=gpkg, osmconf=osmconf_ini, task_uid=task_uid)\\n osmparser.create_geopackage()\\n osmparser.create_default_schema_gpkg()\\n osmparser.update_zindexes()\\n result['result'] = gpkg\\n result['geopackage'] = gpkg\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39f5193f8d782a9cc400e0f88802ec45\",\n \"score\": \"0.43153542\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser(prog=\\\"clustergen\\\")\\n parser.add_argument(\\\"--clustercfg\\\",\\n \\\"-c\\\",\\n metavar=\\\"file\\\",\\n type=argparse.FileType('r'),\\n required=True,\\n help=\\\"A cluster configuration file\\\")\\n parser.add_argument(\\\"--outdir\\\",\\n \\\"-o\\\",\\n type=pathlib.Path,\\n required=True,\\n help=\\\"Target directory.\\\")\\n parser.add_argument(\\\"--wrapper\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Generate Snitch cluster wrapper\\\")\\n parser.add_argument(\\\"--linker\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Generate linker script\\\")\\n parser.add_argument(\\\"--bootdata\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Generate bootdata\\\")\\n parser.add_argument(\\\"--memories\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Generate memories\\\")\\n parser.add_argument(\\\"--template\\\",\\n metavar=\\\"template\\\",\\n help=\\\"Name of the template file\\\")\\n\\n args = parser.parse_args()\\n\\n # Read HJSON description\\n with args.clustercfg as file:\\n try:\\n srcfull = file.read()\\n obj = hjson.loads(srcfull, use_decimal=True)\\n obj = JsonRef.replace_refs(obj)\\n except ValueError:\\n raise SystemExit(sys.exc_info()[1])\\n\\n cluster_tb = SnitchClusterTB(obj)\\n\\n if not args.outdir.is_dir():\\n exit(\\\"Out directory is not a valid path.\\\")\\n\\n outdir = args.outdir\\n outdir.mkdir(parents=True, exist_ok=True)\\n\\n ##############\\n # Misc files #\\n ##############\\n\\n if args.wrapper:\\n with open(outdir / \\\"snitch_cluster_wrapper.sv\\\", \\\"w\\\") as f:\\n f.write(cluster_tb.render_wrapper())\\n\\n if args.linker:\\n with open(outdir / \\\"link.ld\\\", \\\"w\\\") as f:\\n f.write(cluster_tb.render_linker_script())\\n\\n if args.bootdata:\\n with open(outdir / \\\"bootdata.cc\\\", \\\"w\\\") as f:\\n f.write(cluster_tb.render_bootdata())\\n\\n if args.memories:\\n with open(outdir / \\\"memories.json\\\", \\\"w\\\") as f:\\n f.write(cluster_tb.cluster.memory_cfg())\\n\\n ####################\\n # Generic template #\\n ####################\\n\\n kwargs = {\\n \\\"cfg\\\": cluster_tb.cfg,\\n }\\n\\n if args.template:\\n write_template(args.template, outdir, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe7bbba84772305469d9124b8945a4d7\",\n \"score\": \"0.43111256\",\n \"text\": \"def toTopoFile(topo, out, excludeHosts=set()):\\n out.write(SECTION_NODES + '\\\\n')\\n for host in topo.nodes(False):\\n if host in excludeHosts:\\n continue\\n out.write('%s: _' % host)\\n attributes = topo.nodeInfo(host).get('mnndn', dict())\\n for kv in attributes.iteritems():\\n out.write(' %s=%s' % kv)\\n out.write('\\\\n')\\n\\n out.write(SECTION_LINKS + '\\\\n')\\n for (src, dst) in topo.iterLinks():\\n if src in excludeHosts or dst in excludeHosts:\\n continue\\n out.write('%s:%s' % (src, dst))\\n attributes = topo.linkInfo(src, dst).get('mnndn', dict())\\n for kv in attributes.iteritems():\\n out.write(' %s=%s' % kv)\\n out.write('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7c0ebbb5a797f27b90fb951a1feb58a\",\n \"score\": \"0.4310493\",\n \"text\": \"def main():\\n\\n from argparse import ArgumentParser, RawDescriptionHelpFormatter\\n from textwrap import dedent\\n parser = ArgumentParser(description=dedent(main.__doc__),\\n formatter_class=RawDescriptionHelpFormatter)\\n parser.add_argument('--version', action='version', version='%(prog)s 1.1')\\n parser.add_argument('-a', '--atomtypes', help='The atom type numbers you want converted.'\\n ' These should be written in \\\"# element\\\" format.', nargs='+')\\n parser.add_argument('-n', '--newfile', help='New file name.', nargs='+')\\n parser.add_argument('-c', '--convert', help='Convert atom type numbers to element names.',\\n action='store_true', default=False)\\n parser.add_argument('-t', '--topology', help='Create an MS-EVB topology file from the final '\\n 'coordinates of the trajectory.', action='store_true', default=False)\\n parser.add_argument('-e', '--evbin', help='The MS-EVB input file containing atom type information.') \\n parser.add_argument('-d', '--datafile', help='The data file containing atom ID number information.') \\n parser.add_argument('files', help='The files that you want converted.'\\n , nargs='+')\\n parser.add_argument('-k', '--cp2k', help='Create coordinate blocks for CP2K.',\\n action='store_true', default=False)\\n parser.add_argument('-s', '--snapshot', help='Chooses the snapshot of the system for CP2K output.',\\n default=-1)\\n args = parser.parse_args()\\n\\n # Store command line arguments as convenient variables\\n fh = args.files[0]\\n table = args.atomtypes\\n conv = args.convert\\n topology = args.topology\\n evbin = args.evbin\\n datafile = args.datafile\\n cp2k = args.cp2k\\n snapshot = int(args.snapshot)\\n\\n # Check that the user specified either convert or topology. Die here if not.\\n if conv is False and topology is False and cp2k is False:\\n error = 'Must specify the -c, -t, or -k flag. Use -h flag for information.'\\n sys.exit('\\\\n' + '%'*len(error) + '\\\\n' + error + '\\\\n' + '%'*len(error) + '\\\\n')\\n\\n # Check if the user is trying to convert the file. Exit here if -c is specified\\n # without giving the atom type numbers.\\n if conv is True and table is None:\\n error = 'Must use -a flag when converting a trajectory file. Use -h flag for information.'\\n sys.exit('\\\\n' + '%'*len(error) + '\\\\n' + error + '\\\\n' + '%'*len(error) + '\\\\n')\\n\\n # Check if the user is trying to generate a topology file. Exit here if\\n # -t is specified without giving the evbin, par4amber and data files.\\n if topology is True and (evbin is None or datafile is None):\\n error = 'Must use -e and -d flags when generating a topology file. Use -h flag for information.'\\n sys.exit('\\\\n' + '%'*len(error) + '\\\\n' + error + '\\\\n' + '%'*len(error) + '\\\\n')\\n \\n # Convert the table to a dictionary\\n if conv or cp2k:\\n convert = {}\\n for item in table:\\n item = item.split()\\n convert[item[0]] = item[1]\\n\\n # Data blocks\\n data = ['ITEM: TIMESTEP',\\n 'ITEM: NUMBER OF ATOMS',\\n 'ITEM: BOX BOUNDS pp pp pp',\\n 'ITEM: ATOMS id type x y z fx fy fz',\\n 'ITEM: ATOMS id type x y z ix iy iz',\\n 'ITEM: ATOMS id mol type x y z ix iy iz',\\n 'ITEM: ATOMS id type q x y z', \\n 'ITEM: ATOMS id mol type q x y z']\\n\\n # Collect all data into memory for data retention\\n with open(fh) as fl:\\n f = tuple([line.rstrip() for line in fl])\\n\\n # Locate data blocks\\n # Need to find:\\n # 1. Timestep value\\n # 2. Number of atoms\\n # 3. Box bounds\\n # 4. Coordinates/forces for each timestep\\n timestep = []\\n natoms = 0\\n box_bounds = []\\n coord_and_force = []\\n coord_and_index = []\\n coord_and_charge = []\\n coord_molid_and_index = []\\n coord_and_molid = []\\n for i,line in enumerate(f):\\n # Timestep value\\n if 'TIMESTEP' in line:\\n timestep.append(int(f[i+1]))\\n # Number of atoms\\n if natoms == 0:\\n if 'NUMBER OF ATOMS' in line:\\n natoms = int(f[i+1])\\n # Box bounds\\n #if box_bounds == []:\\n if ('BOX BOUNDS pp pp pp' in line or\\n 'BOX BOUNDS ff ff ff' in line or\\n 'BOX BOUNDS fm fm fm' in line or\\n 'BOX BOUNDS mm mm mm' in line):\\n tmp1 = f[i+1].split()\\n tmp2 = f[i+2].split()\\n tmp3 = f[i+3].split()\\n tmp4 = []\\n box_bounds.append([ [float(tmp1[0]),float(tmp1[1])],\\n [float(tmp2[0]),float(tmp2[1])],\\n [float(tmp3[0]),float(tmp3[1])] ])\\n # Coordinates and forces\\n if ('ITEM: ATOMS id type x y z fx fy fz' in line or\\n 'ITEM: ATOMS id type xu yu zu fx fy fz' in line):\\n # Temporary list\\n tmp = []\\n for ix in range(i+1,i+1+natoms):\\n ln = f[ix] # Grab the current line\\n ln = ln.split() # Split on whitespace\\n if conv or cp2k:\\n ln[1] = convert[ln[1]] # Use the conversion table to convert the label to element\\n else:\\n ln[1] = int(ln[1])\\n ln[2] = float(ln[2])\\n ln[3] = float(ln[3])\\n ln[4] = float(ln[4])\\n ln[5] = float(ln[5])\\n ln[6] = float(ln[6])\\n ln[7] = float(ln[7])\\n tmp.append(ln) # Add the line to tmp\\n coord_and_force.append(tmp) # Make coord_and_force a list of lists.\\n # Coordinates and box indices\\n if 'ITEM: ATOMS id type x y z ix iy iz' in line:\\n tmp = []\\n for ix in range(i+1,i+1+natoms):\\n ln = f[ix] # Grab the current line\\n ln = ln.split() # Split on whitespace\\n if conv or cp2k:\\n ln[1] = convert[ln[1]] # Use the conversion table to convert the label to element\\n else:\\n ln[1] = int(ln[1])\\n ln[2] = float(ln[2])\\n ln[3] = float(ln[3])\\n ln[4] = float(ln[4])\\n ln[5] = int(ln[5])\\n ln[6] = int(ln[6])\\n ln[7] = int(ln[7])\\n tmp.append(ln) # Add the line to tmp\\n coord_and_index.append(tmp) # Make coord_and_force a list of lists.\\n # Coordinates, charges\\n if 'ITEM: ATOMS id type q x y z' in line:\\n tmp = []\\n for ix in range(i+1,i+1+natoms):\\n ln = f[ix] # Grab the current line\\n ln = ln.split() # Split on whitespace\\n if conv or cp2k:\\n ln[1] = convert[ln[1]] # Use the conversion table to convert the label to element\\n else:\\n ln[1] = int(ln[1])\\n ln[2] = float(ln[2])\\n ln[3] = float(ln[3])\\n ln[4] = float(ln[4])\\n ln[5] = float(ln[5])\\n tmp.append(ln) # Add the line to tmp\\n coord_and_charge.append(tmp) # Make coord_and_force a list of lists.\\n # Coordinates, mol_id and box indices\\n if 'ITEM: ATOMS id mol type x y z ix iy iz' in line:\\n tmp = []\\n for ix in range(i+1,i+1+natoms):\\n ln = f[ix] # Grab the current line\\n ln = ln.split() # Split on whitespace\\n if conv or cp2k:\\n ln[2] = convert[ln[2]] # Use the conversion table to convert the label to element\\n else:\\n ln[2] = int(ln[2])\\n ln[3] = float(ln[3])\\n ln[4] = float(ln[4])\\n ln[5] = float(ln[5])\\n ln[6] = int(ln[6])\\n ln[7] = int(ln[7])\\n ln[8] = int(ln[8])\\n tmp.append(ln) # Add the line to tmp\\n coord_molid_and_index.append(tmp) # Make coord_molid_and_force a list of lists.\\n # Coordinates and mol_id\\n if 'ITEM: ATOMS id mol type q x y z' in line:\\n tmp = []\\n for ix in range(i+1,i+1+natoms):\\n ln = f[ix] # Grab the current line\\n ln = ln.split() # Split on whitespace\\n if conv or cp2k:\\n ln[2] = convert[ln[2]] # Use the conversion table to convert the label to element\\n else:\\n ln[2] = int(ln[2])\\n ln[3] = float(ln[3])\\n ln[4] = float(ln[4])\\n ln[5] = float(ln[5])\\n ln[6] = float(ln[6])\\n tmp.append(ln) # Add the line to tmp\\n coord_and_molid.append(tmp) # Make coord_molid_and_force a list of lists.\\n\\n # Close the trajectory file and empty the tuple\\n f = () \\n\\n # Convert atom type numbers to element names.\\n if conv:\\n # Number of timesteps\\n ntstep = len(timestep)\\n\\n # Print data to a new file.\\n nfile = []\\n if args.newfile is not None:\\n for item in args.newfile:\\n nfile.append(args.newfile)\\n else:\\n nfile.append(fh.split('.')[0] + '_new.lammpstrj')\\n\\n lstyle1 = '{0:>9.5f} {1:>9.5f}'\\n lstyle2 = '{0:>4} {1:>4} {2:>9.5f} {3:>9.5f} {4:>9.5f} {5:>9.5f} {6:>9.5f} {7:>9.5f}'\\n lstyle3 = '{0:>4} {1:>4} {2:>9.5f} {3:>9.5f} {4:>9.5f} {5:>4} {6:>4} {7:>4}'\\n lstyle4 = '{0:>4} {1:>4} {2:>2} {3:>9.5f} {4:>9.5f} {5:>9.5f} {6:>4} {7:>4} {8:>4}'\\n lstyle5 = '{0:>4} {1:>4} {2:>5.2f} {3:>9.5f} {4:>9.5f} {5:>9.5f}'\\n lstyle6 = '{0:>4} {1:>4} {2:>2} {3:>5.2f} {4:>9.5f} {5:>9.5f} {6:>9.5f}'\\n for item in nfile:\\n fl = open(item, 'w')\\n for ix in range(ntstep):\\n print(data[0], file=fl)\\n print(timestep[ix], file=fl)\\n print(data[1], file=fl)\\n print(natoms, file=fl)\\n print(data[2], file=fl)\\n print(lstyle1.format(box_bounds[ix][0][0], box_bounds[ix][0][1]), file=fl)\\n print(lstyle1.format(box_bounds[ix][1][0], box_bounds[ix][1][1]), file=fl)\\n print(lstyle1.format(box_bounds[ix][2][0], box_bounds[ix][2][1]), file=fl)\\n if coord_and_force != []:\\n print(data[3], file=fl)\\n for ln in coord_and_force[ix]:\\n print(lstyle2.format(ln[0], ln[1], ln[2], ln[3], ln[4], ln[5], ln[6], ln[7]), file=fl)\\n elif coord_and_index != []:\\n print(data[4], file=fl)\\n for ln in coord_and_index[ix]:\\n print(lstyle3.format(ln[0], ln[1], ln[2], ln[3], ln[4], ln[5], ln[6], ln[7]), file=fl)\\n elif coord_and_charge != []:\\n print(data[6], file=fl)\\n for ln in coord_and_charge[ix]:\\n print(lstyle5.format(ln[0], ln[1], ln[2], ln[3], ln[4], ln[5]), file=fl)\\n elif coord_molid_and_index != []:\\n print(data[5], file=fl)\\n for ln in coord_molid_and_index[ix]:\\n print(lstyle4.format(ln[0], ln[1], ln[2], ln[3], ln[4], ln[5], ln[6], ln[7], ln[8]), file=fl)\\n elif coord_and_molid != []:\\n print(data[7], file=fl)\\n for ln in coord_and_molid[ix]:\\n print(lstyle6.format(ln[0], ln[1], ln[2], ln[3], ln[4], ln[5], ln[6]), file=fl)\\n # Generate a topology file for RAPTOR.\\n elif topology:\\n # Trajectory data\\n traj_data = coord_and_force[-1]\\n\\n # /\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\n # Read the input file for the MS-EVB code\\n # /\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\n # This tells us the atom types in RAPTOR related to the LAMMPS atom types\\n with open(evbin) as data:\\n evb = tuple([line.rstrip() for line in data])\\n\\n # Read the LAMMPS types and molecule numbers from the MS-EVB input\\n lammps_type = {}\\n reaction_mol_num = {}\\n for i,line in enumerate(evb):\\n if 'MODEL' in line:\\n s1 = i + 3\\n if 'Reaction' in line:\\n m1 = i + 1\\n if ': atom type' in line:\\n s2 = i + 2\\n if ': bond type' in line:\\n e2 = i - 1\\n \\n # Store the reaction type(s)\\n for data in evb[m1:]:\\n if '#define' in data:\\n tmp = data.split()\\n reaction_mol_num[tmp[1]] = {}\\n # Stop the loop if a blank line is encountered\\n if data == '':\\n break\\n \\n # Store the molecule names and numbers belonging to a reaction type\\n # I assume that the naming convention of reactions has correspondence\\n # with the naming of the molecular model\\n for i,data in enumerate(evb[s1:m1]):\\n # This part is pretty convoluted. At least it works.\\n if '#define' in data:\\n count = s1 + i + 1\\n tmp = data.split()\\n # Here we loop through the reaction types we stored above\\n for reaction in reaction_mol_num.keys():\\n if tmp[1] in reaction.split('_'):\\n reaction_mol_num[reaction][tmp[1]] = {}\\n nloop = 0\\n # Here we look through lines greater and stash the molecule\\n # number information. \\n for line in evb[count:]:\\n mol = line.split()\\n if '#define' in mol:\\n break\\n elif mol == []:\\n break\\n else:\\n reaction_mol_num[reaction][tmp[1]][mol[1]] = int(mol[2])\\n\\n # Store the LAMMPS types\\n for data in evb[s2:e2]:\\n if '#define' in data:\\n tmp = data.split()\\n lammps_type[tmp[1]] = int(tmp[2])\\n\\n # Clear out the tuple (free memory)\\n evb = ()\\n\\n # /\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\n # Read the data file from the LAMMPS output\\n # /\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\\\/\\n # This tells us the atom topology information\\n with open(datafile) as data:\\n df = tuple([line.rstrip() for line in data])\\n\\n # Collect the 'Atoms' information from the data file.\\n for i,line in enumerate(df):\\n if 'Atoms' in line:\\n s = i + 2\\n elif 'Velocities' in line:\\n e = i - 1\\n elif 'Bonds' in line:\\n s1 = i + 2\\n elif 'Angles' in line:\\n e1 = i - 1\\n\\n # Store the atom number and atom type.\\n natoms = len(df[s:e])\\n atomdata = {}\\n for atom in df[s:e]:\\n tmp = atom.split()\\n atomdata[tmp[0]] = [int(tmp[2]),[]]\\n\\n # Also store the bond topology. Note that only only the \\\"central\\\"\\n # atom in the molecule has bond information.\\n for atom in df[s1:e1]:\\n tmp = atom.split()\\n atomdata[tmp[2]][1].append(int(tmp[3])) \\n\\n # Clear out the tuple (free memory)\\n df = ()\\n\\n # What we have now\\n # 1. lammps_type is a dictionary containing the correspondence between \\n # RAPTOR atom types and LAMMPS atom ID numbers\\n # 2. reaction_mol_num defines the correspondence between reaction names,\\n # molecule names, and molecule numbers (\\\"kernels\\\").\\n # 3. atomdata defines the atom number, its LAMMPS atom ID, and which atoms\\n # it is bonded to (if it is a central atom in the molecule).\\n # \\n # This information can be used to print the bond topology file. The columns\\n # in the topology file are:\\n # atom number | molecule number | RAPTOR atom type\\n\\n # Molecule to atom name correspondence\\n mol2atom = { 'OW' : 'H2O',\\n 'HW' : 'H2O',\\n 'OH' : 'H3O', \\n 'HO' : 'H3O' }\\n\\n # Based on the atomtype of the central atom, it is now possible to determine\\n # the types of the terminal atoms.\\n ltop = [[0,0,0] for row in range(natoms)]\\n for key in atomdata.keys():\\n ltop[int(key)-1][0] = int(key)\\n\\n #fi = open('top.tmp', 'w')\\n\\n # Based on the bonding topology, we can determine the molecule number (\\\"kernel\\\").\\n for key in atomdata.keys():\\n # Identify central atoms. These contain bonding information.\\n if atomdata[key][1] != []:\\n #print(key, atomdata[key], file=fi)\\n # List of bonded partners\\n tmp = atomdata[key][1]\\n # Type of the central atom\\n ctype = atomdata[key][0]\\n # Central atom\\n # Here, we match ctype with what is contained in lammps_type.\\n for key2 in lammps_type.keys():\\n if lammps_type[key2] == ctype:\\n mol = mol2atom[key2]\\n # Subtraction is accounting for Python numbering.\\n ltop[int(key)-1][1] = mol\\n # Central atom is assumed to always have index 1 for convenience.\\n # If this wasn't true, this part would be a lot more complicated.\\n ltop[int(key)-1][2] = 1\\n # Terminal atoms. Start the counter at 2 since the central atom is 1.\\n count = 2\\n for item in tmp:\\n # Account for Python numbering\\n i = item - 1\\n # Terminal atom type to be compared with lammps_type\\n ttype = atomdata[str(item)][0]\\n for key3 in lammps_type.keys():\\n if lammps_type[key3] == ttype:\\n mol = mol2atom[key3]\\n # Temporarily place the molecule name in the list.\\n ltop[i][1] = mol\\n # Place the counter in the list. This is trivial as\\n # long as the terminal atoms are identical. We must\\n # be careful in situations where this isn't true.\\n ltop[i][2] = count\\n count += 1\\n\\n # Finally we convert molecule strings to molecule numbers\\n for i in range(len(ltop)):\\n for reaction in reaction_mol_num.keys():\\n for key in reaction_mol_num[reaction].keys():\\n ltop[i][1] = reaction_mol_num[reaction][key][ltop[i][1]] \\n\\n # Output data to a file\\n stop = 'lammps_msevb.top'\\n top = open(stop, 'w')\\n \\n fmt = '{0:>4} {1:>2} {2:>2}'\\n for i in range(len(ltop)):\\n print(fmt.format(ltop[i][0], ltop[i][1], ltop[i][2]), file=top)\\n elif cp2k:\\n # Trajectory data\\n traj_data = coord_and_force[snapshot]\\n\\n # Print out box bounds\\n bb = box_bounds[snapshot]\\n x = bb[0][1] - bb[0][0]\\n y = bb[1][1] - bb[1][0]\\n z = bb[2][1] - bb[2][0]\\n fmt = ' ABC {0:7.5f} {1:7.5f} {2:7.5f}'\\n print('&CELL')\\n print(fmt.format(x,y,z))\\n print('&END CELL')\\n print('#')\\n print('&COORD')\\n lnstyle = ' {0:2} {1:10.5f} {2:10.5f} {3:10.5f}'\\n for ln in traj_data:\\n print(lnstyle.format(ln[1],ln[2],ln[3],ln[4]))\\n print('&END COORD')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8477e9d7f1edbb350c4aa3147a9da9c\",\n \"score\": \"0.4306115\",\n \"text\": \"def create_subtop(self, label_type, label):\\n from gmso.parameterization.molecule_utils import (\\n molecule_angles,\\n molecule_bonds,\\n molecule_dihedrals,\\n molecule_impropers,\\n )\\n\\n of_group = True if label_type == \\\"group\\\" else False\\n sites_dict = {\\n site: (idx, site.clone())\\n for idx, site in enumerate(self.iter_sites(label_type, label))\\n }\\n bonds_dict = {\\n bond: tuple(\\n sites_dict[bond.connection_members[i]][0] for i in range(2)\\n )\\n for bond in molecule_bonds(self, label, of_group)\\n }\\n\\n angles_dict = {\\n angle: tuple(\\n sites_dict[angle.connection_members[i]][0] for i in range(3)\\n )\\n for angle in molecule_angles(self, label, of_group)\\n }\\n\\n dihedrals_dict = {\\n dihedral: tuple(\\n sites_dict[dihedral.connection_members[i]][0] for i in range(4)\\n )\\n for dihedral in molecule_dihedrals(self, label, of_group)\\n }\\n\\n impropers_dict = {\\n improper: tuple(\\n sites_dict[improper.connection_members[i]][0] for i in range(4)\\n )\\n for improper in molecule_impropers(self, label, of_group)\\n }\\n\\n new_top = gmso.Topology(\\n name=label if isinstance(label, str) else label[0]\\n )\\n\\n for ref_site, new_site in sites_dict.items():\\n new_top.add_site(new_site[1])\\n for ref_conn, conn_idx in bonds_dict.items():\\n bond = gmso.Bond(\\n connection_members=[\\n new_top.sites[conn_idx[i]] for i in range(2)\\n ],\\n bond_type=None\\n if not ref_conn.connection_type\\n else ref_conn.connection_type.clone(),\\n )\\n new_top.add_connection(bond)\\n for ref_conn, conn_idx in angles_dict.items():\\n angle = gmso.Angle(\\n connection_members=[\\n new_top.sites[conn_idx[i]] for i in range(3)\\n ],\\n angle_type=None\\n if not ref_conn.connection_type\\n else ref_conn.connection_type.clone(),\\n )\\n new_top.add_connection(angle)\\n for ref_conn, conn_idx in dihedrals_dict.items():\\n dihedral = gmso.Dihedral(\\n connection_members=[\\n new_top.sites[conn_idx[i]] for i in range(4)\\n ],\\n dihedral_type=None\\n if not ref_conn.connection_type\\n else ref_conn.connection_type.clone(),\\n )\\n new_top.add_connection(dihedral)\\n for ref_conn, conn_idx in impropers_dict.items():\\n improper = gmso.Improper(\\n connection_members=[\\n new_top.sites[conn_idx[i]] for i in range(4)\\n ],\\n improper_type=None\\n if not ref_conn.connection_type\\n else ref_conn.connection_type.clone(),\\n )\\n new_top.add_connection(improper)\\n\\n new_top.update_topology()\\n return new_top\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861c27a581961250817921baa484d8fb\",\n \"score\": \"0.4301693\",\n \"text\": \"def topologytemplate_spec(request, template):\\n return HttpResponse(template.specification, mimetype=\\\"text/plain\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e5242a8f0213f785b0690639ec44fcf\",\n \"score\": \"0.42926726\",\n \"text\": \"def inject_label_matchers(self, expression, topology) -> str:\\n if not topology:\\n return expression\\n if not self.path:\\n logger.debug(\\\"`cos-tool` unavailable. Leaving expression unchanged: %s\\\", expression)\\n return expression\\n args = [str(self.path), \\\"transform\\\"]\\n args.extend(\\n [\\\"--label-matcher={}={}\\\".format(key, value) for key, value in topology.items()]\\n )\\n\\n args.extend([\\\"{}\\\".format(expression)])\\n # noinspection PyBroadException\\n try:\\n return self._exec(args)\\n except subprocess.CalledProcessError as e:\\n logger.debug('Applying the expression failed: \\\"%s\\\", falling back to the original', e)\\n return expression\",\n \"title\": \"\"\n },\n {\n \"docid\": \"555319af7520c988a93c5100170d8cc5\",\n \"score\": \"0.4291545\",\n \"text\": \"def _write_loader_script(path, state_name, package, top):\\r\\n if state_name.startswith(package):\\r\\n pkg_arg = ''\\r\\n else:\\r\\n pkg_arg = \\\", package='%s'\\\" % package\\r\\n\\r\\n if top:\\r\\n top_arg = ''\\r\\n else:\\r\\n top_arg = ', top_obj=False'\\r\\n\\r\\n out = open(path, 'w')\\r\\n out.write(\\\"\\\"\\\"\\\\\\r\\nimport os\\r\\nimport sys\\r\\nif not '.' in sys.path:\\r\\n sys.path.append('.')\\r\\n\\r\\ntry:\\r\\n from openmdao.main.api import Component, SAVE_CPICKLE\\r\\nexcept ImportError:\\r\\n print 'No OpenMDAO distribution available.'\\r\\n if __name__ != '__main__':\\r\\n print 'You can unzip the egg to access the enclosed files.'\\r\\n print 'To get OpenMDAO, please visit openmdao.org'\\r\\n sys.exit(1)\\r\\n\\r\\ndef load(**kwargs):\\r\\n '''Create object(s) from state file.'''\\r\\n return Component.load('%(name)s', SAVE_CPICKLE%(pkg)s%(top)s, **kwargs)\\r\\n\\r\\ndef main():\\r\\n '''Load state and run.'''\\r\\n model = load()\\r\\n model.run()\\r\\n\\r\\nif __name__ == '__main__':\\r\\n main()\\r\\n\\\"\\\"\\\" % {'name': state_name, 'pkg': pkg_arg, 'top': top_arg})\\r\\n out.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"334ad27ce5271dc9a26655e8f23fce5a\",\n \"score\": \"0.4276431\",\n \"text\": \"def main(server=None, input=None):\\n if not input:\\n input = {}\\n\\n try:\\n # CUSTOM_SCRIPT00019\\n #\\n # Created by Matthew Misenhimer\\n #\\n def make_timestamp():\\n #Makes a Timestamp for postgres\\n import datetime\\n now = datetime.datetime.now()\\n return now.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n \\n def are_no_hackpipes_preceding(sob, ignore_code):\\n #Checks to see if there are any manually inserted/non-pipeline Projects or Work Orders that lead in to the sob that are not completed yet\\n #If there are incompleted hackpipe work orders or projs that precede the sob, then this will return false.\\n #Ignore code is usually the code of the task just completed. We don't care about looking at it's status in determining this. \\n boolio = True\\n matcher = '' #This is the type of sob (PROJ or WORK_ORDER) that we care about looking at\\n if 'PROJ' in sob.get('code'):\\n matcher = 'PROJ'\\n elif 'WORK_ORDER' in sob.get('code'):\\n matcher = 'WORK_ORDER'\\n pre_hacks_expr = \\\"@SOBJECT(twog/hackpipe_out['out_to','%s'])\\\" % sob.get('code') #See what hackpipes lead in to this sob\\n pre_hacks = server.eval(pre_hacks_expr)\\n for ph in pre_hacks:\\n if matcher in ph.get('lookup_code') and ignore_code not in ph.get('lookup_code'): #If it's the type we care about and it isn't the main sob\\n ph_task = server.eval(\\\"@SOBJECT(sthpw/task['lookup_code','%s'])\\\" % ph.get('lookup_code'))\\n if ph_task:\\n ph_task = ph_task[0]\\n if ph_task.get('status') != 'Completed': #If it hasn't been completed, then there is an incomplete hackpipe preceding sob, so return false\\n boolio = False\\n return boolio\\n \\n def block_manual_status_adjust_for_inactive_hackup(sob, task):\\n #This was created because people used to be able to change the status of inactive work orders\\n #I think this is prevented in other ways now, but I am keeping it in just in case it is still needed.\\n from pyasm.common import TacticException\\n good = True\\n if sob.get('creation_type') == 'hackup':\\n if task.get('active') not in [True,'true','t',1,'1']:\\n raise TacticException('This needs to be active in order to change the status')\\n return good\\n \\n from pyasm.common import TacticException, Environment\\n # input and server are assumed variables\\n # define some contants here\\n #print \\\"\\\\n\\\\nIN KICKOFF\\\"\\n COMPLETE = 'Completed'\\n READY = 'Ready'\\n PENDING = 'Pending'\\n sobj = input.get('sobject')\\n this_process = sobj.get('process')\\n this_lookup = sobj.get('lookup_code')\\n sk = input.get('search_key')\\n task_code = sobj.get('code')\\n update_data = input.get('update_data') #These are the new values\\n prev_data = {}\\n old_status = ''\\n if 'prev_data' in input.keys():\\n prev_data = input.get('prev_data') #These are the old values\\n if 'status' in prev_data.keys():\\n old_status = prev_data.get('status')\\n new_status = update_data.get('status')\\n login = Environment.get_login()\\n user_name = login.get_login()\\n assigned_login_group = sobj.get('assigned_login_group')\\n parent_obj = None\\n title = None\\n order = None\\n if 'PROJ' in this_lookup:\\n parent_obj = server.eval(\\\"@SOBJECT(twog/proj['code','%s'])\\\" % this_lookup)[0] #Parent Obj is the Proj attached to the task\\n proj = parent_obj\\n title = server.eval(\\\"@SOBJECT(twog/title['code','%s'])\\\" % parent_obj.get('title_code'))[0]\\n order = server.eval(\\\"@SOBJECT(twog/order['code','%s'])\\\" % title.get('order_code'))[0] \\n elif 'WORK_ORDER' in this_lookup:\\n parent_obj = server.eval(\\\"@SOBJECT(twog/work_order['code','%s'])\\\" % this_lookup)[0] #Parent Obj is the Work Order attached to the task\\n work_order = parent_obj\\n proj = server.eval(\\\"@SOBJECT(twog/proj['code','%s'])\\\" % work_order.get('proj_code'))[0]\\n title = server.eval(\\\"@SOBJECT(twog/proj['code','%s'].twog/title)\\\" % parent_obj.get('proj_code'))[0] \\n order = server.eval(\\\"@SOBJECT(twog/order['code','%s'])\\\" % title.get('order_code'))[0] \\n \\n if 'PROJ' in this_lookup and title.get('priority_triggers') != 'No':\\n #If the new status for this Proj is ready, then grab the priority attached to he proj and give it to the Title\\n #This is to control priority order per department\\n if new_status == 'Ready':\\n server.update(title.get('__search_key__'), {'priority': parent_obj.get('priority')}, triggers=False)\\n elif 'WORK_ORDER' in this_lookup:\\n t_wo_completed = title.get('wo_completed') #This is for the completion ratio on title\\n o_wo_completed = order.get('wo_completed') #This is for the completion ratio on order\\n if new_status == COMPLETE:\\n title_str = title.get('title') #This is for a potential alert/exception\\n if title.get('episode') not in [None,'']:\\n title_str = '%s: %s' % (title_str, title.get('episode'))\\n #Block QC and Edel from completing their work orders if the TRT or TRT w/Textless are not filled in\\n if 'qc' in assigned_login_group or 'edeliveries' in assigned_login_group:\\n total_program_runtime = title.get('total_program_runtime')\\n total_runtime_w_textless = title.get('total_runtime_w_textless')\\n say_str = ''\\n say_str2 = ''\\n if total_program_runtime in [None,''] or total_runtime_w_textless in [None,'']:\\n if total_program_runtime in [None,'']:\\n say_str = 'Total Program Runtime has' \\n if total_runtime_w_textless in [None,'']:\\n if say_str == '': \\n say_str = 'Total Runtime With Textless has'\\n else:\\n say_str = '%s and Total Runtime With Textless have' % (say_str[:-4]) \\n say_str2 = \\\"%s (%s)'s %s not been filled. You must enter this data before trying to complete this work order.\\\" % (title_str, title.get('code'), say_str)\\n if 'qc' in assigned_login_group:\\n if total_program_runtime in [None,''] or total_runtime_w_textless in [None,'']:\\n raise TacticException(say_str2)\\n else:\\n #They were filled in, so finish completing the task and send a note\\n from pyasm.biz import Note\\n from pyasm.search import Search\\n title_obj2 = Search.get_by_search_key(title.get('__search_key__')) #This is the type of object required for Note creation\\n note_text = '%s (%s) has been Passed and Completed by %s in QC' % (sobj.get('process'), this_lookup, user_name)\\n note = Note.create(title_obj2, note_text, context='QC Completed', process='QC Completed')\\n elif 'edeliveries' in assigned_login_group and (total_program_runtime in [None,''] or total_runtime_w_textless in [None,'']):\\n raise TacticException(say_str2)\\n #This section is turned off due to logistical problems with it. \\n #It intended to block machine room, edit, and compression from completing a work order unless the pulled_blacks had been filled out.\\n #if 'machine_room' in assigned_login_group or 'edit' in assigned_login_group or 'compression' in assigned_login_group:\\n # pulled_blacks = title.get('pulled_blacks')\\n # if pulled_blacks in [None,'','0']:\\n # raise TacticException(\\\"%s (%s)'s pulled_blacks has not been filled, or is still '0'.\\\" % (title_str, title.get('code'))) \\n t_wo_completed = t_wo_completed + 1\\n o_wo_completed = o_wo_completed + 1\\n #Update the completion ratios attached, since there were no blocking exceptions \\n server.update(title.get('__search_key__'), {'wo_completed': t_wo_completed})\\n server.update(order.get('__search_key__'), {'wo_completed': o_wo_completed})\\n elif old_status == COMPLETE:\\n t_wo_completed = t_wo_completed - 1\\n o_wo_completed = o_wo_completed - 1\\n #Reduce the completion ratio, since it was completed but has now been taken off that status\\n server.update(title.get('__search_key__'), {'wo_completed': t_wo_completed})\\n server.update(order.get('__search_key__'), {'wo_completed': o_wo_completed})\\n #Still doing this, but don't know if it's neccessary anymore \\n mmkay = block_manual_status_adjust_for_inactive_hackup(parent_obj, sobj)\\n if new_status == COMPLETE and 'PROJ' not in this_lookup:\\n #Make sure they have set the assigned person to the work order.\\n if sobj.get('assigned') in [None,'']:\\n task_assigned_expr = \\\"@GET(sthpw/task['code','%s'].assigned)\\\" % sobj.get('code') #MTM: Do I need to retrieve the task again, or can I just use the sobj's \\\"assigned\\\"? \\n task_assigned = server.eval(task_assigned_expr)\\n if task_assigned:\\n task_assigned = task_assigned[0]\\n if task_assigned in [None,'']:\\n raise TacticException('Before completing a work order, someone must be assigned to it.')\\n #Make sure they have added work hours. If not, error out.\\n whs_expr = \\\"@SOBJECT(sthpw/work_hour['task_code','%s'])\\\" % task_code\\n whs = server.eval(whs_expr)\\n sum = 0\\n for wh in whs:\\n straight_time = wh.get('straight_time')\\n if straight_time in [None,'']:\\n straight_time = 0\\n else:\\n straight_time = float(straight_time)\\n sum = float(sum) + straight_time\\n sum = str(sum)\\n if sum in ['0','',0,0.0]:\\n raise TacticException('You need to save the hours you worked on this before you can set the status to \\\"Completed\\\".')\\n \\n now_timestamp = make_timestamp() \\n #Since there have been no blocking exceptions, record the status change\\n server.insert('twog/status_log', {'login': user_name, 'timestamp': now_timestamp, 'from_status': old_status, 'status': new_status, 'task_code': task_code, 'lookup_code': this_lookup, 'order_code': sobj.get('order_code'), 'title_code': sobj.get('title_code'), 'process': this_process})\\n if new_status == COMPLETE:\\n #Record the completion date on the work order, and take it off the BigBoard\\n import datetime\\n now = datetime.datetime.now()\\n timestamp_str = '%s-%s-%s %s:%s:%s' % (now.year, now.month, now.day, now.hour, now.minute, now.second)\\n updict = {'actual_end_date': timestamp_str}\\n if 'WORK_ORDER' in this_lookup:\\n updict['bigboard'] = False\\n server.update(sk, updict)\\n elif new_status not in ['Pending','Ready','Completed'] and 'WORK_ORDER' in this_lookup:\\n server.update(server.build_search_key('sthpw/task', proj.get('task_code')), {'status': new_status}) #NEW\\n if 'PROJ' in sobj.get('lookup_code'):\\n #MTM: This annoying section is for passing Proj's their task's status.\\n #I don't know if this is needed at all anymore. Will have to check other triggers and reports.\\n #The \\\"tripwire\\\" stuff was just to keep it from infinitely passing statuses from proj to task, task to proj\\n do_it = True\\n if 'tripwire' in update_data.keys():\\n if update_data.get('tripwire') == 'No Send Back': #?\\n do_it = False\\n server.update(input.get('search_key'), {'tripwire': ''}, triggers=False) #Empty the tripwire and do nothing\\n server.update(proj.get('__search_key__'), {'tripwire': '', 'status': sobj.get('status')}, triggers=False) #?\\n if do_it:\\n if proj:\\n server.update(proj.get('__search_key__'), {'status': new_status})\\n if title.get('priority_triggers') != 'No':\\n #Update Title Priority for On Hold Status, or having that status removed -- BEGIN\\n if sobj.get('status') in ['On_Hold','On Hold']:\\n title_priority = title.get('priority')\\n server.update(title.get('__search_key__'), {'saved_priority': title_priority, 'priority': 200}, triggers=False) \\n else:\\n if old_status in ['On_Hold','On Hold']:\\n saved_priority = title.get('saved_priority')\\n server.update(title.get('__search_key__'), {'priority': saved_priority}, triggers=False) \\n #Update Title Priority for On Hold Status, or having that status removed -- END\\n \\n #Update Title Priority for Client Response Status, or having that status removed -- BEGIN\\n if sobj.get('status') == 'Client Response':\\n title_priority = title.get('priority')\\n crc = title.get('client_response_count')\\n crc_num = 0\\n if crc not in [None,'']:\\n crc_num = int(crc)\\n crc_num = crc_num + 1 \\n server.update(title.get('__search_key__'), {'saved_priority': title_priority, 'priority': 300, 'client_response_count': crc_num}, triggers=False) \\n else:\\n if old_status == 'Client Response':\\n saved_priority = title.get('saved_priority')\\n crc = title.get('client_response_count')\\n crc_num = 0\\n if crc not in [None,'']:\\n crc_num = int(crc)\\n if crc_num > 0:\\n crc_num = crc_num - 1 \\n server.update(title.get('__search_key__'), {'priority': saved_priority, 'client_response_count': crc_num}, triggers=False) \\n #Update Title Priority for Client Response Status, or having that status removed -- END\\n \\n if sobj.get('status') in ['In_Progress','In Progress','DR In_Progress','DR In Progress', 'Amberfin01_In_Progress', 'Amberfin01 In Progress', 'Amberfin02_In_Progress', 'Amberfin02 In Progress','BATON In_Progress','BATON In Progress','Export In_Progress','Export In Progress','Buddy Check In_Progress','Buddy Check In Progress','Need Buddy Check','Completed'] and old_status not in ['In_Progress','DR In_Progress','DR In Progress','Amberfin01_In_Progress','Amberfin01 In Progress', 'Amberfin02_In_Progress','Amberfin02 In Progress','BATON In_Progress','BATON In Progress','Export In_Progress','Export In Progress','Buddy Check In_Progress','Buddy Check In Progress','Need Buddy Check','In Progress']:\\n #Update the actual start date if they just set the status to 'In Progress'\\n if sobj.get('actual_start_date') in ['',None]:\\n now_timestamp = make_timestamp() \\n server.update(sk, {'actual_start_date': now_timestamp})\\n if sobj.get('status') in ['Ready','In_Progress','In Progress'] and 'WORK_ORDER' in sobj.get('lookup_code'):\\n if title.get('client_status') != 'In Production':\\n server.update(title.get('__search_key__'), {'client_status': 'In Production', 'status': 'In Production'})\\n elif sobj.get('status') in ['Rejected','Fix Needed'] and 'WORK_ORDER' in sobj.get('lookup_code'):\\n from pyasm.biz import Note\\n from pyasm.search import Search\\n server.insert('twog/production_error', {'error_type': sobj.get('status'), 'process': sobj.get('process'), 'work_order_code': sobj.get('lookup_code'), 'title': title.get('title'), 'episode': title.get('episode'), 'title_code': title.get('code'), 'order_code': order.get('code'), 'order_name': order.get('name'), 'po_number': order.get('po_number'), 'proj_code': proj.get('code'), 'scheduler_login': sobj.get('creator_login'), 'operator_login': user_name, 'login': user_name}) \\n if sobj.get('status') == 'Rejected':\\n server.update(title.get('__search_key__'), {'client_status': 'QC Rejected'})\\n if title.get('priority_triggers') != 'No':\\n server.update(title.get('__search_key__'), {'priority': 90}, triggers=False)\\n title_obj2 = Search.get_by_search_key(title.get('__search_key__')) #This is the type of object required for Note creation\\n note_text = '%s (%s) has been Rejected, as marked by %s' % (sobj.get('process'), this_lookup, user_name)\\n note = Note.create(title_obj2, note_text, context='QC Rejected', process='QC Rejected')\\n \\n if sobj.get('status') == COMPLETE and title.get('status_triggers') != 'No':\\n # Now we need to set the next task(s) statuses to 'Ready'\\n parent = server.get_parent(sk)\\n # Get all process information from the pipeline regarding processes linked to this process in the normal pipeline\\n info = server.get_pipeline_processes_info(parent.get('__search_key__'), related_process=this_process)\\n input_processes = info.get('input_processes')\\n output_processes = info.get('output_processes')\\n # this combines all other input_processes and this process\\n # including this_process in case this process has more than 1 task\\n ready = False\\n if input_processes:\\n input_processes.append(this_process)\\n ready = True\\n input_tasks = server.query('sthpw/task', filters = [('search_type', sobj.get('search_type')), ('search_id', sobj.get('search_id')), ('process', input_processes), ('title_code',sobj.get('title_code'))])\\n for task in input_tasks:\\n if task.get('status') != COMPLETE:\\n ready = False\\n else:\\n ready = True\\n #Now we need to check the manually entered work orders and projs \\n #This section may be replaced with the are_no_hackpipes_preceding function, but for the sake of stability, I won't do it until I test it again\\n hack_outs_expr = \\\"@SOBJECT(twog/hackpipe_out['out_to','%s'])\\\" % sobj.get('lookup_code')\\n hack_outs = server.eval(hack_outs_expr)\\n stype = ''\\n stype_st = ''\\n if 'PROJ' in sobj.get('lookup_code'):\\n stype = 'PROJ'\\n stype_st = 'twog/proj'\\n elif 'WORK_ORDER' in sobj.get('lookup_code'):\\n stype = 'WORK_ORDER'\\n stype_st = 'twog/work_order'\\n for ho in hack_outs:\\n lookup_code = ho.get('lookup_code')\\n if stype in lookup_code:\\n hacktasks = server.eval(\\\"@SOBJECT(sthpw/task['lookup_code','%s'])\\\" % lookup_code)\\n for hacktask in hacktasks:\\n if hacktask.get('status') != COMPLETE:\\n ready = False\\n \\n # make the next process ready\\n if ready == True:\\n output_tasks = server.query('sthpw/task', filters = [('search_type', sobj.get('search_type')), ('search_id', sobj.get('search_id')), ('process', output_processes), ('title_code',sobj.get('title_code'))])\\n update_data = {}\\n \\n for task in output_tasks:\\n if task.get('lookup_code') not in [None,'']:\\n if task.get('status') == 'Pending':\\n #Need to make sure other tasks leading into this one (an output task for the triggered task) are also complete - both ways, with pipeline and hackpipe - before allowing this status update \\n \\n out_info = server.get_pipeline_processes_info(parent.get('__search_key__'), related_process=task.get('process'))\\n input_to_out = out_info.get('input_processes') \\n if this_process in input_to_out:\\n prc_idx = input_to_out.index(this_process)\\n input_to_out.pop(prc_idx)\\n ready2 = False\\n if input_to_out:\\n #input_to_out.append(task.get('process'))\\n ts_st = ''\\n if 'PROJ' in task.get('lookup_code'):\\n ts_st = 'twog/proj'\\n elif 'WORK_ORDER' in task.get('lookup_code'):\\n ts_st = 'twog/work_order'\\n tsob = server.eval(\\\"@SOBJECT(%s['code','%s'])\\\" % (ts_st, task.get('lookup_code')))[0] \\n ready2 = are_no_hackpipes_preceding(tsob, sobj.get('lookup_code'))\\n into_out_tasks = server.query('sthpw/task', filters = [('search_type', task.get('search_type')), ('search_id', task.get('search_id')), ('process', input_to_out)])\\n for iotask in into_out_tasks:\\n # If preceding tasks have not yet been set to 'Completed', do not change the next task's status\\n if iotask.get('status') != COMPLETE:\\n ready2 = False\\n else:\\n ready2 = True\\n hacks_expr = \\\"@SOBJECT(twog/hackpipe_out['out_to','%s'])\\\" % task.get('lookup_code')\\n hacks = server.eval(hacks_expr)\\n for hack in hacks:\\n lu_code = hack.get('lookup_code') \\n hacktasks = server.eval(\\\"@SOBJECT(sthpw/task['lookup_code','%s'])\\\" % lu_code)\\n for hacktask in hacktasks:\\n if hacktask.get('status') != COMPLETE:\\n ready2 = False\\n if ready2 == True:\\n update_data[task.get('__search_key__')] = { 'status': READY }\\n if 'PROJ' in task.get('lookup_code'):\\n get_proj_expr = \\\"@SOBJECT(twog/proj['code','%s'])\\\" % task.get('lookup_code')\\n proj = server.eval(get_proj_expr)[0]\\n # FIND ALL NON HACK WOS\\n wos = server.eval(\\\"@SOBJECT(twog/work_order['proj_code','%s'])\\\" % proj.get('code'))\\n for wo in wos: \\n if wo.get('creation_type') not in ['hackpipe','hackup']:\\n this_processer = wo.get('process')\\n info2 = server.get_pipeline_processes_info(proj.get('__search_key__'), related_process=this_processer)\\n input_processes2 = info2.get('input_processes')\\n okayed = are_no_hackpipes_preceding(wo, sobj.get('lookup_code'))\\n len_proc = 0\\n if input_processes2 not in ['',{},[],None]:\\n len_proc = len(input_processes2)\\n if len_proc < 1 and okayed:\\n task2 = server.eval(\\\"@SOBJECT(sthpw/task['code','%s'])\\\" % wo.get('task_code')) \\n if task2:\\n task2 = task2[0]\\n if task2.get('status') == 'Pending':\\n server.update(task2.get('__search_key__'), {'status': READY}) #?\\n # FIND ALL HACK WOS HERE.........\\n hack_dudes = server.eval(\\\"@SOBJECT(twog/hackpipe_out['lookup_code','%s'])\\\" % proj.get('code'))\\n for ho in hack_dudes:\\n ready3 = True\\n out_to = ho.get('out_to')\\n label = ''\\n if 'PROJ' in out_to:\\n label = 'twog/proj'\\n elif 'WORK_ORDER' in out_to:\\n label = 'twog/work_order'\\n sob_guy = server.eval(\\\"@SOBJECT(%s['code','%s'])\\\" % (label, out_to))\\n for sobby in sob_guy:\\n ready3 = are_no_hackpipes_preceding(sobby, sobj.get('lookup_code'))\\n if ready3:\\n htask = server.eval(\\\"@SOBJECT(sthpw/task['lookup_code','%s'])\\\" % out_to)\\n if htask:\\n htask = htask[0]\\n if htask.get('status') == 'Pending':\\n server.update(htask.get('__search_key__'), {'status': READY})\\n \\n hackers = server.eval(\\\"@SOBJECT(twog/hackpipe_out['lookup_code','%s'])\\\" % sobj.get('lookup_code'))\\n for hack in hackers:\\n if stype in hack.get('out_to'):\\n out_to = hack.get('out_to')\\n out_sob = server.eval(\\\"@SOBJECT(%s['code','%s'])\\\" % (stype_st, out_to))\\n if out_sob:\\n out_sob = out_sob[0] \\n ready4 = are_no_hackpipes_preceding(out_sob, sobj.get('lookup_code'))\\n if ready4:\\n tasker = server.eval(\\\"@SOBJECT(sthpw/task['code','%s'])\\\" % out_sob.get('task_code'))\\n if tasker:\\n tasker = tasker[0]\\n if tasker.get('status') == 'Pending':\\n server.update(tasker.get('__search_key__'), {'status': READY})\\n if stype == 'PROJ':\\n # Need to do the same thing here, looking at pipeline and hackpipe\\n hack_wos = server.eval(\\\"@SOBJECT(twog/hackpipe_out['lookup_code','%s'])\\\" % out_to)\\n for hos in hack_wos:\\n if 'PROJ' not in hos.get('out_to'):\\n ho_wo = server.eval(\\\"@SOBJECT(twog/work_order['code','%s'])\\\" % hos.get('out_to'))\\n if ho_wo:\\n ho_wo = ho_wo[0]\\n ready5 = are_no_hackpipes_preceding(ho_wo, out_sob.get('code'))\\n if ready5:\\n ho_wo_task_sk = server.build_search_key('sthpw/task', ho_wo.get('task_code'))\\n #7/10/2014 --- WAIT. WTF. THERE IS NO STATUS ON WOS, JUST THE TASK..... MTMMTMMTM!!!\\n if ho_wo.get('status') == 'Pending':\\n server.update(ho_wo_task_sk, {'status': READY})\\n # NEED TO LOOK AT PROJ PIPELINE NOW\\n proj_sk = server.build_search_key('twog/proj', out_to)\\n pipe_wos = server.eval(\\\"@SOBJECT(twog/work_order['proj_code','%s'])\\\" % out_to)\\n for pwos in pipe_wos:\\n if pwos.get('creation_type') not in ['hackpipe','hackup']:\\n info2 = server.get_pipeline_processes_info(proj_sk, related_process=pwos.get('process'))\\n if 'input_processes' in info2.keys():\\n input_processes2 = info2.get('input_processes')\\n whack_says = are_no_hackpipes_preceding(pwos, out_to)\\n # If there are no input processes, it must be a work order that should also be set to ready - as long as hackpipe says it's ok\\n len_proc2 = 0\\n if input_processes2 not in ['',{},[],None]:\\n len_proc2 = len(input_processes2)\\n if len_proc2 < 1 and whack_says:\\n wtask_code = pwos.get('task_code')\\n # Get the task sobject associated with this work order\\n wtask = server.eval(\\\"@SOBJECT(sthpw/task['code','%s'])\\\" % wtask_code)\\n if wtask:\\n wtask = wtask[0]\\n # If the task's status has not been touched yet ('Pending') and active is set to true, update the status with 'Ready'\\n if wtask.get('status') == 'Pending':\\n wdata = {}\\n wdata['status'] = 'Ready'\\n if wtask.get('status') == 'Pending':\\n server.update(wtask.get('__search_key__'), wdata)\\n \\n \\n \\n #\\n # HERE NEED TO FIND ANY OTHER TASKS THIS GOES 'OUT_TO' and make sure they don't also depend on another task's status (if so, make sure it is completed)\\n #\\n \\n # this is optional, for simplicity, turn off triggers for these updates\\n if update_data != {} and title.get('priority_triggers') != 'No':\\n #make title priority the proj priority, if proj is becoming \\\"Ready\\\"\\n for tkey in update_data.keys():\\n record = update_data.get(tkey)\\n tkcode = tkey.split('code=')[1]\\n if 'status' in record.keys():\\n if record.get('status') == 'Ready':\\n ttt = server.eval(\\\"@GET(sthpw/task['code','%s'].lookup_code)\\\" % tkcode)[0]\\n if 'PROJ' in ttt:\\n pjj = server.eval(\\\"@SOBJECT(twog/proj['code','%s'])\\\" % ttt)[0]\\n proj_title_code = pjj.get('title_code')\\n proj_prio = pjj.get('priority')\\n server.update(server.build_search_key('twog/title',proj_title_code), {'priority': proj_prio}, triggers=False)\\n server.update_multiple(update_data, triggers=False) #? Should triggers=False?\\n \\n \\n # Now see if all wos under proj or all projs under title are completed. If so, make their parent's status completed\\n all_wos_completed = False\\n all_wos_pending = False\\n prj = None\\n if new_status in [COMPLETE,PENDING]: \\n if 'WORK_ORDER' in this_lookup:\\n wo = server.eval(\\\"@SOBJECT(twog/work_order['code','%s'])\\\" % sobj.get('lookup_code'))\\n wo = wo[0]\\n other_wotasks_expr = \\\"@SOBJECT(twog/proj['code','%s'].twog/work_order.WT:sthpw/task)\\\" % wo.get('proj_code')\\n other_wo_tasks = server.eval(other_wotasks_expr)\\n all_wos_completed = True\\n all_wos_pending = True\\n if new_status == PENDING:\\n all_wos_completed = False\\n else:\\n all_wos_pending = False\\n for owt in other_wo_tasks:\\n if owt.get('lookup_code') != wo.get('code'):\\n if owt.get('status') != COMPLETE:\\n all_wos_completed = False\\n if owt.get('status') != PENDING:\\n all_wos_pending = False\\n prj = server.eval(\\\"@SOBJECT(twog/proj['code','%s'])\\\" % wo.get('proj_code'))\\n if len(prj) > 0:\\n prj = prj[0]\\n else:\\n prj = None\\n if (all_wos_completed or all_wos_pending) and prj not in [None,'']:\\n if title.get('status_triggers') != 'No' or all_wos_pending == True:\\n prj_task = server.eval(\\\"@SOBJECT(sthpw/task['lookup_code','%s'])\\\" % prj.get('code'))\\n if prj_task:\\n prj_task = prj_task[0]\\n server.update(prj_task.get('__search_key__'), {'status': new_status})\\n elif 'PROJ' in this_lookup:\\n prj = server.eval(\\\"@SOBJECT(twog/proj['code','%s'])\\\" % this_lookup)\\n if prj:\\n prj = prj[0]\\n else:\\n prj = None\\n \\n all_projs_completed = True\\n all_projs_pending = True\\n all_titles_completed = False\\n all_titles_pending = False\\n if prj not in [None,'']:\\n title_proj_tasks = server.eval(\\\"@SOBJECT(twog/title['code','%s'].twog/proj.PT:sthpw/task)\\\" % prj.get('title_code'))\\n for tpt in title_proj_tasks:\\n if tpt.get('status') != COMPLETE:\\n all_projs_completed = False\\n if tpt.get('status') != PENDING:\\n all_projs_pending = False\\n title_updated = False\\n if all_projs_completed:\\n title_sk = server.build_search_key('twog/title', prj.get('title_code'))\\n if title.get('priority_triggers') != 'No' and title.get('status_triggers') != 'No':\\n server.update(title_sk, {'status': COMPLETE, 'bigboard': False, 'priority': 5000})\\n titles_completed = order.get('titles_completed')\\n title_codes_completed = order.get('title_codes_completed')\\n if title.get('code') not in title_codes_completed:\\n if titles_completed in [None,'']:\\n titles_completed = 0\\n else:\\n titles_completed = int(titles_completed)\\n titles_completed = titles_completed + 1\\n if title_codes_completed == '':\\n title_codes_completed = title.get('code')\\n else:\\n title_codes_completed = '%s,%s' % (title_codes_completed, title.get('code'))\\n server.update(order.get('__search_key__'), {'titles_completed': titles_completed, 'title_codes_completed': title_codes_completed}) \\n title_updated = True\\n all_titles_completed = True\\n title = server.eval(\\\"@SOBJECT(twog/title['code','%s'])\\\" % prj.get('title_code'))\\n if title and title_updated:\\n title = title[0]\\n other_titles = server.eval(\\\"@SOBJECT(twog/order['code','%s'].twog/title)\\\" % title.get('order_code'))\\n for ot in other_titles:\\n if title.get('code') != ot.get('code'):\\n if ot.get('status') != COMPLETE:\\n all_titles_completed = False\\n else:\\n all_titles_completed = False\\n if all_projs_pending:\\n title_sk = server.build_search_key('twog/title', prj.get('title_code'))\\n if title.get('priority_triggers') != 'No' and title.get('status_triggers') != 'No':\\n server.update(title_sk, {'status': '', 'bigboard': False})\\n title_codes_completed = order.get('title_codes_completed')\\n if title.get('code') in title_codes_completed:\\n title_codes_completed = title_codes_completed.replace(',%s' % title.get('code'),'').replace('%s,' % title.get('code'),'').replace('%s' % title.get('code'),'')\\n titles_completed = order.get('titles_completed')\\n if titles_completed in [None,'']:\\n titles_completed = 0\\n else:\\n titles_completed = int(titles_completed) - 1\\n server.update(order.get('__search_key__'), {'titles_completed': titles_completed, 'title_codes_completed': title_codes_completed})\\n title_updated = True\\n all_titles_pending = True\\n title = server.eval(\\\"@SOBJECT(twog/title['code','%s'])\\\" % prj.get('title_code'))\\n if title and title_updated:\\n title = title[0]\\n other_titles = server.eval(\\\"@SOBJECT(twog/order['code','%s'].twog/title)\\\" % title.get('order_code'))\\n for ot in other_titles:\\n if title.get('code') != ot.get('code'):\\n if ot.get('status') != '':\\n all_titles_pending = False\\n else:\\n all_titles_pending = False\\n if all_titles_pending:\\n server.update(server.build_search_key('twog/order', title.get('order_code')), {'needs_completion_review': False}) \\n if all_titles_completed:\\n server.update(server.build_search_key('twog/order', title.get('order_code')), {'needs_completion_review': True}) \\n \\n #print \\\"LEAVING KICKOFF\\\"\\n except AttributeError as e:\\n traceback.print_exc()\\n print str(e) + '\\\\nMost likely the server object does not exist.'\\n raise e\\n except KeyError as e:\\n traceback.print_exc()\\n print str(e) + '\\\\nMost likely the input dictionary does not exist.'\\n raise e\\n except Exception as e:\\n traceback.print_exc()\\n print str(e)\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fec6355e967e199b1e27d6e5414498ca\",\n \"score\": \"0.42743507\",\n \"text\": \"def _substitute(self, tpl):\\n p = self.params\\n # We do not yet know NFS_SERVER_IP_ADDRESS so leave it a template.\\n # Same with DB_IDENTIFIER\\n return tpl.render(CLUSTERNAME=p['kubernetes_cluster_name'],\\n OAUTH_PROVIDER=self.encode_value(\\n 'oauth_provider'),\\n OAUTH_CLIENT_ID=self.encode_value(\\n 'oauth_client_id'),\\n OAUTH_SECRET=self.encode_value(\\n 'oauth_secret'),\\n OAUTH_CALLBACK_URL=self.encode_value(\\n 'oauth_callback_url'),\\n GITHUB_ORGANIZATION_WHITELIST=self.encode_value(\\n 'github_organization_whitelist'),\\n CILOGON_GROUP_WHITELIST=self.encode_value(\\n 'cilogon_group_whitelist'),\\n GITHUB_ORGANIZATION_DENYLIST=self.encode_value(\\n 'github_organization_denylist'),\\n CILOGON_GROUP_DENYLIST=self.encode_value(\\n 'cilogon_group_denylist'),\\n SESSION_DB_URL=self.encode_value(\\n 'session_db_url'),\\n JUPYTERHUB_CRYPTO_KEY=self.encode_value(\\n 'crypto_key'),\\n CONFIGPROXY_AUTH_TOKEN=self.encode_value(\\n 'configproxy_auth_token'),\\n CLUSTER_IDENTIFIER=p[\\n 'kubernetes_cluster_namespace'],\\n SHARED_VOLUME_SIZE=p[\\n 'nfs_volume_size'],\\n PHYSICAL_SHARED_VOLUME_SIZE=p[\\n 'volume_size'],\\n ROOT_CHAIN_PEM=self.encode_file('tls_root_chain'),\\n DHPARAM_PEM=self.encode_value(\\\"dhparams\\\"),\\n TLS_CRT=self.encode_file('tls_cert'),\\n TLS_KEY=self.encode_file('tls_key'),\\n HOSTNAME=p['hostname'],\\n FQDN=p['hostname'],\\n CA_CERTIFICATE=self.encode_file('beats_ca'),\\n BEATS_CERTIFICATE=self.encode_file('beats_cert'),\\n BEATS_KEY=self.encode_file('beats_key'),\\n SHIPPER_NAME=p['log_shipper_name'],\\n RABBITMQ_PAN_PASSWORD=self.encode_value(\\n 'rabbitmq_pan_password'),\\n RABBITMQ_TARGET_HOST=p['rabbitmq_target_host'],\\n RABBITMQ_TARGET_VHOST=p['rabbitmq_target_vhost'],\\n DEBUG=p['debug'],\\n PREPULLER_IMAGE_LIST=p['prepuller_image_list'],\\n PREPULLER_NO_SCAN=p['prepuller_no_scan'],\\n PREPULLER_REPO=p['prepuller_repo'],\\n PREPULLER_OWNER=p['prepuller_owner'],\\n PREPULLER_IMAGE_NAME=p['prepuller_image_name'],\\n PREPULLER_EXPERIMENTALS=p['prepuller_experimentals'],\\n PREPULLER_DAILIES=p['prepuller_dailies'],\\n PREPULLER_WEEKLIES=p['prepuller_weeklies'],\\n PREPULLER_RELEASES=p['prepuller_releases'],\\n PREPULLER_PORT=p['prepuller_port'],\\n PREPULLER_SORT_FIELD=p['prepuller_sort_field'],\\n PREPULLER_COMMAND=p['prepuller_command'],\\n PREPULLER_NAMESPACE=p['prepuller_namespace'],\\n PREPULLER_MINUTE=p['prepuller_minute'],\\n LAB_REPO_HOST=p['lab_repo_host'],\\n LAB_REPO_OWNER=p['lab_repo_owner'],\\n LAB_REPO_NAME=p['lab_repo_name'],\\n LAB_IMAGE=p['lab_image'],\\n LAB_SELECTOR_TITLE=p['lab_selector_title'],\\n LAB_IDLE_TIMEOUT=p['lab_idle_timeout'],\\n LAB_MEM_LIMIT=p['lab_mem_limit'],\\n LAB_CPU_LIMIT=p['lab_cpu_limit'],\\n LAB_MEM_GUARANTEE=p['lab_mem_guarantee'],\\n LAB_CPU_GUARANTEE=p['lab_cpu_guarantee'],\\n LAB_NODEJS_MAX_MEM=p['lab_nodejs_max_mem'],\\n TINY_MAX_CPU=p['tiny_max_cpu'],\\n MB_PER_CPU=p['mb_per_cpu'],\\n LAB_SIZE_RANGE=p['lab_size_range'],\\n AUTO_REPO_URLS=p['auto_repo_urls'],\\n ALLOW_DASK_SPAWN=p['allow_dask_spawn'],\\n MAX_DASK_WORKERS=p['max_dask_workers'],\\n SIZE_INDEX=p['size_index'],\\n HUB_ROUTE=p['hub_route'],\\n FIREFLY_ADMIN_PASSWORD=self.encode_value(\\n 'firefly_admin_password'),\\n FIREFLY_REPLICAS=p['firefly_replicas'],\\n FIREFLY_CONTAINER_MEM_LIMIT=p[\\n 'firefly_container_mem_limit'],\\n FIREFLY_CONTAINER_CPU_LIMIT=p[\\n 'firefly_container_cpu_limit'],\\n FIREFLY_MAX_JVM_SIZE=p['firefly_max_jvm_size'],\\n FIREFLY_UID=p['firefly_uid'],\\n FIREFLY_ROUTE=p['firefly_route'],\\n JS9_ROUTE=p['js9_route'],\\n API_ROUTE=p['api_route'],\\n TAP_ROUTE=p['tap_route'],\\n SODA_ROUTE=p['soda_route'],\\n EXTERNAL_INSTANCE_URL=p['external_instance_url'],\\n EXTERNAL_FIREFLY_URL=p['external_firefly_url'],\\n EXTERNAL_JS9_URL=p['external_js9_url'],\\n EXTERNAL_API_URL=p['external_api_url'],\\n EXTERNAL_TAP_URL=p['external_tap_url'],\\n EXTERNAL_SODA_URL=p['external_soda_url'],\\n EXTERNAL_URL=p['external_soda_url'],\\n RESTRICT_DASK_NODES=p['restrict_dask_nodes'],\\n RESTRICT_LAB_NODES=p['restrict_lab_nodes'],\\n MAX_HTTP_HEADER_SIZE=p['max_http_header_size'],\\n DB_IDENTIFIER='{{DB_IDENTIFIER}}',\\n NFS_SERVER_IP_ADDRESS='{{NFS_SERVER_IP_ADDRESS}}',\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c20f2bb9733fea4788200595a686f03\",\n \"score\": \"0.4263419\",\n \"text\": \"def tree(input_file, output_file):\\n\\n\\ttree=\\\"muscle -maketree -in \\\"+input_file+\\\" -out \\\"+output_file+ \\\\\\n\\t\\t\\\" -cluster neighborjoining \\\"+ \\\" 2>/dev/null \\\"\\n\\tos.system(tree)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3eae7d3f04468a6936ad7e5af7cbbdf\",\n \"score\": \"0.4260095\",\n \"text\": \"def Initialise_plan():\\n rp = rospkg.RosPack()\\n pkg_path = rp.get_path('eyantra_task')\\n arg_file_path = pkg_path + '/config/ur5_2_trajectories/'\\n\\n saved_plans_to_homes = { 'zero': {'home1': 'zero_to_home1.yaml' , \\n 'home3':'zero_to_home3.yaml'},\\n 'red' : {'home1': 'red_to_home1.yaml' , \\n 'home2': 'red_to_home2.yaml' , \\n 'home3' : 'red_to_home3.yaml'} ,\\n 'yellow': {'home1': 'yellow_to_home1.yaml' , \\n 'home2': 'yellow_to_home2.yaml' , \\n 'home3' : 'yellow_to_home3.yaml'},\\n 'green': { 'home1': 'green_to_home1.yaml' , \\n 'home2': 'green_to_home2.yaml' , \\n 'home3' : 'green_to_home3.yaml'}}\\n\\n saved_homes_to_bin = { 'home1' : { 'red' : 'home1_to_red.yaml' , \\n 'yellow': 'home1_to_yellow.yaml' } ,\\n 'home2' : { 'red' : 'home2_to_red.yaml' , \\n 'yellow': 'home2_to_yellow.yaml' } ,\\n 'home3' : { 'green' : 'home3_to_green.yaml' } }\\n\\n\\n loaded_home = { 'zero': { 'home1': ' ' , 'home3': ' '} , \\n 'red' : { 'home1': ' ' , 'home2': ' ' , 'home3': ' ' } ,\\n 'yellow' : { 'home1': ' ' , 'home2': ' ' , 'home3': ' ' } ,\\n 'green': { 'home1': ' ' , 'home2': ' ' , 'home3': ' ' } }\\n\\n loaded_pkg = { 'home1': {'red' : ' ' , 'green': ' ' } , 'home2':{'red':' ' , 'green': ' '} , 'home3': { 'green':' '} }\\n\\n saved_plans = [ saved_plans_to_homes , saved_homes_to_bin ]\\n loaded_plans = [ loaded_home , loaded_pkg ]\\n\\n for i in range(len(saved_plans)):\\n temp_keys = list(saved_plans[i].keys())\\n for keys in temp_keys:\\n temp_keys2 = list(saved_plans[i][keys].keys())\\n for keys2 in temp_keys2:\\n file_name = saved_plans[i][keys][keys2]\\n file_path = arg_file_path + file_name\\n with open(file_path, 'r') as file_open:\\n loaded_plan = yaml.load(file_open)\\n loaded_plans[i][keys][keys2] = loaded_plan\\n\\n rospy.loginfo(\\\"Loaded the plans....\\\")\\n return loaded_plans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49162f00f04e935c6f5bf0caf8df634f\",\n \"score\": \"0.42598808\",\n \"text\": \"def generate(c):\\n check_main_jsonnet_here()\\n res = c.run(\\\"tk eval .\\\", hide=True)\\n data = json.loads(res.stdout)\\n for stack_name, files_cfg in data.items():\\n for fname, content in files_cfg.items():\\n path = DEPLOY_PATH / stack_name / fname\\n path.parent.mkdir(parents=True, exist_ok=True)\\n path.write_text(content, encoding=\\\"utf-8\\\")\\n print(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8e15e15a5bafb64267fd149eb21c64d\",\n \"score\": \"0.42580685\",\n \"text\": \"def _generateScripts(simconfig, curcomp):\\n from supercomputer import JobConfig\\n from datetime import time\\n from os.path import join\\n from shutil import copy\\n #Configure job\\n jconfig = JobConfig()\\n jconfig.nodes = simconfig.node_count\\n jconfig.mpn = simconfig.mpn\\n jconfig.omp = simconfig.omp_thds\\n jconfig.label = simconfig.run_label\\n jconfig.time = time(4,0,0) \\n jconfig.exe = simconfig.exe\\n jconfig.inputs = simconfig.inputs\\n\\n #Make strings\\n outpath = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Runs',\\n simconfig.run_label, 'run')\\n tpath = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Templates',\\n 'titan.run.template')\\n\\n #Generate job script\\n curcomp.generateJobScript(outpath, jconfig, tpath)\\n\\n #Copy process script over\\n proc_temp = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Templates',\\n 'process.titan.template')\\n proc_dst = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Runs',\\n simconfig.run_label, 'run', 'process.titan')\\n copy(proc_temp, proc_dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3c8eee520b6fd06d2613bbe4cf74624\",\n \"score\": \"0.42566642\",\n \"text\": \"def convert_from_backup(backup_file, output_file=None):\\n if not os.path.exists(backup_file):\\n raise ValueError(\\\"%s does not exists.\\\" % backup_file)\\n\\n tree = ET.parse(backup_file)\\n root = tree.getroot()\\n\\n script_output = _generate_query(root)\\n imports_str = _generate_imports(import_list)\\n boilerplate_start_str = \\\"\\\"\\\"#!/usr/bin/env python\\n\\nfrom ucsmsdk.ucshandle import UcsHandle\\n\\nhandle = UcsHandle(ip=\\\"\\\", username=\\\"\\\", password=\\\"\\\")\\nhandle.login()\\n\\n\\\"\\\"\\\"\\n\\n boilerplate_end_str = \\\"\\\"\\\"handle.logout()\\\"\\\"\\\"\\n\\n final_str = boilerplate_start_str + imports_str + script_output + boilerplate_end_str\\n if output_file:\\n _outfile = open(output_file, 'w')\\n _outfile.write(final_str)\\n _outfile.close()\\n else:\\n print(final_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24cd93a75cd645c67a51996d385bd54f\",\n \"score\": \"0.42557374\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser(usage=\\\"python generate_scene_desc.py --pb=<path> --tmpl=<scene-template>\\\")\\n parser.add_argument('--tmpl', type=str, help='Scene template json file.')\\n parser.add_argument('--pb', type=str, help='Path to the protobuf file.')\\n parser.add_argument('--out', type=str, required=True, help='Output directory')\\n parser.add_argument('--data-root', type=str, default='./', help='Data root.')\\n\\n args = parser.parse_args()\\n print(args)\\n\\n protobuf_path = os.path.expanduser(args.pb)\\n data_root_path = os.path.expanduser(args.data_root)\\n out_dir = os.path.expanduser(args.out)\\n\\n if not os.path.exists(out_dir):\\n os.makedirs(out_dir)\\n\\n trajectories = sn.Trajectories()\\n try:\\n with open(protobuf_path, 'rb') as f:\\n trajectories.ParseFromString(f.read())\\n except IOError:\\n print('Scenenet protobuf data not found at location:{0}'.format(data_root_path))\\n\\n print('Number of trajectories:{0}'.format(len(trajectories.trajectories)))\\n\\n for traj_no, traj in enumerate(trajectories.trajectories):\\n layout_type = sn.SceneLayout.LayoutType.Name(traj.layout.layout_type)\\n layout_path = traj.layout.model\\n\\n print('=' * 20)\\n print('Render path:{0}'.format(traj.render_path))\\n print('Layout type:{0} path:{1}'.format(layout_type, layout_path))\\n print('=' * 20)\\n obj_filename = 'trajectory_{}.obj'.format(traj_no)\\n print(obj_filename)\\n scene_desc = {\\\"file-format\\\": {\\\"type\\\": \\\"diffrend\\\", \\\"version\\\": \\\"0.2\\\"},\\n \\\"glsl\\\": {\\\"vertex\\\": \\\"../shaders/phong/vs.glsl\\\", \\\"fragment\\\": \\\"../shaders/phong/fs.glsl\\\"},\\n \\\"camera\\\": {\\n \\\"proj_type\\\": \\\"perspective\\\",\\n \\\"viewport\\\": [0, 0, 640, 480],\\n \\\"fovy\\\": 1.04,\\n \\\"focal_length\\\": 1.0,\\n \\\"eye\\\": [3.5, 2.0, 3.5, 1.0],\\n \\\"up\\\": [0.0, 1.0, 0.0, 0.0],\\n \\\"at\\\": [0.0, 1.0, 0.0, 1.0],\\n \\\"near\\\": 0.1, \\\"far\\\": 1000.0\\n },\\n \\\"lights\\\": {\\\"pos\\\": [],\\n \\\"color_idx\\\": [],\\n \\\"attenuation\\\": [],\\n \\\"ambient\\\": [0.01, 0.01, 0.01]\\n },\\n \\\"colors\\\": [],\\n \\\"materials\\\": {\\n \\\"albedo\\\": [[0.0, 0.0, 0.0], [0.1, 0.1, 0.1], [0.2, 0.2, 0.2], [0.5, 0.5, 0.5],\\n [0.8, 0.8, 0.8], [0.44, 0.55, 0.64]],\\n \\\"coeffs\\\": [[1.0, 0.0, 0.0], [1.0, 0.0, 0.0], [1.0, 0.0, 0.0], [1.0, 0.0, 0.0],\\n [1.0, 0.0, 0.0], [1.0, 0.0, 0.0]]\\n },\\n \\\"objects\\\": {\\\"obj\\\": [{\\\"path\\\": \\\"../objs/scenenetrgbd/\\\" + obj_filename, \\\"material_idx\\\": 4,\\n \\\"scale\\\": [1.0, 1.0, 1.0]\\n }\\n ]\\n },\\n \\\"tonemap\\\": {\\\"type\\\": \\\"gamma\\\", \\\"gamma\\\": [1.0]}\\n }\\n for instance in traj.instances:\\n if instance.instance_type == sn.Instance.LIGHT_OBJECT:\\n light = instance.light_info\\n light_type = sn.LightInfo.LightType.Name(light.light_type)\\n light_intensity = [light.light_output.r, light.light_output.g, light.light_output.b]\\n light_pos = [light.position.x, light.position.y, light.position.z]\\n print('=' * 20)\\n print('Light type:{0}'.format(light_type))\\n print(light)\\n print('emission: ', light_intensity)\\n print('pos: ', light_pos)\\n scene_desc[\\\"colors\\\"].append(light_intensity)\\n scene_desc['lights'][\\\"color_idx\\\"].append(len(scene_desc[\\\"colors\\\"]) - 1)\\n scene_desc['lights'][\\\"pos\\\"].append(light_pos)\\n scene_desc['lights'][\\\"attenuation\\\"].append([0.0, 0.0, 0.4])\\n\\n print(scene_desc)\\n outfilename = 'scenenet_{:04}.json'.format(traj_no)\\n print(outfilename)\\n with open(os.path.join(out_dir, outfilename), 'w') as fid:\\n json.dump(scene_desc, fid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2bad0ed2603a1d075b46b3e09430b00\",\n \"score\": \"0.42413738\",\n \"text\": \"def write_tcl(options, A, B, pairs, tag=\\\"\\\", center=False):\\n AA = deepcopy(A)\\n BB = deepcopy(B)\\n if pairs != None and len(pairs) > 0:\\n for p in pairs:\\n ia = p[0]\\n ib = p[1]\\n AA[ia].pos = p[3]\\n BB[ib].pos = p[4]\\n\\n with open(\\\"POSCAR_A.%s\\\" % tag, \\\"w\\\") as f: pcwrite.poscar(AA, f, vasp5=True)\\n with open(\\\"POSCAR_B.%s\\\" % tag, \\\"w\\\") as f: pcwrite.poscar(BB, f, vasp5=True)\\n write_xyz(options, AA, \\\"A.%s\\\" % tag,options.output_tiles)\\n write_xyz(options, BB, \\\"B.%s\\\" % tag,options.output_tiles)\\n\\n fout = file(\\\"plotpairs.%s.tcl\\\" % tag, \\\"w\\\")\\n write_struct(fout, AA, \\\"A.%s.xyz\\\" % tag, 0, center)\\n write_struct(fout, BB, \\\"B.%s.xyz\\\" %tag, 1, center)\\n\\n linestr = \\\"draw color green; graphics top line {%f %f %f} {%f %f %f} width 3 style dashed\\\\n\\\"\\n# sum = 0\\n for p in pairs:\\n print p\\n ia = p[0]\\n ib = p[1]\\n p1 = AA.scale * AA[ia].pos\\n p2 = BB.scale * BB[ib].pos\\n## double check! print \\\"TCL types: %s %s\\\" % (A[ia].type, B[ib].type)\\n# val = npl.norm(np.array(p1)-np.array(p2))\\n# sum += val\\n# print \\\"write_tcl pairing: \\\", p1, p2, val\\n fout.write(linestr % (p1[0], p1[1], p1[2], p2[0], p2[1], p2[2]))\\n# print \\\"write_tcl total pairing dist: \\\", sum\\n\\n fout.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61d95e842da2c0d243564c422879ccf9\",\n \"score\": \"0.4233163\",\n \"text\": \"def create_topology(nc, num):\\n rolls = ['Input', 'Output']\\n for i in range(num):\\n NEAT.GeneticOperation.add_hidden(nc, rolls[np.random.randint(0, 2)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"187e0437b755b557a9ee96c680de76da\",\n \"score\": \"0.42321697\",\n \"text\": \"def generate_test_topology():\\n config = FBOSSTestTopology(\\\"rsw1aj.20.snc1\\\")\\n\\n \\\"\\\"\\\" This example has two testing hosts \\\"\\\"\\\"\\n f154 = TestHost(\\\"fboss154.20.snc1\\\")\\n f154.add_interface(\\\"eth0\\\", \\\"2401:db00:111:400a::154\\\")\\n config.add_host(f154)\\n\\n f155 = TestHost(\\\"fboss155.20.snc1\\\")\\n f155.add_interface(\\\"eth0\\\", \\\"2401:db00:111:400a::155\\\")\\n config.add_host(f155)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd1168a769b7395c168ad1580cb3e005\",\n \"score\": \"0.4226332\",\n \"text\": \"def build_tree(script):\\n return ast.parse(script,mode=\\\"exec\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c3b837db91a3e356985d1b8d94ed16d\",\n \"score\": \"0.4226303\",\n \"text\": \"def notify_topology_update(self):\\n name = 'kytos/topology.updated'\\n event = KytosEvent(name=name, content={'topology': self.topology})\\n self.controller.buffers.app.put(event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c4bdaac2dae6e2288734e103617b789\",\n \"score\": \"0.422377\",\n \"text\": \"def _build_network_definition_using_topology(self):\\n log.debug(\\\"%r: _build_network_definition_using_topology: %s\\\",\\n self._platform_id, self._topology)\\n\\n def build(platform_id, children):\\n \\\"\\\"\\\"\\n Returns the root NNode for the given platform_id with its\\n children according to the given list.\\n \\\"\\\"\\\"\\n nnode = NNode(platform_id)\\n if self._agent_device_map:\\n self._set_attributes_and_ports_from_agent_device_map(nnode)\\n\\n log.debug('Created NNode for %r', platform_id)\\n\\n for subplatform_id in children:\\n subplatform_children = self._topology.get(subplatform_id, [])\\n sub_nnode = build(subplatform_id, subplatform_children)\\n nnode.add_subplatform(sub_nnode)\\n\\n return nnode\\n\\n children = self._topology.get(self._platform_id, [])\\n return build(self._platform_id, children)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e8f498922156d0d2c106d55511a4497\",\n \"score\": \"0.4215529\",\n \"text\": \"def do_topo_subgraph(self, args):\\n\\n # logger.info(\\\"Topo initialized with sec IDs %s\\\" % sec_ids)\\n\\n # delimiter of node in dot file\\n delim_node = '_'\\n\\n args_cleaned = re.sub(r\\\"\\\\s+\\\", ' ', args).strip()\\n # Show subgraphs if given no argments\\n if (args_cleaned == ''):\\n if len(self.spp_topo.subgraphs) == 0:\\n print(\\\"No subgraph.\\\")\\n else:\\n for label, subg in self.spp_topo.subgraphs.items():\\n print('label: %s\\\\tsubgraph: \\\"%s\\\"' % (label, subg))\\n else: # add or del\\n tokens = args_cleaned.split(' ')\\n # Add subgraph\\n if tokens[0] == 'add':\\n if len(tokens) == 3:\\n label = tokens[1]\\n subg = tokens[2]\\n if ',' in subg:\\n subg = re.sub(r'%s' % delim_node, ':', subg)\\n subg = re.sub(r\\\",\\\", \\\";\\\", subg)\\n\\n # TODO(yasufum) add validation for subgraph\\n self.spp_topo.subgraphs[label] = subg\\n print(\\\"Add subgraph '%s'\\\" % label)\\n else:\\n print(\\\"Invalid syntax '%s'!\\\" % args_cleaned)\\n # Delete subgraph\\n elif ((tokens[0] == 'del') or\\n (tokens[0] == 'delete') or\\n (tokens[0] == 'remove')):\\n del(self.spp_topo.subgraphs[tokens[1]])\\n print(\\\"Delete subgraph '%s'\\\" % tokens[1])\\n\\n else:\\n print(\\\"Ivalid subcommand '%s'!\\\" % tokens[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4d7ab3893642a4825fc02c383858933\",\n \"score\": \"0.4205685\",\n \"text\": \"def get_topology(self):\\n return json.dumps(self.topology.to_json())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b139a4ba887e5b907890a7a41d3efc90\",\n \"score\": \"0.4204748\",\n \"text\": \"def main():\\n args = prepare_argparser(allowed_formats)\\n inp_format, inp_loc = filter_dict(\\\"input\\\", args.__dict__)\\n out_format, out_loc = filter_dict(\\\"output\\\", args.__dict__)\\n output_nodes = args.output_nodes.split(',')\\n convert(inp_format, inp_loc, out_format, out_loc, output_nodes,\\n args.ngbackend)\\n print('Converted the model. Exiting now')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ff53d6dca043bfefcbf28508ebded68\",\n \"score\": \"0.42006487\",\n \"text\": \"def create_vm(self, name, conn, template):\\n xml = \\\"\\\"\\n tree = ElementTree.parse(template)\\n root = tree.getroot()\\n tree.find('.//name').text = name\\n xml = ElementTree.tostring(root, encoding='utf8', method='xml')\\n vm = conn.defineXML(xml)\\n return vm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71eb8d4147fea5ad60498aab0fff6c5c\",\n \"score\": \"0.4198723\",\n \"text\": \"def generate(self, namespace):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c875bd57eca71458b3ba5dcaa68f9c42\",\n \"score\": \"0.4195698\",\n \"text\": \"def create_root(parser: ConfigParser):\\n slsoptions = parser[\\\"sls\\\"]\\n\\n sls_source = slsoptions[\\\"sls_source\\\"]\\n mount_point = Path(slsoptions[\\\"mount_point\\\"])\\n disk_path = slsoptions[\\\"disk_path\\\"]\\n tarfile = slsoptions[\\\"tarfile\\\"]\\n newfs = str(Path(sls_source, \\\"tools\\\", \\\"newfs_sls\\\", \\\"newfs_sls\\\"))\\n\\n bashcmd([newfs, disk_path])\\n bashcmd([\\\"mount\\\", \\\"-t\\\", \\\"slsfs\\\", disk_path, str(mount_point)])\\n bashcmd([\\\"tar\\\", \\\"-xf\\\", tarfile, \\\"-C\\\", mount_point])\\n bashcmd([\\\"mount\\\", \\\"-t\\\", \\\"devfs\\\", \\\"devfs\\\", str(mount_point / \\\"dev\\\")])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d4138f4f892dfe00691614705265d02\",\n \"score\": \"0.41942257\",\n \"text\": \"def j2template (self, template_file):\\n expansion = {}\\n for host in self.hosts():\\n expansion[host] = ansible.utils.template.template_from_file (\\n None,\\n template_file,\\n self.hostvars(host)\\n )\\n return expansion\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9b4f1364a1f9d99f912b951ca1bfc8f\",\n \"score\": \"0.41930586\",\n \"text\": \"def CASE102( self, main ):\\n main.HA.startTopology( main )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9975a53c433dd8b35f917e1896375275\",\n \"score\": \"0.4192809\",\n \"text\": \"def __init__(self,itpname,groname):\\n Topology.__init__(self)\\n fid = open(itpname)\\n topology = fid.readlines()\\n fid.close()\\n fid = open(groname)\\n grofile = fid.readlines()\\n grodata = grofile[2:(len(grofile)-1)]\\n box = grofile[len(grofile)-1].split()\\n self.box = [float(box[0]),float(box[1]),float(box[2])]\\n fid.close()\\n flag = 'notes'\\n for line in topology:\\n if line.strip() == '[ moleculetype ]':\\n flag = 'moltype'\\n continue\\n if line.strip() == '[ atoms ]':\\n flag = 'atoms'\\n continue\\n if line.strip() == '[ bonds ]':\\n flag = 'bonds'\\n continue\\n if line.strip() == '[ constraints ]':\\n flag = 'cons'\\n continue\\n if line.strip() == '[ angles ]':\\n flag = 'angles'\\n continue\\n if line.strip() == '[ dihedrals ]':\\n flag = 'dihs'\\n continue\\n if flag == 'notes':\\n self.title += line\\n else:\\n if line[0] == ';' or line == '\\\\n' or line[0] == '#':\\n continue\\n else:\\n spline = line.split()\\n if flag == 'moltype':\\n self.moltype[0] = spline[0]\\n self.moltype[1] = int(spline[1])\\n elif flag == 'atoms':\\n resNo = int(spline[2])\\n resname = spline[3]\\n beadname = spline[4]\\n beadno = int(spline[0])\\n beadtype = spline[1]\\n gline = grodata[beadno-1]\\n spgline = gline.split()\\n pos = np.array([float(spgline[3]),float(spgline[4]),\\n float(spgline[5])])\\n vel = np.array([0.,0.,0.])\\n A = Bead(resNo,resname,beadname,beadno,pos,vel,\\n beadtype)\\n self.atomlist.append(A)\\n elif flag == 'bonds':\\n try:\\n (beads,params,notes) = self.__bangle__(spline,2)\\n except:\\n pdb.set_trace()\\n B = Bond(beads,params,notes)\\n self.bondlist.append(B)\\n elif flag == 'cons':\\n (beads,params,notes) = self.__bangle__(spline,2)\\n C = Bond(beads,params,notes)\\n self.conlist.append(C)\\n elif flag == 'angles':\\n (beads,params,notes) = self.__bangle__(spline,3)\\n A = Angle(beads,params,notes)\\n self.anglist.append(A)\\n elif flag == 'dihs':\\n (beads,params,notes) = self.__bangle__(spline,4)\\n D = Dihedral(beads,params,notes)\\n self.dihlist.append(D)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61a9f042ff6c709c4f5d9380a9ec8f6a\",\n \"score\": \"0.4184373\",\n \"text\": \"def transform_object(obj_name, t=None, r=None, s=None):\\n t = \\\"\\\" if t is None else (\\\"T=(%f %f %f)\\\" % (t[0], t[1], t[2]))\\n r = \\\"\\\" if r is None else (\\\"R=(%f %f %f)\\\" % (r[0], r[1], r[2]))\\n s = \\\"\\\" if s is None else (\\\"S=(%f %f %f)\\\" % (s[0], s[1], s[2]))\\n msg = (\\\"TransformObject {name} {t} {r} {s}\\\"\\n .format(name=obj_name, t=t, r=r, s=s))\\n connection.send_message(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ef855f1ce211e92cd665286f68e7b3\",\n \"score\": \"0.41660273\",\n \"text\": \"def main():\\n args = prepare_argparser(allowed_formats)\\n inp_format, inp_loc = filter_dict(\\\"input\\\", args.__dict__)\\n out_format, out_loc = filter_dict(\\\"output\\\", args.__dict__)\\n output_nodes = args.output_nodes.split(',')\\n extra_params = parse_extra_params_string(args.extra_params)\\n convert(inp_format, inp_loc, out_format, out_loc, output_nodes,\\n args.ng_backend, args.device_id, extra_params)\\n print('Converted the model. Exiting now')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c12e7aa9a130cfe410569818f50af000\",\n \"score\": \"0.41633734\",\n \"text\": \"def t2_mapping(filename):\\n\\n scan_key = filename.split('-')[0]\\n mkdir(scan_key)\\n command = 'mv {}-* {};' \\\\\\n 'cd {};' \\\\\\n 'fslsplit {}-5T2s echo -t;' \\\\\\n ''.format(scan_key, scan_key, scan_key, scan_key)\\n for i in range(1,5):\\n command += f'fslmaths echo{i:04} -add 0 echo{(i-1):04} -odt float;'\\n command += 'gunzip echo0*;' \\\\\\n 't2_map_mask_float 4 40 60 80 100;' \\\\\\n 'gzip chi2.nii mask.nii t2vol.nii pdw.nii;' \\\\\\n 'bet echo0000 brain -S;' \\\\\\n 'fslmaths brain_mask -fillh26 brain_mask -odt char;' \\\\\\n 'fslmaths t2vol -mas brain_mask -abs t2vol1;' \\\\\\n 'fslmaths t2vol1 -thr 250 -bin mask250;' \\\\\\n 'fslmaths brain_mask -sub mask250 -ero chi2mask -odt char;' \\\\\\n 'rm echo0* brain.nii.gz brain_skull.nii.gz mask250.nii.gz t2vol1.nii.gz;' \\\\\\n 'mv chi2.nii.gz ../../QA/chi2/{}-chi2.nii.gz;' \\\\\\n 'mv mask.nii.gz ../../t2s/masks/{}-headmask.nii.gz;' \\\\\\n 'mv t2vol.nii.gz ../../t2s/raw/{}-rawT2.nii.gz;' \\\\\\n 'mv brain_mask.nii.gz ../../t2s/masks/{}-brainmask.nii.gz;' \\\\\\n 'mv chi2mask.nii.gz ../../QA/chi2/{}-chi2mask.nii.gz;' \\\\\\n 'mv pdw.nii.gz ../../pdw/raw/{}-rawPDw.nii.gz;' \\\\\\n 'fslmaths ../../pdw/raw/{}-rawPDw -mas ../../t2s/masks/{}-headmask ../../pdw/{}-pdw;' \\\\\\n 'fslmaths ../../t2s/raw/{}-rawT2 -mas ../../t2s/masks/{}-headmask -thr 0 ../../t2s/{}-t2;' \\\\\\n 'mv *-5T2s.nii.gz ..;' \\\\\\n 'cd ..;' \\\\\\n 'rmdir {}' \\\\\\n ''.format(scan_key, scan_key, scan_key, scan_key, scan_key,\\n scan_key, scan_key, scan_key, scan_key, scan_key,\\n scan_key, scan_key, scan_key)\\n\\n result = run(command, stdout=PIPE, stderr=STDOUT, check=True, shell=True)\\n if result.stdout.decode('utf-8') != '':\\n with open(f'{scan_key}.log', 'wb') as logfile:\\n logfile.write(result.stdout)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda91de728bf59c213eaef2fd349e3d3\",\n \"score\": \"0.4162229\",\n \"text\": \"def save_topology(name):\\n if not request.is_json:\\n return json.dumps('{\\\"error\\\": \\\"gt was not a JSON request\\\"}'), 400\\n topology = request.get_json()\\n with open(join(settings.TOPOLOGY_DIR, name + '.json'), 'w') as outfile:\\n json.dump(topology, outfile)\\n return json.dumps({'response': 'Saved'}), 201\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab9b4eee4d0582845eaacf04541b042\",\n \"score\": \"0.41589352\",\n \"text\": \"def write_bd_tcl(tclFileName, force=False):\\n params = []\\n if force:\\n params.append('-force')\\n return \\\"write_bd_tcl %s %s\\\" % (' '.join(params), tclFileName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e4e791123a8163d4e227767c7283d2\",\n \"score\": \"0.41488856\",\n \"text\": \"def setrun(claw_pkg='geoclaw'):\\n#------------------------------\\n\\n from clawpack.clawutil import data\\n\\n assert claw_pkg.lower() == 'geoclaw', \\\"Expected claw_pkg = 'geoclaw'\\\"\\n\\n num_dim = 2\\n\\n rundata = data.ClawRunData(claw_pkg, num_dim)\\n\\n topofile = 'topos/TetonLarge.topo'\\n\\n #------------------------------------------------------------------\\n # GeoClaw specific parameters:\\n #------------------------------------------------------------------\\n rundata = setgeo(rundata)\\n\\n #------------------------------------------------------------------\\n # Standard Clawpack parameters to be written to claw.data:\\n # (or to amr2ez.data for AMR)\\n #------------------------------------------------------------------\\n clawdata = rundata.clawdata # initialized when rundata instantiated\\n\\n\\n # Set single grid parameters first.\\n # See below for AMR parameters.\\n\\n\\n # ---------------\\n # Spatial domain:\\n # ---------------\\n\\n # Number of space dimensions:\\n clawdata.num_dim = num_dim\\n\\n m_topo,n_topo,xllcorner,yllcorner,cellsize = tools.read_topo_data(topofile)\\n\\n\\n # Topo info (TetonDamLatLong.topo)\\n # m_topo = 4180\\n # n_topo = 1464\\n # xllcorner = -112.390734400000\\n # yllcorner = 43.581746970335\\n # cellsize = 0.000277729665\\n\\n # Derived info from the topo map\\n mx_topo = m_topo - 1\\n my_topo = n_topo - 1\\n xurcorner = xllcorner + cellsize*mx_topo\\n yurcorner = yllcorner + cellsize*my_topo\\n ll_topo = np.array([xllcorner, yllcorner])\\n ur_topo = np.array([xurcorner, yurcorner])\\n\\n print(\\\"\\\")\\n print(\\\"Topo domain\\\")\\n print(\\\"%-12s (%14.8f, %12.8f)\\\" % (\\\"Lower left\\\",ll_topo[0],ll_topo[1]))\\n print(\\\"%-12s (%14.8f, %12.8f)\\\" % (\\\"Upper right\\\",ur_topo[0],ur_topo[1]))\\n print(\\\"\\\")\\n\\n dims_topo = ur_topo - ll_topo\\n\\n # Try to match aspect ratio of topo map\\n clawdata.num_cells[0] = 54\\n clawdata.num_cells[1] = 54\\n\\n dim_topo = ur_topo - ll_topo\\n mdpt_topo = ll_topo + 0.5*dim_topo\\n\\n dim_comp = 0.975*dim_topo # Shrink domain inside of given bathymetry.\\n\\n clawdata.lower[0] = mdpt_topo[0] - dim_comp[0]/2.0\\n clawdata.upper[0] = mdpt_topo[0] + dim_comp[0]/2.0\\n\\n clawdata.lower[1] = mdpt_topo[1] - dim_comp[1]/2.0\\n clawdata.upper[1] = mdpt_topo[1] + dim_comp[1]/2.0\\n\\n print(\\\"\\\")\\n print(\\\"Computational domain\\\")\\n print(\\\"%-12s (%14.8f, %12.8f)\\\" % (\\\"Lower left\\\",clawdata.lower[0],clawdata.lower[1]))\\n print(\\\"%-12s (%14.8f, %12.8f)\\\" % (\\\"Upper right\\\",clawdata.upper[0],clawdata.upper[1]))\\n print(\\\"\\\")\\n\\n print(\\\"Approximate aspect ratio : {0:16.8f}\\\".format(float(clawdata.num_cells[0])/clawdata.num_cells[1]))\\n print(\\\"Actual aspect ratio : {0:16.8f}\\\".format(dims_topo[0]/dims_topo[1]))\\n\\n # print \\\"[{0:20.12f},{1:20.12f}]\\\".format(*clawdata.lower)\\n # print \\\"[{0:20.12f},{1:20.12f}]\\\".format(*clawdata.upper)\\n\\n dims_computed = np.array([clawdata.upper[0]-clawdata.lower[0], clawdata.upper[1]-clawdata.lower[1]])\\n print(\\\"Computed aspect ratio : {0:20.12f}\\\".format(dims_computed[0]/dims_computed[1]))\\n\\n print(\\\"\\\")\\n print(\\\"Details in km : \\\") \\n\\n lon = np.array([clawdata.lower[0],clawdata.upper[0]])\\n lat = np.array([clawdata.lower[1],clawdata.upper[1]])\\n d = tools.compute_distances(lon,lat)\\n \\n # ---------------\\n # Size of system:\\n # ---------------\\n\\n # Number of equations in the system:\\n clawdata.num_eqn = 3\\n\\n # Number of auxiliary variables in the aux array (initialized in setaux)\\n clawdata.num_aux = 3\\n\\n # Index of aux array corresponding to capacity function, if there is one:\\n clawdata.capa_index = 2\\n\\n # -------------\\n # Initial time:\\n # -------------\\n\\n clawdata.t0 = 0.0\\n\\n\\n # Restart from checkpoint file of a previous run?\\n # Note: If restarting, you must also change the Makefile to set:\\n # RESTART = True\\n # If restarting, t0 above should be from original run, and the\\n # restart_file 'fort.chkNNNNN' specified below should be in\\n # the OUTDIR indicated in Makefile.\\n\\n clawdata.restart = False # True to restart from prior results\\n clawdata.restart_file = 'fort.chk00006' # File to use for restart data\\n\\n # -------------\\n # Output times:\\n #--------------\\n\\n # Specify at what times the results should be written to fort.q files.\\n # Note that the time integration stops after the final output time.\\n # The solution at initial time t0 is always written in addition.\\n\\n clawdata.output_style = 1\\n\\n if clawdata.output_style == 1:\\n # Output nout frames at equally spaced times up to tfinal:\\n n_hours = 2.0\\n frames_per_minute = 60.0/5.0 # Frames every 5 seconds\\n clawdata.num_output_times = int(frames_per_minute*60*n_hours) # Plot every 10 seconds\\n clawdata.tfinal = 60*60*n_hours\\n clawdata.output_t0 = True # output at initial (or restart) time?\\n\\n elif clawdata.output_style == 2:\\n # Specify a list of output times.\\n clawdata.output_times = [0.5, 1.0]\\n\\n elif clawdata.output_style == 3:\\n # Output every iout timesteps with a total of ntot time steps:\\n clawdata.output_step_interval = 1\\n clawdata.total_steps = 1\\n clawdata.output_t0 = True\\n\\n\\n clawdata.output_format = 'ascii' # 'ascii' or 'netcdf'\\n\\n clawdata.output_q_components = 'all' # could be list such as [True,True]\\n clawdata.output_aux_components = 'none' # could be list\\n clawdata.output_aux_onlyonce = True # output aux arrays only at t0\\n\\n\\n\\n # ---------------------------------------------------\\n # Verbosity of messages to screen during integration:\\n # ---------------------------------------------------\\n\\n # The current t, dt, and cfl will be printed every time step\\n # at AMR levels <= verbosity. Set verbosity = 0 for no printing.\\n # (E.g. verbosity == 2 means print only on levels 1 and 2.)\\n clawdata.verbosity = 1\\n\\n\\n\\n # --------------\\n # Time stepping:\\n # --------------\\n\\n # if dt_variable==1: variable time steps used based on cfl_desired,\\n # if dt_variable==0: fixed time steps dt = dt_initial will always be used.\\n clawdata.dt_variable = True\\n\\n # Initial time step for variable dt.\\n # If dt_variable==0 then dt=dt_initial for all steps:\\n clawdata.dt_initial = 0.0001\\n\\n # Max time step to be allowed if variable dt used:\\n clawdata.dt_max = 1e+99\\n\\n # Desired Courant number if variable dt used, and max to allow without\\n # retaking step with a smaller dt:\\n clawdata.cfl_desired = 0.75\\n clawdata.cfl_max = 1.0\\n\\n # Maximum number of time steps to allow between output times:\\n clawdata.steps_max = 5000\\n\\n\\n\\n\\n # ------------------\\n # Method to be used:\\n # ------------------\\n\\n # Order of accuracy: 1 => Godunov, 2 => Lax-Wendroff plus limiters\\n clawdata.order = 2\\n\\n # Use dimensional splitting? (not yet available for AMR)\\n clawdata.dimensional_split = 'unsplit'\\n\\n # For unsplit method, transverse_waves can be\\n # 0 or 'none' ==> donor cell (only normal solver used)\\n # 1 or 'increment' ==> corner transport of waves\\n # 2 or 'all' ==> corner transport of 2nd order corrections too\\n clawdata.transverse_waves = 2\\n\\n # Number of waves in the Riemann solution:\\n clawdata.num_waves = 3\\n\\n # List of limiters to use for each wave family:\\n # Required: len(limiter) == num_waves\\n # Some options:\\n # 0 or 'none' ==> no limiter (Lax-Wendroff)\\n # 1 or 'minmod' ==> minmod\\n # 2 or 'superbee' ==> superbee\\n # 3 or 'mc' ==> MC limiter\\n # 4 or 'vanleer' ==> van Leer\\n clawdata.limiter = ['mc', 'mc', 'mc']\\n\\n clawdata.use_fwaves = True # True ==> use f-wave version of algorithms\\n\\n # Source terms splitting:\\n # src_split == 0 or 'none' ==> no source term (src routine never called)\\n # src_split == 1 or 'godunov' ==> Godunov (1st order) splitting used,\\n # src_split == 2 or 'strang' ==> Strang (2nd order) splitting used, not recommended.\\n clawdata.source_split = 'godunov'\\n\\n\\n # --------------------\\n # Boundary conditions:\\n # --------------------\\n\\n # Number of ghost cells (usually 2)\\n clawdata.num_ghost = 2\\n\\n # Choice of BCs at xlower and xupper:\\n # 0 => user specified (must modify bcN.f to use this option)\\n # 1 => extrapolation (non-reflecting outflow)\\n # 2 => periodic (must specify this at both boundaries)\\n # 3 => solid wall for systems where q(2) is normal velocity\\n\\n clawdata.bc_lower[0] = 'extrap'\\n clawdata.bc_upper[0] = 'extrap'\\n\\n clawdata.bc_lower[1] = 'extrap'\\n clawdata.bc_upper[1] = 'extrap'\\n\\n # Specify when checkpoint files should be created that can be\\n # used to restart a computation.\\n\\n clawdata.checkpt_style = 0\\n\\n if clawdata.checkpt_style == 0:\\n # Do not checkpoint at all\\n pass\\n\\n elif clawdata.checkpt_style == 1:\\n # Checkpoint only at tfinal.\\n pass\\n\\n elif clawdata.checkpt_style == 2:\\n # Specify a list of checkpoint times.\\n clawdata.checkpt_times = [0.1,0.15]\\n\\n elif clawdata.checkpt_style == 3:\\n # Checkpoint every checkpt_interval timesteps (on Level 1)\\n # and at the final time.\\n clawdata.checkpt_interval = 5\\n\\n\\n # -----------------------------------------------\\n # AMR parameters:\\n # -----------------------------------------------\\n amrdata = rundata.amrdata\\n\\n maxlevel = 7\\n\\n amrdata.amr_levels_max = maxlevel # Set to 3 for best results\\n amrdata.refinement_ratios_x = [2]*7\\n amrdata.refinement_ratios_y = [2]*7\\n amrdata.refinement_ratios_t = [2]*7\\n # rundata.tol = -1\\n # rundata.tolsp = 0.001\\n\\n # Specify type of each aux variable in amrdata.auxtype.\\n # This must be a list of length maux, each element of which is one of:\\n # 'center', 'capacity', 'xleft', or 'yleft' (see documentation).\\n\\n amrdata.aux_type = ['center','capacity','yleft']\\n\\n\\n # Flag using refinement routine flag2refine rather than richardson error\\n amrdata.flag_richardson = False # use Richardson?\\n amrdata.flag2refine = True\\n amrdata.regrid_interval = 3\\n amrdata.regrid_buffer_width = 2\\n amrdata.clustering_cutoff = 0.700000\\n amrdata.verbosity_regrid = 0\\n\\n # -----------------------------------------------\\n # INL Regions\\n # Regions to be refined :\\n # (1) Refine initial reservoir to level 4\\n # (otherwise, we won't resolve valley, and\\n # won't fill the reservoir properly)\\n # (2) Refine around nuclear power plant (indicated by gauge\\n # 100, 101, ..., 115, below)\\n # (3) Computational domain, with maxlevel=4\\n #\\n # To specify regions of refinement append lines of the form\\n # regions.append([minlevel,maxlevel,t1,t2,x1,x2,y1,y2])\\n\\n # -----------------------------------------------\\n regions = rundata.regiondata.regions\\n\\n # Region containing initial reservoir\\n regions.append([maxlevel,maxlevel, 0, 1.e10,-111.543,-111.24,43.88, 43.965])\\n\\n # Box containing gauge location locations\\n\\n# xll = [-111.64, 43.913661] # From email\\n# xur = [-111.60, 43.92] # from email\\n# region_lower, region_upper,_ = tools.region_coords(xll,xur,\\n# clawdata.num_cells,\\n# clawdata.lower,\\n# clawdata.upper)\\n#\\n# regions.append([maxlevel,maxlevel,0, 1e10, region_lower[0],region_upper[0],\\n# region_lower[1],region_upper[1]])\\n\\n # Computational domain. With exception of region above, don't go beyond level 4\\n regions.append([0,maxlevel-1,0, 1e10, clawdata.lower[0],clawdata.upper[0],\\n clawdata.lower[1],clawdata.upper[1]])\\n\\n # -------------------------------------------------------\\n # INL Gauges\\n # -- Set gauges at Teton City and Wilford\\n # -- Remaining gauges build border around power plant\\n #\\n # For gauges append lines of the form [gaugeno, x, y, t1, t2]\\n # -------------------------------------------------------\\n\\n # Wilford\\n xc,yc = [-111.672222,43.914444]\\n rundata.gaugedata.gauges.append([1,xc,yc,0.,clawdata.tfinal]) # Wilford\\n\\n # Teton City\\n xc,yc = [-111.669167,43.887778]\\n rundata.gaugedata.gauges.append([2,xc,yc,0.,clawdata.tfinal]) # Teton City\\n\\n # Power plant, with border constructed of 4*m gauges\\n # Start at SW corner; build gauges in counter-clockwise order in a\\n # square around the region [xll,xur].\\n\\n# m = 2 # Gauge spacing along one edge (m=4 --> edge divided into four sections)\\n# gauge_counter = 100\\n#\\n# # South West corner of power plant\\n# xll = [-111.623926, 43.913661] # From email\\n#\\n# # North East corner of power plant\\n# xur = [-111.620150, 43.916382] # from email\\n#\\n# s = np.linspace(0,1.,m+1)\\n# for i in range(0,m):\\n# x = xll[0] + (xur[0] - xll[0])*s[i]\\n# rundata.gaugedata.gauges.append([gauge_counter,x,xll[1],0.,clawdata.tfinal])\\n# gauge_counter = gauge_counter + 1\\n#\\n# for i in range(0,m):\\n# y = xll[1] + (xur[1] - xll[1])*s[i]\\n# rundata.gaugedata.gauges.append([gauge_counter,xur[0],y,0.,clawdata.tfinal])\\n# gauge_counter = gauge_counter + 1\\n#\\n# for i in range(0,m):\\n# x = xur[0] + (xll[0] - xur[0])*s[i]\\n# rundata.gaugedata.gauges.append([gauge_counter,x,xur[1],0.,clawdata.tfinal])\\n# gauge_counter = gauge_counter + 1\\n#\\n# for i in range(0,m):\\n# y = xur[1] + (xll[1] - xur[1])*s[i]\\n# rundata.gaugedata.gauges.append([gauge_counter,xll[0],y,0.,clawdata.tfinal])\\n# gauge_counter = gauge_counter + 1\\n\\n\\n # -------------------------------------------------------\\n # For developers\\n # -- Toggle debugging print statements:\\n # -------------------------------------------------------\\n amrdata.dprint = False # print domain flags\\n amrdata.eprint = False # print err est flags\\n amrdata.edebug = False # even more err est flags\\n amrdata.gprint = False # grid bisection/clustering\\n amrdata.nprint = False # proper nesting output\\n amrdata.pprint = False # proj. of tagged points\\n amrdata.rprint = False # print regridding summary\\n amrdata.sprint = False # space/memory output\\n amrdata.tprint = True # time step reporting each level\\n amrdata.uprint = False # update/upbnd reporting\\n\\n\\n return rundata\\n # end of function setrun\\n # ----------------------\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0522bf7c5e5b77e0cef1cfb67b4a936a\",\n \"score\": \"0.4146013\",\n \"text\": \"def script(self):\\n text = []\\n\\n # Load waveforms\\n if self.waveforms:\\n par_string = \\\" \\\".join([f\\\"constants.{wfm.name}={wfm.name}\\\" for wfm in self.waveforms])\\n text.append(\\n f\\\".exec\\\\n\\\"\\n f\\\"load {self.wfm_file}\\\\n\\\"\\n f\\\"let constants.tm=tm {par_string}\\\\n\\\"\\n f\\\".endc\\\"\\n )\\n\\n # Declare all subcircuits\\n for name,subckt in self.subcircuits.items():\\n text.append(subckt.script())\\n # Declare models\\n for name, model in self.models.items():\\n line = [\\\".model \\\", name, ' ',model[\\\"type\\\"],'(']\\n pams = []\\n for k,v in model[\\\"params\\\"].items():\\n pams.append(\\\"%s=%s\\\" %(str(k),str(v)))\\n line.append(','.join(pams))\\n line.append(')')\\n text.append(''.join(line))\\n # Declare components\\n for k,comp in self.components.items():\\n text.append(comp.script())\\n # Extra scripts\\n if len(self._extrascript)>0:\\n text.append(self._extrascript)\\n return \\\"\\\\n\\\".join(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c5dea2a65f2331c88844a437b30ccfb\",\n \"score\": \"0.4136075\",\n \"text\": \"def tasks_merge_cluster_and_release(self):\\n self.show_step(1) # Revert snapshot\\n self.env.revert_snapshot('pre_provision_ubuntu_slaves_3')\\n graph_type = 'custom_graph'\\n\\n self.show_step(2) # Create cluster\\n cluster_id = self.fuel_web.create_cluster(name=self.__class__.__name__)\\n rel_id = self.fuel_web.get_cluster_release_id(cluster_id)\\n self.move_ubuntu_target_image(rel_id, cluster_id)\\n\\n self.show_step(3) # Upload 'custom_graph' tasks to release\\n rel_tasks_filename = 'release_custom_tasks.yaml'\\n local_tasks_file = os.path.join(os.path.dirname(fuelweb_test.__file__),\\n 'config_templates',\\n rel_tasks_filename)\\n with open(local_tasks_file, 'r') as yaml_file:\\n release_tasks_yaml_data = yaml.load(yaml_file)\\n upload_tasks_path = '/tmp/{}'.format(rel_tasks_filename)\\n\\n self.ssh_manager.upload_to_remote(\\n ip=self.ssh_manager.admin_ip,\\n source=local_tasks_file,\\n target=upload_tasks_path)\\n upload_tasks_cmd = 'fuel2 graph upload -e {cluster_id} -t ' \\\\\\n '{graph_type} -f {path}'.format(\\n cluster_id=cluster_id,\\n graph_type=graph_type,\\n path=upload_tasks_path\\n )\\n self.ssh_manager.execute_on_remote(\\n ip=self.ssh_manager.admin_ip,\\n cmd=upload_tasks_cmd)\\n\\n self.show_step(4) # Upload 'custom_graph' tasks to cluster\\n c_tasks_filename = 'custom_graph_tasks.yaml'\\n local_tasks_file = os.path.join(os.path.dirname(fuelweb_test.__file__),\\n 'config_templates',\\n c_tasks_filename)\\n with open(local_tasks_file, 'r') as yaml_file:\\n cluster_tasks_yaml_data = yaml.load(yaml_file)\\n upload_tasks_path = '/tmp/{}'.format(rel_tasks_filename)\\n\\n self.ssh_manager.upload_to_remote(\\n ip=self.ssh_manager.admin_ip,\\n source=local_tasks_file,\\n target=upload_tasks_path)\\n upload_tasks_cmd = 'fuel2 graph upload -e {cluster_id} -t ' \\\\\\n '{graph_type} -f {path}'.format(\\n cluster_id=cluster_id,\\n graph_type=graph_type,\\n path=upload_tasks_path\\n )\\n self.ssh_manager.execute_on_remote(\\n ip=self.ssh_manager.admin_ip,\\n cmd=upload_tasks_cmd)\\n\\n self.show_step(5) # Download 'custom_graph' deployment graph\\n custom_tasks = \\\\\\n self.fuel_web.client.get_custom_cluster_deployment_tasks(\\n cluster_id,\\n graph_type)\\n\\n self.show_step(6) # 'custom_graph' is a merge of release and cluster.\\n generated_names = set([t['task_name'] for t in custom_tasks])\\n uploaded_names = set(\\n [t['id'] for t in release_tasks_yaml_data] +\\n [t['id'] for t in cluster_tasks_yaml_data])\\n diff = generated_names - uploaded_names\\n assert_equal(diff, set([]), 'Tasks are not result of merge!')\\n\\n self.show_step(7) # Create snapshot 'tasks_diff'\\n self.env.make_snapshot('tasks_diff')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc5b467462eca59fc358e848492095e1\",\n \"score\": \"0.41351083\",\n \"text\": \"def config_composer(self, model, hostname, junos_on_box_version):\\n if (\\\"SRX3\\\" in model) or (\\\"vsrx\\\" in model):\\n template_filename = \\\"SRX_template.j2\\\"\\n network_parameter_filename = \\\"SRX_networkParameters.yaml\\\"\\n elif \\\"EX\\\" in model:\\n template_filename = \\\"EX_template.j2\\\"\\n network_parameter_filename = \\\"EX_networkParameters.yaml\\\"\\n complete_path = os.path.join(os.getcwd(), 'Config')\\n ENV = jinja2.Environment(loader=jinja2.FileSystemLoader(complete_path))\\n template = ENV.get_template(template_filename)\\n with open(complete_path + \\\"/\\\" + network_parameter_filename) as yamlfile:\\n dict = yaml.load(yamlfile) # yaml file is loaded as a dictionary with key value pairs\\n addition = {\\\"hostname\\\": hostname, \\\"version\\\": junos_on_box_version}\\n dict.update(addition)\\n # print dict\\n content = template.render(dict)\\n # print content\\n target = open(\\\"Config_History/\\\" + hostname + \\\".set\\\", 'w')\\n target.write(content)\\n target.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ce304f3414997de3316712e68a7af5d\",\n \"score\": \"0.41311955\",\n \"text\": \"def main():\\n\\n #Example usage:\\n #rosrun tf2seds.py -b <path_to_tf_bag_files> -o <path_to_seds_bagfile>\\n\\n\\n # rospy gets first crack at sys.argv\\n rospy.myargv(argv=sys.argv)\\n (options,args) = getopt.getopt(sys.argv[1:], 'b:o:s:t:', ['bags=','ouptut=','source=','target='])\\n\\n rospy.init_node(\\\"tf2seds\\\")\\n\\n # should be in tf2seds namespace\\n source_frameid = rospy.get_param(\\\"/tf2seds/source_frameid\\\",\\\"torso_lift_link\\\")\\n target_frameid = rospy.get_param(\\\"/tf2seds/target_frameid\\\",\\\"r_gripper_tool_frame\\\")\\n path = rospy.get_param(\\\"/tf2seds/source_directory\\\", None)\\n outfile = rospy.get_param(\\\"/tf2seds/outputfile\\\", None)\\n\\n # or specify any of these options on the command line\\n for o,a in options:\\n if o in ('-b','--bags'):\\n path = a\\n elif o in ('-o','--output'):\\n outfile = a\\n elif o in ('-s','--source'):\\n source_frameid = a\\n elif o in ('-t','--target'):\\n target_frameid = a\\n\\n if not path or not outfile:\\n raise Exception, \\\"You must specify bagfile input and output locations.\\\"\\n\\n bagfiles = glob.glob(os.path.normpath(path + \\\"/*.bag\\\"))\\n bagfiles.sort() # given the default naming these should be sorted by time\\n\\n rospy.loginfo(\\\"dir: %s out: %s sf: %s tf: %s\\\", path, outfile, source_frameid, target_frameid)\\n\\n\\n process_bags(outfile, bagfiles, source_frameid,target_frameid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c524b2440cf453469e52ff6b59e4c599\",\n \"score\": \"0.41297665\",\n \"text\": \"def generate_launch_description():\\n\\n tensorrt_yolo3_dir = get_package_share_directory('tensorrt_yolo3')\\n # Arguments\\n prototxt_file = DeclareLaunchArgument(\\n 'prototxt_file',\\n default_value=os.path.join(tensorrt_yolo3_dir, 'data/yolov3_416_trt.prototxt'),\\n description='prototxt file for tensorrt yolo3'\\n )\\n\\n caffemodel_file = DeclareLaunchArgument(\\n 'caffemodel_file',\\n default_value=os.path.join(tensorrt_yolo3_dir, 'data/yolov3_416.caffemodel'),\\n description='caffemodel file for tensorrt yolo3'\\n )\\n\\n input_topic = DeclareLaunchArgument(\\n 'input_topic',\\n default_value='/image_raw',\\n description='input topic name'\\n )\\n\\n output_topic = DeclareLaunchArgument(\\n 'output_topic',\\n default_value='rois',\\n description='output topic name'\\n )\\n\\n # Nodes\\n tensorrt_yolo3 = Node(\\n package='tensorrt_yolo3',\\n node_executable='tensorrt_yolo3_node_exe',\\n node_name='tensorrt_yolo3_node',\\n node_namespace='',\\n output='screen',\\n parameters=[{'prototxt_file': LaunchConfiguration('prototxt_file'),\\n 'caffemodel_file': LaunchConfiguration('caffemodel_file'), }],\\n remappings=[\\n (\\\"in_image_base_topic\\\", LaunchConfiguration('input_topic')),\\n (\\\"out_image_base_topic\\\", [LaunchConfiguration('output_topic'), '/debug/image']),\\n (\\\"out_objects_topic\\\", LaunchConfiguration('output_topic'))\\n ]\\n )\\n\\n return LaunchDescription([\\n prototxt_file,\\n caffemodel_file,\\n input_topic,\\n output_topic,\\n tensorrt_yolo3\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d99c5fbd13bad88498da44b3a83fd1\",\n \"score\": \"0.41294044\",\n \"text\": \"def populate_from_topology(graph):\\n topology = Topology()\\n topology.hosts = graph.find(is_a=Host)\\n # TODO(cs): broken: can't set attribute\\n topology.switches = graph.find(is_a=SoftwareSwitch)\\n topology.access_links = [AccessLink(host, switch[0], switch[1][0], switch[1][1])\\n for host in topology.hosts\\n for switch in filter(\\n lambda n: isinstance(n[1][0], SoftwareSwitch),\\n graph.ports_for_node(host).iteritems())]\\n return topology\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd9b69f1582e19e6edcf5af4cdf2a3f9\",\n \"score\": \"0.41284353\",\n \"text\": \"def main(args):\\n\\n optparser = argparse.ArgumentParser(description=\\\"Generate OWL from Comments in an ICD-11 file\\\")\\n optparser.add_argument('owlfile', help=\\\"Input OWL file\\\")\\n optparser.add_argument('-f', '--fullydefined', help=\\\"Definitions are fully defined\\\", action=\\\"store_true\\\")\\n optparser.add_argument('-p', '--port', help=\\\"SCT Converter gateway port\\\", type=int)\\n optparser.add_argument('-o', '--out', help=\\\"Output file\\\", required=True)\\n optparser.add_argument('-s', '--shorturi', help=\\\"Shorten URI's for readability\\\", action=\\\"store_true\\\")\\n optparser.add_argument('-r', '--removesctid', help=\\\"Remove the SCT class declarations\\\", action=\\\"store_true\\\")\\n\\n opts = optparser.parse_args(args)\\n\\n gw = SCTConverterGateway(opts.port) if opts.port else SCTConverterGateway()\\n\\n g = Graph()\\n target_graph = add_namespaces(Graph())\\n g.parse(opts.owlfile)\\n target_graph.parse(opts.owlfile)\\n\\n # Iterate over the comments with the compositional expressions\\n for subj, desc in list(g.subject_objects(icdf_comments)):\\n if cgre1.search(desc):\\n cgexpr = cgre1.sub(r'\\\\2', desc)\\n elif cgre2.search(desc):\\n cgexpr = cgre2.sub(r'\\\\2', desc)\\n else:\\n cgexpr = None\\n\\n if cgexpr:\\n if not parse_and_load(gw, subj, not bool(opts.fullydefined), cgexpr, target_graph):\\n print(\\\"Conversion error on %s (%s)\\\" % (subj, cgexpr), file=sys.stderr)\\n else:\\n print(\\\"No conversion available for %s (%s)\\\" % (subj, desc), file=sys.stderr)\\n target = serialize_graph(target_graph, removesctid=opts.removesctid, shorturi=opts.shorturi)\\n open(opts.out, 'w').write(target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb387bdc0789d7bb01742e074fd4ce4d\",\n \"score\": \"0.41276404\",\n \"text\": \"def convert_stp_files_to_neutronics_geometry():\\n\\n os.system(\\\"trelis -batch -nographics make_faceteted_neutronics_model.py\\\")\\n\\n os.system(\\\"make_watertight dagmc_notwatertight.h5m -o dagmc.h5m\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"584509ba00369834101cb5dc6af0c7df\",\n \"score\": \"0.41266462\",\n \"text\": \"def create_script(self):\\n\\n # script prep, headers and variable assignments\\n self.align_script = self.script_init()\\n self.align_script += self.script_set_vars()\\n\\n # do some actual work\\n self.align_script += self.script_results_dir()\\n self.align_script += self.script_copy_data()\\n if self.uvars.align_centers == 'yes':\\n self.align_script += self.script_align_centers()\\n self.align_script += self.script_align_datasets()\\n\\n # add commands ...\\n\\n if len(self.errors) > 0: return # if any errors so far, give up\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d82916bdbda8b06a443feddd5c899bf\",\n \"score\": \"0.41259828\",\n \"text\": \"def model_to_python(pipeline: Pipeline) -> str:\\n imports = []\\n for name, step in pipeline.steps:\\n imports.append(f\\\"from {step.__module__} import {step.__class__.__name__}\\\")\\n\\n script = (\\\"from sklearn.pipeline import Pipeline\\\\n\\\" +\\n '\\\\n'.join(imports) + '\\\\n\\\\n' +\\n 'pipeline = ' + str(pipeline) + '\\\\n')\\n\\n return script\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6af484b5dc21648ba50fa9fc7cb4df6\",\n \"score\": \"0.41259146\",\n \"text\": \"def create_exec_stmt(self, script):\\n return 'exec \\\"{0}\\\"'.format(script)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bba512a0742004032fcd93b1c514d82\",\n \"score\": \"0.41226125\",\n \"text\": \"def main(ymlfile):\\n\\n with ymlfile as f:\\n cfg = yaml.load(f, Loader)\\n\\n kwargs = {}\\n cfg['params']['config_file'] = ymlfile.name\\n kwargs['params'] = cfg['params']\\n root = cfg['params']['data_dir']\\n kwargs['data'] = complete_paths(cfg['data'], root)\\n kwargs['velocity'] = complete_vel_paths(cfg['velocity'], root)\\n kwargs['layers'] = complete_map_paths(cfg['map_layers'], root)\\n kwargs['potfields'] = complete_pot_paths(cfg['potfields'], root)\\n\\n tc = TransectContainer(**kwargs)\\n\\n tc.plot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faab9fa27f38a3099e6625863955b81b\",\n \"score\": \"0.41208908\",\n \"text\": \"def make_dbt_task(self, node_name, dbt_verb):\\n\\n model_name = node_name.split('.')[-1]\\n if dbt_verb == 'test':\\n node_name = node_name.replace('model', 'test') # Just a cosmetic renaming of the task\\n task_group = self.dbt_test_group\\n else:\\n task_group = self.dbt_run_group\\n\\n dbt_task = BashOperator(\\n task_id=node_name,\\n task_group=task_group,\\n bash_command=f'\\\\\\ndbt {self.dbt_global_cli_flags} {dbt_verb} --target {self.dbt_target} --models {model_name} \\\\\\n--profiles-dir {self.dbt_profiles_dir} --project-dir {self.dbt_project_dir}\\\\\\n ',\\n dag=self.dag,\\n )\\n # Keeping the log output, it's convenient to see when testing the python code outside of Airflow\\n logging.info('Created task: %s', node_name)\\n return dbt_task\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70386385f58597a916719d311b96c081\",\n \"score\": \"0.41149753\",\n \"text\": \"def template_to_binary(self, template):\\n\\t\\tleft = Chem.MolFromSmarts(template.split('>>')[0]).ToBinary()\\n\\t\\tright = Chem.MolFromSmarts(template.split('>>')[1]).ToBinary()\\n\\t\\treturn b'>>'.join([left, right])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"223150bc4d587d947c9d3ce6229401c7\",\n \"score\": \"0.4113279\",\n \"text\": \"def create_tcl_for_talb(base_dir):\\n\\n binding_residues = ['179', '226', '224', '130', '152', '174', '15', '33', '240', '30', '93']\\n base_dir_ligand = ''.join([base_dir, '/TalB/3_MD_post_dock2016/'])\\n file_names_list = [''.join([base_dir_ligand, '1_APO/1_S7P_linear/cl101/0_complex_prep/TALB_WT_APO-103_receptor_S7P_linear_docked.pdb']),\\n ''.join([base_dir_ligand, '1_APO/1_S7P_linear/cl101_2/0_complex_prep/TALB_WT_APO-515_receptor_S7P_docked.pdb']),\\n ''.join([base_dir_ligand, '1_APO/1_S7P_linear/cl102/0_complex_prep/TALB_WT_APO-091_receptor_S7P_linear_docked.pdb']),\\n ''.join([base_dir_ligand, '1_APO/1_S7P_linear/cl102_2/0_complex_prep/TALB_WT_APO-535_receptor_S7P_docked.pdb']),\\n ''.join([base_dir_ligand, '1_APO/1_S7P_linear/cl212/0_complex_prep/TALB_WT_APO-036_receptor_S7P_linear_docked.pdb']),\\n ''.join([base_dir_ligand, '1_APO/1_S7P_linear/cl212_2/0_complex_prep/TALB_WT_APO-777_receptor_S7P_docked.pdb']),\\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/1_S7P_linear/cl101/0_complex_prep/TALB_WT_HALO_S7P_remS7P-237_receptor_S7P_linear_docked.pdb']),\\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/1_S7P_linear/cl101_2/0_complex_prep/TALB_WT_HALO_S7P_remS7P-260_receptor_S7P_docked.pdb']),\\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/1_S7P_linear/cl302/0_complex_prep/TALB_WT_HALO_S7P_remS7P-091_receptor_S7P_linear_docked.pdb']),\\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/1_S7P_linear/cl302_2/0_complex_prep/TALB_WT_HALO_S7P_remS7P-492_receptor_S7P_docked.pdb']),\\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/1_S7P_linear/cl322/0_complex_prep/TALB_WT_HALO_S7P_remS7P-042_receptor_S7P_linear_docked.pdb']),\\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/1_S7P_linear/cl322_2/0_complex_prep/TALB_WT_HALO_S7P_remS7P-184_receptor_S7P_docked.pdb']),\\n ''.join([base_dir, '/TalB/0_Crystalline_structures/1UCW_wt_S7P_watbox.pdb'])]\\n\\n file_out = ''.join([base_dir, '/TalB/3_MD_post_dock2016/load_s7p_frames.tcl'])\\n ligand_list = ['S7P']\\n create_vmd_file(file_names_list, file_out, ligand_list, binding_residues)\\n\\n\\n base_dir_ligand = ''.join([base_dir, '/TalB/3_MD_post_dock2016/'])\\n file_names_list = [''.join([base_dir_ligand, '1_APO/2_F6P/cl202/0_complex_prep/TALB_WT_APO-533_receptor_F6P_docked.pdb']),\\n ''.join([base_dir_ligand, '1_APO/2_F6P/cl202_2/0_complex_prep/TALB_WT_APO-314_receptor_F6P_docked.pdb']),\\n ''.join([base_dir_ligand, '1_APO/2_F6P/cl212/0_complex_prep/TALB_WT_APO-139_receptor_F6P_docked.pdb']),\\n ''.join([base_dir_ligand, '1_APO/2_F6P/cl212_2/0_complex_prep/TALB_WT_APO-464_receptor_F6P_docked.pdb']),\\n ''.join([base_dir_ligand, '1_APO/2_F6P/cl321/0_complex_prep/TALB_WT_APO-799_receptor_F6P_docked.pdb']),\\n ''.join([base_dir_ligand, '1_APO/2_F6P/cl321_2/0_complex_prep/TALB_WT_APO-003_receptor_F6P_docked.pdb']),\\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/2_F6P/cl101/0_complex_prep/TALB_WT_HALO_S7P_remS7P-492_receptor_F6P_docked.pdb']),\\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/2_F6P/cl101_2/0_complex_prep/TALB_WT_HALO_S7P_remS7P-056_receptor_F6P_docked.pdb']),\\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/2_F6P/cl201/0_complex_prep/TALB_WT_HALO_S7P_remS7P-381_receptor_F6P_docked.pdb']),\\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/2_F6P/cl201_2/0_complex_prep/TALB_WT_HALO_S7P_remS7P-117_receptor_F6P_docked.pdb']),\\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/2_F6P/cl311/0_complex_prep/TALB_WT_HALO_S7P_remS7P-014_receptor_F6P_docked.pdb']),\\n ''.join([base_dir_ligand, '2_HALO_S7P_remS7P/2_F6P/cl311_2/0_complex_prep/TALB_WT_HALO_S7P_remS7P-222_receptor_F6P_docked.pdb']),\\n ''.join([base_dir, '/TalB/0_Crystalline_structures/1UCW_wt_S7P_watbox.pdb'])]\\n\\n\\n file_out = ''.join([base_dir, '/TalB/3_MD_post_dock2016/load_f6p_frames.tcl'])\\n ligand_list = ['F6P']\\n create_vmd_file(file_names_list, file_out, ligand_list, binding_residues)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54f3c8d20c155c9fb4f0a77e33a5fe8d\",\n \"score\": \"0.4107464\",\n \"text\": \"def topology(self, name):\\n if request.method == 'POST':\\n return self.save_topology(name)\\n return self.get_topology(name)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994088,"cells":{"query_id":{"kind":"string","value":"4cca531af85e252640b38a122ef3ef97"},"query":{"kind":"string","value":"Creates a new archive_file (hdf5) with groups for gpt and distgen. Calls .archive method of GPT and Distgen objects, into these groups."},"positive_passages":{"kind":"list like","value":[{"docid":"5cf03ca819bee0c064fc670222362c11","score":"0.76831263","text":"def archive_gpt_with_distgen(gpt_object,\n distgen_object,\n archive_file=None,\n gpt_group ='gpt',\n distgen_group ='distgen'):\n \n h5 = File(archive_file, 'w')\n \n #fingerprint = tools.fingerprint(astra_object.input.update(distgen.input))\n \n g = h5.create_group(distgen_group)\n distgen_object.archive(g)\n \n g = h5.create_group(gpt_group)\n gpt_object.archive(g)\n \n h5.close()","title":""}],"string":"[\n {\n \"docid\": \"5cf03ca819bee0c064fc670222362c11\",\n \"score\": \"0.76831263\",\n \"text\": \"def archive_gpt_with_distgen(gpt_object,\\n distgen_object,\\n archive_file=None,\\n gpt_group ='gpt',\\n distgen_group ='distgen'):\\n \\n h5 = File(archive_file, 'w')\\n \\n #fingerprint = tools.fingerprint(astra_object.input.update(distgen.input))\\n \\n g = h5.create_group(distgen_group)\\n distgen_object.archive(g)\\n \\n g = h5.create_group(gpt_group)\\n gpt_object.archive(g)\\n \\n h5.close()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"095f84ec623087ed69bf071f02b382e4","score":"0.604668","text":"def write_hdf5(out_file, data):\n gmu_proc_file = h5py.File(out_file, 'w')\n print(gmu_proc_file)\n for g in data:\n group = gmu_proc_file.create_group(g)\n for datum in data[g]:\n try:\n group.create_dataset(\"mel_spects_{}\".format(datum[0]),\n data=datum[1])\n except Exception as e:\n print(group.name, datum[0], e)\n\n gmu_proc_file.close()\n exit()","title":""},{"docid":"11d07c00908b6787f7b49c996368fbaf","score":"0.58894116","text":"def make_fasta_for_groups(dict_groups, dict_fasta, path_name):\n if os.path.isdir(path_name):\n shutil.rmtree(path_name)\n os.makedirs(path_name)\n for group_name, group in dict_groups.iteritems():\n records = [dict_fasta[member] for member in group.members]\n write_fasta(path_name + group_name, records)","title":""},{"docid":"363821f753ef4460b9f520da6011eed7","score":"0.58364344","text":"def save_groups(fname, gtable):\n np.save(fname, table_to_group_list(gtable))","title":""},{"docid":"e809cff4c7d178bfc464bd4130b4d1c4","score":"0.5739024","text":"def write_groups(out_file, groupname):\r\n print(\"To create a single group please just enter the main group name i.e. Group Name\")\r\n print('To create a subgroup to an exisitng group, please enter /Group Name/Subgroup Name/etc/etc/')\r\n print() \r\n attributes = {}\r\n print(\"Enter attributes for\", groupname)\r\n meta = input(\"Is there a metadata file? (Y/N): \")\r\n if meta == \"Y\" or meta == \"y\":\r\n metapath = input(\"Enter metadata file path: \")\r\n with open(metapath, 'r') as metafile:\r\n for line in metafile:\r\n line = line.split('\\t')\r\n item = line[0].strip('\\n')\r\n value = line[-1].strip('\\n')\r\n if item in attributes.keys():\r\n attributes[item].append(value)\r\n else:\r\n attributes[item] = [value]\r\n else:\r\n input_attributes = input(\"Enter an attribute followed by a value. i.e. Project Name: iknowit, Date: 04-11-2019: \")\r\n for attribute in input_attributes.split(','):\r\n attribute = attribute.split(':')\r\n attributes[attribute[0].strip(' ')] = attribute[1].strip(' ')\r\n data_file = h5py.File(out_file, 'a')\r\n dset = data_file.create_group(groupname)\r\n for k, v in attributes.items():\r\n dset.attrs[k] = v","title":""},{"docid":"0d90e8720ae0c70ea2b513271db63034","score":"0.57272446","text":"def write_func(in_files, out_file, groups):\r\n data_file = h5py.File(out_file, 'a')\r\n image_extensions = ['jpg', 'jpeg', 'png', 'bmp', 'tiff']\r\n count = 0\r\n try:\r\n for in_file in in_files:\r\n if in_file.split('.')[-1] not in image_extensions:\r\n try:\r\n with open(in_file) as ocf:\r\n data = ocf.read()\r\n str_type = h5py.special_dtype(vlen=str)\r\n dset = data_file.create_dataset(\r\n groups[count] + in_file.split('/')[-1],\r\n data=data, shape=(1,),\r\n dtype=str_type\r\n )\r\n attributes = generate_attributes_to_add(\r\n groups[count] + in_file.split('/')[-1])\r\n for k, v in attributes.items():\r\n dset.attrs[k] = v\r\n except FileNotFoundError:\r\n print(in_file, \"not found\")\r\n else:\r\n dset = image_to_hdf5(in_file, data_file, groups[count])\r\n attributes = generate_attributes_to_add(\r\n groups[count] + in_file.split('/')[-1])\r\n for k, v in attributes.items():\r\n dset.attrs[k] = v\r\n if len(groups) == 1:\r\n count = 0\r\n else:\r\n count += 1\r\n except RuntimeError:\r\n pass","title":""},{"docid":"d243c65ab03e1df39a736a3ea2592385","score":"0.5663915","text":"def to_hdf(self, hdf=None, group_name=None):\n super().to_hdf(hdf=hdf, group_name=group_name)\n self.input.to_hdf(hdf=self.project_hdf5)\n self.potential.to_hdf(hdf=self.project_hdf5)\n self.structures.to_hdf(hdf=self.project_hdf5)\n self.output.to_hdf(hdf=self.project_hdf5)","title":""},{"docid":"5276f57c5b2208e31ba280da31513c61","score":"0.5663336","text":"def write_h5_stack(npy_vol, fn, group='stack', compression=None, chunks=None,\n shuffle=None, attrs=None):\n fn = os.path.expanduser(fn)\n fout = h5py.File(fn, 'a')\n if group in fout:\n del fout[group]\n fout.create_dataset(group, data=npy_vol, compression=compression,\n chunks=chunks, shuffle=shuffle)\n if attrs is not None:\n for attr, value in attrs.items():\n fout[group].attrs[attr] = value\n fout.close()","title":""},{"docid":"e0966f0dca7fd6474c392e4b03bc4ab2","score":"0.56151164","text":"def export_set(output_dir, name, data, labels, classes):\n\n assert len(data) == len(labels)\n\n # Variable-length datatypes for encoded png streams and label names\n dt_int = h5py.vlen_dtype(np.dtype('uint8'))\n dt_str = h5py.string_dtype(encoding='utf-8')\n\n # Initialize hdf5 file pointer\n f = h5py.File(f\"{output_dir}/{name}_{len(data)}.h5\", \"w\")\n\n # Create group and store data/labels\n x = f.create_dataset(\"data\", (len(data),), dtype=dt_int, data=data)\n y = f.create_dataset(\"label\", data=np.array(labels, dtype=int))\n\n # Store <mapping from (0, 1 ...) to class names> as group attribute\n y.attrs.create(\"class_names\", data=np.array(classes, dtype=dt_str))\n\n f.close()","title":""},{"docid":"84d667309fb7e68e1ed0fd66c315d755","score":"0.55947095","text":"def writing_to_output_file(mockgal_pd, mockgroup_pd, zz_mock, \n param_dict, proj_dict, output_fmt = 'hdf5', perf_catl=False):\n ## Keys\n gal_key = '/gal_catl'\n group_key = '/group_catl'\n ## Filenames\n if perf_catl:\n ## Perfect Galaxy catalogue\n gal_file = os.path.join(proj_dict['mock_cat_mc_perf'],\n '{0}_cat_{1}_{2}_memb_cat_perf.{3}'.format(\n param_dict['survey'], zz_mock, \n param_dict['cosmo_choice'], output_fmt))\n ## Perfect Group catalogue\n group_file = os.path.join(proj_dict['mock_cat_gc_perf'],\n '{0}_cat_{1}_{2}_group_cat_perf.{3}'.format(\n param_dict['survey'], zz_mock,\n param_dict['cosmo_choice'], output_fmt))\n else:\n ## Normal galaxy catalogue\n gal_file = os.path.join(proj_dict['mock_cat_mc'],\n '{0}_cat_{1}_{2}_memb_cat.{3}'.format(\n param_dict['survey'], zz_mock,\n param_dict['cosmo_choice'], output_fmt))\n ## Normal group catalogue\n group_file = os.path.join(proj_dict['mock_cat_gc'],\n '{0}_cat_{1}_{2}_group_cat.{3}'.format(\n param_dict['survey'], zz_mock,\n param_dict['cosmo_choice'], output_fmt))\n ##\n ## Saving DataFrames to files\n # Member catalogue\n cu.pandas_df_to_hdf5_file(mockgal_pd, gal_file, key=gal_key)\n # Group catalogue\n cu.pandas_df_to_hdf5_file(mockgroup_pd, group_file, key=group_key)\n ##\n ## Checking for file's existence\n cu.File_Exists(gal_file)\n cu.File_Exists(group_file)\n print('{0} gal_file : {1}'.format(param_dict['Prog_msg'], gal_file))\n print('{0} group_file: {1}'.format(param_dict['Prog_msg'], group_file))","title":""},{"docid":"2c4d3780e809abec0808ba417109f9f6","score":"0.55542684","text":"def to_hdf(self, hdf, group_name=\"structure\"):\n # import time\n with hdf.open(group_name) as hdf_structure:\n # time_start = time.time()\n hdf_structure[\"TYPE\"] = str(type(self))\n for el in self.species:\n if isinstance(el.tags, dict):\n with hdf_structure.open(\"new_species\") as hdf_species:\n el.to_hdf(hdf_species)\n hdf_structure[\"species\"] = [el.Abbreviation for el in self.species]\n hdf_structure[\"indices\"] = self.indices\n\n with hdf_structure.open(\"tags\") as hdf_tags:\n for tag in self._tag_list.keys():\n tag_value = self._tag_list[tag]\n if isinstance(tag_value, SparseList):\n tag_value.to_hdf(hdf_tags, tag)\n hdf_structure[\"units\"] = self.units\n hdf_structure[\"dimension\"] = self.dimension\n\n if self.cell is not None:\n with hdf_structure.open(\"cell\") as hdf_cell:\n # Convert ASE cell object to numpy array before storing\n hdf_cell[\"cell\"] = np.array(self.cell)\n hdf_cell[\"pbc\"] = self.pbc\n\n # hdf_structure[\"coordinates\"] = self.positions # \"Atomic coordinates\"\n hdf_structure[\"positions\"] = self.positions # \"Atomic coordinates\"\n\n # potentials with explicit bonds (TIP3P, harmonic, etc.)\n if self.bonds is not None:\n hdf_structure[\"explicit_bonds\"] = self.bonds\n\n # print ('time in atoms.to_hdf: ', time.time() - time_start)\n\n if self._high_symmetry_points is not None:\n hdf_structure[\"high_symmetry_points\"] = self._high_symmetry_points\n\n if self._high_symmetry_path is not None:\n hdf_structure[\"high_symmetry_path\"] = self._high_symmetry_path\n\n hdf_structure[\"info\"] = self.info","title":""},{"docid":"1177ebb729269e891d16e45e5ae0a0b3","score":"0.55385166","text":"def pack_waveforms_to_hdf5(args):\n\n # Arguments & parameters\n audios_dir = args.audios_dir\n csv_path = args.csv_path\n waveforms_hdf5_path = args.waveforms_hdf5_path\n mini_data = args.mini_data\n\n split_dir = args.split_dir\n split_to_test_id = args.split_to_test_id\n\n clip_samples = config.clip_samples\n classes_num = config.classes_num\n sample_rate = config.sample_rate\n id_to_ix = config.id_to_ix\n\n\n\n #id_dict = {'screaming':'/m/03qc9zr','crying_sobbing':'/m/0463cq4','chatter':'/m/07rkbfh','motor_vehicle_(road)':'/m/012f08','emergency_vehicle':'/m/03j1ly','siren':'/m/03kmc9','explosion':'/m/014zdl','gunshot_gunfire':'/m/032s66', 'breaking': '/m/07pc8lb'}\n\n\n\n\n sample_rate = config.sample_rate\n id_to_ix = config.id_to_ix\n\n\n\n id_dict = {'screaming':'/m/03qc9zr','crying_sobbing':'/m/0463cq4','chatter':'/m/07rkbfh','motor_vehicle_(road)':'/m/012f08','emergency_vehicle':'/m/03j1ly','siren':'/m/03kmc9','explosion':'/m/014zdl','gunshot_gunfire':'/m/032s66', 'breaking': '/m/07pc8lb'}\n\n\n\n\n # Paths\n if mini_data:\n prefix = 'mini_'\n waveforms_hdf5_path += '.mini'\n else:\n prefix = ''\n\n create_folder(os.path.dirname(waveforms_hdf5_path))\n\n logs_dir = '_logs/pack_waveforms_to_hdf5/{}{}'.format(prefix, get_filename(csv_path))\n create_folder(logs_dir)\n create_logging(logs_dir, filemode='w')\n logging.info('Write logs to {}'.format(logs_dir))\n\n # Read csv file\n split_file = open(split_dir+\"/fold_\"+str(split_to_test_id)+\".txt\",\"r\")\n #split_file = open(split_dir+\"/fold_filter_\"+str(split_to_test_id)+\".txt\",\"r\")\n lines = split_file.readlines()\n audio_names = []\n target_names = []\n for line in lines:\n audio_names.append(line.split(' ')[0]+'.wav')\n if line.split(' ')[2][0:-1]==\"others\":\n target_names.append(\"others\")\n else:\n target_names.append(id_dict[line.split(' ')[2][0:-1]])\n\n\n\n\n\n meta_dict = read_metadata(csv_path, classes_num, id_to_ix)\n\n '''if mini_data:\n mini_num = 10\n for key in meta_dict.keys():\n meta_dict[key] = meta_dict[key][0 : mini_num]'''\n\n #audios_num = len(meta_dict['audio_name'])\n audios_num = len(lines)\n\n\n\n # Pack waveform to hdf5\n total_time = time.time()\n\n with h5py.File(waveforms_hdf5_path, 'w') as hf:\n hf.create_dataset('audio_name', shape=((audios_num,)), dtype='S20')\n hf.create_dataset('waveform', shape=((audios_num, clip_samples)), dtype=np.int16)\n hf.create_dataset('target', shape=((audios_num, classes_num)), dtype=np.bool)\n hf.attrs.create('sample_rate', data=sample_rate, dtype=np.int32)\n\n targets = np.zeros((audios_num, classes_num), dtype=np.bool)\n\n # Pack waveform & target of several audio clips to a single hdf5 file\n for n in range(audios_num):\n audio_path = os.path.join(audios_dir, audio_names[n])\n\n if os.path.isfile(audio_path):\n logging.info('{} {}'.format(n, audio_path))\n (audio, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True)\n audio = pad_or_truncate(audio, clip_samples)\n\n hf['audio_name'][n] = audio_names[n].encode()\n hf['waveform'][n] = float32_to_int16(audio)\n ix = id_to_ix[target_names[n]]\n targets[n, ix] = 1\n #print(targets[n])\n hf['target'][n] = targets[n]\n else:\n logging.info('{} File does not exist! {}'.format(n, audio_path))\n\n logging.info('Write to {}'.format(waveforms_hdf5_path))\n logging.info('Pack hdf5 time: {:.3f}'.format(time.time() - total_time))","title":""},{"docid":"5d035cede7bd46397be40298c6d9fd68","score":"0.55122924","text":"def image_to_hdf5(filename, f, group):\r\n img = Image.open(filename)\r\n image = np.array(img.getdata()).reshape(img.size[1], img.size[0], -1)\r\n data = np.array(image)\r\n dset = f.create_dataset(group + filename.split('/')\r\n [-1], shape=(4, img.size[1], img.size[0], 4))\r\n dset[0] = data\r\n im = Image.fromarray(dset[0].astype('uint8'))\r\n im.save(filename)\r\n return dset","title":""},{"docid":"5533f5f19d0b74ec6473df1e509a92bd","score":"0.5512146","text":"def createGroup(filename, group, subgroups = []):\n\n #deleteGroup(filename, group)\n FILE = h5py.File(filename, \"r+\")\n try:\n GROUP = FILE.create_group(group)\n print(\"[CREATE]: <{:s}> group created.\".format(group))\n\n for sg in subgroups:\n GROUP.create_group(sg)\n except:\n print(\"Group exists, so not creating a new one.\")\n pass\n\n try:\n FILE.close()\n except:\n pass","title":""},{"docid":"0999e38982a0b581097a9cf4ea0ef139","score":"0.55031323","text":"def createArchiveGenTask(self):\n archiveTask = author.Task()\n archiveTask.title = 'Generate {} files'.format(self.archiveExt)\n archiveTask.service = self.service\n\n archiveTaskCmd = author.Command()\n archiveTaskCmd.argv = [os.path.join(self.houdiniBinPath, 'hbatch.exe').replace('\\\\', '/')]\n archiveTaskCmd.argv.append('-c')\n # hscriptCmd = 'tcur {}; render -w -i -V 1 -f {} {} {}; quit'.format(\n hscriptCmd = 'render -w -i -V 2 -f {} {} {}; quit'.format(\n self.start,\n self.end,\n self.outputDriverPath\n )\n archiveTaskCmd.argv.append(hscriptCmd)\n archiveTaskCmd.argv.append(self.snapshotScene)\n archiveTaskCmd.retryrc = range(0, 11) # WATCHME : exit codes that will restart the task\n archiveTask.addCommand(archiveTaskCmd)\n\n # Add archive files to be deleted by cleanup task\n archiveFiles = [\n self.archiveOutput.replace('$F4', str(frame).zfill(4))\n for frame in range(self.start, self.end + 1)\n ]\n\n self.toDelete.extend(archiveFiles)\n\n return archiveTask","title":""},{"docid":"1ad02e77510cb35a15e0d2991413f733","score":"0.546978","text":"def pack_project_waveforms_to_hdf5(args):\n\n # Arguments & parameters\n audios_dir = args.audios_dir\n csv_path = args.csv_path\n waveforms_hdf5_path = args.waveforms_hdf5_path\n mini_data = args.mini_data\n\n split_dir = args.split_dir\n split_to_test_id = args.split_to_test_id\n\n clip_samples = config.clip_samples\n #classes_num = config.classes_num\n classes_num = 9\n sample_rate = config.sample_rate\n #id_to_ix = config.id_to_ix\n id_to_ix = {'screaming':0,'crying_sobbing':1,'chatter':2,'motor_vehicle_(road)':3,'emergency_vehicle':4,'siren':5,'explosion':6,'gunshot_gunfire':7, 'breaking':8 ,'others': 9}\n\n\n id_dict = {'screaming':'/m/03qc9zr','crying_sobbing':'/m/0463cq4','chatter':'/m/07rkbfh','motor_vehicle_(road)':'/m/012f08','emergency_vehicle':'/m/03j1ly','siren':'/m/03kmc9','explosion':'/m/014zdl','gunshot_gunfire':'/m/032s66', 'breaking': '/m/07pc8lb'} \n\n\n\n\n # Paths\n if mini_data:\n prefix = 'mini_'\n waveforms_hdf5_path += '.mini'\n else:\n prefix = ''\n\n create_folder(os.path.dirname(waveforms_hdf5_path))\n\n logs_dir = '_logs/pack_waveforms_to_hdf5/{}{}'.format(prefix, get_filename(csv_path))\n create_folder(logs_dir)\n create_logging(logs_dir, filemode='w')\n logging.info('Write logs to {}'.format(logs_dir))\n \n\t# Read csv file\n \n #split_file = open(split_dir+\"/fold_filter_\"+str(split_to_test_id)+\".txt\",\"r\")\n split_file = open(split_dir+\"/fold_\"+str(split_to_test_id)+\".txt\",\"r\")\n lines = split_file.readlines()\n audio_names = []\n target_names = []\n for line in lines:\n audio_names.append(line.split(' ')[0]+'.wav')\n target_names.append(id_to_ix[line.split(' ')[2][0:-1]])\n\n\n\n\n\n #meta_dict = read_metadata(csv_path, classes_num, id_to_ix)\n\n '''if mini_data:\n mini_num = 10\n for key in meta_dict.keys():\n meta_dict[key] = meta_dict[key][0 : mini_num]'''\n\n #audios_num = len(meta_dict['audio_name'])\n audios_num = len(lines)\n\n\n\n # Pack waveform to hdf5\n total_time = time.time()\n\n with h5py.File(waveforms_hdf5_path, 'w') as hf:\n hf.create_dataset('audio_name', shape=((audios_num,)), dtype='S20')\n hf.create_dataset('waveform', shape=((audios_num, clip_samples)), dtype=np.int16)\n hf.create_dataset('target', shape=((audios_num, classes_num)), dtype=np.bool)\n hf.attrs.create('sample_rate', data=sample_rate, dtype=np.int32)\n\n targets = np.zeros((audios_num, classes_num), dtype=np.bool)\n\n # Pack waveform & target of several audio clips to a single hdf5 file\n for n in range(audios_num):\n audio_path = os.path.join(audios_dir, audio_names[n])\n if os.path.isfile(audio_path):\n logging.info('{} {}'.format(n, audio_path))\n (audio, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True)\n audio = pad_or_truncate(audio, clip_samples)\n\n hf['audio_name'][n] = audio_names[n].encode()\n hf['waveform'][n] = float32_to_int16(audio)\n \n ix = target_names[n]\n if ix < classes_num:\n targets[n, ix] = 1\n logging.info('{}'.format(targets[n]))\n hf['target'][n] = targets[n]\n else:\n logging.info('{} File does not exist! {}'.format(n, audio_path))\n\n logging.info('Write to {}'.format(waveforms_hdf5_path))\n logging.info('Pack hdf5 time: {:.3f}'.format(time.time() - total_time))","title":""},{"docid":"3dd42d631e95e3cacfbc4d6c50940ab2","score":"0.54462826","text":"def save_metadata(self, hdf5: h5py.File, group_name: str) -> None:\n pass","title":""},{"docid":"3ce0a608a1e3aa0296337311022976b1","score":"0.5441013","text":"def createFileGroups(self):\n self.dataset.buildListOfFiles()\n self.dataset_details['FileGroups'] = dict() \n for fileName in self.dataset.files: \n\n name = removeIndex(fileName) \n if name in self.dataset_details['FileGroups']: \n self.dataset_details['FileGroups'][name]['Files'].append(fileName) \n else: \n instanceGroup = {\"SizeInTB\":None, \n \"IsCrab\":None,\n \"Files\":None,\n \"FileEntries\":None,\n \"PrimaryDatasetFraction\":None,\n \"BadFiles\":None,\n \"NumberBadFiles\":None,\n \"MissingFiles\":None,\n \"NumberMissingFiles\":None,\n \"GoodFiles\":None,\n \"NumberGoodFiles\":None,\n \"TotalJobs\":None}\n instanceGroup['Files']=[]\n instanceGroup['Files'].append(fileName) \n isCrab = isCrabFile( fileName ) \n instanceGroup['IsCrab']=isCrab \n self.dataset_details['FileGroups'][name]=instanceGroup \n for fg in self.dataset_details['FileGroups']: \n self.dataset_details['FileGroups'][fg]['Files'].sort(key=lambda x: int(getIndex(x)))","title":""},{"docid":"937ba09b1bcb1b00953fb95c26703f3b","score":"0.5439032","text":"def xr_save_by_grouping_along_dimension(ds,filepath, filename_prefix, grouping='chunk',parallel=True,dim='time'):\n \n return","title":""},{"docid":"049219d45a71390f71f7457c993530d3","score":"0.5436357","text":"def to_h5(self, out_file, fields=None, group='/', replace=False, nocompression=False, attributes={}, fill_value=None, **kwargs):\n kwargs.setdefault('srs_proj4', None)\n kwargs.setdefault('srs_wkt', None)\n kwargs.setdefault('srs_epsg', None)\n kwargs.setdefault('grid_mapping_name', 'crs')\n # check whether overwriting existing files\n # append to existing files as default\n mode = 'w' if replace else 'a'\n\n if fields is None:\n fields=self.fields\n if group[0] != '/':\n group='/'+group\n\n # update fill values in fields\n if fill_value is not None:\n self.replace_invalid(fields=fields, fill_value=fill_value)\n\n # get crs attributes\n self.crs_attributes(**kwargs)\n with h5py.File(out_file,mode) as h5f:\n # try adding file level attributes\n try:\n for att_name,att_val in attributes['ROOT'].items():\n h5f.attrs[att_name] = att_val\n except Exception:\n pass\n\n # try creating the output group\n try:\n h5f.create_group(group)\n except Exception:\n pass\n\n # try adding group attributes\n try:\n for att_name,att_val in attributes[group].items():\n h5f[group].attrs[att_name] = att_val\n except Exception:\n pass\n\n for field in ['x','y','time', 't'] + fields:\n # if field exists, overwrite it\n if field in h5f[group]:\n if hasattr(self, field):\n h5f[group+'/'+field][...] = getattr(self, field)\n else:\n #Otherwise, try to create the dataset\n try:\n if nocompression or field in ['x','y','time']:\n h5f.create_dataset(group+'/'+field, data=getattr(self, field))\n else:\n\n h5f.create_dataset(group+'/'+field, data=getattr(self, field),\n chunks=True, compression=\"gzip\", fillvalue=self.fill_value)\n except Exception:\n pass\n # try adding field attributes\n try:\n for att_name,att_val in attributes[field].items():\n h5f[group+'/'+field].attrs[att_name] = att_val\n except Exception:\n pass\n # add crs attributes if applicable\n if self.crs:\n # add grid mapping attribute to each grid field\n for field in fields:\n h5f[group+'/'+field].attrs['grid_mapping'] = kwargs['grid_mapping_name']\n # add grid mapping variable with projection attributes\n h5crs = h5f.create_dataset(kwargs['grid_mapping_name'], (), dtype=np.byte)\n for att_name,att_val in self.crs.items():\n h5crs.attrs[att_name] = att_val","title":""},{"docid":"9335eba7a943c66b3289267357718608","score":"0.54333574","text":"def write_by_file(fpath, *objs_list, **kwargs):\n group_size = kwargs.pop('group_size', len(objs_list))\n mode = 'wb'\n if kwargs.get('gzip', True):\n ofs = gzip.open(fpath, mode)\n else:\n ofs = open(fpath, mode)\n with stream.open(fileobj=ofs, mode=mode, buffer_size=group_size,\n **kwargs) as ostream:\n ostream.write(*objs_list)\n ofs.close()","title":""},{"docid":"c0d5008a7deb52ba1830263c9061b685","score":"0.5423882","text":"def produce_bam_custom(kmers_trie, name, label, guides_filename, args,\n offdist, maxoffcount, processes, n, parts):\n guidesfiles = []\n # parts = 256\n tempdir = '%s%s' % (name,'/classifiedfiles/tempfiles')\n\n util.print_log('produce SAM files...')\n samfiles = ['%s/%s.sam' % (tempdir, i) for i in range(parts)]\n # samfiles = [tempfile.NamedTemporaryFile(dir=name, suffix='.sam%s' % i)\n # for i in xrange(parts)]\n # util.print_log('store SAM in these files (gzipped): %s'\n # % (', '.join([basename(f.name) for f in samfiles])))\n\n \n if isinstance(guides_filename, str):\n\n util.print_log('split %s in %s parts...' % (guides_filename, parts))\n guidesfiles = [tempfile.NamedTemporaryFile(dir=name,\n suffix='.guides%s' % i)\n for i in range(parts)]\n util.print_log('store guides in these files: %s'\n % (', '.join([basename(f.name) for f in guidesfiles])))\n guidesfile = gzip.open(guides_filename) \\\n if guides_filename.endswith('.gz') \\\n else open(guides_filename)\n index_num = 0\n guidecount = 0\n for line in guidesfile:\n kmer1 = line.split()[0][0:n]\n index_num = guides.get_num(kmer1, n)\n guidesfiles[index_num].write(line)\n \n guidecount += 1\n \n guidesfile.close()\n for f in guidesfiles:\n f.flush()\n util.print_log('%s guideRNAs to process' % guidecount)\n util.print_log('done')\n\n process_list = []\n all_task = Queue()\n for i in range(parts):\n task = (guides_filename[i].name, samfiles[i].name, i)\n all_task.put(task)\n\n for i in range(processes):\n p = Process(target=process_pool, args=(all_task, kmers_trie, args, offdist, maxoffcount, i, n, parts))\n p.start()\n process_list.append(p)\n\n for p in process_list:\n p.join()\n \n for i in range(parts):\n guidesfiles[i].close()\n\n else: \n process_list = []\n all_task = Queue()\n for i in range(parts):\n task = (guides_filename[i], samfiles[i], i)\n all_task.put(task)\n\n for i in range(processes):\n p = Process(target=process_pool, args=(all_task, kmers_trie, args, offdist, maxoffcount, i, n, parts))\n p.start()\n process_list.append(p)\n\n for p in process_list:\n p.join()\n\n util.print_log('produce sorted BAM files...')\n \n bamfiles = ['%s/%s.bam' % (tempdir, i) for i in range(parts)]\n # bamfiles = [tempfile.NamedTemporaryFile(dir=name, suffix='.bam%s' % i)\n # for i in xrange(parts)]\n # util.print_log('store BAM in these files: %s'\n # % (', '.join([basename(f.name) for f in bamfiles])))\n\n pool = Pool(processes)\n util.print_log('poolSize %s...' % processes)\n index=False\n for i in range(parts):\n pool.apply_async(sam_to_bam,(samfiles[i], bamfiles[i], index,))\n util.print_log('Waiting for all subprocesses done...')\n pool.close()\n pool.join()\n\n # for i in xrange(parts):\n # samfiles[i].close()\n util.print_log('merge into one BAM file...')\n bamfile = '%s/%s_guides%s.bam' % (name, name,\n '_%s' % label if label else '')\n util.print_log('store in %s' % bamfile)\n util.warn_file_exists(bamfile)\n if parts > 1000:\n mid = parts // 2\n bamfiles_temp = [tempfile.NamedTemporaryFile(dir=name, suffix='.bam%s' % i)\n for i in xrange(2)]\n samtools_command1 = 'samtools merge -f %s %s' \\\n % (bamfiles_temp[0].name, ' '.join(bamfiles[0:mid]))\n os.system(samtools_command1)\n\n samtools_command2 = 'samtools merge -f %s %s' \\\n % (bamfiles_temp[1].name, ' '.join(bamfiles[mid:parts]))\n os.system(samtools_command2)\n\n samtools_command = 'samtools merge -f %s %s' \\\n % (bamfile, ' '.join([f.name for f in bamfiles_temp]))\n os.system(samtools_command)\n\n for f in bamfiles_temp:\n f.close()\n\n else:\n samtools_command = 'samtools merge -f %s %s' \\\n % (bamfile, ' '.join(bamfiles))\n # print samtools_command\n os.system(samtools_command)\n samtools_index_command = 'samtools index %s' % bamfile\n # print samtools_index_command\n os.system(samtools_index_command)\n util.print_log('done')\n # for i in xrange(parts):\n # bamfiles[i].close()\n\n for i in range(parts):\n if(os.path.exists(samfiles[i])):\n os.remove(samfiles[i])\n if(os.path.exists(bamfiles[i])):\n os.remove(bamfiles[i])\n\n util.print_log('samtools version')\n samtools_version_command = 'samtools --version'\n print samtools_version_command\n os.system(samtools_version_command)","title":""},{"docid":"2da00a9f5c41e9c6ab49a32f72eda32f","score":"0.54021645","text":"def evaluate_gpt_with_distgen(settings, \n archive_path=None, \n merit_f=None, \n gpt_input_file=None,\n distgen_input_file=None,\n workdir=None, \n use_tempdir=True,\n gpt_bin='$GPT_BIN',\n timeout=2500,\n auto_phase=False,\n verbose=False,\n gpt_verbose=False,\n asci2gdf_bin='$ASCI2GDF_BIN',\n kill_msgs=DEFAULT_KILL_MSGS,\n parse_layout=False):\n \n G = run_gpt_with_distgen(settings=settings,\n gpt_input_file=gpt_input_file,\n distgen_input_file=distgen_input_file,\n workdir=workdir, \n use_tempdir=use_tempdir,\n gpt_bin=gpt_bin,\n timeout=timeout,\n auto_phase=auto_phase,\n verbose=verbose,\n gpt_verbose=gpt_verbose,\n asci2gdf_bin=asci2gdf_bin,\n kill_msgs=kill_msgs,\n parse_layout=parse_layout\n )\n \n if merit_f:\n merit_f = tools.get_function(merit_f)\n output = merit_f(G)\n else:\n output = default_gpt_merit(G)\n \n if output['error']:\n raise ValueError('error occured!')\n \n #Recreate Generator object for fingerprint, proper archiving\n # TODO: make this cleaner\n gen = Generator(G.distgen_input)\n #gen = Generator()\n #gen.input = G.distgen_input \n \n fingerprint = fingerprint_gpt_with_distgen(G, gen)\n output['fingerprint'] = fingerprint \n \n if archive_path:\n path = tools.full_path(archive_path)\n assert os.path.exists(path), f'archive path does not exist: {path}'\n archive_file = os.path.join(path, fingerprint+'.h5')\n output['archive'] = archive_file\n \n # Call the composite archive method\n archive_gpt_with_distgen(G, gen, archive_file=archive_file) \n \n return output","title":""},{"docid":"42c307bd366b33a33a34b314e83be7c0","score":"0.5375089","text":"def rewrite_hdf5(g, data_dict):\n for ddkey, dditem in data_dict.iteritems():\n if ddkey not in g.keys():\n g.create_group(ddkey)\n for attr in dditem.attrs:\n g[ddkey].attrs[attr]=dditem.attrs[attr]\n append=False\n else:\n append=dditem.attrs.get(\"append\", False)\n g[ddkey].attrs[\"append\"]=append\n if isinstance(dditem, dataset): #dataset\n if not append:\n namestr=\"{0}\".format(len(g[ddkey]))\n g[ddkey].create_dataset(namestr, data=dditem.data, dtype=dditem.datatype, maxshape=dditem.maxshape)#chunks=True)\n #g[ddkey][namestr].attrs[\"index\"]=len(dditem.data)-1\n else:\n namestr=\"{0}\".format(len(g[ddkey])-1)\n dset=g[ddkey][namestr]\n #n=dset.attrs[\"index\"]\n #if n>=len(dset)-1:\n n=len(dset)\n a=n+len(dditem.data)\n dset.resize(a,axis=0)\n dset[n:]=dditem.data\n #n+=len(dditem.data)\n #dset.attrs[\"index\"]=n\n elif isinstance(dditem, group):\n rewrite_hdf5(g[ddkey], dditem)","title":""},{"docid":"f041639fea79d2b81b0fb6861c3e2e3c","score":"0.5358876","text":"def save_data(self):\n if not os.path.exists(os.path.dirname(self.filename)):\n os.makedirs(os.path.dirname(self.filename))\n with h5py.File(self.filename, \"w\") as f:\n grid_data = f.create_group('grid')\n self.grid.save_to_h5py(grid_data)\n\n all_species = f.create_group('species')\n for species in self.list_species:\n species_data = all_species.create_group(species.name)\n species.save_to_h5py(species_data)\n\n f.attrs['dt'] = self.dt\n f.attrs['NT'] = self.NT\n f.attrs['run_date'] = self.run_date\n f.attrs['git_version'] = self.git_version\n f.attrs['title'] = self.title\n f.attrs['runtime'] = self.runtime\n f.attrs['considered_large'] = self.considered_large\n print(f\"Saved file to {self.filename}\")\n return self","title":""},{"docid":"4598538961ac7ac35010057b8031a57a","score":"0.5351646","text":"def WriteHDF5(self, fname, basedir, tag, m , nrings=9, fix_xco=False):\n modf = h5py.File(fname, 'w')\n\n if m is not None:\n if fix_xco is False:\n X_CO = np.array([m.values['pi0_H2_'+str(i)] for i in range(1,nrings+1)])\n else: \n X_CO = np.ones(nrings)\n #modf = h5py.File(fname, 'w')\n #try:\n # Generate Groups\n # template_group = modf.create_group(\"templates\")\n # fit_group = modf.create_group(\"fit_results\")\n\n\n # Get data dimensions\n tmp = pyfits.open(basedir+'/bremss_healpix_54_'+tag+'.gz')\n energies = tmp[2].data.field(0)\n tShape = (len(energies), tmp[1].data.shape[0])\n print tShape\n del tmp # free memory\n\n pi0 = modf.create_dataset(\"/templates/pi0\", tShape, dtype='float32',compression=\"gzip\")\n pi0_0 = modf.create_dataset(\"/templates/pi0_0\", tShape, dtype='float32',compression=\"gzip\")\n pi0_1 = modf.create_dataset(\"/templates/pi0_1\", tShape, dtype='float32',compression=\"gzip\")\n brem = modf.create_dataset(\"/templates/brem\", tShape, dtype='float32',compression=\"gzip\")\n brem_0 = modf.create_dataset(\"/templates/brem_0\", tShape, dtype='float32',compression=\"gzip\")\n brem_1 = modf.create_dataset(\"/templates/brem_1\", tShape, dtype='float32',compression=\"gzip\")\n ics_opt = modf.create_dataset(\"/templates/ics_opt\", tShape, dtype='float32',compression=\"gzip\")\n ics_fir = modf.create_dataset(\"/templates/ics_fir\", tShape, dtype='float32',compression=\"gzip\")\n ics_cmb = modf.create_dataset(\"/templates/ics_cmb\", tShape, dtype='float32',compression=\"gzip\")\n modf.create_dataset(\"/templates/energies\", data=energies, dtype='float32',compression=\"gzip\")\n # Now fill in the templates one by one.\n # Add fit metadata.\n # Add galdef metadata.\n\n\n #---------------------------------------------------------------\n # Create Diffuse Template from fitting results.\n def ReadFits(fname, length):\n d = pyfits.open(fname)[1].data\n return np.array([d.field(i) for i in range(length)])\n\n if m is not None:\n for i_ring in range(1,nrings+1):\n print \"Adding HI/HII ring\", i_ring\n\n pi0[...] += m.values['pi0HIHII']*ReadFits(basedir+'/pi0_decay_HIR_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n pi0[...] += m.values['pi0HIHII']*ReadFits(basedir+'/pi0_decay_HII_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n if fix_xco is False:\n pi0[...] += X_CO[i_ring-1]*ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n else:\n pi0[...] += m.values['pi0HIHII']*ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n\n brem[...] += m.values['pi0HIHII']*1.25*ReadFits(basedir+'/bremss_HIR_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n brem[...] += m.values['pi0HIHII']*1.25*ReadFits(basedir+'/bremss_HII_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n if fix_xco is False:\n brem[...] += 1.25*X_CO[i_ring-1]*ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n else: \n brem[...] += m.values['pi0HIHII']*1.25*X_CO[i_ring-1]*ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n \n if i_ring == 1 :\n pi0_0[...] += ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n brem_0[...] += ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n if i_ring == 2:\n pi0_1[...] += ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n brem_1[...] += ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n\n print 'Adding ICS'\n ics_opt[...] += m.values['ics']*ReadFits(basedir+'/ics_isotropic_comp_1_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n ics_fir[...] += m.values['ics']*ReadFits(basedir+'/ics_isotropic_comp_2_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n ics_cmb[...] += m.values['ics']*ReadFits(basedir+'/ics_isotropic_comp_3_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n\n else:\n for i_ring in range(1,nrings+1):\n print \"Adding HI/HII/H2 ring\", i_ring\n\n pi0[...] += ReadFits(basedir+'/pi0_decay_HIR_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n pi0[...] += ReadFits(basedir+'/pi0_decay_HII_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n pi0[...] += ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n\n brem[...] += ReadFits(basedir+'/bremss_HIR_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n brem[...] += ReadFits(basedir+'/bremss_HII_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n brem[...] += ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n\n if i_ring == 1 :\n pi0_0[...] += ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n brem_0[...] += ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n if i_ring == 2:\n pi0_1[...] += ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n brem_1[...] += ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n\n print 'Adding ICS'\n ics_opt[...] += ReadFits(basedir+'/ics_isotropic_comp_1_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n ics_fir[...] += ReadFits(basedir+'/ics_isotropic_comp_2_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n ics_cmb[...] += ReadFits(basedir+'/ics_isotropic_comp_3_healpix_54_'+tag+'.gz', len(energies)).clip(0)\n\n # except:\n # modf.close()\n \n try: \n modf.close()\n print 'Closed HDF5 file.' \n except: \n print 'Failed to close HDF5 file.' \n pass\n return","title":""},{"docid":"edc55435f66927e97fd865b8b5016a59","score":"0.53244704","text":"def write_to_hdf5(data_by_test, filename):\n\twith hdf5.File('{}.hdf5'.format(filename), 'w') as file:\n\t\t# foreach test data\n\t\tfor test_index, test_data in enumerate(data_by_test.values()):\n\t\t\t# convert iterator 'test_data' to the list\n\t\t\tfile.create_dataset(\"test_{}\".format(test_index),\n\t\t\t data=list(test_data), compression=\"gzip\")","title":""},{"docid":"adbad349ce105e54e030dbc519f364c4","score":"0.5301874","text":"def save(self):\n\n with h5py.File(self.filename, \"r+\") as f:\n\n source_handle = f.create_group(\"source\")\n if self.data.source:\n self.data.source.save(source_handle)\n\n uhecr_handle = f.create_group(\"uhecr\")\n if self.data.uhecr:\n self.data.uhecr.save(uhecr_handle)\n\n detector_handle = f.create_group(\"detector\")\n if self.data.detector:\n self.data.detector.save(detector_handle)\n\n model_handle = f.create_group(\"model\")\n if self.model:\n self.model.save(model_handle)\n\n fit_handle = f.create_group(\"fit\")\n if self.fit:\n\n # fit inputs\n fit_input_handle = fit_handle.create_group(\"input\")\n for key, value in self.fit_input.items():\n fit_input_handle.create_dataset(key, data=value)\n\n # samples\n samples = fit_handle.create_group(\"samples\")\n for key, value in self.chain.items():\n samples.create_dataset(key, data=value)\n\n else:\n plotvars_handle = f.create_group(\"plotvars\")\n\n if self.analysis_type == self.gmf_type:\n for key, value in list(self.defl_plotvars.items()):\n plotvars_handle.create_dataset(key, data=value)","title":""},{"docid":"1e9f3d70ea62c4328ad931aea3ce24a4","score":"0.52851725","text":"def save(self):\n \n self.f = hdf.File(\"dbtest3.hdf5\",\"a\")\n dt = hdf.special_dtype(vlen=str)\n name = self.compoundA + \" + \" + self.compoundB + \" @ \" + str(self.temperature)\n subgrp = self.f.create_group(name)\n dset = subgrp.create_dataset(\"Specifics\",(10,),dtype=dt)\n dset[0] = self.compoundA\n dset[1] = self.compoundB\n dset[2] = self.reference\n dset[3] = self.etaA\n dset[4] = self.etaB\n dset[5] = self.rhoA\n dset[6] = self.rhoB\n dset[7] = self.massA\n dset[8] = self.massB\n dset[9] = self.temperature\n \n dset = subgrp.create_dataset(\"Data\",(3,len(self.x1)),dtype=\"f\")\n dset[0] = self.x1\n dset[1] = self.etaSystem\n dset[2] = self.rhoSystem\n print \"Saving data for \" + name\n self.f.close()","title":""},{"docid":"a6516f1de6193780627bfc1ac1a160af","score":"0.5283196","text":"def _output(self):\n t0 = time.monotonic()\n self.sendProgress('Waiting for HDF5 writer lock...')\n with self.h5io.writer(f'Samples/{self.headers[0].title}/{self.headers[0].distance[0]:.2f}') as group:\n t1 = time.monotonic()\n self.sendProgress('Writing HDF5 file...')\n # Set attributes of the <dist> group from the averaged header.\n self.h5io.writeHeader(self.averagedHeader, group)\n self.h5io.writeExposure(self.averagedExposure, group)\n try:\n del group['badfsns']\n except KeyError:\n pass\n group['badfsns'] = np.array(sorted(self.result.badfsns), dtype=np.int)\n try:\n del group['goodindex']\n except KeyError:\n pass\n group['goodindex'] = self.goodindex\n self.h5io.writeCurve(self.averagedCurve, group, 'curve_averaged')\n self.h5io.writeCurve(self.reintegratedCurve, group, 'curve_reintegrated')\n try:\n del group['curve']\n except KeyError:\n pass\n group['curve'] = h5py.SoftLink('curve_averaged')\n self.h5io.writeOutlierTest(group.name, self.outliertest)\n # save all curves\n for groupname in ['allcurves', 'curves']:\n try:\n del group[groupname]\n except KeyError:\n pass\n curvesgroup = group.create_group('allcurves')\n goodcurvesgroup = group.create_group('curves')\n # we will write outlier results per-curve. Do the common calculations beforehand.\n scoreindex = 0\n for i, (h, isgood) in enumerate(zip(self.headers, self.goodindex)):\n self.h5io.writeCurve(self.curves[:, :, i], curvesgroup, str(h.fsn))\n dataset = curvesgroup[str(h.fsn)]\n self.h5io.writeHeader(h, dataset)\n if isgood:\n dataset.attrs['correlmat_bad'] = int(h.fsn in self.result.badfsns)\n dataset.attrs['correlmat_discrp'] = self.outliertest.score[scoreindex]\n scoreindex += 1\n else:\n dataset.attrs['correlmat_bad'] = -1\n dataset.attrs['correlmat_discrp'] = np.nan\n # make links in the 'curves' group to those fsns which were not bad at the beginning of this procedure\n if (h.fsn not in self.result.badfsns) or (h.fsn in self.result.newbadfsns):\n goodcurvesgroup[str(h.fsn)] = h5py.SoftLink(dataset.name)\n\n self.result.time_output_write = time.monotonic() - t1\n self.result.time_output_waitforlock = t1 - t0\n self.result.time_output = time.monotonic() - t0","title":""},{"docid":"295b6199ac04d36bc7052db8ece4d1f2","score":"0.5261463","text":"def ProduceCombinedUnifiedTreeandHaloCatalog(fname,numsnaps,tree,numhalos,halodata,atime,\n\tdescripdata={'Title':'Tree and Halo catalog of sim', 'VELOCIraptor_version':1.15, 'Tree_version':1.1, 'Particle_num_threshold':20, 'Temporal_linking_length':1, 'Flag_gas':False, 'Flag_star':False, 'Flag_bh':False},\n\tcosmodata={'Omega_m':1.0, 'Omega_b':0., 'Omega_Lambda':0., 'Hubble_param':1.0,'BoxSize':1.0, 'Sigma8':1.0},\n\tunitdata={'UnitLength_in_Mpc':1.0, 'UnitVelocity_in_kms':1.0,'UnitMass_in_Msol':1.0, 'Flag_physical_comoving':True,'Flag_hubble_flow':False},\n\tpartdata={'Flag_gas':False, 'Flag_star':False, 'Flag_bh':False},\n\tibuildheadtail=0,ibuildmajormergers=0, TEMPORALHALOIDVAL=1000000000000):\n\n\tif (ibuildheadtail==1):\n\t\tBuildTemporalHeadTail(numsnaps,tree,numhalos,halodata)\n\tif (ibuildmajormergers==1):\n\t\tIdentifyMergers(numsnaps,tree,numhalos,halodata,boxsize,hval,atime)\n\thdffile=h5py.File(fname+\".snap.hdf.data\",'w')\n\theadergrp=hdffile.create_group(\"Header\")\n\t#store useful information such as number of snapshots, halos,\n\t#cosmology (Omega_m,Omega_b,Hubble_param,Omega_Lambda, Box size)\n\t#units (Physical [1/0] for physical/comoving flag, length in Mpc, km/s, solar masses, Gravity\n\t#and TEMPORALHALOIDVAL used to traverse tree information (converting halo ids to haloindex or snapshot), Reverse_order [1/0] for last snap listed first)\n\t#set the attributes of the header\n\theadergrp.attrs[\"NSnaps\"]=numsnaps\n\t#overall description\n\theadergrp.attrs[\"Title\"]=descripdata[\"Title\"]\n\t#simulation box size\n\theadergrp.attrs[\"BoxSize\"]=cosmodata[\"BoxSize\"]\n\tfindergrp=headergrp.create_group(\"HaloFinder\")\n\tfindergrp.attrs[\"Name\"]=\"VELOCIraptor\"\n\tfindergrp.attrs[\"Version\"]=descripdata[\"VELOCIraptor_version\"]\n\tfindergrp.attrs[\"Particle_num_threshold\"]=descripdata[\"Particle_num_threshold\"]\n\n\ttreebuildergrp=headergrp.create_group(\"TreeBuilder\")\n\ttreebuildergrp.attrs[\"Name\"]=\"VELOCIraptor-Tree\"\n\ttreebuildergrp.attrs[\"Version\"]=descripdata[\"Tree_version\"]\n\ttreebuildergrp.attrs[\"Temporal_linking_length\"]=descripdata[\"Temporal_linking_length\"]\n\n\t#cosmological params\n\tcosmogrp=headergrp.create_group(\"Cosmology\")\n\tfor key in cosmodata.keys():\n\t\tif (key!='BoxSize'): cosmogrp.attrs[key]=cosmodata[key]\n\t#unit params\n\tunitgrp=headergrp.create_group(\"Units\")\n\tfor key in unitdata.keys():\n\t\tunitgrp.attrs[key]=unitdata[key]\n\t#particle types\n\tpartgrp=headergrp.create_group(\"Parttypes\")\n\tpartgrp.attrs[\"Flag_gas\"]=descripdata[\"Flag_gas\"]\n\tpartgrp.attrs[\"Flag_star\"]=descripdata[\"Flag_star\"]\n\tpartgrp.attrs[\"Flag_bh\"]=descripdata[\"Flag_bh\"]\n\n\t#now have finished with header\n\n\t#now need to create groups for halos and then a group containing tree information\n\tsnapsgrp=hdffile.create_group(\"Snapshots\")\n\t#internal tree keys\n\ttreekeys=[\"RootHead\", \"RootHeadSnap\", \"Head\", \"HeadSnap\", \"Tail\", \"TailSnap\", \"RootTail\", \"RootTailSnap\", \"Num_progen\"]\n\n\tfor i in range(numsnaps):\n\t\t#note that I normally have information in reverse order so that might be something in the units\n\t\tsnapgrp=snapsgrp.create_group(\"Snap_%03d\"%(numsnaps-1-i))\n\t\tsnapgrp.attrs[\"Snapnum\"]=i\n\t\tsnapgrp.attrs[\"NHalos\"]=numhalos[i]\n\t\tsnapgrp.attrs[\"scalefactor\"]=atime[i]\n\t#now close file and use the pytables interface so as to write the table\n\thdffile.close()\n\t#now write tables using pandas interface\n\tfor i in range(numsnaps):\n\t\t#lets see if we can alter the code to write a table\n\t\tkeys=halodata[i].keys()\n\t\t#remove tree keys\n\t\tfor tkey in treekeys: keys.remove(tkey)\n\t\t#make temp dict\n\t\tdictval=dict()\n\t\tfor key in keys:\n\t\t\tdictval[key]=halodata[i][key]\n\t\t#make a pandas DataFrame using halo dictionary\n\t\tdf=pd.DataFrame.from_dict(dictval)\n\t\tdf.to_hdf(fname+\".snap.hdf.data\",\"Snapshots/Snap_%03d/Halos\"%(numsnaps-1-i), format='table', mode='a')\n\n\t#reopen with h5py interface\n\thdffile=h5py.File(fname+\".snap.hdf.data\",'a')\n\t#then write tree information in separate group\n\ttreegrp=hdffile.create_group(\"MergerTree\")\n\t#Tree group should contain\n\t\"\"\"\n\t\tHaloSnapID\n\t\tHaloSnapNum\n\t\tHaloSnapIndex\n\t\tProgenitorIndex\n\t\tProgenitorSnapnum\n\t\tProgenitorID\n\t\tDescendantIndex\n\t\t..\n\t\t..\n\t\tRootProgenitorIndex\n\t\t..\n\t\t..\n\t\tRootDescendantIndex\n\t\"\"\"\n\t#to save on memory, allocate each block separately\n\t#store halo information\n\ttothalos=sum(numhalos)\n\ttdata=np.zeros(tothalos,dtype=halodata[0][\"ID\"].dtype)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=halodata[i][\"ID\"]\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"HaloSnapID\",data=tdata)\n\ttdata=np.zeros(tothalos,dtype=np.uint32)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=i\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"HaloSnapNum\",data=tdata)\n\ttdata=np.zeros(tothalos,dtype=np.uint64)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=range(int(numhalos[i]))\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"HaloSnapIndex\",data=tdata)\n\t#store progenitors\n\ttdata=np.zeros(tothalos,dtype=halodata[0][\"Tail\"].dtype)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=halodata[i][\"Tail\"]\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"ProgenitorID\",data=tdata)\n\ttdata=np.zeros(tothalos,dtype=halodata[0][\"TailSnap\"].dtype)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=halodata[i][\"TailSnap\"]\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"ProgenitorSnapnum\",data=tdata)\n\ttdata=np.zeros(tothalos,dtype=np.uint64)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=(halodata[i][\"Tail\"]%TEMPORALHALOIDVAL-1)\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"ProgenitorIndex\",data=tdata)\n\t#store descendants\n\ttdata=np.zeros(tothalos,dtype=halodata[0][\"Head\"].dtype)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=halodata[i][\"Head\"]\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"DescendantID\",data=tdata)\n\ttdata=np.zeros(tothalos,dtype=halodata[0][\"HeadSnap\"].dtype)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=halodata[i][\"HeadSnap\"]\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"DescendantSnapnum\",data=tdata)\n\ttdata=np.zeros(tothalos,dtype=np.uint64)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=(halodata[i][\"Head\"]%TEMPORALHALOIDVAL-1)\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"DescendantIndex\",data=tdata)\n\t#store progenitors\n\ttdata=np.zeros(tothalos,dtype=halodata[0][\"RootTail\"].dtype)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=halodata[i][\"RootTail\"]\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"RootProgenitorID\",data=tdata)\n\ttdata=np.zeros(tothalos,dtype=halodata[0][\"RootTailSnap\"].dtype)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=halodata[i][\"RootTailSnap\"]\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"RootProgenitorSnapnum\",data=tdata)\n\ttdata=np.zeros(tothalos,dtype=np.uint64)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=(halodata[i][\"RootTail\"]%TEMPORALHALOIDVAL-1)\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"RootProgenitorIndex\",data=tdata)\n\t#store descendants\n\ttdata=np.zeros(tothalos,dtype=halodata[0][\"RootHead\"].dtype)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=halodata[i][\"RootHead\"]\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"RootDescendantID\",data=tdata)\n\ttdata=np.zeros(tothalos,dtype=halodata[0][\"RootHeadSnap\"].dtype)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=halodata[i][\"RootHeadSnap\"]\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"RootDescendantSnapnum\",data=tdata)\n\ttdata=np.zeros(tothalos,dtype=np.uint64)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=(halodata[i][\"RootHead\"]%TEMPORALHALOIDVAL-1)\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"RootDescendantIndex\",data=tdata)\n\t#store number of progenitors\n\ttdata=np.zeros(tothalos,dtype=np.uint32)\n\tcount=0\n\tfor i in range(numsnaps):\n\t\ttdata[count:int(numhalos[i])+count]=halodata[i][\"Num_progen\"]\n\t\tcount+=int(numhalos[i])\n\ttreegrp.create_dataset(\"NProgen\",data=tdata)\n\n\thdffile.close()","title":""},{"docid":"16445b72f0367edad1d91f339993db9e","score":"0.5249218","text":"def createh5(filename, mode=None, **kwargs):\n if mode is None: \n mode = 'a'\n params = {'libver':'latest', 'track_order':True}\n params.update(kwargs)\n attrs = params.pop('attrs') if 'attrs' in params else dict()\n with h5file(filename, mode, **params) as f:\n f.attrs.update(attrs)\n return Group_autoiter(__h5file__(filename, params), '/')","title":""},{"docid":"ec650ccdd21dd4572ccb4eabfaa1a3a5","score":"0.5233218","text":"def _save_superstar_grids(self, hs_runner):\n ss_dir = join(self.out_dir, \"superstar_grids\")\n if not exists(ss_dir):\n os.mkdir(ss_dir)\n for s in hs_runner.superstar_grids:\n s.grid.write(join(ss_dir, \"superstar_{}.ccp4\".format(s.identifier)))\n shutil.make_archive(ss_dir, 'zip', ss_dir)\n shutil.rmtree(ss_dir)","title":""},{"docid":"2af67287cd549a5b05ba8c242ce5370a","score":"0.51985604","text":"def tarball_create(param_dict, proj_dict, catl_ext='hdf5'):\n Prog_msg = param_dict['Prog_msg' ]\n ## List of Mock catalogues\n catl_path_arr = cu.Index(proj_dict['mock_cat_mc'], catl_ext)\n ## README file\n # Downloading working README file\n readme_file = os.path.join( proj_dict['base_dir'],\n 'references',\n 'README_RTD.pdf')\n cu.File_Download_needed(readme_file, param_dict['readme_url'])\n cu.File_Exists(readme_file)\n # Cartesian coordinates for all mocks\n cart_pos_fig = os.path.join( proj_dict['fig_dir'],\n '{0}_{1}_{2}_xyz_mocks.{3}'.format(\n param_dict['survey'],\n param_dict['halotype'],\n param_dict['cosmo_choice'],\n 'pdf'))\n # Luminosity function\n lum_func_catls = os.path.join( proj_dict['fig_dir'],\n '{0}_{1}_{2}_lum_function_mocks.{3}'.format(\n param_dict['survey'],\n param_dict['halotype'],\n param_dict['cosmo_choice'],\n 'pdf'))\n ## Saving to TAR file\n tar_file_path = os.path.join( proj_dict['tar_dir'],\n '{0}_{1}_catls.tar.gz'.format(\n param_dict['survey_name'],\n param_dict['halotype']))\n # Opening file\n with tarfile.open(tar_file_path, mode='w:gz') as tf:\n # README file\n tf.add(readme_file, arcname=os.path.basename(readme_file))\n # Figures\n tf.add(cart_pos_fig, arcname=os.path.basename(cart_pos_fig))\n tf.add(lum_func_catls, arcname=os.path.basename(lum_func_catls))\n for file_kk in catl_path_arr:\n ## Reading in DataFrame\n gal_pd_kk = cu.read_hdf5_file_to_pandas_DF(file_kk)\n ## DataFrame `without` certain columns\n gal_pd_mod = catl_drop_cols(gal_pd_kk)\n ## Saving modified DataFrame to file\n file_mod_kk = file_kk+'.mod'\n cu.pandas_df_to_hdf5_file(gal_pd_mod, file_mod_kk, key='\\gal_catl')\n cu.File_Exists(file_mod_kk)\n # Saving to Tar-file\n tf.add(file_mod_kk, arcname=os.path.basename(file_kk))\n # Deleting extra file\n os.remove(file_mod_kk)\n tf.close()\n cu.File_Exists(tar_file_path)\n if param_dict['verbose']:\n print('{0} TAR file saved as: {1}'.format(Prog_msg, tar_file_path))","title":""},{"docid":"5c71b274a5e89e859c80d60018925cad","score":"0.5181026","text":"def write_partitioned(ratings, output_dir):\n\n dates = pd.to_datetime(ratings[\"timestamp\"], unit=\"s\")\n\n for (year, month), grp in ratings.groupby([dates.dt.year, dates.dt.month]):\n output_path = output_dir / str(year) / f\"{month:02d}.csv\"\n output_path.parent.mkdir(parents=True, exist_ok=True)\n grp.to_csv(output_path, index=False)","title":""},{"docid":"0e8c61a1752f9dec28e5223291e3efea","score":"0.516821","text":"def create_seg_exports(self):\n try:\n if self.output_dir == None:\n out_dir = \"SEG_Output\"\n os.mkdir(\"SEG_Output\")\n else:\n out_dir = self.output_dir\n os.mkdir(self.output_dir)\n except:\n pass\n\n self.annots_df.apply(self.drop_dupes, axis=1)\n\n for dataset_id in self.annots_df[\"datasetId\"].unique():\n self.dataset_annots = self.annots_df[self.annots_df.datasetId == dataset_id]\n for study_uid in self.studies:\n dicom_hier = self.dicom_hierarchy[study_uid]\n for series_dict in dicom_hier:\n for series_uid in series_dict:\n sops = series_dict[series_uid]\n\n annotations = self.dataset_annots[\n self.dataset_annots.SeriesInstanceUID == series_uid\n ]\n annotations = annotations[annotations[\"scope\"] == \"INSTANCE\"]\n if annotations.empty:\n continue\n\n self.dicom_tags = self.tags_df[\n self.tags_df.SeriesInstanceUID == series_uid\n ].dicomTags.values[0]\n annotators = annotations.createdById.unique()\n instance_uid = pydicom.uid.generate_uid(prefix=None)\n date = datetime.now().strftime(\"%Y%m%d\")\n time = datetime.now().strftime(\"%H%M%S\")\n\n sop_instance_num_map = {}\n for sop in sops:\n sop_dicom_tags = self.tags_df[\n self.tags_df.SOPInstanceUID == sop\n ].dicomTags.values[0]\n if \"InstanceNumber\" in sop_dicom_tags:\n sop_instance_num_map[sop] = sop_dicom_tags[\"InstanceNumber\"]\n else:\n sop_instance_num_map[sop] = \"1\"\n\n def create_instance_number(row):\n return sop_instance_num_map[row[\"SOPInstanceUID\"]]\n\n try:\n annotations[\"instanceNumber\"] = annotations.apply(\n create_instance_number, axis=1\n )\n except:\n continue\n annotations = annotations.sort_values(\n [\"labelGroupName\", \"labelId\", \"instanceNumber\"], ignore_index=True\n ) # sort by label group then annotation then appearance in series\n\n # File meta info data elements\n file_meta = FileMetaDataset()\n file_meta.FileMetaInformationVersion = b\"\\x00\\x01\"\n file_meta.TransferSyntaxUID = \"1.2.840.10008.1.2.1\"\n file_meta.MediaStorageSOPInstanceUID = instance_uid # Create Instance UID # Media Storage SOP Instance UID\n file_meta.ImplementationClassUID = str(\n pydicom.uid.PYDICOM_IMPLEMENTATION_UID\n ) # Implementation Class UID\n file_meta.ImplementationVersionName = str(\n pydicom.__version__\n ) # Implementation Version Name\n file_meta.SourceApplicationEntityTitle = \"POSDA\"\n\n # Main data elements\n ds = Dataset()\n\n ds = self.place_tags(self.dicom_tags, ds, self.dicom_tag_heirarchy, True)\n\n ds.SpecificCharacterSet = \"ISO_IR 192\"\n ds.SOPClassUID = \"1.2.840.10008.5.1.4.1.1.66.4\"\n ds.SOPInstanceUID = instance_uid\n ds.SeriesDate = str(date) # Series Date\n ds.ContentDate = str(date) # Content Date\n ds.SeriesTime = str(time) # Series Time\n ds.ContentTime = str(time) # Series Time\n ds.Manufacturer = \"MDAI\"\n ds.Modality = \"SEG\"\n\n # Referenced Series Sequence\n refd_series_sequence = Sequence()\n ds.ReferencedSeriesSequence = refd_series_sequence\n\n # Referenced Series Sequence: Referenced Series 1\n refd_series1 = Dataset()\n refd_series_sequence.append(refd_series1)\n\n # Referenced Series Sequence: Referenced Series 1\n refd_series1 = Dataset()\n refd_series_sequence.append(refd_series1)\n refd_series1.SeriesInstanceUID = series_uid\n\n # Referenced Instance Sequence\n refd_instance_sequence = Sequence()\n refd_series1.ReferencedInstanceSequence = refd_instance_sequence\n\n ds.SegmentationType = \"BINARY\"\n\n for annotator_id in annotators:\n annotator_annots = annotations[annotations.createdById == annotator_id]\n\n if self.combine:\n label_group_sets = [annotator_annots.labelGroupName.unique()]\n else:\n label_group_sets = [\n [group] for group in annotator_annots.labelGroupName.unique()\n ]\n\n ds.SamplesPerPixel = 1\n ds.PhotometricInterpretation = \"MONOCHROME2\"\n ds.BitsAllocated = 1\n ds.BitsStored = 1\n ds.HighBit = 0\n ds.PixelRepresentation = 0\n ds.LossyImageCompression = \"00\"\n\n for label_group_set in label_group_sets:\n label_group_annots = annotator_annots[\n annotator_annots.labelGroupName.isin(label_group_set)\n ]\n\n # Segment Sequence\n segment_sequence = Sequence()\n ds.SegmentSequence = segment_sequence\n\n self.imgs = []\n self.seen_labels = set()\n self.name_number_map = {}\n self.included_sops = []\n self.unique_sops = set()\n self.label_groups = list(annotations.labelGroupName.unique())\n self.prev_annot = None\n label_group_annots.apply(self.img_insert, args=(ds,), axis=1)\n\n ds.NumberOfFrames = len(\n self.imgs\n ) # create during last parts of SEG file (should equal length of annot_df)\n ds.PixelData = pack_bits(np.array(self.imgs))\n\n for sop in self.unique_sops:\n sop_dicom_tags = self.tags_df[\n self.tags_df.SOPInstanceUID == sop\n ].dicomTags.values[0]\n refd_instance1 = Dataset()\n refd_instance_sequence.append(refd_instance1)\n if \"SOPClassUID\" in sop_dicom_tags:\n refd_instance1.ReferencedSOPClassUID = sop_dicom_tags[\n \"SOPClassUID\"\n ]\n refd_instance1.ReferencedSOPInstanceUID = sop_dicom_tags[\n \"SOPInstanceUID\"\n ]\n\n # Leaving it out for now but if nothing works then maybe try to add it back in blank and then with dummy values\n # Edit: added it back in but still unnecessary.\n # -----------------------------------------------------------------------\n # Dimension Index Sequence\n dimension_index_sequence = Sequence()\n ds.DimensionIndexSequence = dimension_index_sequence\n # -----------------------------------------------------------------------\n\n ds.ContentLabel = \"MDAI_SEG\"\n ds.ContentCreatorName = f\"annotator {annotator_id}\"\n\n # Leaving it out for now but if nothing works then maybe try to add it back in blank and then with dummy values\n # Edit: added it back in but still unnecessary.\n # -----------------------------------------------------------------------\n # Shared Functional Groups Sequence\n shared_functional_groups_sequence = Sequence()\n ds.SharedFunctionalGroupsSequence = (\n shared_functional_groups_sequence\n )\n # -----------------------------------------------------------------------\n\n # Per-frame Functional Groups Sequence\n per_frame_functional_groups_sequence = Sequence()\n ds.PerFrameFunctionalGroupsSequence = (\n per_frame_functional_groups_sequence\n )\n\n # Per-frame Functional Groups Sequence\n per_frame_functional_groups_sequence = Sequence()\n ds.PerFrameFunctionalGroupsSequence = (\n per_frame_functional_groups_sequence\n )\n\n # Per-frame Functional Groups Sequence\n per_frame_functional_groups_sequence = []\n\n # Loop through each frame with an annotation and create unique Per Frame Functional Group Sequence\n # ---------------------------------------------------------------------------\n for segment_number, sop in self.included_sops:\n label_names = \", \".join(\n label_group_annots[\"labelName\"].unique()\n )\n ds.SeriesDescription = (\n f\"Segmentation of {label_names} by annotator {annotator_id}\"\n )\n\n sop_dicom_tags = self.tags_df[\n self.tags_df.SOPInstanceUID == sop\n ].dicomTags.values[0]\n\n # Per-frame Functional Groups Sequence: Per-frame Functional Groups 1\n per_frame_functional_groups1 = Dataset()\n per_frame_functional_groups_sequence.append(\n per_frame_functional_groups1\n )\n\n # Derivation Image Sequence\n derivation_image_sequence = Sequence()\n per_frame_functional_groups1.DerivationImageSequence = (\n derivation_image_sequence\n )\n\n # Derivation Image Sequence: Derivation Image 1\n derivation_image1 = Dataset()\n derivation_image_sequence.append(derivation_image1)\n\n # Source Image Sequence\n source_image_sequence = Sequence()\n derivation_image1.SourceImageSequence = source_image_sequence\n\n # Source Image Sequence: Source Image 1\n source_image1 = Dataset()\n source_image_sequence.append(source_image1)\n if \"SOPClassUID\" in self.dicom_tags:\n source_image1.ReferencedSOPClassUID = self.dicom_tags[\n \"SOPClassUID\"\n ]\n source_image1.ReferencedSOPInstanceUID = self.dicom_tags[\n \"SOPInstanceUID\"\n ]\n\n # Purpose of Reference Code Sequence\n purpose_of_ref_code_sequence = Sequence()\n source_image1.PurposeOfReferenceCodeSequence = (\n purpose_of_ref_code_sequence\n )\n\n # Purpose of Reference Code Sequence: Purpose of Reference Code 1\n purpose_of_ref_code1 = Dataset()\n purpose_of_ref_code_sequence.append(purpose_of_ref_code1)\n purpose_of_ref_code1.CodeValue = \"121322\"\n purpose_of_ref_code1.CodingSchemeDesignator = \"DCM\"\n purpose_of_ref_code1.CodeMeaning = (\n \"Source image for image processing operation\"\n )\n\n # Derivation Code Sequence\n derivation_code_sequence = Sequence()\n derivation_image1.DerivationCodeSequence = (\n derivation_code_sequence\n )\n\n # Derivation Code Sequence: Derivation Code 1\n derivation_code1 = Dataset()\n derivation_code_sequence.append(derivation_code1)\n derivation_code1.CodeValue = \"113076\"\n derivation_code1.CodingSchemeDesignator = \"DCM\"\n derivation_code1.CodeMeaning = \"Segmentation\"\n\n # Segment Identification Sequence\n segment_id_seq = Dataset()\n per_frame_functional_groups1.SegmentIdentificationSequence = [\n segment_id_seq\n ]\n\n # Segment Number\n segment_id_seq.ReferencedSegmentNumber = segment_number\n\n per_frame_functional_groups1 = self.place_tags(\n sop_dicom_tags,\n per_frame_functional_groups1,\n self.dicom_tag_heirarchy[\n \"PerFrameFunctionalGroupsSequence\"\n ],\n False,\n )\n # -------------------------------------------------------------------------\n\n ds.PerFrameFunctionalGroupsSequence = (\n per_frame_functional_groups_sequence\n )\n\n ds.file_meta = file_meta\n ds.is_implicit_VR = False\n ds.is_little_endian = True\n\n if self.included_sops:\n if self.combine:\n ds.save_as(\n f\"{os.getcwd()}/{out_dir}/DICOM_SEG_{dataset_id}_{series_uid}_annotator_{annotator_id}.dcm\",\n False,\n )\n else:\n ds.save_as(\n f\"{os.getcwd()}/{out_dir}/DICOM_SEG_{dataset_id}_label_group_{label_group_set[0]}_series_{series_uid}_annotator_{annotator_id}.dcm\",\n False,\n )\n print(f\"Successfully exported DICOM SEG files into {out_dir}\")","title":""},{"docid":"d9fb4baa1caf9d2e244af1752f0b023a","score":"0.5165111","text":"def compress_all_data():\n\n output_file = 'qbank_data.tar.gz'\n\n output_path = os.path.join(OUTPUT_DIR, 'qbank', output_file)\n\n with tarfile.open(output_path, 'w:gz') as tar:\n\n mongo_dump_dir = os.path.join(OUTPUT_DIR, 'qbank', 'mongo-dump')\n\n activity_dump_dir = os.path.join(OUTPUT_DIR, 'qbank', 'student-activities')\n\n tar.add(mongo_dump_dir, arcname=os.path.basename(mongo_dump_dir))\n\n #tar.add(activity_dump_dir, arcname=os.path.basename(activity_dump_dir))","title":""},{"docid":"4d6011c79cbeceab7a098e4821f4bc3c","score":"0.5148114","text":"def write_by_name(fpath, *objs_list, **kwargs):\n length = len(objs_list)\n group_size = kwargs.pop('group_size', length)\n with stream.open(fpath, 'wb', **kwargs) as ostream:\n cursor = 0\n while cursor < length:\n ostream.write(*objs_list[cursor:cursor+group_size])\n cursor += group_size","title":""},{"docid":"af681386ee088a4b407d27cbc964433f","score":"0.5146606","text":"def save(self, filepath, npf=-1, compression=None, verbose=1, n_jobs=1):\n\n path, name = os.path.split(filepath)\n if name.endswith('.h5'):\n name = '.'.join(name.split('.')[:-1])\n\n start = time.time()\n if npf != -1:\n \n global moddsets\n\n i = begin = end = 0\n args, moddsets = [], []\n while end < len(self.jets_i):\n end = min(end + npf, len(self.jets_i))\n\n arrays = {'jets_i': self.jets_i[begin:end], 'jets_i_cols': self.jets_i_cols,\n 'jets_f': self.jets_f[begin:end], 'jets_f_cols': self.jets_f_cols,\n 'filenames': self.filenames}\n\n if self.store_pfcs:\n arrays['pfcs'] = self.pfcs[begin:end]\n arrays['pfcs_cols'] = self.pfcs_cols\n\n if self.store_gens:\n arrays['gens'] = self.gens[begin:end]\n arrays['gens_cols'] = self.gens_cols\n\n filepath = os.path.join(path, '{}_{}'.format(name, i))\n\n moddset = self.__class__(_dataset=self.dataset, _arrays=arrays)\n\n if n_jobs == 1:\n moddset.save(filepath, compression=compression, verbose=verbose)\n\n else:\n moddsets.append(moddset)\n args.append((i, filepath, compression)) \n\n begin = end\n i += 1\n\n if n_jobs != 1:\n if verbose >= 1:\n l = len(args)\n pf = (l, 's' if l > 1 else '', time.time() - start)\n print('Constructed {} temporary MODDataset{} in {:.3f}s'.format(*pf))\n\n if n_jobs == -1:\n n_jobs = os.cpu_count()\n if n_jobs is None:\n n_jobs = 4\n\n start = time.time()\n with create_pool(processes=min(n_jobs, len(args))) as pool:\n for i,_ in enumerate(pool.imap_unordered(_moddset_save, args, chunksize=1)):\n if verbose >= 1 and ((i+1) % 5 == 0 or i+1 == len(args)):\n pf = (i+1, (i+1)/len(args)*100, time.time() - start)\n print(' Saved {} files, {:.2f}% done in {:.3f}s'.format(*pf))\n\n del moddsets\n \n return\n\n # compression opts\n compression = ({'compression': 'gzip', 'compression_opts': compression} \n if compression is not None else {})\n comp_str = '_compressed' if len(compression) else ''\n\n # ensure directory exists\n if not os.path.exists(path):\n if verbose >= 2:\n print('Creating', path)\n os.mkdir(path)\n\n if verbose >= 2:\n print('Saving to', path)\n\n filename = name + comp_str\n hf = h5py.File(os.path.join(path, filename + '.h5'), 'w')\n\n # jets_i\n jets_i = hf.create_dataset('jets_i', data=self.jets_i, **compression)\n jets_i.attrs.create('cols', np.asarray(self.jets_i_cols, dtype='S'))\n\n # jets_f\n jets_f = hf.create_dataset('jets_f', data=self.jets_f, **compression)\n jets_f.attrs.create('cols', np.asarray(self.jets_f_cols, dtype='S'))\n\n # pfcs\n if self.store_pfcs:\n pfcs = _write_large_object_array_to_h5(hf, 'pfcs', self.pfcs, \n ncols=len(self.pfcs_cols), **compression)\n pfcs.attrs.create('cols', np.asarray(self.pfcs_cols, dtype='S'))\n hf.create_dataset('pfcs_index', data=_make_particles_index(self.pfcs), **compression)\n\n # gens\n if self.store_gens:\n gens = _write_large_object_array_to_h5(hf, 'gens', self.gens, \n ncols=len(self.gens_cols), **compression)\n gens.attrs.create('cols', np.asarray(self.gens_cols, dtype='S'))\n hf.create_dataset('gens_index', data=_make_particles_index(self.gens), **compression)\n\n # filenames\n hf.create_dataset('filenames', data=self.filenames.astype('S'), **compression)\n\n # close\n hf.close()\n\n if verbose >= 1:\n args = (filename, len(self.jets_i), time.time() - start)\n print(' Saved {} with {} jets in {:.3f}s'.format(*args))","title":""},{"docid":"c30f42120ef98b16049f68dfdfc9bc72","score":"0.5138672","text":"def _archive(self, label, files, delete_originals=True):\n if not os.path.exists(self.archive_store):\n os.mkdir(self.archive_store)\n tf = tarfile.open(label + \".tar.gz\",'w:gz')\n logging.info(\"Archiving data to file %s\" % tf.name)\n # Add data files\n archive_paths = []\n for file_path in files:\n archive_path = os.path.join(label, file_path)\n tf.add(os.path.join(self.root, file_path), archive_path)\n archive_paths.append(archive_path)\n tf.close()\n # Move the archive to self.archive_store\n shutil.copy(tf.name, self.archive_store) # shutil.move() doesn't work as expected if dataroot is a symbolic link\n os.remove(tf.name)\n # Delete original files.\n if delete_originals:\n for file_path in files:\n os.remove(os.path.join(self.root, file_path))\n self._last_label = label # useful for testing\n return archive_paths","title":""},{"docid":"48e2e0577c8e31d6e77e6d744202225c","score":"0.5128573","text":"def write_hdf5_file(metadata, out_file, ts_file, tcoh_file, scoh_file, vel_file, mask_file, geom_file):\n ts_obj = timeseries(ts_file)\n ts_obj.open(print_msg=False)\n dateList = ts_obj.dateList\n numDate = len(dateList)\n\n # Open HDF5 File\n f = h5py.File(out_file, 'w')\n print('create HDF5 file: {} with w mode'.format(out_file))\n max_digit = 55\n\n ##### Group - Observation\n gName = 'HDFEOS/GRIDS/timeseries/observation'\n print('create group /{}'.format(gName))\n group = f.create_group(gName)\n\n ## O1 - displacement\n dsName = 'displacement'\n dsShape = (numDate, ts_obj.length, ts_obj.width)\n dsDataType = np.float32\n print(('create dataset /{d:<{w}} of {t:<10} in size of {s}'\n ' with compression={c}').format(d='{}/{}'.format(gName, dsName),\n w=max_digit,\n t='float32',\n s=dsShape,\n c=COMPRESSION))\n dset = group.create_dataset(dsName,\n shape=dsShape,\n maxshape=(None, dsShape[1], dsShape[2]),\n dtype=dsDataType,\n chunks=True,\n compression=COMPRESSION)\n\n print('write data acquition by acquition ...')\n prog_bar = ptime.progressBar(maxValue=numDate)\n for i in range(numDate):\n dset[i, :, :] = readfile.read(ts_file, datasetName=dateList[i])[0]\n prog_bar.update(i+1, suffix='{}/{} {}'.format(i+1, numDate, dateList[i]))\n prog_bar.close()\n\n # attributes\n dset.attrs['Title'] = dsName\n dset.attrs['MissingValue'] = FLOAT_ZERO\n dset.attrs['_FillValue'] = FLOAT_ZERO\n dset.attrs['Units'] = 'meters'\n\n ## O2 - date\n dsName = 'date'\n data = np.array(dateList, dtype=np.string_)\n dset = create_hdf5_dataset(group, dsName, data)\n\n ## O3 - perp baseline\n dsName = 'bperp'\n data = np.array(ts_obj.pbase, dtype=np.float32)\n dset = create_hdf5_dataset(group, dsName, data)\n\n ## O4 - velocity\n dsName = 'velocity'\n data = readfile.read(vel_file)[0]\n dset = create_hdf5_dataset(group, dsName, data)\n # attributes\n dset.attrs['Title'] = dsName\n dset.attrs['MissingValue'] = FLOAT_ZERO\n dset.attrs['_FillValue'] = FLOAT_ZERO\n dset.attrs['Units'] = 'm/yr'\n\n ##### Group - Quality\n gName = 'HDFEOS/GRIDS/timeseries/quality'\n print('create group /{}'.format(gName))\n group = f.create_group(gName)\n\n ## Q1 - temporalCoherence\n dsName = 'temporalCoherence'\n # read\n data = readfile.read(tcoh_file)[0]\n # write\n dset = create_hdf5_dataset(group, dsName, data)\n # attributes\n dset.attrs['Title'] = dsName\n dset.attrs['MissingValue'] = FLOAT_ZERO\n dset.attrs['_FillValue'] = FLOAT_ZERO\n dset.attrs['Units'] = '1'\n\n ## Q2 - avgSpatialCoherence\n dsName = 'avgSpatialCoherence'\n # read\n data = readfile.read(scoh_file)[0]\n # write\n dset = create_hdf5_dataset(group, dsName, data)\n # attributes\n dset.attrs['Title'] = dsName\n dset.attrs['MissingValue'] = FLOAT_ZERO\n dset.attrs['_FillValue'] = FLOAT_ZERO\n dset.attrs['Units'] = '1'\n\n ## Q3 - mask\n dsName = 'mask'\n # read\n data = readfile.read(mask_file, datasetName='mask')[0]\n # write\n dset = create_hdf5_dataset(group, dsName, data)\n # attributes\n dset.attrs['Title'] = dsName\n dset.attrs['MissingValue'] = BOOL_ZERO\n dset.attrs['_FillValue'] = BOOL_ZERO\n dset.attrs['Units'] = '1'\n \n\n ##### Group - Write Geometry\n # Required: height, incidenceAngle\n # Optional: rangeCoord, azimuthCoord, azimuthAngle, slantRangeDistance, waterMask, shadowMask\n gName = 'HDFEOS/GRIDS/timeseries/geometry'\n print('create group /{}'.format(gName))\n group = f.create_group(gName)\n\n geom_obj = geometry(geom_file)\n geom_obj.open(print_msg=False)\n for dsName in geom_obj.datasetNames:\n # read\n data = geom_obj.read(datasetName=dsName, print_msg=False)\n # write\n dset = create_hdf5_dataset(group, dsName, data)\n\n # attributes\n dset.attrs['Title'] = dsName\n if dsName in ['height', 'slantRangeDistance', 'bperp']:\n dset.attrs['MissingValue'] = FLOAT_ZERO\n dset.attrs['_FillValue'] = FLOAT_ZERO\n dset.attrs['Units'] = 'meters'\n\n elif dsName in ['incidenceAngle', 'azimuthAngle', 'latitude', 'longitude']:\n dset.attrs['MissingValue'] = FLOAT_ZERO\n dset.attrs['_FillValue'] = FLOAT_ZERO\n dset.attrs['Units'] = 'degrees'\n\n elif dsName in ['rangeCoord', 'azimuthCoord']:\n dset.attrs['MissingValue'] = FLOAT_ZERO\n dset.attrs['_FillValue'] = FLOAT_ZERO\n dset.attrs['Units'] = '1'\n\n elif dsName in ['waterMask', 'shadowMask']:\n dset.attrs['MissingValue'] = BOOL_ZERO\n dset.attrs['_FillValue'] = BOOL_ZERO\n dset.attrs['Units'] = '1'\n\n # Write Attributes to the HDF File\n print('write metadata to root level')\n for key, value in iter(metadata.items()):\n f.attrs[key] = value\n f.close()\n print('finished writing to {}'.format(out_file))","title":""},{"docid":"dbbd598756eb39ad06f90109f086e14b","score":"0.51182556","text":"def write_group(pf, tag,mdef):\n tbl=pf.get_tbl(tag)\n filename=tag+\".csv\"\n fh=open(filename,\"w+\")\n fh.write('\\\"%s\\\",\\\"%s\\\",\\\"%s\\\",\\\"%s\\\"\\n' % (\"Key\",\"Type\",\"Mutable\",\"Concept\"))\n for k in tbl:\n t=mdef.type(k)\n tstr=\"undefined\"\n if(t==MDtype.Int64):\n tstr=\"int\"\n elif(t==MDtype.Double):\n tstr=\"double\"\n elif(t==MDtype.String):\n tstr=\"string\"\n elif(t==MDtype.Boolean):\n tstr=\"boolean\"\n writeable=mdef.writeable(k)\n wstr=\"undefined\"\n if(writeable):\n wstr=\"Yes\"\n else:\n wstr=\"No\"\n fh.write('\\\"%s\\\",\\\"%s\\\",\\\"%s\\\",\\\"%s\\\"\\n' % (k,tstr,wstr,mdef.concept(k)))\n fh.close()","title":""},{"docid":"9d1c698f9e5670d4528426641511f69d","score":"0.5116573","text":"def create_group(self, name, attrs={}, track_order=True):\n with h5file(self.file.filename, 'a', **self.file.params) as f:\n o = f[self.name]\n g = o.create_group(name, track_order)\n g.attrs.update(attrs)\n return Group_autoiter(self.file, self.name+name+'/')","title":""},{"docid":"464456aa5f2393a86d1b9683afc59df5","score":"0.5110412","text":"def create_group_directory(self, campaign_name, app_dir, group_name, runs,\n max_nprocs, nodes, launch_mode,\n component_subdirs, walltime, node_exclusive,\n timeout, machine,\n sosd_path=None,\n sos_analysis_path=None,\n tau_profiling=False, tau_tracing=False,\n kill_on_partial_failure=False,\n run_post_process_script=None,\n run_post_process_stop_on_failure=False,\n scheduler_options=None,\n run_dir_setup_script=None):\n script_dir = os.path.join(config.CHEETAH_PATH_SCHEDULER,\n self.scheduler_name, 'group')\n if not os.path.isdir(script_dir):\n raise ValueError(\"scheduler '%s' is not yet supported (path '%s')\"\n % (self.scheduler_name, script_dir))\n if scheduler_options is None:\n scheduler_options = {}\n copytree_to_dir(script_dir, self.output_directory)\n\n fobs_path = os.path.join(self.output_directory, 'fobs.json')\n min_nodes = 1\n\n f = open(fobs_path, 'w')\n fob_list = []\n for i, run in enumerate(runs):\n # TODO: abstract this to higher levels\n os.makedirs(run.run_path, exist_ok=True)\n\n # Create working dir for each component\n for rc in run.run_components:\n os.makedirs(rc.working_dir, exist_ok=True)\n\n if run.sosflow_profiling:\n run.insert_sosflow(sosd_path, sos_analysis_path,\n run.run_path,\n machine.processes_per_node)\n\n # Copy the global input files common to all components\n for input_rpath in run.inputs:\n copy_to_dir(input_rpath, run.run_path)\n\n # Copy input files requested by each component\n # save working dirs for later use\n working_dirs = {} # map component name to path\n for rc in run.run_components:\n working_dirs[rc.name] = rc.working_dir\n\n # if rc has an adios xml file, copy it to working dir\n if rc.adios_xml_file:\n copy_to_dir(rc.adios_xml_file, rc.working_dir)\n\n # now copy other inputs marked under component_inputs\n if rc.component_inputs is not None:\n for input_file in rc.component_inputs:\n dest = os.path.join(rc.working_dir,\n os.path.basename(\n input_file))\n # input type is symlink\n if type(input_file) == SymLink:\n os.symlink(input_file, dest)\n\n # input type is a regular file\n elif os.path.isfile(input_file):\n copy_to_dir(input_file, rc.working_dir)\n\n # Input file is a directory\n elif os.path.isdir(input_file):\n copytree_to_dir(input_file, dest)\n\n else:\n raise exc.CheetahException \\\n (\"Could not copy component input {}\"\n .format(input_file))\n\n # ADIOS XML param support\n adios_xml_params = \\\n run.instance.get_parameter_values_by_type(ParamAdiosXML) or \\\n run.instance.get_parameter_values_by_type(ParamADIOS2XML)\n for pv in adios_xml_params:\n working_dir = working_dirs[pv.target]\n\n # dirty way of getting the adios xml filename of the rc\n # that is represented by pv.target\n rc_adios_xml = self._get_rc_adios_xml_filename(\n run, pv.target)\n xml_filepath = os.path.join(working_dir,\n os.path.basename(rc_adios_xml))\n\n # Check if this is adios1 or adios2\n adios_version = get_adios_version(rc_adios_xml)\n\n if adios_version == 1:\n if pv.param_type == \"adios_transform\":\n adios_params.adios_xml_transform(\n xml_filepath,pv.group_name, pv.var_name, pv.value)\n elif pv.param_type == \"adios_transport\":\n # value could be\n # \"MPI_AGGREGATE:num_aggregators=64;num_osts\"\n # extract the method name and the method options\n method_name = pv.value\n method_opts = \"\"\n if \":\" in pv.value:\n value_tokens = pv.value.split(\":\", 1)\n method_name = value_tokens[0]\n method_opts = value_tokens[1]\n\n adios_params.adios_xml_transport(\n xml_filepath, pv.group_name, method_name,\n method_opts)\n else:\n raise exc.CheetahException(\"Unrecognized adios param\")\n\n else: # adios version == 2\n operation_value = list(pv.value.keys())[0]\n if pv.operation_name in ('engine', 'transport'):\n parameters = list(pv.value.values())[0]\n if pv.operation_name == 'engine':\n adios2.set_engine(xml_filepath, pv.io_name,\n operation_value, parameters)\n else:\n adios2.set_transport(xml_filepath, pv.io_name,\n operation_value, parameters)\n else: # operation_name == 'var_operation'\n var_name = list(pv.value.keys())[0]\n var_name_dict = pv.value[var_name]\n var_operation_value = list(var_name_dict.keys())[0]\n var_op_dict = var_name_dict[var_operation_value]\n parameters = var_op_dict\n adios2.set_var_operation(xml_filepath, pv.io_name,\n var_name,\n var_operation_value,\n parameters)\n\n # Calculate the no. of nodes required by this run.\n # This must be done after dataspaces support is added.\n if run.total_nodes > min_nodes:\n min_nodes = run.total_nodes\n\n # Generic config file support. Note: slurps entire\n # config file into memory, requires adding file to\n # campaign 'inputs' option.\n config_params = \\\n run.instance.get_parameter_values_by_type(ParamConfig)\n for pv in config_params:\n working_dir = working_dirs[pv.target]\n src_filepath = relative_or_absolute_path(app_dir,\n pv.config_filename)\n # Allow for relative pathnames in the spec\n src_filename = pv.config_filename\n if pv.config_filename[0] == '/':\n src_filename = os.path.basename(src_filepath)\n config_filepath = os.path.join(working_dir,\n src_filename)\n if not os.path.isfile(config_filepath):\n copy_to_path(src_filepath, config_filepath)\n lines = []\n # read and modify lines\n # hack: handle json files. currently works only on singly\n # nested json files\n if config_filepath.endswith(\".json\"):\n json_config_set_option(config_filepath, pv.match_string,\n pv.value)\n else: # handle other file types\n with open(config_filepath) as config_f:\n for line in config_f:\n line = line.replace(pv.match_string, pv.value)\n lines.append(line)\n # rewrite file with modified lines\n with open(config_filepath, 'w') as config_f:\n config_f.write(\"\".join(lines))\n\n # Key value config file support. Note: slurps entire\n # config file into memory, requires adding file to\n # campaign 'inputs' option.\n kv_params = \\\n run.instance.get_parameter_values_by_type(ParamKeyValue)\n for pv in kv_params:\n working_dir = working_dirs[pv.target]\n src_filepath = relative_or_absolute_path(app_dir,\n pv.config_filename)\n # Allow for relative pathnames in the spec\n src_filename = pv.config_filename\n if pv.config_filename[0] == '/':\n src_filename = os.path.basename(src_filepath)\n kv_filepath = os.path.join(working_dir, src_filename)\n if not os.path.isfile(kv_filepath):\n copy_to_path(src_filepath, kv_filepath)\n lines = []\n # read and modify lines\n key_found = False\n with open(kv_filepath) as kv_f:\n for line in kv_f:\n parts = line.split('=', 1)\n if len(parts) == 2:\n k = parts[0].strip()\n if k == pv.key_name:\n # assume all k=v type formats will\n # support no spaces around equals\n line = k + '=' + str(pv.value)\n # preserve a user comment if it exists\n if '!' in parts[1]:\n line = line + \" !\" + \\\n parts[1].strip().split('!')[1]\n line = line + '\\n'\n key_found = True\n lines.append(line)\n assert key_found, \\\n \"Issue parsing a ParamKeyValue: Could not find key {}\"\\\n \" in config file {}\".format(pv.key_name, src_filepath)\n # rewrite file with modified lines\n with open(kv_filepath, 'w') as kv_f:\n kv_f.write(\"\".join(lines))\n\n # Env var parameter values\n kv_params = run.instance.get_parameter_values_by_type(ParamEnvVar)\n for pv in kv_params:\n rc = run._get_rc_by_name(pv.target)\n rc.env[pv.option] = str(pv.value)\n\n # save code commands as text\n params_path_txt = os.path.join(run.run_path,\n self.run_command_name)\n with open(params_path_txt, 'w') as params_f:\n for rc in run.run_components:\n params_f.write(' '.join(map(shlex.quote,\n [rc.exe] + rc.args)))\n params_f.write('\\n')\n\n # save params as JSON for use in post-processing, more\n # useful for post-processing scripts then the command\n # text\n params_path_json = os.path.join(run.run_path,\n self.run_json_name)\n run_data = run.get_app_param_dict()\n with open(params_path_json, 'w') as params_f:\n json.dump(run_data, params_f, indent=2)\n\n fob_runs = []\n for j, rc in enumerate(run.run_components):\n if timeout is not None:\n rc.timeout = parse_timedelta_seconds(timeout)\n\n fob_runs.append(rc.as_fob_data())\n\n fob = dict(id=run.run_id, launch_mode=launch_mode, runs=fob_runs,\n working_dir=run.run_path, apps_dir=app_dir,\n kill_on_partial_failure=kill_on_partial_failure,\n post_process_script=run_post_process_script,\n post_process_stop_on_failure=\n run_post_process_stop_on_failure,\n post_process_args=[params_path_json],\n node_layout=run.node_layout.serialize_to_dict(),\n total_nodes=run.total_nodes,\n machine_name=machine.name,\n tau_profiling=tau_profiling, tau_tracing=tau_tracing)\n fob_list.append(fob)\n\n # write to file run dir\n run_fob_path = os.path.join(run.run_path,\n \"codar.cheetah.fob.json\")\n with open(run_fob_path, \"w\") as runf:\n runf.write(json.dumps(fob, sort_keys=True, indent=4))\n runf.write(\"\\n\")\n\n if run_dir_setup_script is not None:\n self._execute_run_dir_setup_script(run.run_path,\n run_dir_setup_script)\n\n # Get the size of the run dir. This should be the last step\n # in the creation of the run dir.\n self._get_pre_submit_dir_size(run)\n\n # Write fob_list to group-level json file\n f.write(json.dumps(fob_list, sort_keys=True, indent=4))\n f.close()\n\n if nodes is None:\n nodes = min_nodes\n elif nodes < min_nodes:\n raise exc.CheetahException(\n \"nodes for group is too low, need at least %d, got %d\"\n % (min_nodes, nodes))\n\n # TODO: what case does this handle? should have a test case for\n # it.\n if machine.node_exclusive:\n group_ppn = machine.processes_per_node\n else:\n group_ppn = math.ceil((max_nprocs) / nodes)\n\n env_path = os.path.join(self.output_directory, 'group-env.sh')\n group_env = templates.GROUP_ENV_TEMPLATE.format(\n walltime=parse_timedelta_seconds(walltime),\n max_procs=max_nprocs,\n processes_per_node=group_ppn,\n nodes=nodes,\n node_exclusive=node_exclusive,\n account=scheduler_options.get('project', ''),\n queue=scheduler_options.get('queue', ''),\n reservation=scheduler_options.get('reservation', ''),\n custom=scheduler_options.get('custom', ''),\n # TODO: require name be valid for all schedulers\n campaign_name='codar.cheetah.'+campaign_name,\n group_name=group_name,\n constraint=scheduler_options.get('constraint', ''),\n license=scheduler_options.get('license', ''),\n machine_name=machine.name\n )\n with open(env_path, 'w') as f:\n f.write(group_env)\n\n return nodes","title":""},{"docid":"c0d0b11610732aabe5234e6c18d97dcc","score":"0.51037985","text":"def write_distributional_spf_dataset(data: Dict[str, np.array], full_path: str) -> None:\n file = h5py.File(full_path, 'w')\n for k, v in data.items():\n file.create_dataset(name=k, data=v)\n file.close()","title":""},{"docid":"1ada4bdf303e3599dd657b9b298ede6d","score":"0.51037645","text":"def make_archive(sim_epoch: rebound.Simulation, \n mjd0: int, mjd1: int, \n time_step: int, save_step: int,\n save_elements: bool,\n progbar: bool) -> rebound.SimulationArchive:\n # Get archive filename from simulation\n fname_archive: str = calc_fname_archive(sim_epoch, mjd0, mjd1)\n # Path of archive\n path_archive = os.path.join(dir_archive, fname_archive)\n try:\n # First try to load the named archive\n sa = rebound.SimulationArchive(filename=path_archive)\n print(f'Loaded archive {fname_archive}.')\n except:\n # If the archive is not on disk, save it to disk\n print(f'Generating archive {fname_archive}\\n'\n f'from mjd {mjd0} to {mjd1}, time_step={time_step}, save_step={save_step}...')\n make_archive_impl(sim_epoch=sim_epoch, mjd0=mjd0, mjd1=mjd1, time_step=time_step, \n save_step=save_step, save_elements=save_elements, progbar=progbar)\n # Load the new archive into memory\n sa = rebound.SimulationArchive(filename=path_archive)\n\n # Bind the archive filename to the archive\n sa.fname_archive: str = fname_archive\n sa.fname_np: str = fname_archive.replace('.bin', '.npz')\n sa.body_collection = sim_epoch.body_collection\n # Bind various dates and time steps\n sa.epoch = sim_epoch.epoch\n sa.mjd0: int = mjd0\n sa.mjd1: int = mjd1\n sa.time_step: int = time_step\n # Bind the simulation at the epoch\n sa.sim_epoch = sim_epoch\n\n return sa","title":""},{"docid":"6fda04613d566a0c745a47e068a70c02","score":"0.5094346","text":"def archiveBag(bags,queue):\n grouptasks=[]\n notValid=[]\n valid=[]\n for bag in bags:\n vBag=validateBag(bag,fast=True)\n if vBag['valid']:\n source=bag\n updateBagValidatationMetadata(bag.split('/')[-1],'local',{\"valid\":True,\"timestamp\":datetime.now().isoformat()})\n valid.append({\"bag\":source.split('/')[-1],\"valid\":datetime.now()})\n destination= os.path.join(petaLibrarySubDirectory,source.split('/')[-1])\n grouptasks.append(scpPetaLibrary.si(source,destination).set(queue=queue))\n else:\n updateBagValidatationMetadata(bag,'local',{\"valid\":False,\"timestamp\":datetime.now().isoformat()})\n notValid.append(bag)\n res = group(grouptasks)()\n return {\"subtasks\":len(grouptasks),\"valid\":valid,\"notvalid\":notValid}","title":""},{"docid":"94fed718476a0dbcb6c7272365a02c7d","score":"0.50901425","text":"def ProduceUnifiedTreeandHaloCatalog(fname,numsnaps,tree,numhalos,halodata,atime,\n\tdescripdata={'Title':'Tree and Halo catalog of sim', 'VELOCIraptor_version':1.15, 'Tree_version':1.1, 'Particle_num_threshold':20, 'Temporal_linking_length':1, 'Flag_gas':False, 'Flag_star':False, 'Flag_bh':False},\n\tcosmodata={'Omega_m':1.0, 'Omega_b':0., 'Omega_Lambda':0., 'Hubble_param':1.0,'BoxSize':1.0, 'Sigma8':1.0},\n\tunitdata={'UnitLength_in_Mpc':1.0, 'UnitVelocity_in_kms':1.0,'UnitMass_in_Msol':1.0, 'Flag_physical_comoving':True,'Flag_hubble_flow':False},\n\tpartdata={'Flag_gas':False, 'Flag_star':False, 'Flag_bh':False},\n\tibuildheadtail=0, icombinefile=1):\n\tif (ibuildheadtail==1):\n\t\tBuildTemporalHeadTail(numsnaps,tree,numhalos,halodata)\n\ttotnumhalos=sum(numhalos)\n\tif (icombinefile==1):\n\t\thdffile=h5py.File(fname+\".snap.hdf.data\",'w')\n\t\theadergrp=hdffile.create_group(\"Header\")\n\t\t#store useful information such as number of snapshots, halos,\n\t\t#cosmology (Omega_m,Omega_b,Hubble_param,Omega_Lambda, Box size)\n\t\t#units (Physical [1/0] for physical/comoving flag, length in Mpc, km/s, solar masses, Gravity\n\t\t#and TEMPORALHALOIDVAL used to traverse tree information (converting halo ids to haloindex or snapshot), Reverse_order [1/0] for last snap listed first)\n\t\t#set the attributes of the header\n\t\theadergrp.attrs[\"NSnaps\"]=numsnaps\n\t\t#overall description\n\t\t#simulation box size\n\n\t\t#cosmological params\n\t\tcosmogrp=headergrp.create_group(\"Cosmology\")\n\t\tfor key in cosmodata.keys():\n\t\t\tcosmogrp.attrs[key]=cosmodata[key]\n\t\t#unit params\n\t\tunitgrp=headergrp.create_group(\"Units\")\n\t\tfor key in unitdata.keys():\n\t\t\tunitgrp.attrs[key]=unitdata[key]\n\t\t#particle types\n\t\tpartgrp=headergrp.create_group(\"Parttypes\")\n\t\tpartgrp.attrs[\"Flag_gas\"]=descripdata[\"Flag_gas\"]\n\t\tpartgrp.attrs[\"Flag_star\"]=descripdata[\"Flag_star\"]\n\t\tpartgrp.attrs[\"Flag_bh\"]=descripdata[\"Flag_bh\"]\n\n\t\tfor i in range(numsnaps):\n\t\t\tsnapgrp=hdffile.create_group(\"Snap_%03d\"%(numsnaps-1-i))\n\t\t\tsnapgrp.attrs[\"Snapnum\"]=(numsnaps-1-i)\n\t\t\tsnapgrp.attrs[\"NHalos\"]=numhalos[i]\n\t\t\tsnapgrp.attrs[\"scalefactor\"]=atime[i]\n\t\t\tfor key in halodata[i].keys():\n\t\t\t\tsnapgrp.create_dataset(key,data=halodata[i][key])\n\t\thdffile.close()\n\telse:\n\t\tfor i in range(numsnaps):\n\t\t\thdffile=h5py.File(fname+\".snap_%03d.hdf.data\"%(numsnaps-1-i),'w')\n\t\t\thdffile.create_dataset(\"Snap_value\",data=np.array([numsnaps-1-i],dtype=np.uint32))\n\t\t\thdffile.create_dataset(\"NSnaps\",data=np.array([numsnaps],dtype=np.uint32))\n\t\t\thdffile.create_dataset(\"NHalos\",data=np.array([numhalos[i]],dtype=np.uint64))\n\t\t\thdffile.create_dataset(\"TotalNHalos\",data=np.array([totnumhalos],dtype=np.uint64))\n\t\t\thdffile.create_dataset(\"scalefactor\",data=np.array([atime[i]],dtype=np.float64))\n\t\t\tfor key in halodata[i].keys():\n\t\t\t\thdffile.create_dataset(key,data=halodata[i][key])\n\t\t\thdffile.close()\n\n\thdffile=h5py.File(fname+\".tree.hdf.data\",'w')\n\thdffile.create_dataset(\"NSnaps\",data=np.array([numsnaps],dtype=np.uint32))\n\thdffile.create_dataset(\"TotalNHalos\",data=np.array([totnumhalos],dtype=np.uint64))\n\thdffile.create_dataset(\"NHalos\",data=np.array([numhalos],dtype=np.uint64))\n\tfor i in range(numsnaps):\n\t\tsnapgrp=hdffile.create_group(\"Snap_%03d\"%(numsnaps-1-i))\n\t\tfor key in tree[i].keys():\n\t\t\t\"\"\"\n\t\t\t#to be completed for progenitor list\n\t\t\tif (key==\"Progen\"):\n\t\t\t\tfor j in range(numhalos[i]):\n\t\t\t\t\thalogrp=snapgrp.create_group(\"Halo\"+str(j))\n\t\t\t\t\thalogrp.create_dataset(key,data=tree[i][key][j])\n\t\t\telse:\n\t\t\t\tsnapgrp.create_dataset(key,data=tree[i][key])\n\t\t\t\"\"\"\n\t\t\tif ((key==\"Progen\") | (key==\"Descen\")): continue\n\t\t\tsnapgrp.create_dataset(key,data=tree[i][key])\n\thdffile.close()","title":""},{"docid":"8770ee73f6e6a459bcd63cd52b7503a8","score":"0.5089119","text":"def parse_single_12tped_to_hdf5(in_file_prefix='/home/bv25/data/Ls154/Ls154_12',\n out_file_prefix='/home/bv25/data/Ls154/Ls154_12',\n impute_type='mode', filter_monomorphic_snps=True,\n missing_val_thr=0.1):\n \n print 'Starting to parse genotypes'\n genotype_data = {}\n h5py_file = h5py.File(out_file_prefix + '.hdf5')\n genotype_data['hdf5p_file'] = h5py_file\n genot_group = h5py_file.create_group('genot_data')\n indiv_group = h5py_file.create_group('indiv_data')\n \n \n tot_num_snps = 0\n tot_num_missing_val_snps_removed = 0\n tot_num_ambiguous_loc_removed = 0\n curr_chrom = 1\n print 'Working on chromosome %d' % curr_chrom\n \n g_filename = '%s.tped' % (in_file_prefix) \n s_filename = '%s.bim' % (in_file_prefix)\n i_filename = '%s.tfam' % (in_file_prefix) \n\n \n \n indiv_ids = []\n phenotypes = [] \n sex = []\n print 'Parsing individuals file: %s' % i_filename\n with open(i_filename) as f:\n for line in f:\n l = line.split()\n iid = l[0]\n indiv_ids.append(iid)\n sex.append(int(l[4]))\n phenotypes.append(float(l[5]))\n tot_num_indiv = len(indiv_ids) \n \n print 'Storing individual data in individ. group'\n indiv_group.create_dataset('indiv_ids', data=indiv_ids)\n indiv_group.create_dataset('sex', data=sex)\n indiv_group.create_dataset('phenotypes', data=phenotypes)\n \n \n \n num_indiv = len(indiv_ids)\n print 'Found %d Individuals' % (num_indiv)\n\n print 'Parsing nucleotide map'\n nt_map = {}\n chromsomoes = []\n curr_chrom = 0\n with open(s_filename) as f:\n for line in f:\n l = line.split()\n chrom = l[0]\n if chrom != curr_chrom:\n chromsomoes.append(chrom)\n curr_chrom = chrom\n nt_map[l[1]] = (l[4], l[5]) \n assert len(chromsomoes) == len(set(chromsomoes)), 'Chromosomes need to be in order.'\n curr_chrom = chromsomoes[0]\n \n position = -1\n # Initializing containers.\n snps_mat = [] \n positions = []\n sids = []\n nts_list = []\n nt_counts_list = []\n missing_counts = []\n freqs = []\n num_missing_removed = 0\n num_monomorphic_removed = 0\n num_ambiguous_loc_removed = 0\n t0 = time.time()\n\n print 'Starting to parse SNP files'\n gf = open(g_filename)\n for g_line in gf:\n# if random.random() > 0.01:\n# continue\n gl = g_line.split()\n chrom = gl[0]\n if chrom != curr_chrom:\n \n # Store everything and reset.\n print 'Number of SNPs removed due to too many missing values: %d' % num_missing_removed\n print 'Number of SNPs removed due to ambiguous location: %d' % num_ambiguous_loc_removed\n print 'Number of monomorphic SNPs removed: %d' % num_monomorphic_removed\n print 'Number of SNPs retained: %d' % len(positions)\n print 'Number of individuals: %d' % num_indiv\n snps = sp.array(snps_mat, dtype='int8')\n h5py_chrom_group = genot_group.create_group('chrom_%s' % curr_chrom)\n h5py_chrom_group.create_dataset('raw_snps', compression='lzf', data=snps)\n h5py_chrom_group.create_dataset('positions', compression='lzf', data=positions)\n h5py_chrom_group.create_dataset('nts', compression='lzf', data=nts_list)\n h5py_chrom_group.create_dataset('nt_counts', compression='lzf', data=nt_counts_list)\n h5py_chrom_group.create_dataset('missing_counts', compression='lzf', data=missing_counts)\n h5py_chrom_group.create_dataset('freqs', compression='lzf', data=freqs)\n h5py_chrom_group.create_dataset('snp_ids', compression='lzf', data=sids) \n tot_num_snps += len(positions)\n tot_num_missing_val_snps_removed += num_missing_removed\n tot_num_ambiguous_loc_removed += num_ambiguous_loc_removed\n h5py_file.flush() \n t1 = time.time()\n t = t1 - t0\n print 'It took %d minutes and %0.2f seconds to parse Chromosome %s.' % (t / 60, t % 60, curr_chrom)\n t0 = time.time()\n\n \n\n # Reset containers\n snps_mat = [] \n positions = []\n sids = []\n nts_list = []\n nt_counts_list = []\n missing_counts = []\n freqs = []\n num_missing_removed = 0\n num_ambiguous = 0\n num_monomorphic_removed = 0\n num_ambiguous_loc_removed = 0\n \n curr_chrom = chrom\n\n sid = gl[1]\n prev_position = position\n position = int(gl[3])\n\n # Skipping unmappable locations\n if position == prev_position:\n num_ambiguous_loc_removed += 1\n continue\n if position == 0:\n num_ambiguous_loc_removed += 1\n continue\n\n nt = nt_map[sid]\n \n snp0 = sp.array(map(int, (g_line.strip()).split()[4:]), 'int8')\n a = sp.arange(tot_num_indiv * 2)\n even_map = a % 2 == 0\n odd_map = a % 2 == 1\n snp = snp0[even_map] + snp0[odd_map] - 2\n snp[snp < 0] = 9\n \n bin_counts = sp.bincount(snp)\n \n\n if len(bin_counts) > 3:\n missing_count = bin_counts[-1]\n # Filtering SNPs with too many missing values\n if missing_count > missing_val_thr * 2 * num_indiv:\n num_missing_removed += 1\n continue\n elif impute_type == 'mode':\n nt_counts = bin_counts[:3] \n v = sp.argmax(nt_counts)\n snp[snp == 9] = v\n bin_counts = sp.bincount(snp)\n else:\n raise Exception('Imputation type is unknown')\n else:\n missing_count = 0\n\n assert len(bin_counts) < 4, 'Issues with nucleotides.'\n nt_counts = bin_counts[:3] \n if len(nt_counts) == 2:\n nt_counts = sp.array([nt_counts[0], nt_counts[1], 0])\n elif len(nt_counts) == 1:\n nt_counts = sp.array([nt_counts[0], 0, 0])\n \n\n # Removing monomorphic SNPs\n if filter_monomorphic_snps:\n if max(nt_counts) == sum(nt_counts):\n num_monomorphic_removed += 1\n continue\n \n freq = sp.mean(snp) / 2.0 \n snps_mat.append(snp)\n positions.append(position)\n sids.append(sid)\n nts_list.append(nt)\n nt_counts_list.append(nt_counts)\n missing_counts.append(missing_count)\n freqs.append(freq) \n\n # Store everything and reset.\n print 'Number of SNPs removed due to too many missing values: %d' % num_missing_removed\n print 'Number of SNPs removed due to ambiguous location: %d' % num_ambiguous_loc_removed\n print 'Number of monomorphic SNPs removed: %d' % num_monomorphic_removed\n print 'Number of SNPs retained: %d' % len(positions)\n print 'Number of individuals: %d' % num_indiv\n snps = sp.array(snps_mat, dtype='int8')\n h5py_chrom_group = genot_group.create_group('chrom_%s' % chrom)\n h5py_chrom_group.create_dataset('raw_snps', compression='lzf', data=snps)\n h5py_chrom_group.create_dataset('positions', compression='lzf', data=positions)\n h5py_chrom_group.create_dataset('nts', compression='lzf', data=nts_list)\n h5py_chrom_group.create_dataset('nt_counts', compression='lzf', data=nt_counts_list)\n h5py_chrom_group.create_dataset('missing_counts', compression='lzf', data=missing_counts)\n h5py_chrom_group.create_dataset('freqs', compression='lzf', data=freqs)\n h5py_chrom_group.create_dataset('snp_ids', compression='lzf', data=sids) \n tot_num_snps += len(positions)\n tot_num_missing_val_snps_removed += num_missing_removed\n tot_num_ambiguous_loc_removed += num_ambiguous_loc_removed\n h5py_file.create_dataset('num_snps', data=sp.array(tot_num_snps))\n h5py_file.flush() \n t1 = time.time()\n t = t1 - t0\n print 'It took %d minutes and %0.2f seconds to parse chromosome %s.' % (t / 60, t % 60, chrom)\n\n \n gf.close()\n \n print 'Total number of SNPs parsed successfully was: %d' % tot_num_snps\n print 'Total number of SNPs removed due to too many missing values: %d' % tot_num_missing_val_snps_removed\n print 'Total number of SNPs removed due to ambiguous locations: %d' % tot_num_ambiguous_loc_removed\n h5py_file.close()\n \n print 'Done parsing genotypes.'","title":""},{"docid":"5ae55e0ee0c569e234eaa99af838c30f","score":"0.50849724","text":"def write_hgrps(self, hgrp_list, dirname):\n self.host_group_manager.write_objects(hgrp_list, dirname)","title":""},{"docid":"72d6f1dc1b4441ba7d808587f926551a","score":"0.5082397","text":"def ProduceWalkableHDFTree(fname,numsnaps,tree,numhalos,halodata,atime,\n\tdescripdata={'Title':'Tree catalogue', 'VELOCIraptor_version':1.3, 'Tree_version':1.1, 'Particle_num_threshold':20, 'Temporal_linking_length':1, 'Flag_gas':False, 'Flag_star':False, 'Flag_bh':False}\n\t):\n\thdffile=h5py.File(fname,'w')\n\theadergrp=hdffile.create_group(\"Header\")\n\t#store useful information such as number of snapshots, halos,\n\t#cosmology (Omega_m,Omega_b,Hubble_param,Omega_Lambda, Box size)\n\t#units (Physical [1/0] for physical/comoving flag, length in Mpc, km/s, solar masses, Gravity\n\t#and TEMPORALHALOIDVAL used to traverse tree information (converting halo ids to haloindex or snapshot), Reverse_order [1/0] for last snap listed first)\n\t#set the attributes of the header\n\theadergrp.attrs[\"NSnaps\"]=numsnaps\n\t#overall description\n\theadergrp.attrs[\"Title\"]=descripdata[\"Title\"]\n\tfindergrp=headergrp.create_group(\"HaloFinder\")\n\tfindergrp.attrs[\"Name\"]=\"VELOCIraptor\"\n\tfindergrp.attrs[\"Version\"]=descripdata[\"VELOCIraptor_version\"]\n\tfindergrp.attrs[\"Particle_num_threshold\"]=descripdata[\"Particle_num_threshold\"]\n\n\ttreebuildergrp=headergrp.create_group(\"TreeBuilder\")\n\ttreebuildergrp.attrs[\"Name\"]=\"VELOCIraptor-Tree\"\n\ttreebuildergrp.attrs[\"Version\"]=descripdata[\"Tree_version\"]\n\ttreebuildergrp.attrs[\"Temporal_linking_length\"]=descripdata[\"Temporal_linking_length\"]\n\n\t#now need to create groups for halos and then a group containing tree information\n\tsnapsgrp=hdffile.create_group(\"Snapshots\")\n\t#internal tree keys\n\thalokeys=[\"RootHead\", \"RootHeadSnap\", \"Head\", \"HeadSnap\", \"Tail\", \"TailSnap\", \"RootTail\", \"RootTailSnap\", \"ID\", \"Num_progen\"]\n\n\tfor i in range(numsnaps):\n\t\t#note that I normally have information in reverse order so that might be something in the units\n\t\tsnapgrp=snapsgrp.create_group(\"Snap_%03d\"%i)\n\t\tsnapgrp.attrs[\"Snapnum\"]=i\n\t\tsnapgrp.attrs[\"NHalos\"]=numhalos[i]\n\t\tsnapgrp.attrs[\"scalefactor\"]=atime[i]\n\t\tfor key in halokeys:\n\t\t\tsnapgrp.create_dataset(key,data=halodata[i][key])\n\thdffile.close()","title":""},{"docid":"2665c63042d944fcd7380e9c9c8afa99","score":"0.50810343","text":"def setup_output_content(top_grp, pckt_summary):\n for where, smmry in pckt_summary.items():\n grp = top_grp.create_group(where)\n grp_path = grp.name\n nelems = smmry['count']\n\n if smmry['type'] == 'MIL1553_FMT_1':\n lggr.debug(f'Create HDF5 dataset data[{nelems}] in {grp_path}')\n dtype_1553 = np.dtype(\n [('time', '<i8'),\n ('timestamp', 'S30'),\n ('msg_error', '|u1'),\n ('ttb', '|u1'),\n ('word_error', '|u1'),\n ('sync_error', '|u1'),\n ('word_count_error', '|u1'),\n ('rsp_tout', '|u1'),\n ('format_error', '|u1'),\n ('bus_id', 'S1'),\n ('packet_version', '|u1'),\n ('messages', h5py.special_dtype(vlen=np.dtype('<u2')))])\n dset = grp.create_dataset(\n 'data', shape=(nelems,), chunks=True, dtype=dtype_1553)\n\n name_dtype = np.dtype(\n [('time', 'S30'),\n ('timestamp', 'S30'),\n ('msg_error', 'S30'),\n ('ttb', 'S30'),\n ('word_error', 'S30'),\n ('sync_error', 'S30'),\n ('word_count_error', 'S30'),\n ('rsp_tout', 'S30'),\n ('format_error', 'S30'),\n ('bus_id', 'S30'),\n ('packet_version', 'S30'),\n ('messages', 'S30')])\n names = ('1553 intra-packet time',\n '1553 intra-packet time stamp',\n '1553 message error flag',\n 'time tag bits',\n 'invalid word error',\n 'sync type error',\n 'word count error',\n 'response time out',\n 'format error',\n 'bus id',\n '1553 packet version',\n '1553 packet message data')\n dset.attrs.create('name', np.array(names, dtype=name_dtype))\n\n # Create alias HDF5 paths for created datasets...\n if 'alias' in smmry:\n for where in smmry['alias']:\n grp = top_grp.create_group(where)\n lggr.debug(f'Hard link {dset.name} from {grp.name}')\n grp['data'] = dset\n\n elif smmry['type'] == 'VIDEO_FMT_0':\n lggr.debug(f'Create HDF5 dataset data[{nelems}] in {grp_path}')\n dset = grp.create_dataset('data', shape=(nelems,),\n chunks=True, dtype=np.dtype('|V188'))\n dset.attrs['name'] = 'video transfer stream'","title":""},{"docid":"b54fd67cd61bd5ec2435e4e05ac9693b","score":"0.5071669","text":"def export(gen, directory, file_prefix='{start[uid]}-', **kwargs):\n with Serializer(directory, file_prefix, **kwargs) as serializer:\n for item in gen:\n serializer(*item)\n\n return serializer.artifacts","title":""},{"docid":"d790df9d50ef43ab9367d222729e1221","score":"0.5059398","text":"def write(self,data):\n if not os.path.exists(os.path.dirname(self.outfilename)):\n os.makedirs(os.path.dirname(self.outfilename))\n\n if os.path.exists(self.outfilename):\n self.outfile = h5py.File(self.outfilename,'a')\n else:\n self.outfile = h5py.File(self.outfilename,'w')\n\n # Set permissions and group\n if self.set_permissions:\n os.chmod(self.outfilename,0o664)\n shutil.chown(self.outfilename, group=self.permissions_group)\n\n if self.level2 in self.outfile:\n del self.outfile[self.level2]\n lvl2 = self.outfile.create_group(self.level2)\n\n tod_dset = lvl2.create_dataset('averaged_tod',data=self.avg_tod, dtype=self.avg_tod.dtype)\n tod_dset.attrs['Unit'] = 'K'\n tod_dset.attrs['Calibration'] = '{self.cal_mode}:{self.cal_prefix}'\n\n freq_dset = lvl2.create_dataset('frequency',data=self.avg_frequency, dtype=self.avg_frequency.dtype)\n\n # Link the Level1 data\n data_filename = data['level1'].file.filename\n fname = data['level1'].file.filename.split('/')[-1]\n vane_file = data['level2/Vane'].file.filename\n\n # Copy over the statistics\n if 'Statistics' in lvl2:\n del lvl2['Statistics']\n grp = lvl2.create_group('Statistics')\n for k,v in data['level2/Statistics'].items():\n if isinstance(v,h5py.Group):\n grp2 = grp.create_group(k)\n for k1,v1 in v.items():\n grp2.create_dataset(k1,data=v1,dtype=v1.dtype)\n else:\n grp.create_dataset(k,data=v,dtype=v.dtype)\n\n\n data.close()\n if 'level1' in self.outfile:\n del self.outfile['level1']\n self.outfile['level1'] = h5py.ExternalLink(data_filename,'/')\n lvl2.attrs['version'] = __level2_version__\n\n # Add version info\n lvl2.attrs['pipeline-version'] = comancpipeline.__version__\n\n # Link the Level1 data\n if 'Vane' in lvl2:\n del lvl2['Vane']\n lvl2['Vane'] = h5py.ExternalLink('{}'.format(vane_file),'/')","title":""},{"docid":"356eeae777c7ba5e43f43fe1a16cbda4","score":"0.50575787","text":"def export_grammars(output_dir):\n d = {}\n d['tokenize_and_classify'] = {'classify': ClassifyFst().fst}\n d['verbalize'] = {'verbalize': VerbalizeFst().fst}\n\n for category, graphs in d.items():\n for stage, fst in graphs.items():\n out_dir = os.path.join(output_dir, stage)\n os.makedirs(out_dir, exist_ok=True)\n _generator_main(f\"{out_dir}/{category}_tmp.far\", fst, category.upper())","title":""},{"docid":"63b4f9dc44b348fc611711f0e14a230a","score":"0.5057459","text":"def save_npz_in_hdf5(npz_files_list, hdf5_path=None,\n add_attributes=None, verbose=False):\n # Set default output hdf5 path.\n if hdf5_path is None:\n hdf5_path = Path(npz_files_list[0]).with_suffix('.hdf5')\n\n # Create output hdf5 file\n #\n # Iterate over npz files list. Create a group for each npz file,\n # using relative orders as groups keys.\n output = H5(hdf5_path, 'w')\n for model_pos, npz_path in enumerate(npz_files_list):\n model_no = model_pos + 1\n grp = output.create_group(str(model_no))\n\n # Set values of interest as group attributes.\n if add_attributes:\n for k, value in add_attributes[int(model_no)].items():\n grp.attrs[k] = value\n\n # Load npz arrays and save them as groups data-sets. Keys\n # will be whatever keys were present in each npz file.\n arrays = np.load(npz_path)\n for k, value in arrays.items():\n grp.create_dataset(k, data=value)\n\n # Print output file structure\n if verbose:\n output.show_hdf5_structure()\n return output","title":""},{"docid":"5e1e889856913fee9c3ef9bbae82f21a","score":"0.5057404","text":"def main(args):\n\n group = Group()\n for f in GROUP_PICK_LIST:\n group.append(args.group_dir + '/' + f, check=True)\n \n twitter_info = TwitterInfo()\n twitter_info.load(args.twitter_file)\n\n allshop2pos = map_shop_to_twitter_pos(twitter_info.get_data())\n\n shop2pos = {}\n shopids = map(lambda x: x.strip(), open(args.shop_list).readlines())\n for s in shopids:\n shop2pos[s] = allshop2pos.get(s, set())\n\n result_map = {}\n for shop in shop2pos:\n local_group = {}\n ret = {'total': len(shop2pos[shop]), 'groups': local_group}\n for pos in shop2pos[shop]:\n group_pos = group.get_group(pos)\n if group_pos is None:\n continue\n if group_pos not in local_group:\n local_group[group_pos] = set()\n local_group[group_pos].add(pos)\n result_map[shop] = ret\n\n output_file = args.output\n if not os.path.exists(output_file):\n os.makedirs(output_file)\n\n for shopid, info in result_map.iteritems():\n temp_file = output_file + '/' + shopid + '.org'\n with open(temp_file, \"w\") as fh:\n print >> fh, \"#+ATTR_HTML: target=\\\"_blank\\\" \"\n print >> fh, \"* [[http://www.meilishuo.com/shop/%s][shopid=%s]] 总商品量%s\" % (shopid, shopid, info['total'])\n print >> fh\n\n local_group = info['groups']\n \n #ordered_keys = random.shuffle(local_group.keys())\n ordered_keys = local_group.keys()\n \n n = len(ordered_keys)\n list1 = range(n)[:args.maxgroup]\n list2 = range(n)[args.maxgroup:args.maxline]\n for i in list1:\n group_pos = ordered_keys[i]\n positions = local_group[group_pos]\n sz = output_group_image_table(twitter_info, list(positions), max_show=args.maxshow, max_col=args.max_col, flag=\"店内同款\")\n print >> fh, sz\n total_group_members = group.get_member(group_pos)\n positions2 = total_group_members.difference(positions)\n if positions2:\n sz = output_group_image_table(twitter_info, list(positions2), max_show=args.maxshow2, max_col=args.max_col, flag=\"跨店同款\")\n print >> fh, sz\n\n print >> fh\n\n if list2:\n print >>fh, \"* 避免拖垮浏览器，以下图片省略\"\n \n for i in list2:\n group_pos = ordered_keys[i]\n positions = local_group[group_pos]\n sz = output_group_text_table(twitter_info, list(positions), max_col=args.max_col, flag=\"店内同款\")\n print >> fh, sz\n total_group_members = group.get_member(group_pos)\n positions2 = total_group_members.difference(positions)\n sz = output_group_text_table(twitter_info, list(positions2), max_col=args.max_col, flag=\"跨店同款\")\n print >> fh, sz\n\n print >> fh\n cmd = \" emacs --kill --batch %s -f org-export-as-html \" % temp_file\n os.system(cmd)\n print \"shop %s is done\" % shopid","title":""},{"docid":"7e4ea330f46465b7dc1e898dd814e89b","score":"0.5049522","text":"def export(gen, directory, file_prefix=\"{uid}-\", **kwargs):\n with Serializer(directory, file_prefix, **kwargs) as serializer:\n for item in gen:\n serializer(*item)\n\n return serializer.artifacts","title":""},{"docid":"5e2acbd3d62f52259f015b0f2733f87f","score":"0.5044531","text":"def load_stacked_hourglass(data_dir,subjects,actions,verbose=True):\n data = {}\n for subj in subjects:\n for action in actions:\n if verbose:\n print('Reading subject {0}, action {1}'.format(subj, action))\n # CHANGED: training data directory path based on StackedHourglass folder in the h36m directory\n # dpath = os.path.join( data_dir, 'S{0}'.format(subj), 'post_accept_sh_finetuned_10it/{0}*.h5'.format(action))\n dpath = os.path.join( data_dir, 'S{0}'.format(subj), 'StackedHourglass/{0}*.h5'.format(action) )\n print( dpath )\n fnames = glob.glob( dpath )\n loaded_seqs = 0\n for fname in fnames:\n seqname = os.path.basename( fname )\n seqname = seqname.replace('_',' ')\n if action == \"Sitting\" and seqname.startswith( \"SittingDown\" ):\n continue\n if seqname.startswith( action ):\n # This filters out e.g. walkDog and walkTogether\n if verbose:\n print( fname )\n loaded_seqs = loaded_seqs + 1\n with h5py.File( fname, 'r' ) as h5f:\n poses = h5f['poses'][:]\n permutation_idx = np.array([6,2,1,0,3,4,5,7,8,9,13,14,15,12,11,10])\n ### PERMUTE TO MAKE IT COMPATIBLE with h36m\n poses = poses[:,permutation_idx,:]\n ### Reshape into n times 16*2\n poses = np.reshape(poses,[poses.shape[0],-1])\n poses_final = np.zeros([poses.shape[0],32*2])\n dim_to_use_x = np.array( [0,1,2,3,6,7,8,12,13,15,17,18,19,25,26,27],dtype=np.int32 )*2\n dim_to_use_y = dim_to_use_x+1\n dim_to_use = np.zeros(16*2,dtype=np.int32)\n dim_to_use[0::2] = dim_to_use_x\n dim_to_use[1::2] = dim_to_use_y\n poses_final[:,dim_to_use] = poses\n seqname = seqname+'-sh'\n data[ (subj, action, seqname) ] = poses_final\n\n # Make sure we loaded 8 sequences\n if (subj == 11 and action == 'Directions'): # <-- this video is damaged\n assert loaded_seqs == 7, \"Expecting 7 sequences, found {0} instead. S:{1} {2}\".format(loaded_seqs, subj, action )\n else:\n assert loaded_seqs == 8, \"Expecting 8 sequences, found {0} instead. S:{1} {2}\".format(loaded_seqs, subj, action )\n\n return data","title":""},{"docid":"39deb588840405dd1a0942e93c5e05e4","score":"0.50427383","text":"def _dump_data(self, mode=['props']):\n with h5py.File(self.name + '.hdf5') as f:\n for obj in self:\n for key in list(obj.keys()):\n tempname = obj.name + '|' + '_'.join(key.split('.'))\n arr = obj[key]\n if 'U' in str(obj[key][0].dtype):\n pass\n elif 'all' in key.split('.'):\n pass\n else:\n f.create_dataset(name='/'+tempname, shape=arr.shape,\n dtype=arr.dtype, data=arr)\n for obj in self:\n obj.clear(mode=mode)","title":""},{"docid":"832e72a4673ab2f71201de440f7b3494","score":"0.503768","text":"def create_hdf5_dataset(group, dsName, data, max_digit=55, compression=COMPRESSION):\n msg = 'create dataset {d:<{w}}'.format(d='{}/{}'.format(group.name, dsName), w=max_digit)\n msg += ' of {t:<10} in size of {s}'.format(t=str(data.dtype), s=data.shape)\n msg += ' with compression={c}'.format(c=compression)\n print(msg)\n\n if data.ndim == 1:\n dset = group.create_dataset(dsName,\n data=data,\n compression=compression)\n elif data.ndim == 2:\n dset = group.create_dataset(dsName,\n data=data,\n chunks=True,\n compression=compression)\n return dset","title":""},{"docid":"b056273bcb844d5dc26474751ca077c6","score":"0.5036801","text":"def gen_sigs(cluster_type, sigmeta, cluster, file_directory, signature_directory, fdf):\n print \"Creating signature for cluster: %s from %s samples\" % (cluster, len(fdf.filename.unique()))\n meta = sigmeta.copy()\n filename = fdf.filename.value_counts().index.tolist()[0]\n print filename\n count = 0\n yara_rule_name = cluster_type + \"_cluster_\" + str(cluster)\n for sample in fdf.filename.unique().tolist():\n meta['sample_'+str(count)] = sample\n count += 1\n\n\n file_header_columns = [\"pointer to symbol table\", \"characteristics\", \"number of symbols\", \"size of optional header\",\n \"machine\", \"compile date\", \"number of sections\"]\n\n optional_header_columns = [\"subsystem\", \"major image version\", \n \"major operating system version\", \"section alignment\", \"loader flags\",\n \"minor subsystem version\", \"major linker version\",\n \"size of code\", \"size of image\", \"number of rva and sizes\", \"dll charactersitics\",\n \"file alignment\", \"minor linker version\", \"base of code\",\n \"size uninit data\", \"entry point address\", \"size init data\", \"major subsystem version\",\n \"magic\", \"checksum\", \"minor image version\",\n \"minor operating system version\", \"size of headers\", \"base of data\",\n \"data dir base relocation rva\", \"data dir base relocation size\", \"data dir debug rva\",\n \"data dir debug size\", \"data dir exception table rva\", \"data dir exception table size\",\n \"data dir export table rva\", \"data dir export table size\", \"data dir import address table rva\",\n \"data dir import address table rva\", \"data dir import address table size\",\n \"data dir import table rva\", \"data dir import table size\", \"data dir import table size\",\n \"data dir resource table rva\", \"data dir resource table size\", \"data dir tls table rva\",\n \"data dir tls table size\"]\n\n qword_columns = ['image base', 'size of stack reserve', 'size of stack commit', 'size of heap reserve', 'size of heap commit']\n\n sig = data_hacking.YaraPEGenerator(file_directory + \"/\" + filename, samplename=yara_rule_name, meta=meta)\n\n file_header = []\n optional_header = {}\n\n for col in fdf.columns:\n if len(fdf[col].unique()) == 1:\n if fdf[col].unique()[0] != -1:\n lower = [s for s in col if s.islower()]\n if fdf[col].unique()[0] != -1 or (len(lower) == len(col)):\n if col in file_header_columns:\n file_header.append(col)\n if col in optional_header_columns:\n optional_header[col] = struct.pack(\"<I\", int(fdf[col].unique()[0])).encode('hex')\n if col in qword_columns:\n if fdf['magic'][0] == 0x20b:\n optional_header[col] = struct.pack(\"<Q\", int(fdf[col].unique()[0])).encode('hex')\n else:\n optional_header[col] = struct.pack(\"<I\", int(fdf[col].unique()[0])).encode('hex')\n\n\n if len(fdf[col].unique()) > 1:\n if col not in optional_header_columns:\n continue\n\n if type(fdf[col].unique()[0]) == str or len(fdf[col].unique()) > 9:\n continue\n\n u = []\n z = []\n for value in fdf[col].unique():\n u.append(struct.pack(\"<I\", value).encode(\"hex\"))\n\n for d in zip(*u):\n match = True\n for idx in range(1,len(d)):\n if d[0] != d[idx]:\n match = False\n break\n if match:\n z.append(d[0])\n else:\n z.append('?')\n string = ''.join(z)\n if string != '????????':\n optional_header[col] = string\n\n if len(file_header) > 0:\n sig.add_file_header(file_header)\n\n if len(optional_header) > 0:\n sig.add_optional_header_with_values(optional_header)\n sig.get_signature(filename=signature_directory + '/' + yara_rule_name + '.yara', writesig=True)","title":""},{"docid":"1ea55c513c5663457506c6db8054e96f","score":"0.5035398","text":"def prepH5(outputName):\n with h5py.File(outputName, 'w') as f:\n # Generate top level groups\n f.create_group('data')\n f.create_group('params')\n f.create_group('misc')\n f.create_group('info')\n\n # Write metadata\n # Package format version\n f.create_dataset('info/package_version', data=np.string_('SingFEL v0.2.0'))\n # Contact\n f.create_dataset('info/contact', data=np.string_('Carsten Fortmann-Grote <carsten.grote@xfel.eu>'))\n # Data Description\n f.create_dataset('info/data_description', data=np.string_('This dataset contains diffraction patterns generated using SingFEL.'))\n # Method Description\n f.create_dataset('info/method_description', data=np.string_('Form factors of the radiation damaged molecules are calculated in time slices. At each time slice, the coherent scattering is calculated and incoherently added to the final diffraction pattern (/data/nnnnnnn/diffr). Finally, Poissonian noise is added to the diffraction pattern (/data/nnnnnnn/data).'))\n # Data format version\n f.create_dataset('version', data=np.string_('0.2'))","title":""},{"docid":"e9827420a61f03aecbf99fc7ad1d93e4","score":"0.50345504","text":"def make_heatmap(data: str, group_by, rds, output, process):\n with open(data) as r:\n data = json.load(r)\n\n tasks = []\n\n if not os.path.exists(output):\n os.makedirs(output)\n # rds, prefix, cluster, gene, group = data\n for k, v in data.items():\n tasks.append([rds, output, k, v, group_by])\n\n with Pool(processes=process) as p:\n p.map(run_r, tasks)","title":""},{"docid":"7c74536aed662db5da03fb8569085822","score":"0.50222504","text":"def export_to_hdf5(cls, h5_file, model, eids):\n #comments = []\n pids = []\n nodes = []\n thetas = []\n for eid in eids:\n element = model.elements[eid]\n #comments.append(element.comment)\n pids.append(element.pid)\n nodes.append(element.nodes)\n\n theta = element.theta\n assert isinstance(theta, float), type(theta)\n thetas.append(theta)\n #h5_file.create_dataset('_comment', data=comments)\n h5_file.create_dataset('eid', data=eids)\n h5_file.create_dataset('pid', data=pids)\n h5_file.create_dataset('nodes', data=nodes)\n h5_file.create_dataset('theta', data=thetas)","title":""},{"docid":"3ad35022a5b3e1d9b834080c7b8c1d9f","score":"0.5018338","text":"def createDataset(filename, group, dataset, data):\n\n deleteDataset(filename, group, dataset)\n\n FILE = h5py.File(filename, \"r+\")\n\n GROUP = FILE[group]\n\n GROUP.create_dataset(dataset, data = data)\n\n print(\"[CREATE]: <{:s}> dataset in <{:s}> group created.\".format(dataset, group))\n\n FILE.close()","title":""},{"docid":"ad52655b1bb5f6792af20b2e04f60f47","score":"0.5007411","text":"def save_grouped_data(input_df, output_dir):\n\n df = pd.read_parquet(input_df)\n df[CDR3_LEN_COL_LABEL] = df['cdr3'].str.len()\n\n grouped_df = df.groupby(['v', 'j', CDR3_LEN_COL_LABEL])\n\n if grouped_df:\n path_dir = pathlib.Path(output_dir)\n path_dir.mkdir(parents=True, exist_ok=True)\n\n filenames = []\n for (v, j, cdr3_len), grouped_data in grouped_df:\n filename = f'{v}_{j}_{cdr3_len}'\n\n for char, replacement_char in FILENAME_CHAR_REPLACEMENTS.items():\n filename = filename.replace(char, replacement_char)\n \n filenames.append(filename)\n grouped_data.to_parquet(f'{output_dir}/{filename}.parquet', index=False)\n\n with open(f'{output_dir}/{GROUP_FILES_LIST_FILENAME}', 'w') as groups_list_file:\n groups_list_file.write('\\n'.join(filenames))","title":""},{"docid":"c2309a1b61c6c278aa33a91a3a02f29f","score":"0.49970445","text":"def group_dls_data(directory = '', pattern = '*.ASC', output = 'data', size = 10):\n import os\n from labtools.io.dls import open_dls_group\n files = glob.glob(os.path.join(directory,pattern))\n data = open_dls_group(files, size)\n for i,d in enumerate(data):\n print('creating data')\n dat = DLS_Data_Old(data = d[0], cr = d[1])\n print('opening window')\n dat.configure_traits()\n print('calculating')\n dat.calculate()\n fname = os.path.join(directory, output + str(i) + '.npy')\n print('saving data+')\n dat.save(fname)","title":""},{"docid":"ea006c100c3c9cddcb0768b5bd8c2011","score":"0.4997002","text":"def _export_dags_plugins_and_data(self: typing.Any) -> None:\n logger.info(\"Exporting DAGs, plugins and data...\")\n bucket = self.cp_bucket_name\n command = (\n f\"gsutil -m cp -r gs://{bucket}/dags \"\n f\"gs://{bucket}/export/dirs/dags && \"\n f\"gsutil -m cp -r gs://{bucket}/plugins \"\n f\"gs://{bucket}/export/dirs/plugins\"\n f\" && gsutil -m cp -r gs://{bucket}/data \"\n f\"gs://{bucket}/export/dirs/data\"\n )\n output = EnvironmentUtils.execute_command_in_a_pod(\n self.worker_pod_namespace,\n self.worker_pod_name,\n self.worker_container_name,\n command,\n )\n logger.info(output)","title":""},{"docid":"b0c7ce81a5bc58a0a8e05d57b04e5940","score":"0.4993694","text":"def createOutputFiles():\n print \"\\nCreating outputs\"\n make_path(outPath)\n os.chdir(outPath)\n outFile = open(\"SipprModelling_%s.csv\" % start, \"wb\")\n outFile.write(\"readLength\\tfoldCoverage\\ttarget\\tkmerLength\\tMedianQualityScore\\t\"\n \"QualityScoreSD\\tMedianFoldCoverage\\tFoldCoverageSD\\tMedianPercentID\\tqualityMetric\\n\")\n for rLength in readLength:\n for fCov in foldCoverage:\n for target in targets:\n for size in kmer:\n total1 = 0\n sys.stdout.write('.')\n filename = os.path.split(target)[1]\n fileNoExt = filename.split(\".\")[0]\n megaName = \"rL%s_fC%s_%s_kmer%s\" % (rLength, fCov, fileNoExt, size)\n filePath = \"%s/tmp/rL%s/rL%s_fC%s\" % (path, rLength, rLength, fCov)\n vcfFile = megaName + \"_sorted.vcf\"\n newPath = \"%s/%s\" % (filePath, megaName)\n outputFile = \"%s/%s\" % (newPath, vcfFile)\n # Initialise the counter, which will be used to track lines in the vcf file - if positions in the\n # target are not mapped, then the position field will jump ahead of the counter\n count = 1\n # Initialise the arrays, which will keep track of the appropriate values for each dataset\n arrQual = []\n arrCov = []\n arrSum = []\n output = open(outputFile, \"r\")\n for line in output:\n # vcf files have 36 commented out lines at the top of each file - these are not necessary\n if re.search('#', line):\n pass\n else:\n total1 += 1\n # Format of file\n # CHROM\t POS\tID\tREF\tALT\tQUAL FILTER\tINFO\t FORMAT\n # adk-12\t8\t.\tG\t.\t32.7\t.\tDP=1;AF1=0;AC1=0;DP4=0,1,0,0;MQ=29;FQ=-30.3\tPL\t0\n # data[0] [1] [2] [3] [4] [5] [6] [7]\n data = line.split(\"\\t\")\n #target = data[0]\n pos = data[1]\n refSeq = data[3]\n mapSeq = data[4]\n qual = data[5]\n # Depth of coverage is reported prior to the first \";\"\n dpLine = data[7].split(\";\")[0]\n # For now, I'm skipping lines that indicated the presence of a possible indel\n # - I may return to this later\n if re.search(\"INDEL\", dpLine):\n pass\n else:\n # If the called base (mapSeq) is identical to the reference base (refSeq)\n # - denoted by a \".\", then set seq to equal refSeq, otherwise, pull the\n # value of mapSeq for seq\n avgQual = sum(arrQual)/total1\n if mapSeq == \".\":\n seq = refSeq\n match = 1\n # This section corrects for the fact that during the conversion of bam files to vcf\n # files, SNP calls and ambiguous calls look identical, except for the fact that for\n # SNPs, the qualityScore (qual) tends to be higher than the surrounding bases,\n # while ambiguous calls have a lower qualityScore - this loop screens for quality\n # scores that are at least 10 lower than the score of the previous base\n else:\n if float(arrQual[-1] - 10) >= 0:\n prevValue = float(arrQual[-1] - 10)\n else:\n prevValue = 0\n if float(qual) <= prevValue:\n seq = refSeq\n match = 1\n else:\n # This attempts to catch if there are two ambiguous bases in a row;\n # they will hopefully have the same value\n if float(qual) == prevValue:\n seq = refSeq\n match = 1\n else:\n # \"True\" SNPs seem to have increased qualityScore compared to the\n # surrounding values, this will catch that\n if float(qual) > prevValue:\n seq = mapSeq\n match = 0\n # Strip the \"DP=\" from dpLine\n DP = dpLine.split(\"=\")[1]\n #vcfData[pos] = (fileName, target, refSeq, mapSeq, DP)\n # If pos > count, then there is a gap in the mapping (or a deletion, but ignoring\n # this possibility for now). For my purposes, I want to have data reported for\n # every position, whether it is present in the vcf file or not, so I will use count\n # as the position, \"-\" as the seq, and 0 as the quality and depth of coverage\n if int(pos) > count:\n #print int(pos) - count, pos, count, range(count, int(pos))\n # the number of skipped positions is equal to the value for pos - count\n # For each skipped position (i), set appropriate variables to appropriate values\n for i in range(count, int(pos)):\n posAdj = count\n seqAdj = \"-\"\n matchAdj = 0\n qualAdj = 0\n DPAdj = 0\n #vcfData[fileName][rL][fC][target][size][int(posAdj)][seqAdj][matchAdj][qualAdj] = DP\n arrQual.append(float(qualAdj))\n arrCov.append(float(DPAdj))\n arrSum.append(float(matchAdj))\n count += 1\n if int(pos) == count:\n #vcfData[fileName][rL][fC][target][size][int(pos)][seq][match][qual] = DP\n arrQual.append(float(qual))\n arrCov.append(float(DP))\n arrSum.append(float(match))\n count += 1\n else:\n #vcfData[fileName][rL][fC][target][size][int(pos)][seq][match][qual] = DP\n arrQual.append(float(qual))\n arrCov.append(float(DP))\n arrSum.append(float(match))\n count += 1\n # In the case of no data being present in a file,\n total = count - 1\n if total == 0:\n avgQual = 0\n stdQual = 0\n avgCov = 0\n stdCov = 0\n avgID = 0\n qualMet = 0\n else:\n avgQual = sum(arrQual)/total\n stdQual = numpy.std(arrQual)\n avgCov = sum(arrCov)/total\n stdCov = numpy.std(arrCov)\n avgID = sum(arrSum)/total * 100\n qualMet = avgQual * avgCov\n\n outFile.write(\"%s\\t%s\\t%s\\t%s\\t%s\\t%s\\t%s\\t%s\\t%s\\t%s\\n\"\n % (rLength, fCov, fileNoExt, size, avgQual, stdQual, avgCov, stdCov, avgID, qualMet))\n\n output.close()\n outFile.close()","title":""},{"docid":"ac211780d7535d82eeac4a04791d06a2","score":"0.4985788","text":"def write_checkgroups(groups, path):\n keys = groups.keys()\n keys.sort()\n checkgroups_file = file(path, 'wb')\n for key in keys:\n if len(key) < 8:\n checkgroups_file.write(key + '\\t\\t\\t' + groups[key] + '\\n')\n elif len(key) < 16:\n checkgroups_file.write(key + '\\t\\t' + groups[key] + '\\n')\n else:\n checkgroups_file.write(key + '\\t' + groups[key] + '\\n')\n checkgroups_file.close()\n print 'Checkgroups file written.'\n print","title":""},{"docid":"b42b23e474853ee230bfb101382b3b57","score":"0.49817586","text":"def _dump(py_obj, h_group, name, memo, loader,attrs={} , **kwargs):\n\n py_obj_id = id(py_obj)\n py_obj_ref = memo.get(py_obj_id,None)\n if py_obj_ref is not None:\n\n # py_object already dumped to hdf5 file store a reference to it instead\n # instead of dumping it again.\n #\n # Note: reference dataset share their base_type and py_obj_type with the\n # referenced h5py.Group or h5py.Dataset. On load their h5py.ref_dtype type \n # dtype is used to distinguish them from datasets hosting pickled data.\n h_link = h_group.create_dataset(name,data = py_obj_ref[0].ref,dtype = h5.ref_dtype)\n h_link.attrs.update(attrs)\n return\n\n # Check if loader has already been loaded for the provided py_obj and \n # retrieve the most appropriate method for creating the corresponding\n # representation within HDF5 file\n py_obj_type, (create_dataset, base_type,memoise) = loader.load_loader(py_obj.__class__)\n try:\n h_node,h_subitems = create_dataset(py_obj, h_group, name, **kwargs)\n except NotHicklable:\n h_node,h_subitems = create_pickled_dataset(py_obj, h_group, name, reason = str(NotHicklable), **kwargs)\n else:\n # store base_type and type unless py_obj had to be pickled by create_pickled_dataset\n memo.store_type(h_node,py_obj_type,base_type,**kwargs)\n\n # add additional attributes and prevent modification of 'type' attribute\n h_node.attrs.update((name,attr) for name,attr in attrs.items() if name != 'type' )\n\n # if py_object shall be memoised to properly represent multiple references\n # to it in HDF5 file store it along with created h_node in the memo dictionary.\n # remembering the py_object along with the h_node ensures that py_object_id\n # which represents the memory address of py_obj refers to py_obj until the\n # whole structure is stored within hickle file.\n if memoise:\n memo[py_obj_id] = (h_node,py_obj)\n\n # loop through list of all sub items and recursively dump them\n # to HDF5 file\n for h_subname,py_subobj,h_subattrs,sub_kwargs in h_subitems:\n _dump(py_subobj,h_node,h_subname,memo,loader,h_subattrs,**sub_kwargs)","title":""},{"docid":"09e6e1d03830d87a72f05725f5caf80c","score":"0.49766046","text":"def write_hdf5( self, iteration ):\n # Find the file name\n filename = \"data%08d.h5\" %iteration\n fullpath = os.path.join( self.write_dir, \"hdf5\", filename )\n\n # Create the file and setup its attributes\n zmin = self.top.zgrid + self.w3d.zmmin\n self.create_file_empty_meshes( fullpath, self.top.it,\n self.top.time, self.nz, zmin, self.dz, self.top.dt )\n\n # Open the file again (possibly in parallel), and get the field path\n f = self.open_file( fullpath, parallel_open=self.lparallel_output )\n # (f is None if this processor does not participate in writing data)\n if f is not None:\n field_path = \"/data/%d/fields/\" %iteration\n field_grp = f[field_path]\n else:\n field_grp = None\n\n # Loop over the different quantities that should be written\n for fieldtype in self.fieldtypes:\n # Scalar field\n if fieldtype == \"rho\":\n self.write_dataset( field_grp, \"rho\", \"rho\" )\n # Vector field\n elif fieldtype in [\"E\", \"B\", \"J\"]:\n for coord in self.coords:\n quantity = \"%s%s\" %(fieldtype, coord)\n path = \"%s/%s\" %(fieldtype, coord)\n self.write_dataset( field_grp, path, quantity )\n\n # Close the file\n if f is not None:\n f.close()","title":""},{"docid":"695d51b0144f629dc9497cceee2ed69a","score":"0.49762768","text":"def _DumpGroup(parent, datasets):\n for key, value in datasets.iteritems():\n if isinstance(value, dict):\n child = parent.create_group(key)\n _DumpGroup(child, value)\n elif value is None:\n child = parent.create_group(key)\n else:\n compression_kwargs = {}\n if compression_level:\n compression_kwargs = {'compression': 'gzip',\n 'compression_opts': compression_level}\n parent.create_dataset(key, data=value, **compression_kwargs)","title":""},{"docid":"adf54dc92a147885acc8fc08cad0cc4a","score":"0.49743944","text":"def export_join_data(toil, options, full_ids, clip_ids, clip_stats, filter_ids, idx_maps, og_chrom_ids):\n\n # make a directory for the chromosomal vgs\n if options.chrom_vg:\n clip_base = os.path.join(options.outDir, '{}.chroms'.format(options.outName))\n if not clip_base.startswith('s3://') and not os.path.isdir(clip_base):\n os.makedirs(clip_base)\n\n if 'full' in options.chrom_vg:\n # download the \"full\" vgs\n assert len(options.vg) == len(full_ids)\n for vg_path, full_id, in zip(options.vg, full_ids):\n name = os.path.splitext(vg_path)[0] + '.full.vg'\n toil.exportFile(full_id, makeURL(os.path.join(clip_base, os.path.basename(name))))\n\n if 'clip' in options.chrom_vg:\n # download the \"clip\" vgs\n assert len(options.vg) == len(clip_ids)\n for vg_path, clip_id, in zip(options.vg, clip_ids):\n name = os.path.splitext(vg_path)[0] + '.vg'\n toil.exportFile(clip_id, makeURL(os.path.join(clip_base, os.path.basename(name))))\n\n if 'filter' in options.chrom_vg:\n # download the \"filter\" vgs\n assert len(options.vg) == len(filter_ids)\n for vg_path, filter_id, in zip(options.vg, filter_ids):\n name = os.path.splitext(vg_path)[0] + '.d{}.vg'.format(options.filter)\n toil.exportFile(filter_id, makeURL(os.path.join(clip_base, os.path.basename(name))))\n\n # make a directory for the chromosomal ogs\n if options.chrom_og:\n clip_base = os.path.join(options.outDir, '{}.chroms'.format(options.outName))\n if not clip_base.startswith('s3://') and not os.path.isdir(clip_base):\n os.makedirs(clip_base)\n\n for gtype in options.chrom_og:\n # download all the chromosomal ogs\n tag = 'd{}'.format(options.filter) if gtype == 'filter' else gtype\n og_ids = og_chrom_ids[gtype]['og']\n assert len(options.vg) == len(og_ids)\n for vg_path, og_id in zip(options.vg, og_ids):\n name = os.path.splitext(vg_path)[0] + '{}.og'.format( '.' + tag if tag != 'clip' else '')\n toil.exportFile(og_id, makeURL(os.path.join(clip_base, os.path.basename(name))))\n\n # make a directory for the viz\n if options.viz + options.draw:\n viz_base = os.path.join(options.outDir, '{}.viz'.format(options.outName))\n if not viz_base.startswith('s3://') and not os.path.isdir(viz_base):\n os.makedirs(viz_base)\n\n for gtype in options.viz:\n # download all the chromosomal 1D visualizations\n assert len(options.vg) == len(og_chrom_ids[gtype]['viz'])\n tag = 'd{}'.format(options.filter) if gtype == 'filter' else gtype \n for vg_path, viz_id in zip(options.vg, og_chrom_ids[gtype]['viz']):\n if viz_id:\n viz_name = os.path.splitext(vg_path)[0] + '{}.viz.png'.format('.' + tag if tag != 'clip' else '')\n toil.exportFile(viz_id, makeURL(os.path.join(viz_base, os.path.basename(viz_name))))\n\n for gtype in options.draw:\n # download all the chromosomal 2D visualizations\n assert len(options.vg) == len(og_chrom_ids[gtype]['draw'])\n tag = 'd{}'.format(options.filter) if gtype == 'filter' else gtype \n for vg_path, draw_id in zip(options.vg, og_chrom_ids[gtype]['draw']):\n if draw_id:\n draw_name = os.path.splitext(vg_path)[0] + '{}.draw.png'.format('.' + tag if tag != 'clip' else '')\n toil.exportFile(draw_id, makeURL(os.path.join(viz_base, os.path.basename(draw_name))))\n \n # download the stats files\n if clip_stats:\n for stats_file in clip_stats.keys():\n toil.exportFile(clip_stats[stats_file], makeURL(os.path.join(options.outDir, '{}.{}'.format(options.outName, stats_file))))\n \n # download everything else\n for idx_map in idx_maps:\n for ext, idx_id in idx_map.items():\n # hacky filtering of intermediate indexes that the user doesn't want\n # ex if someone did --vcf clip --gfa full this would filter out clip.gfa etc.\n is_intermediate = False\n for out_ext, out_sel in [('gfa', options.gfa), ('gbz', options.gbz + options.giraffe), ('snarls', options.gbz)]:\n for phase in ['full', 'clip', 'filter']:\n if '{}.{}'.format(phase, out_ext) in ext and phase not in out_sel:\n is_intermediate = True\n if not is_intermediate:\n out_ext = ext\n if 'clip.' in out_ext:\n out_ext = out_ext.replace('clip.', '')\n if 'filter.' in out_ext:\n out_ext = out_ext.replace('filter.', 'd{}.'.format(options.filter))\n toil.exportFile(idx_id, makeURL(os.path.join(options.outDir, '{}.{}'.format(options.outName, out_ext))))","title":""},{"docid":"e2de30753670716534c983083dd676a2","score":"0.49726242","text":"def create_tar_archive(self, outdir=None):\n # for the tar archive filename, we need a timestamp \n timestamp = datetime.now().strftime('%F-%H%M%S') # e.g., '2020-04-21-132052'\n directory_basename = os.path.basename(self.abspath)\n tar_archive_name = f\"{timestamp}-{directory_basename}.tar.gz\"\n try:\n tar_archive_abspath = os.path.join(outdir.abspath, tar_archive_name)\n except AttributeError:\n tar_archive_abspath = os.path.join(self.abspath, tar_archive_name)\n with tarfile.open(tar_archive_abspath, mode=\"w:gz\") as tar:\n print(f\"Creating tar archive {tar_archive_abspath} ...\")\n tar.add(self.abspath, arcname=f\"{timestamp}-{directory_basename}\")\n return tar_archive_abspath","title":""},{"docid":"638f8e8259d7ee7bbd806492d862d438","score":"0.49636325","text":"def upload_usp_family(folder, group_label, group_description, stop_if_existing=True):\n import os\n\n import aiida.common\n\n # from aiida.common import aiidalogger\n from aiida.common import NotExistent, UniquenessError\n from aiida.orm.querybuilder import QueryBuilder\n\n from .otfg import OTFGGroup\n\n files = [\n os.path.realpath(os.path.join(folder, i))\n for i in os.listdir(folder)\n if os.path.isfile(os.path.join(folder, i))\n and (\n i.lower().endswith(\".usp\")\n or i.lower().endswith(\"recpot\")\n or i.lower().endswith(\".uspcc\")\n )\n ]\n\n nfiles = len(files)\n\n try:\n group = OTFGGroup.get(label=group_label)\n group_created = False\n except NotExistent:\n group = OTFGGroup(\n label=group_label,\n )\n group_created = True\n\n # Update the descript even if the group already existed\n group.description = group_description\n\n pseudo_and_created = [] # A list of records (UspData, created)\n\n for f in files:\n\n md5sum = md5_file(f)\n qb = QueryBuilder()\n qb.append(UspData, filters={\"attributes.md5\": {\"==\": md5sum}})\n existing_usp = qb.first()\n\n # Add the file if it is in the database\n if existing_usp is None:\n pseudo, created = UspData.get_or_create(f, use_first=True, store_usp=False)\n pseudo_and_created.append((pseudo, created))\n\n # The same file is there already\n else:\n if stop_if_existing:\n raise ValueError(\n \"A usp/recpot with identical MD5 to\"\n \" {} cannot be added with stop_if_existing\"\n \"\".format(f)\n )\n existing_usp = existing_usp[0]\n pseudo_and_created.append((existing_usp, False))\n\n # Check for unique per element\n elements = [(i[0].element, i[0].md5sum) for i in pseudo_and_created]\n\n # Check if we will duplicate after insertion\n\n if not group_created:\n for aiida_n in group.nodes:\n if not isinstance(aiida_n, UspData):\n continue\n elements.append((aiida_n.element, aiida_n.md5sum))\n\n # Discard duplicated pairs\n elements = set(elements)\n elements_names = [e[0] for e in elements]\n\n # Check the uniqueness of the complete group\n if not len(elements_names) == len(set(elements_names)):\n duplicates = {x for x in elements_names if elements_names.count(x) > 1}\n dup_string = \", \".join(duplicates)\n raise UniquenessError(\n \"More than one usp/recpot found for the elements: \" + dup_string + \".\"\n )\n\n if group_created:\n group.store()\n\n # Save the usp in the database if necessary and add them to the group\n\n for pseudo, created in pseudo_and_created:\n if created:\n pseudo.store()\n # aiidalogger.debug(\"New node {} created for file {}\".format(\n # pseudo.uuid, pseudo.filename))\n else:\n # aiidalogger.debug(\"Reusing node {} for file {}\".format(\n # pseudo.uuid, pseudo.filename))\n pass\n\n nodes_new = [pseduo for pseduo, created in pseudo_and_created if created is True]\n nodes_add = [pseduo for pseduo, created in pseudo_and_created]\n group.add_nodes(nodes_add)\n\n return nfiles, len(nodes_new)","title":""},{"docid":"ab5f865c86861c4d958d1a3f574b4fda","score":"0.49579674","text":"def generate_files_distributed(generator,\n output_name,\n output_dir,\n num_shards=1,\n max_cases=None,\n task_id=0):\n assert task_id < num_shards\n output_filename = sharded_name(output_name, task_id, num_shards)\n output_file = os.path.join(output_dir, output_filename)\n tf.logging.info(\"Writing to file %s\", output_file)\n writer = tf.python_io.TFRecordWriter(output_file)\n\n counter = 0\n for case in generator:\n if counter % 100000 == 0:\n tf.logging.info(\"Generating case %d for %s.\" % (counter, output_name))\n counter += 1\n if max_cases and counter > max_cases:\n break\n example = to_example(case)\n writer.write(example.SerializeToString())\n\n writer.close()\n return output_file","title":""},{"docid":"004a4f09de47aba4be8e696b01ccc681","score":"0.49563918","text":"def write_detections(detections, filename):\n with h5py.File(filename, 'w') as f:\n for d in detections:\n # Compression factor with 'poor, but fast' lzf compression almost \n # factor 10 for 1 dataset, ~factor 35 for 100 datasets\n f.create_dataset(d, data=detections[d], compression='lzf')","title":""},{"docid":"0ea0d30855d7d271d195112a3479f67b","score":"0.49468485","text":"def createArchesFile(input_dataset,out_dir):\n\n ds_name = os.path.basename(input_dataset)\n if os.path.splitext(ds_name)[1] != \"\":\n ds_name = os.path.splitext(ds_name)[0]\n outfile = os.path.join(out_dir,ds_name+\".arches\")\n \n with open(outfile,\"wb\") as arches:\n arches.write(\n \"RESOURCEID|RESOURCETYPE|ATTRIBUTENAME|ATTRIBUTEVALUE|GROUPID\\r\\n\")\n return outfile","title":""},{"docid":"8ba340db2468b7568736c7785fcfd76a","score":"0.49412504","text":"def write_entries_group(bib_entries):\n\n # copy icons\n icons_folder = os.path.join(params['htmlfile_group'], 'Icons')\n params['author_group_Icons'] = icons_folder\n if not os.path.exists(icons_folder):\n os.mkdir(icons_folder)\n if params['icon_www']:\n params['author_group_icon_www'] = os.path.join(icons_folder, os.path.basename(params['icon_www']))\n shutil.copyfile(params['icon_www'], params['author_group_icon_www'])\n if params['icon_pdf']:\n params['author_group_icon_pdf'] = os.path.join(icons_folder, os.path.basename(params['icon_pdf']))\n shutil.copyfile(params['icon_pdf'], params['author_group_icon_pdf'])\n\n # copy css file\n static_folder = os.path.join(params['htmlfile_group'], 'Static')\n params['author_group_Static'] = static_folder\n if not os.path.exists(static_folder):\n os.mkdir(static_folder)\n if params['css_file'] and os.path.exists(params['css_file']):\n params['author_group_css'] = os.path.join(static_folder, os.path.basename(params['css_file']))\n shutil.copyfile(params['css_file'], params['author_group_css'])\n params['css_file'] = params['author_group_css']\n if params['bootstrap_css'] and os.path.exists(params['bootstrap_css']):\n params['author_group_bootstrap_css'] = os.path.join(static_folder, os.path.basename(params['bootstrap_css']))\n shutil.copyfile(params['bootstrap_css'], params['author_group_bootstrap_css'])\n params['bootstrap_css'] = params['author_group_bootstrap_css']\n\n title = params['title']\n\n # write entries selected by authors\n _write_entries_group_author(bib_entries)\n params['dict_title'] = params['dict_title_group']\n\n # write complete-bibliography.html\n params['title'] = title\n _write_entries_group_complete(bib_entries)\n\n # write complete-bibliography.bib\n biblio_folder = os.path.join(params['htmlfile_group'], 'Bibliography')\n params['outbibfile'] = os.path.join(biblio_folder, 'complete-bibliography.bib')\n write_entries_to_bibfile(bib_entries)\n\n # write entries selected by publication venues\n _write_entries_group_venue(bib_entries)\n\n # write entries selected by years\n _write_entries_group_year(bib_entries)\n\n # write entries selected by categories\n _write_entries_group_category(bib_entries)\n\n # write index.html\n params['title'] = title\n _write_entries_group_index(bib_entries)","title":""},{"docid":"10034300e76d05c86bccd28a52048efb","score":"0.4940712","text":"def order_by( self, inspp, outfolder=curr_path, make_csv=True, make_graph=False, make_tree=False, make_tree_x=False, make_tree_y=False, group_every=None, num_classes=None, evenly=False ):\n db_name_short = os.path.basename(self.db_name).replace('.pickle.gz', '')\n\n if inspp not in self.spps:\n print \"requested species(%s) does not exist\" % inspp\n print self.spps\n sys.exit( 1 )\n\n\n pos_extra = \"\"\n spp_extra = \"\"\n\n db_fn = os.path.join( outfolder, db_name_short + '.pickle.gz.' + inspp )\n\n if group_every is not None:\n db_fn += \".every_\" + str(group_every)\n pos_extra += \" - every %dbp\" % group_every\n\n elif num_classes is not None:\n db_fn += \".classes_\" + str(num_classes)\n pos_extra += \" - in %d groups\" % num_classes\n\n elif evenly:\n db_fn += \".evenly\"\n\n if make_tree:\n db_fn += \".tree\"\n spp_extra += \" with clustering\"\n pos_extra += \" with clustering\"\n\n elif make_tree_x:\n db_fn += \".treex\"\n pos_extra += \" with clustering\"\n\n elif make_tree_y:\n db_fn += \".treey\"\n spp_extra += \" with clustering\"\n\n db_fn += '.pickle.gz'\n\n excerpt = {}\n\n if os.path.exists( db_fn ):\n print \"ordering %s :: db file %s exists. reading\" % ( inspp, db_fn )\n excerpt = vcf_walk.loads( db_fn )\n\n else:\n print \"ordering %s :: db file %s does not exists. parsing\" % ( inspp, db_fn )\n excerpt = self.filter_by(inspp, group_every=None, num_classes=None, evenly=False)\n\n #leng = max([len(x) for x in spps])\n #fmt = \" %-\"+str(leng)+\"s: %12d %12d \"\n\n #for chromosome_name in excerpt:\n # print \" \", chromosome_name\n # for start, end, name, line in excerpt[ chromosome_name ]:\n # print \" %-12s %12d %12d %s\" % ( name, start, end, str(line) )\n\n print \"ordering %s :: ordered :: dumping\" % inspp\n vcf_walk.dumps( db_fn, excerpt )\n print \"ordering %s :: ordered :: done\" % inspp\n\n\n sppindex = self.spps[ inspp ]\n sppindexinv = self.getSppIndexInvert( )\n\n\n if make_csv or make_graph:\n print \"ordering %s :: exporting\" % inspp\n\n fhdcsv = None\n if make_csv:\n fhdcsv = open(db_fn + '.csv', 'w')\n\n for chromosome_name in sorted(excerpt):\n hlines = []\n dlines = []\n #print excerpt[chromosome_name]\n\n if make_csv:\n hlines.append( chromosome_name + \"\\n\" )\n hlines.append( \"start,\" )\n hlines.append( \",\".join([ str(x[vcf_walk.DB_START ] ) for x in excerpt[chromosome_name] ]) )\n hlines.append( \"\\n\" )\n\n hlines.append( \"end,\" )\n hlines.append( \",\".join([ str(x[vcf_walk.DB_END ] ) for x in excerpt[chromosome_name] ]) )\n hlines.append( \"\\n\" )\n\n hlines.append( \"num_unities,\" )\n hlines.append( \",\".join([ str(x[vcf_walk.DB_LEN_OBJ] ) for x in excerpt[chromosome_name] ]) )\n hlines.append( \"\\n\" )\n\n hlines.append( \"num_snps,\" )\n hlines.append( \",\".join([ str(x[vcf_walk.DB_LEN_SNP] ) for x in excerpt[chromosome_name] ]) )\n hlines.append( \"\\n\" )\n\n dlines.append( \"name,\" )\n dlines.append( \",\".join([ str(x[vcf_walk.DB_NAME ] ) for x in excerpt[chromosome_name] ]) )\n dlines.append( \"\\n\" )\n\n for spp in sppindexinv:\n sppindex = self.spps[ spp ]\n data = []\n\n for x in excerpt[chromosome_name]:\n xline = x[vcf_walk.DB_LINE]\n val = xline[sppindex]\n data.append( str(val) )\n\n dlines.append( spp + \",\" )\n dlines.append( \",\".join( data ) )\n dlines.append( \"\\n\" )\n\n\n if make_csv:\n print \"ordering %s :: exporting :: exporting chromosome %s to csv\" % (inspp, chromosome_name)\n\n fhdcsv.writelines( hlines )\n fhdcsv.writelines( dlines )\n fhdcsv.write( \"\\n\\n=\\n\\n\" )\n\n\n if make_graph:\n print \"ordering %s :: exporting :: exporting chromosome %s to image\" % (inspp, chromosome_name)\n #Rscript heat.R < /tmp/heat.csv\n\n pos_extra_str = pos_extra\n if evenly:\n db = excerpt[ chromosome_name ]\n dbLen = len(db)\n chromLen = db[-1][vcf_walk.DB_END] - db[0][vcf_walk.DB_START]\n\n num_classes = dbLen\n\n group_every = int(chromLen / num_classes)\n\n pos_extra_str += \" - evenly distributed %dbp in %d groups of %dbp\" % (chromLen, dbLen, group_every)\n\n\n fn_R = db_fn + '.csv.%s.R' % chromosome_name\n fn_R_o = db_fn + '.csv.%s.R.stdout' % chromosome_name\n fn_R_e = db_fn + '.csv.%s.R.stderr' % chromosome_name\n fn_Png = db_fn + '.csv.%s.png' % chromosome_name\n fn_Pdf = db_fn + '.csv.%s.pdf' % chromosome_name\n fn_Svg = db_fn + '.csv.%s.svg' % chromosome_name\n fn_csv = db_fn + '.csv.%s.csv' % chromosome_name\n\n R = genR(chromosome_name, inspp, fn_csv, fn_Png, fn_Pdf, fn_Svg, make_tree=make_tree, make_tree_x=make_tree_x, make_tree_y=make_tree_y, pos_extra=pos_extra_str, spp_extra=spp_extra)\n\n with open( fn_csv, 'w' ) as fhdcsvr:\n fhdcsvr.writelines( dlines )\n\n with open( fn_R, 'w' ) as fhd:\n fhd.write( R )\n\n print \"ordering %s :: exporting :: exporting chromosome %s to image :: running R script: %s\" % (inspp, chromosome_name, fn_R)\n #print R\n\n call([\"Rscript\", fn_R], stdout=open(fn_R_o, 'wb'), stderr=open(fn_R_e, 'wb'))\n\n #to_img( db_name, spps, dlines, inspp )\n\n print \"ordering %s :: exporting :: exported chromosome %s to image\" % (inspp, chromosome_name)\n print\n print \"ordering %s :: exporting :: done\" % inspp","title":""},{"docid":"699ba597180d0c1e0c9fb26a91edf17d","score":"0.49352628","text":"def save_files_to_h5(X, Y, N, labels, output_file):\n\n store = h5py.File(output_file, \"w\")\n store.create_dataset(\n 'data', data=X,\n dtype='float32', compression='gzip', compression_opts=4\n )\n if compute_normals:\n store.create_dataset(\n 'normal', data=N,\n dtype='float32', compression='gzip', compression_opts=4\n )\n store.create_dataset(\n 'label', data=labels,\n dtype='uint8', compression='gzip', compression_opts=1\n )\n store.create_dataset(\n 'pid', data=Y,\n dtype='uint8', compression='gzip', compression_opts=1\n )","title":""},{"docid":"01c9ab9a806582e213e219f00e408405","score":"0.49234766","text":"def create_archive(archive, folder):\n with tarfile.open(archive, \"w:gz\") as tar:\n tar.add(folder, arcname=os.path.basename(folder))","title":""},{"docid":"9e1a15b6cf51d27cad3dd78ca3c6c115","score":"0.49186638","text":"def write_to_file(startdate, gen_output, nc_fname, metadata=None):\n ensemble_forecast = gen_output.get(\"ensemble_forecast\", None)\n deterministic = gen_output.get(\"deterministic\", None)\n motion_field = gen_output.get(\"motion_field\", None)\n ensemble_motion = gen_output.get(\"ensemble_motion\", None)\n\n if metadata is None:\n metadata = dict()\n\n if all((dataset is None for dataset in gen_output.values())):\n print(\"Nothing to store\")\n log(\"warning\", \"Nothing to store into .h5 file. Skipping.\")\n return None\n\n output_options = PD[\"output_options\"]\n # We don't support irregular nowcast outputs here\n nowcast_timestep = get_timesteps()\n\n ensemble_forecast, ens_scale_meta = prepare_data_for_writing(ensemble_forecast)\n deterministic, det_scale_meta = prepare_data_for_writing(deterministic)\n\n with h5py.File(os.path.join(output_options[\"path\"], nc_fname), 'w') as outf:\n if ensemble_forecast is not None and output_options[\"store_ensemble\"]:\n for eidx in range(PD[\"ensemble_size\"]):\n ens_grp = outf.create_group(\"member-{:0>2}\".format(eidx))\n utils.store_timeseries(ens_grp,\n ensemble_forecast[eidx, :, :, :],\n startdate,\n timestep=nowcast_timestep,\n metadata=ens_scale_meta)\n\n if ensemble_motion is not None and output_options[\"store_perturbed_motion\"]:\n for eidx in range(PD[\"ensemble_size\"]):\n try:\n ens_grp = outf[\"member-{:0>2}\".format(eidx)]\n except KeyError:\n ens_grp = outf.create_group(\"member-{:0>2}\".format(eidx))\n ens_grp.create_dataset(\"motion\", data=ensemble_motion[eidx])\n\n if deterministic is not None and output_options[\"store_deterministic\"]:\n det_grp = outf.create_group(\"deterministic\")\n utils.store_timeseries(det_grp, deterministic, startdate,\n timestep=nowcast_timestep,\n metadata=det_scale_meta)\n\n if output_options[\"store_motion\"]:\n outf.create_dataset(\"motion\", data=motion_field)\n\n meta = outf.create_group(\"meta\")\n # configuration \"OUTPUT_TIME_FORMAT\" is removed, new output uses ODIM standard\n meta.attrs[\"nowcast_started\"] = dt.datetime.strftime(metadata[\"time_at_start\"],\n \"%Y-%m-%d %H:%M:%S\")\n meta.attrs[\"nowcast_ended\"] = dt.datetime.strftime(metadata[\"time_at_end\"],\n \"%Y-%m-%d %H:%M:%S\")\n meta.attrs[\"nowcast_units\"] = metadata.get(\"unit\", \"Unknown\")\n meta.attrs[\"nowcast_seed\"] = metadata.get(\"seed\", \"Unknown\")\n meta.attrs[\"nowcast_init_time\"] = dt.datetime.strftime(startdate, \"%Y%m%d%H%M\")\n\n # Old configurations - may be used by postprocessing scripts\n old_style_configs = {\n # Method selections\n #\"DOMAIN\": \"fmi\", # postprocessing defines this instead of reading it here\n \"VALUE_DOMAIN\": \"rrate\" if PD[\"run_options\"][\"forecast_as_quantity\"] == \"RATE\" else \"dbz\", # Unused?\n # Z-R conversion parameters\n \"ZR_A\": PD[\"data_options\"][\"zr_a\"], #\n \"ZR_B\": PD[\"data_options\"][\"zr_b\"], #\n # Nowcasting parameters\n \"NOWCAST_TIMESTEP\": nowcast_timestep, #\n \"MAX_LEADTIME\": PD[\"run_options\"][\"max_leadtime\"], #\n \"NUM_TIMESTEPS\": PD[\"run_options\"][\"leadtimes\"], #\n \"ENSEMBLE_SIZE\": PD[\"ensemble_size\"], #\n \"NUM_CASCADES\": PD[\"nowcast_options\"].get(\"n_cascade_levels\", 6), # Unused?\n \"RAIN_THRESHOLD\": PD[\"out_rain_threshold\"], # Unused?\n \"NORAIN_VALUE\": PD[\"out_norain_value\"], #\n \"KMPERPIXEL\": PD[\"nowcast_options\"][\"kmperpixel\"], # Unused?\n \"CALCULATION_DOMAIN\": PD[\"nowcast_options\"][\"domain\"], # Unused?\n \"VEL_PERT_KWARGS\": PD[\"nowcast_options\"][\"vel_pert_kwargs\"],\n # Storing parameters\n \"FIELD_VALUES\": PD[\"output_options\"][\"as_quantity\"], # Unused?\n \"STORE_DETERMINISTIC\": output_options[\"store_deterministic\"], #\n \"STORE_PERTURBED_MOTION\": output_options[\"store_perturbed_motion\"], #\n }\n\n pd_meta = meta.create_group(\"configuration\")\n for key, value in old_style_configs.items():\n pd_meta.attrs[key] = str(value)\n\n proj_meta = meta.create_group(\"projection\")\n for key, value in metadata[\"projection\"].items():\n proj_meta.attrs[key] = value\n\n return None","title":""},{"docid":"7eff8e1731b2ff5dd37ea11d753b7486","score":"0.4917919","text":"def generate_standard_outputs(self, output_dir, gtfs_day='19700101'):\n gngeojson.generate_standard_outputs_for_schedule(self, output_dir, gtfs_day)\n logging.info('Finished generating standard outputs. Zipping folder.')\n persistence.zip_folder(output_dir)","title":""},{"docid":"eb5c69f8be18fe9723020203513e1a89","score":"0.49162012","text":"def _write_results_file(self, all_boxes, output_dir):\n\n #format_str = \"{}, -1, {}, {}, {}, {}, {}, -1, -1, -1\"\n\n files = {}\n for cls in all_boxes:\n for i, dets in enumerate(cls):\n path = self.image_path_at(i)\n img1, name = osp.split(path)\n # get image number out of name\n frame = int(name.split('.')[0])\n # smth like /train/MOT17-09-FRCNN or /train/MOT17-09\n tmp = osp.dirname(img1)\n # get the folder name of the sequence and split it\n tmp = osp.basename(tmp).split('-')\n # Now get the output name of the file\n out = tmp[0]+'-'+tmp[1]+'.txt'\n outfile = osp.join(output_dir, out)\n\n # check if out in keys and create empty list if not\n if outfile not in files.keys():\n files[outfile] = []\n\n for d in dets:\n x1 = d[0]\n y1 = d[1]\n x2 = d[2]\n y2 = d[3]\n score = d[4]\n files[outfile].append([frame, -1, x1+1, y1+1, x2-x1+1, y2-y1+1, score, -1, -1, -1])\n\n for k,v in files.items():\n #outfile = osp.join(output_dir, out)\n with open(k, \"w\") as of:\n writer = csv.writer(of, delimiter=',')\n for d in v:\n writer.writerow(d)","title":""},{"docid":"e10a87863efd30bd513a6b3af03cf2d1","score":"0.49148518","text":"def create_hdf5(\n bigwig_paths, chrom_sizes_path, out_path, chunk_size, batch_size=100\n):\n bigwig_readers = [pyBigWig.open(path) for path in bigwig_paths]\n \n # Read in chromosome sizes\n with open(chrom_sizes_path, \"r\") as f:\n chrom_sizes = {}\n for line in f:\n tokens = line.strip().split(\"\\t\")\n chrom_sizes[tokens[0]] = int(tokens[1])\n \n # Convert batch size to be in terms of rows, not number of chunks\n batch_size = batch_size * chunk_size\n\n with h5py.File(out_path, \"w\") as f:\n # Store source paths\n f.create_dataset(\"bigwig_paths\", data=np.array(bigwig_paths, dtype=\"S\"))\n for chrom in sorted(chrom_sizes.keys()):\n chrom_size = chrom_sizes[chrom]\n num_batches = int(np.ceil(chrom_size / batch_size))\n chrom_dset = f.create_dataset(\n chrom, (chrom_size, len(bigwig_paths), 1), dtype=\"f\",\n compression=\"gzip\", chunks=(chunk_size, len(bigwig_paths), 1)\n )\n for i in tqdm.trange(num_batches, desc=chrom):\n start = i * batch_size\n end = min(chrom_size, (i + 1) * batch_size)\n\n values = np.stack([\n np.stack([\n np.nan_to_num(reader.values(chrom, start, end))\n ], axis=1) for reader in bigwig_readers\n ], axis=1)\n\n chrom_dset[start : end] = values","title":""},{"docid":"256f02c777a0d1c72ad15dc7cd4b7905","score":"0.49132812","text":"def write(self, fname, **kwargs):\n\n f = fits.HDUList()\n\n f.append(fits.PrimaryHDU())\n\n thdr = fits.table_to_hdu(self.gal.get_table())\n thdr.name = 'GALAXY'\n f.append(thdr)\n\n if self.spec is not None:\n f[0].header['HASSPEC'] = True\n f.append(fits.ImageHDU(name='SPEC_DATA', data=self.spec.data.data))\n f.append(fits.ImageHDU(name='SPEC_VAR', data=self.spec.var.data))\n else:\n f[0].header['HASSPEC'] = False\n\n if self.ddata is not None:\n f[0].header['HASDATA'] = True\n f[0].header['NDSETS'] = len(self.datasets)\n\n f.append(fits.ImageHDU(name='DATA', data=self.ddata))\n for ds in self.datasets:\n f.append(fits.ImageHDU(**ds))\n\n fig = Figure()\n canvas = Canvas(fig)\n self.plot(fig.gca())\n canvas.draw()\n\n w, h = canvas.get_width_height()\n plot = np.fromstring(canvas.tostring_argb(), dtype='uint8').reshape(\n (h, w, 4))\n\n f.append(fits.ImageHDU(name='PLOT', data=plot))\n else:\n f[0].header['HASDATA'] = False\n\n f.writeto(fname, **kwargs)","title":""},{"docid":"aff7c0cbddf3f4dd9273903d2593fdf8","score":"0.49125293","text":"def genGroupFromTraffic(self, trafficfile):\n pass","title":""},{"docid":"6ad2b4afc7cb8c46a9619fb5ab83c86e","score":"0.49042654","text":"def open_halo_output(self):\n \n \n try:\n self.halo_output_file = h5.File(self.HDF_output_filepath, \"w\")\n\n except OSError:\n for obj in gc.get_objects(): # Browse through ALL objects\n if isinstance(obj, h5.File): # Just HDF5 files\n try:\n obj.close()\n except:\n pass # Was already closed \n self.halo_output_file = h5.File(self.HDF_output_filepath, \"w\")\n\n self.halo_output_dataset = self.halo_output_file.create_dataset(\n \"halo_data\", (0,), maxshape=(None,), dtype=self.dtype_halo, compression=\"gzip\"\n )\n \n self.subhalo_output_dataset = self.halo_output_file.create_dataset(\n \"subhalo_data\", (0,), maxshape=(None,), dtype=self.subhalo_dtype, compression=\"gzip\"\n )\n \n return None","title":""},{"docid":"f12875305def5e7c09b75e00e041148b","score":"0.4899638","text":"def generate_files(self, output_dir: str) -> None:\n self._write_file(output_dir, self._OUTPUT_FILE, self._generate_zones())","title":""},{"docid":"45e2e6d4f38e8aa157c0b1be0610871a","score":"0.48976526","text":"def process_set_metadata(self, data, set_name):\n hdf5_handler = self.hdf5_manager.get_group(set_name)\n image_filenames = []\n keypoints = []\n object_id = []\n object_fields = [\"image_filenames\", \"keypoints\"]\n\n if self.verbose:\n print('> Adding data to default group:')\n prgbar = progressbar.ProgressBar(max_value=len(data))\n\n for i, annot in enumerate(data):\n image_filenames.append(annot[\"filename\"])\n keypoints.append(annot[\"joints\"])\n\n object_id.append([i, i])\n\n # update progressbar\n if self.verbose:\n prgbar.update(i)\n\n # update progressbar\n if self.verbose:\n prgbar.finish()\n\n hdf5_write_data(hdf5_handler, 'image_filenames',\n str2ascii(image_filenames),\n dtype=np.uint8, fillvalue=0)\n hdf5_write_data(hdf5_handler, 'keypoints',\n np.array(keypoints, dtype=np.float),\n fillvalue=-1)\n hdf5_write_data(hdf5_handler, 'keypoint_names',\n str2ascii(self.keypoints_labels),\n dtype=np.uint8, fillvalue=0)\n hdf5_write_data(hdf5_handler, 'object_ids',\n np.array(object_id, dtype=np.int32),\n fillvalue=-1)\n hdf5_write_data(hdf5_handler, 'object_fields',\n str2ascii(object_fields),\n dtype=np.uint8, fillvalue=0)","title":""},{"docid":"f27d38b86522261688a78a7346bc5e2f","score":"0.48972598","text":"def save_gtfs(filename,tables):\n\twith zipfile.ZipFile(filename,\"w\",compression=zipfile.ZIP_DEFLATED) as zipf:\n\t\tfor (name,table) in tables.items():\n\t\t\tfieldnames = table[0].keys()\n\t\t\ttablef = tempfile.NamedTemporaryFile(mode=\"w\",encoding=\"utf-8\")\n\t\t\t#FIXME: extrasaction is set to ignore due to bug in csv.py in Python3.6\n\t\t\twriter=csv.DictWriter(tablef,fieldnames,extrasaction=\"ignore\")\n\t\t\twriter.writeheader()\n\t\t\twriter.writerows(table)\n\t\t\ttablef.flush()\n\t\t\tzipf.write(tablef.name,name+\".txt\")\n\t\t\ttablef.close()","title":""},{"docid":"6c59439b20e0ca626247f05fc7b5185e","score":"0.48950222","text":"def _save_results(self, zipdata, outdir, module, gmt, rank_metric, permutation_type):\n\n res = OrderedDict()\n for gs,gseale,ind,RES in zipdata:\n rdict = OrderedDict()\n rdict['es'] = gseale[0]\n rdict['nes'] = gseale[1]\n rdict['pval'] = gseale[2]\n rdict['fdr'] = gseale[3]\n rdict['gene_set_size'] = len(gmt[gs])\n rdict['matched_size'] = len(ind)\n #reformat gene list.\n _genes = rank_metric.iloc[ind, rank_metric.columns.get_loc('gene_name')]\n _genes = _genes.to_string(header=False, index=False).split(\"\\n\")\n rdict['genes'] = \",\".join([ g.strip() for g in _genes ])\n\n rdict['rank_ES'] = RES\n rdict['hit_index'] = ind\n res[gs] = rdict\n\n res_df = pd.DataFrame.from_dict(res, orient='index')\n res_df.index.name = 'Term'\n res_df.sort_values(by='fdr', inplace=True)\n res_df.drop(['rank_ES','hit_index'], axis=1, inplace=True)\n res_df.to_csv('{a}/gseapy.{b}.{c}.report.csv'.format(a=outdir, b=module, c=permutation_type),\n float_format ='%.7f')\n\n self.res2d = res_df\n self.results = res\n return","title":""},{"docid":"5510a4f25e94771ef37f98b8d6c9c6b0","score":"0.48928303","text":"def addhdf5(self, HDF5group):\n HDF5group.attrs['type'] = self.__class__.__name__\n HDF5group['Nvstars'] = self.Nvstars\n HDF5group['vecposlist'], HDF5group['vecposindex'] = doublelist2flatlistindex(self.vecpos)\n HDF5group['vecveclist'], HDF5group['vecvecindex'] = doublelist2flatlistindex(self.vecvec)\n HDF5group['outer'] = self.outer","title":""},{"docid":"ca6d4ca8628fa2a2bb2384bf5667b503","score":"0.48912898","text":"def background_group():\n obs_table = config.obs_table.copy()\n\n # Define observation groups\n # zenith_bins = np.array([0, 20, 30, 40, 50, 90])\n zenith_bins = np.array([0, 49, 90])\n # zenith_bins = np.array([0, 30, 90]) # for testing\n axes = [ObservationGroupAxis('ZEN_PNT', zenith_bins, 'bin_edges')]\n obs_groups = ObservationGroups(axes)\n log.info(obs_groups.info)\n\n # Apply observation grouping\n obs_table = obs_groups.group_observation_table(obs_table)\n\n # Store the results\n filename = config.obs_table_grouped_filename\n log.info('Writing {}'.format(filename))\n obs_table.write(str(filename), format='ascii.ecsv')\n\n filename = config.group_def_filename\n log.info('Writing {}'.format(filename))\n obs_groups.obs_groups_table.write(str(filename), format='ascii.ecsv')","title":""},{"docid":"8ad1397340c695b5f0c7684be27b06a2","score":"0.48905203","text":"def main(outfolder = './gazette_data/', outtype = sys.argv[1]):\n \n # Check if gazette data exists (whether gazette.py has been run) #\n data_exists_check()\n # Check whether save directories exists, and if not, create them #\n save_directory_check()\n # Files that have extra spaces, indents, etc. and won't run smoothly through.\n bad_dates = ['2006/18_Sep30','2006/17_Sep15','2016/18_Sept30','2017/11_Jun15','2018/10_May31']\n # Instantiate empty lists for keeping data before saving out.\n incorporations, namechanges = [], [] \n # Format default output as CSVs if no sys.argv[1] provided\n if not sys.argv[1]:\n outtype = 'csv'\n \n # Begin parsing files by year #\n for year in range(2006,2018+1):\n files = os.listdir('cache/gazette/'+str(year))\n for filename in files:\n if filename.endswith('.txt'):\n date_string = str(year) + '/' + filename[:-4]\n if date_string not in bad_dates:\n # The following returns pandas DataFrames if outtype is 'csv', list of pickles if outtype is 'pickles' #\n inc, nmch = make_incorporations_and_namechange(date_string,outfolder = outfolder,outtype = outtype)\n if outtype == 'csv':\n incorporations.append(inc)\n namechanges.append(nmch)\n elif outtype == 'pickles':\n incorporations += inc\n namechanges += nmch\n \n # Write out files #\n \n if outtype == 'csv': \n make_master_tables(incorporations,namechanges)\n \n elif outtype =='pickles': \n # Incorporations #\n pickle_out = open(outfolder + \"incorporations_2006-2018.pickle\",\"wb\")\n pickle.dump(incorporations, pickle_out)\n pickle_out.close()\n # Namechanges #\n pickle_out = open(outfolder + \"namechanges_2006-2018.pickle\",\"wb\")\n pickle.dump(namechanges, pickle_out)\n pickle_out.close()","title":""},{"docid":"8464957a2a23a652b7aeb986e25b2aa8","score":"0.48893464","text":"def createAgents(self, group):\n for ty in group.agentTypes:\n for ag in ty.agents:\n self.newagent(ag, ty.name, group.freezeAnimation)","title":""},{"docid":"98b46f1f5f759ce89b74d373aba562c5","score":"0.4879788","text":"def main(args):\n\n\n data = pandas.read_csv(args.infile,index_col=0)\n if not args.nocls:\n data = data[args.group1+args.group2]\n\n samples = data.columns.tolist()\n genes = data.index.tolist()\n\n descriptions = ['NA']*data.shape[0]\n temp = ['NAME','DESCRIPTION'] + samples\n\n gct = pandas.DataFrame(columns=temp, index=data.index)\n gct['NAME'] = genes\n gct['DESCRIPTION'] = descriptions\n for i in samples:\n gct[i] = data[i]\n gct.to_csv('temp.txt',sep='\\t',index=False)\n\n gct = open('temp.txt', 'r').read()\n fout = open(args.prefix+'.gct', 'w')\n fout.write('#1.2\\n')\n fout.write(str(data.shape[0]) + '\\t' + str(len(samples)) + '\\n')\n fout.write(gct)\n fout.close()\n\n if not args.nocls:\n fout = open(args.prefix + '.cls','w')\n fout.write(str(len(args.group1) + len(args.group2)) + ' 2 1\\n')\n fout.write('# ' + args.phenotypes[0] + ' ' + args.phenotypes[1] + '\\n')\n fout.write(' '.join(['0']*len(args.group1)) + ' ' + ' '.join(['1']*len(args.group2)) + '\\n')\n fout.close()","title":""},{"docid":"9a1524f71c467eaf3639777623947e2f","score":"0.48788592","text":"def _save_reconstructions(self, reconstructions):\n self.out_dir.mkdir(exist_ok=False) # Throw an error to prevent overwriting.\n gzip = dict(compression='gzip', compression_opts=1, shuffle=True, fletcher32=True)\n for file_name, recons in tqdm(reconstructions.items()):\n with h5py.File(self.out_dir / file_name, mode='x', libver='latest') as f: # Overwriting throws an error.\n f.create_dataset('reconstruction', data=recons, **gzip)","title":""}],"string":"[\n {\n \"docid\": \"095f84ec623087ed69bf071f02b382e4\",\n \"score\": \"0.604668\",\n \"text\": \"def write_hdf5(out_file, data):\\n gmu_proc_file = h5py.File(out_file, 'w')\\n print(gmu_proc_file)\\n for g in data:\\n group = gmu_proc_file.create_group(g)\\n for datum in data[g]:\\n try:\\n group.create_dataset(\\\"mel_spects_{}\\\".format(datum[0]),\\n data=datum[1])\\n except Exception as e:\\n print(group.name, datum[0], e)\\n\\n gmu_proc_file.close()\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11d07c00908b6787f7b49c996368fbaf\",\n \"score\": \"0.58894116\",\n \"text\": \"def make_fasta_for_groups(dict_groups, dict_fasta, path_name):\\n if os.path.isdir(path_name):\\n shutil.rmtree(path_name)\\n os.makedirs(path_name)\\n for group_name, group in dict_groups.iteritems():\\n records = [dict_fasta[member] for member in group.members]\\n write_fasta(path_name + group_name, records)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"363821f753ef4460b9f520da6011eed7\",\n \"score\": \"0.58364344\",\n \"text\": \"def save_groups(fname, gtable):\\n np.save(fname, table_to_group_list(gtable))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e809cff4c7d178bfc464bd4130b4d1c4\",\n \"score\": \"0.5739024\",\n \"text\": \"def write_groups(out_file, groupname):\\r\\n print(\\\"To create a single group please just enter the main group name i.e. Group Name\\\")\\r\\n print('To create a subgroup to an exisitng group, please enter /Group Name/Subgroup Name/etc/etc/')\\r\\n print() \\r\\n attributes = {}\\r\\n print(\\\"Enter attributes for\\\", groupname)\\r\\n meta = input(\\\"Is there a metadata file? (Y/N): \\\")\\r\\n if meta == \\\"Y\\\" or meta == \\\"y\\\":\\r\\n metapath = input(\\\"Enter metadata file path: \\\")\\r\\n with open(metapath, 'r') as metafile:\\r\\n for line in metafile:\\r\\n line = line.split('\\\\t')\\r\\n item = line[0].strip('\\\\n')\\r\\n value = line[-1].strip('\\\\n')\\r\\n if item in attributes.keys():\\r\\n attributes[item].append(value)\\r\\n else:\\r\\n attributes[item] = [value]\\r\\n else:\\r\\n input_attributes = input(\\\"Enter an attribute followed by a value. i.e. Project Name: iknowit, Date: 04-11-2019: \\\")\\r\\n for attribute in input_attributes.split(','):\\r\\n attribute = attribute.split(':')\\r\\n attributes[attribute[0].strip(' ')] = attribute[1].strip(' ')\\r\\n data_file = h5py.File(out_file, 'a')\\r\\n dset = data_file.create_group(groupname)\\r\\n for k, v in attributes.items():\\r\\n dset.attrs[k] = v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d90e8720ae0c70ea2b513271db63034\",\n \"score\": \"0.57272446\",\n \"text\": \"def write_func(in_files, out_file, groups):\\r\\n data_file = h5py.File(out_file, 'a')\\r\\n image_extensions = ['jpg', 'jpeg', 'png', 'bmp', 'tiff']\\r\\n count = 0\\r\\n try:\\r\\n for in_file in in_files:\\r\\n if in_file.split('.')[-1] not in image_extensions:\\r\\n try:\\r\\n with open(in_file) as ocf:\\r\\n data = ocf.read()\\r\\n str_type = h5py.special_dtype(vlen=str)\\r\\n dset = data_file.create_dataset(\\r\\n groups[count] + in_file.split('/')[-1],\\r\\n data=data, shape=(1,),\\r\\n dtype=str_type\\r\\n )\\r\\n attributes = generate_attributes_to_add(\\r\\n groups[count] + in_file.split('/')[-1])\\r\\n for k, v in attributes.items():\\r\\n dset.attrs[k] = v\\r\\n except FileNotFoundError:\\r\\n print(in_file, \\\"not found\\\")\\r\\n else:\\r\\n dset = image_to_hdf5(in_file, data_file, groups[count])\\r\\n attributes = generate_attributes_to_add(\\r\\n groups[count] + in_file.split('/')[-1])\\r\\n for k, v in attributes.items():\\r\\n dset.attrs[k] = v\\r\\n if len(groups) == 1:\\r\\n count = 0\\r\\n else:\\r\\n count += 1\\r\\n except RuntimeError:\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d243c65ab03e1df39a736a3ea2592385\",\n \"score\": \"0.5663915\",\n \"text\": \"def to_hdf(self, hdf=None, group_name=None):\\n super().to_hdf(hdf=hdf, group_name=group_name)\\n self.input.to_hdf(hdf=self.project_hdf5)\\n self.potential.to_hdf(hdf=self.project_hdf5)\\n self.structures.to_hdf(hdf=self.project_hdf5)\\n self.output.to_hdf(hdf=self.project_hdf5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5276f57c5b2208e31ba280da31513c61\",\n \"score\": \"0.5663336\",\n \"text\": \"def write_h5_stack(npy_vol, fn, group='stack', compression=None, chunks=None,\\n shuffle=None, attrs=None):\\n fn = os.path.expanduser(fn)\\n fout = h5py.File(fn, 'a')\\n if group in fout:\\n del fout[group]\\n fout.create_dataset(group, data=npy_vol, compression=compression,\\n chunks=chunks, shuffle=shuffle)\\n if attrs is not None:\\n for attr, value in attrs.items():\\n fout[group].attrs[attr] = value\\n fout.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0966f0dca7fd6474c392e4b03bc4ab2\",\n \"score\": \"0.56151164\",\n \"text\": \"def export_set(output_dir, name, data, labels, classes):\\n\\n assert len(data) == len(labels)\\n\\n # Variable-length datatypes for encoded png streams and label names\\n dt_int = h5py.vlen_dtype(np.dtype('uint8'))\\n dt_str = h5py.string_dtype(encoding='utf-8')\\n\\n # Initialize hdf5 file pointer\\n f = h5py.File(f\\\"{output_dir}/{name}_{len(data)}.h5\\\", \\\"w\\\")\\n\\n # Create group and store data/labels\\n x = f.create_dataset(\\\"data\\\", (len(data),), dtype=dt_int, data=data)\\n y = f.create_dataset(\\\"label\\\", data=np.array(labels, dtype=int))\\n\\n # Store <mapping from (0, 1 ...) to class names> as group attribute\\n y.attrs.create(\\\"class_names\\\", data=np.array(classes, dtype=dt_str))\\n\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84d667309fb7e68e1ed0fd66c315d755\",\n \"score\": \"0.55947095\",\n \"text\": \"def writing_to_output_file(mockgal_pd, mockgroup_pd, zz_mock, \\n param_dict, proj_dict, output_fmt = 'hdf5', perf_catl=False):\\n ## Keys\\n gal_key = '/gal_catl'\\n group_key = '/group_catl'\\n ## Filenames\\n if perf_catl:\\n ## Perfect Galaxy catalogue\\n gal_file = os.path.join(proj_dict['mock_cat_mc_perf'],\\n '{0}_cat_{1}_{2}_memb_cat_perf.{3}'.format(\\n param_dict['survey'], zz_mock, \\n param_dict['cosmo_choice'], output_fmt))\\n ## Perfect Group catalogue\\n group_file = os.path.join(proj_dict['mock_cat_gc_perf'],\\n '{0}_cat_{1}_{2}_group_cat_perf.{3}'.format(\\n param_dict['survey'], zz_mock,\\n param_dict['cosmo_choice'], output_fmt))\\n else:\\n ## Normal galaxy catalogue\\n gal_file = os.path.join(proj_dict['mock_cat_mc'],\\n '{0}_cat_{1}_{2}_memb_cat.{3}'.format(\\n param_dict['survey'], zz_mock,\\n param_dict['cosmo_choice'], output_fmt))\\n ## Normal group catalogue\\n group_file = os.path.join(proj_dict['mock_cat_gc'],\\n '{0}_cat_{1}_{2}_group_cat.{3}'.format(\\n param_dict['survey'], zz_mock,\\n param_dict['cosmo_choice'], output_fmt))\\n ##\\n ## Saving DataFrames to files\\n # Member catalogue\\n cu.pandas_df_to_hdf5_file(mockgal_pd, gal_file, key=gal_key)\\n # Group catalogue\\n cu.pandas_df_to_hdf5_file(mockgroup_pd, group_file, key=group_key)\\n ##\\n ## Checking for file's existence\\n cu.File_Exists(gal_file)\\n cu.File_Exists(group_file)\\n print('{0} gal_file : {1}'.format(param_dict['Prog_msg'], gal_file))\\n print('{0} group_file: {1}'.format(param_dict['Prog_msg'], group_file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c4d3780e809abec0808ba417109f9f6\",\n \"score\": \"0.55542684\",\n \"text\": \"def to_hdf(self, hdf, group_name=\\\"structure\\\"):\\n # import time\\n with hdf.open(group_name) as hdf_structure:\\n # time_start = time.time()\\n hdf_structure[\\\"TYPE\\\"] = str(type(self))\\n for el in self.species:\\n if isinstance(el.tags, dict):\\n with hdf_structure.open(\\\"new_species\\\") as hdf_species:\\n el.to_hdf(hdf_species)\\n hdf_structure[\\\"species\\\"] = [el.Abbreviation for el in self.species]\\n hdf_structure[\\\"indices\\\"] = self.indices\\n\\n with hdf_structure.open(\\\"tags\\\") as hdf_tags:\\n for tag in self._tag_list.keys():\\n tag_value = self._tag_list[tag]\\n if isinstance(tag_value, SparseList):\\n tag_value.to_hdf(hdf_tags, tag)\\n hdf_structure[\\\"units\\\"] = self.units\\n hdf_structure[\\\"dimension\\\"] = self.dimension\\n\\n if self.cell is not None:\\n with hdf_structure.open(\\\"cell\\\") as hdf_cell:\\n # Convert ASE cell object to numpy array before storing\\n hdf_cell[\\\"cell\\\"] = np.array(self.cell)\\n hdf_cell[\\\"pbc\\\"] = self.pbc\\n\\n # hdf_structure[\\\"coordinates\\\"] = self.positions # \\\"Atomic coordinates\\\"\\n hdf_structure[\\\"positions\\\"] = self.positions # \\\"Atomic coordinates\\\"\\n\\n # potentials with explicit bonds (TIP3P, harmonic, etc.)\\n if self.bonds is not None:\\n hdf_structure[\\\"explicit_bonds\\\"] = self.bonds\\n\\n # print ('time in atoms.to_hdf: ', time.time() - time_start)\\n\\n if self._high_symmetry_points is not None:\\n hdf_structure[\\\"high_symmetry_points\\\"] = self._high_symmetry_points\\n\\n if self._high_symmetry_path is not None:\\n hdf_structure[\\\"high_symmetry_path\\\"] = self._high_symmetry_path\\n\\n hdf_structure[\\\"info\\\"] = self.info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1177ebb729269e891d16e45e5ae0a0b3\",\n \"score\": \"0.55385166\",\n \"text\": \"def pack_waveforms_to_hdf5(args):\\n\\n # Arguments & parameters\\n audios_dir = args.audios_dir\\n csv_path = args.csv_path\\n waveforms_hdf5_path = args.waveforms_hdf5_path\\n mini_data = args.mini_data\\n\\n split_dir = args.split_dir\\n split_to_test_id = args.split_to_test_id\\n\\n clip_samples = config.clip_samples\\n classes_num = config.classes_num\\n sample_rate = config.sample_rate\\n id_to_ix = config.id_to_ix\\n\\n\\n\\n #id_dict = {'screaming':'/m/03qc9zr','crying_sobbing':'/m/0463cq4','chatter':'/m/07rkbfh','motor_vehicle_(road)':'/m/012f08','emergency_vehicle':'/m/03j1ly','siren':'/m/03kmc9','explosion':'/m/014zdl','gunshot_gunfire':'/m/032s66', 'breaking': '/m/07pc8lb'}\\n\\n\\n\\n\\n sample_rate = config.sample_rate\\n id_to_ix = config.id_to_ix\\n\\n\\n\\n id_dict = {'screaming':'/m/03qc9zr','crying_sobbing':'/m/0463cq4','chatter':'/m/07rkbfh','motor_vehicle_(road)':'/m/012f08','emergency_vehicle':'/m/03j1ly','siren':'/m/03kmc9','explosion':'/m/014zdl','gunshot_gunfire':'/m/032s66', 'breaking': '/m/07pc8lb'}\\n\\n\\n\\n\\n # Paths\\n if mini_data:\\n prefix = 'mini_'\\n waveforms_hdf5_path += '.mini'\\n else:\\n prefix = ''\\n\\n create_folder(os.path.dirname(waveforms_hdf5_path))\\n\\n logs_dir = '_logs/pack_waveforms_to_hdf5/{}{}'.format(prefix, get_filename(csv_path))\\n create_folder(logs_dir)\\n create_logging(logs_dir, filemode='w')\\n logging.info('Write logs to {}'.format(logs_dir))\\n\\n # Read csv file\\n split_file = open(split_dir+\\\"/fold_\\\"+str(split_to_test_id)+\\\".txt\\\",\\\"r\\\")\\n #split_file = open(split_dir+\\\"/fold_filter_\\\"+str(split_to_test_id)+\\\".txt\\\",\\\"r\\\")\\n lines = split_file.readlines()\\n audio_names = []\\n target_names = []\\n for line in lines:\\n audio_names.append(line.split(' ')[0]+'.wav')\\n if line.split(' ')[2][0:-1]==\\\"others\\\":\\n target_names.append(\\\"others\\\")\\n else:\\n target_names.append(id_dict[line.split(' ')[2][0:-1]])\\n\\n\\n\\n\\n\\n meta_dict = read_metadata(csv_path, classes_num, id_to_ix)\\n\\n '''if mini_data:\\n mini_num = 10\\n for key in meta_dict.keys():\\n meta_dict[key] = meta_dict[key][0 : mini_num]'''\\n\\n #audios_num = len(meta_dict['audio_name'])\\n audios_num = len(lines)\\n\\n\\n\\n # Pack waveform to hdf5\\n total_time = time.time()\\n\\n with h5py.File(waveforms_hdf5_path, 'w') as hf:\\n hf.create_dataset('audio_name', shape=((audios_num,)), dtype='S20')\\n hf.create_dataset('waveform', shape=((audios_num, clip_samples)), dtype=np.int16)\\n hf.create_dataset('target', shape=((audios_num, classes_num)), dtype=np.bool)\\n hf.attrs.create('sample_rate', data=sample_rate, dtype=np.int32)\\n\\n targets = np.zeros((audios_num, classes_num), dtype=np.bool)\\n\\n # Pack waveform & target of several audio clips to a single hdf5 file\\n for n in range(audios_num):\\n audio_path = os.path.join(audios_dir, audio_names[n])\\n\\n if os.path.isfile(audio_path):\\n logging.info('{} {}'.format(n, audio_path))\\n (audio, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True)\\n audio = pad_or_truncate(audio, clip_samples)\\n\\n hf['audio_name'][n] = audio_names[n].encode()\\n hf['waveform'][n] = float32_to_int16(audio)\\n ix = id_to_ix[target_names[n]]\\n targets[n, ix] = 1\\n #print(targets[n])\\n hf['target'][n] = targets[n]\\n else:\\n logging.info('{} File does not exist! {}'.format(n, audio_path))\\n\\n logging.info('Write to {}'.format(waveforms_hdf5_path))\\n logging.info('Pack hdf5 time: {:.3f}'.format(time.time() - total_time))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d035cede7bd46397be40298c6d9fd68\",\n \"score\": \"0.55122924\",\n \"text\": \"def image_to_hdf5(filename, f, group):\\r\\n img = Image.open(filename)\\r\\n image = np.array(img.getdata()).reshape(img.size[1], img.size[0], -1)\\r\\n data = np.array(image)\\r\\n dset = f.create_dataset(group + filename.split('/')\\r\\n [-1], shape=(4, img.size[1], img.size[0], 4))\\r\\n dset[0] = data\\r\\n im = Image.fromarray(dset[0].astype('uint8'))\\r\\n im.save(filename)\\r\\n return dset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5533f5f19d0b74ec6473df1e509a92bd\",\n \"score\": \"0.5512146\",\n \"text\": \"def createGroup(filename, group, subgroups = []):\\n\\n #deleteGroup(filename, group)\\n FILE = h5py.File(filename, \\\"r+\\\")\\n try:\\n GROUP = FILE.create_group(group)\\n print(\\\"[CREATE]: <{:s}> group created.\\\".format(group))\\n\\n for sg in subgroups:\\n GROUP.create_group(sg)\\n except:\\n print(\\\"Group exists, so not creating a new one.\\\")\\n pass\\n\\n try:\\n FILE.close()\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0999e38982a0b581097a9cf4ea0ef139\",\n \"score\": \"0.55031323\",\n \"text\": \"def createArchiveGenTask(self):\\n archiveTask = author.Task()\\n archiveTask.title = 'Generate {} files'.format(self.archiveExt)\\n archiveTask.service = self.service\\n\\n archiveTaskCmd = author.Command()\\n archiveTaskCmd.argv = [os.path.join(self.houdiniBinPath, 'hbatch.exe').replace('\\\\\\\\', '/')]\\n archiveTaskCmd.argv.append('-c')\\n # hscriptCmd = 'tcur {}; render -w -i -V 1 -f {} {} {}; quit'.format(\\n hscriptCmd = 'render -w -i -V 2 -f {} {} {}; quit'.format(\\n self.start,\\n self.end,\\n self.outputDriverPath\\n )\\n archiveTaskCmd.argv.append(hscriptCmd)\\n archiveTaskCmd.argv.append(self.snapshotScene)\\n archiveTaskCmd.retryrc = range(0, 11) # WATCHME : exit codes that will restart the task\\n archiveTask.addCommand(archiveTaskCmd)\\n\\n # Add archive files to be deleted by cleanup task\\n archiveFiles = [\\n self.archiveOutput.replace('$F4', str(frame).zfill(4))\\n for frame in range(self.start, self.end + 1)\\n ]\\n\\n self.toDelete.extend(archiveFiles)\\n\\n return archiveTask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ad02e77510cb35a15e0d2991413f733\",\n \"score\": \"0.546978\",\n \"text\": \"def pack_project_waveforms_to_hdf5(args):\\n\\n # Arguments & parameters\\n audios_dir = args.audios_dir\\n csv_path = args.csv_path\\n waveforms_hdf5_path = args.waveforms_hdf5_path\\n mini_data = args.mini_data\\n\\n split_dir = args.split_dir\\n split_to_test_id = args.split_to_test_id\\n\\n clip_samples = config.clip_samples\\n #classes_num = config.classes_num\\n classes_num = 9\\n sample_rate = config.sample_rate\\n #id_to_ix = config.id_to_ix\\n id_to_ix = {'screaming':0,'crying_sobbing':1,'chatter':2,'motor_vehicle_(road)':3,'emergency_vehicle':4,'siren':5,'explosion':6,'gunshot_gunfire':7, 'breaking':8 ,'others': 9}\\n\\n\\n id_dict = {'screaming':'/m/03qc9zr','crying_sobbing':'/m/0463cq4','chatter':'/m/07rkbfh','motor_vehicle_(road)':'/m/012f08','emergency_vehicle':'/m/03j1ly','siren':'/m/03kmc9','explosion':'/m/014zdl','gunshot_gunfire':'/m/032s66', 'breaking': '/m/07pc8lb'} \\n\\n\\n\\n\\n # Paths\\n if mini_data:\\n prefix = 'mini_'\\n waveforms_hdf5_path += '.mini'\\n else:\\n prefix = ''\\n\\n create_folder(os.path.dirname(waveforms_hdf5_path))\\n\\n logs_dir = '_logs/pack_waveforms_to_hdf5/{}{}'.format(prefix, get_filename(csv_path))\\n create_folder(logs_dir)\\n create_logging(logs_dir, filemode='w')\\n logging.info('Write logs to {}'.format(logs_dir))\\n \\n\\t# Read csv file\\n \\n #split_file = open(split_dir+\\\"/fold_filter_\\\"+str(split_to_test_id)+\\\".txt\\\",\\\"r\\\")\\n split_file = open(split_dir+\\\"/fold_\\\"+str(split_to_test_id)+\\\".txt\\\",\\\"r\\\")\\n lines = split_file.readlines()\\n audio_names = []\\n target_names = []\\n for line in lines:\\n audio_names.append(line.split(' ')[0]+'.wav')\\n target_names.append(id_to_ix[line.split(' ')[2][0:-1]])\\n\\n\\n\\n\\n\\n #meta_dict = read_metadata(csv_path, classes_num, id_to_ix)\\n\\n '''if mini_data:\\n mini_num = 10\\n for key in meta_dict.keys():\\n meta_dict[key] = meta_dict[key][0 : mini_num]'''\\n\\n #audios_num = len(meta_dict['audio_name'])\\n audios_num = len(lines)\\n\\n\\n\\n # Pack waveform to hdf5\\n total_time = time.time()\\n\\n with h5py.File(waveforms_hdf5_path, 'w') as hf:\\n hf.create_dataset('audio_name', shape=((audios_num,)), dtype='S20')\\n hf.create_dataset('waveform', shape=((audios_num, clip_samples)), dtype=np.int16)\\n hf.create_dataset('target', shape=((audios_num, classes_num)), dtype=np.bool)\\n hf.attrs.create('sample_rate', data=sample_rate, dtype=np.int32)\\n\\n targets = np.zeros((audios_num, classes_num), dtype=np.bool)\\n\\n # Pack waveform & target of several audio clips to a single hdf5 file\\n for n in range(audios_num):\\n audio_path = os.path.join(audios_dir, audio_names[n])\\n if os.path.isfile(audio_path):\\n logging.info('{} {}'.format(n, audio_path))\\n (audio, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True)\\n audio = pad_or_truncate(audio, clip_samples)\\n\\n hf['audio_name'][n] = audio_names[n].encode()\\n hf['waveform'][n] = float32_to_int16(audio)\\n \\n ix = target_names[n]\\n if ix < classes_num:\\n targets[n, ix] = 1\\n logging.info('{}'.format(targets[n]))\\n hf['target'][n] = targets[n]\\n else:\\n logging.info('{} File does not exist! {}'.format(n, audio_path))\\n\\n logging.info('Write to {}'.format(waveforms_hdf5_path))\\n logging.info('Pack hdf5 time: {:.3f}'.format(time.time() - total_time))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dd42d631e95e3cacfbc4d6c50940ab2\",\n \"score\": \"0.54462826\",\n \"text\": \"def save_metadata(self, hdf5: h5py.File, group_name: str) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ce0a608a1e3aa0296337311022976b1\",\n \"score\": \"0.5441013\",\n \"text\": \"def createFileGroups(self):\\n self.dataset.buildListOfFiles()\\n self.dataset_details['FileGroups'] = dict() \\n for fileName in self.dataset.files: \\n\\n name = removeIndex(fileName) \\n if name in self.dataset_details['FileGroups']: \\n self.dataset_details['FileGroups'][name]['Files'].append(fileName) \\n else: \\n instanceGroup = {\\\"SizeInTB\\\":None, \\n \\\"IsCrab\\\":None,\\n \\\"Files\\\":None,\\n \\\"FileEntries\\\":None,\\n \\\"PrimaryDatasetFraction\\\":None,\\n \\\"BadFiles\\\":None,\\n \\\"NumberBadFiles\\\":None,\\n \\\"MissingFiles\\\":None,\\n \\\"NumberMissingFiles\\\":None,\\n \\\"GoodFiles\\\":None,\\n \\\"NumberGoodFiles\\\":None,\\n \\\"TotalJobs\\\":None}\\n instanceGroup['Files']=[]\\n instanceGroup['Files'].append(fileName) \\n isCrab = isCrabFile( fileName ) \\n instanceGroup['IsCrab']=isCrab \\n self.dataset_details['FileGroups'][name]=instanceGroup \\n for fg in self.dataset_details['FileGroups']: \\n self.dataset_details['FileGroups'][fg]['Files'].sort(key=lambda x: int(getIndex(x)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"937ba09b1bcb1b00953fb95c26703f3b\",\n \"score\": \"0.5439032\",\n \"text\": \"def xr_save_by_grouping_along_dimension(ds,filepath, filename_prefix, grouping='chunk',parallel=True,dim='time'):\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"049219d45a71390f71f7457c993530d3\",\n \"score\": \"0.5436357\",\n \"text\": \"def to_h5(self, out_file, fields=None, group='/', replace=False, nocompression=False, attributes={}, fill_value=None, **kwargs):\\n kwargs.setdefault('srs_proj4', None)\\n kwargs.setdefault('srs_wkt', None)\\n kwargs.setdefault('srs_epsg', None)\\n kwargs.setdefault('grid_mapping_name', 'crs')\\n # check whether overwriting existing files\\n # append to existing files as default\\n mode = 'w' if replace else 'a'\\n\\n if fields is None:\\n fields=self.fields\\n if group[0] != '/':\\n group='/'+group\\n\\n # update fill values in fields\\n if fill_value is not None:\\n self.replace_invalid(fields=fields, fill_value=fill_value)\\n\\n # get crs attributes\\n self.crs_attributes(**kwargs)\\n with h5py.File(out_file,mode) as h5f:\\n # try adding file level attributes\\n try:\\n for att_name,att_val in attributes['ROOT'].items():\\n h5f.attrs[att_name] = att_val\\n except Exception:\\n pass\\n\\n # try creating the output group\\n try:\\n h5f.create_group(group)\\n except Exception:\\n pass\\n\\n # try adding group attributes\\n try:\\n for att_name,att_val in attributes[group].items():\\n h5f[group].attrs[att_name] = att_val\\n except Exception:\\n pass\\n\\n for field in ['x','y','time', 't'] + fields:\\n # if field exists, overwrite it\\n if field in h5f[group]:\\n if hasattr(self, field):\\n h5f[group+'/'+field][...] = getattr(self, field)\\n else:\\n #Otherwise, try to create the dataset\\n try:\\n if nocompression or field in ['x','y','time']:\\n h5f.create_dataset(group+'/'+field, data=getattr(self, field))\\n else:\\n\\n h5f.create_dataset(group+'/'+field, data=getattr(self, field),\\n chunks=True, compression=\\\"gzip\\\", fillvalue=self.fill_value)\\n except Exception:\\n pass\\n # try adding field attributes\\n try:\\n for att_name,att_val in attributes[field].items():\\n h5f[group+'/'+field].attrs[att_name] = att_val\\n except Exception:\\n pass\\n # add crs attributes if applicable\\n if self.crs:\\n # add grid mapping attribute to each grid field\\n for field in fields:\\n h5f[group+'/'+field].attrs['grid_mapping'] = kwargs['grid_mapping_name']\\n # add grid mapping variable with projection attributes\\n h5crs = h5f.create_dataset(kwargs['grid_mapping_name'], (), dtype=np.byte)\\n for att_name,att_val in self.crs.items():\\n h5crs.attrs[att_name] = att_val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9335eba7a943c66b3289267357718608\",\n \"score\": \"0.54333574\",\n \"text\": \"def write_by_file(fpath, *objs_list, **kwargs):\\n group_size = kwargs.pop('group_size', len(objs_list))\\n mode = 'wb'\\n if kwargs.get('gzip', True):\\n ofs = gzip.open(fpath, mode)\\n else:\\n ofs = open(fpath, mode)\\n with stream.open(fileobj=ofs, mode=mode, buffer_size=group_size,\\n **kwargs) as ostream:\\n ostream.write(*objs_list)\\n ofs.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d5008a7deb52ba1830263c9061b685\",\n \"score\": \"0.5423882\",\n \"text\": \"def produce_bam_custom(kmers_trie, name, label, guides_filename, args,\\n offdist, maxoffcount, processes, n, parts):\\n guidesfiles = []\\n # parts = 256\\n tempdir = '%s%s' % (name,'/classifiedfiles/tempfiles')\\n\\n util.print_log('produce SAM files...')\\n samfiles = ['%s/%s.sam' % (tempdir, i) for i in range(parts)]\\n # samfiles = [tempfile.NamedTemporaryFile(dir=name, suffix='.sam%s' % i)\\n # for i in xrange(parts)]\\n # util.print_log('store SAM in these files (gzipped): %s'\\n # % (', '.join([basename(f.name) for f in samfiles])))\\n\\n \\n if isinstance(guides_filename, str):\\n\\n util.print_log('split %s in %s parts...' % (guides_filename, parts))\\n guidesfiles = [tempfile.NamedTemporaryFile(dir=name,\\n suffix='.guides%s' % i)\\n for i in range(parts)]\\n util.print_log('store guides in these files: %s'\\n % (', '.join([basename(f.name) for f in guidesfiles])))\\n guidesfile = gzip.open(guides_filename) \\\\\\n if guides_filename.endswith('.gz') \\\\\\n else open(guides_filename)\\n index_num = 0\\n guidecount = 0\\n for line in guidesfile:\\n kmer1 = line.split()[0][0:n]\\n index_num = guides.get_num(kmer1, n)\\n guidesfiles[index_num].write(line)\\n \\n guidecount += 1\\n \\n guidesfile.close()\\n for f in guidesfiles:\\n f.flush()\\n util.print_log('%s guideRNAs to process' % guidecount)\\n util.print_log('done')\\n\\n process_list = []\\n all_task = Queue()\\n for i in range(parts):\\n task = (guides_filename[i].name, samfiles[i].name, i)\\n all_task.put(task)\\n\\n for i in range(processes):\\n p = Process(target=process_pool, args=(all_task, kmers_trie, args, offdist, maxoffcount, i, n, parts))\\n p.start()\\n process_list.append(p)\\n\\n for p in process_list:\\n p.join()\\n \\n for i in range(parts):\\n guidesfiles[i].close()\\n\\n else: \\n process_list = []\\n all_task = Queue()\\n for i in range(parts):\\n task = (guides_filename[i], samfiles[i], i)\\n all_task.put(task)\\n\\n for i in range(processes):\\n p = Process(target=process_pool, args=(all_task, kmers_trie, args, offdist, maxoffcount, i, n, parts))\\n p.start()\\n process_list.append(p)\\n\\n for p in process_list:\\n p.join()\\n\\n util.print_log('produce sorted BAM files...')\\n \\n bamfiles = ['%s/%s.bam' % (tempdir, i) for i in range(parts)]\\n # bamfiles = [tempfile.NamedTemporaryFile(dir=name, suffix='.bam%s' % i)\\n # for i in xrange(parts)]\\n # util.print_log('store BAM in these files: %s'\\n # % (', '.join([basename(f.name) for f in bamfiles])))\\n\\n pool = Pool(processes)\\n util.print_log('poolSize %s...' % processes)\\n index=False\\n for i in range(parts):\\n pool.apply_async(sam_to_bam,(samfiles[i], bamfiles[i], index,))\\n util.print_log('Waiting for all subprocesses done...')\\n pool.close()\\n pool.join()\\n\\n # for i in xrange(parts):\\n # samfiles[i].close()\\n util.print_log('merge into one BAM file...')\\n bamfile = '%s/%s_guides%s.bam' % (name, name,\\n '_%s' % label if label else '')\\n util.print_log('store in %s' % bamfile)\\n util.warn_file_exists(bamfile)\\n if parts > 1000:\\n mid = parts // 2\\n bamfiles_temp = [tempfile.NamedTemporaryFile(dir=name, suffix='.bam%s' % i)\\n for i in xrange(2)]\\n samtools_command1 = 'samtools merge -f %s %s' \\\\\\n % (bamfiles_temp[0].name, ' '.join(bamfiles[0:mid]))\\n os.system(samtools_command1)\\n\\n samtools_command2 = 'samtools merge -f %s %s' \\\\\\n % (bamfiles_temp[1].name, ' '.join(bamfiles[mid:parts]))\\n os.system(samtools_command2)\\n\\n samtools_command = 'samtools merge -f %s %s' \\\\\\n % (bamfile, ' '.join([f.name for f in bamfiles_temp]))\\n os.system(samtools_command)\\n\\n for f in bamfiles_temp:\\n f.close()\\n\\n else:\\n samtools_command = 'samtools merge -f %s %s' \\\\\\n % (bamfile, ' '.join(bamfiles))\\n # print samtools_command\\n os.system(samtools_command)\\n samtools_index_command = 'samtools index %s' % bamfile\\n # print samtools_index_command\\n os.system(samtools_index_command)\\n util.print_log('done')\\n # for i in xrange(parts):\\n # bamfiles[i].close()\\n\\n for i in range(parts):\\n if(os.path.exists(samfiles[i])):\\n os.remove(samfiles[i])\\n if(os.path.exists(bamfiles[i])):\\n os.remove(bamfiles[i])\\n\\n util.print_log('samtools version')\\n samtools_version_command = 'samtools --version'\\n print samtools_version_command\\n os.system(samtools_version_command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2da00a9f5c41e9c6ab49a32f72eda32f\",\n \"score\": \"0.54021645\",\n \"text\": \"def evaluate_gpt_with_distgen(settings, \\n archive_path=None, \\n merit_f=None, \\n gpt_input_file=None,\\n distgen_input_file=None,\\n workdir=None, \\n use_tempdir=True,\\n gpt_bin='$GPT_BIN',\\n timeout=2500,\\n auto_phase=False,\\n verbose=False,\\n gpt_verbose=False,\\n asci2gdf_bin='$ASCI2GDF_BIN',\\n kill_msgs=DEFAULT_KILL_MSGS,\\n parse_layout=False):\\n \\n G = run_gpt_with_distgen(settings=settings,\\n gpt_input_file=gpt_input_file,\\n distgen_input_file=distgen_input_file,\\n workdir=workdir, \\n use_tempdir=use_tempdir,\\n gpt_bin=gpt_bin,\\n timeout=timeout,\\n auto_phase=auto_phase,\\n verbose=verbose,\\n gpt_verbose=gpt_verbose,\\n asci2gdf_bin=asci2gdf_bin,\\n kill_msgs=kill_msgs,\\n parse_layout=parse_layout\\n )\\n \\n if merit_f:\\n merit_f = tools.get_function(merit_f)\\n output = merit_f(G)\\n else:\\n output = default_gpt_merit(G)\\n \\n if output['error']:\\n raise ValueError('error occured!')\\n \\n #Recreate Generator object for fingerprint, proper archiving\\n # TODO: make this cleaner\\n gen = Generator(G.distgen_input)\\n #gen = Generator()\\n #gen.input = G.distgen_input \\n \\n fingerprint = fingerprint_gpt_with_distgen(G, gen)\\n output['fingerprint'] = fingerprint \\n \\n if archive_path:\\n path = tools.full_path(archive_path)\\n assert os.path.exists(path), f'archive path does not exist: {path}'\\n archive_file = os.path.join(path, fingerprint+'.h5')\\n output['archive'] = archive_file\\n \\n # Call the composite archive method\\n archive_gpt_with_distgen(G, gen, archive_file=archive_file) \\n \\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42c307bd366b33a33a34b314e83be7c0\",\n \"score\": \"0.5375089\",\n \"text\": \"def rewrite_hdf5(g, data_dict):\\n for ddkey, dditem in data_dict.iteritems():\\n if ddkey not in g.keys():\\n g.create_group(ddkey)\\n for attr in dditem.attrs:\\n g[ddkey].attrs[attr]=dditem.attrs[attr]\\n append=False\\n else:\\n append=dditem.attrs.get(\\\"append\\\", False)\\n g[ddkey].attrs[\\\"append\\\"]=append\\n if isinstance(dditem, dataset): #dataset\\n if not append:\\n namestr=\\\"{0}\\\".format(len(g[ddkey]))\\n g[ddkey].create_dataset(namestr, data=dditem.data, dtype=dditem.datatype, maxshape=dditem.maxshape)#chunks=True)\\n #g[ddkey][namestr].attrs[\\\"index\\\"]=len(dditem.data)-1\\n else:\\n namestr=\\\"{0}\\\".format(len(g[ddkey])-1)\\n dset=g[ddkey][namestr]\\n #n=dset.attrs[\\\"index\\\"]\\n #if n>=len(dset)-1:\\n n=len(dset)\\n a=n+len(dditem.data)\\n dset.resize(a,axis=0)\\n dset[n:]=dditem.data\\n #n+=len(dditem.data)\\n #dset.attrs[\\\"index\\\"]=n\\n elif isinstance(dditem, group):\\n rewrite_hdf5(g[ddkey], dditem)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f041639fea79d2b81b0fb6861c3e2e3c\",\n \"score\": \"0.5358876\",\n \"text\": \"def save_data(self):\\n if not os.path.exists(os.path.dirname(self.filename)):\\n os.makedirs(os.path.dirname(self.filename))\\n with h5py.File(self.filename, \\\"w\\\") as f:\\n grid_data = f.create_group('grid')\\n self.grid.save_to_h5py(grid_data)\\n\\n all_species = f.create_group('species')\\n for species in self.list_species:\\n species_data = all_species.create_group(species.name)\\n species.save_to_h5py(species_data)\\n\\n f.attrs['dt'] = self.dt\\n f.attrs['NT'] = self.NT\\n f.attrs['run_date'] = self.run_date\\n f.attrs['git_version'] = self.git_version\\n f.attrs['title'] = self.title\\n f.attrs['runtime'] = self.runtime\\n f.attrs['considered_large'] = self.considered_large\\n print(f\\\"Saved file to {self.filename}\\\")\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4598538961ac7ac35010057b8031a57a\",\n \"score\": \"0.5351646\",\n \"text\": \"def WriteHDF5(self, fname, basedir, tag, m , nrings=9, fix_xco=False):\\n modf = h5py.File(fname, 'w')\\n\\n if m is not None:\\n if fix_xco is False:\\n X_CO = np.array([m.values['pi0_H2_'+str(i)] for i in range(1,nrings+1)])\\n else: \\n X_CO = np.ones(nrings)\\n #modf = h5py.File(fname, 'w')\\n #try:\\n # Generate Groups\\n # template_group = modf.create_group(\\\"templates\\\")\\n # fit_group = modf.create_group(\\\"fit_results\\\")\\n\\n\\n # Get data dimensions\\n tmp = pyfits.open(basedir+'/bremss_healpix_54_'+tag+'.gz')\\n energies = tmp[2].data.field(0)\\n tShape = (len(energies), tmp[1].data.shape[0])\\n print tShape\\n del tmp # free memory\\n\\n pi0 = modf.create_dataset(\\\"/templates/pi0\\\", tShape, dtype='float32',compression=\\\"gzip\\\")\\n pi0_0 = modf.create_dataset(\\\"/templates/pi0_0\\\", tShape, dtype='float32',compression=\\\"gzip\\\")\\n pi0_1 = modf.create_dataset(\\\"/templates/pi0_1\\\", tShape, dtype='float32',compression=\\\"gzip\\\")\\n brem = modf.create_dataset(\\\"/templates/brem\\\", tShape, dtype='float32',compression=\\\"gzip\\\")\\n brem_0 = modf.create_dataset(\\\"/templates/brem_0\\\", tShape, dtype='float32',compression=\\\"gzip\\\")\\n brem_1 = modf.create_dataset(\\\"/templates/brem_1\\\", tShape, dtype='float32',compression=\\\"gzip\\\")\\n ics_opt = modf.create_dataset(\\\"/templates/ics_opt\\\", tShape, dtype='float32',compression=\\\"gzip\\\")\\n ics_fir = modf.create_dataset(\\\"/templates/ics_fir\\\", tShape, dtype='float32',compression=\\\"gzip\\\")\\n ics_cmb = modf.create_dataset(\\\"/templates/ics_cmb\\\", tShape, dtype='float32',compression=\\\"gzip\\\")\\n modf.create_dataset(\\\"/templates/energies\\\", data=energies, dtype='float32',compression=\\\"gzip\\\")\\n # Now fill in the templates one by one.\\n # Add fit metadata.\\n # Add galdef metadata.\\n\\n\\n #---------------------------------------------------------------\\n # Create Diffuse Template from fitting results.\\n def ReadFits(fname, length):\\n d = pyfits.open(fname)[1].data\\n return np.array([d.field(i) for i in range(length)])\\n\\n if m is not None:\\n for i_ring in range(1,nrings+1):\\n print \\\"Adding HI/HII ring\\\", i_ring\\n\\n pi0[...] += m.values['pi0HIHII']*ReadFits(basedir+'/pi0_decay_HIR_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n pi0[...] += m.values['pi0HIHII']*ReadFits(basedir+'/pi0_decay_HII_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n if fix_xco is False:\\n pi0[...] += X_CO[i_ring-1]*ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n else:\\n pi0[...] += m.values['pi0HIHII']*ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n\\n brem[...] += m.values['pi0HIHII']*1.25*ReadFits(basedir+'/bremss_HIR_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n brem[...] += m.values['pi0HIHII']*1.25*ReadFits(basedir+'/bremss_HII_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n if fix_xco is False:\\n brem[...] += 1.25*X_CO[i_ring-1]*ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n else: \\n brem[...] += m.values['pi0HIHII']*1.25*X_CO[i_ring-1]*ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n \\n if i_ring == 1 :\\n pi0_0[...] += ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n brem_0[...] += ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n if i_ring == 2:\\n pi0_1[...] += ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n brem_1[...] += ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n\\n print 'Adding ICS'\\n ics_opt[...] += m.values['ics']*ReadFits(basedir+'/ics_isotropic_comp_1_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n ics_fir[...] += m.values['ics']*ReadFits(basedir+'/ics_isotropic_comp_2_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n ics_cmb[...] += m.values['ics']*ReadFits(basedir+'/ics_isotropic_comp_3_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n\\n else:\\n for i_ring in range(1,nrings+1):\\n print \\\"Adding HI/HII/H2 ring\\\", i_ring\\n\\n pi0[...] += ReadFits(basedir+'/pi0_decay_HIR_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n pi0[...] += ReadFits(basedir+'/pi0_decay_HII_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n pi0[...] += ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n\\n brem[...] += ReadFits(basedir+'/bremss_HIR_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n brem[...] += ReadFits(basedir+'/bremss_HII_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n brem[...] += ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n\\n if i_ring == 1 :\\n pi0_0[...] += ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n brem_0[...] += ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n if i_ring == 2:\\n pi0_1[...] += ReadFits(basedir+'/pi0_decay_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n brem_1[...] += ReadFits(basedir+'/bremss_H2R_ring_'+str(i_ring)+'_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n\\n print 'Adding ICS'\\n ics_opt[...] += ReadFits(basedir+'/ics_isotropic_comp_1_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n ics_fir[...] += ReadFits(basedir+'/ics_isotropic_comp_2_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n ics_cmb[...] += ReadFits(basedir+'/ics_isotropic_comp_3_healpix_54_'+tag+'.gz', len(energies)).clip(0)\\n\\n # except:\\n # modf.close()\\n \\n try: \\n modf.close()\\n print 'Closed HDF5 file.' \\n except: \\n print 'Failed to close HDF5 file.' \\n pass\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edc55435f66927e97fd865b8b5016a59\",\n \"score\": \"0.53244704\",\n \"text\": \"def write_to_hdf5(data_by_test, filename):\\n\\twith hdf5.File('{}.hdf5'.format(filename), 'w') as file:\\n\\t\\t# foreach test data\\n\\t\\tfor test_index, test_data in enumerate(data_by_test.values()):\\n\\t\\t\\t# convert iterator 'test_data' to the list\\n\\t\\t\\tfile.create_dataset(\\\"test_{}\\\".format(test_index),\\n\\t\\t\\t data=list(test_data), compression=\\\"gzip\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adbad349ce105e54e030dbc519f364c4\",\n \"score\": \"0.5301874\",\n \"text\": \"def save(self):\\n\\n with h5py.File(self.filename, \\\"r+\\\") as f:\\n\\n source_handle = f.create_group(\\\"source\\\")\\n if self.data.source:\\n self.data.source.save(source_handle)\\n\\n uhecr_handle = f.create_group(\\\"uhecr\\\")\\n if self.data.uhecr:\\n self.data.uhecr.save(uhecr_handle)\\n\\n detector_handle = f.create_group(\\\"detector\\\")\\n if self.data.detector:\\n self.data.detector.save(detector_handle)\\n\\n model_handle = f.create_group(\\\"model\\\")\\n if self.model:\\n self.model.save(model_handle)\\n\\n fit_handle = f.create_group(\\\"fit\\\")\\n if self.fit:\\n\\n # fit inputs\\n fit_input_handle = fit_handle.create_group(\\\"input\\\")\\n for key, value in self.fit_input.items():\\n fit_input_handle.create_dataset(key, data=value)\\n\\n # samples\\n samples = fit_handle.create_group(\\\"samples\\\")\\n for key, value in self.chain.items():\\n samples.create_dataset(key, data=value)\\n\\n else:\\n plotvars_handle = f.create_group(\\\"plotvars\\\")\\n\\n if self.analysis_type == self.gmf_type:\\n for key, value in list(self.defl_plotvars.items()):\\n plotvars_handle.create_dataset(key, data=value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9f3d70ea62c4328ad931aea3ce24a4\",\n \"score\": \"0.52851725\",\n \"text\": \"def save(self):\\n \\n self.f = hdf.File(\\\"dbtest3.hdf5\\\",\\\"a\\\")\\n dt = hdf.special_dtype(vlen=str)\\n name = self.compoundA + \\\" + \\\" + self.compoundB + \\\" @ \\\" + str(self.temperature)\\n subgrp = self.f.create_group(name)\\n dset = subgrp.create_dataset(\\\"Specifics\\\",(10,),dtype=dt)\\n dset[0] = self.compoundA\\n dset[1] = self.compoundB\\n dset[2] = self.reference\\n dset[3] = self.etaA\\n dset[4] = self.etaB\\n dset[5] = self.rhoA\\n dset[6] = self.rhoB\\n dset[7] = self.massA\\n dset[8] = self.massB\\n dset[9] = self.temperature\\n \\n dset = subgrp.create_dataset(\\\"Data\\\",(3,len(self.x1)),dtype=\\\"f\\\")\\n dset[0] = self.x1\\n dset[1] = self.etaSystem\\n dset[2] = self.rhoSystem\\n print \\\"Saving data for \\\" + name\\n self.f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6516f1de6193780627bfc1ac1a160af\",\n \"score\": \"0.5283196\",\n \"text\": \"def _output(self):\\n t0 = time.monotonic()\\n self.sendProgress('Waiting for HDF5 writer lock...')\\n with self.h5io.writer(f'Samples/{self.headers[0].title}/{self.headers[0].distance[0]:.2f}') as group:\\n t1 = time.monotonic()\\n self.sendProgress('Writing HDF5 file...')\\n # Set attributes of the <dist> group from the averaged header.\\n self.h5io.writeHeader(self.averagedHeader, group)\\n self.h5io.writeExposure(self.averagedExposure, group)\\n try:\\n del group['badfsns']\\n except KeyError:\\n pass\\n group['badfsns'] = np.array(sorted(self.result.badfsns), dtype=np.int)\\n try:\\n del group['goodindex']\\n except KeyError:\\n pass\\n group['goodindex'] = self.goodindex\\n self.h5io.writeCurve(self.averagedCurve, group, 'curve_averaged')\\n self.h5io.writeCurve(self.reintegratedCurve, group, 'curve_reintegrated')\\n try:\\n del group['curve']\\n except KeyError:\\n pass\\n group['curve'] = h5py.SoftLink('curve_averaged')\\n self.h5io.writeOutlierTest(group.name, self.outliertest)\\n # save all curves\\n for groupname in ['allcurves', 'curves']:\\n try:\\n del group[groupname]\\n except KeyError:\\n pass\\n curvesgroup = group.create_group('allcurves')\\n goodcurvesgroup = group.create_group('curves')\\n # we will write outlier results per-curve. Do the common calculations beforehand.\\n scoreindex = 0\\n for i, (h, isgood) in enumerate(zip(self.headers, self.goodindex)):\\n self.h5io.writeCurve(self.curves[:, :, i], curvesgroup, str(h.fsn))\\n dataset = curvesgroup[str(h.fsn)]\\n self.h5io.writeHeader(h, dataset)\\n if isgood:\\n dataset.attrs['correlmat_bad'] = int(h.fsn in self.result.badfsns)\\n dataset.attrs['correlmat_discrp'] = self.outliertest.score[scoreindex]\\n scoreindex += 1\\n else:\\n dataset.attrs['correlmat_bad'] = -1\\n dataset.attrs['correlmat_discrp'] = np.nan\\n # make links in the 'curves' group to those fsns which were not bad at the beginning of this procedure\\n if (h.fsn not in self.result.badfsns) or (h.fsn in self.result.newbadfsns):\\n goodcurvesgroup[str(h.fsn)] = h5py.SoftLink(dataset.name)\\n\\n self.result.time_output_write = time.monotonic() - t1\\n self.result.time_output_waitforlock = t1 - t0\\n self.result.time_output = time.monotonic() - t0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"295b6199ac04d36bc7052db8ece4d1f2\",\n \"score\": \"0.5261463\",\n \"text\": \"def ProduceCombinedUnifiedTreeandHaloCatalog(fname,numsnaps,tree,numhalos,halodata,atime,\\n\\tdescripdata={'Title':'Tree and Halo catalog of sim', 'VELOCIraptor_version':1.15, 'Tree_version':1.1, 'Particle_num_threshold':20, 'Temporal_linking_length':1, 'Flag_gas':False, 'Flag_star':False, 'Flag_bh':False},\\n\\tcosmodata={'Omega_m':1.0, 'Omega_b':0., 'Omega_Lambda':0., 'Hubble_param':1.0,'BoxSize':1.0, 'Sigma8':1.0},\\n\\tunitdata={'UnitLength_in_Mpc':1.0, 'UnitVelocity_in_kms':1.0,'UnitMass_in_Msol':1.0, 'Flag_physical_comoving':True,'Flag_hubble_flow':False},\\n\\tpartdata={'Flag_gas':False, 'Flag_star':False, 'Flag_bh':False},\\n\\tibuildheadtail=0,ibuildmajormergers=0, TEMPORALHALOIDVAL=1000000000000):\\n\\n\\tif (ibuildheadtail==1):\\n\\t\\tBuildTemporalHeadTail(numsnaps,tree,numhalos,halodata)\\n\\tif (ibuildmajormergers==1):\\n\\t\\tIdentifyMergers(numsnaps,tree,numhalos,halodata,boxsize,hval,atime)\\n\\thdffile=h5py.File(fname+\\\".snap.hdf.data\\\",'w')\\n\\theadergrp=hdffile.create_group(\\\"Header\\\")\\n\\t#store useful information such as number of snapshots, halos,\\n\\t#cosmology (Omega_m,Omega_b,Hubble_param,Omega_Lambda, Box size)\\n\\t#units (Physical [1/0] for physical/comoving flag, length in Mpc, km/s, solar masses, Gravity\\n\\t#and TEMPORALHALOIDVAL used to traverse tree information (converting halo ids to haloindex or snapshot), Reverse_order [1/0] for last snap listed first)\\n\\t#set the attributes of the header\\n\\theadergrp.attrs[\\\"NSnaps\\\"]=numsnaps\\n\\t#overall description\\n\\theadergrp.attrs[\\\"Title\\\"]=descripdata[\\\"Title\\\"]\\n\\t#simulation box size\\n\\theadergrp.attrs[\\\"BoxSize\\\"]=cosmodata[\\\"BoxSize\\\"]\\n\\tfindergrp=headergrp.create_group(\\\"HaloFinder\\\")\\n\\tfindergrp.attrs[\\\"Name\\\"]=\\\"VELOCIraptor\\\"\\n\\tfindergrp.attrs[\\\"Version\\\"]=descripdata[\\\"VELOCIraptor_version\\\"]\\n\\tfindergrp.attrs[\\\"Particle_num_threshold\\\"]=descripdata[\\\"Particle_num_threshold\\\"]\\n\\n\\ttreebuildergrp=headergrp.create_group(\\\"TreeBuilder\\\")\\n\\ttreebuildergrp.attrs[\\\"Name\\\"]=\\\"VELOCIraptor-Tree\\\"\\n\\ttreebuildergrp.attrs[\\\"Version\\\"]=descripdata[\\\"Tree_version\\\"]\\n\\ttreebuildergrp.attrs[\\\"Temporal_linking_length\\\"]=descripdata[\\\"Temporal_linking_length\\\"]\\n\\n\\t#cosmological params\\n\\tcosmogrp=headergrp.create_group(\\\"Cosmology\\\")\\n\\tfor key in cosmodata.keys():\\n\\t\\tif (key!='BoxSize'): cosmogrp.attrs[key]=cosmodata[key]\\n\\t#unit params\\n\\tunitgrp=headergrp.create_group(\\\"Units\\\")\\n\\tfor key in unitdata.keys():\\n\\t\\tunitgrp.attrs[key]=unitdata[key]\\n\\t#particle types\\n\\tpartgrp=headergrp.create_group(\\\"Parttypes\\\")\\n\\tpartgrp.attrs[\\\"Flag_gas\\\"]=descripdata[\\\"Flag_gas\\\"]\\n\\tpartgrp.attrs[\\\"Flag_star\\\"]=descripdata[\\\"Flag_star\\\"]\\n\\tpartgrp.attrs[\\\"Flag_bh\\\"]=descripdata[\\\"Flag_bh\\\"]\\n\\n\\t#now have finished with header\\n\\n\\t#now need to create groups for halos and then a group containing tree information\\n\\tsnapsgrp=hdffile.create_group(\\\"Snapshots\\\")\\n\\t#internal tree keys\\n\\ttreekeys=[\\\"RootHead\\\", \\\"RootHeadSnap\\\", \\\"Head\\\", \\\"HeadSnap\\\", \\\"Tail\\\", \\\"TailSnap\\\", \\\"RootTail\\\", \\\"RootTailSnap\\\", \\\"Num_progen\\\"]\\n\\n\\tfor i in range(numsnaps):\\n\\t\\t#note that I normally have information in reverse order so that might be something in the units\\n\\t\\tsnapgrp=snapsgrp.create_group(\\\"Snap_%03d\\\"%(numsnaps-1-i))\\n\\t\\tsnapgrp.attrs[\\\"Snapnum\\\"]=i\\n\\t\\tsnapgrp.attrs[\\\"NHalos\\\"]=numhalos[i]\\n\\t\\tsnapgrp.attrs[\\\"scalefactor\\\"]=atime[i]\\n\\t#now close file and use the pytables interface so as to write the table\\n\\thdffile.close()\\n\\t#now write tables using pandas interface\\n\\tfor i in range(numsnaps):\\n\\t\\t#lets see if we can alter the code to write a table\\n\\t\\tkeys=halodata[i].keys()\\n\\t\\t#remove tree keys\\n\\t\\tfor tkey in treekeys: keys.remove(tkey)\\n\\t\\t#make temp dict\\n\\t\\tdictval=dict()\\n\\t\\tfor key in keys:\\n\\t\\t\\tdictval[key]=halodata[i][key]\\n\\t\\t#make a pandas DataFrame using halo dictionary\\n\\t\\tdf=pd.DataFrame.from_dict(dictval)\\n\\t\\tdf.to_hdf(fname+\\\".snap.hdf.data\\\",\\\"Snapshots/Snap_%03d/Halos\\\"%(numsnaps-1-i), format='table', mode='a')\\n\\n\\t#reopen with h5py interface\\n\\thdffile=h5py.File(fname+\\\".snap.hdf.data\\\",'a')\\n\\t#then write tree information in separate group\\n\\ttreegrp=hdffile.create_group(\\\"MergerTree\\\")\\n\\t#Tree group should contain\\n\\t\\\"\\\"\\\"\\n\\t\\tHaloSnapID\\n\\t\\tHaloSnapNum\\n\\t\\tHaloSnapIndex\\n\\t\\tProgenitorIndex\\n\\t\\tProgenitorSnapnum\\n\\t\\tProgenitorID\\n\\t\\tDescendantIndex\\n\\t\\t..\\n\\t\\t..\\n\\t\\tRootProgenitorIndex\\n\\t\\t..\\n\\t\\t..\\n\\t\\tRootDescendantIndex\\n\\t\\\"\\\"\\\"\\n\\t#to save on memory, allocate each block separately\\n\\t#store halo information\\n\\ttothalos=sum(numhalos)\\n\\ttdata=np.zeros(tothalos,dtype=halodata[0][\\\"ID\\\"].dtype)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=halodata[i][\\\"ID\\\"]\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"HaloSnapID\\\",data=tdata)\\n\\ttdata=np.zeros(tothalos,dtype=np.uint32)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=i\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"HaloSnapNum\\\",data=tdata)\\n\\ttdata=np.zeros(tothalos,dtype=np.uint64)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=range(int(numhalos[i]))\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"HaloSnapIndex\\\",data=tdata)\\n\\t#store progenitors\\n\\ttdata=np.zeros(tothalos,dtype=halodata[0][\\\"Tail\\\"].dtype)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=halodata[i][\\\"Tail\\\"]\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"ProgenitorID\\\",data=tdata)\\n\\ttdata=np.zeros(tothalos,dtype=halodata[0][\\\"TailSnap\\\"].dtype)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=halodata[i][\\\"TailSnap\\\"]\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"ProgenitorSnapnum\\\",data=tdata)\\n\\ttdata=np.zeros(tothalos,dtype=np.uint64)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=(halodata[i][\\\"Tail\\\"]%TEMPORALHALOIDVAL-1)\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"ProgenitorIndex\\\",data=tdata)\\n\\t#store descendants\\n\\ttdata=np.zeros(tothalos,dtype=halodata[0][\\\"Head\\\"].dtype)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=halodata[i][\\\"Head\\\"]\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"DescendantID\\\",data=tdata)\\n\\ttdata=np.zeros(tothalos,dtype=halodata[0][\\\"HeadSnap\\\"].dtype)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=halodata[i][\\\"HeadSnap\\\"]\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"DescendantSnapnum\\\",data=tdata)\\n\\ttdata=np.zeros(tothalos,dtype=np.uint64)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=(halodata[i][\\\"Head\\\"]%TEMPORALHALOIDVAL-1)\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"DescendantIndex\\\",data=tdata)\\n\\t#store progenitors\\n\\ttdata=np.zeros(tothalos,dtype=halodata[0][\\\"RootTail\\\"].dtype)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=halodata[i][\\\"RootTail\\\"]\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"RootProgenitorID\\\",data=tdata)\\n\\ttdata=np.zeros(tothalos,dtype=halodata[0][\\\"RootTailSnap\\\"].dtype)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=halodata[i][\\\"RootTailSnap\\\"]\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"RootProgenitorSnapnum\\\",data=tdata)\\n\\ttdata=np.zeros(tothalos,dtype=np.uint64)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=(halodata[i][\\\"RootTail\\\"]%TEMPORALHALOIDVAL-1)\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"RootProgenitorIndex\\\",data=tdata)\\n\\t#store descendants\\n\\ttdata=np.zeros(tothalos,dtype=halodata[0][\\\"RootHead\\\"].dtype)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=halodata[i][\\\"RootHead\\\"]\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"RootDescendantID\\\",data=tdata)\\n\\ttdata=np.zeros(tothalos,dtype=halodata[0][\\\"RootHeadSnap\\\"].dtype)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=halodata[i][\\\"RootHeadSnap\\\"]\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"RootDescendantSnapnum\\\",data=tdata)\\n\\ttdata=np.zeros(tothalos,dtype=np.uint64)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=(halodata[i][\\\"RootHead\\\"]%TEMPORALHALOIDVAL-1)\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"RootDescendantIndex\\\",data=tdata)\\n\\t#store number of progenitors\\n\\ttdata=np.zeros(tothalos,dtype=np.uint32)\\n\\tcount=0\\n\\tfor i in range(numsnaps):\\n\\t\\ttdata[count:int(numhalos[i])+count]=halodata[i][\\\"Num_progen\\\"]\\n\\t\\tcount+=int(numhalos[i])\\n\\ttreegrp.create_dataset(\\\"NProgen\\\",data=tdata)\\n\\n\\thdffile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16445b72f0367edad1d91f339993db9e\",\n \"score\": \"0.5249218\",\n \"text\": \"def createh5(filename, mode=None, **kwargs):\\n if mode is None: \\n mode = 'a'\\n params = {'libver':'latest', 'track_order':True}\\n params.update(kwargs)\\n attrs = params.pop('attrs') if 'attrs' in params else dict()\\n with h5file(filename, mode, **params) as f:\\n f.attrs.update(attrs)\\n return Group_autoiter(__h5file__(filename, params), '/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec650ccdd21dd4572ccb4eabfaa1a3a5\",\n \"score\": \"0.5233218\",\n \"text\": \"def _save_superstar_grids(self, hs_runner):\\n ss_dir = join(self.out_dir, \\\"superstar_grids\\\")\\n if not exists(ss_dir):\\n os.mkdir(ss_dir)\\n for s in hs_runner.superstar_grids:\\n s.grid.write(join(ss_dir, \\\"superstar_{}.ccp4\\\".format(s.identifier)))\\n shutil.make_archive(ss_dir, 'zip', ss_dir)\\n shutil.rmtree(ss_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2af67287cd549a5b05ba8c242ce5370a\",\n \"score\": \"0.51985604\",\n \"text\": \"def tarball_create(param_dict, proj_dict, catl_ext='hdf5'):\\n Prog_msg = param_dict['Prog_msg' ]\\n ## List of Mock catalogues\\n catl_path_arr = cu.Index(proj_dict['mock_cat_mc'], catl_ext)\\n ## README file\\n # Downloading working README file\\n readme_file = os.path.join( proj_dict['base_dir'],\\n 'references',\\n 'README_RTD.pdf')\\n cu.File_Download_needed(readme_file, param_dict['readme_url'])\\n cu.File_Exists(readme_file)\\n # Cartesian coordinates for all mocks\\n cart_pos_fig = os.path.join( proj_dict['fig_dir'],\\n '{0}_{1}_{2}_xyz_mocks.{3}'.format(\\n param_dict['survey'],\\n param_dict['halotype'],\\n param_dict['cosmo_choice'],\\n 'pdf'))\\n # Luminosity function\\n lum_func_catls = os.path.join( proj_dict['fig_dir'],\\n '{0}_{1}_{2}_lum_function_mocks.{3}'.format(\\n param_dict['survey'],\\n param_dict['halotype'],\\n param_dict['cosmo_choice'],\\n 'pdf'))\\n ## Saving to TAR file\\n tar_file_path = os.path.join( proj_dict['tar_dir'],\\n '{0}_{1}_catls.tar.gz'.format(\\n param_dict['survey_name'],\\n param_dict['halotype']))\\n # Opening file\\n with tarfile.open(tar_file_path, mode='w:gz') as tf:\\n # README file\\n tf.add(readme_file, arcname=os.path.basename(readme_file))\\n # Figures\\n tf.add(cart_pos_fig, arcname=os.path.basename(cart_pos_fig))\\n tf.add(lum_func_catls, arcname=os.path.basename(lum_func_catls))\\n for file_kk in catl_path_arr:\\n ## Reading in DataFrame\\n gal_pd_kk = cu.read_hdf5_file_to_pandas_DF(file_kk)\\n ## DataFrame `without` certain columns\\n gal_pd_mod = catl_drop_cols(gal_pd_kk)\\n ## Saving modified DataFrame to file\\n file_mod_kk = file_kk+'.mod'\\n cu.pandas_df_to_hdf5_file(gal_pd_mod, file_mod_kk, key='\\\\gal_catl')\\n cu.File_Exists(file_mod_kk)\\n # Saving to Tar-file\\n tf.add(file_mod_kk, arcname=os.path.basename(file_kk))\\n # Deleting extra file\\n os.remove(file_mod_kk)\\n tf.close()\\n cu.File_Exists(tar_file_path)\\n if param_dict['verbose']:\\n print('{0} TAR file saved as: {1}'.format(Prog_msg, tar_file_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c71b274a5e89e859c80d60018925cad\",\n \"score\": \"0.5181026\",\n \"text\": \"def write_partitioned(ratings, output_dir):\\n\\n dates = pd.to_datetime(ratings[\\\"timestamp\\\"], unit=\\\"s\\\")\\n\\n for (year, month), grp in ratings.groupby([dates.dt.year, dates.dt.month]):\\n output_path = output_dir / str(year) / f\\\"{month:02d}.csv\\\"\\n output_path.parent.mkdir(parents=True, exist_ok=True)\\n grp.to_csv(output_path, index=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e8c61a1752f9dec28e5223291e3efea\",\n \"score\": \"0.516821\",\n \"text\": \"def create_seg_exports(self):\\n try:\\n if self.output_dir == None:\\n out_dir = \\\"SEG_Output\\\"\\n os.mkdir(\\\"SEG_Output\\\")\\n else:\\n out_dir = self.output_dir\\n os.mkdir(self.output_dir)\\n except:\\n pass\\n\\n self.annots_df.apply(self.drop_dupes, axis=1)\\n\\n for dataset_id in self.annots_df[\\\"datasetId\\\"].unique():\\n self.dataset_annots = self.annots_df[self.annots_df.datasetId == dataset_id]\\n for study_uid in self.studies:\\n dicom_hier = self.dicom_hierarchy[study_uid]\\n for series_dict in dicom_hier:\\n for series_uid in series_dict:\\n sops = series_dict[series_uid]\\n\\n annotations = self.dataset_annots[\\n self.dataset_annots.SeriesInstanceUID == series_uid\\n ]\\n annotations = annotations[annotations[\\\"scope\\\"] == \\\"INSTANCE\\\"]\\n if annotations.empty:\\n continue\\n\\n self.dicom_tags = self.tags_df[\\n self.tags_df.SeriesInstanceUID == series_uid\\n ].dicomTags.values[0]\\n annotators = annotations.createdById.unique()\\n instance_uid = pydicom.uid.generate_uid(prefix=None)\\n date = datetime.now().strftime(\\\"%Y%m%d\\\")\\n time = datetime.now().strftime(\\\"%H%M%S\\\")\\n\\n sop_instance_num_map = {}\\n for sop in sops:\\n sop_dicom_tags = self.tags_df[\\n self.tags_df.SOPInstanceUID == sop\\n ].dicomTags.values[0]\\n if \\\"InstanceNumber\\\" in sop_dicom_tags:\\n sop_instance_num_map[sop] = sop_dicom_tags[\\\"InstanceNumber\\\"]\\n else:\\n sop_instance_num_map[sop] = \\\"1\\\"\\n\\n def create_instance_number(row):\\n return sop_instance_num_map[row[\\\"SOPInstanceUID\\\"]]\\n\\n try:\\n annotations[\\\"instanceNumber\\\"] = annotations.apply(\\n create_instance_number, axis=1\\n )\\n except:\\n continue\\n annotations = annotations.sort_values(\\n [\\\"labelGroupName\\\", \\\"labelId\\\", \\\"instanceNumber\\\"], ignore_index=True\\n ) # sort by label group then annotation then appearance in series\\n\\n # File meta info data elements\\n file_meta = FileMetaDataset()\\n file_meta.FileMetaInformationVersion = b\\\"\\\\x00\\\\x01\\\"\\n file_meta.TransferSyntaxUID = \\\"1.2.840.10008.1.2.1\\\"\\n file_meta.MediaStorageSOPInstanceUID = instance_uid # Create Instance UID # Media Storage SOP Instance UID\\n file_meta.ImplementationClassUID = str(\\n pydicom.uid.PYDICOM_IMPLEMENTATION_UID\\n ) # Implementation Class UID\\n file_meta.ImplementationVersionName = str(\\n pydicom.__version__\\n ) # Implementation Version Name\\n file_meta.SourceApplicationEntityTitle = \\\"POSDA\\\"\\n\\n # Main data elements\\n ds = Dataset()\\n\\n ds = self.place_tags(self.dicom_tags, ds, self.dicom_tag_heirarchy, True)\\n\\n ds.SpecificCharacterSet = \\\"ISO_IR 192\\\"\\n ds.SOPClassUID = \\\"1.2.840.10008.5.1.4.1.1.66.4\\\"\\n ds.SOPInstanceUID = instance_uid\\n ds.SeriesDate = str(date) # Series Date\\n ds.ContentDate = str(date) # Content Date\\n ds.SeriesTime = str(time) # Series Time\\n ds.ContentTime = str(time) # Series Time\\n ds.Manufacturer = \\\"MDAI\\\"\\n ds.Modality = \\\"SEG\\\"\\n\\n # Referenced Series Sequence\\n refd_series_sequence = Sequence()\\n ds.ReferencedSeriesSequence = refd_series_sequence\\n\\n # Referenced Series Sequence: Referenced Series 1\\n refd_series1 = Dataset()\\n refd_series_sequence.append(refd_series1)\\n\\n # Referenced Series Sequence: Referenced Series 1\\n refd_series1 = Dataset()\\n refd_series_sequence.append(refd_series1)\\n refd_series1.SeriesInstanceUID = series_uid\\n\\n # Referenced Instance Sequence\\n refd_instance_sequence = Sequence()\\n refd_series1.ReferencedInstanceSequence = refd_instance_sequence\\n\\n ds.SegmentationType = \\\"BINARY\\\"\\n\\n for annotator_id in annotators:\\n annotator_annots = annotations[annotations.createdById == annotator_id]\\n\\n if self.combine:\\n label_group_sets = [annotator_annots.labelGroupName.unique()]\\n else:\\n label_group_sets = [\\n [group] for group in annotator_annots.labelGroupName.unique()\\n ]\\n\\n ds.SamplesPerPixel = 1\\n ds.PhotometricInterpretation = \\\"MONOCHROME2\\\"\\n ds.BitsAllocated = 1\\n ds.BitsStored = 1\\n ds.HighBit = 0\\n ds.PixelRepresentation = 0\\n ds.LossyImageCompression = \\\"00\\\"\\n\\n for label_group_set in label_group_sets:\\n label_group_annots = annotator_annots[\\n annotator_annots.labelGroupName.isin(label_group_set)\\n ]\\n\\n # Segment Sequence\\n segment_sequence = Sequence()\\n ds.SegmentSequence = segment_sequence\\n\\n self.imgs = []\\n self.seen_labels = set()\\n self.name_number_map = {}\\n self.included_sops = []\\n self.unique_sops = set()\\n self.label_groups = list(annotations.labelGroupName.unique())\\n self.prev_annot = None\\n label_group_annots.apply(self.img_insert, args=(ds,), axis=1)\\n\\n ds.NumberOfFrames = len(\\n self.imgs\\n ) # create during last parts of SEG file (should equal length of annot_df)\\n ds.PixelData = pack_bits(np.array(self.imgs))\\n\\n for sop in self.unique_sops:\\n sop_dicom_tags = self.tags_df[\\n self.tags_df.SOPInstanceUID == sop\\n ].dicomTags.values[0]\\n refd_instance1 = Dataset()\\n refd_instance_sequence.append(refd_instance1)\\n if \\\"SOPClassUID\\\" in sop_dicom_tags:\\n refd_instance1.ReferencedSOPClassUID = sop_dicom_tags[\\n \\\"SOPClassUID\\\"\\n ]\\n refd_instance1.ReferencedSOPInstanceUID = sop_dicom_tags[\\n \\\"SOPInstanceUID\\\"\\n ]\\n\\n # Leaving it out for now but if nothing works then maybe try to add it back in blank and then with dummy values\\n # Edit: added it back in but still unnecessary.\\n # -----------------------------------------------------------------------\\n # Dimension Index Sequence\\n dimension_index_sequence = Sequence()\\n ds.DimensionIndexSequence = dimension_index_sequence\\n # -----------------------------------------------------------------------\\n\\n ds.ContentLabel = \\\"MDAI_SEG\\\"\\n ds.ContentCreatorName = f\\\"annotator {annotator_id}\\\"\\n\\n # Leaving it out for now but if nothing works then maybe try to add it back in blank and then with dummy values\\n # Edit: added it back in but still unnecessary.\\n # -----------------------------------------------------------------------\\n # Shared Functional Groups Sequence\\n shared_functional_groups_sequence = Sequence()\\n ds.SharedFunctionalGroupsSequence = (\\n shared_functional_groups_sequence\\n )\\n # -----------------------------------------------------------------------\\n\\n # Per-frame Functional Groups Sequence\\n per_frame_functional_groups_sequence = Sequence()\\n ds.PerFrameFunctionalGroupsSequence = (\\n per_frame_functional_groups_sequence\\n )\\n\\n # Per-frame Functional Groups Sequence\\n per_frame_functional_groups_sequence = Sequence()\\n ds.PerFrameFunctionalGroupsSequence = (\\n per_frame_functional_groups_sequence\\n )\\n\\n # Per-frame Functional Groups Sequence\\n per_frame_functional_groups_sequence = []\\n\\n # Loop through each frame with an annotation and create unique Per Frame Functional Group Sequence\\n # ---------------------------------------------------------------------------\\n for segment_number, sop in self.included_sops:\\n label_names = \\\", \\\".join(\\n label_group_annots[\\\"labelName\\\"].unique()\\n )\\n ds.SeriesDescription = (\\n f\\\"Segmentation of {label_names} by annotator {annotator_id}\\\"\\n )\\n\\n sop_dicom_tags = self.tags_df[\\n self.tags_df.SOPInstanceUID == sop\\n ].dicomTags.values[0]\\n\\n # Per-frame Functional Groups Sequence: Per-frame Functional Groups 1\\n per_frame_functional_groups1 = Dataset()\\n per_frame_functional_groups_sequence.append(\\n per_frame_functional_groups1\\n )\\n\\n # Derivation Image Sequence\\n derivation_image_sequence = Sequence()\\n per_frame_functional_groups1.DerivationImageSequence = (\\n derivation_image_sequence\\n )\\n\\n # Derivation Image Sequence: Derivation Image 1\\n derivation_image1 = Dataset()\\n derivation_image_sequence.append(derivation_image1)\\n\\n # Source Image Sequence\\n source_image_sequence = Sequence()\\n derivation_image1.SourceImageSequence = source_image_sequence\\n\\n # Source Image Sequence: Source Image 1\\n source_image1 = Dataset()\\n source_image_sequence.append(source_image1)\\n if \\\"SOPClassUID\\\" in self.dicom_tags:\\n source_image1.ReferencedSOPClassUID = self.dicom_tags[\\n \\\"SOPClassUID\\\"\\n ]\\n source_image1.ReferencedSOPInstanceUID = self.dicom_tags[\\n \\\"SOPInstanceUID\\\"\\n ]\\n\\n # Purpose of Reference Code Sequence\\n purpose_of_ref_code_sequence = Sequence()\\n source_image1.PurposeOfReferenceCodeSequence = (\\n purpose_of_ref_code_sequence\\n )\\n\\n # Purpose of Reference Code Sequence: Purpose of Reference Code 1\\n purpose_of_ref_code1 = Dataset()\\n purpose_of_ref_code_sequence.append(purpose_of_ref_code1)\\n purpose_of_ref_code1.CodeValue = \\\"121322\\\"\\n purpose_of_ref_code1.CodingSchemeDesignator = \\\"DCM\\\"\\n purpose_of_ref_code1.CodeMeaning = (\\n \\\"Source image for image processing operation\\\"\\n )\\n\\n # Derivation Code Sequence\\n derivation_code_sequence = Sequence()\\n derivation_image1.DerivationCodeSequence = (\\n derivation_code_sequence\\n )\\n\\n # Derivation Code Sequence: Derivation Code 1\\n derivation_code1 = Dataset()\\n derivation_code_sequence.append(derivation_code1)\\n derivation_code1.CodeValue = \\\"113076\\\"\\n derivation_code1.CodingSchemeDesignator = \\\"DCM\\\"\\n derivation_code1.CodeMeaning = \\\"Segmentation\\\"\\n\\n # Segment Identification Sequence\\n segment_id_seq = Dataset()\\n per_frame_functional_groups1.SegmentIdentificationSequence = [\\n segment_id_seq\\n ]\\n\\n # Segment Number\\n segment_id_seq.ReferencedSegmentNumber = segment_number\\n\\n per_frame_functional_groups1 = self.place_tags(\\n sop_dicom_tags,\\n per_frame_functional_groups1,\\n self.dicom_tag_heirarchy[\\n \\\"PerFrameFunctionalGroupsSequence\\\"\\n ],\\n False,\\n )\\n # -------------------------------------------------------------------------\\n\\n ds.PerFrameFunctionalGroupsSequence = (\\n per_frame_functional_groups_sequence\\n )\\n\\n ds.file_meta = file_meta\\n ds.is_implicit_VR = False\\n ds.is_little_endian = True\\n\\n if self.included_sops:\\n if self.combine:\\n ds.save_as(\\n f\\\"{os.getcwd()}/{out_dir}/DICOM_SEG_{dataset_id}_{series_uid}_annotator_{annotator_id}.dcm\\\",\\n False,\\n )\\n else:\\n ds.save_as(\\n f\\\"{os.getcwd()}/{out_dir}/DICOM_SEG_{dataset_id}_label_group_{label_group_set[0]}_series_{series_uid}_annotator_{annotator_id}.dcm\\\",\\n False,\\n )\\n print(f\\\"Successfully exported DICOM SEG files into {out_dir}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9fb4baa1caf9d2e244af1752f0b023a\",\n \"score\": \"0.5165111\",\n \"text\": \"def compress_all_data():\\n\\n output_file = 'qbank_data.tar.gz'\\n\\n output_path = os.path.join(OUTPUT_DIR, 'qbank', output_file)\\n\\n with tarfile.open(output_path, 'w:gz') as tar:\\n\\n mongo_dump_dir = os.path.join(OUTPUT_DIR, 'qbank', 'mongo-dump')\\n\\n activity_dump_dir = os.path.join(OUTPUT_DIR, 'qbank', 'student-activities')\\n\\n tar.add(mongo_dump_dir, arcname=os.path.basename(mongo_dump_dir))\\n\\n #tar.add(activity_dump_dir, arcname=os.path.basename(activity_dump_dir))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d6011c79cbeceab7a098e4821f4bc3c\",\n \"score\": \"0.5148114\",\n \"text\": \"def write_by_name(fpath, *objs_list, **kwargs):\\n length = len(objs_list)\\n group_size = kwargs.pop('group_size', length)\\n with stream.open(fpath, 'wb', **kwargs) as ostream:\\n cursor = 0\\n while cursor < length:\\n ostream.write(*objs_list[cursor:cursor+group_size])\\n cursor += group_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af681386ee088a4b407d27cbc964433f\",\n \"score\": \"0.5146606\",\n \"text\": \"def save(self, filepath, npf=-1, compression=None, verbose=1, n_jobs=1):\\n\\n path, name = os.path.split(filepath)\\n if name.endswith('.h5'):\\n name = '.'.join(name.split('.')[:-1])\\n\\n start = time.time()\\n if npf != -1:\\n \\n global moddsets\\n\\n i = begin = end = 0\\n args, moddsets = [], []\\n while end < len(self.jets_i):\\n end = min(end + npf, len(self.jets_i))\\n\\n arrays = {'jets_i': self.jets_i[begin:end], 'jets_i_cols': self.jets_i_cols,\\n 'jets_f': self.jets_f[begin:end], 'jets_f_cols': self.jets_f_cols,\\n 'filenames': self.filenames}\\n\\n if self.store_pfcs:\\n arrays['pfcs'] = self.pfcs[begin:end]\\n arrays['pfcs_cols'] = self.pfcs_cols\\n\\n if self.store_gens:\\n arrays['gens'] = self.gens[begin:end]\\n arrays['gens_cols'] = self.gens_cols\\n\\n filepath = os.path.join(path, '{}_{}'.format(name, i))\\n\\n moddset = self.__class__(_dataset=self.dataset, _arrays=arrays)\\n\\n if n_jobs == 1:\\n moddset.save(filepath, compression=compression, verbose=verbose)\\n\\n else:\\n moddsets.append(moddset)\\n args.append((i, filepath, compression)) \\n\\n begin = end\\n i += 1\\n\\n if n_jobs != 1:\\n if verbose >= 1:\\n l = len(args)\\n pf = (l, 's' if l > 1 else '', time.time() - start)\\n print('Constructed {} temporary MODDataset{} in {:.3f}s'.format(*pf))\\n\\n if n_jobs == -1:\\n n_jobs = os.cpu_count()\\n if n_jobs is None:\\n n_jobs = 4\\n\\n start = time.time()\\n with create_pool(processes=min(n_jobs, len(args))) as pool:\\n for i,_ in enumerate(pool.imap_unordered(_moddset_save, args, chunksize=1)):\\n if verbose >= 1 and ((i+1) % 5 == 0 or i+1 == len(args)):\\n pf = (i+1, (i+1)/len(args)*100, time.time() - start)\\n print(' Saved {} files, {:.2f}% done in {:.3f}s'.format(*pf))\\n\\n del moddsets\\n \\n return\\n\\n # compression opts\\n compression = ({'compression': 'gzip', 'compression_opts': compression} \\n if compression is not None else {})\\n comp_str = '_compressed' if len(compression) else ''\\n\\n # ensure directory exists\\n if not os.path.exists(path):\\n if verbose >= 2:\\n print('Creating', path)\\n os.mkdir(path)\\n\\n if verbose >= 2:\\n print('Saving to', path)\\n\\n filename = name + comp_str\\n hf = h5py.File(os.path.join(path, filename + '.h5'), 'w')\\n\\n # jets_i\\n jets_i = hf.create_dataset('jets_i', data=self.jets_i, **compression)\\n jets_i.attrs.create('cols', np.asarray(self.jets_i_cols, dtype='S'))\\n\\n # jets_f\\n jets_f = hf.create_dataset('jets_f', data=self.jets_f, **compression)\\n jets_f.attrs.create('cols', np.asarray(self.jets_f_cols, dtype='S'))\\n\\n # pfcs\\n if self.store_pfcs:\\n pfcs = _write_large_object_array_to_h5(hf, 'pfcs', self.pfcs, \\n ncols=len(self.pfcs_cols), **compression)\\n pfcs.attrs.create('cols', np.asarray(self.pfcs_cols, dtype='S'))\\n hf.create_dataset('pfcs_index', data=_make_particles_index(self.pfcs), **compression)\\n\\n # gens\\n if self.store_gens:\\n gens = _write_large_object_array_to_h5(hf, 'gens', self.gens, \\n ncols=len(self.gens_cols), **compression)\\n gens.attrs.create('cols', np.asarray(self.gens_cols, dtype='S'))\\n hf.create_dataset('gens_index', data=_make_particles_index(self.gens), **compression)\\n\\n # filenames\\n hf.create_dataset('filenames', data=self.filenames.astype('S'), **compression)\\n\\n # close\\n hf.close()\\n\\n if verbose >= 1:\\n args = (filename, len(self.jets_i), time.time() - start)\\n print(' Saved {} with {} jets in {:.3f}s'.format(*args))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c30f42120ef98b16049f68dfdfc9bc72\",\n \"score\": \"0.5138672\",\n \"text\": \"def _archive(self, label, files, delete_originals=True):\\n if not os.path.exists(self.archive_store):\\n os.mkdir(self.archive_store)\\n tf = tarfile.open(label + \\\".tar.gz\\\",'w:gz')\\n logging.info(\\\"Archiving data to file %s\\\" % tf.name)\\n # Add data files\\n archive_paths = []\\n for file_path in files:\\n archive_path = os.path.join(label, file_path)\\n tf.add(os.path.join(self.root, file_path), archive_path)\\n archive_paths.append(archive_path)\\n tf.close()\\n # Move the archive to self.archive_store\\n shutil.copy(tf.name, self.archive_store) # shutil.move() doesn't work as expected if dataroot is a symbolic link\\n os.remove(tf.name)\\n # Delete original files.\\n if delete_originals:\\n for file_path in files:\\n os.remove(os.path.join(self.root, file_path))\\n self._last_label = label # useful for testing\\n return archive_paths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48e2e0577c8e31d6e77e6d744202225c\",\n \"score\": \"0.5128573\",\n \"text\": \"def write_hdf5_file(metadata, out_file, ts_file, tcoh_file, scoh_file, vel_file, mask_file, geom_file):\\n ts_obj = timeseries(ts_file)\\n ts_obj.open(print_msg=False)\\n dateList = ts_obj.dateList\\n numDate = len(dateList)\\n\\n # Open HDF5 File\\n f = h5py.File(out_file, 'w')\\n print('create HDF5 file: {} with w mode'.format(out_file))\\n max_digit = 55\\n\\n ##### Group - Observation\\n gName = 'HDFEOS/GRIDS/timeseries/observation'\\n print('create group /{}'.format(gName))\\n group = f.create_group(gName)\\n\\n ## O1 - displacement\\n dsName = 'displacement'\\n dsShape = (numDate, ts_obj.length, ts_obj.width)\\n dsDataType = np.float32\\n print(('create dataset /{d:<{w}} of {t:<10} in size of {s}'\\n ' with compression={c}').format(d='{}/{}'.format(gName, dsName),\\n w=max_digit,\\n t='float32',\\n s=dsShape,\\n c=COMPRESSION))\\n dset = group.create_dataset(dsName,\\n shape=dsShape,\\n maxshape=(None, dsShape[1], dsShape[2]),\\n dtype=dsDataType,\\n chunks=True,\\n compression=COMPRESSION)\\n\\n print('write data acquition by acquition ...')\\n prog_bar = ptime.progressBar(maxValue=numDate)\\n for i in range(numDate):\\n dset[i, :, :] = readfile.read(ts_file, datasetName=dateList[i])[0]\\n prog_bar.update(i+1, suffix='{}/{} {}'.format(i+1, numDate, dateList[i]))\\n prog_bar.close()\\n\\n # attributes\\n dset.attrs['Title'] = dsName\\n dset.attrs['MissingValue'] = FLOAT_ZERO\\n dset.attrs['_FillValue'] = FLOAT_ZERO\\n dset.attrs['Units'] = 'meters'\\n\\n ## O2 - date\\n dsName = 'date'\\n data = np.array(dateList, dtype=np.string_)\\n dset = create_hdf5_dataset(group, dsName, data)\\n\\n ## O3 - perp baseline\\n dsName = 'bperp'\\n data = np.array(ts_obj.pbase, dtype=np.float32)\\n dset = create_hdf5_dataset(group, dsName, data)\\n\\n ## O4 - velocity\\n dsName = 'velocity'\\n data = readfile.read(vel_file)[0]\\n dset = create_hdf5_dataset(group, dsName, data)\\n # attributes\\n dset.attrs['Title'] = dsName\\n dset.attrs['MissingValue'] = FLOAT_ZERO\\n dset.attrs['_FillValue'] = FLOAT_ZERO\\n dset.attrs['Units'] = 'm/yr'\\n\\n ##### Group - Quality\\n gName = 'HDFEOS/GRIDS/timeseries/quality'\\n print('create group /{}'.format(gName))\\n group = f.create_group(gName)\\n\\n ## Q1 - temporalCoherence\\n dsName = 'temporalCoherence'\\n # read\\n data = readfile.read(tcoh_file)[0]\\n # write\\n dset = create_hdf5_dataset(group, dsName, data)\\n # attributes\\n dset.attrs['Title'] = dsName\\n dset.attrs['MissingValue'] = FLOAT_ZERO\\n dset.attrs['_FillValue'] = FLOAT_ZERO\\n dset.attrs['Units'] = '1'\\n\\n ## Q2 - avgSpatialCoherence\\n dsName = 'avgSpatialCoherence'\\n # read\\n data = readfile.read(scoh_file)[0]\\n # write\\n dset = create_hdf5_dataset(group, dsName, data)\\n # attributes\\n dset.attrs['Title'] = dsName\\n dset.attrs['MissingValue'] = FLOAT_ZERO\\n dset.attrs['_FillValue'] = FLOAT_ZERO\\n dset.attrs['Units'] = '1'\\n\\n ## Q3 - mask\\n dsName = 'mask'\\n # read\\n data = readfile.read(mask_file, datasetName='mask')[0]\\n # write\\n dset = create_hdf5_dataset(group, dsName, data)\\n # attributes\\n dset.attrs['Title'] = dsName\\n dset.attrs['MissingValue'] = BOOL_ZERO\\n dset.attrs['_FillValue'] = BOOL_ZERO\\n dset.attrs['Units'] = '1'\\n \\n\\n ##### Group - Write Geometry\\n # Required: height, incidenceAngle\\n # Optional: rangeCoord, azimuthCoord, azimuthAngle, slantRangeDistance, waterMask, shadowMask\\n gName = 'HDFEOS/GRIDS/timeseries/geometry'\\n print('create group /{}'.format(gName))\\n group = f.create_group(gName)\\n\\n geom_obj = geometry(geom_file)\\n geom_obj.open(print_msg=False)\\n for dsName in geom_obj.datasetNames:\\n # read\\n data = geom_obj.read(datasetName=dsName, print_msg=False)\\n # write\\n dset = create_hdf5_dataset(group, dsName, data)\\n\\n # attributes\\n dset.attrs['Title'] = dsName\\n if dsName in ['height', 'slantRangeDistance', 'bperp']:\\n dset.attrs['MissingValue'] = FLOAT_ZERO\\n dset.attrs['_FillValue'] = FLOAT_ZERO\\n dset.attrs['Units'] = 'meters'\\n\\n elif dsName in ['incidenceAngle', 'azimuthAngle', 'latitude', 'longitude']:\\n dset.attrs['MissingValue'] = FLOAT_ZERO\\n dset.attrs['_FillValue'] = FLOAT_ZERO\\n dset.attrs['Units'] = 'degrees'\\n\\n elif dsName in ['rangeCoord', 'azimuthCoord']:\\n dset.attrs['MissingValue'] = FLOAT_ZERO\\n dset.attrs['_FillValue'] = FLOAT_ZERO\\n dset.attrs['Units'] = '1'\\n\\n elif dsName in ['waterMask', 'shadowMask']:\\n dset.attrs['MissingValue'] = BOOL_ZERO\\n dset.attrs['_FillValue'] = BOOL_ZERO\\n dset.attrs['Units'] = '1'\\n\\n # Write Attributes to the HDF File\\n print('write metadata to root level')\\n for key, value in iter(metadata.items()):\\n f.attrs[key] = value\\n f.close()\\n print('finished writing to {}'.format(out_file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbbd598756eb39ad06f90109f086e14b\",\n \"score\": \"0.51182556\",\n \"text\": \"def write_group(pf, tag,mdef):\\n tbl=pf.get_tbl(tag)\\n filename=tag+\\\".csv\\\"\\n fh=open(filename,\\\"w+\\\")\\n fh.write('\\\\\\\"%s\\\\\\\",\\\\\\\"%s\\\\\\\",\\\\\\\"%s\\\\\\\",\\\\\\\"%s\\\\\\\"\\\\n' % (\\\"Key\\\",\\\"Type\\\",\\\"Mutable\\\",\\\"Concept\\\"))\\n for k in tbl:\\n t=mdef.type(k)\\n tstr=\\\"undefined\\\"\\n if(t==MDtype.Int64):\\n tstr=\\\"int\\\"\\n elif(t==MDtype.Double):\\n tstr=\\\"double\\\"\\n elif(t==MDtype.String):\\n tstr=\\\"string\\\"\\n elif(t==MDtype.Boolean):\\n tstr=\\\"boolean\\\"\\n writeable=mdef.writeable(k)\\n wstr=\\\"undefined\\\"\\n if(writeable):\\n wstr=\\\"Yes\\\"\\n else:\\n wstr=\\\"No\\\"\\n fh.write('\\\\\\\"%s\\\\\\\",\\\\\\\"%s\\\\\\\",\\\\\\\"%s\\\\\\\",\\\\\\\"%s\\\\\\\"\\\\n' % (k,tstr,wstr,mdef.concept(k)))\\n fh.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d1c698f9e5670d4528426641511f69d\",\n \"score\": \"0.5116573\",\n \"text\": \"def create_group(self, name, attrs={}, track_order=True):\\n with h5file(self.file.filename, 'a', **self.file.params) as f:\\n o = f[self.name]\\n g = o.create_group(name, track_order)\\n g.attrs.update(attrs)\\n return Group_autoiter(self.file, self.name+name+'/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"464456aa5f2393a86d1b9683afc59df5\",\n \"score\": \"0.5110412\",\n \"text\": \"def create_group_directory(self, campaign_name, app_dir, group_name, runs,\\n max_nprocs, nodes, launch_mode,\\n component_subdirs, walltime, node_exclusive,\\n timeout, machine,\\n sosd_path=None,\\n sos_analysis_path=None,\\n tau_profiling=False, tau_tracing=False,\\n kill_on_partial_failure=False,\\n run_post_process_script=None,\\n run_post_process_stop_on_failure=False,\\n scheduler_options=None,\\n run_dir_setup_script=None):\\n script_dir = os.path.join(config.CHEETAH_PATH_SCHEDULER,\\n self.scheduler_name, 'group')\\n if not os.path.isdir(script_dir):\\n raise ValueError(\\\"scheduler '%s' is not yet supported (path '%s')\\\"\\n % (self.scheduler_name, script_dir))\\n if scheduler_options is None:\\n scheduler_options = {}\\n copytree_to_dir(script_dir, self.output_directory)\\n\\n fobs_path = os.path.join(self.output_directory, 'fobs.json')\\n min_nodes = 1\\n\\n f = open(fobs_path, 'w')\\n fob_list = []\\n for i, run in enumerate(runs):\\n # TODO: abstract this to higher levels\\n os.makedirs(run.run_path, exist_ok=True)\\n\\n # Create working dir for each component\\n for rc in run.run_components:\\n os.makedirs(rc.working_dir, exist_ok=True)\\n\\n if run.sosflow_profiling:\\n run.insert_sosflow(sosd_path, sos_analysis_path,\\n run.run_path,\\n machine.processes_per_node)\\n\\n # Copy the global input files common to all components\\n for input_rpath in run.inputs:\\n copy_to_dir(input_rpath, run.run_path)\\n\\n # Copy input files requested by each component\\n # save working dirs for later use\\n working_dirs = {} # map component name to path\\n for rc in run.run_components:\\n working_dirs[rc.name] = rc.working_dir\\n\\n # if rc has an adios xml file, copy it to working dir\\n if rc.adios_xml_file:\\n copy_to_dir(rc.adios_xml_file, rc.working_dir)\\n\\n # now copy other inputs marked under component_inputs\\n if rc.component_inputs is not None:\\n for input_file in rc.component_inputs:\\n dest = os.path.join(rc.working_dir,\\n os.path.basename(\\n input_file))\\n # input type is symlink\\n if type(input_file) == SymLink:\\n os.symlink(input_file, dest)\\n\\n # input type is a regular file\\n elif os.path.isfile(input_file):\\n copy_to_dir(input_file, rc.working_dir)\\n\\n # Input file is a directory\\n elif os.path.isdir(input_file):\\n copytree_to_dir(input_file, dest)\\n\\n else:\\n raise exc.CheetahException \\\\\\n (\\\"Could not copy component input {}\\\"\\n .format(input_file))\\n\\n # ADIOS XML param support\\n adios_xml_params = \\\\\\n run.instance.get_parameter_values_by_type(ParamAdiosXML) or \\\\\\n run.instance.get_parameter_values_by_type(ParamADIOS2XML)\\n for pv in adios_xml_params:\\n working_dir = working_dirs[pv.target]\\n\\n # dirty way of getting the adios xml filename of the rc\\n # that is represented by pv.target\\n rc_adios_xml = self._get_rc_adios_xml_filename(\\n run, pv.target)\\n xml_filepath = os.path.join(working_dir,\\n os.path.basename(rc_adios_xml))\\n\\n # Check if this is adios1 or adios2\\n adios_version = get_adios_version(rc_adios_xml)\\n\\n if adios_version == 1:\\n if pv.param_type == \\\"adios_transform\\\":\\n adios_params.adios_xml_transform(\\n xml_filepath,pv.group_name, pv.var_name, pv.value)\\n elif pv.param_type == \\\"adios_transport\\\":\\n # value could be\\n # \\\"MPI_AGGREGATE:num_aggregators=64;num_osts\\\"\\n # extract the method name and the method options\\n method_name = pv.value\\n method_opts = \\\"\\\"\\n if \\\":\\\" in pv.value:\\n value_tokens = pv.value.split(\\\":\\\", 1)\\n method_name = value_tokens[0]\\n method_opts = value_tokens[1]\\n\\n adios_params.adios_xml_transport(\\n xml_filepath, pv.group_name, method_name,\\n method_opts)\\n else:\\n raise exc.CheetahException(\\\"Unrecognized adios param\\\")\\n\\n else: # adios version == 2\\n operation_value = list(pv.value.keys())[0]\\n if pv.operation_name in ('engine', 'transport'):\\n parameters = list(pv.value.values())[0]\\n if pv.operation_name == 'engine':\\n adios2.set_engine(xml_filepath, pv.io_name,\\n operation_value, parameters)\\n else:\\n adios2.set_transport(xml_filepath, pv.io_name,\\n operation_value, parameters)\\n else: # operation_name == 'var_operation'\\n var_name = list(pv.value.keys())[0]\\n var_name_dict = pv.value[var_name]\\n var_operation_value = list(var_name_dict.keys())[0]\\n var_op_dict = var_name_dict[var_operation_value]\\n parameters = var_op_dict\\n adios2.set_var_operation(xml_filepath, pv.io_name,\\n var_name,\\n var_operation_value,\\n parameters)\\n\\n # Calculate the no. of nodes required by this run.\\n # This must be done after dataspaces support is added.\\n if run.total_nodes > min_nodes:\\n min_nodes = run.total_nodes\\n\\n # Generic config file support. Note: slurps entire\\n # config file into memory, requires adding file to\\n # campaign 'inputs' option.\\n config_params = \\\\\\n run.instance.get_parameter_values_by_type(ParamConfig)\\n for pv in config_params:\\n working_dir = working_dirs[pv.target]\\n src_filepath = relative_or_absolute_path(app_dir,\\n pv.config_filename)\\n # Allow for relative pathnames in the spec\\n src_filename = pv.config_filename\\n if pv.config_filename[0] == '/':\\n src_filename = os.path.basename(src_filepath)\\n config_filepath = os.path.join(working_dir,\\n src_filename)\\n if not os.path.isfile(config_filepath):\\n copy_to_path(src_filepath, config_filepath)\\n lines = []\\n # read and modify lines\\n # hack: handle json files. currently works only on singly\\n # nested json files\\n if config_filepath.endswith(\\\".json\\\"):\\n json_config_set_option(config_filepath, pv.match_string,\\n pv.value)\\n else: # handle other file types\\n with open(config_filepath) as config_f:\\n for line in config_f:\\n line = line.replace(pv.match_string, pv.value)\\n lines.append(line)\\n # rewrite file with modified lines\\n with open(config_filepath, 'w') as config_f:\\n config_f.write(\\\"\\\".join(lines))\\n\\n # Key value config file support. Note: slurps entire\\n # config file into memory, requires adding file to\\n # campaign 'inputs' option.\\n kv_params = \\\\\\n run.instance.get_parameter_values_by_type(ParamKeyValue)\\n for pv in kv_params:\\n working_dir = working_dirs[pv.target]\\n src_filepath = relative_or_absolute_path(app_dir,\\n pv.config_filename)\\n # Allow for relative pathnames in the spec\\n src_filename = pv.config_filename\\n if pv.config_filename[0] == '/':\\n src_filename = os.path.basename(src_filepath)\\n kv_filepath = os.path.join(working_dir, src_filename)\\n if not os.path.isfile(kv_filepath):\\n copy_to_path(src_filepath, kv_filepath)\\n lines = []\\n # read and modify lines\\n key_found = False\\n with open(kv_filepath) as kv_f:\\n for line in kv_f:\\n parts = line.split('=', 1)\\n if len(parts) == 2:\\n k = parts[0].strip()\\n if k == pv.key_name:\\n # assume all k=v type formats will\\n # support no spaces around equals\\n line = k + '=' + str(pv.value)\\n # preserve a user comment if it exists\\n if '!' in parts[1]:\\n line = line + \\\" !\\\" + \\\\\\n parts[1].strip().split('!')[1]\\n line = line + '\\\\n'\\n key_found = True\\n lines.append(line)\\n assert key_found, \\\\\\n \\\"Issue parsing a ParamKeyValue: Could not find key {}\\\"\\\\\\n \\\" in config file {}\\\".format(pv.key_name, src_filepath)\\n # rewrite file with modified lines\\n with open(kv_filepath, 'w') as kv_f:\\n kv_f.write(\\\"\\\".join(lines))\\n\\n # Env var parameter values\\n kv_params = run.instance.get_parameter_values_by_type(ParamEnvVar)\\n for pv in kv_params:\\n rc = run._get_rc_by_name(pv.target)\\n rc.env[pv.option] = str(pv.value)\\n\\n # save code commands as text\\n params_path_txt = os.path.join(run.run_path,\\n self.run_command_name)\\n with open(params_path_txt, 'w') as params_f:\\n for rc in run.run_components:\\n params_f.write(' '.join(map(shlex.quote,\\n [rc.exe] + rc.args)))\\n params_f.write('\\\\n')\\n\\n # save params as JSON for use in post-processing, more\\n # useful for post-processing scripts then the command\\n # text\\n params_path_json = os.path.join(run.run_path,\\n self.run_json_name)\\n run_data = run.get_app_param_dict()\\n with open(params_path_json, 'w') as params_f:\\n json.dump(run_data, params_f, indent=2)\\n\\n fob_runs = []\\n for j, rc in enumerate(run.run_components):\\n if timeout is not None:\\n rc.timeout = parse_timedelta_seconds(timeout)\\n\\n fob_runs.append(rc.as_fob_data())\\n\\n fob = dict(id=run.run_id, launch_mode=launch_mode, runs=fob_runs,\\n working_dir=run.run_path, apps_dir=app_dir,\\n kill_on_partial_failure=kill_on_partial_failure,\\n post_process_script=run_post_process_script,\\n post_process_stop_on_failure=\\n run_post_process_stop_on_failure,\\n post_process_args=[params_path_json],\\n node_layout=run.node_layout.serialize_to_dict(),\\n total_nodes=run.total_nodes,\\n machine_name=machine.name,\\n tau_profiling=tau_profiling, tau_tracing=tau_tracing)\\n fob_list.append(fob)\\n\\n # write to file run dir\\n run_fob_path = os.path.join(run.run_path,\\n \\\"codar.cheetah.fob.json\\\")\\n with open(run_fob_path, \\\"w\\\") as runf:\\n runf.write(json.dumps(fob, sort_keys=True, indent=4))\\n runf.write(\\\"\\\\n\\\")\\n\\n if run_dir_setup_script is not None:\\n self._execute_run_dir_setup_script(run.run_path,\\n run_dir_setup_script)\\n\\n # Get the size of the run dir. This should be the last step\\n # in the creation of the run dir.\\n self._get_pre_submit_dir_size(run)\\n\\n # Write fob_list to group-level json file\\n f.write(json.dumps(fob_list, sort_keys=True, indent=4))\\n f.close()\\n\\n if nodes is None:\\n nodes = min_nodes\\n elif nodes < min_nodes:\\n raise exc.CheetahException(\\n \\\"nodes for group is too low, need at least %d, got %d\\\"\\n % (min_nodes, nodes))\\n\\n # TODO: what case does this handle? should have a test case for\\n # it.\\n if machine.node_exclusive:\\n group_ppn = machine.processes_per_node\\n else:\\n group_ppn = math.ceil((max_nprocs) / nodes)\\n\\n env_path = os.path.join(self.output_directory, 'group-env.sh')\\n group_env = templates.GROUP_ENV_TEMPLATE.format(\\n walltime=parse_timedelta_seconds(walltime),\\n max_procs=max_nprocs,\\n processes_per_node=group_ppn,\\n nodes=nodes,\\n node_exclusive=node_exclusive,\\n account=scheduler_options.get('project', ''),\\n queue=scheduler_options.get('queue', ''),\\n reservation=scheduler_options.get('reservation', ''),\\n custom=scheduler_options.get('custom', ''),\\n # TODO: require name be valid for all schedulers\\n campaign_name='codar.cheetah.'+campaign_name,\\n group_name=group_name,\\n constraint=scheduler_options.get('constraint', ''),\\n license=scheduler_options.get('license', ''),\\n machine_name=machine.name\\n )\\n with open(env_path, 'w') as f:\\n f.write(group_env)\\n\\n return nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d0b11610732aabe5234e6c18d97dcc\",\n \"score\": \"0.51037985\",\n \"text\": \"def write_distributional_spf_dataset(data: Dict[str, np.array], full_path: str) -> None:\\n file = h5py.File(full_path, 'w')\\n for k, v in data.items():\\n file.create_dataset(name=k, data=v)\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ada4bdf303e3599dd657b9b298ede6d\",\n \"score\": \"0.51037645\",\n \"text\": \"def make_archive(sim_epoch: rebound.Simulation, \\n mjd0: int, mjd1: int, \\n time_step: int, save_step: int,\\n save_elements: bool,\\n progbar: bool) -> rebound.SimulationArchive:\\n # Get archive filename from simulation\\n fname_archive: str = calc_fname_archive(sim_epoch, mjd0, mjd1)\\n # Path of archive\\n path_archive = os.path.join(dir_archive, fname_archive)\\n try:\\n # First try to load the named archive\\n sa = rebound.SimulationArchive(filename=path_archive)\\n print(f'Loaded archive {fname_archive}.')\\n except:\\n # If the archive is not on disk, save it to disk\\n print(f'Generating archive {fname_archive}\\\\n'\\n f'from mjd {mjd0} to {mjd1}, time_step={time_step}, save_step={save_step}...')\\n make_archive_impl(sim_epoch=sim_epoch, mjd0=mjd0, mjd1=mjd1, time_step=time_step, \\n save_step=save_step, save_elements=save_elements, progbar=progbar)\\n # Load the new archive into memory\\n sa = rebound.SimulationArchive(filename=path_archive)\\n\\n # Bind the archive filename to the archive\\n sa.fname_archive: str = fname_archive\\n sa.fname_np: str = fname_archive.replace('.bin', '.npz')\\n sa.body_collection = sim_epoch.body_collection\\n # Bind various dates and time steps\\n sa.epoch = sim_epoch.epoch\\n sa.mjd0: int = mjd0\\n sa.mjd1: int = mjd1\\n sa.time_step: int = time_step\\n # Bind the simulation at the epoch\\n sa.sim_epoch = sim_epoch\\n\\n return sa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fda04613d566a0c745a47e068a70c02\",\n \"score\": \"0.5094346\",\n \"text\": \"def archiveBag(bags,queue):\\n grouptasks=[]\\n notValid=[]\\n valid=[]\\n for bag in bags:\\n vBag=validateBag(bag,fast=True)\\n if vBag['valid']:\\n source=bag\\n updateBagValidatationMetadata(bag.split('/')[-1],'local',{\\\"valid\\\":True,\\\"timestamp\\\":datetime.now().isoformat()})\\n valid.append({\\\"bag\\\":source.split('/')[-1],\\\"valid\\\":datetime.now()})\\n destination= os.path.join(petaLibrarySubDirectory,source.split('/')[-1])\\n grouptasks.append(scpPetaLibrary.si(source,destination).set(queue=queue))\\n else:\\n updateBagValidatationMetadata(bag,'local',{\\\"valid\\\":False,\\\"timestamp\\\":datetime.now().isoformat()})\\n notValid.append(bag)\\n res = group(grouptasks)()\\n return {\\\"subtasks\\\":len(grouptasks),\\\"valid\\\":valid,\\\"notvalid\\\":notValid}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94fed718476a0dbcb6c7272365a02c7d\",\n \"score\": \"0.50901425\",\n \"text\": \"def ProduceUnifiedTreeandHaloCatalog(fname,numsnaps,tree,numhalos,halodata,atime,\\n\\tdescripdata={'Title':'Tree and Halo catalog of sim', 'VELOCIraptor_version':1.15, 'Tree_version':1.1, 'Particle_num_threshold':20, 'Temporal_linking_length':1, 'Flag_gas':False, 'Flag_star':False, 'Flag_bh':False},\\n\\tcosmodata={'Omega_m':1.0, 'Omega_b':0., 'Omega_Lambda':0., 'Hubble_param':1.0,'BoxSize':1.0, 'Sigma8':1.0},\\n\\tunitdata={'UnitLength_in_Mpc':1.0, 'UnitVelocity_in_kms':1.0,'UnitMass_in_Msol':1.0, 'Flag_physical_comoving':True,'Flag_hubble_flow':False},\\n\\tpartdata={'Flag_gas':False, 'Flag_star':False, 'Flag_bh':False},\\n\\tibuildheadtail=0, icombinefile=1):\\n\\tif (ibuildheadtail==1):\\n\\t\\tBuildTemporalHeadTail(numsnaps,tree,numhalos,halodata)\\n\\ttotnumhalos=sum(numhalos)\\n\\tif (icombinefile==1):\\n\\t\\thdffile=h5py.File(fname+\\\".snap.hdf.data\\\",'w')\\n\\t\\theadergrp=hdffile.create_group(\\\"Header\\\")\\n\\t\\t#store useful information such as number of snapshots, halos,\\n\\t\\t#cosmology (Omega_m,Omega_b,Hubble_param,Omega_Lambda, Box size)\\n\\t\\t#units (Physical [1/0] for physical/comoving flag, length in Mpc, km/s, solar masses, Gravity\\n\\t\\t#and TEMPORALHALOIDVAL used to traverse tree information (converting halo ids to haloindex or snapshot), Reverse_order [1/0] for last snap listed first)\\n\\t\\t#set the attributes of the header\\n\\t\\theadergrp.attrs[\\\"NSnaps\\\"]=numsnaps\\n\\t\\t#overall description\\n\\t\\t#simulation box size\\n\\n\\t\\t#cosmological params\\n\\t\\tcosmogrp=headergrp.create_group(\\\"Cosmology\\\")\\n\\t\\tfor key in cosmodata.keys():\\n\\t\\t\\tcosmogrp.attrs[key]=cosmodata[key]\\n\\t\\t#unit params\\n\\t\\tunitgrp=headergrp.create_group(\\\"Units\\\")\\n\\t\\tfor key in unitdata.keys():\\n\\t\\t\\tunitgrp.attrs[key]=unitdata[key]\\n\\t\\t#particle types\\n\\t\\tpartgrp=headergrp.create_group(\\\"Parttypes\\\")\\n\\t\\tpartgrp.attrs[\\\"Flag_gas\\\"]=descripdata[\\\"Flag_gas\\\"]\\n\\t\\tpartgrp.attrs[\\\"Flag_star\\\"]=descripdata[\\\"Flag_star\\\"]\\n\\t\\tpartgrp.attrs[\\\"Flag_bh\\\"]=descripdata[\\\"Flag_bh\\\"]\\n\\n\\t\\tfor i in range(numsnaps):\\n\\t\\t\\tsnapgrp=hdffile.create_group(\\\"Snap_%03d\\\"%(numsnaps-1-i))\\n\\t\\t\\tsnapgrp.attrs[\\\"Snapnum\\\"]=(numsnaps-1-i)\\n\\t\\t\\tsnapgrp.attrs[\\\"NHalos\\\"]=numhalos[i]\\n\\t\\t\\tsnapgrp.attrs[\\\"scalefactor\\\"]=atime[i]\\n\\t\\t\\tfor key in halodata[i].keys():\\n\\t\\t\\t\\tsnapgrp.create_dataset(key,data=halodata[i][key])\\n\\t\\thdffile.close()\\n\\telse:\\n\\t\\tfor i in range(numsnaps):\\n\\t\\t\\thdffile=h5py.File(fname+\\\".snap_%03d.hdf.data\\\"%(numsnaps-1-i),'w')\\n\\t\\t\\thdffile.create_dataset(\\\"Snap_value\\\",data=np.array([numsnaps-1-i],dtype=np.uint32))\\n\\t\\t\\thdffile.create_dataset(\\\"NSnaps\\\",data=np.array([numsnaps],dtype=np.uint32))\\n\\t\\t\\thdffile.create_dataset(\\\"NHalos\\\",data=np.array([numhalos[i]],dtype=np.uint64))\\n\\t\\t\\thdffile.create_dataset(\\\"TotalNHalos\\\",data=np.array([totnumhalos],dtype=np.uint64))\\n\\t\\t\\thdffile.create_dataset(\\\"scalefactor\\\",data=np.array([atime[i]],dtype=np.float64))\\n\\t\\t\\tfor key in halodata[i].keys():\\n\\t\\t\\t\\thdffile.create_dataset(key,data=halodata[i][key])\\n\\t\\t\\thdffile.close()\\n\\n\\thdffile=h5py.File(fname+\\\".tree.hdf.data\\\",'w')\\n\\thdffile.create_dataset(\\\"NSnaps\\\",data=np.array([numsnaps],dtype=np.uint32))\\n\\thdffile.create_dataset(\\\"TotalNHalos\\\",data=np.array([totnumhalos],dtype=np.uint64))\\n\\thdffile.create_dataset(\\\"NHalos\\\",data=np.array([numhalos],dtype=np.uint64))\\n\\tfor i in range(numsnaps):\\n\\t\\tsnapgrp=hdffile.create_group(\\\"Snap_%03d\\\"%(numsnaps-1-i))\\n\\t\\tfor key in tree[i].keys():\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t#to be completed for progenitor list\\n\\t\\t\\tif (key==\\\"Progen\\\"):\\n\\t\\t\\t\\tfor j in range(numhalos[i]):\\n\\t\\t\\t\\t\\thalogrp=snapgrp.create_group(\\\"Halo\\\"+str(j))\\n\\t\\t\\t\\t\\thalogrp.create_dataset(key,data=tree[i][key][j])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tsnapgrp.create_dataset(key,data=tree[i][key])\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tif ((key==\\\"Progen\\\") | (key==\\\"Descen\\\")): continue\\n\\t\\t\\tsnapgrp.create_dataset(key,data=tree[i][key])\\n\\thdffile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8770ee73f6e6a459bcd63cd52b7503a8\",\n \"score\": \"0.5089119\",\n \"text\": \"def parse_single_12tped_to_hdf5(in_file_prefix='/home/bv25/data/Ls154/Ls154_12',\\n out_file_prefix='/home/bv25/data/Ls154/Ls154_12',\\n impute_type='mode', filter_monomorphic_snps=True,\\n missing_val_thr=0.1):\\n \\n print 'Starting to parse genotypes'\\n genotype_data = {}\\n h5py_file = h5py.File(out_file_prefix + '.hdf5')\\n genotype_data['hdf5p_file'] = h5py_file\\n genot_group = h5py_file.create_group('genot_data')\\n indiv_group = h5py_file.create_group('indiv_data')\\n \\n \\n tot_num_snps = 0\\n tot_num_missing_val_snps_removed = 0\\n tot_num_ambiguous_loc_removed = 0\\n curr_chrom = 1\\n print 'Working on chromosome %d' % curr_chrom\\n \\n g_filename = '%s.tped' % (in_file_prefix) \\n s_filename = '%s.bim' % (in_file_prefix)\\n i_filename = '%s.tfam' % (in_file_prefix) \\n\\n \\n \\n indiv_ids = []\\n phenotypes = [] \\n sex = []\\n print 'Parsing individuals file: %s' % i_filename\\n with open(i_filename) as f:\\n for line in f:\\n l = line.split()\\n iid = l[0]\\n indiv_ids.append(iid)\\n sex.append(int(l[4]))\\n phenotypes.append(float(l[5]))\\n tot_num_indiv = len(indiv_ids) \\n \\n print 'Storing individual data in individ. group'\\n indiv_group.create_dataset('indiv_ids', data=indiv_ids)\\n indiv_group.create_dataset('sex', data=sex)\\n indiv_group.create_dataset('phenotypes', data=phenotypes)\\n \\n \\n \\n num_indiv = len(indiv_ids)\\n print 'Found %d Individuals' % (num_indiv)\\n\\n print 'Parsing nucleotide map'\\n nt_map = {}\\n chromsomoes = []\\n curr_chrom = 0\\n with open(s_filename) as f:\\n for line in f:\\n l = line.split()\\n chrom = l[0]\\n if chrom != curr_chrom:\\n chromsomoes.append(chrom)\\n curr_chrom = chrom\\n nt_map[l[1]] = (l[4], l[5]) \\n assert len(chromsomoes) == len(set(chromsomoes)), 'Chromosomes need to be in order.'\\n curr_chrom = chromsomoes[0]\\n \\n position = -1\\n # Initializing containers.\\n snps_mat = [] \\n positions = []\\n sids = []\\n nts_list = []\\n nt_counts_list = []\\n missing_counts = []\\n freqs = []\\n num_missing_removed = 0\\n num_monomorphic_removed = 0\\n num_ambiguous_loc_removed = 0\\n t0 = time.time()\\n\\n print 'Starting to parse SNP files'\\n gf = open(g_filename)\\n for g_line in gf:\\n# if random.random() > 0.01:\\n# continue\\n gl = g_line.split()\\n chrom = gl[0]\\n if chrom != curr_chrom:\\n \\n # Store everything and reset.\\n print 'Number of SNPs removed due to too many missing values: %d' % num_missing_removed\\n print 'Number of SNPs removed due to ambiguous location: %d' % num_ambiguous_loc_removed\\n print 'Number of monomorphic SNPs removed: %d' % num_monomorphic_removed\\n print 'Number of SNPs retained: %d' % len(positions)\\n print 'Number of individuals: %d' % num_indiv\\n snps = sp.array(snps_mat, dtype='int8')\\n h5py_chrom_group = genot_group.create_group('chrom_%s' % curr_chrom)\\n h5py_chrom_group.create_dataset('raw_snps', compression='lzf', data=snps)\\n h5py_chrom_group.create_dataset('positions', compression='lzf', data=positions)\\n h5py_chrom_group.create_dataset('nts', compression='lzf', data=nts_list)\\n h5py_chrom_group.create_dataset('nt_counts', compression='lzf', data=nt_counts_list)\\n h5py_chrom_group.create_dataset('missing_counts', compression='lzf', data=missing_counts)\\n h5py_chrom_group.create_dataset('freqs', compression='lzf', data=freqs)\\n h5py_chrom_group.create_dataset('snp_ids', compression='lzf', data=sids) \\n tot_num_snps += len(positions)\\n tot_num_missing_val_snps_removed += num_missing_removed\\n tot_num_ambiguous_loc_removed += num_ambiguous_loc_removed\\n h5py_file.flush() \\n t1 = time.time()\\n t = t1 - t0\\n print 'It took %d minutes and %0.2f seconds to parse Chromosome %s.' % (t / 60, t % 60, curr_chrom)\\n t0 = time.time()\\n\\n \\n\\n # Reset containers\\n snps_mat = [] \\n positions = []\\n sids = []\\n nts_list = []\\n nt_counts_list = []\\n missing_counts = []\\n freqs = []\\n num_missing_removed = 0\\n num_ambiguous = 0\\n num_monomorphic_removed = 0\\n num_ambiguous_loc_removed = 0\\n \\n curr_chrom = chrom\\n\\n sid = gl[1]\\n prev_position = position\\n position = int(gl[3])\\n\\n # Skipping unmappable locations\\n if position == prev_position:\\n num_ambiguous_loc_removed += 1\\n continue\\n if position == 0:\\n num_ambiguous_loc_removed += 1\\n continue\\n\\n nt = nt_map[sid]\\n \\n snp0 = sp.array(map(int, (g_line.strip()).split()[4:]), 'int8')\\n a = sp.arange(tot_num_indiv * 2)\\n even_map = a % 2 == 0\\n odd_map = a % 2 == 1\\n snp = snp0[even_map] + snp0[odd_map] - 2\\n snp[snp < 0] = 9\\n \\n bin_counts = sp.bincount(snp)\\n \\n\\n if len(bin_counts) > 3:\\n missing_count = bin_counts[-1]\\n # Filtering SNPs with too many missing values\\n if missing_count > missing_val_thr * 2 * num_indiv:\\n num_missing_removed += 1\\n continue\\n elif impute_type == 'mode':\\n nt_counts = bin_counts[:3] \\n v = sp.argmax(nt_counts)\\n snp[snp == 9] = v\\n bin_counts = sp.bincount(snp)\\n else:\\n raise Exception('Imputation type is unknown')\\n else:\\n missing_count = 0\\n\\n assert len(bin_counts) < 4, 'Issues with nucleotides.'\\n nt_counts = bin_counts[:3] \\n if len(nt_counts) == 2:\\n nt_counts = sp.array([nt_counts[0], nt_counts[1], 0])\\n elif len(nt_counts) == 1:\\n nt_counts = sp.array([nt_counts[0], 0, 0])\\n \\n\\n # Removing monomorphic SNPs\\n if filter_monomorphic_snps:\\n if max(nt_counts) == sum(nt_counts):\\n num_monomorphic_removed += 1\\n continue\\n \\n freq = sp.mean(snp) / 2.0 \\n snps_mat.append(snp)\\n positions.append(position)\\n sids.append(sid)\\n nts_list.append(nt)\\n nt_counts_list.append(nt_counts)\\n missing_counts.append(missing_count)\\n freqs.append(freq) \\n\\n # Store everything and reset.\\n print 'Number of SNPs removed due to too many missing values: %d' % num_missing_removed\\n print 'Number of SNPs removed due to ambiguous location: %d' % num_ambiguous_loc_removed\\n print 'Number of monomorphic SNPs removed: %d' % num_monomorphic_removed\\n print 'Number of SNPs retained: %d' % len(positions)\\n print 'Number of individuals: %d' % num_indiv\\n snps = sp.array(snps_mat, dtype='int8')\\n h5py_chrom_group = genot_group.create_group('chrom_%s' % chrom)\\n h5py_chrom_group.create_dataset('raw_snps', compression='lzf', data=snps)\\n h5py_chrom_group.create_dataset('positions', compression='lzf', data=positions)\\n h5py_chrom_group.create_dataset('nts', compression='lzf', data=nts_list)\\n h5py_chrom_group.create_dataset('nt_counts', compression='lzf', data=nt_counts_list)\\n h5py_chrom_group.create_dataset('missing_counts', compression='lzf', data=missing_counts)\\n h5py_chrom_group.create_dataset('freqs', compression='lzf', data=freqs)\\n h5py_chrom_group.create_dataset('snp_ids', compression='lzf', data=sids) \\n tot_num_snps += len(positions)\\n tot_num_missing_val_snps_removed += num_missing_removed\\n tot_num_ambiguous_loc_removed += num_ambiguous_loc_removed\\n h5py_file.create_dataset('num_snps', data=sp.array(tot_num_snps))\\n h5py_file.flush() \\n t1 = time.time()\\n t = t1 - t0\\n print 'It took %d minutes and %0.2f seconds to parse chromosome %s.' % (t / 60, t % 60, chrom)\\n\\n \\n gf.close()\\n \\n print 'Total number of SNPs parsed successfully was: %d' % tot_num_snps\\n print 'Total number of SNPs removed due to too many missing values: %d' % tot_num_missing_val_snps_removed\\n print 'Total number of SNPs removed due to ambiguous locations: %d' % tot_num_ambiguous_loc_removed\\n h5py_file.close()\\n \\n print 'Done parsing genotypes.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ae55e0ee0c569e234eaa99af838c30f\",\n \"score\": \"0.50849724\",\n \"text\": \"def write_hgrps(self, hgrp_list, dirname):\\n self.host_group_manager.write_objects(hgrp_list, dirname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d6f1dc1b4441ba7d808587f926551a\",\n \"score\": \"0.5082397\",\n \"text\": \"def ProduceWalkableHDFTree(fname,numsnaps,tree,numhalos,halodata,atime,\\n\\tdescripdata={'Title':'Tree catalogue', 'VELOCIraptor_version':1.3, 'Tree_version':1.1, 'Particle_num_threshold':20, 'Temporal_linking_length':1, 'Flag_gas':False, 'Flag_star':False, 'Flag_bh':False}\\n\\t):\\n\\thdffile=h5py.File(fname,'w')\\n\\theadergrp=hdffile.create_group(\\\"Header\\\")\\n\\t#store useful information such as number of snapshots, halos,\\n\\t#cosmology (Omega_m,Omega_b,Hubble_param,Omega_Lambda, Box size)\\n\\t#units (Physical [1/0] for physical/comoving flag, length in Mpc, km/s, solar masses, Gravity\\n\\t#and TEMPORALHALOIDVAL used to traverse tree information (converting halo ids to haloindex or snapshot), Reverse_order [1/0] for last snap listed first)\\n\\t#set the attributes of the header\\n\\theadergrp.attrs[\\\"NSnaps\\\"]=numsnaps\\n\\t#overall description\\n\\theadergrp.attrs[\\\"Title\\\"]=descripdata[\\\"Title\\\"]\\n\\tfindergrp=headergrp.create_group(\\\"HaloFinder\\\")\\n\\tfindergrp.attrs[\\\"Name\\\"]=\\\"VELOCIraptor\\\"\\n\\tfindergrp.attrs[\\\"Version\\\"]=descripdata[\\\"VELOCIraptor_version\\\"]\\n\\tfindergrp.attrs[\\\"Particle_num_threshold\\\"]=descripdata[\\\"Particle_num_threshold\\\"]\\n\\n\\ttreebuildergrp=headergrp.create_group(\\\"TreeBuilder\\\")\\n\\ttreebuildergrp.attrs[\\\"Name\\\"]=\\\"VELOCIraptor-Tree\\\"\\n\\ttreebuildergrp.attrs[\\\"Version\\\"]=descripdata[\\\"Tree_version\\\"]\\n\\ttreebuildergrp.attrs[\\\"Temporal_linking_length\\\"]=descripdata[\\\"Temporal_linking_length\\\"]\\n\\n\\t#now need to create groups for halos and then a group containing tree information\\n\\tsnapsgrp=hdffile.create_group(\\\"Snapshots\\\")\\n\\t#internal tree keys\\n\\thalokeys=[\\\"RootHead\\\", \\\"RootHeadSnap\\\", \\\"Head\\\", \\\"HeadSnap\\\", \\\"Tail\\\", \\\"TailSnap\\\", \\\"RootTail\\\", \\\"RootTailSnap\\\", \\\"ID\\\", \\\"Num_progen\\\"]\\n\\n\\tfor i in range(numsnaps):\\n\\t\\t#note that I normally have information in reverse order so that might be something in the units\\n\\t\\tsnapgrp=snapsgrp.create_group(\\\"Snap_%03d\\\"%i)\\n\\t\\tsnapgrp.attrs[\\\"Snapnum\\\"]=i\\n\\t\\tsnapgrp.attrs[\\\"NHalos\\\"]=numhalos[i]\\n\\t\\tsnapgrp.attrs[\\\"scalefactor\\\"]=atime[i]\\n\\t\\tfor key in halokeys:\\n\\t\\t\\tsnapgrp.create_dataset(key,data=halodata[i][key])\\n\\thdffile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2665c63042d944fcd7380e9c9c8afa99\",\n \"score\": \"0.50810343\",\n \"text\": \"def setup_output_content(top_grp, pckt_summary):\\n for where, smmry in pckt_summary.items():\\n grp = top_grp.create_group(where)\\n grp_path = grp.name\\n nelems = smmry['count']\\n\\n if smmry['type'] == 'MIL1553_FMT_1':\\n lggr.debug(f'Create HDF5 dataset data[{nelems}] in {grp_path}')\\n dtype_1553 = np.dtype(\\n [('time', '<i8'),\\n ('timestamp', 'S30'),\\n ('msg_error', '|u1'),\\n ('ttb', '|u1'),\\n ('word_error', '|u1'),\\n ('sync_error', '|u1'),\\n ('word_count_error', '|u1'),\\n ('rsp_tout', '|u1'),\\n ('format_error', '|u1'),\\n ('bus_id', 'S1'),\\n ('packet_version', '|u1'),\\n ('messages', h5py.special_dtype(vlen=np.dtype('<u2')))])\\n dset = grp.create_dataset(\\n 'data', shape=(nelems,), chunks=True, dtype=dtype_1553)\\n\\n name_dtype = np.dtype(\\n [('time', 'S30'),\\n ('timestamp', 'S30'),\\n ('msg_error', 'S30'),\\n ('ttb', 'S30'),\\n ('word_error', 'S30'),\\n ('sync_error', 'S30'),\\n ('word_count_error', 'S30'),\\n ('rsp_tout', 'S30'),\\n ('format_error', 'S30'),\\n ('bus_id', 'S30'),\\n ('packet_version', 'S30'),\\n ('messages', 'S30')])\\n names = ('1553 intra-packet time',\\n '1553 intra-packet time stamp',\\n '1553 message error flag',\\n 'time tag bits',\\n 'invalid word error',\\n 'sync type error',\\n 'word count error',\\n 'response time out',\\n 'format error',\\n 'bus id',\\n '1553 packet version',\\n '1553 packet message data')\\n dset.attrs.create('name', np.array(names, dtype=name_dtype))\\n\\n # Create alias HDF5 paths for created datasets...\\n if 'alias' in smmry:\\n for where in smmry['alias']:\\n grp = top_grp.create_group(where)\\n lggr.debug(f'Hard link {dset.name} from {grp.name}')\\n grp['data'] = dset\\n\\n elif smmry['type'] == 'VIDEO_FMT_0':\\n lggr.debug(f'Create HDF5 dataset data[{nelems}] in {grp_path}')\\n dset = grp.create_dataset('data', shape=(nelems,),\\n chunks=True, dtype=np.dtype('|V188'))\\n dset.attrs['name'] = 'video transfer stream'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b54fd67cd61bd5ec2435e4e05ac9693b\",\n \"score\": \"0.5071669\",\n \"text\": \"def export(gen, directory, file_prefix='{start[uid]}-', **kwargs):\\n with Serializer(directory, file_prefix, **kwargs) as serializer:\\n for item in gen:\\n serializer(*item)\\n\\n return serializer.artifacts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d790df9d50ef43ab9367d222729e1221\",\n \"score\": \"0.5059398\",\n \"text\": \"def write(self,data):\\n if not os.path.exists(os.path.dirname(self.outfilename)):\\n os.makedirs(os.path.dirname(self.outfilename))\\n\\n if os.path.exists(self.outfilename):\\n self.outfile = h5py.File(self.outfilename,'a')\\n else:\\n self.outfile = h5py.File(self.outfilename,'w')\\n\\n # Set permissions and group\\n if self.set_permissions:\\n os.chmod(self.outfilename,0o664)\\n shutil.chown(self.outfilename, group=self.permissions_group)\\n\\n if self.level2 in self.outfile:\\n del self.outfile[self.level2]\\n lvl2 = self.outfile.create_group(self.level2)\\n\\n tod_dset = lvl2.create_dataset('averaged_tod',data=self.avg_tod, dtype=self.avg_tod.dtype)\\n tod_dset.attrs['Unit'] = 'K'\\n tod_dset.attrs['Calibration'] = '{self.cal_mode}:{self.cal_prefix}'\\n\\n freq_dset = lvl2.create_dataset('frequency',data=self.avg_frequency, dtype=self.avg_frequency.dtype)\\n\\n # Link the Level1 data\\n data_filename = data['level1'].file.filename\\n fname = data['level1'].file.filename.split('/')[-1]\\n vane_file = data['level2/Vane'].file.filename\\n\\n # Copy over the statistics\\n if 'Statistics' in lvl2:\\n del lvl2['Statistics']\\n grp = lvl2.create_group('Statistics')\\n for k,v in data['level2/Statistics'].items():\\n if isinstance(v,h5py.Group):\\n grp2 = grp.create_group(k)\\n for k1,v1 in v.items():\\n grp2.create_dataset(k1,data=v1,dtype=v1.dtype)\\n else:\\n grp.create_dataset(k,data=v,dtype=v.dtype)\\n\\n\\n data.close()\\n if 'level1' in self.outfile:\\n del self.outfile['level1']\\n self.outfile['level1'] = h5py.ExternalLink(data_filename,'/')\\n lvl2.attrs['version'] = __level2_version__\\n\\n # Add version info\\n lvl2.attrs['pipeline-version'] = comancpipeline.__version__\\n\\n # Link the Level1 data\\n if 'Vane' in lvl2:\\n del lvl2['Vane']\\n lvl2['Vane'] = h5py.ExternalLink('{}'.format(vane_file),'/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"356eeae777c7ba5e43f43fe1a16cbda4\",\n \"score\": \"0.50575787\",\n \"text\": \"def export_grammars(output_dir):\\n d = {}\\n d['tokenize_and_classify'] = {'classify': ClassifyFst().fst}\\n d['verbalize'] = {'verbalize': VerbalizeFst().fst}\\n\\n for category, graphs in d.items():\\n for stage, fst in graphs.items():\\n out_dir = os.path.join(output_dir, stage)\\n os.makedirs(out_dir, exist_ok=True)\\n _generator_main(f\\\"{out_dir}/{category}_tmp.far\\\", fst, category.upper())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63b4f9dc44b348fc611711f0e14a230a\",\n \"score\": \"0.5057459\",\n \"text\": \"def save_npz_in_hdf5(npz_files_list, hdf5_path=None,\\n add_attributes=None, verbose=False):\\n # Set default output hdf5 path.\\n if hdf5_path is None:\\n hdf5_path = Path(npz_files_list[0]).with_suffix('.hdf5')\\n\\n # Create output hdf5 file\\n #\\n # Iterate over npz files list. Create a group for each npz file,\\n # using relative orders as groups keys.\\n output = H5(hdf5_path, 'w')\\n for model_pos, npz_path in enumerate(npz_files_list):\\n model_no = model_pos + 1\\n grp = output.create_group(str(model_no))\\n\\n # Set values of interest as group attributes.\\n if add_attributes:\\n for k, value in add_attributes[int(model_no)].items():\\n grp.attrs[k] = value\\n\\n # Load npz arrays and save them as groups data-sets. Keys\\n # will be whatever keys were present in each npz file.\\n arrays = np.load(npz_path)\\n for k, value in arrays.items():\\n grp.create_dataset(k, data=value)\\n\\n # Print output file structure\\n if verbose:\\n output.show_hdf5_structure()\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e1e889856913fee9c3ef9bbae82f21a\",\n \"score\": \"0.5057404\",\n \"text\": \"def main(args):\\n\\n group = Group()\\n for f in GROUP_PICK_LIST:\\n group.append(args.group_dir + '/' + f, check=True)\\n \\n twitter_info = TwitterInfo()\\n twitter_info.load(args.twitter_file)\\n\\n allshop2pos = map_shop_to_twitter_pos(twitter_info.get_data())\\n\\n shop2pos = {}\\n shopids = map(lambda x: x.strip(), open(args.shop_list).readlines())\\n for s in shopids:\\n shop2pos[s] = allshop2pos.get(s, set())\\n\\n result_map = {}\\n for shop in shop2pos:\\n local_group = {}\\n ret = {'total': len(shop2pos[shop]), 'groups': local_group}\\n for pos in shop2pos[shop]:\\n group_pos = group.get_group(pos)\\n if group_pos is None:\\n continue\\n if group_pos not in local_group:\\n local_group[group_pos] = set()\\n local_group[group_pos].add(pos)\\n result_map[shop] = ret\\n\\n output_file = args.output\\n if not os.path.exists(output_file):\\n os.makedirs(output_file)\\n\\n for shopid, info in result_map.iteritems():\\n temp_file = output_file + '/' + shopid + '.org'\\n with open(temp_file, \\\"w\\\") as fh:\\n print >> fh, \\\"#+ATTR_HTML: target=\\\\\\\"_blank\\\\\\\" \\\"\\n print >> fh, \\\"* [[http://www.meilishuo.com/shop/%s][shopid=%s]] 总商品量%s\\\" % (shopid, shopid, info['total'])\\n print >> fh\\n\\n local_group = info['groups']\\n \\n #ordered_keys = random.shuffle(local_group.keys())\\n ordered_keys = local_group.keys()\\n \\n n = len(ordered_keys)\\n list1 = range(n)[:args.maxgroup]\\n list2 = range(n)[args.maxgroup:args.maxline]\\n for i in list1:\\n group_pos = ordered_keys[i]\\n positions = local_group[group_pos]\\n sz = output_group_image_table(twitter_info, list(positions), max_show=args.maxshow, max_col=args.max_col, flag=\\\"店内同款\\\")\\n print >> fh, sz\\n total_group_members = group.get_member(group_pos)\\n positions2 = total_group_members.difference(positions)\\n if positions2:\\n sz = output_group_image_table(twitter_info, list(positions2), max_show=args.maxshow2, max_col=args.max_col, flag=\\\"跨店同款\\\")\\n print >> fh, sz\\n\\n print >> fh\\n\\n if list2:\\n print >>fh, \\\"* 避免拖垮浏览器，以下图片省略\\\"\\n \\n for i in list2:\\n group_pos = ordered_keys[i]\\n positions = local_group[group_pos]\\n sz = output_group_text_table(twitter_info, list(positions), max_col=args.max_col, flag=\\\"店内同款\\\")\\n print >> fh, sz\\n total_group_members = group.get_member(group_pos)\\n positions2 = total_group_members.difference(positions)\\n sz = output_group_text_table(twitter_info, list(positions2), max_col=args.max_col, flag=\\\"跨店同款\\\")\\n print >> fh, sz\\n\\n print >> fh\\n cmd = \\\" emacs --kill --batch %s -f org-export-as-html \\\" % temp_file\\n os.system(cmd)\\n print \\\"shop %s is done\\\" % shopid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e4ea330f46465b7dc1e898dd814e89b\",\n \"score\": \"0.5049522\",\n \"text\": \"def export(gen, directory, file_prefix=\\\"{uid}-\\\", **kwargs):\\n with Serializer(directory, file_prefix, **kwargs) as serializer:\\n for item in gen:\\n serializer(*item)\\n\\n return serializer.artifacts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e2acbd3d62f52259f015b0f2733f87f\",\n \"score\": \"0.5044531\",\n \"text\": \"def load_stacked_hourglass(data_dir,subjects,actions,verbose=True):\\n data = {}\\n for subj in subjects:\\n for action in actions:\\n if verbose:\\n print('Reading subject {0}, action {1}'.format(subj, action))\\n # CHANGED: training data directory path based on StackedHourglass folder in the h36m directory\\n # dpath = os.path.join( data_dir, 'S{0}'.format(subj), 'post_accept_sh_finetuned_10it/{0}*.h5'.format(action))\\n dpath = os.path.join( data_dir, 'S{0}'.format(subj), 'StackedHourglass/{0}*.h5'.format(action) )\\n print( dpath )\\n fnames = glob.glob( dpath )\\n loaded_seqs = 0\\n for fname in fnames:\\n seqname = os.path.basename( fname )\\n seqname = seqname.replace('_',' ')\\n if action == \\\"Sitting\\\" and seqname.startswith( \\\"SittingDown\\\" ):\\n continue\\n if seqname.startswith( action ):\\n # This filters out e.g. walkDog and walkTogether\\n if verbose:\\n print( fname )\\n loaded_seqs = loaded_seqs + 1\\n with h5py.File( fname, 'r' ) as h5f:\\n poses = h5f['poses'][:]\\n permutation_idx = np.array([6,2,1,0,3,4,5,7,8,9,13,14,15,12,11,10])\\n ### PERMUTE TO MAKE IT COMPATIBLE with h36m\\n poses = poses[:,permutation_idx,:]\\n ### Reshape into n times 16*2\\n poses = np.reshape(poses,[poses.shape[0],-1])\\n poses_final = np.zeros([poses.shape[0],32*2])\\n dim_to_use_x = np.array( [0,1,2,3,6,7,8,12,13,15,17,18,19,25,26,27],dtype=np.int32 )*2\\n dim_to_use_y = dim_to_use_x+1\\n dim_to_use = np.zeros(16*2,dtype=np.int32)\\n dim_to_use[0::2] = dim_to_use_x\\n dim_to_use[1::2] = dim_to_use_y\\n poses_final[:,dim_to_use] = poses\\n seqname = seqname+'-sh'\\n data[ (subj, action, seqname) ] = poses_final\\n\\n # Make sure we loaded 8 sequences\\n if (subj == 11 and action == 'Directions'): # <-- this video is damaged\\n assert loaded_seqs == 7, \\\"Expecting 7 sequences, found {0} instead. S:{1} {2}\\\".format(loaded_seqs, subj, action )\\n else:\\n assert loaded_seqs == 8, \\\"Expecting 8 sequences, found {0} instead. S:{1} {2}\\\".format(loaded_seqs, subj, action )\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39deb588840405dd1a0942e93c5e05e4\",\n \"score\": \"0.50427383\",\n \"text\": \"def _dump_data(self, mode=['props']):\\n with h5py.File(self.name + '.hdf5') as f:\\n for obj in self:\\n for key in list(obj.keys()):\\n tempname = obj.name + '|' + '_'.join(key.split('.'))\\n arr = obj[key]\\n if 'U' in str(obj[key][0].dtype):\\n pass\\n elif 'all' in key.split('.'):\\n pass\\n else:\\n f.create_dataset(name='/'+tempname, shape=arr.shape,\\n dtype=arr.dtype, data=arr)\\n for obj in self:\\n obj.clear(mode=mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"832e72a4673ab2f71201de440f7b3494\",\n \"score\": \"0.503768\",\n \"text\": \"def create_hdf5_dataset(group, dsName, data, max_digit=55, compression=COMPRESSION):\\n msg = 'create dataset {d:<{w}}'.format(d='{}/{}'.format(group.name, dsName), w=max_digit)\\n msg += ' of {t:<10} in size of {s}'.format(t=str(data.dtype), s=data.shape)\\n msg += ' with compression={c}'.format(c=compression)\\n print(msg)\\n\\n if data.ndim == 1:\\n dset = group.create_dataset(dsName,\\n data=data,\\n compression=compression)\\n elif data.ndim == 2:\\n dset = group.create_dataset(dsName,\\n data=data,\\n chunks=True,\\n compression=compression)\\n return dset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b056273bcb844d5dc26474751ca077c6\",\n \"score\": \"0.5036801\",\n \"text\": \"def gen_sigs(cluster_type, sigmeta, cluster, file_directory, signature_directory, fdf):\\n print \\\"Creating signature for cluster: %s from %s samples\\\" % (cluster, len(fdf.filename.unique()))\\n meta = sigmeta.copy()\\n filename = fdf.filename.value_counts().index.tolist()[0]\\n print filename\\n count = 0\\n yara_rule_name = cluster_type + \\\"_cluster_\\\" + str(cluster)\\n for sample in fdf.filename.unique().tolist():\\n meta['sample_'+str(count)] = sample\\n count += 1\\n\\n\\n file_header_columns = [\\\"pointer to symbol table\\\", \\\"characteristics\\\", \\\"number of symbols\\\", \\\"size of optional header\\\",\\n \\\"machine\\\", \\\"compile date\\\", \\\"number of sections\\\"]\\n\\n optional_header_columns = [\\\"subsystem\\\", \\\"major image version\\\", \\n \\\"major operating system version\\\", \\\"section alignment\\\", \\\"loader flags\\\",\\n \\\"minor subsystem version\\\", \\\"major linker version\\\",\\n \\\"size of code\\\", \\\"size of image\\\", \\\"number of rva and sizes\\\", \\\"dll charactersitics\\\",\\n \\\"file alignment\\\", \\\"minor linker version\\\", \\\"base of code\\\",\\n \\\"size uninit data\\\", \\\"entry point address\\\", \\\"size init data\\\", \\\"major subsystem version\\\",\\n \\\"magic\\\", \\\"checksum\\\", \\\"minor image version\\\",\\n \\\"minor operating system version\\\", \\\"size of headers\\\", \\\"base of data\\\",\\n \\\"data dir base relocation rva\\\", \\\"data dir base relocation size\\\", \\\"data dir debug rva\\\",\\n \\\"data dir debug size\\\", \\\"data dir exception table rva\\\", \\\"data dir exception table size\\\",\\n \\\"data dir export table rva\\\", \\\"data dir export table size\\\", \\\"data dir import address table rva\\\",\\n \\\"data dir import address table rva\\\", \\\"data dir import address table size\\\",\\n \\\"data dir import table rva\\\", \\\"data dir import table size\\\", \\\"data dir import table size\\\",\\n \\\"data dir resource table rva\\\", \\\"data dir resource table size\\\", \\\"data dir tls table rva\\\",\\n \\\"data dir tls table size\\\"]\\n\\n qword_columns = ['image base', 'size of stack reserve', 'size of stack commit', 'size of heap reserve', 'size of heap commit']\\n\\n sig = data_hacking.YaraPEGenerator(file_directory + \\\"/\\\" + filename, samplename=yara_rule_name, meta=meta)\\n\\n file_header = []\\n optional_header = {}\\n\\n for col in fdf.columns:\\n if len(fdf[col].unique()) == 1:\\n if fdf[col].unique()[0] != -1:\\n lower = [s for s in col if s.islower()]\\n if fdf[col].unique()[0] != -1 or (len(lower) == len(col)):\\n if col in file_header_columns:\\n file_header.append(col)\\n if col in optional_header_columns:\\n optional_header[col] = struct.pack(\\\"<I\\\", int(fdf[col].unique()[0])).encode('hex')\\n if col in qword_columns:\\n if fdf['magic'][0] == 0x20b:\\n optional_header[col] = struct.pack(\\\"<Q\\\", int(fdf[col].unique()[0])).encode('hex')\\n else:\\n optional_header[col] = struct.pack(\\\"<I\\\", int(fdf[col].unique()[0])).encode('hex')\\n\\n\\n if len(fdf[col].unique()) > 1:\\n if col not in optional_header_columns:\\n continue\\n\\n if type(fdf[col].unique()[0]) == str or len(fdf[col].unique()) > 9:\\n continue\\n\\n u = []\\n z = []\\n for value in fdf[col].unique():\\n u.append(struct.pack(\\\"<I\\\", value).encode(\\\"hex\\\"))\\n\\n for d in zip(*u):\\n match = True\\n for idx in range(1,len(d)):\\n if d[0] != d[idx]:\\n match = False\\n break\\n if match:\\n z.append(d[0])\\n else:\\n z.append('?')\\n string = ''.join(z)\\n if string != '????????':\\n optional_header[col] = string\\n\\n if len(file_header) > 0:\\n sig.add_file_header(file_header)\\n\\n if len(optional_header) > 0:\\n sig.add_optional_header_with_values(optional_header)\\n sig.get_signature(filename=signature_directory + '/' + yara_rule_name + '.yara', writesig=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ea55c513c5663457506c6db8054e96f\",\n \"score\": \"0.5035398\",\n \"text\": \"def prepH5(outputName):\\n with h5py.File(outputName, 'w') as f:\\n # Generate top level groups\\n f.create_group('data')\\n f.create_group('params')\\n f.create_group('misc')\\n f.create_group('info')\\n\\n # Write metadata\\n # Package format version\\n f.create_dataset('info/package_version', data=np.string_('SingFEL v0.2.0'))\\n # Contact\\n f.create_dataset('info/contact', data=np.string_('Carsten Fortmann-Grote <carsten.grote@xfel.eu>'))\\n # Data Description\\n f.create_dataset('info/data_description', data=np.string_('This dataset contains diffraction patterns generated using SingFEL.'))\\n # Method Description\\n f.create_dataset('info/method_description', data=np.string_('Form factors of the radiation damaged molecules are calculated in time slices. At each time slice, the coherent scattering is calculated and incoherently added to the final diffraction pattern (/data/nnnnnnn/diffr). Finally, Poissonian noise is added to the diffraction pattern (/data/nnnnnnn/data).'))\\n # Data format version\\n f.create_dataset('version', data=np.string_('0.2'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9827420a61f03aecbf99fc7ad1d93e4\",\n \"score\": \"0.50345504\",\n \"text\": \"def make_heatmap(data: str, group_by, rds, output, process):\\n with open(data) as r:\\n data = json.load(r)\\n\\n tasks = []\\n\\n if not os.path.exists(output):\\n os.makedirs(output)\\n # rds, prefix, cluster, gene, group = data\\n for k, v in data.items():\\n tasks.append([rds, output, k, v, group_by])\\n\\n with Pool(processes=process) as p:\\n p.map(run_r, tasks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c74536aed662db5da03fb8569085822\",\n \"score\": \"0.50222504\",\n \"text\": \"def export_to_hdf5(cls, h5_file, model, eids):\\n #comments = []\\n pids = []\\n nodes = []\\n thetas = []\\n for eid in eids:\\n element = model.elements[eid]\\n #comments.append(element.comment)\\n pids.append(element.pid)\\n nodes.append(element.nodes)\\n\\n theta = element.theta\\n assert isinstance(theta, float), type(theta)\\n thetas.append(theta)\\n #h5_file.create_dataset('_comment', data=comments)\\n h5_file.create_dataset('eid', data=eids)\\n h5_file.create_dataset('pid', data=pids)\\n h5_file.create_dataset('nodes', data=nodes)\\n h5_file.create_dataset('theta', data=thetas)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ad35022a5b3e1d9b834080c7b8c1d9f\",\n \"score\": \"0.5018338\",\n \"text\": \"def createDataset(filename, group, dataset, data):\\n\\n deleteDataset(filename, group, dataset)\\n\\n FILE = h5py.File(filename, \\\"r+\\\")\\n\\n GROUP = FILE[group]\\n\\n GROUP.create_dataset(dataset, data = data)\\n\\n print(\\\"[CREATE]: <{:s}> dataset in <{:s}> group created.\\\".format(dataset, group))\\n\\n FILE.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad52655b1bb5f6792af20b2e04f60f47\",\n \"score\": \"0.5007411\",\n \"text\": \"def save_grouped_data(input_df, output_dir):\\n\\n df = pd.read_parquet(input_df)\\n df[CDR3_LEN_COL_LABEL] = df['cdr3'].str.len()\\n\\n grouped_df = df.groupby(['v', 'j', CDR3_LEN_COL_LABEL])\\n\\n if grouped_df:\\n path_dir = pathlib.Path(output_dir)\\n path_dir.mkdir(parents=True, exist_ok=True)\\n\\n filenames = []\\n for (v, j, cdr3_len), grouped_data in grouped_df:\\n filename = f'{v}_{j}_{cdr3_len}'\\n\\n for char, replacement_char in FILENAME_CHAR_REPLACEMENTS.items():\\n filename = filename.replace(char, replacement_char)\\n \\n filenames.append(filename)\\n grouped_data.to_parquet(f'{output_dir}/{filename}.parquet', index=False)\\n\\n with open(f'{output_dir}/{GROUP_FILES_LIST_FILENAME}', 'w') as groups_list_file:\\n groups_list_file.write('\\\\n'.join(filenames))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2309a1b61c6c278aa33a91a3a02f29f\",\n \"score\": \"0.49970445\",\n \"text\": \"def group_dls_data(directory = '', pattern = '*.ASC', output = 'data', size = 10):\\n import os\\n from labtools.io.dls import open_dls_group\\n files = glob.glob(os.path.join(directory,pattern))\\n data = open_dls_group(files, size)\\n for i,d in enumerate(data):\\n print('creating data')\\n dat = DLS_Data_Old(data = d[0], cr = d[1])\\n print('opening window')\\n dat.configure_traits()\\n print('calculating')\\n dat.calculate()\\n fname = os.path.join(directory, output + str(i) + '.npy')\\n print('saving data+')\\n dat.save(fname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea006c100c3c9cddcb0768b5bd8c2011\",\n \"score\": \"0.4997002\",\n \"text\": \"def _export_dags_plugins_and_data(self: typing.Any) -> None:\\n logger.info(\\\"Exporting DAGs, plugins and data...\\\")\\n bucket = self.cp_bucket_name\\n command = (\\n f\\\"gsutil -m cp -r gs://{bucket}/dags \\\"\\n f\\\"gs://{bucket}/export/dirs/dags && \\\"\\n f\\\"gsutil -m cp -r gs://{bucket}/plugins \\\"\\n f\\\"gs://{bucket}/export/dirs/plugins\\\"\\n f\\\" && gsutil -m cp -r gs://{bucket}/data \\\"\\n f\\\"gs://{bucket}/export/dirs/data\\\"\\n )\\n output = EnvironmentUtils.execute_command_in_a_pod(\\n self.worker_pod_namespace,\\n self.worker_pod_name,\\n self.worker_container_name,\\n command,\\n )\\n logger.info(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0c7ce81a5bc58a0a8e05d57b04e5940\",\n \"score\": \"0.4993694\",\n \"text\": \"def createOutputFiles():\\n print \\\"\\\\nCreating outputs\\\"\\n make_path(outPath)\\n os.chdir(outPath)\\n outFile = open(\\\"SipprModelling_%s.csv\\\" % start, \\\"wb\\\")\\n outFile.write(\\\"readLength\\\\tfoldCoverage\\\\ttarget\\\\tkmerLength\\\\tMedianQualityScore\\\\t\\\"\\n \\\"QualityScoreSD\\\\tMedianFoldCoverage\\\\tFoldCoverageSD\\\\tMedianPercentID\\\\tqualityMetric\\\\n\\\")\\n for rLength in readLength:\\n for fCov in foldCoverage:\\n for target in targets:\\n for size in kmer:\\n total1 = 0\\n sys.stdout.write('.')\\n filename = os.path.split(target)[1]\\n fileNoExt = filename.split(\\\".\\\")[0]\\n megaName = \\\"rL%s_fC%s_%s_kmer%s\\\" % (rLength, fCov, fileNoExt, size)\\n filePath = \\\"%s/tmp/rL%s/rL%s_fC%s\\\" % (path, rLength, rLength, fCov)\\n vcfFile = megaName + \\\"_sorted.vcf\\\"\\n newPath = \\\"%s/%s\\\" % (filePath, megaName)\\n outputFile = \\\"%s/%s\\\" % (newPath, vcfFile)\\n # Initialise the counter, which will be used to track lines in the vcf file - if positions in the\\n # target are not mapped, then the position field will jump ahead of the counter\\n count = 1\\n # Initialise the arrays, which will keep track of the appropriate values for each dataset\\n arrQual = []\\n arrCov = []\\n arrSum = []\\n output = open(outputFile, \\\"r\\\")\\n for line in output:\\n # vcf files have 36 commented out lines at the top of each file - these are not necessary\\n if re.search('#', line):\\n pass\\n else:\\n total1 += 1\\n # Format of file\\n # CHROM\\t POS\\tID\\tREF\\tALT\\tQUAL FILTER\\tINFO\\t FORMAT\\n # adk-12\\t8\\t.\\tG\\t.\\t32.7\\t.\\tDP=1;AF1=0;AC1=0;DP4=0,1,0,0;MQ=29;FQ=-30.3\\tPL\\t0\\n # data[0] [1] [2] [3] [4] [5] [6] [7]\\n data = line.split(\\\"\\\\t\\\")\\n #target = data[0]\\n pos = data[1]\\n refSeq = data[3]\\n mapSeq = data[4]\\n qual = data[5]\\n # Depth of coverage is reported prior to the first \\\";\\\"\\n dpLine = data[7].split(\\\";\\\")[0]\\n # For now, I'm skipping lines that indicated the presence of a possible indel\\n # - I may return to this later\\n if re.search(\\\"INDEL\\\", dpLine):\\n pass\\n else:\\n # If the called base (mapSeq) is identical to the reference base (refSeq)\\n # - denoted by a \\\".\\\", then set seq to equal refSeq, otherwise, pull the\\n # value of mapSeq for seq\\n avgQual = sum(arrQual)/total1\\n if mapSeq == \\\".\\\":\\n seq = refSeq\\n match = 1\\n # This section corrects for the fact that during the conversion of bam files to vcf\\n # files, SNP calls and ambiguous calls look identical, except for the fact that for\\n # SNPs, the qualityScore (qual) tends to be higher than the surrounding bases,\\n # while ambiguous calls have a lower qualityScore - this loop screens for quality\\n # scores that are at least 10 lower than the score of the previous base\\n else:\\n if float(arrQual[-1] - 10) >= 0:\\n prevValue = float(arrQual[-1] - 10)\\n else:\\n prevValue = 0\\n if float(qual) <= prevValue:\\n seq = refSeq\\n match = 1\\n else:\\n # This attempts to catch if there are two ambiguous bases in a row;\\n # they will hopefully have the same value\\n if float(qual) == prevValue:\\n seq = refSeq\\n match = 1\\n else:\\n # \\\"True\\\" SNPs seem to have increased qualityScore compared to the\\n # surrounding values, this will catch that\\n if float(qual) > prevValue:\\n seq = mapSeq\\n match = 0\\n # Strip the \\\"DP=\\\" from dpLine\\n DP = dpLine.split(\\\"=\\\")[1]\\n #vcfData[pos] = (fileName, target, refSeq, mapSeq, DP)\\n # If pos > count, then there is a gap in the mapping (or a deletion, but ignoring\\n # this possibility for now). For my purposes, I want to have data reported for\\n # every position, whether it is present in the vcf file or not, so I will use count\\n # as the position, \\\"-\\\" as the seq, and 0 as the quality and depth of coverage\\n if int(pos) > count:\\n #print int(pos) - count, pos, count, range(count, int(pos))\\n # the number of skipped positions is equal to the value for pos - count\\n # For each skipped position (i), set appropriate variables to appropriate values\\n for i in range(count, int(pos)):\\n posAdj = count\\n seqAdj = \\\"-\\\"\\n matchAdj = 0\\n qualAdj = 0\\n DPAdj = 0\\n #vcfData[fileName][rL][fC][target][size][int(posAdj)][seqAdj][matchAdj][qualAdj] = DP\\n arrQual.append(float(qualAdj))\\n arrCov.append(float(DPAdj))\\n arrSum.append(float(matchAdj))\\n count += 1\\n if int(pos) == count:\\n #vcfData[fileName][rL][fC][target][size][int(pos)][seq][match][qual] = DP\\n arrQual.append(float(qual))\\n arrCov.append(float(DP))\\n arrSum.append(float(match))\\n count += 1\\n else:\\n #vcfData[fileName][rL][fC][target][size][int(pos)][seq][match][qual] = DP\\n arrQual.append(float(qual))\\n arrCov.append(float(DP))\\n arrSum.append(float(match))\\n count += 1\\n # In the case of no data being present in a file,\\n total = count - 1\\n if total == 0:\\n avgQual = 0\\n stdQual = 0\\n avgCov = 0\\n stdCov = 0\\n avgID = 0\\n qualMet = 0\\n else:\\n avgQual = sum(arrQual)/total\\n stdQual = numpy.std(arrQual)\\n avgCov = sum(arrCov)/total\\n stdCov = numpy.std(arrCov)\\n avgID = sum(arrSum)/total * 100\\n qualMet = avgQual * avgCov\\n\\n outFile.write(\\\"%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\n\\\"\\n % (rLength, fCov, fileNoExt, size, avgQual, stdQual, avgCov, stdCov, avgID, qualMet))\\n\\n output.close()\\n outFile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac211780d7535d82eeac4a04791d06a2\",\n \"score\": \"0.4985788\",\n \"text\": \"def write_checkgroups(groups, path):\\n keys = groups.keys()\\n keys.sort()\\n checkgroups_file = file(path, 'wb')\\n for key in keys:\\n if len(key) < 8:\\n checkgroups_file.write(key + '\\\\t\\\\t\\\\t' + groups[key] + '\\\\n')\\n elif len(key) < 16:\\n checkgroups_file.write(key + '\\\\t\\\\t' + groups[key] + '\\\\n')\\n else:\\n checkgroups_file.write(key + '\\\\t' + groups[key] + '\\\\n')\\n checkgroups_file.close()\\n print 'Checkgroups file written.'\\n print\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b42b23e474853ee230bfb101382b3b57\",\n \"score\": \"0.49817586\",\n \"text\": \"def _dump(py_obj, h_group, name, memo, loader,attrs={} , **kwargs):\\n\\n py_obj_id = id(py_obj)\\n py_obj_ref = memo.get(py_obj_id,None)\\n if py_obj_ref is not None:\\n\\n # py_object already dumped to hdf5 file store a reference to it instead\\n # instead of dumping it again.\\n #\\n # Note: reference dataset share their base_type and py_obj_type with the\\n # referenced h5py.Group or h5py.Dataset. On load their h5py.ref_dtype type \\n # dtype is used to distinguish them from datasets hosting pickled data.\\n h_link = h_group.create_dataset(name,data = py_obj_ref[0].ref,dtype = h5.ref_dtype)\\n h_link.attrs.update(attrs)\\n return\\n\\n # Check if loader has already been loaded for the provided py_obj and \\n # retrieve the most appropriate method for creating the corresponding\\n # representation within HDF5 file\\n py_obj_type, (create_dataset, base_type,memoise) = loader.load_loader(py_obj.__class__)\\n try:\\n h_node,h_subitems = create_dataset(py_obj, h_group, name, **kwargs)\\n except NotHicklable:\\n h_node,h_subitems = create_pickled_dataset(py_obj, h_group, name, reason = str(NotHicklable), **kwargs)\\n else:\\n # store base_type and type unless py_obj had to be pickled by create_pickled_dataset\\n memo.store_type(h_node,py_obj_type,base_type,**kwargs)\\n\\n # add additional attributes and prevent modification of 'type' attribute\\n h_node.attrs.update((name,attr) for name,attr in attrs.items() if name != 'type' )\\n\\n # if py_object shall be memoised to properly represent multiple references\\n # to it in HDF5 file store it along with created h_node in the memo dictionary.\\n # remembering the py_object along with the h_node ensures that py_object_id\\n # which represents the memory address of py_obj refers to py_obj until the\\n # whole structure is stored within hickle file.\\n if memoise:\\n memo[py_obj_id] = (h_node,py_obj)\\n\\n # loop through list of all sub items and recursively dump them\\n # to HDF5 file\\n for h_subname,py_subobj,h_subattrs,sub_kwargs in h_subitems:\\n _dump(py_subobj,h_node,h_subname,memo,loader,h_subattrs,**sub_kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09e6e1d03830d87a72f05725f5caf80c\",\n \"score\": \"0.49766046\",\n \"text\": \"def write_hdf5( self, iteration ):\\n # Find the file name\\n filename = \\\"data%08d.h5\\\" %iteration\\n fullpath = os.path.join( self.write_dir, \\\"hdf5\\\", filename )\\n\\n # Create the file and setup its attributes\\n zmin = self.top.zgrid + self.w3d.zmmin\\n self.create_file_empty_meshes( fullpath, self.top.it,\\n self.top.time, self.nz, zmin, self.dz, self.top.dt )\\n\\n # Open the file again (possibly in parallel), and get the field path\\n f = self.open_file( fullpath, parallel_open=self.lparallel_output )\\n # (f is None if this processor does not participate in writing data)\\n if f is not None:\\n field_path = \\\"/data/%d/fields/\\\" %iteration\\n field_grp = f[field_path]\\n else:\\n field_grp = None\\n\\n # Loop over the different quantities that should be written\\n for fieldtype in self.fieldtypes:\\n # Scalar field\\n if fieldtype == \\\"rho\\\":\\n self.write_dataset( field_grp, \\\"rho\\\", \\\"rho\\\" )\\n # Vector field\\n elif fieldtype in [\\\"E\\\", \\\"B\\\", \\\"J\\\"]:\\n for coord in self.coords:\\n quantity = \\\"%s%s\\\" %(fieldtype, coord)\\n path = \\\"%s/%s\\\" %(fieldtype, coord)\\n self.write_dataset( field_grp, path, quantity )\\n\\n # Close the file\\n if f is not None:\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"695d51b0144f629dc9497cceee2ed69a\",\n \"score\": \"0.49762768\",\n \"text\": \"def _DumpGroup(parent, datasets):\\n for key, value in datasets.iteritems():\\n if isinstance(value, dict):\\n child = parent.create_group(key)\\n _DumpGroup(child, value)\\n elif value is None:\\n child = parent.create_group(key)\\n else:\\n compression_kwargs = {}\\n if compression_level:\\n compression_kwargs = {'compression': 'gzip',\\n 'compression_opts': compression_level}\\n parent.create_dataset(key, data=value, **compression_kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adf54dc92a147885acc8fc08cad0cc4a\",\n \"score\": \"0.49743944\",\n \"text\": \"def export_join_data(toil, options, full_ids, clip_ids, clip_stats, filter_ids, idx_maps, og_chrom_ids):\\n\\n # make a directory for the chromosomal vgs\\n if options.chrom_vg:\\n clip_base = os.path.join(options.outDir, '{}.chroms'.format(options.outName))\\n if not clip_base.startswith('s3://') and not os.path.isdir(clip_base):\\n os.makedirs(clip_base)\\n\\n if 'full' in options.chrom_vg:\\n # download the \\\"full\\\" vgs\\n assert len(options.vg) == len(full_ids)\\n for vg_path, full_id, in zip(options.vg, full_ids):\\n name = os.path.splitext(vg_path)[0] + '.full.vg'\\n toil.exportFile(full_id, makeURL(os.path.join(clip_base, os.path.basename(name))))\\n\\n if 'clip' in options.chrom_vg:\\n # download the \\\"clip\\\" vgs\\n assert len(options.vg) == len(clip_ids)\\n for vg_path, clip_id, in zip(options.vg, clip_ids):\\n name = os.path.splitext(vg_path)[0] + '.vg'\\n toil.exportFile(clip_id, makeURL(os.path.join(clip_base, os.path.basename(name))))\\n\\n if 'filter' in options.chrom_vg:\\n # download the \\\"filter\\\" vgs\\n assert len(options.vg) == len(filter_ids)\\n for vg_path, filter_id, in zip(options.vg, filter_ids):\\n name = os.path.splitext(vg_path)[0] + '.d{}.vg'.format(options.filter)\\n toil.exportFile(filter_id, makeURL(os.path.join(clip_base, os.path.basename(name))))\\n\\n # make a directory for the chromosomal ogs\\n if options.chrom_og:\\n clip_base = os.path.join(options.outDir, '{}.chroms'.format(options.outName))\\n if not clip_base.startswith('s3://') and not os.path.isdir(clip_base):\\n os.makedirs(clip_base)\\n\\n for gtype in options.chrom_og:\\n # download all the chromosomal ogs\\n tag = 'd{}'.format(options.filter) if gtype == 'filter' else gtype\\n og_ids = og_chrom_ids[gtype]['og']\\n assert len(options.vg) == len(og_ids)\\n for vg_path, og_id in zip(options.vg, og_ids):\\n name = os.path.splitext(vg_path)[0] + '{}.og'.format( '.' + tag if tag != 'clip' else '')\\n toil.exportFile(og_id, makeURL(os.path.join(clip_base, os.path.basename(name))))\\n\\n # make a directory for the viz\\n if options.viz + options.draw:\\n viz_base = os.path.join(options.outDir, '{}.viz'.format(options.outName))\\n if not viz_base.startswith('s3://') and not os.path.isdir(viz_base):\\n os.makedirs(viz_base)\\n\\n for gtype in options.viz:\\n # download all the chromosomal 1D visualizations\\n assert len(options.vg) == len(og_chrom_ids[gtype]['viz'])\\n tag = 'd{}'.format(options.filter) if gtype == 'filter' else gtype \\n for vg_path, viz_id in zip(options.vg, og_chrom_ids[gtype]['viz']):\\n if viz_id:\\n viz_name = os.path.splitext(vg_path)[0] + '{}.viz.png'.format('.' + tag if tag != 'clip' else '')\\n toil.exportFile(viz_id, makeURL(os.path.join(viz_base, os.path.basename(viz_name))))\\n\\n for gtype in options.draw:\\n # download all the chromosomal 2D visualizations\\n assert len(options.vg) == len(og_chrom_ids[gtype]['draw'])\\n tag = 'd{}'.format(options.filter) if gtype == 'filter' else gtype \\n for vg_path, draw_id in zip(options.vg, og_chrom_ids[gtype]['draw']):\\n if draw_id:\\n draw_name = os.path.splitext(vg_path)[0] + '{}.draw.png'.format('.' + tag if tag != 'clip' else '')\\n toil.exportFile(draw_id, makeURL(os.path.join(viz_base, os.path.basename(draw_name))))\\n \\n # download the stats files\\n if clip_stats:\\n for stats_file in clip_stats.keys():\\n toil.exportFile(clip_stats[stats_file], makeURL(os.path.join(options.outDir, '{}.{}'.format(options.outName, stats_file))))\\n \\n # download everything else\\n for idx_map in idx_maps:\\n for ext, idx_id in idx_map.items():\\n # hacky filtering of intermediate indexes that the user doesn't want\\n # ex if someone did --vcf clip --gfa full this would filter out clip.gfa etc.\\n is_intermediate = False\\n for out_ext, out_sel in [('gfa', options.gfa), ('gbz', options.gbz + options.giraffe), ('snarls', options.gbz)]:\\n for phase in ['full', 'clip', 'filter']:\\n if '{}.{}'.format(phase, out_ext) in ext and phase not in out_sel:\\n is_intermediate = True\\n if not is_intermediate:\\n out_ext = ext\\n if 'clip.' in out_ext:\\n out_ext = out_ext.replace('clip.', '')\\n if 'filter.' in out_ext:\\n out_ext = out_ext.replace('filter.', 'd{}.'.format(options.filter))\\n toil.exportFile(idx_id, makeURL(os.path.join(options.outDir, '{}.{}'.format(options.outName, out_ext))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2de30753670716534c983083dd676a2\",\n \"score\": \"0.49726242\",\n \"text\": \"def create_tar_archive(self, outdir=None):\\n # for the tar archive filename, we need a timestamp \\n timestamp = datetime.now().strftime('%F-%H%M%S') # e.g., '2020-04-21-132052'\\n directory_basename = os.path.basename(self.abspath)\\n tar_archive_name = f\\\"{timestamp}-{directory_basename}.tar.gz\\\"\\n try:\\n tar_archive_abspath = os.path.join(outdir.abspath, tar_archive_name)\\n except AttributeError:\\n tar_archive_abspath = os.path.join(self.abspath, tar_archive_name)\\n with tarfile.open(tar_archive_abspath, mode=\\\"w:gz\\\") as tar:\\n print(f\\\"Creating tar archive {tar_archive_abspath} ...\\\")\\n tar.add(self.abspath, arcname=f\\\"{timestamp}-{directory_basename}\\\")\\n return tar_archive_abspath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"638f8e8259d7ee7bbd806492d862d438\",\n \"score\": \"0.49636325\",\n \"text\": \"def upload_usp_family(folder, group_label, group_description, stop_if_existing=True):\\n import os\\n\\n import aiida.common\\n\\n # from aiida.common import aiidalogger\\n from aiida.common import NotExistent, UniquenessError\\n from aiida.orm.querybuilder import QueryBuilder\\n\\n from .otfg import OTFGGroup\\n\\n files = [\\n os.path.realpath(os.path.join(folder, i))\\n for i in os.listdir(folder)\\n if os.path.isfile(os.path.join(folder, i))\\n and (\\n i.lower().endswith(\\\".usp\\\")\\n or i.lower().endswith(\\\"recpot\\\")\\n or i.lower().endswith(\\\".uspcc\\\")\\n )\\n ]\\n\\n nfiles = len(files)\\n\\n try:\\n group = OTFGGroup.get(label=group_label)\\n group_created = False\\n except NotExistent:\\n group = OTFGGroup(\\n label=group_label,\\n )\\n group_created = True\\n\\n # Update the descript even if the group already existed\\n group.description = group_description\\n\\n pseudo_and_created = [] # A list of records (UspData, created)\\n\\n for f in files:\\n\\n md5sum = md5_file(f)\\n qb = QueryBuilder()\\n qb.append(UspData, filters={\\\"attributes.md5\\\": {\\\"==\\\": md5sum}})\\n existing_usp = qb.first()\\n\\n # Add the file if it is in the database\\n if existing_usp is None:\\n pseudo, created = UspData.get_or_create(f, use_first=True, store_usp=False)\\n pseudo_and_created.append((pseudo, created))\\n\\n # The same file is there already\\n else:\\n if stop_if_existing:\\n raise ValueError(\\n \\\"A usp/recpot with identical MD5 to\\\"\\n \\\" {} cannot be added with stop_if_existing\\\"\\n \\\"\\\".format(f)\\n )\\n existing_usp = existing_usp[0]\\n pseudo_and_created.append((existing_usp, False))\\n\\n # Check for unique per element\\n elements = [(i[0].element, i[0].md5sum) for i in pseudo_and_created]\\n\\n # Check if we will duplicate after insertion\\n\\n if not group_created:\\n for aiida_n in group.nodes:\\n if not isinstance(aiida_n, UspData):\\n continue\\n elements.append((aiida_n.element, aiida_n.md5sum))\\n\\n # Discard duplicated pairs\\n elements = set(elements)\\n elements_names = [e[0] for e in elements]\\n\\n # Check the uniqueness of the complete group\\n if not len(elements_names) == len(set(elements_names)):\\n duplicates = {x for x in elements_names if elements_names.count(x) > 1}\\n dup_string = \\\", \\\".join(duplicates)\\n raise UniquenessError(\\n \\\"More than one usp/recpot found for the elements: \\\" + dup_string + \\\".\\\"\\n )\\n\\n if group_created:\\n group.store()\\n\\n # Save the usp in the database if necessary and add them to the group\\n\\n for pseudo, created in pseudo_and_created:\\n if created:\\n pseudo.store()\\n # aiidalogger.debug(\\\"New node {} created for file {}\\\".format(\\n # pseudo.uuid, pseudo.filename))\\n else:\\n # aiidalogger.debug(\\\"Reusing node {} for file {}\\\".format(\\n # pseudo.uuid, pseudo.filename))\\n pass\\n\\n nodes_new = [pseduo for pseduo, created in pseudo_and_created if created is True]\\n nodes_add = [pseduo for pseduo, created in pseudo_and_created]\\n group.add_nodes(nodes_add)\\n\\n return nfiles, len(nodes_new)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab5f865c86861c4d958d1a3f574b4fda\",\n \"score\": \"0.49579674\",\n \"text\": \"def generate_files_distributed(generator,\\n output_name,\\n output_dir,\\n num_shards=1,\\n max_cases=None,\\n task_id=0):\\n assert task_id < num_shards\\n output_filename = sharded_name(output_name, task_id, num_shards)\\n output_file = os.path.join(output_dir, output_filename)\\n tf.logging.info(\\\"Writing to file %s\\\", output_file)\\n writer = tf.python_io.TFRecordWriter(output_file)\\n\\n counter = 0\\n for case in generator:\\n if counter % 100000 == 0:\\n tf.logging.info(\\\"Generating case %d for %s.\\\" % (counter, output_name))\\n counter += 1\\n if max_cases and counter > max_cases:\\n break\\n example = to_example(case)\\n writer.write(example.SerializeToString())\\n\\n writer.close()\\n return output_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"004a4f09de47aba4be8e696b01ccc681\",\n \"score\": \"0.49563918\",\n \"text\": \"def write_detections(detections, filename):\\n with h5py.File(filename, 'w') as f:\\n for d in detections:\\n # Compression factor with 'poor, but fast' lzf compression almost \\n # factor 10 for 1 dataset, ~factor 35 for 100 datasets\\n f.create_dataset(d, data=detections[d], compression='lzf')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea0d30855d7d271d195112a3479f67b\",\n \"score\": \"0.49468485\",\n \"text\": \"def createArchesFile(input_dataset,out_dir):\\n\\n ds_name = os.path.basename(input_dataset)\\n if os.path.splitext(ds_name)[1] != \\\"\\\":\\n ds_name = os.path.splitext(ds_name)[0]\\n outfile = os.path.join(out_dir,ds_name+\\\".arches\\\")\\n \\n with open(outfile,\\\"wb\\\") as arches:\\n arches.write(\\n \\\"RESOURCEID|RESOURCETYPE|ATTRIBUTENAME|ATTRIBUTEVALUE|GROUPID\\\\r\\\\n\\\")\\n return outfile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba340db2468b7568736c7785fcfd76a\",\n \"score\": \"0.49412504\",\n \"text\": \"def write_entries_group(bib_entries):\\n\\n # copy icons\\n icons_folder = os.path.join(params['htmlfile_group'], 'Icons')\\n params['author_group_Icons'] = icons_folder\\n if not os.path.exists(icons_folder):\\n os.mkdir(icons_folder)\\n if params['icon_www']:\\n params['author_group_icon_www'] = os.path.join(icons_folder, os.path.basename(params['icon_www']))\\n shutil.copyfile(params['icon_www'], params['author_group_icon_www'])\\n if params['icon_pdf']:\\n params['author_group_icon_pdf'] = os.path.join(icons_folder, os.path.basename(params['icon_pdf']))\\n shutil.copyfile(params['icon_pdf'], params['author_group_icon_pdf'])\\n\\n # copy css file\\n static_folder = os.path.join(params['htmlfile_group'], 'Static')\\n params['author_group_Static'] = static_folder\\n if not os.path.exists(static_folder):\\n os.mkdir(static_folder)\\n if params['css_file'] and os.path.exists(params['css_file']):\\n params['author_group_css'] = os.path.join(static_folder, os.path.basename(params['css_file']))\\n shutil.copyfile(params['css_file'], params['author_group_css'])\\n params['css_file'] = params['author_group_css']\\n if params['bootstrap_css'] and os.path.exists(params['bootstrap_css']):\\n params['author_group_bootstrap_css'] = os.path.join(static_folder, os.path.basename(params['bootstrap_css']))\\n shutil.copyfile(params['bootstrap_css'], params['author_group_bootstrap_css'])\\n params['bootstrap_css'] = params['author_group_bootstrap_css']\\n\\n title = params['title']\\n\\n # write entries selected by authors\\n _write_entries_group_author(bib_entries)\\n params['dict_title'] = params['dict_title_group']\\n\\n # write complete-bibliography.html\\n params['title'] = title\\n _write_entries_group_complete(bib_entries)\\n\\n # write complete-bibliography.bib\\n biblio_folder = os.path.join(params['htmlfile_group'], 'Bibliography')\\n params['outbibfile'] = os.path.join(biblio_folder, 'complete-bibliography.bib')\\n write_entries_to_bibfile(bib_entries)\\n\\n # write entries selected by publication venues\\n _write_entries_group_venue(bib_entries)\\n\\n # write entries selected by years\\n _write_entries_group_year(bib_entries)\\n\\n # write entries selected by categories\\n _write_entries_group_category(bib_entries)\\n\\n # write index.html\\n params['title'] = title\\n _write_entries_group_index(bib_entries)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10034300e76d05c86bccd28a52048efb\",\n \"score\": \"0.4940712\",\n \"text\": \"def order_by( self, inspp, outfolder=curr_path, make_csv=True, make_graph=False, make_tree=False, make_tree_x=False, make_tree_y=False, group_every=None, num_classes=None, evenly=False ):\\n db_name_short = os.path.basename(self.db_name).replace('.pickle.gz', '')\\n\\n if inspp not in self.spps:\\n print \\\"requested species(%s) does not exist\\\" % inspp\\n print self.spps\\n sys.exit( 1 )\\n\\n\\n pos_extra = \\\"\\\"\\n spp_extra = \\\"\\\"\\n\\n db_fn = os.path.join( outfolder, db_name_short + '.pickle.gz.' + inspp )\\n\\n if group_every is not None:\\n db_fn += \\\".every_\\\" + str(group_every)\\n pos_extra += \\\" - every %dbp\\\" % group_every\\n\\n elif num_classes is not None:\\n db_fn += \\\".classes_\\\" + str(num_classes)\\n pos_extra += \\\" - in %d groups\\\" % num_classes\\n\\n elif evenly:\\n db_fn += \\\".evenly\\\"\\n\\n if make_tree:\\n db_fn += \\\".tree\\\"\\n spp_extra += \\\" with clustering\\\"\\n pos_extra += \\\" with clustering\\\"\\n\\n elif make_tree_x:\\n db_fn += \\\".treex\\\"\\n pos_extra += \\\" with clustering\\\"\\n\\n elif make_tree_y:\\n db_fn += \\\".treey\\\"\\n spp_extra += \\\" with clustering\\\"\\n\\n db_fn += '.pickle.gz'\\n\\n excerpt = {}\\n\\n if os.path.exists( db_fn ):\\n print \\\"ordering %s :: db file %s exists. reading\\\" % ( inspp, db_fn )\\n excerpt = vcf_walk.loads( db_fn )\\n\\n else:\\n print \\\"ordering %s :: db file %s does not exists. parsing\\\" % ( inspp, db_fn )\\n excerpt = self.filter_by(inspp, group_every=None, num_classes=None, evenly=False)\\n\\n #leng = max([len(x) for x in spps])\\n #fmt = \\\" %-\\\"+str(leng)+\\\"s: %12d %12d \\\"\\n\\n #for chromosome_name in excerpt:\\n # print \\\" \\\", chromosome_name\\n # for start, end, name, line in excerpt[ chromosome_name ]:\\n # print \\\" %-12s %12d %12d %s\\\" % ( name, start, end, str(line) )\\n\\n print \\\"ordering %s :: ordered :: dumping\\\" % inspp\\n vcf_walk.dumps( db_fn, excerpt )\\n print \\\"ordering %s :: ordered :: done\\\" % inspp\\n\\n\\n sppindex = self.spps[ inspp ]\\n sppindexinv = self.getSppIndexInvert( )\\n\\n\\n if make_csv or make_graph:\\n print \\\"ordering %s :: exporting\\\" % inspp\\n\\n fhdcsv = None\\n if make_csv:\\n fhdcsv = open(db_fn + '.csv', 'w')\\n\\n for chromosome_name in sorted(excerpt):\\n hlines = []\\n dlines = []\\n #print excerpt[chromosome_name]\\n\\n if make_csv:\\n hlines.append( chromosome_name + \\\"\\\\n\\\" )\\n hlines.append( \\\"start,\\\" )\\n hlines.append( \\\",\\\".join([ str(x[vcf_walk.DB_START ] ) for x in excerpt[chromosome_name] ]) )\\n hlines.append( \\\"\\\\n\\\" )\\n\\n hlines.append( \\\"end,\\\" )\\n hlines.append( \\\",\\\".join([ str(x[vcf_walk.DB_END ] ) for x in excerpt[chromosome_name] ]) )\\n hlines.append( \\\"\\\\n\\\" )\\n\\n hlines.append( \\\"num_unities,\\\" )\\n hlines.append( \\\",\\\".join([ str(x[vcf_walk.DB_LEN_OBJ] ) for x in excerpt[chromosome_name] ]) )\\n hlines.append( \\\"\\\\n\\\" )\\n\\n hlines.append( \\\"num_snps,\\\" )\\n hlines.append( \\\",\\\".join([ str(x[vcf_walk.DB_LEN_SNP] ) for x in excerpt[chromosome_name] ]) )\\n hlines.append( \\\"\\\\n\\\" )\\n\\n dlines.append( \\\"name,\\\" )\\n dlines.append( \\\",\\\".join([ str(x[vcf_walk.DB_NAME ] ) for x in excerpt[chromosome_name] ]) )\\n dlines.append( \\\"\\\\n\\\" )\\n\\n for spp in sppindexinv:\\n sppindex = self.spps[ spp ]\\n data = []\\n\\n for x in excerpt[chromosome_name]:\\n xline = x[vcf_walk.DB_LINE]\\n val = xline[sppindex]\\n data.append( str(val) )\\n\\n dlines.append( spp + \\\",\\\" )\\n dlines.append( \\\",\\\".join( data ) )\\n dlines.append( \\\"\\\\n\\\" )\\n\\n\\n if make_csv:\\n print \\\"ordering %s :: exporting :: exporting chromosome %s to csv\\\" % (inspp, chromosome_name)\\n\\n fhdcsv.writelines( hlines )\\n fhdcsv.writelines( dlines )\\n fhdcsv.write( \\\"\\\\n\\\\n=\\\\n\\\\n\\\" )\\n\\n\\n if make_graph:\\n print \\\"ordering %s :: exporting :: exporting chromosome %s to image\\\" % (inspp, chromosome_name)\\n #Rscript heat.R < /tmp/heat.csv\\n\\n pos_extra_str = pos_extra\\n if evenly:\\n db = excerpt[ chromosome_name ]\\n dbLen = len(db)\\n chromLen = db[-1][vcf_walk.DB_END] - db[0][vcf_walk.DB_START]\\n\\n num_classes = dbLen\\n\\n group_every = int(chromLen / num_classes)\\n\\n pos_extra_str += \\\" - evenly distributed %dbp in %d groups of %dbp\\\" % (chromLen, dbLen, group_every)\\n\\n\\n fn_R = db_fn + '.csv.%s.R' % chromosome_name\\n fn_R_o = db_fn + '.csv.%s.R.stdout' % chromosome_name\\n fn_R_e = db_fn + '.csv.%s.R.stderr' % chromosome_name\\n fn_Png = db_fn + '.csv.%s.png' % chromosome_name\\n fn_Pdf = db_fn + '.csv.%s.pdf' % chromosome_name\\n fn_Svg = db_fn + '.csv.%s.svg' % chromosome_name\\n fn_csv = db_fn + '.csv.%s.csv' % chromosome_name\\n\\n R = genR(chromosome_name, inspp, fn_csv, fn_Png, fn_Pdf, fn_Svg, make_tree=make_tree, make_tree_x=make_tree_x, make_tree_y=make_tree_y, pos_extra=pos_extra_str, spp_extra=spp_extra)\\n\\n with open( fn_csv, 'w' ) as fhdcsvr:\\n fhdcsvr.writelines( dlines )\\n\\n with open( fn_R, 'w' ) as fhd:\\n fhd.write( R )\\n\\n print \\\"ordering %s :: exporting :: exporting chromosome %s to image :: running R script: %s\\\" % (inspp, chromosome_name, fn_R)\\n #print R\\n\\n call([\\\"Rscript\\\", fn_R], stdout=open(fn_R_o, 'wb'), stderr=open(fn_R_e, 'wb'))\\n\\n #to_img( db_name, spps, dlines, inspp )\\n\\n print \\\"ordering %s :: exporting :: exported chromosome %s to image\\\" % (inspp, chromosome_name)\\n print\\n print \\\"ordering %s :: exporting :: done\\\" % inspp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"699ba597180d0c1e0c9fb26a91edf17d\",\n \"score\": \"0.49352628\",\n \"text\": \"def save_files_to_h5(X, Y, N, labels, output_file):\\n\\n store = h5py.File(output_file, \\\"w\\\")\\n store.create_dataset(\\n 'data', data=X,\\n dtype='float32', compression='gzip', compression_opts=4\\n )\\n if compute_normals:\\n store.create_dataset(\\n 'normal', data=N,\\n dtype='float32', compression='gzip', compression_opts=4\\n )\\n store.create_dataset(\\n 'label', data=labels,\\n dtype='uint8', compression='gzip', compression_opts=1\\n )\\n store.create_dataset(\\n 'pid', data=Y,\\n dtype='uint8', compression='gzip', compression_opts=1\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01c9ab9a806582e213e219f00e408405\",\n \"score\": \"0.49234766\",\n \"text\": \"def create_archive(archive, folder):\\n with tarfile.open(archive, \\\"w:gz\\\") as tar:\\n tar.add(folder, arcname=os.path.basename(folder))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e1a15b6cf51d27cad3dd78ca3c6c115\",\n \"score\": \"0.49186638\",\n \"text\": \"def write_to_file(startdate, gen_output, nc_fname, metadata=None):\\n ensemble_forecast = gen_output.get(\\\"ensemble_forecast\\\", None)\\n deterministic = gen_output.get(\\\"deterministic\\\", None)\\n motion_field = gen_output.get(\\\"motion_field\\\", None)\\n ensemble_motion = gen_output.get(\\\"ensemble_motion\\\", None)\\n\\n if metadata is None:\\n metadata = dict()\\n\\n if all((dataset is None for dataset in gen_output.values())):\\n print(\\\"Nothing to store\\\")\\n log(\\\"warning\\\", \\\"Nothing to store into .h5 file. Skipping.\\\")\\n return None\\n\\n output_options = PD[\\\"output_options\\\"]\\n # We don't support irregular nowcast outputs here\\n nowcast_timestep = get_timesteps()\\n\\n ensemble_forecast, ens_scale_meta = prepare_data_for_writing(ensemble_forecast)\\n deterministic, det_scale_meta = prepare_data_for_writing(deterministic)\\n\\n with h5py.File(os.path.join(output_options[\\\"path\\\"], nc_fname), 'w') as outf:\\n if ensemble_forecast is not None and output_options[\\\"store_ensemble\\\"]:\\n for eidx in range(PD[\\\"ensemble_size\\\"]):\\n ens_grp = outf.create_group(\\\"member-{:0>2}\\\".format(eidx))\\n utils.store_timeseries(ens_grp,\\n ensemble_forecast[eidx, :, :, :],\\n startdate,\\n timestep=nowcast_timestep,\\n metadata=ens_scale_meta)\\n\\n if ensemble_motion is not None and output_options[\\\"store_perturbed_motion\\\"]:\\n for eidx in range(PD[\\\"ensemble_size\\\"]):\\n try:\\n ens_grp = outf[\\\"member-{:0>2}\\\".format(eidx)]\\n except KeyError:\\n ens_grp = outf.create_group(\\\"member-{:0>2}\\\".format(eidx))\\n ens_grp.create_dataset(\\\"motion\\\", data=ensemble_motion[eidx])\\n\\n if deterministic is not None and output_options[\\\"store_deterministic\\\"]:\\n det_grp = outf.create_group(\\\"deterministic\\\")\\n utils.store_timeseries(det_grp, deterministic, startdate,\\n timestep=nowcast_timestep,\\n metadata=det_scale_meta)\\n\\n if output_options[\\\"store_motion\\\"]:\\n outf.create_dataset(\\\"motion\\\", data=motion_field)\\n\\n meta = outf.create_group(\\\"meta\\\")\\n # configuration \\\"OUTPUT_TIME_FORMAT\\\" is removed, new output uses ODIM standard\\n meta.attrs[\\\"nowcast_started\\\"] = dt.datetime.strftime(metadata[\\\"time_at_start\\\"],\\n \\\"%Y-%m-%d %H:%M:%S\\\")\\n meta.attrs[\\\"nowcast_ended\\\"] = dt.datetime.strftime(metadata[\\\"time_at_end\\\"],\\n \\\"%Y-%m-%d %H:%M:%S\\\")\\n meta.attrs[\\\"nowcast_units\\\"] = metadata.get(\\\"unit\\\", \\\"Unknown\\\")\\n meta.attrs[\\\"nowcast_seed\\\"] = metadata.get(\\\"seed\\\", \\\"Unknown\\\")\\n meta.attrs[\\\"nowcast_init_time\\\"] = dt.datetime.strftime(startdate, \\\"%Y%m%d%H%M\\\")\\n\\n # Old configurations - may be used by postprocessing scripts\\n old_style_configs = {\\n # Method selections\\n #\\\"DOMAIN\\\": \\\"fmi\\\", # postprocessing defines this instead of reading it here\\n \\\"VALUE_DOMAIN\\\": \\\"rrate\\\" if PD[\\\"run_options\\\"][\\\"forecast_as_quantity\\\"] == \\\"RATE\\\" else \\\"dbz\\\", # Unused?\\n # Z-R conversion parameters\\n \\\"ZR_A\\\": PD[\\\"data_options\\\"][\\\"zr_a\\\"], #\\n \\\"ZR_B\\\": PD[\\\"data_options\\\"][\\\"zr_b\\\"], #\\n # Nowcasting parameters\\n \\\"NOWCAST_TIMESTEP\\\": nowcast_timestep, #\\n \\\"MAX_LEADTIME\\\": PD[\\\"run_options\\\"][\\\"max_leadtime\\\"], #\\n \\\"NUM_TIMESTEPS\\\": PD[\\\"run_options\\\"][\\\"leadtimes\\\"], #\\n \\\"ENSEMBLE_SIZE\\\": PD[\\\"ensemble_size\\\"], #\\n \\\"NUM_CASCADES\\\": PD[\\\"nowcast_options\\\"].get(\\\"n_cascade_levels\\\", 6), # Unused?\\n \\\"RAIN_THRESHOLD\\\": PD[\\\"out_rain_threshold\\\"], # Unused?\\n \\\"NORAIN_VALUE\\\": PD[\\\"out_norain_value\\\"], #\\n \\\"KMPERPIXEL\\\": PD[\\\"nowcast_options\\\"][\\\"kmperpixel\\\"], # Unused?\\n \\\"CALCULATION_DOMAIN\\\": PD[\\\"nowcast_options\\\"][\\\"domain\\\"], # Unused?\\n \\\"VEL_PERT_KWARGS\\\": PD[\\\"nowcast_options\\\"][\\\"vel_pert_kwargs\\\"],\\n # Storing parameters\\n \\\"FIELD_VALUES\\\": PD[\\\"output_options\\\"][\\\"as_quantity\\\"], # Unused?\\n \\\"STORE_DETERMINISTIC\\\": output_options[\\\"store_deterministic\\\"], #\\n \\\"STORE_PERTURBED_MOTION\\\": output_options[\\\"store_perturbed_motion\\\"], #\\n }\\n\\n pd_meta = meta.create_group(\\\"configuration\\\")\\n for key, value in old_style_configs.items():\\n pd_meta.attrs[key] = str(value)\\n\\n proj_meta = meta.create_group(\\\"projection\\\")\\n for key, value in metadata[\\\"projection\\\"].items():\\n proj_meta.attrs[key] = value\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eff8e1731b2ff5dd37ea11d753b7486\",\n \"score\": \"0.4917919\",\n \"text\": \"def generate_standard_outputs(self, output_dir, gtfs_day='19700101'):\\n gngeojson.generate_standard_outputs_for_schedule(self, output_dir, gtfs_day)\\n logging.info('Finished generating standard outputs. Zipping folder.')\\n persistence.zip_folder(output_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb5c69f8be18fe9723020203513e1a89\",\n \"score\": \"0.49162012\",\n \"text\": \"def _write_results_file(self, all_boxes, output_dir):\\n\\n #format_str = \\\"{}, -1, {}, {}, {}, {}, {}, -1, -1, -1\\\"\\n\\n files = {}\\n for cls in all_boxes:\\n for i, dets in enumerate(cls):\\n path = self.image_path_at(i)\\n img1, name = osp.split(path)\\n # get image number out of name\\n frame = int(name.split('.')[0])\\n # smth like /train/MOT17-09-FRCNN or /train/MOT17-09\\n tmp = osp.dirname(img1)\\n # get the folder name of the sequence and split it\\n tmp = osp.basename(tmp).split('-')\\n # Now get the output name of the file\\n out = tmp[0]+'-'+tmp[1]+'.txt'\\n outfile = osp.join(output_dir, out)\\n\\n # check if out in keys and create empty list if not\\n if outfile not in files.keys():\\n files[outfile] = []\\n\\n for d in dets:\\n x1 = d[0]\\n y1 = d[1]\\n x2 = d[2]\\n y2 = d[3]\\n score = d[4]\\n files[outfile].append([frame, -1, x1+1, y1+1, x2-x1+1, y2-y1+1, score, -1, -1, -1])\\n\\n for k,v in files.items():\\n #outfile = osp.join(output_dir, out)\\n with open(k, \\\"w\\\") as of:\\n writer = csv.writer(of, delimiter=',')\\n for d in v:\\n writer.writerow(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e10a87863efd30bd513a6b3af03cf2d1\",\n \"score\": \"0.49148518\",\n \"text\": \"def create_hdf5(\\n bigwig_paths, chrom_sizes_path, out_path, chunk_size, batch_size=100\\n):\\n bigwig_readers = [pyBigWig.open(path) for path in bigwig_paths]\\n \\n # Read in chromosome sizes\\n with open(chrom_sizes_path, \\\"r\\\") as f:\\n chrom_sizes = {}\\n for line in f:\\n tokens = line.strip().split(\\\"\\\\t\\\")\\n chrom_sizes[tokens[0]] = int(tokens[1])\\n \\n # Convert batch size to be in terms of rows, not number of chunks\\n batch_size = batch_size * chunk_size\\n\\n with h5py.File(out_path, \\\"w\\\") as f:\\n # Store source paths\\n f.create_dataset(\\\"bigwig_paths\\\", data=np.array(bigwig_paths, dtype=\\\"S\\\"))\\n for chrom in sorted(chrom_sizes.keys()):\\n chrom_size = chrom_sizes[chrom]\\n num_batches = int(np.ceil(chrom_size / batch_size))\\n chrom_dset = f.create_dataset(\\n chrom, (chrom_size, len(bigwig_paths), 1), dtype=\\\"f\\\",\\n compression=\\\"gzip\\\", chunks=(chunk_size, len(bigwig_paths), 1)\\n )\\n for i in tqdm.trange(num_batches, desc=chrom):\\n start = i * batch_size\\n end = min(chrom_size, (i + 1) * batch_size)\\n\\n values = np.stack([\\n np.stack([\\n np.nan_to_num(reader.values(chrom, start, end))\\n ], axis=1) for reader in bigwig_readers\\n ], axis=1)\\n\\n chrom_dset[start : end] = values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"256f02c777a0d1c72ad15dc7cd4b7905\",\n \"score\": \"0.49132812\",\n \"text\": \"def write(self, fname, **kwargs):\\n\\n f = fits.HDUList()\\n\\n f.append(fits.PrimaryHDU())\\n\\n thdr = fits.table_to_hdu(self.gal.get_table())\\n thdr.name = 'GALAXY'\\n f.append(thdr)\\n\\n if self.spec is not None:\\n f[0].header['HASSPEC'] = True\\n f.append(fits.ImageHDU(name='SPEC_DATA', data=self.spec.data.data))\\n f.append(fits.ImageHDU(name='SPEC_VAR', data=self.spec.var.data))\\n else:\\n f[0].header['HASSPEC'] = False\\n\\n if self.ddata is not None:\\n f[0].header['HASDATA'] = True\\n f[0].header['NDSETS'] = len(self.datasets)\\n\\n f.append(fits.ImageHDU(name='DATA', data=self.ddata))\\n for ds in self.datasets:\\n f.append(fits.ImageHDU(**ds))\\n\\n fig = Figure()\\n canvas = Canvas(fig)\\n self.plot(fig.gca())\\n canvas.draw()\\n\\n w, h = canvas.get_width_height()\\n plot = np.fromstring(canvas.tostring_argb(), dtype='uint8').reshape(\\n (h, w, 4))\\n\\n f.append(fits.ImageHDU(name='PLOT', data=plot))\\n else:\\n f[0].header['HASDATA'] = False\\n\\n f.writeto(fname, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aff7c0cbddf3f4dd9273903d2593fdf8\",\n \"score\": \"0.49125293\",\n \"text\": \"def genGroupFromTraffic(self, trafficfile):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ad2b4afc7cb8c46a9619fb5ab83c86e\",\n \"score\": \"0.49042654\",\n \"text\": \"def open_halo_output(self):\\n \\n \\n try:\\n self.halo_output_file = h5.File(self.HDF_output_filepath, \\\"w\\\")\\n\\n except OSError:\\n for obj in gc.get_objects(): # Browse through ALL objects\\n if isinstance(obj, h5.File): # Just HDF5 files\\n try:\\n obj.close()\\n except:\\n pass # Was already closed \\n self.halo_output_file = h5.File(self.HDF_output_filepath, \\\"w\\\")\\n\\n self.halo_output_dataset = self.halo_output_file.create_dataset(\\n \\\"halo_data\\\", (0,), maxshape=(None,), dtype=self.dtype_halo, compression=\\\"gzip\\\"\\n )\\n \\n self.subhalo_output_dataset = self.halo_output_file.create_dataset(\\n \\\"subhalo_data\\\", (0,), maxshape=(None,), dtype=self.subhalo_dtype, compression=\\\"gzip\\\"\\n )\\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f12875305def5e7c09b75e00e041148b\",\n \"score\": \"0.4899638\",\n \"text\": \"def generate_files(self, output_dir: str) -> None:\\n self._write_file(output_dir, self._OUTPUT_FILE, self._generate_zones())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e2e6d4f38e8aa157c0b1be0610871a\",\n \"score\": \"0.48976526\",\n \"text\": \"def process_set_metadata(self, data, set_name):\\n hdf5_handler = self.hdf5_manager.get_group(set_name)\\n image_filenames = []\\n keypoints = []\\n object_id = []\\n object_fields = [\\\"image_filenames\\\", \\\"keypoints\\\"]\\n\\n if self.verbose:\\n print('> Adding data to default group:')\\n prgbar = progressbar.ProgressBar(max_value=len(data))\\n\\n for i, annot in enumerate(data):\\n image_filenames.append(annot[\\\"filename\\\"])\\n keypoints.append(annot[\\\"joints\\\"])\\n\\n object_id.append([i, i])\\n\\n # update progressbar\\n if self.verbose:\\n prgbar.update(i)\\n\\n # update progressbar\\n if self.verbose:\\n prgbar.finish()\\n\\n hdf5_write_data(hdf5_handler, 'image_filenames',\\n str2ascii(image_filenames),\\n dtype=np.uint8, fillvalue=0)\\n hdf5_write_data(hdf5_handler, 'keypoints',\\n np.array(keypoints, dtype=np.float),\\n fillvalue=-1)\\n hdf5_write_data(hdf5_handler, 'keypoint_names',\\n str2ascii(self.keypoints_labels),\\n dtype=np.uint8, fillvalue=0)\\n hdf5_write_data(hdf5_handler, 'object_ids',\\n np.array(object_id, dtype=np.int32),\\n fillvalue=-1)\\n hdf5_write_data(hdf5_handler, 'object_fields',\\n str2ascii(object_fields),\\n dtype=np.uint8, fillvalue=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f27d38b86522261688a78a7346bc5e2f\",\n \"score\": \"0.48972598\",\n \"text\": \"def save_gtfs(filename,tables):\\n\\twith zipfile.ZipFile(filename,\\\"w\\\",compression=zipfile.ZIP_DEFLATED) as zipf:\\n\\t\\tfor (name,table) in tables.items():\\n\\t\\t\\tfieldnames = table[0].keys()\\n\\t\\t\\ttablef = tempfile.NamedTemporaryFile(mode=\\\"w\\\",encoding=\\\"utf-8\\\")\\n\\t\\t\\t#FIXME: extrasaction is set to ignore due to bug in csv.py in Python3.6\\n\\t\\t\\twriter=csv.DictWriter(tablef,fieldnames,extrasaction=\\\"ignore\\\")\\n\\t\\t\\twriter.writeheader()\\n\\t\\t\\twriter.writerows(table)\\n\\t\\t\\ttablef.flush()\\n\\t\\t\\tzipf.write(tablef.name,name+\\\".txt\\\")\\n\\t\\t\\ttablef.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c59439b20e0ca626247f05fc7b5185e\",\n \"score\": \"0.48950222\",\n \"text\": \"def _save_results(self, zipdata, outdir, module, gmt, rank_metric, permutation_type):\\n\\n res = OrderedDict()\\n for gs,gseale,ind,RES in zipdata:\\n rdict = OrderedDict()\\n rdict['es'] = gseale[0]\\n rdict['nes'] = gseale[1]\\n rdict['pval'] = gseale[2]\\n rdict['fdr'] = gseale[3]\\n rdict['gene_set_size'] = len(gmt[gs])\\n rdict['matched_size'] = len(ind)\\n #reformat gene list.\\n _genes = rank_metric.iloc[ind, rank_metric.columns.get_loc('gene_name')]\\n _genes = _genes.to_string(header=False, index=False).split(\\\"\\\\n\\\")\\n rdict['genes'] = \\\",\\\".join([ g.strip() for g in _genes ])\\n\\n rdict['rank_ES'] = RES\\n rdict['hit_index'] = ind\\n res[gs] = rdict\\n\\n res_df = pd.DataFrame.from_dict(res, orient='index')\\n res_df.index.name = 'Term'\\n res_df.sort_values(by='fdr', inplace=True)\\n res_df.drop(['rank_ES','hit_index'], axis=1, inplace=True)\\n res_df.to_csv('{a}/gseapy.{b}.{c}.report.csv'.format(a=outdir, b=module, c=permutation_type),\\n float_format ='%.7f')\\n\\n self.res2d = res_df\\n self.results = res\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5510a4f25e94771ef37f98b8d6c9c6b0\",\n \"score\": \"0.48928303\",\n \"text\": \"def addhdf5(self, HDF5group):\\n HDF5group.attrs['type'] = self.__class__.__name__\\n HDF5group['Nvstars'] = self.Nvstars\\n HDF5group['vecposlist'], HDF5group['vecposindex'] = doublelist2flatlistindex(self.vecpos)\\n HDF5group['vecveclist'], HDF5group['vecvecindex'] = doublelist2flatlistindex(self.vecvec)\\n HDF5group['outer'] = self.outer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca6d4ca8628fa2a2bb2384bf5667b503\",\n \"score\": \"0.48912898\",\n \"text\": \"def background_group():\\n obs_table = config.obs_table.copy()\\n\\n # Define observation groups\\n # zenith_bins = np.array([0, 20, 30, 40, 50, 90])\\n zenith_bins = np.array([0, 49, 90])\\n # zenith_bins = np.array([0, 30, 90]) # for testing\\n axes = [ObservationGroupAxis('ZEN_PNT', zenith_bins, 'bin_edges')]\\n obs_groups = ObservationGroups(axes)\\n log.info(obs_groups.info)\\n\\n # Apply observation grouping\\n obs_table = obs_groups.group_observation_table(obs_table)\\n\\n # Store the results\\n filename = config.obs_table_grouped_filename\\n log.info('Writing {}'.format(filename))\\n obs_table.write(str(filename), format='ascii.ecsv')\\n\\n filename = config.group_def_filename\\n log.info('Writing {}'.format(filename))\\n obs_groups.obs_groups_table.write(str(filename), format='ascii.ecsv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ad1397340c695b5f0c7684be27b06a2\",\n \"score\": \"0.48905203\",\n \"text\": \"def main(outfolder = './gazette_data/', outtype = sys.argv[1]):\\n \\n # Check if gazette data exists (whether gazette.py has been run) #\\n data_exists_check()\\n # Check whether save directories exists, and if not, create them #\\n save_directory_check()\\n # Files that have extra spaces, indents, etc. and won't run smoothly through.\\n bad_dates = ['2006/18_Sep30','2006/17_Sep15','2016/18_Sept30','2017/11_Jun15','2018/10_May31']\\n # Instantiate empty lists for keeping data before saving out.\\n incorporations, namechanges = [], [] \\n # Format default output as CSVs if no sys.argv[1] provided\\n if not sys.argv[1]:\\n outtype = 'csv'\\n \\n # Begin parsing files by year #\\n for year in range(2006,2018+1):\\n files = os.listdir('cache/gazette/'+str(year))\\n for filename in files:\\n if filename.endswith('.txt'):\\n date_string = str(year) + '/' + filename[:-4]\\n if date_string not in bad_dates:\\n # The following returns pandas DataFrames if outtype is 'csv', list of pickles if outtype is 'pickles' #\\n inc, nmch = make_incorporations_and_namechange(date_string,outfolder = outfolder,outtype = outtype)\\n if outtype == 'csv':\\n incorporations.append(inc)\\n namechanges.append(nmch)\\n elif outtype == 'pickles':\\n incorporations += inc\\n namechanges += nmch\\n \\n # Write out files #\\n \\n if outtype == 'csv': \\n make_master_tables(incorporations,namechanges)\\n \\n elif outtype =='pickles': \\n # Incorporations #\\n pickle_out = open(outfolder + \\\"incorporations_2006-2018.pickle\\\",\\\"wb\\\")\\n pickle.dump(incorporations, pickle_out)\\n pickle_out.close()\\n # Namechanges #\\n pickle_out = open(outfolder + \\\"namechanges_2006-2018.pickle\\\",\\\"wb\\\")\\n pickle.dump(namechanges, pickle_out)\\n pickle_out.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8464957a2a23a652b7aeb986e25b2aa8\",\n \"score\": \"0.48893464\",\n \"text\": \"def createAgents(self, group):\\n for ty in group.agentTypes:\\n for ag in ty.agents:\\n self.newagent(ag, ty.name, group.freezeAnimation)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98b46f1f5f759ce89b74d373aba562c5\",\n \"score\": \"0.4879788\",\n \"text\": \"def main(args):\\n\\n\\n data = pandas.read_csv(args.infile,index_col=0)\\n if not args.nocls:\\n data = data[args.group1+args.group2]\\n\\n samples = data.columns.tolist()\\n genes = data.index.tolist()\\n\\n descriptions = ['NA']*data.shape[0]\\n temp = ['NAME','DESCRIPTION'] + samples\\n\\n gct = pandas.DataFrame(columns=temp, index=data.index)\\n gct['NAME'] = genes\\n gct['DESCRIPTION'] = descriptions\\n for i in samples:\\n gct[i] = data[i]\\n gct.to_csv('temp.txt',sep='\\\\t',index=False)\\n\\n gct = open('temp.txt', 'r').read()\\n fout = open(args.prefix+'.gct', 'w')\\n fout.write('#1.2\\\\n')\\n fout.write(str(data.shape[0]) + '\\\\t' + str(len(samples)) + '\\\\n')\\n fout.write(gct)\\n fout.close()\\n\\n if not args.nocls:\\n fout = open(args.prefix + '.cls','w')\\n fout.write(str(len(args.group1) + len(args.group2)) + ' 2 1\\\\n')\\n fout.write('# ' + args.phenotypes[0] + ' ' + args.phenotypes[1] + '\\\\n')\\n fout.write(' '.join(['0']*len(args.group1)) + ' ' + ' '.join(['1']*len(args.group2)) + '\\\\n')\\n fout.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a1524f71c467eaf3639777623947e2f\",\n \"score\": \"0.48788592\",\n \"text\": \"def _save_reconstructions(self, reconstructions):\\n self.out_dir.mkdir(exist_ok=False) # Throw an error to prevent overwriting.\\n gzip = dict(compression='gzip', compression_opts=1, shuffle=True, fletcher32=True)\\n for file_name, recons in tqdm(reconstructions.items()):\\n with h5py.File(self.out_dir / file_name, mode='x', libver='latest') as f: # Overwriting throws an error.\\n f.create_dataset('reconstruction', data=recons, **gzip)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994089,"cells":{"query_id":{"kind":"string","value":"92a56c7f57a736b69d2267ad2bc5e9d5"},"query":{"kind":"string","value":"Authenticates a challenge message and responds to the requestor."},"positive_passages":{"kind":"list like","value":[{"docid":"2b049151ea4c4fa28d8780963a4016be","score":"0.8119253","text":"def authenticate_challenge(self, message: Dict[Any, Any]):\n\n challenge = message[\"challenge\"]\n log.info(\"Authenticating a challenge from the server\")\n\n try:\n signed_challenge = sign_challenge(\n base64.b64decode(challenge), self.private_key\n )\n\n message = {\n \"ChallengeResponse\": {\n \"email\": self.email,\n \"model_name\": self.model_name,\n \"response\": base64.b64encode(signed_challenge).decode(\"utf-8\"),\n }\n }\n\n self._send_message(message)\n except KeyError:\n log.error(\"Failed to find the private key in the environment\")","title":""}],"string":"[\n {\n \"docid\": \"2b049151ea4c4fa28d8780963a4016be\",\n \"score\": \"0.8119253\",\n \"text\": \"def authenticate_challenge(self, message: Dict[Any, Any]):\\n\\n challenge = message[\\\"challenge\\\"]\\n log.info(\\\"Authenticating a challenge from the server\\\")\\n\\n try:\\n signed_challenge = sign_challenge(\\n base64.b64decode(challenge), self.private_key\\n )\\n\\n message = {\\n \\\"ChallengeResponse\\\": {\\n \\\"email\\\": self.email,\\n \\\"model_name\\\": self.model_name,\\n \\\"response\\\": base64.b64encode(signed_challenge).decode(\\\"utf-8\\\"),\\n }\\n }\\n\\n self._send_message(message)\\n except KeyError:\\n log.error(\\\"Failed to find the private key in the environment\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e7ab498cb458cdcb5097216cbfed59d8","score":"0.7524756","text":"def process_challenge(self, message: str) -> None:\n\t\tvalue = message.get('val', None)\n\n\t\tif value != self.challenge_ans:\n\t\t\tlogger.error(\"Authentication failed\")\n\t\t\treturn False\n\t\t\n\n\t\tself.state = STATE_AUTH\n\t\tself._send({'type':'OK'})\n\n\t\treturn True","title":""},{"docid":"b1dccbd12c98a4b96df5fc9bf408840a","score":"0.74329895","text":"def process_challenge_response(self, message):\n\t\tusername = message['credentials']['username']\n\t\t\n\t\tif username not in self.registered_users or 'A1' not in self.registered_users[username][1]:\n\t\t\tlogger.info(\"{} authenticated denied.\".format(username))\n\t\t\tmessage = {'type': 'AUTH_RESPONSE', 'status': 'DENIED'}\n\t\t\tsecure_message = self.encrypt_payload(message)\n\t\t\tself._send(secure_message)\n\t\t\tself.send_mac()\n\t\t\treturn False\n\n\t\tsigned_challenge = message['credentials']['signed_challenge']\n\t\tpw, permissions = self.registered_users[username][0], self.registered_users[username][1]\t\n\t\tsignature_verification = self.crypto.rsa_signature_verification(base64.b64decode(signed_challenge.encode()), (str(self.client_nonce) + pw + str(self.crypto.auth_nonce)).encode(), self.crypto.load_public_key(self.client_public_key))\n\n\t\tif signature_verification:\n\t\t\tlogger.info(\"{} authenticated with success!\".format(username))\n\t\t\tmessage = {'type': 'AUTH_RESPONSE', 'status': 'SUCCESS', 'username': username}\n\t\t\tsecure_message = self.encrypt_payload(message)\n\t\t\tself._send(secure_message)\n\t\t\tself.send_mac()\n\t\t\tself.authenticated_user = [username, permissions]\n\n\t\telse:\n\t\t\tself.authentication_tries += 1\n\t\t\tif self.authentication_tries == 3:\n\t\t\t\t# remove authentication permission\n\t\t\t\tself.registered_users[username][1] = self.registered_users[username][1].replace('1', '0')\n\t\t\t\tself.update_users()\n\t\t\t\tlogger.info(\"{} authenticated denied.\".format(username))\n\t\t\t\tmessage = {'type': 'AUTH_RESPONSE', 'status': 'DENIED'}\n\t\t\t\tsecure_message = self.encrypt_payload(message)\n\t\t\t\tself._send(secure_message)\n\t\t\t\tself.send_mac()\n\t\t\t\treturn False\n\n\t\t\tlogger.info(\"{} authenticated failed.\".format(username))\n\t\t\tmessage = {'type': 'AUTH_RESPONSE', 'status': 'FAILED'}\n\t\t\tsecure_message = self.encrypt_payload(message)\n\t\t\tself._send(secure_message)\n\t\t\tself.send_mac()\n\t\t\treturn True\n\t\t\n\t\treturn True","title":""},{"docid":"72e890d5341676f0833fa204f2e0b630","score":"0.70976585","text":"def handle_challenge(self, challenge: bytes) -> bytes:\n raise NotImplementedError","title":""},{"docid":"d8168a98462fb6a6ee4ee17d23912830","score":"0.6681826","text":"def triggerChallenge(self):\r\n self.send(\"\"\"<challenge xmlns='urn:ietf:params:xml:ns:xmpp-sasl'>cmVhbG09ImNoZXNzcGFyay5jb20iLG5vbmNlPSJ0YUhIM0FHQkpQSE40eXNvNEt5cFlBPT0iLHFvcD0iYXV0aCxhdXRoLWludCIsY2hhcnNldD11dGYtOCxhbGdvcml0aG09bWQ1LXNlc3M=</challenge>\"\"\")","title":""},{"docid":"03ac3aeb12e0f5e43dfb3056dd6c38bd","score":"0.65662795","text":"def auth_challenge(self):\n\n self.set_header(\"WWW-Authenticate\", \"Basic realm=pyjojo\")\n self.set_status(401)\n self.finish()","title":""},{"docid":"ef73f81cf0acac1e8ff79a7e900ced45","score":"0.6551384","text":"def challenge(self):\n if self._challenge is None:\n self.sock.sendto(a2s_getchallenge_req(), self.srvaddr)\n inforsp = recvresponse(self.sock)\n self._challenge = a2s_getchallenge_rsp(inforsp)[\"challenge\"]\n return self._challenge","title":""},{"docid":"a634fd589381dd25bc3923e4a8944744","score":"0.65054107","text":"def challenge_response(self, challenge):\n key_1 = self.request.headers.get(\"Sec-Websocket-Key1\")\n key_2 = self.request.headers.get(\"Sec-Websocket-Key2\")\n try:\n part_1 = self._calculate_part(key_1)\n part_2 = self._calculate_part(key_2)\n except ValueError:\n raise ValueError(\"Invalid Keys/Challenge\")\n return self._generate_challenge_response(part_1, part_2, challenge)","title":""},{"docid":"744d0279df8a34881c33353d10f98161","score":"0.6491226","text":"def get(self):\n self.response.out.write(self.request.get('challenge'))","title":""},{"docid":"0195fd9e3a1aadd9a0a16d130010962a","score":"0.6347538","text":"def challenge(self, challenge):\n # pylint: disable=R0911\n if not challenge:\n logger.debug(\"Empty challenge\")\n return Failure(\"bad-challenge\")\n\n if self._server_first_message:\n return self._final_challenge(challenge)\n\n match = SERVER_FIRST_MESSAGE_RE.match(challenge)\n if not match:\n logger.debug(\"Bad challenge syntax: {0!r}\".format(challenge))\n return Failure(\"bad-challenge\")\n\n self._server_first_message = challenge\n\n mext = match.group(\"mext\")\n if mext:\n logger.debug(\"Unsupported extension received: {0!r}\".format(mext))\n return Failure(\"bad-challenge\")\n\n nonce = match.group(\"nonce\")\n if not nonce.startswith(self._c_nonce):\n logger.debug(\"Nonce does not start with our nonce\")\n return Failure(\"bad-challenge\")\n\n salt = match.group(\"salt\")\n try:\n salt = a2b_base64(salt)\n except ValueError:\n logger.debug(\"Bad base64 encoding for salt: {0!r}\".format(salt))\n return Failure(\"bad-challenge\")\n\n iteration_count = match.group(\"iteration_count\")\n try:\n iteration_count = int(iteration_count)\n except ValueError:\n logger.debug(\"Bad iteration_count: {0!r}\".format(iteration_count))\n return Failure(\"bad-challenge\")\n\n return self._make_response(nonce, salt, iteration_count)","title":""},{"docid":"eb37ac6baf8d896b4b48e9b095b6a6fb","score":"0.63438255","text":"def process_login_request(self, message):\n\t\tself.state = STATE_CLIENT_AUTH\n\t\tself.client_nonce = base64.b64decode(message['nonce'].encode())\n\t\tself.crypto.auth_nonce = os.urandom(16)\n\t\tself.client_public_key = base64.b64decode(message['public_key'].encode())\n\t\tmessage = {'type': 'CHALLENGE_REQUEST', 'nonce': base64.b64encode(self.crypto.auth_nonce).decode()}\n\t\tsecure_message = self.encrypt_payload(message)\n\t\tself._send(secure_message)\n\t\tself.send_mac()\n\t\t\n\t\treturn True","title":""},{"docid":"44428eaeb0244f85fb46d45692463873","score":"0.6340404","text":"def _send_challenge_result(\n request, session_id, challenge_id, client_input, access_token\n):\n body = {\n \"sessionId\": session_id,\n \"challengeId\": challenge_id,\n \"action\": \"RESPOND\",\n \"proposalResponse\": client_input,\n }\n metrics_header = {metrics.API_CLIENT_HEADER: metrics.reauth_continue()}\n\n return _client._token_endpoint_request(\n request,\n _REAUTH_API + \"/{}:continue\".format(session_id),\n body,\n access_token=access_token,\n use_json=True,\n headers=metrics_header,\n )","title":""},{"docid":"80ddf053e89f40e99b58b0b9fc052d75","score":"0.6324714","text":"def start_responding(self, ignore, challenge, response):\n self.putChild(\n challenge.encode('token').encode('utf-8'),\n StaticTextResource(response.key_authorization),\n )","title":""},{"docid":"7df5b38e6c4ef96a36abb96ccb9cc353","score":"0.6309285","text":"def authenticate(self, message):\n if self.authenticated == self.auth_statuses[\"auth_complete\"]:\n return\n\n if not self.is_auth() and message.strip() in [CLIENT_HELLO, INJECTOR_HELLO, ADMIN_HELLO]:\n self.change_auth_status(self.auth_statuses[\"auth_ok\"])\n self.send(SERVER_HELLO)\n if message.strip() == CLIENT_HELLO:\n self.client_type = self.client_types[\"hook\"]\n elif message.strip() == INJECTOR_HELLO:\n self.client_type = self.client_types[\"injector\"]\n elif message.strip() == ADMIN_HELLO:\n self.client_type = self.client_types[\"admin\"]\n return\n\n if self.client_type == self.client_types[\"admin\"]:\n admin = AdminAuthentication(self, self.db)\n admin.run(message)\n return\n\n if self.authenticated == self.auth_statuses[\"auth_ok\"]:\n response = message.splitlines()\n self.status, uid = response[0].strip().split(\" \")\n if int(self.status) != (PROTOCOL_STATUS_CODES[\"authentication\"]):\n return\n\n self.change_auth_status(self.auth_statuses[\"auth_complete\"])\n self.uid = uid\n\n self.send(str(PROTOCOL_STATUS_CODES[\"ok\"]) + \" Authentication complete\")\n LOGGER.info(\"{client_info} has authenticated\".format(client_info=self))\n return\n return","title":""},{"docid":"784e0272e7533c1cfa73dfc053142893","score":"0.61799854","text":"def make_basic_challenge(self, realm, message=None):\n\n if message is None:\n message = \"Authentication is required\"\n\n authenticate = 'Basic realm=\"%s\"' % realm\n\n return Response(message, 401, {\"WWW-Authenticate\": authenticate})","title":""},{"docid":"7e7a563e89802aa268fe7f4cb79a0fd4","score":"0.6168865","text":"def init_challenge(self, ignored):\n if not self.challenge_loaded:\n print('You must first send a challenge to the server.')\n return\n\n self.write_line('CHALLENGE_INITIATE')\n response = self.read_line()\n if response == 'CHALLENGE_NOT_FOUND':\n print(' No challenge has been sent to the server yet.')\n return\n elif response == 'CHALLENGE_ALREADY_ACTIVE':\n print(' Another challenge is already active.')\n return\n elif response == 'NO_PARTICIPANTS_CONNECTED':\n print(' There are no participants connected to the server.')\n return\n elif response == 'CHALLENGE_ERROR':\n print(' Unknown error')\n return\n \n while response != 'PARTICIPANT_LIST_FINISHED':\n participant_name = self.read_line()\n participant_editor = self.read_line()\n print('Participant \"{}\" accepted the challenge (editor \"{}\").'\n .format(participant_name, participant_editor))\n response = self.read_line()\n \n num_participants_accepting = self.read_line()\n num_participants_total = self.read_line()\n print('{} of {} participants accepted to do the challenge.'\n .format(num_participants_accepting, num_participants_total))\n \n print('Would you like to start the challenge? [Y/n]')\n answer = input(' > ')\n if answer.lower() != 'y':\n self.write_line('CHALLENGE_CANCEL')\n return\n \n self.write_line('CHALLENGE_START')\n response = self.read_line()\n if response == 'CHALLENGE_FINISH':\n print('Challenge successfully completed.')\n else:\n print('There was a problem with the challenge.')","title":""},{"docid":"39140a527fd0cad46d112824311ee2f3","score":"0.61504436","text":"def _challenge_response(self, challenge, mode, slot, variable, may_block):\n # Check length and pad challenge if appropriate\n if mode == 'HMAC':\n if len(challenge) > yubikey_defs.SHA1_MAX_BLOCK_SIZE:\n raise yubico_exception.InputError('Mode HMAC challenge too big (%i/%i)' \\\n % (yubikey_defs.SHA1_MAX_BLOCK_SIZE, len(challenge)))\n if len(challenge) < yubikey_defs.SHA1_MAX_BLOCK_SIZE:\n pad_with = b'\\0'\n if variable and challenge[-1:] == pad_with:\n pad_with = b'\\xff'\n challenge = challenge.ljust(yubikey_defs.SHA1_MAX_BLOCK_SIZE, pad_with)\n response_len = yubikey_defs.SHA1_DIGEST_SIZE\n elif mode == 'OTP':\n if len(challenge) != yubikey_defs.UID_SIZE:\n raise yubico_exception.InputError('Mode OTP challenge must be %i bytes (got %i)' \\\n % (yubikey_defs.UID_SIZE, len(challenge)))\n challenge = challenge.ljust(yubikey_defs.SHA1_MAX_BLOCK_SIZE, b'\\0')\n response_len = 16\n else:\n raise yubico_exception.InputError('Invalid mode supplied (%s, valid values are HMAC and OTP)' \\\n % (mode))\n\n try:\n command = _CMD_CHALLENGE[mode][slot]\n except:\n raise yubico_exception.InputError('Invalid slot specified (%s)' % (slot))\n\n frame = yubikey_frame.YubiKeyFrame(command=command, payload=challenge)\n self._device._write(frame)\n response = self._device._read_response(may_block=may_block)\n if not yubico_util.validate_crc16(response[:response_len + 2]):\n raise YubiKeyUSBHIDError(\"Read from device failed CRC check\")\n return response[:response_len]","title":""},{"docid":"e6dac9a3fac712c06bcdb9732a3060e4","score":"0.61374694","text":"def challenge(self, code):\n\n data = {\n 'code': code\n }\n token = self.base_request.request(\n 'auth/totp/verify', 'POST', data=data,\n endpoint=self.settings.get('api_endpoint'), login=True\n )\n self.settings.set(TOKEN_KEY, token)","title":""},{"docid":"c5cbdbfc5382b824b20fbf775d0fc6ba","score":"0.60651004","text":"def process_server_auth(self, message):\n\t\tself.crypto.server_cert=self.crypto.load_cert(\"server_cert/secure_server.pem\")\n\t\tself.crypto.server_ca_cert=self.crypto.load_cert(\"server_roots/Secure_Server_CA.pem\")\n\t\tself.crypto.rsa_public_key=self.crypto.server_cert.public_key()\n\t\tself.crypto.rsa_private_key=self.crypto.load_key_from_file(\"server_key/server_key.pem\")\n\t\tnonce=base64.b64decode(message['nonce'].encode())\n\n\t\t# Encrypt NONCE received by client\n\t\tself.crypto.signature = self.crypto.rsa_signing(nonce, self.crypto.rsa_private_key)\n\n\t\tlogger.info(\"Sending certificates for validation\")\n\t\tmessage={'type':'SERVER_AUTH_RESPONSE','signature':base64.b64encode(self.crypto.signature).decode(),'server_cert':base64.b64encode(self.crypto.get_certificate_bytes(self.crypto.server_cert)).decode(),'server_roots':base64.b64encode(self.crypto.get_certificate_bytes(self.crypto.server_ca_cert)).decode()}\n\t\tsecure_message = self.encrypt_payload(message)\n\t\tself._send(secure_message)\n\t\tself.send_mac()\n\t\treturn True","title":""},{"docid":"eaf10f9d00fed8445c64634c25ab1aa4","score":"0.59784365","text":"def getChallenge(self, request):\n return {'realm': self.authenticationRealm}","title":""},{"docid":"1ffa8572df93759f903eb0eb84d96f36","score":"0.59590465","text":"def getChallenge(self):\n\t\tchallengeXML = self.requestFactory.generateQuestion()\n\t\tself.sessionSocket.sendall(challengeXML)\n\t\txmlString = self.sessionSocket.recv(1024).strip().decode('utf-8')\n\t\tprint self.responseHandler.handle(xmlString)","title":""},{"docid":"8a58f15bebb5272615c8074de6a4e9e1","score":"0.5938506","text":"def auth_proceed(bot, trigger):\n if bot.config.core.auth_method == 'sasl':\n mech = bot.config.core.auth_target or 'PLAIN'\n elif bot.config.core.server_auth_method == 'sasl':\n mech = bot.config.core.server_auth_sasl_mech or 'PLAIN'\n else:\n return\n\n if mech == 'EXTERNAL':\n if trigger.args[0] != '+':\n # not an expected response from the server; abort SASL\n token = '*'\n else:\n token = '+'\n\n bot.write(('AUTHENTICATE', token))\n return\n\n if bot.config.core.auth_method == 'sasl':\n sasl_username = bot.config.core.auth_username\n sasl_password = bot.config.core.auth_password\n elif bot.config.core.server_auth_method == 'sasl':\n sasl_username = bot.config.core.server_auth_username\n sasl_password = bot.config.core.server_auth_password\n else:\n # How did we get here? I am not good with computer\n return\n\n sasl_username = sasl_username or bot.nick\n\n if mech == 'PLAIN':\n if trigger.args[0] == '+':\n sasl_token = _make_sasl_plain_token(sasl_username, sasl_password)\n LOGGER.info(\"Sending SASL Auth token.\")\n send_authenticate(bot, sasl_token)\n return\n else:\n # Not an expected response from the server\n LOGGER.warning(\n 'Aborting SASL: unexpected server reply \"%s\"', trigger,\n )\n # Send `authenticate-abort` command\n # See https://ircv3.net/specs/extensions/sasl-3.1#the-authenticate-command\n bot.write(('AUTHENTICATE', '*'))\n return\n\n # TODO: Implement SCRAM challenges","title":""},{"docid":"c4cd3a2ea8d5fffb267d8d4e84d373b6","score":"0.58901256","text":"def authenticate(self, msg):\n\t\tif msg.command != 'AUTH':\n\t\t\traise TypeError('Must be AUTH command!')\n\t\tself.__auth_id = msg.id\n\t\tself.request(msg)","title":""},{"docid":"dea5b4ccdf3d7b91489fcb22268a1ae6","score":"0.5881792","text":"def auth(self, irc, msg, args, nick):\r\n self._removeExpiredRequests()\r\n userdata = self.db.getByNick(nick)\r\n if len(userdata) == 0:\r\n irc.error(\"This nick is not registered. Please register.\")\r\n return\r\n keyid = userdata[0][1]\r\n fingerprint = userdata[0][2]\r\n if keyid is None:\r\n irc.error(\"You have not registered a GPG key. Try using bcauth instead, or register a GPG key first.\")\r\n return\r\n challenge = \"freenode:#bitcoin-otc:\" + hashlib.sha256(os.urandom(128)).hexdigest()[:-8]\r\n request = {msg.prefix: {'nick':userdata[0][5],\r\n 'expiry':time.time(), 'keyid':keyid,\r\n 'type':'auth', 'challenge':challenge,\r\n 'fingerprint':fingerprint}}\r\n self.pending_auth.update(request)\r\n self.authlog.info(\"auth request from hostmask %s for user %s, keyid %s.\" %\\\r\n (msg.prefix, nick, keyid, ))\r\n irc.reply(\"Request successful for user %s, hostmask %s. Your challenge string is: %s\" %\\\r\n (nick, msg.prefix, challenge,))","title":""},{"docid":"fcccb39d57d9761bd333af094ae1d29f","score":"0.58794737","text":"def challenge_post(self, environ, start_response):\n # request_info = cgi.parse_qs(environ['wsgi.input'].read(int(environ['CONTENT_LENGTH'])))\n # redirect = request_info.get('tiddlyweb_redirect', ['/'])[0]\n query = environ['tiddlyweb.query']\n redirect = query.get('tiddlyweb_redirect', ['/'])[0]\n\n try:\n user = query['user'][0]\n password = query['password'][0]\n valid=self._validate_and_redirect(environ, start_response, user, password, redirect)\n if valid:\n self._ensure_bags(environ, user)\n self._ensure_recipe(environ, user)\n return valid\n except KeyError:\n return self._send_cookie_form(environ, start_response, redirect, '401 Unauthorized')","title":""},{"docid":"d440a6e27a32c253b40039ac0a0db55b","score":"0.5851626","text":"def challenge_client(sock: socket) -> bool:\n nonce = os.urandom(32)\n sock.send(nonce)\n response = sock.recv(32)\n expected_response = hashlib.sha256(nonce + settings.PASSPHRASE).digest()\n return response == expected_response","title":""},{"docid":"6c251cafda5d43f257abc49b9ce16c08","score":"0.58377445","text":"def calculate_response(challenge: bytes):\n\tsecret = challenge[3:6]\n\tresponse = struct.pack('BBBB', challenge[2], *[secret[i] ^ XOR_KEY[i] for i in range(len(secret))])\n\treturn format_message(response)","title":""},{"docid":"ea9f7d7734ddb326707599334b1978a6","score":"0.58345133","text":"def _final_challenge(self, challenge):\n if self._finished:\n return Failure(\"extra-challenge\")\n\n match = SERVER_FINAL_MESSAGE_RE.match(challenge)\n if not match:\n logger.debug(\"Bad final message syntax: {0!r}\".format(challenge))\n return Failure(\"bad-challenge\")\n\n error = match.group(\"error\")\n if error:\n logger.debug(\"Server returned SCRAM error: {0!r}\".format(error))\n return Failure(u\"scram-\" + error.decode(\"utf-8\"))\n\n verifier = match.group(\"verifier\")\n if not verifier:\n logger.debug(\"No verifier value in the final message\")\n return Failure(\"bad-succes\")\n\n server_key = self.HMAC(self._salted_password, b\"Server Key\")\n server_signature = self.HMAC(server_key, self._auth_message)\n if server_signature != a2b_base64(verifier):\n logger.debug(\"Server verifier does not match\")\n return Failure(\"bad-succes\")\n\n self._finished = True\n return Response(None)","title":""},{"docid":"fda05d5552c95d652d81fe3237cd7b16","score":"0.5816291","text":"def make_digest_challenge(self, realm, message=None):\n\n if message is None:\n message = \"Authentication is required\"\n\n param_dict = {\n \"realm\": realm,\n \"nonce\": gen_nonce(16),\n \"opaque\": gen_nonce(16),\n }\n\n parameters = \", \".join('%s=\"%s\"' % t for t in param_dict.items())\n\n authenticate = \"Digest %s\" % parameters\n\n return Response(message, 401, {\"WWW-Authenticate\": authenticate})","title":""},{"docid":"6ec43bc6f2ed99c0d94ca6033cc37654","score":"0.5812959","text":"def challenge(self) -> bool:\n return self.__challenge","title":""},{"docid":"e946bef569e81aca055ff13409099d0b","score":"0.5784814","text":"def authenticate_request(self, request, content):\n json_request = {\n \"method\": request.method,\n \"uri\": request.uri,\n \"destination_is\": self.sydent.server_name,\n \"signatures\": {},\n }\n\n if content is not None:\n json_request[\"content\"] = content\n\n origin = None\n\n def parse_auth_header(header_str):\n \"\"\"\n Extracts a server name, signing key and payload signature from an\n authentication header.\n\n :param header_str: The content of the header\n :type header_str: unicode\n\n :return: The server name, the signing key, and the payload signature.\n :rtype: tuple[unicode]\n \"\"\"\n try:\n params = header_str.split(u\" \")[1].split(u\",\")\n param_dict = dict(kv.split(u\"=\") for kv in params)\n\n def strip_quotes(value):\n if value.startswith(u\"\\\"\"):\n return value[1:-1]\n else:\n return value\n\n origin = strip_quotes(param_dict[\"origin\"])\n key = strip_quotes(param_dict[\"key\"])\n sig = strip_quotes(param_dict[\"sig\"])\n return origin, key, sig\n except Exception:\n raise SignatureVerifyException(\"Malformed Authorization header\")\n\n auth_headers = request.requestHeaders.getRawHeaders(u\"Authorization\")\n\n if not auth_headers:\n raise NoAuthenticationError(\"Missing Authorization headers\")\n\n for auth in auth_headers:\n if auth.startswith(u\"X-Matrix\"):\n (origin, key, sig) = parse_auth_header(auth)\n json_request[\"origin\"] = origin\n json_request[\"signatures\"].setdefault(origin, {})[key] = sig\n\n if not json_request[\"signatures\"]:\n raise NoAuthenticationError(\"Missing X-Matrix Authorization header\")\n\n yield self.verifyServerSignedJson(json_request, [origin])\n\n logger.info(\"Verified request from HS %s\", origin)\n\n defer.returnValue(origin)","title":""},{"docid":"a218690d8a08a13ec66970712510aaa2","score":"0.57790285","text":"def authenticate(self, handler):","title":""},{"docid":"1e151d44187b0970d8225f0b5b76c31c","score":"0.57646435","text":"def auth_step(socket, password, header):\n send(socket, b'EHLO ' + SMTP_DOMAINNAME.encode('ASCII'))\n response = read_line(socket)\n while '-' in response:\n response = read_line(socket)\n if response[:3] != '250':\n raise Exception('EHLO Response not OK')\n auth_login = b'AUTH LOGIN'\n user_name = base64.b64encode(b'betances-leblancc@msoe.edu') # rip inbox\n encode_pass = base64.b64encode(password.encode('ASCII'))\n send(socket, auth_login)\n response_user = read_line(socket)\n if response_user[:3] != '334':\n raise Exception('Username question expected')\n send(socket, user_name)\n response_pass_ask = read_line(socket)\n if response_pass_ask[:3] != '334':\n raise Exception('Password Request Expected')\n send(socket, encode_pass)\n response_success = read_line(socket)\n if response_success[:3] != '235':\n print(response_success)\n return -1\n # raise Exception('Authentication was NOT successful')\n print('Authentication Successful')\n return 0","title":""},{"docid":"176ecd63705a39b7662d6838e84bac8e","score":"0.5752457","text":"def authenticate():\n return Response('Could not verify your access level for that URL.\\n''You have to login with proper credentials', 401, {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"3419696577a5db795993722803f03fec","score":"0.57397336","text":"def challenge_response(self, challenge, mode='HMAC', slot=1, variable=True, may_block=True):\n if not self.capabilities.have_challenge_response(mode):\n raise yubikey_base.YubiKeyVersionError(\"%s challenge-response unsupported in YubiKey %s\" % (mode, self.version()) )\n return self._challenge_response(challenge, mode, slot, variable, may_block)","title":""},{"docid":"ea7f076fd66805b2cf6005d0a1f189a0","score":"0.57264584","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"66478b2d497eadcfc6c5a19c3f63b3a4","score":"0.57178414","text":"def everify(self, irc, msg, args, otp):\r\n self._removeExpiredRequests()\r\n if not self._testPresenceInChannels(irc, msg.nick):\r\n irc.error(\"In order to authenticate, you must be present in one \"\r\n \"of the following channels: %s\" % (self.registryValue('channels'),))\r\n return\r\n try:\r\n authrequest = self.pending_auth[msg.prefix]\r\n except KeyError:\r\n irc.error(\"Could not find a pending authentication request from your hostmask. \"\r\n \"Either it expired, or you changed hostmask, or you haven't made one.\")\r\n return\r\n if authrequest['type'] not in ['eregister','eauth','echangekey']:\r\n irc.error(\"No outstanding encryption-based request found.\")\r\n return\r\n if authrequest['challenge'] != otp:\r\n irc.error(\"Incorrect one-time password. Try again.\")\r\n return\r\n\r\n response = \"\"\r\n if authrequest['type'] == 'eregister':\r\n if self.db.getByNick(authrequest['nick']) or self.db.getByKey(authrequest['keyid']):\r\n irc.error(\"Username or key already in the database.\")\r\n return\r\n self.db.register(authrequest['keyid'], authrequest['fingerprint'], None,\r\n time.time(), authrequest['nick'])\r\n response = \"Registration successful. \"\r\n elif authrequest['type'] == 'echangekey':\r\n gpgauth = self._ident(msg.prefix)\r\n if gpgauth is None:\r\n irc.error(\"You must be authenticated in order to change your registered key.\")\r\n return\r\n if self.db.getByKey(authrequest['keyid']):\r\n irc.error(\"This key id already registered. Try a different key.\")\r\n return\r\n self.db.changekey(gpgauth['nick'], gpgauth['keyid'], authrequest['keyid'], authrequest['fingerprint'])\r\n response = \"Successfully changed key for user %s from %s to %s. \" %\\\r\n (gpgauth['nick'], gpgauth['keyid'], authrequest['keyid'],)\r\n userdata = self.db.getByNick(authrequest['nick'])\r\n self.authed_users[msg.prefix] = {'timestamp':time.time(),\r\n 'keyid': authrequest['keyid'], 'nick':authrequest['nick'],\r\n 'bitcoinaddress':userdata[0][3],\r\n 'fingerprint':authrequest['fingerprint']}\r\n del self.pending_auth[msg.prefix]\r\n logmsg = \"everify success from hostmask %s for user %s, keyid %s.\" %\\\r\n (msg.prefix, authrequest['nick'], authrequest['keyid'],) + response\r\n self.authlog.info(logmsg)\r\n self.db.update_auth_date(userdata[0][0], time.time())\r\n self.db.set_auth_status(userdata[0][5], 1)\r\n if not world.testing:\r\n irc.queueMsg(ircmsgs.privmsg(\"#bitcoin-otc-auth\", logmsg))\r\n irc.reply(response + \"You are now authenticated for user %s with key %s\" %\\\r\n (authrequest['nick'], authrequest['keyid']))","title":""},{"docid":"d8c26245bcc8568f233aa5ce00ddee39","score":"0.56832886","text":"def authenticate():\n return Response(\n \"Could not verify your access level for that URL.\\n\"\n \"You have to login with proper credentials\",\n 401,\n {\"WWW-Authenticate\": 'Basic realm=\"Login Required\"'},\n )","title":""},{"docid":"d8c26245bcc8568f233aa5ce00ddee39","score":"0.56832886","text":"def authenticate():\n return Response(\n \"Could not verify your access level for that URL.\\n\"\n \"You have to login with proper credentials\",\n 401,\n {\"WWW-Authenticate\": 'Basic realm=\"Login Required\"'},\n )","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"bd1e3484726a4bc4129e5e53552d7a9d","score":"0.56745934","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e612bfdff49d8f441a9e258775d0c52","score":"0.56672156","text":"def run(self, message):\n if self.parse_response(message):\n if self.validate_credentials():\n self.client.change_auth_status(self.client.auth_statuses[\"auth_complete\"])\n else:\n LOGGER.warning(\"Username, password or unique id incorrect at client: {client}\".format(\n client=str(self.client)))\n return\n else:\n LOGGER.warning(\"Error receiving admin credentials: {client}\".format(\n client=str(self.client)))\n return","title":""},{"docid":"73a6e71a7ed72d6bec05f19f8202d1e5","score":"0.5665175","text":"def authenticate():\n return Response(\n 'Could not verify your access level for this URL.\\n'\n 'Login credentials required.', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"f402242170ddeba635205a946f3e6b39","score":"0.56613606","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n { 'WWW-Authenticate': 'Basic realm=\"Login Required\"' }\n )","title":""},{"docid":"e931527e6c09b961a935455a809800c8","score":"0.5657155","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"15d8a611bda1649971f213b486ec6897","score":"0.5644863","text":"def authenticate():\n return Response('', 401, {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e9da2ce0b082d844de498283bf87d54","score":"0.562817","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e9da2ce0b082d844de498283bf87d54","score":"0.562817","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e9da2ce0b082d844de498283bf87d54","score":"0.562817","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e9da2ce0b082d844de498283bf87d54","score":"0.562817","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e9da2ce0b082d844de498283bf87d54","score":"0.562817","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e9da2ce0b082d844de498283bf87d54","score":"0.562817","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e9da2ce0b082d844de498283bf87d54","score":"0.562817","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e9da2ce0b082d844de498283bf87d54","score":"0.562817","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e9da2ce0b082d844de498283bf87d54","score":"0.562817","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e9da2ce0b082d844de498283bf87d54","score":"0.562817","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e9da2ce0b082d844de498283bf87d54","score":"0.562817","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e9da2ce0b082d844de498283bf87d54","score":"0.562817","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"1e9da2ce0b082d844de498283bf87d54","score":"0.562817","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"81a2f233dfda1b716720252f8325bee0","score":"0.562801","text":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'}\n )","title":""},{"docid":"d787347051c1dcc8d0b9a977e7547cf1","score":"0.56100833","text":"def authenticate():\n message = {'message': \"Authenticate.\"}\n resp = jsonify(message) # create a json object\n\n resp.status_code = 401\n resp.headers['WWW-Authenticate'] = 'Basic realm=\"Main\"'\n\n return resp","title":""},{"docid":"02445dc3743fbc536768a9e8071dbd0a","score":"0.56082314","text":"def authenticate():\r\n\treturn Response(\r\n\t'Could not verify your access level for that URL.\\n'\r\n\t'You have to login with proper credentials', 401,\r\n\t{'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"9131c830ad6f6c798b92075c52eec195","score":"0.55819196","text":"def __authenticate(self, *args, **kwargs):\n\n # If priv_key is None, then master.key didn't get loaded correctly.\n if self.controller.priv_key is None:\n raise ControllerException(CONTROLLER_ERROR_NO_RSA_KEY)\n\n challenge_func = WebControllerFunction(self.controller, 'authenticate')\n challenge = challenge_func().__str__()\n\n # re-encrypt using servers key and then sha hash it before sending it\n # back\n response_encode = self.controller.priv_key.encrypt(challenge, None)\n response_hash = hashlib.sha512(response_encode[0]).hexdigest()\n \n challenge_func = WebControllerFunction(self.controller, \\\n 'challenge_response')\n if challenge_func(response_hash):\n return self(*args, **kwargs)\n\n raise ControllerException(CONTROLLER_ERROR_AUTH_FAIL)","title":""},{"docid":"2a5fcbcaa0eddcad2fc682233c111859","score":"0.5580618","text":"def verify (self, response, challenge, v):\r\n\t\ttouple = self.check(response, challenge, v)\r\n\t\treturn touple[0]==touple[1]","title":""},{"docid":"15af5994a08f565053f437475caf5d27","score":"0.55725276","text":"def auth_ok_message():\n return {\"type\": TYPE_AUTH_OK, \"ha_version\": __version__}","title":""},{"docid":"20d028fdf571e87e2c6daa523e6e8459","score":"0.5563835","text":"def authenticate():\n return Response(\n 'Login Required', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","title":""},{"docid":"3bd1ce8c3cdf0f4fd90f8ac7e4753dfc","score":"0.55371714","text":"def messenger_webhook():\n verify_token = request.query.get('hub.verify_token')\n # check whether the verify tokens match\n if verify_token == FB_VERIFY_TOKEN:\n # respond with the challenge to confirm\n challenge = request.query.get('hub.challenge')\n return challenge\n else:\n return 'Invalid Request or Verification Token'","title":""},{"docid":"3bd1ce8c3cdf0f4fd90f8ac7e4753dfc","score":"0.55371714","text":"def messenger_webhook():\n verify_token = request.query.get('hub.verify_token')\n # check whether the verify tokens match\n if verify_token == FB_VERIFY_TOKEN:\n # respond with the challenge to confirm\n challenge = request.query.get('hub.challenge')\n return challenge\n else:\n return 'Invalid Request or Verification Token'","title":""},{"docid":"288bd984225cff35a310b32ebceda0ca","score":"0.5536558","text":"def authenticate(self):\n log(2,\"Checking for authentication\")\n if self.request.user:#\n log(2,\"Authentication OK\")\n return True\n else:\n log(2,\"Authentication not OK, send 401\")\n self.response.add_header(\"WWW-Authenticate\", 'RestBasic realm=\"We need your password.\"')\n self.response.add_header('Content-Type', 'application/json')\n raise StopProcessing(401,json.dumps({\"error\":\"Authentiction required.\"}))","title":""},{"docid":"110515db4636d2f7efad6c53953b3957","score":"0.55330414","text":"def basic_challenge(realm, content='Authorization Required'):\n response = HttpResponse(content, content_type=\"text/plain\")\n response['WWW-Authenticate'] = 'Basic realm=\"%s\"' % (realm, )\n response.status_code = 401\n return response","title":""},{"docid":"5372fc9b27d87c5f4cea6a2d4d0e1f91","score":"0.5510157","text":"def authenticate(self, data):\n\n data = data.strip().split('%')\n if len(data) != 3:\n raise Exception(\"Invalid Message\")\n try:\n privateid = self.factory.authkeys[data[0]].encode('utf-8', 'ignore')\n except:\n raise Exception(\"Invalid Authkey\")\n message = data[1].encode('utf-8', 'ignore')\n checksum = hashlib.md5(message + privateid).hexdigest()\n if data[2] == checksum:\n return message.decode('utf-8', 'ignore')\n else:\n raise Exception(\"Failed auth\")","title":""},{"docid":"7e7e1cf5272ece535e6af297b722b6e5","score":"0.55049473","text":"def authenticate(self, tree):\n\n # Currently RFC specifies only SASL as supported way of auth'ing\n handler = SASLAuthHandler()\n if tree.get('xmlns') != handler.namespace:\n raise MalformedRequestError\n handler.process(tree)\n self.connection.parser.reset()\n self.jid = JID(\"@\".join([handler.authenticated_user,\n self.hostname]))\n self.authenticated = True\n response_element = ET.Element(\"success\")\n response_element.set(\"xmlns\", handler.namespace)\n self.send_element(response_element)","title":""},{"docid":"6477851370b338ccb1d08baa7d5df877","score":"0.54741734","text":"def authenticate():\n return flask.Response('Login required', 401, {\n 'WWW-Authenticate': 'Basic realm=\"Login required\"',\n })","title":""},{"docid":"681e4c4b0db31e87d9e3bb22648fed0c","score":"0.54555166","text":"def authenticate(self, req, resp, resource):\n raise NotImplementedError(\".authenticate() must be overridden.\")","title":""},{"docid":"f269c18a478e8bbf3fa00d2cdeb06c0e","score":"0.5446857","text":"def challenge(self, request, response, **kw):\n user_expired = response.getHeader(\"user_expired\")\n if user_expired:\n portal_url = api.portal.get_tool(name=\"portal_url\")()\n IStatusMessage(request).add(\n _(u\"Your password has expired.\"), type=\"error\"\n )\n response.redirect(\n \"%s/mail_password_form?userid=%s\" % (portal_url, user_expired),\n lock=1,\n )\n return 1\n return 0","title":""},{"docid":"8e52eda84df4d0f232949dd3a95c7428","score":"0.54455614","text":"def authorization(self):\n pr_req = Request(RequestAction.PRESENCE, self.user.username)\n self.__send_request(pr_req)\n resp = self.__get_response()\n if resp is None:\n return Response(SERVER_ERROR)\n if resp.code != AUTH:\n return resp\n enc_pass = encrypt_rsa(\n import_pub_key(resp.message.encode()),\n self.user.password)\n auth_req = Request(RequestAction.AUTH, enc_pass.decode())\n self.__send_request(auth_req)\n return self.__get_response()","title":""},{"docid":"ab644ede42a4c845ac2ae73a47c9fb22","score":"0.5432318","text":"def authenticate_request(\n self,\n request: 'CoreRequest'\n ) -> Success | Failure | Response | None:","title":""},{"docid":"3be545bac3c2fba1cef46d1eace66f42","score":"0.5428773","text":"def exec_get_challenge(self):\n\n\t\tapdu = [\n\t\t\t0x00, # CLA\n\t\t\t0x84, # INS = C/R INT AUTH\n\t\t\t0x00, # P1 = 0x00\n\t\t\t0x00, # P2 = 0x00\n\t\t\t0x08,\n\t\t]\n\n\t\td, s, t = self.transmit(apdu)\n\t\treturn d","title":""},{"docid":"79c0157f68832652bfc3b62a89d208cf","score":"0.5420126","text":"def authenticate():\n return Response(\"Could not authenticate. Please provide valid Globus credentials.\",\n 401, {\"WWW-Authenticate\": 'Basic realm=\"Login Required\"'})","title":""},{"docid":"e1cd8db06076f610ff1a8742a0760690","score":"0.5399056","text":"async def __handleHandshake(self, path: str, headers):\n parts = path.split(\"?\")\n _pathname = parts[0]\n _query = \"\"\n\n if len(parts) >= 2:\n _query = \"?\".join(parts[1:])\n\n if self.protocol != \"ws+unix:\" and _pathname != self.pathname:\n return (http.HTTPStatus.NOT_FOUND, [], bytes([]))\n\n query = parse_qs(_query or \"\")\n clientId = str(query.get(\"id\") and query.get(\"id\")[0] or \"\")\n secret = str(query.get(\"secret\")\n and query.get(\"secret\")[0] or \"\")\n\n if not clientId or (self.secret and secret != self.secret):\n return (http.HTTPStatus.UNAUTHORIZED, [], bytes([]))","title":""},{"docid":"8e8311598ba08f156aed434e86c23c10","score":"0.5394339","text":"def authenticate(socket, config):\n # Generate the auth string\n auth_str = config[\"user\"] + \";\" + config[\"pass\"]\n \n # Send the string\n send_mess(socket,auth_str)\n\n # Return response\n return get_bool_response(socket)","title":""},{"docid":"46fa4d854805b344642819095f77e3b3","score":"0.5392686","text":"def response_auth(status, message, token, status_code):\n return make_response(jsonify({\n 'status': status,\n 'message': message,\n 'auth_token': token.decode(\"utf-8\")\n })), status_code","title":""},{"docid":"e7b9d44eacb858ce821ecc6888ce0d4c","score":"0.53925836","text":"def logAuthenticate200(r):\n log.debug(\"Authenticate Response: \")\n log.debug(json.dumps(r.json(),indent=10,sort_keys=True))\n log.debug(\"Authenticate Response Headers: \")\n for h in r.headers:\n log.debug(\" \" + h + \" : \" + r.headers[h])\n token = r.json()[\"token\"]\n try:\n decoded_token = jwt.decode(token, server_env[\"CLIENT_SECRET\"], algorithms=[\"HS256\"], audience=server_env[\"CLIENT_ID\"])\n log.debug(\"Decoded token: \")\n log.debug(json.dumps(decoded_token, indent=30, sort_keys=True))\n header = base64.b64decode(token.split('.')[0]).decode('utf-8')\n log.debug(\"header:\")\n log.debug(json.dumps(json.loads(header), indent=4, sort_keys=True))\n payload = base64.b64decode(token.split('.')[1]+\"==\").decode('utf-8')\n log.debug(\"payload:\")\n log.debug(json.dumps(json.loads(payload), indent=4, sort_keys=True))\n except jwt.exceptions.InvalidSignatureError as err:\n log.warn(\"Unable to decode token: \" + str(err))\n return None","title":""},{"docid":"63f88460cc1d063dc9e9e91c2a5ce439","score":"0.53920156","text":"def challenge_get(self, environ, start_response):\n redirect = environ['tiddlyweb.query'].get('tiddlyweb_redirect', ['/'])[0]\n # print \"REDIRECT\"\n # print redirect\n return self._send_cookie_form(environ, start_response, redirect)","title":""},{"docid":"b85f31245b709456ab54cba9979c09da","score":"0.53745955","text":"def login(): \n headers = { 'X-Requested-With': 'XMLHttpRequest' }\n data = { 'callback': 'getChallenge', 'action': 'challenge'}\n\n response = session.post(URL + 'login', headers=headers, data=data)\n if response.status_code != 200:\n sys.exit(\"login(): Impossible de récuperer le challenge\")\n\n d = pq( response.content )\n challenge = d.find('challenge').text().encode('utf-8')\n\n hash1 = sha256('admin'.encode('utf-8')).hexdigest().encode('utf-8')\n hash1 = hmac.new(challenge, hash1, sha256).hexdigest()\n\n hash2 = sha256(KEY.encode('utf-8')).hexdigest().encode('utf-8')\n hash2 = hmac.new(challenge, hash2, sha256).hexdigest()\n \n data = {\n 'method' : 'passwd',\n 'page_ref': '',\n 'zsid' : challenge,\n 'hash' : hash1 + hash2\n }\n\n response = session.post( URL + 'login', data=data )\n if response.status_code != 200:\n sys.exit(\"login(): Impossible de se connecter\")","title":""},{"docid":"c637b7d442ef84d4dbf262e51207ac77","score":"0.53692544","text":"def _run_next_challenge(msg, request, access_token):\n for challenge in msg[\"challenges\"]:\n if challenge[\"status\"] != \"READY\":\n # Skip non-activated challenges.\n continue\n c = challenges.AVAILABLE_CHALLENGES.get(challenge[\"challengeType\"], None)\n if not c:\n raise exceptions.ReauthFailError(\n \"Unsupported challenge type {0}. Supported types: {1}\".format(\n challenge[\"challengeType\"],\n \",\".join(list(challenges.AVAILABLE_CHALLENGES.keys())),\n )\n )\n if not c.is_locally_eligible:\n raise exceptions.ReauthFailError(\n \"Challenge {0} is not locally eligible\".format(\n challenge[\"challengeType\"]\n )\n )\n client_input = c.obtain_challenge_input(challenge)\n if not client_input:\n return None\n return _send_challenge_result(\n request,\n msg[\"sessionId\"],\n challenge[\"challengeId\"],\n client_input,\n access_token,\n )\n return None","title":""},{"docid":"469d9b87f5ef0856de126c5e9ca4ef9b","score":"0.5326108","text":"def __authenticate(self):\n log.trace(\n \"Attempting authentication against {} with key [{}...{}]\",\n self.url,\n self.api_key[:2],\n self.api_key[-2:],\n )\n self.status[\"auth_state\"] = \"attempting\"\n response = self.request(\"auth\")\n log.trace(f\"Authenticate response: {response.text}\")\n self.status[\"auth_response\"] = response.text\n self.status[\"auth_httpstatus\"] = response.status_code\n\n # NOTE: an unauthorized request has already been caught be the request() method\n # above. Our legacy code was additionally testing for 'error' in the response\n # text - however, it is unclear if PUMAPI ever returns this:\n if \"error\" in response.text.lower():\n self.status[\"auth_state\"] = \"FAILED-ERROR\"\n msg = f\"Authentication failed with an error: {response.text}\"\n log.error(msg)\n raise requests.exceptions.ConnectionError(msg)\n\n status_ok = requests.codes.ok # pylint: disable-msg=no-member\n\n if response.status_code != status_ok:\n # NOTE: branch excluded from coverage as we don't have a known way\n # to produce such a response from the API\n log.warning(\n \"Unexpected combination of response [{}] and status code [{}], it's \"\n \"unclear if authentication succeeded (assuming it didn't)\",\n response.status_code,\n response.text,\n )\n self.status[\"auth_state\"] = \"FAILED-UNKNOWN\"\n\n msg = (\n f\"Authenticating against {self.url} with key \"\n f\"[{self.api_key[:2]}...{self.api_key[-2:]}] FAILED!\"\n )\n log.error(msg)\n raise requests.exceptions.ConnectionError(msg)\n\n log.trace(\n \"Authentication succeeded, response=[{}], http_status=[{}]\",\n response.text,\n response.status_code,\n )\n self.status[\"auth_state\"] = \"good\"","title":""}],"string":"[\n {\n \"docid\": \"e7ab498cb458cdcb5097216cbfed59d8\",\n \"score\": \"0.7524756\",\n \"text\": \"def process_challenge(self, message: str) -> None:\\n\\t\\tvalue = message.get('val', None)\\n\\n\\t\\tif value != self.challenge_ans:\\n\\t\\t\\tlogger.error(\\\"Authentication failed\\\")\\n\\t\\t\\treturn False\\n\\t\\t\\n\\n\\t\\tself.state = STATE_AUTH\\n\\t\\tself._send({'type':'OK'})\\n\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1dccbd12c98a4b96df5fc9bf408840a\",\n \"score\": \"0.74329895\",\n \"text\": \"def process_challenge_response(self, message):\\n\\t\\tusername = message['credentials']['username']\\n\\t\\t\\n\\t\\tif username not in self.registered_users or 'A1' not in self.registered_users[username][1]:\\n\\t\\t\\tlogger.info(\\\"{} authenticated denied.\\\".format(username))\\n\\t\\t\\tmessage = {'type': 'AUTH_RESPONSE', 'status': 'DENIED'}\\n\\t\\t\\tsecure_message = self.encrypt_payload(message)\\n\\t\\t\\tself._send(secure_message)\\n\\t\\t\\tself.send_mac()\\n\\t\\t\\treturn False\\n\\n\\t\\tsigned_challenge = message['credentials']['signed_challenge']\\n\\t\\tpw, permissions = self.registered_users[username][0], self.registered_users[username][1]\\t\\n\\t\\tsignature_verification = self.crypto.rsa_signature_verification(base64.b64decode(signed_challenge.encode()), (str(self.client_nonce) + pw + str(self.crypto.auth_nonce)).encode(), self.crypto.load_public_key(self.client_public_key))\\n\\n\\t\\tif signature_verification:\\n\\t\\t\\tlogger.info(\\\"{} authenticated with success!\\\".format(username))\\n\\t\\t\\tmessage = {'type': 'AUTH_RESPONSE', 'status': 'SUCCESS', 'username': username}\\n\\t\\t\\tsecure_message = self.encrypt_payload(message)\\n\\t\\t\\tself._send(secure_message)\\n\\t\\t\\tself.send_mac()\\n\\t\\t\\tself.authenticated_user = [username, permissions]\\n\\n\\t\\telse:\\n\\t\\t\\tself.authentication_tries += 1\\n\\t\\t\\tif self.authentication_tries == 3:\\n\\t\\t\\t\\t# remove authentication permission\\n\\t\\t\\t\\tself.registered_users[username][1] = self.registered_users[username][1].replace('1', '0')\\n\\t\\t\\t\\tself.update_users()\\n\\t\\t\\t\\tlogger.info(\\\"{} authenticated denied.\\\".format(username))\\n\\t\\t\\t\\tmessage = {'type': 'AUTH_RESPONSE', 'status': 'DENIED'}\\n\\t\\t\\t\\tsecure_message = self.encrypt_payload(message)\\n\\t\\t\\t\\tself._send(secure_message)\\n\\t\\t\\t\\tself.send_mac()\\n\\t\\t\\t\\treturn False\\n\\n\\t\\t\\tlogger.info(\\\"{} authenticated failed.\\\".format(username))\\n\\t\\t\\tmessage = {'type': 'AUTH_RESPONSE', 'status': 'FAILED'}\\n\\t\\t\\tsecure_message = self.encrypt_payload(message)\\n\\t\\t\\tself._send(secure_message)\\n\\t\\t\\tself.send_mac()\\n\\t\\t\\treturn True\\n\\t\\t\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72e890d5341676f0833fa204f2e0b630\",\n \"score\": \"0.70976585\",\n \"text\": \"def handle_challenge(self, challenge: bytes) -> bytes:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8168a98462fb6a6ee4ee17d23912830\",\n \"score\": \"0.6681826\",\n \"text\": \"def triggerChallenge(self):\\r\\n self.send(\\\"\\\"\\\"<challenge xmlns='urn:ietf:params:xml:ns:xmpp-sasl'>cmVhbG09ImNoZXNzcGFyay5jb20iLG5vbmNlPSJ0YUhIM0FHQkpQSE40eXNvNEt5cFlBPT0iLHFvcD0iYXV0aCxhdXRoLWludCIsY2hhcnNldD11dGYtOCxhbGdvcml0aG09bWQ1LXNlc3M=</challenge>\\\"\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03ac3aeb12e0f5e43dfb3056dd6c38bd\",\n \"score\": \"0.65662795\",\n \"text\": \"def auth_challenge(self):\\n\\n self.set_header(\\\"WWW-Authenticate\\\", \\\"Basic realm=pyjojo\\\")\\n self.set_status(401)\\n self.finish()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef73f81cf0acac1e8ff79a7e900ced45\",\n \"score\": \"0.6551384\",\n \"text\": \"def challenge(self):\\n if self._challenge is None:\\n self.sock.sendto(a2s_getchallenge_req(), self.srvaddr)\\n inforsp = recvresponse(self.sock)\\n self._challenge = a2s_getchallenge_rsp(inforsp)[\\\"challenge\\\"]\\n return self._challenge\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a634fd589381dd25bc3923e4a8944744\",\n \"score\": \"0.65054107\",\n \"text\": \"def challenge_response(self, challenge):\\n key_1 = self.request.headers.get(\\\"Sec-Websocket-Key1\\\")\\n key_2 = self.request.headers.get(\\\"Sec-Websocket-Key2\\\")\\n try:\\n part_1 = self._calculate_part(key_1)\\n part_2 = self._calculate_part(key_2)\\n except ValueError:\\n raise ValueError(\\\"Invalid Keys/Challenge\\\")\\n return self._generate_challenge_response(part_1, part_2, challenge)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"744d0279df8a34881c33353d10f98161\",\n \"score\": \"0.6491226\",\n \"text\": \"def get(self):\\n self.response.out.write(self.request.get('challenge'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0195fd9e3a1aadd9a0a16d130010962a\",\n \"score\": \"0.6347538\",\n \"text\": \"def challenge(self, challenge):\\n # pylint: disable=R0911\\n if not challenge:\\n logger.debug(\\\"Empty challenge\\\")\\n return Failure(\\\"bad-challenge\\\")\\n\\n if self._server_first_message:\\n return self._final_challenge(challenge)\\n\\n match = SERVER_FIRST_MESSAGE_RE.match(challenge)\\n if not match:\\n logger.debug(\\\"Bad challenge syntax: {0!r}\\\".format(challenge))\\n return Failure(\\\"bad-challenge\\\")\\n\\n self._server_first_message = challenge\\n\\n mext = match.group(\\\"mext\\\")\\n if mext:\\n logger.debug(\\\"Unsupported extension received: {0!r}\\\".format(mext))\\n return Failure(\\\"bad-challenge\\\")\\n\\n nonce = match.group(\\\"nonce\\\")\\n if not nonce.startswith(self._c_nonce):\\n logger.debug(\\\"Nonce does not start with our nonce\\\")\\n return Failure(\\\"bad-challenge\\\")\\n\\n salt = match.group(\\\"salt\\\")\\n try:\\n salt = a2b_base64(salt)\\n except ValueError:\\n logger.debug(\\\"Bad base64 encoding for salt: {0!r}\\\".format(salt))\\n return Failure(\\\"bad-challenge\\\")\\n\\n iteration_count = match.group(\\\"iteration_count\\\")\\n try:\\n iteration_count = int(iteration_count)\\n except ValueError:\\n logger.debug(\\\"Bad iteration_count: {0!r}\\\".format(iteration_count))\\n return Failure(\\\"bad-challenge\\\")\\n\\n return self._make_response(nonce, salt, iteration_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb37ac6baf8d896b4b48e9b095b6a6fb\",\n \"score\": \"0.63438255\",\n \"text\": \"def process_login_request(self, message):\\n\\t\\tself.state = STATE_CLIENT_AUTH\\n\\t\\tself.client_nonce = base64.b64decode(message['nonce'].encode())\\n\\t\\tself.crypto.auth_nonce = os.urandom(16)\\n\\t\\tself.client_public_key = base64.b64decode(message['public_key'].encode())\\n\\t\\tmessage = {'type': 'CHALLENGE_REQUEST', 'nonce': base64.b64encode(self.crypto.auth_nonce).decode()}\\n\\t\\tsecure_message = self.encrypt_payload(message)\\n\\t\\tself._send(secure_message)\\n\\t\\tself.send_mac()\\n\\t\\t\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44428eaeb0244f85fb46d45692463873\",\n \"score\": \"0.6340404\",\n \"text\": \"def _send_challenge_result(\\n request, session_id, challenge_id, client_input, access_token\\n):\\n body = {\\n \\\"sessionId\\\": session_id,\\n \\\"challengeId\\\": challenge_id,\\n \\\"action\\\": \\\"RESPOND\\\",\\n \\\"proposalResponse\\\": client_input,\\n }\\n metrics_header = {metrics.API_CLIENT_HEADER: metrics.reauth_continue()}\\n\\n return _client._token_endpoint_request(\\n request,\\n _REAUTH_API + \\\"/{}:continue\\\".format(session_id),\\n body,\\n access_token=access_token,\\n use_json=True,\\n headers=metrics_header,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ddf053e89f40e99b58b0b9fc052d75\",\n \"score\": \"0.6324714\",\n \"text\": \"def start_responding(self, ignore, challenge, response):\\n self.putChild(\\n challenge.encode('token').encode('utf-8'),\\n StaticTextResource(response.key_authorization),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7df5b38e6c4ef96a36abb96ccb9cc353\",\n \"score\": \"0.6309285\",\n \"text\": \"def authenticate(self, message):\\n if self.authenticated == self.auth_statuses[\\\"auth_complete\\\"]:\\n return\\n\\n if not self.is_auth() and message.strip() in [CLIENT_HELLO, INJECTOR_HELLO, ADMIN_HELLO]:\\n self.change_auth_status(self.auth_statuses[\\\"auth_ok\\\"])\\n self.send(SERVER_HELLO)\\n if message.strip() == CLIENT_HELLO:\\n self.client_type = self.client_types[\\\"hook\\\"]\\n elif message.strip() == INJECTOR_HELLO:\\n self.client_type = self.client_types[\\\"injector\\\"]\\n elif message.strip() == ADMIN_HELLO:\\n self.client_type = self.client_types[\\\"admin\\\"]\\n return\\n\\n if self.client_type == self.client_types[\\\"admin\\\"]:\\n admin = AdminAuthentication(self, self.db)\\n admin.run(message)\\n return\\n\\n if self.authenticated == self.auth_statuses[\\\"auth_ok\\\"]:\\n response = message.splitlines()\\n self.status, uid = response[0].strip().split(\\\" \\\")\\n if int(self.status) != (PROTOCOL_STATUS_CODES[\\\"authentication\\\"]):\\n return\\n\\n self.change_auth_status(self.auth_statuses[\\\"auth_complete\\\"])\\n self.uid = uid\\n\\n self.send(str(PROTOCOL_STATUS_CODES[\\\"ok\\\"]) + \\\" Authentication complete\\\")\\n LOGGER.info(\\\"{client_info} has authenticated\\\".format(client_info=self))\\n return\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"784e0272e7533c1cfa73dfc053142893\",\n \"score\": \"0.61799854\",\n \"text\": \"def make_basic_challenge(self, realm, message=None):\\n\\n if message is None:\\n message = \\\"Authentication is required\\\"\\n\\n authenticate = 'Basic realm=\\\"%s\\\"' % realm\\n\\n return Response(message, 401, {\\\"WWW-Authenticate\\\": authenticate})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e7a563e89802aa268fe7f4cb79a0fd4\",\n \"score\": \"0.6168865\",\n \"text\": \"def init_challenge(self, ignored):\\n if not self.challenge_loaded:\\n print('You must first send a challenge to the server.')\\n return\\n\\n self.write_line('CHALLENGE_INITIATE')\\n response = self.read_line()\\n if response == 'CHALLENGE_NOT_FOUND':\\n print(' No challenge has been sent to the server yet.')\\n return\\n elif response == 'CHALLENGE_ALREADY_ACTIVE':\\n print(' Another challenge is already active.')\\n return\\n elif response == 'NO_PARTICIPANTS_CONNECTED':\\n print(' There are no participants connected to the server.')\\n return\\n elif response == 'CHALLENGE_ERROR':\\n print(' Unknown error')\\n return\\n \\n while response != 'PARTICIPANT_LIST_FINISHED':\\n participant_name = self.read_line()\\n participant_editor = self.read_line()\\n print('Participant \\\"{}\\\" accepted the challenge (editor \\\"{}\\\").'\\n .format(participant_name, participant_editor))\\n response = self.read_line()\\n \\n num_participants_accepting = self.read_line()\\n num_participants_total = self.read_line()\\n print('{} of {} participants accepted to do the challenge.'\\n .format(num_participants_accepting, num_participants_total))\\n \\n print('Would you like to start the challenge? [Y/n]')\\n answer = input(' > ')\\n if answer.lower() != 'y':\\n self.write_line('CHALLENGE_CANCEL')\\n return\\n \\n self.write_line('CHALLENGE_START')\\n response = self.read_line()\\n if response == 'CHALLENGE_FINISH':\\n print('Challenge successfully completed.')\\n else:\\n print('There was a problem with the challenge.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39140a527fd0cad46d112824311ee2f3\",\n \"score\": \"0.61504436\",\n \"text\": \"def _challenge_response(self, challenge, mode, slot, variable, may_block):\\n # Check length and pad challenge if appropriate\\n if mode == 'HMAC':\\n if len(challenge) > yubikey_defs.SHA1_MAX_BLOCK_SIZE:\\n raise yubico_exception.InputError('Mode HMAC challenge too big (%i/%i)' \\\\\\n % (yubikey_defs.SHA1_MAX_BLOCK_SIZE, len(challenge)))\\n if len(challenge) < yubikey_defs.SHA1_MAX_BLOCK_SIZE:\\n pad_with = b'\\\\0'\\n if variable and challenge[-1:] == pad_with:\\n pad_with = b'\\\\xff'\\n challenge = challenge.ljust(yubikey_defs.SHA1_MAX_BLOCK_SIZE, pad_with)\\n response_len = yubikey_defs.SHA1_DIGEST_SIZE\\n elif mode == 'OTP':\\n if len(challenge) != yubikey_defs.UID_SIZE:\\n raise yubico_exception.InputError('Mode OTP challenge must be %i bytes (got %i)' \\\\\\n % (yubikey_defs.UID_SIZE, len(challenge)))\\n challenge = challenge.ljust(yubikey_defs.SHA1_MAX_BLOCK_SIZE, b'\\\\0')\\n response_len = 16\\n else:\\n raise yubico_exception.InputError('Invalid mode supplied (%s, valid values are HMAC and OTP)' \\\\\\n % (mode))\\n\\n try:\\n command = _CMD_CHALLENGE[mode][slot]\\n except:\\n raise yubico_exception.InputError('Invalid slot specified (%s)' % (slot))\\n\\n frame = yubikey_frame.YubiKeyFrame(command=command, payload=challenge)\\n self._device._write(frame)\\n response = self._device._read_response(may_block=may_block)\\n if not yubico_util.validate_crc16(response[:response_len + 2]):\\n raise YubiKeyUSBHIDError(\\\"Read from device failed CRC check\\\")\\n return response[:response_len]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6dac9a3fac712c06bcdb9732a3060e4\",\n \"score\": \"0.61374694\",\n \"text\": \"def challenge(self, code):\\n\\n data = {\\n 'code': code\\n }\\n token = self.base_request.request(\\n 'auth/totp/verify', 'POST', data=data,\\n endpoint=self.settings.get('api_endpoint'), login=True\\n )\\n self.settings.set(TOKEN_KEY, token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5cbdbfc5382b824b20fbf775d0fc6ba\",\n \"score\": \"0.60651004\",\n \"text\": \"def process_server_auth(self, message):\\n\\t\\tself.crypto.server_cert=self.crypto.load_cert(\\\"server_cert/secure_server.pem\\\")\\n\\t\\tself.crypto.server_ca_cert=self.crypto.load_cert(\\\"server_roots/Secure_Server_CA.pem\\\")\\n\\t\\tself.crypto.rsa_public_key=self.crypto.server_cert.public_key()\\n\\t\\tself.crypto.rsa_private_key=self.crypto.load_key_from_file(\\\"server_key/server_key.pem\\\")\\n\\t\\tnonce=base64.b64decode(message['nonce'].encode())\\n\\n\\t\\t# Encrypt NONCE received by client\\n\\t\\tself.crypto.signature = self.crypto.rsa_signing(nonce, self.crypto.rsa_private_key)\\n\\n\\t\\tlogger.info(\\\"Sending certificates for validation\\\")\\n\\t\\tmessage={'type':'SERVER_AUTH_RESPONSE','signature':base64.b64encode(self.crypto.signature).decode(),'server_cert':base64.b64encode(self.crypto.get_certificate_bytes(self.crypto.server_cert)).decode(),'server_roots':base64.b64encode(self.crypto.get_certificate_bytes(self.crypto.server_ca_cert)).decode()}\\n\\t\\tsecure_message = self.encrypt_payload(message)\\n\\t\\tself._send(secure_message)\\n\\t\\tself.send_mac()\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaf10f9d00fed8445c64634c25ab1aa4\",\n \"score\": \"0.59784365\",\n \"text\": \"def getChallenge(self, request):\\n return {'realm': self.authenticationRealm}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ffa8572df93759f903eb0eb84d96f36\",\n \"score\": \"0.59590465\",\n \"text\": \"def getChallenge(self):\\n\\t\\tchallengeXML = self.requestFactory.generateQuestion()\\n\\t\\tself.sessionSocket.sendall(challengeXML)\\n\\t\\txmlString = self.sessionSocket.recv(1024).strip().decode('utf-8')\\n\\t\\tprint self.responseHandler.handle(xmlString)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a58f15bebb5272615c8074de6a4e9e1\",\n \"score\": \"0.5938506\",\n \"text\": \"def auth_proceed(bot, trigger):\\n if bot.config.core.auth_method == 'sasl':\\n mech = bot.config.core.auth_target or 'PLAIN'\\n elif bot.config.core.server_auth_method == 'sasl':\\n mech = bot.config.core.server_auth_sasl_mech or 'PLAIN'\\n else:\\n return\\n\\n if mech == 'EXTERNAL':\\n if trigger.args[0] != '+':\\n # not an expected response from the server; abort SASL\\n token = '*'\\n else:\\n token = '+'\\n\\n bot.write(('AUTHENTICATE', token))\\n return\\n\\n if bot.config.core.auth_method == 'sasl':\\n sasl_username = bot.config.core.auth_username\\n sasl_password = bot.config.core.auth_password\\n elif bot.config.core.server_auth_method == 'sasl':\\n sasl_username = bot.config.core.server_auth_username\\n sasl_password = bot.config.core.server_auth_password\\n else:\\n # How did we get here? I am not good with computer\\n return\\n\\n sasl_username = sasl_username or bot.nick\\n\\n if mech == 'PLAIN':\\n if trigger.args[0] == '+':\\n sasl_token = _make_sasl_plain_token(sasl_username, sasl_password)\\n LOGGER.info(\\\"Sending SASL Auth token.\\\")\\n send_authenticate(bot, sasl_token)\\n return\\n else:\\n # Not an expected response from the server\\n LOGGER.warning(\\n 'Aborting SASL: unexpected server reply \\\"%s\\\"', trigger,\\n )\\n # Send `authenticate-abort` command\\n # See https://ircv3.net/specs/extensions/sasl-3.1#the-authenticate-command\\n bot.write(('AUTHENTICATE', '*'))\\n return\\n\\n # TODO: Implement SCRAM challenges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4cd3a2ea8d5fffb267d8d4e84d373b6\",\n \"score\": \"0.58901256\",\n \"text\": \"def authenticate(self, msg):\\n\\t\\tif msg.command != 'AUTH':\\n\\t\\t\\traise TypeError('Must be AUTH command!')\\n\\t\\tself.__auth_id = msg.id\\n\\t\\tself.request(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dea5b4ccdf3d7b91489fcb22268a1ae6\",\n \"score\": \"0.5881792\",\n \"text\": \"def auth(self, irc, msg, args, nick):\\r\\n self._removeExpiredRequests()\\r\\n userdata = self.db.getByNick(nick)\\r\\n if len(userdata) == 0:\\r\\n irc.error(\\\"This nick is not registered. Please register.\\\")\\r\\n return\\r\\n keyid = userdata[0][1]\\r\\n fingerprint = userdata[0][2]\\r\\n if keyid is None:\\r\\n irc.error(\\\"You have not registered a GPG key. Try using bcauth instead, or register a GPG key first.\\\")\\r\\n return\\r\\n challenge = \\\"freenode:#bitcoin-otc:\\\" + hashlib.sha256(os.urandom(128)).hexdigest()[:-8]\\r\\n request = {msg.prefix: {'nick':userdata[0][5],\\r\\n 'expiry':time.time(), 'keyid':keyid,\\r\\n 'type':'auth', 'challenge':challenge,\\r\\n 'fingerprint':fingerprint}}\\r\\n self.pending_auth.update(request)\\r\\n self.authlog.info(\\\"auth request from hostmask %s for user %s, keyid %s.\\\" %\\\\\\r\\n (msg.prefix, nick, keyid, ))\\r\\n irc.reply(\\\"Request successful for user %s, hostmask %s. Your challenge string is: %s\\\" %\\\\\\r\\n (nick, msg.prefix, challenge,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcccb39d57d9761bd333af094ae1d29f\",\n \"score\": \"0.58794737\",\n \"text\": \"def challenge_post(self, environ, start_response):\\n # request_info = cgi.parse_qs(environ['wsgi.input'].read(int(environ['CONTENT_LENGTH'])))\\n # redirect = request_info.get('tiddlyweb_redirect', ['/'])[0]\\n query = environ['tiddlyweb.query']\\n redirect = query.get('tiddlyweb_redirect', ['/'])[0]\\n\\n try:\\n user = query['user'][0]\\n password = query['password'][0]\\n valid=self._validate_and_redirect(environ, start_response, user, password, redirect)\\n if valid:\\n self._ensure_bags(environ, user)\\n self._ensure_recipe(environ, user)\\n return valid\\n except KeyError:\\n return self._send_cookie_form(environ, start_response, redirect, '401 Unauthorized')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d440a6e27a32c253b40039ac0a0db55b\",\n \"score\": \"0.5851626\",\n \"text\": \"def challenge_client(sock: socket) -> bool:\\n nonce = os.urandom(32)\\n sock.send(nonce)\\n response = sock.recv(32)\\n expected_response = hashlib.sha256(nonce + settings.PASSPHRASE).digest()\\n return response == expected_response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c251cafda5d43f257abc49b9ce16c08\",\n \"score\": \"0.58377445\",\n \"text\": \"def calculate_response(challenge: bytes):\\n\\tsecret = challenge[3:6]\\n\\tresponse = struct.pack('BBBB', challenge[2], *[secret[i] ^ XOR_KEY[i] for i in range(len(secret))])\\n\\treturn format_message(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea9f7d7734ddb326707599334b1978a6\",\n \"score\": \"0.58345133\",\n \"text\": \"def _final_challenge(self, challenge):\\n if self._finished:\\n return Failure(\\\"extra-challenge\\\")\\n\\n match = SERVER_FINAL_MESSAGE_RE.match(challenge)\\n if not match:\\n logger.debug(\\\"Bad final message syntax: {0!r}\\\".format(challenge))\\n return Failure(\\\"bad-challenge\\\")\\n\\n error = match.group(\\\"error\\\")\\n if error:\\n logger.debug(\\\"Server returned SCRAM error: {0!r}\\\".format(error))\\n return Failure(u\\\"scram-\\\" + error.decode(\\\"utf-8\\\"))\\n\\n verifier = match.group(\\\"verifier\\\")\\n if not verifier:\\n logger.debug(\\\"No verifier value in the final message\\\")\\n return Failure(\\\"bad-succes\\\")\\n\\n server_key = self.HMAC(self._salted_password, b\\\"Server Key\\\")\\n server_signature = self.HMAC(server_key, self._auth_message)\\n if server_signature != a2b_base64(verifier):\\n logger.debug(\\\"Server verifier does not match\\\")\\n return Failure(\\\"bad-succes\\\")\\n\\n self._finished = True\\n return Response(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fda05d5552c95d652d81fe3237cd7b16\",\n \"score\": \"0.5816291\",\n \"text\": \"def make_digest_challenge(self, realm, message=None):\\n\\n if message is None:\\n message = \\\"Authentication is required\\\"\\n\\n param_dict = {\\n \\\"realm\\\": realm,\\n \\\"nonce\\\": gen_nonce(16),\\n \\\"opaque\\\": gen_nonce(16),\\n }\\n\\n parameters = \\\", \\\".join('%s=\\\"%s\\\"' % t for t in param_dict.items())\\n\\n authenticate = \\\"Digest %s\\\" % parameters\\n\\n return Response(message, 401, {\\\"WWW-Authenticate\\\": authenticate})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ec43bc6f2ed99c0d94ca6033cc37654\",\n \"score\": \"0.5812959\",\n \"text\": \"def challenge(self) -> bool:\\n return self.__challenge\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e946bef569e81aca055ff13409099d0b\",\n \"score\": \"0.5784814\",\n \"text\": \"def authenticate_request(self, request, content):\\n json_request = {\\n \\\"method\\\": request.method,\\n \\\"uri\\\": request.uri,\\n \\\"destination_is\\\": self.sydent.server_name,\\n \\\"signatures\\\": {},\\n }\\n\\n if content is not None:\\n json_request[\\\"content\\\"] = content\\n\\n origin = None\\n\\n def parse_auth_header(header_str):\\n \\\"\\\"\\\"\\n Extracts a server name, signing key and payload signature from an\\n authentication header.\\n\\n :param header_str: The content of the header\\n :type header_str: unicode\\n\\n :return: The server name, the signing key, and the payload signature.\\n :rtype: tuple[unicode]\\n \\\"\\\"\\\"\\n try:\\n params = header_str.split(u\\\" \\\")[1].split(u\\\",\\\")\\n param_dict = dict(kv.split(u\\\"=\\\") for kv in params)\\n\\n def strip_quotes(value):\\n if value.startswith(u\\\"\\\\\\\"\\\"):\\n return value[1:-1]\\n else:\\n return value\\n\\n origin = strip_quotes(param_dict[\\\"origin\\\"])\\n key = strip_quotes(param_dict[\\\"key\\\"])\\n sig = strip_quotes(param_dict[\\\"sig\\\"])\\n return origin, key, sig\\n except Exception:\\n raise SignatureVerifyException(\\\"Malformed Authorization header\\\")\\n\\n auth_headers = request.requestHeaders.getRawHeaders(u\\\"Authorization\\\")\\n\\n if not auth_headers:\\n raise NoAuthenticationError(\\\"Missing Authorization headers\\\")\\n\\n for auth in auth_headers:\\n if auth.startswith(u\\\"X-Matrix\\\"):\\n (origin, key, sig) = parse_auth_header(auth)\\n json_request[\\\"origin\\\"] = origin\\n json_request[\\\"signatures\\\"].setdefault(origin, {})[key] = sig\\n\\n if not json_request[\\\"signatures\\\"]:\\n raise NoAuthenticationError(\\\"Missing X-Matrix Authorization header\\\")\\n\\n yield self.verifyServerSignedJson(json_request, [origin])\\n\\n logger.info(\\\"Verified request from HS %s\\\", origin)\\n\\n defer.returnValue(origin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a218690d8a08a13ec66970712510aaa2\",\n \"score\": \"0.57790285\",\n \"text\": \"def authenticate(self, handler):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e151d44187b0970d8225f0b5b76c31c\",\n \"score\": \"0.57646435\",\n \"text\": \"def auth_step(socket, password, header):\\n send(socket, b'EHLO ' + SMTP_DOMAINNAME.encode('ASCII'))\\n response = read_line(socket)\\n while '-' in response:\\n response = read_line(socket)\\n if response[:3] != '250':\\n raise Exception('EHLO Response not OK')\\n auth_login = b'AUTH LOGIN'\\n user_name = base64.b64encode(b'betances-leblancc@msoe.edu') # rip inbox\\n encode_pass = base64.b64encode(password.encode('ASCII'))\\n send(socket, auth_login)\\n response_user = read_line(socket)\\n if response_user[:3] != '334':\\n raise Exception('Username question expected')\\n send(socket, user_name)\\n response_pass_ask = read_line(socket)\\n if response_pass_ask[:3] != '334':\\n raise Exception('Password Request Expected')\\n send(socket, encode_pass)\\n response_success = read_line(socket)\\n if response_success[:3] != '235':\\n print(response_success)\\n return -1\\n # raise Exception('Authentication was NOT successful')\\n print('Authentication Successful')\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"176ecd63705a39b7662d6838e84bac8e\",\n \"score\": \"0.5752457\",\n \"text\": \"def authenticate():\\n return Response('Could not verify your access level for that URL.\\\\n''You have to login with proper credentials', 401, {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3419696577a5db795993722803f03fec\",\n \"score\": \"0.57397336\",\n \"text\": \"def challenge_response(self, challenge, mode='HMAC', slot=1, variable=True, may_block=True):\\n if not self.capabilities.have_challenge_response(mode):\\n raise yubikey_base.YubiKeyVersionError(\\\"%s challenge-response unsupported in YubiKey %s\\\" % (mode, self.version()) )\\n return self._challenge_response(challenge, mode, slot, variable, may_block)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea7f076fd66805b2cf6005d0a1f189a0\",\n \"score\": \"0.57264584\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66478b2d497eadcfc6c5a19c3f63b3a4\",\n \"score\": \"0.57178414\",\n \"text\": \"def everify(self, irc, msg, args, otp):\\r\\n self._removeExpiredRequests()\\r\\n if not self._testPresenceInChannels(irc, msg.nick):\\r\\n irc.error(\\\"In order to authenticate, you must be present in one \\\"\\r\\n \\\"of the following channels: %s\\\" % (self.registryValue('channels'),))\\r\\n return\\r\\n try:\\r\\n authrequest = self.pending_auth[msg.prefix]\\r\\n except KeyError:\\r\\n irc.error(\\\"Could not find a pending authentication request from your hostmask. \\\"\\r\\n \\\"Either it expired, or you changed hostmask, or you haven't made one.\\\")\\r\\n return\\r\\n if authrequest['type'] not in ['eregister','eauth','echangekey']:\\r\\n irc.error(\\\"No outstanding encryption-based request found.\\\")\\r\\n return\\r\\n if authrequest['challenge'] != otp:\\r\\n irc.error(\\\"Incorrect one-time password. Try again.\\\")\\r\\n return\\r\\n\\r\\n response = \\\"\\\"\\r\\n if authrequest['type'] == 'eregister':\\r\\n if self.db.getByNick(authrequest['nick']) or self.db.getByKey(authrequest['keyid']):\\r\\n irc.error(\\\"Username or key already in the database.\\\")\\r\\n return\\r\\n self.db.register(authrequest['keyid'], authrequest['fingerprint'], None,\\r\\n time.time(), authrequest['nick'])\\r\\n response = \\\"Registration successful. \\\"\\r\\n elif authrequest['type'] == 'echangekey':\\r\\n gpgauth = self._ident(msg.prefix)\\r\\n if gpgauth is None:\\r\\n irc.error(\\\"You must be authenticated in order to change your registered key.\\\")\\r\\n return\\r\\n if self.db.getByKey(authrequest['keyid']):\\r\\n irc.error(\\\"This key id already registered. Try a different key.\\\")\\r\\n return\\r\\n self.db.changekey(gpgauth['nick'], gpgauth['keyid'], authrequest['keyid'], authrequest['fingerprint'])\\r\\n response = \\\"Successfully changed key for user %s from %s to %s. \\\" %\\\\\\r\\n (gpgauth['nick'], gpgauth['keyid'], authrequest['keyid'],)\\r\\n userdata = self.db.getByNick(authrequest['nick'])\\r\\n self.authed_users[msg.prefix] = {'timestamp':time.time(),\\r\\n 'keyid': authrequest['keyid'], 'nick':authrequest['nick'],\\r\\n 'bitcoinaddress':userdata[0][3],\\r\\n 'fingerprint':authrequest['fingerprint']}\\r\\n del self.pending_auth[msg.prefix]\\r\\n logmsg = \\\"everify success from hostmask %s for user %s, keyid %s.\\\" %\\\\\\r\\n (msg.prefix, authrequest['nick'], authrequest['keyid'],) + response\\r\\n self.authlog.info(logmsg)\\r\\n self.db.update_auth_date(userdata[0][0], time.time())\\r\\n self.db.set_auth_status(userdata[0][5], 1)\\r\\n if not world.testing:\\r\\n irc.queueMsg(ircmsgs.privmsg(\\\"#bitcoin-otc-auth\\\", logmsg))\\r\\n irc.reply(response + \\\"You are now authenticated for user %s with key %s\\\" %\\\\\\r\\n (authrequest['nick'], authrequest['keyid']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8c26245bcc8568f233aa5ce00ddee39\",\n \"score\": \"0.56832886\",\n \"text\": \"def authenticate():\\n return Response(\\n \\\"Could not verify your access level for that URL.\\\\n\\\"\\n \\\"You have to login with proper credentials\\\",\\n 401,\\n {\\\"WWW-Authenticate\\\": 'Basic realm=\\\"Login Required\\\"'},\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8c26245bcc8568f233aa5ce00ddee39\",\n \"score\": \"0.56832886\",\n \"text\": \"def authenticate():\\n return Response(\\n \\\"Could not verify your access level for that URL.\\\\n\\\"\\n \\\"You have to login with proper credentials\\\",\\n 401,\\n {\\\"WWW-Authenticate\\\": 'Basic realm=\\\"Login Required\\\"'},\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1e3484726a4bc4129e5e53552d7a9d\",\n \"score\": \"0.56745934\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e612bfdff49d8f441a9e258775d0c52\",\n \"score\": \"0.56672156\",\n \"text\": \"def run(self, message):\\n if self.parse_response(message):\\n if self.validate_credentials():\\n self.client.change_auth_status(self.client.auth_statuses[\\\"auth_complete\\\"])\\n else:\\n LOGGER.warning(\\\"Username, password or unique id incorrect at client: {client}\\\".format(\\n client=str(self.client)))\\n return\\n else:\\n LOGGER.warning(\\\"Error receiving admin credentials: {client}\\\".format(\\n client=str(self.client)))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73a6e71a7ed72d6bec05f19f8202d1e5\",\n \"score\": \"0.5665175\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for this URL.\\\\n'\\n 'Login credentials required.', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f402242170ddeba635205a946f3e6b39\",\n \"score\": \"0.56613606\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n { 'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"' }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e931527e6c09b961a935455a809800c8\",\n \"score\": \"0.5657155\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15d8a611bda1649971f213b486ec6897\",\n \"score\": \"0.5644863\",\n \"text\": \"def authenticate():\\n return Response('', 401, {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9da2ce0b082d844de498283bf87d54\",\n \"score\": \"0.562817\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9da2ce0b082d844de498283bf87d54\",\n \"score\": \"0.562817\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9da2ce0b082d844de498283bf87d54\",\n \"score\": \"0.562817\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9da2ce0b082d844de498283bf87d54\",\n \"score\": \"0.562817\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9da2ce0b082d844de498283bf87d54\",\n \"score\": \"0.562817\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9da2ce0b082d844de498283bf87d54\",\n \"score\": \"0.562817\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9da2ce0b082d844de498283bf87d54\",\n \"score\": \"0.562817\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9da2ce0b082d844de498283bf87d54\",\n \"score\": \"0.562817\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9da2ce0b082d844de498283bf87d54\",\n \"score\": \"0.562817\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9da2ce0b082d844de498283bf87d54\",\n \"score\": \"0.562817\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9da2ce0b082d844de498283bf87d54\",\n \"score\": \"0.562817\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9da2ce0b082d844de498283bf87d54\",\n \"score\": \"0.562817\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9da2ce0b082d844de498283bf87d54\",\n \"score\": \"0.562817\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81a2f233dfda1b716720252f8325bee0\",\n \"score\": \"0.562801\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'}\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d787347051c1dcc8d0b9a977e7547cf1\",\n \"score\": \"0.56100833\",\n \"text\": \"def authenticate():\\n message = {'message': \\\"Authenticate.\\\"}\\n resp = jsonify(message) # create a json object\\n\\n resp.status_code = 401\\n resp.headers['WWW-Authenticate'] = 'Basic realm=\\\"Main\\\"'\\n\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02445dc3743fbc536768a9e8071dbd0a\",\n \"score\": \"0.56082314\",\n \"text\": \"def authenticate():\\r\\n\\treturn Response(\\r\\n\\t'Could not verify your access level for that URL.\\\\n'\\r\\n\\t'You have to login with proper credentials', 401,\\r\\n\\t{'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9131c830ad6f6c798b92075c52eec195\",\n \"score\": \"0.55819196\",\n \"text\": \"def __authenticate(self, *args, **kwargs):\\n\\n # If priv_key is None, then master.key didn't get loaded correctly.\\n if self.controller.priv_key is None:\\n raise ControllerException(CONTROLLER_ERROR_NO_RSA_KEY)\\n\\n challenge_func = WebControllerFunction(self.controller, 'authenticate')\\n challenge = challenge_func().__str__()\\n\\n # re-encrypt using servers key and then sha hash it before sending it\\n # back\\n response_encode = self.controller.priv_key.encrypt(challenge, None)\\n response_hash = hashlib.sha512(response_encode[0]).hexdigest()\\n \\n challenge_func = WebControllerFunction(self.controller, \\\\\\n 'challenge_response')\\n if challenge_func(response_hash):\\n return self(*args, **kwargs)\\n\\n raise ControllerException(CONTROLLER_ERROR_AUTH_FAIL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a5fcbcaa0eddcad2fc682233c111859\",\n \"score\": \"0.5580618\",\n \"text\": \"def verify (self, response, challenge, v):\\r\\n\\t\\ttouple = self.check(response, challenge, v)\\r\\n\\t\\treturn touple[0]==touple[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15af5994a08f565053f437475caf5d27\",\n \"score\": \"0.55725276\",\n \"text\": \"def auth_ok_message():\\n return {\\\"type\\\": TYPE_AUTH_OK, \\\"ha_version\\\": __version__}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20d028fdf571e87e2c6daa523e6e8459\",\n \"score\": \"0.5563835\",\n \"text\": \"def authenticate():\\n return Response(\\n 'Login Required', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bd1ce8c3cdf0f4fd90f8ac7e4753dfc\",\n \"score\": \"0.55371714\",\n \"text\": \"def messenger_webhook():\\n verify_token = request.query.get('hub.verify_token')\\n # check whether the verify tokens match\\n if verify_token == FB_VERIFY_TOKEN:\\n # respond with the challenge to confirm\\n challenge = request.query.get('hub.challenge')\\n return challenge\\n else:\\n return 'Invalid Request or Verification Token'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bd1ce8c3cdf0f4fd90f8ac7e4753dfc\",\n \"score\": \"0.55371714\",\n \"text\": \"def messenger_webhook():\\n verify_token = request.query.get('hub.verify_token')\\n # check whether the verify tokens match\\n if verify_token == FB_VERIFY_TOKEN:\\n # respond with the challenge to confirm\\n challenge = request.query.get('hub.challenge')\\n return challenge\\n else:\\n return 'Invalid Request or Verification Token'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"288bd984225cff35a310b32ebceda0ca\",\n \"score\": \"0.5536558\",\n \"text\": \"def authenticate(self):\\n log(2,\\\"Checking for authentication\\\")\\n if self.request.user:#\\n log(2,\\\"Authentication OK\\\")\\n return True\\n else:\\n log(2,\\\"Authentication not OK, send 401\\\")\\n self.response.add_header(\\\"WWW-Authenticate\\\", 'RestBasic realm=\\\"We need your password.\\\"')\\n self.response.add_header('Content-Type', 'application/json')\\n raise StopProcessing(401,json.dumps({\\\"error\\\":\\\"Authentiction required.\\\"}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"110515db4636d2f7efad6c53953b3957\",\n \"score\": \"0.55330414\",\n \"text\": \"def basic_challenge(realm, content='Authorization Required'):\\n response = HttpResponse(content, content_type=\\\"text/plain\\\")\\n response['WWW-Authenticate'] = 'Basic realm=\\\"%s\\\"' % (realm, )\\n response.status_code = 401\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5372fc9b27d87c5f4cea6a2d4d0e1f91\",\n \"score\": \"0.5510157\",\n \"text\": \"def authenticate(self, data):\\n\\n data = data.strip().split('%')\\n if len(data) != 3:\\n raise Exception(\\\"Invalid Message\\\")\\n try:\\n privateid = self.factory.authkeys[data[0]].encode('utf-8', 'ignore')\\n except:\\n raise Exception(\\\"Invalid Authkey\\\")\\n message = data[1].encode('utf-8', 'ignore')\\n checksum = hashlib.md5(message + privateid).hexdigest()\\n if data[2] == checksum:\\n return message.decode('utf-8', 'ignore')\\n else:\\n raise Exception(\\\"Failed auth\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e7e1cf5272ece535e6af297b722b6e5\",\n \"score\": \"0.55049473\",\n \"text\": \"def authenticate(self, tree):\\n\\n # Currently RFC specifies only SASL as supported way of auth'ing\\n handler = SASLAuthHandler()\\n if tree.get('xmlns') != handler.namespace:\\n raise MalformedRequestError\\n handler.process(tree)\\n self.connection.parser.reset()\\n self.jid = JID(\\\"@\\\".join([handler.authenticated_user,\\n self.hostname]))\\n self.authenticated = True\\n response_element = ET.Element(\\\"success\\\")\\n response_element.set(\\\"xmlns\\\", handler.namespace)\\n self.send_element(response_element)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6477851370b338ccb1d08baa7d5df877\",\n \"score\": \"0.54741734\",\n \"text\": \"def authenticate():\\n return flask.Response('Login required', 401, {\\n 'WWW-Authenticate': 'Basic realm=\\\"Login required\\\"',\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"681e4c4b0db31e87d9e3bb22648fed0c\",\n \"score\": \"0.54555166\",\n \"text\": \"def authenticate(self, req, resp, resource):\\n raise NotImplementedError(\\\".authenticate() must be overridden.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f269c18a478e8bbf3fa00d2cdeb06c0e\",\n \"score\": \"0.5446857\",\n \"text\": \"def challenge(self, request, response, **kw):\\n user_expired = response.getHeader(\\\"user_expired\\\")\\n if user_expired:\\n portal_url = api.portal.get_tool(name=\\\"portal_url\\\")()\\n IStatusMessage(request).add(\\n _(u\\\"Your password has expired.\\\"), type=\\\"error\\\"\\n )\\n response.redirect(\\n \\\"%s/mail_password_form?userid=%s\\\" % (portal_url, user_expired),\\n lock=1,\\n )\\n return 1\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e52eda84df4d0f232949dd3a95c7428\",\n \"score\": \"0.54455614\",\n \"text\": \"def authorization(self):\\n pr_req = Request(RequestAction.PRESENCE, self.user.username)\\n self.__send_request(pr_req)\\n resp = self.__get_response()\\n if resp is None:\\n return Response(SERVER_ERROR)\\n if resp.code != AUTH:\\n return resp\\n enc_pass = encrypt_rsa(\\n import_pub_key(resp.message.encode()),\\n self.user.password)\\n auth_req = Request(RequestAction.AUTH, enc_pass.decode())\\n self.__send_request(auth_req)\\n return self.__get_response()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab644ede42a4c845ac2ae73a47c9fb22\",\n \"score\": \"0.5432318\",\n \"text\": \"def authenticate_request(\\n self,\\n request: 'CoreRequest'\\n ) -> Success | Failure | Response | None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3be545bac3c2fba1cef46d1eace66f42\",\n \"score\": \"0.5428773\",\n \"text\": \"def exec_get_challenge(self):\\n\\n\\t\\tapdu = [\\n\\t\\t\\t0x00, # CLA\\n\\t\\t\\t0x84, # INS = C/R INT AUTH\\n\\t\\t\\t0x00, # P1 = 0x00\\n\\t\\t\\t0x00, # P2 = 0x00\\n\\t\\t\\t0x08,\\n\\t\\t]\\n\\n\\t\\td, s, t = self.transmit(apdu)\\n\\t\\treturn d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c0157f68832652bfc3b62a89d208cf\",\n \"score\": \"0.5420126\",\n \"text\": \"def authenticate():\\n return Response(\\\"Could not authenticate. Please provide valid Globus credentials.\\\",\\n 401, {\\\"WWW-Authenticate\\\": 'Basic realm=\\\"Login Required\\\"'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1cd8db06076f610ff1a8742a0760690\",\n \"score\": \"0.5399056\",\n \"text\": \"async def __handleHandshake(self, path: str, headers):\\n parts = path.split(\\\"?\\\")\\n _pathname = parts[0]\\n _query = \\\"\\\"\\n\\n if len(parts) >= 2:\\n _query = \\\"?\\\".join(parts[1:])\\n\\n if self.protocol != \\\"ws+unix:\\\" and _pathname != self.pathname:\\n return (http.HTTPStatus.NOT_FOUND, [], bytes([]))\\n\\n query = parse_qs(_query or \\\"\\\")\\n clientId = str(query.get(\\\"id\\\") and query.get(\\\"id\\\")[0] or \\\"\\\")\\n secret = str(query.get(\\\"secret\\\")\\n and query.get(\\\"secret\\\")[0] or \\\"\\\")\\n\\n if not clientId or (self.secret and secret != self.secret):\\n return (http.HTTPStatus.UNAUTHORIZED, [], bytes([]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e8311598ba08f156aed434e86c23c10\",\n \"score\": \"0.5394339\",\n \"text\": \"def authenticate(socket, config):\\n # Generate the auth string\\n auth_str = config[\\\"user\\\"] + \\\";\\\" + config[\\\"pass\\\"]\\n \\n # Send the string\\n send_mess(socket,auth_str)\\n\\n # Return response\\n return get_bool_response(socket)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46fa4d854805b344642819095f77e3b3\",\n \"score\": \"0.5392686\",\n \"text\": \"def response_auth(status, message, token, status_code):\\n return make_response(jsonify({\\n 'status': status,\\n 'message': message,\\n 'auth_token': token.decode(\\\"utf-8\\\")\\n })), status_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7b9d44eacb858ce821ecc6888ce0d4c\",\n \"score\": \"0.53925836\",\n \"text\": \"def logAuthenticate200(r):\\n log.debug(\\\"Authenticate Response: \\\")\\n log.debug(json.dumps(r.json(),indent=10,sort_keys=True))\\n log.debug(\\\"Authenticate Response Headers: \\\")\\n for h in r.headers:\\n log.debug(\\\" \\\" + h + \\\" : \\\" + r.headers[h])\\n token = r.json()[\\\"token\\\"]\\n try:\\n decoded_token = jwt.decode(token, server_env[\\\"CLIENT_SECRET\\\"], algorithms=[\\\"HS256\\\"], audience=server_env[\\\"CLIENT_ID\\\"])\\n log.debug(\\\"Decoded token: \\\")\\n log.debug(json.dumps(decoded_token, indent=30, sort_keys=True))\\n header = base64.b64decode(token.split('.')[0]).decode('utf-8')\\n log.debug(\\\"header:\\\")\\n log.debug(json.dumps(json.loads(header), indent=4, sort_keys=True))\\n payload = base64.b64decode(token.split('.')[1]+\\\"==\\\").decode('utf-8')\\n log.debug(\\\"payload:\\\")\\n log.debug(json.dumps(json.loads(payload), indent=4, sort_keys=True))\\n except jwt.exceptions.InvalidSignatureError as err:\\n log.warn(\\\"Unable to decode token: \\\" + str(err))\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63f88460cc1d063dc9e9e91c2a5ce439\",\n \"score\": \"0.53920156\",\n \"text\": \"def challenge_get(self, environ, start_response):\\n redirect = environ['tiddlyweb.query'].get('tiddlyweb_redirect', ['/'])[0]\\n # print \\\"REDIRECT\\\"\\n # print redirect\\n return self._send_cookie_form(environ, start_response, redirect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b85f31245b709456ab54cba9979c09da\",\n \"score\": \"0.53745955\",\n \"text\": \"def login(): \\n headers = { 'X-Requested-With': 'XMLHttpRequest' }\\n data = { 'callback': 'getChallenge', 'action': 'challenge'}\\n\\n response = session.post(URL + 'login', headers=headers, data=data)\\n if response.status_code != 200:\\n sys.exit(\\\"login(): Impossible de récuperer le challenge\\\")\\n\\n d = pq( response.content )\\n challenge = d.find('challenge').text().encode('utf-8')\\n\\n hash1 = sha256('admin'.encode('utf-8')).hexdigest().encode('utf-8')\\n hash1 = hmac.new(challenge, hash1, sha256).hexdigest()\\n\\n hash2 = sha256(KEY.encode('utf-8')).hexdigest().encode('utf-8')\\n hash2 = hmac.new(challenge, hash2, sha256).hexdigest()\\n \\n data = {\\n 'method' : 'passwd',\\n 'page_ref': '',\\n 'zsid' : challenge,\\n 'hash' : hash1 + hash2\\n }\\n\\n response = session.post( URL + 'login', data=data )\\n if response.status_code != 200:\\n sys.exit(\\\"login(): Impossible de se connecter\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c637b7d442ef84d4dbf262e51207ac77\",\n \"score\": \"0.53692544\",\n \"text\": \"def _run_next_challenge(msg, request, access_token):\\n for challenge in msg[\\\"challenges\\\"]:\\n if challenge[\\\"status\\\"] != \\\"READY\\\":\\n # Skip non-activated challenges.\\n continue\\n c = challenges.AVAILABLE_CHALLENGES.get(challenge[\\\"challengeType\\\"], None)\\n if not c:\\n raise exceptions.ReauthFailError(\\n \\\"Unsupported challenge type {0}. Supported types: {1}\\\".format(\\n challenge[\\\"challengeType\\\"],\\n \\\",\\\".join(list(challenges.AVAILABLE_CHALLENGES.keys())),\\n )\\n )\\n if not c.is_locally_eligible:\\n raise exceptions.ReauthFailError(\\n \\\"Challenge {0} is not locally eligible\\\".format(\\n challenge[\\\"challengeType\\\"]\\n )\\n )\\n client_input = c.obtain_challenge_input(challenge)\\n if not client_input:\\n return None\\n return _send_challenge_result(\\n request,\\n msg[\\\"sessionId\\\"],\\n challenge[\\\"challengeId\\\"],\\n client_input,\\n access_token,\\n )\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"469d9b87f5ef0856de126c5e9ca4ef9b\",\n \"score\": \"0.5326108\",\n \"text\": \"def __authenticate(self):\\n log.trace(\\n \\\"Attempting authentication against {} with key [{}...{}]\\\",\\n self.url,\\n self.api_key[:2],\\n self.api_key[-2:],\\n )\\n self.status[\\\"auth_state\\\"] = \\\"attempting\\\"\\n response = self.request(\\\"auth\\\")\\n log.trace(f\\\"Authenticate response: {response.text}\\\")\\n self.status[\\\"auth_response\\\"] = response.text\\n self.status[\\\"auth_httpstatus\\\"] = response.status_code\\n\\n # NOTE: an unauthorized request has already been caught be the request() method\\n # above. Our legacy code was additionally testing for 'error' in the response\\n # text - however, it is unclear if PUMAPI ever returns this:\\n if \\\"error\\\" in response.text.lower():\\n self.status[\\\"auth_state\\\"] = \\\"FAILED-ERROR\\\"\\n msg = f\\\"Authentication failed with an error: {response.text}\\\"\\n log.error(msg)\\n raise requests.exceptions.ConnectionError(msg)\\n\\n status_ok = requests.codes.ok # pylint: disable-msg=no-member\\n\\n if response.status_code != status_ok:\\n # NOTE: branch excluded from coverage as we don't have a known way\\n # to produce such a response from the API\\n log.warning(\\n \\\"Unexpected combination of response [{}] and status code [{}], it's \\\"\\n \\\"unclear if authentication succeeded (assuming it didn't)\\\",\\n response.status_code,\\n response.text,\\n )\\n self.status[\\\"auth_state\\\"] = \\\"FAILED-UNKNOWN\\\"\\n\\n msg = (\\n f\\\"Authenticating against {self.url} with key \\\"\\n f\\\"[{self.api_key[:2]}...{self.api_key[-2:]}] FAILED!\\\"\\n )\\n log.error(msg)\\n raise requests.exceptions.ConnectionError(msg)\\n\\n log.trace(\\n \\\"Authentication succeeded, response=[{}], http_status=[{}]\\\",\\n response.text,\\n response.status_code,\\n )\\n self.status[\\\"auth_state\\\"] = \\\"good\\\"\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994090,"cells":{"query_id":{"kind":"string","value":"6a373c6e7a1bd455aa1ed75f652c7e26"},"query":{"kind":"string","value":"Tests the method which let us set the URL to work from for the case that the given URL is not a supported URL."},"positive_passages":{"kind":"list like","value":[{"docid":"20831f84b83e2b3651fbe1de70c2a13f","score":"0.0","text":"def test_set_url_base_ends_with_slash(self) -> None:\n\n given = \"http://example.org/\"\n expected = \"http://example.org\"\n\n self.query_tool.url_base = given\n actual = self.query_tool.url_base\n\n self.assertEqual(expected, actual)","title":""}],"string":"[\n {\n \"docid\": \"20831f84b83e2b3651fbe1de70c2a13f\",\n \"score\": \"0.0\",\n \"text\": \"def test_set_url_base_ends_with_slash(self) -> None:\\n\\n given = \\\"http://example.org/\\\"\\n expected = \\\"http://example.org\\\"\\n\\n self.query_tool.url_base = given\\n actual = self.query_tool.url_base\\n\\n self.assertEqual(expected, actual)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6be824e2daffbb7a6dd84fc0d7cbc843","score":"0.7581493","text":"def set_broken_url():","title":""},{"docid":"e02dcdf95f707f20fc80126e7cd69e75","score":"0.7048977","text":"def _set_url(self, url):\n ...","title":""},{"docid":"1079ed216e18a353496465c0d18685c3","score":"0.67172503","text":"def test_set_url_base_not_url(self) -> None:\n\n given = \"example.org\"\n\n self.assertRaises(ValueError, lambda: self.query_tool.set_url_base(given))","title":""},{"docid":"448572db4b03d97afd57398c53b3c0d7","score":"0.6544353","text":"def test_bad_url_double_scheme(self):\r\n index = setuptools.package_index.PackageIndex(\r\n hosts=('www.example.com',)\r\n )\r\n\r\n # issue 20\r\n url = 'http://http://svn.pythonpaste.org/Paste/wphp/trunk'\r\n try:\r\n index.open_url(url)\r\n except distutils.errors.DistutilsError:\r\n error = sys.exc_info()[1]\r\n msg = unicode(error)\r\n assert 'nonnumeric port' in msg or 'getaddrinfo failed' in msg or 'Name or service not known' in msg\r\n return\r\n raise RuntimeError(\"Did not raise\")","title":""},{"docid":"3c512c5c48eb16b3045a8bbe06d231ab","score":"0.64755464","text":"def _validate_url(self, url):\n return","title":""},{"docid":"cee220758bc5d3b3f2173da9b5715242","score":"0.6451268","text":"def _is_valid_url(self):\n try:\n self.req_page_obj = requests.get(self.url)\n except requests.exceptions.ConnectionError:\n return False\n else:\n return True","title":""},{"docid":"ab0bd2aec17985dcbf73f700c6b4677c","score":"0.64381975","text":"def get_broken_url():","title":""},{"docid":"c374a92ef66ebb5089b5b1609d7c6256","score":"0.640715","text":"def test_13_no_url(self):\n self.base_13_no_url()","title":""},{"docid":"a4d366be29c75aad8d973a4212db5fc0","score":"0.6349455","text":"def suitable(url):\n return False","title":""},{"docid":"d46785f5ec01d61d957cd4617b1fef28","score":"0.63463944","text":"def _fix_url(self): \n\t\tif self.scheme not in self.allowed_schemes:\n\t\t\tif not self.auto_fix:\n\t\t\t\traise SchemeError(self.scheme, self.allowed_schemes)\n\t\t\tself.scheme = self.allowed_schemes[0]\n\n\n\t\tif not self.host:\n\t\t\tif not self.auto_fix:\n\t\t\t\traise HostError()\n\n\t\t\telif self.path:\n\t\t\t\tpieces = self.path.split('/')\n\t\t\t\t\n\t\t\t\tif len(pieces) == 1:\n\t\t\t\t\tif not pieces[0]: raise HostError()\n\t\t\t\t\telse: self.host = pieces.pop(0)\n\t\t\t\telif pieces[0]:\n\t\t\t\t\tself.host = pieces.pop(0)\n\t\t\t\telif pieces[1]:\n\t\t\t\t\tself.host = pieces.pop(1)\n\t\t\t\telse:\n\t\t\t\t\traise HostError()\n\t\t\t\tself.path = '/'.join(pieces)\n\n\t\tself.join_url()","title":""},{"docid":"a7a6e1679a199426d2fd489a2c217b71","score":"0.6336512","text":"def test_invalid_url(self):\n validator = zope.component.getUtility(IURLValidator)\n with self.assertRaises(ValueError):\n validator.validate(\"?\")","title":""},{"docid":"aeee4a38066e463bb4b417312db4dc86","score":"0.63004094","text":"def test_invalid_url(self):\n self.data['url'] = 'notarepo'\n self.assertInvalid()","title":""},{"docid":"fb6a2ea6f44a65b64942455a3be491d2","score":"0.6286197","text":"def is_my_url(url):\n raise NotImplementedError","title":""},{"docid":"292b1f905f747a59b3f94a2e9acb875d","score":"0.62741524","text":"def test_guess_and_set_url_base_not_str(self) -> None:\n\n config_loader = ConfigLoader()\n config_loader.set_custom_config({\"collection\": {\"url_base\": False}}).start()\n\n self.query_tool.guess_and_set_url_base()\n\n expected = \"http://localhost:8001\"\n actual = self.query_tool.url_base\n\n self.assertEqual(expected, actual)\n\n del config_loader","title":""},{"docid":"17b4483ec9d8dd31fe4c6b2fadd749bd","score":"0.6204555","text":"def test_wrong_url(self):\n print(\"(\"+self.test_wrong_url.__name__+\")\", self.test_wrong_url.__doc__)\n resp = self.client.get(self.url_wrong)\n self.assertEqual(resp.status_code, 404)","title":""},{"docid":"376360eb4c2bf85f32c1198668405eda","score":"0.61899644","text":"def test_check_release_check_url(self):\n self.assertEqual(checks.check_release_check_url(None), [checks.E004])","title":""},{"docid":"a6984825977b8d93b69113d4caf6b8ca","score":"0.6174316","text":"def _url_callback(url: str) -> Union[str, None]:\n if url == \"latest\":\n return url\n if validators.url(url):\n return url\n else:\n raise typer.BadParameter(f\"Please check {url} is a valid url\")","title":""},{"docid":"400a259b89cdda5126376eb7ebe4ac6f","score":"0.61433077","text":"def test_set_url_base_method(self) -> None:\n\n given = \"https://example.org\"\n expected = given\n\n self.query_tool.set_url_base(given)\n actual = self.query_tool.url_base\n\n self.assertEqual(expected, actual)","title":""},{"docid":"35666a00df4dc8b7fd5c3beb7c6aafad","score":"0.6134628","text":"def test_url_format_check(self):\n\n assert not is_url(\"www.google.com\")\n assert is_url(\"http://www.google.com\")\n assert is_url(\"https://www.google.com\")\n assert is_url(\"http://www.google.com/info.txt\")\n assert is_url(\"http://www.google.com/child/info.txt\")\n\n assert not is_url(\"10.120.1.23\")\n assert is_url(\"http://10.120.1.23\")\n assert is_url(\"http://10.120.1.23/info.txt\")\n assert is_url(\"http://10.120.1.23/child/info.txt\")\n\n assert is_url(\"http://127.0.0.1:8080\")\n assert is_url(\"http://127.0.0.1:8080/child/info.txt\")\n\n assert is_url(\"http://port:8080\")\n assert is_url(\"http://port:8080/child/info.txt\")\n\n assert is_url(\"http://hello\")\n assert not is_url(\"http://hello.\")\n assert is_url(\"http://hello.i\")\n assert is_url(\"http://hello.io\")\n assert is_url(\"http://hello/child/info.txt\")\n\n assert is_url(\"http://hel-lo\")\n assert is_url(\"http://hel_lo\")\n assert not is_url(\"http://hel lo\")\n assert is_url(\"http://hello/\")\n assert is_url(\"http://hello/.\")\n assert is_url(\"http://hello/.txt\")","title":""},{"docid":"a3a3ca01af52f576a24288e34d983d05","score":"0.61344594","text":"def test_bad_url(self):\n resp = requests.get('http://localhost:8000/asdf')\n self.assertEquals(resp.status_code, 200)","title":""},{"docid":"38743008d99972e9b2f459731e4c1793","score":"0.61343056","text":"def validate_url(self, url: str) -> bool:\n if url.startswith(\"http://\") or url.startswith(\"https://\") or url.startswith(\"ftp://\"):\n return True\n self.logger.info(f\"GetURL: Unsupported URL prefix: {url}\")\n raise PluginException(\n preset=PluginException.Preset.UNKNOWN,\n assistance=f\"GetURL: Unsupported URL prefix: {url}\",\n )","title":""},{"docid":"9dab804bc225261246b8a6ceb25a3e56","score":"0.61172897","text":"def test_url_no_url_type(self, mock_get, request):\n mock_get.return_value = self.url_check_get_response\n request.json = {\"landing_url\": \"unimportant\"}\n response = post_source_parameter(SOURCE_ID, \"landing_url\", self.database)\n self.assertEqual(response, {\"ok\": True, \"nr_sources_mass_edited\": 0, \"availability\": []})\n mock_get.assert_not_called()","title":""},{"docid":"d10862b17eb12b6524848ed5e1260ae2","score":"0.6089364","text":"def checkurl(self):\n url = self.fields.get('url',False)\n if url:\n if url.endswith('.'):\n url = url[:-1]\n self.fields['url'] = url\n\n if url != None and url.count(' ')>0:\n self.errors.append(\"space in url\")\n nonsites = ('ebrary','degruyter','doi','myilibrary','academia','ebscohost')\n for n in nonsites:\n if url != None and n in url:\n self.errors.append(\"%s: urls should only be given for true repositories or for material not available elsewhere\"%url)","title":""},{"docid":"3e0aca29482e3cdabe025a148ee81cfa","score":"0.60806435","text":"def __check_url(self, url) -> None:\n\n if requests.head(url).status_code != 200:\n raise VersionUrlError('error: Invalid url')","title":""},{"docid":"3ec42dc67576e5f985715919f733e253","score":"0.6080406","text":"def handles_url(self, url):","title":""},{"docid":"7f6ac4119cc3282618540a786a22cc99","score":"0.60770726","text":"def test_url(url):\n http_url = url.replace(\"ftp://\", \"http://\")\n if http_url[-1] != \"/\":\n http_url += \"/\"\n\n res = requests.head(http_url)\n if res.status_code != 200:\n raise requests.HTTPError(f\"Unable to connect to URL: {url}\")\n\n return url","title":""},{"docid":"eb431c9b62859f5b2f1ee79ee148fc3d","score":"0.6057759","text":"def OpenURL(self, type, url):","title":""},{"docid":"a32d8e0abc4708f7f23b7df7e255d6fa","score":"0.6049299","text":"def test_match_normal_url(self):\n self.assertTrue(match(self.obj_url))","title":""},{"docid":"f6f8477ea7186f05a46b283dc638b120","score":"0.6046837","text":"def test_set_url_base_not_str(self) -> None:\n\n given = [\"Hello\", \"World!\"]\n\n self.assertRaises(TypeError, lambda: self.query_tool.set_url_base(given))","title":""},{"docid":"df286c561ba05bc1910af18ad33400f5","score":"0.60453475","text":"def _get_url(self):\n ...","title":""},{"docid":"a034b58c5aca01a4dc25b05611b77d13","score":"0.60043675","text":"def test_is_url():\n assert utils.is_url(\"http://mydomain.com/foo/bar/bat?asdf=1234&qewr=ooo\")\n assert utils.is_url(\"http://xkcd.com/1193/\")\n assert not utils.is_url(\"syn123445\")\n assert not utils.is_url(\"wasssuuuup???\")\n assert utils.is_url(\"file://foo.com/path/to/file.xyz\")\n assert utils.is_url(\"file:///path/to/file.xyz\")\n assert utils.is_url(\"file:/path/to/file.xyz\")\n assert utils.is_url(\"file:///c:/WINDOWS/clock.avi\")\n assert utils.is_url(\"file:c:/WINDOWS/clock.avi\")\n assert not utils.is_url(\"c:/WINDOWS/ugh/ugh.ugh\")","title":""},{"docid":"c82c4d56d599fb3c00138a7f83ddd2f1","score":"0.6002521","text":"def test_check_empty_url(self):\n self.empty_url = \"\"\n get_url_response = get_url(self.empty_url)\n self.assertEqual(get_url_response, None)","title":""},{"docid":"e678efca97550b4cf16ce6e5a5146390","score":"0.5997677","text":"def test_good_url(self):\n rv = self.submit_url('www.python.org')\n resp = str(rv.data, encoding='utf8')\n assert 'Short url is ' in resp","title":""},{"docid":"463844b2006068616c36266e93532417","score":"0.59973145","text":"def test_guess_and_set_url_base(self) -> None:\n\n config_loader = ConfigLoader()\n config_loader.set_custom_config(\n {\"collection\": {\"url_base\": \"https://example.org:8443\"}}\n ).start()\n\n self.query_tool.guess_and_set_url_base()\n\n expected = \"https://example.org:8443\"\n actual = self.query_tool.url_base\n\n self.assertEqual(expected, actual)\n\n del config_loader","title":""},{"docid":"788b29cc67d2ed37c8b2773c1253b9c8","score":"0.5980256","text":"def test_url_sanitization(self):\n expected_url = 'http://foo'\n\n api = AerospikeRestApi('http://foo/')\n self.assertEqual(api.base_url, expected_url)\n\n api = AerospikeRestApi('foo/')\n self.assertEqual(api.base_url, expected_url)","title":""},{"docid":"c33b067cee7c31123b6efbd895bc60de","score":"0.59763426","text":"def test_match_not_valid_url(self):\n self.assertFalse(match('http://not.valid.url/'))","title":""},{"docid":"0ab72e65c82b85c94a9f08b124874fe4","score":"0.5975472","text":"def test_compose_url_from_with_properurl(self):\n href = \"http://www.gc.com\"\n url = None\n actual = SiteMap().compose_url_from_href(url, href)\n expected = 'http://www.gc.com'\n self.assertEqual(expected, actual)","title":""},{"docid":"fc4cd78f250b2e207cf0b28f47e2a417","score":"0.5965903","text":"def test_run_non_existing_url(self):\n\n with patch(\"validators.url\", return_value=True), patch(\n \"streamlit.cli._main_run\"\n ), requests_mock.mock() as m:\n\n m.get(\"http://url\", exc=requests.exceptions.RequestException)\n with patch(\"tempfile.NamedTemporaryFile\"):\n result = self.runner.invoke(cli, [\"run\", \"http://url\"])\n\n self.assertNotEqual(0, result.exit_code)\n self.assertTrue(\"Unable to fetch\" in result.output)","title":""},{"docid":"926316492dfcedc8770cec4fd4ac31c8","score":"0.5939719","text":"def test_url_http_error(self, mock_get, request):\n mock_get.side_effect = requests.exceptions.RequestException\n request.json = {\"url\": self.url}\n response = post_source_parameter(SOURCE_ID, \"url\", self.database)\n self.assert_url_check(response, -1, \"RequestException\")\n updated_report = self.database.reports.insert_one.call_args[0][0]\n url = self.url\n self.assert_delta(\n f\"url of source 'Source' of metric 'Metric' of subject 'Subject' in report 'Report' from '' to '{url}'\",\n report=updated_report,\n )","title":""},{"docid":"891a203ff0a164af63a0322f7dc3d885","score":"0.59220636","text":"def url(self):\n raise NotImplemented()","title":""},{"docid":"b51e6f28c47e72676eee6d4e3820cac6","score":"0.59185445","text":"def test_isUrl(self):\n cases = [\n (\"http://example.com:8080/path/to/file\", True),\n (\"https://example.com:8080/path/to/file\", True),\n (\"ftp://example.com:8080/path/to/file\", False),\n (\"/path/to/my/file\", False)\n ]\n for case in cases:\n url, expected = case\n result = Utils.isUrl(url)\n self.assertEquals(expected, result)","title":""},{"docid":"1501cbd4109743eca9ded6285d573d2b","score":"0.5914192","text":"def test_13a_no_url(self):\n self.base_13_no_url(use_config_cache=True)","title":""},{"docid":"1408064edde210cfa3ca8d5beca847a0","score":"0.59105694","text":"def url(self):\n raise NotImplementedError('url') # pragma no cover","title":""},{"docid":"2468d04a0dddb63373dc6cd92da0ef3e","score":"0.5906903","text":"def is_supported(self, url):\r\n return self.regex.match(url) is not None","title":""},{"docid":"cee6c17828b577438ff8f637c4ab346e","score":"0.5883006","text":"def testIsFeedURLValid(self):\n #invalid: not a feed url\n self.assertFalse(validate.isFeedURLValid('http://www.google.com'))\n\n self.assertFalse(validate.isFeedURLValid(''))\n\n #valid feed url\n self.assertTrue(validate.isFeedURLValid(\n 'http://googlesummerofcode.blogspot.com/feeds/posts/default'))\n\n #invalid: wrong protocol\n self.assertFalse(validate.isFeedURLValid(\n 'htp://googlesummerofcode.blogspot.com/feeds/posts/default'))","title":""},{"docid":"530b845e524da9af9dbec02b5d6dd6be","score":"0.5873142","text":"def _validate_url(self):\n\n accepted_pattern = [\n r\"^https:\\/\\/player.vimeo.com\\/video\\/(\\d+)$\",\n r\"^https:\\/\\/vimeo.com\\/(\\d+)$\",\n r\"^https://vimeo.com/groups/.+?/videos/(\\d+)$\",\n r\"^https://vimeo.com/manage/videos/(\\d+)$\"\n ]\n for pattern in accepted_pattern:\n match = re.findall(pattern, self._url)\n if match:\n return match[0]\n # If none of the patterns is matched exception is raised\n raise URLNotSupported(\n f\"{self._url} is not supported. Make sure you don't include query parameters in the url\"\n )","title":""},{"docid":"2065e9a3db6e28e683e2c6dc46996964","score":"0.58643746","text":"def test_domain_url_error(self):\n self._test_failed_domain_url_fetch(raise_exception=True)","title":""},{"docid":"8019927ff0d0994980292cb877094508","score":"0.5853208","text":"def check_url(self, c):\n try:\n if c['src'][0:4] == 'http': \n self._comic_url = c['src'].replace(\" \", \"%20\")\n self._state = \"URL found\"\n else: \n self._comic_url = (self._url + c['src']).replace(\" \", \"%20\")\n self._state = \"URL found\"\n except:\n self._state = None\n self._comic_url = None","title":""},{"docid":"9cfed8d95a1778b37ba9392a4a00de17","score":"0.5842438","text":"def test_run_valid_url(self):\n\n with patch(\"validators.url\", return_value=True), patch(\n \"streamlit.cli._main_run\"\n ), requests_mock.mock() as m:\n\n m.get(\"http://url\", content=b\"content\")\n with patch(\"tempfile.NamedTemporaryFile\"):\n result = self.runner.invoke(cli, [\"run\", \"http://url\"])\n\n self.assertEqual(0, result.exit_code)","title":""},{"docid":"e588fa519ae8db817d2f18d79aa145fc","score":"0.58424115","text":"def checks(self):\n if not requests.head(self._url_page).status_code:\n raise NotImplementedError('incorrect url.')","title":""},{"docid":"a4bde13df7c07cc2aaecb4b057fa4c7f","score":"0.58409846","text":"def test_empty_url(self, mock_get, request):\n self.sources[SOURCE_ID][\"parameters\"][\"url\"] = self.url\n request.json = {\"url\": \"\"}\n response = post_source_parameter(SOURCE_ID, \"url\", self.database)\n self.assertEqual(response, {\"ok\": True, \"nr_sources_mass_edited\": 0, \"availability\": []})\n mock_get.assert_not_called()","title":""},{"docid":"51a99d5e07753a59f17e57aa1ff1266b","score":"0.5838431","text":"def test_schemes_allow_only_supported_protocols():\n pass","title":""},{"docid":"af300a463789d3f0a22a9968374711fb","score":"0.5825196","text":"def bad_url(url: str, e: Exception):\n print(\"\\nProblem found with \" + url + \" via \" + str(e) + \"\\n\")\n print(\"Check if the datasource's URL has changed.\\n\")","title":""},{"docid":"86c68f1c3946f6159f539deaeeca7d14","score":"0.5824091","text":"def test_url_socket_error(self, mock_get, request):\n mock_get.side_effect = socket.gaierror(\"This is some text that should be ignored ([Errno 1234] Error message)\")\n request.json = {\"url\": self.url}\n response = post_source_parameter(SOURCE_ID, \"url\", self.database)\n self.assert_url_check(response, -1, \"[Errno 1234] Error message\")\n updated_report = self.database.reports.insert_one.call_args[0][0]\n url = self.url\n self.assert_delta(\n f\"url of source 'Source' of metric 'Metric' of subject 'Subject' in report 'Report' from '' to '{url}'\",\n report=updated_report,\n )","title":""},{"docid":"e68e31128996ff8fdeac261d70715a48","score":"0.5820514","text":"def url(self, name):\r\n raise NotImplementedError()","title":""},{"docid":"37c39712801bae191d50d12c65090f99","score":"0.5803945","text":"def test_set_url_base_return(self) -> None:\n\n given = \"https://example.org\"\n\n actual = self.query_tool.set_url_base(given)\n\n self.assertIsInstance(actual, CollectionQueryTool)","title":""},{"docid":"adb072453491e5a345fa5f87de540501","score":"0.57885486","text":"def get_checked_url(url_to_use):\n if not URL_FORMAT_PATTERN.match(url_to_use):\n write_out(\"Url '{}', is invalid\".format(url_to_use), False)\n return get_error_data(url_to_use, \"Invalid URL\", True)","title":""},{"docid":"89c1a4055974ff48f6e0e9c9eb4f4d45","score":"0.5785253","text":"def test_index_url_resolve(self):\n self.assertEqual(self.url, '/home')","title":""},{"docid":"1f5396dcf8c17aef8f41c7f718472fc2","score":"0.57816863","text":"def test_set_preferred_status_origin_not_supported(self) -> None:\n\n given = \"hello\"\n\n self.assertRaises(\n ValueError, lambda: self.query_tool.set_preferred_status_origin(given)\n )","title":""},{"docid":"a1bee7b3d99ee58ade81ed4ea324dbb9","score":"0.57625926","text":"def test_malformed_url_property(self):\n self.activities[0]['object'].update({\n 'content': 'post content without backlinks',\n 'url': 'https://fa.ke/post/url',\n })\n\n # malformed u-url, should skip it without an unhashable dict error\n self.expect_requests_get('http://author/', \"\"\"\n<html class=\"h-feed\">\n <div class=\"h-entry\">\n <a class=\"u-url h-cite\" href=\"/permalink\">this is a strange permalink</a>\n </div>\n</html>\"\"\")\n\n self.mox.ReplayAll()\n self.assert_discover([])","title":""},{"docid":"199385a73eade92cd64cd1c5869f542e","score":"0.57601875","text":"def check_url_is_allowed(self, value: str) -> str:\n\n url = parse_url(value.strip())\n\n if url.scheme in self.blacklisted_url_schemes:\n raise ValueError(\n \"Scheme: {scheme} is blacklisted\".format(scheme=url.scheme)\n )\n\n return url.url","title":""},{"docid":"8cb13824bee11063c2b7bfbd96696f5c","score":"0.57587177","text":"def fetch(self, url):\n raise Exception(\"NotImplementedException\")","title":""},{"docid":"9788060f2bf84b0864f11c2bc72117bf","score":"0.5745475","text":"def test_set_url_base_attribute(self) -> None:\n\n given = \"https://example.org\"\n expected = given\n\n self.query_tool.url_base = given\n actual = self.query_tool.url_base\n\n self.assertEqual(expected, actual)","title":""},{"docid":"4d3808359fe9d1e9e40b2e64373beb83","score":"0.5741907","text":"def test_domain_url_not_found(self):\n self._test_failed_domain_url_fetch(raise_exception=False)","title":""},{"docid":"9b4c68df862e78aa6debce276dbe0009","score":"0.57383","text":"def url(self, url):\n self.__url = url","title":""},{"docid":"babdf1ea09f4f9a052de475d76cb4bbd","score":"0.57349","text":"def test_unknownschemehost_noport(self):\n exc = exceptions.HttpError\n match = \"no.*'port'\"\n with pytest.raises(exc, match=match):\n axonapi.http.ParserUrl(\"httpx://host\")","title":""},{"docid":"3ecca72aaa0e35b34f98c7f73f78a729","score":"0.5722408","text":"def test_url_get(self):\n m = Maltiverse()\n item = m.url_get('https://www.welsfagmary-online.com/')\n self.assertTrue(isinstance(str(item), str))\n self.assertTrue(isinstance(item, dict))\n self.assertTrue('url' in item)","title":""},{"docid":"53957a5032e8e4b81d6ced09d18518aa","score":"0.57180303","text":"def test_invalid_URL(self):\n #Invalid URL\n #Invalid URL\n count = 0\n try:\n e = DocManager(\"http://doesntexist.cskjdfhskdjfhdsom\")\n except SystemError:\n count += 1\n self.assertTrue(count == 1)\n print(\"PASSED INVALID URL\")","title":""},{"docid":"9b7850dadebde1b3f1c688339b61dd5a","score":"0.57177603","text":"def test_user_tries_access_invalid_link():","title":""},{"docid":"63316f34b4cbb7ff63edf96e27e42d4f","score":"0.57032394","text":"def try_url(url):\n try:\n urllib2.urlopen(url)\n return True\n except urllib2.URLError:\n return False","title":""},{"docid":"9d23e0d794d8044f5bae528259d1ec11","score":"0.5701769","text":"def set_url(self, url):\n super(BatchIteration, self).set_url(url)","title":""},{"docid":"20c793e321142a78ed50933fbb8eee99","score":"0.56998813","text":"def test_url(self):\n #NOTE: self.shortDescription() shuould work.\n print(\"(\"+self.test_url.__name__+\")\", self.test_url.__doc__, end=' ')\n with resources.app.test_request_context(self.url):\n rule = flask.request.url_rule\n view_point = resources.app.view_functions[rule.endpoint].view_class\n self.assertEqual(view_point, resources.MachineTypes)","title":""},{"docid":"4d64e2cce4e21a5fc7daef6aa81a0cd7","score":"0.5695838","text":"def is_url(self, _input: str):\r\n if validators.url(_input):\r\n return True\r\n return False","title":""},{"docid":"ad95864d6b06e2de862464c925c02388","score":"0.5688012","text":"def test_schemehost_noport80(self):\n u = axonapi.http.ParserUrl(\"http://host\")\n assert u.hostname == \"host\"\n assert u.port == 80\n assert u.scheme == \"http\"","title":""},{"docid":"4c471eead7ff009e0804559ee2fbcd9d","score":"0.5686827","text":"def __init__(self, url):\n self.url = url\n self.parsed = requests.utils.urlparse(url)\n\n self.clean_netloc()\n\n if not self.parsed.netloc:\n raise ValueError('Wrong URL (Make sure \"http(s)://\" included)')\n\n self.adjust_url()","title":""},{"docid":"37589a38d942d6a0fff7c12ecb2a7ced","score":"0.56847954","text":"def supports_uri_scheme():\n\n return True","title":""},{"docid":"653dc6e238476f6b2666d06c68cb49a1","score":"0.56796396","text":"def try_url(URL, wsdl_url, code, version):\n # set schema doctor to fix missing schemas\n d = get_doctor(code, version)\n if not d: return\n try:\n # FIRST -- try the provided url\n client = Client(URL, plugins=[d])\n print(client.__str__())\n if 'Methods' not in client.__str__():\n # need to try this on first call, self loops have this as False and will return empty\n if wsdl_url == False:\n return\n else:\n # SECOND -- add ?wsdl to provided url\n url_with_wsdl = URL + '?wsdl'\n URL = try_url(url_with_wsdl, False, code, version)\n if URL:\n return URL\n else:\n # FINALLY -- try the wsdl url provided using the scheme and domain in the regular url\n URL = try_url(wsdl_url, False, code, version)\n if URL:\n return URL\n else:\n return\n except Exception as e:\n print(e)\n if wsdl_url != False:\n url_with_wsdl = URL + '?wsdl'\n URL = False\n try:\n URL = try_url(url_with_wsdl, False, code, version)\n if not URL:\n try:\n URL = try_url(wsdl_url, False, code, version)\n except Exception as e:\n print(e)\n return\n except Exception as e:\n print(e)\n try:\n URL = try_url(wsdl_url, False, code, version)\n except Exception as e:\n print(e)\n return\n return URL","title":""},{"docid":"4803ca9b67ef302059a334b1bf6bd04c","score":"0.56785655","text":"def seturl(self) -> None:\n url = self.session.options.get(\"stdurl\", self.DEFAULT_SDT_URL)\n self.url = urlparse(url)\n self.address = (self.url.hostname, self.url.port)\n self.protocol = self.url.scheme","title":""},{"docid":"c6807f1d20c2c6ca0403eba3365d21ab","score":"0.5669546","text":"def test_url(self, mock_get, request):\n mock_get.return_value = self.url_check_get_response\n request.json = {\"url\": self.url}\n response = post_source_parameter(SOURCE_ID, \"url\", self.database)\n self.assert_url_check(response)\n mock_get.assert_called_once_with(self.url, auth=(\"username\", \"\"), headers={}, timeout=10)\n updated_report = self.database.reports.insert_one.call_args[0][0]\n url = self.url\n self.assert_delta(\n f\"url of source 'Source' of metric 'Metric' of subject 'Subject' in report 'Report' from '' to '{url}'\",\n report=updated_report,\n )","title":""},{"docid":"ea394ab60b7dc02d19954589f8d94171","score":"0.5667569","text":"def test_get_internal_api_url_with_microsite_override(self):\n self.assert_get_internal_api_url_value('foo')","title":""},{"docid":"6bcfffe2c894fee4ec6654dbcd49efef","score":"0.5664153","text":"def url(self):\n raise NotImplementedError","title":""},{"docid":"6189ec3ca1fd9c90c192587b52a59712","score":"0.5647506","text":"def prepare_url(self, url, params):\r\n #: Accept objects that have string representations.\r\n try:\r\n url = unicode(url)\r\n except NameError:\r\n # We're on Python 3.\r\n url = str(url)\r\n except UnicodeDecodeError:\r\n pass\r\n\r\n # Don't do any URL preparation for oddball schemes\r\n if ':' in url and not url.lower().startswith('http'):\r\n self.url = url\r\n return\r\n\r\n # Support for unicode domain names and paths.\r\n scheme, auth, host, port, path, query, fragment = parse_url(url)\r\n\r\n if not scheme:\r\n raise MissingSchema(\"Invalid URL {0!r}: No schema supplied. \"\r\n \"Perhaps you meant http://{0}?\".format(url))\r\n\r\n if not host:\r\n raise InvalidURL(\"Invalid URL %r: No host supplied\" % url)\r\n\r\n # Only want to apply IDNA to the hostname\r\n try:\r\n host = host.encode('idna').decode('utf-8')\r\n except UnicodeError:\r\n raise InvalidURL('URL has an invalid label.')\r\n\r\n # Carefully reconstruct the network location\r\n netloc = auth or ''\r\n if netloc:\r\n netloc += '@'\r\n netloc += host\r\n if port:\r\n netloc += ':' + str(port)\r\n\r\n # Bare domains aren't valid URLs.\r\n if not path:\r\n path = '/'\r\n\r\n if is_py2:\r\n if isinstance(scheme, str):\r\n scheme = scheme.encode('utf-8')\r\n if isinstance(netloc, str):\r\n netloc = netloc.encode('utf-8')\r\n if isinstance(path, str):\r\n path = path.encode('utf-8')\r\n if isinstance(query, str):\r\n query = query.encode('utf-8')\r\n if isinstance(fragment, str):\r\n fragment = fragment.encode('utf-8')\r\n\r\n enc_params = self._encode_params(params)\r\n if enc_params:\r\n if query:\r\n query = '%s&%s' % (query, enc_params)\r\n else:\r\n query = enc_params\r\n\r\n url = requote_uri(urlunparse([scheme, netloc, path, None, query, fragment]))\r\n self.url = url","title":""},{"docid":"140df832cb85bee9ee1657476d5299c7","score":"0.5647161","text":"def _checkurl(self,url):\n\n try:\n util.request(url+'.dds')\n return True\n except KeyboardInterrupt:\n raise KeyboardInterrupt\n except:\n return False","title":""},{"docid":"9ba2c959f29953db8ecce1795012d493","score":"0.5644463","text":"def check_url_availability(cmd_args):\n if cmd_args.source:\n url = cmd_args.source\n try:\n requests.get(url)\n LOGGER.info('Check the URL availability.')\n except Exception:\n raise er.UnreachableURLError(\"URL is invalid.\")\n else:\n LOGGER.info('URL is valid. Connection established.')\n return True\n else:\n raise er.UndefinedURL('URL is required')","title":""},{"docid":"cf3aaf2102094d040a0d138d9bae5f93","score":"0.5643659","text":"def test_proxies_import_from_url_post(self):\n pass","title":""},{"docid":"d8b02d34679b9ab3dd870ab00a060c47","score":"0.56435233","text":"def url(self, value):\n self.__url = value","title":""},{"docid":"b3a7558b3d460ab0705511debc17703c","score":"0.5638695","text":"def __init__(self, url: str = None):\n self.url = url","title":""},{"docid":"b3a7558b3d460ab0705511debc17703c","score":"0.5638695","text":"def __init__(self, url: str = None):\n self.url = url","title":""},{"docid":"056c7b077bed214d544534fbbcc579b1","score":"0.5633176","text":"def set_url(self, url):\n o = urlparse.urlparse(url)\n self.path = o.path\n self.scheme = o.scheme or self.scheme\n self.host = o.netloc or self.host\n\n for (name, value) in urlparse.parse_qs(o.query).items():\n assert len(value) == 1\n self.set(name, value[0])","title":""},{"docid":"cb2f5328ef4a51b4f721396b4109f423","score":"0.56305605","text":"def validServerURL(option, opt_str, value, parser):\n if value is not None:\n if not validURL(value):\n raise OptionValueError(\"%s option value '%s' not valid.\" % (opt_str, value))\n setattr(parser.values, option.dest, value)\n else:\n setattr(parser.values, option.dest, option.default)","title":""},{"docid":"572e36c7f1037d59d3c0a9f6bc37e3e6","score":"0.56234425","text":"def test_urlparse_exception(self) -> None:\n\n address = \"[DOUBLEQUOTE]\"\n url = Url(address)\n self.assertEqual(url.domain, \"\")","title":""},{"docid":"b91a58b48d67b4c44a941f3d4457f88d","score":"0.5618582","text":"def _detect(self):\n self.available = self._check_required_args(['urls'])","title":""},{"docid":"04a81ca064231fd3796774c3257723de","score":"0.5616055","text":"def invalid_url(url):\n r = requests.get(url)\n if r.status_code >= 400 or r.text == '404 File Not Found':\n raise ValueError\n return","title":""},{"docid":"b166e41867c21b5789b13e14939df6b5","score":"0.5609505","text":"def test_url_starts_with_https(self):\n self.assertIsNotNone(self.get_url_response)","title":""},{"docid":"bac88bf8a2724a01b7c385c76c70db01","score":"0.5605313","text":"def url(self, value):\r\n self.logger.warn(\"Setting values on url will NOT update the remote Canvas instance.\")\r\n self._url = value","title":""},{"docid":"00fa017725117babe58b82a768b044e4","score":"0.5604786","text":"def setQueryAPIURL(self, url):\n if self.useTestAPI:\n self.url_orderAPI_demo = url\n self.log(\"Changed DEMO URL for query API to:\", url)\n else:\n self.url_orderAPI = url\n self.log(\"Changed PRODUCTION URL for query API to:\", url)","title":""},{"docid":"2c9fdfb77489ca3c3cc6a7f6ed29789a","score":"0.5602213","text":"def test_replace_not_valid_url(self):\n res = parse('<a class=\"external-link embedlylink\" href=\"http://not.valid.url/11\"></a>')\n self.assertFalse(res.startswith('<div class=\"embed\">'))","title":""},{"docid":"68e34ce1f92e1bd34dbce0f7423ee99a","score":"0.55987066","text":"def test_fetch_url(self):\n # This should return a 200\n result = fetch_url(\n 'https://zoek.officielebekendmakingen.nl/rss/dossier/31981'\n )\n\n self.assertTrue(result)\n\n # This should yield a timeout - and thus empty data\n result = fetch_url(\n 'http://10.255.255.1/'\n )\n\n self.assertFalse(result)","title":""},{"docid":"5d0cb7bb4a4445ab5938df18ea6364b2","score":"0.559571","text":"def test_bad_url(self):\n rv = self.submit_url('50 cent')\n resp = str(rv.data, encoding='utf8')\n assert 'Sorry, but' in resp","title":""},{"docid":"3b8aa4a14c288035ca286ae5e4e6d195","score":"0.5593122","text":"def test_uri(self):\n with pytest.deprecated_call():\n assert \"https://www.structlog.org/\" == structlog.__uri__","title":""},{"docid":"3ab3413c0ffcee3ddb2d9dff293b9528","score":"0.5588141","text":"def test_invalid_url(self):\n base_url = 'http://invalid_url'\n username = self.admin_username\n password = self.admin_password\n language = self.language\n with self.assertRaises(ConnectionError, msg='Exception not raised when invalid URL was passed.'):\n Session(base_url, username, password, language)","title":""}],"string":"[\n {\n \"docid\": \"6be824e2daffbb7a6dd84fc0d7cbc843\",\n \"score\": \"0.7581493\",\n \"text\": \"def set_broken_url():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e02dcdf95f707f20fc80126e7cd69e75\",\n \"score\": \"0.7048977\",\n \"text\": \"def _set_url(self, url):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1079ed216e18a353496465c0d18685c3\",\n \"score\": \"0.67172503\",\n \"text\": \"def test_set_url_base_not_url(self) -> None:\\n\\n given = \\\"example.org\\\"\\n\\n self.assertRaises(ValueError, lambda: self.query_tool.set_url_base(given))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"448572db4b03d97afd57398c53b3c0d7\",\n \"score\": \"0.6544353\",\n \"text\": \"def test_bad_url_double_scheme(self):\\r\\n index = setuptools.package_index.PackageIndex(\\r\\n hosts=('www.example.com',)\\r\\n )\\r\\n\\r\\n # issue 20\\r\\n url = 'http://http://svn.pythonpaste.org/Paste/wphp/trunk'\\r\\n try:\\r\\n index.open_url(url)\\r\\n except distutils.errors.DistutilsError:\\r\\n error = sys.exc_info()[1]\\r\\n msg = unicode(error)\\r\\n assert 'nonnumeric port' in msg or 'getaddrinfo failed' in msg or 'Name or service not known' in msg\\r\\n return\\r\\n raise RuntimeError(\\\"Did not raise\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c512c5c48eb16b3045a8bbe06d231ab\",\n \"score\": \"0.64755464\",\n \"text\": \"def _validate_url(self, url):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cee220758bc5d3b3f2173da9b5715242\",\n \"score\": \"0.6451268\",\n \"text\": \"def _is_valid_url(self):\\n try:\\n self.req_page_obj = requests.get(self.url)\\n except requests.exceptions.ConnectionError:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab0bd2aec17985dcbf73f700c6b4677c\",\n \"score\": \"0.64381975\",\n \"text\": \"def get_broken_url():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c374a92ef66ebb5089b5b1609d7c6256\",\n \"score\": \"0.640715\",\n \"text\": \"def test_13_no_url(self):\\n self.base_13_no_url()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d366be29c75aad8d973a4212db5fc0\",\n \"score\": \"0.6349455\",\n \"text\": \"def suitable(url):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46785f5ec01d61d957cd4617b1fef28\",\n \"score\": \"0.63463944\",\n \"text\": \"def _fix_url(self): \\n\\t\\tif self.scheme not in self.allowed_schemes:\\n\\t\\t\\tif not self.auto_fix:\\n\\t\\t\\t\\traise SchemeError(self.scheme, self.allowed_schemes)\\n\\t\\t\\tself.scheme = self.allowed_schemes[0]\\n\\n\\n\\t\\tif not self.host:\\n\\t\\t\\tif not self.auto_fix:\\n\\t\\t\\t\\traise HostError()\\n\\n\\t\\t\\telif self.path:\\n\\t\\t\\t\\tpieces = self.path.split('/')\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif len(pieces) == 1:\\n\\t\\t\\t\\t\\tif not pieces[0]: raise HostError()\\n\\t\\t\\t\\t\\telse: self.host = pieces.pop(0)\\n\\t\\t\\t\\telif pieces[0]:\\n\\t\\t\\t\\t\\tself.host = pieces.pop(0)\\n\\t\\t\\t\\telif pieces[1]:\\n\\t\\t\\t\\t\\tself.host = pieces.pop(1)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\traise HostError()\\n\\t\\t\\t\\tself.path = '/'.join(pieces)\\n\\n\\t\\tself.join_url()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a6e1679a199426d2fd489a2c217b71\",\n \"score\": \"0.6336512\",\n \"text\": \"def test_invalid_url(self):\\n validator = zope.component.getUtility(IURLValidator)\\n with self.assertRaises(ValueError):\\n validator.validate(\\\"?\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeee4a38066e463bb4b417312db4dc86\",\n \"score\": \"0.63004094\",\n \"text\": \"def test_invalid_url(self):\\n self.data['url'] = 'notarepo'\\n self.assertInvalid()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb6a2ea6f44a65b64942455a3be491d2\",\n \"score\": \"0.6286197\",\n \"text\": \"def is_my_url(url):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"292b1f905f747a59b3f94a2e9acb875d\",\n \"score\": \"0.62741524\",\n \"text\": \"def test_guess_and_set_url_base_not_str(self) -> None:\\n\\n config_loader = ConfigLoader()\\n config_loader.set_custom_config({\\\"collection\\\": {\\\"url_base\\\": False}}).start()\\n\\n self.query_tool.guess_and_set_url_base()\\n\\n expected = \\\"http://localhost:8001\\\"\\n actual = self.query_tool.url_base\\n\\n self.assertEqual(expected, actual)\\n\\n del config_loader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17b4483ec9d8dd31fe4c6b2fadd749bd\",\n \"score\": \"0.6204555\",\n \"text\": \"def test_wrong_url(self):\\n print(\\\"(\\\"+self.test_wrong_url.__name__+\\\")\\\", self.test_wrong_url.__doc__)\\n resp = self.client.get(self.url_wrong)\\n self.assertEqual(resp.status_code, 404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"376360eb4c2bf85f32c1198668405eda\",\n \"score\": \"0.61899644\",\n \"text\": \"def test_check_release_check_url(self):\\n self.assertEqual(checks.check_release_check_url(None), [checks.E004])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6984825977b8d93b69113d4caf6b8ca\",\n \"score\": \"0.6174316\",\n \"text\": \"def _url_callback(url: str) -> Union[str, None]:\\n if url == \\\"latest\\\":\\n return url\\n if validators.url(url):\\n return url\\n else:\\n raise typer.BadParameter(f\\\"Please check {url} is a valid url\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"400a259b89cdda5126376eb7ebe4ac6f\",\n \"score\": \"0.61433077\",\n \"text\": \"def test_set_url_base_method(self) -> None:\\n\\n given = \\\"https://example.org\\\"\\n expected = given\\n\\n self.query_tool.set_url_base(given)\\n actual = self.query_tool.url_base\\n\\n self.assertEqual(expected, actual)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35666a00df4dc8b7fd5c3beb7c6aafad\",\n \"score\": \"0.6134628\",\n \"text\": \"def test_url_format_check(self):\\n\\n assert not is_url(\\\"www.google.com\\\")\\n assert is_url(\\\"http://www.google.com\\\")\\n assert is_url(\\\"https://www.google.com\\\")\\n assert is_url(\\\"http://www.google.com/info.txt\\\")\\n assert is_url(\\\"http://www.google.com/child/info.txt\\\")\\n\\n assert not is_url(\\\"10.120.1.23\\\")\\n assert is_url(\\\"http://10.120.1.23\\\")\\n assert is_url(\\\"http://10.120.1.23/info.txt\\\")\\n assert is_url(\\\"http://10.120.1.23/child/info.txt\\\")\\n\\n assert is_url(\\\"http://127.0.0.1:8080\\\")\\n assert is_url(\\\"http://127.0.0.1:8080/child/info.txt\\\")\\n\\n assert is_url(\\\"http://port:8080\\\")\\n assert is_url(\\\"http://port:8080/child/info.txt\\\")\\n\\n assert is_url(\\\"http://hello\\\")\\n assert not is_url(\\\"http://hello.\\\")\\n assert is_url(\\\"http://hello.i\\\")\\n assert is_url(\\\"http://hello.io\\\")\\n assert is_url(\\\"http://hello/child/info.txt\\\")\\n\\n assert is_url(\\\"http://hel-lo\\\")\\n assert is_url(\\\"http://hel_lo\\\")\\n assert not is_url(\\\"http://hel lo\\\")\\n assert is_url(\\\"http://hello/\\\")\\n assert is_url(\\\"http://hello/.\\\")\\n assert is_url(\\\"http://hello/.txt\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a3ca01af52f576a24288e34d983d05\",\n \"score\": \"0.61344594\",\n \"text\": \"def test_bad_url(self):\\n resp = requests.get('http://localhost:8000/asdf')\\n self.assertEquals(resp.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38743008d99972e9b2f459731e4c1793\",\n \"score\": \"0.61343056\",\n \"text\": \"def validate_url(self, url: str) -> bool:\\n if url.startswith(\\\"http://\\\") or url.startswith(\\\"https://\\\") or url.startswith(\\\"ftp://\\\"):\\n return True\\n self.logger.info(f\\\"GetURL: Unsupported URL prefix: {url}\\\")\\n raise PluginException(\\n preset=PluginException.Preset.UNKNOWN,\\n assistance=f\\\"GetURL: Unsupported URL prefix: {url}\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dab804bc225261246b8a6ceb25a3e56\",\n \"score\": \"0.61172897\",\n \"text\": \"def test_url_no_url_type(self, mock_get, request):\\n mock_get.return_value = self.url_check_get_response\\n request.json = {\\\"landing_url\\\": \\\"unimportant\\\"}\\n response = post_source_parameter(SOURCE_ID, \\\"landing_url\\\", self.database)\\n self.assertEqual(response, {\\\"ok\\\": True, \\\"nr_sources_mass_edited\\\": 0, \\\"availability\\\": []})\\n mock_get.assert_not_called()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d10862b17eb12b6524848ed5e1260ae2\",\n \"score\": \"0.6089364\",\n \"text\": \"def checkurl(self):\\n url = self.fields.get('url',False)\\n if url:\\n if url.endswith('.'):\\n url = url[:-1]\\n self.fields['url'] = url\\n\\n if url != None and url.count(' ')>0:\\n self.errors.append(\\\"space in url\\\")\\n nonsites = ('ebrary','degruyter','doi','myilibrary','academia','ebscohost')\\n for n in nonsites:\\n if url != None and n in url:\\n self.errors.append(\\\"%s: urls should only be given for true repositories or for material not available elsewhere\\\"%url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e0aca29482e3cdabe025a148ee81cfa\",\n \"score\": \"0.60806435\",\n \"text\": \"def __check_url(self, url) -> None:\\n\\n if requests.head(url).status_code != 200:\\n raise VersionUrlError('error: Invalid url')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ec42dc67576e5f985715919f733e253\",\n \"score\": \"0.6080406\",\n \"text\": \"def handles_url(self, url):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f6ac4119cc3282618540a786a22cc99\",\n \"score\": \"0.60770726\",\n \"text\": \"def test_url(url):\\n http_url = url.replace(\\\"ftp://\\\", \\\"http://\\\")\\n if http_url[-1] != \\\"/\\\":\\n http_url += \\\"/\\\"\\n\\n res = requests.head(http_url)\\n if res.status_code != 200:\\n raise requests.HTTPError(f\\\"Unable to connect to URL: {url}\\\")\\n\\n return url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb431c9b62859f5b2f1ee79ee148fc3d\",\n \"score\": \"0.6057759\",\n \"text\": \"def OpenURL(self, type, url):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a32d8e0abc4708f7f23b7df7e255d6fa\",\n \"score\": \"0.6049299\",\n \"text\": \"def test_match_normal_url(self):\\n self.assertTrue(match(self.obj_url))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6f8477ea7186f05a46b283dc638b120\",\n \"score\": \"0.6046837\",\n \"text\": \"def test_set_url_base_not_str(self) -> None:\\n\\n given = [\\\"Hello\\\", \\\"World!\\\"]\\n\\n self.assertRaises(TypeError, lambda: self.query_tool.set_url_base(given))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df286c561ba05bc1910af18ad33400f5\",\n \"score\": \"0.60453475\",\n \"text\": \"def _get_url(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a034b58c5aca01a4dc25b05611b77d13\",\n \"score\": \"0.60043675\",\n \"text\": \"def test_is_url():\\n assert utils.is_url(\\\"http://mydomain.com/foo/bar/bat?asdf=1234&qewr=ooo\\\")\\n assert utils.is_url(\\\"http://xkcd.com/1193/\\\")\\n assert not utils.is_url(\\\"syn123445\\\")\\n assert not utils.is_url(\\\"wasssuuuup???\\\")\\n assert utils.is_url(\\\"file://foo.com/path/to/file.xyz\\\")\\n assert utils.is_url(\\\"file:///path/to/file.xyz\\\")\\n assert utils.is_url(\\\"file:/path/to/file.xyz\\\")\\n assert utils.is_url(\\\"file:///c:/WINDOWS/clock.avi\\\")\\n assert utils.is_url(\\\"file:c:/WINDOWS/clock.avi\\\")\\n assert not utils.is_url(\\\"c:/WINDOWS/ugh/ugh.ugh\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c82c4d56d599fb3c00138a7f83ddd2f1\",\n \"score\": \"0.6002521\",\n \"text\": \"def test_check_empty_url(self):\\n self.empty_url = \\\"\\\"\\n get_url_response = get_url(self.empty_url)\\n self.assertEqual(get_url_response, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e678efca97550b4cf16ce6e5a5146390\",\n \"score\": \"0.5997677\",\n \"text\": \"def test_good_url(self):\\n rv = self.submit_url('www.python.org')\\n resp = str(rv.data, encoding='utf8')\\n assert 'Short url is ' in resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"463844b2006068616c36266e93532417\",\n \"score\": \"0.59973145\",\n \"text\": \"def test_guess_and_set_url_base(self) -> None:\\n\\n config_loader = ConfigLoader()\\n config_loader.set_custom_config(\\n {\\\"collection\\\": {\\\"url_base\\\": \\\"https://example.org:8443\\\"}}\\n ).start()\\n\\n self.query_tool.guess_and_set_url_base()\\n\\n expected = \\\"https://example.org:8443\\\"\\n actual = self.query_tool.url_base\\n\\n self.assertEqual(expected, actual)\\n\\n del config_loader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"788b29cc67d2ed37c8b2773c1253b9c8\",\n \"score\": \"0.5980256\",\n \"text\": \"def test_url_sanitization(self):\\n expected_url = 'http://foo'\\n\\n api = AerospikeRestApi('http://foo/')\\n self.assertEqual(api.base_url, expected_url)\\n\\n api = AerospikeRestApi('foo/')\\n self.assertEqual(api.base_url, expected_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c33b067cee7c31123b6efbd895bc60de\",\n \"score\": \"0.59763426\",\n \"text\": \"def test_match_not_valid_url(self):\\n self.assertFalse(match('http://not.valid.url/'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab72e65c82b85c94a9f08b124874fe4\",\n \"score\": \"0.5975472\",\n \"text\": \"def test_compose_url_from_with_properurl(self):\\n href = \\\"http://www.gc.com\\\"\\n url = None\\n actual = SiteMap().compose_url_from_href(url, href)\\n expected = 'http://www.gc.com'\\n self.assertEqual(expected, actual)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc4cd78f250b2e207cf0b28f47e2a417\",\n \"score\": \"0.5965903\",\n \"text\": \"def test_run_non_existing_url(self):\\n\\n with patch(\\\"validators.url\\\", return_value=True), patch(\\n \\\"streamlit.cli._main_run\\\"\\n ), requests_mock.mock() as m:\\n\\n m.get(\\\"http://url\\\", exc=requests.exceptions.RequestException)\\n with patch(\\\"tempfile.NamedTemporaryFile\\\"):\\n result = self.runner.invoke(cli, [\\\"run\\\", \\\"http://url\\\"])\\n\\n self.assertNotEqual(0, result.exit_code)\\n self.assertTrue(\\\"Unable to fetch\\\" in result.output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926316492dfcedc8770cec4fd4ac31c8\",\n \"score\": \"0.5939719\",\n \"text\": \"def test_url_http_error(self, mock_get, request):\\n mock_get.side_effect = requests.exceptions.RequestException\\n request.json = {\\\"url\\\": self.url}\\n response = post_source_parameter(SOURCE_ID, \\\"url\\\", self.database)\\n self.assert_url_check(response, -1, \\\"RequestException\\\")\\n updated_report = self.database.reports.insert_one.call_args[0][0]\\n url = self.url\\n self.assert_delta(\\n f\\\"url of source 'Source' of metric 'Metric' of subject 'Subject' in report 'Report' from '' to '{url}'\\\",\\n report=updated_report,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"891a203ff0a164af63a0322f7dc3d885\",\n \"score\": \"0.59220636\",\n \"text\": \"def url(self):\\n raise NotImplemented()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b51e6f28c47e72676eee6d4e3820cac6\",\n \"score\": \"0.59185445\",\n \"text\": \"def test_isUrl(self):\\n cases = [\\n (\\\"http://example.com:8080/path/to/file\\\", True),\\n (\\\"https://example.com:8080/path/to/file\\\", True),\\n (\\\"ftp://example.com:8080/path/to/file\\\", False),\\n (\\\"/path/to/my/file\\\", False)\\n ]\\n for case in cases:\\n url, expected = case\\n result = Utils.isUrl(url)\\n self.assertEquals(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1501cbd4109743eca9ded6285d573d2b\",\n \"score\": \"0.5914192\",\n \"text\": \"def test_13a_no_url(self):\\n self.base_13_no_url(use_config_cache=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1408064edde210cfa3ca8d5beca847a0\",\n \"score\": \"0.59105694\",\n \"text\": \"def url(self):\\n raise NotImplementedError('url') # pragma no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2468d04a0dddb63373dc6cd92da0ef3e\",\n \"score\": \"0.5906903\",\n \"text\": \"def is_supported(self, url):\\r\\n return self.regex.match(url) is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cee6c17828b577438ff8f637c4ab346e\",\n \"score\": \"0.5883006\",\n \"text\": \"def testIsFeedURLValid(self):\\n #invalid: not a feed url\\n self.assertFalse(validate.isFeedURLValid('http://www.google.com'))\\n\\n self.assertFalse(validate.isFeedURLValid(''))\\n\\n #valid feed url\\n self.assertTrue(validate.isFeedURLValid(\\n 'http://googlesummerofcode.blogspot.com/feeds/posts/default'))\\n\\n #invalid: wrong protocol\\n self.assertFalse(validate.isFeedURLValid(\\n 'htp://googlesummerofcode.blogspot.com/feeds/posts/default'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"530b845e524da9af9dbec02b5d6dd6be\",\n \"score\": \"0.5873142\",\n \"text\": \"def _validate_url(self):\\n\\n accepted_pattern = [\\n r\\\"^https:\\\\/\\\\/player.vimeo.com\\\\/video\\\\/(\\\\d+)$\\\",\\n r\\\"^https:\\\\/\\\\/vimeo.com\\\\/(\\\\d+)$\\\",\\n r\\\"^https://vimeo.com/groups/.+?/videos/(\\\\d+)$\\\",\\n r\\\"^https://vimeo.com/manage/videos/(\\\\d+)$\\\"\\n ]\\n for pattern in accepted_pattern:\\n match = re.findall(pattern, self._url)\\n if match:\\n return match[0]\\n # If none of the patterns is matched exception is raised\\n raise URLNotSupported(\\n f\\\"{self._url} is not supported. Make sure you don't include query parameters in the url\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2065e9a3db6e28e683e2c6dc46996964\",\n \"score\": \"0.58643746\",\n \"text\": \"def test_domain_url_error(self):\\n self._test_failed_domain_url_fetch(raise_exception=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8019927ff0d0994980292cb877094508\",\n \"score\": \"0.5853208\",\n \"text\": \"def check_url(self, c):\\n try:\\n if c['src'][0:4] == 'http': \\n self._comic_url = c['src'].replace(\\\" \\\", \\\"%20\\\")\\n self._state = \\\"URL found\\\"\\n else: \\n self._comic_url = (self._url + c['src']).replace(\\\" \\\", \\\"%20\\\")\\n self._state = \\\"URL found\\\"\\n except:\\n self._state = None\\n self._comic_url = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cfed8d95a1778b37ba9392a4a00de17\",\n \"score\": \"0.5842438\",\n \"text\": \"def test_run_valid_url(self):\\n\\n with patch(\\\"validators.url\\\", return_value=True), patch(\\n \\\"streamlit.cli._main_run\\\"\\n ), requests_mock.mock() as m:\\n\\n m.get(\\\"http://url\\\", content=b\\\"content\\\")\\n with patch(\\\"tempfile.NamedTemporaryFile\\\"):\\n result = self.runner.invoke(cli, [\\\"run\\\", \\\"http://url\\\"])\\n\\n self.assertEqual(0, result.exit_code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e588fa519ae8db817d2f18d79aa145fc\",\n \"score\": \"0.58424115\",\n \"text\": \"def checks(self):\\n if not requests.head(self._url_page).status_code:\\n raise NotImplementedError('incorrect url.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4bde13df7c07cc2aaecb4b057fa4c7f\",\n \"score\": \"0.58409846\",\n \"text\": \"def test_empty_url(self, mock_get, request):\\n self.sources[SOURCE_ID][\\\"parameters\\\"][\\\"url\\\"] = self.url\\n request.json = {\\\"url\\\": \\\"\\\"}\\n response = post_source_parameter(SOURCE_ID, \\\"url\\\", self.database)\\n self.assertEqual(response, {\\\"ok\\\": True, \\\"nr_sources_mass_edited\\\": 0, \\\"availability\\\": []})\\n mock_get.assert_not_called()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51a99d5e07753a59f17e57aa1ff1266b\",\n \"score\": \"0.5838431\",\n \"text\": \"def test_schemes_allow_only_supported_protocols():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af300a463789d3f0a22a9968374711fb\",\n \"score\": \"0.5825196\",\n \"text\": \"def bad_url(url: str, e: Exception):\\n print(\\\"\\\\nProblem found with \\\" + url + \\\" via \\\" + str(e) + \\\"\\\\n\\\")\\n print(\\\"Check if the datasource's URL has changed.\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86c68f1c3946f6159f539deaeeca7d14\",\n \"score\": \"0.5824091\",\n \"text\": \"def test_url_socket_error(self, mock_get, request):\\n mock_get.side_effect = socket.gaierror(\\\"This is some text that should be ignored ([Errno 1234] Error message)\\\")\\n request.json = {\\\"url\\\": self.url}\\n response = post_source_parameter(SOURCE_ID, \\\"url\\\", self.database)\\n self.assert_url_check(response, -1, \\\"[Errno 1234] Error message\\\")\\n updated_report = self.database.reports.insert_one.call_args[0][0]\\n url = self.url\\n self.assert_delta(\\n f\\\"url of source 'Source' of metric 'Metric' of subject 'Subject' in report 'Report' from '' to '{url}'\\\",\\n report=updated_report,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e68e31128996ff8fdeac261d70715a48\",\n \"score\": \"0.5820514\",\n \"text\": \"def url(self, name):\\r\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37c39712801bae191d50d12c65090f99\",\n \"score\": \"0.5803945\",\n \"text\": \"def test_set_url_base_return(self) -> None:\\n\\n given = \\\"https://example.org\\\"\\n\\n actual = self.query_tool.set_url_base(given)\\n\\n self.assertIsInstance(actual, CollectionQueryTool)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adb072453491e5a345fa5f87de540501\",\n \"score\": \"0.57885486\",\n \"text\": \"def get_checked_url(url_to_use):\\n if not URL_FORMAT_PATTERN.match(url_to_use):\\n write_out(\\\"Url '{}', is invalid\\\".format(url_to_use), False)\\n return get_error_data(url_to_use, \\\"Invalid URL\\\", True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89c1a4055974ff48f6e0e9c9eb4f4d45\",\n \"score\": \"0.5785253\",\n \"text\": \"def test_index_url_resolve(self):\\n self.assertEqual(self.url, '/home')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5396dcf8c17aef8f41c7f718472fc2\",\n \"score\": \"0.57816863\",\n \"text\": \"def test_set_preferred_status_origin_not_supported(self) -> None:\\n\\n given = \\\"hello\\\"\\n\\n self.assertRaises(\\n ValueError, lambda: self.query_tool.set_preferred_status_origin(given)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1bee7b3d99ee58ade81ed4ea324dbb9\",\n \"score\": \"0.57625926\",\n \"text\": \"def test_malformed_url_property(self):\\n self.activities[0]['object'].update({\\n 'content': 'post content without backlinks',\\n 'url': 'https://fa.ke/post/url',\\n })\\n\\n # malformed u-url, should skip it without an unhashable dict error\\n self.expect_requests_get('http://author/', \\\"\\\"\\\"\\n<html class=\\\"h-feed\\\">\\n <div class=\\\"h-entry\\\">\\n <a class=\\\"u-url h-cite\\\" href=\\\"/permalink\\\">this is a strange permalink</a>\\n </div>\\n</html>\\\"\\\"\\\")\\n\\n self.mox.ReplayAll()\\n self.assert_discover([])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"199385a73eade92cd64cd1c5869f542e\",\n \"score\": \"0.57601875\",\n \"text\": \"def check_url_is_allowed(self, value: str) -> str:\\n\\n url = parse_url(value.strip())\\n\\n if url.scheme in self.blacklisted_url_schemes:\\n raise ValueError(\\n \\\"Scheme: {scheme} is blacklisted\\\".format(scheme=url.scheme)\\n )\\n\\n return url.url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cb13824bee11063c2b7bfbd96696f5c\",\n \"score\": \"0.57587177\",\n \"text\": \"def fetch(self, url):\\n raise Exception(\\\"NotImplementedException\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9788060f2bf84b0864f11c2bc72117bf\",\n \"score\": \"0.5745475\",\n \"text\": \"def test_set_url_base_attribute(self) -> None:\\n\\n given = \\\"https://example.org\\\"\\n expected = given\\n\\n self.query_tool.url_base = given\\n actual = self.query_tool.url_base\\n\\n self.assertEqual(expected, actual)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d3808359fe9d1e9e40b2e64373beb83\",\n \"score\": \"0.5741907\",\n \"text\": \"def test_domain_url_not_found(self):\\n self._test_failed_domain_url_fetch(raise_exception=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b4c68df862e78aa6debce276dbe0009\",\n \"score\": \"0.57383\",\n \"text\": \"def url(self, url):\\n self.__url = url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"babdf1ea09f4f9a052de475d76cb4bbd\",\n \"score\": \"0.57349\",\n \"text\": \"def test_unknownschemehost_noport(self):\\n exc = exceptions.HttpError\\n match = \\\"no.*'port'\\\"\\n with pytest.raises(exc, match=match):\\n axonapi.http.ParserUrl(\\\"httpx://host\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ecca72aaa0e35b34f98c7f73f78a729\",\n \"score\": \"0.5722408\",\n \"text\": \"def test_url_get(self):\\n m = Maltiverse()\\n item = m.url_get('https://www.welsfagmary-online.com/')\\n self.assertTrue(isinstance(str(item), str))\\n self.assertTrue(isinstance(item, dict))\\n self.assertTrue('url' in item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53957a5032e8e4b81d6ced09d18518aa\",\n \"score\": \"0.57180303\",\n \"text\": \"def test_invalid_URL(self):\\n #Invalid URL\\n #Invalid URL\\n count = 0\\n try:\\n e = DocManager(\\\"http://doesntexist.cskjdfhskdjfhdsom\\\")\\n except SystemError:\\n count += 1\\n self.assertTrue(count == 1)\\n print(\\\"PASSED INVALID URL\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b7850dadebde1b3f1c688339b61dd5a\",\n \"score\": \"0.57177603\",\n \"text\": \"def test_user_tries_access_invalid_link():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63316f34b4cbb7ff63edf96e27e42d4f\",\n \"score\": \"0.57032394\",\n \"text\": \"def try_url(url):\\n try:\\n urllib2.urlopen(url)\\n return True\\n except urllib2.URLError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d23e0d794d8044f5bae528259d1ec11\",\n \"score\": \"0.5701769\",\n \"text\": \"def set_url(self, url):\\n super(BatchIteration, self).set_url(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20c793e321142a78ed50933fbb8eee99\",\n \"score\": \"0.56998813\",\n \"text\": \"def test_url(self):\\n #NOTE: self.shortDescription() shuould work.\\n print(\\\"(\\\"+self.test_url.__name__+\\\")\\\", self.test_url.__doc__, end=' ')\\n with resources.app.test_request_context(self.url):\\n rule = flask.request.url_rule\\n view_point = resources.app.view_functions[rule.endpoint].view_class\\n self.assertEqual(view_point, resources.MachineTypes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d64e2cce4e21a5fc7daef6aa81a0cd7\",\n \"score\": \"0.5695838\",\n \"text\": \"def is_url(self, _input: str):\\r\\n if validators.url(_input):\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad95864d6b06e2de862464c925c02388\",\n \"score\": \"0.5688012\",\n \"text\": \"def test_schemehost_noport80(self):\\n u = axonapi.http.ParserUrl(\\\"http://host\\\")\\n assert u.hostname == \\\"host\\\"\\n assert u.port == 80\\n assert u.scheme == \\\"http\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c471eead7ff009e0804559ee2fbcd9d\",\n \"score\": \"0.5686827\",\n \"text\": \"def __init__(self, url):\\n self.url = url\\n self.parsed = requests.utils.urlparse(url)\\n\\n self.clean_netloc()\\n\\n if not self.parsed.netloc:\\n raise ValueError('Wrong URL (Make sure \\\"http(s)://\\\" included)')\\n\\n self.adjust_url()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37589a38d942d6a0fff7c12ecb2a7ced\",\n \"score\": \"0.56847954\",\n \"text\": \"def supports_uri_scheme():\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"653dc6e238476f6b2666d06c68cb49a1\",\n \"score\": \"0.56796396\",\n \"text\": \"def try_url(URL, wsdl_url, code, version):\\n # set schema doctor to fix missing schemas\\n d = get_doctor(code, version)\\n if not d: return\\n try:\\n # FIRST -- try the provided url\\n client = Client(URL, plugins=[d])\\n print(client.__str__())\\n if 'Methods' not in client.__str__():\\n # need to try this on first call, self loops have this as False and will return empty\\n if wsdl_url == False:\\n return\\n else:\\n # SECOND -- add ?wsdl to provided url\\n url_with_wsdl = URL + '?wsdl'\\n URL = try_url(url_with_wsdl, False, code, version)\\n if URL:\\n return URL\\n else:\\n # FINALLY -- try the wsdl url provided using the scheme and domain in the regular url\\n URL = try_url(wsdl_url, False, code, version)\\n if URL:\\n return URL\\n else:\\n return\\n except Exception as e:\\n print(e)\\n if wsdl_url != False:\\n url_with_wsdl = URL + '?wsdl'\\n URL = False\\n try:\\n URL = try_url(url_with_wsdl, False, code, version)\\n if not URL:\\n try:\\n URL = try_url(wsdl_url, False, code, version)\\n except Exception as e:\\n print(e)\\n return\\n except Exception as e:\\n print(e)\\n try:\\n URL = try_url(wsdl_url, False, code, version)\\n except Exception as e:\\n print(e)\\n return\\n return URL\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4803ca9b67ef302059a334b1bf6bd04c\",\n \"score\": \"0.56785655\",\n \"text\": \"def seturl(self) -> None:\\n url = self.session.options.get(\\\"stdurl\\\", self.DEFAULT_SDT_URL)\\n self.url = urlparse(url)\\n self.address = (self.url.hostname, self.url.port)\\n self.protocol = self.url.scheme\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6807f1d20c2c6ca0403eba3365d21ab\",\n \"score\": \"0.5669546\",\n \"text\": \"def test_url(self, mock_get, request):\\n mock_get.return_value = self.url_check_get_response\\n request.json = {\\\"url\\\": self.url}\\n response = post_source_parameter(SOURCE_ID, \\\"url\\\", self.database)\\n self.assert_url_check(response)\\n mock_get.assert_called_once_with(self.url, auth=(\\\"username\\\", \\\"\\\"), headers={}, timeout=10)\\n updated_report = self.database.reports.insert_one.call_args[0][0]\\n url = self.url\\n self.assert_delta(\\n f\\\"url of source 'Source' of metric 'Metric' of subject 'Subject' in report 'Report' from '' to '{url}'\\\",\\n report=updated_report,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea394ab60b7dc02d19954589f8d94171\",\n \"score\": \"0.5667569\",\n \"text\": \"def test_get_internal_api_url_with_microsite_override(self):\\n self.assert_get_internal_api_url_value('foo')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bcfffe2c894fee4ec6654dbcd49efef\",\n \"score\": \"0.5664153\",\n \"text\": \"def url(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6189ec3ca1fd9c90c192587b52a59712\",\n \"score\": \"0.5647506\",\n \"text\": \"def prepare_url(self, url, params):\\r\\n #: Accept objects that have string representations.\\r\\n try:\\r\\n url = unicode(url)\\r\\n except NameError:\\r\\n # We're on Python 3.\\r\\n url = str(url)\\r\\n except UnicodeDecodeError:\\r\\n pass\\r\\n\\r\\n # Don't do any URL preparation for oddball schemes\\r\\n if ':' in url and not url.lower().startswith('http'):\\r\\n self.url = url\\r\\n return\\r\\n\\r\\n # Support for unicode domain names and paths.\\r\\n scheme, auth, host, port, path, query, fragment = parse_url(url)\\r\\n\\r\\n if not scheme:\\r\\n raise MissingSchema(\\\"Invalid URL {0!r}: No schema supplied. \\\"\\r\\n \\\"Perhaps you meant http://{0}?\\\".format(url))\\r\\n\\r\\n if not host:\\r\\n raise InvalidURL(\\\"Invalid URL %r: No host supplied\\\" % url)\\r\\n\\r\\n # Only want to apply IDNA to the hostname\\r\\n try:\\r\\n host = host.encode('idna').decode('utf-8')\\r\\n except UnicodeError:\\r\\n raise InvalidURL('URL has an invalid label.')\\r\\n\\r\\n # Carefully reconstruct the network location\\r\\n netloc = auth or ''\\r\\n if netloc:\\r\\n netloc += '@'\\r\\n netloc += host\\r\\n if port:\\r\\n netloc += ':' + str(port)\\r\\n\\r\\n # Bare domains aren't valid URLs.\\r\\n if not path:\\r\\n path = '/'\\r\\n\\r\\n if is_py2:\\r\\n if isinstance(scheme, str):\\r\\n scheme = scheme.encode('utf-8')\\r\\n if isinstance(netloc, str):\\r\\n netloc = netloc.encode('utf-8')\\r\\n if isinstance(path, str):\\r\\n path = path.encode('utf-8')\\r\\n if isinstance(query, str):\\r\\n query = query.encode('utf-8')\\r\\n if isinstance(fragment, str):\\r\\n fragment = fragment.encode('utf-8')\\r\\n\\r\\n enc_params = self._encode_params(params)\\r\\n if enc_params:\\r\\n if query:\\r\\n query = '%s&%s' % (query, enc_params)\\r\\n else:\\r\\n query = enc_params\\r\\n\\r\\n url = requote_uri(urlunparse([scheme, netloc, path, None, query, fragment]))\\r\\n self.url = url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"140df832cb85bee9ee1657476d5299c7\",\n \"score\": \"0.5647161\",\n \"text\": \"def _checkurl(self,url):\\n\\n try:\\n util.request(url+'.dds')\\n return True\\n except KeyboardInterrupt:\\n raise KeyboardInterrupt\\n except:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ba2c959f29953db8ecce1795012d493\",\n \"score\": \"0.5644463\",\n \"text\": \"def check_url_availability(cmd_args):\\n if cmd_args.source:\\n url = cmd_args.source\\n try:\\n requests.get(url)\\n LOGGER.info('Check the URL availability.')\\n except Exception:\\n raise er.UnreachableURLError(\\\"URL is invalid.\\\")\\n else:\\n LOGGER.info('URL is valid. Connection established.')\\n return True\\n else:\\n raise er.UndefinedURL('URL is required')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf3aaf2102094d040a0d138d9bae5f93\",\n \"score\": \"0.5643659\",\n \"text\": \"def test_proxies_import_from_url_post(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8b02d34679b9ab3dd870ab00a060c47\",\n \"score\": \"0.56435233\",\n \"text\": \"def url(self, value):\\n self.__url = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3a7558b3d460ab0705511debc17703c\",\n \"score\": \"0.5638695\",\n \"text\": \"def __init__(self, url: str = None):\\n self.url = url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3a7558b3d460ab0705511debc17703c\",\n \"score\": \"0.5638695\",\n \"text\": \"def __init__(self, url: str = None):\\n self.url = url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"056c7b077bed214d544534fbbcc579b1\",\n \"score\": \"0.5633176\",\n \"text\": \"def set_url(self, url):\\n o = urlparse.urlparse(url)\\n self.path = o.path\\n self.scheme = o.scheme or self.scheme\\n self.host = o.netloc or self.host\\n\\n for (name, value) in urlparse.parse_qs(o.query).items():\\n assert len(value) == 1\\n self.set(name, value[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb2f5328ef4a51b4f721396b4109f423\",\n \"score\": \"0.56305605\",\n \"text\": \"def validServerURL(option, opt_str, value, parser):\\n if value is not None:\\n if not validURL(value):\\n raise OptionValueError(\\\"%s option value '%s' not valid.\\\" % (opt_str, value))\\n setattr(parser.values, option.dest, value)\\n else:\\n setattr(parser.values, option.dest, option.default)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"572e36c7f1037d59d3c0a9f6bc37e3e6\",\n \"score\": \"0.56234425\",\n \"text\": \"def test_urlparse_exception(self) -> None:\\n\\n address = \\\"[DOUBLEQUOTE]\\\"\\n url = Url(address)\\n self.assertEqual(url.domain, \\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b91a58b48d67b4c44a941f3d4457f88d\",\n \"score\": \"0.5618582\",\n \"text\": \"def _detect(self):\\n self.available = self._check_required_args(['urls'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04a81ca064231fd3796774c3257723de\",\n \"score\": \"0.5616055\",\n \"text\": \"def invalid_url(url):\\n r = requests.get(url)\\n if r.status_code >= 400 or r.text == '404 File Not Found':\\n raise ValueError\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b166e41867c21b5789b13e14939df6b5\",\n \"score\": \"0.5609505\",\n \"text\": \"def test_url_starts_with_https(self):\\n self.assertIsNotNone(self.get_url_response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bac88bf8a2724a01b7c385c76c70db01\",\n \"score\": \"0.5605313\",\n \"text\": \"def url(self, value):\\r\\n self.logger.warn(\\\"Setting values on url will NOT update the remote Canvas instance.\\\")\\r\\n self._url = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00fa017725117babe58b82a768b044e4\",\n \"score\": \"0.5604786\",\n \"text\": \"def setQueryAPIURL(self, url):\\n if self.useTestAPI:\\n self.url_orderAPI_demo = url\\n self.log(\\\"Changed DEMO URL for query API to:\\\", url)\\n else:\\n self.url_orderAPI = url\\n self.log(\\\"Changed PRODUCTION URL for query API to:\\\", url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c9fdfb77489ca3c3cc6a7f6ed29789a\",\n \"score\": \"0.5602213\",\n \"text\": \"def test_replace_not_valid_url(self):\\n res = parse('<a class=\\\"external-link embedlylink\\\" href=\\\"http://not.valid.url/11\\\"></a>')\\n self.assertFalse(res.startswith('<div class=\\\"embed\\\">'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68e34ce1f92e1bd34dbce0f7423ee99a\",\n \"score\": \"0.55987066\",\n \"text\": \"def test_fetch_url(self):\\n # This should return a 200\\n result = fetch_url(\\n 'https://zoek.officielebekendmakingen.nl/rss/dossier/31981'\\n )\\n\\n self.assertTrue(result)\\n\\n # This should yield a timeout - and thus empty data\\n result = fetch_url(\\n 'http://10.255.255.1/'\\n )\\n\\n self.assertFalse(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d0cb7bb4a4445ab5938df18ea6364b2\",\n \"score\": \"0.559571\",\n \"text\": \"def test_bad_url(self):\\n rv = self.submit_url('50 cent')\\n resp = str(rv.data, encoding='utf8')\\n assert 'Sorry, but' in resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b8aa4a14c288035ca286ae5e4e6d195\",\n \"score\": \"0.5593122\",\n \"text\": \"def test_uri(self):\\n with pytest.deprecated_call():\\n assert \\\"https://www.structlog.org/\\\" == structlog.__uri__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ab3413c0ffcee3ddb2d9dff293b9528\",\n \"score\": \"0.5588141\",\n \"text\": \"def test_invalid_url(self):\\n base_url = 'http://invalid_url'\\n username = self.admin_username\\n password = self.admin_password\\n language = self.language\\n with self.assertRaises(ConnectionError, msg='Exception not raised when invalid URL was passed.'):\\n Session(base_url, username, password, language)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994091,"cells":{"query_id":{"kind":"string","value":"7732897d672fe036c1d2b58e44dd262c"},"query":{"kind":"string","value":"Make a random matrix with elements in range (lowhigh)"},"positive_passages":{"kind":"list like","value":[{"docid":"e27867629904731d44dbbc8c2a9a695c","score":"0.76756394","text":"def makeRandom(cls, m, n, low=0, high=10):\n\n obj = Matrix(m, n)\n for x in range(m):\n obj.rows[x] = [random.randrange(low, high) for i in range(obj.csize)]\n\n return obj","title":""}],"string":"[\n {\n \"docid\": \"e27867629904731d44dbbc8c2a9a695c\",\n \"score\": \"0.76756394\",\n \"text\": \"def makeRandom(cls, m, n, low=0, high=10):\\n\\n obj = Matrix(m, n)\\n for x in range(m):\\n obj.rows[x] = [random.randrange(low, high) for i in range(obj.csize)]\\n\\n return obj\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b5e8a62cd84fd0814bfb38c042749abe","score":"0.7580365","text":"def randmatrix(m, n, lower=-0.5, upper=0.5):\n return np.array([random.uniform(lower, upper) for i in range(m*n)]).reshape(m, n)","title":""},{"docid":"4154e7b54e17a3fc806a6f9e15ecbed0","score":"0.7443374","text":"def generate_matrix(upper_bound, length, width, height):\n\n return np.random.rand(length, width, height) * upper_bound","title":""},{"docid":"d7f26b5c98de7a94af8309092dcdec73","score":"0.73389655","text":"def agx_matrix_new_random(row, column, min, max):\n return _angka.agx_matrix_new_random(row, column, min, max)","title":""},{"docid":"e61bc6787d87c0b7857a807738987fe3","score":"0.7003225","text":"def uniform(min_, max_, dims):\r\n return mdp.numx_rand.random(dims)*(max_-min_)+min_","title":""},{"docid":"06f1f2cbcee60f5bd10e5fdcb9dcf810","score":"0.69823575","text":"def random(self, min, max):\n return _angka.AgxMatrix_random(self, min, max)","title":""},{"docid":"ac450d32595256138b717bd01fde5841","score":"0.6828775","text":"def create_random_matrix(columns, rows):\n return [[random.randrange(10) for c in range(columns)] for r in range(rows)]","title":""},{"docid":"81323769c9501c5694b3c3f338707318","score":"0.6759446","text":"def randomMatrix(matrix):\n for i in range(len(matrix)):\n matrix[i] = random.randrange(2)\n return matrix","title":""},{"docid":"0af832b08703340e2d1be1b42e67efaa","score":"0.6697226","text":"def randomUniformSampled(low, high):\n\treturn np.random.uniform(low, high)","title":""},{"docid":"a14f2663ff5afb3127ac1b9c6ae255ab","score":"0.6590932","text":"def generate_matrix_num(rows, cols, low, high, value_type):\r\n\r\n matrix = []\r\n\r\n if value_type == 'int':\r\n for _ in range(rows):\r\n subList = []\r\n for _ in range(cols):\r\n b = randint(low, high)\r\n subList.append(b)\r\n matrix.append(subList)\r\n elif value_type == 'float':\r\n for _ in range(rows):\r\n subList = []\r\n for _ in range(cols):\r\n a = uniform(low, high)\r\n subList.append(a)\r\n matrix.append(subList)\r\n return matrix","title":""},{"docid":"4b7966d2db6275367bd539b141c196a6","score":"0.65716064","text":"def rand_matrix(n: int ,m: int) -> np.matrix:\n\n matrix = np.matrix(np.random.random_integers(0,100, (n,m)))\n return matrix","title":""},{"docid":"4b7966d2db6275367bd539b141c196a6","score":"0.65716064","text":"def rand_matrix(n: int ,m: int) -> np.matrix:\n\n matrix = np.matrix(np.random.random_integers(0,100, (n,m)))\n return matrix","title":""},{"docid":"6de5856bc181a65140ebb8f3b5d81dc5","score":"0.65316176","text":"def randomized_matrix(self, a=0, b=255):\n matrix = []\n for _ in range(self.dim):\n row = []\n for _ in range(self.dim):\n row.append(np.random.randint(a, b))\n matrix.append(row)\n return matrix","title":""},{"docid":"b2a0fff53a18d19d0578726bc812a711","score":"0.6514424","text":"def set_matrix(n, m):\n new_matrix = []\n for i in range(n):\n new_matrix.append(sample(range(10), m))\n return new_matrix","title":""},{"docid":"7ebbc0a2ed1473ba82453b169606e918","score":"0.64943236","text":"def _randrange(n, vmin, vmax):\n return (vmax - vmin) * np.random.rand(n) + vmin","title":""},{"docid":"a1fd993ca3075e6294f2fb2066ff285c","score":"0.64659387","text":"def uniform(low, high):\n\n return abs(high-low) * random() + low","title":""},{"docid":"b025e518f01d7ebf5fa3526273742a85","score":"0.6450461","text":"def randomUniformSampledList(low, high, size):\n\treturn np.random.uniform(low, high, size)","title":""},{"docid":"957099743023c5750c7f990e5e388299","score":"0.6440226","text":"def generate_random_matrix(n): # checked; works\r\n return np.random.random((n, n))","title":""},{"docid":"3b279578857700d0f5d25a7de2033bcf","score":"0.64094037","text":"def sampleUniform(min, max):\n\treturn randint(min, max)","title":""},{"docid":"9704cbf3516ed8e78119ea57aaa63fb2","score":"0.6395663","text":"def randomProblem(rows = 10, columns = 10, max = 1000):\n result = []\n\n for i in range(rows):\n resultRow = []\n\n for j in range(columns):\n resultRow.append(random.randint(0, max))\n\n result.append(resultRow)\n\n return result","title":""},{"docid":"87e4a06e4c1cf72a0523275f9d5f958e","score":"0.6314613","text":"def _uniform(val_range):\n return np.random.uniform(val_range[0], val_range[1])","title":""},{"docid":"6a3338ced95f9753c656d41e513bd855","score":"0.62933385","text":"def random_mat(m):\n return np.random.randn(m,m) / m**.5","title":""},{"docid":"b05168f678e98cb75c92235b94f1b316","score":"0.62763226","text":"def random_upper_triangular(m):\n M = random_mat(m)\n return np.triu(M)","title":""},{"docid":"4f559b065e2fc4fe91add3eed48db609","score":"0.62369907","text":"def uniform(low, high, size=None):\n if low == high:\n if size is None:\n return low\n else:\n return np.ones(size) * low\n else:\n return np.random.uniform(low, high, size)","title":""},{"docid":"db604e36f9016c50681d8fac49b42121","score":"0.62349445","text":"def randtn(minlim=-3., maxlim=3.):\n rn = minlim-1 # initialize out of bounds to get the while loop going\n while (rn<minlim) or (rn>maxlim):\n rn = np.random.randn()\n return rn","title":""},{"docid":"916d51b515e26e6cc7d0b103e8533615","score":"0.62154245","text":"def randrange(self, start, stop, step=None):\n pass","title":""},{"docid":"ef9095d58770709aabdf49d6d23f0bda","score":"0.6196181","text":"def gen_random(self, n, M):\n m = []\n for i in range(0, n):\n m.append([])\n for j in range(0, n):\n m[i].append(random.randint(0, M-1))\n\n self.matrix = m","title":""},{"docid":"39477c0f9357381e6a1d24057e311e11","score":"0.6189386","text":"def uniform(min_bound: int, max_bound: int):\n\n def apply_uniform(_):\n \"\"\" Inner function for appling random.uniform()\n \"\"\"\n return int(np.random.uniform(min_bound, max_bound))\n\n return tune.sample_from(apply_uniform)","title":""},{"docid":"ffbce9a02c526ca9daa7da8c1a157be6","score":"0.6188085","text":"def randomCells(width,height):\n A = createBoard(width, height)\n for row in range(height):\n for col in range(width):\n if row == 0 or row == height-1 or col == 0 or col == width-1:\n A[row][col] = 0\n else:\n A[row][col] = random.choice([0,1])\n return A","title":""},{"docid":"f63e052ebbb25e6e5b0a7c43e857ec49","score":"0.617113","text":"def randrange(self, start, stop, step=None):\n pass","title":""},{"docid":"f63e052ebbb25e6e5b0a7c43e857ec49","score":"0.617113","text":"def randrange(self, start, stop, step=None):\n pass","title":""},{"docid":"f63e052ebbb25e6e5b0a7c43e857ec49","score":"0.617113","text":"def randrange(self, start, stop, step=None):\n pass","title":""},{"docid":"4f9cbd789790a2eac9f9dc90b6f612cb","score":"0.6159474","text":"def rand_lo_hi( lo , hi ):\n return random() * ( hi - lo ) + lo","title":""},{"docid":"73281f819f43a7fe5d895114d877f584","score":"0.6134226","text":"def create_random_array(start=-50000, end=1000000, length=1000):\n\n from random import randint\n return [randint(start,end ) for i in range(length)]","title":""},{"docid":"2fe62c817582adf962f18b17355b0b0d","score":"0.61241114","text":"def rand_in_range(lower, upper):\n return (random() * (upper - lower)) + lower","title":""},{"docid":"c10859dd8c1218fad7762a46990e353d","score":"0.6123463","text":"def RANDBETWEEN(low, high):\n return random.randrange(low, high + 1)","title":""},{"docid":"c561e05c540117b68babf76f5a9f527d","score":"0.6104389","text":"def generate_random_value(low_bound, upp_bound, identificator=None):\n if identificator == 't' or identificator == 's':\n return np.random.uniform(low_bound, upp_bound)\n mode = 1.0\n if low_bound >= mode:\n sample = np.random.triangular(low_bound, low_bound, upp_bound)\n elif upp_bound <= mode:\n sample = np.random.triangular(low_bound, upp_bound, upp_bound)\n else:\n sample = np.random.triangular(low_bound, mode, upp_bound)\n return sample","title":""},{"docid":"8a432a49cd58ee5ab43115e94530483c","score":"0.610209","text":"def choose_pivot(start, end):\n pivot = randint(start, end)\n return pivot","title":""},{"docid":"e2c0854024e8c59bf028f59289f7bd03","score":"0.60778135","text":"def random_matrix(size,conn):\n mtx = np.zeros((size,size))\n \n for i in range(size):\n for j in range(size):\n tmp = random.random()\n if tmp < conn:\n mtx[i,j] = 1 - tmp\n return mtx","title":""},{"docid":"13b1da5eaf30c7574ec04a2884c5200f","score":"0.6062179","text":"def sample_Z(m, n):\n Z = np.random.uniform(-1, 1, size=[m, n])\n return Z","title":""},{"docid":"c1e03ead24fe1fbdace278c56ed9f497","score":"0.604987","text":"def produce_board():\n base_range = range(base)\n rows = [x * base + y for x in shuffle(base_range) for y in shuffle(base_range)]\n cols = [x * base + z for x in shuffle(base_range) for z in shuffle(base_range)]\n nums = shuffle(range(1, base * base + 1))\n\n board = [[nums[pattern(r, c)] for c in cols] for r in rows]\n\n for line in board:\n num_one = randint(0, 8)\n num_two = randint(0, 8)\n while abs(num_one - num_two) < 1:\n # make sure range is greater 1\n num_one = randint(0, 8)\n num_two = randint(0, 8)\n\n if num_one > num_two:\n for i in range(num_two, num_one):\n line[i] = 0\n elif num_two > num_one:\n for j in range(num_one, num_two):\n line[j] = 0\n\n return board","title":""},{"docid":"e72558b50085552319a2623e320d5dc5","score":"0.60326946","text":"def createIndividual(min, max, length):\n return np.random.randint(min, max, length)","title":""},{"docid":"d958fec97177cf369a8c9febfbc1fa04","score":"0.60246074","text":"def initGrid(self, cols, rows, array):\r\n\r\n for i in range(rows):\r\n arrayRow = []\r\n for j in range(cols):\r\n if (i == 0 or j == 0 or (i == rows - 1) or (j == cols - 1)):\r\n arrayRow += [-1]\r\n else:\r\n ran = random.randint(0,3)\r\n if ran == 0:\r\n arrayRow += [1]\r\n else:\r\n arrayRow += [0]\r\n array += [arrayRow]","title":""},{"docid":"ad4d3a99a3de2d9500d5fea8073fbb96","score":"0.6014742","text":"def samplemat(dims):\n aa = np.zeros(dims)\n for i in range(min(dims)):\n aa[i, i] = i\n return aa","title":""},{"docid":"4801a52a22d3a8d837ca0f9318a8aadb","score":"0.60018975","text":"def gen_abnormal_sensor_data():\r\n abnormal_range = range(45, 60)\r\n return random.choice(abnormal_range)","title":""},{"docid":"ac82a81e79f808fa7198ef6b135a0dfd","score":"0.5997376","text":"def randomCells(w,h):\n A = createBoard(w, h)\n\n for row in range(1,h-1):\n for col in range(1,w-1):\n rand= random.randrange(0,2)\n A[row][col] = rand\n return A","title":""},{"docid":"b72ad201779a44772d7befe143f108ac","score":"0.5994065","text":"def fill_rng_2d(\n min=ScalarDef(F64),\n max=ScalarDef(F64),\n seed=ScalarDef(I32),\n O=TensorDef(T, S.M, S.N, output=True),\n):\n domain(D.m, D.n)\n multiplier = TypeFn.cast_signed(I32, const(1103515245))\n increment = TypeFn.cast_signed(I32, const(12345))\n rand1 = (TypeFn.cast_signed(I32, index(D.m)) + seed) * multiplier + increment\n rand2 = (TypeFn.cast_signed(I32, index(D.n)) + rand1) * multiplier + increment\n inv_range = TypeFn.cast_signed(F64, const(2.3283064e-10))\n offset = TypeFn.cast_signed(F64, const(2147483647))\n scaling = (max - min) * inv_range\n O[D.m, D.n] = TypeFn.cast_signed(\n T, (offset + TypeFn.cast_signed(F64, rand2)) * scaling + min\n )","title":""},{"docid":"d08b51b58442f93d9a7ac648f53e102a","score":"0.59930384","text":"def draw_uniform(size=5, low=0., high=1., seed=42):\n np.random.seed(seed)\n return np.random.uniform(low, high, size)","title":""},{"docid":"2a7a4a0094d12a6ad07bd9f455b54f05","score":"0.5989579","text":"def coord_gen(coord_limit):\n rand = []\n for i in range(tr.info_len()):\n value = np.random.randint(0, coord_limit)\n rand = np.append(rand, value)\n return rand","title":""},{"docid":"3f70cb236ec24fe6bea7a68841b2294f","score":"0.598252","text":"def new_tile(self):\n # replace with your code\n #print self.matrix\n node = []\n for i in range(self.get_grid_height()):\n for j in range(self.get_grid_width()):\n if self.matrix[i][j] == 0:\n node += [(i,j)]\n\n rand_node = random.choice(node)\n row = rand_node[0]\n col = rand_node[1]\n if self.matrix[row][col] == 0:\n if random.randrange(0, 10) == 9:\n self.matrix[row][col] = 4\n else:\n self.matrix[row][col] = 2\n #print self.matrix\n #print","title":""},{"docid":"322c4fcd1d4bff051f0e06064682bb39","score":"0.59822","text":"def create_weight_matrix(nrows, ncols):\n return np.random.default_rng().normal(loc=0, scale=1/(nrows*ncols), size=(nrows, ncols))","title":""},{"docid":"d33aef9db3bc3326fd2d745593818d15","score":"0.59617436","text":"def generate_uniform_distribution(lower, upper, number_of_values):\n\n return np.longdouble(np.array(list(np.random.uniform(lower, upper, number_of_values))))","title":""},{"docid":"304710d1859e53feb2db5b1b76b720b6","score":"0.59546643","text":"def sample_Z(m, n):\n\n return np.random.uniform(-1., 1., size=(m, n))","title":""},{"docid":"e214be498c11c02f4304e5f1b4b628e2","score":"0.5952451","text":"def randomGrid(N):\r\n return np.random.choice(vals, N*N, p=[0.1,0.9]).reshape(N, N)","title":""},{"docid":"935fe15558cd0f7f0b01f2832358a8b0","score":"0.59476155","text":"def uniform(self, low, high, size=None):\n return self.rng.uniform(low, high, size)","title":""},{"docid":"c05a48f1b9b8905ec8c943a028d11c87","score":"0.59428656","text":"def test_get_random_range(self):\n return\n rand_seq = []\n hot_random = HotRandom()\n for j in range( 0, 200 ):\n rand_num = hot_random.hot_rand( 91, 37 )\n rand_seq.append( rand_num )\n assert are_items_in_range( rand_seq, 91, 37 ) , \"Got an out of range number\"\n rand_seq = []\n for j in range( 0, 200 ):\n rand_num = hot_random.hot_rand( 19, 0 )\n rand_seq.append( rand_num )\n assert are_items_in_range( rand_seq, 19, 0 ) , \"Got an out of range number\"\n rand_seq = []\n for j in range( 0, 200 ):\n rand_num = hot_random.hot_rand( 61, 4 )\n rand_seq.append( rand_num )\n assert are_items_in_range( rand_seq, 61, 4 ) , \"Got an out of range number\"","title":""},{"docid":"d9ec1c0e21f3826cb2d780769c9671c9","score":"0.59331393","text":"def make_random_ints(num, lower_bound, upper_bound):\n rng = random.Random() # Create a random number generator\n result = []\n for i in range(num):\n result.append(rng.randrange(lower_bound, upper_bound))\n # result.append(random.randrange(lower_bound, upper_bound))\n return result","title":""},{"docid":"1fb1f5c265915290c9a281eb666fb2e4","score":"0.59292376","text":"def generate_matrix(m, n, distr=scipy.stats.norm()):\n return distr.rvs((m,n))","title":""},{"docid":"95c541ec81775366168027e6c5090be5","score":"0.59199923","text":"def randomGrid(N):\n return np.random.choice(vals, N*N, p=[0.2, 0.8]).reshape(N, N)","title":""},{"docid":"4530dddc1e3eaffdd239d544848bdc24","score":"0.5919806","text":"def matrix_fill(rows,cols):\r\n return [[x for x in range(1,rows*cols+1)][i:i+cols] for i in range(0,rows*cols,cols)]","title":""},{"docid":"820e13f4fd7eedb6a02ad5610940f357","score":"0.59172094","text":"def uniform_random_points(bounds, num_points):\n internal_bounds = [sorted(b) for b in bounds]\n # Generate rows for each of the coordinates according to the given bounds, stack into an array, \n # and split into a list of points.\n mat = np.vstack([np.random.uniform(b[0], b[1], num_points) for b in internal_bounds])\n return list(mat[:len(bounds)].T)","title":""},{"docid":"dd31bfdbab2b269bf91eec157f191390","score":"0.591685","text":"def genera_lunghezza():\n return randint(0, min(args.N, args.M)//2)","title":""},{"docid":"21504544f38166ebd4c3d49418ffd5ba","score":"0.5907893","text":"def randomGrid(N):\n return np.random.choice(vals, N * N, p=[0.2, 0.8]).reshape(N, N)","title":""},{"docid":"21504544f38166ebd4c3d49418ffd5ba","score":"0.5907893","text":"def randomGrid(N):\n return np.random.choice(vals, N * N, p=[0.2, 0.8]).reshape(N, N)","title":""},{"docid":"fae1f2421ebc8cfb2792117b1d1a4d68","score":"0.59039974","text":"def genMatrix(max = 10):\n matrix = []\n for i in range(getExp(start), getExp(end)):\n # print(str(bin(i)))\n matrix.append(str(bin(i))[-end+1:])\n # print(matrix[j])\n max -= 1\n if max == 0:\n break\n return matrix","title":""},{"docid":"0ae130cbd1de5c5779a1e3262df2854f","score":"0.58732146","text":"def generate_random(self, shape: list, val_range: list, dtype=\"float\", seed=datetime.now()):\n random.seed(seed)\n self.shape[0] = shape[0]\n self.shape[1] = shape[1]\n\n for x in range(shape[0]):\n self.matrix.append([])\n for y in range(shape[1]):\n # self.matrix[x].append(random.randint(val_range[0], val_range[1]) + random.random() if dtype == \"float\"\n # else random.randint(val_range[0], val_range[1]))\n if dtype == \"float\":\n self.matrix[x].append(float(random.randint(val_range[0], val_range[1]) + round(random.random(), 4)))\n elif dtype == \"int\":\n self.matrix[x].append(random.randint(val_range[0], val_range[1]))","title":""},{"docid":"397d151e76a8f74031d5c9e5f153fe4e","score":"0.5872394","text":"def random_RGB_matrix(self,\n a=0,\n b=255):\n matrix = []\n for _ in range(self.dim):\n row = []\n for _ in range(self.dim):\n pixel = [x for x in np.random.randint(a, b, 3)]\n row.append(pixel)\n matrix.append(row)\n return matrix","title":""},{"docid":"5d4ecca61015b9c55cf1995945c318fe","score":"0.5868767","text":"def make_random_ints(num, lower_bound, upper_bound):\n rng = random.Random() # Create a random number generator\n result = []\n for i in range(num):\n result.append(rng.randrange(lower_bound, upper_bound))\n return result","title":""},{"docid":"7991741abea7df7326ae15afcf632296","score":"0.58624685","text":"def randomGrid(N):\n return np.random.choice(vals, N*N, p=[0.7, 0.2, 0.1]).reshape(N, N)","title":""},{"docid":"9de6110d1bb3eaf5e8e8b5f7c23189a0","score":"0.5855373","text":"def randomGrid(N):\n return np.random.choice(vals, N*N, p=[0.3, 0.7]).reshape(N, N)","title":""},{"docid":"643cb8d4b86abed6ee943dbd845eb319","score":"0.58393186","text":"def gen_nox(self):\n new_nox = np.random.randint( low = matrix_min,\n high = matrix_max-handicap,\n size = (1) )\n return new_nox","title":""},{"docid":"bf52bafce8f98ff98ac3dc98a3881811","score":"0.58380544","text":"def create_random(cls, low=0, high=1.):\n res = cls()\n res.random(low, high)\n return res","title":""},{"docid":"d771a2d06655f0fb1beef1aa80c2ea64","score":"0.5808646","text":"def random_unit_vector_within_limits(self, lower_limit=0.0, upper_limit=1.0):\r\n return list(np.random.uniform(low=lower_limit, high=upper_limit, size=self.prior_count))","title":""},{"docid":"960377cc5a4c09a8dc12acaab1fc6d4e","score":"0.57926625","text":"def randomize(self, xsize, ysize):\n res = []\n for y in range(ysize):\n row = []\n for x in range(xsize):\n row.append(random.sample([0]*20 + [1]*2,1)[0])\n res.append(row)\n self._data = res","title":""},{"docid":"e795d3a8b8b73ad42a650ead8c2cb59c","score":"0.57916695","text":"def get_random_invertible_matrix(m):\n d = 2\n while d>1:\n M = Matrix([[randint(0,m-1), randint(0,m-1)],[randint(0,m-1), randint(0,m-1)]])\n d = gcd(M.det(), m)\n return M","title":""},{"docid":"203ff4eabe57ccd1e8c98e59cf7150f3","score":"0.5784927","text":"def genMatrix(size=200, value=10):\n\n matrix = [[value for col in range(0,size)] for row in range(0,size)]\n\n return matrix","title":""},{"docid":"c001423b00d45e71ed6f3c9d29fdc2d5","score":"0.5784199","text":"def rand_uniform(self, arr_shape, min_elem=0, max_elem=1):\n rand_arr = np.random.rand(*arr_shape)\n rand_arr = min_elem + rand_arr * (max_elem - min_elem)\n return rand_arr","title":""},{"docid":"1ea5d4444d40cab632cef455ebd277f9","score":"0.5774231","text":"def embaralha(m,v,n):\n m = ceil(m)\n if m > 0 and m <= n:\n mi = (n-m) // 2\n mf = (n+m) // 2\n for num in range(mi,mf):\n i = np.random.randint(mi,mf)\n j = np.random.randint(mi,mf)\n t = v[i]\n v[i] = v[j]\n v[j] = t\n return v","title":""},{"docid":"21b259c0b0ec5eb2c840b1350ca0548f","score":"0.57656854","text":"def get_random_corners(var_lower, var_upper):\n assert(isinstance(var_lower, np.ndarray))\n assert(isinstance(var_upper, np.ndarray))\n assert(len(var_lower) == len(var_upper))\n\n n = len(var_lower)\n limits = np.vstack((var_upper, var_lower))\n node_pos = np.atleast_2d(limits[np.random.randint(2, size=n), \n np.arange(n)])\n while (len(node_pos) < n+1):\n point = limits[np.random.randint(2, size=n), np.arange(n)]\n if (get_min_distance(point, node_pos) > 0):\n node_pos = np.vstack((node_pos, point))\n\n return np.array(node_pos, np.float_)","title":""},{"docid":"04c31ffd48dcffc937dc432ac7e053a4","score":"0.57642174","text":"def new_tile(self):\n # replace with your code\n value = random.choice(([2] * 90) + ([4] * 10))\n row = random.randrange(0, self._grid_height)\n col = random.randrange(0, self._grid_width)\n while self._grid[row][col] != 0:\n row = random.randrange(0, self._grid_height)\n col = random.randrange(0, self._grid_width)\n self._grid[row][col] = value","title":""},{"docid":"10ead1e548380a86bdaf04d202b919a9","score":"0.57620573","text":"def generate_random(min=1, max=100, nums=10):\r\n rng = default_rng()\r\n numbers = rng.choice(np.arange(min, max), size=nums, replace=False)\r\n return numbers","title":""},{"docid":"067b0c9331fe9d6f358c985551d189e8","score":"0.57606566","text":"def random_board(self):\n\n board = np.random.randint(0, 11, (4, 4))\n for i in range(len(board)):\n for j in range(len(board[i])):\n if board[i, j] != 0:\n board[i, j] = 2**board[i, j]\n\n return board","title":""},{"docid":"a53462efd8cd44e882681569c190c16b","score":"0.5738326","text":"def _random_binary_string_matrix(rows, cols, max_length):\n return [[_random_binary_string_gen(max_length) for _ in range(cols)] for _ in range(rows)]","title":""},{"docid":"d35526d1bf0b825c860d350e1ac10bc6","score":"0.5732991","text":"def random_nums(num, mn, mx):\n array = []\n while len(array) < num:\n array.append(random.randrange(mn, mx))\n return array","title":""},{"docid":"bef418b0a73e8106cd9eb06f05c5fd17","score":"0.57216287","text":"def create_uniform_grid(width, height):\n grid_size = (10, 10)\n num_bins_horizontal = int(round(width / grid_size[0]))\n num_bins_vertical = int(round(height / grid_size[1]))\n bins = (num_bins_horizontal, num_bins_vertical)\n low = [0, 0]\n high = [width, height]\n grid = [np.linspace(low[dim], high[dim], bins[dim] + 1)[1:-1] for dim in range(len(bins))]\n\n return grid","title":""},{"docid":"8eb3792c26297b15a21efcef87db0a30","score":"0.57181585","text":"def random_init(n, initial, bounds, use_point=False, problem=None):\n population = np.ascontiguousarray(problem.randomize(n))\n if use_point:\n population[0] = clip(initial, *bounds)\n return population","title":""},{"docid":"14698691c4c5b9c503f4ad0dd636a9b2","score":"0.571791","text":"def between(min, max):\n return random.random() * (max - min) + min","title":""},{"docid":"cfba3ffb732bbcbc4f6b8d65e3d0b236","score":"0.57167524","text":"def make_uniform_grid(xmin, xmax, ncells, nghost=2):\n dx = (xmax - xmin)/(ncells - 1)\n xf = xmin + dx*np.arange(-nghost, ncells+nghost)\n return Grid(xf, nghost)","title":""},{"docid":"871d393eda57d80211983a9d6a371272","score":"0.57135993","text":"def pick(arr, m):\r\n if m > len(arr):\r\n raise ValueError(\"not enough items to fill all m slots\")\r\n res = arr[:m]\r\n for i in range(m, len(arr)):\r\n k = randint(0, i)\r\n if k < m: # k must not equal m, else index out of bounds\r\n res[k] = arr[i]\r\n return res","title":""},{"docid":"9d01779782b949870f6786ae3608d27a","score":"0.57105386","text":"def initialize(X, k):\n # cases\n if not isinstance(X, np.ndarray) or len(X.shape) != 2:\n return None\n if not isinstance(k, int) or k <= 0:\n return None\n\n # Setting min and max values per col\n n, d = X.shape\n X_min = X.min(axis=0)\n X_max = X.max(axis=0)\n\n # return multivariate uniform distribution\n return np.random.uniform(X_min, X_max, size=(k, d))","title":""},{"docid":"cbf63516ecb02fb8b9bb6e45c127cd7f","score":"0.5704827","text":"def set_signal(low=-1, high=1, N=10):\r\n s = np.random.uniform(low=low, high=high, size=(N, 1))\r\n return s","title":""},{"docid":"2037e8fe5f46db94c7ae1d2c4ea90639","score":"0.5690302","text":"def genMatrix2(size=200, value=5):\n\n matrix = np.asarray([ np.asarray([value for col in range(0,size)]) for row in range(0,size)])\n\n return matrix","title":""},{"docid":"f9be7e2035c2be7ce56d938c165118b0","score":"0.56721246","text":"def new_tile(self):\n cell_value = 1\n while cell_value != 0:\n row = random.randint(0, self._height -1 )\n column = random.randint(0, self._width -1)\n cell_value = self._grid[row][column]\n \n random_value = random.randint(1, 10)\n if random_value <= 9:\n self._grid[row][column] = 2\n else:\n self._grid[row][column] = 4","title":""},{"docid":"524d47fee9bf75d608299e90ceab4177","score":"0.56651396","text":"def make_matrix(height, width, fill_value):\n return [[fill_value for dummy_col in range(width)] for \n dummy_row in range(height)]","title":""},{"docid":"f8d9aba0d4376eba79830ebab1e872f8","score":"0.56586415","text":"def mt_rand (low = 0, high = sys.maxint):\n return random.randint (low, high)","title":""},{"docid":"dfe57dcc9769fc9fa0f79c0f13bc10b9","score":"0.56497943","text":"def generate_list():\n upper_bound = int(input(\"-> Please choose the Upper Bound of the array: \"))\n size = int(input(\"-> Please choose the size of the array: \"))\n\n # return upperBound, size, np.random.randint(0, upperBound, size)\n\n rand_arr = []\n for _ in range(size):\n rand_arr.append(pcg32bounded(upper_bound))\n\n return upper_bound, size, rand_arr","title":""},{"docid":"db70b12d1f1d4ff0b1029a7c2f2bd4ed","score":"0.56410575","text":"def generate_function(limit):\r\n w0 = random.uniform(-limit, limit)\r\n w1 = random.uniform(-limit, limit)\r\n w2 = random.uniform(-limit, limit)\r\n return np.array([w0, w1, w2])","title":""},{"docid":"739e28b3f79887822c1142806d82199e","score":"0.5627422","text":"def _random_vector(min, max, prng=DEFAULT_PRNG):\n min = np.array(min)\n max = np.array(max)\n assert min.shape == max.shape\n assert len(min.shape) == 1\n return prng.uniform(min, max)","title":""},{"docid":"a89e331ec96e57eba296d4dab8507303","score":"0.5625954","text":"def random_uniform(low=0.0, high=0.0, size=None, dev='cpu', f=None):\n return _get_framework(f=f).random_uniform(low, high, size, dev)","title":""},{"docid":"c14d79510049f0d7914b9a234dc2b100","score":"0.56223214","text":"def random_x_gen(self):\n if isinstance(self.min_x, list):\n x_list = []\n for i,j in zip(self.min_x, self.max_x):\n x_list.append(uniform(i,j))\n return x_list\n else:\n x_list = [uniform(self.min_x, self.max_x)]\n return x_list","title":""},{"docid":"1b4515e37546eb3ae4dfa5ca3bbfedf2","score":"0.56164277","text":"def gen_pool_matrix(size=25,mu=.0,sigma=1.,gamma=-.5):\n\n if gamma==0:\n asymm=0\n else:\n asymm=(1- np.sqrt(1-gamma**2) )/gamma\n\n aij=(np.random.normal(0,1./np.sqrt(size),(size,size)) )\n\n aij= (aij+asymm * aij.T) / np.sqrt(1+ asymm**2 )\n\n A=-mu/size + sigma*aij\n np.fill_diagonal(A,-1)\n return A","title":""}],"string":"[\n {\n \"docid\": \"b5e8a62cd84fd0814bfb38c042749abe\",\n \"score\": \"0.7580365\",\n \"text\": \"def randmatrix(m, n, lower=-0.5, upper=0.5):\\n return np.array([random.uniform(lower, upper) for i in range(m*n)]).reshape(m, n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4154e7b54e17a3fc806a6f9e15ecbed0\",\n \"score\": \"0.7443374\",\n \"text\": \"def generate_matrix(upper_bound, length, width, height):\\n\\n return np.random.rand(length, width, height) * upper_bound\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7f26b5c98de7a94af8309092dcdec73\",\n \"score\": \"0.73389655\",\n \"text\": \"def agx_matrix_new_random(row, column, min, max):\\n return _angka.agx_matrix_new_random(row, column, min, max)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e61bc6787d87c0b7857a807738987fe3\",\n \"score\": \"0.7003225\",\n \"text\": \"def uniform(min_, max_, dims):\\r\\n return mdp.numx_rand.random(dims)*(max_-min_)+min_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06f1f2cbcee60f5bd10e5fdcb9dcf810\",\n \"score\": \"0.69823575\",\n \"text\": \"def random(self, min, max):\\n return _angka.AgxMatrix_random(self, min, max)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac450d32595256138b717bd01fde5841\",\n \"score\": \"0.6828775\",\n \"text\": \"def create_random_matrix(columns, rows):\\n return [[random.randrange(10) for c in range(columns)] for r in range(rows)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81323769c9501c5694b3c3f338707318\",\n \"score\": \"0.6759446\",\n \"text\": \"def randomMatrix(matrix):\\n for i in range(len(matrix)):\\n matrix[i] = random.randrange(2)\\n return matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af832b08703340e2d1be1b42e67efaa\",\n \"score\": \"0.6697226\",\n \"text\": \"def randomUniformSampled(low, high):\\n\\treturn np.random.uniform(low, high)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a14f2663ff5afb3127ac1b9c6ae255ab\",\n \"score\": \"0.6590932\",\n \"text\": \"def generate_matrix_num(rows, cols, low, high, value_type):\\r\\n\\r\\n matrix = []\\r\\n\\r\\n if value_type == 'int':\\r\\n for _ in range(rows):\\r\\n subList = []\\r\\n for _ in range(cols):\\r\\n b = randint(low, high)\\r\\n subList.append(b)\\r\\n matrix.append(subList)\\r\\n elif value_type == 'float':\\r\\n for _ in range(rows):\\r\\n subList = []\\r\\n for _ in range(cols):\\r\\n a = uniform(low, high)\\r\\n subList.append(a)\\r\\n matrix.append(subList)\\r\\n return matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b7966d2db6275367bd539b141c196a6\",\n \"score\": \"0.65716064\",\n \"text\": \"def rand_matrix(n: int ,m: int) -> np.matrix:\\n\\n matrix = np.matrix(np.random.random_integers(0,100, (n,m)))\\n return matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b7966d2db6275367bd539b141c196a6\",\n \"score\": \"0.65716064\",\n \"text\": \"def rand_matrix(n: int ,m: int) -> np.matrix:\\n\\n matrix = np.matrix(np.random.random_integers(0,100, (n,m)))\\n return matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6de5856bc181a65140ebb8f3b5d81dc5\",\n \"score\": \"0.65316176\",\n \"text\": \"def randomized_matrix(self, a=0, b=255):\\n matrix = []\\n for _ in range(self.dim):\\n row = []\\n for _ in range(self.dim):\\n row.append(np.random.randint(a, b))\\n matrix.append(row)\\n return matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2a0fff53a18d19d0578726bc812a711\",\n \"score\": \"0.6514424\",\n \"text\": \"def set_matrix(n, m):\\n new_matrix = []\\n for i in range(n):\\n new_matrix.append(sample(range(10), m))\\n return new_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ebbc0a2ed1473ba82453b169606e918\",\n \"score\": \"0.64943236\",\n \"text\": \"def _randrange(n, vmin, vmax):\\n return (vmax - vmin) * np.random.rand(n) + vmin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1fd993ca3075e6294f2fb2066ff285c\",\n \"score\": \"0.64659387\",\n \"text\": \"def uniform(low, high):\\n\\n return abs(high-low) * random() + low\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b025e518f01d7ebf5fa3526273742a85\",\n \"score\": \"0.6450461\",\n \"text\": \"def randomUniformSampledList(low, high, size):\\n\\treturn np.random.uniform(low, high, size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"957099743023c5750c7f990e5e388299\",\n \"score\": \"0.6440226\",\n \"text\": \"def generate_random_matrix(n): # checked; works\\r\\n return np.random.random((n, n))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b279578857700d0f5d25a7de2033bcf\",\n \"score\": \"0.64094037\",\n \"text\": \"def sampleUniform(min, max):\\n\\treturn randint(min, max)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9704cbf3516ed8e78119ea57aaa63fb2\",\n \"score\": \"0.6395663\",\n \"text\": \"def randomProblem(rows = 10, columns = 10, max = 1000):\\n result = []\\n\\n for i in range(rows):\\n resultRow = []\\n\\n for j in range(columns):\\n resultRow.append(random.randint(0, max))\\n\\n result.append(resultRow)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87e4a06e4c1cf72a0523275f9d5f958e\",\n \"score\": \"0.6314613\",\n \"text\": \"def _uniform(val_range):\\n return np.random.uniform(val_range[0], val_range[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a3338ced95f9753c656d41e513bd855\",\n \"score\": \"0.62933385\",\n \"text\": \"def random_mat(m):\\n return np.random.randn(m,m) / m**.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b05168f678e98cb75c92235b94f1b316\",\n \"score\": \"0.62763226\",\n \"text\": \"def random_upper_triangular(m):\\n M = random_mat(m)\\n return np.triu(M)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f559b065e2fc4fe91add3eed48db609\",\n \"score\": \"0.62369907\",\n \"text\": \"def uniform(low, high, size=None):\\n if low == high:\\n if size is None:\\n return low\\n else:\\n return np.ones(size) * low\\n else:\\n return np.random.uniform(low, high, size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db604e36f9016c50681d8fac49b42121\",\n \"score\": \"0.62349445\",\n \"text\": \"def randtn(minlim=-3., maxlim=3.):\\n rn = minlim-1 # initialize out of bounds to get the while loop going\\n while (rn<minlim) or (rn>maxlim):\\n rn = np.random.randn()\\n return rn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"916d51b515e26e6cc7d0b103e8533615\",\n \"score\": \"0.62154245\",\n \"text\": \"def randrange(self, start, stop, step=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef9095d58770709aabdf49d6d23f0bda\",\n \"score\": \"0.6196181\",\n \"text\": \"def gen_random(self, n, M):\\n m = []\\n for i in range(0, n):\\n m.append([])\\n for j in range(0, n):\\n m[i].append(random.randint(0, M-1))\\n\\n self.matrix = m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39477c0f9357381e6a1d24057e311e11\",\n \"score\": \"0.6189386\",\n \"text\": \"def uniform(min_bound: int, max_bound: int):\\n\\n def apply_uniform(_):\\n \\\"\\\"\\\" Inner function for appling random.uniform()\\n \\\"\\\"\\\"\\n return int(np.random.uniform(min_bound, max_bound))\\n\\n return tune.sample_from(apply_uniform)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffbce9a02c526ca9daa7da8c1a157be6\",\n \"score\": \"0.6188085\",\n \"text\": \"def randomCells(width,height):\\n A = createBoard(width, height)\\n for row in range(height):\\n for col in range(width):\\n if row == 0 or row == height-1 or col == 0 or col == width-1:\\n A[row][col] = 0\\n else:\\n A[row][col] = random.choice([0,1])\\n return A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f63e052ebbb25e6e5b0a7c43e857ec49\",\n \"score\": \"0.617113\",\n \"text\": \"def randrange(self, start, stop, step=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f63e052ebbb25e6e5b0a7c43e857ec49\",\n \"score\": \"0.617113\",\n \"text\": \"def randrange(self, start, stop, step=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f63e052ebbb25e6e5b0a7c43e857ec49\",\n \"score\": \"0.617113\",\n \"text\": \"def randrange(self, start, stop, step=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9cbd789790a2eac9f9dc90b6f612cb\",\n \"score\": \"0.6159474\",\n \"text\": \"def rand_lo_hi( lo , hi ):\\n return random() * ( hi - lo ) + lo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73281f819f43a7fe5d895114d877f584\",\n \"score\": \"0.6134226\",\n \"text\": \"def create_random_array(start=-50000, end=1000000, length=1000):\\n\\n from random import randint\\n return [randint(start,end ) for i in range(length)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fe62c817582adf962f18b17355b0b0d\",\n \"score\": \"0.61241114\",\n \"text\": \"def rand_in_range(lower, upper):\\n return (random() * (upper - lower)) + lower\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c10859dd8c1218fad7762a46990e353d\",\n \"score\": \"0.6123463\",\n \"text\": \"def RANDBETWEEN(low, high):\\n return random.randrange(low, high + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c561e05c540117b68babf76f5a9f527d\",\n \"score\": \"0.6104389\",\n \"text\": \"def generate_random_value(low_bound, upp_bound, identificator=None):\\n if identificator == 't' or identificator == 's':\\n return np.random.uniform(low_bound, upp_bound)\\n mode = 1.0\\n if low_bound >= mode:\\n sample = np.random.triangular(low_bound, low_bound, upp_bound)\\n elif upp_bound <= mode:\\n sample = np.random.triangular(low_bound, upp_bound, upp_bound)\\n else:\\n sample = np.random.triangular(low_bound, mode, upp_bound)\\n return sample\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a432a49cd58ee5ab43115e94530483c\",\n \"score\": \"0.610209\",\n \"text\": \"def choose_pivot(start, end):\\n pivot = randint(start, end)\\n return pivot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c0854024e8c59bf028f59289f7bd03\",\n \"score\": \"0.60778135\",\n \"text\": \"def random_matrix(size,conn):\\n mtx = np.zeros((size,size))\\n \\n for i in range(size):\\n for j in range(size):\\n tmp = random.random()\\n if tmp < conn:\\n mtx[i,j] = 1 - tmp\\n return mtx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13b1da5eaf30c7574ec04a2884c5200f\",\n \"score\": \"0.6062179\",\n \"text\": \"def sample_Z(m, n):\\n Z = np.random.uniform(-1, 1, size=[m, n])\\n return Z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1e03ead24fe1fbdace278c56ed9f497\",\n \"score\": \"0.604987\",\n \"text\": \"def produce_board():\\n base_range = range(base)\\n rows = [x * base + y for x in shuffle(base_range) for y in shuffle(base_range)]\\n cols = [x * base + z for x in shuffle(base_range) for z in shuffle(base_range)]\\n nums = shuffle(range(1, base * base + 1))\\n\\n board = [[nums[pattern(r, c)] for c in cols] for r in rows]\\n\\n for line in board:\\n num_one = randint(0, 8)\\n num_two = randint(0, 8)\\n while abs(num_one - num_two) < 1:\\n # make sure range is greater 1\\n num_one = randint(0, 8)\\n num_two = randint(0, 8)\\n\\n if num_one > num_two:\\n for i in range(num_two, num_one):\\n line[i] = 0\\n elif num_two > num_one:\\n for j in range(num_one, num_two):\\n line[j] = 0\\n\\n return board\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e72558b50085552319a2623e320d5dc5\",\n \"score\": \"0.60326946\",\n \"text\": \"def createIndividual(min, max, length):\\n return np.random.randint(min, max, length)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d958fec97177cf369a8c9febfbc1fa04\",\n \"score\": \"0.60246074\",\n \"text\": \"def initGrid(self, cols, rows, array):\\r\\n\\r\\n for i in range(rows):\\r\\n arrayRow = []\\r\\n for j in range(cols):\\r\\n if (i == 0 or j == 0 or (i == rows - 1) or (j == cols - 1)):\\r\\n arrayRow += [-1]\\r\\n else:\\r\\n ran = random.randint(0,3)\\r\\n if ran == 0:\\r\\n arrayRow += [1]\\r\\n else:\\r\\n arrayRow += [0]\\r\\n array += [arrayRow]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad4d3a99a3de2d9500d5fea8073fbb96\",\n \"score\": \"0.6014742\",\n \"text\": \"def samplemat(dims):\\n aa = np.zeros(dims)\\n for i in range(min(dims)):\\n aa[i, i] = i\\n return aa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4801a52a22d3a8d837ca0f9318a8aadb\",\n \"score\": \"0.60018975\",\n \"text\": \"def gen_abnormal_sensor_data():\\r\\n abnormal_range = range(45, 60)\\r\\n return random.choice(abnormal_range)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac82a81e79f808fa7198ef6b135a0dfd\",\n \"score\": \"0.5997376\",\n \"text\": \"def randomCells(w,h):\\n A = createBoard(w, h)\\n\\n for row in range(1,h-1):\\n for col in range(1,w-1):\\n rand= random.randrange(0,2)\\n A[row][col] = rand\\n return A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b72ad201779a44772d7befe143f108ac\",\n \"score\": \"0.5994065\",\n \"text\": \"def fill_rng_2d(\\n min=ScalarDef(F64),\\n max=ScalarDef(F64),\\n seed=ScalarDef(I32),\\n O=TensorDef(T, S.M, S.N, output=True),\\n):\\n domain(D.m, D.n)\\n multiplier = TypeFn.cast_signed(I32, const(1103515245))\\n increment = TypeFn.cast_signed(I32, const(12345))\\n rand1 = (TypeFn.cast_signed(I32, index(D.m)) + seed) * multiplier + increment\\n rand2 = (TypeFn.cast_signed(I32, index(D.n)) + rand1) * multiplier + increment\\n inv_range = TypeFn.cast_signed(F64, const(2.3283064e-10))\\n offset = TypeFn.cast_signed(F64, const(2147483647))\\n scaling = (max - min) * inv_range\\n O[D.m, D.n] = TypeFn.cast_signed(\\n T, (offset + TypeFn.cast_signed(F64, rand2)) * scaling + min\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d08b51b58442f93d9a7ac648f53e102a\",\n \"score\": \"0.59930384\",\n \"text\": \"def draw_uniform(size=5, low=0., high=1., seed=42):\\n np.random.seed(seed)\\n return np.random.uniform(low, high, size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a7a4a0094d12a6ad07bd9f455b54f05\",\n \"score\": \"0.5989579\",\n \"text\": \"def coord_gen(coord_limit):\\n rand = []\\n for i in range(tr.info_len()):\\n value = np.random.randint(0, coord_limit)\\n rand = np.append(rand, value)\\n return rand\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f70cb236ec24fe6bea7a68841b2294f\",\n \"score\": \"0.598252\",\n \"text\": \"def new_tile(self):\\n # replace with your code\\n #print self.matrix\\n node = []\\n for i in range(self.get_grid_height()):\\n for j in range(self.get_grid_width()):\\n if self.matrix[i][j] == 0:\\n node += [(i,j)]\\n\\n rand_node = random.choice(node)\\n row = rand_node[0]\\n col = rand_node[1]\\n if self.matrix[row][col] == 0:\\n if random.randrange(0, 10) == 9:\\n self.matrix[row][col] = 4\\n else:\\n self.matrix[row][col] = 2\\n #print self.matrix\\n #print\",\n \"title\": \"\"\n },\n {\n \"docid\": \"322c4fcd1d4bff051f0e06064682bb39\",\n \"score\": \"0.59822\",\n \"text\": \"def create_weight_matrix(nrows, ncols):\\n return np.random.default_rng().normal(loc=0, scale=1/(nrows*ncols), size=(nrows, ncols))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d33aef9db3bc3326fd2d745593818d15\",\n \"score\": \"0.59617436\",\n \"text\": \"def generate_uniform_distribution(lower, upper, number_of_values):\\n\\n return np.longdouble(np.array(list(np.random.uniform(lower, upper, number_of_values))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"304710d1859e53feb2db5b1b76b720b6\",\n \"score\": \"0.59546643\",\n \"text\": \"def sample_Z(m, n):\\n\\n return np.random.uniform(-1., 1., size=(m, n))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e214be498c11c02f4304e5f1b4b628e2\",\n \"score\": \"0.5952451\",\n \"text\": \"def randomGrid(N):\\r\\n return np.random.choice(vals, N*N, p=[0.1,0.9]).reshape(N, N)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"935fe15558cd0f7f0b01f2832358a8b0\",\n \"score\": \"0.59476155\",\n \"text\": \"def uniform(self, low, high, size=None):\\n return self.rng.uniform(low, high, size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c05a48f1b9b8905ec8c943a028d11c87\",\n \"score\": \"0.59428656\",\n \"text\": \"def test_get_random_range(self):\\n return\\n rand_seq = []\\n hot_random = HotRandom()\\n for j in range( 0, 200 ):\\n rand_num = hot_random.hot_rand( 91, 37 )\\n rand_seq.append( rand_num )\\n assert are_items_in_range( rand_seq, 91, 37 ) , \\\"Got an out of range number\\\"\\n rand_seq = []\\n for j in range( 0, 200 ):\\n rand_num = hot_random.hot_rand( 19, 0 )\\n rand_seq.append( rand_num )\\n assert are_items_in_range( rand_seq, 19, 0 ) , \\\"Got an out of range number\\\"\\n rand_seq = []\\n for j in range( 0, 200 ):\\n rand_num = hot_random.hot_rand( 61, 4 )\\n rand_seq.append( rand_num )\\n assert are_items_in_range( rand_seq, 61, 4 ) , \\\"Got an out of range number\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9ec1c0e21f3826cb2d780769c9671c9\",\n \"score\": \"0.59331393\",\n \"text\": \"def make_random_ints(num, lower_bound, upper_bound):\\n rng = random.Random() # Create a random number generator\\n result = []\\n for i in range(num):\\n result.append(rng.randrange(lower_bound, upper_bound))\\n # result.append(random.randrange(lower_bound, upper_bound))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb1f5c265915290c9a281eb666fb2e4\",\n \"score\": \"0.59292376\",\n \"text\": \"def generate_matrix(m, n, distr=scipy.stats.norm()):\\n return distr.rvs((m,n))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95c541ec81775366168027e6c5090be5\",\n \"score\": \"0.59199923\",\n \"text\": \"def randomGrid(N):\\n return np.random.choice(vals, N*N, p=[0.2, 0.8]).reshape(N, N)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4530dddc1e3eaffdd239d544848bdc24\",\n \"score\": \"0.5919806\",\n \"text\": \"def matrix_fill(rows,cols):\\r\\n return [[x for x in range(1,rows*cols+1)][i:i+cols] for i in range(0,rows*cols,cols)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"820e13f4fd7eedb6a02ad5610940f357\",\n \"score\": \"0.59172094\",\n \"text\": \"def uniform_random_points(bounds, num_points):\\n internal_bounds = [sorted(b) for b in bounds]\\n # Generate rows for each of the coordinates according to the given bounds, stack into an array, \\n # and split into a list of points.\\n mat = np.vstack([np.random.uniform(b[0], b[1], num_points) for b in internal_bounds])\\n return list(mat[:len(bounds)].T)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd31bfdbab2b269bf91eec157f191390\",\n \"score\": \"0.591685\",\n \"text\": \"def genera_lunghezza():\\n return randint(0, min(args.N, args.M)//2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21504544f38166ebd4c3d49418ffd5ba\",\n \"score\": \"0.5907893\",\n \"text\": \"def randomGrid(N):\\n return np.random.choice(vals, N * N, p=[0.2, 0.8]).reshape(N, N)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21504544f38166ebd4c3d49418ffd5ba\",\n \"score\": \"0.5907893\",\n \"text\": \"def randomGrid(N):\\n return np.random.choice(vals, N * N, p=[0.2, 0.8]).reshape(N, N)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fae1f2421ebc8cfb2792117b1d1a4d68\",\n \"score\": \"0.59039974\",\n \"text\": \"def genMatrix(max = 10):\\n matrix = []\\n for i in range(getExp(start), getExp(end)):\\n # print(str(bin(i)))\\n matrix.append(str(bin(i))[-end+1:])\\n # print(matrix[j])\\n max -= 1\\n if max == 0:\\n break\\n return matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ae130cbd1de5c5779a1e3262df2854f\",\n \"score\": \"0.58732146\",\n \"text\": \"def generate_random(self, shape: list, val_range: list, dtype=\\\"float\\\", seed=datetime.now()):\\n random.seed(seed)\\n self.shape[0] = shape[0]\\n self.shape[1] = shape[1]\\n\\n for x in range(shape[0]):\\n self.matrix.append([])\\n for y in range(shape[1]):\\n # self.matrix[x].append(random.randint(val_range[0], val_range[1]) + random.random() if dtype == \\\"float\\\"\\n # else random.randint(val_range[0], val_range[1]))\\n if dtype == \\\"float\\\":\\n self.matrix[x].append(float(random.randint(val_range[0], val_range[1]) + round(random.random(), 4)))\\n elif dtype == \\\"int\\\":\\n self.matrix[x].append(random.randint(val_range[0], val_range[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"397d151e76a8f74031d5c9e5f153fe4e\",\n \"score\": \"0.5872394\",\n \"text\": \"def random_RGB_matrix(self,\\n a=0,\\n b=255):\\n matrix = []\\n for _ in range(self.dim):\\n row = []\\n for _ in range(self.dim):\\n pixel = [x for x in np.random.randint(a, b, 3)]\\n row.append(pixel)\\n matrix.append(row)\\n return matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d4ecca61015b9c55cf1995945c318fe\",\n \"score\": \"0.5868767\",\n \"text\": \"def make_random_ints(num, lower_bound, upper_bound):\\n rng = random.Random() # Create a random number generator\\n result = []\\n for i in range(num):\\n result.append(rng.randrange(lower_bound, upper_bound))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7991741abea7df7326ae15afcf632296\",\n \"score\": \"0.58624685\",\n \"text\": \"def randomGrid(N):\\n return np.random.choice(vals, N*N, p=[0.7, 0.2, 0.1]).reshape(N, N)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9de6110d1bb3eaf5e8e8b5f7c23189a0\",\n \"score\": \"0.5855373\",\n \"text\": \"def randomGrid(N):\\n return np.random.choice(vals, N*N, p=[0.3, 0.7]).reshape(N, N)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"643cb8d4b86abed6ee943dbd845eb319\",\n \"score\": \"0.58393186\",\n \"text\": \"def gen_nox(self):\\n new_nox = np.random.randint( low = matrix_min,\\n high = matrix_max-handicap,\\n size = (1) )\\n return new_nox\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf52bafce8f98ff98ac3dc98a3881811\",\n \"score\": \"0.58380544\",\n \"text\": \"def create_random(cls, low=0, high=1.):\\n res = cls()\\n res.random(low, high)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d771a2d06655f0fb1beef1aa80c2ea64\",\n \"score\": \"0.5808646\",\n \"text\": \"def random_unit_vector_within_limits(self, lower_limit=0.0, upper_limit=1.0):\\r\\n return list(np.random.uniform(low=lower_limit, high=upper_limit, size=self.prior_count))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"960377cc5a4c09a8dc12acaab1fc6d4e\",\n \"score\": \"0.57926625\",\n \"text\": \"def randomize(self, xsize, ysize):\\n res = []\\n for y in range(ysize):\\n row = []\\n for x in range(xsize):\\n row.append(random.sample([0]*20 + [1]*2,1)[0])\\n res.append(row)\\n self._data = res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e795d3a8b8b73ad42a650ead8c2cb59c\",\n \"score\": \"0.57916695\",\n \"text\": \"def get_random_invertible_matrix(m):\\n d = 2\\n while d>1:\\n M = Matrix([[randint(0,m-1), randint(0,m-1)],[randint(0,m-1), randint(0,m-1)]])\\n d = gcd(M.det(), m)\\n return M\",\n \"title\": \"\"\n },\n {\n \"docid\": \"203ff4eabe57ccd1e8c98e59cf7150f3\",\n \"score\": \"0.5784927\",\n \"text\": \"def genMatrix(size=200, value=10):\\n\\n matrix = [[value for col in range(0,size)] for row in range(0,size)]\\n\\n return matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c001423b00d45e71ed6f3c9d29fdc2d5\",\n \"score\": \"0.5784199\",\n \"text\": \"def rand_uniform(self, arr_shape, min_elem=0, max_elem=1):\\n rand_arr = np.random.rand(*arr_shape)\\n rand_arr = min_elem + rand_arr * (max_elem - min_elem)\\n return rand_arr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ea5d4444d40cab632cef455ebd277f9\",\n \"score\": \"0.5774231\",\n \"text\": \"def embaralha(m,v,n):\\n m = ceil(m)\\n if m > 0 and m <= n:\\n mi = (n-m) // 2\\n mf = (n+m) // 2\\n for num in range(mi,mf):\\n i = np.random.randint(mi,mf)\\n j = np.random.randint(mi,mf)\\n t = v[i]\\n v[i] = v[j]\\n v[j] = t\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21b259c0b0ec5eb2c840b1350ca0548f\",\n \"score\": \"0.57656854\",\n \"text\": \"def get_random_corners(var_lower, var_upper):\\n assert(isinstance(var_lower, np.ndarray))\\n assert(isinstance(var_upper, np.ndarray))\\n assert(len(var_lower) == len(var_upper))\\n\\n n = len(var_lower)\\n limits = np.vstack((var_upper, var_lower))\\n node_pos = np.atleast_2d(limits[np.random.randint(2, size=n), \\n np.arange(n)])\\n while (len(node_pos) < n+1):\\n point = limits[np.random.randint(2, size=n), np.arange(n)]\\n if (get_min_distance(point, node_pos) > 0):\\n node_pos = np.vstack((node_pos, point))\\n\\n return np.array(node_pos, np.float_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c31ffd48dcffc937dc432ac7e053a4\",\n \"score\": \"0.57642174\",\n \"text\": \"def new_tile(self):\\n # replace with your code\\n value = random.choice(([2] * 90) + ([4] * 10))\\n row = random.randrange(0, self._grid_height)\\n col = random.randrange(0, self._grid_width)\\n while self._grid[row][col] != 0:\\n row = random.randrange(0, self._grid_height)\\n col = random.randrange(0, self._grid_width)\\n self._grid[row][col] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ead1e548380a86bdaf04d202b919a9\",\n \"score\": \"0.57620573\",\n \"text\": \"def generate_random(min=1, max=100, nums=10):\\r\\n rng = default_rng()\\r\\n numbers = rng.choice(np.arange(min, max), size=nums, replace=False)\\r\\n return numbers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"067b0c9331fe9d6f358c985551d189e8\",\n \"score\": \"0.57606566\",\n \"text\": \"def random_board(self):\\n\\n board = np.random.randint(0, 11, (4, 4))\\n for i in range(len(board)):\\n for j in range(len(board[i])):\\n if board[i, j] != 0:\\n board[i, j] = 2**board[i, j]\\n\\n return board\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a53462efd8cd44e882681569c190c16b\",\n \"score\": \"0.5738326\",\n \"text\": \"def _random_binary_string_matrix(rows, cols, max_length):\\n return [[_random_binary_string_gen(max_length) for _ in range(cols)] for _ in range(rows)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d35526d1bf0b825c860d350e1ac10bc6\",\n \"score\": \"0.5732991\",\n \"text\": \"def random_nums(num, mn, mx):\\n array = []\\n while len(array) < num:\\n array.append(random.randrange(mn, mx))\\n return array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bef418b0a73e8106cd9eb06f05c5fd17\",\n \"score\": \"0.57216287\",\n \"text\": \"def create_uniform_grid(width, height):\\n grid_size = (10, 10)\\n num_bins_horizontal = int(round(width / grid_size[0]))\\n num_bins_vertical = int(round(height / grid_size[1]))\\n bins = (num_bins_horizontal, num_bins_vertical)\\n low = [0, 0]\\n high = [width, height]\\n grid = [np.linspace(low[dim], high[dim], bins[dim] + 1)[1:-1] for dim in range(len(bins))]\\n\\n return grid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eb3792c26297b15a21efcef87db0a30\",\n \"score\": \"0.57181585\",\n \"text\": \"def random_init(n, initial, bounds, use_point=False, problem=None):\\n population = np.ascontiguousarray(problem.randomize(n))\\n if use_point:\\n population[0] = clip(initial, *bounds)\\n return population\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14698691c4c5b9c503f4ad0dd636a9b2\",\n \"score\": \"0.571791\",\n \"text\": \"def between(min, max):\\n return random.random() * (max - min) + min\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfba3ffb732bbcbc4f6b8d65e3d0b236\",\n \"score\": \"0.57167524\",\n \"text\": \"def make_uniform_grid(xmin, xmax, ncells, nghost=2):\\n dx = (xmax - xmin)/(ncells - 1)\\n xf = xmin + dx*np.arange(-nghost, ncells+nghost)\\n return Grid(xf, nghost)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"871d393eda57d80211983a9d6a371272\",\n \"score\": \"0.57135993\",\n \"text\": \"def pick(arr, m):\\r\\n if m > len(arr):\\r\\n raise ValueError(\\\"not enough items to fill all m slots\\\")\\r\\n res = arr[:m]\\r\\n for i in range(m, len(arr)):\\r\\n k = randint(0, i)\\r\\n if k < m: # k must not equal m, else index out of bounds\\r\\n res[k] = arr[i]\\r\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d01779782b949870f6786ae3608d27a\",\n \"score\": \"0.57105386\",\n \"text\": \"def initialize(X, k):\\n # cases\\n if not isinstance(X, np.ndarray) or len(X.shape) != 2:\\n return None\\n if not isinstance(k, int) or k <= 0:\\n return None\\n\\n # Setting min and max values per col\\n n, d = X.shape\\n X_min = X.min(axis=0)\\n X_max = X.max(axis=0)\\n\\n # return multivariate uniform distribution\\n return np.random.uniform(X_min, X_max, size=(k, d))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbf63516ecb02fb8b9bb6e45c127cd7f\",\n \"score\": \"0.5704827\",\n \"text\": \"def set_signal(low=-1, high=1, N=10):\\r\\n s = np.random.uniform(low=low, high=high, size=(N, 1))\\r\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2037e8fe5f46db94c7ae1d2c4ea90639\",\n \"score\": \"0.5690302\",\n \"text\": \"def genMatrix2(size=200, value=5):\\n\\n matrix = np.asarray([ np.asarray([value for col in range(0,size)]) for row in range(0,size)])\\n\\n return matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9be7e2035c2be7ce56d938c165118b0\",\n \"score\": \"0.56721246\",\n \"text\": \"def new_tile(self):\\n cell_value = 1\\n while cell_value != 0:\\n row = random.randint(0, self._height -1 )\\n column = random.randint(0, self._width -1)\\n cell_value = self._grid[row][column]\\n \\n random_value = random.randint(1, 10)\\n if random_value <= 9:\\n self._grid[row][column] = 2\\n else:\\n self._grid[row][column] = 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"524d47fee9bf75d608299e90ceab4177\",\n \"score\": \"0.56651396\",\n \"text\": \"def make_matrix(height, width, fill_value):\\n return [[fill_value for dummy_col in range(width)] for \\n dummy_row in range(height)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8d9aba0d4376eba79830ebab1e872f8\",\n \"score\": \"0.56586415\",\n \"text\": \"def mt_rand (low = 0, high = sys.maxint):\\n return random.randint (low, high)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfe57dcc9769fc9fa0f79c0f13bc10b9\",\n \"score\": \"0.56497943\",\n \"text\": \"def generate_list():\\n upper_bound = int(input(\\\"-> Please choose the Upper Bound of the array: \\\"))\\n size = int(input(\\\"-> Please choose the size of the array: \\\"))\\n\\n # return upperBound, size, np.random.randint(0, upperBound, size)\\n\\n rand_arr = []\\n for _ in range(size):\\n rand_arr.append(pcg32bounded(upper_bound))\\n\\n return upper_bound, size, rand_arr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db70b12d1f1d4ff0b1029a7c2f2bd4ed\",\n \"score\": \"0.56410575\",\n \"text\": \"def generate_function(limit):\\r\\n w0 = random.uniform(-limit, limit)\\r\\n w1 = random.uniform(-limit, limit)\\r\\n w2 = random.uniform(-limit, limit)\\r\\n return np.array([w0, w1, w2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"739e28b3f79887822c1142806d82199e\",\n \"score\": \"0.5627422\",\n \"text\": \"def _random_vector(min, max, prng=DEFAULT_PRNG):\\n min = np.array(min)\\n max = np.array(max)\\n assert min.shape == max.shape\\n assert len(min.shape) == 1\\n return prng.uniform(min, max)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a89e331ec96e57eba296d4dab8507303\",\n \"score\": \"0.5625954\",\n \"text\": \"def random_uniform(low=0.0, high=0.0, size=None, dev='cpu', f=None):\\n return _get_framework(f=f).random_uniform(low, high, size, dev)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c14d79510049f0d7914b9a234dc2b100\",\n \"score\": \"0.56223214\",\n \"text\": \"def random_x_gen(self):\\n if isinstance(self.min_x, list):\\n x_list = []\\n for i,j in zip(self.min_x, self.max_x):\\n x_list.append(uniform(i,j))\\n return x_list\\n else:\\n x_list = [uniform(self.min_x, self.max_x)]\\n return x_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b4515e37546eb3ae4dfa5ca3bbfedf2\",\n \"score\": \"0.56164277\",\n \"text\": \"def gen_pool_matrix(size=25,mu=.0,sigma=1.,gamma=-.5):\\n\\n if gamma==0:\\n asymm=0\\n else:\\n asymm=(1- np.sqrt(1-gamma**2) )/gamma\\n\\n aij=(np.random.normal(0,1./np.sqrt(size),(size,size)) )\\n\\n aij= (aij+asymm * aij.T) / np.sqrt(1+ asymm**2 )\\n\\n A=-mu/size + sigma*aij\\n np.fill_diagonal(A,-1)\\n return A\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994092,"cells":{"query_id":{"kind":"string","value":"68d870d5d23b867b8cde3c54962e3557"},"query":{"kind":"string","value":"Creates the temporary html_to_pdf.html file that will be located in the rssparser/data/ folder. With pdfkit library converts this html file to the pdf file specified in topdf arg. And removes the temporary html file."},"positive_passages":{"kind":"list like","value":[{"docid":"38fd8232ae791da34cff94ddf52be21a","score":"0.7395331","text":"def to_pdf(items, path_to_save_pdf):\n to_html(items, 'rssparser/data/html_to_pdf.html')\n pdfkit.from_file('rssparser/data/html_to_pdf.html', path_to_save_pdf)\n os.remove('rssparser/data/html_to_pdf.html')","title":""}],"string":"[\n {\n \"docid\": \"38fd8232ae791da34cff94ddf52be21a\",\n \"score\": \"0.7395331\",\n \"text\": \"def to_pdf(items, path_to_save_pdf):\\n to_html(items, 'rssparser/data/html_to_pdf.html')\\n pdfkit.from_file('rssparser/data/html_to_pdf.html', path_to_save_pdf)\\n os.remove('rssparser/data/html_to_pdf.html')\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0294fe5f674f0b44652a9c10498ec7d8","score":"0.73789454","text":"def write_pdf(self, html):\n try:\n f = tempfile.NamedTemporaryFile(delete=False, suffix='.html')\n f.write(html.encode('utf_8', 'xmlcharrefreplace'))\n f.close()\n except Exception:\n raise IOError(u\"Unable to create temporary file, aborting\")\n\n dummy_fh = open(os.path.devnull, 'w')\n\n try:\n command = [\"prince\", f.name, \"-o\", self.destination_file]\n\n Popen(command, stderr=dummy_fh).communicate()\n except Exception:\n raise EnvironmentError(u\"Unable to generate PDF file using \"\n \"prince. Is it installed and available?\")\n finally:\n dummy_fh.close()","title":""},{"docid":"d32ba824fcf4a80ccfc2eb14e49d67f6","score":"0.7002694","text":"def pdftotxt(pdfdata):\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\n print pdffout.name\n pdffout.write(pdfdata)\n pdffout.flush()\n\n xmlin = tempfile.NamedTemporaryFile(mode='r', suffix='.xml')\n tmpxml = xmlin.name # \"temph.xml\"\n #cmd = '/usr/bin/pdftohtml -nodrm -enc UTF-8 \"%s\" \"%s\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\n cmd = '/usr/bin/pdftohtml -xml -nodrm -zoom 1.5 -enc UTF-8 -noframes \"%s\" \"%s\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\n cmd = cmd + \" >/dev/null 2>&1\" # can't turn off output, so throw away even stderr yeuch\n os.system(cmd)\n\n pdffout.close()\n #xmlfin = open(tmpxml)\n xmldata = xmlin.read()\n xmlin.close()\n return xmldata","title":""},{"docid":"d32ba824fcf4a80ccfc2eb14e49d67f6","score":"0.7002694","text":"def pdftotxt(pdfdata):\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\n print pdffout.name\n pdffout.write(pdfdata)\n pdffout.flush()\n\n xmlin = tempfile.NamedTemporaryFile(mode='r', suffix='.xml')\n tmpxml = xmlin.name # \"temph.xml\"\n #cmd = '/usr/bin/pdftohtml -nodrm -enc UTF-8 \"%s\" \"%s\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\n cmd = '/usr/bin/pdftohtml -xml -nodrm -zoom 1.5 -enc UTF-8 -noframes \"%s\" \"%s\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\n cmd = cmd + \" >/dev/null 2>&1\" # can't turn off output, so throw away even stderr yeuch\n os.system(cmd)\n\n pdffout.close()\n #xmlfin = open(tmpxml)\n xmldata = xmlin.read()\n xmlin.close()\n return xmldata","title":""},{"docid":"6c196d6b87e3e5b19398d2194ee877ad","score":"0.68640995","text":"def create_pdf(html):\n\n # Define options\n pdf_options = {\n \"page-size\": \"A4\",\n \"orientation\": \"portrait\"\n }\n\n if selectedTemplate == \"FWF\":\n pdf_options[\"orientation\"] = \"landscape\"\n\n config = pdfkit.configuration(wkhtmltopdf=PATH_WKHTMLTOPDF)\n\n pdfkit.from_file(html, html.replace(\".html\", \".pdf\"), configuration=config, options=pdf_options)\n return html.replace(\".html\", \".pdf\")","title":""},{"docid":"a0eb8dbaf78e05f9f89ad04fa2b6edba","score":"0.6609989","text":"def test_put_convert_html_to_pdf(self):\n # Already in storage\n name = \"test1.html\"\n result_name = \"putHtmlToPdf.pdf\"\n test_folder = \"HtmlTestDoc\"\n test_out_path = test_folder + \"/\" + result_name\n\n try:\n # Convert document to pdf in storage\n self.api.put_convert_document_to_pdf(\n name, out_path=test_out_path, width=800, height=1000, left_margin=50, right_margin=100,\n top_margin=150, bottom_margin=200, folder=test_folder, storage=\"\")\n\n # Download result\n res = TestHelper.download_file(test_out_path)\n save_file = TestHelper.test_dst + result_name\n TestHelper.move_file(res, save_file)\n except ApiException as ex:\n print(\"Exception\")\n print(\"Info: \" + str(ex))\n raise ex","title":""},{"docid":"35f0546e6d277ecc65ddd9c430c67fdb","score":"0.6557726","text":"def pdftoxml(pdfdata):\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\n pdffout.write(pdfdata)\n pdffout.flush()\n\n xmlin = tempfile.NamedTemporaryFile(mode='r', suffix='.xml')\n tmpxml = xmlin.name # \"temph.xml\"\n cmd = 'pdftohtml -xml -nodrm -zoom 1.5 -enc UTF-8 -noframes \"%s\" \"%s\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\n cmd = cmd + \" >/dev/null 2>&1\" # can't turn off output, so throw away even stderr yeuch\n os.system(cmd)\n\n pdffout.close()\n #xmlfin = open(tmpxml)\n xmldata = xmlin.read()\n xmlin.close()\n return xmldata","title":""},{"docid":"3dfddfcdddaa2569c5ab0f399a7a0c9b","score":"0.65014225","text":"def convert_html2pdf(detector):\n # get a list of all the html files in the calwebb_spec2_pytests dir\n latest_htmlfile = get_latest_file(\"*\" + detector + \"*.html\")\n # create the pdf output name\n pdf_file = latest_htmlfile.replace(\".html\", \".pdf\")\n # convert the html report into a pdf file\n # import pdfkit\n # options = {'dpi': 96}\n # pdfkit.from_file(latest_htmlfile, pdf_file, options=options)\n print(\"\\n Converted \", latest_htmlfile, \" to \", pdf_file, \". Both files are available in current directory. \\n\")","title":""},{"docid":"45d93f989fbde2e3edb5c3b4ce84ee8a","score":"0.6468519","text":"def write_to_pdf(self, html, config, ofname=\"\"):\n pdf = None\n try:\n if ofname == \"\":\n pdf = pdfkit.from_string(html, False, configuration=config)\n # pdfkit.from_string(html, ofname, configuration=config)\n else:\n pdfkit.from_string(html, ofname, configuration=config)\n # pdfkit.from_string(html, ofname)\n finally:\n self.grouped_failure.close()\n self.grouped_risk.close()\n self.overall_failure.close()\n self.overall_risk.close()\n return pdf","title":""},{"docid":"d87ec27ca5227f0fbaf58a93a928598a","score":"0.646367","text":"def pdftoxml(pdfdata):\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\n pdffout.write(pdfdata)\n pdffout.flush()\n\n xmlin = tempfile.NamedTemporaryFile(mode='r', suffix='.xml')\n tmpxml = xmlin.name # \"temph.xml\"\n cmd = '/usr/bin/pdftohtml -xml -nodrm -zoom 1.5 -enc UTF-8 -noframes \"%s\" \"%s\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\n cmd = cmd + \" >/dev/null 2>&1\" # can't turn off output, so throw away even stderr yeuch\n os.system(cmd)\n\n pdffout.close()\n #xmlfin = open(tmpxml)\n xmldata = xmlin.read()\n xmlin.close()\n return xmldata","title":""},{"docid":"d87ec27ca5227f0fbaf58a93a928598a","score":"0.646367","text":"def pdftoxml(pdfdata):\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\n pdffout.write(pdfdata)\n pdffout.flush()\n\n xmlin = tempfile.NamedTemporaryFile(mode='r', suffix='.xml')\n tmpxml = xmlin.name # \"temph.xml\"\n cmd = '/usr/bin/pdftohtml -xml -nodrm -zoom 1.5 -enc UTF-8 -noframes \"%s\" \"%s\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\n cmd = cmd + \" >/dev/null 2>&1\" # can't turn off output, so throw away even stderr yeuch\n os.system(cmd)\n\n pdffout.close()\n #xmlfin = open(tmpxml)\n xmldata = xmlin.read()\n xmlin.close()\n return xmldata","title":""},{"docid":"00a399a9e9d6ec6d4ad9e1a3d7dab1e3","score":"0.62859905","text":"def create_html_file(html):\r\n tmp_fd, url = tempfile.mkstemp(suffix='.html')\r\n tmp_file = os.fdopen(tmp_fd, 'w')\r\n tmp_file.write(html)\r\n tmp_file.close()\r\n return url","title":""},{"docid":"2eff7e819359036aa0747cd7814dcccb","score":"0.62706757","text":"def test_post_convert_html_in_request_to_pdf(self):\n name = \"postHtmlToPdfInReq.pdf\"\n test_out_path = \"HtmlTestDoc/\" + name\n test_file = TestHelper.test_src + \"test1.html\"\n try:\n # Upload and convert document to pdf\n self.api.post_convert_document_in_request_to_pdf(\n out_path=test_out_path, file=test_file, width=800, height=1000, left_margin=50, right_margin=100,\n top_margin=150, bottom_margin=200)\n\n # Download result\n res = TestHelper.download_file(test_out_path)\n save_file = TestHelper.test_dst + name\n TestHelper.move_file(res, save_file)\n except ApiException as ex:\n print(\"Exception\")\n print(\"Info: \" + str(ex))\n raise ex","title":""},{"docid":"b275724231056372124a1e8b27e79337","score":"0.6114608","text":"def test_get_convert_html_to_pdf(self):\n name = \"test1.html\"\n try:\n # Upload file to storage\n res = TestHelper.upload_file(name)\n self.assertTrue(len(res.uploaded) == 1)\n self.assertTrue(len(res.errors) == 0)\n\n # Convert document to pdf\n res = self.api.get_convert_document_to_pdf(\n name, width=800, height=1000, left_margin=50, right_margin=100, top_margin=150, bottom_margin=200,\n folder=TestHelper.folder, storage=\"\"\n )\n self.assertTrue(isinstance(res, str), \"Error convert html to pdf\")\n\n # Move to test folder\n TestHelper.move_file(str(res), TestHelper.test_dst)\n except ApiException as ex:\n print(\"Exception\")\n print(\"Info: \" + str(ex))\n raise ex","title":""},{"docid":"176018c473850f440b9fdd512a2624f6","score":"0.6098932","text":"def save_pdf(htmls, file_name):\n options = {\n 'page-size': 'Letter',\n 'margin-top': '0.75in',\n 'margin-right': '0.75in',\n 'margin-bottom': '0.75in',\n 'margin-left': '0.75in',\n 'encoding': \"UTF-8\",\n 'custom-header': [\n ('Accept-Encoding', 'gzip')\n ],\n 'cookie': [\n ('cookie-name1', 'cookie-value1'),\n ('cookie-name2', 'cookie-value2'),\n ],\n 'outline-depth': 10,\n }\n pdfkit.from_file(htmls, file_name, options=options)","title":""},{"docid":"38e1ce71cdbb72451e8c10ad70d8a187","score":"0.60953766","text":"def generate_pdf(html): \n result = StringIO.StringIO()\n pdf = pisa.pisaDocument(StringIO.StringIO(html.encode(\"UTF-8\")), result)\n if not pdf.err:\n return HttpResponse(result.getvalue(), content_type='application/pdf')\n return HttpResponse('Error al generar el PDF: %s' % cgi.escape(html))","title":""},{"docid":"d6c614441db22ad8704f78516e5ea146","score":"0.6012346","text":"def write_pdf(self):\n try:\n #f = tempfile.NamedTemporaryFile(delete=False, suffix='.tex')\n f = open(self.output_file.replace('.pdf', '.tex'), 'w')\n for a in self.content:\n f.write(a)\n f.close()\n except Exception:\n raise IOError(u\"Unable to create temporary file, aborting\")\n\n #dummy_fh = open(os.path.devnull, 'w')\n\n try:\n command = [\"pdflatex\", \"-shell-escape\", f.name]\n Popen(command).communicate()\n except Exception:\n raise EnvironmentError(u\"Unable to generate PDF file using \"\n \"pdflatex. Is it installed and available?\")\n finally:\n #dummy_fh.close()\n name = self.output_file.replace('.pdf', '')\n latex_trash = ['.out', '.log', '.aux', '.snm', '.nav', '.toc']\n for a in latex_trash:\n #print self.source_abspath+'/'+name+a\n if os.path.isfile(self.source_abspath+'/'+name+a):\n os.remove(self.source_abspath+'/'+name+a)","title":""},{"docid":"0860851ea29bb2f310e2cf9b95fb2370","score":"0.5933459","text":"def html_to_pdf(html, as_http_response=False, name=None):\n # weasyprint is an extra dependency\n import weasyprint\n\n def url_fetcher(url, timeout=10, ssl_context=None):\n return weasyprint.default_url_fetcher(\n \"file://\" + url.replace(\"file://\", settings.BASE_DIR), timeout, ssl_context\n )\n\n ret = weasyprint.HTML(string=html, base_url=\"\", url_fetcher=url_fetcher).write_pdf()\n if as_http_response:\n ret = HttpResponse(ret, content_type=\"application/pdf\")\n ret[\"Content-Disposition\"] = f\"inline; filename={name}.pdf\"\n return ret","title":""},{"docid":"5eab72ba84cb310f145f8c77fa281d36","score":"0.59234834","text":"def test_save_news_in_pdf(self, pisa, html_saver):\n pisa.return_value = ''\n path = os.path.abspath(os.curdir)\n mock_logger = MagicMock(return_value=None)\n html_file = os.path.join(path, 'test.html')\n with open(html_file, 'x', encoding='utf-8') as html:\n html.write(self.html)\n html_saver.return_value = html\n pdf = functions.save_news_in_pdf_file(self.article_a, path, mock_logger)\n self.assertTrue(os.path.join(path, 'rss_news.pdf'))\n os.remove(pdf.name)","title":""},{"docid":"2e5590425036d5778bca7b7b0cc78c96","score":"0.5892757","text":"def pdftoetree(pdforig, options=''):\n xmlin = tempfile.NamedTemporaryFile(mode='r', suffix='.xml')\n tmpxml = xmlin.name # \"temph.xml\"\n cmd = '/usr/bin/pdftohtml -xml -nodrm -zoom 1.5 -enc UTF-8 -noframes %s \"%s\" \"%s\"' % (options, pdforig, os.path.splitext(tmpxml)[0])\n cmd = cmd + \" >/dev/null 2>&1\" # can't turn off output, so throw away even stderr yeuch\n os.system(cmd)\n tree = lxml.etree.parse(xmlin)\n xmlin.close()\n return tree","title":""},{"docid":"9a61eb38b2a2578876b9f58f885a1dc0","score":"0.5879228","text":"def _write_pdf_report(self):\n LOGGER.info('Generating HTML for writing pdf report...')\n pypandoc.convert_file(self.path, 'html',\n outputfile=self._REPORT_TMP_HTML_PATH,\n extra_args=['-V', 'geometry:margin=1.5cm', '--columns', '1000'])\n LOGGER.info('Metamorphosising HTML to PDF...')\n weasyprint.HTML(self._REPORT_TMP_HTML_PATH) \\\n .write_pdf(self.path_pdf, stylesheets=[weasyprint.CSS(self._REPORT_CSS_PATH)])","title":""},{"docid":"bf8a7e53828d8044b506b13602db0bf1","score":"0.5771483","text":"def convert(file, path):\n try:\n process = subprocess.Popen(['pdftohtml -c -nodrm -hidden -xml ' + file + ' ' + path], shell=True)\n process.wait()\n except Exception as exception:\n error_logger(traceback.format_exc())\n error_logger(str(exception))","title":""},{"docid":"14441f9b6dafc757d682e0f257f898d6","score":"0.5673843","text":"def create_pdf(text: str, output_file: str, font_type: str, font_size: int,\n remove_cms: bool = True, remove_tgs: bool = True) -> None:\n\n # Install reportlab if not installed\n try:\n import reportlab\n except ImportError:\n import pip\n pip.main(['install', 'reportlab', '--user'])\n import reportlab\n\n from reportlab.pdfgen import canvas\n from reportlab.pdfbase import pdfmetrics\n from reportlab.pdfbase.ttfonts import TTFont\n from reportlab.platypus import Paragraph\n from reportlab.lib.styles import ParagraphStyle\n from reportlab.lib.styles import getSampleStyleSheet\n from reportlab.platypus.flowables import KeepInFrame\n from reportlab.lib import pagesizes\n\n canvas = canvas.Canvas(output_file, pagesize=pagesizes.A4)\n\n # Get default fonts into a dictionary\n default_fonts = {font.upper(): font for font in canvas.getAvailableFonts()}\n\n # Get fonts in fonts/ directory into a dictionary\n program_fonts = {}\n for root, dirs, files in os.walk(\"fonts/\"):\n for file in files:\n if file.endswith('.ttf') or file.endswith('.otf'):\n program_fonts[re.sub('\\.[ot]tf$', '', file).upper()] = os.path.join(root, file)\n\n # Try to register font if not one of the defaults, fails otherwise\n try:\n # Check if font is in the fonts/ directory, register if true\n if font_type.upper() in program_fonts:\n pdfmetrics.registerFont(TTFont(font_type, program_fonts.get(font_type.upper())))\n # Else check if path to font is provided, register if true\n elif os.path.isfile(font_type) and font_type.endswith('.ttf') or font_type.endswith('.otf'):\n pdfmetrics.registerFont(TTFont(font_type, font_type))\n # Else check if the provided font is one of the default fonts, rename font\n elif font_type.upper() in default_fonts:\n font_type = default_fonts.get(font_type.upper())\n # Otherwise fail\n else:\n raise ValueError('Not a valid font file.')\n except (ValueError, reportlab.pdfbase.ttfonts.TTFError) as ex:\n print(ex)\n print('Can not use font: ' + os.path.abspath(font_type) + '.')\n print('You can use one of the default fonts:')\n [print(font, end=\" \") for font in default_fonts.values()]\n print()\n print('or the fonts in the \"fonts/\" directory:')\n [print(font, end=\" \") for font in program_fonts.keys()]\n print()\n print('Alternatively, you can provide a path to a TrueType font file.')\n sys.exit(1)\n\n # Styling\n stylesheet = getSampleStyleSheet()\n stylesheet.add(ParagraphStyle(name='custom', fontName=font_type, fontSize=font_size, leading=font_size * 1.3))\n\n # Positioning\n width, height = pagesizes.A4\n width -= 40 # drawing area width\n height -= 30 # drawing area height\n\n tlx = 30 # offset from left\n tly = height + 10 # offset from bottom\n\n # Remove comments and/or tags\n if remove_cms:\n text = remove_comments(text)\n if remove_tgs:\n text = remove_tags(text)\n\n # Pagination\n # Pages are split on empty lines (can contain whitespace)\n pages = re.split('(?:[\\n][ \\t]*){2,}', text.strip())\n for index, page in enumerate(pages):\n frame = KeepInFrame(width, height, [Paragraph(page.replace('\\n', '<br/>\\n'), stylesheet[\"custom\"])])\n w, h = frame.wrapOn(canvas, width, height)\n frame.drawOn(canvas, tlx, tly - h)\n\n # Insert page break for all but the last page\n if index != len(pages):\n canvas.showPage()\n\n # Produce pdf\n canvas.save()","title":""},{"docid":"89f97a9e6d3a0bcad906a5da4aed9b2e","score":"0.56649834","text":"def convert_pdf(converter, build_path: pathlib.Path, settings: Settings, variables: Variables) -> None:\n build_path = get_build_path(settings)\n pdf_build_filename = build_path / pathlib.Path(settings[\"OutputFormats\"][\"pdf\"][\"OutputFile\"])\n html_filename = build_path / pathlib.Path(settings[\"InputFile\"]).with_suffix(\".html\").name\n html_builder = HTMLContentBuilder(html_filename, build_path)\n converter.convert(html_builder)\n html_cover_filename = build_path / pathlib.Path(settings[\"InputFile\"]).with_suffix(\".cover.html\").name\n html_cover_builder = HTMLCoverBuilder(html_cover_filename, build_path)\n converter.convert(html_cover_builder)\n with open(\"DocumentDefinitions/Shared/header.html\", encoding=\"utf-8\") as header_template_file:\n header_contents = header_template_file.read() % variables[\"KWALITEITSAANPAK\"]\n header_filename = build_path / \"header.html\"\n with open(header_filename, mode=\"w\", encoding=\"utf-8\") as header_file:\n header_file.write(header_contents)\n pdf_build_filename = build_path / pathlib.Path(settings[\"OutputFormats\"][\"pdf\"][\"OutputFile\"])\n os.system(\n f\"\"\"wkhtmltopdf \\\n --enable-local-file-access \\\n --footer-html DocumentDefinitions/Shared/footer.html --footer-spacing 10 \\\n --header-html {header_filename} --header-spacing 10 \\\n --margin-bottom 27 --margin-left 34 --margin-right 34 --margin-top 27 \\\n --title '{settings[\"Title\"]}' \\\n cover {html_cover_filename} \\\n {\"toc --xsl-style-sheet DocumentDefinitions/Shared/toc.xsl\" if settings[\"IncludeTableOfContents\"] else \"\"} \\\n {html_filename} {pdf_build_filename}\"\"\"\n )\n pdf_build_filename2 = build_path / pathlib.Path(settings[\"OutputFormats\"][\"pdf\"][\"OutputFile\"] + \".step2\")\n os.system(f\"gs -o {pdf_build_filename2} -sDEVICE=pdfwrite -dPrinted=false -f {pdf_build_filename} src/pdfmark.txt\")\n copy_output(pdf_build_filename2, settings, \"pdf\")","title":""},{"docid":"d0e2715f8118b67b3edf30b83ffd6975","score":"0.5664913","text":"def test_put_convert_html_to_xps(self):\n # Already in storage\n name = \"test1.html\"\n result_name = \"putHtmlToXps.xps\"\n test_folder = \"HtmlTestDoc\"\n test_out_path = test_folder + \"/\" + result_name\n\n try:\n # Convert document to pdf in storage\n self.api.put_convert_document_to_xps(\n name, out_path=test_out_path, width=800, height=1000, left_margin=50, right_margin=100,\n top_margin=150, bottom_margin=200, folder=test_folder, storage=\"\")\n\n # Download result\n res = TestHelper.download_file(test_out_path)\n save_file = TestHelper.test_dst + result_name\n TestHelper.move_file(res, save_file)\n except ApiException as ex:\n print(\"Exception\")\n print(\"Info: \" + str(ex))\n raise ex","title":""},{"docid":"cfd815c2e0a434f97518f60ed56d6dc4","score":"0.55401945","text":"def save_pdf(self, url: str, file_name: str):\n if not url.startswith('http'):\n url = 'http://' + url\n # TODO replace\n file_name = file_name.replace(\".html\", \"\")\n file_name = f'{path_join(self.root, file_name)}'\n if exists(file_name):\n os.remove(file_name)\n try:\n pdfkit.from_url(url, file_name, options=self.options)\n except OSError as e:\n logger.error(f'Pdf not saved: {url}.\\n{e}')\n os.remove(file_name)","title":""},{"docid":"4c08ab5016c03208202e11ed2e154698","score":"0.55357","text":"def tmpHtmlFile(dir_path):\n\n #\n # Create a temporary file for logging the folder\n #\n fd, filename = tempfile.mkstemp(suffix='.htm', dir=dir_path)\n os.close(fd)\n f = open(filename, 'w')\n return f","title":""},{"docid":"2a7e8c7ed9c51d95c1a4249b2faa73f1","score":"0.5509825","text":"def serve_pdf_from_html(\n html: str, offered_filename: str = \"test.pdf\", **kwargs\n) -> HttpResponse:\n pdf = get_pdf_from_html(html, **kwargs)\n return serve_buffer(\n pdf,\n offered_filename=offered_filename,\n content_type=MimeType.PDF,\n as_attachment=False,\n as_inline=True,\n )","title":""},{"docid":"f77d9f22578ebf7a1e053b7270d3f152","score":"0.54926306","text":"def build_html_report(options, xml_file):\n options.xml_file = xml_file\n options.html = True\n xml_parser = ReportBuilder.FunkLoadXmlParser()\n xml_parser.parse(xml_file)\n utils.trace(\"Creating html report ...\")\n html_path = ReportRenderHtml.RenderHtml(\n xml_parser.config, xml_parser.stats,\n xml_parser.error, xml_parser.monitor,\n options)()\n report = os.path.dirname(html_path)\n os.rename(xml_file[:-4]+'.log',\n os.path.join(report, 'funkload.log'))\n assert os.path.isfile(os.path.join(report, 'funkload.xml'))\n os.remove(xml_file)\n utils.trace(\"done: \\n\")\n utils.trace(\"file://%s\\n\" % html_path)\n return report","title":""},{"docid":"0b17b9da643de205fff5be0910d9748b","score":"0.547525","text":"def pdf_generation(request):\n html_template = render_to_string('bill.html')\n BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n css = [os.path.join(BASE_DIR, 'static/css/', 'bill.css')]\n pdf_file = HTML(string=html_template).write_pdf(\n target='/home/kobey/Desktop/mypdf.pdf', stylesheets=css) # this line creates pdf\n fs = FileSystemStorage('/home/kobey/Desktop')\n\n with fs.open('mypdf.pdf') as pdf:\n response = HttpResponse(pdf, content_type='application/pdf')\n response['Content-Disposition'] = 'inline; filename=\"mypdf.pdf\"'\n return response\n # return render(request,'bill.html')\n return response","title":""},{"docid":"ac112bcfe74d53987cff233ea9b2c369","score":"0.5449141","text":"def bytes(self):\n html_bytes = self.html_format.bytes\n\n pdf_bytes = pdfkit.from_string(\n html_bytes.decode(self.encoding),\n False,\n options={\n \"load-error-handling\": \"ignore\",\n \"load-media-error-handling\": \"ignore\",\n \"margin-left\": self.pdf_margin_left,\n \"margin-right\": self.pdf_margin_right,\n \"margin-top\": self.pdf_margin_top,\n \"margin-bottom\": self.pdf_margin_bottom,\n \"zoom\": self.pdf_output_zoom,\n # 'disable-smart-shrinking': '',\n },\n )\n\n return pdf_bytes","title":""},{"docid":"d3ce3f507c7b7ece2d179cc3cf679f0c","score":"0.5428952","text":"def pdf_html(view):","title":""},{"docid":"bcdf35dc11be71f9f351df5482af0ef2","score":"0.53816795","text":"def create_html(markdown_file):\n html_file = html_name_from_markdown(markdown_file)\n # Convert from Github-flavored Markdown to HTML\n cmd = f'pandoc -f gfm -o {html_file} {markdown_file}'\n # Use pandoc to generate HTML from Markdown\n process = subprocess.run(\n cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT,\n shell=True,\n encoding=\"utf-8\",\n universal_newlines=True\n )\n return process","title":""},{"docid":"2d47ebd79efdd4a750fb98bd79b27073","score":"0.53535813","text":"def write_html(html_text, filename):\n\ttmpfolder = os.path.join(CURDIR, '../../tmp')\n\tif not os.path.exists(tmpfolder):\n\t\tos.makedirs(tmpfolder)\n\ttagfile = os.path.join(tmpfolder, '%s.html' % filename)\n\twith codecs.open(tagfile, 'w') as f:\n\t\tsoup = BeautifulSoup(html_text, 'html.parser')\n\t\tf.write(soup.prettify())","title":""},{"docid":"745ad3f26e45a974dbb18cc9552430ba","score":"0.5352487","text":"def pdf_response_xp(self, html, filename, download=False):\n disposition = f'filename=\"{filename}\"'\n if download:\n disposition = 'attachment;' + disposition\n\n response = HttpResponse(content_type='application/pdf')\n response['Content-Disposition'] = disposition\n\n pisa_status = pisa.CreatePDF(html, dest=response)\n\n if pisa_status.err:\n message = (f\"Unable to generate PDF from <hl><pre>{html}</pre>\")\n return HttpResponse(message, status=500)\n\n return response","title":""},{"docid":"128f66c5ed05b808f175f6b85d9797e5","score":"0.5317638","text":"def write_pdf(\n inkscape: Path | str,\n pdf: Path | str,\n xml: EtreeElement,\n stylesheet: Path | str | None = None,\n) -> str:\n with NamedTemporaryFile(mode=\"wb\", delete=False) as svg_file:\n svg = write_svg(svg_file, xml, stylesheet)\n _ = write_pdf_from_svg(inkscape, svg, pdf)\n os.unlink(svg)\n return str(pdf)","title":""},{"docid":"157bcc73badec34aaf0404662fd29f63","score":"0.52880245","text":"def test_get_convert_html_to_pdf_by_url(self):\n source_url = \"https://stallman.org/articles/anonymous-payments-thru-phones.html\"\n try:\n\n # Convert url to pdf\n res = self.api.get_convert_document_to_pdf_by_url(\n source_url, width=800, height=1000, left_margin=50, right_margin=100, top_margin=150,\n bottom_margin=200,\n folder=TestHelper.folder, storage=\"\"\n )\n self.assertTrue(isinstance(res, str), \"Error convert url to pdf\")\n\n # Move to test folder\n TestHelper.move_file(str(res), TestHelper.test_dst)\n except ApiException as ex:\n print(\"Exception\")\n print(\"Info: \" + str(ex))\n raise ex","title":""},{"docid":"25c0027263b8baf8f6862720e05dd64f","score":"0.5274468","text":"def create_pdf(self) -> None:\n print(f\"making {self.pdf.title}\")\n if self.pdf.exists():\n self.pdf.path.unlink()\n\n canvas = Canvas(str(self.pdf.path))\n canvas.setAuthor(\"Luca Ercole\")\n\n for page in self.pages:\n drawing = page.drawing\n canvas.setPageSize((drawing.width, drawing.height))\n\n renderPDF.draw(drawing, canvas, 0, 0)\n\n for bookmark in page.bookmarks:\n exact_ts = bookmark.dtime\n title = exact_ts.replace(minute=30).isoformat()[:16]\n pos = bookmark.position\n\n canvas.bookmarkPage(key=str(exact_ts), fit=\"XYZ\", top=pos)\n # canvas.addOutlineEntry(title=title, key=title)\n canvas.addOutlineEntry(title=title, key=str(exact_ts))\n # canvas.drawString(0, pos, str(pos))\n\n canvas.showPage()\n\n canvas.save()\n print(\"Done\")","title":""},{"docid":"2e3775d2948a79bc9b4f40bc3bb7ec60","score":"0.526846","text":"def render_pdf(\n template,\n file_,\n url_fetcher=staticfiles_url_fetcher,\n context=None,\n):\n context = context or {}\n\n html = get_template(template).render(context)\n HTML(\n string=html,\n base_url='not-used://',\n url_fetcher=url_fetcher,\n ).write_pdf(\n target=file_,\n )","title":""},{"docid":"4a21aec5ddb7c8dd1fd3ecd2d307a0eb","score":"0.5261352","text":"async def convert_body(request: Request):\n data = await request.body()\n\n if not data:\n return Response(\"ERROR: No body\", status_code=400)\n\n with TemporaryDirectory() as tmpdirname:\n outfile = os.path.join(tmpdirname, \"out.html\")\n\n with open(outfile, \"w\") as fh:\n fh.write(data.decode(\"utf-8\"))\n\n bytes = await execute_wkhtmltopdf(outfile)\n\n return Response(bytes, media_type=\"application/pdf\")","title":""},{"docid":"3ef788c9e61eef0953f8e61f0b1d26e5","score":"0.52207357","text":"def exportToHTMLFile(self, filePath):\n\n if os.path.exists(filePath) and os.path.isfile(filePath): #if the file path provided exists\n htmlDocument = self.htmlRenderer( self.noteEditor.toPlainText() ) #convert the note to HTML\n htmlFile = open(filePath, \"w\") #open the provided file path\n htmlFile.write(htmlDocument) #write the HTML content to the file\n htmlFile.close() #close the file after writing to it","title":""},{"docid":"11f9b846961763ac38e7783a82354e1f","score":"0.52157754","text":"def get_to_file(self, *args, **kwargs):\n import os\n from tempfile import mkstemp\n fd, name = mkstemp()\n fp = os.fdopen(fd, 'wb')\n fp.write(self.get_html(*args, **kwargs))\n fp.close()\n return name","title":""},{"docid":"b75b3ed28d39bb01fd607d1ba1a7a81d","score":"0.52083796","text":"def to_pdf(paper_dir, template_dir=None, use_shell_escape=False, flatten=False, keep_comments=False):\n template_dir = template_dir or scriptorium.CONFIG['TEMPLATE_DIR']\n\n paper_dir = os.path.abspath(paper_dir)\n if os.path.isdir(paper_dir):\n fname = paper_root(paper_dir)\n elif os.path.isfile(paper_dir):\n fname = paper_dir\n paper_dir = os.path.dirname(paper_dir)\n else:\n raise IOError(\"{0} is not a valid directory\".format(paper_dir))\n\n old_cwd = os.getcwd()\n if old_cwd != paper_dir:\n os.chdir(paper_dir)\n\n if not fname:\n raise IOError(\"{0} has no obvious root.\".format(paper_dir))\n\n #Convert all auxillary MMD files to LaTeX\n for mmd in _list_files(paper_dir):\n bname = os.path.basename(mmd).split('.')[0]\n with open(mmd, 'r') as mmd_fp, open('{0}.tex'.format(bname), 'w') as tex_fp:\n tex_fp.write(pymmd.convert(mmd_fp.read(), fmt=pymmd.LATEX, dname=mmd, ext=pymmd.SMART))\n\n pdf_cmd, new_env = _build_latex_cmd(fname, template_dir, use_shell_escape)\n\n bname = os.path.basename(fname).split('.')[0]\n if flatten:\n tname = '{0}.tex'.format(bname)\n fargs = '--keep-comments' if keep_comments else ''\n with tempfile.NamedTemporaryFile() as tmp:\n subprocess.check_call(['latexpand', '-o', tmp.name, tname, fargs], env=new_env)\n shutil.copyfile(tmp.name, tname)\n\n try:\n subprocess.check_output(pdf_cmd, env=new_env)\n if os.path.exists(os.path.join(paper_dir, '{0}.xdy'.format(bname))):\n subprocess.check_output(['makeglossaries', bname], env=new_env)\n except subprocess.CalledProcessError as exc:\n raise IOError(decodeCPEError(exc.output))\n\n _process_bib(fname, new_env)\n\n try:\n subprocess.check_output(pdf_cmd, env=new_env)\n except subprocess.CalledProcessError as exc:\n raise IOError(decodeCPEError(exc.output))\n try:\n subprocess.check_output(pdf_cmd, env=new_env)\n except subprocess.CalledProcessError as exc:\n raise IOError(decodeCPEError(exc.output))\n\n # Revert working directory\n if os.getcwd() != old_cwd:\n os.chdir(old_cwd)\n\n return os.path.join(paper_dir, '{0}.pdf'.format(bname))","title":""},{"docid":"94509dbaa2f6a18c6dc1e05251e1c2bd","score":"0.51938945","text":"def render_to_pdf(template_src, context_dict={}):\n template = get_template(template_src)\n html = template.render(context_dict)\n result = BytesIO()\n pdf = pisa.pisaDocument(BytesIO(html.encode(\"UTF-8\")), result, link_callback=link_callback)\n if not pdf.err:\n return HttpResponse(result.getvalue(), content_type='application/pdf')\n return HttpResponse(\"Error Rendering PDF\", status=400)","title":""},{"docid":"5ed75e370b200466ebdd2320004ff903","score":"0.517688","text":"def pdftotext(pdfdata):\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\n pdffout.write(pdfdata)\n pdffout.flush()\n\n textin = tempfile.NamedTemporaryFile(mode='r', suffix='.txt')\n tmptext = textin.name\n cmd = '/usr/bin/pdftotext -layout -enc UTF-8 \"%s\" \"%s\"' % (pdffout.name, tmptext)\n cmd = cmd + \" >/dev/null 2>&1\" # can't turn off output, so throw away even stderr yeuch\n os.system(cmd)\n\n pdffout.close()\n text = textin.read()\n textin.close()\n return text","title":""},{"docid":"5ed75e370b200466ebdd2320004ff903","score":"0.517688","text":"def pdftotext(pdfdata):\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\n pdffout.write(pdfdata)\n pdffout.flush()\n\n textin = tempfile.NamedTemporaryFile(mode='r', suffix='.txt')\n tmptext = textin.name\n cmd = '/usr/bin/pdftotext -layout -enc UTF-8 \"%s\" \"%s\"' % (pdffout.name, tmptext)\n cmd = cmd + \" >/dev/null 2>&1\" # can't turn off output, so throw away even stderr yeuch\n os.system(cmd)\n\n pdffout.close()\n text = textin.read()\n textin.close()\n return text","title":""},{"docid":"5ed75e370b200466ebdd2320004ff903","score":"0.517688","text":"def pdftotext(pdfdata):\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\n pdffout.write(pdfdata)\n pdffout.flush()\n\n textin = tempfile.NamedTemporaryFile(mode='r', suffix='.txt')\n tmptext = textin.name\n cmd = '/usr/bin/pdftotext -layout -enc UTF-8 \"%s\" \"%s\"' % (pdffout.name, tmptext)\n cmd = cmd + \" >/dev/null 2>&1\" # can't turn off output, so throw away even stderr yeuch\n os.system(cmd)\n\n pdffout.close()\n text = textin.read()\n textin.close()\n return text","title":""},{"docid":"3c7276167a8be937af616b6973946ad0","score":"0.51666015","text":"def generate_pdf(tex_content, out_file):\n out_file = Path(out_file)\n with TemporaryDirectory() as tmpdir:\n tmpdir = Path(tmpdir)\n tex_file = tmpdir/f'{out_file.stem}.tex'\n\n with tex_file.open('w') as f:\n f.write(tex_content)\n\n p = subprocess.run(['pdflatex', '-interaction', 'nonstopmode', f'-output-directory={tmpdir}', tex_file.name])\n\n shutil.copy(tmpdir/f'{tex_file.stem}.pdf', out_file)","title":""},{"docid":"2890795c6ceb3173a18c489371b326f2","score":"0.51657754","text":"def create_pdf(pdf_data):\n pdf = BytesIO()\n pisa.CreatePDF(BytesIO(pdf_data.encode('utf-8')), pdf)\n return pdf","title":""},{"docid":"f58fcf9f9894f89e5ddf6face1d4fedf","score":"0.5107512","text":"def create_html_and_punc(html_dir, punc_dir, plain_dir, stm_dir, rawpunc_dir, puncstm_dir, speaker):\n\n # Perform link reading and html conversion if raw_transcript does not exist\n try:\n if not os.path.exists(os.path.join(punc_dir, speaker + \".txt\")):\n # exceptions, some speakers result in error\n if speaker == 'KarenThompsonWalker_2012G':\n return\n # Convert stm into a plain file of transcription (the transcription does not have punctuation or capitalization)\n plain_file = get_plain_with_stm(plain_dir, stm_dir, speaker)\n # Get the html_file that contains transcript that is the closest to the plain file (if there is anything close)\n best_link = write_html_with_name(html_dir, plain_file, speaker)\n\n # Write a punctuated transcript into punc_dir\n raw_transcript = write_raw_punc_with_name(rawpunc_dir, best_link, speaker)\n\n write_clean_punc_with_name(punc_dir, rawpunc_dir, speaker)\n except:\n pass","title":""},{"docid":"974e5f80464a485cd6ba7a2366181d30","score":"0.5073336","text":"def wk_pdf_file( sourcefile, destfile, wk_executable = \"wkhtmltopdf\", \n options = [], del_source = False,\n ):\n if not destfile.endswith('.pdf'):\n destfile += '.pdf'\n try:\n rc = call( [ wk_executable ] + options + [ sourcefile, destfile ] )\n os.remove( sourcefile ) \n return destfile\n except Exception, e:\n if settings.MODULE_ADMINLOG:\n AdminLog( by = 'gdjet.utils.pdf.wk_pdf_file',\n message = 'Error writing PDF File: %s\\n%s' % (destfile, e) ).save()\n else:\n raise\n return False","title":""},{"docid":"edde6ac7b446727f6fc19ff87f4b5a72","score":"0.5070975","text":"def write_html(outdir, path, html):\n with open(os.path.join(outdir, path), 'w') as buf:\n buf.write(html)","title":""},{"docid":"05e5404a4a2dac9f611c81d6fc10d174","score":"0.50464195","text":"def to_html(doc, output=\"/tmp\", style=\"dep\"):\n # generate filename from first six non-punct tokens\n file_name = \"test_extract.html\"\n html = displacy.render(doc, style=style, page=True) # render markup\n if output is not None:\n output_path = Path(output)\n if not output_path.exists():\n output_path.mkdir()\n output_file = Path(output) / file_name\n output_file.open(\"a\", encoding=\"utf-8\").write(html) # save to file\n print(\"Saved HTML to {}\".format(output_file))\n else:\n print(html)","title":""},{"docid":"0c6a0bec22c1297e3af5fb0e9516c418","score":"0.50404537","text":"def outline_pdf2html(pdf_fp,\n fp_dir_pdf2htmlEx=\"C:\\\\pdf2htmlEX\",\n fp_outdir=\"C:\\\\CloudStation\\\\CBitter_-_PHD\\\\CloudStation\\\\PHDLiterature\\\\AHP_scopus\\\\html\",\n outline_file_name=\"outline.html\",\n copy_to_outline_file=True,\n verbose=False):\n assert (isinstance(pdf_fp, str))\n assert (isinstance(fp_outdir, str))\n assert (isinstance(fp_dir_pdf2htmlEx, str))\n assert (isinstance(outline_file_name, str))\n\n if not os.path.isdir(fp_outdir):\n raise ValueError(\"outline_pdf2html - fp_outdir does not exist?\")\n\n if not os.path.isdir(fp_dir_pdf2htmlEx):\n raise ValueError(\"outline_pdf2html - pdf2htmlex does not exist?\")\n\n if not os.path.isfile(pdf_fp):\n raise ValueError(\"outline_pdf2html - pdf file does not exist?\")\n\n if not outline_file_name:\n raise ValueError(\"outline_pdf2html - outline file name not provided\")\n\n outline_fp = os.path.join(fp_outdir, outline_file_name)\n\n # we generate the content into a temp dir\n # then take the outline file and stuff its contents into the outline_file\n pdf_fname, _ = path.splitext(path.basename(pdf_fp))\n outline_fname= \"%s.outline\" % pdf_fname\n\n ret_code = pdf2html(fp_pdf=pdf_fp,\n generated_html_filename=\"generated.html\",\n fp_outdir=fp_outdir,\n splitPages=False,\n optimizeText=False,\n processNonText=True,\n preprocessToPS=False,\n options={\n \"process-outline\": \"1\"\n },\n debug=verbose)\n\n tmp_outline_fp = os.path.join(fp_outdir, outline_fname)\n\n if copy_to_outline_file:\n shutil.copy(tmp_outline_fp, outline_fp)\n else:\n outline_fp = tmp_outline_fp\n\n if verbose:\n print(\"outline html file: %s (%s)\" % (outline_fp, ret_code))\n\n if ret_code:\n return outline_fp\n else:\n return None","title":""},{"docid":"8576b9bbf7ed2ae4c085a4e241fbf49b","score":"0.50296617","text":"def test_to_pdf_create_file(converter_instance):\n converter_instance.to_pdf()\n assert (os.path.exists(os.path.join(converter_instance.folder_path, \"news.pdf\")) is True)","title":""},{"docid":"c80657ba20afb25195013e4407941c6e","score":"0.5018169","text":"async def execute_wkhtmltopdf(uri: str) -> bytes:\n cmd = [\n \"wkhtmltopdf\",\n \"--log-level\",\n \"none\",\n uri,\n \"-\",\n ]\n return check_output(cmd)","title":""},{"docid":"5fe4e36f80d325aad7df8ce8fb1bfd5e","score":"0.50049746","text":"def generate_pdf(template_name, context, params, request=None, fix_static_paths=True, stream=True):\n html = render_to_string(template_name, context=context, request=request)\n if fix_static_paths:\n replacement = get_absolute_static_path(request)\n html = html.replace(settings.STATIC_URL, replacement)\n\n r = requests.post(\n settings.PDF_SERVICE_ENDPOINT,\n json={\n \"html\": html\n },\n params=params,\n headers={\n \"x-api-key\": settings.PDF_SERVICE_TOKEN,\n },\n stream=stream\n )\n r.raise_for_status()\n return r","title":""},{"docid":"8097d95c1ec5b77121f5c8291341b8b0","score":"0.498684","text":"def test_to_html_create_file(converter_instance):\n converter_instance.to_html()\n assert (os.path.exists(os.path.join(converter_instance.folder_path, \"news.html\")) is True)","title":""},{"docid":"2053447ac98da626c60de7b5cd15511f","score":"0.49583066","text":"def store_html(self, path: str = f\"html{os.sep}result.html\", allow_header_links: bool = False,\n skip_tables: bool = False):\n with open(path, \"w\") as result_file:\n result_file.write(self.to_html(allow_header_links=allow_header_links, skip_tables=skip_tables))\n result_file.write(\"\\n\")\n result_file.flush()","title":""},{"docid":"d5264c302886e9a3626ccb5e033f72eb","score":"0.49577415","text":"def render_to_pdf(template_src, context_dict):\n template = get_template(template_src)\n context = Context(context_dict)\n html = template.render(context)\n result = StringIO.StringIO()\n\n pdf = pisa.pisaDocument(StringIO.StringIO(html.encode(\"UTF-8\")),\n dest=result,\n encoding='UTF-8',\n link_callback=fetch_resources)\n if not pdf.err:\n response = HttpResponse(result.getvalue(),\n mimetype='application/pdf')\n\n return response\n\n return HttpResponse('We had some errors<pre>%s</pre>' % escape(html))","title":""},{"docid":"a743a40fa4293ed78b9314c281e88473","score":"0.49230415","text":"def create_txt():\n\t(_, week, year) = get_date()\n\tpdf_path = get_pdf_path()\n\ttxt_path = BASE_TXT_PATH.format(year, week)\n\tsubprocess.call(['pdftotext', pdf_path, txt_path])","title":""},{"docid":"1a07e12ddc82feb7cb48115b74ab5ea1","score":"0.49225733","text":"def __init__(\n self,\n encoding=\"utf-8\",\n pdf_margin_left=\"0\",\n pdf_margin_right=\"0\",\n pdf_margin_top=\"0\",\n pdf_margin_bottom=\"0\",\n pdf_output_zoom=\"1.6\",\n pdf_input_zoom=\"1.0\",\n pdftohtml_path=\"pdftohtml\",\n wkhtmltopdf_path=\"wkhtmltopdf\",\n ):\n super().__init__()\n self.encoding = encoding\n self.html_format = HtmlFormat()\n self.pdf_margin_left = pdf_margin_left\n self.pdf_margin_right = pdf_margin_right\n self.pdf_margin_top = pdf_margin_top\n self.pdf_margin_bottom = pdf_margin_bottom\n self.pdf_output_zoom = pdf_output_zoom\n self.pdf_input_zoom = pdf_input_zoom\n self.pdftohtml_path = pdftohtml_path\n self.wkhtmltopdf_path = wkhtmltopdf_path","title":""},{"docid":"e4651e9c583a34c952eaa6ec4ab5831b","score":"0.49170125","text":"def saveDMP(event=None):\n f = filedialog.asksaveasfile(mode='w', defaultextension=\".pdf\")\n if f is None: # asksaveasfile return `None` if dialog closed with \"cancel\".\n return\n\n html = create_html(template=selectedTemplate, output=f.name.replace(\".pdf\", \"\"))\n print(\"Stored HTML successfully as \" + str(html))\n pdf = create_pdf(html=html)\n print(\"Stored PDF successfully as \" + str(pdf))","title":""},{"docid":"4769277cd3da1e5a1e6572ef0b07a02b","score":"0.49056256","text":"def make_pdf():\n d = datetime.utcnow()\n report_date = d.strftime(\"%Y%m%d%H%M%S\")\n report_name = \"report-\" + report_date + \".pdf\"\n pdf = canvas.Canvas(report_name, pagesize=letter)\n pdf.setFont(\"Courier\", 50)\n pdf.setStrokeColorRGB(1, 0, 0)\n pdf.setFillColorRGB(1, 0, 0)\n pdf.drawCentredString(letter[0] / 2, inch * 7, 'FANCY REPORT')\n pdf.drawCentredString(letter[0] / 2, inch * 6, 'CLASSIFIED')\n pdf.drawCentredString(letter[0] / 2, inch * 5, 'For Your Eyes Only')\n pdf.setFont(\"Courier\", 20)\n pdf.drawCentredString(letter[0] / 2, inch * 4, 'Created: ' + d.strftime(\"%B %d, %Y %H:%M:%S\"))\n\n pdf.showPage()\n pdf.save()\n return report_name","title":""},{"docid":"5b3f9db2fbba9ad8e8cc088581170c69","score":"0.49039894","text":"def _get_report_pdf(self, report):\n pdf = StringIO()\n pisa.CreatePDF(StringIO(self._get_render_template(report)), pdf)\n return pdf","title":""},{"docid":"12b3953e593046ba7db9d6531441a611","score":"0.48923507","text":"def _merge_pdf(self, documents):\n writer = PdfFileWriter()\n streams = [] # We have to close the streams *after* PdfFilWriter's call to write()\n for document in documents:\n pdfreport = file(document, 'rb')\n streams.append(pdfreport)\n reader = PdfFileReader(pdfreport)\n for page in range(0, reader.getNumPages()):\n writer.addPage(reader.getPage(page))\n\n merged_file_fd, merged_file_path = tempfile.mkstemp(suffix='.pdf', prefix='report.merged.tmp.')\n with closing(os.fdopen(merged_file_fd, 'w')) as merged_file:\n writer.write(merged_file)\n\n for stream in streams:\n stream.close()\n\n return merged_file_path","title":""},{"docid":"6e9b3441ef63a23768c73ceb77384fe9","score":"0.48896754","text":"def generate_pdf(output_dir='.', doujinshi_obj=None, rm_origin_dir=False):\n if doujinshi_obj is not None:\n doujinshi_dir = os.path.join(output_dir, doujinshi_obj.filename)\n pdf_filename = os.path.join(\n os.path.join(doujinshi_dir, '..'),\n '{}.pdf'.format(doujinshi_obj.filename)\n )\n else:\n pdf_filename = './doujinshi.pdf'\n doujinshi_dir = '.'\n\n file_list = os.listdir(doujinshi_dir)\n file_list.sort()\n\n logger.info('Writing PDF file to path: {}'.format(pdf_filename))\n with open(pdf_filename, 'wb') as pdf_f:\n full_path_list = (\n [os.path.join(doujinshi_dir, image) for image in file_list]\n )\n pdf_f.write(img2pdf.convert(full_path_list))\n\n if rm_origin_dir:\n shutil.rmtree(doujinshi_dir, ignore_errors=True)\n\n logger.log(15, 'PDF file has been written to \\'{0}\\''.format(doujinshi_dir))","title":""},{"docid":"2b8ab0b323e14a8c6b5bd2340eeb3416","score":"0.4878805","text":"def outputPDF(self, file, writer):\n pass","title":""},{"docid":"5bf1cd5194512ca2b1d7b1511b805b7c","score":"0.48548716","text":"def create_raw_html(year, house, bill, html):\n raw_files_folder = os.path.join(default_path,'bill_files', 'raw', str(year))\n if not os.path.exists(raw_files_folder):\n os.mkdir(raw_files_folder)\n if check_bill(html):\n file = open(os.path.join(raw_files_folder, '{}{}{}.html'.format(year, house, bill)), 'w',\n encoding='utf-8')\n file.write(html)\n file.close()\n return html\n else:\n return None","title":""},{"docid":"97e3e1c72d8531045cbe4b8a97734306","score":"0.4854084","text":"def make_send_pdf(self):\n slim_content = edit_content(page.get_content(page.get_summary()), \"Weblinks\", \"Literatur\", \"Einzelnachweise\",\n \"Sonstiges\", \"Filme\", \"Auszeichnungen\", \"Filmdokumentationen\", \"Anmerkungen\",\n \"Biografien\",\n \"Weitere Texte\") # cuts out these sections from the article\n\n to_mail = 'send_to_this_mail@gmail.com' # testing email\n try:\n print(\"Progress:\")\n # create the word page\n add_title()\n print(\"Added title...\")\n add_logo()\n print(\"Added image...\")\n add_summary()\n print(\"Added summary...\")\n add_content(slim_content)\n print(\"Added content...\")\n document.save(get_doc_name() + \".docx\")\n print(\"Saving file...\")\n delete_pic_in_outer_folder()\n word_to_pdf.docx_to_pdf(get_doc_name() + \".docx\", get_doc_name())\n move_word_files()\n print(\"Sending email...\")\n email_sender.send_email(to_mail, get_doc_name() + \".pdf\", str(page.get_title())) # uncomment to send email\n word_to_pdf.move_pdf_to_folder()\n print(\"===Finished===\")\n except PermissionError:\n print(\n \"Bitte schließen sie Microsoft Word und versuchen sie es erneut.\") # \"Please close Microsoft Word and try again\"","title":""},{"docid":"9f8dfd98ea039984867401866adc896e","score":"0.48486796","text":"def convert_html(converter, build_path: pathlib.Path, settings: Settings) -> None:\n html_build_filename = build_path / settings[\"OutputFormats\"][\"html\"][\"OutputFile\"]\n html_builder = HTMLBuilder(html_build_filename, pathlib.Path(\"\"))\n converter.convert(html_builder)\n copy_output(html_build_filename, settings, \"html\")","title":""},{"docid":"ce203683853ee854a16b6684b592734b","score":"0.48462394","text":"def render_html_to_file(filename: str, output: str):\n logging.info(\"Writing html to {}\".format(filename))\n\n with open(filename, \"w\", encoding=\"utf8\") as fh:\n fh.write(output)\n\n return filename","title":""},{"docid":"19fd1ea086e1d74c28a5d7728308b60b","score":"0.4846071","text":"def write(self):\n html = self.render()\n\n if self.file_type == 'pdf':\n self.write_pdf(html)\n else:\n with codecs.open(self.destination_file, 'w',\n encoding='utf_8') as outfile:\n outfile.write(html)","title":""},{"docid":"db50bccef34fa2cde35523c7a57ff994","score":"0.4844008","text":"def generate_pdf(self):\n filepath = os.path.join(self.m_output_dir, self.m_run_prefix+\"-summary\")\n self.m_doc.generate_pdf(clean_tex=False, filepath=filepath)","title":""},{"docid":"8fcc29f1571cb3bf5ee3b46f1255c3a4","score":"0.48339328","text":"def write_graphs_to_pdf(graphs, out_dir=Path('./')):\n out_dir.mkdir(parents=True, exist_ok=True)\n print('Writing graphs to pdf an png files...')\n for graph_file in tqdm(graphs):\n graphs[graph_file].write_pdf(out_dir / (graph_file + '.pdf'))\n graphs[graph_file].write_png(out_dir / (graph_file + '.png'))","title":""},{"docid":"59d771725ea870dc6728128ba359bdb1","score":"0.48289427","text":"def create_pdf(catalog, template):\n RML_DIR = 'rml'\n templateName = os.path.join(RML_DIR, template)\n template = preppy.getModule(templateName)\n namespace = {\n 'products':catalog,\n 'RML_DIR': RML_DIR,\n 'IMG_DIR': 'img'\n\n }\n rml = template.getOutput(namespace,quoteFunc=preppy.stdQuote)\n open(os.path.join(DATA_DIR,'latest.rml'), 'wb').write(asBytes(rml))\n buf = BytesIO()\n rml2pdf.go(asBytes(rml), outputFileName=buf)\n return buf.getvalue()","title":""},{"docid":"d1968c959db56a6186b5a0d52bcb0d5c","score":"0.48108557","text":"def getPDF(self, params, template):\n output = template.render(Context(params))\n byteString = weasyprint.HTML(string = output).write_pdf()\n pdfString = base64.b64encode(byteString)\n return pdfString","title":""},{"docid":"58aa2decd2a6e9c1e9094b492d5e8937","score":"0.4808302","text":"def pdf_to_text(filename,prefix=\"\"):\n global pdftotextProgram,pdftotextOptions\n\n notfound = \"\"\"\\\nCould not find '%s', which is needed for pdf to text conversion.\n%s is part of the 'xPdf' suite of programs, obtainable at:\n http://www.foolabs.com/xpdf/\n\"\"\" % (pdftotextProgram,pdftotextProgram)\n (fout,output) = apply_command_temp(pdftotextProgram,pdftotextOptions,notfound,filename,prefix,\".txt\")\n return fout","title":""},{"docid":"7a568e4e033abbee304191ba90f8f4e2","score":"0.4804926","text":"def _create_document(self, link, html, title):\n self.chunk.compute_file_header_value(len(self.chunk.header))\n self.chunk.create_document(link, html, title)","title":""},{"docid":"78382dc1d4dff5fd655acb2923b6454f","score":"0.47853962","text":"def temp_html_output(self, plot_object, backend=None):\n save_plot(\n \"temp_plot\", plot_object, self.rootdir, save_as=[\"html\"], backend=backend\n )\n return os.path.join(self.rootdir, \"html\", \"temp_plot.html\")","title":""},{"docid":"16a129296e4028a73355f299ab0f62b4","score":"0.47735542","text":"def gera_pdf(imgdir, rodada, outpath=os.getcwd(),**kwargs):\n\n # TODO: Organizar arquivos em grupos de pdfs, config aqui dentro, texto descritivo vem como arg\n # TODO: Colocar para receber exemplos de dados para escrever nos textos. Receber texto exemplo pronto de fora\n # TODO: colocar exemplo como caption da tabela com styler do pandas\n\n textos ={\"cedidos_#{rodada:d}\": \"PONTOS CEDIDOS: Média dos pontos cedidos pelas equipes aos adversários no \"\n \"Cartola em {ano}, considerando o mando de campo. Em destaque, as equipes com\"\n \"desvio acima padrão.\",\n \"cedidos(semMando)_#{rodada:d}\": \"PONTOS CEDIDOS SEM MANDO: Média dos pontos cedidos pelas equipes aos adversários no \"\n \"Cartola em {ano}, considerando todos seus jogos SEM distinção de mando de campo. Em destaque, \"\n \"as equipes com maiores distância para média do campeoanto.\",\n \"liqCed_#{rodada:d}\": \"PONTOS CEDIDOS LÍQUIDOS (EXCLUSIVO): Média dos pontos cedidos pelas equipes \"\n \"aos adversários no Cartola em {ano}, tirando da conta as pontuações de gols, \"\n \"assistências e SG. A tabela não considera o mando de campo. Em destaque, as \"\n \"equipes com desvio acima padrão.\",\n \"ptosPos_#{rodada:d}\": \"PONTOS GANHOS (EXCLUSIVO): Média de pontos ganhos pelas equipes, separados por \"\n \"posição, no Cartola FC. A tabela não faz a distinção de mando de campo.\",\n \"apostas_#{rodada:d}\": \"CASAS DE APOSTAS (PRIVADO): Análise da probabilidade de vitória, SG e gols dos \"\n \"confrontos nas principais casas de apostas.\",\n \"rbCed_#{rodada:d}\": \"DSs e DDs CEDIDAS: Média dos desarmes e defesas difíceis (goleiros) cedidos pelas \"\n \"equipes aos adversários no Cartola em {ano}, sem considerar o mando de campo. Em destaque, \"\n \"as equipes com desvio acima do padrão.\",\n \"desempenhoClubes_#{rodada:d}\": \"DESEMPENHO DOS TIMES: Desempenho dos times no Cartola FC em {ano}, sem levar em \"\n \"consideração o mando de campo. As informações são a média de: SG, Gols Pró, Gols\"\n \" Sofridos e SG Cedidos.\",\n 'goleiros_#{rodada:d}': \"GOLEIROS (EXCLUSIVO): Médias por jogo dos goleiros no Cartola FC em {ano}. Todos os valores\"\n \" em média por jogo. A tabela conta com distinção de mando de campo.\",\n 'GS_Forma_#{rodada:d}': \"GOLS SOFRIDOS POR FORMA: Tabela com os tipos de gols sofridos por equipe do Campeonato \"\n \"Brasileiro {ano}. A tabela conta todos os jogos (casa e fora).\",\n 'GS_posicao_#{rodada:d}': \"GOLS SOFRIDOS POR POSIÇÃO (EXCLUSIVO): Tabela com os gols sofridos por equipe do Campeonato \"\n \"Brasileiro {ano}, separados pela posição do jogador adversário que marcou o gol. A tabela conta\"\n \" todos os jogos (casa e fora).\",\n }\n \n doc=Document(\".\\\\pytabelas\\\\socios.docx\")\n p = doc.add_paragraph()\n r = p.add_run()\n font = r.font\n font.name=\"Industry-Black\"\n font.size = Pt(36)\n p.paragraph_format.alignment = WD_ALIGN_PARAGRAPH.CENTER\n r.add_text(\"ARQUIVO DOS SÓCIOS RODADA \" +str(rodada))\n\n for prod, decr in textos.items():\n r.add_break(WD_BREAK.PAGE)\n p = doc.add_paragraph()\n r = p.add_run()\n font = r.font\n font.name=\"Industry-Black\"\n font.size = Pt(24)\n r.add_text(decr.format(rodada=rodada, **kwargs))\n p = doc.add_paragraph()\n r = p.add_run()\n r.add_picture(os.path.join(imgdir, prod.format(rodada=rodada, **kwargs)+\".jpg\"), width= Inches(6.1))\n \n \n doc.save(os.path.join(outpath, 'socios #{}'.format(rodada)+'.docx'))","title":""},{"docid":"14a58f574464d90e4ca06b82dbfe70c6","score":"0.47671464","text":"def pickle_pdf_xml(arguments):\n file = arguments[0]\n path = arguments[1]\n try:\n xml = parser.from_file(file, xmlContent = True)\n replace_string = file.split('\\\\')[-1].replace('.pdf', '')\n save_string = path + replace_string + '.pkl'\n print(save_string)\n pickle.dump(xml, open(save_string, \"wb\"))\n except: \n return file","title":""},{"docid":"d279f1b01da67e0f559cfdfe5755a989","score":"0.47637767","text":"def word_to_pdf(self, file, path):\n convert(file, path + \"\\\\\" + \"resultat\")","title":""},{"docid":"f2c4d3d87cded7336b01ba35a9322859","score":"0.4760178","text":"def WriteRedirectHtmlFile(from_html_path, to_html_path):\n to_html_relative_path = GetRelativePathToDirectoryOfFile(\n to_html_path, from_html_path)\n content = (\"\"\"\n <!DOCTYPE html>\n <html>\n <head>\n <!-- HTML meta refresh URL redirection -->\n <meta http-equiv=\"refresh\" content=\"0; url=%s\">\n </head>\n </html>\"\"\" % to_html_relative_path)\n with open(from_html_path, 'w') as f:\n f.write(content)","title":""},{"docid":"97670acd16ffb183fc86fc028aa73b19","score":"0.47423863","text":"def genpdf(source, des, rm=False):\n command = [\"pdflatex\", \"-output-directory\", des, source]\n subprocess.call(command)\n if rm:\n from os import listdir, remove\n from os.path import isfile, join, splitext, basename\n name = splitext(basename(source))[0]\n for f in listdir(des):\n if isfile(join(des, f)):\n fname, ext = splitext(f)\n if fname == name:\n if ext == '.aux' or ext == '.log' or ext == \".tex\":\n remove(os.path.join(des, f))","title":""},{"docid":"3ce87e257d73c517742749a739b2d386","score":"0.47367832","text":"def test_put_convert_html_to_image(self):\n # Already in storage\n name = \"test1.html\"\n result_name = \"putHtmlToImg.zip\"\n test_folder = \"HtmlTestDoc\"\n test_out_path = test_folder + \"/\" + result_name\n\n try:\n # Convert document to image in storage\n self.api.put_convert_document_to_image(\n name, out_path=test_out_path, out_format=\"tiff\", width=800, height=1000, left_margin=50, right_margin=100,\n top_margin=150, bottom_margin=200, resolution=300, folder=test_folder, storage=\"\")\n\n # Download result\n res = TestHelper.download_file(test_out_path)\n save_file = TestHelper.test_dst + result_name\n TestHelper.move_file(res, save_file)\n except ApiException as ex:\n print(\"Exception\")\n print(\"Info: \" + str(ex))\n raise ex","title":""},{"docid":"3ddf1b24f730594f35e8f63ad7e9be6f","score":"0.46994278","text":"def pdf_to_text_using_tikka(filepath, filename, output_path):\n output_file_name = filename + '_tikka.txt'\n output_file_path = output_path + output_file_name\n parsed_file = parser.from_file(filepath) # Parsing PDF file\n content = parsed_file['content'] # Extracting content\n\n with open(output_file_path, 'w') as f:\n res = f.write(content.strip())\n\n return res","title":""},{"docid":"60d1d2177e14741fa3d0ab824dc37873","score":"0.4697682","text":"def html_str_to_file(text, filename):\n output = open(os.path.join(outputs_path,filename), \"w\", encoding=\"utf-8\")\n output.write(text)\n output.close()\n return","title":""},{"docid":"82efd033b9b5ec1cba6c756bac314bc6","score":"0.46915892","text":"def genPDF(filename):\n newlines = 'echo -e \"' + '\\\\n' * 100 + '\"' # Hack to continue when pdflatex halts.\n cmd = newlines + \" | pdflatex \" + filename + \" --shell-escape 2>/dev/null >/dev/null\"\n os.system(cmd)","title":""},{"docid":"a0dcfed2234da2d5402137e36516e3c8","score":"0.46886522","text":"def test_put_convert_html_to_markdown(self):\n # Already in storage\n name = \"test_md.html\"\n result_name = \"putConvertToMarkdownPy.md\"\n test_folder = \"HtmlTestDoc\"\n test_out_path = test_folder + \"/\" + result_name\n\n try:\n # Convert document to markdown in storage\n self.api.put_convert_document_to_markdown(\n name, out_path=test_out_path, use_git=\"false\", folder=test_folder, storage=\"\")\n\n # Download result\n res = TestHelper.download_file(test_out_path)\n save_file = TestHelper.test_dst + result_name\n TestHelper.move_file(res, save_file)\n except ApiException as ex:\n print(\"Exception\")\n print(\"Info: \" + str(ex))\n raise ex","title":""},{"docid":"410ece57c4f4db8b0ebe89f1cb2ac99b","score":"0.46829975","text":"def _convert_to_pdf(\n self,\n file_content: typing.IO[bytes],\n input_extension: str,\n cache_path: str,\n output_filepath: str,\n mimetype: str,\n ) -> BytesIO:\n\n raise NotImplementedError","title":""},{"docid":"7ef313c3078e29dc10b3d0de64a54aec","score":"0.4661873","text":"def save_as_pdf(self, text: str, file_name: str = \"Podcast.pdf\"):\n text_encoded = text.encode('latin-1', 'replace').decode('latin-1')\n text = text_encoded.replace(\"?\", \".\").replace(\"[\", \"\").replace(\"]\", \"\").replace(\"'\", \"\")\n\n pdf = FPDF(orientation='P', unit='mm', format='A4')\n pdf.set_auto_page_break(True, margin=10)\n pdf.add_page()\n pdf.set_font(family='Courier', size=12)\n splitter = text.split('\\n')\n\n for line in splitter:\n lines = textwrap.wrap(line, 75)\n if len(lines) == 0:\n pdf.ln()\n for wrap in lines:\n pdf.cell(0, 4, wrap, ln=1)\n pdf.output(file_name, 'F')","title":""},{"docid":"a15e60e99e381a95edfceb96a7d8f65f","score":"0.46592584","text":"def test_post_merge_html_template(self):\n template_name = \"HtmlTemplate.html\"\n template_data = \"XmlSourceData.xml\"\n result_name = \"PostMergeHtmlTemplatePython.html\"\n options = \"\"\n folder = \"HtmlTestDoc\"\n storage = \"\"\n data_file = TestHelper.test_src + template_data\n out_path = folder + \"/\" + result_name\n\n try:\n # Convert document to image\n self.api.post_merge_html_template(template_name=template_name, out_path=out_path, file=data_file,\n options=options, folder=folder, storage=storage)\n # Download result\n res = TestHelper.download_file(out_path)\n save_file = TestHelper.test_dst + result_name\n TestHelper.move_file(res, save_file)\n except ApiException as ex:\n print(\"Exception\")\n print(\"Info: \" + str(ex))\n raise ex","title":""},{"docid":"a4836404b9518e2c2482df8997b18135","score":"0.46539325","text":"def render_to_pdf(request, template_name, renderdict, filename):\n rendered_html = get_template(template_name).render(Context(renderdict))\n\n response = HttpResponse(content_type=\"application/pdf\")\n response['Content-Disposition'] = 'attachment; filename=\"' + filename + '\"'\n\n success = pisa.CreatePDF(rendered_html, dest=response)\n\n if not success:\n return HttpResponseServerError()\n return response","title":""},{"docid":"3127f91de047cec24c9fdc3fef4aaf23","score":"0.46349818","text":"def page_extractor_pdftotext(file_name):\n \n import subprocess\n tmpfile = tempfile.NamedTemporaryFile()\n subprocess.call('pdftotext -l 3 \"%s\" \"%s\"' % (file_name, tmpfile.name), shell=True)\n text = tmpfile.read()\n return text","title":""},{"docid":"0a4aa23649bb5e1bffbb345cd3752302","score":"0.4619672","text":"def generate_main_html(output_dir='./', html=False):\n\n image_html = ''\n\n main = readfile('viewer/main.html')\n css = readfile('viewer/main.css')\n js = readfile('viewer/main.js')\n\n element = '\\n\\\n <div class=\"gallery-favorite\">\\n\\\n <div class=\"gallery\">\\n\\\n <a href=\"./{FOLDER}/index.html\" class=\"cover\" style=\"padding:0 0 141.6% 0\"><img\\n\\\n src=\"./{FOLDER}/{IMAGE}\" />\\n\\\n <div class=\"caption\">{TITLE}</div>\\n\\\n </a>\\n\\\n </div>\\n\\\n </div>\\n'\n\n os.chdir(output_dir)\n doujinshi_dirs = next(os.walk('.'))[1]\n\n for folder in doujinshi_dirs:\n if html is True and folder[0] == '[':\n class Foo:\n pass\n foo = Foo()\n foo.name = folder\n foo.filename = folder\n generate_html(doujinshi_obj=foo)\n\n files = os.listdir(folder)\n files.sort()\n\n if 'index.html' in files:\n logger.info('Add doujinshi \\'{}\\''.format(folder))\n else:\n continue\n\n image = files[0] # 001.jpg or 001.png\n if folder is not None:\n title = folder.replace('_', ' ')\n else:\n title = 'nHentai HTML Viewer'\n\n image_html += element.format(FOLDER=folder, IMAGE=image, TITLE=title)\n if image_html == '':\n logger.warning('No index.html found, --gen-main paused.')\n return\n try:\n data = main.format(STYLES=css, SCRIPTS=js, PICTURE=image_html)\n if sys.version_info < (3, 0):\n with open('./main.html', 'w') as f:\n f.write(data)\n else:\n with open('./main.html', 'wb') as f:\n f.write(data.encode('utf-8'))\n shutil.copy(os.path.dirname(__file__)+'/viewer/logo.png', './')\n set_js_database()\n logger.log(\n 15, 'Main Viewer has been written to \\'{0}main.html\\''.format(output_dir))\n except Exception as e:\n logger.warning('Writing Main Viewer failed ({})'.format(str(e)))","title":""},{"docid":"f4360982e2178d4069d811379b54c9fd","score":"0.4611201","text":"def image_to_pdf(self, file):\n image1 = Image.open(file)\n im1 = image1.convert('RGB')\n im1.save(path + \"\\\\\" + \"resultat\" + \"\\\\\" + filename + \".pdf\")","title":""},{"docid":"e9ece8f5929ff929939ee07bd37c6e99","score":"0.46092772","text":"def merge_temporary_pdfs(self,\n description: str=\"No description\",\n ) -> None:\n\n temporary_pdfs = self.find_temporary_pdfs()\n\n filename = self.get_filename()\n save_path = self.get_save_path(filename)\n\n self._merge_and_save(temporary_pdfs, save_path)\n\n self.temporary_counter.zero()\n for temp_pdf in temporary_pdfs:\n os.remove(temp_pdf)\n\n pdf_hash = self.get_file_hash(save_path)\n\n self.save_to_database(\n pdf_hash=pdf_hash,\n filename=filename,\n description=description\n )","title":""},{"docid":"5d3ab1c5e0d594dba140df15b8d6c922","score":"0.45995933","text":"def build_pdf():\n print(\"=== Build PDF Docs from ReST Files ===\")\n subprocess.check_call([\n \"sphinx-build\",\n \"-b\", \"latex\", # Builder name. TODO: accept as arg to setup.py.\n \"-d\", joinpath(SPHINX_BUILD, \"doctrees\"),\n SPHINX_SOURCE,\n joinpath(SPHINX_BUILD, \"latex\")\n ])\n\n LATEXDIR = joinpath(SPHINX_BUILD, \"latex\")\n\n def pdflatex():\n subprocess.call([\"pdflatex\", \"SasView.tex\"], cwd=LATEXDIR)\n\n # Note: pdflatex requires multiple passes to resolve cross-references correctly\n pdflatex()\n pdflatex()\n pdflatex()\n subprocess.call([\"makeindex\", \"-s\", \"python.ist\", \"SasView.idx\"], cwd=LATEXDIR)\n pdflatex()\n pdflatex()\n\n print(\"=== Copy PDF to HTML Directory ===\")\n source = joinpath(LATEXDIR, \"SasView.pdf\")\n target = joinpath(SASVIEW_DOC_TARGET, \"SasView.pdf\")\n shutil.copyfile(source, target)","title":""},{"docid":"32992730a290607665ebdbd18af5310c","score":"0.45934606","text":"def create_html(template, output):\n templateLoader = jinja2.FileSystemLoader(searchpath=\"../templates\")\n templateEnv = jinja2.Environment(loader=templateLoader)\n TEMPLATE_FILE = \"template_\" + template.lower() + \".html\"\n real_template = templateEnv.get_template(TEMPLATE_FILE)\n\n if template == \"FWF\":\n outputText = real_template.render(data_officer=dataOfficerText.get(1.0,END).replace(\"\\n\", \"<p />\"),\n data_characteristics=dataDescriptionText1.get(1.0,END).replace(\"\\n\", \"<p />\"),\n meta_standards=docMetaText1.get(1.0,END).replace(\"\\n\", \"<p />\"),\n data_documentation=docMetaText2.get(1.0,END).replace(\"\\n\", \"<p />\"),\n data_quality=docMetaText3.get(1.0,END).replace(\"\\n\", \"<p />\"),\n data_sharing=dataSharingText1.get(1.0,END).replace(\"\\n\", \"<p />\"),\n data_storage=dataSharingText2.get(1.0,END).replace(\"\\n\", \"<p />\"),\n legal=ethicalText1.get(1.0,END).replace(\"\\n\", \"<p />\"),\n ethical=ethicalText2.get(1.0,END).replace(\"\\n\", \"<p />\"),\n generation=generatedDataText.get(1.0,END).replace(\"\\n\", \"<p />\"))\n else:\n global title\n outputText = real_template.render(title=title,\n abstract=abstractText.get(1.0,END).replace(\"\\n\", \"<p />\"),\n data_summary=dataDescriptionText1.get(1.0,END).replace(\"\\n\", \"<p />\"),\n fair_findable=fairText1.get(1.0,END).replace(\"\\n\", \"<p />\"),\n fair_accessible=fairText2.get(1.0,END).replace(\"\\n\", \"<p />\"),\n fair_interoperable=fairText3.get(1.0,END).replace(\"\\n\", \"<p />\"),\n fair_reuse=fairText4.get(1.0,END).replace(\"\\n\", \"<p />\"),\n resource=resourceText.get(1.0,END).replace(\"\\n\", \"<p />\"),\n security=securityText.get(1.0,END).replace(\"\\n\", \"<p />\"),\n ethical=ethicalText.get(1.0,END).replace(\"\\n\", \"<p />\"),\n other=otherText.get(1.0,END).replace(\"\\n\", \"<p />\"))\n\n html_file = open(output + \".html\", \"w\")\n html_file.write(outputText)\n html_file.close()\n\n return output + \".html\"","title":""},{"docid":"00c0d5d7d427a6c3024f4a4e759cda50","score":"0.4584902","text":"def makeFiles(titlestring, generaldescription, containerlist, runlist, foldername,\n makeIndex = True, makeTables = True, makePlotOverview = True, plottuples = None,\n fullperRunDF = None, fullRunCompDF = None, ZperRunDF = None, ZRunCompDF = None,\n LadderperRunDF = None, LadderRunCompDF = None, cfgname = None,\n linkTeX = False, linkCSV = False, linkCFG = False):\n from ConfigParser import SafeConfigParser\n logging.info(\"Generating HTML files\")\n ####################################################################\n # General HTML headers\n styleconfig = SafeConfigParser()\n logging.debug(\"Loading style config\")\n styleconfig.read(\"configs/style.cfg\")\n\n header = \"<!DOCTYPE html> \\n <html> \\n <body> \\n\"\n style = \"<style> \\n table, th, td {{\\nborder: {0} solid black;\\n border-collapse: collapse;\\n}}\\nth, td {{ padding: {1}; }}\\n</style>\\n\".format(styleconfig.get(\"Tables\",\"bordersize\"),styleconfig.get(\"Tables\",\"padding\"))\n footnote = \"<br><br><small>Generated: {0} by K. Schweiger, korbinian.schweiger@cern.ch</small>\\n\".format(datetime.now())\n footer = \"</body> \\n </html> \\n\"\n htmltemplatetuple = (header, style, footnote, footer)\n ####################################################################\n if makeTables:\n #Check if DFs are passed -> Used to speed up the script\n if fullperRunDF is None or fullRunCompDF is None or ZperRunDF is None or LadderperRunDF is None or LadderRunCompDF is None:\n DFstopass = None\n else:\n DFstopass = (fullperRunDF, fullRunCompDF, ZperRunDF, ZRunCompDF, LadderperRunDF, LadderRunCompDF)\n makeComparisonFiles(titlestring, generaldescription, containerlist, runlist, foldername,\n htmltemplates = htmltemplatetuple, DFs = DFstopass, linkTeX = linkTeX, linkCSV = linkCSV, linkCFG = linkCFG)\n if makeIndex: #Make a landing page called index.html linking tables and plots (if passed as plottuples)\n commentsite = makeRunCommentPage(titlestring, runlist, foldername, htmltemplatetuple, containerlist)\n makeLandingPage(titlestring, runlist, foldername, htmltemplatetuple, makePlotOverview, cfgname, commentsite)\n if makePlotOverview:\n if plottuples is None:\n logging.error(\"No plotlists and names are given! No adding plots to index.\")\n else:\n for plottuple in plottuples: #Go through all groups\n plotlist, group = plottuple\n makePlotOverviewFile(titlestring, generaldescription, plotlist, runlist, foldername, group)","title":""}],"string":"[\n {\n \"docid\": \"0294fe5f674f0b44652a9c10498ec7d8\",\n \"score\": \"0.73789454\",\n \"text\": \"def write_pdf(self, html):\\n try:\\n f = tempfile.NamedTemporaryFile(delete=False, suffix='.html')\\n f.write(html.encode('utf_8', 'xmlcharrefreplace'))\\n f.close()\\n except Exception:\\n raise IOError(u\\\"Unable to create temporary file, aborting\\\")\\n\\n dummy_fh = open(os.path.devnull, 'w')\\n\\n try:\\n command = [\\\"prince\\\", f.name, \\\"-o\\\", self.destination_file]\\n\\n Popen(command, stderr=dummy_fh).communicate()\\n except Exception:\\n raise EnvironmentError(u\\\"Unable to generate PDF file using \\\"\\n \\\"prince. Is it installed and available?\\\")\\n finally:\\n dummy_fh.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d32ba824fcf4a80ccfc2eb14e49d67f6\",\n \"score\": \"0.7002694\",\n \"text\": \"def pdftotxt(pdfdata):\\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\\n print pdffout.name\\n pdffout.write(pdfdata)\\n pdffout.flush()\\n\\n xmlin = tempfile.NamedTemporaryFile(mode='r', suffix='.xml')\\n tmpxml = xmlin.name # \\\"temph.xml\\\"\\n #cmd = '/usr/bin/pdftohtml -nodrm -enc UTF-8 \\\"%s\\\" \\\"%s\\\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\\n cmd = '/usr/bin/pdftohtml -xml -nodrm -zoom 1.5 -enc UTF-8 -noframes \\\"%s\\\" \\\"%s\\\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\\n cmd = cmd + \\\" >/dev/null 2>&1\\\" # can't turn off output, so throw away even stderr yeuch\\n os.system(cmd)\\n\\n pdffout.close()\\n #xmlfin = open(tmpxml)\\n xmldata = xmlin.read()\\n xmlin.close()\\n return xmldata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d32ba824fcf4a80ccfc2eb14e49d67f6\",\n \"score\": \"0.7002694\",\n \"text\": \"def pdftotxt(pdfdata):\\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\\n print pdffout.name\\n pdffout.write(pdfdata)\\n pdffout.flush()\\n\\n xmlin = tempfile.NamedTemporaryFile(mode='r', suffix='.xml')\\n tmpxml = xmlin.name # \\\"temph.xml\\\"\\n #cmd = '/usr/bin/pdftohtml -nodrm -enc UTF-8 \\\"%s\\\" \\\"%s\\\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\\n cmd = '/usr/bin/pdftohtml -xml -nodrm -zoom 1.5 -enc UTF-8 -noframes \\\"%s\\\" \\\"%s\\\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\\n cmd = cmd + \\\" >/dev/null 2>&1\\\" # can't turn off output, so throw away even stderr yeuch\\n os.system(cmd)\\n\\n pdffout.close()\\n #xmlfin = open(tmpxml)\\n xmldata = xmlin.read()\\n xmlin.close()\\n return xmldata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c196d6b87e3e5b19398d2194ee877ad\",\n \"score\": \"0.68640995\",\n \"text\": \"def create_pdf(html):\\n\\n # Define options\\n pdf_options = {\\n \\\"page-size\\\": \\\"A4\\\",\\n \\\"orientation\\\": \\\"portrait\\\"\\n }\\n\\n if selectedTemplate == \\\"FWF\\\":\\n pdf_options[\\\"orientation\\\"] = \\\"landscape\\\"\\n\\n config = pdfkit.configuration(wkhtmltopdf=PATH_WKHTMLTOPDF)\\n\\n pdfkit.from_file(html, html.replace(\\\".html\\\", \\\".pdf\\\"), configuration=config, options=pdf_options)\\n return html.replace(\\\".html\\\", \\\".pdf\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0eb8dbaf78e05f9f89ad04fa2b6edba\",\n \"score\": \"0.6609989\",\n \"text\": \"def test_put_convert_html_to_pdf(self):\\n # Already in storage\\n name = \\\"test1.html\\\"\\n result_name = \\\"putHtmlToPdf.pdf\\\"\\n test_folder = \\\"HtmlTestDoc\\\"\\n test_out_path = test_folder + \\\"/\\\" + result_name\\n\\n try:\\n # Convert document to pdf in storage\\n self.api.put_convert_document_to_pdf(\\n name, out_path=test_out_path, width=800, height=1000, left_margin=50, right_margin=100,\\n top_margin=150, bottom_margin=200, folder=test_folder, storage=\\\"\\\")\\n\\n # Download result\\n res = TestHelper.download_file(test_out_path)\\n save_file = TestHelper.test_dst + result_name\\n TestHelper.move_file(res, save_file)\\n except ApiException as ex:\\n print(\\\"Exception\\\")\\n print(\\\"Info: \\\" + str(ex))\\n raise ex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35f0546e6d277ecc65ddd9c430c67fdb\",\n \"score\": \"0.6557726\",\n \"text\": \"def pdftoxml(pdfdata):\\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\\n pdffout.write(pdfdata)\\n pdffout.flush()\\n\\n xmlin = tempfile.NamedTemporaryFile(mode='r', suffix='.xml')\\n tmpxml = xmlin.name # \\\"temph.xml\\\"\\n cmd = 'pdftohtml -xml -nodrm -zoom 1.5 -enc UTF-8 -noframes \\\"%s\\\" \\\"%s\\\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\\n cmd = cmd + \\\" >/dev/null 2>&1\\\" # can't turn off output, so throw away even stderr yeuch\\n os.system(cmd)\\n\\n pdffout.close()\\n #xmlfin = open(tmpxml)\\n xmldata = xmlin.read()\\n xmlin.close()\\n return xmldata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dfddfcdddaa2569c5ab0f399a7a0c9b\",\n \"score\": \"0.65014225\",\n \"text\": \"def convert_html2pdf(detector):\\n # get a list of all the html files in the calwebb_spec2_pytests dir\\n latest_htmlfile = get_latest_file(\\\"*\\\" + detector + \\\"*.html\\\")\\n # create the pdf output name\\n pdf_file = latest_htmlfile.replace(\\\".html\\\", \\\".pdf\\\")\\n # convert the html report into a pdf file\\n # import pdfkit\\n # options = {'dpi': 96}\\n # pdfkit.from_file(latest_htmlfile, pdf_file, options=options)\\n print(\\\"\\\\n Converted \\\", latest_htmlfile, \\\" to \\\", pdf_file, \\\". Both files are available in current directory. \\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45d93f989fbde2e3edb5c3b4ce84ee8a\",\n \"score\": \"0.6468519\",\n \"text\": \"def write_to_pdf(self, html, config, ofname=\\\"\\\"):\\n pdf = None\\n try:\\n if ofname == \\\"\\\":\\n pdf = pdfkit.from_string(html, False, configuration=config)\\n # pdfkit.from_string(html, ofname, configuration=config)\\n else:\\n pdfkit.from_string(html, ofname, configuration=config)\\n # pdfkit.from_string(html, ofname)\\n finally:\\n self.grouped_failure.close()\\n self.grouped_risk.close()\\n self.overall_failure.close()\\n self.overall_risk.close()\\n return pdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d87ec27ca5227f0fbaf58a93a928598a\",\n \"score\": \"0.646367\",\n \"text\": \"def pdftoxml(pdfdata):\\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\\n pdffout.write(pdfdata)\\n pdffout.flush()\\n\\n xmlin = tempfile.NamedTemporaryFile(mode='r', suffix='.xml')\\n tmpxml = xmlin.name # \\\"temph.xml\\\"\\n cmd = '/usr/bin/pdftohtml -xml -nodrm -zoom 1.5 -enc UTF-8 -noframes \\\"%s\\\" \\\"%s\\\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\\n cmd = cmd + \\\" >/dev/null 2>&1\\\" # can't turn off output, so throw away even stderr yeuch\\n os.system(cmd)\\n\\n pdffout.close()\\n #xmlfin = open(tmpxml)\\n xmldata = xmlin.read()\\n xmlin.close()\\n return xmldata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d87ec27ca5227f0fbaf58a93a928598a\",\n \"score\": \"0.646367\",\n \"text\": \"def pdftoxml(pdfdata):\\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\\n pdffout.write(pdfdata)\\n pdffout.flush()\\n\\n xmlin = tempfile.NamedTemporaryFile(mode='r', suffix='.xml')\\n tmpxml = xmlin.name # \\\"temph.xml\\\"\\n cmd = '/usr/bin/pdftohtml -xml -nodrm -zoom 1.5 -enc UTF-8 -noframes \\\"%s\\\" \\\"%s\\\"' % (pdffout.name, os.path.splitext(tmpxml)[0])\\n cmd = cmd + \\\" >/dev/null 2>&1\\\" # can't turn off output, so throw away even stderr yeuch\\n os.system(cmd)\\n\\n pdffout.close()\\n #xmlfin = open(tmpxml)\\n xmldata = xmlin.read()\\n xmlin.close()\\n return xmldata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00a399a9e9d6ec6d4ad9e1a3d7dab1e3\",\n \"score\": \"0.62859905\",\n \"text\": \"def create_html_file(html):\\r\\n tmp_fd, url = tempfile.mkstemp(suffix='.html')\\r\\n tmp_file = os.fdopen(tmp_fd, 'w')\\r\\n tmp_file.write(html)\\r\\n tmp_file.close()\\r\\n return url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eff7e819359036aa0747cd7814dcccb\",\n \"score\": \"0.62706757\",\n \"text\": \"def test_post_convert_html_in_request_to_pdf(self):\\n name = \\\"postHtmlToPdfInReq.pdf\\\"\\n test_out_path = \\\"HtmlTestDoc/\\\" + name\\n test_file = TestHelper.test_src + \\\"test1.html\\\"\\n try:\\n # Upload and convert document to pdf\\n self.api.post_convert_document_in_request_to_pdf(\\n out_path=test_out_path, file=test_file, width=800, height=1000, left_margin=50, right_margin=100,\\n top_margin=150, bottom_margin=200)\\n\\n # Download result\\n res = TestHelper.download_file(test_out_path)\\n save_file = TestHelper.test_dst + name\\n TestHelper.move_file(res, save_file)\\n except ApiException as ex:\\n print(\\\"Exception\\\")\\n print(\\\"Info: \\\" + str(ex))\\n raise ex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b275724231056372124a1e8b27e79337\",\n \"score\": \"0.6114608\",\n \"text\": \"def test_get_convert_html_to_pdf(self):\\n name = \\\"test1.html\\\"\\n try:\\n # Upload file to storage\\n res = TestHelper.upload_file(name)\\n self.assertTrue(len(res.uploaded) == 1)\\n self.assertTrue(len(res.errors) == 0)\\n\\n # Convert document to pdf\\n res = self.api.get_convert_document_to_pdf(\\n name, width=800, height=1000, left_margin=50, right_margin=100, top_margin=150, bottom_margin=200,\\n folder=TestHelper.folder, storage=\\\"\\\"\\n )\\n self.assertTrue(isinstance(res, str), \\\"Error convert html to pdf\\\")\\n\\n # Move to test folder\\n TestHelper.move_file(str(res), TestHelper.test_dst)\\n except ApiException as ex:\\n print(\\\"Exception\\\")\\n print(\\\"Info: \\\" + str(ex))\\n raise ex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"176018c473850f440b9fdd512a2624f6\",\n \"score\": \"0.6098932\",\n \"text\": \"def save_pdf(htmls, file_name):\\n options = {\\n 'page-size': 'Letter',\\n 'margin-top': '0.75in',\\n 'margin-right': '0.75in',\\n 'margin-bottom': '0.75in',\\n 'margin-left': '0.75in',\\n 'encoding': \\\"UTF-8\\\",\\n 'custom-header': [\\n ('Accept-Encoding', 'gzip')\\n ],\\n 'cookie': [\\n ('cookie-name1', 'cookie-value1'),\\n ('cookie-name2', 'cookie-value2'),\\n ],\\n 'outline-depth': 10,\\n }\\n pdfkit.from_file(htmls, file_name, options=options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38e1ce71cdbb72451e8c10ad70d8a187\",\n \"score\": \"0.60953766\",\n \"text\": \"def generate_pdf(html): \\n result = StringIO.StringIO()\\n pdf = pisa.pisaDocument(StringIO.StringIO(html.encode(\\\"UTF-8\\\")), result)\\n if not pdf.err:\\n return HttpResponse(result.getvalue(), content_type='application/pdf')\\n return HttpResponse('Error al generar el PDF: %s' % cgi.escape(html))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6c614441db22ad8704f78516e5ea146\",\n \"score\": \"0.6012346\",\n \"text\": \"def write_pdf(self):\\n try:\\n #f = tempfile.NamedTemporaryFile(delete=False, suffix='.tex')\\n f = open(self.output_file.replace('.pdf', '.tex'), 'w')\\n for a in self.content:\\n f.write(a)\\n f.close()\\n except Exception:\\n raise IOError(u\\\"Unable to create temporary file, aborting\\\")\\n\\n #dummy_fh = open(os.path.devnull, 'w')\\n\\n try:\\n command = [\\\"pdflatex\\\", \\\"-shell-escape\\\", f.name]\\n Popen(command).communicate()\\n except Exception:\\n raise EnvironmentError(u\\\"Unable to generate PDF file using \\\"\\n \\\"pdflatex. Is it installed and available?\\\")\\n finally:\\n #dummy_fh.close()\\n name = self.output_file.replace('.pdf', '')\\n latex_trash = ['.out', '.log', '.aux', '.snm', '.nav', '.toc']\\n for a in latex_trash:\\n #print self.source_abspath+'/'+name+a\\n if os.path.isfile(self.source_abspath+'/'+name+a):\\n os.remove(self.source_abspath+'/'+name+a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0860851ea29bb2f310e2cf9b95fb2370\",\n \"score\": \"0.5933459\",\n \"text\": \"def html_to_pdf(html, as_http_response=False, name=None):\\n # weasyprint is an extra dependency\\n import weasyprint\\n\\n def url_fetcher(url, timeout=10, ssl_context=None):\\n return weasyprint.default_url_fetcher(\\n \\\"file://\\\" + url.replace(\\\"file://\\\", settings.BASE_DIR), timeout, ssl_context\\n )\\n\\n ret = weasyprint.HTML(string=html, base_url=\\\"\\\", url_fetcher=url_fetcher).write_pdf()\\n if as_http_response:\\n ret = HttpResponse(ret, content_type=\\\"application/pdf\\\")\\n ret[\\\"Content-Disposition\\\"] = f\\\"inline; filename={name}.pdf\\\"\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eab72ba84cb310f145f8c77fa281d36\",\n \"score\": \"0.59234834\",\n \"text\": \"def test_save_news_in_pdf(self, pisa, html_saver):\\n pisa.return_value = ''\\n path = os.path.abspath(os.curdir)\\n mock_logger = MagicMock(return_value=None)\\n html_file = os.path.join(path, 'test.html')\\n with open(html_file, 'x', encoding='utf-8') as html:\\n html.write(self.html)\\n html_saver.return_value = html\\n pdf = functions.save_news_in_pdf_file(self.article_a, path, mock_logger)\\n self.assertTrue(os.path.join(path, 'rss_news.pdf'))\\n os.remove(pdf.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e5590425036d5778bca7b7b0cc78c96\",\n \"score\": \"0.5892757\",\n \"text\": \"def pdftoetree(pdforig, options=''):\\n xmlin = tempfile.NamedTemporaryFile(mode='r', suffix='.xml')\\n tmpxml = xmlin.name # \\\"temph.xml\\\"\\n cmd = '/usr/bin/pdftohtml -xml -nodrm -zoom 1.5 -enc UTF-8 -noframes %s \\\"%s\\\" \\\"%s\\\"' % (options, pdforig, os.path.splitext(tmpxml)[0])\\n cmd = cmd + \\\" >/dev/null 2>&1\\\" # can't turn off output, so throw away even stderr yeuch\\n os.system(cmd)\\n tree = lxml.etree.parse(xmlin)\\n xmlin.close()\\n return tree\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a61eb38b2a2578876b9f58f885a1dc0\",\n \"score\": \"0.5879228\",\n \"text\": \"def _write_pdf_report(self):\\n LOGGER.info('Generating HTML for writing pdf report...')\\n pypandoc.convert_file(self.path, 'html',\\n outputfile=self._REPORT_TMP_HTML_PATH,\\n extra_args=['-V', 'geometry:margin=1.5cm', '--columns', '1000'])\\n LOGGER.info('Metamorphosising HTML to PDF...')\\n weasyprint.HTML(self._REPORT_TMP_HTML_PATH) \\\\\\n .write_pdf(self.path_pdf, stylesheets=[weasyprint.CSS(self._REPORT_CSS_PATH)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8a7e53828d8044b506b13602db0bf1\",\n \"score\": \"0.5771483\",\n \"text\": \"def convert(file, path):\\n try:\\n process = subprocess.Popen(['pdftohtml -c -nodrm -hidden -xml ' + file + ' ' + path], shell=True)\\n process.wait()\\n except Exception as exception:\\n error_logger(traceback.format_exc())\\n error_logger(str(exception))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14441f9b6dafc757d682e0f257f898d6\",\n \"score\": \"0.5673843\",\n \"text\": \"def create_pdf(text: str, output_file: str, font_type: str, font_size: int,\\n remove_cms: bool = True, remove_tgs: bool = True) -> None:\\n\\n # Install reportlab if not installed\\n try:\\n import reportlab\\n except ImportError:\\n import pip\\n pip.main(['install', 'reportlab', '--user'])\\n import reportlab\\n\\n from reportlab.pdfgen import canvas\\n from reportlab.pdfbase import pdfmetrics\\n from reportlab.pdfbase.ttfonts import TTFont\\n from reportlab.platypus import Paragraph\\n from reportlab.lib.styles import ParagraphStyle\\n from reportlab.lib.styles import getSampleStyleSheet\\n from reportlab.platypus.flowables import KeepInFrame\\n from reportlab.lib import pagesizes\\n\\n canvas = canvas.Canvas(output_file, pagesize=pagesizes.A4)\\n\\n # Get default fonts into a dictionary\\n default_fonts = {font.upper(): font for font in canvas.getAvailableFonts()}\\n\\n # Get fonts in fonts/ directory into a dictionary\\n program_fonts = {}\\n for root, dirs, files in os.walk(\\\"fonts/\\\"):\\n for file in files:\\n if file.endswith('.ttf') or file.endswith('.otf'):\\n program_fonts[re.sub('\\\\.[ot]tf$', '', file).upper()] = os.path.join(root, file)\\n\\n # Try to register font if not one of the defaults, fails otherwise\\n try:\\n # Check if font is in the fonts/ directory, register if true\\n if font_type.upper() in program_fonts:\\n pdfmetrics.registerFont(TTFont(font_type, program_fonts.get(font_type.upper())))\\n # Else check if path to font is provided, register if true\\n elif os.path.isfile(font_type) and font_type.endswith('.ttf') or font_type.endswith('.otf'):\\n pdfmetrics.registerFont(TTFont(font_type, font_type))\\n # Else check if the provided font is one of the default fonts, rename font\\n elif font_type.upper() in default_fonts:\\n font_type = default_fonts.get(font_type.upper())\\n # Otherwise fail\\n else:\\n raise ValueError('Not a valid font file.')\\n except (ValueError, reportlab.pdfbase.ttfonts.TTFError) as ex:\\n print(ex)\\n print('Can not use font: ' + os.path.abspath(font_type) + '.')\\n print('You can use one of the default fonts:')\\n [print(font, end=\\\" \\\") for font in default_fonts.values()]\\n print()\\n print('or the fonts in the \\\"fonts/\\\" directory:')\\n [print(font, end=\\\" \\\") for font in program_fonts.keys()]\\n print()\\n print('Alternatively, you can provide a path to a TrueType font file.')\\n sys.exit(1)\\n\\n # Styling\\n stylesheet = getSampleStyleSheet()\\n stylesheet.add(ParagraphStyle(name='custom', fontName=font_type, fontSize=font_size, leading=font_size * 1.3))\\n\\n # Positioning\\n width, height = pagesizes.A4\\n width -= 40 # drawing area width\\n height -= 30 # drawing area height\\n\\n tlx = 30 # offset from left\\n tly = height + 10 # offset from bottom\\n\\n # Remove comments and/or tags\\n if remove_cms:\\n text = remove_comments(text)\\n if remove_tgs:\\n text = remove_tags(text)\\n\\n # Pagination\\n # Pages are split on empty lines (can contain whitespace)\\n pages = re.split('(?:[\\\\n][ \\\\t]*){2,}', text.strip())\\n for index, page in enumerate(pages):\\n frame = KeepInFrame(width, height, [Paragraph(page.replace('\\\\n', '<br/>\\\\n'), stylesheet[\\\"custom\\\"])])\\n w, h = frame.wrapOn(canvas, width, height)\\n frame.drawOn(canvas, tlx, tly - h)\\n\\n # Insert page break for all but the last page\\n if index != len(pages):\\n canvas.showPage()\\n\\n # Produce pdf\\n canvas.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89f97a9e6d3a0bcad906a5da4aed9b2e\",\n \"score\": \"0.56649834\",\n \"text\": \"def convert_pdf(converter, build_path: pathlib.Path, settings: Settings, variables: Variables) -> None:\\n build_path = get_build_path(settings)\\n pdf_build_filename = build_path / pathlib.Path(settings[\\\"OutputFormats\\\"][\\\"pdf\\\"][\\\"OutputFile\\\"])\\n html_filename = build_path / pathlib.Path(settings[\\\"InputFile\\\"]).with_suffix(\\\".html\\\").name\\n html_builder = HTMLContentBuilder(html_filename, build_path)\\n converter.convert(html_builder)\\n html_cover_filename = build_path / pathlib.Path(settings[\\\"InputFile\\\"]).with_suffix(\\\".cover.html\\\").name\\n html_cover_builder = HTMLCoverBuilder(html_cover_filename, build_path)\\n converter.convert(html_cover_builder)\\n with open(\\\"DocumentDefinitions/Shared/header.html\\\", encoding=\\\"utf-8\\\") as header_template_file:\\n header_contents = header_template_file.read() % variables[\\\"KWALITEITSAANPAK\\\"]\\n header_filename = build_path / \\\"header.html\\\"\\n with open(header_filename, mode=\\\"w\\\", encoding=\\\"utf-8\\\") as header_file:\\n header_file.write(header_contents)\\n pdf_build_filename = build_path / pathlib.Path(settings[\\\"OutputFormats\\\"][\\\"pdf\\\"][\\\"OutputFile\\\"])\\n os.system(\\n f\\\"\\\"\\\"wkhtmltopdf \\\\\\n --enable-local-file-access \\\\\\n --footer-html DocumentDefinitions/Shared/footer.html --footer-spacing 10 \\\\\\n --header-html {header_filename} --header-spacing 10 \\\\\\n --margin-bottom 27 --margin-left 34 --margin-right 34 --margin-top 27 \\\\\\n --title '{settings[\\\"Title\\\"]}' \\\\\\n cover {html_cover_filename} \\\\\\n {\\\"toc --xsl-style-sheet DocumentDefinitions/Shared/toc.xsl\\\" if settings[\\\"IncludeTableOfContents\\\"] else \\\"\\\"} \\\\\\n {html_filename} {pdf_build_filename}\\\"\\\"\\\"\\n )\\n pdf_build_filename2 = build_path / pathlib.Path(settings[\\\"OutputFormats\\\"][\\\"pdf\\\"][\\\"OutputFile\\\"] + \\\".step2\\\")\\n os.system(f\\\"gs -o {pdf_build_filename2} -sDEVICE=pdfwrite -dPrinted=false -f {pdf_build_filename} src/pdfmark.txt\\\")\\n copy_output(pdf_build_filename2, settings, \\\"pdf\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0e2715f8118b67b3edf30b83ffd6975\",\n \"score\": \"0.5664913\",\n \"text\": \"def test_put_convert_html_to_xps(self):\\n # Already in storage\\n name = \\\"test1.html\\\"\\n result_name = \\\"putHtmlToXps.xps\\\"\\n test_folder = \\\"HtmlTestDoc\\\"\\n test_out_path = test_folder + \\\"/\\\" + result_name\\n\\n try:\\n # Convert document to pdf in storage\\n self.api.put_convert_document_to_xps(\\n name, out_path=test_out_path, width=800, height=1000, left_margin=50, right_margin=100,\\n top_margin=150, bottom_margin=200, folder=test_folder, storage=\\\"\\\")\\n\\n # Download result\\n res = TestHelper.download_file(test_out_path)\\n save_file = TestHelper.test_dst + result_name\\n TestHelper.move_file(res, save_file)\\n except ApiException as ex:\\n print(\\\"Exception\\\")\\n print(\\\"Info: \\\" + str(ex))\\n raise ex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfd815c2e0a434f97518f60ed56d6dc4\",\n \"score\": \"0.55401945\",\n \"text\": \"def save_pdf(self, url: str, file_name: str):\\n if not url.startswith('http'):\\n url = 'http://' + url\\n # TODO replace\\n file_name = file_name.replace(\\\".html\\\", \\\"\\\")\\n file_name = f'{path_join(self.root, file_name)}'\\n if exists(file_name):\\n os.remove(file_name)\\n try:\\n pdfkit.from_url(url, file_name, options=self.options)\\n except OSError as e:\\n logger.error(f'Pdf not saved: {url}.\\\\n{e}')\\n os.remove(file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c08ab5016c03208202e11ed2e154698\",\n \"score\": \"0.55357\",\n \"text\": \"def tmpHtmlFile(dir_path):\\n\\n #\\n # Create a temporary file for logging the folder\\n #\\n fd, filename = tempfile.mkstemp(suffix='.htm', dir=dir_path)\\n os.close(fd)\\n f = open(filename, 'w')\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a7e8c7ed9c51d95c1a4249b2faa73f1\",\n \"score\": \"0.5509825\",\n \"text\": \"def serve_pdf_from_html(\\n html: str, offered_filename: str = \\\"test.pdf\\\", **kwargs\\n) -> HttpResponse:\\n pdf = get_pdf_from_html(html, **kwargs)\\n return serve_buffer(\\n pdf,\\n offered_filename=offered_filename,\\n content_type=MimeType.PDF,\\n as_attachment=False,\\n as_inline=True,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f77d9f22578ebf7a1e053b7270d3f152\",\n \"score\": \"0.54926306\",\n \"text\": \"def build_html_report(options, xml_file):\\n options.xml_file = xml_file\\n options.html = True\\n xml_parser = ReportBuilder.FunkLoadXmlParser()\\n xml_parser.parse(xml_file)\\n utils.trace(\\\"Creating html report ...\\\")\\n html_path = ReportRenderHtml.RenderHtml(\\n xml_parser.config, xml_parser.stats,\\n xml_parser.error, xml_parser.monitor,\\n options)()\\n report = os.path.dirname(html_path)\\n os.rename(xml_file[:-4]+'.log',\\n os.path.join(report, 'funkload.log'))\\n assert os.path.isfile(os.path.join(report, 'funkload.xml'))\\n os.remove(xml_file)\\n utils.trace(\\\"done: \\\\n\\\")\\n utils.trace(\\\"file://%s\\\\n\\\" % html_path)\\n return report\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b17b9da643de205fff5be0910d9748b\",\n \"score\": \"0.547525\",\n \"text\": \"def pdf_generation(request):\\n html_template = render_to_string('bill.html')\\n BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\\n css = [os.path.join(BASE_DIR, 'static/css/', 'bill.css')]\\n pdf_file = HTML(string=html_template).write_pdf(\\n target='/home/kobey/Desktop/mypdf.pdf', stylesheets=css) # this line creates pdf\\n fs = FileSystemStorage('/home/kobey/Desktop')\\n\\n with fs.open('mypdf.pdf') as pdf:\\n response = HttpResponse(pdf, content_type='application/pdf')\\n response['Content-Disposition'] = 'inline; filename=\\\"mypdf.pdf\\\"'\\n return response\\n # return render(request,'bill.html')\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac112bcfe74d53987cff233ea9b2c369\",\n \"score\": \"0.5449141\",\n \"text\": \"def bytes(self):\\n html_bytes = self.html_format.bytes\\n\\n pdf_bytes = pdfkit.from_string(\\n html_bytes.decode(self.encoding),\\n False,\\n options={\\n \\\"load-error-handling\\\": \\\"ignore\\\",\\n \\\"load-media-error-handling\\\": \\\"ignore\\\",\\n \\\"margin-left\\\": self.pdf_margin_left,\\n \\\"margin-right\\\": self.pdf_margin_right,\\n \\\"margin-top\\\": self.pdf_margin_top,\\n \\\"margin-bottom\\\": self.pdf_margin_bottom,\\n \\\"zoom\\\": self.pdf_output_zoom,\\n # 'disable-smart-shrinking': '',\\n },\\n )\\n\\n return pdf_bytes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ce3f507c7b7ece2d179cc3cf679f0c\",\n \"score\": \"0.5428952\",\n \"text\": \"def pdf_html(view):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcdf35dc11be71f9f351df5482af0ef2\",\n \"score\": \"0.53816795\",\n \"text\": \"def create_html(markdown_file):\\n html_file = html_name_from_markdown(markdown_file)\\n # Convert from Github-flavored Markdown to HTML\\n cmd = f'pandoc -f gfm -o {html_file} {markdown_file}'\\n # Use pandoc to generate HTML from Markdown\\n process = subprocess.run(\\n cmd,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.STDOUT,\\n shell=True,\\n encoding=\\\"utf-8\\\",\\n universal_newlines=True\\n )\\n return process\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d47ebd79efdd4a750fb98bd79b27073\",\n \"score\": \"0.53535813\",\n \"text\": \"def write_html(html_text, filename):\\n\\ttmpfolder = os.path.join(CURDIR, '../../tmp')\\n\\tif not os.path.exists(tmpfolder):\\n\\t\\tos.makedirs(tmpfolder)\\n\\ttagfile = os.path.join(tmpfolder, '%s.html' % filename)\\n\\twith codecs.open(tagfile, 'w') as f:\\n\\t\\tsoup = BeautifulSoup(html_text, 'html.parser')\\n\\t\\tf.write(soup.prettify())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"745ad3f26e45a974dbb18cc9552430ba\",\n \"score\": \"0.5352487\",\n \"text\": \"def pdf_response_xp(self, html, filename, download=False):\\n disposition = f'filename=\\\"{filename}\\\"'\\n if download:\\n disposition = 'attachment;' + disposition\\n\\n response = HttpResponse(content_type='application/pdf')\\n response['Content-Disposition'] = disposition\\n\\n pisa_status = pisa.CreatePDF(html, dest=response)\\n\\n if pisa_status.err:\\n message = (f\\\"Unable to generate PDF from <hl><pre>{html}</pre>\\\")\\n return HttpResponse(message, status=500)\\n\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128f66c5ed05b808f175f6b85d9797e5\",\n \"score\": \"0.5317638\",\n \"text\": \"def write_pdf(\\n inkscape: Path | str,\\n pdf: Path | str,\\n xml: EtreeElement,\\n stylesheet: Path | str | None = None,\\n) -> str:\\n with NamedTemporaryFile(mode=\\\"wb\\\", delete=False) as svg_file:\\n svg = write_svg(svg_file, xml, stylesheet)\\n _ = write_pdf_from_svg(inkscape, svg, pdf)\\n os.unlink(svg)\\n return str(pdf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"157bcc73badec34aaf0404662fd29f63\",\n \"score\": \"0.52880245\",\n \"text\": \"def test_get_convert_html_to_pdf_by_url(self):\\n source_url = \\\"https://stallman.org/articles/anonymous-payments-thru-phones.html\\\"\\n try:\\n\\n # Convert url to pdf\\n res = self.api.get_convert_document_to_pdf_by_url(\\n source_url, width=800, height=1000, left_margin=50, right_margin=100, top_margin=150,\\n bottom_margin=200,\\n folder=TestHelper.folder, storage=\\\"\\\"\\n )\\n self.assertTrue(isinstance(res, str), \\\"Error convert url to pdf\\\")\\n\\n # Move to test folder\\n TestHelper.move_file(str(res), TestHelper.test_dst)\\n except ApiException as ex:\\n print(\\\"Exception\\\")\\n print(\\\"Info: \\\" + str(ex))\\n raise ex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25c0027263b8baf8f6862720e05dd64f\",\n \"score\": \"0.5274468\",\n \"text\": \"def create_pdf(self) -> None:\\n print(f\\\"making {self.pdf.title}\\\")\\n if self.pdf.exists():\\n self.pdf.path.unlink()\\n\\n canvas = Canvas(str(self.pdf.path))\\n canvas.setAuthor(\\\"Luca Ercole\\\")\\n\\n for page in self.pages:\\n drawing = page.drawing\\n canvas.setPageSize((drawing.width, drawing.height))\\n\\n renderPDF.draw(drawing, canvas, 0, 0)\\n\\n for bookmark in page.bookmarks:\\n exact_ts = bookmark.dtime\\n title = exact_ts.replace(minute=30).isoformat()[:16]\\n pos = bookmark.position\\n\\n canvas.bookmarkPage(key=str(exact_ts), fit=\\\"XYZ\\\", top=pos)\\n # canvas.addOutlineEntry(title=title, key=title)\\n canvas.addOutlineEntry(title=title, key=str(exact_ts))\\n # canvas.drawString(0, pos, str(pos))\\n\\n canvas.showPage()\\n\\n canvas.save()\\n print(\\\"Done\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e3775d2948a79bc9b4f40bc3bb7ec60\",\n \"score\": \"0.526846\",\n \"text\": \"def render_pdf(\\n template,\\n file_,\\n url_fetcher=staticfiles_url_fetcher,\\n context=None,\\n):\\n context = context or {}\\n\\n html = get_template(template).render(context)\\n HTML(\\n string=html,\\n base_url='not-used://',\\n url_fetcher=url_fetcher,\\n ).write_pdf(\\n target=file_,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a21aec5ddb7c8dd1fd3ecd2d307a0eb\",\n \"score\": \"0.5261352\",\n \"text\": \"async def convert_body(request: Request):\\n data = await request.body()\\n\\n if not data:\\n return Response(\\\"ERROR: No body\\\", status_code=400)\\n\\n with TemporaryDirectory() as tmpdirname:\\n outfile = os.path.join(tmpdirname, \\\"out.html\\\")\\n\\n with open(outfile, \\\"w\\\") as fh:\\n fh.write(data.decode(\\\"utf-8\\\"))\\n\\n bytes = await execute_wkhtmltopdf(outfile)\\n\\n return Response(bytes, media_type=\\\"application/pdf\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ef788c9e61eef0953f8e61f0b1d26e5\",\n \"score\": \"0.52207357\",\n \"text\": \"def exportToHTMLFile(self, filePath):\\n\\n if os.path.exists(filePath) and os.path.isfile(filePath): #if the file path provided exists\\n htmlDocument = self.htmlRenderer( self.noteEditor.toPlainText() ) #convert the note to HTML\\n htmlFile = open(filePath, \\\"w\\\") #open the provided file path\\n htmlFile.write(htmlDocument) #write the HTML content to the file\\n htmlFile.close() #close the file after writing to it\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11f9b846961763ac38e7783a82354e1f\",\n \"score\": \"0.52157754\",\n \"text\": \"def get_to_file(self, *args, **kwargs):\\n import os\\n from tempfile import mkstemp\\n fd, name = mkstemp()\\n fp = os.fdopen(fd, 'wb')\\n fp.write(self.get_html(*args, **kwargs))\\n fp.close()\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b75b3ed28d39bb01fd607d1ba1a7a81d\",\n \"score\": \"0.52083796\",\n \"text\": \"def to_pdf(paper_dir, template_dir=None, use_shell_escape=False, flatten=False, keep_comments=False):\\n template_dir = template_dir or scriptorium.CONFIG['TEMPLATE_DIR']\\n\\n paper_dir = os.path.abspath(paper_dir)\\n if os.path.isdir(paper_dir):\\n fname = paper_root(paper_dir)\\n elif os.path.isfile(paper_dir):\\n fname = paper_dir\\n paper_dir = os.path.dirname(paper_dir)\\n else:\\n raise IOError(\\\"{0} is not a valid directory\\\".format(paper_dir))\\n\\n old_cwd = os.getcwd()\\n if old_cwd != paper_dir:\\n os.chdir(paper_dir)\\n\\n if not fname:\\n raise IOError(\\\"{0} has no obvious root.\\\".format(paper_dir))\\n\\n #Convert all auxillary MMD files to LaTeX\\n for mmd in _list_files(paper_dir):\\n bname = os.path.basename(mmd).split('.')[0]\\n with open(mmd, 'r') as mmd_fp, open('{0}.tex'.format(bname), 'w') as tex_fp:\\n tex_fp.write(pymmd.convert(mmd_fp.read(), fmt=pymmd.LATEX, dname=mmd, ext=pymmd.SMART))\\n\\n pdf_cmd, new_env = _build_latex_cmd(fname, template_dir, use_shell_escape)\\n\\n bname = os.path.basename(fname).split('.')[0]\\n if flatten:\\n tname = '{0}.tex'.format(bname)\\n fargs = '--keep-comments' if keep_comments else ''\\n with tempfile.NamedTemporaryFile() as tmp:\\n subprocess.check_call(['latexpand', '-o', tmp.name, tname, fargs], env=new_env)\\n shutil.copyfile(tmp.name, tname)\\n\\n try:\\n subprocess.check_output(pdf_cmd, env=new_env)\\n if os.path.exists(os.path.join(paper_dir, '{0}.xdy'.format(bname))):\\n subprocess.check_output(['makeglossaries', bname], env=new_env)\\n except subprocess.CalledProcessError as exc:\\n raise IOError(decodeCPEError(exc.output))\\n\\n _process_bib(fname, new_env)\\n\\n try:\\n subprocess.check_output(pdf_cmd, env=new_env)\\n except subprocess.CalledProcessError as exc:\\n raise IOError(decodeCPEError(exc.output))\\n try:\\n subprocess.check_output(pdf_cmd, env=new_env)\\n except subprocess.CalledProcessError as exc:\\n raise IOError(decodeCPEError(exc.output))\\n\\n # Revert working directory\\n if os.getcwd() != old_cwd:\\n os.chdir(old_cwd)\\n\\n return os.path.join(paper_dir, '{0}.pdf'.format(bname))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94509dbaa2f6a18c6dc1e05251e1c2bd\",\n \"score\": \"0.51938945\",\n \"text\": \"def render_to_pdf(template_src, context_dict={}):\\n template = get_template(template_src)\\n html = template.render(context_dict)\\n result = BytesIO()\\n pdf = pisa.pisaDocument(BytesIO(html.encode(\\\"UTF-8\\\")), result, link_callback=link_callback)\\n if not pdf.err:\\n return HttpResponse(result.getvalue(), content_type='application/pdf')\\n return HttpResponse(\\\"Error Rendering PDF\\\", status=400)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed75e370b200466ebdd2320004ff903\",\n \"score\": \"0.517688\",\n \"text\": \"def pdftotext(pdfdata):\\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\\n pdffout.write(pdfdata)\\n pdffout.flush()\\n\\n textin = tempfile.NamedTemporaryFile(mode='r', suffix='.txt')\\n tmptext = textin.name\\n cmd = '/usr/bin/pdftotext -layout -enc UTF-8 \\\"%s\\\" \\\"%s\\\"' % (pdffout.name, tmptext)\\n cmd = cmd + \\\" >/dev/null 2>&1\\\" # can't turn off output, so throw away even stderr yeuch\\n os.system(cmd)\\n\\n pdffout.close()\\n text = textin.read()\\n textin.close()\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed75e370b200466ebdd2320004ff903\",\n \"score\": \"0.517688\",\n \"text\": \"def pdftotext(pdfdata):\\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\\n pdffout.write(pdfdata)\\n pdffout.flush()\\n\\n textin = tempfile.NamedTemporaryFile(mode='r', suffix='.txt')\\n tmptext = textin.name\\n cmd = '/usr/bin/pdftotext -layout -enc UTF-8 \\\"%s\\\" \\\"%s\\\"' % (pdffout.name, tmptext)\\n cmd = cmd + \\\" >/dev/null 2>&1\\\" # can't turn off output, so throw away even stderr yeuch\\n os.system(cmd)\\n\\n pdffout.close()\\n text = textin.read()\\n textin.close()\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed75e370b200466ebdd2320004ff903\",\n \"score\": \"0.517688\",\n \"text\": \"def pdftotext(pdfdata):\\n pdffout = tempfile.NamedTemporaryFile(suffix='.pdf')\\n pdffout.write(pdfdata)\\n pdffout.flush()\\n\\n textin = tempfile.NamedTemporaryFile(mode='r', suffix='.txt')\\n tmptext = textin.name\\n cmd = '/usr/bin/pdftotext -layout -enc UTF-8 \\\"%s\\\" \\\"%s\\\"' % (pdffout.name, tmptext)\\n cmd = cmd + \\\" >/dev/null 2>&1\\\" # can't turn off output, so throw away even stderr yeuch\\n os.system(cmd)\\n\\n pdffout.close()\\n text = textin.read()\\n textin.close()\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c7276167a8be937af616b6973946ad0\",\n \"score\": \"0.51666015\",\n \"text\": \"def generate_pdf(tex_content, out_file):\\n out_file = Path(out_file)\\n with TemporaryDirectory() as tmpdir:\\n tmpdir = Path(tmpdir)\\n tex_file = tmpdir/f'{out_file.stem}.tex'\\n\\n with tex_file.open('w') as f:\\n f.write(tex_content)\\n\\n p = subprocess.run(['pdflatex', '-interaction', 'nonstopmode', f'-output-directory={tmpdir}', tex_file.name])\\n\\n shutil.copy(tmpdir/f'{tex_file.stem}.pdf', out_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2890795c6ceb3173a18c489371b326f2\",\n \"score\": \"0.51657754\",\n \"text\": \"def create_pdf(pdf_data):\\n pdf = BytesIO()\\n pisa.CreatePDF(BytesIO(pdf_data.encode('utf-8')), pdf)\\n return pdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f58fcf9f9894f89e5ddf6face1d4fedf\",\n \"score\": \"0.5107512\",\n \"text\": \"def create_html_and_punc(html_dir, punc_dir, plain_dir, stm_dir, rawpunc_dir, puncstm_dir, speaker):\\n\\n # Perform link reading and html conversion if raw_transcript does not exist\\n try:\\n if not os.path.exists(os.path.join(punc_dir, speaker + \\\".txt\\\")):\\n # exceptions, some speakers result in error\\n if speaker == 'KarenThompsonWalker_2012G':\\n return\\n # Convert stm into a plain file of transcription (the transcription does not have punctuation or capitalization)\\n plain_file = get_plain_with_stm(plain_dir, stm_dir, speaker)\\n # Get the html_file that contains transcript that is the closest to the plain file (if there is anything close)\\n best_link = write_html_with_name(html_dir, plain_file, speaker)\\n\\n # Write a punctuated transcript into punc_dir\\n raw_transcript = write_raw_punc_with_name(rawpunc_dir, best_link, speaker)\\n\\n write_clean_punc_with_name(punc_dir, rawpunc_dir, speaker)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"974e5f80464a485cd6ba7a2366181d30\",\n \"score\": \"0.5073336\",\n \"text\": \"def wk_pdf_file( sourcefile, destfile, wk_executable = \\\"wkhtmltopdf\\\", \\n options = [], del_source = False,\\n ):\\n if not destfile.endswith('.pdf'):\\n destfile += '.pdf'\\n try:\\n rc = call( [ wk_executable ] + options + [ sourcefile, destfile ] )\\n os.remove( sourcefile ) \\n return destfile\\n except Exception, e:\\n if settings.MODULE_ADMINLOG:\\n AdminLog( by = 'gdjet.utils.pdf.wk_pdf_file',\\n message = 'Error writing PDF File: %s\\\\n%s' % (destfile, e) ).save()\\n else:\\n raise\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edde6ac7b446727f6fc19ff87f4b5a72\",\n \"score\": \"0.5070975\",\n \"text\": \"def write_html(outdir, path, html):\\n with open(os.path.join(outdir, path), 'w') as buf:\\n buf.write(html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05e5404a4a2dac9f611c81d6fc10d174\",\n \"score\": \"0.50464195\",\n \"text\": \"def to_html(doc, output=\\\"/tmp\\\", style=\\\"dep\\\"):\\n # generate filename from first six non-punct tokens\\n file_name = \\\"test_extract.html\\\"\\n html = displacy.render(doc, style=style, page=True) # render markup\\n if output is not None:\\n output_path = Path(output)\\n if not output_path.exists():\\n output_path.mkdir()\\n output_file = Path(output) / file_name\\n output_file.open(\\\"a\\\", encoding=\\\"utf-8\\\").write(html) # save to file\\n print(\\\"Saved HTML to {}\\\".format(output_file))\\n else:\\n print(html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c6a0bec22c1297e3af5fb0e9516c418\",\n \"score\": \"0.50404537\",\n \"text\": \"def outline_pdf2html(pdf_fp,\\n fp_dir_pdf2htmlEx=\\\"C:\\\\\\\\pdf2htmlEX\\\",\\n fp_outdir=\\\"C:\\\\\\\\CloudStation\\\\\\\\CBitter_-_PHD\\\\\\\\CloudStation\\\\\\\\PHDLiterature\\\\\\\\AHP_scopus\\\\\\\\html\\\",\\n outline_file_name=\\\"outline.html\\\",\\n copy_to_outline_file=True,\\n verbose=False):\\n assert (isinstance(pdf_fp, str))\\n assert (isinstance(fp_outdir, str))\\n assert (isinstance(fp_dir_pdf2htmlEx, str))\\n assert (isinstance(outline_file_name, str))\\n\\n if not os.path.isdir(fp_outdir):\\n raise ValueError(\\\"outline_pdf2html - fp_outdir does not exist?\\\")\\n\\n if not os.path.isdir(fp_dir_pdf2htmlEx):\\n raise ValueError(\\\"outline_pdf2html - pdf2htmlex does not exist?\\\")\\n\\n if not os.path.isfile(pdf_fp):\\n raise ValueError(\\\"outline_pdf2html - pdf file does not exist?\\\")\\n\\n if not outline_file_name:\\n raise ValueError(\\\"outline_pdf2html - outline file name not provided\\\")\\n\\n outline_fp = os.path.join(fp_outdir, outline_file_name)\\n\\n # we generate the content into a temp dir\\n # then take the outline file and stuff its contents into the outline_file\\n pdf_fname, _ = path.splitext(path.basename(pdf_fp))\\n outline_fname= \\\"%s.outline\\\" % pdf_fname\\n\\n ret_code = pdf2html(fp_pdf=pdf_fp,\\n generated_html_filename=\\\"generated.html\\\",\\n fp_outdir=fp_outdir,\\n splitPages=False,\\n optimizeText=False,\\n processNonText=True,\\n preprocessToPS=False,\\n options={\\n \\\"process-outline\\\": \\\"1\\\"\\n },\\n debug=verbose)\\n\\n tmp_outline_fp = os.path.join(fp_outdir, outline_fname)\\n\\n if copy_to_outline_file:\\n shutil.copy(tmp_outline_fp, outline_fp)\\n else:\\n outline_fp = tmp_outline_fp\\n\\n if verbose:\\n print(\\\"outline html file: %s (%s)\\\" % (outline_fp, ret_code))\\n\\n if ret_code:\\n return outline_fp\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8576b9bbf7ed2ae4c085a4e241fbf49b\",\n \"score\": \"0.50296617\",\n \"text\": \"def test_to_pdf_create_file(converter_instance):\\n converter_instance.to_pdf()\\n assert (os.path.exists(os.path.join(converter_instance.folder_path, \\\"news.pdf\\\")) is True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c80657ba20afb25195013e4407941c6e\",\n \"score\": \"0.5018169\",\n \"text\": \"async def execute_wkhtmltopdf(uri: str) -> bytes:\\n cmd = [\\n \\\"wkhtmltopdf\\\",\\n \\\"--log-level\\\",\\n \\\"none\\\",\\n uri,\\n \\\"-\\\",\\n ]\\n return check_output(cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fe4e36f80d325aad7df8ce8fb1bfd5e\",\n \"score\": \"0.50049746\",\n \"text\": \"def generate_pdf(template_name, context, params, request=None, fix_static_paths=True, stream=True):\\n html = render_to_string(template_name, context=context, request=request)\\n if fix_static_paths:\\n replacement = get_absolute_static_path(request)\\n html = html.replace(settings.STATIC_URL, replacement)\\n\\n r = requests.post(\\n settings.PDF_SERVICE_ENDPOINT,\\n json={\\n \\\"html\\\": html\\n },\\n params=params,\\n headers={\\n \\\"x-api-key\\\": settings.PDF_SERVICE_TOKEN,\\n },\\n stream=stream\\n )\\n r.raise_for_status()\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8097d95c1ec5b77121f5c8291341b8b0\",\n \"score\": \"0.498684\",\n \"text\": \"def test_to_html_create_file(converter_instance):\\n converter_instance.to_html()\\n assert (os.path.exists(os.path.join(converter_instance.folder_path, \\\"news.html\\\")) is True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2053447ac98da626c60de7b5cd15511f\",\n \"score\": \"0.49583066\",\n \"text\": \"def store_html(self, path: str = f\\\"html{os.sep}result.html\\\", allow_header_links: bool = False,\\n skip_tables: bool = False):\\n with open(path, \\\"w\\\") as result_file:\\n result_file.write(self.to_html(allow_header_links=allow_header_links, skip_tables=skip_tables))\\n result_file.write(\\\"\\\\n\\\")\\n result_file.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5264c302886e9a3626ccb5e033f72eb\",\n \"score\": \"0.49577415\",\n \"text\": \"def render_to_pdf(template_src, context_dict):\\n template = get_template(template_src)\\n context = Context(context_dict)\\n html = template.render(context)\\n result = StringIO.StringIO()\\n\\n pdf = pisa.pisaDocument(StringIO.StringIO(html.encode(\\\"UTF-8\\\")),\\n dest=result,\\n encoding='UTF-8',\\n link_callback=fetch_resources)\\n if not pdf.err:\\n response = HttpResponse(result.getvalue(),\\n mimetype='application/pdf')\\n\\n return response\\n\\n return HttpResponse('We had some errors<pre>%s</pre>' % escape(html))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a743a40fa4293ed78b9314c281e88473\",\n \"score\": \"0.49230415\",\n \"text\": \"def create_txt():\\n\\t(_, week, year) = get_date()\\n\\tpdf_path = get_pdf_path()\\n\\ttxt_path = BASE_TXT_PATH.format(year, week)\\n\\tsubprocess.call(['pdftotext', pdf_path, txt_path])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a07e12ddc82feb7cb48115b74ab5ea1\",\n \"score\": \"0.49225733\",\n \"text\": \"def __init__(\\n self,\\n encoding=\\\"utf-8\\\",\\n pdf_margin_left=\\\"0\\\",\\n pdf_margin_right=\\\"0\\\",\\n pdf_margin_top=\\\"0\\\",\\n pdf_margin_bottom=\\\"0\\\",\\n pdf_output_zoom=\\\"1.6\\\",\\n pdf_input_zoom=\\\"1.0\\\",\\n pdftohtml_path=\\\"pdftohtml\\\",\\n wkhtmltopdf_path=\\\"wkhtmltopdf\\\",\\n ):\\n super().__init__()\\n self.encoding = encoding\\n self.html_format = HtmlFormat()\\n self.pdf_margin_left = pdf_margin_left\\n self.pdf_margin_right = pdf_margin_right\\n self.pdf_margin_top = pdf_margin_top\\n self.pdf_margin_bottom = pdf_margin_bottom\\n self.pdf_output_zoom = pdf_output_zoom\\n self.pdf_input_zoom = pdf_input_zoom\\n self.pdftohtml_path = pdftohtml_path\\n self.wkhtmltopdf_path = wkhtmltopdf_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4651e9c583a34c952eaa6ec4ab5831b\",\n \"score\": \"0.49170125\",\n \"text\": \"def saveDMP(event=None):\\n f = filedialog.asksaveasfile(mode='w', defaultextension=\\\".pdf\\\")\\n if f is None: # asksaveasfile return `None` if dialog closed with \\\"cancel\\\".\\n return\\n\\n html = create_html(template=selectedTemplate, output=f.name.replace(\\\".pdf\\\", \\\"\\\"))\\n print(\\\"Stored HTML successfully as \\\" + str(html))\\n pdf = create_pdf(html=html)\\n print(\\\"Stored PDF successfully as \\\" + str(pdf))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4769277cd3da1e5a1e6572ef0b07a02b\",\n \"score\": \"0.49056256\",\n \"text\": \"def make_pdf():\\n d = datetime.utcnow()\\n report_date = d.strftime(\\\"%Y%m%d%H%M%S\\\")\\n report_name = \\\"report-\\\" + report_date + \\\".pdf\\\"\\n pdf = canvas.Canvas(report_name, pagesize=letter)\\n pdf.setFont(\\\"Courier\\\", 50)\\n pdf.setStrokeColorRGB(1, 0, 0)\\n pdf.setFillColorRGB(1, 0, 0)\\n pdf.drawCentredString(letter[0] / 2, inch * 7, 'FANCY REPORT')\\n pdf.drawCentredString(letter[0] / 2, inch * 6, 'CLASSIFIED')\\n pdf.drawCentredString(letter[0] / 2, inch * 5, 'For Your Eyes Only')\\n pdf.setFont(\\\"Courier\\\", 20)\\n pdf.drawCentredString(letter[0] / 2, inch * 4, 'Created: ' + d.strftime(\\\"%B %d, %Y %H:%M:%S\\\"))\\n\\n pdf.showPage()\\n pdf.save()\\n return report_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b3f9db2fbba9ad8e8cc088581170c69\",\n \"score\": \"0.49039894\",\n \"text\": \"def _get_report_pdf(self, report):\\n pdf = StringIO()\\n pisa.CreatePDF(StringIO(self._get_render_template(report)), pdf)\\n return pdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12b3953e593046ba7db9d6531441a611\",\n \"score\": \"0.48923507\",\n \"text\": \"def _merge_pdf(self, documents):\\n writer = PdfFileWriter()\\n streams = [] # We have to close the streams *after* PdfFilWriter's call to write()\\n for document in documents:\\n pdfreport = file(document, 'rb')\\n streams.append(pdfreport)\\n reader = PdfFileReader(pdfreport)\\n for page in range(0, reader.getNumPages()):\\n writer.addPage(reader.getPage(page))\\n\\n merged_file_fd, merged_file_path = tempfile.mkstemp(suffix='.pdf', prefix='report.merged.tmp.')\\n with closing(os.fdopen(merged_file_fd, 'w')) as merged_file:\\n writer.write(merged_file)\\n\\n for stream in streams:\\n stream.close()\\n\\n return merged_file_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e9b3441ef63a23768c73ceb77384fe9\",\n \"score\": \"0.48896754\",\n \"text\": \"def generate_pdf(output_dir='.', doujinshi_obj=None, rm_origin_dir=False):\\n if doujinshi_obj is not None:\\n doujinshi_dir = os.path.join(output_dir, doujinshi_obj.filename)\\n pdf_filename = os.path.join(\\n os.path.join(doujinshi_dir, '..'),\\n '{}.pdf'.format(doujinshi_obj.filename)\\n )\\n else:\\n pdf_filename = './doujinshi.pdf'\\n doujinshi_dir = '.'\\n\\n file_list = os.listdir(doujinshi_dir)\\n file_list.sort()\\n\\n logger.info('Writing PDF file to path: {}'.format(pdf_filename))\\n with open(pdf_filename, 'wb') as pdf_f:\\n full_path_list = (\\n [os.path.join(doujinshi_dir, image) for image in file_list]\\n )\\n pdf_f.write(img2pdf.convert(full_path_list))\\n\\n if rm_origin_dir:\\n shutil.rmtree(doujinshi_dir, ignore_errors=True)\\n\\n logger.log(15, 'PDF file has been written to \\\\'{0}\\\\''.format(doujinshi_dir))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b8ab0b323e14a8c6b5bd2340eeb3416\",\n \"score\": \"0.4878805\",\n \"text\": \"def outputPDF(self, file, writer):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf1cd5194512ca2b1d7b1511b805b7c\",\n \"score\": \"0.48548716\",\n \"text\": \"def create_raw_html(year, house, bill, html):\\n raw_files_folder = os.path.join(default_path,'bill_files', 'raw', str(year))\\n if not os.path.exists(raw_files_folder):\\n os.mkdir(raw_files_folder)\\n if check_bill(html):\\n file = open(os.path.join(raw_files_folder, '{}{}{}.html'.format(year, house, bill)), 'w',\\n encoding='utf-8')\\n file.write(html)\\n file.close()\\n return html\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97e3e1c72d8531045cbe4b8a97734306\",\n \"score\": \"0.4854084\",\n \"text\": \"def make_send_pdf(self):\\n slim_content = edit_content(page.get_content(page.get_summary()), \\\"Weblinks\\\", \\\"Literatur\\\", \\\"Einzelnachweise\\\",\\n \\\"Sonstiges\\\", \\\"Filme\\\", \\\"Auszeichnungen\\\", \\\"Filmdokumentationen\\\", \\\"Anmerkungen\\\",\\n \\\"Biografien\\\",\\n \\\"Weitere Texte\\\") # cuts out these sections from the article\\n\\n to_mail = 'send_to_this_mail@gmail.com' # testing email\\n try:\\n print(\\\"Progress:\\\")\\n # create the word page\\n add_title()\\n print(\\\"Added title...\\\")\\n add_logo()\\n print(\\\"Added image...\\\")\\n add_summary()\\n print(\\\"Added summary...\\\")\\n add_content(slim_content)\\n print(\\\"Added content...\\\")\\n document.save(get_doc_name() + \\\".docx\\\")\\n print(\\\"Saving file...\\\")\\n delete_pic_in_outer_folder()\\n word_to_pdf.docx_to_pdf(get_doc_name() + \\\".docx\\\", get_doc_name())\\n move_word_files()\\n print(\\\"Sending email...\\\")\\n email_sender.send_email(to_mail, get_doc_name() + \\\".pdf\\\", str(page.get_title())) # uncomment to send email\\n word_to_pdf.move_pdf_to_folder()\\n print(\\\"===Finished===\\\")\\n except PermissionError:\\n print(\\n \\\"Bitte schließen sie Microsoft Word und versuchen sie es erneut.\\\") # \\\"Please close Microsoft Word and try again\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f8dfd98ea039984867401866adc896e\",\n \"score\": \"0.48486796\",\n \"text\": \"def convert_html(converter, build_path: pathlib.Path, settings: Settings) -> None:\\n html_build_filename = build_path / settings[\\\"OutputFormats\\\"][\\\"html\\\"][\\\"OutputFile\\\"]\\n html_builder = HTMLBuilder(html_build_filename, pathlib.Path(\\\"\\\"))\\n converter.convert(html_builder)\\n copy_output(html_build_filename, settings, \\\"html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce203683853ee854a16b6684b592734b\",\n \"score\": \"0.48462394\",\n \"text\": \"def render_html_to_file(filename: str, output: str):\\n logging.info(\\\"Writing html to {}\\\".format(filename))\\n\\n with open(filename, \\\"w\\\", encoding=\\\"utf8\\\") as fh:\\n fh.write(output)\\n\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19fd1ea086e1d74c28a5d7728308b60b\",\n \"score\": \"0.4846071\",\n \"text\": \"def write(self):\\n html = self.render()\\n\\n if self.file_type == 'pdf':\\n self.write_pdf(html)\\n else:\\n with codecs.open(self.destination_file, 'w',\\n encoding='utf_8') as outfile:\\n outfile.write(html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db50bccef34fa2cde35523c7a57ff994\",\n \"score\": \"0.4844008\",\n \"text\": \"def generate_pdf(self):\\n filepath = os.path.join(self.m_output_dir, self.m_run_prefix+\\\"-summary\\\")\\n self.m_doc.generate_pdf(clean_tex=False, filepath=filepath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fcc29f1571cb3bf5ee3b46f1255c3a4\",\n \"score\": \"0.48339328\",\n \"text\": \"def write_graphs_to_pdf(graphs, out_dir=Path('./')):\\n out_dir.mkdir(parents=True, exist_ok=True)\\n print('Writing graphs to pdf an png files...')\\n for graph_file in tqdm(graphs):\\n graphs[graph_file].write_pdf(out_dir / (graph_file + '.pdf'))\\n graphs[graph_file].write_png(out_dir / (graph_file + '.png'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59d771725ea870dc6728128ba359bdb1\",\n \"score\": \"0.48289427\",\n \"text\": \"def create_pdf(catalog, template):\\n RML_DIR = 'rml'\\n templateName = os.path.join(RML_DIR, template)\\n template = preppy.getModule(templateName)\\n namespace = {\\n 'products':catalog,\\n 'RML_DIR': RML_DIR,\\n 'IMG_DIR': 'img'\\n\\n }\\n rml = template.getOutput(namespace,quoteFunc=preppy.stdQuote)\\n open(os.path.join(DATA_DIR,'latest.rml'), 'wb').write(asBytes(rml))\\n buf = BytesIO()\\n rml2pdf.go(asBytes(rml), outputFileName=buf)\\n return buf.getvalue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1968c959db56a6186b5a0d52bcb0d5c\",\n \"score\": \"0.48108557\",\n \"text\": \"def getPDF(self, params, template):\\n output = template.render(Context(params))\\n byteString = weasyprint.HTML(string = output).write_pdf()\\n pdfString = base64.b64encode(byteString)\\n return pdfString\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58aa2decd2a6e9c1e9094b492d5e8937\",\n \"score\": \"0.4808302\",\n \"text\": \"def pdf_to_text(filename,prefix=\\\"\\\"):\\n global pdftotextProgram,pdftotextOptions\\n\\n notfound = \\\"\\\"\\\"\\\\\\nCould not find '%s', which is needed for pdf to text conversion.\\n%s is part of the 'xPdf' suite of programs, obtainable at:\\n http://www.foolabs.com/xpdf/\\n\\\"\\\"\\\" % (pdftotextProgram,pdftotextProgram)\\n (fout,output) = apply_command_temp(pdftotextProgram,pdftotextOptions,notfound,filename,prefix,\\\".txt\\\")\\n return fout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a568e4e033abbee304191ba90f8f4e2\",\n \"score\": \"0.4804926\",\n \"text\": \"def _create_document(self, link, html, title):\\n self.chunk.compute_file_header_value(len(self.chunk.header))\\n self.chunk.create_document(link, html, title)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78382dc1d4dff5fd655acb2923b6454f\",\n \"score\": \"0.47853962\",\n \"text\": \"def temp_html_output(self, plot_object, backend=None):\\n save_plot(\\n \\\"temp_plot\\\", plot_object, self.rootdir, save_as=[\\\"html\\\"], backend=backend\\n )\\n return os.path.join(self.rootdir, \\\"html\\\", \\\"temp_plot.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a129296e4028a73355f299ab0f62b4\",\n \"score\": \"0.47735542\",\n \"text\": \"def gera_pdf(imgdir, rodada, outpath=os.getcwd(),**kwargs):\\n\\n # TODO: Organizar arquivos em grupos de pdfs, config aqui dentro, texto descritivo vem como arg\\n # TODO: Colocar para receber exemplos de dados para escrever nos textos. Receber texto exemplo pronto de fora\\n # TODO: colocar exemplo como caption da tabela com styler do pandas\\n\\n textos ={\\\"cedidos_#{rodada:d}\\\": \\\"PONTOS CEDIDOS: Média dos pontos cedidos pelas equipes aos adversários no \\\"\\n \\\"Cartola em {ano}, considerando o mando de campo. Em destaque, as equipes com\\\"\\n \\\"desvio acima padrão.\\\",\\n \\\"cedidos(semMando)_#{rodada:d}\\\": \\\"PONTOS CEDIDOS SEM MANDO: Média dos pontos cedidos pelas equipes aos adversários no \\\"\\n \\\"Cartola em {ano}, considerando todos seus jogos SEM distinção de mando de campo. Em destaque, \\\"\\n \\\"as equipes com maiores distância para média do campeoanto.\\\",\\n \\\"liqCed_#{rodada:d}\\\": \\\"PONTOS CEDIDOS LÍQUIDOS (EXCLUSIVO): Média dos pontos cedidos pelas equipes \\\"\\n \\\"aos adversários no Cartola em {ano}, tirando da conta as pontuações de gols, \\\"\\n \\\"assistências e SG. A tabela não considera o mando de campo. Em destaque, as \\\"\\n \\\"equipes com desvio acima padrão.\\\",\\n \\\"ptosPos_#{rodada:d}\\\": \\\"PONTOS GANHOS (EXCLUSIVO): Média de pontos ganhos pelas equipes, separados por \\\"\\n \\\"posição, no Cartola FC. A tabela não faz a distinção de mando de campo.\\\",\\n \\\"apostas_#{rodada:d}\\\": \\\"CASAS DE APOSTAS (PRIVADO): Análise da probabilidade de vitória, SG e gols dos \\\"\\n \\\"confrontos nas principais casas de apostas.\\\",\\n \\\"rbCed_#{rodada:d}\\\": \\\"DSs e DDs CEDIDAS: Média dos desarmes e defesas difíceis (goleiros) cedidos pelas \\\"\\n \\\"equipes aos adversários no Cartola em {ano}, sem considerar o mando de campo. Em destaque, \\\"\\n \\\"as equipes com desvio acima do padrão.\\\",\\n \\\"desempenhoClubes_#{rodada:d}\\\": \\\"DESEMPENHO DOS TIMES: Desempenho dos times no Cartola FC em {ano}, sem levar em \\\"\\n \\\"consideração o mando de campo. As informações são a média de: SG, Gols Pró, Gols\\\"\\n \\\" Sofridos e SG Cedidos.\\\",\\n 'goleiros_#{rodada:d}': \\\"GOLEIROS (EXCLUSIVO): Médias por jogo dos goleiros no Cartola FC em {ano}. Todos os valores\\\"\\n \\\" em média por jogo. A tabela conta com distinção de mando de campo.\\\",\\n 'GS_Forma_#{rodada:d}': \\\"GOLS SOFRIDOS POR FORMA: Tabela com os tipos de gols sofridos por equipe do Campeonato \\\"\\n \\\"Brasileiro {ano}. A tabela conta todos os jogos (casa e fora).\\\",\\n 'GS_posicao_#{rodada:d}': \\\"GOLS SOFRIDOS POR POSIÇÃO (EXCLUSIVO): Tabela com os gols sofridos por equipe do Campeonato \\\"\\n \\\"Brasileiro {ano}, separados pela posição do jogador adversário que marcou o gol. A tabela conta\\\"\\n \\\" todos os jogos (casa e fora).\\\",\\n }\\n \\n doc=Document(\\\".\\\\\\\\pytabelas\\\\\\\\socios.docx\\\")\\n p = doc.add_paragraph()\\n r = p.add_run()\\n font = r.font\\n font.name=\\\"Industry-Black\\\"\\n font.size = Pt(36)\\n p.paragraph_format.alignment = WD_ALIGN_PARAGRAPH.CENTER\\n r.add_text(\\\"ARQUIVO DOS SÓCIOS RODADA \\\" +str(rodada))\\n\\n for prod, decr in textos.items():\\n r.add_break(WD_BREAK.PAGE)\\n p = doc.add_paragraph()\\n r = p.add_run()\\n font = r.font\\n font.name=\\\"Industry-Black\\\"\\n font.size = Pt(24)\\n r.add_text(decr.format(rodada=rodada, **kwargs))\\n p = doc.add_paragraph()\\n r = p.add_run()\\n r.add_picture(os.path.join(imgdir, prod.format(rodada=rodada, **kwargs)+\\\".jpg\\\"), width= Inches(6.1))\\n \\n \\n doc.save(os.path.join(outpath, 'socios #{}'.format(rodada)+'.docx'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14a58f574464d90e4ca06b82dbfe70c6\",\n \"score\": \"0.47671464\",\n \"text\": \"def pickle_pdf_xml(arguments):\\n file = arguments[0]\\n path = arguments[1]\\n try:\\n xml = parser.from_file(file, xmlContent = True)\\n replace_string = file.split('\\\\\\\\')[-1].replace('.pdf', '')\\n save_string = path + replace_string + '.pkl'\\n print(save_string)\\n pickle.dump(xml, open(save_string, \\\"wb\\\"))\\n except: \\n return file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d279f1b01da67e0f559cfdfe5755a989\",\n \"score\": \"0.47637767\",\n \"text\": \"def word_to_pdf(self, file, path):\\n convert(file, path + \\\"\\\\\\\\\\\" + \\\"resultat\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2c4d3d87cded7336b01ba35a9322859\",\n \"score\": \"0.4760178\",\n \"text\": \"def WriteRedirectHtmlFile(from_html_path, to_html_path):\\n to_html_relative_path = GetRelativePathToDirectoryOfFile(\\n to_html_path, from_html_path)\\n content = (\\\"\\\"\\\"\\n <!DOCTYPE html>\\n <html>\\n <head>\\n <!-- HTML meta refresh URL redirection -->\\n <meta http-equiv=\\\"refresh\\\" content=\\\"0; url=%s\\\">\\n </head>\\n </html>\\\"\\\"\\\" % to_html_relative_path)\\n with open(from_html_path, 'w') as f:\\n f.write(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97670acd16ffb183fc86fc028aa73b19\",\n \"score\": \"0.47423863\",\n \"text\": \"def genpdf(source, des, rm=False):\\n command = [\\\"pdflatex\\\", \\\"-output-directory\\\", des, source]\\n subprocess.call(command)\\n if rm:\\n from os import listdir, remove\\n from os.path import isfile, join, splitext, basename\\n name = splitext(basename(source))[0]\\n for f in listdir(des):\\n if isfile(join(des, f)):\\n fname, ext = splitext(f)\\n if fname == name:\\n if ext == '.aux' or ext == '.log' or ext == \\\".tex\\\":\\n remove(os.path.join(des, f))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ce87e257d73c517742749a739b2d386\",\n \"score\": \"0.47367832\",\n \"text\": \"def test_put_convert_html_to_image(self):\\n # Already in storage\\n name = \\\"test1.html\\\"\\n result_name = \\\"putHtmlToImg.zip\\\"\\n test_folder = \\\"HtmlTestDoc\\\"\\n test_out_path = test_folder + \\\"/\\\" + result_name\\n\\n try:\\n # Convert document to image in storage\\n self.api.put_convert_document_to_image(\\n name, out_path=test_out_path, out_format=\\\"tiff\\\", width=800, height=1000, left_margin=50, right_margin=100,\\n top_margin=150, bottom_margin=200, resolution=300, folder=test_folder, storage=\\\"\\\")\\n\\n # Download result\\n res = TestHelper.download_file(test_out_path)\\n save_file = TestHelper.test_dst + result_name\\n TestHelper.move_file(res, save_file)\\n except ApiException as ex:\\n print(\\\"Exception\\\")\\n print(\\\"Info: \\\" + str(ex))\\n raise ex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ddf1b24f730594f35e8f63ad7e9be6f\",\n \"score\": \"0.46994278\",\n \"text\": \"def pdf_to_text_using_tikka(filepath, filename, output_path):\\n output_file_name = filename + '_tikka.txt'\\n output_file_path = output_path + output_file_name\\n parsed_file = parser.from_file(filepath) # Parsing PDF file\\n content = parsed_file['content'] # Extracting content\\n\\n with open(output_file_path, 'w') as f:\\n res = f.write(content.strip())\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60d1d2177e14741fa3d0ab824dc37873\",\n \"score\": \"0.4697682\",\n \"text\": \"def html_str_to_file(text, filename):\\n output = open(os.path.join(outputs_path,filename), \\\"w\\\", encoding=\\\"utf-8\\\")\\n output.write(text)\\n output.close()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82efd033b9b5ec1cba6c756bac314bc6\",\n \"score\": \"0.46915892\",\n \"text\": \"def genPDF(filename):\\n newlines = 'echo -e \\\"' + '\\\\\\\\n' * 100 + '\\\"' # Hack to continue when pdflatex halts.\\n cmd = newlines + \\\" | pdflatex \\\" + filename + \\\" --shell-escape 2>/dev/null >/dev/null\\\"\\n os.system(cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0dcfed2234da2d5402137e36516e3c8\",\n \"score\": \"0.46886522\",\n \"text\": \"def test_put_convert_html_to_markdown(self):\\n # Already in storage\\n name = \\\"test_md.html\\\"\\n result_name = \\\"putConvertToMarkdownPy.md\\\"\\n test_folder = \\\"HtmlTestDoc\\\"\\n test_out_path = test_folder + \\\"/\\\" + result_name\\n\\n try:\\n # Convert document to markdown in storage\\n self.api.put_convert_document_to_markdown(\\n name, out_path=test_out_path, use_git=\\\"false\\\", folder=test_folder, storage=\\\"\\\")\\n\\n # Download result\\n res = TestHelper.download_file(test_out_path)\\n save_file = TestHelper.test_dst + result_name\\n TestHelper.move_file(res, save_file)\\n except ApiException as ex:\\n print(\\\"Exception\\\")\\n print(\\\"Info: \\\" + str(ex))\\n raise ex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"410ece57c4f4db8b0ebe89f1cb2ac99b\",\n \"score\": \"0.46829975\",\n \"text\": \"def _convert_to_pdf(\\n self,\\n file_content: typing.IO[bytes],\\n input_extension: str,\\n cache_path: str,\\n output_filepath: str,\\n mimetype: str,\\n ) -> BytesIO:\\n\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ef313c3078e29dc10b3d0de64a54aec\",\n \"score\": \"0.4661873\",\n \"text\": \"def save_as_pdf(self, text: str, file_name: str = \\\"Podcast.pdf\\\"):\\n text_encoded = text.encode('latin-1', 'replace').decode('latin-1')\\n text = text_encoded.replace(\\\"?\\\", \\\".\\\").replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\").replace(\\\"'\\\", \\\"\\\")\\n\\n pdf = FPDF(orientation='P', unit='mm', format='A4')\\n pdf.set_auto_page_break(True, margin=10)\\n pdf.add_page()\\n pdf.set_font(family='Courier', size=12)\\n splitter = text.split('\\\\n')\\n\\n for line in splitter:\\n lines = textwrap.wrap(line, 75)\\n if len(lines) == 0:\\n pdf.ln()\\n for wrap in lines:\\n pdf.cell(0, 4, wrap, ln=1)\\n pdf.output(file_name, 'F')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a15e60e99e381a95edfceb96a7d8f65f\",\n \"score\": \"0.46592584\",\n \"text\": \"def test_post_merge_html_template(self):\\n template_name = \\\"HtmlTemplate.html\\\"\\n template_data = \\\"XmlSourceData.xml\\\"\\n result_name = \\\"PostMergeHtmlTemplatePython.html\\\"\\n options = \\\"\\\"\\n folder = \\\"HtmlTestDoc\\\"\\n storage = \\\"\\\"\\n data_file = TestHelper.test_src + template_data\\n out_path = folder + \\\"/\\\" + result_name\\n\\n try:\\n # Convert document to image\\n self.api.post_merge_html_template(template_name=template_name, out_path=out_path, file=data_file,\\n options=options, folder=folder, storage=storage)\\n # Download result\\n res = TestHelper.download_file(out_path)\\n save_file = TestHelper.test_dst + result_name\\n TestHelper.move_file(res, save_file)\\n except ApiException as ex:\\n print(\\\"Exception\\\")\\n print(\\\"Info: \\\" + str(ex))\\n raise ex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4836404b9518e2c2482df8997b18135\",\n \"score\": \"0.46539325\",\n \"text\": \"def render_to_pdf(request, template_name, renderdict, filename):\\n rendered_html = get_template(template_name).render(Context(renderdict))\\n\\n response = HttpResponse(content_type=\\\"application/pdf\\\")\\n response['Content-Disposition'] = 'attachment; filename=\\\"' + filename + '\\\"'\\n\\n success = pisa.CreatePDF(rendered_html, dest=response)\\n\\n if not success:\\n return HttpResponseServerError()\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3127f91de047cec24c9fdc3fef4aaf23\",\n \"score\": \"0.46349818\",\n \"text\": \"def page_extractor_pdftotext(file_name):\\n \\n import subprocess\\n tmpfile = tempfile.NamedTemporaryFile()\\n subprocess.call('pdftotext -l 3 \\\"%s\\\" \\\"%s\\\"' % (file_name, tmpfile.name), shell=True)\\n text = tmpfile.read()\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4aa23649bb5e1bffbb345cd3752302\",\n \"score\": \"0.4619672\",\n \"text\": \"def generate_main_html(output_dir='./', html=False):\\n\\n image_html = ''\\n\\n main = readfile('viewer/main.html')\\n css = readfile('viewer/main.css')\\n js = readfile('viewer/main.js')\\n\\n element = '\\\\n\\\\\\n <div class=\\\"gallery-favorite\\\">\\\\n\\\\\\n <div class=\\\"gallery\\\">\\\\n\\\\\\n <a href=\\\"./{FOLDER}/index.html\\\" class=\\\"cover\\\" style=\\\"padding:0 0 141.6% 0\\\"><img\\\\n\\\\\\n src=\\\"./{FOLDER}/{IMAGE}\\\" />\\\\n\\\\\\n <div class=\\\"caption\\\">{TITLE}</div>\\\\n\\\\\\n </a>\\\\n\\\\\\n </div>\\\\n\\\\\\n </div>\\\\n'\\n\\n os.chdir(output_dir)\\n doujinshi_dirs = next(os.walk('.'))[1]\\n\\n for folder in doujinshi_dirs:\\n if html is True and folder[0] == '[':\\n class Foo:\\n pass\\n foo = Foo()\\n foo.name = folder\\n foo.filename = folder\\n generate_html(doujinshi_obj=foo)\\n\\n files = os.listdir(folder)\\n files.sort()\\n\\n if 'index.html' in files:\\n logger.info('Add doujinshi \\\\'{}\\\\''.format(folder))\\n else:\\n continue\\n\\n image = files[0] # 001.jpg or 001.png\\n if folder is not None:\\n title = folder.replace('_', ' ')\\n else:\\n title = 'nHentai HTML Viewer'\\n\\n image_html += element.format(FOLDER=folder, IMAGE=image, TITLE=title)\\n if image_html == '':\\n logger.warning('No index.html found, --gen-main paused.')\\n return\\n try:\\n data = main.format(STYLES=css, SCRIPTS=js, PICTURE=image_html)\\n if sys.version_info < (3, 0):\\n with open('./main.html', 'w') as f:\\n f.write(data)\\n else:\\n with open('./main.html', 'wb') as f:\\n f.write(data.encode('utf-8'))\\n shutil.copy(os.path.dirname(__file__)+'/viewer/logo.png', './')\\n set_js_database()\\n logger.log(\\n 15, 'Main Viewer has been written to \\\\'{0}main.html\\\\''.format(output_dir))\\n except Exception as e:\\n logger.warning('Writing Main Viewer failed ({})'.format(str(e)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4360982e2178d4069d811379b54c9fd\",\n \"score\": \"0.4611201\",\n \"text\": \"def image_to_pdf(self, file):\\n image1 = Image.open(file)\\n im1 = image1.convert('RGB')\\n im1.save(path + \\\"\\\\\\\\\\\" + \\\"resultat\\\" + \\\"\\\\\\\\\\\" + filename + \\\".pdf\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ece8f5929ff929939ee07bd37c6e99\",\n \"score\": \"0.46092772\",\n \"text\": \"def merge_temporary_pdfs(self,\\n description: str=\\\"No description\\\",\\n ) -> None:\\n\\n temporary_pdfs = self.find_temporary_pdfs()\\n\\n filename = self.get_filename()\\n save_path = self.get_save_path(filename)\\n\\n self._merge_and_save(temporary_pdfs, save_path)\\n\\n self.temporary_counter.zero()\\n for temp_pdf in temporary_pdfs:\\n os.remove(temp_pdf)\\n\\n pdf_hash = self.get_file_hash(save_path)\\n\\n self.save_to_database(\\n pdf_hash=pdf_hash,\\n filename=filename,\\n description=description\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d3ab1c5e0d594dba140df15b8d6c922\",\n \"score\": \"0.45995933\",\n \"text\": \"def build_pdf():\\n print(\\\"=== Build PDF Docs from ReST Files ===\\\")\\n subprocess.check_call([\\n \\\"sphinx-build\\\",\\n \\\"-b\\\", \\\"latex\\\", # Builder name. TODO: accept as arg to setup.py.\\n \\\"-d\\\", joinpath(SPHINX_BUILD, \\\"doctrees\\\"),\\n SPHINX_SOURCE,\\n joinpath(SPHINX_BUILD, \\\"latex\\\")\\n ])\\n\\n LATEXDIR = joinpath(SPHINX_BUILD, \\\"latex\\\")\\n\\n def pdflatex():\\n subprocess.call([\\\"pdflatex\\\", \\\"SasView.tex\\\"], cwd=LATEXDIR)\\n\\n # Note: pdflatex requires multiple passes to resolve cross-references correctly\\n pdflatex()\\n pdflatex()\\n pdflatex()\\n subprocess.call([\\\"makeindex\\\", \\\"-s\\\", \\\"python.ist\\\", \\\"SasView.idx\\\"], cwd=LATEXDIR)\\n pdflatex()\\n pdflatex()\\n\\n print(\\\"=== Copy PDF to HTML Directory ===\\\")\\n source = joinpath(LATEXDIR, \\\"SasView.pdf\\\")\\n target = joinpath(SASVIEW_DOC_TARGET, \\\"SasView.pdf\\\")\\n shutil.copyfile(source, target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32992730a290607665ebdbd18af5310c\",\n \"score\": \"0.45934606\",\n \"text\": \"def create_html(template, output):\\n templateLoader = jinja2.FileSystemLoader(searchpath=\\\"../templates\\\")\\n templateEnv = jinja2.Environment(loader=templateLoader)\\n TEMPLATE_FILE = \\\"template_\\\" + template.lower() + \\\".html\\\"\\n real_template = templateEnv.get_template(TEMPLATE_FILE)\\n\\n if template == \\\"FWF\\\":\\n outputText = real_template.render(data_officer=dataOfficerText.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n data_characteristics=dataDescriptionText1.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n meta_standards=docMetaText1.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n data_documentation=docMetaText2.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n data_quality=docMetaText3.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n data_sharing=dataSharingText1.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n data_storage=dataSharingText2.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n legal=ethicalText1.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n ethical=ethicalText2.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n generation=generatedDataText.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"))\\n else:\\n global title\\n outputText = real_template.render(title=title,\\n abstract=abstractText.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n data_summary=dataDescriptionText1.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n fair_findable=fairText1.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n fair_accessible=fairText2.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n fair_interoperable=fairText3.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n fair_reuse=fairText4.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n resource=resourceText.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n security=securityText.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n ethical=ethicalText.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"),\\n other=otherText.get(1.0,END).replace(\\\"\\\\n\\\", \\\"<p />\\\"))\\n\\n html_file = open(output + \\\".html\\\", \\\"w\\\")\\n html_file.write(outputText)\\n html_file.close()\\n\\n return output + \\\".html\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00c0d5d7d427a6c3024f4a4e759cda50\",\n \"score\": \"0.4584902\",\n \"text\": \"def makeFiles(titlestring, generaldescription, containerlist, runlist, foldername,\\n makeIndex = True, makeTables = True, makePlotOverview = True, plottuples = None,\\n fullperRunDF = None, fullRunCompDF = None, ZperRunDF = None, ZRunCompDF = None,\\n LadderperRunDF = None, LadderRunCompDF = None, cfgname = None,\\n linkTeX = False, linkCSV = False, linkCFG = False):\\n from ConfigParser import SafeConfigParser\\n logging.info(\\\"Generating HTML files\\\")\\n ####################################################################\\n # General HTML headers\\n styleconfig = SafeConfigParser()\\n logging.debug(\\\"Loading style config\\\")\\n styleconfig.read(\\\"configs/style.cfg\\\")\\n\\n header = \\\"<!DOCTYPE html> \\\\n <html> \\\\n <body> \\\\n\\\"\\n style = \\\"<style> \\\\n table, th, td {{\\\\nborder: {0} solid black;\\\\n border-collapse: collapse;\\\\n}}\\\\nth, td {{ padding: {1}; }}\\\\n</style>\\\\n\\\".format(styleconfig.get(\\\"Tables\\\",\\\"bordersize\\\"),styleconfig.get(\\\"Tables\\\",\\\"padding\\\"))\\n footnote = \\\"<br><br><small>Generated: {0} by K. Schweiger, korbinian.schweiger@cern.ch</small>\\\\n\\\".format(datetime.now())\\n footer = \\\"</body> \\\\n </html> \\\\n\\\"\\n htmltemplatetuple = (header, style, footnote, footer)\\n ####################################################################\\n if makeTables:\\n #Check if DFs are passed -> Used to speed up the script\\n if fullperRunDF is None or fullRunCompDF is None or ZperRunDF is None or LadderperRunDF is None or LadderRunCompDF is None:\\n DFstopass = None\\n else:\\n DFstopass = (fullperRunDF, fullRunCompDF, ZperRunDF, ZRunCompDF, LadderperRunDF, LadderRunCompDF)\\n makeComparisonFiles(titlestring, generaldescription, containerlist, runlist, foldername,\\n htmltemplates = htmltemplatetuple, DFs = DFstopass, linkTeX = linkTeX, linkCSV = linkCSV, linkCFG = linkCFG)\\n if makeIndex: #Make a landing page called index.html linking tables and plots (if passed as plottuples)\\n commentsite = makeRunCommentPage(titlestring, runlist, foldername, htmltemplatetuple, containerlist)\\n makeLandingPage(titlestring, runlist, foldername, htmltemplatetuple, makePlotOverview, cfgname, commentsite)\\n if makePlotOverview:\\n if plottuples is None:\\n logging.error(\\\"No plotlists and names are given! No adding plots to index.\\\")\\n else:\\n for plottuple in plottuples: #Go through all groups\\n plotlist, group = plottuple\\n makePlotOverviewFile(titlestring, generaldescription, plotlist, runlist, foldername, group)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994093,"cells":{"query_id":{"kind":"string","value":"bc1345310a64780e09c789847d11b327"},"query":{"kind":"string","value":"Classifies the given inputs (images) by comparing the processing\\ results of the images to the target classes targets."},"positive_passages":{"kind":"list like","value":[{"docid":"6840b3a9df26313e808670b73b4439d1","score":"0.79305094","text":"def classify(self, inputs, targets):\n assert inputs.shape[0] == targets.shape[0], \\\n 'Different number of inputs and targets provided. inputs.shape[0] ' \\\n 'and targets.shape[0] must be identical.'\n assert inputs.shape[1] == self.imageSize, \\\n 'inputs have incorrect size. inputs.shape[1] must be %d.' % (\n self.imageSize)\n\n self._inputs = inputs\n self._targets = targets\n self._classifications = np.zeros(self._targets.shape, dtype=int)\n\n self._configureSNIPs()\n self._configureRun()\n\n # Compute number of batches\n assert self.numSamples % self.batchSize == 0, 'numSamples/targets ' \\\n 'must be multiple of ' \\\n 'batchSize.'\n\n imgIds = range(0, self.numSamples)\n self._start(imgIds)\n\n self.finish()","title":""}],"string":"[\n {\n \"docid\": \"6840b3a9df26313e808670b73b4439d1\",\n \"score\": \"0.79305094\",\n \"text\": \"def classify(self, inputs, targets):\\n assert inputs.shape[0] == targets.shape[0], \\\\\\n 'Different number of inputs and targets provided. inputs.shape[0] ' \\\\\\n 'and targets.shape[0] must be identical.'\\n assert inputs.shape[1] == self.imageSize, \\\\\\n 'inputs have incorrect size. inputs.shape[1] must be %d.' % (\\n self.imageSize)\\n\\n self._inputs = inputs\\n self._targets = targets\\n self._classifications = np.zeros(self._targets.shape, dtype=int)\\n\\n self._configureSNIPs()\\n self._configureRun()\\n\\n # Compute number of batches\\n assert self.numSamples % self.batchSize == 0, 'numSamples/targets ' \\\\\\n 'must be multiple of ' \\\\\\n 'batchSize.'\\n\\n imgIds = range(0, self.numSamples)\\n self._start(imgIds)\\n\\n self.finish()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8a295023de0f3cc752c3de5964aeb74a","score":"0.6684749","text":"def postprocess(results, filenames, batch_size):\n if len(results) != 1:\n raise Exception(\"expected 1 result, got {}\".format(len(results)))\n\n batched_result = results[0].batch_classes\n if len(batched_result) != batch_size:\n raise Exception(\"expected {} results, got {}\".format(batch_size, len(batched_result)))\n if len(filenames) != batch_size:\n raise Exception(\"expected {} filenames, got {}\".format(batch_size, len(filenames)))\n\n label, score = [], []\n # batch size is always 1 here, need to modify if were to larger batch_size\n for (index, result) in enumerate(batched_result):\n print(\"Image '{}':\".format(filenames[index]))\n for cls in result.cls:\n label.append(cls.label)\n score += [{\"index\": cls.label, \"val\": cls.value}]\n print(\" {} ({}) = {}\".format(cls.idx, cls.label, cls.value))\n return label[0], score","title":""},{"docid":"73c23595c939de51150afd1ef98e92c8","score":"0.6672096","text":"def classified_images(wildcards):\n outrule, i = classify_or_test(wildcards, return_int=True)\n if i == 3:\n checkpoint_output = checkpoints.create_split_truth_data.get(**wildcards).output.test\n elif i:\n checkpoint_output = checkpoints.create_truth_data.get(**wildcards).output[0]\n else:\n checkpoint_output = checkpoints.rev_transform.get(**wildcards).output[0]\n return expand(\n outrule.output[0],\n sample=wildcards.sample,\n image=glob_wildcards(\n os.path.join(\n checkpoint_output,\n \"{image}\"+(\".tsv\" if i else \".json\")\n )\n ).image\n )","title":""},{"docid":"314d3bc358652b13a3fb28ebe2290a18","score":"0.65816724","text":"def compute(self, images_true, images_pred, images_score):\n if not (len(images_true) == len(images_pred) == len(images_score)):\n raise ValueError('Dimensions must correspond.')\n\n for image_true, image_pred, image_score in itertools.izip(images_true, images_pred, images_score):\n groundtruths = np.argwhere(np.asarray(image_true) > 0)\n predictions = np.argwhere(np.asarray(image_pred) > 0)\n\n n_groundtruths = float(groundtruths.shape[0])\n n_predictions = float(predictions.shape[0])\n width = float(image_true.shape[0])\n height = float(image_true.shape[1])\n raw_count = np.sum(image_score / np.float(self._raw_factor), dtype=np.float64)\n\n # Sort detections by score in descending order such that the first detection\n # linked to a ground truths is the more confident one\n scores = image_score[tuple(predictions.T)]\n predictions = predictions[np.argsort(scores, kind='quicksort')]\n\n n_tp = 0\n if n_predictions > 0:\n kdtree = KDTree(predictions, metric='euclidean')\n pred_idxss, distancess = kdtree.query_radius(groundtruths, self._epsilon, return_distance=True)\n free = np.array([True] * predictions.shape[0])\n for pred_idxs, distances in itertools.izip(pred_idxss, distancess):\n pred_idxs = pred_idxs[free]\n if len(pred_idxs) > 0 and n_tp <= int(n_predictions):\n pred_idx = pred_idxs[np.argmax(pred_idxs)]\n free[pred_idx] = False\n self.distance_errors.append(distances[np.argmax(pred_idxs)])\n n_tp += 1\n\n self.cm.incrementTP(n_tp)\n self.cm.incrementFP(int(n_predictions - n_tp))\n self.cm.incrementFN(int(n_groundtruths - n_tp))\n\n self.count_errors.append(np.abs(n_groundtruths - n_predictions))\n self.raw_count_errors.append(np.abs(n_groundtruths - raw_count))\n\n self.density_errors.append(np.abs((n_groundtruths / (width * height)) - (n_predictions / (width * height))))\n self.raw_density_errors.append(np.abs((n_groundtruths / (width * height)) - (raw_count / (width * height))))\n self.count += n_groundtruths","title":""},{"docid":"3637cb7c0f56c7e3a52c6380e2c71bc3","score":"0.6534657","text":"def classify(self, image_messages):\n pass","title":""},{"docid":"545a58f7cb2334582c2c240b6a715500","score":"0.6500155","text":"def classify(self, caffemodel, deploy_file, image_files,\n mean_file=None, labels_file=None, batch_size=None, use_gpu=True):\n # Load the model and images\n net = self.get_net(caffemodel, deploy_file, use_gpu)\n transformer = self.get_transformer(deploy_file, mean_file)\n _, channels, height, width = transformer.inputs['data']\n if channels == 3:\n mode = 'RGB'\n elif channels == 1:\n mode = 'L'\n else:\n raise ValueError('Invalid number for channels: %s' % channels)\n images = [self.load_image(image_file, height, width, mode) for image_file in image_files]\n labels = self.read_labels(labels_file)\n\n # Classify the image\n scores = self.forward_pass(images, net, transformer, batch_size=batch_size)\n\n # Process the results\n indices = (-scores).argsort()[:, :5] # take top 5 results\n classifications = []\n for image_index, index_list in enumerate(indices):\n result = []\n for i in index_list:\n # 'i' is a category in labels and also an index into scores\n if labels is None:\n label = 'Class #%s' % i\n else:\n label = labels[i]\n result.append((label, round(100.0 * scores[image_index, i], 4)))\n classifications.append(result)\n\n # classifications is in the form of [('Occupied', 100.0), ('Empty', 0.0)]\n # originally this function just printed out results. modified to send an array with the results and labels\n # for use elsewhere in the program\n return classifications","title":""},{"docid":"bfa059044b8f64e812d9da961b2ec168","score":"0.6481143","text":"def compare_results(input_url_images, input_url_results_labels, input_url_gt_labels, output_url, colors_gt,\n colors_result, save, visualize):\n data = os.listdir(input_url_gt_labels)\n\n for d in data:\n file = open(os.path.join(input_url_results_labels, d))\n file_gt = open(os.path.join(input_url_gt_labels, d))\n\n img = cv2.imread(os.path.join(input_url_images, d.split('.')[0] + \".png\"))\n if img is None:\n img = cv2.imread(os.path.join(input_url_images, d.split('.')[0] + \".jpg\"))\n\n img = get_labels(file_gt, img, 5, colors_gt, \"yolo\")\n img = get_labels(file, img, 5, colors_result, \"det\")\n\n if visualize:\n visualize_data(img)\n\n if save:\n save_data(img, output_url, d)","title":""},{"docid":"5220d165b6421ad63622ce02844b740f","score":"0.6467733","text":"def _f_image(self, images: np.ndarray):\n out = np.zeros((len(images), 2)) # output same shape\n\n # inputs neeeds to be [PIL.Image.Image]; if not try to transform\n if not isinstance(images[0], Image.Image):\n images = utils.arr_to_img(images)\n\n for i, (image, text) in enumerate(zip(images, self._fixed_texts)):\n # classify, output is a tupe (index, score)\n ind, score = self.model.classify(image, text).values()\n out[i][ind] = score\n out[i][1 - ind] = 1 - score\n\n return out\n # test if only output is the probability of being hateful\n # return out[:, 1][:, np.newaxis]","title":""},{"docid":"d67624d11d4986b95a2c60a8d75749fc","score":"0.6464209","text":"def classify(self, inputs):\n return self.predict(self.params, inputs)","title":""},{"docid":"cebb62ab3ca63abff427e62afe3d02a9","score":"0.6442733","text":"def detect(self, images, verbose=0):\n assert self.mode == \"inference\", \"Create model in inference mode.\"\n assert len(\n images) == self.config.BATCH_SIZE, \"len(images) must be equal to BATCH_SIZE\"\n\n if verbose:\n log(\"Processing {} images\".format(len(images)))\n for image in images:\n log(\"image\", image)\n\n # Mold inputs to format expected by the neural network\n molded_images, image_metas, windows = self.mold_inputs(images)\n\n # Validate image sizes\n # All images in a batch MUST be of the same size\n image_shape = molded_images[0].shape\n for g in molded_images[1:]:\n assert g.shape == image_shape, \\\n \"After resizing, all images must have the same size. Check IMAGE_RESIZE_MODE and image sizes.\"\n\n # Anchors\n anchors = self.get_anchors(image_shape)\n # Duplicate across the batch dimension because Keras requires it\n # TODO: can this be optimized to avoid duplicating the anchors?\n anchors = np.broadcast_to(anchors, (self.config.BATCH_SIZE,) + anchors.shape)\n\n if verbose:\n log(\"molded_images\", molded_images)\n log(\"image_metas\", image_metas)\n log(\"anchors\", anchors)\n # Run object detection\n detections, _, _, mrcnn_mask, _, _, _ = \\\n self.keras_model.predict([molded_images, image_metas, anchors], verbose=0)\n # Process detections\n results = []\n for i, image in enumerate(images):\n final_rois, final_class_ids, final_scores, final_masks = \\\n self.unmold_detections(detections[i], mrcnn_mask[i],\n image.shape, molded_images[i].shape,\n windows[i])\n results.append({\n \"rois\": final_rois,\n \"class_ids\": final_class_ids,\n \"scores\": final_scores,\n \"masks\": final_masks,\n })\n return results","title":""},{"docid":"4afe0b5fd3d9d76a532ff5a493ede9eb","score":"0.6397793","text":"def classify(test_Q1, test_Q2, y, threshold, model, vocab, data_generator=data_generator, batch_size=64):\n accuracy = 0\n ### START CODE HERE (Replace instances of 'None' with your code) ###\n for i in range(0, len(test_Q1), batch_size):\n # Call the data generator (built in Ex 01) with shuffle=False using next()\n # use batch size chuncks of questions as Q1 & Q2 arguments of the data generator. e.g x[i:i + batch_size]\n # Hint: use `vocab['<PAD>']` for the `pad` argument of the data generator\n q1, q2 = next(data_generator(\n test_Q1[i:i + batch_size], test_Q2[i:i + batch_size], batch_size, vocab[\"<PAD>\"], shuffle=False))\n # use batch size chuncks of actual output targets (same syntax as example above)\n y_test = y[i:i + batch_size]\n # Call the model\n v1, v2 = model((q1, q2))\n\n for j in range(batch_size):\n # take dot product to compute cos similarity of each pair of entries, v1[j], v2[j]\n # don't forget to transpose the second argument\n d = fastnp.dot(v1[j], v2[j].T)\n # is d greater than the threshold?\n res = d > threshold\n # increment accurancy if y_test is equal `res`\n accuracy += (y_test[j] == res)\n # compute accuracy using accuracy and total length of test questions\n accuracy = accuracy / len(test_Q1)\n ### END CODE HERE ###\n\n return accuracy","title":""},{"docid":"e710c78d9059839bc92198551226f4df","score":"0.63741803","text":"def _check_images(self, abs_output_directory):\n # Make output directory/directories\n if len(set(self.class_labels)) <= 1:\n if not os.path.exists(abs_output_directory):\n try:\n os.makedirs(abs_output_directory)\n except IOError:\n logging.error(\"Insufficient rights to read or write output directory ({})\"\n .format(abs_output_directory))\n else:\n for class_label in self.class_labels:\n if not os.path.exists(os.path.join(abs_output_directory, str(class_label[0]))):\n try:\n os.makedirs(os.path.join(abs_output_directory, str(class_label[0])))\n except IOError:\n logging.error(\"Insufficient rights to read or write output directory ({})\"\n .format(abs_output_directory))\n # Check the images, read their dimensions, and remove them if they cannot be read\n for augmentor_image in self.augmentor_images:\n try:\n opened_image = np.load(augmentor_image.image_path)\n self.distinct_dimensions.add(opened_image.shape)\n except IOError as e:\n logging.error(\"There is a problem with image {} in the source directory: {}\"\n .format(augmentor_image.image_path, e.message))\n self.augmentor_images.remove(augmentor_image)\n\n logging.info(\"Initialised with {} image(s) found.\\n\".format(len(self.augmentor_images)))\n logging.info(\"Output directory set to {}.\".format(abs_output_directory))","title":""},{"docid":"ea3f8f306170c37feb4fc50130240941","score":"0.6322083","text":"def test_network_monger(dirs, image_dims):\r\n start_time = time.time()\r\n read_write_time = 0.0\r\n n_classes = len(dirs) # assumes number of classes is the same as number of input directories\r\n master_list_encoded = []\r\n for i in range(len(dirs)):\r\n imagenames = return_images(dirs[i])\r\n image_encoded = list(zip(imagenames, [i] * len(imagenames)))\r\n master_list_encoded += image_encoded\r\n total_num_images = len(master_list_encoded)\r\n all_image_data = read_images(master_list_encoded, image_dims)\r\n image_indices = list(range(total_num_images))\r\n px_values, true_classes = batch_create(all_image_data, image_indices, image_dims, n_classes) # see batch_create\r\n input_dict = {image_data: px_values, true_class: true_classes,\r\n im_x: image_dims[0], im_y: image_dims[1], im_z: image_dims[2],\r\n num_classes: n_classes, keep_prob: 1.0}\r\n read_write_time = read_write_time + time.time() - start_time\r\n run_time_start = time.time()\r\n \"\"\"Now, the network isn't being optimized, so instead the calculated raw class values and binary class\r\n predictions are returned for all the test images\"\"\"\r\n decimal_classes, pred_classes = session.run([calculated_value, predicted_class], feed_dict=input_dict)\r\n run_time = time.time() - run_time_start\r\n current_time = time.time()\r\n pred_classes = np.ravel(pred_classes) # munge munge munge\r\n pred_classes = pred_classes.tolist() # munge munge munge munge munge\r\n decimal_classes.tolist() # munge munge munge munge munge munge munge munge munge munge\r\n all_test_classes = list(all_image_data[:, 0].astype(int))\r\n correct_predictions = [i == j for i, j in zip(all_test_classes, pred_classes)] # boolean of correct predictions\r\n lists = [all_test_classes, pred_classes, decimal_classes]\r\n names = ['true', 'pred', 'raw']\r\n for i in range(3):\r\n \"\"\"see readme for description of these output files\"\"\"\r\n file = open('out'+names[i]+'.txt', 'w')\r\n for line in lists[i]:\r\n file.write(str(line)+' \\n')\r\n file.close()\r\n total_correct_predictions = sum(correct_predictions) # total number of correct test predictions\r\n accuracy = round((float(total_correct_predictions) / float(total_num_images))*100, 2)\r\n read_write_time = read_write_time + time.time() - current_time\r\n return read_write_time, run_time, accuracy","title":""},{"docid":"2fbf94146eae5aa78b71322062f1f1b7","score":"0.6163668","text":"def _input_format_classification(preds: torch.Tensor, target: torch.Tensor, threshold: float):\n if not (len(preds.shape) == len(target.shape) or len(preds.shape) == len(target.shape) + 1):\n raise ValueError(\n \"preds and target must have same number of dimensions, or one additional dimension for preds\"\n )\n\n if len(preds.shape) == len(target.shape) + 1:\n # multi class probabilites\n preds = torch.argmax(preds, dim=1)\n\n if len(preds.shape) == len(target.shape) and preds.dtype == torch.float:\n # binary or multilabel probablities\n preds = (preds >= threshold).long()\n return preds, target","title":""},{"docid":"887fec5b52e14b5025918edd69b8be70","score":"0.6161768","text":"def step_fn(inputs):\n images, labels = inputs\n logits = self.model(images, training=False)\n\n if self.test_loss:\n if self.one_hot:\n loss = tf.keras.losses.categorical_crossentropy(\n labels, logits, label_smoothing=self.label_smoothing)\n else:\n loss = tf.keras.losses.sparse_categorical_crossentropy(labels, logits)\n loss = tf.reduce_sum(loss) * (1.0 / self.flags_obj.batch_size)\n self.test_loss.update_state(loss)\n\n if self.test_accuracy:\n self.test_accuracy.update_state(labels, logits)\n # tf.print('labels.shape: ', labels.shape,\n # ', logits.shape: ', logits.shape,\n # ', result: ', self.test_accuracy.result())\n # self.test_corrects.update_state(\n # tf.cast(\n # tf.reduce_sum(\n # tf.cast(\n # tf.equal(\n # tf.cast(tf.argmax(logits, axis=1), labels.dtype),\n # labels), tf.int32)), tf.float32))","title":""},{"docid":"3323a1edd08d744a074e45eb6bec075f","score":"0.61310375","text":"def test_attack(self, adv_images, orig_images, true_labels,\n target_labels=None, targeted=False):\n score = self.model.evaluate(adv_images, true_labels, verbose=0)\n print('Test loss: {:.2f}'.format(score[0]))\n print('Successfully moved out of source class: {:.2f}'.format(\n 1 - score[1]))\n\n if targeted:\n score = self.model.evaluate(adv_images, target_labels, verbose=0)\n print('Test loss: {:.2f}'.format(score[0]))\n print('Successfully perturbed to target class: {:.2f}'.format(\n score[1]))\n\n dist = np.mean(np.sqrt(\n np.mean(np.square(adv_images - orig_images), axis=(1, 2, 3))))\n print('Mean perturbation distance: {:.2f}'.format(dist))\n return score[0], 1 - score[1], dist","title":""},{"docid":"84897f663417004410fb56682d96d400","score":"0.61264914","text":"def main():\n os.chdir(os.path.dirname(__file__))\n cur_dir=os.getcwd()\n \n training_data_path_ants=cur_dir+\"\\\\ant\"\n training_data_path_lobsters=cur_dir+\"\\\\lobster\"\n test_data_path_unknown=cur_dir+\"\\\\test\";\n \n processor=ImageProcessor();\n train_X1=processor.fetch_all_jpg_files_as_numpy_array(training_data_path_ants);\"\"\" pictures of ants--class 0\"\"\"\n train_X2=processor.fetch_all_jpg_files_as_numpy_array(training_data_path_lobsters);\"\"\" pictures of lobsters--class 1\"\"\"\n \n train_Y1=[];\n train_Y2=[];\n \n for image in train_X1:\n train_Y1.append(0);\"\"\"class 0\"\"\"\n for image in train_X2:\n train_Y2.append(1);\"\"\"class 1\"\"\"\n \n train_Y=train_Y1+train_Y2;\"\"\"all class labels--> Y\"\"\"\n train_X_temp=train_X1+train_X2; \"\"\"all training data--variables X\"\"\"\n \n \"\"\"********************************************\n our training set is ready now: \n let us have a test data to find if the test image is ant or lobster.\"\"\"\n \n \n \"\"\"pad zeros at the end\"\"\"\n \n train_X=processor.pad_list(train_X_temp)\n \n dim1=int(math.sqrt(len(train_X[0])/3))\n dim2=dim1\n test_X=processor.fetch_with_resize(test_data_path_unknown,dim1,dim2)\n test_X=test_X[0].tolist() \n \"\"\" set up the deep network with 1 hidden layer defined above\"\"\"\n \n deep_network=DeepNet(train_X,train_Y)\n \n deep_network.train()\n output=deep_network.predict(test_X)\n \n if(output<0.5):\n print (\"ant\")\n else:\n print (\"lobster\")\n \n print(\"The end\")","title":""},{"docid":"faab65fc3e395f22ee9d04e2296a64fb","score":"0.61191386","text":"def apply_preprocessing(self):\n # Images preprocessing\n preparator = ImagePreparation(\n self.images_paths, self.args['model_type']\n )\n images = preparator.apply_basic_processing()\n\n # Labels Encoding\n encoder = LabelClassifier(self.labels)\n labels_cat, label_classes = encoder.get_categorical_labels()\n\n return images, labels_cat, label_classes","title":""},{"docid":"7b3ca84d4f190cbaeceaa2d22f46d443","score":"0.6116314","text":"def _classifyImages(self):\n processor = ImageProcessor(self._filePath)\n labels = processor.classifyImages()\n if(len(labels) == 0):\n raise ValueError(\"There are no images in that folder\")\n return labels","title":""},{"docid":"1a08421180cd0f65d5228bc4584f5938","score":"0.61113167","text":"def categorize(self, protos):\n corpus = self.imagefeed.feed_location\n mask = ChooseTrainingSet(get_labels(corpus=self.imagefeed.feed_location), train_size=0.5)\n categories = classify_images(protos, get_images(corpus, mask=mask), \n get_labels(corpus, mask=mask), get_images(corpus, mask=~mask), corpus)\n reset_directory(self.sorted_location, self.imagefeed.image_location)\n self.imagefeed.move_images(categories, self.sorted_location)","title":""},{"docid":"ed7d97656d6a3cf21340b81790c5e062","score":"0.6108216","text":"def classify_jpegs():\n args = sys.argv\n\n # ensure we have the right number of arguments\n if not len(args) == 2:\n print 'Usage: %s [directory]' % (args[0])\n print 'Classifies each JPEG image in the given directory.'\n return\n\n classifier = classify.read_classifier()\n\n directory = args[1]\n old_cwd = os.getcwd()\n os.chdir(directory)\n\n # find all images in directory\n image_paths = glob.glob('*.jpg') + glob.glob('*.jpeg') + glob.glob('*.png')\n\n # read and classify each image\n for image_path in image_paths:\n image = cv2.imread(image_path)\n features = classify.compute_features(image, preprocess=True)\n\n prediction = classifier.predict(features)[0]\n print image_path, chr(prediction)\n\n os.chdir(old_cwd)","title":""},{"docid":"6ba4591a43635749242407f02c19cbcf","score":"0.61065733","text":"def detection_as_classification(model, test_generator):\n i = 0\n TP = 0\n FP = 0\n \n for X,Y in test_generator:\n if i >= len(test_generator):\n break # otherwise will run indefinitely\n X = rgb2bgr(X)\n X = preprocess_image(X)\n boxes, scores, labels = model.predict_on_batch(X)\n tp, fp = evaluate(filter(scores, labels, score_threshold), Y)\n i += 1\n TP += tp\n FP += fp\n\n return TP, FP","title":""},{"docid":"6165e3379545522b2a36fc331be8a2d1","score":"0.61028713","text":"def training(\n positive_iis, \n negative_iis, \n num_classifiers, \n ns, \n Ddesired=0.9, \n Edesired = 0.05\n ):\n logger = getLogger('Train')\n \n num_pos = len(positive_iis)\n num_neg = len(negative_iis)\n num_imgs = num_pos + num_neg\n img_height, img_width = positive_iis[0].shape\n \n pos_weights = np.ones(num_pos) * 1. / (2 * num_pos)\n neg_weights = np.ones(num_neg) * 1. / (2 * num_neg)\n weights = np.hstack((pos_weights, neg_weights))\n labels = np.hstack((np.ones(num_pos), -np.ones(num_neg)))\n \n images = positive_iis + negative_iis\n \n logger.info('Stage 1: Creating features')\n features = create_dynamic_features(\n (img_width, img_height), \n ns,\n 0\n )\n num_features = len(features)\n num_classifiers = num_features if num_classifiers == -1 else num_classifiers\n \n logger.info('Generated features: {0}'.format(num_features))\n logger.info('Stage 2: Calculating thresholds')\n \n bar = ProgressBar()\n for i in bar(range(num_features)):\n feature = features[i]\n scores = np.fromiter(\n (feature.get_score(img) for img in images), \n np.float\n )\n feature.threshold = get_appropriet_threshold(\n scores[:num_pos], \n scores[num_pos:], \n Ddesired, \n Edesired\n )\n \n logger.info('Stage 3: Calculating scores for all images')\n votes = np.zeros((num_imgs, num_features))\n bar = ProgressBar()\n # Use as many workers as there are CPUs\n pool = Pool(processes=None)\n for i in bar(range(num_imgs)):\n features_matrix = np.array( \n list(\n pool.map(\n #map( \n partial(_get_feature_values, image=images[i]), \n features\n )))\n # Convert matrix to vector\n votes[i, :] = features_matrix\n \n logger.info('Stage 4: Selecting classifiers')\n classifiers = list()\n feature_indexes = list(range(num_features))\n bar = ProgressBar()\n for _ in bar(range(num_classifiers)):\n\n classification_errors = np.zeros(len(feature_indexes))\n # normalize weights\n weights *= 1. / np.sum(weights)\n\n # select best classifier based on the weighted error\n for f in range(len(feature_indexes)):\n f_idx = feature_indexes[f]\n # classifier error is the sum of image weights where the classifier\n # is right\n error = sum( map(\n lambda img_idx: weights[img_idx] if labels[img_idx] != votes[img_idx, f_idx] else 0, \n range(num_imgs)\n ))\n classification_errors[f] = error\n\n # get best feature, i.e. with smallest error\n min_error_idx = np.argmin(classification_errors)\n best_error = classification_errors[min_error_idx]\n best_feature_idx = feature_indexes[min_error_idx]\n\n # set feature weight\n best_feature = features[best_feature_idx]\n eb_eq = (1-best_error)/best_error\n feature_weight = 0.5 * np.log((1 - best_error) / best_error)\n best_feature.weight = feature_weight\n\n classifiers.append(best_feature)\n \n weights = np.array(\n list(\n map(\n lambda img_idx: weights[img_idx] * np.sqrt(eb_eq if (labels[img_idx] != votes[img_idx, best_feature_idx]) else 1/eb_eq),\n range(num_imgs)\n )\n )\n )\n \n feature_indexes.remove(best_feature_idx)\n \n logger.info(\n 'Training finished. {0} week classifiers formed'.format(len(classifiers))\n )\n return classifiers","title":""},{"docid":"0d5339fbadddabf29188f5f26d8daabd","score":"0.6089755","text":"def process_batch(self, detections, labels):\n detections = detections[detections[:, 4] > self.conf]\n gt_classes = labels[:, 0].int()\n detection_classes = detections[:, 5].int()\n iou = box_iou(labels[:, 1:], detections[:, :4])\n\n x = torch.where(iou > self.iou_thres)\n if x[0].shape[0]:\n matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()\n if x[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n else:\n matches = np.zeros((0, 3))\n\n n = matches.shape[0] > 0\n m0, m1, _ = matches.transpose().astype(int)\n for i, gc in enumerate(gt_classes):\n j = m0 == i\n if n and sum(j) == 1:\n self.matrix[detection_classes[m1[j]], gc] += 1 # correct\n else:\n self.matrix[self.nc, gc] += 1 # background FP\n\n if n:\n for i, dc in enumerate(detection_classes):\n if not any(m1 == i):\n self.matrix[dc, self.nc] += 1 # background FN","title":""},{"docid":"3bd5c7e71502f132bb8fdadbf9e2ba93","score":"0.6076005","text":"def inference(self, batch_inputs, images, pred_centers, pred_regions, pred_weights, encode_feat):\n results = batch_inputs\n processed_results = []\n for img_idx, result_img in enumerate(results):\n if \"instances\" in result_img.keys():\n img_shape = result_img[\"instances\"].image_size\n else:\n img_shape = result_img[\"image\"].shape[-2:]\n ori_shape = (result_img[\"height\"], result_img[\"width\"])\n encode_feat = encode_feat[img_idx].unsqueeze(0)\n feat_shape = encode_feat.shape\n encode_feat = encode_feat.reshape(*feat_shape[:2], -1)\n result_instance = None\n\n pred_regions = [_pred[img_idx].unsqueeze(0) for _pred in pred_regions]\n pred_weights = [_pred[img_idx].unsqueeze(0) for _pred in pred_weights]\n pred_centers = [_pred[img_idx].unsqueeze(0) for _pred in pred_centers]\n pool_size = [3,3,3,5,5]\n idx_feat_th, class_ths, score_ths, thing_num, idx_feat_st, score_sts, class_sts, stuff_num = \\\n multi_apply(self.inference_single_level, pred_centers,\\\n pred_regions, pred_weights, pool_size)\n \n thing_num = sum(thing_num)\n if thing_num == 0:\n result_instance = Instances(ori_shape, pred_masks=[], pred_boxes=[], \n pred_classes=[], scores=[])\n else:\n class_ths = [_class for _class in class_ths if len(_class)>0]\n score_ths = [_score for _score in score_ths if len(_score)>0]\n idx_feat_th = [_feat for _feat in idx_feat_th if len(_feat)>0]\n class_ths = torch.cat(class_ths, dim=0)\n score_ths = torch.cat(score_ths, dim=0)\n idx_feat_th = torch.cat(idx_feat_th, dim=2)\n keep = torch.argsort(score_ths, descending=True)\n idx_feat_th = idx_feat_th[:,:,keep]\n score_ths = score_ths[keep]\n class_ths = class_ths[keep]\n\n stuff_num = sum(stuff_num)\n if stuff_num == 0:\n class_sts, idx_feat_st, score_sts = [], [], []\n else:\n score_sts = [_score for _score in score_sts if len(_score)>0]\n class_sts = [_cate_sem for _cate_sem in class_sts if len(_cate_sem)>0]\n idx_feat_st = [_feat for _feat in idx_feat_st if len(_feat)>0]\n score_sts = torch.cat(score_sts, dim=0)\n class_sts = torch.cat(class_sts, dim=0)\n idx_feat_st = torch.cat(idx_feat_st, dim=0)\n\n pred_thing, [class_ths, score_ths] = self.thing_generator(encode_feat, feat_shape, idx_feat_th, thing_num, class_ths, score_ths)\n pred_stuff, [class_sts, score_sts] = self.stuff_generator(encode_feat, feat_shape, idx_feat_st, stuff_num, class_sts, score_sts)\n pred_stuff = pred_stuff.sigmoid()\n \n if result_instance is None:\n result_instance, pred_mask, class_ths, score_ths = self.process_inst(\n class_ths, score_ths, pred_thing, img_shape, ori_shape) \n else:\n pred_mask, class_ths, score_ths = None, None, None\n if self.sem_with_thing or self.cfg.MODEL.POSITION_HEAD.STUFF.ALL_CLASSES:\n sem_classes = self.sem_classes\n else:\n sem_classes = self.sem_classes + 1\n\n pred_stuff = F.interpolate(pred_stuff, scale_factor=self.common_stride, mode=\"bilinear\", \n align_corners=False)[...,:img_shape[0],:img_shape[1]]\n pred_stuff = F.interpolate(pred_stuff, size=ori_shape, mode=\"bilinear\", align_corners=False)[0]\n pred_sem_seg = torch.zeros(sem_classes, *pred_stuff.shape[-2:], device=self.device)\n pred_sem_seg[class_sts] += pred_stuff\n processed_results.append({\"sem_seg\": pred_sem_seg, \"instances\": result_instance})\n\n if self.panoptic_combine:\n result_panoptic = self.combine_thing_and_stuff(\n [pred_mask, class_ths, score_ths],\n pred_sem_seg.argmax(dim=0),\n self.panoptic_overlap_thrs,\n self.panoptic_stuff_limit,\n self.panoptic_inst_thrs)\n processed_results[-1][\"panoptic_seg\"] = result_panoptic\n\n return processed_results","title":""},{"docid":"8d8e18c099a271baf52f0e63c258c43e","score":"0.60603863","text":"def classify_images(images_dir,petlabel_dic,model):\n #classifier()\n images_dir_filename = listdir(images_dir)\n #model_petlabel_dic = dict()\n results_dic = dict()\n # for block to pass values into classifer method and get model label\n # model lable will be added to a dictionary\n for key in petlabel_dic:\n file_name = images_dir+key\n # print(file_name)\n model_label = classifier(file_name,model)\n #print(model_label)\n model_label = model_label.lower()\n model_label = model_label.strip()\n truth = petlabel_dic[key]\n found = model_label.find(truth)\n if found>=0:\n if( (found ==0 and len(truth)==len(model_label) ) or\n ( ( (found==0) or (model_label[found-1]==\" \") ) and\n ( (found + len(truth)==len(model_label)) or\n (model_label[found + len(truth):found+len(truth)+1] in\n (\",\",\" \") )\n )\n )\n ):\n if key not in results_dic:\n results_dic[key] = [truth,model_label,1]\n\n else:\n if key not in result_dic:\n results_dic[key] = [truth,model_label,0]\n else:\n if key not in results_dic:\n results_dic[key] = [truth,model_label,0]\n return(results_dic)","title":""},{"docid":"bacf24a7bca36483f1bd00eee184f5a2","score":"0.605324","text":"def imageClassify(net, image_name):\n\n # Load the image uploaded by user..\n im_file = os.path.join('./assets', image_name)\n im = cv2.imread(im_file)\n\n # Detect all object classes and regress object bounds\n timer = Timer()\n timer.tic()\n scores, boxes = im_detect(net, im)\n timer.toc()\n print ('Detection took {:.3f}s for '\n '{:d} object proposals').format(timer.total_time, boxes.shape[0])\n\n # Visualize detections for each class\n CONF_THRESH = 0.8\n NMS_THRESH = 0.3\n fw = open(im_file+\".txt\", 'w')\n fw.write(\"{\\\"objects\\\": [\\n\")\n for cls_ind, cls in enumerate(CLASSES[1:]):\n cls_ind += 1 # because we skipped background\n cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\n cls_scores = scores[:, cls_ind]\n dets = np.hstack((cls_boxes,\n cls_scores[:, np.newaxis])).astype(np.float32)\n keep = nms(dets, NMS_THRESH)\n dets = dets[keep, :]\n# fw.write(\"{extent:[\"+str(bbox[0])+\",\"+str(bbox[1])+','+str(bbox[2])+\",\"+str(bbox[3])+\"],\\n\")\n# fw.write(\"class:\" + class_name + \",\\n\")\n# fw.write(\"score:\" + score + \"},\\n\")\n\n vis_detections(im, cls, dets, im_file, fw, thresh=CONF_THRESH)\n fw.write(\"{}]}\")\n fw.close()","title":""},{"docid":"c9fcde7c822832a4adc922842d561494","score":"0.60296136","text":"def prepare_images(self):\n\n self.model.resize_data(Structure.CategoricalNeuralNetwork, Environment.TRAIN)\n self.model.resize_data(Structure.CategoricalNeuralNetwork, Environment.TEST)\n\n n_classes = self.model.convert_to_one_hot_data()\n\n return n_classes","title":""},{"docid":"6c111a0ba9e1382e7af399588679d4d7","score":"0.60258794","text":"def run_classifier(self, sets=[\"train\", \"valid\"], class_threshold=0, preprocess_func=None, label_func=None, pwds=None, classify_func=None, **kwargs):\n\n print(\"\\nRunning Classifier:\", self.name)\n\n for data_set in sets:\n\n assert data_set in self.dataset, \"%s not in dataset\" % data_set\n print(\"Currently classifying {} with {} datapoints\".format(data_set, len(self.dataset[data_set][\"data\"])))\n\n preds = []\n\n data = self.dataset[data_set][\"data\"]\n\n self.dataset[data_set][\"matches\"] = []\n self.dataset[data_set][\"scores\"] = []\n\n for datum in data:\n capture_scores = defaultdict(int)\n captures = {}\n class_matches = {}\n\n #Forced to do this because self.regexes stored as {\"class\": [Regex Objects list]}\n for class_name in self.regexes:\n\n matches = {}\n #Ask handler to get capture_scores, captures, and matches\n if len(self.regexes[class_name]) > 0:\n matches, captures, capture_scores = self.handler.score_data(datum, self.regexes[class_name],\n pwds=pwds, preprocess_func=preprocess_func,\n capture_convert=label_func)\n #Storing matches in object\n class_matches[class_name] = matches\n\n #Adding biases\n for bias in self.capture_biases:\n capture_scores[bias] += self.capture_biases[bias]\n\n self.dataset[data_set][\"matches\"].append(class_matches)\n self.dataset[data_set][\"scores\"].append(capture_scores)\n\n #getting prediction\n\n if not classify_func:\n preds.append(self.classify(capture_scores, threshold=class_threshold)[0])\n else:\n preds.append(classify_func(class_matches, captures, capture_scores, **kwargs))\n\n preds = np.array(preds)\n self.dataset[data_set][\"preds\"] = preds","title":""},{"docid":"d8fb745ccba717175f00be5ca6dada34","score":"0.60210526","text":"def _predict_images(self, images):\n img_transform = transforms.Compose([\n transforms.Resize((197, 197)),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ])\n \n\n reconstructed_image_tensor_array = []\n for (image, shape) in zip(images['image'], images['shape']):\n imagebytes = bytearray(image, encoding=\"utf8\")\n deserialized_bytes = base64.decodebytes(imagebytes)\n deserialized_bytes = np.frombuffer(deserialized_bytes, dtype=np.uint8)\n img_nparray = np.reshape(deserialized_bytes, newshape=shape)\n img = PIL.Image.fromarray(np.uint8(img_nparray), mode=\"RGB\")\n imgTensor = img_transform(img)\n reconstructed_image_tensor_array.append(imgTensor)\n \n reconstructed_image_tensor = torch.stack(reconstructed_image_tensor_array, dim=0)\n with torch.no_grad():\n output = self._model(reconstructed_image_tensor).squeeze(-1).squeeze(-1)\n return output","title":""},{"docid":"7ef940ab70e617f83d12c0bfd7e0239e","score":"0.6019942","text":"def run_images(self):\n car_list_train, car_list_test, noncar_list_train, noncar_list_test = self.read_data()\n X_train, X_test, y_train, y_test, self.scaler = self.get_datasets(car_list_train, car_list_test,\n noncar_list_train, noncar_list_test)\n self.classifier = self.train_classifier(X_train, y_train, X_test, y_test)\n\n tests = glob.glob('./test_images/*.jpg')\n fig = plt.figure()\n idx = 1\n for img in tests:\n test_img = cv2.imread(img)\n t= time.time()\n self.heatmap = []\n img=self.find_cars(test_img, return_image=True)\n print('Time prediction: ', time.time()-t)\n fig.add_subplot(len(tests), 2, idx)\n idx+=1\n plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n plt.axis('off')\n fig.add_subplot(len(tests), 2, idx)\n idx+=1\n plt.imshow(self.heatmap[-1], cmap='hot')\n plt.axis('off')\n plt.subplots_adjust(left=0.28, bottom=0.01, right=0.69, top=0.98, wspace=0.03, hspace=0.03)\n plt.savefig('./output_images/test_heatmap.png', dpi=400)\n plt.show()","title":""},{"docid":"8f51986f12154b7f7f018265c5c9f3d4","score":"0.60119885","text":"def test(self):\n\n\t\ttotaltime = 0\n\t\tfiles = 0\n\n\t\ttp = 0\n\t\tfp = 0\n\t\ttn = 0\n\t\tfn = 0\n\n\t\tself.addr = \"http://\" + self.helpers.credentials[\"server\"][\"ip\"] + \\\n\t\t\t':'+str(self.helpers.credentials[\"server\"][\"port\"]) + '/Inference'\n\t\tself.headers = {'content-type': 'image/jpeg'}\n\n\t\tfor testFile in os.listdir(self.model.testing_dir):\n\t\t\tif os.path.splitext(testFile)[1] in self.model.valid:\n\n\t\t\t\tstart = time.time()\n\t\t\t\tprediction = self.request(self.model.testing_dir + \"/\" + testFile)\n\t\t\t\tprint(prediction)\n\t\t\t\tend = time.time()\n\t\t\t\tbenchmark = end - start\n\t\t\t\ttotaltime += benchmark\n\n\t\t\t\tmsg = \"\"\n\t\t\t\tstatus = \"\"\n\t\t\t\toutcome = \"\"\n\n\t\t\t\tif prediction[\"Diagnosis\"] == \"Positive\" and \"Non-Covid\" in testFile:\n\t\t\t\t\tfp += 1\n\t\t\t\t\tstatus = \"incorrectly\"\n\t\t\t\t\toutcome = \"(False Positive)\"\n\t\t\t\telif prediction[\"Diagnosis\"] == \"Negative\" and \"Non-Covid\" in testFile:\n\t\t\t\t\ttn += 1\n\t\t\t\t\tstatus = \"correctly\"\n\t\t\t\t\toutcome = \"(True Negative)\"\n\t\t\t\telif prediction[\"Diagnosis\"] == \"Positive\" and \"Covid\" in testFile:\n\t\t\t\t\ttp += 1\n\t\t\t\t\tstatus = \"correctly\"\n\t\t\t\t\toutcome = \"(True Positive)\"\n\t\t\t\telif prediction[\"Diagnosis\"] == \"Negative\" and \"Covid\" in testFile:\n\t\t\t\t\tfn += 1\n\t\t\t\t\tstatus = \"incorrectly\"\n\t\t\t\t\toutcome = \"(False Negative)\"\n\n\t\t\t\tfiles += 1\n\t\t\t\tself.helpers.logger.info(\"COVID-19 xDNN Classifier \" + status +\n\t\t\t\t\t\t\t\t\t\" detected \" + outcome + \" in \" + str(benchmark) + \" seconds.\")\n\n\t\tself.helpers.logger.info(\"Images Classified: \" + str(files))\n\t\tself.helpers.logger.info(\"True Positives: \" + str(tp))\n\t\tself.helpers.logger.info(\"False Positives: \" + str(fp))\n\t\tself.helpers.logger.info(\"True Negatives: \" + str(tn))\n\t\tself.helpers.logger.info(\"False Negatives: \" + str(fn))\n\t\tself.helpers.logger.info(\"Total Time Taken: \" + str(totaltime))","title":""},{"docid":"6a49817fea99179942d072738408b915","score":"0.60117257","text":"def classify(image: Img, classifier: callable):\n\treturn classifier(image.arr1d)","title":""},{"docid":"9497acef7cfac114ef930578ebafe53d","score":"0.6011085","text":"def predict(self, instances, **kwargs):\n imgs = map(self.decode_img, instances)\n inputs = np.asarray(imgs)\n outputs = self._model.predict(imgs)\n\n # If outputs are not in same order as inputs, consider that TF likely\n # runs things in parallel for multi-input???\n\n return [self.threshold(output) for output in outputs]","title":""},{"docid":"1aea73709799f838a213d8a1cee919e6","score":"0.5998706","text":"def generate_images_pred(self, inputs, outputs):\n for scale in self.opt.scales:\n disp = outputs[(\"disp\", scale)]\n disp = F.interpolate(disp, [self.opt.height, self.opt.width], mode=\"bilinear\", align_corners=False)\n _, depth = disp_to_depth(disp, self.opt.min_depth, self.opt.max_depth)\n outputs[(\"depth\", scale)] = depth * 5.4\n\n # Compute Reprojection loss\n occmaskrec = dict()\n for k in inputs['imgr'].keys():\n if k == 0:\n continue\n occmaskrec[k] = (self.sod(inputs['intrinsic'], inputs['poser'][k], outputs[(\"depth\", 0)], float(1e10)) == 0).float()\n outputs['occmaskrec'] = occmaskrec\n\n # Compute Reconstruction image\n for scale in self.opt.scales:\n outputs['reconImgs', scale] = self.reconimg(inputs['imgr'], outputs[(\"depth\", scale)], inputs['intrinsic'], inputs['poser'])\n\n return outputs","title":""},{"docid":"59ed4bd69fa8387dba2c91593270cde1","score":"0.5981367","text":"def matching_batch(\n iou_fn: Callable[[np.ndarray, np.ndarray], np.ndarray], \n iou_thresholds: Sequence[float], pred_boxes: Sequence[np.ndarray],\n pred_classes: Sequence[np.ndarray], pred_scores: Sequence[np.ndarray],\n gt_boxes: Sequence[np.ndarray], gt_classes: Sequence[np.ndarray],\n gt_ignore: Sequence[Sequence[bool]], max_detections: int = 100,\n ) -> List[Dict[int, Dict[str, np.ndarray]]]:\n results = []\n # iterate over images/batches\n for pboxes, pclasses, pscores, gboxes, gclasses, gignore in zip(\n pred_boxes, pred_classes, pred_scores, gt_boxes, gt_classes, gt_ignore):\n img_classes = np.union1d(pclasses, gclasses)\n result = {} # dict contains results for each class in one image\n for c in img_classes:\n pred_mask = pclasses == c # mask predictions with current class\n gt_mask = gclasses == c # mask ground trtuh with current class\n\n if not np.any(gt_mask): # no ground truth\n result[c] = _matching_no_gt(\n iou_thresholds=iou_thresholds,\n pred_scores=pscores[pred_mask],\n max_detections=max_detections)\n elif not np.any(pred_mask): # no predictions\n result[c] = _matching_no_pred(\n iou_thresholds=iou_thresholds,\n gt_ignore=gignore[gt_mask],\n )\n else: # at least one prediction and one ground truth\n result[c] = _matching_single_image_single_class(\n iou_fn=iou_fn,\n pred_boxes=pboxes[pred_mask],\n pred_scores=pscores[pred_mask],\n gt_boxes=gboxes[gt_mask],\n gt_ignore=gignore[gt_mask],\n max_detections=max_detections,\n iou_thresholds=iou_thresholds,\n )\n results.append(result)\n return results","title":""},{"docid":"128a24d7a2976c59240fff985d365c36","score":"0.59797955","text":"def forward(self, outputs, processed_sizes, target_sizes=None):\n if target_sizes is None:\n target_sizes = processed_sizes\n assert len(processed_sizes) == len(target_sizes)\n out_logits, raw_masks, raw_boxes = outputs['pred_logits'], outputs['pred_masks'], outputs['pred_boxes']\n assert len(out_logits) == len(raw_masks) == len(target_sizes)\n preds = []\n\n def to_tuple(tup):\n if isinstance(tup, tuple):\n return tup\n return tuple(tup.cpu().tolist())\n for cur_logits, cur_masks, cur_boxes, size, target_size in zip(out_logits, raw_masks, raw_boxes, processed_sizes, target_sizes):\n scores, labels = cur_logits.softmax(-1).max(-1)\n keep = labels.ne(outputs['pred_logits'].shape[-1] - 1) & (scores > self.threshold)\n cur_scores, cur_classes = cur_logits.softmax(-1).max(-1)\n cur_scores = cur_scores[keep]\n cur_classes = cur_classes[keep]\n cur_masks = cur_masks[keep]\n cur_masks = interpolate(cur_masks[None], to_tuple(size), mode='bilinear').squeeze(0)\n cur_boxes = box_ops.box_cxcywh_to_xyxy(cur_boxes[keep])\n h, w = cur_masks.shape[-2:]\n assert len(cur_boxes) == len(cur_classes)\n cur_masks = cur_masks.flatten(1)\n stuff_equiv_classes = defaultdict(lambda : [])\n for k, label in enumerate(cur_classes):\n if not self.is_thing_map[label.item()]:\n stuff_equiv_classes[label.item()].append(k)\n\n def get_ids_area(masks, scores, dedup=False):\n m_id = masks.transpose(0, 1).softmax(-1)\n if m_id.shape[-1] == 0:\n m_id = torch.zeros((h, w), dtype=torch.long, device=m_id.device)\n else:\n m_id = m_id.argmax(-1).view(h, w)\n if dedup:\n for equiv in stuff_equiv_classes.values():\n if len(equiv) > 1:\n for eq_id in equiv:\n m_id.masked_fill_(m_id.eq(eq_id), equiv[0])\n final_h, final_w = to_tuple(target_size)\n seg_img = Image.fromarray(id2rgb(m_id.view(h, w).cpu().numpy()))\n seg_img = seg_img.resize(size=(final_w, final_h), resample=Image.NEAREST)\n np_seg_img = torch.ByteTensor(torch.ByteStorage.from_buffer(seg_img.tobytes())).view(final_h, final_w, 3).numpy()\n m_id = torch.from_numpy(rgb2id(np_seg_img))\n area = []\n for i in range(len(scores)):\n area.append(m_id.eq(i).sum().item())\n return area, seg_img\n area, seg_img = get_ids_area(cur_masks, cur_scores, dedup=True)\n if cur_classes.numel() > 0:\n while True:\n filtered_small = torch.as_tensor([(area[i] <= 4) for i, c in enumerate(cur_classes)], dtype=torch.bool, device=keep.device)\n if filtered_small.any().item():\n cur_scores = cur_scores[~filtered_small]\n cur_classes = cur_classes[~filtered_small]\n cur_masks = cur_masks[~filtered_small]\n area, seg_img = get_ids_area(cur_masks, cur_scores)\n else:\n break\n else:\n cur_classes = torch.ones(1, dtype=torch.long, device=cur_classes.device)\n segments_info = []\n for i, a in enumerate(area):\n cat = cur_classes[i].item()\n segments_info.append({'id': i, 'isthing': self.is_thing_map[cat], 'category_id': cat, 'area': a})\n del cur_classes\n with io.BytesIO() as out:\n seg_img.save(out, format='PNG')\n predictions = {'png_string': out.getvalue(), 'segments_info': segments_info}\n preds.append(predictions)\n return preds","title":""},{"docid":"1ef887b0bf7877ca1b96c62bd65d89e3","score":"0.5957035","text":"def classify(self, source1, source2):\n \n scores1 = source1.similarity_scores(self)\n scores2 = source2.similarity_scores(self)\n \n print('scores for ' +source1.name+ ':', scores1)\n print('scores for ' +source2.name+ ':', scores2)\n print()\n \n sum1 = 0\n sum2 = 0\n total1 = 0\n total2 = 0\n weights = [10,7,5,7,1,3]\n for i in range(len(scores1)):\n if scores1[i] > scores2[i]:\n sum1 += 1\n else:\n sum2 += 1\n total1 += weights[i]*scores1[i]\n total2 += weights[i]*scores2[i]\n \n if sum1 > sum2:\n print(self.name + ' is more likely to come from ' + source1.name)\n print(sum1,sum2)\n print()\n elif sum2 > sum1:\n print(self.name + ' is more likely to come from ' + source2.name)\n print(sum1,sum2)\n print()\n else:\n if total1 > total2:\n print(self.name + ' is more likely to come from ' + source1.name)\n print(total1,total2)\n print()\n else:\n print(self.name + ' is more likely to come from ' + source2.name)\n print(total1,total2)\n print()","title":""},{"docid":"2fddec0a4c39982b575b90f73d4787fe","score":"0.59546","text":"def classify_photo(pic=None, destination=None):\n if pic is not None:\n img = open_image(pic)\n else:\n img = open_image(destination)\n classifier_path = \"app/models/cnn_classifier/\"\n classifier = load_learner(classifier_path)\n pred_class, pred_idx, outputs = classifier.predict(img)\n\n if max(outputs) < 0.92:\n\n # if destination is not None:\n # img_data = cv2.imread(destination)\n # else:\n # img_data = cv2.imread(pic)\n\n # with global_graph.as_default():\n # img_paths = detector.predict(img_data, destination)\n # img_paths.append(destination)\n\n # classifier_outputs = []\n\n # for path in img_paths:\n # img = open_image(path)\n # pred_class, pred_idx, outputs = classifier.predict(img)\n # classifier_outputs.append(\n # [max(outputs), pred_class, pred_idx, outputs])\n # classifier_outputs.sort(key=lambda x: x[0], reverse=True)\n\n # pred_class = classifier_outputs[0][1]\n # pred_idx = classifier_outputs[0][2]\n # outputs = classifier_outputs[0][3]\n\n return pred_class, pred_idx, outputs\n\n return pred_class, pred_idx, outputs","title":""},{"docid":"856a51177793c543f0e6030c0663c678","score":"0.595266","text":"def check_classification_targets(y):\n ...","title":""},{"docid":"20911ed85bc9ebf34fc364567400be7f","score":"0.5915451","text":"def image_preprocessing():\n\t# generate tags for images, if they do not exis\tt\n\tif not os.path.isfile(images_folder + \"/train_tags.csv\"):\n\t\tprint(\"Extracting tags...\")\n\t\tio.extract_tags(raw_images_folder + \"/train\",images_folder,\"train_\")\n\t\tprint(\"Done!\\n\")\n\tif not os.path.isfile(images_folder + \"/val_tags.csv\"):\n\t\tprint(\"Extracting tags...\")\n\t\tio.extract_tags(raw_images_folder + \"/validation\", images_folder,\"val_\")\n\t\tprint(\"Done!\\n\")\n\n\t# resize the images to a particular size, if not already done\n\t# Let's assume its already done if a particular directory \n\t# has images in it...\n\t'''if len(os.listdir(resized_images_folder)) == 0:\n\t\tprint(\"Resizing images...this could take awhile!\\n\")\n\t\tio.resize_images(raw_images_folder,resized_images_folder,resize_dimensions)\n\t'''\n\tif(len(os.listdir(transformed_images_folder + \"/train\")) + len(os.listdir(transformed_images_folder + \"/validation\")) > 0):\t\n\t\tprint(\"clearing out previously transformed images\")\t\n\t\tos.system(\"rm ./data/transformed/train/* ./data/transformed/validation/*\")\n\t\t\n\t# generate X and Y, in the format keras needs for the model\n\t# and return\n\t#return io.partition_image_data(resized_images_folder,(150,150),3)\n\tprint(\"obtaining model inputs\")\n\treturn io.format_data(resize_dimensions, 3)","title":""},{"docid":"8382faa30e2d300c3d951dad0930a0ba","score":"0.5909281","text":"def compute_class_weights(image_files, label_values):\n\n # image_files = [os.path.join(labels_dir, file) for file in os.listdir(labels_dir) if file.endswith('.png')]\n num_classes = len(label_values)\n class_pixels = np.zeros(num_classes)\n\n total_pixels = 0.0\n\n for n in range(len(image_files)):\n image = imread(image_files[n])\n\n for index, colour in enumerate(label_values):\n class_map = np.all(np.equal(image, colour), axis=-1)\n class_map = class_map.astype(np.float32)\n class_pixels[index] += np.sum(class_map)\n\n print(\"\\rProcessing image: \" + str(n) + \" / \" + str(len(image_files)), end=\"\")\n sys.stdout.flush()\n\n total_pixels = float(np.sum(class_pixels))\n index_to_delete = np.argwhere(class_pixels == 0.0)\n class_pixels = np.delete(class_pixels, index_to_delete)\n\n class_weights = total_pixels / class_pixels\n class_weights = class_weights / np.sum(class_weights)\n\n return class_weights","title":""},{"docid":"8be6173f0a97bc23d9661ba1438c9277","score":"0.5903825","text":"def few_label_classification(self, list_num_shots=[1, 10, 100, -1], num_runs=10, batch_size=1000, classifier='logistic'):\n if not os.path.exists('results/few_label/'):\n os.makedirs('results/few_label/')\n results = {'Num_Shots': [], 'Mean': [], 'Std': []}\n train_loader, im_h, im_w, im_channels = setup_data_loader(data=self.data, \n data_dir=self.data_dir, \n batch_size=batch_size, \n train=True, \n normalize=False if self.model_name in ['VAE', 'VAE_GMM'] else True, \n shuffle=False, \n shot_random_seed=None)\n zs_train, ys_train = self.encode_dataset(train_loader)\n \n test_loader, im_h, im_w, im_channels = setup_data_loader(data=self.data, \n data_dir=self.data_dir, \n batch_size=batch_size, \n train=False, \n normalize=False if self.model_name in ['VAE', 'VAE_GMM'] else True, \n shuffle=False, \n shot_random_seed=None)\n zs_test, ys_test = self.encode_dataset(test_loader)\n \n num_classes = len(np.unique(ys_train))\n \n for num_shots in tqdm(list_num_shots):\n Accuracy = []\n if num_shots == -1:\n clf = LogisticRegression(random_state=0, \n multi_class='auto', \n solver='liblinear', \n max_iter=10000).fit(zs_train, ys_train)\n Accuracy.append(np.array([clf.score(zs_test, ys_test)])) \n else:\n for i in tqdm(range(num_runs)):\n # torch.cuda.empty_cache()\n zs_train_selected = []\n ys_train_selected = []\n set_seed(i)\n for k in range(num_classes):\n indk = np.argwhere(ys_train == k)[:,0]\n ind_of_indk = np.random.permutation(len(indk))[:num_shots]\n indk_selected = indk[ind_of_indk]\n zs_train_selected.append(zs_train[indk_selected])\n ys_train_selected.append(ys_train[indk_selected])\n zs_train_selected = np.concatenate(zs_train_selected, 0)\n ys_train_selected = np.concatenate(ys_train_selected, 0)\n\n# print(indk_selected)\n \n clf = LogisticRegression(random_state=0, \n multi_class='auto', \n solver='liblinear', \n max_iter=10000).fit(zs_train_selected, ys_train_selected)\n Accuracy.append(np.array([clf.score(zs_test, ys_test)])) \n# return 0\n Accuracy = np.concatenate(Accuracy)\n results['Num_Shots'].append(num_shots)\n results['Mean'].append(Accuracy.mean())\n results['Std'].append(Accuracy.std())\n pd.DataFrame.from_dict(results).to_csv('results/few_label/{}-{}-runs={}.csv'.format(self.model_name, self.data, num_runs), index=False)\n return results","title":""},{"docid":"948198389e8b010b65ed4eb1e6d7e23a","score":"0.58989424","text":"def process_outputs(self, outputs, image_size):\n boxes = []\n confidences = []\n class_proba = []\n img_H = image_size[0]\n img_W = image_size[1]\n for output in outputs:\n boxes.append(output[..., 0:4])\n confidences.append(self.sigmoid(output[..., 4, np.newaxis]))\n class_proba.append(self.sigmoid(output[..., 5:]))\n for i, box in enumerate(boxes):\n g_h, g_w, achors_box, _ = box.shape\n coordidate = np.zeros((g_h, g_w, achors_box))\n idx_y = np.arange(g_h)\n idx_y = idx_y.reshape(g_h, 1, 1)\n idx_x = np.arange(g_w)\n idx_x = idx_x.reshape(1, g_w, 1)\n C_x = coordidate + idx_x\n C_y = coordidate + idx_y\n centerX = box[..., 0]\n centerY = box[..., 1]\n width = box[..., 2]\n height = box[..., 3]\n bx = (self.sigmoid(centerX) + C_x) / g_w\n by = (self.sigmoid(centerY) + C_y) / g_h\n pw = self.anchors[i, :, 0]\n ph = self.anchors[i, :, 1]\n bw = (np.exp(width) * pw) / self.model.input.shape[1].value\n bh = (np.exp(height) * ph) / self.model.input.shape[2].value\n x1 = bx - bw / 2\n y1 = by - bh / 2\n x2 = x1 + bw\n y2 = y1 + bh\n box[..., 0] = x1 * img_W\n box[..., 1] = y1 * img_H\n box[..., 2] = x2 * img_W\n box[..., 3] = y2 * img_H\n return boxes, confidences, class_proba","title":""},{"docid":"5573952e335fb188d5885ee893550fc4","score":"0.5890267","text":"def forward(self, batched_inputs):\n images = self.preprocess_image(batched_inputs)\n if self.training:\n batch_size, _, H, W = images.tensor.shape\n img_masks = images.tensor.new_ones(batch_size, H, W)\n for img_id in range(batch_size):\n img_h, img_w = batched_inputs[img_id]['instances'].image_size\n img_masks[img_id, :img_h, :img_w] = 0\n else:\n batch_size, _, H, W = images.tensor.shape\n img_masks = images.tensor.new_zeros(batch_size, H, W)\n features = self.backbone(images.tensor)[self.in_features[-1]]\n features = self.input_proj(features)\n img_masks = F.interpolate(img_masks[None], size=features.shape[-2:])[0]\n pos_embed = self.position_embedding(img_masks)\n hidden_states, _ = self.transformer(features, img_masks, self.query_embed.weight, pos_embed)\n outputs_class = self.class_embed(hidden_states)\n outputs_coord = self.bbox_embed(hidden_states).sigmoid()\n output = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1]}\n if self.aux_loss:\n output['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord)\n if self.training:\n gt_instances = [x['instances'] for x in batched_inputs]\n targets = self.prepare_targets(gt_instances)\n loss_dict = self.criterion(output, targets)\n weight_dict = self.criterion.weight_dict\n for k in loss_dict.keys():\n if k in weight_dict:\n loss_dict[k] *= weight_dict[k]\n return loss_dict\n else:\n box_cls = output['pred_logits']\n box_pred = output['pred_boxes']\n results = self.inference(box_cls, box_pred, images.image_sizes)\n processed_results = []\n for results_per_image, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes):\n height = input_per_image.get('height', image_size[0])\n width = input_per_image.get('width', image_size[1])\n r = detector_postprocess(results_per_image, height, width)\n processed_results.append({'instances': r})\n return processed_results","title":""},{"docid":"cf45f6d088fd003c6b52bf2faa15c5d7","score":"0.58813107","text":"def classify(model, images):\n probabilities = model.predict(images)\n labels = np.argmax(probabilities, axis=1)\n\n # Load the label encoder\n encoder_dir = \\\n Path(__file__).absolute().parents[2].joinpath('generated_data',\n 'encoders')\n encoder_path = encoder_dir.joinpath('encoder.pkl')\n\n with encoder_path.open('rb') as f:\n label_encoder = pickle.load(f)\n\n labels = label_encoder.inverse_transform(labels)\n\n return labels, probabilities","title":""},{"docid":"2098c9d59de4b26efd20cedc8ab5d1f9","score":"0.58700335","text":"def _compute_targets(entry):\n # Indices of ground-truth ROIs\n rois = entry['boxes']\n overlaps = entry['max_overlaps']\n labels = entry['max_classes']\n gt_inds = np.where((entry['gt_classes'] > 0) & (entry['is_crowd'] == 0))[0]\n # Targets has format (class, tx, ty, tw, th)\n targets = np.zeros((rois.shape[0], 5), dtype=np.float32)\n if len(gt_inds) == 0:\n # Bail if the image has no ground-truth ROIs\n return targets\n\n # Indices of examples for which we try to make predictions\n ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]\n\n # Get IoU overlap between each ex ROI and gt ROI\n ex_gt_overlaps = box_utils.bbox_overlaps(\n rois[ex_inds, :].astype(dtype=np.float32, copy=False),\n rois[gt_inds, :].astype(dtype=np.float32, copy=False))\n\n # Find which gt ROI each ex ROI has max overlap with:\n # this will be the ex ROI's gt target\n gt_assignment = ex_gt_overlaps.argmax(axis=1)\n gt_rois = rois[gt_inds[gt_assignment], :]\n ex_rois = rois[ex_inds, :]\n # Use class \"1\" for all boxes if using class_agnostic_bbox_reg\n targets[ex_inds, 0] = (1 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG else labels[ex_inds])\n targets[ex_inds, 1:] = box_utils.bbox_transform_inv(ex_rois, gt_rois, cfg.MODEL.BBOX_REG_WEIGHTS)\n return targets","title":""},{"docid":"1c2edebd92e2553520422621b3530a15","score":"0.5838647","text":"def forward(self, batched_inputs):\n images = self.preprocess_image(batched_inputs)\n if self.training:\n batch_size, _, H, W = images.tensor.shape\n img_masks = images.tensor.new_ones(batch_size, H, W)\n for img_id in range(batch_size):\n img_h, img_w = batched_inputs[img_id]['instances'].image_size\n img_masks[img_id, :img_h, :img_w] = 0\n else:\n batch_size, _, H, W = images.tensor.shape\n img_masks = images.tensor.new_zeros(batch_size, H, W)\n features = self.backbone(images.tensor)[self.in_features[-1]]\n features = self.input_proj(features)\n img_masks = F.interpolate(img_masks[None], size=features.shape[-2:])[0]\n pos_embed = self.position_embedding(img_masks)\n hidden_states, reference = self.transformer(features, img_masks, self.query_embed.weight, pos_embed)\n reference_before_sigmoid = inverse_sigmoid(reference)\n outputs_coords = []\n for lvl in range(hidden_states.shape[0]):\n tmp = self.bbox_embed(hidden_states[lvl])\n tmp[..., :2] += reference_before_sigmoid\n outputs_coord = tmp.sigmoid()\n outputs_coords.append(outputs_coord)\n outputs_coord = torch.stack(outputs_coords)\n outputs_class = self.class_embed(hidden_states)\n output = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1]}\n if self.aux_loss:\n output['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord)\n if self.training:\n gt_instances = [x['instances'] for x in batched_inputs]\n targets = self.prepare_targets(gt_instances)\n loss_dict = self.criterion(output, targets)\n weight_dict = self.criterion.weight_dict\n for k in loss_dict.keys():\n if k in weight_dict:\n loss_dict[k] *= weight_dict[k]\n return loss_dict\n else:\n box_cls = output['pred_logits']\n box_pred = output['pred_boxes']\n results = self.inference(box_cls, box_pred, images.image_sizes)\n processed_results = []\n for results_per_image, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes):\n height = input_per_image.get('height', image_size[0])\n width = input_per_image.get('width', image_size[1])\n r = detector_postprocess(results_per_image, height, width)\n processed_results.append({'instances': r})\n return processed_results","title":""},{"docid":"753a71ea40064c5a62b82727b8fe906c","score":"0.58297926","text":"def visualize_detections(images, ground_truths, detections, id2code,\n label_codes, label_names, out_dir='/tmp'):\n max_id = max(id2code.values())\n name_range = range(len(label_names))\n\n for i in range(0, np.shape(detections)[0]):\n fig = plt.figure(figsize=(17, 17))\n\n # original image\n ax1 = fig.add_subplot(2, 2, 1)\n # TODO: expect also other data than S2\n a = np.stack((images[i][:, :, 3], images[i][:, :, 2],\n images[i][:, :, 1]), axis=2)\n ax1.imshow((255 / a.max() * a).astype(np.uint8))\n ax1.title.set_text('Actual image')\n\n # ground truths\n ax3 = fig.add_subplot(2, 2, 3)\n ax3.set_title('Ground truth labels')\n gt_labels = ground_truths[i]\n gt_labels = onehot_decode(gt_labels, id2code)\n ax3.imshow(gt_labels * 4)\n\n # detections\n ax4 = fig.add_subplot(2, 2, 4)\n ax4.set_title('Predicted labels')\n pred_labels = onehot_decode(detections[i], id2code)\n ax4.imshow(pred_labels * 4)\n\n # confusion matrix\n ax2 = fig.add_subplot(2, 2, 2)\n ax2.set_title('Confusion matrix')\n conf_matrix = confusion_matrix(\n gt_labels[:, :, 0].flatten(), pred_labels[:, :, 0].flatten(),\n max_id + 1)\n # subset to existing classes\n conf_matrix = conf_matrix.numpy()[label_codes][:, label_codes]\n # normalize the confusion matrix\n row_sums = conf_matrix.sum(axis=1)[:, np.newaxis]\n # TODO: solve division by 0\n cm_norm = np.around(conf_matrix.astype('float') / row_sums, decimals=2)\n # visualize\n ax2.imshow(cm_norm, cmap=plt.cm.Blues)\n y_labels = ['{}\\n{}'.format(label_names[j], row_sums[j]) for j in\n name_range]\n plt.xticks(name_range, label_names)\n plt.yticks(name_range, y_labels)\n plt.xlabel('Predicted label')\n plt.ylabel('True label')\n # write percentage values (0.00 -- 1.00) into the confusion matrix\n threshold = cm_norm.max() / 2. # used to decide for the font colour\n for row in range(len(conf_matrix)):\n for col in range(len(conf_matrix)):\n if cm_norm[col, row] > threshold:\n colour = 'white'\n else:\n colour = 'black'\n # TODO: class names, not codes\n ax2.text(row, col, cm_norm[col, row], color=colour,\n horizontalalignment='center')\n\n plt.savefig(os.path.join(out_dir, str(i)))\n plt.close()","title":""},{"docid":"98b5eeb8aba439c41040b45c0c28aad7","score":"0.58281577","text":"def classify():\n classifier = load_model(\"kerasmodel.h5\")\n im = cv2.imread(\"result.png\", 0)\n im2 = cv2.resize(im, (28, 28))\n im = im2.reshape(28, 28, -1)\n im = im.reshape(1, 784)\n im = cv2.bitwise_not(im)\n plt.imshow(im.reshape(28, 28), cmap='Greys')\n result = classifier.predict(im)\n a = np.argmax(result)\n return a","title":""},{"docid":"54b57f9eeb41d312252fbf8a69bb72d9","score":"0.58160615","text":"def classify(self, data, features, target, **kwargs):\n raise NotImplementedError","title":""},{"docid":"88a4a74128dce86df26c0e1d336c08ad","score":"0.5811792","text":"def classify(labelled_files, unlabelled_files, labels):\n # Extract features using CountVectorizer (Bag of Words)\n count_vect = CountVectorizer()\n counts = count_vect.fit_transform(labelled_files)\n\n # Fit classifier to training data (user labels)\n clf = LogisticRegression().fit(counts, labels)\n\n # Transform CountVectorizer to unlabelled files\n new_counts = count_vect.transform(unlabelled_files)\n\n # Run prediction on new counts\n predicted_labels = clf.predict(new_counts)\n probabilities = clf.predict_proba(new_counts)\n\n # Reduce probabilities to only show for predicted label\n probabilities = np.amax(probabilities, axis=1)\n\n return predicted_labels, probabilities","title":""},{"docid":"d3a789a6339e5f1b97dbb45d1c654cf3","score":"0.5810821","text":"def classify(self, elements):\r\n if elements is None:\r\n return\r\n for element in elements:\r\n was_classified = False\r\n image_added = False\r\n for op in self.ops():\r\n if op.operation == \"Raster\":\r\n if image_added:\r\n continue # already added to an image operation, is not added here.\r\n if element.stroke is not None and op.color == abs(element.stroke):\r\n op.append(element)\r\n was_classified = True\r\n elif isinstance(element, SVGImage):\r\n op.append(element)\r\n was_classified = True\r\n elif isinstance(element, SVGText):\r\n op.append(element)\r\n was_classified = True\r\n elif element.fill is not None and element.fill.value is not None:\r\n op.append(element)\r\n was_classified = True\r\n elif op.operation in (\"Engrave\", \"Cut\") and element.stroke is not None and \\\r\n op.color == abs(element.stroke):\r\n op.append(element)\r\n was_classified = True\r\n elif op.operation == 'Image' and isinstance(element, SVGImage):\r\n op.append(element)\r\n was_classified = True\r\n image_added = True\r\n if not was_classified:\r\n if element.stroke is not None and element.stroke.value is not None:\r\n op = LaserOperation(operation=\"Engrave\", color=element.stroke, speed=35.0)\r\n op.append(element)\r\n self.add_op(op)","title":""},{"docid":"fbf7c0c480361a30f46110b320853329","score":"0.58092415","text":"def predict(self, images):\n\n molded_images, image_metas = mold_inputs(images, self.config)\n\n # Convert to Tensor\n molded_images = torch.from_numpy(molded_images).float().to(self.device)\n\n # Features Extraction\n features = self.extractor(molded_images)\n\n print(\"features\", features.size())\n\n # RPN\n rpn_logits, rpn_probs, rpn_bbox = self.rpn(features)\n\n print(\"rpn_probs\", rpn_probs.size(), \"rpn_bbox\", rpn_bbox.size())\n\n # Perform proposal_layer on each image in batch\n n_batch = features.size()[0]\n\n for i in range(n_batch):\n print(\"batch\", i, rpn_probs[i].size(), rpn_bbox[i].size())\n # proposal_layer\n\n # ROI Pooling / ROI Align\n\n # Classification","title":""},{"docid":"1f917eea7c9833496c8df885a215d720","score":"0.5807556","text":"def pt_cls_preprocess(self, images):\n mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1)).astype(np.float32)\n std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1)).astype(np.float32)\n output = []\n for image in images:\n for param in self.xform:\n param_0 = param[0]\n param_1 = param[1]\n if param_0 == \"resize\":\n size = param_1\n # image = cv2.resize(image, (size[1], size[0]), \\\n # interpolation=cv2.INTER_LINEAR)\n # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n org_h, org_w = image.shape[:2]\n scale = 256.0 / min(org_h, org_w)\n resize_h, resize_w = int(scale * org_h), int(scale * org_w)\n image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n image = np.asarray(image.resize((resize_w, resize_h), \\\n resample=Image.BILINEAR))\n # image = cv2.resize(image, (tw, th), interpolation=cv2.INTER_LINEAR)\n offset_h, offset_w = (resize_h - size[0]) // 2, (resize_w - size[1]) // 2\n image = image[offset_h:offset_h + size[0], offset_w:offset_w + size[1]]\n image = np.transpose(image, (2, 0, 1)).astype(np.float32)\n image /= 255.0\n image = (image - mean) / std\n output.append(image)\n return np.array(output)","title":""},{"docid":"7316ffc702978a09d7e85f78e2ba52a7","score":"0.5806382","text":"def process_batch(self, inputs):\n for key, ipt in inputs.items():\n if key == 'tag':\n continue\n elif key == 'imgr' or key == 'poser':\n for k, kipt in inputs[key].items():\n inputs[key][k] = kipt.to(self.device)\n else:\n inputs[key] = ipt.to(self.device)\n\n features = self.models[\"encoder\"](inputs['inrgb_augmented'])\n outputs = self.models[\"depth\"](features)\n\n outputs = self.generate_images_pred(inputs, outputs)\n losses = self.compute_losses(inputs, outputs)\n\n return outputs, losses","title":""},{"docid":"1e3934bf7cc91231733ff198af2287b4","score":"0.5800077","text":"def prepare_targets(self, batch_inputs):\n images, bboxes, labels, points, has_pts, instance_mask = batch_inputs\n h, w = images[0].shape[1:]\n image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float)\n target_images = images.to(self.device)\n target_classes = labels.to(self.device)\n # normalize bboxes with wdith and height and convert the x1, y1, x2, y2 to centerx, centery, w, h\n target_boxes = bboxes / image_size_xyxy\n target_boxes_cxcywh = box_xyxy_to_cxcywh(target_boxes).to(self.device)\n # x1, y1, x2, y2 bboxes\n bboxes = bboxes.to(self.device)\n # images w, h, w, h tensor shape -> (batch, Num)\n target_image_size_xyxy = torch.stack([torch.cat([image_size_xyxy]) for _ in range(images.shape[0])], dim=0)\n target_image_size_xyxy = target_image_size_xyxy.to(self.device)\n # instance mask\n target_instance_mask = instance_mask.to(self.device)\n if points[0] is not None:\n target_points = points.to(self.device)\n else:\n target_points = points\n\n if has_pts[0] is not None:\n target_has_pts = has_pts.to(self.device)\n else:\n target_has_pts = has_pts\n\n targets = {}\n targets['images'] = target_images\n targets['labels'] = target_classes\n targets['bboxes'] = target_boxes_cxcywh # (centerx, centery, w, h)\n targets['boxes_xyxy'] = bboxes # (x1, y1, x2, y2)\n targets['image_size_xyxy'] = target_image_size_xyxy\n targets['pts'] = target_points\n targets['pts_mask'] = has_pts\n targets['instance_mask'] = target_instance_mask\n\n return targets","title":""},{"docid":"bbf3e95b916face8d17ca00b50a7ce8f","score":"0.5798492","text":"def classify_input(wildcards, return_int=False):\n if check_config('truth') and check_config(wildcards.sample, place=config['truth']):\n image_ending = \"/{image}.tsv\" if check_config('parallel') else ''\n if check_config('train_all', place=config['truth'][wildcards.sample]):\n if return_int:\n return 1\n return [\n checkpoints.create_truth_data.get(**wildcards).output[0]+image_ending,\n rules.train.output[0]\n ]\n if check_config('model'):\n if return_int:\n return 2\n return [\n checkpoints.create_truth_data.get(**wildcards).output[0]+image_ending,\n config['model']\n ]\n else:\n if return_int:\n return 3\n return [\n checkpoints.create_split_truth_data.get(**wildcards).output.test+image_ending,\n rules.train.output[0]\n ]\n else:\n if return_int:\n return 0\n if check_config('model'):\n return [rules.extract_features.output[0], config['model']]\n else:\n raise ValueError(\"If you don't specify any truth sets, you must provide a pre-trained model.\")","title":""},{"docid":"33e267c0a4ea7eaa5ceebe7be7076767","score":"0.5786576","text":"def forward(self, batched_inputs):\n images = self.preprocess_image(batched_inputs)\n if self.training:\n batch_size, _, H, W = images.tensor.shape\n img_masks = images.tensor.new_ones(batch_size, H, W)\n for img_id in range(batch_size):\n img_h, img_w = batched_inputs[img_id]['instances'].image_size\n img_masks[img_id, :img_h, :img_w] = 0\n else:\n batch_size, _, H, W = images.tensor.shape\n img_masks = images.tensor.new_zeros(batch_size, H, W)\n features = self.backbone(images.tensor)[self.in_features[-1]]\n features = self.input_proj(features)\n img_masks = F.interpolate(img_masks[None], size=features.shape[-2:])[0]\n pos_embed = self.position_embedding(img_masks)\n dynamic_anchor_boxes = self.anchor_box_embed.weight\n hidden_states, reference_boxes = self.transformer(features, img_masks, dynamic_anchor_boxes, pos_embed)\n reference_boxes = inverse_sigmoid(reference_boxes)\n anchor_box_offsets = self.bbox_embed(hidden_states)\n outputs_coord = (reference_boxes + anchor_box_offsets).sigmoid()\n outputs_class = self.class_embed(hidden_states)\n output = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1]}\n if self.aux_loss:\n output['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord)\n if self.training:\n gt_instances = [x['instances'] for x in batched_inputs]\n targets = self.prepare_targets(gt_instances)\n loss_dict = self.criterion(output, targets)\n weight_dict = self.criterion.weight_dict\n for k in loss_dict.keys():\n if k in weight_dict:\n loss_dict[k] *= weight_dict[k]\n return loss_dict\n else:\n box_cls = output['pred_logits']\n box_pred = output['pred_boxes']\n results = self.inference(box_cls, box_pred, images.image_sizes)\n processed_results = []\n for results_per_image, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes):\n height = input_per_image.get('height', image_size[0])\n width = input_per_image.get('width', image_size[1])\n r = detector_postprocess(results_per_image, height, width)\n processed_results.append({'instances': r})\n return processed_results","title":""},{"docid":"c4eed7eac26f5a0035d5aea2098d3c08","score":"0.5782558","text":"def classify(self, X):","title":""},{"docid":"d7adf2a8278f81d95f2c53608e0e6ef0","score":"0.5781525","text":"def postprocess_per_class(params, cls_outputs, box_outputs, image_scales=None):\n cls_outputs = to_list(cls_outputs)\n box_outputs = to_list(box_outputs)\n boxes, scores, classes = pre_nms(params, cls_outputs, box_outputs)\n return per_class_nms(params, boxes, scores, classes, image_scales)","title":""},{"docid":"996d20b0cf818c8e254b2c8922fb02ac","score":"0.577933","text":"def run_inference(image_files):\n\t# Read image files, resize them, convert to numpy arrays w/ dtype=uint8\n\timages = []\n\tfor image_file in image_files:\n\t\timage = Image.open(image_file)\n\t\timage = image.convert('RGB')\n\t\timage = image.resize((IMG_SIZE, IMG_SIZE), Image.ANTIALIAS)\n\t\timage = np.array(list(image.getdata()), dtype='uint8')\n\t\timage = np.reshape(image, (32, 32, 3))\n\n\t\timages.append(image)\n\timages = np.array(images, dtype='uint8')\n\n\t# Pre-process the image (don't care about label, put dummy labels)\n\timages, _ = preprocess_data(images, np.array([0 for _ in range(images.shape[0])]))\n\n\twith tf.Graph().as_default(), tf.Session() as sess:\n\t\t# Instantiate the CNN model\n\t\tx, y, keep_prob, logits, optimizer, predictions, accuracy = neural_network()\n\n\t\t# Load trained weights\n\t\tsaver = tf.train.Saver()\n\t\tsaver.restore(sess, MODEL_SAVE_PATH)\n\n\t\t# Run inference on CNN to make predictions\n\t\tpreds = sess.run(predictions, feed_dict={x: images, keep_prob: 1.})\n\n\t# Load signnames.csv to map label number to sign string\n\tlabel_map = {}\n\twith open('signnames.csv', 'r') as f:\n\t\tfirst_line = True\n\t\tfor line in f:\n\t\t\t# Ignore first line\n\t\t\tif first_line:\n\t\t\t\tfirst_line = False\n\t\t\t\tcontinue\n\n\t\t\t# Populate label_map\n\t\t\tlabel_int, label_string = line.split(',')\n\t\t\tlabel_int = int(label_int)\n\n\t\t\tlabel_map[label_int] = label_string\n\n\tfinal_preds = [label_map[pred] for pred in preds]\n\n\treturn final_preds","title":""},{"docid":"32851f705877c52bbbf48b0a2b7de63c","score":"0.576555","text":"def forward(self, batched_inputs):\n images = self.preprocess_image(batched_inputs)\n if self.training:\n batch_size, _, H, W = images.tensor.shape\n img_masks = images.tensor.new_ones(batch_size, H, W)\n for img_id in range(batch_size):\n img_h, img_w = batched_inputs[img_id]['instances'].image_size\n img_masks[img_id, :img_h, :img_w] = 0\n else:\n batch_size, _, H, W = images.tensor.shape\n img_masks = images.tensor.new_zeros(batch_size, H, W)\n features = self.backbone(images.tensor)[self.in_features[-1]]\n features = self.input_proj(features)\n img_masks = F.interpolate(img_masks[None], size=features.shape[-2:])[0]\n pos_embed = self.position_embedding(img_masks)\n if self.training:\n gt_instances = [x['instances'] for x in batched_inputs]\n targets = self.prepare_targets(gt_instances)\n gt_labels_list = [t['labels'] for t in targets]\n gt_boxes_list = [t['boxes'] for t in targets]\n else:\n targets = None\n matching_label_query = self.denoising_generator.label_encoder(torch.tensor(self.num_classes)).repeat(self.num_queries, 1)\n indicator_for_matching_part = torch.zeros([self.num_queries, 1])\n matching_label_query = torch.cat([matching_label_query, indicator_for_matching_part], 1).repeat(batch_size, 1, 1)\n matching_box_query = self.anchor_box_embed.weight.repeat(batch_size, 1, 1)\n if targets is None:\n input_label_query = matching_label_query.transpose(0, 1)\n input_box_query = matching_box_query.transpose(0, 1)\n attn_mask = None\n denoising_groups = self.denoising_groups\n max_gt_num_per_image = 0\n else:\n noised_label_queries, noised_box_queries, attn_mask, denoising_groups, max_gt_num_per_image = self.denoising_generator(gt_labels_list, gt_boxes_list)\n input_label_query = torch.cat([noised_label_queries, matching_label_query], 1).transpose(0, 1)\n input_box_query = torch.cat([noised_box_queries, matching_box_query], 1).transpose(0, 1)\n hidden_states, reference_boxes = self.transformer(features, img_masks, input_box_query, pos_embed, target=input_label_query, attn_mask=[attn_mask, None])\n reference_boxes = inverse_sigmoid(reference_boxes)\n anchor_box_offsets = self.bbox_embed(hidden_states)\n outputs_coord = (reference_boxes + anchor_box_offsets).sigmoid()\n outputs_class = self.class_embed(hidden_states)\n output = {'denoising_groups': torch.tensor(denoising_groups), 'max_gt_num_per_image': torch.tensor(max_gt_num_per_image)}\n outputs_class, outputs_coord = self.dn_post_process(outputs_class, outputs_coord, output)\n output.update({'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1]})\n if self.aux_loss:\n output['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord)\n if self.training:\n loss_dict = self.criterion(output, targets)\n weight_dict = self.criterion.weight_dict\n for k in loss_dict.keys():\n if k in weight_dict:\n loss_dict[k] *= weight_dict[k]\n return loss_dict\n else:\n box_cls = output['pred_logits']\n box_pred = output['pred_boxes']\n results = self.inference(box_cls, box_pred, images.image_sizes)\n processed_results = []\n for results_per_image, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes):\n height = input_per_image.get('height', image_size[0])\n width = input_per_image.get('width', image_size[1])\n r = detector_postprocess(results_per_image, height, width)\n processed_results.append({'instances': r})\n return processed_results","title":""},{"docid":"c94c9f61562de9b0b532bd15dc023de4","score":"0.5765359","text":"def compute_classif_metrics(y_true, y_pred, metric_averages=METRIC_AVERAGES):\n y_true = np.array(y_true)\n y_pred = np.array(y_pred)\n if y_true.shape != y_pred.shape:\n raise ValueError('prediction (%i) and annotation (%i) should be equal' % (len(y_true), len(y_pred)))\n logging.debug('unique lbs true: %r, predict %r', np.unique(y_true), np.unique(y_pred))\n\n uq_labels = np.unique(np.hstack((y_true, y_pred)))\n # in case there are just two classes, relabel them as [0, 1], sklearn error:\n # \"ValueError: pos_label=1 is not a valid label: array([ 0, 255])\"\n if len(uq_labels) <= 2:\n # NOTE, this is temporal just for purposes of computing statistic\n y_true = relabel_sequential(y_true, uq_labels)\n y_pred = relabel_sequential(y_pred, uq_labels)\n\n # http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html\n eval_str = 'EVALUATION: {:<2} PRE: {:.3f} REC: {:.3f} F1: {:.3f} S: {:>6}'\n try:\n p, r, f, s = metrics.precision_recall_fscore_support(y_true, y_pred)\n for lb, _ in enumerate(p):\n logging.debug(eval_str.format(lb, p[lb], r[lb], f[lb], s[lb]))\n except Exception:\n logging.exception('metrics.precision_recall_fscore_support')\n\n dict_metrics = {\n 'ARS': metrics.adjusted_rand_score(y_true, y_pred),\n # 'F1': metrics.f1_score(y_true, y_pred),\n 'accuracy': metrics.accuracy_score(y_true, y_pred),\n # 'precision': metrics.precision_score(y_true, y_pred),\n 'confusion': metrics.confusion_matrix(y_true, y_pred).tolist(),\n # 'report': metrics.classification_report(labels, predicted),\n }\n # compute aggregated precision, recall, f-score, support\n names = ['precision', 'recall', 'f1', 'support']\n for avg in metric_averages:\n try:\n mtr = metrics.precision_recall_fscore_support(y_true, y_pred, average=avg)\n res = dict(zip(['{}_{}'.format(n, avg) for n in names], mtr))\n except Exception:\n logging.exception('metrics.precision_recall_fscore_support')\n res = dict(zip(['{}_{}'.format(n, avg) for n in names], [-1] * 4))\n dict_metrics.update(res)\n return dict_metrics","title":""},{"docid":"cdf079c045a45e9cd378172d3875e8f3","score":"0.576438","text":"def mark(self, images: List[np.ndarray]):\n\n n_images = len(images)\n stack_shape = (n_images, 3) + TRANSFORM_SIZE\n image_stack = np.zeros(stack_shape, dtype=np.float32)\n image_sizes = np.zeros((n_images, 2), dtype=np.float32)\n\n for i in range(n_images):\n image_stack[i, :, :, :] = _image_transform(images[i])\n image_sizes[i, :] = images[i].shape[-2::-1]\n\n image_stack = torch.from_numpy(image_stack)\n if self.gpu is not None:\n image_stack = image_stack.to(self.gpu)\n\n with torch.no_grad():\n marks, offsets, scores = self.onet(image_stack)\n\n if self.gpu is not None:\n marks = marks.cpu().data.numpy()\n scores = scores.cpu().data.numpy()\n else:\n marks = marks.data.numpy()\n scores = scores.data.numpy()\n\n marks[:, 0:5] = np.expand_dims(image_sizes[:, 0], 1) * marks[:, 0:5]\n marks[:, 5:10] = np.expand_dims(image_sizes[:, 1], 1) * marks[:, 5:10]\n\n scores = scores[:, 1].reshape((-1,))\n marks = marks.reshape(-1, 2, 5).transpose(0, 2, 1).astype(np.int32)\n\n return marks, scores","title":""},{"docid":"8d3b118ad335956281f2c387376eede6","score":"0.57475436","text":"def test(self,dataset):\n outputs = self.use(dataset)\n \n costs = np.ones((len(outputs),1))\n # Compute classification error\n for xy,pred,cost in zip(dataset,outputs,costs):\n x,y = xy\n if y == pred[0]:\n cost[0] = 0\n\n return outputs,costs","title":""},{"docid":"3cc15c3cfc6db2e710c8c2d4dacd5d9c","score":"0.5742178","text":"def evaluate(self, input_data, target_data):\n\n self.confusion_matrix = np.zeros(\n [target_data.shape[-1], target_data.shape[-1]])\n \n # Compute confusion matrix (one data point at a time)\n for i in np.arange(len(input_data)):\n output = self.run(input_data[i])\n true_label = np.array(target_data[i], ndmin=2).T\n \n # Compute result and add to confusion matrix \n # (true label in rows, prediction in columns)\n confusion_result = true_label @ output.T\n confusion_result[confusion_result == np.max(confusion_result)] = 1\n confusion_result[confusion_result != 1] = 0\n self.confusion_matrix += confusion_result\n \n total_predictions = np.sum(self.confusion_matrix)\n correct_predictions = np.sum(np.diag(self.confusion_matrix))\n false_predictions = total_predictions - correct_predictions\n \n # Accuracy\n self.accuracy = correct_predictions / total_predictions\n \n # Recall (per class)\n self.recall = np.array([])\n for i in np.arange(target_data.shape[-1]):\n self.recall = np.append(\n self.recall, \n self.confusion_matrix[i,i] / np.sum(self.confusion_matrix[i,:]),\n )\n \n # Precision (per class)\n self.precision = np.array([])\n for i in np.arange(target_data.shape[-1]):\n self.precision = np.append(\n self.precision, \n self.confusion_matrix[i,i] / np.sum(self.confusion_matrix[:,i]),\n )\n \n # Print accuracy measures\n print('Accuracy: ' + str('%.2f' % (self.accuracy * 100)) + '%')\n for i in np.arange(target_data.shape[-1]): \n print('Recall for ' + str(i) + ': ' \n + str('%.2f' % (self.recall[i] * 100)) + '%')\n print('Precision for ' + str(i) + ': ' \n + str('%.2f' % (self.precision[i] * 100)) + '%')","title":""},{"docid":"77d124ebe7be840095fcba6b88eb08cc","score":"0.57381797","text":"def get_classifier(images: tp.List[Image.Image], _model_path: str, class_dict: tp.Dict) -> np.ndarray:\n # noinspection PyTypeChecker\n images = [np.asarray(x, dtype=\"float32\") / 255 for x in images]\n images = np.stack(images)\n\n _model = load_model(_model_path)\n test_preds_raw = _model.predict(images, verbose=0)\n\n test_preds = np.argmax(test_preds_raw, axis=1)\n\n # convert (als mapping) prediction from number to str\n return np.vectorize(lambda x: class_dict[x])(test_preds)","title":""},{"docid":"e4f8cf7b39a126aa3d41d37dd1406475","score":"0.57213855","text":"def classify(img_path, parameters):\n\n classes = np.array([b'non-cat', b'cat'])\n num_px = 64\n\n img = np.array(ndimage.imread(img_path, flatten=False))\n img_resized = scipy.misc.imresize(img, size=(num_px,num_px)).reshape((num_px*num_px*3,1))\n\n img_pred = predict(img_resized, parameters)\n\n img_class = np.squeeze(img_pred)\n img_result = classes[int(img_class),].decode(\"utf-8\")\n\n return img_result","title":""},{"docid":"38e813fae4023682802fb700aed8102e","score":"0.57198906","text":"def attack(self, imgs, targets, input_data_format=CHANNELS_LAST, output_data_format=CHANNELS_LAST):\n assert input_data_format in (CHANNELS_FIRST, CHANNELS_LAST)\n assert output_data_format in (CHANNELS_FIRST, CHANNELS_LAST)\n r = []\n count = len(imgs)\n print('go up to', len(imgs))\n # tranpose the input data format to fit the data format of the attack model\n if input_data_format != self.data_format:\n if input_data_format == CHANNELS_LAST:\n # input is channels_last, transpose to channels_first\n imgs = np.transpose(imgs, [0, 3, 1, 2])\n else:\n # input is channels_first, transpose to channels_last\n imgs = np.transpose(imgs, [0, 2, 3, 1])\n for i in range(0, count, self.batch_size):\n print('tick', i)\n attack_imgs = imgs[i:i + self.batch_size]\n attack_targets = targets[i:i + self.batch_size]\n attack_len = len(attack_targets)\n\n if attack_len < self.batch_size:\n img_shape = np.asarray(attack_imgs.shape)\n img_shape[0] = self.batch_size - attack_len\n target_shape = np.asarray(attack_targets.shape)\n target_shape[0] = self.batch_size - attack_len\n attack_imgs = np.append(attack_imgs, np.zeros(img_shape), axis=0)\n attack_targets = np.append(attack_targets, np.zeros(target_shape), axis=0)\n r.extend(self.attack_batch(attack_imgs, attack_targets))\n\n output = np.array(r)[0: count]\n # tranpose the output data format of the attack model to fit the output data format\n if output_data_format != self.data_format:\n if output_data_format == CHANNELS_LAST:\n # attack model output is channels_first, transpose to channels_last\n output = np.transpose(output, [0, 2, 3, 1])\n else:\n # attack model output is channels_last, transpose to channels_first\n output = np.transpose(output, [0, 3, 1, 2])\n return output","title":""},{"docid":"c70134d62d8b66e1851414062f0ec5d6","score":"0.5719786","text":"def make_1class_predictions(model, images, device, score_threshold=0.5):\n images = torch.stack(images).to(device).float()\n box_list = []\n score_list = []\n with torch.no_grad():\n det = model(images, torch.tensor([1] * images.shape[0]).to(device).float())\n for i in range(images.shape[0]):\n boxes = det[i].detach().cpu().numpy()[:, :4]\n scores = det[i].detach().cpu().numpy()[:, 4]\n # for higher threshold\n indexes = np.where((scores > score_threshold))[0]\n boxes[:, 2] = boxes[:, 2] + boxes[:, 0]\n boxes[:, 3] = boxes[:, 3] + boxes[:, 1]\n box_list.append(boxes[indexes])\n score_list.append(scores[indexes])\n\n return box_list, score_list","title":""},{"docid":"3fdb901f79d3594053552bb4800f5f4d","score":"0.5715709","text":"def train(self, patches, labels_patches, imgs, labels):\n opts = self._options\n\n labels_patches = (labels_patches >= 0.5) * 1.\n labels = (labels >= 0.5) * 1.\n\n num_train_patches = patches.shape[0]\n\n indices = np.arange(0, num_train_patches)\n np.random.shuffle(indices)\n\n num_errors = 0\n total = 0\n\n for batch_i, offset in enumerate(range(0, num_train_patches - opts.batch_size, opts.batch_size)):\n batch_indices = indices[offset:offset + opts.batch_size]\n feed_dict = {\n self._patches_node: patches[batch_indices, :, :, :],\n self._labels_node: labels_patches[batch_indices],\n self._dropout_keep: opts.dropout,\n self._image_augmentation: opts.image_augmentation,\n }\n\n summary_str, _, l, predictions, predictions, step = self._session.run(\n [self.summary_op, self._train, self._loss, self._predict_logits, self._predictions,\n self._global_step],\n feed_dict=feed_dict)\n\n print(\"Batch {} Step {}\".format(batch_i, step), end=\"\\r\")\n self._summary.add(summary_str, global_step=step)\n\n num_errors += np.abs(labels_patches[batch_indices] - predictions).sum()\n total += opts.batch_size\n self._summary.add_to_pixel_missclassification_summary(num_errors, total, self._global_step)\n\n # from time to time do full prediction on some images\n if step > 0 and step % opts.eval_every == 0:\n print()\n\n images_to_predict = imgs[:opts.num_eval_images, :, :, :]\n masks = self.predict(images_to_predict)\n overlays = images.overlays(images_to_predict, masks)\n pred_masks = ((masks > 0.5) * 1).squeeze()\n true_masks = labels[:opts.num_eval_images, :, :].squeeze()\n\n self._summary.add_to_eval_summary(masks, overlays, labels, self._global_step)\n self._summary.add_to_overlap_summary(true_masks, pred_masks, self._global_step)\n\n if step > 0 and step % opts.train_score_every == 0:\n self._summary.add_to_training_summary(self.predict(imgs), labels, self._global_step)\n\n self._summary.flush()","title":""},{"docid":"3a4f2d95a3386886796ee3a2c306f0eb","score":"0.5712749","text":"def predict(self, instances, **kwargs):\n imgs = map(self.decode_img, instances)\n inputs = np.asarray(imgs)\n outputs = self._model.predict(imgs)\n\n return [self.threshold(output) for output in outputs]","title":""},{"docid":"d75acd21d5c2b89f4addb30e9bd003ec","score":"0.57005554","text":"def performance_evaluation(self, metrics):\n\n metrics_indicator = valid_input_perform_eval(metrics)\n if (metrics_indicator == None):\n print(\"Invalid metrics!\")\n return\n else:\n results = np.zeros((len(self.comp_img), len(metrics)))\n overlap_measures_filter = sitk.LabelOverlapMeasuresImageFilter()\n hausdorff_distance_filter = sitk.HausdorffDistanceImageFilter()\n\n for i in range(len(self.comp_img)):\n # Cannot compute for unmatched image files\n if (self.comp_img[i].GetSpacing() != self.radio_img[i].GetSpacing()):\n print(\n \"Spacing of corresponding image files ({}) don't match!\".format(self.filename[i]))\n results[i] = [float(\"NaN\")] * len(metrics)\n else: \n overlap_measures_filter.Execute(\n self.comp_img[i], self.radio_img[i])\n Fp = overlap_measures_filter.GetFalsePositiveError()\n Tn = 1 - Fp\n Fn = overlap_measures_filter.GetFalseNegativeError()\n Tp = 1 - Fn\n if (metrics_indicator[1]):\n results[i, 1] = overlap_measures_filter.GetDiceCoefficient()\n if (metrics_indicator[3]):\n results[i, 3] = overlap_measures_filter.GetJaccardCoefficient()\n if (metrics_indicator[4]):\n results[i, 4] = (\n Tp * Tn - Fp * Fn) / np.sqrt((Tp + Fp) * (Tp + Fn) * (Tn + Fp) * (Tn + Fn))\n if (metrics_indicator[0]):\n results[i, 0] = (Tp + Tn) / (Tp + Fp + Tn + Fn)\n hausdorff_distance_filter.Execute(\n self.comp_img[i], self.radio_img[i])\n if (metrics_indicator[2]):\n results[i, 2] = hausdorff_distance_filter.GetHausdorffDistance()\n \n results_df = pd.DataFrame(\n data=results, index=self.filename, columns=sorted(metrics))\n results_df.to_csv(self.export_path + '/results.csv')","title":""},{"docid":"e2a7f015c4dc28ea47e75b144f30e0ba","score":"0.56978613","text":"def augment_images(self, images, labels, folder_names, target, current_step, total_steps):\n unq, unq_inv, unq_cnt = np.unique(labels, return_inverse=True, return_counts=True)\n\n print(\"___________________________________\")\n print(\"augment_images...\")\n print(\"Labels:\", labels)\n print(\"Labels Unique:\", unq)\n print(\"Labels Unique Invers:\", unq_inv)\n print(\"Labels Unique Count:\", unq_cnt)\n\n # count the number of images that already exist for each user\n unique_class_indices = np.split(np.argsort(unq_inv), np.cumsum(unq_cnt[:-1]))\n\n target_x = images\n target_y = labels\n i = 0\n for unique_class in unq:\n print(\"Unique Class:\", unique_class)\n indices = unique_class_indices[unique_class]\n #print(\"Unique Class Indeces:\", indices)\n\n folder_name = folder_names[unique_class]\n print(\"Gallery:\", folder_name)\n\n number_of_missing_images = target - len(indices)\n\n current_step += 1\n self.update_status('STARTED', {'step': current_step,\n 'total_steps': total_steps,\n 'description': 'Augmenting'})\n\n print(\"Number of missing Images: \", number_of_missing_images)\n batch_x = augmenter.augment_array_target(folder_name, number_of_missing_images)\n batch_y = np.full(number_of_missing_images, unique_class)\n print(\" \")\n print(\"batch_y:\", batch_y)\n\n target_x.extend(batch_x)\n target_y.extend(batch_y)\n i += 1\n return target_x, target_y","title":""},{"docid":"443d500467409c119cda9d54be802a27","score":"0.5693967","text":"def test_images(self, gt, pred):\n avg_psnr = 0\n avg_ssim = 0\n individual_psnr = []\n individual_ssim = []\n\n print('Length of gt: %s'%len(gt))\n print('Length of pred: %s'%len(pred))\n for i in range(len(pred)):\n # compare to gt\n psnr = self.PSNR(self.luminance(gt[i]), self.luminance(pred[i]))\n ssim = self.SSIM(self.luminance(gt[i]), self.luminance(pred[i]))\n # save results to log_path ex: 'results/experiment1/Set5/baby/1000.png'\n # if save_images:\n # path = os.path.join(log_path, self.name, self.names[i])\n # gather results\n individual_psnr.append(psnr)\n individual_ssim.append(ssim)\n avg_psnr += psnr\n avg_ssim += ssim\n\n avg_psnr /= len(pred)\n avg_ssim /= len(pred)\n return avg_psnr, avg_ssim, individual_psnr, individual_ssim","title":""},{"docid":"b0dbd367f2d4d61e5ca47b1e12dc9dff","score":"0.5690589","text":"def im_detect(net, im, boxes, num_classes, num_subclasses):\n\n if boxes.shape[0] == 0:\n scores = np.zeros((0, num_classes))\n pred_boxes = np.zeros((0, 4*num_classes))\n pred_views = np.zeros((0, 3*num_classes))\n scores_subcls = np.zeros((0, num_subclasses))\n return scores, pred_boxes, scores_subcls, pred_views\n\n blobs, unused_im_scale_factors = _get_blobs(im, boxes)\n\n # When mapping from image ROIs to feature map ROIs, there's some aliasing\n # (some distinct image ROIs get mapped to the same feature ROI).\n # Here, we identify duplicate feature ROIs, so we only compute features\n # on the unique subset.\n if cfg.DEDUP_BOXES > 0:\n v = np.array([1, 1e3, 1e6, 1e9, 1e12])\n hashes = np.round(blobs['rois'] * cfg.DEDUP_BOXES).dot(v)\n _, index, inv_index = np.unique(hashes, return_index=True,\n return_inverse=True)\n blobs['rois'] = blobs['rois'][index, :]\n boxes = boxes[index, :]\n\n # reshape network inputs\n net.blobs['data'].reshape(*(blobs['data'].shape))\n net.blobs['rois'].reshape(*(blobs['rois'].shape))\n blobs_out = net.forward(data=blobs['data'].astype(np.float32, copy=False),\n rois=blobs['rois'].astype(np.float32, copy=False))\n\n #for layer_name, blob in net.blobs.iteritems():\n # print layer_name+'\\t'+ str(blob.data.shape)\n if cfg.TEST.SVM:\n # use the raw scores before softmax under the assumption they\n # were trained as linear SVMs\n scores = net.blobs['cls_score'].data\n else:\n # use softmax estimated probabilities\n scores = blobs_out['cls_prob']\n\n if cfg.TEST.SUBCLS:\n scores_subcls = blobs_out['subcls_prob']\n else:\n # just use class scores\n scores_subcls = scores\n\n if cfg.TEST.BBOX_REG:\n # Apply bounding-box regression deltas\n box_deltas = blobs_out['bbox_pred']\n pred_boxes = _bbox_pred(boxes, box_deltas)\n pred_boxes = _clip_boxes(pred_boxes, im.shape)\n else:\n # Simply repeat the boxes, once for each class\n pred_boxes = np.tile(boxes, (1, scores.shape[1]))\n\n if cfg.TEST.VIEWPOINT:\n # Apply bounding-box regression deltas\n pred_views = blobs_out['view_pred']\n else:\n # set to zeros\n pred_views = np.zeros((boxes.shape[0], 3*num_classes))\n\n if cfg.DEDUP_BOXES > 0 and not cfg.TEST.IS_PATCH:\n # Map scores and predictions back to the original set of boxes\n scores = scores[inv_index, :]\n scores_subcls = scores_subcls[inv_index, :]\n pred_boxes = pred_boxes[inv_index, :]\n pred_views = pred_views[inv_index, :]\n\n return scores, pred_boxes, scores_subcls, pred_views","title":""},{"docid":"28d395fb5073aac957852355b1933aa6","score":"0.5686527","text":"def run_batch_inference(self, images):\n if len(images.shape) == 3:\n logger.info(\"input is a single image. Use batch size 1.\")\n images = np.expand_dims(images, axis=0)\n output_tensor_dict = self._output_tensor_dict\n image_tensor = self._graph.get_tensor_by_name('image_tensor:0')\n\n # Run inference\n output_dict = self._sess.run(output_tensor_dict,\n feed_dict={image_tensor: images})\n\n # all outputs are float32 numpy arrays, so convert types as appropriate\n output_dict['num_detections'] = output_dict['num_detections'].astype(np.uint)\n output_dict['detection_classes'] = output_dict[\n 'detection_classes'].astype(np.uint8)\n output_dict['detection_boxes'] = output_dict['detection_boxes']\n output_dict['detection_scores'] = output_dict['detection_scores']\n return output_dict","title":""},{"docid":"389aee3b8584e90914b7583fc383d57c","score":"0.56823885","text":"def _run_inference_on_image_tensors(self, images: tf.Tensor):\n model_params = self._params.task.model\n with tf.device('cpu:0'):\n images = tf.cast(images, dtype=tf.float32)\n\n # Tensor Specs for map_fn outputs (images, anchor_boxes, and image_info).\n images_spec = tf.TensorSpec(shape=self._input_image_size + [3],\n dtype=tf.float32)\n\n num_anchors = model_params.anchor.num_scales * len(\n model_params.anchor.aspect_ratios) * 4\n anchor_shapes = []\n for level in range(model_params.min_level, model_params.max_level + 1):\n anchor_level_spec = tf.TensorSpec(\n shape=[\n self._input_image_size[0] // 2**level,\n self._input_image_size[1] // 2**level, num_anchors\n ],\n dtype=tf.float32)\n anchor_shapes.append((str(level), anchor_level_spec))\n\n image_shape_spec = tf.TensorSpec(shape=[2,], dtype=tf.float32)\n\n images, anchor_boxes, image_shape = tf.nest.map_structure(\n tf.identity,\n tf.map_fn(\n self._build_inputs,\n elems=images,\n fn_output_signature=(images_spec, dict(anchor_shapes),\n image_shape_spec),\n parallel_iterations=32))\n\n detections = self._model.call(\n images=images,\n image_shape=image_shape,\n anchor_boxes=anchor_boxes,\n training=False)\n\n final_outputs = {\n 'detection_boxes': detections['detection_boxes'],\n 'detection_scores': detections['detection_scores'],\n 'detection_classes': detections['detection_classes'],\n 'num_detections': detections['num_detections']\n }\n if 'detection_masks' in detections.keys():\n final_outputs['detection_masks'] = detections['detection_masks']\n\n return final_outputs","title":""},{"docid":"7bb77db78dc33f23a39c5affb6f6d02a","score":"0.5681441","text":"def _check_targets(y_true, y_pred):\n check_consistent_length(y_true, y_pred)\n type_true = type_of_target(y_true)\n type_pred = type_of_target(y_pred)\n\n y_type = set([type_true, type_pred])\n if y_type == set([\"binary\", \"multiclass\"]):\n y_type = set([\"multiclass\"])\n\n if y_type == set([\"multiclass-multioutput\", \"multilabel-indicator\"]):\n y_type = set([\"multiclass-multioutput\"])\n\n if len(y_type) > 1:\n raise ValueError(\"Classification metrics can't handle a mix of {0} \"\n \"and {1} targets\".format(type_true, type_pred))\n\n # We can't have more than one value on y_type => The set is no more needed\n y_type = y_type.pop()\n\n if (y_type not in [\"binary\", \"multiclass\", \"multilabel-indicator\",\n \"multiclass-multioutput\"]):\n raise ValueError(\"{0} is not supported\".format(y_type))\n\n if y_type in [\"binary\", \"multiclass\"]:\n y_true = column_or_1d(y_true)\n y_pred = column_or_1d(y_pred)\n if y_type == \"binary\":\n unique_values = np.union1d(y_true, y_pred)\n if len(unique_values) > 2:\n y_type = \"multiclass\"\n\n if y_type.startswith('multilabel'):\n y_type = 'multilabel-indicator'\n\n return y_type, y_true, y_pred","title":""},{"docid":"2ae5e1eabf3713ac8881f4d42af0d5fb","score":"0.5680647","text":"def recognize(self, images: typing.List[typing.Union[np.ndarray, str]]) -> typing.List[str]:\n height, width = self.model.input_shape[1:-1]\n images = [tools.read_and_fit(image, width=width, height=height) for image in images]\n for index, image in enumerate(images):\n # It's a grayscale image with no\n # channel dimension so we need to add a\n # dimension first or possibly convert to color.\n if len(image.shape) == 2 and not self.color:\n images[index] = image[..., np.newaxis]\n continue\n # It's grayscale but we need color.\n if len(image.shape) == 2 and self.color:\n images[index] = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\n continue\n\n # It's a color image but we need grayscale\n if image.shape[2] == 3 and not self.color:\n images[index] = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)[..., np.newaxis]\n continue\n\n # It's a grayscale image with a color dimension\n # but we need color.\n if image.shape[2] == 1 and self.color:\n images[index] = cv2.cvtColor(image[..., 0], cv2.COLOR_GRAY2RGB)\n\n images = np.array(images)\n X = keras_applications.imagenet_utils.preprocess_input(images,\n mode=self.preprocessing_mode,\n data_format='channels_last',\n backend=keras.backend)\n predictions = self.prediction_model.predict(X)\n return decode(predictions=predictions, alphabet=self.alphabet)","title":""},{"docid":"a4723c6104635da5600eeddd7283cb41","score":"0.5679709","text":"def process_images_for_prediction(chess_piece_images):\n processed_chess_piece_images = []\n for chess_piece_image in chess_piece_images:\n processed_chess_piece_image = utils.resize_given_image(chess_piece_image, IMAGE_DIMS)\n processed_chess_piece_images.append(processed_chess_piece_image)\n\n processed_chess_piece_images = np.array(processed_chess_piece_images)\n processed_chess_piece_images = processed_chess_piece_images.astype('float32')\n processed_chess_piece_images /= 255\n\n return processed_chess_piece_images","title":""},{"docid":"884e2689fcb76cdb71241aea19c8aa24","score":"0.56754535","text":"def preprocess_validate(images_names, color_data, tensors, softmax_layer,\n session, y_true, model):\n # load, remove mean, crop images \n dataset = []\n for image_name in images_names:\n im = cv2.imread(image_name)\n im[0] = im[0] - color_data['r_mean']\n im[1] = im[1] - color_data['g_mean']\n im[2] = im[2] - color_data['b_mean']\n pad_crop_im = pad_crop(im, model=model)\n pad_crop_bgr_im = cv2.cvtColor(np.uint8(pad_crop_im), cv2.COLOR_RGB2BGR)\n dataset.append(pad_crop_bgr_im)\n # convert list of images to 4-D array\n dataset = np.array(dataset)\n # normalize images\n dataset = norm_data(dataset)\n\n # predict labels for provided images names\n predictions = session.run(softmax_layer, feed_dict={tensors['input_layer']: dataset,\n tensors['labels_tensor']: y_true,\n tensors['hold_prob']: 1})\n return predictions","title":""},{"docid":"b22d68c06a7d96ff9b93a480055c68eb","score":"0.5671083","text":"def im_test(self, detector: Detector, image: np.ndarray, labels: dict) -> tuple:\n start = datetime.now()\n objects = detector.predict(image)\n # initiate a dictionary for found labels, and lists for accuracies and probabilities.\n time = datetime.now() - start\n logging.info(\n f\"Run time is {time.total_seconds()}, the threshold is {self.thresholds['runtime'].total_seconds()}\")\n test_dict = {}\n tp = 0\n fp = 0\n fn = 0\n probabilities = []\n # fill test dictionary with labels found.\n for dic in objects:\n if dic['label'] in test_dict:\n test_dict[dic['label']] += 1\n else:\n test_dict[dic['label']] = 1\n probabilities.append(dic['prob'])\n # check false negative.\n for key in labels.keys():\n if key in test_dict:\n if test_dict[key] - labels[key] == 0:\n tp += labels[key]\n elif labels[key] > test_dict[key]:\n tp += test_dict[key]\n fn += labels[key] - test_dict[key]\n else:\n tp += labels[key]\n fp += test_dict[key] - labels[key]\n logging.info(f\"Found {test_dict[key]} {key}s out of {labels[key]}.\")\n else:\n fn += labels[key]\n logging.warning(f\"Found {0} {key}s out of {labels[key]}.\")\n # check false positive\n for key in test_dict.keys():\n if key not in labels:\n fp += test_dict[key]\n logging.warning(f\"False positive label was detected.\")\n # calculate recall and probability.\n recall = tp / (tp + fn)\n precision = tp / (tp + fp)\n mean_prob = mean(probabilities)\n logging.info(f\"Recall is {round(recall, 2)}, the threshold is {self.thresholds['recall']}.\")\n logging.info(f\"Precision is {round(precision, 2)}, the threshold is {self.thresholds['precision']}.\")\n logging.info(f\"Mean probability is {round(mean_prob, 2)}, the threshold is {self.thresholds['probability']}.\")\n if recall >= self.thresholds['recall'] and mean_prob >= self.thresholds['probability'] and time <= \\\n self.thresholds['runtime']:\n logging.warning(\"The test has passed successfully.\")\n else:\n logging.warning(\"The test has failed.\")\n\n return recall, mean_prob, precision, time","title":""},{"docid":"aa752013b4b43a06c77ed23d5deeec39","score":"0.5670382","text":"def _eval_classifier(self):\n\n y_pred_prior = self.model_prior.predict(self.X_test_prior)\n y_pred_post = self.model_post.predict(self.X_test_post)\n\n y_test_ = pd.DataFrame(self.y_test)\n y_pred_prior = pd.DataFrame(y_pred_prior, columns=y_test.columns)\n y_pred_post = pd.DataFrame(y_pred_post, columns=y_test.columns)\n\n y_pred_prior['source'] = \"prior\"\n y_pred_post['source'] = \"post\"\n y_test['source'] = \"test\"\n\n y_ = pd.concat([y_pred_prior, y_pred_post, y_test])\n cols = [col for col in y_.columns if col != \"source\"]\n y_ = pd.get_dummies(y_, columns=cols)\n\n y_pred_prior = y_[y_.source == 'prior'].drop('source', axis=1).values\n y_pred_post = y_[y_.source == 'post'].drop('source', axis=1).values\n y_test = y_[y_.source == 'test'].drop('source', axis=1).values\n\n y_.drop('source', axis=1, inplace=True)\n class_labels = y_.columns\n\n res = pd.DataFrame([])\n\n if (len(y_test[0]) == 2):\n # for binary classification\n # only take position 1, assuming position 1 is the true label\n iters = [1]\n else:\n # for multiclass classification\n iters = range(len(y_test[0]))\n\n for i in iters:\n\n precision_prior = precision_score(y_test[:,i], y_pred_prior[:,i])\n recall_prior = recall_score(y_test[:,i], y_pred_prior[:,i])\n acc_prior = accuracy_score(y_test[:,i], y_pred_prior[:,i])\n f1_prior = f1_score(y_test[:,i], y_pred_prior[:,i])\n try:\n auc_prior = roc_auc_score(y_test[:,i], y_pred_prior[:,i])\n except ValueError:\n auc_prior = \"NA\"\n\n precision_post = precision_score(y_test[:,i], y_pred_post[:,i])\n recall_post = recall_score(y_test[:,i], y_pred_post[:,i])\n acc_post = accuracy_score(y_test[:,i], y_pred_post[:,i])\n f1_post = f1_score(y_test[:,i], y_pred_post[:,i])\n try:\n auc_post = roc_auc_score(y_test[:,i], y_pred_post[:,i])\n except ValueError:\n auc_post = \"NA\"\n\n multiindex = [(str(class_labels[i]), 'Prior'),\n (str(class_labels[i]), 'Post')]\n\n index = pd.MultiIndex.from_tuples(multiindex,\n names=['Class', 'Data Type'])\n\n score = pd.DataFrame({\n 'Accuracy': [acc_prior, acc_post],\n 'Precision': [precision_prior, precision_post],\n 'Recall': [recall_prior, recall_post],\n 'F1': [f1_prior, f1_post],\n 'AUC': [auc_prior, auc_post]\n },\n index=index\n )\n\n res = pd.concat([res, score])\n\n self.ml_report = res","title":""},{"docid":"1b88f7d1e0692e0c8f6185bb880d15d6","score":"0.56663907","text":"def accuracy(classifications, targets):\n return (classifications == targets).mean()","title":""},{"docid":"d4acd753d0c664fac050befba38ed9ae","score":"0.5664122","text":"def inference(\n net, images, device=\"cuda\", prob_thresh=0.05, nms_iou_thresh=0.3,\n resize=True\n):\n if not isinstance(images, list):\n images = [images]\n\n orig_image_shapes = [image.shape for image in images]\n\n # Resize input images to match shape of images on which net was trained.\n if resize:\n net_image_shape = (net.net_info[\"height\"], net.net_info[\"width\"])\n images = [\n cv2.resize(image, net_image_shape)\n if image.shape[:2] != net_image_shape\n else image for image in images\n ]\n\n # Stack images along new batch axis, flip channel axis so channels are RGB\n # instead of BGR, transpose so channel axis comes before row/column axes,\n # and convert pixel values to FP32. Do this in one step to ensure array\n # is contiguous before passing to torch tensor constructor.\n inp = np.transpose(np.flip(np.stack(images), 3), (0, 3, 1, 2)).astype(\n np.float32) / 255.0\n\n inp = torch.tensor(inp, device=device)\n out = net.forward(inp)\n\n bbox_xywh = out[\"bbox_xywh\"].detach().cpu().numpy()\n class_prob = out[\"class_prob\"].cpu().numpy()\n class_idx = out[\"class_idx\"].cpu().numpy()\n\n thresh_mask = class_prob >= prob_thresh\n\n # Perform post-processing on each image in the batch and return results.\n results = []\n for i in range(bbox_xywh.shape[0]):\n image_bbox_xywh = bbox_xywh[i, thresh_mask[i, :], :]\n image_class_prob = class_prob[i, thresh_mask[i, :]]\n image_class_idx = class_idx[i, thresh_mask[i, :]]\n\n image_bbox_xywh[:, [0, 2]] *= orig_image_shapes[i][1]\n image_bbox_xywh[:, [1, 3]] *= orig_image_shapes[i][0]\n image_bbox_tlbr = cxywh_to_tlbr(image_bbox_xywh.astype(np.int))\n\n idxs_to_keep = non_max_suppression(\n image_bbox_tlbr, image_class_prob, class_idx=image_class_idx,\n iou_thresh=nms_iou_thresh\n )\n\n results.append(\n [\n image_bbox_tlbr[idxs_to_keep, :],\n image_class_prob[idxs_to_keep],\n image_class_idx[idxs_to_keep]\n ]\n )\n\n return results","title":""},{"docid":"058723c29ce82a331bc0d26241be5169","score":"0.56624264","text":"def run_inference_on_images(image_list, output_dir):\n image_to_labels = defaultdict(list)\n\n create_graph()\n\n with tf.compat.v1.Session() as sess:\n # Some useful tensors:\n # 'softmax:0': A tensor containing the normalized prediction across\n # 1000 labels.\n # 'pool_3:0': A tensor containing the next-to-last layer containing 2048\n # float description of the image.\n # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG\n # encoding of the image.\n # Runs the softmax tensor by feeding the image_data as input to the graph.\n softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')\n\n for image_index, image in enumerate(image_list):\n try:\n print(\"parsing\", image_index, image, \"\\n\")\n if not tf.io.gfile.exists(image):\n tf.logging.fatal('File does not exist %s', image)\n\n with tf.io.gfile.GFile(image, 'rb') as f:\n image_data = f.read()\n\n process = psutil.Process(os.getpid())\n mem3 = process.memory_info().rss\n print('Memory After reading file', mem3 / (1024 ** 2), 'MB')\n\n predictions = sess.run(softmax_tensor,\n {'DecodeJpeg/contents:0': image_data})\n\n predictions = np.squeeze(predictions)\n\n ###\n # Get penultimate layer weights\n ###\n\n feature_tensor = sess.graph.get_tensor_by_name('pool_3:0')\n feature_set = sess.run(feature_tensor,\n {'DecodeJpeg/contents:0': image_data})\n feature_vector = np.squeeze(feature_set)\n outfile_name = os.path.basename(image) + \".npz\"\n out_path = os.path.join(output_dir, outfile_name)\n np.savetxt(out_path, feature_vector, delimiter=',')\n\n # Creates node ID --> English string lookup.\n node_lookup = NodeLookup()\n\n process = psutil.Process(os.getpid())\n mem4 = process.memory_info().rss\n print('Memory before prediction', mem4 / (1024 ** 2), 'MB')\n\n top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]\n for node_id in top_k:\n human_string = node_lookup.id_to_string(node_id)\n score = predictions[node_id]\n print(\"results for\", image)\n print('%s (score = %.5f)' % (human_string, score))\n print(\"\\n\")\n\n image_to_labels['image_labels'].append(\n {\n \"labels\": human_string,\n \"score\": str(score)\n }\n )\n process = psutil.Process(os.getpid())\n mem5 = process.memory_info().rss\n print('Memory After Prediction', mem5 / (1024 ** 2), 'MB')\n\n # # detect number of faces\n num_faces = detect_num_faces(image)\n image_to_labels['number_of_faces'].append(num_faces)\n\n process = psutil.Process(os.getpid())\n mem6 = process.memory_info().rss\n print('Memory After Face Detection', mem6 / (1024 ** 2), 'MB')\n\n # close the open file handlers\n proc = psutil.Process()\n open_files = proc.open_files()\n\n for open_file in open_files:\n file_handler = getattr(open_file, \"fd\")\n os.close(file_handler)\n except:\n print('could not process image index', image_index, 'image', image)\n\n return image_to_labels","title":""},{"docid":"e372f3e44e6ba24d3b98edb532d1ce63","score":"0.5662352","text":"def _process_batch(sess, original_images, semantic_predictions, probs, image_names,\n image_heights, image_widths, image_id_offset, save_dir,\n raw_save_dir, train_id_to_eval_id=None):\n (original_images,\n semantic_predictions,\n probs,\n image_names,\n image_heights,\n image_widths) = sess.run([original_images, semantic_predictions, probs,\n image_names, image_heights, image_widths])\n\n num_image = semantic_predictions.shape[0]\n for i in range(num_image):\n image_height = np.squeeze(image_heights[i])\n image_width = np.squeeze(image_widths[i])\n semantic_prediction = np.squeeze(semantic_predictions[i])\n crop_semantic_prediction = semantic_prediction[:image_height, :image_width]\n img_name = f\"{image_names[i].decode().split('/')[-1].split('.')[0]}\"\n\n if FLAGS.also_save_raw_predictions:\n image_filename = os.path.basename(image_names[i])\n\n if train_id_to_eval_id is not None:\n crop_semantic_prediction = _convert_train_id_to_eval_id(\n crop_semantic_prediction,\n train_id_to_eval_id)\n\n save_annotation.save_annotation(\n prediction_with_COCO(crop_semantic_prediction, probs[i], concept_dict[f\"{FLAGS.folder}/{img_name}.jpg\"]),\n raw_save_dir,\n f\"{img_name}\",\n add_colormap=False)","title":""},{"docid":"d37b79ca8c7a21575d2f346b7a9f68e0","score":"0.5660186","text":"def visualize_training(self, batched_inputs, results, feedbacks=None):\r\n from detectron2.utils.visualizer import Visualizer\r\n\r\n assert len(batched_inputs) == len(\r\n results\r\n ), \"Cannot visualize inputs and results of different sizes\"\r\n storage = get_event_storage()\r\n max_boxes = 20\r\n\r\n image_index = 0 # only visualize a single image\r\n img = batched_inputs[image_index][\"image\"]\r\n img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format)\r\n v_gt = Visualizer(img, None)\r\n v_gt = v_gt.overlay_instances(boxes=batched_inputs[image_index][\"instances\"].gt_boxes)\r\n anno_img = v_gt.get_image()\r\n processed_results = detector_postprocess(results[image_index], img.shape[0], img.shape[1])\r\n predicted_boxes = processed_results.pred_boxes.tensor.detach().cpu().numpy()\r\n\r\n v_pred = Visualizer(img, None)\r\n v_pred = v_pred.overlay_instances(boxes=predicted_boxes[0:max_boxes])\r\n prop_img = v_pred.get_image()\r\n\r\n num_classes = self.num_classes\r\n if feedbacks is not None:\r\n v_feedback_gt = Visualizer(img, None)\r\n instance = feedbacks[0].to(torch.device(\"cpu\"))\r\n v_feedback_gt = v_feedback_gt.overlay_instances(\r\n boxes=instance.proposal_boxes[instance.gt_classes != num_classes])\r\n feedback_gt_img = v_feedback_gt.get_image()\r\n\r\n v_feedback_gf = Visualizer(img, None)\r\n v_feedback_gf = v_feedback_gf.overlay_instances(\r\n boxes=instance.proposal_boxes[instance.gt_classes == num_classes])\r\n feedback_gf_img = v_feedback_gf.get_image()\r\n\r\n vis_img = np.vstack((anno_img, prop_img, feedback_gt_img, feedback_gf_img))\r\n vis_img = vis_img.transpose(2, 0, 1)\r\n vis_name = f\"Top: GT; Middle: Pred; Bottom: Feedback GT, Feedback GF\"\r\n else:\r\n vis_img = np.concatenate((anno_img, prop_img), axis=1)\r\n vis_img = vis_img.transpose(2, 0, 1)\r\n vis_name = \"Left: GT bounding boxes; Right: Predicted proposals\"\r\n\r\n storage.put_image(vis_name, vis_img)","title":""},{"docid":"ef6c8ecd231cec6e87fb351b6ca61dfe","score":"0.5654193","text":"def process(self):\n print('Processing images...')\n processed = self.model.predict(self.source['process'], batch_size=self.batch_size, verbose=1)\n save_img(self.res_dir, processed)\n print('Complete')","title":""},{"docid":"85f10becdce5d3df79a6f19e658a365f","score":"0.5653114","text":"def postprocess(results, filenames, batch_size):\n if len(results) != 1:\n raise Exception(\"expected 1 result, got {}\".format(len(results)))\n\n batched_result = list(results.values())[0]\n if len(batched_result) != batch_size:\n raise Exception(\"expected {} results, got {}\".format(batch_size, len(batched_result)))\n if len(filenames) != batch_size:\n raise Exception(\"expected {} filenames, got {}\".format(batch_size, len(filenames)))\n\n for (index, result) in enumerate(batched_result):\n print(\"Image '{}':\".format(filenames[index]))\n for cls in result:\n print(\" {} ({}) = {}\".format(cls[0], cls[2], cls[1]))","title":""},{"docid":"c8a3392028eda8c4acc7fb3037eb3c8e","score":"0.56511486","text":"def inference_single_image(self, pred_labels, anchors):\n pred_classes = pred_labels[\"class\"]\n pred_bbox_offsets = pred_labels[\"bbox_offset\"]\n pred_dim_offsets = pred_labels[\"dim_offset\"]\n pred_orient_offsets = pred_labels[\"orient_offset\"]\n pred_bin_confs = pred_labels[\"bin_conf\"]\n pred_classes_all = []\n pred_scores_all = []\n pred_bboxes_all = []\n pred_dims_all = []\n pred_orients_all = []\n\n # Iterate over every feature level\n for i, anchors_i in enumerate(anchors):\n pred_classes_i = pred_classes[i]\n pred_bbox_offsets_i = pred_bbox_offsets[i]\n pred_dim_offsets_i = pred_dim_offsets[i]\n pred_orient_offsets_i = pred_orient_offsets[i]\n pred_bin_confs_i = pred_bin_confs[i]\n # (HxWxAxK,)\n pred_classes_i = pred_classes_i.flatten().sigmoid()\n anchors_i = anchors_i.to(pred_classes_i.device)\n # Keep top k top scoring indices only.\n num_topk = min(self.topk_candidates, pred_bbox_offsets_i.size(0))\n # torch.sort is actually faster than .topk (at least on GPUs)\n pred_scores_i, topk_idxs = pred_classes_i.sort(descending=True)\n pred_scores_i = pred_scores_i[:num_topk]\n topk_idxs = topk_idxs[:num_topk]\n\n # filter out the proposals with low confidence score\n keep_idxs = pred_scores_i > self.score_threshold\n if keep_idxs.float().sum() <= 0:\n continue\n pred_scores_i = pred_scores_i[keep_idxs]\n topk_idxs = topk_idxs[keep_idxs]\n\n anchor_idxs = topk_idxs // self.num_classes\n classes_idxs = topk_idxs % self.num_classes\n\n pred_bbox_offsets_i = pred_bbox_offsets_i[anchor_idxs]\n anchors_i = anchors_i[anchor_idxs]\n pred_dim_offsets_i = pred_dim_offsets_i[anchor_idxs]\n pred_orient_offsets_i = pred_orient_offsets_i[anchor_idxs]\n pred_bin_confs_i = pred_bin_confs_i[anchor_idxs]\n # predict boxes\n pred_bbox_i = self._ground_truth_coder.decode_bbox(pred_bbox_offsets_i, anchors_i.tensor)\n # predict dimensions\n pred_dims_i = self._ground_truth_coder.decode_dimension(pred_dim_offsets_i, classes_idxs)\n # predict orientation\n pred_orients_i = self._ground_truth_coder.decode_orient(pred_orient_offsets_i, pred_bin_confs_i)\n pred_bboxes_all.append(pred_bbox_i)\n pred_scores_all.append(pred_scores_i)\n pred_classes_all.append(classes_idxs)\n pred_dims_all.append(pred_dims_i)\n pred_orients_all.append(pred_orients_i)\n\n result = ParamList(self.in_size, is_train=False)\n if len(pred_bboxes_all) > 0:\n pred_bboxes_all, pred_scores_all, pred_classes_all, pred_dims_all, pred_orients_all = [\n torch.cat(x) for x in [pred_bboxes_all, pred_scores_all, pred_classes_all, pred_dims_all, pred_orients_all]\n ]\n keep = batched_nms(pred_bboxes_all, pred_scores_all, pred_classes_all, self.nms_threshold)\n keep = keep[: self.max_detections_per_image]\n result.add_field(\"class\", pred_classes_all[keep])\n result.add_field(\"score\", pred_scores_all[keep])\n result.add_field(\"bbox\", pred_bboxes_all[keep])\n result.add_field(\"dimension\", pred_dims_all[keep])\n result.add_field(\"orientation\", pred_orients_all[keep])\n else:\n result.add_field(\"class\", pred_classes_all)\n result.add_field(\"score\", pred_scores_all)\n result.add_field(\"bbox\", pred_bboxes_all)\n result.add_field(\"dimension\", pred_dims_all)\n result.add_field(\"orientation\", pred_orients_all)\n return result","title":""},{"docid":"9ebfa5991161e0a8595de7f1b8f3f192","score":"0.5649904","text":"def preprocess_image(self, batched_inputs):\n images = [x[\"image\"].to(self.device) / 255. for x in batched_inputs]\n images = [(x - self.pixel_mean) / self.pixel_std for x in images]\n images, masks = ImageList.from_tensors(images, self.backbone.size_divisibility)\n return images, masks","title":""},{"docid":"e049c72db629467f0b847333deda2842","score":"0.56466806","text":"def save_interm_result(imgs, outputs, targets, save_dir, epoch):\n def _denormalize_img(img):\n return (img*127.5) + 127.5\n\n num_imgs = len(imgs)\n img_size = imgs.shape[-2], imgs.shape[-1]\n output_size = outputs.shape[-2], outputs.shape[-1]\n\n num_rows = math.ceil(num_imgs**0.5)\n num_cols = round(num_imgs**0.5)\n\n img_mask = np.zeros([img_size[0]*num_rows, img_size[1]*num_cols])\n output_mask = np.zeros([output_size[0]*num_rows, output_size[1]*num_cols])\n target_mask = np.zeros([output_size[0]*num_rows, output_size[1]*num_cols])\n\n imgs_arr = imgs.cpu().data.numpy()[:, 0, :, :]\n targets_arr = targets.cpu().data.numpy()[:, 0, :, :]\n outputs_arr = outputs.cpu().data.numpy()[:, 0, :, :]\n\n for i, (img, target, output) in enumerate(zip(imgs_arr, targets_arr, outputs_arr)):\n row = i // num_cols\n col = i % num_cols\n img_mask[row*img_size[0]: (row+1)*img_size[0], col*img_size[1]:(col+1)*img_size[1]] = img.copy()\n output_mask[row*output_size[0]: (row+1)*output_size[0], col*output_size[1]:(col+1)*output_size[1]] = output.copy()\n target_mask[row*output_size[0]: (row+1)*output_size[0], col*output_size[1]:(col+1)*output_size[1]] = target.copy()\n\n\n for i, mask in enumerate([img_mask, output_mask, target_mask]):\n save_path = os.path.join(save_dir, f'{epoch}_{i}.jpg')\n cv2.imwrite(save_path, _denormalize_img(mask))\n return img_mask, output_mask, target_mask","title":""},{"docid":"f1e6b970fff5215fc10cbbf808de8406","score":"0.5643073","text":"def classify(data, label, weights):\n\tdataMat, _ = normalize(data); labelMat= np.mat(label).T\n\tm, n = np.shape(dataMat)\n\th = sigmoid(dataMat * weights)\n\ty = np.float32(h > 0.5)\n\taccuracy = np.sum(y == labelMat) / m\n\tprint('Accuracy {:.2f}%'.format(accuracy * 100))","title":""},{"docid":"5eac0ab26285d507b370f09ef2888594","score":"0.5642408","text":"def train_from_images(self, images):\n # TODO: Allow filenames to be a list of actual image objects too?\n raw_patches, raw_originals = self._random_patches_from_images(images)\n if len(raw_patches) == 0:\n raise Exception(\"No patches found, maybe your thresholds are too strict?\")\n mixture = ag.stats.BernoulliMixture(self.num_parts, raw_patches, init_seed=0)\n # Also store these in \"settings\"\n mixture.run_EM(1e-8, min_probability=self.settings['min_probability'])\n ag.info(\"Done.\")\n\n # Reject weak parts\n scores = np.empty(self.num_parts) \n for i in range(self.num_parts):\n part = mixture.templates[i]\n sh = part.shape\n p = part.reshape((sh[0]*sh[1], sh[2]))\n \n #import ipdb; ipdb.set_trace()\n pec = p.mean(axis=0)\n \n N = np.sum(p * np.log(p/pec) + (1-p)*np.log((1-p)/(1-pec)))\n D = np.sqrt(np.sum(np.log(p/(1-p))**2 * p * (1-p)))\n\n scores[i] = N/D \n\n # Only keep with a certain score\n visparts = mixture.remix(raw_originals)\n \n self.parts = mixture.templates[scores > 1]\n self.visparts = visparts[scores > 1]\n self.num_parts = self.parts.shape[0]\n \n # Update num_parts\n \n # Store the stuff in the instance\n #self.parts = mixture.templates\n #self.visparts = mixture.remix(raw_originals)\n\n self._preprocess_logs()","title":""},{"docid":"7269b0039d93fe959000a1f2230a2dce","score":"0.563852","text":"def __call__(self, *args, **kwargs):\n image = kwargs.get('image')\n class_ids, scores, _ = self.detect_objects(image, show=False)\n labels = self.get_object_labels(class_ids, scores)\n return labels","title":""},{"docid":"15a6f69e87a58c2a8d487d5289ca9805","score":"0.5634711","text":"def images_to_predicted_labels(self, network, images):\n predicted_densities, predicted_counts = network(images)\n return predicted_densities, predicted_counts","title":""},{"docid":"8426042ea9369652e4089e4776618336","score":"0.56341326","text":"def image_analysis(self, *args, **kwargs):","title":""},{"docid":"91b87ed616f51ddd9668767836d16f11","score":"0.5632717","text":"def postprocess(self, prediction_dict, true_image_shapes, **params):\n with tf.variable_scope('PostprocessorInference'):\n detections_dict = {}\n\n semantic_prediction, semantic_prediction_probability = \\\n self._postprocess_logits(\n prediction_dict['semantic_predictions'], true_image_shapes)\n detections_dict[\n fields.DetectionResultFields.detection_semantic] \\\n = semantic_prediction\n detections_dict[\n fields.DetectionResultFields.detection_semantic_heatmap] \\\n = semantic_prediction_probability\n\n instance_prediction, instance_prediction_probability = \\\n self._postprocess_logits(\n prediction_dict['instance_predictions'], true_image_shapes)\n detections_dict[\n fields.DetectionResultFields.detection_masks] \\\n = instance_prediction\n detections_dict[\n fields.DetectionResultFields.detection_masks_heatmap] \\\n = instance_prediction_probability\n\n # [Combining Instances] ************************************************\n mask_image = tf.cast(instance_prediction, dtype=tf.uint8)\n semantic_image = tf.cast(semantic_prediction, dtype=tf.uint8)\n zero_image = tf.zeros_like(mask_image, dtype=tf.uint8)\n mask_image = tf.concat([mask_image, zero_image, zero_image], axis=-1)\n semantic_image = tf.concat([zero_image, zero_image, semantic_image],\n axis=-1)\n panoptic_image = mask_image + semantic_image\n detections_dict[\n fields.DetectionResultFields.detection_masks_image] = \\\n mask_image\n detections_dict[\n fields.DetectionResultFields.detection_panoptic_image] = \\\n panoptic_image\n params = (mask_image, panoptic_image)\n self._add_summary_panoptic_image(params)\n # ************************************************ [Combining Instances]\n return detections_dict","title":""}],"string":"[\n {\n \"docid\": \"8a295023de0f3cc752c3de5964aeb74a\",\n \"score\": \"0.6684749\",\n \"text\": \"def postprocess(results, filenames, batch_size):\\n if len(results) != 1:\\n raise Exception(\\\"expected 1 result, got {}\\\".format(len(results)))\\n\\n batched_result = results[0].batch_classes\\n if len(batched_result) != batch_size:\\n raise Exception(\\\"expected {} results, got {}\\\".format(batch_size, len(batched_result)))\\n if len(filenames) != batch_size:\\n raise Exception(\\\"expected {} filenames, got {}\\\".format(batch_size, len(filenames)))\\n\\n label, score = [], []\\n # batch size is always 1 here, need to modify if were to larger batch_size\\n for (index, result) in enumerate(batched_result):\\n print(\\\"Image '{}':\\\".format(filenames[index]))\\n for cls in result.cls:\\n label.append(cls.label)\\n score += [{\\\"index\\\": cls.label, \\\"val\\\": cls.value}]\\n print(\\\" {} ({}) = {}\\\".format(cls.idx, cls.label, cls.value))\\n return label[0], score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73c23595c939de51150afd1ef98e92c8\",\n \"score\": \"0.6672096\",\n \"text\": \"def classified_images(wildcards):\\n outrule, i = classify_or_test(wildcards, return_int=True)\\n if i == 3:\\n checkpoint_output = checkpoints.create_split_truth_data.get(**wildcards).output.test\\n elif i:\\n checkpoint_output = checkpoints.create_truth_data.get(**wildcards).output[0]\\n else:\\n checkpoint_output = checkpoints.rev_transform.get(**wildcards).output[0]\\n return expand(\\n outrule.output[0],\\n sample=wildcards.sample,\\n image=glob_wildcards(\\n os.path.join(\\n checkpoint_output,\\n \\\"{image}\\\"+(\\\".tsv\\\" if i else \\\".json\\\")\\n )\\n ).image\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"314d3bc358652b13a3fb28ebe2290a18\",\n \"score\": \"0.65816724\",\n \"text\": \"def compute(self, images_true, images_pred, images_score):\\n if not (len(images_true) == len(images_pred) == len(images_score)):\\n raise ValueError('Dimensions must correspond.')\\n\\n for image_true, image_pred, image_score in itertools.izip(images_true, images_pred, images_score):\\n groundtruths = np.argwhere(np.asarray(image_true) > 0)\\n predictions = np.argwhere(np.asarray(image_pred) > 0)\\n\\n n_groundtruths = float(groundtruths.shape[0])\\n n_predictions = float(predictions.shape[0])\\n width = float(image_true.shape[0])\\n height = float(image_true.shape[1])\\n raw_count = np.sum(image_score / np.float(self._raw_factor), dtype=np.float64)\\n\\n # Sort detections by score in descending order such that the first detection\\n # linked to a ground truths is the more confident one\\n scores = image_score[tuple(predictions.T)]\\n predictions = predictions[np.argsort(scores, kind='quicksort')]\\n\\n n_tp = 0\\n if n_predictions > 0:\\n kdtree = KDTree(predictions, metric='euclidean')\\n pred_idxss, distancess = kdtree.query_radius(groundtruths, self._epsilon, return_distance=True)\\n free = np.array([True] * predictions.shape[0])\\n for pred_idxs, distances in itertools.izip(pred_idxss, distancess):\\n pred_idxs = pred_idxs[free]\\n if len(pred_idxs) > 0 and n_tp <= int(n_predictions):\\n pred_idx = pred_idxs[np.argmax(pred_idxs)]\\n free[pred_idx] = False\\n self.distance_errors.append(distances[np.argmax(pred_idxs)])\\n n_tp += 1\\n\\n self.cm.incrementTP(n_tp)\\n self.cm.incrementFP(int(n_predictions - n_tp))\\n self.cm.incrementFN(int(n_groundtruths - n_tp))\\n\\n self.count_errors.append(np.abs(n_groundtruths - n_predictions))\\n self.raw_count_errors.append(np.abs(n_groundtruths - raw_count))\\n\\n self.density_errors.append(np.abs((n_groundtruths / (width * height)) - (n_predictions / (width * height))))\\n self.raw_density_errors.append(np.abs((n_groundtruths / (width * height)) - (raw_count / (width * height))))\\n self.count += n_groundtruths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3637cb7c0f56c7e3a52c6380e2c71bc3\",\n \"score\": \"0.6534657\",\n \"text\": \"def classify(self, image_messages):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"545a58f7cb2334582c2c240b6a715500\",\n \"score\": \"0.6500155\",\n \"text\": \"def classify(self, caffemodel, deploy_file, image_files,\\n mean_file=None, labels_file=None, batch_size=None, use_gpu=True):\\n # Load the model and images\\n net = self.get_net(caffemodel, deploy_file, use_gpu)\\n transformer = self.get_transformer(deploy_file, mean_file)\\n _, channels, height, width = transformer.inputs['data']\\n if channels == 3:\\n mode = 'RGB'\\n elif channels == 1:\\n mode = 'L'\\n else:\\n raise ValueError('Invalid number for channels: %s' % channels)\\n images = [self.load_image(image_file, height, width, mode) for image_file in image_files]\\n labels = self.read_labels(labels_file)\\n\\n # Classify the image\\n scores = self.forward_pass(images, net, transformer, batch_size=batch_size)\\n\\n # Process the results\\n indices = (-scores).argsort()[:, :5] # take top 5 results\\n classifications = []\\n for image_index, index_list in enumerate(indices):\\n result = []\\n for i in index_list:\\n # 'i' is a category in labels and also an index into scores\\n if labels is None:\\n label = 'Class #%s' % i\\n else:\\n label = labels[i]\\n result.append((label, round(100.0 * scores[image_index, i], 4)))\\n classifications.append(result)\\n\\n # classifications is in the form of [('Occupied', 100.0), ('Empty', 0.0)]\\n # originally this function just printed out results. modified to send an array with the results and labels\\n # for use elsewhere in the program\\n return classifications\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfa059044b8f64e812d9da961b2ec168\",\n \"score\": \"0.6481143\",\n \"text\": \"def compare_results(input_url_images, input_url_results_labels, input_url_gt_labels, output_url, colors_gt,\\n colors_result, save, visualize):\\n data = os.listdir(input_url_gt_labels)\\n\\n for d in data:\\n file = open(os.path.join(input_url_results_labels, d))\\n file_gt = open(os.path.join(input_url_gt_labels, d))\\n\\n img = cv2.imread(os.path.join(input_url_images, d.split('.')[0] + \\\".png\\\"))\\n if img is None:\\n img = cv2.imread(os.path.join(input_url_images, d.split('.')[0] + \\\".jpg\\\"))\\n\\n img = get_labels(file_gt, img, 5, colors_gt, \\\"yolo\\\")\\n img = get_labels(file, img, 5, colors_result, \\\"det\\\")\\n\\n if visualize:\\n visualize_data(img)\\n\\n if save:\\n save_data(img, output_url, d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5220d165b6421ad63622ce02844b740f\",\n \"score\": \"0.6467733\",\n \"text\": \"def _f_image(self, images: np.ndarray):\\n out = np.zeros((len(images), 2)) # output same shape\\n\\n # inputs neeeds to be [PIL.Image.Image]; if not try to transform\\n if not isinstance(images[0], Image.Image):\\n images = utils.arr_to_img(images)\\n\\n for i, (image, text) in enumerate(zip(images, self._fixed_texts)):\\n # classify, output is a tupe (index, score)\\n ind, score = self.model.classify(image, text).values()\\n out[i][ind] = score\\n out[i][1 - ind] = 1 - score\\n\\n return out\\n # test if only output is the probability of being hateful\\n # return out[:, 1][:, np.newaxis]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d67624d11d4986b95a2c60a8d75749fc\",\n \"score\": \"0.6464209\",\n \"text\": \"def classify(self, inputs):\\n return self.predict(self.params, inputs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cebb62ab3ca63abff427e62afe3d02a9\",\n \"score\": \"0.6442733\",\n \"text\": \"def detect(self, images, verbose=0):\\n assert self.mode == \\\"inference\\\", \\\"Create model in inference mode.\\\"\\n assert len(\\n images) == self.config.BATCH_SIZE, \\\"len(images) must be equal to BATCH_SIZE\\\"\\n\\n if verbose:\\n log(\\\"Processing {} images\\\".format(len(images)))\\n for image in images:\\n log(\\\"image\\\", image)\\n\\n # Mold inputs to format expected by the neural network\\n molded_images, image_metas, windows = self.mold_inputs(images)\\n\\n # Validate image sizes\\n # All images in a batch MUST be of the same size\\n image_shape = molded_images[0].shape\\n for g in molded_images[1:]:\\n assert g.shape == image_shape, \\\\\\n \\\"After resizing, all images must have the same size. Check IMAGE_RESIZE_MODE and image sizes.\\\"\\n\\n # Anchors\\n anchors = self.get_anchors(image_shape)\\n # Duplicate across the batch dimension because Keras requires it\\n # TODO: can this be optimized to avoid duplicating the anchors?\\n anchors = np.broadcast_to(anchors, (self.config.BATCH_SIZE,) + anchors.shape)\\n\\n if verbose:\\n log(\\\"molded_images\\\", molded_images)\\n log(\\\"image_metas\\\", image_metas)\\n log(\\\"anchors\\\", anchors)\\n # Run object detection\\n detections, _, _, mrcnn_mask, _, _, _ = \\\\\\n self.keras_model.predict([molded_images, image_metas, anchors], verbose=0)\\n # Process detections\\n results = []\\n for i, image in enumerate(images):\\n final_rois, final_class_ids, final_scores, final_masks = \\\\\\n self.unmold_detections(detections[i], mrcnn_mask[i],\\n image.shape, molded_images[i].shape,\\n windows[i])\\n results.append({\\n \\\"rois\\\": final_rois,\\n \\\"class_ids\\\": final_class_ids,\\n \\\"scores\\\": final_scores,\\n \\\"masks\\\": final_masks,\\n })\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4afe0b5fd3d9d76a532ff5a493ede9eb\",\n \"score\": \"0.6397793\",\n \"text\": \"def classify(test_Q1, test_Q2, y, threshold, model, vocab, data_generator=data_generator, batch_size=64):\\n accuracy = 0\\n ### START CODE HERE (Replace instances of 'None' with your code) ###\\n for i in range(0, len(test_Q1), batch_size):\\n # Call the data generator (built in Ex 01) with shuffle=False using next()\\n # use batch size chuncks of questions as Q1 & Q2 arguments of the data generator. e.g x[i:i + batch_size]\\n # Hint: use `vocab['<PAD>']` for the `pad` argument of the data generator\\n q1, q2 = next(data_generator(\\n test_Q1[i:i + batch_size], test_Q2[i:i + batch_size], batch_size, vocab[\\\"<PAD>\\\"], shuffle=False))\\n # use batch size chuncks of actual output targets (same syntax as example above)\\n y_test = y[i:i + batch_size]\\n # Call the model\\n v1, v2 = model((q1, q2))\\n\\n for j in range(batch_size):\\n # take dot product to compute cos similarity of each pair of entries, v1[j], v2[j]\\n # don't forget to transpose the second argument\\n d = fastnp.dot(v1[j], v2[j].T)\\n # is d greater than the threshold?\\n res = d > threshold\\n # increment accurancy if y_test is equal `res`\\n accuracy += (y_test[j] == res)\\n # compute accuracy using accuracy and total length of test questions\\n accuracy = accuracy / len(test_Q1)\\n ### END CODE HERE ###\\n\\n return accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e710c78d9059839bc92198551226f4df\",\n \"score\": \"0.63741803\",\n \"text\": \"def _check_images(self, abs_output_directory):\\n # Make output directory/directories\\n if len(set(self.class_labels)) <= 1:\\n if not os.path.exists(abs_output_directory):\\n try:\\n os.makedirs(abs_output_directory)\\n except IOError:\\n logging.error(\\\"Insufficient rights to read or write output directory ({})\\\"\\n .format(abs_output_directory))\\n else:\\n for class_label in self.class_labels:\\n if not os.path.exists(os.path.join(abs_output_directory, str(class_label[0]))):\\n try:\\n os.makedirs(os.path.join(abs_output_directory, str(class_label[0])))\\n except IOError:\\n logging.error(\\\"Insufficient rights to read or write output directory ({})\\\"\\n .format(abs_output_directory))\\n # Check the images, read their dimensions, and remove them if they cannot be read\\n for augmentor_image in self.augmentor_images:\\n try:\\n opened_image = np.load(augmentor_image.image_path)\\n self.distinct_dimensions.add(opened_image.shape)\\n except IOError as e:\\n logging.error(\\\"There is a problem with image {} in the source directory: {}\\\"\\n .format(augmentor_image.image_path, e.message))\\n self.augmentor_images.remove(augmentor_image)\\n\\n logging.info(\\\"Initialised with {} image(s) found.\\\\n\\\".format(len(self.augmentor_images)))\\n logging.info(\\\"Output directory set to {}.\\\".format(abs_output_directory))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea3f8f306170c37feb4fc50130240941\",\n \"score\": \"0.6322083\",\n \"text\": \"def test_network_monger(dirs, image_dims):\\r\\n start_time = time.time()\\r\\n read_write_time = 0.0\\r\\n n_classes = len(dirs) # assumes number of classes is the same as number of input directories\\r\\n master_list_encoded = []\\r\\n for i in range(len(dirs)):\\r\\n imagenames = return_images(dirs[i])\\r\\n image_encoded = list(zip(imagenames, [i] * len(imagenames)))\\r\\n master_list_encoded += image_encoded\\r\\n total_num_images = len(master_list_encoded)\\r\\n all_image_data = read_images(master_list_encoded, image_dims)\\r\\n image_indices = list(range(total_num_images))\\r\\n px_values, true_classes = batch_create(all_image_data, image_indices, image_dims, n_classes) # see batch_create\\r\\n input_dict = {image_data: px_values, true_class: true_classes,\\r\\n im_x: image_dims[0], im_y: image_dims[1], im_z: image_dims[2],\\r\\n num_classes: n_classes, keep_prob: 1.0}\\r\\n read_write_time = read_write_time + time.time() - start_time\\r\\n run_time_start = time.time()\\r\\n \\\"\\\"\\\"Now, the network isn't being optimized, so instead the calculated raw class values and binary class\\r\\n predictions are returned for all the test images\\\"\\\"\\\"\\r\\n decimal_classes, pred_classes = session.run([calculated_value, predicted_class], feed_dict=input_dict)\\r\\n run_time = time.time() - run_time_start\\r\\n current_time = time.time()\\r\\n pred_classes = np.ravel(pred_classes) # munge munge munge\\r\\n pred_classes = pred_classes.tolist() # munge munge munge munge munge\\r\\n decimal_classes.tolist() # munge munge munge munge munge munge munge munge munge munge\\r\\n all_test_classes = list(all_image_data[:, 0].astype(int))\\r\\n correct_predictions = [i == j for i, j in zip(all_test_classes, pred_classes)] # boolean of correct predictions\\r\\n lists = [all_test_classes, pred_classes, decimal_classes]\\r\\n names = ['true', 'pred', 'raw']\\r\\n for i in range(3):\\r\\n \\\"\\\"\\\"see readme for description of these output files\\\"\\\"\\\"\\r\\n file = open('out'+names[i]+'.txt', 'w')\\r\\n for line in lists[i]:\\r\\n file.write(str(line)+' \\\\n')\\r\\n file.close()\\r\\n total_correct_predictions = sum(correct_predictions) # total number of correct test predictions\\r\\n accuracy = round((float(total_correct_predictions) / float(total_num_images))*100, 2)\\r\\n read_write_time = read_write_time + time.time() - current_time\\r\\n return read_write_time, run_time, accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fbf94146eae5aa78b71322062f1f1b7\",\n \"score\": \"0.6163668\",\n \"text\": \"def _input_format_classification(preds: torch.Tensor, target: torch.Tensor, threshold: float):\\n if not (len(preds.shape) == len(target.shape) or len(preds.shape) == len(target.shape) + 1):\\n raise ValueError(\\n \\\"preds and target must have same number of dimensions, or one additional dimension for preds\\\"\\n )\\n\\n if len(preds.shape) == len(target.shape) + 1:\\n # multi class probabilites\\n preds = torch.argmax(preds, dim=1)\\n\\n if len(preds.shape) == len(target.shape) and preds.dtype == torch.float:\\n # binary or multilabel probablities\\n preds = (preds >= threshold).long()\\n return preds, target\",\n \"title\": \"\"\n },\n {\n \"docid\": \"887fec5b52e14b5025918edd69b8be70\",\n \"score\": \"0.6161768\",\n \"text\": \"def step_fn(inputs):\\n images, labels = inputs\\n logits = self.model(images, training=False)\\n\\n if self.test_loss:\\n if self.one_hot:\\n loss = tf.keras.losses.categorical_crossentropy(\\n labels, logits, label_smoothing=self.label_smoothing)\\n else:\\n loss = tf.keras.losses.sparse_categorical_crossentropy(labels, logits)\\n loss = tf.reduce_sum(loss) * (1.0 / self.flags_obj.batch_size)\\n self.test_loss.update_state(loss)\\n\\n if self.test_accuracy:\\n self.test_accuracy.update_state(labels, logits)\\n # tf.print('labels.shape: ', labels.shape,\\n # ', logits.shape: ', logits.shape,\\n # ', result: ', self.test_accuracy.result())\\n # self.test_corrects.update_state(\\n # tf.cast(\\n # tf.reduce_sum(\\n # tf.cast(\\n # tf.equal(\\n # tf.cast(tf.argmax(logits, axis=1), labels.dtype),\\n # labels), tf.int32)), tf.float32))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3323a1edd08d744a074e45eb6bec075f\",\n \"score\": \"0.61310375\",\n \"text\": \"def test_attack(self, adv_images, orig_images, true_labels,\\n target_labels=None, targeted=False):\\n score = self.model.evaluate(adv_images, true_labels, verbose=0)\\n print('Test loss: {:.2f}'.format(score[0]))\\n print('Successfully moved out of source class: {:.2f}'.format(\\n 1 - score[1]))\\n\\n if targeted:\\n score = self.model.evaluate(adv_images, target_labels, verbose=0)\\n print('Test loss: {:.2f}'.format(score[0]))\\n print('Successfully perturbed to target class: {:.2f}'.format(\\n score[1]))\\n\\n dist = np.mean(np.sqrt(\\n np.mean(np.square(adv_images - orig_images), axis=(1, 2, 3))))\\n print('Mean perturbation distance: {:.2f}'.format(dist))\\n return score[0], 1 - score[1], dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84897f663417004410fb56682d96d400\",\n \"score\": \"0.61264914\",\n \"text\": \"def main():\\n os.chdir(os.path.dirname(__file__))\\n cur_dir=os.getcwd()\\n \\n training_data_path_ants=cur_dir+\\\"\\\\\\\\ant\\\"\\n training_data_path_lobsters=cur_dir+\\\"\\\\\\\\lobster\\\"\\n test_data_path_unknown=cur_dir+\\\"\\\\\\\\test\\\";\\n \\n processor=ImageProcessor();\\n train_X1=processor.fetch_all_jpg_files_as_numpy_array(training_data_path_ants);\\\"\\\"\\\" pictures of ants--class 0\\\"\\\"\\\"\\n train_X2=processor.fetch_all_jpg_files_as_numpy_array(training_data_path_lobsters);\\\"\\\"\\\" pictures of lobsters--class 1\\\"\\\"\\\"\\n \\n train_Y1=[];\\n train_Y2=[];\\n \\n for image in train_X1:\\n train_Y1.append(0);\\\"\\\"\\\"class 0\\\"\\\"\\\"\\n for image in train_X2:\\n train_Y2.append(1);\\\"\\\"\\\"class 1\\\"\\\"\\\"\\n \\n train_Y=train_Y1+train_Y2;\\\"\\\"\\\"all class labels--> Y\\\"\\\"\\\"\\n train_X_temp=train_X1+train_X2; \\\"\\\"\\\"all training data--variables X\\\"\\\"\\\"\\n \\n \\\"\\\"\\\"********************************************\\n our training set is ready now: \\n let us have a test data to find if the test image is ant or lobster.\\\"\\\"\\\"\\n \\n \\n \\\"\\\"\\\"pad zeros at the end\\\"\\\"\\\"\\n \\n train_X=processor.pad_list(train_X_temp)\\n \\n dim1=int(math.sqrt(len(train_X[0])/3))\\n dim2=dim1\\n test_X=processor.fetch_with_resize(test_data_path_unknown,dim1,dim2)\\n test_X=test_X[0].tolist() \\n \\\"\\\"\\\" set up the deep network with 1 hidden layer defined above\\\"\\\"\\\"\\n \\n deep_network=DeepNet(train_X,train_Y)\\n \\n deep_network.train()\\n output=deep_network.predict(test_X)\\n \\n if(output<0.5):\\n print (\\\"ant\\\")\\n else:\\n print (\\\"lobster\\\")\\n \\n print(\\\"The end\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faab65fc3e395f22ee9d04e2296a64fb\",\n \"score\": \"0.61191386\",\n \"text\": \"def apply_preprocessing(self):\\n # Images preprocessing\\n preparator = ImagePreparation(\\n self.images_paths, self.args['model_type']\\n )\\n images = preparator.apply_basic_processing()\\n\\n # Labels Encoding\\n encoder = LabelClassifier(self.labels)\\n labels_cat, label_classes = encoder.get_categorical_labels()\\n\\n return images, labels_cat, label_classes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3ca84d4f190cbaeceaa2d22f46d443\",\n \"score\": \"0.6116314\",\n \"text\": \"def _classifyImages(self):\\n processor = ImageProcessor(self._filePath)\\n labels = processor.classifyImages()\\n if(len(labels) == 0):\\n raise ValueError(\\\"There are no images in that folder\\\")\\n return labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a08421180cd0f65d5228bc4584f5938\",\n \"score\": \"0.61113167\",\n \"text\": \"def categorize(self, protos):\\n corpus = self.imagefeed.feed_location\\n mask = ChooseTrainingSet(get_labels(corpus=self.imagefeed.feed_location), train_size=0.5)\\n categories = classify_images(protos, get_images(corpus, mask=mask), \\n get_labels(corpus, mask=mask), get_images(corpus, mask=~mask), corpus)\\n reset_directory(self.sorted_location, self.imagefeed.image_location)\\n self.imagefeed.move_images(categories, self.sorted_location)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed7d97656d6a3cf21340b81790c5e062\",\n \"score\": \"0.6108216\",\n \"text\": \"def classify_jpegs():\\n args = sys.argv\\n\\n # ensure we have the right number of arguments\\n if not len(args) == 2:\\n print 'Usage: %s [directory]' % (args[0])\\n print 'Classifies each JPEG image in the given directory.'\\n return\\n\\n classifier = classify.read_classifier()\\n\\n directory = args[1]\\n old_cwd = os.getcwd()\\n os.chdir(directory)\\n\\n # find all images in directory\\n image_paths = glob.glob('*.jpg') + glob.glob('*.jpeg') + glob.glob('*.png')\\n\\n # read and classify each image\\n for image_path in image_paths:\\n image = cv2.imread(image_path)\\n features = classify.compute_features(image, preprocess=True)\\n\\n prediction = classifier.predict(features)[0]\\n print image_path, chr(prediction)\\n\\n os.chdir(old_cwd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ba4591a43635749242407f02c19cbcf\",\n \"score\": \"0.61065733\",\n \"text\": \"def detection_as_classification(model, test_generator):\\n i = 0\\n TP = 0\\n FP = 0\\n \\n for X,Y in test_generator:\\n if i >= len(test_generator):\\n break # otherwise will run indefinitely\\n X = rgb2bgr(X)\\n X = preprocess_image(X)\\n boxes, scores, labels = model.predict_on_batch(X)\\n tp, fp = evaluate(filter(scores, labels, score_threshold), Y)\\n i += 1\\n TP += tp\\n FP += fp\\n\\n return TP, FP\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6165e3379545522b2a36fc331be8a2d1\",\n \"score\": \"0.61028713\",\n \"text\": \"def training(\\n positive_iis, \\n negative_iis, \\n num_classifiers, \\n ns, \\n Ddesired=0.9, \\n Edesired = 0.05\\n ):\\n logger = getLogger('Train')\\n \\n num_pos = len(positive_iis)\\n num_neg = len(negative_iis)\\n num_imgs = num_pos + num_neg\\n img_height, img_width = positive_iis[0].shape\\n \\n pos_weights = np.ones(num_pos) * 1. / (2 * num_pos)\\n neg_weights = np.ones(num_neg) * 1. / (2 * num_neg)\\n weights = np.hstack((pos_weights, neg_weights))\\n labels = np.hstack((np.ones(num_pos), -np.ones(num_neg)))\\n \\n images = positive_iis + negative_iis\\n \\n logger.info('Stage 1: Creating features')\\n features = create_dynamic_features(\\n (img_width, img_height), \\n ns,\\n 0\\n )\\n num_features = len(features)\\n num_classifiers = num_features if num_classifiers == -1 else num_classifiers\\n \\n logger.info('Generated features: {0}'.format(num_features))\\n logger.info('Stage 2: Calculating thresholds')\\n \\n bar = ProgressBar()\\n for i in bar(range(num_features)):\\n feature = features[i]\\n scores = np.fromiter(\\n (feature.get_score(img) for img in images), \\n np.float\\n )\\n feature.threshold = get_appropriet_threshold(\\n scores[:num_pos], \\n scores[num_pos:], \\n Ddesired, \\n Edesired\\n )\\n \\n logger.info('Stage 3: Calculating scores for all images')\\n votes = np.zeros((num_imgs, num_features))\\n bar = ProgressBar()\\n # Use as many workers as there are CPUs\\n pool = Pool(processes=None)\\n for i in bar(range(num_imgs)):\\n features_matrix = np.array( \\n list(\\n pool.map(\\n #map( \\n partial(_get_feature_values, image=images[i]), \\n features\\n )))\\n # Convert matrix to vector\\n votes[i, :] = features_matrix\\n \\n logger.info('Stage 4: Selecting classifiers')\\n classifiers = list()\\n feature_indexes = list(range(num_features))\\n bar = ProgressBar()\\n for _ in bar(range(num_classifiers)):\\n\\n classification_errors = np.zeros(len(feature_indexes))\\n # normalize weights\\n weights *= 1. / np.sum(weights)\\n\\n # select best classifier based on the weighted error\\n for f in range(len(feature_indexes)):\\n f_idx = feature_indexes[f]\\n # classifier error is the sum of image weights where the classifier\\n # is right\\n error = sum( map(\\n lambda img_idx: weights[img_idx] if labels[img_idx] != votes[img_idx, f_idx] else 0, \\n range(num_imgs)\\n ))\\n classification_errors[f] = error\\n\\n # get best feature, i.e. with smallest error\\n min_error_idx = np.argmin(classification_errors)\\n best_error = classification_errors[min_error_idx]\\n best_feature_idx = feature_indexes[min_error_idx]\\n\\n # set feature weight\\n best_feature = features[best_feature_idx]\\n eb_eq = (1-best_error)/best_error\\n feature_weight = 0.5 * np.log((1 - best_error) / best_error)\\n best_feature.weight = feature_weight\\n\\n classifiers.append(best_feature)\\n \\n weights = np.array(\\n list(\\n map(\\n lambda img_idx: weights[img_idx] * np.sqrt(eb_eq if (labels[img_idx] != votes[img_idx, best_feature_idx]) else 1/eb_eq),\\n range(num_imgs)\\n )\\n )\\n )\\n \\n feature_indexes.remove(best_feature_idx)\\n \\n logger.info(\\n 'Training finished. {0} week classifiers formed'.format(len(classifiers))\\n )\\n return classifiers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d5339fbadddabf29188f5f26d8daabd\",\n \"score\": \"0.6089755\",\n \"text\": \"def process_batch(self, detections, labels):\\n detections = detections[detections[:, 4] > self.conf]\\n gt_classes = labels[:, 0].int()\\n detection_classes = detections[:, 5].int()\\n iou = box_iou(labels[:, 1:], detections[:, :4])\\n\\n x = torch.where(iou > self.iou_thres)\\n if x[0].shape[0]:\\n matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()\\n if x[0].shape[0] > 1:\\n matches = matches[matches[:, 2].argsort()[::-1]]\\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\\n matches = matches[matches[:, 2].argsort()[::-1]]\\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\\n else:\\n matches = np.zeros((0, 3))\\n\\n n = matches.shape[0] > 0\\n m0, m1, _ = matches.transpose().astype(int)\\n for i, gc in enumerate(gt_classes):\\n j = m0 == i\\n if n and sum(j) == 1:\\n self.matrix[detection_classes[m1[j]], gc] += 1 # correct\\n else:\\n self.matrix[self.nc, gc] += 1 # background FP\\n\\n if n:\\n for i, dc in enumerate(detection_classes):\\n if not any(m1 == i):\\n self.matrix[dc, self.nc] += 1 # background FN\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bd5c7e71502f132bb8fdadbf9e2ba93\",\n \"score\": \"0.6076005\",\n \"text\": \"def inference(self, batch_inputs, images, pred_centers, pred_regions, pred_weights, encode_feat):\\n results = batch_inputs\\n processed_results = []\\n for img_idx, result_img in enumerate(results):\\n if \\\"instances\\\" in result_img.keys():\\n img_shape = result_img[\\\"instances\\\"].image_size\\n else:\\n img_shape = result_img[\\\"image\\\"].shape[-2:]\\n ori_shape = (result_img[\\\"height\\\"], result_img[\\\"width\\\"])\\n encode_feat = encode_feat[img_idx].unsqueeze(0)\\n feat_shape = encode_feat.shape\\n encode_feat = encode_feat.reshape(*feat_shape[:2], -1)\\n result_instance = None\\n\\n pred_regions = [_pred[img_idx].unsqueeze(0) for _pred in pred_regions]\\n pred_weights = [_pred[img_idx].unsqueeze(0) for _pred in pred_weights]\\n pred_centers = [_pred[img_idx].unsqueeze(0) for _pred in pred_centers]\\n pool_size = [3,3,3,5,5]\\n idx_feat_th, class_ths, score_ths, thing_num, idx_feat_st, score_sts, class_sts, stuff_num = \\\\\\n multi_apply(self.inference_single_level, pred_centers,\\\\\\n pred_regions, pred_weights, pool_size)\\n \\n thing_num = sum(thing_num)\\n if thing_num == 0:\\n result_instance = Instances(ori_shape, pred_masks=[], pred_boxes=[], \\n pred_classes=[], scores=[])\\n else:\\n class_ths = [_class for _class in class_ths if len(_class)>0]\\n score_ths = [_score for _score in score_ths if len(_score)>0]\\n idx_feat_th = [_feat for _feat in idx_feat_th if len(_feat)>0]\\n class_ths = torch.cat(class_ths, dim=0)\\n score_ths = torch.cat(score_ths, dim=0)\\n idx_feat_th = torch.cat(idx_feat_th, dim=2)\\n keep = torch.argsort(score_ths, descending=True)\\n idx_feat_th = idx_feat_th[:,:,keep]\\n score_ths = score_ths[keep]\\n class_ths = class_ths[keep]\\n\\n stuff_num = sum(stuff_num)\\n if stuff_num == 0:\\n class_sts, idx_feat_st, score_sts = [], [], []\\n else:\\n score_sts = [_score for _score in score_sts if len(_score)>0]\\n class_sts = [_cate_sem for _cate_sem in class_sts if len(_cate_sem)>0]\\n idx_feat_st = [_feat for _feat in idx_feat_st if len(_feat)>0]\\n score_sts = torch.cat(score_sts, dim=0)\\n class_sts = torch.cat(class_sts, dim=0)\\n idx_feat_st = torch.cat(idx_feat_st, dim=0)\\n\\n pred_thing, [class_ths, score_ths] = self.thing_generator(encode_feat, feat_shape, idx_feat_th, thing_num, class_ths, score_ths)\\n pred_stuff, [class_sts, score_sts] = self.stuff_generator(encode_feat, feat_shape, idx_feat_st, stuff_num, class_sts, score_sts)\\n pred_stuff = pred_stuff.sigmoid()\\n \\n if result_instance is None:\\n result_instance, pred_mask, class_ths, score_ths = self.process_inst(\\n class_ths, score_ths, pred_thing, img_shape, ori_shape) \\n else:\\n pred_mask, class_ths, score_ths = None, None, None\\n if self.sem_with_thing or self.cfg.MODEL.POSITION_HEAD.STUFF.ALL_CLASSES:\\n sem_classes = self.sem_classes\\n else:\\n sem_classes = self.sem_classes + 1\\n\\n pred_stuff = F.interpolate(pred_stuff, scale_factor=self.common_stride, mode=\\\"bilinear\\\", \\n align_corners=False)[...,:img_shape[0],:img_shape[1]]\\n pred_stuff = F.interpolate(pred_stuff, size=ori_shape, mode=\\\"bilinear\\\", align_corners=False)[0]\\n pred_sem_seg = torch.zeros(sem_classes, *pred_stuff.shape[-2:], device=self.device)\\n pred_sem_seg[class_sts] += pred_stuff\\n processed_results.append({\\\"sem_seg\\\": pred_sem_seg, \\\"instances\\\": result_instance})\\n\\n if self.panoptic_combine:\\n result_panoptic = self.combine_thing_and_stuff(\\n [pred_mask, class_ths, score_ths],\\n pred_sem_seg.argmax(dim=0),\\n self.panoptic_overlap_thrs,\\n self.panoptic_stuff_limit,\\n self.panoptic_inst_thrs)\\n processed_results[-1][\\\"panoptic_seg\\\"] = result_panoptic\\n\\n return processed_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d8e18c099a271baf52f0e63c258c43e\",\n \"score\": \"0.60603863\",\n \"text\": \"def classify_images(images_dir,petlabel_dic,model):\\n #classifier()\\n images_dir_filename = listdir(images_dir)\\n #model_petlabel_dic = dict()\\n results_dic = dict()\\n # for block to pass values into classifer method and get model label\\n # model lable will be added to a dictionary\\n for key in petlabel_dic:\\n file_name = images_dir+key\\n # print(file_name)\\n model_label = classifier(file_name,model)\\n #print(model_label)\\n model_label = model_label.lower()\\n model_label = model_label.strip()\\n truth = petlabel_dic[key]\\n found = model_label.find(truth)\\n if found>=0:\\n if( (found ==0 and len(truth)==len(model_label) ) or\\n ( ( (found==0) or (model_label[found-1]==\\\" \\\") ) and\\n ( (found + len(truth)==len(model_label)) or\\n (model_label[found + len(truth):found+len(truth)+1] in\\n (\\\",\\\",\\\" \\\") )\\n )\\n )\\n ):\\n if key not in results_dic:\\n results_dic[key] = [truth,model_label,1]\\n\\n else:\\n if key not in result_dic:\\n results_dic[key] = [truth,model_label,0]\\n else:\\n if key not in results_dic:\\n results_dic[key] = [truth,model_label,0]\\n return(results_dic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bacf24a7bca36483f1bd00eee184f5a2\",\n \"score\": \"0.605324\",\n \"text\": \"def imageClassify(net, image_name):\\n\\n # Load the image uploaded by user..\\n im_file = os.path.join('./assets', image_name)\\n im = cv2.imread(im_file)\\n\\n # Detect all object classes and regress object bounds\\n timer = Timer()\\n timer.tic()\\n scores, boxes = im_detect(net, im)\\n timer.toc()\\n print ('Detection took {:.3f}s for '\\n '{:d} object proposals').format(timer.total_time, boxes.shape[0])\\n\\n # Visualize detections for each class\\n CONF_THRESH = 0.8\\n NMS_THRESH = 0.3\\n fw = open(im_file+\\\".txt\\\", 'w')\\n fw.write(\\\"{\\\\\\\"objects\\\\\\\": [\\\\n\\\")\\n for cls_ind, cls in enumerate(CLASSES[1:]):\\n cls_ind += 1 # because we skipped background\\n cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\\n cls_scores = scores[:, cls_ind]\\n dets = np.hstack((cls_boxes,\\n cls_scores[:, np.newaxis])).astype(np.float32)\\n keep = nms(dets, NMS_THRESH)\\n dets = dets[keep, :]\\n# fw.write(\\\"{extent:[\\\"+str(bbox[0])+\\\",\\\"+str(bbox[1])+','+str(bbox[2])+\\\",\\\"+str(bbox[3])+\\\"],\\\\n\\\")\\n# fw.write(\\\"class:\\\" + class_name + \\\",\\\\n\\\")\\n# fw.write(\\\"score:\\\" + score + \\\"},\\\\n\\\")\\n\\n vis_detections(im, cls, dets, im_file, fw, thresh=CONF_THRESH)\\n fw.write(\\\"{}]}\\\")\\n fw.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9fcde7c822832a4adc922842d561494\",\n \"score\": \"0.60296136\",\n \"text\": \"def prepare_images(self):\\n\\n self.model.resize_data(Structure.CategoricalNeuralNetwork, Environment.TRAIN)\\n self.model.resize_data(Structure.CategoricalNeuralNetwork, Environment.TEST)\\n\\n n_classes = self.model.convert_to_one_hot_data()\\n\\n return n_classes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c111a0ba9e1382e7af399588679d4d7\",\n \"score\": \"0.60258794\",\n \"text\": \"def run_classifier(self, sets=[\\\"train\\\", \\\"valid\\\"], class_threshold=0, preprocess_func=None, label_func=None, pwds=None, classify_func=None, **kwargs):\\n\\n print(\\\"\\\\nRunning Classifier:\\\", self.name)\\n\\n for data_set in sets:\\n\\n assert data_set in self.dataset, \\\"%s not in dataset\\\" % data_set\\n print(\\\"Currently classifying {} with {} datapoints\\\".format(data_set, len(self.dataset[data_set][\\\"data\\\"])))\\n\\n preds = []\\n\\n data = self.dataset[data_set][\\\"data\\\"]\\n\\n self.dataset[data_set][\\\"matches\\\"] = []\\n self.dataset[data_set][\\\"scores\\\"] = []\\n\\n for datum in data:\\n capture_scores = defaultdict(int)\\n captures = {}\\n class_matches = {}\\n\\n #Forced to do this because self.regexes stored as {\\\"class\\\": [Regex Objects list]}\\n for class_name in self.regexes:\\n\\n matches = {}\\n #Ask handler to get capture_scores, captures, and matches\\n if len(self.regexes[class_name]) > 0:\\n matches, captures, capture_scores = self.handler.score_data(datum, self.regexes[class_name],\\n pwds=pwds, preprocess_func=preprocess_func,\\n capture_convert=label_func)\\n #Storing matches in object\\n class_matches[class_name] = matches\\n\\n #Adding biases\\n for bias in self.capture_biases:\\n capture_scores[bias] += self.capture_biases[bias]\\n\\n self.dataset[data_set][\\\"matches\\\"].append(class_matches)\\n self.dataset[data_set][\\\"scores\\\"].append(capture_scores)\\n\\n #getting prediction\\n\\n if not classify_func:\\n preds.append(self.classify(capture_scores, threshold=class_threshold)[0])\\n else:\\n preds.append(classify_func(class_matches, captures, capture_scores, **kwargs))\\n\\n preds = np.array(preds)\\n self.dataset[data_set][\\\"preds\\\"] = preds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8fb745ccba717175f00be5ca6dada34\",\n \"score\": \"0.60210526\",\n \"text\": \"def _predict_images(self, images):\\n img_transform = transforms.Compose([\\n transforms.Resize((197, 197)),\\n transforms.ToTensor(),\\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\\n ])\\n \\n\\n reconstructed_image_tensor_array = []\\n for (image, shape) in zip(images['image'], images['shape']):\\n imagebytes = bytearray(image, encoding=\\\"utf8\\\")\\n deserialized_bytes = base64.decodebytes(imagebytes)\\n deserialized_bytes = np.frombuffer(deserialized_bytes, dtype=np.uint8)\\n img_nparray = np.reshape(deserialized_bytes, newshape=shape)\\n img = PIL.Image.fromarray(np.uint8(img_nparray), mode=\\\"RGB\\\")\\n imgTensor = img_transform(img)\\n reconstructed_image_tensor_array.append(imgTensor)\\n \\n reconstructed_image_tensor = torch.stack(reconstructed_image_tensor_array, dim=0)\\n with torch.no_grad():\\n output = self._model(reconstructed_image_tensor).squeeze(-1).squeeze(-1)\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ef940ab70e617f83d12c0bfd7e0239e\",\n \"score\": \"0.6019942\",\n \"text\": \"def run_images(self):\\n car_list_train, car_list_test, noncar_list_train, noncar_list_test = self.read_data()\\n X_train, X_test, y_train, y_test, self.scaler = self.get_datasets(car_list_train, car_list_test,\\n noncar_list_train, noncar_list_test)\\n self.classifier = self.train_classifier(X_train, y_train, X_test, y_test)\\n\\n tests = glob.glob('./test_images/*.jpg')\\n fig = plt.figure()\\n idx = 1\\n for img in tests:\\n test_img = cv2.imread(img)\\n t= time.time()\\n self.heatmap = []\\n img=self.find_cars(test_img, return_image=True)\\n print('Time prediction: ', time.time()-t)\\n fig.add_subplot(len(tests), 2, idx)\\n idx+=1\\n plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\\n plt.axis('off')\\n fig.add_subplot(len(tests), 2, idx)\\n idx+=1\\n plt.imshow(self.heatmap[-1], cmap='hot')\\n plt.axis('off')\\n plt.subplots_adjust(left=0.28, bottom=0.01, right=0.69, top=0.98, wspace=0.03, hspace=0.03)\\n plt.savefig('./output_images/test_heatmap.png', dpi=400)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f51986f12154b7f7f018265c5c9f3d4\",\n \"score\": \"0.60119885\",\n \"text\": \"def test(self):\\n\\n\\t\\ttotaltime = 0\\n\\t\\tfiles = 0\\n\\n\\t\\ttp = 0\\n\\t\\tfp = 0\\n\\t\\ttn = 0\\n\\t\\tfn = 0\\n\\n\\t\\tself.addr = \\\"http://\\\" + self.helpers.credentials[\\\"server\\\"][\\\"ip\\\"] + \\\\\\n\\t\\t\\t':'+str(self.helpers.credentials[\\\"server\\\"][\\\"port\\\"]) + '/Inference'\\n\\t\\tself.headers = {'content-type': 'image/jpeg'}\\n\\n\\t\\tfor testFile in os.listdir(self.model.testing_dir):\\n\\t\\t\\tif os.path.splitext(testFile)[1] in self.model.valid:\\n\\n\\t\\t\\t\\tstart = time.time()\\n\\t\\t\\t\\tprediction = self.request(self.model.testing_dir + \\\"/\\\" + testFile)\\n\\t\\t\\t\\tprint(prediction)\\n\\t\\t\\t\\tend = time.time()\\n\\t\\t\\t\\tbenchmark = end - start\\n\\t\\t\\t\\ttotaltime += benchmark\\n\\n\\t\\t\\t\\tmsg = \\\"\\\"\\n\\t\\t\\t\\tstatus = \\\"\\\"\\n\\t\\t\\t\\toutcome = \\\"\\\"\\n\\n\\t\\t\\t\\tif prediction[\\\"Diagnosis\\\"] == \\\"Positive\\\" and \\\"Non-Covid\\\" in testFile:\\n\\t\\t\\t\\t\\tfp += 1\\n\\t\\t\\t\\t\\tstatus = \\\"incorrectly\\\"\\n\\t\\t\\t\\t\\toutcome = \\\"(False Positive)\\\"\\n\\t\\t\\t\\telif prediction[\\\"Diagnosis\\\"] == \\\"Negative\\\" and \\\"Non-Covid\\\" in testFile:\\n\\t\\t\\t\\t\\ttn += 1\\n\\t\\t\\t\\t\\tstatus = \\\"correctly\\\"\\n\\t\\t\\t\\t\\toutcome = \\\"(True Negative)\\\"\\n\\t\\t\\t\\telif prediction[\\\"Diagnosis\\\"] == \\\"Positive\\\" and \\\"Covid\\\" in testFile:\\n\\t\\t\\t\\t\\ttp += 1\\n\\t\\t\\t\\t\\tstatus = \\\"correctly\\\"\\n\\t\\t\\t\\t\\toutcome = \\\"(True Positive)\\\"\\n\\t\\t\\t\\telif prediction[\\\"Diagnosis\\\"] == \\\"Negative\\\" and \\\"Covid\\\" in testFile:\\n\\t\\t\\t\\t\\tfn += 1\\n\\t\\t\\t\\t\\tstatus = \\\"incorrectly\\\"\\n\\t\\t\\t\\t\\toutcome = \\\"(False Negative)\\\"\\n\\n\\t\\t\\t\\tfiles += 1\\n\\t\\t\\t\\tself.helpers.logger.info(\\\"COVID-19 xDNN Classifier \\\" + status +\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\\" detected \\\" + outcome + \\\" in \\\" + str(benchmark) + \\\" seconds.\\\")\\n\\n\\t\\tself.helpers.logger.info(\\\"Images Classified: \\\" + str(files))\\n\\t\\tself.helpers.logger.info(\\\"True Positives: \\\" + str(tp))\\n\\t\\tself.helpers.logger.info(\\\"False Positives: \\\" + str(fp))\\n\\t\\tself.helpers.logger.info(\\\"True Negatives: \\\" + str(tn))\\n\\t\\tself.helpers.logger.info(\\\"False Negatives: \\\" + str(fn))\\n\\t\\tself.helpers.logger.info(\\\"Total Time Taken: \\\" + str(totaltime))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a49817fea99179942d072738408b915\",\n \"score\": \"0.60117257\",\n \"text\": \"def classify(image: Img, classifier: callable):\\n\\treturn classifier(image.arr1d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9497acef7cfac114ef930578ebafe53d\",\n \"score\": \"0.6011085\",\n \"text\": \"def predict(self, instances, **kwargs):\\n imgs = map(self.decode_img, instances)\\n inputs = np.asarray(imgs)\\n outputs = self._model.predict(imgs)\\n\\n # If outputs are not in same order as inputs, consider that TF likely\\n # runs things in parallel for multi-input???\\n\\n return [self.threshold(output) for output in outputs]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aea73709799f838a213d8a1cee919e6\",\n \"score\": \"0.5998706\",\n \"text\": \"def generate_images_pred(self, inputs, outputs):\\n for scale in self.opt.scales:\\n disp = outputs[(\\\"disp\\\", scale)]\\n disp = F.interpolate(disp, [self.opt.height, self.opt.width], mode=\\\"bilinear\\\", align_corners=False)\\n _, depth = disp_to_depth(disp, self.opt.min_depth, self.opt.max_depth)\\n outputs[(\\\"depth\\\", scale)] = depth * 5.4\\n\\n # Compute Reprojection loss\\n occmaskrec = dict()\\n for k in inputs['imgr'].keys():\\n if k == 0:\\n continue\\n occmaskrec[k] = (self.sod(inputs['intrinsic'], inputs['poser'][k], outputs[(\\\"depth\\\", 0)], float(1e10)) == 0).float()\\n outputs['occmaskrec'] = occmaskrec\\n\\n # Compute Reconstruction image\\n for scale in self.opt.scales:\\n outputs['reconImgs', scale] = self.reconimg(inputs['imgr'], outputs[(\\\"depth\\\", scale)], inputs['intrinsic'], inputs['poser'])\\n\\n return outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59ed4bd69fa8387dba2c91593270cde1\",\n \"score\": \"0.5981367\",\n \"text\": \"def matching_batch(\\n iou_fn: Callable[[np.ndarray, np.ndarray], np.ndarray], \\n iou_thresholds: Sequence[float], pred_boxes: Sequence[np.ndarray],\\n pred_classes: Sequence[np.ndarray], pred_scores: Sequence[np.ndarray],\\n gt_boxes: Sequence[np.ndarray], gt_classes: Sequence[np.ndarray],\\n gt_ignore: Sequence[Sequence[bool]], max_detections: int = 100,\\n ) -> List[Dict[int, Dict[str, np.ndarray]]]:\\n results = []\\n # iterate over images/batches\\n for pboxes, pclasses, pscores, gboxes, gclasses, gignore in zip(\\n pred_boxes, pred_classes, pred_scores, gt_boxes, gt_classes, gt_ignore):\\n img_classes = np.union1d(pclasses, gclasses)\\n result = {} # dict contains results for each class in one image\\n for c in img_classes:\\n pred_mask = pclasses == c # mask predictions with current class\\n gt_mask = gclasses == c # mask ground trtuh with current class\\n\\n if not np.any(gt_mask): # no ground truth\\n result[c] = _matching_no_gt(\\n iou_thresholds=iou_thresholds,\\n pred_scores=pscores[pred_mask],\\n max_detections=max_detections)\\n elif not np.any(pred_mask): # no predictions\\n result[c] = _matching_no_pred(\\n iou_thresholds=iou_thresholds,\\n gt_ignore=gignore[gt_mask],\\n )\\n else: # at least one prediction and one ground truth\\n result[c] = _matching_single_image_single_class(\\n iou_fn=iou_fn,\\n pred_boxes=pboxes[pred_mask],\\n pred_scores=pscores[pred_mask],\\n gt_boxes=gboxes[gt_mask],\\n gt_ignore=gignore[gt_mask],\\n max_detections=max_detections,\\n iou_thresholds=iou_thresholds,\\n )\\n results.append(result)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128a24d7a2976c59240fff985d365c36\",\n \"score\": \"0.59797955\",\n \"text\": \"def forward(self, outputs, processed_sizes, target_sizes=None):\\n if target_sizes is None:\\n target_sizes = processed_sizes\\n assert len(processed_sizes) == len(target_sizes)\\n out_logits, raw_masks, raw_boxes = outputs['pred_logits'], outputs['pred_masks'], outputs['pred_boxes']\\n assert len(out_logits) == len(raw_masks) == len(target_sizes)\\n preds = []\\n\\n def to_tuple(tup):\\n if isinstance(tup, tuple):\\n return tup\\n return tuple(tup.cpu().tolist())\\n for cur_logits, cur_masks, cur_boxes, size, target_size in zip(out_logits, raw_masks, raw_boxes, processed_sizes, target_sizes):\\n scores, labels = cur_logits.softmax(-1).max(-1)\\n keep = labels.ne(outputs['pred_logits'].shape[-1] - 1) & (scores > self.threshold)\\n cur_scores, cur_classes = cur_logits.softmax(-1).max(-1)\\n cur_scores = cur_scores[keep]\\n cur_classes = cur_classes[keep]\\n cur_masks = cur_masks[keep]\\n cur_masks = interpolate(cur_masks[None], to_tuple(size), mode='bilinear').squeeze(0)\\n cur_boxes = box_ops.box_cxcywh_to_xyxy(cur_boxes[keep])\\n h, w = cur_masks.shape[-2:]\\n assert len(cur_boxes) == len(cur_classes)\\n cur_masks = cur_masks.flatten(1)\\n stuff_equiv_classes = defaultdict(lambda : [])\\n for k, label in enumerate(cur_classes):\\n if not self.is_thing_map[label.item()]:\\n stuff_equiv_classes[label.item()].append(k)\\n\\n def get_ids_area(masks, scores, dedup=False):\\n m_id = masks.transpose(0, 1).softmax(-1)\\n if m_id.shape[-1] == 0:\\n m_id = torch.zeros((h, w), dtype=torch.long, device=m_id.device)\\n else:\\n m_id = m_id.argmax(-1).view(h, w)\\n if dedup:\\n for equiv in stuff_equiv_classes.values():\\n if len(equiv) > 1:\\n for eq_id in equiv:\\n m_id.masked_fill_(m_id.eq(eq_id), equiv[0])\\n final_h, final_w = to_tuple(target_size)\\n seg_img = Image.fromarray(id2rgb(m_id.view(h, w).cpu().numpy()))\\n seg_img = seg_img.resize(size=(final_w, final_h), resample=Image.NEAREST)\\n np_seg_img = torch.ByteTensor(torch.ByteStorage.from_buffer(seg_img.tobytes())).view(final_h, final_w, 3).numpy()\\n m_id = torch.from_numpy(rgb2id(np_seg_img))\\n area = []\\n for i in range(len(scores)):\\n area.append(m_id.eq(i).sum().item())\\n return area, seg_img\\n area, seg_img = get_ids_area(cur_masks, cur_scores, dedup=True)\\n if cur_classes.numel() > 0:\\n while True:\\n filtered_small = torch.as_tensor([(area[i] <= 4) for i, c in enumerate(cur_classes)], dtype=torch.bool, device=keep.device)\\n if filtered_small.any().item():\\n cur_scores = cur_scores[~filtered_small]\\n cur_classes = cur_classes[~filtered_small]\\n cur_masks = cur_masks[~filtered_small]\\n area, seg_img = get_ids_area(cur_masks, cur_scores)\\n else:\\n break\\n else:\\n cur_classes = torch.ones(1, dtype=torch.long, device=cur_classes.device)\\n segments_info = []\\n for i, a in enumerate(area):\\n cat = cur_classes[i].item()\\n segments_info.append({'id': i, 'isthing': self.is_thing_map[cat], 'category_id': cat, 'area': a})\\n del cur_classes\\n with io.BytesIO() as out:\\n seg_img.save(out, format='PNG')\\n predictions = {'png_string': out.getvalue(), 'segments_info': segments_info}\\n preds.append(predictions)\\n return preds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ef887b0bf7877ca1b96c62bd65d89e3\",\n \"score\": \"0.5957035\",\n \"text\": \"def classify(self, source1, source2):\\n \\n scores1 = source1.similarity_scores(self)\\n scores2 = source2.similarity_scores(self)\\n \\n print('scores for ' +source1.name+ ':', scores1)\\n print('scores for ' +source2.name+ ':', scores2)\\n print()\\n \\n sum1 = 0\\n sum2 = 0\\n total1 = 0\\n total2 = 0\\n weights = [10,7,5,7,1,3]\\n for i in range(len(scores1)):\\n if scores1[i] > scores2[i]:\\n sum1 += 1\\n else:\\n sum2 += 1\\n total1 += weights[i]*scores1[i]\\n total2 += weights[i]*scores2[i]\\n \\n if sum1 > sum2:\\n print(self.name + ' is more likely to come from ' + source1.name)\\n print(sum1,sum2)\\n print()\\n elif sum2 > sum1:\\n print(self.name + ' is more likely to come from ' + source2.name)\\n print(sum1,sum2)\\n print()\\n else:\\n if total1 > total2:\\n print(self.name + ' is more likely to come from ' + source1.name)\\n print(total1,total2)\\n print()\\n else:\\n print(self.name + ' is more likely to come from ' + source2.name)\\n print(total1,total2)\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fddec0a4c39982b575b90f73d4787fe\",\n \"score\": \"0.59546\",\n \"text\": \"def classify_photo(pic=None, destination=None):\\n if pic is not None:\\n img = open_image(pic)\\n else:\\n img = open_image(destination)\\n classifier_path = \\\"app/models/cnn_classifier/\\\"\\n classifier = load_learner(classifier_path)\\n pred_class, pred_idx, outputs = classifier.predict(img)\\n\\n if max(outputs) < 0.92:\\n\\n # if destination is not None:\\n # img_data = cv2.imread(destination)\\n # else:\\n # img_data = cv2.imread(pic)\\n\\n # with global_graph.as_default():\\n # img_paths = detector.predict(img_data, destination)\\n # img_paths.append(destination)\\n\\n # classifier_outputs = []\\n\\n # for path in img_paths:\\n # img = open_image(path)\\n # pred_class, pred_idx, outputs = classifier.predict(img)\\n # classifier_outputs.append(\\n # [max(outputs), pred_class, pred_idx, outputs])\\n # classifier_outputs.sort(key=lambda x: x[0], reverse=True)\\n\\n # pred_class = classifier_outputs[0][1]\\n # pred_idx = classifier_outputs[0][2]\\n # outputs = classifier_outputs[0][3]\\n\\n return pred_class, pred_idx, outputs\\n\\n return pred_class, pred_idx, outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"856a51177793c543f0e6030c0663c678\",\n \"score\": \"0.595266\",\n \"text\": \"def check_classification_targets(y):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20911ed85bc9ebf34fc364567400be7f\",\n \"score\": \"0.5915451\",\n \"text\": \"def image_preprocessing():\\n\\t# generate tags for images, if they do not exis\\tt\\n\\tif not os.path.isfile(images_folder + \\\"/train_tags.csv\\\"):\\n\\t\\tprint(\\\"Extracting tags...\\\")\\n\\t\\tio.extract_tags(raw_images_folder + \\\"/train\\\",images_folder,\\\"train_\\\")\\n\\t\\tprint(\\\"Done!\\\\n\\\")\\n\\tif not os.path.isfile(images_folder + \\\"/val_tags.csv\\\"):\\n\\t\\tprint(\\\"Extracting tags...\\\")\\n\\t\\tio.extract_tags(raw_images_folder + \\\"/validation\\\", images_folder,\\\"val_\\\")\\n\\t\\tprint(\\\"Done!\\\\n\\\")\\n\\n\\t# resize the images to a particular size, if not already done\\n\\t# Let's assume its already done if a particular directory \\n\\t# has images in it...\\n\\t'''if len(os.listdir(resized_images_folder)) == 0:\\n\\t\\tprint(\\\"Resizing images...this could take awhile!\\\\n\\\")\\n\\t\\tio.resize_images(raw_images_folder,resized_images_folder,resize_dimensions)\\n\\t'''\\n\\tif(len(os.listdir(transformed_images_folder + \\\"/train\\\")) + len(os.listdir(transformed_images_folder + \\\"/validation\\\")) > 0):\\t\\n\\t\\tprint(\\\"clearing out previously transformed images\\\")\\t\\n\\t\\tos.system(\\\"rm ./data/transformed/train/* ./data/transformed/validation/*\\\")\\n\\t\\t\\n\\t# generate X and Y, in the format keras needs for the model\\n\\t# and return\\n\\t#return io.partition_image_data(resized_images_folder,(150,150),3)\\n\\tprint(\\\"obtaining model inputs\\\")\\n\\treturn io.format_data(resize_dimensions, 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8382faa30e2d300c3d951dad0930a0ba\",\n \"score\": \"0.5909281\",\n \"text\": \"def compute_class_weights(image_files, label_values):\\n\\n # image_files = [os.path.join(labels_dir, file) for file in os.listdir(labels_dir) if file.endswith('.png')]\\n num_classes = len(label_values)\\n class_pixels = np.zeros(num_classes)\\n\\n total_pixels = 0.0\\n\\n for n in range(len(image_files)):\\n image = imread(image_files[n])\\n\\n for index, colour in enumerate(label_values):\\n class_map = np.all(np.equal(image, colour), axis=-1)\\n class_map = class_map.astype(np.float32)\\n class_pixels[index] += np.sum(class_map)\\n\\n print(\\\"\\\\rProcessing image: \\\" + str(n) + \\\" / \\\" + str(len(image_files)), end=\\\"\\\")\\n sys.stdout.flush()\\n\\n total_pixels = float(np.sum(class_pixels))\\n index_to_delete = np.argwhere(class_pixels == 0.0)\\n class_pixels = np.delete(class_pixels, index_to_delete)\\n\\n class_weights = total_pixels / class_pixels\\n class_weights = class_weights / np.sum(class_weights)\\n\\n return class_weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8be6173f0a97bc23d9661ba1438c9277\",\n \"score\": \"0.5903825\",\n \"text\": \"def few_label_classification(self, list_num_shots=[1, 10, 100, -1], num_runs=10, batch_size=1000, classifier='logistic'):\\n if not os.path.exists('results/few_label/'):\\n os.makedirs('results/few_label/')\\n results = {'Num_Shots': [], 'Mean': [], 'Std': []}\\n train_loader, im_h, im_w, im_channels = setup_data_loader(data=self.data, \\n data_dir=self.data_dir, \\n batch_size=batch_size, \\n train=True, \\n normalize=False if self.model_name in ['VAE', 'VAE_GMM'] else True, \\n shuffle=False, \\n shot_random_seed=None)\\n zs_train, ys_train = self.encode_dataset(train_loader)\\n \\n test_loader, im_h, im_w, im_channels = setup_data_loader(data=self.data, \\n data_dir=self.data_dir, \\n batch_size=batch_size, \\n train=False, \\n normalize=False if self.model_name in ['VAE', 'VAE_GMM'] else True, \\n shuffle=False, \\n shot_random_seed=None)\\n zs_test, ys_test = self.encode_dataset(test_loader)\\n \\n num_classes = len(np.unique(ys_train))\\n \\n for num_shots in tqdm(list_num_shots):\\n Accuracy = []\\n if num_shots == -1:\\n clf = LogisticRegression(random_state=0, \\n multi_class='auto', \\n solver='liblinear', \\n max_iter=10000).fit(zs_train, ys_train)\\n Accuracy.append(np.array([clf.score(zs_test, ys_test)])) \\n else:\\n for i in tqdm(range(num_runs)):\\n # torch.cuda.empty_cache()\\n zs_train_selected = []\\n ys_train_selected = []\\n set_seed(i)\\n for k in range(num_classes):\\n indk = np.argwhere(ys_train == k)[:,0]\\n ind_of_indk = np.random.permutation(len(indk))[:num_shots]\\n indk_selected = indk[ind_of_indk]\\n zs_train_selected.append(zs_train[indk_selected])\\n ys_train_selected.append(ys_train[indk_selected])\\n zs_train_selected = np.concatenate(zs_train_selected, 0)\\n ys_train_selected = np.concatenate(ys_train_selected, 0)\\n\\n# print(indk_selected)\\n \\n clf = LogisticRegression(random_state=0, \\n multi_class='auto', \\n solver='liblinear', \\n max_iter=10000).fit(zs_train_selected, ys_train_selected)\\n Accuracy.append(np.array([clf.score(zs_test, ys_test)])) \\n# return 0\\n Accuracy = np.concatenate(Accuracy)\\n results['Num_Shots'].append(num_shots)\\n results['Mean'].append(Accuracy.mean())\\n results['Std'].append(Accuracy.std())\\n pd.DataFrame.from_dict(results).to_csv('results/few_label/{}-{}-runs={}.csv'.format(self.model_name, self.data, num_runs), index=False)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"948198389e8b010b65ed4eb1e6d7e23a\",\n \"score\": \"0.58989424\",\n \"text\": \"def process_outputs(self, outputs, image_size):\\n boxes = []\\n confidences = []\\n class_proba = []\\n img_H = image_size[0]\\n img_W = image_size[1]\\n for output in outputs:\\n boxes.append(output[..., 0:4])\\n confidences.append(self.sigmoid(output[..., 4, np.newaxis]))\\n class_proba.append(self.sigmoid(output[..., 5:]))\\n for i, box in enumerate(boxes):\\n g_h, g_w, achors_box, _ = box.shape\\n coordidate = np.zeros((g_h, g_w, achors_box))\\n idx_y = np.arange(g_h)\\n idx_y = idx_y.reshape(g_h, 1, 1)\\n idx_x = np.arange(g_w)\\n idx_x = idx_x.reshape(1, g_w, 1)\\n C_x = coordidate + idx_x\\n C_y = coordidate + idx_y\\n centerX = box[..., 0]\\n centerY = box[..., 1]\\n width = box[..., 2]\\n height = box[..., 3]\\n bx = (self.sigmoid(centerX) + C_x) / g_w\\n by = (self.sigmoid(centerY) + C_y) / g_h\\n pw = self.anchors[i, :, 0]\\n ph = self.anchors[i, :, 1]\\n bw = (np.exp(width) * pw) / self.model.input.shape[1].value\\n bh = (np.exp(height) * ph) / self.model.input.shape[2].value\\n x1 = bx - bw / 2\\n y1 = by - bh / 2\\n x2 = x1 + bw\\n y2 = y1 + bh\\n box[..., 0] = x1 * img_W\\n box[..., 1] = y1 * img_H\\n box[..., 2] = x2 * img_W\\n box[..., 3] = y2 * img_H\\n return boxes, confidences, class_proba\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5573952e335fb188d5885ee893550fc4\",\n \"score\": \"0.5890267\",\n \"text\": \"def forward(self, batched_inputs):\\n images = self.preprocess_image(batched_inputs)\\n if self.training:\\n batch_size, _, H, W = images.tensor.shape\\n img_masks = images.tensor.new_ones(batch_size, H, W)\\n for img_id in range(batch_size):\\n img_h, img_w = batched_inputs[img_id]['instances'].image_size\\n img_masks[img_id, :img_h, :img_w] = 0\\n else:\\n batch_size, _, H, W = images.tensor.shape\\n img_masks = images.tensor.new_zeros(batch_size, H, W)\\n features = self.backbone(images.tensor)[self.in_features[-1]]\\n features = self.input_proj(features)\\n img_masks = F.interpolate(img_masks[None], size=features.shape[-2:])[0]\\n pos_embed = self.position_embedding(img_masks)\\n hidden_states, _ = self.transformer(features, img_masks, self.query_embed.weight, pos_embed)\\n outputs_class = self.class_embed(hidden_states)\\n outputs_coord = self.bbox_embed(hidden_states).sigmoid()\\n output = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1]}\\n if self.aux_loss:\\n output['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord)\\n if self.training:\\n gt_instances = [x['instances'] for x in batched_inputs]\\n targets = self.prepare_targets(gt_instances)\\n loss_dict = self.criterion(output, targets)\\n weight_dict = self.criterion.weight_dict\\n for k in loss_dict.keys():\\n if k in weight_dict:\\n loss_dict[k] *= weight_dict[k]\\n return loss_dict\\n else:\\n box_cls = output['pred_logits']\\n box_pred = output['pred_boxes']\\n results = self.inference(box_cls, box_pred, images.image_sizes)\\n processed_results = []\\n for results_per_image, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes):\\n height = input_per_image.get('height', image_size[0])\\n width = input_per_image.get('width', image_size[1])\\n r = detector_postprocess(results_per_image, height, width)\\n processed_results.append({'instances': r})\\n return processed_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf45f6d088fd003c6b52bf2faa15c5d7\",\n \"score\": \"0.58813107\",\n \"text\": \"def classify(model, images):\\n probabilities = model.predict(images)\\n labels = np.argmax(probabilities, axis=1)\\n\\n # Load the label encoder\\n encoder_dir = \\\\\\n Path(__file__).absolute().parents[2].joinpath('generated_data',\\n 'encoders')\\n encoder_path = encoder_dir.joinpath('encoder.pkl')\\n\\n with encoder_path.open('rb') as f:\\n label_encoder = pickle.load(f)\\n\\n labels = label_encoder.inverse_transform(labels)\\n\\n return labels, probabilities\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2098c9d59de4b26efd20cedc8ab5d1f9\",\n \"score\": \"0.58700335\",\n \"text\": \"def _compute_targets(entry):\\n # Indices of ground-truth ROIs\\n rois = entry['boxes']\\n overlaps = entry['max_overlaps']\\n labels = entry['max_classes']\\n gt_inds = np.where((entry['gt_classes'] > 0) & (entry['is_crowd'] == 0))[0]\\n # Targets has format (class, tx, ty, tw, th)\\n targets = np.zeros((rois.shape[0], 5), dtype=np.float32)\\n if len(gt_inds) == 0:\\n # Bail if the image has no ground-truth ROIs\\n return targets\\n\\n # Indices of examples for which we try to make predictions\\n ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]\\n\\n # Get IoU overlap between each ex ROI and gt ROI\\n ex_gt_overlaps = box_utils.bbox_overlaps(\\n rois[ex_inds, :].astype(dtype=np.float32, copy=False),\\n rois[gt_inds, :].astype(dtype=np.float32, copy=False))\\n\\n # Find which gt ROI each ex ROI has max overlap with:\\n # this will be the ex ROI's gt target\\n gt_assignment = ex_gt_overlaps.argmax(axis=1)\\n gt_rois = rois[gt_inds[gt_assignment], :]\\n ex_rois = rois[ex_inds, :]\\n # Use class \\\"1\\\" for all boxes if using class_agnostic_bbox_reg\\n targets[ex_inds, 0] = (1 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG else labels[ex_inds])\\n targets[ex_inds, 1:] = box_utils.bbox_transform_inv(ex_rois, gt_rois, cfg.MODEL.BBOX_REG_WEIGHTS)\\n return targets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c2edebd92e2553520422621b3530a15\",\n \"score\": \"0.5838647\",\n \"text\": \"def forward(self, batched_inputs):\\n images = self.preprocess_image(batched_inputs)\\n if self.training:\\n batch_size, _, H, W = images.tensor.shape\\n img_masks = images.tensor.new_ones(batch_size, H, W)\\n for img_id in range(batch_size):\\n img_h, img_w = batched_inputs[img_id]['instances'].image_size\\n img_masks[img_id, :img_h, :img_w] = 0\\n else:\\n batch_size, _, H, W = images.tensor.shape\\n img_masks = images.tensor.new_zeros(batch_size, H, W)\\n features = self.backbone(images.tensor)[self.in_features[-1]]\\n features = self.input_proj(features)\\n img_masks = F.interpolate(img_masks[None], size=features.shape[-2:])[0]\\n pos_embed = self.position_embedding(img_masks)\\n hidden_states, reference = self.transformer(features, img_masks, self.query_embed.weight, pos_embed)\\n reference_before_sigmoid = inverse_sigmoid(reference)\\n outputs_coords = []\\n for lvl in range(hidden_states.shape[0]):\\n tmp = self.bbox_embed(hidden_states[lvl])\\n tmp[..., :2] += reference_before_sigmoid\\n outputs_coord = tmp.sigmoid()\\n outputs_coords.append(outputs_coord)\\n outputs_coord = torch.stack(outputs_coords)\\n outputs_class = self.class_embed(hidden_states)\\n output = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1]}\\n if self.aux_loss:\\n output['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord)\\n if self.training:\\n gt_instances = [x['instances'] for x in batched_inputs]\\n targets = self.prepare_targets(gt_instances)\\n loss_dict = self.criterion(output, targets)\\n weight_dict = self.criterion.weight_dict\\n for k in loss_dict.keys():\\n if k in weight_dict:\\n loss_dict[k] *= weight_dict[k]\\n return loss_dict\\n else:\\n box_cls = output['pred_logits']\\n box_pred = output['pred_boxes']\\n results = self.inference(box_cls, box_pred, images.image_sizes)\\n processed_results = []\\n for results_per_image, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes):\\n height = input_per_image.get('height', image_size[0])\\n width = input_per_image.get('width', image_size[1])\\n r = detector_postprocess(results_per_image, height, width)\\n processed_results.append({'instances': r})\\n return processed_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"753a71ea40064c5a62b82727b8fe906c\",\n \"score\": \"0.58297926\",\n \"text\": \"def visualize_detections(images, ground_truths, detections, id2code,\\n label_codes, label_names, out_dir='/tmp'):\\n max_id = max(id2code.values())\\n name_range = range(len(label_names))\\n\\n for i in range(0, np.shape(detections)[0]):\\n fig = plt.figure(figsize=(17, 17))\\n\\n # original image\\n ax1 = fig.add_subplot(2, 2, 1)\\n # TODO: expect also other data than S2\\n a = np.stack((images[i][:, :, 3], images[i][:, :, 2],\\n images[i][:, :, 1]), axis=2)\\n ax1.imshow((255 / a.max() * a).astype(np.uint8))\\n ax1.title.set_text('Actual image')\\n\\n # ground truths\\n ax3 = fig.add_subplot(2, 2, 3)\\n ax3.set_title('Ground truth labels')\\n gt_labels = ground_truths[i]\\n gt_labels = onehot_decode(gt_labels, id2code)\\n ax3.imshow(gt_labels * 4)\\n\\n # detections\\n ax4 = fig.add_subplot(2, 2, 4)\\n ax4.set_title('Predicted labels')\\n pred_labels = onehot_decode(detections[i], id2code)\\n ax4.imshow(pred_labels * 4)\\n\\n # confusion matrix\\n ax2 = fig.add_subplot(2, 2, 2)\\n ax2.set_title('Confusion matrix')\\n conf_matrix = confusion_matrix(\\n gt_labels[:, :, 0].flatten(), pred_labels[:, :, 0].flatten(),\\n max_id + 1)\\n # subset to existing classes\\n conf_matrix = conf_matrix.numpy()[label_codes][:, label_codes]\\n # normalize the confusion matrix\\n row_sums = conf_matrix.sum(axis=1)[:, np.newaxis]\\n # TODO: solve division by 0\\n cm_norm = np.around(conf_matrix.astype('float') / row_sums, decimals=2)\\n # visualize\\n ax2.imshow(cm_norm, cmap=plt.cm.Blues)\\n y_labels = ['{}\\\\n{}'.format(label_names[j], row_sums[j]) for j in\\n name_range]\\n plt.xticks(name_range, label_names)\\n plt.yticks(name_range, y_labels)\\n plt.xlabel('Predicted label')\\n plt.ylabel('True label')\\n # write percentage values (0.00 -- 1.00) into the confusion matrix\\n threshold = cm_norm.max() / 2. # used to decide for the font colour\\n for row in range(len(conf_matrix)):\\n for col in range(len(conf_matrix)):\\n if cm_norm[col, row] > threshold:\\n colour = 'white'\\n else:\\n colour = 'black'\\n # TODO: class names, not codes\\n ax2.text(row, col, cm_norm[col, row], color=colour,\\n horizontalalignment='center')\\n\\n plt.savefig(os.path.join(out_dir, str(i)))\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98b5eeb8aba439c41040b45c0c28aad7\",\n \"score\": \"0.58281577\",\n \"text\": \"def classify():\\n classifier = load_model(\\\"kerasmodel.h5\\\")\\n im = cv2.imread(\\\"result.png\\\", 0)\\n im2 = cv2.resize(im, (28, 28))\\n im = im2.reshape(28, 28, -1)\\n im = im.reshape(1, 784)\\n im = cv2.bitwise_not(im)\\n plt.imshow(im.reshape(28, 28), cmap='Greys')\\n result = classifier.predict(im)\\n a = np.argmax(result)\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54b57f9eeb41d312252fbf8a69bb72d9\",\n \"score\": \"0.58160615\",\n \"text\": \"def classify(self, data, features, target, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88a4a74128dce86df26c0e1d336c08ad\",\n \"score\": \"0.5811792\",\n \"text\": \"def classify(labelled_files, unlabelled_files, labels):\\n # Extract features using CountVectorizer (Bag of Words)\\n count_vect = CountVectorizer()\\n counts = count_vect.fit_transform(labelled_files)\\n\\n # Fit classifier to training data (user labels)\\n clf = LogisticRegression().fit(counts, labels)\\n\\n # Transform CountVectorizer to unlabelled files\\n new_counts = count_vect.transform(unlabelled_files)\\n\\n # Run prediction on new counts\\n predicted_labels = clf.predict(new_counts)\\n probabilities = clf.predict_proba(new_counts)\\n\\n # Reduce probabilities to only show for predicted label\\n probabilities = np.amax(probabilities, axis=1)\\n\\n return predicted_labels, probabilities\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3a789a6339e5f1b97dbb45d1c654cf3\",\n \"score\": \"0.5810821\",\n \"text\": \"def classify(self, elements):\\r\\n if elements is None:\\r\\n return\\r\\n for element in elements:\\r\\n was_classified = False\\r\\n image_added = False\\r\\n for op in self.ops():\\r\\n if op.operation == \\\"Raster\\\":\\r\\n if image_added:\\r\\n continue # already added to an image operation, is not added here.\\r\\n if element.stroke is not None and op.color == abs(element.stroke):\\r\\n op.append(element)\\r\\n was_classified = True\\r\\n elif isinstance(element, SVGImage):\\r\\n op.append(element)\\r\\n was_classified = True\\r\\n elif isinstance(element, SVGText):\\r\\n op.append(element)\\r\\n was_classified = True\\r\\n elif element.fill is not None and element.fill.value is not None:\\r\\n op.append(element)\\r\\n was_classified = True\\r\\n elif op.operation in (\\\"Engrave\\\", \\\"Cut\\\") and element.stroke is not None and \\\\\\r\\n op.color == abs(element.stroke):\\r\\n op.append(element)\\r\\n was_classified = True\\r\\n elif op.operation == 'Image' and isinstance(element, SVGImage):\\r\\n op.append(element)\\r\\n was_classified = True\\r\\n image_added = True\\r\\n if not was_classified:\\r\\n if element.stroke is not None and element.stroke.value is not None:\\r\\n op = LaserOperation(operation=\\\"Engrave\\\", color=element.stroke, speed=35.0)\\r\\n op.append(element)\\r\\n self.add_op(op)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbf7c0c480361a30f46110b320853329\",\n \"score\": \"0.58092415\",\n \"text\": \"def predict(self, images):\\n\\n molded_images, image_metas = mold_inputs(images, self.config)\\n\\n # Convert to Tensor\\n molded_images = torch.from_numpy(molded_images).float().to(self.device)\\n\\n # Features Extraction\\n features = self.extractor(molded_images)\\n\\n print(\\\"features\\\", features.size())\\n\\n # RPN\\n rpn_logits, rpn_probs, rpn_bbox = self.rpn(features)\\n\\n print(\\\"rpn_probs\\\", rpn_probs.size(), \\\"rpn_bbox\\\", rpn_bbox.size())\\n\\n # Perform proposal_layer on each image in batch\\n n_batch = features.size()[0]\\n\\n for i in range(n_batch):\\n print(\\\"batch\\\", i, rpn_probs[i].size(), rpn_bbox[i].size())\\n # proposal_layer\\n\\n # ROI Pooling / ROI Align\\n\\n # Classification\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f917eea7c9833496c8df885a215d720\",\n \"score\": \"0.5807556\",\n \"text\": \"def pt_cls_preprocess(self, images):\\n mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1)).astype(np.float32)\\n std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1)).astype(np.float32)\\n output = []\\n for image in images:\\n for param in self.xform:\\n param_0 = param[0]\\n param_1 = param[1]\\n if param_0 == \\\"resize\\\":\\n size = param_1\\n # image = cv2.resize(image, (size[1], size[0]), \\\\\\n # interpolation=cv2.INTER_LINEAR)\\n # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n org_h, org_w = image.shape[:2]\\n scale = 256.0 / min(org_h, org_w)\\n resize_h, resize_w = int(scale * org_h), int(scale * org_w)\\n image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\\n image = np.asarray(image.resize((resize_w, resize_h), \\\\\\n resample=Image.BILINEAR))\\n # image = cv2.resize(image, (tw, th), interpolation=cv2.INTER_LINEAR)\\n offset_h, offset_w = (resize_h - size[0]) // 2, (resize_w - size[1]) // 2\\n image = image[offset_h:offset_h + size[0], offset_w:offset_w + size[1]]\\n image = np.transpose(image, (2, 0, 1)).astype(np.float32)\\n image /= 255.0\\n image = (image - mean) / std\\n output.append(image)\\n return np.array(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7316ffc702978a09d7e85f78e2ba52a7\",\n \"score\": \"0.5806382\",\n \"text\": \"def process_batch(self, inputs):\\n for key, ipt in inputs.items():\\n if key == 'tag':\\n continue\\n elif key == 'imgr' or key == 'poser':\\n for k, kipt in inputs[key].items():\\n inputs[key][k] = kipt.to(self.device)\\n else:\\n inputs[key] = ipt.to(self.device)\\n\\n features = self.models[\\\"encoder\\\"](inputs['inrgb_augmented'])\\n outputs = self.models[\\\"depth\\\"](features)\\n\\n outputs = self.generate_images_pred(inputs, outputs)\\n losses = self.compute_losses(inputs, outputs)\\n\\n return outputs, losses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e3934bf7cc91231733ff198af2287b4\",\n \"score\": \"0.5800077\",\n \"text\": \"def prepare_targets(self, batch_inputs):\\n images, bboxes, labels, points, has_pts, instance_mask = batch_inputs\\n h, w = images[0].shape[1:]\\n image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float)\\n target_images = images.to(self.device)\\n target_classes = labels.to(self.device)\\n # normalize bboxes with wdith and height and convert the x1, y1, x2, y2 to centerx, centery, w, h\\n target_boxes = bboxes / image_size_xyxy\\n target_boxes_cxcywh = box_xyxy_to_cxcywh(target_boxes).to(self.device)\\n # x1, y1, x2, y2 bboxes\\n bboxes = bboxes.to(self.device)\\n # images w, h, w, h tensor shape -> (batch, Num)\\n target_image_size_xyxy = torch.stack([torch.cat([image_size_xyxy]) for _ in range(images.shape[0])], dim=0)\\n target_image_size_xyxy = target_image_size_xyxy.to(self.device)\\n # instance mask\\n target_instance_mask = instance_mask.to(self.device)\\n if points[0] is not None:\\n target_points = points.to(self.device)\\n else:\\n target_points = points\\n\\n if has_pts[0] is not None:\\n target_has_pts = has_pts.to(self.device)\\n else:\\n target_has_pts = has_pts\\n\\n targets = {}\\n targets['images'] = target_images\\n targets['labels'] = target_classes\\n targets['bboxes'] = target_boxes_cxcywh # (centerx, centery, w, h)\\n targets['boxes_xyxy'] = bboxes # (x1, y1, x2, y2)\\n targets['image_size_xyxy'] = target_image_size_xyxy\\n targets['pts'] = target_points\\n targets['pts_mask'] = has_pts\\n targets['instance_mask'] = target_instance_mask\\n\\n return targets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbf3e95b916face8d17ca00b50a7ce8f\",\n \"score\": \"0.5798492\",\n \"text\": \"def classify_input(wildcards, return_int=False):\\n if check_config('truth') and check_config(wildcards.sample, place=config['truth']):\\n image_ending = \\\"/{image}.tsv\\\" if check_config('parallel') else ''\\n if check_config('train_all', place=config['truth'][wildcards.sample]):\\n if return_int:\\n return 1\\n return [\\n checkpoints.create_truth_data.get(**wildcards).output[0]+image_ending,\\n rules.train.output[0]\\n ]\\n if check_config('model'):\\n if return_int:\\n return 2\\n return [\\n checkpoints.create_truth_data.get(**wildcards).output[0]+image_ending,\\n config['model']\\n ]\\n else:\\n if return_int:\\n return 3\\n return [\\n checkpoints.create_split_truth_data.get(**wildcards).output.test+image_ending,\\n rules.train.output[0]\\n ]\\n else:\\n if return_int:\\n return 0\\n if check_config('model'):\\n return [rules.extract_features.output[0], config['model']]\\n else:\\n raise ValueError(\\\"If you don't specify any truth sets, you must provide a pre-trained model.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33e267c0a4ea7eaa5ceebe7be7076767\",\n \"score\": \"0.5786576\",\n \"text\": \"def forward(self, batched_inputs):\\n images = self.preprocess_image(batched_inputs)\\n if self.training:\\n batch_size, _, H, W = images.tensor.shape\\n img_masks = images.tensor.new_ones(batch_size, H, W)\\n for img_id in range(batch_size):\\n img_h, img_w = batched_inputs[img_id]['instances'].image_size\\n img_masks[img_id, :img_h, :img_w] = 0\\n else:\\n batch_size, _, H, W = images.tensor.shape\\n img_masks = images.tensor.new_zeros(batch_size, H, W)\\n features = self.backbone(images.tensor)[self.in_features[-1]]\\n features = self.input_proj(features)\\n img_masks = F.interpolate(img_masks[None], size=features.shape[-2:])[0]\\n pos_embed = self.position_embedding(img_masks)\\n dynamic_anchor_boxes = self.anchor_box_embed.weight\\n hidden_states, reference_boxes = self.transformer(features, img_masks, dynamic_anchor_boxes, pos_embed)\\n reference_boxes = inverse_sigmoid(reference_boxes)\\n anchor_box_offsets = self.bbox_embed(hidden_states)\\n outputs_coord = (reference_boxes + anchor_box_offsets).sigmoid()\\n outputs_class = self.class_embed(hidden_states)\\n output = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1]}\\n if self.aux_loss:\\n output['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord)\\n if self.training:\\n gt_instances = [x['instances'] for x in batched_inputs]\\n targets = self.prepare_targets(gt_instances)\\n loss_dict = self.criterion(output, targets)\\n weight_dict = self.criterion.weight_dict\\n for k in loss_dict.keys():\\n if k in weight_dict:\\n loss_dict[k] *= weight_dict[k]\\n return loss_dict\\n else:\\n box_cls = output['pred_logits']\\n box_pred = output['pred_boxes']\\n results = self.inference(box_cls, box_pred, images.image_sizes)\\n processed_results = []\\n for results_per_image, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes):\\n height = input_per_image.get('height', image_size[0])\\n width = input_per_image.get('width', image_size[1])\\n r = detector_postprocess(results_per_image, height, width)\\n processed_results.append({'instances': r})\\n return processed_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4eed7eac26f5a0035d5aea2098d3c08\",\n \"score\": \"0.5782558\",\n \"text\": \"def classify(self, X):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7adf2a8278f81d95f2c53608e0e6ef0\",\n \"score\": \"0.5781525\",\n \"text\": \"def postprocess_per_class(params, cls_outputs, box_outputs, image_scales=None):\\n cls_outputs = to_list(cls_outputs)\\n box_outputs = to_list(box_outputs)\\n boxes, scores, classes = pre_nms(params, cls_outputs, box_outputs)\\n return per_class_nms(params, boxes, scores, classes, image_scales)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"996d20b0cf818c8e254b2c8922fb02ac\",\n \"score\": \"0.577933\",\n \"text\": \"def run_inference(image_files):\\n\\t# Read image files, resize them, convert to numpy arrays w/ dtype=uint8\\n\\timages = []\\n\\tfor image_file in image_files:\\n\\t\\timage = Image.open(image_file)\\n\\t\\timage = image.convert('RGB')\\n\\t\\timage = image.resize((IMG_SIZE, IMG_SIZE), Image.ANTIALIAS)\\n\\t\\timage = np.array(list(image.getdata()), dtype='uint8')\\n\\t\\timage = np.reshape(image, (32, 32, 3))\\n\\n\\t\\timages.append(image)\\n\\timages = np.array(images, dtype='uint8')\\n\\n\\t# Pre-process the image (don't care about label, put dummy labels)\\n\\timages, _ = preprocess_data(images, np.array([0 for _ in range(images.shape[0])]))\\n\\n\\twith tf.Graph().as_default(), tf.Session() as sess:\\n\\t\\t# Instantiate the CNN model\\n\\t\\tx, y, keep_prob, logits, optimizer, predictions, accuracy = neural_network()\\n\\n\\t\\t# Load trained weights\\n\\t\\tsaver = tf.train.Saver()\\n\\t\\tsaver.restore(sess, MODEL_SAVE_PATH)\\n\\n\\t\\t# Run inference on CNN to make predictions\\n\\t\\tpreds = sess.run(predictions, feed_dict={x: images, keep_prob: 1.})\\n\\n\\t# Load signnames.csv to map label number to sign string\\n\\tlabel_map = {}\\n\\twith open('signnames.csv', 'r') as f:\\n\\t\\tfirst_line = True\\n\\t\\tfor line in f:\\n\\t\\t\\t# Ignore first line\\n\\t\\t\\tif first_line:\\n\\t\\t\\t\\tfirst_line = False\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t# Populate label_map\\n\\t\\t\\tlabel_int, label_string = line.split(',')\\n\\t\\t\\tlabel_int = int(label_int)\\n\\n\\t\\t\\tlabel_map[label_int] = label_string\\n\\n\\tfinal_preds = [label_map[pred] for pred in preds]\\n\\n\\treturn final_preds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32851f705877c52bbbf48b0a2b7de63c\",\n \"score\": \"0.576555\",\n \"text\": \"def forward(self, batched_inputs):\\n images = self.preprocess_image(batched_inputs)\\n if self.training:\\n batch_size, _, H, W = images.tensor.shape\\n img_masks = images.tensor.new_ones(batch_size, H, W)\\n for img_id in range(batch_size):\\n img_h, img_w = batched_inputs[img_id]['instances'].image_size\\n img_masks[img_id, :img_h, :img_w] = 0\\n else:\\n batch_size, _, H, W = images.tensor.shape\\n img_masks = images.tensor.new_zeros(batch_size, H, W)\\n features = self.backbone(images.tensor)[self.in_features[-1]]\\n features = self.input_proj(features)\\n img_masks = F.interpolate(img_masks[None], size=features.shape[-2:])[0]\\n pos_embed = self.position_embedding(img_masks)\\n if self.training:\\n gt_instances = [x['instances'] for x in batched_inputs]\\n targets = self.prepare_targets(gt_instances)\\n gt_labels_list = [t['labels'] for t in targets]\\n gt_boxes_list = [t['boxes'] for t in targets]\\n else:\\n targets = None\\n matching_label_query = self.denoising_generator.label_encoder(torch.tensor(self.num_classes)).repeat(self.num_queries, 1)\\n indicator_for_matching_part = torch.zeros([self.num_queries, 1])\\n matching_label_query = torch.cat([matching_label_query, indicator_for_matching_part], 1).repeat(batch_size, 1, 1)\\n matching_box_query = self.anchor_box_embed.weight.repeat(batch_size, 1, 1)\\n if targets is None:\\n input_label_query = matching_label_query.transpose(0, 1)\\n input_box_query = matching_box_query.transpose(0, 1)\\n attn_mask = None\\n denoising_groups = self.denoising_groups\\n max_gt_num_per_image = 0\\n else:\\n noised_label_queries, noised_box_queries, attn_mask, denoising_groups, max_gt_num_per_image = self.denoising_generator(gt_labels_list, gt_boxes_list)\\n input_label_query = torch.cat([noised_label_queries, matching_label_query], 1).transpose(0, 1)\\n input_box_query = torch.cat([noised_box_queries, matching_box_query], 1).transpose(0, 1)\\n hidden_states, reference_boxes = self.transformer(features, img_masks, input_box_query, pos_embed, target=input_label_query, attn_mask=[attn_mask, None])\\n reference_boxes = inverse_sigmoid(reference_boxes)\\n anchor_box_offsets = self.bbox_embed(hidden_states)\\n outputs_coord = (reference_boxes + anchor_box_offsets).sigmoid()\\n outputs_class = self.class_embed(hidden_states)\\n output = {'denoising_groups': torch.tensor(denoising_groups), 'max_gt_num_per_image': torch.tensor(max_gt_num_per_image)}\\n outputs_class, outputs_coord = self.dn_post_process(outputs_class, outputs_coord, output)\\n output.update({'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1]})\\n if self.aux_loss:\\n output['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord)\\n if self.training:\\n loss_dict = self.criterion(output, targets)\\n weight_dict = self.criterion.weight_dict\\n for k in loss_dict.keys():\\n if k in weight_dict:\\n loss_dict[k] *= weight_dict[k]\\n return loss_dict\\n else:\\n box_cls = output['pred_logits']\\n box_pred = output['pred_boxes']\\n results = self.inference(box_cls, box_pred, images.image_sizes)\\n processed_results = []\\n for results_per_image, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes):\\n height = input_per_image.get('height', image_size[0])\\n width = input_per_image.get('width', image_size[1])\\n r = detector_postprocess(results_per_image, height, width)\\n processed_results.append({'instances': r})\\n return processed_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c94c9f61562de9b0b532bd15dc023de4\",\n \"score\": \"0.5765359\",\n \"text\": \"def compute_classif_metrics(y_true, y_pred, metric_averages=METRIC_AVERAGES):\\n y_true = np.array(y_true)\\n y_pred = np.array(y_pred)\\n if y_true.shape != y_pred.shape:\\n raise ValueError('prediction (%i) and annotation (%i) should be equal' % (len(y_true), len(y_pred)))\\n logging.debug('unique lbs true: %r, predict %r', np.unique(y_true), np.unique(y_pred))\\n\\n uq_labels = np.unique(np.hstack((y_true, y_pred)))\\n # in case there are just two classes, relabel them as [0, 1], sklearn error:\\n # \\\"ValueError: pos_label=1 is not a valid label: array([ 0, 255])\\\"\\n if len(uq_labels) <= 2:\\n # NOTE, this is temporal just for purposes of computing statistic\\n y_true = relabel_sequential(y_true, uq_labels)\\n y_pred = relabel_sequential(y_pred, uq_labels)\\n\\n # http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html\\n eval_str = 'EVALUATION: {:<2} PRE: {:.3f} REC: {:.3f} F1: {:.3f} S: {:>6}'\\n try:\\n p, r, f, s = metrics.precision_recall_fscore_support(y_true, y_pred)\\n for lb, _ in enumerate(p):\\n logging.debug(eval_str.format(lb, p[lb], r[lb], f[lb], s[lb]))\\n except Exception:\\n logging.exception('metrics.precision_recall_fscore_support')\\n\\n dict_metrics = {\\n 'ARS': metrics.adjusted_rand_score(y_true, y_pred),\\n # 'F1': metrics.f1_score(y_true, y_pred),\\n 'accuracy': metrics.accuracy_score(y_true, y_pred),\\n # 'precision': metrics.precision_score(y_true, y_pred),\\n 'confusion': metrics.confusion_matrix(y_true, y_pred).tolist(),\\n # 'report': metrics.classification_report(labels, predicted),\\n }\\n # compute aggregated precision, recall, f-score, support\\n names = ['precision', 'recall', 'f1', 'support']\\n for avg in metric_averages:\\n try:\\n mtr = metrics.precision_recall_fscore_support(y_true, y_pred, average=avg)\\n res = dict(zip(['{}_{}'.format(n, avg) for n in names], mtr))\\n except Exception:\\n logging.exception('metrics.precision_recall_fscore_support')\\n res = dict(zip(['{}_{}'.format(n, avg) for n in names], [-1] * 4))\\n dict_metrics.update(res)\\n return dict_metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdf079c045a45e9cd378172d3875e8f3\",\n \"score\": \"0.576438\",\n \"text\": \"def mark(self, images: List[np.ndarray]):\\n\\n n_images = len(images)\\n stack_shape = (n_images, 3) + TRANSFORM_SIZE\\n image_stack = np.zeros(stack_shape, dtype=np.float32)\\n image_sizes = np.zeros((n_images, 2), dtype=np.float32)\\n\\n for i in range(n_images):\\n image_stack[i, :, :, :] = _image_transform(images[i])\\n image_sizes[i, :] = images[i].shape[-2::-1]\\n\\n image_stack = torch.from_numpy(image_stack)\\n if self.gpu is not None:\\n image_stack = image_stack.to(self.gpu)\\n\\n with torch.no_grad():\\n marks, offsets, scores = self.onet(image_stack)\\n\\n if self.gpu is not None:\\n marks = marks.cpu().data.numpy()\\n scores = scores.cpu().data.numpy()\\n else:\\n marks = marks.data.numpy()\\n scores = scores.data.numpy()\\n\\n marks[:, 0:5] = np.expand_dims(image_sizes[:, 0], 1) * marks[:, 0:5]\\n marks[:, 5:10] = np.expand_dims(image_sizes[:, 1], 1) * marks[:, 5:10]\\n\\n scores = scores[:, 1].reshape((-1,))\\n marks = marks.reshape(-1, 2, 5).transpose(0, 2, 1).astype(np.int32)\\n\\n return marks, scores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d3b118ad335956281f2c387376eede6\",\n \"score\": \"0.57475436\",\n \"text\": \"def test(self,dataset):\\n outputs = self.use(dataset)\\n \\n costs = np.ones((len(outputs),1))\\n # Compute classification error\\n for xy,pred,cost in zip(dataset,outputs,costs):\\n x,y = xy\\n if y == pred[0]:\\n cost[0] = 0\\n\\n return outputs,costs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cc15c3cfc6db2e710c8c2d4dacd5d9c\",\n \"score\": \"0.5742178\",\n \"text\": \"def evaluate(self, input_data, target_data):\\n\\n self.confusion_matrix = np.zeros(\\n [target_data.shape[-1], target_data.shape[-1]])\\n \\n # Compute confusion matrix (one data point at a time)\\n for i in np.arange(len(input_data)):\\n output = self.run(input_data[i])\\n true_label = np.array(target_data[i], ndmin=2).T\\n \\n # Compute result and add to confusion matrix \\n # (true label in rows, prediction in columns)\\n confusion_result = true_label @ output.T\\n confusion_result[confusion_result == np.max(confusion_result)] = 1\\n confusion_result[confusion_result != 1] = 0\\n self.confusion_matrix += confusion_result\\n \\n total_predictions = np.sum(self.confusion_matrix)\\n correct_predictions = np.sum(np.diag(self.confusion_matrix))\\n false_predictions = total_predictions - correct_predictions\\n \\n # Accuracy\\n self.accuracy = correct_predictions / total_predictions\\n \\n # Recall (per class)\\n self.recall = np.array([])\\n for i in np.arange(target_data.shape[-1]):\\n self.recall = np.append(\\n self.recall, \\n self.confusion_matrix[i,i] / np.sum(self.confusion_matrix[i,:]),\\n )\\n \\n # Precision (per class)\\n self.precision = np.array([])\\n for i in np.arange(target_data.shape[-1]):\\n self.precision = np.append(\\n self.precision, \\n self.confusion_matrix[i,i] / np.sum(self.confusion_matrix[:,i]),\\n )\\n \\n # Print accuracy measures\\n print('Accuracy: ' + str('%.2f' % (self.accuracy * 100)) + '%')\\n for i in np.arange(target_data.shape[-1]): \\n print('Recall for ' + str(i) + ': ' \\n + str('%.2f' % (self.recall[i] * 100)) + '%')\\n print('Precision for ' + str(i) + ': ' \\n + str('%.2f' % (self.precision[i] * 100)) + '%')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d124ebe7be840095fcba6b88eb08cc\",\n \"score\": \"0.57381797\",\n \"text\": \"def get_classifier(images: tp.List[Image.Image], _model_path: str, class_dict: tp.Dict) -> np.ndarray:\\n # noinspection PyTypeChecker\\n images = [np.asarray(x, dtype=\\\"float32\\\") / 255 for x in images]\\n images = np.stack(images)\\n\\n _model = load_model(_model_path)\\n test_preds_raw = _model.predict(images, verbose=0)\\n\\n test_preds = np.argmax(test_preds_raw, axis=1)\\n\\n # convert (als mapping) prediction from number to str\\n return np.vectorize(lambda x: class_dict[x])(test_preds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4f8cf7b39a126aa3d41d37dd1406475\",\n \"score\": \"0.57213855\",\n \"text\": \"def classify(img_path, parameters):\\n\\n classes = np.array([b'non-cat', b'cat'])\\n num_px = 64\\n\\n img = np.array(ndimage.imread(img_path, flatten=False))\\n img_resized = scipy.misc.imresize(img, size=(num_px,num_px)).reshape((num_px*num_px*3,1))\\n\\n img_pred = predict(img_resized, parameters)\\n\\n img_class = np.squeeze(img_pred)\\n img_result = classes[int(img_class),].decode(\\\"utf-8\\\")\\n\\n return img_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38e813fae4023682802fb700aed8102e\",\n \"score\": \"0.57198906\",\n \"text\": \"def attack(self, imgs, targets, input_data_format=CHANNELS_LAST, output_data_format=CHANNELS_LAST):\\n assert input_data_format in (CHANNELS_FIRST, CHANNELS_LAST)\\n assert output_data_format in (CHANNELS_FIRST, CHANNELS_LAST)\\n r = []\\n count = len(imgs)\\n print('go up to', len(imgs))\\n # tranpose the input data format to fit the data format of the attack model\\n if input_data_format != self.data_format:\\n if input_data_format == CHANNELS_LAST:\\n # input is channels_last, transpose to channels_first\\n imgs = np.transpose(imgs, [0, 3, 1, 2])\\n else:\\n # input is channels_first, transpose to channels_last\\n imgs = np.transpose(imgs, [0, 2, 3, 1])\\n for i in range(0, count, self.batch_size):\\n print('tick', i)\\n attack_imgs = imgs[i:i + self.batch_size]\\n attack_targets = targets[i:i + self.batch_size]\\n attack_len = len(attack_targets)\\n\\n if attack_len < self.batch_size:\\n img_shape = np.asarray(attack_imgs.shape)\\n img_shape[0] = self.batch_size - attack_len\\n target_shape = np.asarray(attack_targets.shape)\\n target_shape[0] = self.batch_size - attack_len\\n attack_imgs = np.append(attack_imgs, np.zeros(img_shape), axis=0)\\n attack_targets = np.append(attack_targets, np.zeros(target_shape), axis=0)\\n r.extend(self.attack_batch(attack_imgs, attack_targets))\\n\\n output = np.array(r)[0: count]\\n # tranpose the output data format of the attack model to fit the output data format\\n if output_data_format != self.data_format:\\n if output_data_format == CHANNELS_LAST:\\n # attack model output is channels_first, transpose to channels_last\\n output = np.transpose(output, [0, 2, 3, 1])\\n else:\\n # attack model output is channels_last, transpose to channels_first\\n output = np.transpose(output, [0, 3, 1, 2])\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c70134d62d8b66e1851414062f0ec5d6\",\n \"score\": \"0.5719786\",\n \"text\": \"def make_1class_predictions(model, images, device, score_threshold=0.5):\\n images = torch.stack(images).to(device).float()\\n box_list = []\\n score_list = []\\n with torch.no_grad():\\n det = model(images, torch.tensor([1] * images.shape[0]).to(device).float())\\n for i in range(images.shape[0]):\\n boxes = det[i].detach().cpu().numpy()[:, :4]\\n scores = det[i].detach().cpu().numpy()[:, 4]\\n # for higher threshold\\n indexes = np.where((scores > score_threshold))[0]\\n boxes[:, 2] = boxes[:, 2] + boxes[:, 0]\\n boxes[:, 3] = boxes[:, 3] + boxes[:, 1]\\n box_list.append(boxes[indexes])\\n score_list.append(scores[indexes])\\n\\n return box_list, score_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fdb901f79d3594053552bb4800f5f4d\",\n \"score\": \"0.5715709\",\n \"text\": \"def train(self, patches, labels_patches, imgs, labels):\\n opts = self._options\\n\\n labels_patches = (labels_patches >= 0.5) * 1.\\n labels = (labels >= 0.5) * 1.\\n\\n num_train_patches = patches.shape[0]\\n\\n indices = np.arange(0, num_train_patches)\\n np.random.shuffle(indices)\\n\\n num_errors = 0\\n total = 0\\n\\n for batch_i, offset in enumerate(range(0, num_train_patches - opts.batch_size, opts.batch_size)):\\n batch_indices = indices[offset:offset + opts.batch_size]\\n feed_dict = {\\n self._patches_node: patches[batch_indices, :, :, :],\\n self._labels_node: labels_patches[batch_indices],\\n self._dropout_keep: opts.dropout,\\n self._image_augmentation: opts.image_augmentation,\\n }\\n\\n summary_str, _, l, predictions, predictions, step = self._session.run(\\n [self.summary_op, self._train, self._loss, self._predict_logits, self._predictions,\\n self._global_step],\\n feed_dict=feed_dict)\\n\\n print(\\\"Batch {} Step {}\\\".format(batch_i, step), end=\\\"\\\\r\\\")\\n self._summary.add(summary_str, global_step=step)\\n\\n num_errors += np.abs(labels_patches[batch_indices] - predictions).sum()\\n total += opts.batch_size\\n self._summary.add_to_pixel_missclassification_summary(num_errors, total, self._global_step)\\n\\n # from time to time do full prediction on some images\\n if step > 0 and step % opts.eval_every == 0:\\n print()\\n\\n images_to_predict = imgs[:opts.num_eval_images, :, :, :]\\n masks = self.predict(images_to_predict)\\n overlays = images.overlays(images_to_predict, masks)\\n pred_masks = ((masks > 0.5) * 1).squeeze()\\n true_masks = labels[:opts.num_eval_images, :, :].squeeze()\\n\\n self._summary.add_to_eval_summary(masks, overlays, labels, self._global_step)\\n self._summary.add_to_overlap_summary(true_masks, pred_masks, self._global_step)\\n\\n if step > 0 and step % opts.train_score_every == 0:\\n self._summary.add_to_training_summary(self.predict(imgs), labels, self._global_step)\\n\\n self._summary.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a4f2d95a3386886796ee3a2c306f0eb\",\n \"score\": \"0.5712749\",\n \"text\": \"def predict(self, instances, **kwargs):\\n imgs = map(self.decode_img, instances)\\n inputs = np.asarray(imgs)\\n outputs = self._model.predict(imgs)\\n\\n return [self.threshold(output) for output in outputs]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d75acd21d5c2b89f4addb30e9bd003ec\",\n \"score\": \"0.57005554\",\n \"text\": \"def performance_evaluation(self, metrics):\\n\\n metrics_indicator = valid_input_perform_eval(metrics)\\n if (metrics_indicator == None):\\n print(\\\"Invalid metrics!\\\")\\n return\\n else:\\n results = np.zeros((len(self.comp_img), len(metrics)))\\n overlap_measures_filter = sitk.LabelOverlapMeasuresImageFilter()\\n hausdorff_distance_filter = sitk.HausdorffDistanceImageFilter()\\n\\n for i in range(len(self.comp_img)):\\n # Cannot compute for unmatched image files\\n if (self.comp_img[i].GetSpacing() != self.radio_img[i].GetSpacing()):\\n print(\\n \\\"Spacing of corresponding image files ({}) don't match!\\\".format(self.filename[i]))\\n results[i] = [float(\\\"NaN\\\")] * len(metrics)\\n else: \\n overlap_measures_filter.Execute(\\n self.comp_img[i], self.radio_img[i])\\n Fp = overlap_measures_filter.GetFalsePositiveError()\\n Tn = 1 - Fp\\n Fn = overlap_measures_filter.GetFalseNegativeError()\\n Tp = 1 - Fn\\n if (metrics_indicator[1]):\\n results[i, 1] = overlap_measures_filter.GetDiceCoefficient()\\n if (metrics_indicator[3]):\\n results[i, 3] = overlap_measures_filter.GetJaccardCoefficient()\\n if (metrics_indicator[4]):\\n results[i, 4] = (\\n Tp * Tn - Fp * Fn) / np.sqrt((Tp + Fp) * (Tp + Fn) * (Tn + Fp) * (Tn + Fn))\\n if (metrics_indicator[0]):\\n results[i, 0] = (Tp + Tn) / (Tp + Fp + Tn + Fn)\\n hausdorff_distance_filter.Execute(\\n self.comp_img[i], self.radio_img[i])\\n if (metrics_indicator[2]):\\n results[i, 2] = hausdorff_distance_filter.GetHausdorffDistance()\\n \\n results_df = pd.DataFrame(\\n data=results, index=self.filename, columns=sorted(metrics))\\n results_df.to_csv(self.export_path + '/results.csv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2a7f015c4dc28ea47e75b144f30e0ba\",\n \"score\": \"0.56978613\",\n \"text\": \"def augment_images(self, images, labels, folder_names, target, current_step, total_steps):\\n unq, unq_inv, unq_cnt = np.unique(labels, return_inverse=True, return_counts=True)\\n\\n print(\\\"___________________________________\\\")\\n print(\\\"augment_images...\\\")\\n print(\\\"Labels:\\\", labels)\\n print(\\\"Labels Unique:\\\", unq)\\n print(\\\"Labels Unique Invers:\\\", unq_inv)\\n print(\\\"Labels Unique Count:\\\", unq_cnt)\\n\\n # count the number of images that already exist for each user\\n unique_class_indices = np.split(np.argsort(unq_inv), np.cumsum(unq_cnt[:-1]))\\n\\n target_x = images\\n target_y = labels\\n i = 0\\n for unique_class in unq:\\n print(\\\"Unique Class:\\\", unique_class)\\n indices = unique_class_indices[unique_class]\\n #print(\\\"Unique Class Indeces:\\\", indices)\\n\\n folder_name = folder_names[unique_class]\\n print(\\\"Gallery:\\\", folder_name)\\n\\n number_of_missing_images = target - len(indices)\\n\\n current_step += 1\\n self.update_status('STARTED', {'step': current_step,\\n 'total_steps': total_steps,\\n 'description': 'Augmenting'})\\n\\n print(\\\"Number of missing Images: \\\", number_of_missing_images)\\n batch_x = augmenter.augment_array_target(folder_name, number_of_missing_images)\\n batch_y = np.full(number_of_missing_images, unique_class)\\n print(\\\" \\\")\\n print(\\\"batch_y:\\\", batch_y)\\n\\n target_x.extend(batch_x)\\n target_y.extend(batch_y)\\n i += 1\\n return target_x, target_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"443d500467409c119cda9d54be802a27\",\n \"score\": \"0.5693967\",\n \"text\": \"def test_images(self, gt, pred):\\n avg_psnr = 0\\n avg_ssim = 0\\n individual_psnr = []\\n individual_ssim = []\\n\\n print('Length of gt: %s'%len(gt))\\n print('Length of pred: %s'%len(pred))\\n for i in range(len(pred)):\\n # compare to gt\\n psnr = self.PSNR(self.luminance(gt[i]), self.luminance(pred[i]))\\n ssim = self.SSIM(self.luminance(gt[i]), self.luminance(pred[i]))\\n # save results to log_path ex: 'results/experiment1/Set5/baby/1000.png'\\n # if save_images:\\n # path = os.path.join(log_path, self.name, self.names[i])\\n # gather results\\n individual_psnr.append(psnr)\\n individual_ssim.append(ssim)\\n avg_psnr += psnr\\n avg_ssim += ssim\\n\\n avg_psnr /= len(pred)\\n avg_ssim /= len(pred)\\n return avg_psnr, avg_ssim, individual_psnr, individual_ssim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dbd367f2d4d61e5ca47b1e12dc9dff\",\n \"score\": \"0.5690589\",\n \"text\": \"def im_detect(net, im, boxes, num_classes, num_subclasses):\\n\\n if boxes.shape[0] == 0:\\n scores = np.zeros((0, num_classes))\\n pred_boxes = np.zeros((0, 4*num_classes))\\n pred_views = np.zeros((0, 3*num_classes))\\n scores_subcls = np.zeros((0, num_subclasses))\\n return scores, pred_boxes, scores_subcls, pred_views\\n\\n blobs, unused_im_scale_factors = _get_blobs(im, boxes)\\n\\n # When mapping from image ROIs to feature map ROIs, there's some aliasing\\n # (some distinct image ROIs get mapped to the same feature ROI).\\n # Here, we identify duplicate feature ROIs, so we only compute features\\n # on the unique subset.\\n if cfg.DEDUP_BOXES > 0:\\n v = np.array([1, 1e3, 1e6, 1e9, 1e12])\\n hashes = np.round(blobs['rois'] * cfg.DEDUP_BOXES).dot(v)\\n _, index, inv_index = np.unique(hashes, return_index=True,\\n return_inverse=True)\\n blobs['rois'] = blobs['rois'][index, :]\\n boxes = boxes[index, :]\\n\\n # reshape network inputs\\n net.blobs['data'].reshape(*(blobs['data'].shape))\\n net.blobs['rois'].reshape(*(blobs['rois'].shape))\\n blobs_out = net.forward(data=blobs['data'].astype(np.float32, copy=False),\\n rois=blobs['rois'].astype(np.float32, copy=False))\\n\\n #for layer_name, blob in net.blobs.iteritems():\\n # print layer_name+'\\\\t'+ str(blob.data.shape)\\n if cfg.TEST.SVM:\\n # use the raw scores before softmax under the assumption they\\n # were trained as linear SVMs\\n scores = net.blobs['cls_score'].data\\n else:\\n # use softmax estimated probabilities\\n scores = blobs_out['cls_prob']\\n\\n if cfg.TEST.SUBCLS:\\n scores_subcls = blobs_out['subcls_prob']\\n else:\\n # just use class scores\\n scores_subcls = scores\\n\\n if cfg.TEST.BBOX_REG:\\n # Apply bounding-box regression deltas\\n box_deltas = blobs_out['bbox_pred']\\n pred_boxes = _bbox_pred(boxes, box_deltas)\\n pred_boxes = _clip_boxes(pred_boxes, im.shape)\\n else:\\n # Simply repeat the boxes, once for each class\\n pred_boxes = np.tile(boxes, (1, scores.shape[1]))\\n\\n if cfg.TEST.VIEWPOINT:\\n # Apply bounding-box regression deltas\\n pred_views = blobs_out['view_pred']\\n else:\\n # set to zeros\\n pred_views = np.zeros((boxes.shape[0], 3*num_classes))\\n\\n if cfg.DEDUP_BOXES > 0 and not cfg.TEST.IS_PATCH:\\n # Map scores and predictions back to the original set of boxes\\n scores = scores[inv_index, :]\\n scores_subcls = scores_subcls[inv_index, :]\\n pred_boxes = pred_boxes[inv_index, :]\\n pred_views = pred_views[inv_index, :]\\n\\n return scores, pred_boxes, scores_subcls, pred_views\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d395fb5073aac957852355b1933aa6\",\n \"score\": \"0.5686527\",\n \"text\": \"def run_batch_inference(self, images):\\n if len(images.shape) == 3:\\n logger.info(\\\"input is a single image. Use batch size 1.\\\")\\n images = np.expand_dims(images, axis=0)\\n output_tensor_dict = self._output_tensor_dict\\n image_tensor = self._graph.get_tensor_by_name('image_tensor:0')\\n\\n # Run inference\\n output_dict = self._sess.run(output_tensor_dict,\\n feed_dict={image_tensor: images})\\n\\n # all outputs are float32 numpy arrays, so convert types as appropriate\\n output_dict['num_detections'] = output_dict['num_detections'].astype(np.uint)\\n output_dict['detection_classes'] = output_dict[\\n 'detection_classes'].astype(np.uint8)\\n output_dict['detection_boxes'] = output_dict['detection_boxes']\\n output_dict['detection_scores'] = output_dict['detection_scores']\\n return output_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"389aee3b8584e90914b7583fc383d57c\",\n \"score\": \"0.56823885\",\n \"text\": \"def _run_inference_on_image_tensors(self, images: tf.Tensor):\\n model_params = self._params.task.model\\n with tf.device('cpu:0'):\\n images = tf.cast(images, dtype=tf.float32)\\n\\n # Tensor Specs for map_fn outputs (images, anchor_boxes, and image_info).\\n images_spec = tf.TensorSpec(shape=self._input_image_size + [3],\\n dtype=tf.float32)\\n\\n num_anchors = model_params.anchor.num_scales * len(\\n model_params.anchor.aspect_ratios) * 4\\n anchor_shapes = []\\n for level in range(model_params.min_level, model_params.max_level + 1):\\n anchor_level_spec = tf.TensorSpec(\\n shape=[\\n self._input_image_size[0] // 2**level,\\n self._input_image_size[1] // 2**level, num_anchors\\n ],\\n dtype=tf.float32)\\n anchor_shapes.append((str(level), anchor_level_spec))\\n\\n image_shape_spec = tf.TensorSpec(shape=[2,], dtype=tf.float32)\\n\\n images, anchor_boxes, image_shape = tf.nest.map_structure(\\n tf.identity,\\n tf.map_fn(\\n self._build_inputs,\\n elems=images,\\n fn_output_signature=(images_spec, dict(anchor_shapes),\\n image_shape_spec),\\n parallel_iterations=32))\\n\\n detections = self._model.call(\\n images=images,\\n image_shape=image_shape,\\n anchor_boxes=anchor_boxes,\\n training=False)\\n\\n final_outputs = {\\n 'detection_boxes': detections['detection_boxes'],\\n 'detection_scores': detections['detection_scores'],\\n 'detection_classes': detections['detection_classes'],\\n 'num_detections': detections['num_detections']\\n }\\n if 'detection_masks' in detections.keys():\\n final_outputs['detection_masks'] = detections['detection_masks']\\n\\n return final_outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bb77db78dc33f23a39c5affb6f6d02a\",\n \"score\": \"0.5681441\",\n \"text\": \"def _check_targets(y_true, y_pred):\\n check_consistent_length(y_true, y_pred)\\n type_true = type_of_target(y_true)\\n type_pred = type_of_target(y_pred)\\n\\n y_type = set([type_true, type_pred])\\n if y_type == set([\\\"binary\\\", \\\"multiclass\\\"]):\\n y_type = set([\\\"multiclass\\\"])\\n\\n if y_type == set([\\\"multiclass-multioutput\\\", \\\"multilabel-indicator\\\"]):\\n y_type = set([\\\"multiclass-multioutput\\\"])\\n\\n if len(y_type) > 1:\\n raise ValueError(\\\"Classification metrics can't handle a mix of {0} \\\"\\n \\\"and {1} targets\\\".format(type_true, type_pred))\\n\\n # We can't have more than one value on y_type => The set is no more needed\\n y_type = y_type.pop()\\n\\n if (y_type not in [\\\"binary\\\", \\\"multiclass\\\", \\\"multilabel-indicator\\\",\\n \\\"multiclass-multioutput\\\"]):\\n raise ValueError(\\\"{0} is not supported\\\".format(y_type))\\n\\n if y_type in [\\\"binary\\\", \\\"multiclass\\\"]:\\n y_true = column_or_1d(y_true)\\n y_pred = column_or_1d(y_pred)\\n if y_type == \\\"binary\\\":\\n unique_values = np.union1d(y_true, y_pred)\\n if len(unique_values) > 2:\\n y_type = \\\"multiclass\\\"\\n\\n if y_type.startswith('multilabel'):\\n y_type = 'multilabel-indicator'\\n\\n return y_type, y_true, y_pred\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ae5e1eabf3713ac8881f4d42af0d5fb\",\n \"score\": \"0.5680647\",\n \"text\": \"def recognize(self, images: typing.List[typing.Union[np.ndarray, str]]) -> typing.List[str]:\\n height, width = self.model.input_shape[1:-1]\\n images = [tools.read_and_fit(image, width=width, height=height) for image in images]\\n for index, image in enumerate(images):\\n # It's a grayscale image with no\\n # channel dimension so we need to add a\\n # dimension first or possibly convert to color.\\n if len(image.shape) == 2 and not self.color:\\n images[index] = image[..., np.newaxis]\\n continue\\n # It's grayscale but we need color.\\n if len(image.shape) == 2 and self.color:\\n images[index] = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\\n continue\\n\\n # It's a color image but we need grayscale\\n if image.shape[2] == 3 and not self.color:\\n images[index] = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)[..., np.newaxis]\\n continue\\n\\n # It's a grayscale image with a color dimension\\n # but we need color.\\n if image.shape[2] == 1 and self.color:\\n images[index] = cv2.cvtColor(image[..., 0], cv2.COLOR_GRAY2RGB)\\n\\n images = np.array(images)\\n X = keras_applications.imagenet_utils.preprocess_input(images,\\n mode=self.preprocessing_mode,\\n data_format='channels_last',\\n backend=keras.backend)\\n predictions = self.prediction_model.predict(X)\\n return decode(predictions=predictions, alphabet=self.alphabet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4723c6104635da5600eeddd7283cb41\",\n \"score\": \"0.5679709\",\n \"text\": \"def process_images_for_prediction(chess_piece_images):\\n processed_chess_piece_images = []\\n for chess_piece_image in chess_piece_images:\\n processed_chess_piece_image = utils.resize_given_image(chess_piece_image, IMAGE_DIMS)\\n processed_chess_piece_images.append(processed_chess_piece_image)\\n\\n processed_chess_piece_images = np.array(processed_chess_piece_images)\\n processed_chess_piece_images = processed_chess_piece_images.astype('float32')\\n processed_chess_piece_images /= 255\\n\\n return processed_chess_piece_images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"884e2689fcb76cdb71241aea19c8aa24\",\n \"score\": \"0.56754535\",\n \"text\": \"def preprocess_validate(images_names, color_data, tensors, softmax_layer,\\n session, y_true, model):\\n # load, remove mean, crop images \\n dataset = []\\n for image_name in images_names:\\n im = cv2.imread(image_name)\\n im[0] = im[0] - color_data['r_mean']\\n im[1] = im[1] - color_data['g_mean']\\n im[2] = im[2] - color_data['b_mean']\\n pad_crop_im = pad_crop(im, model=model)\\n pad_crop_bgr_im = cv2.cvtColor(np.uint8(pad_crop_im), cv2.COLOR_RGB2BGR)\\n dataset.append(pad_crop_bgr_im)\\n # convert list of images to 4-D array\\n dataset = np.array(dataset)\\n # normalize images\\n dataset = norm_data(dataset)\\n\\n # predict labels for provided images names\\n predictions = session.run(softmax_layer, feed_dict={tensors['input_layer']: dataset,\\n tensors['labels_tensor']: y_true,\\n tensors['hold_prob']: 1})\\n return predictions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b22d68c06a7d96ff9b93a480055c68eb\",\n \"score\": \"0.5671083\",\n \"text\": \"def im_test(self, detector: Detector, image: np.ndarray, labels: dict) -> tuple:\\n start = datetime.now()\\n objects = detector.predict(image)\\n # initiate a dictionary for found labels, and lists for accuracies and probabilities.\\n time = datetime.now() - start\\n logging.info(\\n f\\\"Run time is {time.total_seconds()}, the threshold is {self.thresholds['runtime'].total_seconds()}\\\")\\n test_dict = {}\\n tp = 0\\n fp = 0\\n fn = 0\\n probabilities = []\\n # fill test dictionary with labels found.\\n for dic in objects:\\n if dic['label'] in test_dict:\\n test_dict[dic['label']] += 1\\n else:\\n test_dict[dic['label']] = 1\\n probabilities.append(dic['prob'])\\n # check false negative.\\n for key in labels.keys():\\n if key in test_dict:\\n if test_dict[key] - labels[key] == 0:\\n tp += labels[key]\\n elif labels[key] > test_dict[key]:\\n tp += test_dict[key]\\n fn += labels[key] - test_dict[key]\\n else:\\n tp += labels[key]\\n fp += test_dict[key] - labels[key]\\n logging.info(f\\\"Found {test_dict[key]} {key}s out of {labels[key]}.\\\")\\n else:\\n fn += labels[key]\\n logging.warning(f\\\"Found {0} {key}s out of {labels[key]}.\\\")\\n # check false positive\\n for key in test_dict.keys():\\n if key not in labels:\\n fp += test_dict[key]\\n logging.warning(f\\\"False positive label was detected.\\\")\\n # calculate recall and probability.\\n recall = tp / (tp + fn)\\n precision = tp / (tp + fp)\\n mean_prob = mean(probabilities)\\n logging.info(f\\\"Recall is {round(recall, 2)}, the threshold is {self.thresholds['recall']}.\\\")\\n logging.info(f\\\"Precision is {round(precision, 2)}, the threshold is {self.thresholds['precision']}.\\\")\\n logging.info(f\\\"Mean probability is {round(mean_prob, 2)}, the threshold is {self.thresholds['probability']}.\\\")\\n if recall >= self.thresholds['recall'] and mean_prob >= self.thresholds['probability'] and time <= \\\\\\n self.thresholds['runtime']:\\n logging.warning(\\\"The test has passed successfully.\\\")\\n else:\\n logging.warning(\\\"The test has failed.\\\")\\n\\n return recall, mean_prob, precision, time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa752013b4b43a06c77ed23d5deeec39\",\n \"score\": \"0.5670382\",\n \"text\": \"def _eval_classifier(self):\\n\\n y_pred_prior = self.model_prior.predict(self.X_test_prior)\\n y_pred_post = self.model_post.predict(self.X_test_post)\\n\\n y_test_ = pd.DataFrame(self.y_test)\\n y_pred_prior = pd.DataFrame(y_pred_prior, columns=y_test.columns)\\n y_pred_post = pd.DataFrame(y_pred_post, columns=y_test.columns)\\n\\n y_pred_prior['source'] = \\\"prior\\\"\\n y_pred_post['source'] = \\\"post\\\"\\n y_test['source'] = \\\"test\\\"\\n\\n y_ = pd.concat([y_pred_prior, y_pred_post, y_test])\\n cols = [col for col in y_.columns if col != \\\"source\\\"]\\n y_ = pd.get_dummies(y_, columns=cols)\\n\\n y_pred_prior = y_[y_.source == 'prior'].drop('source', axis=1).values\\n y_pred_post = y_[y_.source == 'post'].drop('source', axis=1).values\\n y_test = y_[y_.source == 'test'].drop('source', axis=1).values\\n\\n y_.drop('source', axis=1, inplace=True)\\n class_labels = y_.columns\\n\\n res = pd.DataFrame([])\\n\\n if (len(y_test[0]) == 2):\\n # for binary classification\\n # only take position 1, assuming position 1 is the true label\\n iters = [1]\\n else:\\n # for multiclass classification\\n iters = range(len(y_test[0]))\\n\\n for i in iters:\\n\\n precision_prior = precision_score(y_test[:,i], y_pred_prior[:,i])\\n recall_prior = recall_score(y_test[:,i], y_pred_prior[:,i])\\n acc_prior = accuracy_score(y_test[:,i], y_pred_prior[:,i])\\n f1_prior = f1_score(y_test[:,i], y_pred_prior[:,i])\\n try:\\n auc_prior = roc_auc_score(y_test[:,i], y_pred_prior[:,i])\\n except ValueError:\\n auc_prior = \\\"NA\\\"\\n\\n precision_post = precision_score(y_test[:,i], y_pred_post[:,i])\\n recall_post = recall_score(y_test[:,i], y_pred_post[:,i])\\n acc_post = accuracy_score(y_test[:,i], y_pred_post[:,i])\\n f1_post = f1_score(y_test[:,i], y_pred_post[:,i])\\n try:\\n auc_post = roc_auc_score(y_test[:,i], y_pred_post[:,i])\\n except ValueError:\\n auc_post = \\\"NA\\\"\\n\\n multiindex = [(str(class_labels[i]), 'Prior'),\\n (str(class_labels[i]), 'Post')]\\n\\n index = pd.MultiIndex.from_tuples(multiindex,\\n names=['Class', 'Data Type'])\\n\\n score = pd.DataFrame({\\n 'Accuracy': [acc_prior, acc_post],\\n 'Precision': [precision_prior, precision_post],\\n 'Recall': [recall_prior, recall_post],\\n 'F1': [f1_prior, f1_post],\\n 'AUC': [auc_prior, auc_post]\\n },\\n index=index\\n )\\n\\n res = pd.concat([res, score])\\n\\n self.ml_report = res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b88f7d1e0692e0c8f6185bb880d15d6\",\n \"score\": \"0.56663907\",\n \"text\": \"def accuracy(classifications, targets):\\n return (classifications == targets).mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4acd753d0c664fac050befba38ed9ae\",\n \"score\": \"0.5664122\",\n \"text\": \"def inference(\\n net, images, device=\\\"cuda\\\", prob_thresh=0.05, nms_iou_thresh=0.3,\\n resize=True\\n):\\n if not isinstance(images, list):\\n images = [images]\\n\\n orig_image_shapes = [image.shape for image in images]\\n\\n # Resize input images to match shape of images on which net was trained.\\n if resize:\\n net_image_shape = (net.net_info[\\\"height\\\"], net.net_info[\\\"width\\\"])\\n images = [\\n cv2.resize(image, net_image_shape)\\n if image.shape[:2] != net_image_shape\\n else image for image in images\\n ]\\n\\n # Stack images along new batch axis, flip channel axis so channels are RGB\\n # instead of BGR, transpose so channel axis comes before row/column axes,\\n # and convert pixel values to FP32. Do this in one step to ensure array\\n # is contiguous before passing to torch tensor constructor.\\n inp = np.transpose(np.flip(np.stack(images), 3), (0, 3, 1, 2)).astype(\\n np.float32) / 255.0\\n\\n inp = torch.tensor(inp, device=device)\\n out = net.forward(inp)\\n\\n bbox_xywh = out[\\\"bbox_xywh\\\"].detach().cpu().numpy()\\n class_prob = out[\\\"class_prob\\\"].cpu().numpy()\\n class_idx = out[\\\"class_idx\\\"].cpu().numpy()\\n\\n thresh_mask = class_prob >= prob_thresh\\n\\n # Perform post-processing on each image in the batch and return results.\\n results = []\\n for i in range(bbox_xywh.shape[0]):\\n image_bbox_xywh = bbox_xywh[i, thresh_mask[i, :], :]\\n image_class_prob = class_prob[i, thresh_mask[i, :]]\\n image_class_idx = class_idx[i, thresh_mask[i, :]]\\n\\n image_bbox_xywh[:, [0, 2]] *= orig_image_shapes[i][1]\\n image_bbox_xywh[:, [1, 3]] *= orig_image_shapes[i][0]\\n image_bbox_tlbr = cxywh_to_tlbr(image_bbox_xywh.astype(np.int))\\n\\n idxs_to_keep = non_max_suppression(\\n image_bbox_tlbr, image_class_prob, class_idx=image_class_idx,\\n iou_thresh=nms_iou_thresh\\n )\\n\\n results.append(\\n [\\n image_bbox_tlbr[idxs_to_keep, :],\\n image_class_prob[idxs_to_keep],\\n image_class_idx[idxs_to_keep]\\n ]\\n )\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"058723c29ce82a331bc0d26241be5169\",\n \"score\": \"0.56624264\",\n \"text\": \"def run_inference_on_images(image_list, output_dir):\\n image_to_labels = defaultdict(list)\\n\\n create_graph()\\n\\n with tf.compat.v1.Session() as sess:\\n # Some useful tensors:\\n # 'softmax:0': A tensor containing the normalized prediction across\\n # 1000 labels.\\n # 'pool_3:0': A tensor containing the next-to-last layer containing 2048\\n # float description of the image.\\n # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG\\n # encoding of the image.\\n # Runs the softmax tensor by feeding the image_data as input to the graph.\\n softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')\\n\\n for image_index, image in enumerate(image_list):\\n try:\\n print(\\\"parsing\\\", image_index, image, \\\"\\\\n\\\")\\n if not tf.io.gfile.exists(image):\\n tf.logging.fatal('File does not exist %s', image)\\n\\n with tf.io.gfile.GFile(image, 'rb') as f:\\n image_data = f.read()\\n\\n process = psutil.Process(os.getpid())\\n mem3 = process.memory_info().rss\\n print('Memory After reading file', mem3 / (1024 ** 2), 'MB')\\n\\n predictions = sess.run(softmax_tensor,\\n {'DecodeJpeg/contents:0': image_data})\\n\\n predictions = np.squeeze(predictions)\\n\\n ###\\n # Get penultimate layer weights\\n ###\\n\\n feature_tensor = sess.graph.get_tensor_by_name('pool_3:0')\\n feature_set = sess.run(feature_tensor,\\n {'DecodeJpeg/contents:0': image_data})\\n feature_vector = np.squeeze(feature_set)\\n outfile_name = os.path.basename(image) + \\\".npz\\\"\\n out_path = os.path.join(output_dir, outfile_name)\\n np.savetxt(out_path, feature_vector, delimiter=',')\\n\\n # Creates node ID --> English string lookup.\\n node_lookup = NodeLookup()\\n\\n process = psutil.Process(os.getpid())\\n mem4 = process.memory_info().rss\\n print('Memory before prediction', mem4 / (1024 ** 2), 'MB')\\n\\n top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]\\n for node_id in top_k:\\n human_string = node_lookup.id_to_string(node_id)\\n score = predictions[node_id]\\n print(\\\"results for\\\", image)\\n print('%s (score = %.5f)' % (human_string, score))\\n print(\\\"\\\\n\\\")\\n\\n image_to_labels['image_labels'].append(\\n {\\n \\\"labels\\\": human_string,\\n \\\"score\\\": str(score)\\n }\\n )\\n process = psutil.Process(os.getpid())\\n mem5 = process.memory_info().rss\\n print('Memory After Prediction', mem5 / (1024 ** 2), 'MB')\\n\\n # # detect number of faces\\n num_faces = detect_num_faces(image)\\n image_to_labels['number_of_faces'].append(num_faces)\\n\\n process = psutil.Process(os.getpid())\\n mem6 = process.memory_info().rss\\n print('Memory After Face Detection', mem6 / (1024 ** 2), 'MB')\\n\\n # close the open file handlers\\n proc = psutil.Process()\\n open_files = proc.open_files()\\n\\n for open_file in open_files:\\n file_handler = getattr(open_file, \\\"fd\\\")\\n os.close(file_handler)\\n except:\\n print('could not process image index', image_index, 'image', image)\\n\\n return image_to_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e372f3e44e6ba24d3b98edb532d1ce63\",\n \"score\": \"0.5662352\",\n \"text\": \"def _process_batch(sess, original_images, semantic_predictions, probs, image_names,\\n image_heights, image_widths, image_id_offset, save_dir,\\n raw_save_dir, train_id_to_eval_id=None):\\n (original_images,\\n semantic_predictions,\\n probs,\\n image_names,\\n image_heights,\\n image_widths) = sess.run([original_images, semantic_predictions, probs,\\n image_names, image_heights, image_widths])\\n\\n num_image = semantic_predictions.shape[0]\\n for i in range(num_image):\\n image_height = np.squeeze(image_heights[i])\\n image_width = np.squeeze(image_widths[i])\\n semantic_prediction = np.squeeze(semantic_predictions[i])\\n crop_semantic_prediction = semantic_prediction[:image_height, :image_width]\\n img_name = f\\\"{image_names[i].decode().split('/')[-1].split('.')[0]}\\\"\\n\\n if FLAGS.also_save_raw_predictions:\\n image_filename = os.path.basename(image_names[i])\\n\\n if train_id_to_eval_id is not None:\\n crop_semantic_prediction = _convert_train_id_to_eval_id(\\n crop_semantic_prediction,\\n train_id_to_eval_id)\\n\\n save_annotation.save_annotation(\\n prediction_with_COCO(crop_semantic_prediction, probs[i], concept_dict[f\\\"{FLAGS.folder}/{img_name}.jpg\\\"]),\\n raw_save_dir,\\n f\\\"{img_name}\\\",\\n add_colormap=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d37b79ca8c7a21575d2f346b7a9f68e0\",\n \"score\": \"0.5660186\",\n \"text\": \"def visualize_training(self, batched_inputs, results, feedbacks=None):\\r\\n from detectron2.utils.visualizer import Visualizer\\r\\n\\r\\n assert len(batched_inputs) == len(\\r\\n results\\r\\n ), \\\"Cannot visualize inputs and results of different sizes\\\"\\r\\n storage = get_event_storage()\\r\\n max_boxes = 20\\r\\n\\r\\n image_index = 0 # only visualize a single image\\r\\n img = batched_inputs[image_index][\\\"image\\\"]\\r\\n img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format)\\r\\n v_gt = Visualizer(img, None)\\r\\n v_gt = v_gt.overlay_instances(boxes=batched_inputs[image_index][\\\"instances\\\"].gt_boxes)\\r\\n anno_img = v_gt.get_image()\\r\\n processed_results = detector_postprocess(results[image_index], img.shape[0], img.shape[1])\\r\\n predicted_boxes = processed_results.pred_boxes.tensor.detach().cpu().numpy()\\r\\n\\r\\n v_pred = Visualizer(img, None)\\r\\n v_pred = v_pred.overlay_instances(boxes=predicted_boxes[0:max_boxes])\\r\\n prop_img = v_pred.get_image()\\r\\n\\r\\n num_classes = self.num_classes\\r\\n if feedbacks is not None:\\r\\n v_feedback_gt = Visualizer(img, None)\\r\\n instance = feedbacks[0].to(torch.device(\\\"cpu\\\"))\\r\\n v_feedback_gt = v_feedback_gt.overlay_instances(\\r\\n boxes=instance.proposal_boxes[instance.gt_classes != num_classes])\\r\\n feedback_gt_img = v_feedback_gt.get_image()\\r\\n\\r\\n v_feedback_gf = Visualizer(img, None)\\r\\n v_feedback_gf = v_feedback_gf.overlay_instances(\\r\\n boxes=instance.proposal_boxes[instance.gt_classes == num_classes])\\r\\n feedback_gf_img = v_feedback_gf.get_image()\\r\\n\\r\\n vis_img = np.vstack((anno_img, prop_img, feedback_gt_img, feedback_gf_img))\\r\\n vis_img = vis_img.transpose(2, 0, 1)\\r\\n vis_name = f\\\"Top: GT; Middle: Pred; Bottom: Feedback GT, Feedback GF\\\"\\r\\n else:\\r\\n vis_img = np.concatenate((anno_img, prop_img), axis=1)\\r\\n vis_img = vis_img.transpose(2, 0, 1)\\r\\n vis_name = \\\"Left: GT bounding boxes; Right: Predicted proposals\\\"\\r\\n\\r\\n storage.put_image(vis_name, vis_img)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef6c8ecd231cec6e87fb351b6ca61dfe\",\n \"score\": \"0.5654193\",\n \"text\": \"def process(self):\\n print('Processing images...')\\n processed = self.model.predict(self.source['process'], batch_size=self.batch_size, verbose=1)\\n save_img(self.res_dir, processed)\\n print('Complete')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85f10becdce5d3df79a6f19e658a365f\",\n \"score\": \"0.5653114\",\n \"text\": \"def postprocess(results, filenames, batch_size):\\n if len(results) != 1:\\n raise Exception(\\\"expected 1 result, got {}\\\".format(len(results)))\\n\\n batched_result = list(results.values())[0]\\n if len(batched_result) != batch_size:\\n raise Exception(\\\"expected {} results, got {}\\\".format(batch_size, len(batched_result)))\\n if len(filenames) != batch_size:\\n raise Exception(\\\"expected {} filenames, got {}\\\".format(batch_size, len(filenames)))\\n\\n for (index, result) in enumerate(batched_result):\\n print(\\\"Image '{}':\\\".format(filenames[index]))\\n for cls in result:\\n print(\\\" {} ({}) = {}\\\".format(cls[0], cls[2], cls[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8a3392028eda8c4acc7fb3037eb3c8e\",\n \"score\": \"0.56511486\",\n \"text\": \"def inference_single_image(self, pred_labels, anchors):\\n pred_classes = pred_labels[\\\"class\\\"]\\n pred_bbox_offsets = pred_labels[\\\"bbox_offset\\\"]\\n pred_dim_offsets = pred_labels[\\\"dim_offset\\\"]\\n pred_orient_offsets = pred_labels[\\\"orient_offset\\\"]\\n pred_bin_confs = pred_labels[\\\"bin_conf\\\"]\\n pred_classes_all = []\\n pred_scores_all = []\\n pred_bboxes_all = []\\n pred_dims_all = []\\n pred_orients_all = []\\n\\n # Iterate over every feature level\\n for i, anchors_i in enumerate(anchors):\\n pred_classes_i = pred_classes[i]\\n pred_bbox_offsets_i = pred_bbox_offsets[i]\\n pred_dim_offsets_i = pred_dim_offsets[i]\\n pred_orient_offsets_i = pred_orient_offsets[i]\\n pred_bin_confs_i = pred_bin_confs[i]\\n # (HxWxAxK,)\\n pred_classes_i = pred_classes_i.flatten().sigmoid()\\n anchors_i = anchors_i.to(pred_classes_i.device)\\n # Keep top k top scoring indices only.\\n num_topk = min(self.topk_candidates, pred_bbox_offsets_i.size(0))\\n # torch.sort is actually faster than .topk (at least on GPUs)\\n pred_scores_i, topk_idxs = pred_classes_i.sort(descending=True)\\n pred_scores_i = pred_scores_i[:num_topk]\\n topk_idxs = topk_idxs[:num_topk]\\n\\n # filter out the proposals with low confidence score\\n keep_idxs = pred_scores_i > self.score_threshold\\n if keep_idxs.float().sum() <= 0:\\n continue\\n pred_scores_i = pred_scores_i[keep_idxs]\\n topk_idxs = topk_idxs[keep_idxs]\\n\\n anchor_idxs = topk_idxs // self.num_classes\\n classes_idxs = topk_idxs % self.num_classes\\n\\n pred_bbox_offsets_i = pred_bbox_offsets_i[anchor_idxs]\\n anchors_i = anchors_i[anchor_idxs]\\n pred_dim_offsets_i = pred_dim_offsets_i[anchor_idxs]\\n pred_orient_offsets_i = pred_orient_offsets_i[anchor_idxs]\\n pred_bin_confs_i = pred_bin_confs_i[anchor_idxs]\\n # predict boxes\\n pred_bbox_i = self._ground_truth_coder.decode_bbox(pred_bbox_offsets_i, anchors_i.tensor)\\n # predict dimensions\\n pred_dims_i = self._ground_truth_coder.decode_dimension(pred_dim_offsets_i, classes_idxs)\\n # predict orientation\\n pred_orients_i = self._ground_truth_coder.decode_orient(pred_orient_offsets_i, pred_bin_confs_i)\\n pred_bboxes_all.append(pred_bbox_i)\\n pred_scores_all.append(pred_scores_i)\\n pred_classes_all.append(classes_idxs)\\n pred_dims_all.append(pred_dims_i)\\n pred_orients_all.append(pred_orients_i)\\n\\n result = ParamList(self.in_size, is_train=False)\\n if len(pred_bboxes_all) > 0:\\n pred_bboxes_all, pred_scores_all, pred_classes_all, pred_dims_all, pred_orients_all = [\\n torch.cat(x) for x in [pred_bboxes_all, pred_scores_all, pred_classes_all, pred_dims_all, pred_orients_all]\\n ]\\n keep = batched_nms(pred_bboxes_all, pred_scores_all, pred_classes_all, self.nms_threshold)\\n keep = keep[: self.max_detections_per_image]\\n result.add_field(\\\"class\\\", pred_classes_all[keep])\\n result.add_field(\\\"score\\\", pred_scores_all[keep])\\n result.add_field(\\\"bbox\\\", pred_bboxes_all[keep])\\n result.add_field(\\\"dimension\\\", pred_dims_all[keep])\\n result.add_field(\\\"orientation\\\", pred_orients_all[keep])\\n else:\\n result.add_field(\\\"class\\\", pred_classes_all)\\n result.add_field(\\\"score\\\", pred_scores_all)\\n result.add_field(\\\"bbox\\\", pred_bboxes_all)\\n result.add_field(\\\"dimension\\\", pred_dims_all)\\n result.add_field(\\\"orientation\\\", pred_orients_all)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ebfa5991161e0a8595de7f1b8f3f192\",\n \"score\": \"0.5649904\",\n \"text\": \"def preprocess_image(self, batched_inputs):\\n images = [x[\\\"image\\\"].to(self.device) / 255. for x in batched_inputs]\\n images = [(x - self.pixel_mean) / self.pixel_std for x in images]\\n images, masks = ImageList.from_tensors(images, self.backbone.size_divisibility)\\n return images, masks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e049c72db629467f0b847333deda2842\",\n \"score\": \"0.56466806\",\n \"text\": \"def save_interm_result(imgs, outputs, targets, save_dir, epoch):\\n def _denormalize_img(img):\\n return (img*127.5) + 127.5\\n\\n num_imgs = len(imgs)\\n img_size = imgs.shape[-2], imgs.shape[-1]\\n output_size = outputs.shape[-2], outputs.shape[-1]\\n\\n num_rows = math.ceil(num_imgs**0.5)\\n num_cols = round(num_imgs**0.5)\\n\\n img_mask = np.zeros([img_size[0]*num_rows, img_size[1]*num_cols])\\n output_mask = np.zeros([output_size[0]*num_rows, output_size[1]*num_cols])\\n target_mask = np.zeros([output_size[0]*num_rows, output_size[1]*num_cols])\\n\\n imgs_arr = imgs.cpu().data.numpy()[:, 0, :, :]\\n targets_arr = targets.cpu().data.numpy()[:, 0, :, :]\\n outputs_arr = outputs.cpu().data.numpy()[:, 0, :, :]\\n\\n for i, (img, target, output) in enumerate(zip(imgs_arr, targets_arr, outputs_arr)):\\n row = i // num_cols\\n col = i % num_cols\\n img_mask[row*img_size[0]: (row+1)*img_size[0], col*img_size[1]:(col+1)*img_size[1]] = img.copy()\\n output_mask[row*output_size[0]: (row+1)*output_size[0], col*output_size[1]:(col+1)*output_size[1]] = output.copy()\\n target_mask[row*output_size[0]: (row+1)*output_size[0], col*output_size[1]:(col+1)*output_size[1]] = target.copy()\\n\\n\\n for i, mask in enumerate([img_mask, output_mask, target_mask]):\\n save_path = os.path.join(save_dir, f'{epoch}_{i}.jpg')\\n cv2.imwrite(save_path, _denormalize_img(mask))\\n return img_mask, output_mask, target_mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1e6b970fff5215fc10cbbf808de8406\",\n \"score\": \"0.5643073\",\n \"text\": \"def classify(data, label, weights):\\n\\tdataMat, _ = normalize(data); labelMat= np.mat(label).T\\n\\tm, n = np.shape(dataMat)\\n\\th = sigmoid(dataMat * weights)\\n\\ty = np.float32(h > 0.5)\\n\\taccuracy = np.sum(y == labelMat) / m\\n\\tprint('Accuracy {:.2f}%'.format(accuracy * 100))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eac0ab26285d507b370f09ef2888594\",\n \"score\": \"0.5642408\",\n \"text\": \"def train_from_images(self, images):\\n # TODO: Allow filenames to be a list of actual image objects too?\\n raw_patches, raw_originals = self._random_patches_from_images(images)\\n if len(raw_patches) == 0:\\n raise Exception(\\\"No patches found, maybe your thresholds are too strict?\\\")\\n mixture = ag.stats.BernoulliMixture(self.num_parts, raw_patches, init_seed=0)\\n # Also store these in \\\"settings\\\"\\n mixture.run_EM(1e-8, min_probability=self.settings['min_probability'])\\n ag.info(\\\"Done.\\\")\\n\\n # Reject weak parts\\n scores = np.empty(self.num_parts) \\n for i in range(self.num_parts):\\n part = mixture.templates[i]\\n sh = part.shape\\n p = part.reshape((sh[0]*sh[1], sh[2]))\\n \\n #import ipdb; ipdb.set_trace()\\n pec = p.mean(axis=0)\\n \\n N = np.sum(p * np.log(p/pec) + (1-p)*np.log((1-p)/(1-pec)))\\n D = np.sqrt(np.sum(np.log(p/(1-p))**2 * p * (1-p)))\\n\\n scores[i] = N/D \\n\\n # Only keep with a certain score\\n visparts = mixture.remix(raw_originals)\\n \\n self.parts = mixture.templates[scores > 1]\\n self.visparts = visparts[scores > 1]\\n self.num_parts = self.parts.shape[0]\\n \\n # Update num_parts\\n \\n # Store the stuff in the instance\\n #self.parts = mixture.templates\\n #self.visparts = mixture.remix(raw_originals)\\n\\n self._preprocess_logs()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7269b0039d93fe959000a1f2230a2dce\",\n \"score\": \"0.563852\",\n \"text\": \"def __call__(self, *args, **kwargs):\\n image = kwargs.get('image')\\n class_ids, scores, _ = self.detect_objects(image, show=False)\\n labels = self.get_object_labels(class_ids, scores)\\n return labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15a6f69e87a58c2a8d487d5289ca9805\",\n \"score\": \"0.5634711\",\n \"text\": \"def images_to_predicted_labels(self, network, images):\\n predicted_densities, predicted_counts = network(images)\\n return predicted_densities, predicted_counts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8426042ea9369652e4089e4776618336\",\n \"score\": \"0.56341326\",\n \"text\": \"def image_analysis(self, *args, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91b87ed616f51ddd9668767836d16f11\",\n \"score\": \"0.5632717\",\n \"text\": \"def postprocess(self, prediction_dict, true_image_shapes, **params):\\n with tf.variable_scope('PostprocessorInference'):\\n detections_dict = {}\\n\\n semantic_prediction, semantic_prediction_probability = \\\\\\n self._postprocess_logits(\\n prediction_dict['semantic_predictions'], true_image_shapes)\\n detections_dict[\\n fields.DetectionResultFields.detection_semantic] \\\\\\n = semantic_prediction\\n detections_dict[\\n fields.DetectionResultFields.detection_semantic_heatmap] \\\\\\n = semantic_prediction_probability\\n\\n instance_prediction, instance_prediction_probability = \\\\\\n self._postprocess_logits(\\n prediction_dict['instance_predictions'], true_image_shapes)\\n detections_dict[\\n fields.DetectionResultFields.detection_masks] \\\\\\n = instance_prediction\\n detections_dict[\\n fields.DetectionResultFields.detection_masks_heatmap] \\\\\\n = instance_prediction_probability\\n\\n # [Combining Instances] ************************************************\\n mask_image = tf.cast(instance_prediction, dtype=tf.uint8)\\n semantic_image = tf.cast(semantic_prediction, dtype=tf.uint8)\\n zero_image = tf.zeros_like(mask_image, dtype=tf.uint8)\\n mask_image = tf.concat([mask_image, zero_image, zero_image], axis=-1)\\n semantic_image = tf.concat([zero_image, zero_image, semantic_image],\\n axis=-1)\\n panoptic_image = mask_image + semantic_image\\n detections_dict[\\n fields.DetectionResultFields.detection_masks_image] = \\\\\\n mask_image\\n detections_dict[\\n fields.DetectionResultFields.detection_panoptic_image] = \\\\\\n panoptic_image\\n params = (mask_image, panoptic_image)\\n self._add_summary_panoptic_image(params)\\n # ************************************************ [Combining Instances]\\n return detections_dict\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994094,"cells":{"query_id":{"kind":"string","value":"3209a5dac48fd4a7f382b7f2a4c9652b"},"query":{"kind":"string","value":"Test eq as arrayarray for failing with different data positions in array 2 Array code d."},"positive_passages":{"kind":"list like","value":[{"docid":"ced6c869f5ee002089761d83cdfd7e8a","score":"0.79888207","text":"def test_eq_numpos_array_array_c2(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1, self.data2fail)])\n\n\t\t\t\tresult = arrayfunc.eq(self.data1, self.data2fail)\n\n\t\t\t\tself.assertFalse(result)\n\t\t\t\tself.assertIsInstance(result, bool)\n\t\t\t\tself.assertEqual(expected, result)\n\n\t\t\t\n\t\t\t# Shift the data one position.\n\t\t\tself.data2.append(self.data2.pop(0))","title":""}],"string":"[\n {\n \"docid\": \"ced6c869f5ee002089761d83cdfd7e8a\",\n \"score\": \"0.79888207\",\n \"text\": \"def test_eq_numpos_array_array_c2(self):\\n\\t\\tfor testpos in range(self.testarraylen):\\n\\t\\t\\twith self.subTest(msg='Failed with posistion', testpos = testpos):\\n\\n\\t\\t\\t\\texpected = all([x == y for x,y in zip(self.data1, self.data2fail)])\\n\\n\\t\\t\\t\\tresult = arrayfunc.eq(self.data1, self.data2fail)\\n\\n\\t\\t\\t\\tself.assertFalse(result)\\n\\t\\t\\t\\tself.assertIsInstance(result, bool)\\n\\t\\t\\t\\tself.assertEqual(expected, result)\\n\\n\\t\\t\\t\\n\\t\\t\\t# Shift the data one position.\\n\\t\\t\\tself.data2.append(self.data2.pop(0))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"71d41f5200c765aa5f06fb50a38ef57b","score":"0.8023773","text":"def test_eq_numpos_array_array_c1(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\n\n\t\t\t\tresult = arrayfunc.eq(self.data1fail, self.data2)\n\n\t\t\t\tself.assertFalse(result)\n\t\t\t\tself.assertIsInstance(result, bool)\n\t\t\t\tself.assertEqual(expected, result)\n\n\t\t\t\n\t\t\t# Shift the data one position.\n\t\t\tself.data1.append(self.data1.pop(0))","title":""},{"docid":"71d41f5200c765aa5f06fb50a38ef57b","score":"0.8023773","text":"def test_eq_numpos_array_array_c1(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\n\n\t\t\t\tresult = arrayfunc.eq(self.data1fail, self.data2)\n\n\t\t\t\tself.assertFalse(result)\n\t\t\t\tself.assertIsInstance(result, bool)\n\t\t\t\tself.assertEqual(expected, result)\n\n\t\t\t\n\t\t\t# Shift the data one position.\n\t\t\tself.data1.append(self.data1.pop(0))","title":""},{"docid":"71d41f5200c765aa5f06fb50a38ef57b","score":"0.8023773","text":"def test_eq_numpos_array_array_c1(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\n\n\t\t\t\tresult = arrayfunc.eq(self.data1fail, self.data2)\n\n\t\t\t\tself.assertFalse(result)\n\t\t\t\tself.assertIsInstance(result, bool)\n\t\t\t\tself.assertEqual(expected, result)\n\n\t\t\t\n\t\t\t# Shift the data one position.\n\t\t\tself.data1.append(self.data1.pop(0))","title":""},{"docid":"71d41f5200c765aa5f06fb50a38ef57b","score":"0.8023773","text":"def test_eq_numpos_array_array_c1(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\n\n\t\t\t\tresult = arrayfunc.eq(self.data1fail, self.data2)\n\n\t\t\t\tself.assertFalse(result)\n\t\t\t\tself.assertIsInstance(result, bool)\n\t\t\t\tself.assertEqual(expected, result)\n\n\t\t\t\n\t\t\t# Shift the data one position.\n\t\t\tself.data1.append(self.data1.pop(0))","title":""},{"docid":"71d41f5200c765aa5f06fb50a38ef57b","score":"0.8023773","text":"def test_eq_numpos_array_array_c1(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\n\n\t\t\t\tresult = arrayfunc.eq(self.data1fail, self.data2)\n\n\t\t\t\tself.assertFalse(result)\n\t\t\t\tself.assertIsInstance(result, bool)\n\t\t\t\tself.assertEqual(expected, result)\n\n\t\t\t\n\t\t\t# Shift the data one position.\n\t\t\tself.data1.append(self.data1.pop(0))","title":""},{"docid":"71d41f5200c765aa5f06fb50a38ef57b","score":"0.8023773","text":"def test_eq_numpos_array_array_c1(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\n\n\t\t\t\tresult = arrayfunc.eq(self.data1fail, self.data2)\n\n\t\t\t\tself.assertFalse(result)\n\t\t\t\tself.assertIsInstance(result, bool)\n\t\t\t\tself.assertEqual(expected, result)\n\n\t\t\t\n\t\t\t# Shift the data one position.\n\t\t\tself.data1.append(self.data1.pop(0))","title":""},{"docid":"71d41f5200c765aa5f06fb50a38ef57b","score":"0.8023773","text":"def test_eq_numpos_array_array_c1(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\n\n\t\t\t\tresult = arrayfunc.eq(self.data1fail, self.data2)\n\n\t\t\t\tself.assertFalse(result)\n\t\t\t\tself.assertIsInstance(result, bool)\n\t\t\t\tself.assertEqual(expected, result)\n\n\t\t\t\n\t\t\t# Shift the data one position.\n\t\t\tself.data1.append(self.data1.pop(0))","title":""},{"docid":"71d41f5200c765aa5f06fb50a38ef57b","score":"0.8023773","text":"def test_eq_numpos_array_array_c1(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\n\n\t\t\t\tresult = arrayfunc.eq(self.data1fail, self.data2)\n\n\t\t\t\tself.assertFalse(result)\n\t\t\t\tself.assertIsInstance(result, bool)\n\t\t\t\tself.assertEqual(expected, result)\n\n\t\t\t\n\t\t\t# Shift the data one position.\n\t\t\tself.data1.append(self.data1.pop(0))","title":""},{"docid":"71d41f5200c765aa5f06fb50a38ef57b","score":"0.8023773","text":"def test_eq_numpos_array_array_c1(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\n\n\t\t\t\tresult = arrayfunc.eq(self.data1fail, self.data2)\n\n\t\t\t\tself.assertFalse(result)\n\t\t\t\tself.assertIsInstance(result, bool)\n\t\t\t\tself.assertEqual(expected, result)\n\n\t\t\t\n\t\t\t# Shift the data one position.\n\t\t\tself.data1.append(self.data1.pop(0))","title":""},{"docid":"71d41f5200c765aa5f06fb50a38ef57b","score":"0.8023773","text":"def test_eq_numpos_array_array_c1(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\n\n\t\t\t\tresult = arrayfunc.eq(self.data1fail, self.data2)\n\n\t\t\t\tself.assertFalse(result)\n\t\t\t\tself.assertIsInstance(result, bool)\n\t\t\t\tself.assertEqual(expected, result)\n\n\t\t\t\n\t\t\t# Shift the data one position.\n\t\t\tself.data1.append(self.data1.pop(0))","title":""},{"docid":"71d41f5200c765aa5f06fb50a38ef57b","score":"0.8023773","text":"def test_eq_numpos_array_array_c1(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\n\n\t\t\t\tresult = arrayfunc.eq(self.data1fail, self.data2)\n\n\t\t\t\tself.assertFalse(result)\n\t\t\t\tself.assertIsInstance(result, bool)\n\t\t\t\tself.assertEqual(expected, result)\n\n\t\t\t\n\t\t\t# Shift the data one position.\n\t\t\tself.data1.append(self.data1.pop(0))","title":""},{"docid":"71d41f5200c765aa5f06fb50a38ef57b","score":"0.8023773","text":"def test_eq_numpos_array_array_c1(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\n\n\t\t\t\tresult = arrayfunc.eq(self.data1fail, self.data2)\n\n\t\t\t\tself.assertFalse(result)\n\t\t\t\tself.assertIsInstance(result, bool)\n\t\t\t\tself.assertEqual(expected, result)\n\n\t\t\t\n\t\t\t# Shift the data one position.\n\t\t\tself.data1.append(self.data1.pop(0))","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"0fa76d60225a9c4713bd998e022b1065","score":"0.8017626","text":"def test_eq_basic_array_array_c2(self):\n\t\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\n\t\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\n\n\t\tself.assertFalse(expected)\n\t\tself.assertFalse(result)\n\t\tself.assertIsInstance(result, bool)\n\t\tself.assertEqual(expected, result)","title":""},{"docid":"7c5f7454c8090ee1e7ece493fc164621","score":"0.79798645","text":"def test_eq_array_num_a2(self):\n\t\tfor testval, badval in zip(self.data2, self.badarray2):\n\t\t\twith self.subTest(msg='Failed with parameter', testval = testval):\n\n\t\t\t\t# Copy the array so we don't change the original data.\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This version is expected to pass.\n\t\t\t\tresult = arrayfunc.eq(data1, testval)\n\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This is the actual test.\n\t\t\t\twith self.assertRaises(TypeError):\n\t\t\t\t\tresult = arrayfunc.eq(data1, badval)","title":""},{"docid":"7c5f7454c8090ee1e7ece493fc164621","score":"0.79798645","text":"def test_eq_array_num_a2(self):\n\t\tfor testval, badval in zip(self.data2, self.badarray2):\n\t\t\twith self.subTest(msg='Failed with parameter', testval = testval):\n\n\t\t\t\t# Copy the array so we don't change the original data.\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This version is expected to pass.\n\t\t\t\tresult = arrayfunc.eq(data1, testval)\n\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This is the actual test.\n\t\t\t\twith self.assertRaises(TypeError):\n\t\t\t\t\tresult = arrayfunc.eq(data1, badval)","title":""},{"docid":"7c5f7454c8090ee1e7ece493fc164621","score":"0.79798645","text":"def test_eq_array_num_a2(self):\n\t\tfor testval, badval in zip(self.data2, self.badarray2):\n\t\t\twith self.subTest(msg='Failed with parameter', testval = testval):\n\n\t\t\t\t# Copy the array so we don't change the original data.\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This version is expected to pass.\n\t\t\t\tresult = arrayfunc.eq(data1, testval)\n\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This is the actual test.\n\t\t\t\twith self.assertRaises(TypeError):\n\t\t\t\t\tresult = arrayfunc.eq(data1, badval)","title":""},{"docid":"7c5f7454c8090ee1e7ece493fc164621","score":"0.79798645","text":"def test_eq_array_num_a2(self):\n\t\tfor testval, badval in zip(self.data2, self.badarray2):\n\t\t\twith self.subTest(msg='Failed with parameter', testval = testval):\n\n\t\t\t\t# Copy the array so we don't change the original data.\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This version is expected to pass.\n\t\t\t\tresult = arrayfunc.eq(data1, testval)\n\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This is the actual test.\n\t\t\t\twith self.assertRaises(TypeError):\n\t\t\t\t\tresult = arrayfunc.eq(data1, badval)","title":""},{"docid":"7c5f7454c8090ee1e7ece493fc164621","score":"0.79798645","text":"def test_eq_array_num_a2(self):\n\t\tfor testval, badval in zip(self.data2, self.badarray2):\n\t\t\twith self.subTest(msg='Failed with parameter', testval = testval):\n\n\t\t\t\t# Copy the array so we don't change the original data.\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This version is expected to pass.\n\t\t\t\tresult = arrayfunc.eq(data1, testval)\n\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This is the actual test.\n\t\t\t\twith self.assertRaises(TypeError):\n\t\t\t\t\tresult = arrayfunc.eq(data1, badval)","title":""},{"docid":"7c5f7454c8090ee1e7ece493fc164621","score":"0.79798645","text":"def test_eq_array_num_a2(self):\n\t\tfor testval, badval in zip(self.data2, self.badarray2):\n\t\t\twith self.subTest(msg='Failed with parameter', testval = testval):\n\n\t\t\t\t# Copy the array so we don't change the original data.\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This version is expected to pass.\n\t\t\t\tresult = arrayfunc.eq(data1, testval)\n\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This is the actual test.\n\t\t\t\twith self.assertRaises(TypeError):\n\t\t\t\t\tresult = arrayfunc.eq(data1, badval)","title":""},{"docid":"7c5f7454c8090ee1e7ece493fc164621","score":"0.79798645","text":"def test_eq_array_num_a2(self):\n\t\tfor testval, badval in zip(self.data2, self.badarray2):\n\t\t\twith self.subTest(msg='Failed with parameter', testval = testval):\n\n\t\t\t\t# Copy the array so we don't change the original data.\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This version is expected to pass.\n\t\t\t\tresult = arrayfunc.eq(data1, testval)\n\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This is the actual test.\n\t\t\t\twith self.assertRaises(TypeError):\n\t\t\t\t\tresult = arrayfunc.eq(data1, badval)","title":""},{"docid":"7c5f7454c8090ee1e7ece493fc164621","score":"0.79798645","text":"def test_eq_array_num_a2(self):\n\t\tfor testval, badval in zip(self.data2, self.badarray2):\n\t\t\twith self.subTest(msg='Failed with parameter', testval = testval):\n\n\t\t\t\t# Copy the array so we don't change the original data.\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This version is expected to pass.\n\t\t\t\tresult = arrayfunc.eq(data1, testval)\n\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This is the actual test.\n\t\t\t\twith self.assertRaises(TypeError):\n\t\t\t\t\tresult = arrayfunc.eq(data1, badval)","title":""},{"docid":"7c5f7454c8090ee1e7ece493fc164621","score":"0.79798645","text":"def test_eq_array_num_a2(self):\n\t\tfor testval, badval in zip(self.data2, self.badarray2):\n\t\t\twith self.subTest(msg='Failed with parameter', testval = testval):\n\n\t\t\t\t# Copy the array so we don't change the original data.\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This version is expected to pass.\n\t\t\t\tresult = arrayfunc.eq(data1, testval)\n\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This is the actual test.\n\t\t\t\twith self.assertRaises(TypeError):\n\t\t\t\t\tresult = arrayfunc.eq(data1, badval)","title":""},{"docid":"7c5f7454c8090ee1e7ece493fc164621","score":"0.79798645","text":"def test_eq_array_num_a2(self):\n\t\tfor testval, badval in zip(self.data2, self.badarray2):\n\t\t\twith self.subTest(msg='Failed with parameter', testval = testval):\n\n\t\t\t\t# Copy the array so we don't change the original data.\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This version is expected to pass.\n\t\t\t\tresult = arrayfunc.eq(data1, testval)\n\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This is the actual test.\n\t\t\t\twith self.assertRaises(TypeError):\n\t\t\t\t\tresult = arrayfunc.eq(data1, badval)","title":""},{"docid":"7c5f7454c8090ee1e7ece493fc164621","score":"0.79798645","text":"def test_eq_array_num_a2(self):\n\t\tfor testval, badval in zip(self.data2, self.badarray2):\n\t\t\twith self.subTest(msg='Failed with parameter', testval = testval):\n\n\t\t\t\t# Copy the array so we don't change the original data.\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This version is expected to pass.\n\t\t\t\tresult = arrayfunc.eq(data1, testval)\n\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This is the actual test.\n\t\t\t\twith self.assertRaises(TypeError):\n\t\t\t\t\tresult = arrayfunc.eq(data1, badval)","title":""},{"docid":"7c5f7454c8090ee1e7ece493fc164621","score":"0.79798645","text":"def test_eq_array_num_a2(self):\n\t\tfor testval, badval in zip(self.data2, self.badarray2):\n\t\t\twith self.subTest(msg='Failed with parameter', testval = testval):\n\n\t\t\t\t# Copy the array so we don't change the original data.\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This version is expected to pass.\n\t\t\t\tresult = arrayfunc.eq(data1, testval)\n\n\t\t\t\tdata1 = copy.copy(self.data1)\n\n\t\t\t\t# This is the actual test.\n\t\t\t\twith self.assertRaises(TypeError):\n\t\t\t\t\tresult = arrayfunc.eq(data1, badval)","title":""},{"docid":"bba0b99bced34543ebd1a101bf47f3f1","score":"0.7969679","text":"def test_eq_array_array_e1(self):\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(data1, self.data2)\n\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(data1, self.badarray2)","title":""},{"docid":"bba0b99bced34543ebd1a101bf47f3f1","score":"0.7969679","text":"def test_eq_array_array_e1(self):\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(data1, self.data2)\n\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(data1, self.badarray2)","title":""},{"docid":"bba0b99bced34543ebd1a101bf47f3f1","score":"0.7969679","text":"def test_eq_array_array_e1(self):\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(data1, self.data2)\n\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(data1, self.badarray2)","title":""},{"docid":"bba0b99bced34543ebd1a101bf47f3f1","score":"0.7969679","text":"def test_eq_array_array_e1(self):\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(data1, self.data2)\n\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(data1, self.badarray2)","title":""},{"docid":"bba0b99bced34543ebd1a101bf47f3f1","score":"0.7969679","text":"def test_eq_array_array_e1(self):\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(data1, self.data2)\n\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(data1, self.badarray2)","title":""},{"docid":"bba0b99bced34543ebd1a101bf47f3f1","score":"0.7969679","text":"def test_eq_array_array_e1(self):\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(data1, self.data2)\n\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(data1, self.badarray2)","title":""},{"docid":"bba0b99bced34543ebd1a101bf47f3f1","score":"0.7969679","text":"def test_eq_array_array_e1(self):\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(data1, self.data2)\n\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(data1, self.badarray2)","title":""},{"docid":"bba0b99bced34543ebd1a101bf47f3f1","score":"0.7969679","text":"def test_eq_array_array_e1(self):\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(data1, self.data2)\n\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(data1, self.badarray2)","title":""},{"docid":"bba0b99bced34543ebd1a101bf47f3f1","score":"0.7969679","text":"def test_eq_array_array_e1(self):\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(data1, self.data2)\n\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(data1, self.badarray2)","title":""},{"docid":"bba0b99bced34543ebd1a101bf47f3f1","score":"0.7969679","text":"def test_eq_array_array_e1(self):\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(data1, self.data2)\n\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(data1, self.badarray2)","title":""},{"docid":"bba0b99bced34543ebd1a101bf47f3f1","score":"0.7969679","text":"def test_eq_array_array_e1(self):\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(data1, self.data2)\n\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(data1, self.badarray2)","title":""},{"docid":"bba0b99bced34543ebd1a101bf47f3f1","score":"0.7969679","text":"def test_eq_array_array_e1(self):\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(data1, self.data2)\n\n\t\t# Copy the array so we don't change the original data.\n\t\tdata1 = copy.copy(self.data1)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(data1, self.badarray2)","title":""},{"docid":"db18b95ae387abe616381f03ce88eb6b","score":"0.79009455","text":"def test_eq_array_array_e2(self):\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(self.data1, self.data2)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(self.badarray1, self.data2)","title":""},{"docid":"db18b95ae387abe616381f03ce88eb6b","score":"0.79009455","text":"def test_eq_array_array_e2(self):\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(self.data1, self.data2)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(self.badarray1, self.data2)","title":""},{"docid":"db18b95ae387abe616381f03ce88eb6b","score":"0.79009455","text":"def test_eq_array_array_e2(self):\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(self.data1, self.data2)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(self.badarray1, self.data2)","title":""},{"docid":"db18b95ae387abe616381f03ce88eb6b","score":"0.79009455","text":"def test_eq_array_array_e2(self):\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(self.data1, self.data2)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(self.badarray1, self.data2)","title":""},{"docid":"db18b95ae387abe616381f03ce88eb6b","score":"0.79009455","text":"def test_eq_array_array_e2(self):\n\t\t# This version is expected to pass.\n\t\tresult = arrayfunc.eq(self.data1, self.data2)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(TypeError):\n\t\t\tresult = arrayfunc.eq(self.badarray1, self.data2)","title":""}],"string":"[\n {\n \"docid\": \"71d41f5200c765aa5f06fb50a38ef57b\",\n \"score\": \"0.8023773\",\n \"text\": \"def test_eq_numpos_array_array_c1(self):\\n\\t\\tfor testpos in range(self.testarraylen):\\n\\t\\t\\twith self.subTest(msg='Failed with posistion', testpos = testpos):\\n\\n\\t\\t\\t\\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\\n\\n\\t\\t\\t\\tresult = arrayfunc.eq(self.data1fail, self.data2)\\n\\n\\t\\t\\t\\tself.assertFalse(result)\\n\\t\\t\\t\\tself.assertIsInstance(result, bool)\\n\\t\\t\\t\\tself.assertEqual(expected, result)\\n\\n\\t\\t\\t\\n\\t\\t\\t# Shift the data one position.\\n\\t\\t\\tself.data1.append(self.data1.pop(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d41f5200c765aa5f06fb50a38ef57b\",\n \"score\": \"0.8023773\",\n \"text\": \"def test_eq_numpos_array_array_c1(self):\\n\\t\\tfor testpos in range(self.testarraylen):\\n\\t\\t\\twith self.subTest(msg='Failed with posistion', testpos = testpos):\\n\\n\\t\\t\\t\\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\\n\\n\\t\\t\\t\\tresult = arrayfunc.eq(self.data1fail, self.data2)\\n\\n\\t\\t\\t\\tself.assertFalse(result)\\n\\t\\t\\t\\tself.assertIsInstance(result, bool)\\n\\t\\t\\t\\tself.assertEqual(expected, result)\\n\\n\\t\\t\\t\\n\\t\\t\\t# Shift the data one position.\\n\\t\\t\\tself.data1.append(self.data1.pop(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d41f5200c765aa5f06fb50a38ef57b\",\n \"score\": \"0.8023773\",\n \"text\": \"def test_eq_numpos_array_array_c1(self):\\n\\t\\tfor testpos in range(self.testarraylen):\\n\\t\\t\\twith self.subTest(msg='Failed with posistion', testpos = testpos):\\n\\n\\t\\t\\t\\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\\n\\n\\t\\t\\t\\tresult = arrayfunc.eq(self.data1fail, self.data2)\\n\\n\\t\\t\\t\\tself.assertFalse(result)\\n\\t\\t\\t\\tself.assertIsInstance(result, bool)\\n\\t\\t\\t\\tself.assertEqual(expected, result)\\n\\n\\t\\t\\t\\n\\t\\t\\t# Shift the data one position.\\n\\t\\t\\tself.data1.append(self.data1.pop(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d41f5200c765aa5f06fb50a38ef57b\",\n \"score\": \"0.8023773\",\n \"text\": \"def test_eq_numpos_array_array_c1(self):\\n\\t\\tfor testpos in range(self.testarraylen):\\n\\t\\t\\twith self.subTest(msg='Failed with posistion', testpos = testpos):\\n\\n\\t\\t\\t\\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\\n\\n\\t\\t\\t\\tresult = arrayfunc.eq(self.data1fail, self.data2)\\n\\n\\t\\t\\t\\tself.assertFalse(result)\\n\\t\\t\\t\\tself.assertIsInstance(result, bool)\\n\\t\\t\\t\\tself.assertEqual(expected, result)\\n\\n\\t\\t\\t\\n\\t\\t\\t# Shift the data one position.\\n\\t\\t\\tself.data1.append(self.data1.pop(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d41f5200c765aa5f06fb50a38ef57b\",\n \"score\": \"0.8023773\",\n \"text\": \"def test_eq_numpos_array_array_c1(self):\\n\\t\\tfor testpos in range(self.testarraylen):\\n\\t\\t\\twith self.subTest(msg='Failed with posistion', testpos = testpos):\\n\\n\\t\\t\\t\\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\\n\\n\\t\\t\\t\\tresult = arrayfunc.eq(self.data1fail, self.data2)\\n\\n\\t\\t\\t\\tself.assertFalse(result)\\n\\t\\t\\t\\tself.assertIsInstance(result, bool)\\n\\t\\t\\t\\tself.assertEqual(expected, result)\\n\\n\\t\\t\\t\\n\\t\\t\\t# Shift the data one position.\\n\\t\\t\\tself.data1.append(self.data1.pop(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d41f5200c765aa5f06fb50a38ef57b\",\n \"score\": \"0.8023773\",\n \"text\": \"def test_eq_numpos_array_array_c1(self):\\n\\t\\tfor testpos in range(self.testarraylen):\\n\\t\\t\\twith self.subTest(msg='Failed with posistion', testpos = testpos):\\n\\n\\t\\t\\t\\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\\n\\n\\t\\t\\t\\tresult = arrayfunc.eq(self.data1fail, self.data2)\\n\\n\\t\\t\\t\\tself.assertFalse(result)\\n\\t\\t\\t\\tself.assertIsInstance(result, bool)\\n\\t\\t\\t\\tself.assertEqual(expected, result)\\n\\n\\t\\t\\t\\n\\t\\t\\t# Shift the data one position.\\n\\t\\t\\tself.data1.append(self.data1.pop(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d41f5200c765aa5f06fb50a38ef57b\",\n \"score\": \"0.8023773\",\n \"text\": \"def test_eq_numpos_array_array_c1(self):\\n\\t\\tfor testpos in range(self.testarraylen):\\n\\t\\t\\twith self.subTest(msg='Failed with posistion', testpos = testpos):\\n\\n\\t\\t\\t\\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\\n\\n\\t\\t\\t\\tresult = arrayfunc.eq(self.data1fail, self.data2)\\n\\n\\t\\t\\t\\tself.assertFalse(result)\\n\\t\\t\\t\\tself.assertIsInstance(result, bool)\\n\\t\\t\\t\\tself.assertEqual(expected, result)\\n\\n\\t\\t\\t\\n\\t\\t\\t# Shift the data one position.\\n\\t\\t\\tself.data1.append(self.data1.pop(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d41f5200c765aa5f06fb50a38ef57b\",\n \"score\": \"0.8023773\",\n \"text\": \"def test_eq_numpos_array_array_c1(self):\\n\\t\\tfor testpos in range(self.testarraylen):\\n\\t\\t\\twith self.subTest(msg='Failed with posistion', testpos = testpos):\\n\\n\\t\\t\\t\\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\\n\\n\\t\\t\\t\\tresult = arrayfunc.eq(self.data1fail, self.data2)\\n\\n\\t\\t\\t\\tself.assertFalse(result)\\n\\t\\t\\t\\tself.assertIsInstance(result, bool)\\n\\t\\t\\t\\tself.assertEqual(expected, result)\\n\\n\\t\\t\\t\\n\\t\\t\\t# Shift the data one position.\\n\\t\\t\\tself.data1.append(self.data1.pop(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d41f5200c765aa5f06fb50a38ef57b\",\n \"score\": \"0.8023773\",\n \"text\": \"def test_eq_numpos_array_array_c1(self):\\n\\t\\tfor testpos in range(self.testarraylen):\\n\\t\\t\\twith self.subTest(msg='Failed with posistion', testpos = testpos):\\n\\n\\t\\t\\t\\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\\n\\n\\t\\t\\t\\tresult = arrayfunc.eq(self.data1fail, self.data2)\\n\\n\\t\\t\\t\\tself.assertFalse(result)\\n\\t\\t\\t\\tself.assertIsInstance(result, bool)\\n\\t\\t\\t\\tself.assertEqual(expected, result)\\n\\n\\t\\t\\t\\n\\t\\t\\t# Shift the data one position.\\n\\t\\t\\tself.data1.append(self.data1.pop(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d41f5200c765aa5f06fb50a38ef57b\",\n \"score\": \"0.8023773\",\n \"text\": \"def test_eq_numpos_array_array_c1(self):\\n\\t\\tfor testpos in range(self.testarraylen):\\n\\t\\t\\twith self.subTest(msg='Failed with posistion', testpos = testpos):\\n\\n\\t\\t\\t\\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\\n\\n\\t\\t\\t\\tresult = arrayfunc.eq(self.data1fail, self.data2)\\n\\n\\t\\t\\t\\tself.assertFalse(result)\\n\\t\\t\\t\\tself.assertIsInstance(result, bool)\\n\\t\\t\\t\\tself.assertEqual(expected, result)\\n\\n\\t\\t\\t\\n\\t\\t\\t# Shift the data one position.\\n\\t\\t\\tself.data1.append(self.data1.pop(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d41f5200c765aa5f06fb50a38ef57b\",\n \"score\": \"0.8023773\",\n \"text\": \"def test_eq_numpos_array_array_c1(self):\\n\\t\\tfor testpos in range(self.testarraylen):\\n\\t\\t\\twith self.subTest(msg='Failed with posistion', testpos = testpos):\\n\\n\\t\\t\\t\\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\\n\\n\\t\\t\\t\\tresult = arrayfunc.eq(self.data1fail, self.data2)\\n\\n\\t\\t\\t\\tself.assertFalse(result)\\n\\t\\t\\t\\tself.assertIsInstance(result, bool)\\n\\t\\t\\t\\tself.assertEqual(expected, result)\\n\\n\\t\\t\\t\\n\\t\\t\\t# Shift the data one position.\\n\\t\\t\\tself.data1.append(self.data1.pop(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d41f5200c765aa5f06fb50a38ef57b\",\n \"score\": \"0.8023773\",\n \"text\": \"def test_eq_numpos_array_array_c1(self):\\n\\t\\tfor testpos in range(self.testarraylen):\\n\\t\\t\\twith self.subTest(msg='Failed with posistion', testpos = testpos):\\n\\n\\t\\t\\t\\texpected = all([x == y for x,y in zip(self.data1fail, self.data2)])\\n\\n\\t\\t\\t\\tresult = arrayfunc.eq(self.data1fail, self.data2)\\n\\n\\t\\t\\t\\tself.assertFalse(result)\\n\\t\\t\\t\\tself.assertIsInstance(result, bool)\\n\\t\\t\\t\\tself.assertEqual(expected, result)\\n\\n\\t\\t\\t\\n\\t\\t\\t# Shift the data one position.\\n\\t\\t\\tself.data1.append(self.data1.pop(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa76d60225a9c4713bd998e022b1065\",\n \"score\": \"0.8017626\",\n \"text\": \"def test_eq_basic_array_array_c2(self):\\n\\t\\texpected = all([x == y for (x, y) in zip(self.data_array_array, self.data_array_array_fail)])\\n\\t\\tresult = arrayfunc.eq(self.data_array_array, self.data_array_array_fail , nosimd=True)\\n\\n\\t\\tself.assertFalse(expected)\\n\\t\\tself.assertFalse(result)\\n\\t\\tself.assertIsInstance(result, bool)\\n\\t\\tself.assertEqual(expected, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5f7454c8090ee1e7ece493fc164621\",\n \"score\": \"0.79798645\",\n \"text\": \"def test_eq_array_num_a2(self):\\n\\t\\tfor testval, badval in zip(self.data2, self.badarray2):\\n\\t\\t\\twith self.subTest(msg='Failed with parameter', testval = testval):\\n\\n\\t\\t\\t\\t# Copy the array so we don't change the original data.\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This version is expected to pass.\\n\\t\\t\\t\\tresult = arrayfunc.eq(data1, testval)\\n\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This is the actual test.\\n\\t\\t\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\t\\t\\tresult = arrayfunc.eq(data1, badval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5f7454c8090ee1e7ece493fc164621\",\n \"score\": \"0.79798645\",\n \"text\": \"def test_eq_array_num_a2(self):\\n\\t\\tfor testval, badval in zip(self.data2, self.badarray2):\\n\\t\\t\\twith self.subTest(msg='Failed with parameter', testval = testval):\\n\\n\\t\\t\\t\\t# Copy the array so we don't change the original data.\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This version is expected to pass.\\n\\t\\t\\t\\tresult = arrayfunc.eq(data1, testval)\\n\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This is the actual test.\\n\\t\\t\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\t\\t\\tresult = arrayfunc.eq(data1, badval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5f7454c8090ee1e7ece493fc164621\",\n \"score\": \"0.79798645\",\n \"text\": \"def test_eq_array_num_a2(self):\\n\\t\\tfor testval, badval in zip(self.data2, self.badarray2):\\n\\t\\t\\twith self.subTest(msg='Failed with parameter', testval = testval):\\n\\n\\t\\t\\t\\t# Copy the array so we don't change the original data.\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This version is expected to pass.\\n\\t\\t\\t\\tresult = arrayfunc.eq(data1, testval)\\n\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This is the actual test.\\n\\t\\t\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\t\\t\\tresult = arrayfunc.eq(data1, badval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5f7454c8090ee1e7ece493fc164621\",\n \"score\": \"0.79798645\",\n \"text\": \"def test_eq_array_num_a2(self):\\n\\t\\tfor testval, badval in zip(self.data2, self.badarray2):\\n\\t\\t\\twith self.subTest(msg='Failed with parameter', testval = testval):\\n\\n\\t\\t\\t\\t# Copy the array so we don't change the original data.\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This version is expected to pass.\\n\\t\\t\\t\\tresult = arrayfunc.eq(data1, testval)\\n\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This is the actual test.\\n\\t\\t\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\t\\t\\tresult = arrayfunc.eq(data1, badval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5f7454c8090ee1e7ece493fc164621\",\n \"score\": \"0.79798645\",\n \"text\": \"def test_eq_array_num_a2(self):\\n\\t\\tfor testval, badval in zip(self.data2, self.badarray2):\\n\\t\\t\\twith self.subTest(msg='Failed with parameter', testval = testval):\\n\\n\\t\\t\\t\\t# Copy the array so we don't change the original data.\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This version is expected to pass.\\n\\t\\t\\t\\tresult = arrayfunc.eq(data1, testval)\\n\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This is the actual test.\\n\\t\\t\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\t\\t\\tresult = arrayfunc.eq(data1, badval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5f7454c8090ee1e7ece493fc164621\",\n \"score\": \"0.79798645\",\n \"text\": \"def test_eq_array_num_a2(self):\\n\\t\\tfor testval, badval in zip(self.data2, self.badarray2):\\n\\t\\t\\twith self.subTest(msg='Failed with parameter', testval = testval):\\n\\n\\t\\t\\t\\t# Copy the array so we don't change the original data.\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This version is expected to pass.\\n\\t\\t\\t\\tresult = arrayfunc.eq(data1, testval)\\n\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This is the actual test.\\n\\t\\t\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\t\\t\\tresult = arrayfunc.eq(data1, badval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5f7454c8090ee1e7ece493fc164621\",\n \"score\": \"0.79798645\",\n \"text\": \"def test_eq_array_num_a2(self):\\n\\t\\tfor testval, badval in zip(self.data2, self.badarray2):\\n\\t\\t\\twith self.subTest(msg='Failed with parameter', testval = testval):\\n\\n\\t\\t\\t\\t# Copy the array so we don't change the original data.\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This version is expected to pass.\\n\\t\\t\\t\\tresult = arrayfunc.eq(data1, testval)\\n\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This is the actual test.\\n\\t\\t\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\t\\t\\tresult = arrayfunc.eq(data1, badval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5f7454c8090ee1e7ece493fc164621\",\n \"score\": \"0.79798645\",\n \"text\": \"def test_eq_array_num_a2(self):\\n\\t\\tfor testval, badval in zip(self.data2, self.badarray2):\\n\\t\\t\\twith self.subTest(msg='Failed with parameter', testval = testval):\\n\\n\\t\\t\\t\\t# Copy the array so we don't change the original data.\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This version is expected to pass.\\n\\t\\t\\t\\tresult = arrayfunc.eq(data1, testval)\\n\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This is the actual test.\\n\\t\\t\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\t\\t\\tresult = arrayfunc.eq(data1, badval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5f7454c8090ee1e7ece493fc164621\",\n \"score\": \"0.79798645\",\n \"text\": \"def test_eq_array_num_a2(self):\\n\\t\\tfor testval, badval in zip(self.data2, self.badarray2):\\n\\t\\t\\twith self.subTest(msg='Failed with parameter', testval = testval):\\n\\n\\t\\t\\t\\t# Copy the array so we don't change the original data.\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This version is expected to pass.\\n\\t\\t\\t\\tresult = arrayfunc.eq(data1, testval)\\n\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This is the actual test.\\n\\t\\t\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\t\\t\\tresult = arrayfunc.eq(data1, badval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5f7454c8090ee1e7ece493fc164621\",\n \"score\": \"0.79798645\",\n \"text\": \"def test_eq_array_num_a2(self):\\n\\t\\tfor testval, badval in zip(self.data2, self.badarray2):\\n\\t\\t\\twith self.subTest(msg='Failed with parameter', testval = testval):\\n\\n\\t\\t\\t\\t# Copy the array so we don't change the original data.\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This version is expected to pass.\\n\\t\\t\\t\\tresult = arrayfunc.eq(data1, testval)\\n\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This is the actual test.\\n\\t\\t\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\t\\t\\tresult = arrayfunc.eq(data1, badval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5f7454c8090ee1e7ece493fc164621\",\n \"score\": \"0.79798645\",\n \"text\": \"def test_eq_array_num_a2(self):\\n\\t\\tfor testval, badval in zip(self.data2, self.badarray2):\\n\\t\\t\\twith self.subTest(msg='Failed with parameter', testval = testval):\\n\\n\\t\\t\\t\\t# Copy the array so we don't change the original data.\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This version is expected to pass.\\n\\t\\t\\t\\tresult = arrayfunc.eq(data1, testval)\\n\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This is the actual test.\\n\\t\\t\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\t\\t\\tresult = arrayfunc.eq(data1, badval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5f7454c8090ee1e7ece493fc164621\",\n \"score\": \"0.79798645\",\n \"text\": \"def test_eq_array_num_a2(self):\\n\\t\\tfor testval, badval in zip(self.data2, self.badarray2):\\n\\t\\t\\twith self.subTest(msg='Failed with parameter', testval = testval):\\n\\n\\t\\t\\t\\t# Copy the array so we don't change the original data.\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This version is expected to pass.\\n\\t\\t\\t\\tresult = arrayfunc.eq(data1, testval)\\n\\n\\t\\t\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t\\t\\t# This is the actual test.\\n\\t\\t\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\t\\t\\tresult = arrayfunc.eq(data1, badval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba0b99bced34543ebd1a101bf47f3f1\",\n \"score\": \"0.7969679\",\n \"text\": \"def test_eq_array_array_e1(self):\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(data1, self.data2)\\n\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(data1, self.badarray2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba0b99bced34543ebd1a101bf47f3f1\",\n \"score\": \"0.7969679\",\n \"text\": \"def test_eq_array_array_e1(self):\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(data1, self.data2)\\n\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(data1, self.badarray2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba0b99bced34543ebd1a101bf47f3f1\",\n \"score\": \"0.7969679\",\n \"text\": \"def test_eq_array_array_e1(self):\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(data1, self.data2)\\n\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(data1, self.badarray2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba0b99bced34543ebd1a101bf47f3f1\",\n \"score\": \"0.7969679\",\n \"text\": \"def test_eq_array_array_e1(self):\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(data1, self.data2)\\n\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(data1, self.badarray2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba0b99bced34543ebd1a101bf47f3f1\",\n \"score\": \"0.7969679\",\n \"text\": \"def test_eq_array_array_e1(self):\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(data1, self.data2)\\n\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(data1, self.badarray2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba0b99bced34543ebd1a101bf47f3f1\",\n \"score\": \"0.7969679\",\n \"text\": \"def test_eq_array_array_e1(self):\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(data1, self.data2)\\n\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(data1, self.badarray2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba0b99bced34543ebd1a101bf47f3f1\",\n \"score\": \"0.7969679\",\n \"text\": \"def test_eq_array_array_e1(self):\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(data1, self.data2)\\n\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(data1, self.badarray2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba0b99bced34543ebd1a101bf47f3f1\",\n \"score\": \"0.7969679\",\n \"text\": \"def test_eq_array_array_e1(self):\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(data1, self.data2)\\n\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(data1, self.badarray2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba0b99bced34543ebd1a101bf47f3f1\",\n \"score\": \"0.7969679\",\n \"text\": \"def test_eq_array_array_e1(self):\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(data1, self.data2)\\n\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(data1, self.badarray2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba0b99bced34543ebd1a101bf47f3f1\",\n \"score\": \"0.7969679\",\n \"text\": \"def test_eq_array_array_e1(self):\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(data1, self.data2)\\n\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(data1, self.badarray2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba0b99bced34543ebd1a101bf47f3f1\",\n \"score\": \"0.7969679\",\n \"text\": \"def test_eq_array_array_e1(self):\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(data1, self.data2)\\n\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(data1, self.badarray2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba0b99bced34543ebd1a101bf47f3f1\",\n \"score\": \"0.7969679\",\n \"text\": \"def test_eq_array_array_e1(self):\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(data1, self.data2)\\n\\n\\t\\t# Copy the array so we don't change the original data.\\n\\t\\tdata1 = copy.copy(self.data1)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(data1, self.badarray2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db18b95ae387abe616381f03ce88eb6b\",\n \"score\": \"0.79009455\",\n \"text\": \"def test_eq_array_array_e2(self):\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(self.data1, self.data2)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(self.badarray1, self.data2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db18b95ae387abe616381f03ce88eb6b\",\n \"score\": \"0.79009455\",\n \"text\": \"def test_eq_array_array_e2(self):\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(self.data1, self.data2)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(self.badarray1, self.data2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db18b95ae387abe616381f03ce88eb6b\",\n \"score\": \"0.79009455\",\n \"text\": \"def test_eq_array_array_e2(self):\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(self.data1, self.data2)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(self.badarray1, self.data2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db18b95ae387abe616381f03ce88eb6b\",\n \"score\": \"0.79009455\",\n \"text\": \"def test_eq_array_array_e2(self):\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(self.data1, self.data2)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(self.badarray1, self.data2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db18b95ae387abe616381f03ce88eb6b\",\n \"score\": \"0.79009455\",\n \"text\": \"def test_eq_array_array_e2(self):\\n\\t\\t# This version is expected to pass.\\n\\t\\tresult = arrayfunc.eq(self.data1, self.data2)\\n\\n\\t\\t# This is the actual test.\\n\\t\\twith self.assertRaises(TypeError):\\n\\t\\t\\tresult = arrayfunc.eq(self.badarray1, self.data2)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994095,"cells":{"query_id":{"kind":"string","value":"a813f98ec27f13401901346feec8a484"},"query":{"kind":"string","value":"Recursively turn the TC logic into TC configuration"},"positive_passages":{"kind":"list like","value":[{"docid":"10a45d1c09ea052b511d7a5891329964","score":"0.0","text":"def make(self):\n tcg = TCCommandGenerator()\n print(\"Make ClassifierFilter\")\n CmdExecutor.run_and_print(tcg.add_classifier_filter(self))","title":""}],"string":"[\n {\n \"docid\": \"10a45d1c09ea052b511d7a5891329964\",\n \"score\": \"0.0\",\n \"text\": \"def make(self):\\n tcg = TCCommandGenerator()\\n print(\\\"Make ClassifierFilter\\\")\\n CmdExecutor.run_and_print(tcg.add_classifier_filter(self))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ef40f7ed175823942ced5d3c9a0f4d46","score":"0.5959538","text":"def process_config(lunch_config):\n def add(domain_name, tree_root, product):\n tree_key = inner_tree.InnerTreeKey(tree_root, product)\n if tree_key in trees:\n tree = trees[tree_key]\n else:\n tree = inner_tree.InnerTree(tree_root, product)\n trees[tree_key] = tree\n domain = api_domain.ApiDomain(domain_name, tree, product)\n domains[domain_name] = domain\n tree.domains[domain_name] = domain\n\n trees = {}\n domains = {}\n\n system_entry = lunch_config.get(\"system\")\n if system_entry:\n add(API_DOMAIN_SYSTEM, system_entry[\"tree\"], system_entry[\"product\"])\n\n vendor_entry = lunch_config.get(\"vendor\")\n if vendor_entry:\n add(API_DOMAIN_VENDOR, vendor_entry[\"tree\"], vendor_entry[\"product\"])\n\n for module_name, module_entry in lunch_config.get(\"modules\", []).items():\n add(module_name, module_entry[\"tree\"], None)\n\n return inner_tree.InnerTrees(trees, domains)","title":""},{"docid":"4430967bdb5e9dc6d25d8eb5e9073881","score":"0.5949084","text":"def traverse(config, workdir, mpc, **kwargs):\n for entry in config:\n #construct a list of tuples, each tuple is of form (file, workdir, ...)\n #where ... are additionnal arguments that will be used to expand the name pattern of the file\n entries = entry.get(workdir, mpc)\n\n #finaly, perform the transformation on the files/arguments constructed above\n for e in entries:\n transformations = entry.trans\n if len(e) == 0:\n continue\n for trans in transformations:\n trans(*e, **kwargs)","title":""},{"docid":"341f2cd6d9d320c3572ce5b2b13f8ab3","score":"0.553948","text":"def expand_config(self, config): \n # First, expand the templates without variables, then expand modes\n # using the vars and expanded templates\n vars = config.get('vars', {})\n templates = config.get('templates', {})\n expanded_templates = self.expand_template(vars, templates, templates)\n\n # Create a new config dict and swap in expanded bits\n #new_config = config\n new_config = OrderedDict()\n\n cruise = config.get('cruise', {})\n if cruise:\n new_config['cruise'] = self.expand_template(vars, cruise,\n expanded_templates)\n loggers = config.get('loggers', {})\n if loggers:\n new_config['loggers'] = self.expand_template(vars, loggers,\n expanded_templates)\n modes = config.get('modes', {})\n if modes:\n new_config['modes'] = self.expand_template(vars, modes,\n expanded_templates)\n default_mode = config.get('default_mode', {})\n if default_mode:\n new_config['default_mode'] = self.expand_template(vars, default_mode,\n expanded_templates)\n configs = config.get('configs', {})\n if configs:\n new_config['configs'] = self.expand_template(vars, configs,\n expanded_templates)\n return new_config","title":""},{"docid":"a9daa34233a77cbeeb46862debca535f","score":"0.55340546","text":"def traverse_cfg(cfg, features_list, handled_set, handled_features_set):\n\n if cfg.control_dep_children: #check for control dependent node\n features_list.append(cfg.name) #added current CFG node in feature\n handled_features_set.add(cfg.id) # Store id from features handled\n get_ast_features(cfg, features_list, handled_features_set) # Handled only once\n for control_dep in cfg.control_dep_children: #Traverse all childeren which has control dependency\n control_flow = control_dep.extremity #gets all dependecy control and statements\n # Otherwise missing CF pointing to a node already analyzed\n if not control_flow.control_dep_children or control_flow.id in handled_set: #if not already handled add and not\n features_list.append(control_flow.name)\n # else: the node name will be added while calling traverse_cfg\n if control_flow.id not in handled_set: #called for all other recurrently\n handled_set.add(control_flow.id)\n handled_features_set.add(control_flow.id) # Store id from features\n get_ast_features(control_flow, features_list, handled_features_set) # Once\n traverse_cfg(control_flow, features_list, handled_set, handled_features_set)","title":""},{"docid":"097acee3b03070c066c0292c3df8b213","score":"0.54283386","text":"def process_config(data):\n config = deepcopy(INITIAL_CONFIG)\n try:\n regexps = data['regexps']\n logs = data['logs']\n actions = data.get('actions') or {}\n except KeyError as exc:\n raise ConfigError('required section missing: {}'.format(exc))\n except TypeError:\n raise ConfigError('config is not a dict: received {}'.format(type(data)))\n config['regexps'] = _process_regexps(regexps)\n config['actions'], auto_actions = _process_actions(actions)\n config['logs'] = _process_logs(logs, config['regexps'], config['actions'], auto_actions)\n return config","title":""},{"docid":"064f2fa416a3eba7a1d122ae8d7af4e0","score":"0.54272306","text":"def apply_conf(self, conf):\n for s in self.steps_data:\n o2 = []\n for o in s[2]:\n co = o.replace(\"%input_dir%\", conf.input_dir)\n co = co.replace(\"%output_dir%\", conf.output_dir)\n co = co.replace(\"%matches_dir%\", conf.matches_dir)\n co = co.replace(\"%reconstruction_dir%\", conf.reconstruction_dir)\n co = co.replace(\"%mvs_dir%\", conf.mvs_dir)\n co = co.replace(\"%camera_file_params%\", conf.camera_file_params)\n o2.append(co)\n s[2] = o2","title":""},{"docid":"1ce0e6137647a97306212b3f31fc312a","score":"0.53981847","text":"def test_walk_config(self):\n n = config_manager.Namespace(doc='top')\n n.add_option('aaa', False, 'the a', short_form='a')\n n.c = config_manager.Namespace(doc='c space')\n n.c.add_option('fred', doc='husband from Flintstones')\n n.c.add_option('wilma', doc='wife from Flintstones')\n n.d = config_manager.Namespace(doc='d space')\n n.d.add_option('fred', doc='male neighbor from I Love Lucy')\n n.d.add_option('ethel', doc='female neighbor from I Love Lucy')\n n.d.x = config_manager.Namespace(doc='x space')\n n.d.x.add_option('size', 100, 'how big in tons', short_form='s')\n n.d.x.add_option('password', 'secrets', 'the password')\n c = config_manager.ConfigurationManager(\n [n],\n use_admin_controls=True,\n #use_config_files=False,\n use_auto_help=False,\n argv_source=[]\n )\n e = [('aaa', 'aaa', n.aaa.name),\n ('c', 'c', n.c._doc),\n ('c.wilma', 'wilma', n.c.wilma.name),\n ('c.fred', 'fred', n.c.fred.name),\n ('d', 'd', n.d._doc),\n ('d.ethel', 'ethel', n.d.ethel.name),\n ('d.fred', 'fred', n.d.fred.name),\n ('d.x', 'x', n.d.x._doc),\n ('d.x.size', 'size', n.d.x.size.name),\n ('d.x.password', 'password', n.d.x.password.name),\n ]\n e.sort()\n r = [(q, k, v.name if isinstance(v, config_manager.Option) else v._doc)\n for q, k, v in c._walk_config()]\n r.sort()\n for expected, received in zip(e, r):\n self.assertEqual(received, expected)","title":""},{"docid":"7e479804ac58246919d4fead8ec6dcfe","score":"0.5370012","text":"def visit_ControlFlowGraph(self, cfg):\n pass","title":""},{"docid":"961f03e57363f56ece817427ef00af50","score":"0.53425205","text":"def get_config_template(self) -> cconfig.Config:\n config = cconfig.Config()\n #\n stage = \"resample_events\"\n config_tmp = config.add_subconfig(self._get_nid(stage))\n config_tmp[\"rule\"] = \"T\"\n config_tmp[\"agg_func\"] = \"mean\"\n #\n stage = \"generate_event_signal\"\n config_tmp = config.add_subconfig(self._get_nid(stage))\n config_kwargs = config_tmp.add_subconfig(\"transformer_kwargs\")\n config_kwargs[\"tau\"] = 8\n config_kwargs[\"max_depth\"] = 3\n #\n stage = \"shift\"\n config_tmp = config.add_subconfig(self._get_nid(stage))\n config_kwargs = config_tmp.add_subconfig(\"method_kwargs\")\n config_kwargs[\"periods\"] = 1\n #\n stage = \"build_local_ts\"\n config_tmp = config.add_subconfig(self._get_nid(stage))\n config_kwargs = config_tmp.add_subconfig(\"connector_kwargs\")\n config_kwargs[\"relative_grid_indices\"] = range(-10, 50)\n #\n stage = \"model\"\n config_tmp = config.add_subconfig(self._get_nid(stage))\n config_tmp[\"x_vars\"] = [\"_DUMMY_\"]\n config_tmp[\"y_vars\"] = [\"_DUMMY_\"]\n config_kwargs = config_tmp.add_subconfig(\"model_kwargs\")\n config_kwargs[\"alpha\"] = 0.5\n return config","title":""},{"docid":"69095991cc633ced222ce7e51d1ff60c","score":"0.5322106","text":"def process_config(config_all):\n print(\"Preparing PATHs for the pipeline...\")\n PATHS = {}\n\n # Set parameters for the pipeline based on config\n if config_all['COMMON']['DATA_LOCATION'] == \"local\":\n PATHS[\"input_data\"] = config_all['LOCAL']['INPUT_DATA_LOCAL']\n PATHS[\"i94_data\"] = config_all['LOCAL']['INPUT_DATA_I94_LOCAL']\n PATHS[\"airport_codes\"] = config_all['LOCAL']['INPUT_DATA_AIRPORT_LOCAL']\n PATHS[\"country_codes_iso\"] = config_all['LOCAL']['INPUT_DATA_COUNTRY_LOCAL']\n PATHS[\"airport_codes_i94\"] = config_all['LOCAL']['INPUT_DATA_AIRPORT_I94_LOCAL']\n PATHS[\"country_codes_i94\"] = config_all['LOCAL']['INPUT_DATA_COUNTRY_I94_LOCAL']\n PATHS[\"output_data\"] = config_all['LOCAL']['OUTPUT_DATA_LOCAL']\n elif config_all['COMMON']['DATA_LOCATION'] == \"server\":\n PATHS[\"input_data\"] = config_all['SERVER']['INPUT_DATA_SERVER']\n PATHS[\"i94_data\"] = config_all['SERVER']['INPUT_DATA_I94_SERVER']\n PATHS[\"airport_codes\"] = config_all['SERVER']['INPUT_DATA_AIRPORT_SERVER']\n PATHS[\"country_codes_iso\"] = config_all['SERVER']['INPUT_DATA_COUNTRY_SERVER']\n PATHS[\"airport_codes_i94\"] = config_all['SERVER']['INPUT_DATA_AIRPORT_I94_SERVER']\n PATHS[\"country_codes_i94\"] = config_all['SERVER']['INPUT_DATA_COUNTRY_I94_SERVER']\n PATHS[\"output_data\"] = config_all['SERVER']['OUTPUT_DATA_SERVER']\n elif config_all['COMMON']['DATA_LOCATION'] == \"aws\":\n PATHS[\"input_data\"] = config_all['AWS']['INPUT_DATA']\n PATHS[\"i94_data\"] = config_all['AWS']['INPUT_DATA_I94']\n PATHS[\"airport_codes\"] = config_all['AWS']['INPUT_DATA_AIRPORT']\n PATHS[\"country_codes_iso\"] = config_all['AWS']['INPUT_DATA_COUNTRY']\n PATHS[\"airport_codes_i94\"] = config_all['AWS']['INPUT_DATA_AIRPORT_I94']\n PATHS[\"country_codes_i94\"] = config_all['AWS']['INPUT_DATA_COUNTRY_I94']\n PATHS[\"output_data\"] = config_all['AWS']['OUTPUT_DATA']\n\n if config_all[\"COMMON\"][\"DATA_STORAGE\"] == \"postgresql\":\n PATHS[\"data_storage\"] = config_all[\"COMMON\"][\"DATA_STORAGE_SQL\"]\n elif config_all[\"COMMON\"][\"DATA_STORAGE\"] == \"parquet\":\n PATHS[\"data_storage\"] = config_all[\"COMMON\"][\"DATA_STORAGE\"]\n\n #print(AWS_ACCESS_KEY_ID)\n #print(AWS_SECRET_ACCESS_KEY)\n\n # Print out paths in PATHS\n print(\"PATHS preparation DONE.\\n\")\n print(\"PATHS:\")\n for path in PATHS:\n print(path)\n\n return PATHS","title":""},{"docid":"19d81fbef3ad071635fb4e99c692dd74","score":"0.53159124","text":"def build_tcno2tcid_mapping(self):\n try:\n test = \"Services_HA_\"\n base_strings = [\"VYOS_EW_\", \"VYOS_NS_\", \"ASAV_EW_\", \"ASAV_NS_\"]\n chains = [\"FW\", \"FW+LB\", \"REMOTE_VPN+FW\", \"REMOTE_VPN+FW+LB\",\n \"S2S_VPN+FW\", \"S2S_VPN+FW+LB\"\n ]\n tc_no = 0\n for bstr in base_strings:\n for chain in chains:\n if \"_EW\" in bstr and \"VPN\" in chain:\n continue\n services = []\n if \"VPN\" in chain:\n services.append(\"VPN\")\n if \"FW\" in chain:\n services.append(\"FW\")\n if \"LB\" in chain:\n services.append(\"LB\")\n # if vpn & fw services are in chain,\n # they shares same service vm.\n if \"VPN\" in chain and \"FW\" in chain:\n services.remove(\"FW\")\n\n # base test no. for service chain insertion.\n base_tc = gbp_config.services_ha_base_test_mapping[\n bstr.lower() + chain.lower()]\n\n # To generate combination of test, where some of the\n # services in chain will in HA mode & some will be in\n # standalone mode and failover will be applied on service\n # vm of service launched in ha mode.\n if len(services) > 1:\n for service_vm in services:\n services_ha = []\n tc_no += 1\n chain_string = chain.replace(\n service_vm, service_vm + '(HA)')\n services_ha.append(service_vm)\n if service_vm == \"VPN\":\n chain_string = chain_string.\\\n replace(\"FW\", \"FW(HA)\")\n services_ha.append('FW')\n\n tc_id = test + bstr + chain_string + \"_FailOver_\"\\\n + service_vm + \"_\" + str(tc_no)\n self.tcno2tcid_mapping[tc_no] = {\n \"tc_id\": tc_id,\n \"base_tc_no\": base_tc,\n \"fail_over\": [service_vm],\n \"services_ha\": services_ha\n }\n\n # To generate combination of test, where all the\n # services in chain will in HA mode. And Failover\n # will be applied on one service vm only.\n for service_vm in services:\n tc_no += 1\n chain_string = '+'.join(map(lambda service: service +\n '(HA)', chain.split('+')))\n tc_id = test + bstr + chain_string + \"_FailOver_\" +\\\n service_vm + \"_\" + str(tc_no)\n services_ha = copy.deepcopy(services)\n if 'VPN' in services:\n services_ha.append('FW')\n\n self.tcno2tcid_mapping[tc_no] = {\n \"tc_id\": tc_id,\n \"base_tc_no\": base_tc,\n \"fail_over\": [service_vm],\n \"services_ha\": services_ha}\n else:\n # tests where all services in chain will be\n # launched in HA mode and failover will be applied on\n # all active service vms of chain one after another.\n # and data path traffic will be verified.\n if len(services) > 1:\n services_ha = copy.deepcopy(services)\n tc_no += 1\n chain_string = '+'.join(map(\n lambda service: service +\n '(HA)', chain.split('+')))\n tc_id = test + bstr + chain_string +\\\n \"_FailOver_All_\" + str(tc_no)\n if 'VPN' in services:\n services_ha.append('FW')\n self.tcno2tcid_mapping[tc_no] = {\n \"tc_id\": tc_id,\n \"base_tc_no\": base_tc,\n \"fail_over\": services,\n \"services_ha\": services_ha}\n\n except Exception as err:\n LOGOBJ.exception(err)\n return \"ATFError: Exception occurred while building test \"\\\n \"cases numbers to test cases id mapping.\"","title":""},{"docid":"fdda980df12fdb6cf4af3f2cc0103937","score":"0.53126025","text":"def _parse_test_cfg(self):","title":""},{"docid":"49bdb39737fe898a8dd50099f8e5c65d","score":"0.53021973","text":"def build_tc(self, tc):\n tc = data.TestCampaigns[tc]\n [self.build_dut_tc(tc, data.DUTs[name]) for name in tc.DUTs]","title":""},{"docid":"bc36f4a729b4aea5d6e30839374a74ed","score":"0.52694374","text":"def create_all_scenarios(self):\r\n for tree in self.trees:\r\n self.parse_tree(tree)","title":""},{"docid":"17cb5e049394fe72aba0c5eb30dd9264","score":"0.5206591","text":"def BuildTC( *args, **kargs ):\n from helpers.tchelper import BuildTC_Helper\n helper = BuildTC_Helper()\n helper.configure( *args, **kargs )\n return helper.execute( *args, **kargs )","title":""},{"docid":"d7f3230bc929d6e8080d78d414c74152","score":"0.5194215","text":"def makeRuleTree(self):\n \n try:\n line = self.command_output[self.index].strip()\n self.index += 1\n except IndexError:\n return\n if len(line) > 0:\n key, value = self.getKeyValue(line)\n if key == \"Subgroup GUID\":\n guid = value.split()[0]\n if value.find(\"(\") == -1:\n name = \"NO NAME\"\n else:\n name = value[value.find(\"(\")+1:-1]\n self.current_subgroup = guid\n self.options[guid] = {\"name\": name}\n #print guid, name, \"(%s)\" % line\n elif key == \"Power Setting GUID\":\n guid = value.split()[0]\n if value.find(\"(\") == -1:\n name = \"NO NAME\"\n else:\n name = value[value.find(\"(\")+1:-1]\n #print \" \" + guid, name\n self.current_pwroption = guid\n self.options[self.current_subgroup][guid] = {\n \"name\": name,\n \"units\": None,\n \"possibilities\": None,\n \"AC\": None,\n \"DC\": None\n }\n else:\n if key == \"Possible Setting Index\":\n self.options[self.current_subgroup][self.current_pwroption][\"possibilities\"] = self.getPossibilities()\n elif key == \"Possible Settings units\":\n self.options[self.current_subgroup][self.current_pwroption][\"units\"] = value\n elif key == \"Current AC Power Setting Index\":\n self.options[self.current_subgroup][self.current_pwroption][\"AC\"] = int(value, 16)\n elif key == \"Current DC Power Setting Index\":\n self.options[self.current_subgroup][self.current_pwroption][\"DC\"] = int(value, 16)\n \n self.makeRuleTree()\n else:\n self.makeRuleTree()","title":""},{"docid":"2a78026e733c6d5b01f34b2f8a306960","score":"0.5173538","text":"def convert_template(config: dict[str, Any]) -> dict[str, Any]:\n my_map = {\n CONF_ROOM_ON: dyn_const.CONF_ROOM_ON,\n CONF_ROOM_OFF: dyn_const.CONF_ROOM_OFF,\n CONF_CHANNEL_COVER: dyn_const.CONF_CHANNEL_COVER,\n CONF_DEVICE_CLASS: dyn_const.CONF_DEVICE_CLASS,\n CONF_OPEN_PRESET: dyn_const.CONF_OPEN_PRESET,\n CONF_CLOSE_PRESET: dyn_const.CONF_CLOSE_PRESET,\n CONF_STOP_PRESET: dyn_const.CONF_STOP_PRESET,\n CONF_DURATION: dyn_const.CONF_DURATION,\n CONF_TILT_TIME: dyn_const.CONF_TILT_TIME,\n }\n return convert_with_map(config, my_map)","title":""},{"docid":"bfb073149d96aa039c513c16c64a89b1","score":"0.5161812","text":"def get_configs(self):\n return {'Runway': self.parse_config(os.path.join(self.template_dir, 'runway.yml')),\n 'Serverless':\n self.parse_config(os.path.join(self.template_dir, 'serverless.yml'))}","title":""},{"docid":"b0d166cac235ecbc65bddb1d5bf78a32","score":"0.5157954","text":"def compile_config(self, config):\n return utils.walk_and_apply(config, self.compile_lookups)","title":""},{"docid":"172e6689cba967981bb5ecb5c3a94ff9","score":"0.5157662","text":"def __configureNodes(self):\n\t\t\"Load all configuration values into an easy to use dict\"\"\"\n\t\tmydict = {}\n\t\tsections = self.Config.sections()\n\t\tprint(\"Configuring nodes for config sections {}\".format(sections))\n\t\tfor section in sections:\n\t\t mydict = dict(mydict.items() + self.Config.items(section))\n\t\treturn mydict","title":""},{"docid":"797cff2743d5f78a6fe37b6924f04a74","score":"0.5151952","text":"def create_configs(\r\n task_filename: str,\r\n configs_folder: str,\r\n parent_config_filename: str,\r\n git_tag: str,\r\n counter_file: str,\r\n results_folder: str,\r\n machine_id: str,\r\n):\r\n list_configs = []\r\n with open(task_filename) as f:\r\n task = json.load(f)\r\n with open(parent_config_filename) as f:\r\n parent_config = json.load(f)\r\n\r\n count = get_starting_number_from_file(counter_file) - 1\r\n cross_scan_gen = (cross_scan_dict for cross_scan_dict in task[\"Task\"][\"CrossScan\"] if cross_scan_dict)\r\n for cross_scan_dict in cross_scan_gen:\r\n patches = (dict(zip(cross_scan_dict.keys(), comb)) for comb in it.product(*cross_scan_dict.values()))\r\n for patch in patches:\r\n count += 1\r\n config_filename = f\"{count}.json\"\r\n save_folder = os.path.join(results_folder, f\"{count}_{machine_id}/\")\r\n if not os.path.exists(save_folder):\r\n os.mkdir(save_folder)\r\n\r\n config = copy.deepcopy(parent_config)\r\n apply_patch(config[\"Configuration\"], patch)\r\n apply_patch(\r\n config,\r\n {\r\n \"Configuration->gitHashShort\": git_tag,\r\n \"Configuration->Name\": str(count),\r\n \"Configuration->LoggerParameters->FilePath\": save_folder,\r\n \"Configuration->ParentConfigName\": parent_config_filename,\r\n \"Configuration->TaskFile\": task_filename,\r\n \"Configuration->ComputerID\": machine_id,\r\n },\r\n )\r\n assign_initial_conditions(config)\r\n\r\n with open(os.path.join(configs_folder, config_filename), \"w\") as f:\r\n json.dump(config, f, sort_keys=True, indent=2)\r\n list_configs.append(config_filename)\r\n\r\n set_counter_file(counter_file, count + 1)\r\n return list_configs","title":""},{"docid":"a0f6fb37fcb1b23794cd6e7f82cfa442","score":"0.51390517","text":"def parse_config(config_path):\n import copy\n from functools import reduce\n import importlib\n import operator\n from ruamel.yaml import YAML\n from ruamel.yaml.comments import CommentedMap, CommentedSeq\n\n yaml = YAML()\n config = yaml.load(open(config_path, 'r'))\n classes = {}\n\n def get_from_dict(data_dict, map_list):\n return reduce(operator.getitem, map_list, data_dict)\n\n def set_in_dict(data_dict, map_list, value):\n get_from_dict(data_dict, map_list[:-1])[map_list[-1]] = value\n\n # assume if a function definition is in the most outer scope, it will not\n # have anywhere in config a nested class\n def walk(node, curr=None, depth=0):\n if curr is None:\n curr = {}\n if isinstance(node, CommentedMap):\n for key, item in node.items():\n curr[depth] = key\n if key in ['object', 'closure']:\n if depth in classes.keys():\n classes[depth].append(copy.deepcopy(curr))\n else:\n classes[depth] = [copy.deepcopy(curr)]\n walk(item, copy.deepcopy(curr), depth + 1)\n elif isinstance(node, CommentedSeq):\n for i, _ in enumerate(node):\n curr[depth] = i\n walk(node[i], copy.deepcopy(curr), depth + 1)\n\n walk(config)\n depths = classes.keys()\n depths = reversed(sorted(depths))\n for depth in depths:\n for keys_dict in classes[depth]:\n keys = list(keys_dict.values())\n item = get_from_dict(config, keys)\n parts = item['name'].split('.')\n module = importlib.import_module('.'.join(parts[:-1]))\n\n # get the constructor for this instance\n ctor = getattr(module, parts[-1])\n # unify branches by setting an empty dict if no params provided\n if 'params' not in item or item['params'] is None:\n item['params'] = {}\n if isinstance(item['params'], CommentedSeq):\n item['params'] = list(item['params'])\n\n try:\n if isinstance(item['params'], dict):\n instance = ctor(**item['params'])\n elif isinstance(item['params'], list):\n instance = ctor(*item['params'])\n except TypeError:\n # pylint: disable=cell-var-from-loop\n instance = lambda *args, f=ctor, params=item['params'], \\\n **kwargs: f(*args, **kwargs, **params)\n # pylint: enable=cell-var-from-loop\n set_in_dict(config, keys[:-1], instance)\n\n return config","title":""},{"docid":"c4165440f0951a5c7e20999b0551f52c","score":"0.5112517","text":"def resolve_cmd_inheritance(self, test_cfg):\n\n for section in ['build', 'run']:\n config = test_cfg.get(section)\n if not config:\n continue\n new_cmd_list = []\n if config.get('prepend_cmds', []):\n new_cmd_list.extend(config.get('prepend_cmds'))\n config['prepend_cmds'] = []\n new_cmd_list += test_cfg[section]['cmds']\n if config.get('append_cmds', []):\n new_cmd_list.extend(config.get('append_cmds'))\n config['append_cmds'] = []\n test_cfg[section]['cmds'] = new_cmd_list\n\n return test_cfg","title":""},{"docid":"d5e0333db6ecb450a4b7cc41c340f2f0","score":"0.5110972","text":"async def cfg(self, ctx):","title":""},{"docid":"dc0ea28355605c29942397ef80a0862e","score":"0.5092078","text":"def config_collection():\n\tshutit = shutit_global.shutit\n\tshutit.log('In config_collection',level=logging.DEBUG)\n\tcfg = shutit.cfg\n\tfor module_id in module_ids():\n\t\t# Default to None so we can interpret as ifneeded\n\t\tshutit.get_config(module_id, 'shutit.core.module.build', None, boolean=True, forcenone=True)\n\t\tshutit.get_config(module_id, 'shutit.core.module.remove', False, boolean=True)\n\t\tshutit.get_config(module_id, 'shutit.core.module.tag', False, boolean=True)\n\t\t# Default to allow any image\n\t\tshutit.get_config(module_id, 'shutit.core.module.allowed_images', [\".*\"])\n\t\tmodule = shutit.shutit_map[module_id]\n\t\tcfg_file = os.path.dirname(module.__module_file) + '/configs/build.cnf'\n\t\tif os.path.isfile(cfg_file):\n\t\t\t# use shutit.get_config, forcing the passed-in default\n\t\t\tconfig_parser = ConfigParser.ConfigParser()\n\t\t\tconfig_parser.read(cfg_file)\n\t\t\tfor section in config_parser.sections():\n\t\t\t\tif section == module_id:\n\t\t\t\t\tfor option in config_parser.options(section):\n\t\t\t\t\t\tif option == 'shutit.core.module.allowed_images':\n\t\t\t\t\t\t\toverride = False\n\t\t\t\t\t\t\tfor mod, opt, val in shutit.build['config_overrides']:\n\t\t\t\t\t\t\t\tval = val # pylint\n\t\t\t\t\t\t\t\t# skip overrides\n\t\t\t\t\t\t\t\tif mod == module_id and opt == option:\n\t\t\t\t\t\t\t\t\toverride = True\n\t\t\t\t\t\t\tif override:\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\tvalue = config_parser.get(section,option)\n\t\t\t\t\t\t\tif option == 'shutit.core.module.allowed_images':\n\t\t\t\t\t\t\t\tvalue = json.loads(value)\n\t\t\t\t\t\t\tshutit.get_config(module_id, option, value, forcedefault=True)\n\t\t# ifneeded will (by default) only take effect if 'build' is not\n\t\t# specified. It can, however, be forced to a value, but this\n\t\t# should be unusual.\n\t\tif cfg[module_id]['shutit.core.module.build'] is None:\n\t\t\tshutit.get_config(module_id, 'shutit.core.module.build_ifneeded', True, boolean=True)\n\t\t\tcfg[module_id]['shutit.core.module.build'] = False\n\t\telse:\n\t\t\tshutit.get_config(module_id, 'shutit.core.module.build_ifneeded', False, boolean=True)","title":""},{"docid":"441d3b079f1d99c6bc1f7616889c4d16","score":"0.5087299","text":"def generate_node_vars(self, config):\n\n self.vars = []\n\n # get location to output individual configuration files to\n configdir = self.get_option(\"config\", section=\"pe\", default=\"configs\")\n configlocation = os.path.join(self.outdir, configdir)\n if not os.path.exists(configlocation):\n os.makedirs(configlocation)\n\n # get results directory\n self.resdir = os.path.join(\n self.outdir, self.get_option(\"results\", default=\"results\"), self.psrname\n )\n if not os.path.exists(self.resdir):\n os.makedirs(self.resdir)\n\n transfer_files = self.submit_options.get(\"should_transfer_files\", \"NO\")\n\n # store expected results file names\n extension = self.sampler_kwargs.get(\"save\", \"hdf5\")\n gzip = self.sampler_kwargs.get(\"gzip\", False)\n self.resultsfiles = []\n\n for i in range(self.n_parallel):\n curconfig = copy.deepcopy(config)\n vardict = {}\n\n label = f\"{self.submit_options.get('name', 'cwinpy_pe')}_{''.join(self.dets)}_{self.psrname}\"\n\n if self.n_parallel > 1:\n configfile = os.path.join(\n configlocation,\n \"{}_{}_{}.ini\".format(\"\".join(self.dets), self.psrname, i),\n )\n\n label += f\"_{i}\"\n else:\n configfile = os.path.join(\n configlocation, \"{}_{}.ini\".format(\"\".join(self.dets), self.psrname)\n )\n\n self.resultsfiles.append(\n bilby.core.result.result_file_name(\n os.path.abspath(self.resdir), label, extension=extension, gzip=gzip\n )\n )\n\n curconfig[\"label\"] = label\n\n # add files for transfer\n if transfer_files == \"YES\":\n transfer_input = []\n\n transfer_input.append(relative_topdir(configfile, self.resdir))\n\n for key in [\n \"par_file\",\n \"inj_par\",\n \"data_file_1f\",\n \"data_file_2f\",\n \"prior\",\n ]:\n if key in list(config.keys()):\n if key in [\"data_file_1f\", \"data_file_2f\"]:\n for detkey in config[key]:\n transfer_input.append(\n relative_topdir(config[key][detkey], self.resdir)\n )\n\n # exclude full path as the transfer directory is flat\n curconfig[key][detkey] = os.path.basename(\n config[key][detkey]\n )\n else:\n transfer_input.append(\n relative_topdir(config[key], self.resdir)\n )\n\n # exclude full path as the transfer directory is flat\n curconfig[key] = os.path.basename(config[key])\n\n # transfer ephemeris files\n for ephem in [\"earth\", \"sun\"]:\n key = f\"{ephem}ephemeris\"\n if key in config:\n if isinstance(config[key], dict):\n for etype in copy.deepcopy(config[key]):\n transfer_input.append(\n relative_topdir(config[key][etype], self.resdir)\n )\n curconfig[key][etype] = os.path.basename(\n config[key][etype]\n )\n else:\n transfer_input.append(\n relative_topdir(config[key], self.resdir)\n )\n curconfig[key] = os.path.basename(config[key])\n\n curconfig[\"outdir\"] = \"results/\"\n\n # add output directory to inputs in case resume file exists\n transfer_input.append(\".\")\n\n vardict[\"ARGS\"] = f\"--config {os.path.basename(configfile)}\"\n vardict[\"INITIALDIR\"] = self.resdir\n vardict[\"TRANSFERINPUT\"] = \",\".join(transfer_input)\n vardict[\"TRANSFEROUTPUT\"] = curconfig[\"outdir\"]\n else:\n vardict[\"ARGS\"] = f\"--config {os.path.basename(configfile)}\"\n\n # set log files\n vardict[\"LOGFILE\"] = os.path.join(\n self.log_directories[\"log\"], f\"{label}.log\"\n )\n vardict[\"OUTPUTFILE\"] = os.path.join(\n self.log_directories[\"out\"], f\"{label}.out\"\n )\n vardict[\"ERRORFILE\"] = os.path.join(\n self.log_directories[\"error\"], f\"{label}.err\"\n )\n\n # write out configuration file\n parseobj = DefaultConfigFileParser()\n with open(configfile, \"w\") as fp:\n fp.write(parseobj.serialize(curconfig))\n\n self.vars.append(vardict)","title":""},{"docid":"dc8a29a64ddfaa262279b1631e7b438c","score":"0.50859463","text":"def _augment_pipeline_cfg(self):","title":""},{"docid":"c55e007ebddca1b90638405690dd0989","score":"0.5085939","text":"def run(self) -> None:\n self.all_commands = []\n # Replaces all $ref entries in config with what they point to\n self.logger.info(\"run():: Replacing references in schemas...\")\n self.recursive_replace_refs(self.openapi_config, self.openapi_config)\n # Now openapi_config contains fully unwrapped configuration specification\n # We can use it as reference dict to update paths\n self.logger.info(\"run():: Replacing references in paths...\")\n self.recursive_replace_refs(self.openapi_paths, self.openapi_config)\n\n # Now we have full configuration starting from API paths with all parameters populated.\n # We can create our command dictionaries\n self.logger.info(\"run():: Building command set...\")\n for endpoint_name, path in self.openapi_paths.items():\n\n # Get full list of parameters for every endpoint (everything in schema)\n endpoint_data = self.recursive_find_dict_key(path, API_DATA_NODE_KEY)\n if endpoint_data != []:\n\n # Now we need to construct a command for each item in endpoint data\n # entry is a dict {\"path\":List, \"data\":Dict}\n # entry[\"data\"] usually contains multiple parameters, e.g.\n # {'systemClass': {'type': 'string', 'readOnly': True, 'nullable': True},\n # 'systemLine': {'type': 'string', 'readOnly': True, 'nullable': True},\n # ...\n # }\n # which have to be split to separate SL2 commands\n # entry[\"path\"] is a node path in JSON schema to the data dict\n for entry in endpoint_data:\n for param_name, param_dict in entry[\"data\"].items():\n # First, get rid of fucking useless oneOf keys\n # They are used for JSON schema validation, absolute PITA\n # for parameters parsing\n self.recursive_reduce(param_dict, \"oneOf\")\n if not isinstance(param_dict, dict):\n self.logger.warning(\"run():: Wrong parameter configuration for %s %s.%s - not a dictionary!\", endpoint_name, '.'.join(entry['path']), param_name)\n continue\n self.all_commands.extend(self.make_command(endpoint_name, entry[\"path\"], param_name, param_dict))\n else:\n self.logger.warning(\"run():: No schema found for endpoint %s\", endpoint_name)","title":""},{"docid":"900cef7ad7ae59279204dc39637ed3fb","score":"0.5076582","text":"def unroll_config(config):\n # get global prefix of run\n prefix = config[\"global\"][\"prefix\"]\n\n # store unrolled configurations here\n configs = {}\n\n # check if we have a single job or need to unroll\n # into multiple jobs\n if config.get(\"batch\", None) is None:\n configs[prefix] = config\n else:\n # go through all specified runs\n for sub_id, delta_config in config[\"batch\"].items():\n # create copy of config and update for current subjob\n sub_config = deepcopy(config)\n\n # create prefix of subjob (may contain / to route\n # subjob output to subfolder)\n sub_prefix = prefix + sub_id\n\n # these are not batch jobs anymore, so deactivate section\n sub_config[\"batch\"] = None\n\n # create full prefix for subjob\n sub_config[\"global\"][\"prefix\"] = sub_prefix\n\n # apply subconfig delta\n # (assuming parameters are nested in two layers)\n for section in delta_config:\n # if dictionary, substitute all items on second level\n if isinstance(delta_config[section], Mapping):\n for param, value in delta_config[section].items():\n sub_config[section][param] = value\n else:\n # substitute entire section (this only affects pipeline stages)\n sub_config[section] = delta_config[section]\n\n configs[sub_prefix] = sub_config\n\n return configs","title":""},{"docid":"1e96bd937ea0d1762878203b150de79b","score":"0.50726634","text":"def build_general_config(self):\n\n raise NotImplementedError()","title":""},{"docid":"46e3364e6acd168f1aa02e6922416a27","score":"0.50712633","text":"def generate_config(cls, tree, special='rethinkdb', override=None):\n\n\t\t# Call the parent\n\t\treturn super().generate_config(tree, special, override);","title":""},{"docid":"a3ceeb4fd3fdc1698dc184ea624153b7","score":"0.50711846","text":"def build_structure(config):\n\n logging.info(\"Building data structure...\")\n\n\n # Build templates\n logging.info(\"Building templates...\")\n templates = {}\n for id_, data in config[\"templates\"].items():\n logging.debug(\"Building template '{0}'\".format(id_))\n try:\n templates[id_] = Template(id_, *data)\n except ConfigError as e:\n logging.warn(\"Problem with config file - {0}. Skipping template '{1}'\".format(e, id_))\n\n # Build items\n logging.info(\"Building items...\")\n items = {}\n for id_, data in config[\"items\"].items():\n logging.debug(\"Building item '{0}'\".format(id_))\n try:\n template_id = data[1]\n if template_id is not None:\n template = templates[template_id]\n else:\n template = None\n items[id_] = Item(id_, data[0], template)\n except KeyError as e:\n logging.warn(\"Problem with config file - Invalid template '{0}'. Skipping item '{1}'\".format(data[1], id_))\n except ConfigError as e:\n logging.warn(\"Problem with config file - {0}. Skipping item '{1}'\".format(e, id_))\n\n # Build users\n logging.info(\"Building users...\")\n users = {}\n for email, item_ids in config[\"users\"].items():\n logging.debug(\"Building user '{0}'\".format(email))\n user_items = []\n\n for item_id in item_ids:\n try:\n user_items.append(items[item_id])\n except KeyError as e:\n logging.warn(\"Problem with config file - Invalid item '{0}' for user '{1}'\".format(item_id, email))\n\n # Check for duplicates\n if len(set(user_items)) != len(user_items):\n logging.warn(\"Problem with config file - Duplicate items for user '{0}'\".format(email))\n\n users[email] = User(user_items)\n\n return users","title":""},{"docid":"adb6409c4556de74340027f11b18a315","score":"0.50702965","text":"def _next_configs(self):\n for state, input, stack in self.data:\n subtable = self.table.get(state, {})\n for (input_prefix, stack_prefix), entries in subtable.items():\n if (input.startswith(input_prefix) and\n stack.startswith(stack_prefix)):\n for next_state, next_stack in entries:\n yield Config(\n next_state,\n input[len(input_prefix):],\n next_stack+stack[len(stack_prefix):])","title":""},{"docid":"c34746248482ba01a6181e25d0e7dfcd","score":"0.5070073","text":"def _create(self):\n parser = _create_field_parser()\n main_rules = parser \\\n .parseFile(self.base_dir + '/' + self.main_config_file,\n parseAll=True)\n rules = main_rules.rules\n includes = main_rules.includes\n already_includes = [self.main_config_file]\n\n #Resolve includes\n for include in includes:\n if include[0] in already_includes:\n continue\n already_includes.append(include[0])\n if os.path.exists(include[0]):\n tmp = parser.parseFile(include[0], parseAll=True)\n else:\n #CHECK: This will raise an IOError if the file doesn't exist\n tmp = parser.parseFile(self.base_dir + '/' + include[0],\n parseAll=True)\n if rules and tmp.rules:\n rules += tmp.rules\n else:\n rules = tmp.rules\n if includes and tmp.includes:\n includes += tmp.includes\n else:\n includes = tmp.includes\n\n #Create config rules\n for rule in rules:\n if rule.override:\n self.__override_rules.append(rule)\n elif rule.extend:\n self.__extend_rules.append(rule)\n elif rule.inherit_from:\n self.__inherit_rules.append(rule)\n else:\n self._create_rule(rule)\n\n self.__resolve_inherit_rules()\n self.__resolve_override_rules()\n self.__resolve_extend_rules()","title":""},{"docid":"54b92469f316d8fdd4e870f1f8a6ce3b","score":"0.506807","text":"def config_func(config):\n\n for node in config.children:\n key = node.key.lower()\n val = node.values[0]\n if key == 'debug':\n obj.debug = pc.convert2boolean(val)\n elif key == 'verbose':\n obj.verbose = pc.convert2boolean(val)\n\n collectd.info('%s: debug=%s, verbose=%s'\n % (PLUGIN, obj.debug, obj.verbose))\n\n return pc.PLUGIN_PASS","title":""},{"docid":"eb4394c5c70ae5af3b4bfa98c0940c29","score":"0.50434846","text":"def applyConfiguration (c):\r\n\r\n def getboolean(name):\r\n value = getattr(config, name)\r\n newvalue = c.config.getBool(name)\r\n if newvalue is not None:\r\n setattr(config, name, newvalue)\r\n \r\n getboolean(\"rst2file\")\r\n getboolean(\"rst2_bodyfilter\")\r\n getboolean(\"rst2_clear_attributes\")\r\n getboolean(\"rst2_http_server_support\")\r\n if config.rst2_http_server_support and not mod_http:\r\n g.es(\"Resetting rst2_http_server_support because mod_http plugin was not imported successfully\", color='red')\r\n config.rst2_http_server_support = False\r\n getboolean(\"rst2_pure_document\")\r\n getboolean(\"rst2_format_headlines\")\r\n # getboolean(\"rst2_warnofdrags\")\r\n getboolean(\"rst2_run_on_window_open\")\r\n \r\n getboolean(\"rst2_debug_handle_endtag\")\r\n getboolean(\"rst2_debug_store_lines\")\r\n getboolean(\"rst2_debug_handle_starttag\")\r\n getboolean(\"rst2_debug_show_unknownattributes\")\r\n getboolean(\"rst2_debug_node_html_1\")\r\n getboolean(\"rst2_debug_anchors\")\r\n getboolean(\"rst2_debug_before_and_after_replacement\")\r\n getboolean(\"rst2_install_menu_item_in_edit_menu\")","title":""},{"docid":"0f2bd2c3a6dcfd4f229e0da4d6382736","score":"0.5012573","text":"def create_config_template():\n import ruamel.yaml\n yaml_str = \"\"\"\\\n# Project definitions (do not edit)\n Task:\n scorer:\n date:\n \\n\n# Project path (change when moving around)\n project_path:\n \\n\n# Annotation data set configuration (and individual video cropping parameters)\n video_sets:\n bodyparts:\n start:\n stop:\n numframes2pick:\n \\n\n# Plotting configuration\n skeleton:\n skeleton_color:\n pcutoff:\n dotsize:\n alphavalue:\n colormap:\n \\n\n# Training,Evaluation and Analysis configuration\n TrainingFraction:\n iteration:\n resnet:\n snapshotindex:\n batch_size:\n \\n\n# Cropping Parameters (for analysis and outlier frame detection)\n cropping:\n#if cropping is true for analysis, then set the values here:\n x1:\n x2:\n y1:\n y2:\n \\n\n# Refinement configuration (parameters from annotation dataset configuration also relevant in this stage)\n corner2move2:\n move2corner:\n \"\"\"\n ruamelFile = ruamel.yaml.YAML()\n cfg_file = ruamelFile.load(yaml_str)\n return(cfg_file,ruamelFile)","title":""},{"docid":"6a1ba6cc584b7e453f363efd562a5b9f","score":"0.50076985","text":"def generate_confs():\n\tglobal_config = config_gen.get_config()\n\tfor section in global_config.keys():\n\t\tif global_config[section].has_key('TEMPLATE_CONFIG'):\n\t\t\tif not global_config[section].has_key('OUTPUT_CONFIG'):\n\t\t\t\texit_error(\"[ERROR] 'OUTPUT_CONFIG' not specified for '\" + section + \"'\")\n\t\t\ttemplate_file = global_config[section]['TEMPLATE_CONFIG']\n\t\t\ttemplate_str = ''\n\t\t\ttry:\n\t\t\t\twith open(template_file) as f:\n\t\t\t\t\ttemplate_str = f.read()\n\t\t\texcept:\n\t\t\t\texit_error(\"[ERROR] Template File for '\" + section + \"', \" + template_file + \" does not exist\") \n\n\t\t\tfor key, val in global_config[section].items():\n\t\t\t\ttemplate_str = template_str.replace('$' + key + '$', val)\n\n\t\t\ttry:\n\t\t\t\twith open(global_config[section]['OUTPUT_CONFIG'], 'wb') as f:\n\t\t\t\t\tprint 'Writing', f.name, '...'\n\t\t\t\t\tf.write(template_str)\n\t\t\texcept:\n\t\t\t\texit_error(\"[ERROR] Failed to open output_config '\" + global_config[section]['OUTPUT_CONFIG'] + \"' in write mode\")\n\t\telif section == 'HOSTAPD':\n\t\t\twrite_hostapd_conf(global_config)","title":""},{"docid":"32322e10328741038c4d57412e8d5eaa","score":"0.5007296","text":"def interpretCfg(self):\n target_node = self.getTargetNode()\n current_node = self.getSourceNode()\n self.visited.append(current_node)\n while current_node != target_node:\n # see if there is actually a decision to take when node is (if|while)\n if current_node in self.cfgparser.labelsIf or \\\n current_node in self.cfgparser.labelsWhile :\n decision_node = current_node\n succ = [s for s in self.cfg.successors(current_node)]\n for s in succ:\n label = self.cfg.edges[decision_node, s]['label']\n if self.interpretCondition(label):\n current_node = s\n # else the edge represents an assigment\n # however if the assigment has a while as successor, it means there is\n # a condition to evaluate\n elif current_node in self.cfgparser.labelsAssigns:\n succ = [s for s in self.cfg.successors(current_node)]\n if len(succ) == 1:\n label = self.cfg.edges[current_node, succ[0]]['label']\n self.interpretAssigments(label)\n current_node = succ[0]\n # else finally the node must BE the empty label with no successfors\n else:\n succ = [s for s in self.cfg.successors(current_node)]\n if len(succ) == 0 :\n current_node = target_node\n else:\n raise Exception('Target node could not be reached')\n self.visited.append(current_node)","title":""},{"docid":"998f5b34efcb0b17f54d73e5546f73c6","score":"0.49866524","text":"def build_config(config: Dict[str, Any]) -> Dict[str, str]:\n result = config.copy()\n # Manage the classifier stable/beta\n is_stable = result.pop(\"is_stable\", False)\n if is_stable:\n result[\"classifier\"] = \"Development Status :: 5 - Production/Stable\"\n else:\n result[\"classifier\"] = \"Development Status :: 4 - Beta\"\n # Manage the nspkg\n package_name = result[\"package_name\"]\n result[\"package_nspkg\"] = result.pop(\"package_nspkg\", package_name[: package_name.rindex(\"-\")] + \"-nspkg\")\n # ARM?\n result[\"is_arm\"] = result.pop(\"is_arm\", True)\n\n # Do I need msrestazure for this package?\n result[\"need_msrestazure\"] = result.pop(\"need_msrestazure\", False)\n\n # Do I need azure-mgmt-core for this package?\n result[\"need_azuremgmtcore\"] = result.pop(\"need_azuremgmtcore\", True)\n\n # Pre-compute some Jinja variable that are complicated to do inside the templates\n package_parts = result[\"package_nspkg\"][: -len(\"-nspkg\")].split(\"-\")\n result[\"nspkg_names\"] = [\".\".join(package_parts[: i + 1]) for i in range(len(package_parts))]\n result[\"init_names\"] = [\"/\".join(package_parts[: i + 1]) + \"/__init__.py\" for i in range(len(package_parts))]\n exclude_folders = result.pop(\"exclude_folders\", \"\")\n result[\"exclude_folders\"] = [item.strip() for item in exclude_folders.split(\",\") if item.strip()]\n\n # Return result\n return result","title":""},{"docid":"dd0ff7d0b17092c8fa77f4ba759bae71","score":"0.49811134","text":"def _python_to_conf(self, x, name='', nesting_context=None, **metadata):\n\n # If this is an atomic type, no parsing is required\n if type(x) in self.VALID_ATOMIC_VALUE_TYPES:\n return x\n elif type(x) in self.VALID_SEQUENCE_VALUE_TYPES:\n # a sequence. apply to elements recursively\n if hasattr(x, 'items'):\n return type(x)((k, self._python_to_conf(v)) for k, v in x.items())\n else:\n return type(x)(self._python_to_conf(elem) for elem in x)\n\n if type(x) == type(inspect):\n\n # the top-level module object --> a ConfigContainer\n raw_attrs = self._get_dunder_dict(x)\n\n # handle the __entry_point___ directive:\n new_x = self._find_entry_point(raw_attrs)\n if new_x is not None:\n # return the result of the recursive call on the new entry point\n return self._python_to_conf(new_x)\n\n elif _is_raw_container(x):\n # a class --> a ConfigContainer\n raw_attrs = self._get_dunder_dict(x)\n\n # handle auto-overlay magic\n if nesting_context is None:\n nesting_context = []\n nesting_context = nesting_context + [(name, x)] # don't use append\n for overlayee in self._gen_overlayees(nesting_context):\n for k, v in self._get_dunder_dict(overlayee).items():\n if k not in raw_attrs:\n raw_attrs[k] = v\n else:\n # type misunderstood\n raise ConfigParsingError('Config construct of unsupported type %r: %s' % (type(x), x))\n\n # Separate raw_attrs to their types. See _prepare_attrs for more details.\n real_attrs, meta_attrs = self._prepare_attrs(raw_attrs)\n\n # if this is an overridet-set, all nested containers are also override sets automatically.\n # we propagate this metadata attribute using the metadata dict:\n if meta_attrs.get('is_override_set', False):\n metadata['is_override_set'] = meta_attrs['is_override_set']\n\n # convert real attr recursively\n attrs = {\n k: self._python_to_conf(v, name=k, nesting_context=nesting_context, **metadata)\n for k, v in real_attrs.items()\n }\n\n # create the container\n metadata_to_apply = merge_dicts(metadata, meta_attrs)\n if metadata_to_apply.get('is_override_set', False):\n container_cls = ConfigOverrideSet\n else:\n container_cls = ConfigContainer\n container = container_cls(attrs)\n\n # set metadata attributes on the container\n container.get_metadata().update(metadata_to_apply)\n\n return container","title":""},{"docid":"38de66c8fdee0b6a4739b56eec38b8af","score":"0.49773577","text":"def parse_config(self) -> None:\n filename = self.nodes\n config = Config()\n cfgdir = os.path.dirname(filename)\n cfg = yaml.safe_load(open(filename))\n nodes = [self.create_node(cfgdir, nodecfg) for nodecfg in cfg['nodes']]\n addrs = []\n for node in nodes:\n addrs.append(node.addr)\n idx = 1\n for node in nodes:\n if not node.addr:\n while addr(idx) in addrs:\n idx += 1\n node.addr = addr(idx)\n addrs.append(node.addr)\n\n if 'wmediumd' in cfg:\n wmd = cfg['wmediumd']\n\n wm_cfg = wmd.get('config', None)\n if wm_cfg is not None:\n assert isinstance(wm_cfg, str)\n config.wmediumd_conf = os.path.join(cfgdir, wm_cfg)\n\n wm_per = wmd.get('per', None)\n if wm_per is not None:\n assert isinstance(wm_per, str)\n config.wmediumd_per = os.path.join(cfgdir, wm_per)\n\n if 'net' in cfg:\n net = cfg['net']\n\n net_delay = net.get('delay', None)\n if net_delay is not None:\n assert isinstance(net_delay, (int, float))\n config.net_delay = float(net_delay)\n\n if 'controller' in cfg:\n config.start_time = cfg['controller'].get('start-time', 0)\n\n config.nodes = nodes\n self.config = config","title":""},{"docid":"587ba2ebe1a59380f6b51d0457f12ca1","score":"0.4937039","text":"def _setup_pipeline_cfg(self):","title":""},{"docid":"f9037a0589aab29e2b5b4aad5aeb2743","score":"0.49354577","text":"def traverse_config(multi_config: dict):\n config_list = [copy.deepcopy(multi_config)]\n queue = [[key] for key in multi_config.keys()]\n sample_functions = []\n while len(queue) > 0:\n key_path = queue.pop(0)\n values = key_path_get_value(multi_config, key_path)\n if key_path[-1] == 'sample':\n assert isinstance(values, dict)\n parent_key_path = key_path[:-1]\n # parent_config = key_path_get_value(multi_config, parent_key_path)\n # the value of the parent_key_path is replaced with the sample in config_list\n # this assertion can be false when the default explain config is merged into the multi config\n # assert len(parent_config) == 1\n sample_config = merge_dicts(SAMPLE_CONFIG, values)\n\n if sample_config['exclude'] is not None:\n def exclude_func(exclude_config: dict):\n return key_path_get_value(exclude_config, sample_config['exclude'])\n else:\n def exclude_func(exclude_config: dict):\n return None\n # TODO: functionality to sample together\n new_config_list = []\n for c in config_list:\n samples = sample(sample_config, exclude_func(c))\n for val in samples:\n config = key_path_set_value(copy.deepcopy(c), parent_key_path, val)\n new_config_list.append(config)\n config_list = new_config_list\n\n elif isinstance(values, dict):\n for key in values.keys():\n queue.append(key_path + [key])\n elif isinstance(values, list) or isinstance(values, tuple):\n for i in range(len(values)):\n queue.append(key_path + [i])\n\n return config_list","title":""},{"docid":"339403b25507ab9284566f4687dbd59d","score":"0.49337658","text":"def ConfigureTests(test_hierarchy: dict, argv):\n\n specific_test = \"\"\n if argv.test is not None:\n specific_test = argv.test\n print(\"specific_test=\" + specific_test)\n\n test_objects = []\n for testdir in test_hierarchy:\n for config_file in ListFilesInDir(testdir, \".json\"):\n sub_test_objs = ParseTestConfiguration(testdir + config_file)\n for obj in sub_test_objs:\n if specific_test != \"\" and obj.filename != specific_test:\n print(\"skipping \" + obj.filename)\n continue\n test_objects.append(obj)\n\n # If the out directory exists then we clear it\n if os.path.isdir(testdir + \"out/\"):\n shutil.rmtree(testdir + \"out/\")\n\n # If the out directory does not exist then we create it\n if not os.path.isdir(testdir + \"out/\"):\n os.mkdir(testdir + \"out/\")\n\n # If the gold directory does not exist then we create it\n if not os.path.isdir(testdir + \"gold/\"):\n os.mkdir(testdir + \"gold/\")\n\n return test_objects","title":""},{"docid":"2b1bb95989620240f195c66942df9983","score":"0.49330992","text":"def GenerateConfig(context):\n\n if context.properties['networkType'] == 'external':\n networkInterfaces = [{\n 'subnetwork': context.properties['subNetwork'],\n 'accessConfigs': [{\n 'name': 'External NAT',\n 'type': 'ONE_TO_ONE_NAT'\n }]\n }]\n else:\n networkInterfaces = [{\n 'subnetwork': context.properties['subNetwork'],\n }]\n if context.properties['mongoType'] == 'dbData':\n importScript = \"mongodata.sh\"\n elif context.properties['mongoType'] == 'dbRouter':\n importScript = \"mongos.sh\"\n elif context.properties['mongoType'] == 'dbConfig':\n if 'a' in context.env['name']:\n networkInterfaces[0]['networkIP'] = '10.0.2.10'\n elif 'b' in context.env['name']:\n networkInterfaces[0]['networkIP'] = '10.0.2.11'\n elif 'c' in context.env['name']:\n networkInterfaces[0]['networkIP'] = '10.0.2.12'\n importScript = \"mongoconf.sh\"\n\n resources = [{\n 'name': context.env['name'],\n 'type': 'compute.v1.instance',\n 'properties': {\n 'zone': context.properties['zone'],\n 'machineType': ''.join([computeBaseUrl, \n 'projects/', context.env['project'],\n '/zones/', context.properties['zone'], \n '/machineTypes/', context.properties['machineType']]),\n 'disks': [{\n 'deviceName': 'boot',\n 'type': 'PERSISTENT',\n 'boot': True,\n 'autoDelete': True,\n 'initializeParams': {\n 'sourceImage': ''.join([computeBaseUrl, 'projects/',\n 'ubuntu-os-cloud/global',\n '/images/family/ubuntu-2004-lts'])\n }\n }],\n 'networkInterfaces': networkInterfaces,\n # Allow the instance to access logging.\n \"serviceAccounts\": [\n {\n \"email\": \"208077196965-compute@developer.gserviceaccount.com\",\n \"scopes\": [\n \"https://www.googleapis.com/auth/devstorage.read_only\",\n \"https://www.googleapis.com/auth/logging.write\",\n \"https://www.googleapis.com/auth/monitoring.write\",\n \"https://www.googleapis.com/auth/servicecontrol\",\n \"https://www.googleapis.com/auth/service.management.readonly\",\n \"https://www.googleapis.com/auth/trace.append\"\n ]\n }\n ],\n # Metadata\n 'metadata': {\n 'items': [{\n # Startup script\n 'key': 'startup-script',\n 'value': context.imports[importScript]\n }]\n }\n }\n }]\n\n return {'resources': resources}","title":""},{"docid":"7111f80dbd0a719c17fa753bdb604826","score":"0.4928377","text":"def to_yaml_tree_transform(self, model, tag, ctx):","title":""},{"docid":"fa4b1586344b171ebcbb8a638d24507c","score":"0.49226865","text":"def get_configs(configs):\n\tshutit = shutit_global.shutit\n\tcp = LayerConfigParser()\n\tfail_str = ''\n\tfiles = []\n\tfor config_file in configs:\n\t\tif isinstance(config_file, tuple):\n\t\t\tcontinue\n\t\tif not is_file_secure(config_file):\n\t\t\tfail_str = fail_str + '\\nchmod 0600 ' + config_file\n\t\t\tfiles.append(config_file)\n\tif fail_str != '':\n\t\tif shutit.build['interactive'] > 1:\n\t\t\tfail_str = 'Files are not secure, mode should be 0600. Running the following commands to correct:\\n' + fail_str + '\\n'\n\t\t\t# Actually show this to the user before failing...\n\t\t\tshutit.log(fail_str)\n\t\t\tshutit.log('Do you want me to run this for you? (input y/n)')\n\t\t\tif shutit.build['interactive'] == 0 or util_raw_input(default='y') == 'y':\n\t\t\t\tfor f in files:\n\t\t\t\t\tshutit.log('Correcting insecure file permissions on: ' + f)\n\t\t\t\t\tos.chmod(f,0o600)\n\t\t\t\t# recurse\n\t\t\t\treturn get_configs(configs)\n\t\telse:\n\t\t\tfor f in files:\n\t\t\t\tshutit.log('Correcting insecure file permissions on: ' + f)\n\t\t\t\tos.chmod(f,0o600)\n\t\t\t# recurse\n\t\t\treturn get_configs(configs)\n\t\tshutit.fail(fail_str)\n\tfor config in configs:\n\t\tif isinstance(config, tuple):\n\t\t\tcp.readfp(config[1], filename=config[0])\n\t\telse:\n\t\t\tcp.read(config)\n\t# Treat allowed_images as a special, additive case\n\tshutit.build['shutit.core.module.allowed_images'] = cp.get_config_set('build', 'shutit.core.module.allowed_images')\n\treturn cp","title":""},{"docid":"09bd34c2e0b1cd33616ba985a97f2ee0","score":"0.48903823","text":"def _custom_tasks(template, info) -> None:\n if template == \"integration\":\n changes = {\"codeowners\": [info.codeowner]}\n\n if info.requirement:\n changes[\"requirements\"] = [info.requirement]\n\n info.update_manifest(**changes)\n\n if template == \"device_trigger\":\n info.update_strings(\n device_automation={\n **info.strings().get(\"device_automation\", {}),\n \"trigger_type\": {\n \"turned_on\": \"{entity_name} turned on\",\n \"turned_off\": \"{entity_name} turned off\",\n },\n }\n )\n\n if template == \"device_condition\":\n info.update_strings(\n device_automation={\n **info.strings().get(\"device_automation\", {}),\n \"condtion_type\": {\n \"is_on\": \"{entity_name} is on\",\n \"is_off\": \"{entity_name} is off\",\n },\n }\n )\n\n if template == \"device_action\":\n info.update_strings(\n device_automation={\n **info.strings().get(\"device_automation\", {}),\n \"action_type\": {\n \"turn_on\": \"Turn on {entity_name}\",\n \"turn_off\": \"Turn off {entity_name}\",\n },\n }\n )\n\n if template == \"config_flow\":\n info.update_manifest(config_flow=True)\n info.update_strings(\n config={\n \"title\": info.name,\n \"step\": {\n \"user\": {\"title\": \"Connect to the device\", \"data\": {\"host\": \"Host\"}}\n },\n \"error\": {\n \"cannot_connect\": \"Failed to connect, please try again\",\n \"invalid_auth\": \"Invalid authentication\",\n \"unknown\": \"Unexpected error\",\n },\n \"abort\": {\"already_configured\": \"Device is already configured\"},\n }\n )\n\n if template == \"config_flow_discovery\":\n info.update_manifest(config_flow=True)\n info.update_strings(\n config={\n \"title\": info.name,\n \"step\": {\n \"confirm\": {\n \"title\": info.name,\n \"description\": f\"Do you want to set up {info.name}?\",\n }\n },\n \"abort\": {\n \"single_instance_allowed\": f\"Only a single configuration of {info.name} is possible.\",\n \"no_devices_found\": f\"No {info.name} devices found on the network.\",\n },\n }\n )\n\n if template in (\"config_flow\", \"config_flow_discovery\"):\n init_file = info.integration_dir / \"__init__.py\"\n init_file.write_text(\n init_file.read_text()\n + \"\"\"\n\nasync def async_setup_entry(hass, entry):\n \\\"\\\"\\\"Set up a config entry for NEW_NAME.\\\"\\\"\\\"\n # TODO forward the entry for each platform that you want to set up.\n # hass.async_create_task(\n # hass.config_entries.async_forward_entry_setup(entry, \"media_player\")\n # )\n\n return True\n\"\"\"\n )","title":""},{"docid":"4965713aa9af60fed320461f01deee92","score":"0.4884857","text":"def generate(physical, fancy, debug):\n\n #\n # Send all the output to a string by redirecting stdout\n #\n import StringIO\n sio = StringIO.StringIO()\n save = sys.stdout\n sys.stdout = sio\n\n #\n #\n #\n print \"<script>\"\n f=open(fancy)\n print f.read()\n f.close()\n print \"</script>\"\n\n\n #\n # Generate the control and main net part of the config\n #\n Defaults(physical, debug).start()\n\n #\n # \n #\n from xtparse import process\n try:\n process(debug, physical, fancy)\n except MyError, error:\n sys.stderr.write(\"%s\\n\" % str(error))\n sys.exit(1)\n\n #\n # Restore stdout\n #\n sys.stdout = save\n\n print \"<testbed_config>\"\n print sio.getvalue(),\n print \"</testbed_config>\"","title":""},{"docid":"b1d714014ee43a630abe4e9df5c6d8ed","score":"0.48782662","text":"def parse_configs(code_config, field_config, time_config):\n # performing basic validation of config paths, obtaining dictionary of \n # config types and correpsonding raw dataframes\n raw_dfs = validate_config_dfs(code_config, field_config, time_config)\n\n # performing additional config-specific validation and parsing \n config_dict = {}\n for config_type, df in raw_dfs.items():\n if config_type in validation_functions:\n validation_functions[config_type](df)\n if config_type in parse_functions:\n config_dict[config_type] = parse_functions[config_type](df)\n else:\n config_dict[config_type] = df\n\n # concatenating code and field configs\n if CODE_CONFIG in config_dict:\n if FIELD_CONFIG in config_dict:\n config_dict[FIELD_CONFIG] = pd.concat([config_dict[CODE_CONFIG],\n config_dict[FIELD_CONFIG]], sort=True)\n else:\n config_dict[FIELD_CONFIG] = config_dict[CODE_CONFIG]\n config_dict.pop(CODE_CONFIG)\n\n return config_dict","title":""},{"docid":"957ecf046c8747fc8eb2d7aa0924c742","score":"0.48711365","text":"async def get_config_to_integration_load() -> dict[str, Any]:\n\n return {\n \"command_line\": [\n {\n \"binary_sensor\": {\n \"name\": \"Test\",\n \"command\": \"echo 1\",\n \"payload_on\": \"1\",\n \"payload_off\": \"0\",\n \"command_timeout\": 15,\n }\n },\n {\n \"cover\": {\n \"name\": \"Test\",\n \"command_state\": \"echo 1\",\n \"command_timeout\": 15,\n }\n },\n {\n \"notify\": {\n \"name\": \"Test\",\n \"command\": \"echo 1\",\n \"command_timeout\": 15,\n }\n },\n {\n \"sensor\": {\n \"name\": \"Test\",\n \"command\": \"echo 5\",\n \"unit_of_measurement\": \"in\",\n \"command_timeout\": 15,\n }\n },\n {\n \"switch\": {\n \"name\": \"Test\",\n \"command_state\": \"echo 1\",\n \"command_timeout\": 15,\n }\n },\n ]\n }","title":""},{"docid":"1bd9fad74c2b8cc0e29460accb8b00e7","score":"0.48693904","text":"def main(argv):\n config_generator = FaucetConfigGenerator()\n filepath = '/tmp/faucet_config_dump'\n egress = 2\n access = 3\n devices = 1\n topo_type = STACK\n argv = argv[1:]\n\n help_msg = \"\"\"\n <python3> build_config.py -e <egress_switches> -a <access_switches> -d <devices per switch>\n -p <config path> -t <topology type (flat, corp, stack)>\n \"\"\"\n\n try:\n opts, _ = getopt.getopt(\n argv, 'he:a:d:p:t:', ['egress=', 'access=', 'devices=', 'path=', 'type='])\n except getopt.GetoptError:\n print(help_msg)\n sys.exit(2)\n for opt, arg in opts:\n if opt == '-h':\n print(help_msg)\n sys.exit()\n elif opt in ('-e', '--egress'):\n egress = int(arg)\n elif opt in ('-a', '--access'):\n access = int(arg)\n elif opt in ('-d', '--devices'):\n devices = int(arg)\n elif opt in ('-p', '--path'):\n filepath = arg\n elif opt in ('-t', '--type'):\n topo_type = arg\n\n if topo_type == FLAT:\n faucet_config = config_generator.create_flat_faucet_config(access, devices)\n elif topo_type == CORP:\n faucet_config = config_generator.create_corp_faucet_config()\n elif topo_type == STACK:\n faucet_config = config_generator.create_scale_faucet_config(egress, access, devices)\n else:\n raise Exception('Unkown topology type: %s' % topo_type)\n\n config_map = cleanup_config(proto_dict(faucet_config))\n\n with open(filepath, 'w') as config_file:\n yaml.dump(config_map, config_file)","title":""},{"docid":"2dd4813e0621c0a08a7ca1dba0e83c3a","score":"0.4855035","text":"def _processCfg(self):\n errors = []\n opts = dict(self.node_cfg)\n cls = get_class(opts.pop('cls'))\n self.dispatcher = cls(opts.pop('name', self._cfgfiles),\n self.log.getChild('dispatcher'), opts, self)\n\n if opts:\n self.dispatcher.errors.append(self.unknown_options(cls, opts))\n self.modules = OrderedDict()\n\n # create and initialize modules\n todos = list(self.module_cfg.items())\n while todos:\n modname, options = todos.pop(0)\n if modname in self.modules:\n # already created by Dispatcher (via Attached)\n continue\n # For Pinata modules: we need to access this in Dispatcher.get_module\n self.module_cfg[modname] = dict(options)\n modobj = self.dispatcher.get_module_instance(modname) # lazy\n if modobj is None:\n self.log.debug('Module %s returned None', modname)\n continue\n self.modules[modname] = modobj\n if isinstance(modobj, Pinata):\n # scan for dynamic devices\n pinata = self.dispatcher.get_module(modname)\n pinata_modules = list(pinata.scanModules())\n for name, _cfg in pinata_modules:\n if name in self.module_cfg:\n self.log.error('Module %s, from pinata %s, already'\n ' exists in config file!', name, modname)\n self.log.info('Pinata %s found %d modules', modname, len(pinata_modules))\n todos.extend(pinata_modules)\n\n # initialize all modules by getting them with Dispatcher.get_module,\n # which is done in the get_descriptive data\n # TODO: caching, to not make this extra work\n self.dispatcher.get_descriptive_data('')\n # =========== All modules are initialized ===========\n\n # all errors from initialization process\n errors = self.dispatcher.errors\n\n if not self._testonly:\n start_events = MultiEvent(default_timeout=30)\n for modname, modobj in self.modules.items():\n # startModule must return either a timeout value or None (default 30 sec)\n start_events.name = f'module {modname}'\n modobj.startModule(start_events)\n if not modobj.startModuleDone:\n errors.append(f'{modobj.startModule.__qualname__} was not called, probably missing super call')\n\n if errors:\n for errtxt in errors:\n for line in errtxt.split('\\n'):\n self.log.error(line)\n # print a list of config errors to stderr\n sys.stderr.write('\\n'.join(errors))\n sys.stderr.write('\\n')\n sys.exit(1)\n\n if self._testonly:\n return\n self.log.info('waiting for modules being started')\n start_events.name = None\n if not start_events.wait():\n # some timeout happened\n for name in start_events.waiting_for():\n self.log.warning('timeout when starting %s', name)\n self.log.info('all modules started')\n history_path = os.environ.get('FRAPPY_HISTORY')\n if history_path:\n from frappy_psi.historywriter import FrappyHistoryWriter # pylint: disable=import-outside-toplevel\n writer = FrappyHistoryWriter(history_path, PREDEFINED_ACCESSIBLES.keys(), self.dispatcher)\n # treat writer as a connection\n self.dispatcher.add_connection(writer)\n writer.init(self.dispatcher.handle_describe(writer, None, None))\n # TODO: if ever somebody wants to implement an other history writer:\n # - a general config file /etc/secp/frappy.conf or <frappy repo>/etc/frappy.conf\n # might be introduced, which contains the log, pid and cfg directory path and\n # the class path implementing the history\n # - or we just add here an other if statement:\n # history_path = os.environ.get('ALTERNATIVE_HISTORY')\n # if history_path:\n # from frappy_<xx>.historywriter import ... etc.","title":""},{"docid":"0e683b5c682146176896ca4a7e7b27fb","score":"0.48546398","text":"def generate_kanon_config(sample: pd.DataFrame, k: int, cn_config: dict, topics: tuple):\n from cn.protect import Protect\n from cn.protect.privacy import KAnonymity\n from cn.protect.hierarchy import DataHierarchy, OrderHierarchy\n import uuid\n import textwrap\n\n protector = Protect(sample, KAnonymity(k))\n\n for prop_name, config in cn_config.items():\n protector.itypes[prop_name], protector.hierarchies[prop_name] = config\n\n private = protector.protect()\n\n tasks = {}\n\n def add_subtask(signature: str, **kwargs):\n # todo Don't add a new item to the dict on each call, but instead group tasks that\n # make use of the same function, e.g.:\n # - signature: drop_keys\n # args:\n # keys: [A, B]\n tasks[f\"{signature}-{uuid.uuid4()}\"] = kwargs\n\n for prop_name, (identifying, hierarchy) in cn_config.items():\n if private[prop_name][0] == \"*\":\n add_subtask(\"drop_keys\", keys=[prop_name])\n elif hierarchy is None:\n pass # no anonymization applied - do nothing\n elif isinstance(hierarchy, OrderHierarchy):\n lower, upper = [float(bound) for bound in private[prop_name][0][1:-1].split(\",\")]\n add_subtask(\"reduce_to_nearest_value\", keys=[prop_name], step_width=upper - lower)\n elif isinstance(hierarchy, DataHierarchy):\n actual_replacements = {}\n possible_replacements = hierarchy.df\n for value in private[prop_name]:\n # todo extract replaced from dataframe\n pass\n add_subtask(\"replace_with\", replacements=actual_replacements)\n else:\n print(\"Warning: Unsupported hierarchy type \" + str(type(hierarchy)))\n\n worker_config = {\n \"task_defaults\": {\n \"input_offset_reset\": \"earliest\",\n \"topic_encoding\": \"utf8\",\n \"storage_mode\": \"memory\"\n },\n \"tasks\": [{\n \"name\": task_name,\n \"function\": {\n \"signature\": task_name.split(\"-\")[0],\n \"args\": task_config\n }\n } for task_name, task_config in tasks.items()]\n }\n worker_config[\"tasks\"][0][\"input_topic\"] = topics[0]\n for task, previous_task in zip(worker_config[\"tasks\"][1:], worker_config[\"tasks\"]):\n task[\"input_topic\"] = previous_task[\"output_topic\"] = f\"{uuid.uuid4()}\"\n worker_config[\"tasks\"][-1][\"output_topic\"] = topics[1]\n\n print(textwrap.dedent(\"\"\"\n To configure k-anonymity in your data processing pipeline, include the following \n configuration snippet in your config.yml:\n ---\n \"\"\"))\n print(yaml.dump(worker_config))\n\n return worker_config","title":""},{"docid":"d7b2e7664cd33e3e9aceb5059c7de0f5","score":"0.48444206","text":"def _get_can_tp_config(self):\r\n tp_configs = self.root.findall(\".//{http://autosar.org/schema/r4.0}CAN-TP-CONFIG\")\r\n for tp_config in tp_configs:\r\n cluster_name = tp_config.find(\"{http://autosar.org/schema/r4.0}COMMUNICATION-CLUSTER-REF\").text.split('/')[-1]\r\n cluster = self.clusters_dict.get(cluster_name)\r\n\r\n # Get TP CONNECTIONS information\r\n tp_connections = tp_config.find(\"{http://autosar.org/schema/r4.0}TP-CONNECTIONS\")\r\n for tp_connection in tp_connections:\r\n current_tpc = _CanTPC()\r\n\r\n current_tpc.addressing_format = tp_connection.find(\"{http://autosar.org/schema/r4.0}ADDRESSING-FORMAT\").text.split('/')[-1]\r\n current_tpc.data_pdu = tp_connection.find(\"{http://autosar.org/schema/r4.0}DATA-PDU-REF\").text.split('/')[-1]\r\n try:\r\n current_tpc.flow_control_pdu = tp_connection.find(\"{http://autosar.org/schema/r4.0}FLOW-CONTROL-PDU-REF\").text.split('/')[-1]\r\n except AttributeError:\r\n pass\r\n current_tpc.tp_sdu = tp_connection.find(\"{http://autosar.org/schema/r4.0}TP-SDU-REF\").text.split('/')[-1]\r\n current_tpc.transmitter = tp_connection.find(\"{http://autosar.org/schema/r4.0}TRANSMITTER-REF\").text.split('/')[-1]\r\n\r\n receiver_refs = tp_connection.find(\"{http://autosar.org/schema/r4.0}RECEIVER-REFS\")\r\n for receiver_ref in receiver_refs:\r\n current_tpc.receiver.append(receiver_ref.text.split('/')[-1])\r\n\r\n cluster.tp_connections.append(current_tpc)","title":""},{"docid":"f273fe558a6582a1584a8baa7daa96dd","score":"0.4841644","text":"def configure(self, configuation):","title":""},{"docid":"6c51d3ee8d7fb6bd6ff8166b91acfcf5","score":"0.48380682","text":"def get_base_config(cfg_parser):\n\tshutit = shutit_global.shutit\n\tshutit.config_parser = cp = cfg_parser\n\t# BEGIN Read from config files\n\t# build - details relating to the build\n\tshutit.build['privileged'] = cp.getboolean('build', 'privileged')\n\tshutit.build['base_image'] = cp.get('build', 'base_image')\n\tshutit.build['dotest'] = cp.get('build', 'dotest')\n\tshutit.build['net'] = cp.get('build', 'net')\n\tshutit.build['secret_words_set'] = set()\n\tshutit.build['completed'] = False\n\tshutit.build['step_through'] = False\n\tshutit.build['ctrlc_stop'] = False\n\tshutit.build['ctrlc_passthrough'] = False\n\tshutit.build['have_read_config_file'] = False\n\t# Width of terminal to set up on login and assume for other cases.\n\tshutit.build['stty_cols'] = 320\n\tshutit.build['vagrant_run_dir'] = None\n\tshutit.build['this_vagrant_run_dir'] = None\n\t# Signals are set here, which is useful for context-switching callbacks.\n\tshutit.shutit_signal['ID'] = 0\n\t# Take a command-line arg if given, else default.\n\tif shutit.build['conn_module'] is None:\n\t\tshutit.build['conn_module'] = cp.get('build', 'conn_module')\n\t# Track logins in a stack and details in logins.\n\tshutit.build['login_stack'] = []\n\tshutit.build['logins'] = {}\n\t# Whether to accept default configs\n\tshutit.build['accept_defaults'] = None\n\t# target - the target of the build, ie the container\n\tshutit.target['hostname'] = cp.get('target', 'hostname')\n\tshutit.target['locale'] = cp.get('target', 'locale')\n\tshutit.target['ports'] = cp.get('target', 'ports')\n\tshutit.target['volumes'] = cp.get('target', 'volumes')\n\tshutit.target['volumes_from'] = cp.get('target', 'volumes_from')\n\tshutit.target['name'] = cp.get('target', 'name')\n\tshutit.target['rm'] = cp.getboolean('target', 'rm')\n\t# host - the host on which the shutit script is run\n\tshutit.host['add_shutit_to_path'] = cp.getboolean('host', 'add_shutit_to_path')\n\tshutit.host['docker_executable'] = cp.get('host', 'docker_executable')\n\tshutit.host['dns'] = cp.get('host', 'dns')\n\tshutit.host['password'] = cp.get('host', 'password')\n\tshutit.build['secret_words_set'].add(shutit.host['password'])\n\tshutit.host['logfile'] = cp.get('host', 'logfile')\n\tshutit.host['shutit_module_path'] = cp.get('host', 'shutit_module_path').split(':')\n\t# repository - information relating to repository/registry\n\tshutit.repository['name'] = cp.get('repository', 'name')\n\tshutit.repository['server'] = cp.get('repository', 'server')\n\tshutit.repository['push'] = cp.getboolean('repository', 'push')\n\tshutit.repository['tag'] = cp.getboolean('repository', 'tag')\n\tshutit.repository['export'] = cp.getboolean('repository', 'export')\n\tshutit.repository['save'] = cp.getboolean('repository', 'save')\n\tshutit.repository['suffix_date'] = cp.getboolean('repository', 'suffix_date')\n\tshutit.repository['suffix_format'] = cp.get('repository', 'suffix_format')\n\tshutit.repository['user'] = cp.get('repository', 'user')\n\tshutit.repository['password'] = cp.get('repository', 'password')\n\tshutit.build['secret_words_set'].add(shutit.repository['password'])\n\tshutit.repository['email'] = cp.get('repository', 'email')\n\tshutit.repository['tag_name'] = cp.get('repository', 'tag_name')\n\t# END Read from config files\n\n\t# BEGIN Standard expects\n\t# It's important that these have '.*' in them at the start, so that the matched data is reliably 'after' in the\n\t# child object. Use these where possible to make things more consistent.\n\t# Attempt to capture any starting prompt (when starting) with this regexp.\n\tshutit.expect_prompts['base_prompt'] = '\\r\\n.*[@#$] '\n\t# END Standard expects\n\n\tif shutit.build['delivery'] in ('bash','ssh'):\n\t\tif shutit.target['docker_image'] != '':\n\t\t\tprint('delivery method specified (' + shutit.build['delivery'] + ') and image_tag argument make no sense')\n\t\t\thandle_exit(exit_code=1)\n\tif shutit.target['docker_image'] == '':\n\t\tshutit.target['docker_image'] = shutit.build['base_image']\n\t# END tidy configs up\n\n\t# BEGIN warnings\n\t# FAILS begins\n\t# rm is incompatible with repository actions\n\tif shutit.target['rm'] and (shutit.repository['tag'] or shutit.repository['push'] or shutit.repository['save'] or shutit.repository['export']):\n\t\tprint(\"Can't have [target]/rm and [repository]/(push/save/export) set to true\")\n\t\thandle_exit(exit_code=1)\n\tif shutit.target['hostname'] != '' and shutit.build['net'] != '' and shutit.build['net'] != 'bridge':\n\t\tprint('\\n\\ntarget/hostname or build/net configs must be blank\\n\\n')\n\t\thandle_exit(exit_code=1)\n\t# FAILS ends","title":""},{"docid":"842e44eebfa47ec517bb41e9a5f9a7c0","score":"0.48260784","text":"def create_c_dict(self):\n c_dict = super().create_c_dict()\n\n et_upper = self.app_name.upper()\n\n # get TCStat data dir/template to read\n c_dict['TC_STAT_INPUT_DIR'] = (\n self.config.getdir('EXTRACT_TILES_TC_STAT_INPUT_DIR', '')\n )\n\n c_dict['TC_STAT_INPUT_TEMPLATE'] = (\n self.config.getraw('filename_templates',\n 'EXTRACT_TILES_TC_STAT_INPUT_TEMPLATE',\n '')\n )\n if not c_dict['TC_STAT_INPUT_TEMPLATE']:\n self.log_error('Must set EXTRACT_TILES_TC_STAT_INPUT_TEMPLATE '\n 'to run ExtractTiles wrapper')\n\n # get gridded input/output directory/template to read\n for data_type in ['FCST', 'OBS']:\n # get [FCST/OBS]_INPUT_DIR\n c_dict[f'{data_type}_INPUT_DIR'] = (\n self.config.getdir(f'{data_type}_EXTRACT_TILES_INPUT_DIR', '')\n )\n if not c_dict[f'{data_type}_INPUT_DIR']:\n self.log_error(f'Must set {data_type}_EXTRACT_TILES_INPUT_DIR to '\n 'run ExtractTiles wrapper')\n\n # get [FCST/OBS]_[INPUT/OUTPUT]_TEMPLATE\n for put in ['INPUT', 'OUTPUT']:\n local_name = f'{data_type}_{put}_TEMPLATE'\n config_name = f'{data_type}_{et_upper}_{put}_TEMPLATE'\n c_dict[local_name] = (\n self.config.getraw('filename_templates',\n config_name)\n )\n if not c_dict[local_name]:\n self.log_error(f\"{config_name} must be set.\")\n\n c_dict['OUTPUT_DIR'] = (\n self.config.getdir('EXTRACT_TILES_OUTPUT_DIR', '')\n )\n if not c_dict['OUTPUT_DIR']:\n self.log_error('Must set EXTRACT_TILES_OUTPUT_DIR to run '\n 'ExtractTiles wrapper')\n\n c_dict['NLAT'] = self.config.getstr('config', 'EXTRACT_TILES_NLAT')\n c_dict['NLON'] = self.config.getstr('config', 'EXTRACT_TILES_NLON')\n c_dict['DLAT'] = self.config.getstr('config', 'EXTRACT_TILES_DLAT')\n c_dict['DLON'] = self.config.getstr('config', 'EXTRACT_TILES_DLON')\n c_dict['LAT_ADJ'] = self.config.getfloat('config',\n 'EXTRACT_TILES_LAT_ADJ')\n c_dict['LON_ADJ'] = self.config.getfloat('config',\n 'EXTRACT_TILES_LON_ADJ')\n\n return c_dict","title":""},{"docid":"67efe9f194b0b81c776b11ac18a3efab","score":"0.4816568","text":"def setup_mtree(nw_src,nw_mcast_dst,inport,controller):\n if nw_mcast_dst == mcast_ip_addr1:\n mtree1_switches = []\n primary_tree = []\n if len(controller.mcast_groups.keys()) == 2:\n mtree1_switches = [10,11,13,12]\n primary_tree = [(13,12),(12,11),(12,10)]\n else:\n mtree1_switches = [7,6,5,4]\n primary_tree = [(7,6),(6,4),(6,5)]\n \n controller.primary_trees[nw_mcast_dst] = primary_tree\n return setup_mtree1_flow_tables(nw_src, nw_mcast_dst, inport,mtree1_switches,controller)\n elif nw_mcast_dst == mcast_ip_addr2:\n mtree2_switches = []\n primary_tree = []\n if len(controller.mcast_groups.keys()) == 2:\n mtree2_switches = [10,14,15]\n primary_tree = [(15,14),(15,10)]\n \n controller.primary_trees[nw_mcast_dst] = primary_tree #TODO REFACTOR !!!!!!!!!!!!!!!!!!!\n return setup_mtree2_flow_tables(nw_src, nw_mcast_dst, inport,mtree2_switches,controller)","title":""},{"docid":"faff659c7dcd6cde063d49fb242f7860","score":"0.48164737","text":"def config(self,conf):\n for key in conf:\n if key in ('startx','starty','start_direction'):\n setattr(__class__,key,conf[key])\n if key in ('puzzle','uid','title','type','theme'):\n setattr(self,key,conf[key])","title":""},{"docid":"502f6c9d32c0fe8c03f8e71df796ee15","score":"0.48163405","text":"def read(prog):\n try:\n with open(str(prog.config), \"r\") as file:\n raw = file.read().splitlines()\n except FileNotFoundError as ex:\n prog.log.error(\"config file '{}' not found\".format(ex.filename))\n return Prog.RetVal.exit_failure\n except OSError as ex:\n prog.log.error(\n \"config file '{}': {}\".format(ex.filename, ex.strerror.lower()))\n return Prog.RetVal.exit_failure\n\n prog.log.info1(\"+++ reading config file '{}'\".format(prog.config))\n\n line_pos = 0\n state = Prog.ConfigState()\n\n active_section = None\n target = None\n\n default_tlsa_list = []\n default_api = None\n\n log_level = None\n\n for l in raw:\n line_pos += 1\n state.line(line_pos)\n\n # matches section: \"[DOMAIN]\"\n match = re.match(r'\\s*\\[\\s*(?P<section>((\\w[a-zA-Z0-9-]*\\w|\\w+)\\.)+\\w+)\\s*\\](\\s*|\\s+#.*)$', l)\n if match:\n active_section = match.group('section').lower()\n prog.log.info3(\" + line {}: section: {}\".format(\n line_pos, active_section))\n for t in prog.target_list:\n if t.matches_domain(active_section):\n target = t\n break\n else:\n prog.target_list += [ Prog.Target(active_section) ]\n target = prog.target_list[-1]\n if default_api:\n target.api = default_api.copy()\n # NOT 'target.api = default_api'. If we do that, then in\n # the following line when we change the api object's\n # domain attribute, it will change the domain in _all_ of\n # the targets. We need to create a NEW object that is\n # (mostly) the same as the default_api object, and we do\n # this with the 'copy' method.\n target.api.set_domain(active_section)\n for tlsa in default_tlsa_list:\n tlsa.domain = active_section\n target.add_tlsa(tlsa)\n continue\n\n # matches parameter: \"param = input\"\n match = re.match(\n r'\\s*(?P<param>\\w+)\\s*=\\s*(?P<input>[^#]*)(\\s*|\\s#.*)$', l)\n if match:\n param = match.group('param')\n try:\n inputs = shlex.split(match.group('input'))\n except ValueError:\n if len(l) > 23:\n state.add_error(prog,\n \"unrecognized command: '{}...'\".format(l[:20]))\n else:\n state.add_error(prog,\n \"unrecognized command: '{}'\".format(l))\n continue\n\n if param == \"tlsa\":\n prog.log.info3(\" + line {}: parameter: {}, inputs: {}\".format(\n line_pos, param, inputs))\n if len(inputs) == 0:\n state.add_error(prog, \"no tlsa data given\")\n elif len(inputs) == 1:\n state.add_error(prog, \"tlsa record given insufficient data\")\n elif len(inputs) > 4:\n state.add_error(prog, \"tlsa record given superfluous data: '{}'\".format(' '.join(inputs[4:])))\n else:\n tlsa = get_tlsa_param(prog, inputs, active_section, state)\n if tlsa:\n if active_section:\n target.add_tlsa(tlsa)\n else:\n default_tlsa_list += [ tlsa ]\n\n elif param == \"api\":\n prog.log.info3(\n \" + line {}: parameter: {}, inputs: ({})...\".format(\n line_pos, param, len(inputs) ))\n if len(inputs) == 0:\n state.add_error(prog, \"api command given no input\")\n continue\n\n if inputs[0] in prog.apis:\n apimod = import_module('alnitak.api.' + inputs[0])\n api = apimod.get_api(prog, active_section,\n inputs[1:], state)\n if api:\n if active_section:\n target.api = api\n else:\n default_api = api\n else:\n state.add_error(prog,\n \"unrecognized api scheme: '{}'\".format(inputs[0]))\n\n elif param == \"dane_directory\":\n prog.log.info3(\" + line {}: parameter: {}, inputs: {}\".format(\n line_pos, param, inputs))\n if len(inputs) == 0:\n state.add_error(\n prog, \"dane_directory command given no input\")\n elif len(inputs) > 1:\n state.add_error(prog, \"dane_directory command given superfluous input: '{}'\".format(' '.join(inputs[1:])))\n else:\n prog.set_dane_directory(inputs[0])\n\n elif param == \"letsencrypt_directory\":\n prog.log.info3(\" + line {}: parameter: {}, inputs: {}\".format(\n line_pos, param, inputs))\n if len(inputs) == 0:\n state.add_error(\n prog, \"letsencrypt_directory command given no input\")\n elif len(inputs) > 1:\n state.add_error(prog, \"letsencrypt_directory command given superfluous input: '{}'\".format(' '.join(inputs[1:])))\n else:\n prog.set_letsencrypt_directory(inputs[0])\n\n elif param == \"log_level\":\n prog.log.info3(\" + line {}: parameter: {}, inputs: {}\".format(\n line_pos, param, inputs))\n if len(inputs) == 0:\n state.add_error(\n prog, \"log_level command given no input\")\n elif len(inputs) > 1:\n state.add_error(prog, \"log_level command given superfluous input: '{}'\".format(' '.join(inputs[1:])))\n else:\n if inputs[0] not in [ 'no', 'normal', 'verbose', 'debug' ]:\n state.add_error(prog, \"\")\n continue\n\n log_level = inputs[0]\n\n elif param == \"ttl\":\n prog.log.info3(\" + line {}: parameter: {}, inputs: {}\".format(\n line_pos, param, inputs))\n if len(inputs) == 0:\n state.add_error(\n prog, \"ttl command given no input\")\n elif len(inputs) > 1:\n state.add_error(prog, \"ttl command given superfluous input: '{}'\".format(' '.join(inputs[1:])))\n else:\n try:\n import alnitak.parser\n # python 3.4: 'from alnitak import parser' will\n # cause an error because of circular imports.\n # Importing like this will work\n ttl_value = alnitak.parser.ttl_check(prog, 0, 'config', inputs[0])\n except Except.Error1013:\n state.add_error(prog, \"ttl value '{}' not an integer\".format(inputs[0]))\n continue\n except Except.Error1100 as ex:\n state.add_error(prog, \"ttl value '{}' exceeds maximum value of '{}'\".format(inputs[0], ex.max))\n continue\n except Except.Error1101 as ex:\n state.add_error(prog, \"ttl value '{}' less than minimum value of '{}'\".format(inputs[0], ex.min))\n continue\n\n prog.set_ttl(ttl_value)\n\n\n else:\n state.add_error(prog,\n \"unrecognized parameter '{}'\".format(param))\n\n continue\n\n # matches empty line\n match = re.match(r'^\\s*(#.*)?$', l)\n if match:\n continue\n\n if len(l) > 23:\n state.add_error(prog,\n \"unrecognized command: '{}...'\".format(l[:20]))\n else:\n state.add_error(prog, \"unrecognized command: '{}'\".format(l))\n\n\n state.lineno = None\n for t in prog.target_list:\n if not t.tlsa:\n state.add_error(\n prog, \"target '{}' has no tlsa record\".format(t.domain))\n if not t.api:\n state.add_error(\n prog, \"target '{}' has no api scheme\".format(t.domain))\n\n if state.errors:\n return Prog.RetVal.config_failure\n\n prog.log.info3(\"+++ targets...\")\n if prog.target_list:\n for t in prog.target_list:\n prog.log.info3(str(t))\n else:\n prog.log.info3(\" + no targets found\")\n prog.log.error(\"config file: no targets given\")\n return Prog.RetVal.config_failure\n\n # set the delayed log level. We don't do this straight away or else we\n # will have mixed logging for this function itself.\n if log_level:\n prog.set_log_level(log_level)\n\n return Prog.RetVal.ok","title":""},{"docid":"6137123851bb0ced08b10e8149dff4f7","score":"0.4812785","text":"def test_config_load_multiple_configs(rule_manager, tmp_path, capsys):\n\n config1 = {}\n config1['rules'] = []\n config1['rules'].append({\n 'name': 'rulepack1.printFilename'\n })\n config1['rules'].append({\n 'name': 'rulepack1.findSingleLineCommentsWith',\n 'settings': {'with_string' : 'the'}\n })\n\n config1_file = tmp_path / \"config1.json\"\n with open(config1_file, 'w') as outfile:\n json.dump(config1, outfile)\n\n\n config2 = {}\n config2['rules'] = []\n config2['rules'].append({\n 'name': 'rulepack1.printFilename'\n })\n config2['rules'].append({\n 'name': 'rulepack1.findSingleLineCommentsWith',\n 'settings': {'with_string' : 'the'}\n })\n config2['rules'].append({\n 'name': 'rulepack1.findSingleLineCommentsWith',\n 'settings': {'with_string' : 'different'}\n })\n config2['rules'].append({\n 'name': 'rulepack1.printLanguage'\n })\n\n config2_file = tmp_path / \"config2.json\"\n with open(config2_file, 'w') as outfile:\n json.dump(config2, outfile)\n\n rule_manager.load_rules([str(config1_file), str(config2_file)], ['./tests'])\n\n captured = capsys.readouterr()\n assert \"Could not load rule\" not in captured.out\n assert 'rulepack1.findSingleLineCommentsWith' in rule_manager._rules_dict\n assert len(rule_manager._rules_dict['rulepack1.findSingleLineCommentsWith']) == 2\n assert 'rulepack1.printFilename' in rule_manager._rules_dict\n assert len(rule_manager._rules_dict['rulepack1.printFilename']) == 1\n assert 'rulepack1.printLanguage' in rule_manager._rules_dict","title":""},{"docid":"533d6192d1391336843ee09221a32ee2","score":"0.48095852","text":"def build_cfg(noop: bool) -> cconfig.Config:\n return cconfig.Config(\n basedir='/base',\n subdir='sub',\n baseurl='/repo',\n branches_file='/conf/test-branches.yaml',\n noop=noop,\n series='weird',\n space='outer',\n repo_auth='jrl:secret',\n )","title":""},{"docid":"49dde00206678594c48413f52c51d290","score":"0.48072264","text":"def create_target_config():\n print (\"Creating target config dir\")\n input_data = target_host1\n app_data = {'mode': 'target'}\n output_data = {'file': 'events.log'}\n create_json_file(\"./target\", input_data, \"inputs.json\")\n create_json_file(\"./target\", app_data, \"app.json\")\n create_json_file(\"./target\", output_data, \"outputs.json\")","title":""},{"docid":"70d4ddfc1a1e777728ce5588568424b4","score":"0.4802051","text":"def va_config_routes(self):\r\n logger.debug(\"Start to config route\")\r\n route_info = self.route_info\r\n\r\n for key in route_info.keys() :\r\n\r\n devobj = self.testdata.va_get_by_uniq_id(key,False)\r\n routes = route_info[key]\r\n node_type = devobj.get_nodetype()\r\n if (node_type == 'linux' or node_type == 'dir') :\r\n #only support linux pc and director now.\r\n devobj = self.testdata.va_get_by_uniq_id(key)\r\n for route in routes :\r\n if (not 'auto_set' in route) or (route['auto_set'] != 1):\r\n continue\r\n else:\r\n # if auto_set =1 , it means need to config route for defined pc\r\n if node_type == 'linux':\r\n result = devobj.config_route(route['dst'],route['netmask'],route['gateway'])\r\n\r\n if node_type == 'dir':\r\n #address netmask format.\r\n if isinstance(route['netmask'],int) :\r\n address = route['dst']+ '/' +route['netmask']\r\n else :\r\n address = route['dst'] + '/' + route['netmask']\r\n result = devobj.va_set_route(ipaddress.IPv4Network(address), route['gateway'],True)\r\n\r\n if not result :\r\n logger.error('Failed to config route')\r\n logger.debug(devobj.show_route())\r\n logger.debug(devobj.show_interface())\r\n return False\r\n\r\n logger.info(\"Completed to config route\")\r\n return True","title":""},{"docid":"8c09b8d7e0e585250c8821646300f60a","score":"0.48007068","text":"def getCommonConf(opts, config):\n\n if not opts:\n raise ProgrammingError(\"opts is None\")\n if not config:\n raise ProgrammingError(\"config is None\")\n \n polltime = POLL_DELAY_SECONDS_DEFAULT\n if opts.polltime:\n polltime = opts.polltime\n \n try:\n sshdkeypath = config.get(\"sshd\", \"generatedkey\")\n hostbasedconfig = config.get(\"sshd\", \"hostbasedconfig\")\n knownhostsconfig = config.get(\"sshd\", \"knownhostsconfig\")\n scratchdir = config.get(\"ctxservice\", \"scratchspacedir\")\n retr_template = config.get(\"ctxservice\", \"retr_template\")\n retr_template2 = config.get(\"ctxservice\", \"retr_template2\")\n err_template = config.get(\"ctxservice\", \"err_template\")\n err_template2 = config.get(\"ctxservice\", \"err_template2\")\n ok_template = config.get(\"ctxservice\", \"ok_template\")\n ok_template2 = config.get(\"ctxservice\", \"ok_template2\")\n ipandhostdir = config.get(\"taskpaths\", \"ipandhostdir\")\n restartdir = config.get(\"taskpaths\", \"restartdir\")\n thishostdir = config.get(\"taskpaths\", \"thishostdir\")\n thishostfinalizedir = config.get(\"taskpaths\", \"thishostfinalizedir\")\n logfilepath = config.get(\"ctxservice\", \"logfilepath\")\n curl = config.get(\"systempaths\", \"curl\")\n hostname = config.get(\"systempaths\", \"hostname\")\n datadir = config.get(\"taskpaths\", \"datadir\")\n etchosts_exe = config.get(\"taskpaths\", \"etchosts\")\n except:\n exception_type = sys.exc_type\n try:\n exceptname = exception_type.__name__ \n except AttributeError:\n exceptname = exception_type\n msg = \"%s: %s\" % (str(exceptname), str(sys.exc_value))\n raise InvalidConfig(msg)\n \n # no evaluate yet, pass False for now\n return CommonConf(opts.trace, False, ipandhostdir, restartdir, polltime, sshdkeypath, scratchdir, retr_template, retr_template2, err_template, err_template2, ok_template, ok_template2, hostbasedconfig, knownhostsconfig, thishostdir, thishostfinalizedir, logfilepath, curl, hostname, datadir, etchosts_exe)","title":""},{"docid":"a10b0ad170662478202a55e64ea83006","score":"0.47946462","text":"def _configure(self):\n # reset group_inputs back to what it was just after self.setup() in case _configure\n # is called multiple times.\n self._group_inputs = self._pre_config_group_inputs.copy()\n for n, lst in self._group_inputs.items():\n self._group_inputs[n] = lst.copy()\n\n for subsys in self._subsystems_myproc:\n subsys._configure()\n subsys._setup_var_data()\n\n self._has_guess |= subsys._has_guess\n self._has_bounds |= subsys._has_bounds\n self.matrix_free |= subsys.matrix_free\n\n conf_info = self._problem_meta['config_info']\n conf_info._reset()\n\n self._problem_meta['setup_status'] = _SetupStatus.POST_CONFIGURE\n self.configure()\n\n # if our configure() has added or promoted any variables, we have to call\n # _setup_var_data again on any modified systems and their ancestors (only those that\n # are our descendents).\n for s in conf_info._modified_system_iter(self):\n s._setup_var_data()","title":""},{"docid":"d6c63e36ea4219369653d5a307e99f78","score":"0.47769693","text":"def read_config(config_data):\n out_lines = []\n df = io.StringIO(config_data.decode('utf-8'))\n odf = io.StringIO()\n in_comment = False\n for line in df.readlines():\n # ack, multiline comments in /objects/generic/statuspod/statuspod.object\n # also in /objects/ancient/hologramgalaxy/hologramgalaxy.object, and\n # unfortunately that one necessitates some stripping (though stripping\n # is no more CPU-intensive than hardcoding the few instances)\n if line.lstrip()[:2] == '/*':\n if line.rstrip()[-2:] != '*/':\n in_comment = True\n else:\n if in_comment:\n if line.lstrip()[:2] == '*/':\n in_comment = False\n else:\n idx = line.find('//')\n if idx == -1:\n print(line, file=odf)\n else:\n print(line[0:idx], file=odf)\n\n # This list of patterns allows us to load all the data we care about\n # (that I'm aware of anyway) but I've moved to just stripping out\n # anything after // automatically. That shaves about a second off of\n # our startup time. Doubtless there are image processing speedups\n # which would probably account for the majority of the loadtime)\n #elif line[:3] != '// ':\n # found_pattern = False\n # for pattern in [\n # ' // ',\n # # special case for /objects/biome/foundry/lavatanklarge/lavatanklarge.object\n # '//FIRE',\n # # special cases for /objects/biome/tentacle/tentaclespawner1/tentaclespawner1.object\n # '//type',\n # '//additional',\n # '//relative',\n # '//[x,y] size',\n # '//total',\n # # special case for /objects/avian/sawblade/sawblade.object\n # '//mollys',\n # # special case for /objects/avian/birdgroundlantern/birdgroundlantern.object\n # '//\"interactive\"',\n # # special cases for /objects/outpost/signstore/signdispenser.object\n # '//\"openSounds\"',\n # '//\"closeSounds\"',\n # # special case for /objects/glitch/medievalspikes/medievalspikes.object\n # '//TODO',\n # # special case for /objects/themed/island/islandhammock/islandhammock.object\n # '//\"sitCoverImage\"',\n # # special case for /objects/protectorate/objects/protectoratewindbanner3/protectoratewindbanner3.object\n # '//\"soundEffect\"',\n # # special cases for /objects/protectorate/objects/protectoratelobbyvending/protectoratelobbyvending.object\n # '//\"onSound\"',\n # '//\"offSound\"',\n # # special case for /objects/spawner/spawners/spawner_human.object\n # '//6000,',\n # # special cases for /objects/spawner/colonydeed/colonydeed.object\n # '//whether',\n # '//delay',\n # '//cooldown',\n # '//scan',\n # '//length',\n # '//seconds',\n # # special cases for /objects/spawner/invisiblemonsterspawner.object\n # '//level',\n # '//options',\n # '//only',\n # # special case for /objects/crafting/upgradeablecraftingobjects/craftingwheel/craftingwheel.object\n # '//this',\n # ]:\n # idx = line.find(pattern)\n # if idx != -1:\n # found_pattern = True\n # break\n # if found_pattern:\n # print(line[0:idx], file=odf)\n # else:\n # print(line, file=odf)\n odf.seek(0)\n return json.load(odf)","title":""},{"docid":"25b08aa81b669ada92f82d37aa4524fc","score":"0.47752577","text":"def create_expected_configs(self, user):","title":""},{"docid":"2f8bee1aa4b6247584816b0ef1708a09","score":"0.47747043","text":"def _buildxml(self, namesonly=False):\n xpath = self._data_dict[self._type]\n self._get_xpath = \"//configuration/\" + xpath\n top = E(\"configuration\")\n dot = top\n for name in xpath.split(\"/\"):\n dot.append(E(name))\n dot = dot[0]\n\n if namesonly is True:\n dot.attrib[\"recurse\"] = \"false\"\n return top","title":""},{"docid":"b667672d18243e5b230c3994d9067eb8","score":"0.47745883","text":"def _prepare_from_cfg(self, model, config: Dict):\n assert isinstance(self.channel_unit_cfg, dict)\n assert 'units' in self.channel_unit_cfg\n config = self.channel_unit_cfg['units']\n if isinstance(config, str):\n config = fileio.load(config)\n assert isinstance(config, dict)\n\n if 'Analyzer' in self.parse_cfg['type']:\n self.parse_cfg.pop('from_cfg')\n tracer = TASK_UTILS.build(self.parse_cfg)\n unit_configs = tracer.analyze(model)\n\n units = []\n for unit_key in config:\n init_args = copy.deepcopy(self.unit_default_args)\n if 'init_args' in config[unit_key]:\n init_args.update(config[unit_key]['init_args'])\n config[unit_key]['init_args'] = init_args\n if 'channels' in config[unit_key]:\n unit = self.unit_class.init_from_cfg(model, config[unit_key])\n unit.name = unit_key\n else:\n try:\n unit = self._prepare_unit_from_init_cfg(\n model, config[unit_key], unit_configs[unit_key])\n except ValueError:\n raise ValueError(\n 'Initializing channel_mutator from the config needs'\n 'to include `channels` or `Analyzer` in the config.')\n units.append(unit)\n return units","title":""},{"docid":"39575d462080125595dc243c5961e201","score":"0.4771972","text":"def convert_area(config: dict[str, Any]) -> dict[str, Any]:\n my_map = {\n CONF_NAME: dyn_const.CONF_NAME,\n CONF_FADE: dyn_const.CONF_FADE,\n CONF_NO_DEFAULT: dyn_const.CONF_NO_DEFAULT,\n CONF_ROOM_ON: dyn_const.CONF_ROOM_ON,\n CONF_ROOM_OFF: dyn_const.CONF_ROOM_OFF,\n CONF_CHANNEL_COVER: dyn_const.CONF_CHANNEL_COVER,\n CONF_DEVICE_CLASS: dyn_const.CONF_DEVICE_CLASS,\n CONF_OPEN_PRESET: dyn_const.CONF_OPEN_PRESET,\n CONF_CLOSE_PRESET: dyn_const.CONF_CLOSE_PRESET,\n CONF_STOP_PRESET: dyn_const.CONF_STOP_PRESET,\n CONF_DURATION: dyn_const.CONF_DURATION,\n CONF_TILT_TIME: dyn_const.CONF_TILT_TIME,\n }\n result = convert_with_map(config, my_map)\n if CONF_CHANNEL in config:\n result[dyn_const.CONF_CHANNEL] = {\n channel: convert_channel(channel_conf)\n for (channel, channel_conf) in config[CONF_CHANNEL].items()\n }\n if CONF_PRESET in config:\n result[dyn_const.CONF_PRESET] = {\n preset: convert_preset(preset_conf)\n for (preset, preset_conf) in config[CONF_PRESET].items()\n }\n if CONF_TEMPLATE in config:\n result[dyn_const.CONF_TEMPLATE] = TEMPLATE_MAP[config[CONF_TEMPLATE]]\n return result","title":""},{"docid":"8820ca0f214d75bd13acc80a805e32ba","score":"0.4770118","text":"def setup_directories(self, config):\n # self.input_dir = os.path.join(config['run_directory'], 'HYDRO_IN')\n # self.output_dir = os.path.join(config['run_directory'], 'HYDRO_OUTPUT')\n self.input_dir = os.path.join(\"..\", \"HYDRO_IN\")\n self.output_dir = os.path.join(\"..\", \"HYDRO_OUTPUT\")\n if os.path.exists(self.input_dir) is False:\n os.mkdir(self.input_dir, 0o755)\n if os.path.exists(self.output_dir) is False:\n os.mkdir(self.output_dir, 0o755)","title":""},{"docid":"583e222f8bd286553c858d1efc3be5bd","score":"0.4767908","text":"def convert_config(\n config: dict[str, Any] | MappingProxyType[str, Any]\n) -> dict[str, Any]:\n my_map = {\n CONF_NAME: dyn_const.CONF_NAME,\n CONF_HOST: dyn_const.CONF_HOST,\n CONF_PORT: dyn_const.CONF_PORT,\n CONF_AUTO_DISCOVER: dyn_const.CONF_AUTO_DISCOVER,\n CONF_POLL_TIMER: dyn_const.CONF_POLL_TIMER,\n }\n result = convert_with_map(config, my_map)\n if CONF_AREA in config:\n result[dyn_const.CONF_AREA] = {\n area: convert_area(area_conf)\n for (area, area_conf) in config[CONF_AREA].items()\n }\n if CONF_DEFAULT in config:\n result[dyn_const.CONF_DEFAULT] = convert_default(config[CONF_DEFAULT])\n if CONF_ACTIVE in config:\n result[dyn_const.CONF_ACTIVE] = ACTIVE_MAP[config[CONF_ACTIVE]]\n if CONF_PRESET in config:\n result[dyn_const.CONF_PRESET] = {\n preset: convert_preset(preset_conf)\n for (preset, preset_conf) in config[CONF_PRESET].items()\n }\n if CONF_TEMPLATE in config:\n result[dyn_const.CONF_TEMPLATE] = {\n TEMPLATE_MAP[template]: convert_template(template_conf)\n for (template, template_conf) in config[CONF_TEMPLATE].items()\n }\n return result","title":""},{"docid":"7d61a5423a7361acd85da28d777ddbf9","score":"0.47649974","text":"def __configure(self):\n\n # load the core configuration\n self.context = zope.configuration.xmlconfig.file(self.confFile,\n execute=False)\n\n # load extensions and plugins\n for epath in p6.app.extension.extPaths():\n for extconf in p6.app.extension.extConfs(epath):\n print extconf\n p6.app.extension.loadExtension(extconf, self.context)\n\n # perform the actions specified by the configuration files\n self.context.execute_actions()\n\n # expand any metadata page groups\n newpages = []\n for page in self.pages:\n if getattr(page, 'expand', False):\n newpages = newpages + [n for n in page(None)]\n else:\n newpages.append(page)\n\n self.pages = newpages\n del newpages","title":""},{"docid":"209e4332bddf24f6df92200144c582c9","score":"0.47578472","text":"def convert_preset(config: dict[str, Any]) -> dict[str, Any]:\n my_map = {\n CONF_NAME: dyn_const.CONF_NAME,\n CONF_FADE: dyn_const.CONF_FADE,\n CONF_LEVEL: dyn_const.CONF_LEVEL,\n }\n return convert_with_map(config, my_map)","title":""},{"docid":"b9380a7021e055edbfc46110674f09a0","score":"0.47564927","text":"def customize_process(group_tree, groupvars, group_hostnames, hostvars, specific_vars):","title":""},{"docid":"fceee2e3bd77da8acbbc1503cdb5530e","score":"0.47562543","text":"def _propagate_to_ctg(meta, new_cycle_acls):\n print \"Propagating roles to cycle task groups\"\n new_ctg_acls = _propagate_to_children(\n meta,\n new_cycle_acls,\n \"CycleTaskGroup\",\n \"cycle_id\",\n \"Cycle\",\n )\n\n _propagate_to_cycle_tasks(meta, new_ctg_acls)","title":""},{"docid":"94e539f5279a0065475a320f1cbdcb7a","score":"0.47546867","text":"def from_yaml_tree_transform(self, node, tag, ctx):","title":""},{"docid":"1551c75fe2645763cd4a9141653b99cc","score":"0.47540516","text":"def run_rendering(self):\n \n if not isinstance(self.config, list):\n # If the config was not a list, just convert this one element into a list\n self.config = [self.config]\n\n for i, c in enumerate(self.config):\n # For each conversion\n if not 'data' in c:\n # Check that the yaml resume file is specified\n error(\"Configuration file has not defined 'data' with resume yaml file\")\n else:\n with open(c['data']) as resume_file:\n self.resume = yaml.load(resume_file)\n\n for output in c['outputs']:\n fmt = output['format']\n # Check that we have a plugin whose classname starts with this format\n assert any([x.startswith(fmt) for x in Plugin.registered])\n template_file = output['template']\n filebasename,filetype = os.path.splitext(template_file)\n if filetype[1:] not in self.allowed_filetypes:\n error(\"File type/extension %s is not one of following: %s\" % (filetype,' '.join(self.allowed_filetypes)))\n output_filename = output['output']\n # Instantiate the required conversion plugin\n print (\"Creating %s ...\" % output_filename, end='')\n text = Plugin.registered['%sResume' % fmt](template_file, self.resume, self.skip)\n text.render(output_filename)\n print (\" done\")","title":""},{"docid":"0e423b542d0f9c5ad93a7d5aa81f56bc","score":"0.47520763","text":"def load_config():\n config = Config(\"config.yaml\")\n # print(config)\n class_setting = dict()\n for item in config[\"dynamic\"][\"inherit\"].values():\n cls_path = item[\"path\"]\n del item[\"path\"]\n class_setting[cls_path] = dict(item)\n\n return class_setting","title":""},{"docid":"b1afebd696a1ae7c310b440158b0c15a","score":"0.4749547","text":"def handle_config(cls, config, base_config):\n if 'include' not in config:\n config['include'] = []\n elif isinstance(config['include'], str):\n config['include'] = [config['include']]\n for p, path in enumerate(config['include']):\n if not os.path.isabs(path):\n config['include'][p] = os.path.abspath(os.path.join(base_config['base_dir'], path))\n load_includes(config['include'])\n\n defaults = {'runner': 'local', 'interface': 'memmap', 'custom': False}\n for key, default in defaults.items():\n if key not in config:\n config[key] = default\n\n if not isinstance(config['runner'], MutableMapping):\n config['runner'] = {'class': config['runner']}\n Runner[config['runner']['class']].handle_subconfig(config['runner'], base_config)\n if not isinstance(config['interface'], MutableMapping):\n config['interface'] = {'class': config['interface']}\n RunnerInterface[config['interface']['class']].handle_config(config['interface'], base_config)\n Worker.handle_config(config, base_config)","title":""},{"docid":"949dc7b05142d1855fc7580314eb17c1","score":"0.47479874","text":"def normalize(conf):\n\n def expand(section_name):\n section = conf.get(section_name)\n if isinstance(section, string_types):\n conf[section_name] = {\n 'search': [section],\n 'register': section,\n 'publish': section\n }\n elif section:\n search = section.get('search')\n if search and not isinstance(search, list):\n section['search'] = [search]\n\n expand('registry')\n expand('ca')\n\n registry = conf.get('registry')\n registry['search'] = map(lambda item: item.rstrip('/'), registry.get('search', []))\n registry['register'] = registry.get('register', '').rstrip('/')\n registry['publish'] = registry.get('publish', '').rstrip('/')\n\n conf['tmp'] = path.abspath(conf['tmp'])\n return conf","title":""},{"docid":"b8ad7d8cdbba28fac64b55cef09e59d6","score":"0.47457963","text":"def post_run_javac(self):\n\n\tpar = {}\n\tfor x in self.inputs:\n\t\tpar[x.parent.id] = x.parent\n\n\tinner = {}\n\tfor k in par.values():\n\t\tpath = k.abspath(self.env)\n\t\tlst = os.listdir(path)\n\n\t\tfor u in lst:\n\t\t\tif u.find('$') >= 0:\n\t\t\t\tinner_class_node = k.find_or_declare(u)\n\t\t\t\tinner[inner_class_node.id] = inner_class_node\n\n\tto_add = set(inner.keys()) - set([x.id for x in self.outputs])\n\tfor x in to_add:\n\t\tself.outputs.append(inner[x])\n\n\treturn Task.Task.post_run(self)","title":""},{"docid":"0b4816f12318d09a78b0efb53530460f","score":"0.4735967","text":"def configure_device():\n config = {}\n\n profile = device_config[\"sys\"][\"device\"].get(\"profile\", ATTR_SWITCH)\n\n for cover_id in range(covers):\n topic, payload = get_cover(cover_id, profile)\n config[topic] = payload\n\n for sensor, description in cover_sensors.items():\n topic, payload = get_sensor(\n sensor, description, profile=profile, cover_id=cover_id\n )\n config[topic] = payload\n\n for relay_id in range(relays):\n consumption_types = [\n item.lower()\n for item in device_config[\"sys\"][\"ui_data\"].get(\"consumption_types\", [])\n ]\n relay_type = get_consumption_type(consumption_types, relay_id)\n\n topic, payload = get_switch(relay_id, relay_type, profile)\n config[topic] = payload\n\n topic, payload = get_light(relay_id, relay_type, profile)\n config[topic] = payload\n\n for sensor, description in relay_sensors.items():\n topic, payload = get_sensor(\n sensor, description, profile=profile, relay_id=relay_id\n )\n config[topic] = payload\n\n for binary_sensor, description in relay_binary_sensors.items():\n topic, payload = get_binary_sensor(\n binary_sensor, description, relay_id, profile=profile\n )\n config[topic] = payload\n\n for input_id in range(inputs):\n input_type = device_config[f\"input:{input_id}\"][\"type\"]\n\n for event in input_events:\n topic, payload = get_input(input_id, input_type, event)\n config[topic] = payload\n\n for binary_sensor, description in input_binary_sensors.items():\n topic, payload = get_binary_sensor(\n binary_sensor,\n description,\n input_id,\n is_input=True,\n input_type=input_type,\n )\n config[topic] = payload\n\n for button, descripton in buttons.items():\n topic, payload = get_button(button, descripton)\n config[topic] = payload\n\n for sensor, description in sensors.items():\n topic, payload = get_sensor(sensor, description)\n config[topic] = payload\n\n for binary_sensor, description in binary_sensors.items():\n topic, payload = get_binary_sensor(binary_sensor, description)\n config[topic] = payload\n\n return config","title":""},{"docid":"53d6aaf01ff66b084ff75781667b1f44","score":"0.4717589","text":"def __init__(self,parser):\n ## TODO: Initialization without parser (e.g. script example)\n self.parser = parser\n \n ## set some vars\n ################################################################\n ## separators\n self.separator = \"## -----------------------------------------------------------------------------\"\n self.verboseseparator = \"## --Verbose--------------------------------------------------------------------\"\n self.debugSeparator = \"## --Debug----------------------------------------------------------------------\"\n \n ## get options and args\n ################################################################\n (self.options, self.args) = parser.parse_args()\n\n ## Setup Logs\n ################################################################\n self.messages = {}\n \n ## main section inside configuration files\n ################################################################\n self.main_section = self.options.section\n ## TODO: set in cfg-file or options or 'main'\n self.sysclass_section = \"sysclass\"\n\n ## parse config files\n ################################################################\n self.config = self.options.config\n ## find path\n if self.config:\n config_files = []\n ## File/Path destination of the configuration\n if not os.path.exists(self.config):\n msg = str(self.config) + \" is not a file or path in current directory. Trying out script location.\"\n self.add_message(msg, level='WARNING')\n if self.config.startswith('./'):\n self.config = os.path.dirname(os.path.abspath(sys.argv[0])) + '/' + self.config.lstrip('./')\n if not os.path.exists(self.config):\n msg = \"Sorry, no configuration file found! Use --no-config if you want to disable configfile usage\"\n self.add_message(msg, level='ERROR')\n raise Exception(msg)\n\n if os.path.isfile(self.config):\n config_files = [self.config]\n elif os.path.isdir(self.config):\n for f in os.listdir(self.config):\n if f.endswith(\".conf\"):\n config_files.append(self.config.rstrip('/') + '/' + f)\n else:\n msg = \"Sorry, \" + str(self.config) + \" is neither a directory, nor a file!\"\n self.add_message(msg, level='ERROR')\n raise Exception(msg)\n ## List of configuration files to be parsed\n self.config_files = config_files\n\n ## ConfigParser object with parsed configuration\n self.configuration = ConfigParser.ConfigParser()\n self.configuration.read(self.config_files)\n else:\n self.add_message(\"Configfile functionality disabled!\", level='SYSINFO')\n\n \n ## Set debugging and the verbosity\n ################################################################\n ## Get verbosity option\n# if options.verbosity:\n if ((self.options.verbosity) or\n (self.config and\n self.configuration.has_option(self.sysclass_section,'verbose') and\n self.configuration.get(self.sysclass_section,'verbose') == 'True')):\n self.verbose = True\n else:\n self.verbose = False\n \n \n ## Get debugging option\n# if options.debug:\n if ((self.options.debug) or\n (self.config and\n self.configuration.has_option(self.sysclass_section,'debug') and\n self.configuration.get(self.sysclass_section,'debug') == 'True')):\n self.debug = True\n self.verbose = True\n else:\n self.debug = False\n if self.verbose:\n self.BeVerbose(\"Getting Options:\\nVerbose:\\t\" + str(self.verbose) + \"\\nDebug:\\t\" + str(self.debug))","title":""},{"docid":"8a27ef88bf7c868d002a1ecf0b90fd0c","score":"0.4716479","text":"def test_configuration(dumbalgo):\n nested_algo = {\"DumbAlgo\": dict(value=6, scoring=5)}\n algo = dumbalgo(8, value=1)\n config = algo.configuration\n assert config == {\n \"dumbalgo\": {\n \"seed\": None,\n \"value\": 1,\n \"scoring\": 0,\n \"judgement\": None,\n \"suspend\": False,\n \"done\": False,\n }\n }","title":""},{"docid":"c5edd682a6eff9d939ae70cedd2f1a73","score":"0.47078672","text":"def _propagate_to_cte(meta, new_ct_acls):\n print \"Propagating roles to cycle task entries\"\n return _propagate_to_children(\n meta,\n new_ct_acls,\n \"CycleTaskEntry\",\n \"cycle_task_group_object_task_id\",\n \"CycleTaskGroupObjectTask\",\n )","title":""},{"docid":"e6449199d42453275cb4ca5819151be5","score":"0.4705307","text":"def _get_conv_configs(dtype_configs):\n conv_configs = []\n observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT\n for convs in [_Conv1dMetadata, _Conv2dMetadata, _Conv3dMetadata]:\n\n # (1) Single conv modules/functions\n # -----------------------------------\n # conv module\n conv_configs.append(\n BackendPatternConfig(convs.root).set_observation_type(\n observation_type) # noqa: E131\n .set_dtype_configs(dtype_configs).set_root_module(\n convs.root).set_reference_quantized_module(\n convs.reference).set_qat_module(convs.qat))\n # conv qat module\n conv_configs.append(\n BackendPatternConfig(convs.qat).set_observation_type(\n observation_type) # noqa: E131\n .set_dtype_configs(dtype_configs).set_root_module(\n convs.root).set_reference_quantized_module(convs.reference))\n # functional conv\n conv_configs.append(\n BackendPatternConfig(convs.func).set_observation_type(\n observation_type) # noqa: E131\n .set_dtype_configs(dtype_configs)._set_input_type_to_index({\n 'weight':\n 1,\n 'bias':\n 2\n }))\n\n # (2) Conv + relu\n # -----------------\n # 2.1 conv module + relu fusion configs\n # conv relu fusion, conv module + relu module\n conv_configs.append(\n BackendPatternConfig(\n (torch.nn.ReLU,\n convs.root)).set_dtype_configs(dtype_configs) # noqa: E131\n .set_fuser_method(\n reverse_sequential_wrapper2(\n convs.fused_conv_relu)).set_fused_module(\n convs.fused_conv_relu))\n # conv relu fusion, conv module + functional relu\n conv_configs.append(\n BackendPatternConfig(\n (F.relu,\n convs.root)).set_dtype_configs(dtype_configs) # noqa: E131\n .set_fuser_method(\n reverse_sequential_wrapper2(\n convs.fused_conv_relu)).set_fused_module(\n convs.fused_conv_relu))\n # 2.2 conv module + relu fused module configs\n # conv relu, fused module\n conv_configs.append(\n BackendPatternConfig(convs.fused_conv_relu).set_observation_type(\n observation_type) # noqa: E131\n .set_dtype_configs(dtype_configs).set_root_module(\n convs.root).set_reference_quantized_module(\n convs.reference).set_qat_module(convs.relu_qat))\n # conv relu, qat fused module\n conv_configs.append(\n BackendPatternConfig(convs.relu_qat).set_observation_type(\n observation_type) # noqa: E131\n .set_dtype_configs(dtype_configs).set_root_module(\n convs.root).set_reference_quantized_module(convs.reference))\n # 2.3 functional conv + relu configs\n # conv relu, functional conv + relu module\n conv_configs.append(\n BackendPatternConfig(\n (torch.nn.ReLU, convs.func)).set_observation_type(\n observation_type) # noqa: E131\n .set_dtype_configs(dtype_configs))\n # conv relu, functional conv + functional relu\n conv_configs.append(\n BackendPatternConfig((F.relu, convs.func)).set_observation_type(\n observation_type) # noqa: E131\n .set_dtype_configs(dtype_configs))\n\n # fused conv relu\n conv_configs.append(\n BackendPatternConfig(convs.fused_conv_relu).set_dtype_configs(\n dtype_configs) # noqa: E131\n .set_qat_module(convs.relu_qat))\n\n conv_configs.append(\n BackendPatternConfig(convs.relu_qat).set_dtype_configs(\n dtype_configs) # noqa: E131\n .set_root_module(convs.root).set_reference_quantized_module(\n convs.reference))\n\n # (3) Conv + batchnorm (+ relu)\n # -------------------------------\n # 3.1 conv bn fusion configs\n # conv + bn fusion\n conv_configs.append(\n BackendPatternConfig(\n (convs.bn,\n convs.root)).set_dtype_configs(dtype_configs) # noqa: E131\n .set_fuser_method(reverse2(fuse_conv_bn)).set_fused_module(\n convs.fused_conv_bn))\n # conv + bn + relu module fusion\n conv_configs.append(\n BackendPatternConfig(\n (nn.ReLU,\n (convs.bn,\n convs.root))).set_dtype_configs(dtype_configs) # noqa: E131\n .set_fuser_method(reverse3(fuse_conv_bn_relu)).set_fused_module(\n convs.fused_conv_bn_relu))\n # conv + bn + relu functional fusion\n conv_configs.append(\n BackendPatternConfig(\n (F.relu,\n (convs.bn,\n convs.root))).set_dtype_configs(dtype_configs) # noqa: E131\n .set_root_module(convs.root).set_fuser_method(\n reverse3(fuse_conv_bn_relu)).set_fused_module(\n convs.fused_conv_bn_relu))\n # TODO: we can add fusion for torch.relu as well\n\n # 3.2 conv + bn (+ relu) fused module configs\n # fused conv bn\n conv_configs.append(\n BackendPatternConfig(convs.fused_conv_bn).set_dtype_configs(\n dtype_configs) # noqa: E131\n .set_qat_module(convs.bn_qat))\n\n # fused conv bn relu\n conv_configs.append(\n BackendPatternConfig(convs.fused_conv_bn_relu).set_dtype_configs(\n dtype_configs) # noqa: E131\n .set_qat_module(convs.bn_relu_qat))\n\n # conv bn, qat fused module\n conv_configs.append(\n BackendPatternConfig(convs.bn_qat).set_observation_type(\n observation_type) # noqa: E131\n .set_dtype_configs(dtype_configs).set_root_module(\n convs.root).set_reference_quantized_module(convs.reference))\n # conv bn relu, qat fused module\n conv_configs.append(\n BackendPatternConfig(convs.bn_relu_qat).set_observation_type(\n observation_type) # noqa: E131\n .set_dtype_configs(dtype_configs).set_root_module(\n convs.root).set_reference_quantized_module(convs.reference))\n\n # (4) conv transpose and its fusion\n # 4.1 conv transpose config\n conv_configs.append(\n BackendPatternConfig(convs.transpose).set_dtype_configs(\n dtype_configs) # noqa: E131\n .set_root_module(convs.transpose).set_reference_quantized_module(\n convs.transpose_reference))\n\n # 4.2 conv transpose + bn fusion\n conv_configs.append(\n BackendPatternConfig(\n (convs.bn, convs.transpose)).set_dtype_configs(\n dtype_configs) # noqa: E131\n .set_fuser_method(reverse2(fuse_convtranspose_bn)).set_root_module(\n convs.transpose).set_reference_quantized_module(\n convs.transpose_reference))\n\n return conv_configs","title":""},{"docid":"1caa3b30e9e06bff1c9c8184508bb5ff","score":"0.47014508","text":"def _state_replaced(self, want, have):\n\n commands = []\n\n # Drill each iteration of want n have and then based on dest and afi tyoe comparison take config call\n for w in want:\n for addr_want in w.get(\"address_families\"):\n for route_want in addr_want.get(\"routes\"):\n check = False\n for h in have:\n if h.get(\"address_families\"):\n for addr_have in h.get(\"address_families\"):\n for route_have in addr_have.get(\"routes\"):\n if (\n route_want.get(\"dest\")\n == route_have.get(\"dest\")\n and addr_want[\"afi\"]\n == addr_have[\"afi\"]\n ):\n check = True\n have_set = set()\n new_hops = []\n for each in route_want.get(\n \"next_hops\"\n ):\n want_set = set()\n new_dict_to_set(\n each, [], want_set, 0\n )\n new_hops.append(want_set)\n new_dict_to_set(\n addr_have, [], have_set, 0\n )\n # Check if the have dict next_hops value is diff from want dict next_hops\n have_dict = filter_dict_having_none_value(\n route_want.get(\"next_hops\")[0],\n route_have.get(\"next_hops\")[0],\n )\n # update the have_dict with forward_router_address\n have_dict.update(\n {\n \"forward_router_address\": route_have.get(\n \"next_hops\"\n )[\n 0\n ].get(\n \"forward_router_address\"\n )\n }\n )\n # updating the have_dict with next_hops val that's not None\n new_have_dict = {}\n for k, v in have_dict.items():\n if v is not None:\n new_have_dict.update({k: v})\n\n # Set the new config from the user provided want config\n cmd = self._set_config(\n w,\n h,\n addr_want,\n route_want,\n route_have,\n new_hops,\n have_set,\n )\n\n if cmd:\n # since inplace update isn't allowed for static routes, preconfigured\n # static routes needs to be deleted before the new want static routes changes\n # are applied\n clear_route_have = copy.deepcopy(\n route_have\n )\n # inplace update is allowed in case of ipv6 static routes, so not deleting it\n # before applying the want changes\n if \":\" not in route_want.get(\n \"dest\"\n ):\n commands.extend(\n self._clear_config(\n {},\n h,\n {},\n addr_have,\n {},\n clear_route_have,\n )\n )\n commands.extend(cmd)\n if check:\n break\n if check:\n break\n if not check:\n # For configuring any non-existing want config\n new_hops = []\n for each in route_want.get(\"next_hops\"):\n want_set = set()\n new_dict_to_set(each, [], want_set, 0)\n new_hops.append(want_set)\n commands.extend(\n self._set_config(\n w,\n {},\n addr_want,\n route_want,\n {},\n new_hops,\n set(),\n )\n )\n commands = [each for each in commands if \"no\" in each] + [\n each for each in commands if \"no\" not in each\n ]\n\n return commands","title":""},{"docid":"e02a8d145f05b26953933f17729d4b01","score":"0.46912372","text":"def start(self):\n\n # tenants is the main structure to be converted into zuul main.yaml\n tenants = {}\n # projects_list is the list of projects used to check for conflicts\n projects_list = {}\n tenant_resources_cache = {}\n\n auth_rules = {}\n\n for tenant_name, tenant_conf in self.main_resources.get(\n \"resources\", {}).get(\"tenants\", {}).items():\n\n # When a tenant_resources already exists\n # It is only set if we loaded tenant resources from\n # a config repo copy (eg. config-check)\n if self.tenant_resources:\n # We only proceed when the args gateway_url\n # match the tenant url\n if tenant_conf['url'] != '%s/manage' % (\n self.gateway_url.rstrip('/')):\n continue\n\n self.log.debug(\n \"--[ Processing %s - %s\" % (tenant_name, tenant_conf))\n\n # First we look for the tenant resources\n if tenant_name != self.default_tenant_name and \\\n tenant_conf[\"url\"] != self.main_resources[\"public-url\"]:\n # check for v2\n tenant_url = tenant_conf['url'].rstrip('/')\n if not tenant_url.endswith('/v2'):\n tenant_url = os.path.join(tenant_url, \"v2\")\n url = os.path.join(tenant_url, 'resources')\n if self.tenant_resources:\n self.log.debug(\"%s: loading resources from workspace\",\n tenant_name)\n tenant_resources = self.tenant_resources\n else:\n self.log.debug(\"%s: loading resources %s\",\n tenant_name, url)\n tenant_resources = get_resources(url)\n else:\n tenant_resources = self.main_resources\n # Fallback to default_tenant_name tenant default connection\n if not tenant_conf.get(\"default-connection\"):\n tenant_conf[\"default-connection\"] = self.main_resources[\n \"resources\"][\"tenants\"][self.default_tenant_name][\n \"default-connection\"]\n\n tenant_resources_cache[tenant_name] = tenant_resources\n\n if not self.utests:\n # Then we pull tenant config repository for legacy zuul\n # flat files\n path = self.fetch_git_repo(\n tenant_name, tenant_resources[\"config-repo\"],\n self.cache_dir)\n tenants_dir = os.path.join(path, 'zuul')\n if not os.path.isdir(tenants_dir):\n continue\n tenants_conf_files = self.discover_yaml_files(tenants_dir)\n # And we load flat files\n self.merge_tenant_from_files(\n tenants, auth_rules, tenants_conf_files,\n tenant_name, projects_list)\n\n # We load project from the resources\n default_conn = tenant_conf[\"default-connection\"]\n self.merge_tenant_from_resources(\n tenants, tenant_resources, tenant_name, projects_list,\n self.main_resources, default_conn, tenant_conf)\n\n for tenant_name, tenant_conf in self.main_resources.get(\n \"resources\", {}).get(\"tenants\", {}).items():\n\n tenant_resources = tenant_resources_cache.get(tenant_name)\n if not tenant_resources:\n continue\n\n default_conn = tenant_conf[\"default-connection\"]\n\n # Finally we add Repos not listed in sr with an include: [] to Zuul\n skip_missing_resources = False\n if tenant_conf[\"url\"] == self.main_resources[\"public-url\"]:\n if tenant_name != self.default_tenant_name:\n # We only add local missing resources to the\n # default_tenant_name tenant\n skip_missing_resources = True\n # Check default_conn is a registered connection\n if default_conn not in self.main_resources[\n 'resources']['connections']:\n # We cannot add repos to Zuul if no valid connection for\n # that tenant\n self.log.debug(\n \"Skip adding missing repos. The tenant has an invalid\"\n \" default connection: %s\" % default_conn)\n continue\n if not skip_missing_resources:\n self.add_missing_repos(\n tenants, tenant_resources, tenant_name, projects_list,\n self.main_resources, default_conn)\n\n self.log.debug(\"]-- Finish processing %s\" % tenant_name)\n\n final_tenant_data = self.final_tenant_merge(tenants)\n auth_rules_data = self.merge_auth_rules(auth_rules)\n final_data = auth_rules_data + final_tenant_data\n return yaml.safe_dump(final_data)","title":""},{"docid":"e24f6f8a3a25a8ed0cab7fc4b0ca09e8","score":"0.46880388","text":"def configure_common_directories(cls, tc_config_files):\n if cls.config_directory is None:\n # Get config directory from properties\n config_directory = cls.get_configured_value('TOOLIUM_CONFIG_DIRECTORY',\n tc_config_files.config_directory, 'conf')\n prop_filenames = cls.get_configured_value('TOOLIUM_CONFIG_PROPERTIES_FILENAMES',\n tc_config_files.config_properties_filenames, 'properties.cfg')\n cls.config_directory = cls._find_parent_directory(config_directory, prop_filenames.split(';')[0])\n\n # Get output directory from properties and create it\n cls.output_directory = cls.get_configured_value('TOOLIUM_OUTPUT_DIRECTORY',\n tc_config_files.output_directory, 'output')\n if not os.path.isabs(cls.output_directory):\n # If output directory is relative, we use the same path as config directory\n cls.output_directory = os.path.join(os.path.dirname(cls.config_directory), cls.output_directory)\n makedirs_safe(cls.output_directory)\n\n # Get visual baseline directory from properties\n default_baseline = os.path.join(cls.output_directory, 'visualtests', 'baseline')\n cls.visual_baseline_directory = cls.get_configured_value('TOOLIUM_VISUAL_BASELINE_DIRECTORY',\n tc_config_files.visual_baseline_directory,\n default_baseline)\n if not os.path.isabs(cls.visual_baseline_directory):\n # If baseline directory is relative, we use the same path as config directory\n cls.visual_baseline_directory = os.path.join(os.path.dirname(cls.config_directory),\n cls.visual_baseline_directory)","title":""},{"docid":"4e9d755a2d1fb2e7bdd6e7c0b88c579d","score":"0.468542","text":"def translate_cfg(self,cfg_str):\n yml = {\"name\": None,\"parameters\": {}, \"cells\": [] }\n # Regex\n re_params = re.compile('^\\s*\\$param{\\s*\"([a-zA-Z0-9_]+)\"\\s*}\\s*=\\s*([^;]+);?\\s*$')\n re_name = re.compile('^\\s*Name\\s*=\\s([a-zA-Z0-9_]+)\\s*$')\n re_cell = re.compile('^\\s*([a-zA-Z0-9_]+)\\s+([RWrw])\\s+([^;\\s]+)\\s+([^;\\s]+)\\s*$')\n for line in cfg_str.split('\\n'): \n p = re_params.search(line)\n n = re_name.search(line)\n c = re_cell.search(line)\n # Check to see which one matched\n if p is not None:\n rhs = self.sub_perl_params(p.group(2))\n rhs = self.attempt_int_conv(rhs)\n yml['parameters'][p.group(1)] = rhs\n elif n is not None:\n yml['name'] = n.group(1)\n elif c is not None:\n width = self.sub_perl_params(c.group(3))\n width = self.attempt_int_conv(width)\n mult = self.sub_perl_params(c.group(4))\n mult = self.attempt_int_conv(mult)\n yml['cells'].append(self.return_cell(c.group(1),c.group(2),width,mult))\n return yaml.dump(yml)","title":""},{"docid":"d75bf4d4785b4f76cc377da141cea37c","score":"0.46834138","text":"def _parse_train_cfg(self):","title":""},{"docid":"1587bdd915b1bef0bf99a5e0ae053992","score":"0.46775225","text":"def prepare_config(config: ConfigType, env_metadata: EnvMetaDataType) -> ConfigType:\n config = config_utils.make_config_mutable(config_utils.unset_struct(config))\n key = \"type_to_select\"\n if key in config.agent.encoder:\n encoder_type_to_select = config.agent.encoder[key]\n # config.agent.encoder = config.agent.encoder[encoder_type_to_select]\n else:\n encoder_type_to_select = config.agent.encoder.type\n if encoder_type_to_select in [\"identity\"]:\n # if the encoder is an identity encoder infer the shape of the input dim.\n config.agent.encoder_feature_dim = env_metadata[\"env_obs_space\"].shape[0]\n\n key = \"ordered_task_list\"\n if key in env_metadata and env_metadata[key]:\n config.env.ordered_task_list = deepcopy(env_metadata[key])\n config = config_utils.make_config_immutable(config)\n\n return config","title":""},{"docid":"a8af68d96081823c8bd0aabe0c6eda2f","score":"0.46763518","text":"def c3k_sysbi_automation_config():\n path = 'apollo.scripts.entsw.automation.auto_control' # Standard path required; link must point to specific module (use run_tests2 >=v2.9.0).\n apollo_config = lib.get_station_configuration()\n uag_production_line = apollo_config.add_production_line(name='UAG_C3K')\n pcbst = uag_production_line.add_area(name='SYSBI')\n station = pcbst.add_test_station(name='Station')\n # Configuration Data\n station.add_configuration_data(key='AUTOMATION', value=dict(enabled=True))\n\n ssh_dictionary = dict(host='localhost', user='gen-apollo', password='Ad@pCr01!', timeout='30')\n\n auto_container = station.add_super_container(name='AUTO')\n auto_container.add_connection(\n name='PC',\n host=\"10.1.1.12\" if '185' in lib.get_hostname() else \"10.1.1.11\",\n port=2027,\n protocol='telnet',\n )\n auto_container.add_connection(name='LOCAL', protocol='ssh', **ssh_dictionary)\n\n auto_container.assign_pre_sequence(sequence_definition='{}.pre_control'.format(path))\n auto_container.add_pid_map(pid='73-*',\n sequence_definition='{}.main_control'.format(path),\n )\n\n for rack in range(1, 3):\n for slot in range(1, 13):\n cell = auto_container.add_container(name='UUT{:02}_{:02}'.format(rack, slot))\n cell.add_connection(\n name='uutTN',\n host=\"10.1.1.11\",\n port=2003 + (rack - 1) * 12 + (slot - 1),\n protocol='telnet',\n )\n cell.add_connection(name='serverSSH', protocol='ssh', **ssh_dictionary)\n cell.assign_pre_sequence(\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_all_pre_sequences.pre_pcbft_gen2'\n )\n cell.add_pid_map(\n pid='WS-C3*',\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_pcbft_run.standard_switch_sysbi'\n )\n\n for rack in range(3, 5):\n for slot in range(1, 13):\n cell = auto_container.add_container(name='UUT{:02}_{:02}'.format(rack, slot))\n cell.add_connection(\n name='uutTN',\n host=\"10.1.1.8\",\n port=2003 + (rack - 3) * 12 + (slot - 1),\n protocol='telnet',\n )\n cell.add_connection(name='serverSSH', protocol='ssh', **ssh_dictionary)\n cell.assign_pre_sequence(\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_all_pre_sequences.pre_pcbft_gen2'\n )\n cell.add_pid_map(\n pid='WS-C3*',\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_pcbft_run.standard_switch_sysbi'\n )\n\n for rack in range(5, 7):\n for slot in range(1, 13):\n cell = auto_container.add_container(name='UUT{:02}_{:02}'.format(rack, slot))\n cell.add_connection(\n name='uutTN',\n host=\"10.1.1.9\",\n port=2003 + (rack - 5) * 12 + (slot - 1),\n protocol='telnet',\n )\n cell.add_connection(name='serverSSH', protocol='ssh', **ssh_dictionary)\n cell.assign_pre_sequence(\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_all_pre_sequences.pre_pcbft_gen2'\n )\n cell.add_pid_map(\n pid='WS-C3*',\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_pcbft_run.standard_switch_sysbi'\n )\n\n for rack in range(7, 9):\n for slot in range(1, 13):\n cell = auto_container.add_container(name='UUT{:02}_{:02}'.format(rack, slot))\n cell.add_connection(\n name='uutTN',\n host=\"10.1.1.10\",\n port=2003 + (rack - 7) * 12 + (slot - 1),\n protocol='telnet',\n )\n cell.add_connection(name='serverSSH', protocol='ssh', **ssh_dictionary)\n cell.assign_pre_sequence(\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_all_pre_sequences.pre_pcbft_gen2'\n )\n cell.add_pid_map(\n pid='WS-C3*',\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_pcbft_run.standard_switch_sysbi'\n )","title":""},{"docid":"daa76f69532dd141363f6cc0d3f11375","score":"0.46760038","text":"def get_config():","title":""},{"docid":"23a827c5a16d63ae28cc7fccb9852083","score":"0.46759373","text":"def test_generic_preprocess(self):\n init_test_data()\n with tempfile.TemporaryDirectory() as tmpdirname:\n scratch_dir = Path(tmpdirname)\n print('scratch dir :', tmpdirname)\n args = [(f'masking_config.masking_config_{mod}.visualisation_path', str(scratch_dir / f'vis_{mod}')) for mod in\n ['anat', 'func']]\n config, workflow_uid = prepare_config(json_config_path=JSON_CONFIG_PATH, scratch_dir=scratch_dir,\n additional_args=args)\n prepare_experiment_result_dataframe(config=config, workflow_uid=workflow_uid)\n config_path = os.path.expanduser(os.path.join(scratch_dir, 'config.json'))\n\n generic(BIDS_BASE,\n '/usr/share/mouse-brain-atlases/dsurqec_200micron.nii',\n registration_mask='/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',\n functional_match={'acquisition': ['EPIlowcov'], },\n structural_match={'acquisition': ['TurboRARElowcov'], },\n out_base='{}/preprocessing'.format(scratch_dir),\n workflow_name='generic',\n keep_work=False,\n subjects=['0000'],\n masking_config_path=config_path,\n )\n assert not os.path.exists(os.path.join(scratch_dir, 'preprocessing/crashdump'))\n for f in ['anat/sub-0000_ses-ofM_acq-TurboRARElowcov_T2w.nii.gz',\n 'func/sub-0000_ses-ofM_task-JogB_acq-EPIlowcov_run-0_bold.nii.gz']:\n out_path = os.path.exists(os.path.join(scratch_dir, 'preprocessing/generic/sub-0000/ses-ofM', f))\n assert out_path, f'Test failed, path {out_path} does not exist.'\n print('finished test_generic_preprocess successfully.')","title":""},{"docid":"35cfbe6673f488b4f1197f563d23a2de","score":"0.46735027","text":"def get_configurations_by_preset(config, model, fq_to_hw_confs, hardware_config):\n\n def _apply_preset_rule(preset_, fq_name, param_type, confs, to_skip=None):\n if param_type == 'weights':\n if preset_ == 'accuracy':\n return confs[-1]\n return confs[0]\n if not to_skip or fq_name not in [fq for _, fqs in to_skip for fq in fqs]:\n if preset_ == 'performance':\n return confs[0]\n return confs[-1]\n return confs\n\n def _intersect_and_apply_preset(preset_, fq_to_hw_confs_, fqs_to_unify_):\n\n def _unify_and_apply_preset(preset_, cur_conf, fqs_to_unify_):\n def _test_shapes(shapes):\n return any([s[0] != shapes[0][0] or len(s) == 1 or s[1] != shapes[0][1] for s in shapes])\n\n for bridges, fqs in fqs_to_unify_:\n res_conf = []\n with_concat = 'Concat' in [get_node_by_name(model, bridge).type for bridge in bridges]\n fq_input_shapes = [get_input_shape(get_node_by_name(model, fq), 0) for fq in fqs]\n unclear_layout = _test_shapes(fq_input_shapes)\n bridge_layers = [get_node_by_name(model, bridge) for bridge in bridges]\n bridge_input_shapes = [get_input_shape(layer, i) for layer in bridge_layers for i in layer.in_ports()]\n broadcasting = _test_shapes(bridge_input_shapes)\n for fq in fqs:\n for key in cur_conf[fq]:\n if key in ACTIVATION_QUANTIZATION_MODES:\n if with_concat or unclear_layout or broadcasting:\n if not isinstance(cur_conf[fq]['activations'], list):\n cur_conf[fq]['activations'] = [cur_conf[fq]['activations']]\n\n configuration = [c for c in cur_conf[fq][key] if c['granularity'] == 'pertensor']\n else:\n configuration = cur_conf[fq][key]\n res_conf = intersect_configs(res_conf, configuration, primary_bitwidth) if res_conf \\\n else configuration\n if not res_conf:\n raise Exception('Fake quantize nodes {} cannot be unified'.format(fqs))\n for fq in fqs:\n for key in cur_conf[fq]:\n if key in ACTIVATION_QUANTIZATION_MODES:\n cur_conf[fq][key] = _apply_preset_rule(preset_, fq, key, res_conf)\n return cur_conf\n\n primary_bitwidth = hardware_config[1]['primary_bitwidth']\n res = {}\n for key, value in fq_to_hw_confs_.items():\n conf = dict()\n for i_type in QUANTIZATION_MODES:\n if i_type in value:\n res_conf = []\n for _, configuration in value[i_type]:\n res_conf = intersect_configs(res_conf, configuration, primary_bitwidth) if res_conf \\\n else configuration\n if not res_conf:\n raise Exception('Fake quantize node {} does not have a suitable configuration'\n ' for layers {}'.format(key, [layer for layer, _ in value[i_type]]))\n conf[i_type] = _apply_preset_rule(preset_, key, i_type, res_conf, fqs_to_unify_)\n res[key] = conf\n res = _unify_and_apply_preset(preset_, res, fqs_to_unify_)\n return res\n\n available_presets = ['accuracy', 'mixed', 'performance']\n preset = config.preset\n if preset not in available_presets:\n raise Exception('Unsupported preset value: {}.'\n ' Supported values are {}'.format(preset, available_presets))\n\n fqs_to_unify = find_fqs_to_unify(model, config)\n result = _intersect_and_apply_preset(preset, fq_to_hw_confs, fqs_to_unify)\n\n return result","title":""},{"docid":"9a5811e1ec6fd0a011b53eb410840557","score":"0.46715492","text":"def do_aws_cf_configure():\n\n # TODO(cmaloney): Move to Config class introduced in https://github.com/dcos/dcos/pull/623\n config = Config(CONFIG_PATH)\n\n # This process is usually ran from a docker container where default boto3 credential\n # method may fail and as such, we allow passing these creds explicitly\n if 'aws_template_storage_access_key_id' in config:\n os.environ['AWS_ACCESS_KEY_ID'] = config['aws_template_storage_access_key_id']\n if 'aws_template_storage_secret_access_key' in config:\n os.environ['AWS_SECRET_ACCESS_KEY'] = config['aws_template_storage_secret_access_key']\n if 'aws_template_storage_region_name' in config:\n os.environ['AWS_DEFAULT_REGION'] = config['aws_template_storage_region_name']\n\n gen_config = config.as_gen_format()\n\n extra_sources = [\n gen.build_deploy.aws.aws_base_source,\n aws_advanced_source,\n gen.build_deploy.aws.groups['master'][1]]\n\n sources, targets, _ = gen.get_dcosconfig_source_target_and_templates(gen_config, [], extra_sources)\n targets.append(get_aws_advanced_target())\n resolver = gen.internals.resolve_configuration(sources, targets)\n # TODO(cmaloney): kill this function and make the API return the structured\n # results api as was always intended rather than the flattened / lossy other\n # format. This will be an API incompatible change. The messages format was\n # specifically so that there wouldn't be this sort of API incompatibility.\n messages = normalize_config_validation(resolver.status_dict)\n if messages:\n print_messages(messages)\n return 1\n\n # TODO(cmaloney): This is really hacky but a lot simpler than merging all the config flows into\n # one currently.\n # Get out the calculated arguments and manually move critical calculated ones to the gen_config\n # object.\n # NOTE: the copying across, as well as validation is guaranteed to succeed because we've already\n # done a validation run.\n full_config = {k: v.value for k, v in resolver.arguments.items()}\n\n # Calculate the config ID and values that depend on it.\n config_id = gen.get_config_id(full_config)\n reproducible_artifact_path = 'config_id/{}'.format(config_id)\n cloudformation_s3_url = '{}/config_id/{}'.format(full_config['bootstrap_url'], config_id)\n cloudformation_s3_url_full = '{}/cloudformation'.format(cloudformation_s3_url)\n\n # TODO(cmaloney): Switch to using the targets\n gen_config['bootstrap_url'] = full_config['bootstrap_url']\n gen_config['provider'] = full_config['provider']\n gen_config['bootstrap_id'] = full_config['bootstrap_id']\n gen_config['package_ids'] = full_config['package_ids']\n gen_config['cloudformation_s3_url_full'] = cloudformation_s3_url_full\n\n # Convert the bootstrap_Variant string we have back to a bootstrap_id as used internally by all\n # the tooling (never has empty string, uses None to say \"no variant\")\n bootstrap_variant = full_config['bootstrap_variant'] if full_config['bootstrap_variant'] else None\n\n artifacts = list()\n for built_resource in list(gen.build_deploy.aws.do_create(\n tag='dcos_generate_config.sh --aws-cloudformation',\n build_name='Custom',\n reproducible_artifact_path=reproducible_artifact_path,\n variant_arguments={bootstrap_variant: gen_config},\n commit=full_config['dcos_image_commit'],\n all_completes=None)):\n artifacts += release.built_resource_to_artifacts(built_resource)\n\n artifacts += list(release.make_bootstrap_artifacts(\n full_config['bootstrap_id'],\n json.loads(full_config['package_ids']),\n bootstrap_variant,\n 'artifacts',\n ))\n\n for package_id in json.loads(full_config['package_ids']):\n package_filename = release.make_package_filename(package_id)\n artifacts.append({\n 'reproducible_path': package_filename,\n 'local_path': 'artifacts/' + package_filename,\n })\n\n # Upload all the artifacts to the config-id path and then print out what\n # the path that should be used is, as well as saving a local json file for\n # easy machine access / processing.\n repository = release.Repository(\n full_config['aws_template_storage_bucket_path'],\n None,\n 'config_id/' + config_id)\n\n storage_commands = repository.make_commands({'core_artifacts': [], 'channel_artifacts': artifacts})\n\n cf_dir = GENCONF_DIR + '/cloudformation'\n log.warning(\"Writing local copies to {}\".format(cf_dir))\n storage_provider = release.storage.local.LocalStorageProvider(cf_dir)\n release.apply_storage_commands({'local': storage_provider}, storage_commands)\n\n log.warning(\n \"Generated templates locally available at %s\",\n cf_dir + \"/\" + reproducible_artifact_path)\n # TODO(cmaloney): Print where the user can find the files locally\n\n if full_config['aws_template_upload'] == 'false':\n return 0\n\n storage_provider = release.storage.aws.S3StorageProvider(\n bucket=full_config['aws_template_storage_bucket'],\n object_prefix=None,\n download_url=cloudformation_s3_url,\n region_name=full_config['aws_template_storage_region_name'],\n access_key_id=full_config['aws_template_storage_access_key_id'],\n secret_access_key=full_config['aws_template_storage_secret_access_key'])\n\n log.warning(\"Uploading to AWS\")\n release.apply_storage_commands({'aws': storage_provider}, storage_commands)\n log.warning(\"AWS CloudFormation templates now available at: {}\".format(cloudformation_s3_url))\n\n # TODO(cmaloney): Print where the user can find the files in AWS\n # TODO(cmaloney): Dump out a JSON with machine paths to make scripting easier.\n return 0","title":""}],"string":"[\n {\n \"docid\": \"ef40f7ed175823942ced5d3c9a0f4d46\",\n \"score\": \"0.5959538\",\n \"text\": \"def process_config(lunch_config):\\n def add(domain_name, tree_root, product):\\n tree_key = inner_tree.InnerTreeKey(tree_root, product)\\n if tree_key in trees:\\n tree = trees[tree_key]\\n else:\\n tree = inner_tree.InnerTree(tree_root, product)\\n trees[tree_key] = tree\\n domain = api_domain.ApiDomain(domain_name, tree, product)\\n domains[domain_name] = domain\\n tree.domains[domain_name] = domain\\n\\n trees = {}\\n domains = {}\\n\\n system_entry = lunch_config.get(\\\"system\\\")\\n if system_entry:\\n add(API_DOMAIN_SYSTEM, system_entry[\\\"tree\\\"], system_entry[\\\"product\\\"])\\n\\n vendor_entry = lunch_config.get(\\\"vendor\\\")\\n if vendor_entry:\\n add(API_DOMAIN_VENDOR, vendor_entry[\\\"tree\\\"], vendor_entry[\\\"product\\\"])\\n\\n for module_name, module_entry in lunch_config.get(\\\"modules\\\", []).items():\\n add(module_name, module_entry[\\\"tree\\\"], None)\\n\\n return inner_tree.InnerTrees(trees, domains)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4430967bdb5e9dc6d25d8eb5e9073881\",\n \"score\": \"0.5949084\",\n \"text\": \"def traverse(config, workdir, mpc, **kwargs):\\n for entry in config:\\n #construct a list of tuples, each tuple is of form (file, workdir, ...)\\n #where ... are additionnal arguments that will be used to expand the name pattern of the file\\n entries = entry.get(workdir, mpc)\\n\\n #finaly, perform the transformation on the files/arguments constructed above\\n for e in entries:\\n transformations = entry.trans\\n if len(e) == 0:\\n continue\\n for trans in transformations:\\n trans(*e, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"341f2cd6d9d320c3572ce5b2b13f8ab3\",\n \"score\": \"0.553948\",\n \"text\": \"def expand_config(self, config): \\n # First, expand the templates without variables, then expand modes\\n # using the vars and expanded templates\\n vars = config.get('vars', {})\\n templates = config.get('templates', {})\\n expanded_templates = self.expand_template(vars, templates, templates)\\n\\n # Create a new config dict and swap in expanded bits\\n #new_config = config\\n new_config = OrderedDict()\\n\\n cruise = config.get('cruise', {})\\n if cruise:\\n new_config['cruise'] = self.expand_template(vars, cruise,\\n expanded_templates)\\n loggers = config.get('loggers', {})\\n if loggers:\\n new_config['loggers'] = self.expand_template(vars, loggers,\\n expanded_templates)\\n modes = config.get('modes', {})\\n if modes:\\n new_config['modes'] = self.expand_template(vars, modes,\\n expanded_templates)\\n default_mode = config.get('default_mode', {})\\n if default_mode:\\n new_config['default_mode'] = self.expand_template(vars, default_mode,\\n expanded_templates)\\n configs = config.get('configs', {})\\n if configs:\\n new_config['configs'] = self.expand_template(vars, configs,\\n expanded_templates)\\n return new_config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9daa34233a77cbeeb46862debca535f\",\n \"score\": \"0.55340546\",\n \"text\": \"def traverse_cfg(cfg, features_list, handled_set, handled_features_set):\\n\\n if cfg.control_dep_children: #check for control dependent node\\n features_list.append(cfg.name) #added current CFG node in feature\\n handled_features_set.add(cfg.id) # Store id from features handled\\n get_ast_features(cfg, features_list, handled_features_set) # Handled only once\\n for control_dep in cfg.control_dep_children: #Traverse all childeren which has control dependency\\n control_flow = control_dep.extremity #gets all dependecy control and statements\\n # Otherwise missing CF pointing to a node already analyzed\\n if not control_flow.control_dep_children or control_flow.id in handled_set: #if not already handled add and not\\n features_list.append(control_flow.name)\\n # else: the node name will be added while calling traverse_cfg\\n if control_flow.id not in handled_set: #called for all other recurrently\\n handled_set.add(control_flow.id)\\n handled_features_set.add(control_flow.id) # Store id from features\\n get_ast_features(control_flow, features_list, handled_features_set) # Once\\n traverse_cfg(control_flow, features_list, handled_set, handled_features_set)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"097acee3b03070c066c0292c3df8b213\",\n \"score\": \"0.54283386\",\n \"text\": \"def process_config(data):\\n config = deepcopy(INITIAL_CONFIG)\\n try:\\n regexps = data['regexps']\\n logs = data['logs']\\n actions = data.get('actions') or {}\\n except KeyError as exc:\\n raise ConfigError('required section missing: {}'.format(exc))\\n except TypeError:\\n raise ConfigError('config is not a dict: received {}'.format(type(data)))\\n config['regexps'] = _process_regexps(regexps)\\n config['actions'], auto_actions = _process_actions(actions)\\n config['logs'] = _process_logs(logs, config['regexps'], config['actions'], auto_actions)\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"064f2fa416a3eba7a1d122ae8d7af4e0\",\n \"score\": \"0.54272306\",\n \"text\": \"def apply_conf(self, conf):\\n for s in self.steps_data:\\n o2 = []\\n for o in s[2]:\\n co = o.replace(\\\"%input_dir%\\\", conf.input_dir)\\n co = co.replace(\\\"%output_dir%\\\", conf.output_dir)\\n co = co.replace(\\\"%matches_dir%\\\", conf.matches_dir)\\n co = co.replace(\\\"%reconstruction_dir%\\\", conf.reconstruction_dir)\\n co = co.replace(\\\"%mvs_dir%\\\", conf.mvs_dir)\\n co = co.replace(\\\"%camera_file_params%\\\", conf.camera_file_params)\\n o2.append(co)\\n s[2] = o2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ce0e6137647a97306212b3f31fc312a\",\n \"score\": \"0.53981847\",\n \"text\": \"def test_walk_config(self):\\n n = config_manager.Namespace(doc='top')\\n n.add_option('aaa', False, 'the a', short_form='a')\\n n.c = config_manager.Namespace(doc='c space')\\n n.c.add_option('fred', doc='husband from Flintstones')\\n n.c.add_option('wilma', doc='wife from Flintstones')\\n n.d = config_manager.Namespace(doc='d space')\\n n.d.add_option('fred', doc='male neighbor from I Love Lucy')\\n n.d.add_option('ethel', doc='female neighbor from I Love Lucy')\\n n.d.x = config_manager.Namespace(doc='x space')\\n n.d.x.add_option('size', 100, 'how big in tons', short_form='s')\\n n.d.x.add_option('password', 'secrets', 'the password')\\n c = config_manager.ConfigurationManager(\\n [n],\\n use_admin_controls=True,\\n #use_config_files=False,\\n use_auto_help=False,\\n argv_source=[]\\n )\\n e = [('aaa', 'aaa', n.aaa.name),\\n ('c', 'c', n.c._doc),\\n ('c.wilma', 'wilma', n.c.wilma.name),\\n ('c.fred', 'fred', n.c.fred.name),\\n ('d', 'd', n.d._doc),\\n ('d.ethel', 'ethel', n.d.ethel.name),\\n ('d.fred', 'fred', n.d.fred.name),\\n ('d.x', 'x', n.d.x._doc),\\n ('d.x.size', 'size', n.d.x.size.name),\\n ('d.x.password', 'password', n.d.x.password.name),\\n ]\\n e.sort()\\n r = [(q, k, v.name if isinstance(v, config_manager.Option) else v._doc)\\n for q, k, v in c._walk_config()]\\n r.sort()\\n for expected, received in zip(e, r):\\n self.assertEqual(received, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e479804ac58246919d4fead8ec6dcfe\",\n \"score\": \"0.5370012\",\n \"text\": \"def visit_ControlFlowGraph(self, cfg):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"961f03e57363f56ece817427ef00af50\",\n \"score\": \"0.53425205\",\n \"text\": \"def get_config_template(self) -> cconfig.Config:\\n config = cconfig.Config()\\n #\\n stage = \\\"resample_events\\\"\\n config_tmp = config.add_subconfig(self._get_nid(stage))\\n config_tmp[\\\"rule\\\"] = \\\"T\\\"\\n config_tmp[\\\"agg_func\\\"] = \\\"mean\\\"\\n #\\n stage = \\\"generate_event_signal\\\"\\n config_tmp = config.add_subconfig(self._get_nid(stage))\\n config_kwargs = config_tmp.add_subconfig(\\\"transformer_kwargs\\\")\\n config_kwargs[\\\"tau\\\"] = 8\\n config_kwargs[\\\"max_depth\\\"] = 3\\n #\\n stage = \\\"shift\\\"\\n config_tmp = config.add_subconfig(self._get_nid(stage))\\n config_kwargs = config_tmp.add_subconfig(\\\"method_kwargs\\\")\\n config_kwargs[\\\"periods\\\"] = 1\\n #\\n stage = \\\"build_local_ts\\\"\\n config_tmp = config.add_subconfig(self._get_nid(stage))\\n config_kwargs = config_tmp.add_subconfig(\\\"connector_kwargs\\\")\\n config_kwargs[\\\"relative_grid_indices\\\"] = range(-10, 50)\\n #\\n stage = \\\"model\\\"\\n config_tmp = config.add_subconfig(self._get_nid(stage))\\n config_tmp[\\\"x_vars\\\"] = [\\\"_DUMMY_\\\"]\\n config_tmp[\\\"y_vars\\\"] = [\\\"_DUMMY_\\\"]\\n config_kwargs = config_tmp.add_subconfig(\\\"model_kwargs\\\")\\n config_kwargs[\\\"alpha\\\"] = 0.5\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69095991cc633ced222ce7e51d1ff60c\",\n \"score\": \"0.5322106\",\n \"text\": \"def process_config(config_all):\\n print(\\\"Preparing PATHs for the pipeline...\\\")\\n PATHS = {}\\n\\n # Set parameters for the pipeline based on config\\n if config_all['COMMON']['DATA_LOCATION'] == \\\"local\\\":\\n PATHS[\\\"input_data\\\"] = config_all['LOCAL']['INPUT_DATA_LOCAL']\\n PATHS[\\\"i94_data\\\"] = config_all['LOCAL']['INPUT_DATA_I94_LOCAL']\\n PATHS[\\\"airport_codes\\\"] = config_all['LOCAL']['INPUT_DATA_AIRPORT_LOCAL']\\n PATHS[\\\"country_codes_iso\\\"] = config_all['LOCAL']['INPUT_DATA_COUNTRY_LOCAL']\\n PATHS[\\\"airport_codes_i94\\\"] = config_all['LOCAL']['INPUT_DATA_AIRPORT_I94_LOCAL']\\n PATHS[\\\"country_codes_i94\\\"] = config_all['LOCAL']['INPUT_DATA_COUNTRY_I94_LOCAL']\\n PATHS[\\\"output_data\\\"] = config_all['LOCAL']['OUTPUT_DATA_LOCAL']\\n elif config_all['COMMON']['DATA_LOCATION'] == \\\"server\\\":\\n PATHS[\\\"input_data\\\"] = config_all['SERVER']['INPUT_DATA_SERVER']\\n PATHS[\\\"i94_data\\\"] = config_all['SERVER']['INPUT_DATA_I94_SERVER']\\n PATHS[\\\"airport_codes\\\"] = config_all['SERVER']['INPUT_DATA_AIRPORT_SERVER']\\n PATHS[\\\"country_codes_iso\\\"] = config_all['SERVER']['INPUT_DATA_COUNTRY_SERVER']\\n PATHS[\\\"airport_codes_i94\\\"] = config_all['SERVER']['INPUT_DATA_AIRPORT_I94_SERVER']\\n PATHS[\\\"country_codes_i94\\\"] = config_all['SERVER']['INPUT_DATA_COUNTRY_I94_SERVER']\\n PATHS[\\\"output_data\\\"] = config_all['SERVER']['OUTPUT_DATA_SERVER']\\n elif config_all['COMMON']['DATA_LOCATION'] == \\\"aws\\\":\\n PATHS[\\\"input_data\\\"] = config_all['AWS']['INPUT_DATA']\\n PATHS[\\\"i94_data\\\"] = config_all['AWS']['INPUT_DATA_I94']\\n PATHS[\\\"airport_codes\\\"] = config_all['AWS']['INPUT_DATA_AIRPORT']\\n PATHS[\\\"country_codes_iso\\\"] = config_all['AWS']['INPUT_DATA_COUNTRY']\\n PATHS[\\\"airport_codes_i94\\\"] = config_all['AWS']['INPUT_DATA_AIRPORT_I94']\\n PATHS[\\\"country_codes_i94\\\"] = config_all['AWS']['INPUT_DATA_COUNTRY_I94']\\n PATHS[\\\"output_data\\\"] = config_all['AWS']['OUTPUT_DATA']\\n\\n if config_all[\\\"COMMON\\\"][\\\"DATA_STORAGE\\\"] == \\\"postgresql\\\":\\n PATHS[\\\"data_storage\\\"] = config_all[\\\"COMMON\\\"][\\\"DATA_STORAGE_SQL\\\"]\\n elif config_all[\\\"COMMON\\\"][\\\"DATA_STORAGE\\\"] == \\\"parquet\\\":\\n PATHS[\\\"data_storage\\\"] = config_all[\\\"COMMON\\\"][\\\"DATA_STORAGE\\\"]\\n\\n #print(AWS_ACCESS_KEY_ID)\\n #print(AWS_SECRET_ACCESS_KEY)\\n\\n # Print out paths in PATHS\\n print(\\\"PATHS preparation DONE.\\\\n\\\")\\n print(\\\"PATHS:\\\")\\n for path in PATHS:\\n print(path)\\n\\n return PATHS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19d81fbef3ad071635fb4e99c692dd74\",\n \"score\": \"0.53159124\",\n \"text\": \"def build_tcno2tcid_mapping(self):\\n try:\\n test = \\\"Services_HA_\\\"\\n base_strings = [\\\"VYOS_EW_\\\", \\\"VYOS_NS_\\\", \\\"ASAV_EW_\\\", \\\"ASAV_NS_\\\"]\\n chains = [\\\"FW\\\", \\\"FW+LB\\\", \\\"REMOTE_VPN+FW\\\", \\\"REMOTE_VPN+FW+LB\\\",\\n \\\"S2S_VPN+FW\\\", \\\"S2S_VPN+FW+LB\\\"\\n ]\\n tc_no = 0\\n for bstr in base_strings:\\n for chain in chains:\\n if \\\"_EW\\\" in bstr and \\\"VPN\\\" in chain:\\n continue\\n services = []\\n if \\\"VPN\\\" in chain:\\n services.append(\\\"VPN\\\")\\n if \\\"FW\\\" in chain:\\n services.append(\\\"FW\\\")\\n if \\\"LB\\\" in chain:\\n services.append(\\\"LB\\\")\\n # if vpn & fw services are in chain,\\n # they shares same service vm.\\n if \\\"VPN\\\" in chain and \\\"FW\\\" in chain:\\n services.remove(\\\"FW\\\")\\n\\n # base test no. for service chain insertion.\\n base_tc = gbp_config.services_ha_base_test_mapping[\\n bstr.lower() + chain.lower()]\\n\\n # To generate combination of test, where some of the\\n # services in chain will in HA mode & some will be in\\n # standalone mode and failover will be applied on service\\n # vm of service launched in ha mode.\\n if len(services) > 1:\\n for service_vm in services:\\n services_ha = []\\n tc_no += 1\\n chain_string = chain.replace(\\n service_vm, service_vm + '(HA)')\\n services_ha.append(service_vm)\\n if service_vm == \\\"VPN\\\":\\n chain_string = chain_string.\\\\\\n replace(\\\"FW\\\", \\\"FW(HA)\\\")\\n services_ha.append('FW')\\n\\n tc_id = test + bstr + chain_string + \\\"_FailOver_\\\"\\\\\\n + service_vm + \\\"_\\\" + str(tc_no)\\n self.tcno2tcid_mapping[tc_no] = {\\n \\\"tc_id\\\": tc_id,\\n \\\"base_tc_no\\\": base_tc,\\n \\\"fail_over\\\": [service_vm],\\n \\\"services_ha\\\": services_ha\\n }\\n\\n # To generate combination of test, where all the\\n # services in chain will in HA mode. And Failover\\n # will be applied on one service vm only.\\n for service_vm in services:\\n tc_no += 1\\n chain_string = '+'.join(map(lambda service: service +\\n '(HA)', chain.split('+')))\\n tc_id = test + bstr + chain_string + \\\"_FailOver_\\\" +\\\\\\n service_vm + \\\"_\\\" + str(tc_no)\\n services_ha = copy.deepcopy(services)\\n if 'VPN' in services:\\n services_ha.append('FW')\\n\\n self.tcno2tcid_mapping[tc_no] = {\\n \\\"tc_id\\\": tc_id,\\n \\\"base_tc_no\\\": base_tc,\\n \\\"fail_over\\\": [service_vm],\\n \\\"services_ha\\\": services_ha}\\n else:\\n # tests where all services in chain will be\\n # launched in HA mode and failover will be applied on\\n # all active service vms of chain one after another.\\n # and data path traffic will be verified.\\n if len(services) > 1:\\n services_ha = copy.deepcopy(services)\\n tc_no += 1\\n chain_string = '+'.join(map(\\n lambda service: service +\\n '(HA)', chain.split('+')))\\n tc_id = test + bstr + chain_string +\\\\\\n \\\"_FailOver_All_\\\" + str(tc_no)\\n if 'VPN' in services:\\n services_ha.append('FW')\\n self.tcno2tcid_mapping[tc_no] = {\\n \\\"tc_id\\\": tc_id,\\n \\\"base_tc_no\\\": base_tc,\\n \\\"fail_over\\\": services,\\n \\\"services_ha\\\": services_ha}\\n\\n except Exception as err:\\n LOGOBJ.exception(err)\\n return \\\"ATFError: Exception occurred while building test \\\"\\\\\\n \\\"cases numbers to test cases id mapping.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdda980df12fdb6cf4af3f2cc0103937\",\n \"score\": \"0.53126025\",\n \"text\": \"def _parse_test_cfg(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49bdb39737fe898a8dd50099f8e5c65d\",\n \"score\": \"0.53021973\",\n \"text\": \"def build_tc(self, tc):\\n tc = data.TestCampaigns[tc]\\n [self.build_dut_tc(tc, data.DUTs[name]) for name in tc.DUTs]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc36f4a729b4aea5d6e30839374a74ed\",\n \"score\": \"0.52694374\",\n \"text\": \"def create_all_scenarios(self):\\r\\n for tree in self.trees:\\r\\n self.parse_tree(tree)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17cb5e049394fe72aba0c5eb30dd9264\",\n \"score\": \"0.5206591\",\n \"text\": \"def BuildTC( *args, **kargs ):\\n from helpers.tchelper import BuildTC_Helper\\n helper = BuildTC_Helper()\\n helper.configure( *args, **kargs )\\n return helper.execute( *args, **kargs )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7f3230bc929d6e8080d78d414c74152\",\n \"score\": \"0.5194215\",\n \"text\": \"def makeRuleTree(self):\\n \\n try:\\n line = self.command_output[self.index].strip()\\n self.index += 1\\n except IndexError:\\n return\\n if len(line) > 0:\\n key, value = self.getKeyValue(line)\\n if key == \\\"Subgroup GUID\\\":\\n guid = value.split()[0]\\n if value.find(\\\"(\\\") == -1:\\n name = \\\"NO NAME\\\"\\n else:\\n name = value[value.find(\\\"(\\\")+1:-1]\\n self.current_subgroup = guid\\n self.options[guid] = {\\\"name\\\": name}\\n #print guid, name, \\\"(%s)\\\" % line\\n elif key == \\\"Power Setting GUID\\\":\\n guid = value.split()[0]\\n if value.find(\\\"(\\\") == -1:\\n name = \\\"NO NAME\\\"\\n else:\\n name = value[value.find(\\\"(\\\")+1:-1]\\n #print \\\" \\\" + guid, name\\n self.current_pwroption = guid\\n self.options[self.current_subgroup][guid] = {\\n \\\"name\\\": name,\\n \\\"units\\\": None,\\n \\\"possibilities\\\": None,\\n \\\"AC\\\": None,\\n \\\"DC\\\": None\\n }\\n else:\\n if key == \\\"Possible Setting Index\\\":\\n self.options[self.current_subgroup][self.current_pwroption][\\\"possibilities\\\"] = self.getPossibilities()\\n elif key == \\\"Possible Settings units\\\":\\n self.options[self.current_subgroup][self.current_pwroption][\\\"units\\\"] = value\\n elif key == \\\"Current AC Power Setting Index\\\":\\n self.options[self.current_subgroup][self.current_pwroption][\\\"AC\\\"] = int(value, 16)\\n elif key == \\\"Current DC Power Setting Index\\\":\\n self.options[self.current_subgroup][self.current_pwroption][\\\"DC\\\"] = int(value, 16)\\n \\n self.makeRuleTree()\\n else:\\n self.makeRuleTree()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a78026e733c6d5b01f34b2f8a306960\",\n \"score\": \"0.5173538\",\n \"text\": \"def convert_template(config: dict[str, Any]) -> dict[str, Any]:\\n my_map = {\\n CONF_ROOM_ON: dyn_const.CONF_ROOM_ON,\\n CONF_ROOM_OFF: dyn_const.CONF_ROOM_OFF,\\n CONF_CHANNEL_COVER: dyn_const.CONF_CHANNEL_COVER,\\n CONF_DEVICE_CLASS: dyn_const.CONF_DEVICE_CLASS,\\n CONF_OPEN_PRESET: dyn_const.CONF_OPEN_PRESET,\\n CONF_CLOSE_PRESET: dyn_const.CONF_CLOSE_PRESET,\\n CONF_STOP_PRESET: dyn_const.CONF_STOP_PRESET,\\n CONF_DURATION: dyn_const.CONF_DURATION,\\n CONF_TILT_TIME: dyn_const.CONF_TILT_TIME,\\n }\\n return convert_with_map(config, my_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfb073149d96aa039c513c16c64a89b1\",\n \"score\": \"0.5161812\",\n \"text\": \"def get_configs(self):\\n return {'Runway': self.parse_config(os.path.join(self.template_dir, 'runway.yml')),\\n 'Serverless':\\n self.parse_config(os.path.join(self.template_dir, 'serverless.yml'))}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0d166cac235ecbc65bddb1d5bf78a32\",\n \"score\": \"0.5157954\",\n \"text\": \"def compile_config(self, config):\\n return utils.walk_and_apply(config, self.compile_lookups)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"172e6689cba967981bb5ecb5c3a94ff9\",\n \"score\": \"0.5157662\",\n \"text\": \"def __configureNodes(self):\\n\\t\\t\\\"Load all configuration values into an easy to use dict\\\"\\\"\\\"\\n\\t\\tmydict = {}\\n\\t\\tsections = self.Config.sections()\\n\\t\\tprint(\\\"Configuring nodes for config sections {}\\\".format(sections))\\n\\t\\tfor section in sections:\\n\\t\\t mydict = dict(mydict.items() + self.Config.items(section))\\n\\t\\treturn mydict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"797cff2743d5f78a6fe37b6924f04a74\",\n \"score\": \"0.5151952\",\n \"text\": \"def create_configs(\\r\\n task_filename: str,\\r\\n configs_folder: str,\\r\\n parent_config_filename: str,\\r\\n git_tag: str,\\r\\n counter_file: str,\\r\\n results_folder: str,\\r\\n machine_id: str,\\r\\n):\\r\\n list_configs = []\\r\\n with open(task_filename) as f:\\r\\n task = json.load(f)\\r\\n with open(parent_config_filename) as f:\\r\\n parent_config = json.load(f)\\r\\n\\r\\n count = get_starting_number_from_file(counter_file) - 1\\r\\n cross_scan_gen = (cross_scan_dict for cross_scan_dict in task[\\\"Task\\\"][\\\"CrossScan\\\"] if cross_scan_dict)\\r\\n for cross_scan_dict in cross_scan_gen:\\r\\n patches = (dict(zip(cross_scan_dict.keys(), comb)) for comb in it.product(*cross_scan_dict.values()))\\r\\n for patch in patches:\\r\\n count += 1\\r\\n config_filename = f\\\"{count}.json\\\"\\r\\n save_folder = os.path.join(results_folder, f\\\"{count}_{machine_id}/\\\")\\r\\n if not os.path.exists(save_folder):\\r\\n os.mkdir(save_folder)\\r\\n\\r\\n config = copy.deepcopy(parent_config)\\r\\n apply_patch(config[\\\"Configuration\\\"], patch)\\r\\n apply_patch(\\r\\n config,\\r\\n {\\r\\n \\\"Configuration->gitHashShort\\\": git_tag,\\r\\n \\\"Configuration->Name\\\": str(count),\\r\\n \\\"Configuration->LoggerParameters->FilePath\\\": save_folder,\\r\\n \\\"Configuration->ParentConfigName\\\": parent_config_filename,\\r\\n \\\"Configuration->TaskFile\\\": task_filename,\\r\\n \\\"Configuration->ComputerID\\\": machine_id,\\r\\n },\\r\\n )\\r\\n assign_initial_conditions(config)\\r\\n\\r\\n with open(os.path.join(configs_folder, config_filename), \\\"w\\\") as f:\\r\\n json.dump(config, f, sort_keys=True, indent=2)\\r\\n list_configs.append(config_filename)\\r\\n\\r\\n set_counter_file(counter_file, count + 1)\\r\\n return list_configs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0f6fb37fcb1b23794cd6e7f82cfa442\",\n \"score\": \"0.51390517\",\n \"text\": \"def parse_config(config_path):\\n import copy\\n from functools import reduce\\n import importlib\\n import operator\\n from ruamel.yaml import YAML\\n from ruamel.yaml.comments import CommentedMap, CommentedSeq\\n\\n yaml = YAML()\\n config = yaml.load(open(config_path, 'r'))\\n classes = {}\\n\\n def get_from_dict(data_dict, map_list):\\n return reduce(operator.getitem, map_list, data_dict)\\n\\n def set_in_dict(data_dict, map_list, value):\\n get_from_dict(data_dict, map_list[:-1])[map_list[-1]] = value\\n\\n # assume if a function definition is in the most outer scope, it will not\\n # have anywhere in config a nested class\\n def walk(node, curr=None, depth=0):\\n if curr is None:\\n curr = {}\\n if isinstance(node, CommentedMap):\\n for key, item in node.items():\\n curr[depth] = key\\n if key in ['object', 'closure']:\\n if depth in classes.keys():\\n classes[depth].append(copy.deepcopy(curr))\\n else:\\n classes[depth] = [copy.deepcopy(curr)]\\n walk(item, copy.deepcopy(curr), depth + 1)\\n elif isinstance(node, CommentedSeq):\\n for i, _ in enumerate(node):\\n curr[depth] = i\\n walk(node[i], copy.deepcopy(curr), depth + 1)\\n\\n walk(config)\\n depths = classes.keys()\\n depths = reversed(sorted(depths))\\n for depth in depths:\\n for keys_dict in classes[depth]:\\n keys = list(keys_dict.values())\\n item = get_from_dict(config, keys)\\n parts = item['name'].split('.')\\n module = importlib.import_module('.'.join(parts[:-1]))\\n\\n # get the constructor for this instance\\n ctor = getattr(module, parts[-1])\\n # unify branches by setting an empty dict if no params provided\\n if 'params' not in item or item['params'] is None:\\n item['params'] = {}\\n if isinstance(item['params'], CommentedSeq):\\n item['params'] = list(item['params'])\\n\\n try:\\n if isinstance(item['params'], dict):\\n instance = ctor(**item['params'])\\n elif isinstance(item['params'], list):\\n instance = ctor(*item['params'])\\n except TypeError:\\n # pylint: disable=cell-var-from-loop\\n instance = lambda *args, f=ctor, params=item['params'], \\\\\\n **kwargs: f(*args, **kwargs, **params)\\n # pylint: enable=cell-var-from-loop\\n set_in_dict(config, keys[:-1], instance)\\n\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4165440f0951a5c7e20999b0551f52c\",\n \"score\": \"0.5112517\",\n \"text\": \"def resolve_cmd_inheritance(self, test_cfg):\\n\\n for section in ['build', 'run']:\\n config = test_cfg.get(section)\\n if not config:\\n continue\\n new_cmd_list = []\\n if config.get('prepend_cmds', []):\\n new_cmd_list.extend(config.get('prepend_cmds'))\\n config['prepend_cmds'] = []\\n new_cmd_list += test_cfg[section]['cmds']\\n if config.get('append_cmds', []):\\n new_cmd_list.extend(config.get('append_cmds'))\\n config['append_cmds'] = []\\n test_cfg[section]['cmds'] = new_cmd_list\\n\\n return test_cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5e0333db6ecb450a4b7cc41c340f2f0\",\n \"score\": \"0.5110972\",\n \"text\": \"async def cfg(self, ctx):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc0ea28355605c29942397ef80a0862e\",\n \"score\": \"0.5092078\",\n \"text\": \"def config_collection():\\n\\tshutit = shutit_global.shutit\\n\\tshutit.log('In config_collection',level=logging.DEBUG)\\n\\tcfg = shutit.cfg\\n\\tfor module_id in module_ids():\\n\\t\\t# Default to None so we can interpret as ifneeded\\n\\t\\tshutit.get_config(module_id, 'shutit.core.module.build', None, boolean=True, forcenone=True)\\n\\t\\tshutit.get_config(module_id, 'shutit.core.module.remove', False, boolean=True)\\n\\t\\tshutit.get_config(module_id, 'shutit.core.module.tag', False, boolean=True)\\n\\t\\t# Default to allow any image\\n\\t\\tshutit.get_config(module_id, 'shutit.core.module.allowed_images', [\\\".*\\\"])\\n\\t\\tmodule = shutit.shutit_map[module_id]\\n\\t\\tcfg_file = os.path.dirname(module.__module_file) + '/configs/build.cnf'\\n\\t\\tif os.path.isfile(cfg_file):\\n\\t\\t\\t# use shutit.get_config, forcing the passed-in default\\n\\t\\t\\tconfig_parser = ConfigParser.ConfigParser()\\n\\t\\t\\tconfig_parser.read(cfg_file)\\n\\t\\t\\tfor section in config_parser.sections():\\n\\t\\t\\t\\tif section == module_id:\\n\\t\\t\\t\\t\\tfor option in config_parser.options(section):\\n\\t\\t\\t\\t\\t\\tif option == 'shutit.core.module.allowed_images':\\n\\t\\t\\t\\t\\t\\t\\toverride = False\\n\\t\\t\\t\\t\\t\\t\\tfor mod, opt, val in shutit.build['config_overrides']:\\n\\t\\t\\t\\t\\t\\t\\t\\tval = val # pylint\\n\\t\\t\\t\\t\\t\\t\\t\\t# skip overrides\\n\\t\\t\\t\\t\\t\\t\\t\\tif mod == module_id and opt == option:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\toverride = True\\n\\t\\t\\t\\t\\t\\t\\tif override:\\n\\t\\t\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\t\\t\\tvalue = config_parser.get(section,option)\\n\\t\\t\\t\\t\\t\\t\\tif option == 'shutit.core.module.allowed_images':\\n\\t\\t\\t\\t\\t\\t\\t\\tvalue = json.loads(value)\\n\\t\\t\\t\\t\\t\\t\\tshutit.get_config(module_id, option, value, forcedefault=True)\\n\\t\\t# ifneeded will (by default) only take effect if 'build' is not\\n\\t\\t# specified. It can, however, be forced to a value, but this\\n\\t\\t# should be unusual.\\n\\t\\tif cfg[module_id]['shutit.core.module.build'] is None:\\n\\t\\t\\tshutit.get_config(module_id, 'shutit.core.module.build_ifneeded', True, boolean=True)\\n\\t\\t\\tcfg[module_id]['shutit.core.module.build'] = False\\n\\t\\telse:\\n\\t\\t\\tshutit.get_config(module_id, 'shutit.core.module.build_ifneeded', False, boolean=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"441d3b079f1d99c6bc1f7616889c4d16\",\n \"score\": \"0.5087299\",\n \"text\": \"def generate_node_vars(self, config):\\n\\n self.vars = []\\n\\n # get location to output individual configuration files to\\n configdir = self.get_option(\\\"config\\\", section=\\\"pe\\\", default=\\\"configs\\\")\\n configlocation = os.path.join(self.outdir, configdir)\\n if not os.path.exists(configlocation):\\n os.makedirs(configlocation)\\n\\n # get results directory\\n self.resdir = os.path.join(\\n self.outdir, self.get_option(\\\"results\\\", default=\\\"results\\\"), self.psrname\\n )\\n if not os.path.exists(self.resdir):\\n os.makedirs(self.resdir)\\n\\n transfer_files = self.submit_options.get(\\\"should_transfer_files\\\", \\\"NO\\\")\\n\\n # store expected results file names\\n extension = self.sampler_kwargs.get(\\\"save\\\", \\\"hdf5\\\")\\n gzip = self.sampler_kwargs.get(\\\"gzip\\\", False)\\n self.resultsfiles = []\\n\\n for i in range(self.n_parallel):\\n curconfig = copy.deepcopy(config)\\n vardict = {}\\n\\n label = f\\\"{self.submit_options.get('name', 'cwinpy_pe')}_{''.join(self.dets)}_{self.psrname}\\\"\\n\\n if self.n_parallel > 1:\\n configfile = os.path.join(\\n configlocation,\\n \\\"{}_{}_{}.ini\\\".format(\\\"\\\".join(self.dets), self.psrname, i),\\n )\\n\\n label += f\\\"_{i}\\\"\\n else:\\n configfile = os.path.join(\\n configlocation, \\\"{}_{}.ini\\\".format(\\\"\\\".join(self.dets), self.psrname)\\n )\\n\\n self.resultsfiles.append(\\n bilby.core.result.result_file_name(\\n os.path.abspath(self.resdir), label, extension=extension, gzip=gzip\\n )\\n )\\n\\n curconfig[\\\"label\\\"] = label\\n\\n # add files for transfer\\n if transfer_files == \\\"YES\\\":\\n transfer_input = []\\n\\n transfer_input.append(relative_topdir(configfile, self.resdir))\\n\\n for key in [\\n \\\"par_file\\\",\\n \\\"inj_par\\\",\\n \\\"data_file_1f\\\",\\n \\\"data_file_2f\\\",\\n \\\"prior\\\",\\n ]:\\n if key in list(config.keys()):\\n if key in [\\\"data_file_1f\\\", \\\"data_file_2f\\\"]:\\n for detkey in config[key]:\\n transfer_input.append(\\n relative_topdir(config[key][detkey], self.resdir)\\n )\\n\\n # exclude full path as the transfer directory is flat\\n curconfig[key][detkey] = os.path.basename(\\n config[key][detkey]\\n )\\n else:\\n transfer_input.append(\\n relative_topdir(config[key], self.resdir)\\n )\\n\\n # exclude full path as the transfer directory is flat\\n curconfig[key] = os.path.basename(config[key])\\n\\n # transfer ephemeris files\\n for ephem in [\\\"earth\\\", \\\"sun\\\"]:\\n key = f\\\"{ephem}ephemeris\\\"\\n if key in config:\\n if isinstance(config[key], dict):\\n for etype in copy.deepcopy(config[key]):\\n transfer_input.append(\\n relative_topdir(config[key][etype], self.resdir)\\n )\\n curconfig[key][etype] = os.path.basename(\\n config[key][etype]\\n )\\n else:\\n transfer_input.append(\\n relative_topdir(config[key], self.resdir)\\n )\\n curconfig[key] = os.path.basename(config[key])\\n\\n curconfig[\\\"outdir\\\"] = \\\"results/\\\"\\n\\n # add output directory to inputs in case resume file exists\\n transfer_input.append(\\\".\\\")\\n\\n vardict[\\\"ARGS\\\"] = f\\\"--config {os.path.basename(configfile)}\\\"\\n vardict[\\\"INITIALDIR\\\"] = self.resdir\\n vardict[\\\"TRANSFERINPUT\\\"] = \\\",\\\".join(transfer_input)\\n vardict[\\\"TRANSFEROUTPUT\\\"] = curconfig[\\\"outdir\\\"]\\n else:\\n vardict[\\\"ARGS\\\"] = f\\\"--config {os.path.basename(configfile)}\\\"\\n\\n # set log files\\n vardict[\\\"LOGFILE\\\"] = os.path.join(\\n self.log_directories[\\\"log\\\"], f\\\"{label}.log\\\"\\n )\\n vardict[\\\"OUTPUTFILE\\\"] = os.path.join(\\n self.log_directories[\\\"out\\\"], f\\\"{label}.out\\\"\\n )\\n vardict[\\\"ERRORFILE\\\"] = os.path.join(\\n self.log_directories[\\\"error\\\"], f\\\"{label}.err\\\"\\n )\\n\\n # write out configuration file\\n parseobj = DefaultConfigFileParser()\\n with open(configfile, \\\"w\\\") as fp:\\n fp.write(parseobj.serialize(curconfig))\\n\\n self.vars.append(vardict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc8a29a64ddfaa262279b1631e7b438c\",\n \"score\": \"0.50859463\",\n \"text\": \"def _augment_pipeline_cfg(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c55e007ebddca1b90638405690dd0989\",\n \"score\": \"0.5085939\",\n \"text\": \"def run(self) -> None:\\n self.all_commands = []\\n # Replaces all $ref entries in config with what they point to\\n self.logger.info(\\\"run():: Replacing references in schemas...\\\")\\n self.recursive_replace_refs(self.openapi_config, self.openapi_config)\\n # Now openapi_config contains fully unwrapped configuration specification\\n # We can use it as reference dict to update paths\\n self.logger.info(\\\"run():: Replacing references in paths...\\\")\\n self.recursive_replace_refs(self.openapi_paths, self.openapi_config)\\n\\n # Now we have full configuration starting from API paths with all parameters populated.\\n # We can create our command dictionaries\\n self.logger.info(\\\"run():: Building command set...\\\")\\n for endpoint_name, path in self.openapi_paths.items():\\n\\n # Get full list of parameters for every endpoint (everything in schema)\\n endpoint_data = self.recursive_find_dict_key(path, API_DATA_NODE_KEY)\\n if endpoint_data != []:\\n\\n # Now we need to construct a command for each item in endpoint data\\n # entry is a dict {\\\"path\\\":List, \\\"data\\\":Dict}\\n # entry[\\\"data\\\"] usually contains multiple parameters, e.g.\\n # {'systemClass': {'type': 'string', 'readOnly': True, 'nullable': True},\\n # 'systemLine': {'type': 'string', 'readOnly': True, 'nullable': True},\\n # ...\\n # }\\n # which have to be split to separate SL2 commands\\n # entry[\\\"path\\\"] is a node path in JSON schema to the data dict\\n for entry in endpoint_data:\\n for param_name, param_dict in entry[\\\"data\\\"].items():\\n # First, get rid of fucking useless oneOf keys\\n # They are used for JSON schema validation, absolute PITA\\n # for parameters parsing\\n self.recursive_reduce(param_dict, \\\"oneOf\\\")\\n if not isinstance(param_dict, dict):\\n self.logger.warning(\\\"run():: Wrong parameter configuration for %s %s.%s - not a dictionary!\\\", endpoint_name, '.'.join(entry['path']), param_name)\\n continue\\n self.all_commands.extend(self.make_command(endpoint_name, entry[\\\"path\\\"], param_name, param_dict))\\n else:\\n self.logger.warning(\\\"run():: No schema found for endpoint %s\\\", endpoint_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"900cef7ad7ae59279204dc39637ed3fb\",\n \"score\": \"0.5076582\",\n \"text\": \"def unroll_config(config):\\n # get global prefix of run\\n prefix = config[\\\"global\\\"][\\\"prefix\\\"]\\n\\n # store unrolled configurations here\\n configs = {}\\n\\n # check if we have a single job or need to unroll\\n # into multiple jobs\\n if config.get(\\\"batch\\\", None) is None:\\n configs[prefix] = config\\n else:\\n # go through all specified runs\\n for sub_id, delta_config in config[\\\"batch\\\"].items():\\n # create copy of config and update for current subjob\\n sub_config = deepcopy(config)\\n\\n # create prefix of subjob (may contain / to route\\n # subjob output to subfolder)\\n sub_prefix = prefix + sub_id\\n\\n # these are not batch jobs anymore, so deactivate section\\n sub_config[\\\"batch\\\"] = None\\n\\n # create full prefix for subjob\\n sub_config[\\\"global\\\"][\\\"prefix\\\"] = sub_prefix\\n\\n # apply subconfig delta\\n # (assuming parameters are nested in two layers)\\n for section in delta_config:\\n # if dictionary, substitute all items on second level\\n if isinstance(delta_config[section], Mapping):\\n for param, value in delta_config[section].items():\\n sub_config[section][param] = value\\n else:\\n # substitute entire section (this only affects pipeline stages)\\n sub_config[section] = delta_config[section]\\n\\n configs[sub_prefix] = sub_config\\n\\n return configs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e96bd937ea0d1762878203b150de79b\",\n \"score\": \"0.50726634\",\n \"text\": \"def build_general_config(self):\\n\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46e3364e6acd168f1aa02e6922416a27\",\n \"score\": \"0.50712633\",\n \"text\": \"def generate_config(cls, tree, special='rethinkdb', override=None):\\n\\n\\t\\t# Call the parent\\n\\t\\treturn super().generate_config(tree, special, override);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3ceeb4fd3fdc1698dc184ea624153b7\",\n \"score\": \"0.50711846\",\n \"text\": \"def build_structure(config):\\n\\n logging.info(\\\"Building data structure...\\\")\\n\\n\\n # Build templates\\n logging.info(\\\"Building templates...\\\")\\n templates = {}\\n for id_, data in config[\\\"templates\\\"].items():\\n logging.debug(\\\"Building template '{0}'\\\".format(id_))\\n try:\\n templates[id_] = Template(id_, *data)\\n except ConfigError as e:\\n logging.warn(\\\"Problem with config file - {0}. Skipping template '{1}'\\\".format(e, id_))\\n\\n # Build items\\n logging.info(\\\"Building items...\\\")\\n items = {}\\n for id_, data in config[\\\"items\\\"].items():\\n logging.debug(\\\"Building item '{0}'\\\".format(id_))\\n try:\\n template_id = data[1]\\n if template_id is not None:\\n template = templates[template_id]\\n else:\\n template = None\\n items[id_] = Item(id_, data[0], template)\\n except KeyError as e:\\n logging.warn(\\\"Problem with config file - Invalid template '{0}'. Skipping item '{1}'\\\".format(data[1], id_))\\n except ConfigError as e:\\n logging.warn(\\\"Problem with config file - {0}. Skipping item '{1}'\\\".format(e, id_))\\n\\n # Build users\\n logging.info(\\\"Building users...\\\")\\n users = {}\\n for email, item_ids in config[\\\"users\\\"].items():\\n logging.debug(\\\"Building user '{0}'\\\".format(email))\\n user_items = []\\n\\n for item_id in item_ids:\\n try:\\n user_items.append(items[item_id])\\n except KeyError as e:\\n logging.warn(\\\"Problem with config file - Invalid item '{0}' for user '{1}'\\\".format(item_id, email))\\n\\n # Check for duplicates\\n if len(set(user_items)) != len(user_items):\\n logging.warn(\\\"Problem with config file - Duplicate items for user '{0}'\\\".format(email))\\n\\n users[email] = User(user_items)\\n\\n return users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adb6409c4556de74340027f11b18a315\",\n \"score\": \"0.50702965\",\n \"text\": \"def _next_configs(self):\\n for state, input, stack in self.data:\\n subtable = self.table.get(state, {})\\n for (input_prefix, stack_prefix), entries in subtable.items():\\n if (input.startswith(input_prefix) and\\n stack.startswith(stack_prefix)):\\n for next_state, next_stack in entries:\\n yield Config(\\n next_state,\\n input[len(input_prefix):],\\n next_stack+stack[len(stack_prefix):])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c34746248482ba01a6181e25d0e7dfcd\",\n \"score\": \"0.5070073\",\n \"text\": \"def _create(self):\\n parser = _create_field_parser()\\n main_rules = parser \\\\\\n .parseFile(self.base_dir + '/' + self.main_config_file,\\n parseAll=True)\\n rules = main_rules.rules\\n includes = main_rules.includes\\n already_includes = [self.main_config_file]\\n\\n #Resolve includes\\n for include in includes:\\n if include[0] in already_includes:\\n continue\\n already_includes.append(include[0])\\n if os.path.exists(include[0]):\\n tmp = parser.parseFile(include[0], parseAll=True)\\n else:\\n #CHECK: This will raise an IOError if the file doesn't exist\\n tmp = parser.parseFile(self.base_dir + '/' + include[0],\\n parseAll=True)\\n if rules and tmp.rules:\\n rules += tmp.rules\\n else:\\n rules = tmp.rules\\n if includes and tmp.includes:\\n includes += tmp.includes\\n else:\\n includes = tmp.includes\\n\\n #Create config rules\\n for rule in rules:\\n if rule.override:\\n self.__override_rules.append(rule)\\n elif rule.extend:\\n self.__extend_rules.append(rule)\\n elif rule.inherit_from:\\n self.__inherit_rules.append(rule)\\n else:\\n self._create_rule(rule)\\n\\n self.__resolve_inherit_rules()\\n self.__resolve_override_rules()\\n self.__resolve_extend_rules()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54b92469f316d8fdd4e870f1f8a6ce3b\",\n \"score\": \"0.506807\",\n \"text\": \"def config_func(config):\\n\\n for node in config.children:\\n key = node.key.lower()\\n val = node.values[0]\\n if key == 'debug':\\n obj.debug = pc.convert2boolean(val)\\n elif key == 'verbose':\\n obj.verbose = pc.convert2boolean(val)\\n\\n collectd.info('%s: debug=%s, verbose=%s'\\n % (PLUGIN, obj.debug, obj.verbose))\\n\\n return pc.PLUGIN_PASS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb4394c5c70ae5af3b4bfa98c0940c29\",\n \"score\": \"0.50434846\",\n \"text\": \"def applyConfiguration (c):\\r\\n\\r\\n def getboolean(name):\\r\\n value = getattr(config, name)\\r\\n newvalue = c.config.getBool(name)\\r\\n if newvalue is not None:\\r\\n setattr(config, name, newvalue)\\r\\n \\r\\n getboolean(\\\"rst2file\\\")\\r\\n getboolean(\\\"rst2_bodyfilter\\\")\\r\\n getboolean(\\\"rst2_clear_attributes\\\")\\r\\n getboolean(\\\"rst2_http_server_support\\\")\\r\\n if config.rst2_http_server_support and not mod_http:\\r\\n g.es(\\\"Resetting rst2_http_server_support because mod_http plugin was not imported successfully\\\", color='red')\\r\\n config.rst2_http_server_support = False\\r\\n getboolean(\\\"rst2_pure_document\\\")\\r\\n getboolean(\\\"rst2_format_headlines\\\")\\r\\n # getboolean(\\\"rst2_warnofdrags\\\")\\r\\n getboolean(\\\"rst2_run_on_window_open\\\")\\r\\n \\r\\n getboolean(\\\"rst2_debug_handle_endtag\\\")\\r\\n getboolean(\\\"rst2_debug_store_lines\\\")\\r\\n getboolean(\\\"rst2_debug_handle_starttag\\\")\\r\\n getboolean(\\\"rst2_debug_show_unknownattributes\\\")\\r\\n getboolean(\\\"rst2_debug_node_html_1\\\")\\r\\n getboolean(\\\"rst2_debug_anchors\\\")\\r\\n getboolean(\\\"rst2_debug_before_and_after_replacement\\\")\\r\\n getboolean(\\\"rst2_install_menu_item_in_edit_menu\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f2bd2c3a6dcfd4f229e0da4d6382736\",\n \"score\": \"0.5012573\",\n \"text\": \"def create_config_template():\\n import ruamel.yaml\\n yaml_str = \\\"\\\"\\\"\\\\\\n# Project definitions (do not edit)\\n Task:\\n scorer:\\n date:\\n \\\\n\\n# Project path (change when moving around)\\n project_path:\\n \\\\n\\n# Annotation data set configuration (and individual video cropping parameters)\\n video_sets:\\n bodyparts:\\n start:\\n stop:\\n numframes2pick:\\n \\\\n\\n# Plotting configuration\\n skeleton:\\n skeleton_color:\\n pcutoff:\\n dotsize:\\n alphavalue:\\n colormap:\\n \\\\n\\n# Training,Evaluation and Analysis configuration\\n TrainingFraction:\\n iteration:\\n resnet:\\n snapshotindex:\\n batch_size:\\n \\\\n\\n# Cropping Parameters (for analysis and outlier frame detection)\\n cropping:\\n#if cropping is true for analysis, then set the values here:\\n x1:\\n x2:\\n y1:\\n y2:\\n \\\\n\\n# Refinement configuration (parameters from annotation dataset configuration also relevant in this stage)\\n corner2move2:\\n move2corner:\\n \\\"\\\"\\\"\\n ruamelFile = ruamel.yaml.YAML()\\n cfg_file = ruamelFile.load(yaml_str)\\n return(cfg_file,ruamelFile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ba6cc584b7e453f363efd562a5b9f\",\n \"score\": \"0.50076985\",\n \"text\": \"def generate_confs():\\n\\tglobal_config = config_gen.get_config()\\n\\tfor section in global_config.keys():\\n\\t\\tif global_config[section].has_key('TEMPLATE_CONFIG'):\\n\\t\\t\\tif not global_config[section].has_key('OUTPUT_CONFIG'):\\n\\t\\t\\t\\texit_error(\\\"[ERROR] 'OUTPUT_CONFIG' not specified for '\\\" + section + \\\"'\\\")\\n\\t\\t\\ttemplate_file = global_config[section]['TEMPLATE_CONFIG']\\n\\t\\t\\ttemplate_str = ''\\n\\t\\t\\ttry:\\n\\t\\t\\t\\twith open(template_file) as f:\\n\\t\\t\\t\\t\\ttemplate_str = f.read()\\n\\t\\t\\texcept:\\n\\t\\t\\t\\texit_error(\\\"[ERROR] Template File for '\\\" + section + \\\"', \\\" + template_file + \\\" does not exist\\\") \\n\\n\\t\\t\\tfor key, val in global_config[section].items():\\n\\t\\t\\t\\ttemplate_str = template_str.replace('$' + key + '$', val)\\n\\n\\t\\t\\ttry:\\n\\t\\t\\t\\twith open(global_config[section]['OUTPUT_CONFIG'], 'wb') as f:\\n\\t\\t\\t\\t\\tprint 'Writing', f.name, '...'\\n\\t\\t\\t\\t\\tf.write(template_str)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\texit_error(\\\"[ERROR] Failed to open output_config '\\\" + global_config[section]['OUTPUT_CONFIG'] + \\\"' in write mode\\\")\\n\\t\\telif section == 'HOSTAPD':\\n\\t\\t\\twrite_hostapd_conf(global_config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32322e10328741038c4d57412e8d5eaa\",\n \"score\": \"0.5007296\",\n \"text\": \"def interpretCfg(self):\\n target_node = self.getTargetNode()\\n current_node = self.getSourceNode()\\n self.visited.append(current_node)\\n while current_node != target_node:\\n # see if there is actually a decision to take when node is (if|while)\\n if current_node in self.cfgparser.labelsIf or \\\\\\n current_node in self.cfgparser.labelsWhile :\\n decision_node = current_node\\n succ = [s for s in self.cfg.successors(current_node)]\\n for s in succ:\\n label = self.cfg.edges[decision_node, s]['label']\\n if self.interpretCondition(label):\\n current_node = s\\n # else the edge represents an assigment\\n # however if the assigment has a while as successor, it means there is\\n # a condition to evaluate\\n elif current_node in self.cfgparser.labelsAssigns:\\n succ = [s for s in self.cfg.successors(current_node)]\\n if len(succ) == 1:\\n label = self.cfg.edges[current_node, succ[0]]['label']\\n self.interpretAssigments(label)\\n current_node = succ[0]\\n # else finally the node must BE the empty label with no successfors\\n else:\\n succ = [s for s in self.cfg.successors(current_node)]\\n if len(succ) == 0 :\\n current_node = target_node\\n else:\\n raise Exception('Target node could not be reached')\\n self.visited.append(current_node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"998f5b34efcb0b17f54d73e5546f73c6\",\n \"score\": \"0.49866524\",\n \"text\": \"def build_config(config: Dict[str, Any]) -> Dict[str, str]:\\n result = config.copy()\\n # Manage the classifier stable/beta\\n is_stable = result.pop(\\\"is_stable\\\", False)\\n if is_stable:\\n result[\\\"classifier\\\"] = \\\"Development Status :: 5 - Production/Stable\\\"\\n else:\\n result[\\\"classifier\\\"] = \\\"Development Status :: 4 - Beta\\\"\\n # Manage the nspkg\\n package_name = result[\\\"package_name\\\"]\\n result[\\\"package_nspkg\\\"] = result.pop(\\\"package_nspkg\\\", package_name[: package_name.rindex(\\\"-\\\")] + \\\"-nspkg\\\")\\n # ARM?\\n result[\\\"is_arm\\\"] = result.pop(\\\"is_arm\\\", True)\\n\\n # Do I need msrestazure for this package?\\n result[\\\"need_msrestazure\\\"] = result.pop(\\\"need_msrestazure\\\", False)\\n\\n # Do I need azure-mgmt-core for this package?\\n result[\\\"need_azuremgmtcore\\\"] = result.pop(\\\"need_azuremgmtcore\\\", True)\\n\\n # Pre-compute some Jinja variable that are complicated to do inside the templates\\n package_parts = result[\\\"package_nspkg\\\"][: -len(\\\"-nspkg\\\")].split(\\\"-\\\")\\n result[\\\"nspkg_names\\\"] = [\\\".\\\".join(package_parts[: i + 1]) for i in range(len(package_parts))]\\n result[\\\"init_names\\\"] = [\\\"/\\\".join(package_parts[: i + 1]) + \\\"/__init__.py\\\" for i in range(len(package_parts))]\\n exclude_folders = result.pop(\\\"exclude_folders\\\", \\\"\\\")\\n result[\\\"exclude_folders\\\"] = [item.strip() for item in exclude_folders.split(\\\",\\\") if item.strip()]\\n\\n # Return result\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd0ff7d0b17092c8fa77f4ba759bae71\",\n \"score\": \"0.49811134\",\n \"text\": \"def _python_to_conf(self, x, name='', nesting_context=None, **metadata):\\n\\n # If this is an atomic type, no parsing is required\\n if type(x) in self.VALID_ATOMIC_VALUE_TYPES:\\n return x\\n elif type(x) in self.VALID_SEQUENCE_VALUE_TYPES:\\n # a sequence. apply to elements recursively\\n if hasattr(x, 'items'):\\n return type(x)((k, self._python_to_conf(v)) for k, v in x.items())\\n else:\\n return type(x)(self._python_to_conf(elem) for elem in x)\\n\\n if type(x) == type(inspect):\\n\\n # the top-level module object --> a ConfigContainer\\n raw_attrs = self._get_dunder_dict(x)\\n\\n # handle the __entry_point___ directive:\\n new_x = self._find_entry_point(raw_attrs)\\n if new_x is not None:\\n # return the result of the recursive call on the new entry point\\n return self._python_to_conf(new_x)\\n\\n elif _is_raw_container(x):\\n # a class --> a ConfigContainer\\n raw_attrs = self._get_dunder_dict(x)\\n\\n # handle auto-overlay magic\\n if nesting_context is None:\\n nesting_context = []\\n nesting_context = nesting_context + [(name, x)] # don't use append\\n for overlayee in self._gen_overlayees(nesting_context):\\n for k, v in self._get_dunder_dict(overlayee).items():\\n if k not in raw_attrs:\\n raw_attrs[k] = v\\n else:\\n # type misunderstood\\n raise ConfigParsingError('Config construct of unsupported type %r: %s' % (type(x), x))\\n\\n # Separate raw_attrs to their types. See _prepare_attrs for more details.\\n real_attrs, meta_attrs = self._prepare_attrs(raw_attrs)\\n\\n # if this is an overridet-set, all nested containers are also override sets automatically.\\n # we propagate this metadata attribute using the metadata dict:\\n if meta_attrs.get('is_override_set', False):\\n metadata['is_override_set'] = meta_attrs['is_override_set']\\n\\n # convert real attr recursively\\n attrs = {\\n k: self._python_to_conf(v, name=k, nesting_context=nesting_context, **metadata)\\n for k, v in real_attrs.items()\\n }\\n\\n # create the container\\n metadata_to_apply = merge_dicts(metadata, meta_attrs)\\n if metadata_to_apply.get('is_override_set', False):\\n container_cls = ConfigOverrideSet\\n else:\\n container_cls = ConfigContainer\\n container = container_cls(attrs)\\n\\n # set metadata attributes on the container\\n container.get_metadata().update(metadata_to_apply)\\n\\n return container\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38de66c8fdee0b6a4739b56eec38b8af\",\n \"score\": \"0.49773577\",\n \"text\": \"def parse_config(self) -> None:\\n filename = self.nodes\\n config = Config()\\n cfgdir = os.path.dirname(filename)\\n cfg = yaml.safe_load(open(filename))\\n nodes = [self.create_node(cfgdir, nodecfg) for nodecfg in cfg['nodes']]\\n addrs = []\\n for node in nodes:\\n addrs.append(node.addr)\\n idx = 1\\n for node in nodes:\\n if not node.addr:\\n while addr(idx) in addrs:\\n idx += 1\\n node.addr = addr(idx)\\n addrs.append(node.addr)\\n\\n if 'wmediumd' in cfg:\\n wmd = cfg['wmediumd']\\n\\n wm_cfg = wmd.get('config', None)\\n if wm_cfg is not None:\\n assert isinstance(wm_cfg, str)\\n config.wmediumd_conf = os.path.join(cfgdir, wm_cfg)\\n\\n wm_per = wmd.get('per', None)\\n if wm_per is not None:\\n assert isinstance(wm_per, str)\\n config.wmediumd_per = os.path.join(cfgdir, wm_per)\\n\\n if 'net' in cfg:\\n net = cfg['net']\\n\\n net_delay = net.get('delay', None)\\n if net_delay is not None:\\n assert isinstance(net_delay, (int, float))\\n config.net_delay = float(net_delay)\\n\\n if 'controller' in cfg:\\n config.start_time = cfg['controller'].get('start-time', 0)\\n\\n config.nodes = nodes\\n self.config = config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"587ba2ebe1a59380f6b51d0457f12ca1\",\n \"score\": \"0.4937039\",\n \"text\": \"def _setup_pipeline_cfg(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9037a0589aab29e2b5b4aad5aeb2743\",\n \"score\": \"0.49354577\",\n \"text\": \"def traverse_config(multi_config: dict):\\n config_list = [copy.deepcopy(multi_config)]\\n queue = [[key] for key in multi_config.keys()]\\n sample_functions = []\\n while len(queue) > 0:\\n key_path = queue.pop(0)\\n values = key_path_get_value(multi_config, key_path)\\n if key_path[-1] == 'sample':\\n assert isinstance(values, dict)\\n parent_key_path = key_path[:-1]\\n # parent_config = key_path_get_value(multi_config, parent_key_path)\\n # the value of the parent_key_path is replaced with the sample in config_list\\n # this assertion can be false when the default explain config is merged into the multi config\\n # assert len(parent_config) == 1\\n sample_config = merge_dicts(SAMPLE_CONFIG, values)\\n\\n if sample_config['exclude'] is not None:\\n def exclude_func(exclude_config: dict):\\n return key_path_get_value(exclude_config, sample_config['exclude'])\\n else:\\n def exclude_func(exclude_config: dict):\\n return None\\n # TODO: functionality to sample together\\n new_config_list = []\\n for c in config_list:\\n samples = sample(sample_config, exclude_func(c))\\n for val in samples:\\n config = key_path_set_value(copy.deepcopy(c), parent_key_path, val)\\n new_config_list.append(config)\\n config_list = new_config_list\\n\\n elif isinstance(values, dict):\\n for key in values.keys():\\n queue.append(key_path + [key])\\n elif isinstance(values, list) or isinstance(values, tuple):\\n for i in range(len(values)):\\n queue.append(key_path + [i])\\n\\n return config_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"339403b25507ab9284566f4687dbd59d\",\n \"score\": \"0.49337658\",\n \"text\": \"def ConfigureTests(test_hierarchy: dict, argv):\\n\\n specific_test = \\\"\\\"\\n if argv.test is not None:\\n specific_test = argv.test\\n print(\\\"specific_test=\\\" + specific_test)\\n\\n test_objects = []\\n for testdir in test_hierarchy:\\n for config_file in ListFilesInDir(testdir, \\\".json\\\"):\\n sub_test_objs = ParseTestConfiguration(testdir + config_file)\\n for obj in sub_test_objs:\\n if specific_test != \\\"\\\" and obj.filename != specific_test:\\n print(\\\"skipping \\\" + obj.filename)\\n continue\\n test_objects.append(obj)\\n\\n # If the out directory exists then we clear it\\n if os.path.isdir(testdir + \\\"out/\\\"):\\n shutil.rmtree(testdir + \\\"out/\\\")\\n\\n # If the out directory does not exist then we create it\\n if not os.path.isdir(testdir + \\\"out/\\\"):\\n os.mkdir(testdir + \\\"out/\\\")\\n\\n # If the gold directory does not exist then we create it\\n if not os.path.isdir(testdir + \\\"gold/\\\"):\\n os.mkdir(testdir + \\\"gold/\\\")\\n\\n return test_objects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b1bb95989620240f195c66942df9983\",\n \"score\": \"0.49330992\",\n \"text\": \"def GenerateConfig(context):\\n\\n if context.properties['networkType'] == 'external':\\n networkInterfaces = [{\\n 'subnetwork': context.properties['subNetwork'],\\n 'accessConfigs': [{\\n 'name': 'External NAT',\\n 'type': 'ONE_TO_ONE_NAT'\\n }]\\n }]\\n else:\\n networkInterfaces = [{\\n 'subnetwork': context.properties['subNetwork'],\\n }]\\n if context.properties['mongoType'] == 'dbData':\\n importScript = \\\"mongodata.sh\\\"\\n elif context.properties['mongoType'] == 'dbRouter':\\n importScript = \\\"mongos.sh\\\"\\n elif context.properties['mongoType'] == 'dbConfig':\\n if 'a' in context.env['name']:\\n networkInterfaces[0]['networkIP'] = '10.0.2.10'\\n elif 'b' in context.env['name']:\\n networkInterfaces[0]['networkIP'] = '10.0.2.11'\\n elif 'c' in context.env['name']:\\n networkInterfaces[0]['networkIP'] = '10.0.2.12'\\n importScript = \\\"mongoconf.sh\\\"\\n\\n resources = [{\\n 'name': context.env['name'],\\n 'type': 'compute.v1.instance',\\n 'properties': {\\n 'zone': context.properties['zone'],\\n 'machineType': ''.join([computeBaseUrl, \\n 'projects/', context.env['project'],\\n '/zones/', context.properties['zone'], \\n '/machineTypes/', context.properties['machineType']]),\\n 'disks': [{\\n 'deviceName': 'boot',\\n 'type': 'PERSISTENT',\\n 'boot': True,\\n 'autoDelete': True,\\n 'initializeParams': {\\n 'sourceImage': ''.join([computeBaseUrl, 'projects/',\\n 'ubuntu-os-cloud/global',\\n '/images/family/ubuntu-2004-lts'])\\n }\\n }],\\n 'networkInterfaces': networkInterfaces,\\n # Allow the instance to access logging.\\n \\\"serviceAccounts\\\": [\\n {\\n \\\"email\\\": \\\"208077196965-compute@developer.gserviceaccount.com\\\",\\n \\\"scopes\\\": [\\n \\\"https://www.googleapis.com/auth/devstorage.read_only\\\",\\n \\\"https://www.googleapis.com/auth/logging.write\\\",\\n \\\"https://www.googleapis.com/auth/monitoring.write\\\",\\n \\\"https://www.googleapis.com/auth/servicecontrol\\\",\\n \\\"https://www.googleapis.com/auth/service.management.readonly\\\",\\n \\\"https://www.googleapis.com/auth/trace.append\\\"\\n ]\\n }\\n ],\\n # Metadata\\n 'metadata': {\\n 'items': [{\\n # Startup script\\n 'key': 'startup-script',\\n 'value': context.imports[importScript]\\n }]\\n }\\n }\\n }]\\n\\n return {'resources': resources}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7111f80dbd0a719c17fa753bdb604826\",\n \"score\": \"0.4928377\",\n \"text\": \"def to_yaml_tree_transform(self, model, tag, ctx):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa4b1586344b171ebcbb8a638d24507c\",\n \"score\": \"0.49226865\",\n \"text\": \"def get_configs(configs):\\n\\tshutit = shutit_global.shutit\\n\\tcp = LayerConfigParser()\\n\\tfail_str = ''\\n\\tfiles = []\\n\\tfor config_file in configs:\\n\\t\\tif isinstance(config_file, tuple):\\n\\t\\t\\tcontinue\\n\\t\\tif not is_file_secure(config_file):\\n\\t\\t\\tfail_str = fail_str + '\\\\nchmod 0600 ' + config_file\\n\\t\\t\\tfiles.append(config_file)\\n\\tif fail_str != '':\\n\\t\\tif shutit.build['interactive'] > 1:\\n\\t\\t\\tfail_str = 'Files are not secure, mode should be 0600. Running the following commands to correct:\\\\n' + fail_str + '\\\\n'\\n\\t\\t\\t# Actually show this to the user before failing...\\n\\t\\t\\tshutit.log(fail_str)\\n\\t\\t\\tshutit.log('Do you want me to run this for you? (input y/n)')\\n\\t\\t\\tif shutit.build['interactive'] == 0 or util_raw_input(default='y') == 'y':\\n\\t\\t\\t\\tfor f in files:\\n\\t\\t\\t\\t\\tshutit.log('Correcting insecure file permissions on: ' + f)\\n\\t\\t\\t\\t\\tos.chmod(f,0o600)\\n\\t\\t\\t\\t# recurse\\n\\t\\t\\t\\treturn get_configs(configs)\\n\\t\\telse:\\n\\t\\t\\tfor f in files:\\n\\t\\t\\t\\tshutit.log('Correcting insecure file permissions on: ' + f)\\n\\t\\t\\t\\tos.chmod(f,0o600)\\n\\t\\t\\t# recurse\\n\\t\\t\\treturn get_configs(configs)\\n\\t\\tshutit.fail(fail_str)\\n\\tfor config in configs:\\n\\t\\tif isinstance(config, tuple):\\n\\t\\t\\tcp.readfp(config[1], filename=config[0])\\n\\t\\telse:\\n\\t\\t\\tcp.read(config)\\n\\t# Treat allowed_images as a special, additive case\\n\\tshutit.build['shutit.core.module.allowed_images'] = cp.get_config_set('build', 'shutit.core.module.allowed_images')\\n\\treturn cp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09bd34c2e0b1cd33616ba985a97f2ee0\",\n \"score\": \"0.48903823\",\n \"text\": \"def _custom_tasks(template, info) -> None:\\n if template == \\\"integration\\\":\\n changes = {\\\"codeowners\\\": [info.codeowner]}\\n\\n if info.requirement:\\n changes[\\\"requirements\\\"] = [info.requirement]\\n\\n info.update_manifest(**changes)\\n\\n if template == \\\"device_trigger\\\":\\n info.update_strings(\\n device_automation={\\n **info.strings().get(\\\"device_automation\\\", {}),\\n \\\"trigger_type\\\": {\\n \\\"turned_on\\\": \\\"{entity_name} turned on\\\",\\n \\\"turned_off\\\": \\\"{entity_name} turned off\\\",\\n },\\n }\\n )\\n\\n if template == \\\"device_condition\\\":\\n info.update_strings(\\n device_automation={\\n **info.strings().get(\\\"device_automation\\\", {}),\\n \\\"condtion_type\\\": {\\n \\\"is_on\\\": \\\"{entity_name} is on\\\",\\n \\\"is_off\\\": \\\"{entity_name} is off\\\",\\n },\\n }\\n )\\n\\n if template == \\\"device_action\\\":\\n info.update_strings(\\n device_automation={\\n **info.strings().get(\\\"device_automation\\\", {}),\\n \\\"action_type\\\": {\\n \\\"turn_on\\\": \\\"Turn on {entity_name}\\\",\\n \\\"turn_off\\\": \\\"Turn off {entity_name}\\\",\\n },\\n }\\n )\\n\\n if template == \\\"config_flow\\\":\\n info.update_manifest(config_flow=True)\\n info.update_strings(\\n config={\\n \\\"title\\\": info.name,\\n \\\"step\\\": {\\n \\\"user\\\": {\\\"title\\\": \\\"Connect to the device\\\", \\\"data\\\": {\\\"host\\\": \\\"Host\\\"}}\\n },\\n \\\"error\\\": {\\n \\\"cannot_connect\\\": \\\"Failed to connect, please try again\\\",\\n \\\"invalid_auth\\\": \\\"Invalid authentication\\\",\\n \\\"unknown\\\": \\\"Unexpected error\\\",\\n },\\n \\\"abort\\\": {\\\"already_configured\\\": \\\"Device is already configured\\\"},\\n }\\n )\\n\\n if template == \\\"config_flow_discovery\\\":\\n info.update_manifest(config_flow=True)\\n info.update_strings(\\n config={\\n \\\"title\\\": info.name,\\n \\\"step\\\": {\\n \\\"confirm\\\": {\\n \\\"title\\\": info.name,\\n \\\"description\\\": f\\\"Do you want to set up {info.name}?\\\",\\n }\\n },\\n \\\"abort\\\": {\\n \\\"single_instance_allowed\\\": f\\\"Only a single configuration of {info.name} is possible.\\\",\\n \\\"no_devices_found\\\": f\\\"No {info.name} devices found on the network.\\\",\\n },\\n }\\n )\\n\\n if template in (\\\"config_flow\\\", \\\"config_flow_discovery\\\"):\\n init_file = info.integration_dir / \\\"__init__.py\\\"\\n init_file.write_text(\\n init_file.read_text()\\n + \\\"\\\"\\\"\\n\\nasync def async_setup_entry(hass, entry):\\n \\\\\\\"\\\\\\\"\\\\\\\"Set up a config entry for NEW_NAME.\\\\\\\"\\\\\\\"\\\\\\\"\\n # TODO forward the entry for each platform that you want to set up.\\n # hass.async_create_task(\\n # hass.config_entries.async_forward_entry_setup(entry, \\\"media_player\\\")\\n # )\\n\\n return True\\n\\\"\\\"\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4965713aa9af60fed320461f01deee92\",\n \"score\": \"0.4884857\",\n \"text\": \"def generate(physical, fancy, debug):\\n\\n #\\n # Send all the output to a string by redirecting stdout\\n #\\n import StringIO\\n sio = StringIO.StringIO()\\n save = sys.stdout\\n sys.stdout = sio\\n\\n #\\n #\\n #\\n print \\\"<script>\\\"\\n f=open(fancy)\\n print f.read()\\n f.close()\\n print \\\"</script>\\\"\\n\\n\\n #\\n # Generate the control and main net part of the config\\n #\\n Defaults(physical, debug).start()\\n\\n #\\n # \\n #\\n from xtparse import process\\n try:\\n process(debug, physical, fancy)\\n except MyError, error:\\n sys.stderr.write(\\\"%s\\\\n\\\" % str(error))\\n sys.exit(1)\\n\\n #\\n # Restore stdout\\n #\\n sys.stdout = save\\n\\n print \\\"<testbed_config>\\\"\\n print sio.getvalue(),\\n print \\\"</testbed_config>\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1d714014ee43a630abe4e9df5c6d8ed\",\n \"score\": \"0.48782662\",\n \"text\": \"def parse_configs(code_config, field_config, time_config):\\n # performing basic validation of config paths, obtaining dictionary of \\n # config types and correpsonding raw dataframes\\n raw_dfs = validate_config_dfs(code_config, field_config, time_config)\\n\\n # performing additional config-specific validation and parsing \\n config_dict = {}\\n for config_type, df in raw_dfs.items():\\n if config_type in validation_functions:\\n validation_functions[config_type](df)\\n if config_type in parse_functions:\\n config_dict[config_type] = parse_functions[config_type](df)\\n else:\\n config_dict[config_type] = df\\n\\n # concatenating code and field configs\\n if CODE_CONFIG in config_dict:\\n if FIELD_CONFIG in config_dict:\\n config_dict[FIELD_CONFIG] = pd.concat([config_dict[CODE_CONFIG],\\n config_dict[FIELD_CONFIG]], sort=True)\\n else:\\n config_dict[FIELD_CONFIG] = config_dict[CODE_CONFIG]\\n config_dict.pop(CODE_CONFIG)\\n\\n return config_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"957ecf046c8747fc8eb2d7aa0924c742\",\n \"score\": \"0.48711365\",\n \"text\": \"async def get_config_to_integration_load() -> dict[str, Any]:\\n\\n return {\\n \\\"command_line\\\": [\\n {\\n \\\"binary_sensor\\\": {\\n \\\"name\\\": \\\"Test\\\",\\n \\\"command\\\": \\\"echo 1\\\",\\n \\\"payload_on\\\": \\\"1\\\",\\n \\\"payload_off\\\": \\\"0\\\",\\n \\\"command_timeout\\\": 15,\\n }\\n },\\n {\\n \\\"cover\\\": {\\n \\\"name\\\": \\\"Test\\\",\\n \\\"command_state\\\": \\\"echo 1\\\",\\n \\\"command_timeout\\\": 15,\\n }\\n },\\n {\\n \\\"notify\\\": {\\n \\\"name\\\": \\\"Test\\\",\\n \\\"command\\\": \\\"echo 1\\\",\\n \\\"command_timeout\\\": 15,\\n }\\n },\\n {\\n \\\"sensor\\\": {\\n \\\"name\\\": \\\"Test\\\",\\n \\\"command\\\": \\\"echo 5\\\",\\n \\\"unit_of_measurement\\\": \\\"in\\\",\\n \\\"command_timeout\\\": 15,\\n }\\n },\\n {\\n \\\"switch\\\": {\\n \\\"name\\\": \\\"Test\\\",\\n \\\"command_state\\\": \\\"echo 1\\\",\\n \\\"command_timeout\\\": 15,\\n }\\n },\\n ]\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bd9fad74c2b8cc0e29460accb8b00e7\",\n \"score\": \"0.48693904\",\n \"text\": \"def main(argv):\\n config_generator = FaucetConfigGenerator()\\n filepath = '/tmp/faucet_config_dump'\\n egress = 2\\n access = 3\\n devices = 1\\n topo_type = STACK\\n argv = argv[1:]\\n\\n help_msg = \\\"\\\"\\\"\\n <python3> build_config.py -e <egress_switches> -a <access_switches> -d <devices per switch>\\n -p <config path> -t <topology type (flat, corp, stack)>\\n \\\"\\\"\\\"\\n\\n try:\\n opts, _ = getopt.getopt(\\n argv, 'he:a:d:p:t:', ['egress=', 'access=', 'devices=', 'path=', 'type='])\\n except getopt.GetoptError:\\n print(help_msg)\\n sys.exit(2)\\n for opt, arg in opts:\\n if opt == '-h':\\n print(help_msg)\\n sys.exit()\\n elif opt in ('-e', '--egress'):\\n egress = int(arg)\\n elif opt in ('-a', '--access'):\\n access = int(arg)\\n elif opt in ('-d', '--devices'):\\n devices = int(arg)\\n elif opt in ('-p', '--path'):\\n filepath = arg\\n elif opt in ('-t', '--type'):\\n topo_type = arg\\n\\n if topo_type == FLAT:\\n faucet_config = config_generator.create_flat_faucet_config(access, devices)\\n elif topo_type == CORP:\\n faucet_config = config_generator.create_corp_faucet_config()\\n elif topo_type == STACK:\\n faucet_config = config_generator.create_scale_faucet_config(egress, access, devices)\\n else:\\n raise Exception('Unkown topology type: %s' % topo_type)\\n\\n config_map = cleanup_config(proto_dict(faucet_config))\\n\\n with open(filepath, 'w') as config_file:\\n yaml.dump(config_map, config_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dd4813e0621c0a08a7ca1dba0e83c3a\",\n \"score\": \"0.4855035\",\n \"text\": \"def _processCfg(self):\\n errors = []\\n opts = dict(self.node_cfg)\\n cls = get_class(opts.pop('cls'))\\n self.dispatcher = cls(opts.pop('name', self._cfgfiles),\\n self.log.getChild('dispatcher'), opts, self)\\n\\n if opts:\\n self.dispatcher.errors.append(self.unknown_options(cls, opts))\\n self.modules = OrderedDict()\\n\\n # create and initialize modules\\n todos = list(self.module_cfg.items())\\n while todos:\\n modname, options = todos.pop(0)\\n if modname in self.modules:\\n # already created by Dispatcher (via Attached)\\n continue\\n # For Pinata modules: we need to access this in Dispatcher.get_module\\n self.module_cfg[modname] = dict(options)\\n modobj = self.dispatcher.get_module_instance(modname) # lazy\\n if modobj is None:\\n self.log.debug('Module %s returned None', modname)\\n continue\\n self.modules[modname] = modobj\\n if isinstance(modobj, Pinata):\\n # scan for dynamic devices\\n pinata = self.dispatcher.get_module(modname)\\n pinata_modules = list(pinata.scanModules())\\n for name, _cfg in pinata_modules:\\n if name in self.module_cfg:\\n self.log.error('Module %s, from pinata %s, already'\\n ' exists in config file!', name, modname)\\n self.log.info('Pinata %s found %d modules', modname, len(pinata_modules))\\n todos.extend(pinata_modules)\\n\\n # initialize all modules by getting them with Dispatcher.get_module,\\n # which is done in the get_descriptive data\\n # TODO: caching, to not make this extra work\\n self.dispatcher.get_descriptive_data('')\\n # =========== All modules are initialized ===========\\n\\n # all errors from initialization process\\n errors = self.dispatcher.errors\\n\\n if not self._testonly:\\n start_events = MultiEvent(default_timeout=30)\\n for modname, modobj in self.modules.items():\\n # startModule must return either a timeout value or None (default 30 sec)\\n start_events.name = f'module {modname}'\\n modobj.startModule(start_events)\\n if not modobj.startModuleDone:\\n errors.append(f'{modobj.startModule.__qualname__} was not called, probably missing super call')\\n\\n if errors:\\n for errtxt in errors:\\n for line in errtxt.split('\\\\n'):\\n self.log.error(line)\\n # print a list of config errors to stderr\\n sys.stderr.write('\\\\n'.join(errors))\\n sys.stderr.write('\\\\n')\\n sys.exit(1)\\n\\n if self._testonly:\\n return\\n self.log.info('waiting for modules being started')\\n start_events.name = None\\n if not start_events.wait():\\n # some timeout happened\\n for name in start_events.waiting_for():\\n self.log.warning('timeout when starting %s', name)\\n self.log.info('all modules started')\\n history_path = os.environ.get('FRAPPY_HISTORY')\\n if history_path:\\n from frappy_psi.historywriter import FrappyHistoryWriter # pylint: disable=import-outside-toplevel\\n writer = FrappyHistoryWriter(history_path, PREDEFINED_ACCESSIBLES.keys(), self.dispatcher)\\n # treat writer as a connection\\n self.dispatcher.add_connection(writer)\\n writer.init(self.dispatcher.handle_describe(writer, None, None))\\n # TODO: if ever somebody wants to implement an other history writer:\\n # - a general config file /etc/secp/frappy.conf or <frappy repo>/etc/frappy.conf\\n # might be introduced, which contains the log, pid and cfg directory path and\\n # the class path implementing the history\\n # - or we just add here an other if statement:\\n # history_path = os.environ.get('ALTERNATIVE_HISTORY')\\n # if history_path:\\n # from frappy_<xx>.historywriter import ... etc.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e683b5c682146176896ca4a7e7b27fb\",\n \"score\": \"0.48546398\",\n \"text\": \"def generate_kanon_config(sample: pd.DataFrame, k: int, cn_config: dict, topics: tuple):\\n from cn.protect import Protect\\n from cn.protect.privacy import KAnonymity\\n from cn.protect.hierarchy import DataHierarchy, OrderHierarchy\\n import uuid\\n import textwrap\\n\\n protector = Protect(sample, KAnonymity(k))\\n\\n for prop_name, config in cn_config.items():\\n protector.itypes[prop_name], protector.hierarchies[prop_name] = config\\n\\n private = protector.protect()\\n\\n tasks = {}\\n\\n def add_subtask(signature: str, **kwargs):\\n # todo Don't add a new item to the dict on each call, but instead group tasks that\\n # make use of the same function, e.g.:\\n # - signature: drop_keys\\n # args:\\n # keys: [A, B]\\n tasks[f\\\"{signature}-{uuid.uuid4()}\\\"] = kwargs\\n\\n for prop_name, (identifying, hierarchy) in cn_config.items():\\n if private[prop_name][0] == \\\"*\\\":\\n add_subtask(\\\"drop_keys\\\", keys=[prop_name])\\n elif hierarchy is None:\\n pass # no anonymization applied - do nothing\\n elif isinstance(hierarchy, OrderHierarchy):\\n lower, upper = [float(bound) for bound in private[prop_name][0][1:-1].split(\\\",\\\")]\\n add_subtask(\\\"reduce_to_nearest_value\\\", keys=[prop_name], step_width=upper - lower)\\n elif isinstance(hierarchy, DataHierarchy):\\n actual_replacements = {}\\n possible_replacements = hierarchy.df\\n for value in private[prop_name]:\\n # todo extract replaced from dataframe\\n pass\\n add_subtask(\\\"replace_with\\\", replacements=actual_replacements)\\n else:\\n print(\\\"Warning: Unsupported hierarchy type \\\" + str(type(hierarchy)))\\n\\n worker_config = {\\n \\\"task_defaults\\\": {\\n \\\"input_offset_reset\\\": \\\"earliest\\\",\\n \\\"topic_encoding\\\": \\\"utf8\\\",\\n \\\"storage_mode\\\": \\\"memory\\\"\\n },\\n \\\"tasks\\\": [{\\n \\\"name\\\": task_name,\\n \\\"function\\\": {\\n \\\"signature\\\": task_name.split(\\\"-\\\")[0],\\n \\\"args\\\": task_config\\n }\\n } for task_name, task_config in tasks.items()]\\n }\\n worker_config[\\\"tasks\\\"][0][\\\"input_topic\\\"] = topics[0]\\n for task, previous_task in zip(worker_config[\\\"tasks\\\"][1:], worker_config[\\\"tasks\\\"]):\\n task[\\\"input_topic\\\"] = previous_task[\\\"output_topic\\\"] = f\\\"{uuid.uuid4()}\\\"\\n worker_config[\\\"tasks\\\"][-1][\\\"output_topic\\\"] = topics[1]\\n\\n print(textwrap.dedent(\\\"\\\"\\\"\\n To configure k-anonymity in your data processing pipeline, include the following \\n configuration snippet in your config.yml:\\n ---\\n \\\"\\\"\\\"))\\n print(yaml.dump(worker_config))\\n\\n return worker_config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7b2e7664cd33e3e9aceb5059c7de0f5\",\n \"score\": \"0.48444206\",\n \"text\": \"def _get_can_tp_config(self):\\r\\n tp_configs = self.root.findall(\\\".//{http://autosar.org/schema/r4.0}CAN-TP-CONFIG\\\")\\r\\n for tp_config in tp_configs:\\r\\n cluster_name = tp_config.find(\\\"{http://autosar.org/schema/r4.0}COMMUNICATION-CLUSTER-REF\\\").text.split('/')[-1]\\r\\n cluster = self.clusters_dict.get(cluster_name)\\r\\n\\r\\n # Get TP CONNECTIONS information\\r\\n tp_connections = tp_config.find(\\\"{http://autosar.org/schema/r4.0}TP-CONNECTIONS\\\")\\r\\n for tp_connection in tp_connections:\\r\\n current_tpc = _CanTPC()\\r\\n\\r\\n current_tpc.addressing_format = tp_connection.find(\\\"{http://autosar.org/schema/r4.0}ADDRESSING-FORMAT\\\").text.split('/')[-1]\\r\\n current_tpc.data_pdu = tp_connection.find(\\\"{http://autosar.org/schema/r4.0}DATA-PDU-REF\\\").text.split('/')[-1]\\r\\n try:\\r\\n current_tpc.flow_control_pdu = tp_connection.find(\\\"{http://autosar.org/schema/r4.0}FLOW-CONTROL-PDU-REF\\\").text.split('/')[-1]\\r\\n except AttributeError:\\r\\n pass\\r\\n current_tpc.tp_sdu = tp_connection.find(\\\"{http://autosar.org/schema/r4.0}TP-SDU-REF\\\").text.split('/')[-1]\\r\\n current_tpc.transmitter = tp_connection.find(\\\"{http://autosar.org/schema/r4.0}TRANSMITTER-REF\\\").text.split('/')[-1]\\r\\n\\r\\n receiver_refs = tp_connection.find(\\\"{http://autosar.org/schema/r4.0}RECEIVER-REFS\\\")\\r\\n for receiver_ref in receiver_refs:\\r\\n current_tpc.receiver.append(receiver_ref.text.split('/')[-1])\\r\\n\\r\\n cluster.tp_connections.append(current_tpc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f273fe558a6582a1584a8baa7daa96dd\",\n \"score\": \"0.4841644\",\n \"text\": \"def configure(self, configuation):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c51d3ee8d7fb6bd6ff8166b91acfcf5\",\n \"score\": \"0.48380682\",\n \"text\": \"def get_base_config(cfg_parser):\\n\\tshutit = shutit_global.shutit\\n\\tshutit.config_parser = cp = cfg_parser\\n\\t# BEGIN Read from config files\\n\\t# build - details relating to the build\\n\\tshutit.build['privileged'] = cp.getboolean('build', 'privileged')\\n\\tshutit.build['base_image'] = cp.get('build', 'base_image')\\n\\tshutit.build['dotest'] = cp.get('build', 'dotest')\\n\\tshutit.build['net'] = cp.get('build', 'net')\\n\\tshutit.build['secret_words_set'] = set()\\n\\tshutit.build['completed'] = False\\n\\tshutit.build['step_through'] = False\\n\\tshutit.build['ctrlc_stop'] = False\\n\\tshutit.build['ctrlc_passthrough'] = False\\n\\tshutit.build['have_read_config_file'] = False\\n\\t# Width of terminal to set up on login and assume for other cases.\\n\\tshutit.build['stty_cols'] = 320\\n\\tshutit.build['vagrant_run_dir'] = None\\n\\tshutit.build['this_vagrant_run_dir'] = None\\n\\t# Signals are set here, which is useful for context-switching callbacks.\\n\\tshutit.shutit_signal['ID'] = 0\\n\\t# Take a command-line arg if given, else default.\\n\\tif shutit.build['conn_module'] is None:\\n\\t\\tshutit.build['conn_module'] = cp.get('build', 'conn_module')\\n\\t# Track logins in a stack and details in logins.\\n\\tshutit.build['login_stack'] = []\\n\\tshutit.build['logins'] = {}\\n\\t# Whether to accept default configs\\n\\tshutit.build['accept_defaults'] = None\\n\\t# target - the target of the build, ie the container\\n\\tshutit.target['hostname'] = cp.get('target', 'hostname')\\n\\tshutit.target['locale'] = cp.get('target', 'locale')\\n\\tshutit.target['ports'] = cp.get('target', 'ports')\\n\\tshutit.target['volumes'] = cp.get('target', 'volumes')\\n\\tshutit.target['volumes_from'] = cp.get('target', 'volumes_from')\\n\\tshutit.target['name'] = cp.get('target', 'name')\\n\\tshutit.target['rm'] = cp.getboolean('target', 'rm')\\n\\t# host - the host on which the shutit script is run\\n\\tshutit.host['add_shutit_to_path'] = cp.getboolean('host', 'add_shutit_to_path')\\n\\tshutit.host['docker_executable'] = cp.get('host', 'docker_executable')\\n\\tshutit.host['dns'] = cp.get('host', 'dns')\\n\\tshutit.host['password'] = cp.get('host', 'password')\\n\\tshutit.build['secret_words_set'].add(shutit.host['password'])\\n\\tshutit.host['logfile'] = cp.get('host', 'logfile')\\n\\tshutit.host['shutit_module_path'] = cp.get('host', 'shutit_module_path').split(':')\\n\\t# repository - information relating to repository/registry\\n\\tshutit.repository['name'] = cp.get('repository', 'name')\\n\\tshutit.repository['server'] = cp.get('repository', 'server')\\n\\tshutit.repository['push'] = cp.getboolean('repository', 'push')\\n\\tshutit.repository['tag'] = cp.getboolean('repository', 'tag')\\n\\tshutit.repository['export'] = cp.getboolean('repository', 'export')\\n\\tshutit.repository['save'] = cp.getboolean('repository', 'save')\\n\\tshutit.repository['suffix_date'] = cp.getboolean('repository', 'suffix_date')\\n\\tshutit.repository['suffix_format'] = cp.get('repository', 'suffix_format')\\n\\tshutit.repository['user'] = cp.get('repository', 'user')\\n\\tshutit.repository['password'] = cp.get('repository', 'password')\\n\\tshutit.build['secret_words_set'].add(shutit.repository['password'])\\n\\tshutit.repository['email'] = cp.get('repository', 'email')\\n\\tshutit.repository['tag_name'] = cp.get('repository', 'tag_name')\\n\\t# END Read from config files\\n\\n\\t# BEGIN Standard expects\\n\\t# It's important that these have '.*' in them at the start, so that the matched data is reliably 'after' in the\\n\\t# child object. Use these where possible to make things more consistent.\\n\\t# Attempt to capture any starting prompt (when starting) with this regexp.\\n\\tshutit.expect_prompts['base_prompt'] = '\\\\r\\\\n.*[@#$] '\\n\\t# END Standard expects\\n\\n\\tif shutit.build['delivery'] in ('bash','ssh'):\\n\\t\\tif shutit.target['docker_image'] != '':\\n\\t\\t\\tprint('delivery method specified (' + shutit.build['delivery'] + ') and image_tag argument make no sense')\\n\\t\\t\\thandle_exit(exit_code=1)\\n\\tif shutit.target['docker_image'] == '':\\n\\t\\tshutit.target['docker_image'] = shutit.build['base_image']\\n\\t# END tidy configs up\\n\\n\\t# BEGIN warnings\\n\\t# FAILS begins\\n\\t# rm is incompatible with repository actions\\n\\tif shutit.target['rm'] and (shutit.repository['tag'] or shutit.repository['push'] or shutit.repository['save'] or shutit.repository['export']):\\n\\t\\tprint(\\\"Can't have [target]/rm and [repository]/(push/save/export) set to true\\\")\\n\\t\\thandle_exit(exit_code=1)\\n\\tif shutit.target['hostname'] != '' and shutit.build['net'] != '' and shutit.build['net'] != 'bridge':\\n\\t\\tprint('\\\\n\\\\ntarget/hostname or build/net configs must be blank\\\\n\\\\n')\\n\\t\\thandle_exit(exit_code=1)\\n\\t# FAILS ends\",\n \"title\": \"\"\n },\n {\n \"docid\": \"842e44eebfa47ec517bb41e9a5f9a7c0\",\n \"score\": \"0.48260784\",\n \"text\": \"def create_c_dict(self):\\n c_dict = super().create_c_dict()\\n\\n et_upper = self.app_name.upper()\\n\\n # get TCStat data dir/template to read\\n c_dict['TC_STAT_INPUT_DIR'] = (\\n self.config.getdir('EXTRACT_TILES_TC_STAT_INPUT_DIR', '')\\n )\\n\\n c_dict['TC_STAT_INPUT_TEMPLATE'] = (\\n self.config.getraw('filename_templates',\\n 'EXTRACT_TILES_TC_STAT_INPUT_TEMPLATE',\\n '')\\n )\\n if not c_dict['TC_STAT_INPUT_TEMPLATE']:\\n self.log_error('Must set EXTRACT_TILES_TC_STAT_INPUT_TEMPLATE '\\n 'to run ExtractTiles wrapper')\\n\\n # get gridded input/output directory/template to read\\n for data_type in ['FCST', 'OBS']:\\n # get [FCST/OBS]_INPUT_DIR\\n c_dict[f'{data_type}_INPUT_DIR'] = (\\n self.config.getdir(f'{data_type}_EXTRACT_TILES_INPUT_DIR', '')\\n )\\n if not c_dict[f'{data_type}_INPUT_DIR']:\\n self.log_error(f'Must set {data_type}_EXTRACT_TILES_INPUT_DIR to '\\n 'run ExtractTiles wrapper')\\n\\n # get [FCST/OBS]_[INPUT/OUTPUT]_TEMPLATE\\n for put in ['INPUT', 'OUTPUT']:\\n local_name = f'{data_type}_{put}_TEMPLATE'\\n config_name = f'{data_type}_{et_upper}_{put}_TEMPLATE'\\n c_dict[local_name] = (\\n self.config.getraw('filename_templates',\\n config_name)\\n )\\n if not c_dict[local_name]:\\n self.log_error(f\\\"{config_name} must be set.\\\")\\n\\n c_dict['OUTPUT_DIR'] = (\\n self.config.getdir('EXTRACT_TILES_OUTPUT_DIR', '')\\n )\\n if not c_dict['OUTPUT_DIR']:\\n self.log_error('Must set EXTRACT_TILES_OUTPUT_DIR to run '\\n 'ExtractTiles wrapper')\\n\\n c_dict['NLAT'] = self.config.getstr('config', 'EXTRACT_TILES_NLAT')\\n c_dict['NLON'] = self.config.getstr('config', 'EXTRACT_TILES_NLON')\\n c_dict['DLAT'] = self.config.getstr('config', 'EXTRACT_TILES_DLAT')\\n c_dict['DLON'] = self.config.getstr('config', 'EXTRACT_TILES_DLON')\\n c_dict['LAT_ADJ'] = self.config.getfloat('config',\\n 'EXTRACT_TILES_LAT_ADJ')\\n c_dict['LON_ADJ'] = self.config.getfloat('config',\\n 'EXTRACT_TILES_LON_ADJ')\\n\\n return c_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67efe9f194b0b81c776b11ac18a3efab\",\n \"score\": \"0.4816568\",\n \"text\": \"def setup_mtree(nw_src,nw_mcast_dst,inport,controller):\\n if nw_mcast_dst == mcast_ip_addr1:\\n mtree1_switches = []\\n primary_tree = []\\n if len(controller.mcast_groups.keys()) == 2:\\n mtree1_switches = [10,11,13,12]\\n primary_tree = [(13,12),(12,11),(12,10)]\\n else:\\n mtree1_switches = [7,6,5,4]\\n primary_tree = [(7,6),(6,4),(6,5)]\\n \\n controller.primary_trees[nw_mcast_dst] = primary_tree\\n return setup_mtree1_flow_tables(nw_src, nw_mcast_dst, inport,mtree1_switches,controller)\\n elif nw_mcast_dst == mcast_ip_addr2:\\n mtree2_switches = []\\n primary_tree = []\\n if len(controller.mcast_groups.keys()) == 2:\\n mtree2_switches = [10,14,15]\\n primary_tree = [(15,14),(15,10)]\\n \\n controller.primary_trees[nw_mcast_dst] = primary_tree #TODO REFACTOR !!!!!!!!!!!!!!!!!!!\\n return setup_mtree2_flow_tables(nw_src, nw_mcast_dst, inport,mtree2_switches,controller)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faff659c7dcd6cde063d49fb242f7860\",\n \"score\": \"0.48164737\",\n \"text\": \"def config(self,conf):\\n for key in conf:\\n if key in ('startx','starty','start_direction'):\\n setattr(__class__,key,conf[key])\\n if key in ('puzzle','uid','title','type','theme'):\\n setattr(self,key,conf[key])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"502f6c9d32c0fe8c03f8e71df796ee15\",\n \"score\": \"0.48163405\",\n \"text\": \"def read(prog):\\n try:\\n with open(str(prog.config), \\\"r\\\") as file:\\n raw = file.read().splitlines()\\n except FileNotFoundError as ex:\\n prog.log.error(\\\"config file '{}' not found\\\".format(ex.filename))\\n return Prog.RetVal.exit_failure\\n except OSError as ex:\\n prog.log.error(\\n \\\"config file '{}': {}\\\".format(ex.filename, ex.strerror.lower()))\\n return Prog.RetVal.exit_failure\\n\\n prog.log.info1(\\\"+++ reading config file '{}'\\\".format(prog.config))\\n\\n line_pos = 0\\n state = Prog.ConfigState()\\n\\n active_section = None\\n target = None\\n\\n default_tlsa_list = []\\n default_api = None\\n\\n log_level = None\\n\\n for l in raw:\\n line_pos += 1\\n state.line(line_pos)\\n\\n # matches section: \\\"[DOMAIN]\\\"\\n match = re.match(r'\\\\s*\\\\[\\\\s*(?P<section>((\\\\w[a-zA-Z0-9-]*\\\\w|\\\\w+)\\\\.)+\\\\w+)\\\\s*\\\\](\\\\s*|\\\\s+#.*)$', l)\\n if match:\\n active_section = match.group('section').lower()\\n prog.log.info3(\\\" + line {}: section: {}\\\".format(\\n line_pos, active_section))\\n for t in prog.target_list:\\n if t.matches_domain(active_section):\\n target = t\\n break\\n else:\\n prog.target_list += [ Prog.Target(active_section) ]\\n target = prog.target_list[-1]\\n if default_api:\\n target.api = default_api.copy()\\n # NOT 'target.api = default_api'. If we do that, then in\\n # the following line when we change the api object's\\n # domain attribute, it will change the domain in _all_ of\\n # the targets. We need to create a NEW object that is\\n # (mostly) the same as the default_api object, and we do\\n # this with the 'copy' method.\\n target.api.set_domain(active_section)\\n for tlsa in default_tlsa_list:\\n tlsa.domain = active_section\\n target.add_tlsa(tlsa)\\n continue\\n\\n # matches parameter: \\\"param = input\\\"\\n match = re.match(\\n r'\\\\s*(?P<param>\\\\w+)\\\\s*=\\\\s*(?P<input>[^#]*)(\\\\s*|\\\\s#.*)$', l)\\n if match:\\n param = match.group('param')\\n try:\\n inputs = shlex.split(match.group('input'))\\n except ValueError:\\n if len(l) > 23:\\n state.add_error(prog,\\n \\\"unrecognized command: '{}...'\\\".format(l[:20]))\\n else:\\n state.add_error(prog,\\n \\\"unrecognized command: '{}'\\\".format(l))\\n continue\\n\\n if param == \\\"tlsa\\\":\\n prog.log.info3(\\\" + line {}: parameter: {}, inputs: {}\\\".format(\\n line_pos, param, inputs))\\n if len(inputs) == 0:\\n state.add_error(prog, \\\"no tlsa data given\\\")\\n elif len(inputs) == 1:\\n state.add_error(prog, \\\"tlsa record given insufficient data\\\")\\n elif len(inputs) > 4:\\n state.add_error(prog, \\\"tlsa record given superfluous data: '{}'\\\".format(' '.join(inputs[4:])))\\n else:\\n tlsa = get_tlsa_param(prog, inputs, active_section, state)\\n if tlsa:\\n if active_section:\\n target.add_tlsa(tlsa)\\n else:\\n default_tlsa_list += [ tlsa ]\\n\\n elif param == \\\"api\\\":\\n prog.log.info3(\\n \\\" + line {}: parameter: {}, inputs: ({})...\\\".format(\\n line_pos, param, len(inputs) ))\\n if len(inputs) == 0:\\n state.add_error(prog, \\\"api command given no input\\\")\\n continue\\n\\n if inputs[0] in prog.apis:\\n apimod = import_module('alnitak.api.' + inputs[0])\\n api = apimod.get_api(prog, active_section,\\n inputs[1:], state)\\n if api:\\n if active_section:\\n target.api = api\\n else:\\n default_api = api\\n else:\\n state.add_error(prog,\\n \\\"unrecognized api scheme: '{}'\\\".format(inputs[0]))\\n\\n elif param == \\\"dane_directory\\\":\\n prog.log.info3(\\\" + line {}: parameter: {}, inputs: {}\\\".format(\\n line_pos, param, inputs))\\n if len(inputs) == 0:\\n state.add_error(\\n prog, \\\"dane_directory command given no input\\\")\\n elif len(inputs) > 1:\\n state.add_error(prog, \\\"dane_directory command given superfluous input: '{}'\\\".format(' '.join(inputs[1:])))\\n else:\\n prog.set_dane_directory(inputs[0])\\n\\n elif param == \\\"letsencrypt_directory\\\":\\n prog.log.info3(\\\" + line {}: parameter: {}, inputs: {}\\\".format(\\n line_pos, param, inputs))\\n if len(inputs) == 0:\\n state.add_error(\\n prog, \\\"letsencrypt_directory command given no input\\\")\\n elif len(inputs) > 1:\\n state.add_error(prog, \\\"letsencrypt_directory command given superfluous input: '{}'\\\".format(' '.join(inputs[1:])))\\n else:\\n prog.set_letsencrypt_directory(inputs[0])\\n\\n elif param == \\\"log_level\\\":\\n prog.log.info3(\\\" + line {}: parameter: {}, inputs: {}\\\".format(\\n line_pos, param, inputs))\\n if len(inputs) == 0:\\n state.add_error(\\n prog, \\\"log_level command given no input\\\")\\n elif len(inputs) > 1:\\n state.add_error(prog, \\\"log_level command given superfluous input: '{}'\\\".format(' '.join(inputs[1:])))\\n else:\\n if inputs[0] not in [ 'no', 'normal', 'verbose', 'debug' ]:\\n state.add_error(prog, \\\"\\\")\\n continue\\n\\n log_level = inputs[0]\\n\\n elif param == \\\"ttl\\\":\\n prog.log.info3(\\\" + line {}: parameter: {}, inputs: {}\\\".format(\\n line_pos, param, inputs))\\n if len(inputs) == 0:\\n state.add_error(\\n prog, \\\"ttl command given no input\\\")\\n elif len(inputs) > 1:\\n state.add_error(prog, \\\"ttl command given superfluous input: '{}'\\\".format(' '.join(inputs[1:])))\\n else:\\n try:\\n import alnitak.parser\\n # python 3.4: 'from alnitak import parser' will\\n # cause an error because of circular imports.\\n # Importing like this will work\\n ttl_value = alnitak.parser.ttl_check(prog, 0, 'config', inputs[0])\\n except Except.Error1013:\\n state.add_error(prog, \\\"ttl value '{}' not an integer\\\".format(inputs[0]))\\n continue\\n except Except.Error1100 as ex:\\n state.add_error(prog, \\\"ttl value '{}' exceeds maximum value of '{}'\\\".format(inputs[0], ex.max))\\n continue\\n except Except.Error1101 as ex:\\n state.add_error(prog, \\\"ttl value '{}' less than minimum value of '{}'\\\".format(inputs[0], ex.min))\\n continue\\n\\n prog.set_ttl(ttl_value)\\n\\n\\n else:\\n state.add_error(prog,\\n \\\"unrecognized parameter '{}'\\\".format(param))\\n\\n continue\\n\\n # matches empty line\\n match = re.match(r'^\\\\s*(#.*)?$', l)\\n if match:\\n continue\\n\\n if len(l) > 23:\\n state.add_error(prog,\\n \\\"unrecognized command: '{}...'\\\".format(l[:20]))\\n else:\\n state.add_error(prog, \\\"unrecognized command: '{}'\\\".format(l))\\n\\n\\n state.lineno = None\\n for t in prog.target_list:\\n if not t.tlsa:\\n state.add_error(\\n prog, \\\"target '{}' has no tlsa record\\\".format(t.domain))\\n if not t.api:\\n state.add_error(\\n prog, \\\"target '{}' has no api scheme\\\".format(t.domain))\\n\\n if state.errors:\\n return Prog.RetVal.config_failure\\n\\n prog.log.info3(\\\"+++ targets...\\\")\\n if prog.target_list:\\n for t in prog.target_list:\\n prog.log.info3(str(t))\\n else:\\n prog.log.info3(\\\" + no targets found\\\")\\n prog.log.error(\\\"config file: no targets given\\\")\\n return Prog.RetVal.config_failure\\n\\n # set the delayed log level. We don't do this straight away or else we\\n # will have mixed logging for this function itself.\\n if log_level:\\n prog.set_log_level(log_level)\\n\\n return Prog.RetVal.ok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6137123851bb0ced08b10e8149dff4f7\",\n \"score\": \"0.4812785\",\n \"text\": \"def test_config_load_multiple_configs(rule_manager, tmp_path, capsys):\\n\\n config1 = {}\\n config1['rules'] = []\\n config1['rules'].append({\\n 'name': 'rulepack1.printFilename'\\n })\\n config1['rules'].append({\\n 'name': 'rulepack1.findSingleLineCommentsWith',\\n 'settings': {'with_string' : 'the'}\\n })\\n\\n config1_file = tmp_path / \\\"config1.json\\\"\\n with open(config1_file, 'w') as outfile:\\n json.dump(config1, outfile)\\n\\n\\n config2 = {}\\n config2['rules'] = []\\n config2['rules'].append({\\n 'name': 'rulepack1.printFilename'\\n })\\n config2['rules'].append({\\n 'name': 'rulepack1.findSingleLineCommentsWith',\\n 'settings': {'with_string' : 'the'}\\n })\\n config2['rules'].append({\\n 'name': 'rulepack1.findSingleLineCommentsWith',\\n 'settings': {'with_string' : 'different'}\\n })\\n config2['rules'].append({\\n 'name': 'rulepack1.printLanguage'\\n })\\n\\n config2_file = tmp_path / \\\"config2.json\\\"\\n with open(config2_file, 'w') as outfile:\\n json.dump(config2, outfile)\\n\\n rule_manager.load_rules([str(config1_file), str(config2_file)], ['./tests'])\\n\\n captured = capsys.readouterr()\\n assert \\\"Could not load rule\\\" not in captured.out\\n assert 'rulepack1.findSingleLineCommentsWith' in rule_manager._rules_dict\\n assert len(rule_manager._rules_dict['rulepack1.findSingleLineCommentsWith']) == 2\\n assert 'rulepack1.printFilename' in rule_manager._rules_dict\\n assert len(rule_manager._rules_dict['rulepack1.printFilename']) == 1\\n assert 'rulepack1.printLanguage' in rule_manager._rules_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533d6192d1391336843ee09221a32ee2\",\n \"score\": \"0.48095852\",\n \"text\": \"def build_cfg(noop: bool) -> cconfig.Config:\\n return cconfig.Config(\\n basedir='/base',\\n subdir='sub',\\n baseurl='/repo',\\n branches_file='/conf/test-branches.yaml',\\n noop=noop,\\n series='weird',\\n space='outer',\\n repo_auth='jrl:secret',\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49dde00206678594c48413f52c51d290\",\n \"score\": \"0.48072264\",\n \"text\": \"def create_target_config():\\n print (\\\"Creating target config dir\\\")\\n input_data = target_host1\\n app_data = {'mode': 'target'}\\n output_data = {'file': 'events.log'}\\n create_json_file(\\\"./target\\\", input_data, \\\"inputs.json\\\")\\n create_json_file(\\\"./target\\\", app_data, \\\"app.json\\\")\\n create_json_file(\\\"./target\\\", output_data, \\\"outputs.json\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d4ddfc1a1e777728ce5588568424b4\",\n \"score\": \"0.4802051\",\n \"text\": \"def va_config_routes(self):\\r\\n logger.debug(\\\"Start to config route\\\")\\r\\n route_info = self.route_info\\r\\n\\r\\n for key in route_info.keys() :\\r\\n\\r\\n devobj = self.testdata.va_get_by_uniq_id(key,False)\\r\\n routes = route_info[key]\\r\\n node_type = devobj.get_nodetype()\\r\\n if (node_type == 'linux' or node_type == 'dir') :\\r\\n #only support linux pc and director now.\\r\\n devobj = self.testdata.va_get_by_uniq_id(key)\\r\\n for route in routes :\\r\\n if (not 'auto_set' in route) or (route['auto_set'] != 1):\\r\\n continue\\r\\n else:\\r\\n # if auto_set =1 , it means need to config route for defined pc\\r\\n if node_type == 'linux':\\r\\n result = devobj.config_route(route['dst'],route['netmask'],route['gateway'])\\r\\n\\r\\n if node_type == 'dir':\\r\\n #address netmask format.\\r\\n if isinstance(route['netmask'],int) :\\r\\n address = route['dst']+ '/' +route['netmask']\\r\\n else :\\r\\n address = route['dst'] + '/' + route['netmask']\\r\\n result = devobj.va_set_route(ipaddress.IPv4Network(address), route['gateway'],True)\\r\\n\\r\\n if not result :\\r\\n logger.error('Failed to config route')\\r\\n logger.debug(devobj.show_route())\\r\\n logger.debug(devobj.show_interface())\\r\\n return False\\r\\n\\r\\n logger.info(\\\"Completed to config route\\\")\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c09b8d7e0e585250c8821646300f60a\",\n \"score\": \"0.48007068\",\n \"text\": \"def getCommonConf(opts, config):\\n\\n if not opts:\\n raise ProgrammingError(\\\"opts is None\\\")\\n if not config:\\n raise ProgrammingError(\\\"config is None\\\")\\n \\n polltime = POLL_DELAY_SECONDS_DEFAULT\\n if opts.polltime:\\n polltime = opts.polltime\\n \\n try:\\n sshdkeypath = config.get(\\\"sshd\\\", \\\"generatedkey\\\")\\n hostbasedconfig = config.get(\\\"sshd\\\", \\\"hostbasedconfig\\\")\\n knownhostsconfig = config.get(\\\"sshd\\\", \\\"knownhostsconfig\\\")\\n scratchdir = config.get(\\\"ctxservice\\\", \\\"scratchspacedir\\\")\\n retr_template = config.get(\\\"ctxservice\\\", \\\"retr_template\\\")\\n retr_template2 = config.get(\\\"ctxservice\\\", \\\"retr_template2\\\")\\n err_template = config.get(\\\"ctxservice\\\", \\\"err_template\\\")\\n err_template2 = config.get(\\\"ctxservice\\\", \\\"err_template2\\\")\\n ok_template = config.get(\\\"ctxservice\\\", \\\"ok_template\\\")\\n ok_template2 = config.get(\\\"ctxservice\\\", \\\"ok_template2\\\")\\n ipandhostdir = config.get(\\\"taskpaths\\\", \\\"ipandhostdir\\\")\\n restartdir = config.get(\\\"taskpaths\\\", \\\"restartdir\\\")\\n thishostdir = config.get(\\\"taskpaths\\\", \\\"thishostdir\\\")\\n thishostfinalizedir = config.get(\\\"taskpaths\\\", \\\"thishostfinalizedir\\\")\\n logfilepath = config.get(\\\"ctxservice\\\", \\\"logfilepath\\\")\\n curl = config.get(\\\"systempaths\\\", \\\"curl\\\")\\n hostname = config.get(\\\"systempaths\\\", \\\"hostname\\\")\\n datadir = config.get(\\\"taskpaths\\\", \\\"datadir\\\")\\n etchosts_exe = config.get(\\\"taskpaths\\\", \\\"etchosts\\\")\\n except:\\n exception_type = sys.exc_type\\n try:\\n exceptname = exception_type.__name__ \\n except AttributeError:\\n exceptname = exception_type\\n msg = \\\"%s: %s\\\" % (str(exceptname), str(sys.exc_value))\\n raise InvalidConfig(msg)\\n \\n # no evaluate yet, pass False for now\\n return CommonConf(opts.trace, False, ipandhostdir, restartdir, polltime, sshdkeypath, scratchdir, retr_template, retr_template2, err_template, err_template2, ok_template, ok_template2, hostbasedconfig, knownhostsconfig, thishostdir, thishostfinalizedir, logfilepath, curl, hostname, datadir, etchosts_exe)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a10b0ad170662478202a55e64ea83006\",\n \"score\": \"0.47946462\",\n \"text\": \"def _configure(self):\\n # reset group_inputs back to what it was just after self.setup() in case _configure\\n # is called multiple times.\\n self._group_inputs = self._pre_config_group_inputs.copy()\\n for n, lst in self._group_inputs.items():\\n self._group_inputs[n] = lst.copy()\\n\\n for subsys in self._subsystems_myproc:\\n subsys._configure()\\n subsys._setup_var_data()\\n\\n self._has_guess |= subsys._has_guess\\n self._has_bounds |= subsys._has_bounds\\n self.matrix_free |= subsys.matrix_free\\n\\n conf_info = self._problem_meta['config_info']\\n conf_info._reset()\\n\\n self._problem_meta['setup_status'] = _SetupStatus.POST_CONFIGURE\\n self.configure()\\n\\n # if our configure() has added or promoted any variables, we have to call\\n # _setup_var_data again on any modified systems and their ancestors (only those that\\n # are our descendents).\\n for s in conf_info._modified_system_iter(self):\\n s._setup_var_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6c63e36ea4219369653d5a307e99f78\",\n \"score\": \"0.47769693\",\n \"text\": \"def read_config(config_data):\\n out_lines = []\\n df = io.StringIO(config_data.decode('utf-8'))\\n odf = io.StringIO()\\n in_comment = False\\n for line in df.readlines():\\n # ack, multiline comments in /objects/generic/statuspod/statuspod.object\\n # also in /objects/ancient/hologramgalaxy/hologramgalaxy.object, and\\n # unfortunately that one necessitates some stripping (though stripping\\n # is no more CPU-intensive than hardcoding the few instances)\\n if line.lstrip()[:2] == '/*':\\n if line.rstrip()[-2:] != '*/':\\n in_comment = True\\n else:\\n if in_comment:\\n if line.lstrip()[:2] == '*/':\\n in_comment = False\\n else:\\n idx = line.find('//')\\n if idx == -1:\\n print(line, file=odf)\\n else:\\n print(line[0:idx], file=odf)\\n\\n # This list of patterns allows us to load all the data we care about\\n # (that I'm aware of anyway) but I've moved to just stripping out\\n # anything after // automatically. That shaves about a second off of\\n # our startup time. Doubtless there are image processing speedups\\n # which would probably account for the majority of the loadtime)\\n #elif line[:3] != '// ':\\n # found_pattern = False\\n # for pattern in [\\n # ' // ',\\n # # special case for /objects/biome/foundry/lavatanklarge/lavatanklarge.object\\n # '//FIRE',\\n # # special cases for /objects/biome/tentacle/tentaclespawner1/tentaclespawner1.object\\n # '//type',\\n # '//additional',\\n # '//relative',\\n # '//[x,y] size',\\n # '//total',\\n # # special case for /objects/avian/sawblade/sawblade.object\\n # '//mollys',\\n # # special case for /objects/avian/birdgroundlantern/birdgroundlantern.object\\n # '//\\\"interactive\\\"',\\n # # special cases for /objects/outpost/signstore/signdispenser.object\\n # '//\\\"openSounds\\\"',\\n # '//\\\"closeSounds\\\"',\\n # # special case for /objects/glitch/medievalspikes/medievalspikes.object\\n # '//TODO',\\n # # special case for /objects/themed/island/islandhammock/islandhammock.object\\n # '//\\\"sitCoverImage\\\"',\\n # # special case for /objects/protectorate/objects/protectoratewindbanner3/protectoratewindbanner3.object\\n # '//\\\"soundEffect\\\"',\\n # # special cases for /objects/protectorate/objects/protectoratelobbyvending/protectoratelobbyvending.object\\n # '//\\\"onSound\\\"',\\n # '//\\\"offSound\\\"',\\n # # special case for /objects/spawner/spawners/spawner_human.object\\n # '//6000,',\\n # # special cases for /objects/spawner/colonydeed/colonydeed.object\\n # '//whether',\\n # '//delay',\\n # '//cooldown',\\n # '//scan',\\n # '//length',\\n # '//seconds',\\n # # special cases for /objects/spawner/invisiblemonsterspawner.object\\n # '//level',\\n # '//options',\\n # '//only',\\n # # special case for /objects/crafting/upgradeablecraftingobjects/craftingwheel/craftingwheel.object\\n # '//this',\\n # ]:\\n # idx = line.find(pattern)\\n # if idx != -1:\\n # found_pattern = True\\n # break\\n # if found_pattern:\\n # print(line[0:idx], file=odf)\\n # else:\\n # print(line, file=odf)\\n odf.seek(0)\\n return json.load(odf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25b08aa81b669ada92f82d37aa4524fc\",\n \"score\": \"0.47752577\",\n \"text\": \"def create_expected_configs(self, user):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f8bee1aa4b6247584816b0ef1708a09\",\n \"score\": \"0.47747043\",\n \"text\": \"def _buildxml(self, namesonly=False):\\n xpath = self._data_dict[self._type]\\n self._get_xpath = \\\"//configuration/\\\" + xpath\\n top = E(\\\"configuration\\\")\\n dot = top\\n for name in xpath.split(\\\"/\\\"):\\n dot.append(E(name))\\n dot = dot[0]\\n\\n if namesonly is True:\\n dot.attrib[\\\"recurse\\\"] = \\\"false\\\"\\n return top\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b667672d18243e5b230c3994d9067eb8\",\n \"score\": \"0.47745883\",\n \"text\": \"def _prepare_from_cfg(self, model, config: Dict):\\n assert isinstance(self.channel_unit_cfg, dict)\\n assert 'units' in self.channel_unit_cfg\\n config = self.channel_unit_cfg['units']\\n if isinstance(config, str):\\n config = fileio.load(config)\\n assert isinstance(config, dict)\\n\\n if 'Analyzer' in self.parse_cfg['type']:\\n self.parse_cfg.pop('from_cfg')\\n tracer = TASK_UTILS.build(self.parse_cfg)\\n unit_configs = tracer.analyze(model)\\n\\n units = []\\n for unit_key in config:\\n init_args = copy.deepcopy(self.unit_default_args)\\n if 'init_args' in config[unit_key]:\\n init_args.update(config[unit_key]['init_args'])\\n config[unit_key]['init_args'] = init_args\\n if 'channels' in config[unit_key]:\\n unit = self.unit_class.init_from_cfg(model, config[unit_key])\\n unit.name = unit_key\\n else:\\n try:\\n unit = self._prepare_unit_from_init_cfg(\\n model, config[unit_key], unit_configs[unit_key])\\n except ValueError:\\n raise ValueError(\\n 'Initializing channel_mutator from the config needs'\\n 'to include `channels` or `Analyzer` in the config.')\\n units.append(unit)\\n return units\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39575d462080125595dc243c5961e201\",\n \"score\": \"0.4771972\",\n \"text\": \"def convert_area(config: dict[str, Any]) -> dict[str, Any]:\\n my_map = {\\n CONF_NAME: dyn_const.CONF_NAME,\\n CONF_FADE: dyn_const.CONF_FADE,\\n CONF_NO_DEFAULT: dyn_const.CONF_NO_DEFAULT,\\n CONF_ROOM_ON: dyn_const.CONF_ROOM_ON,\\n CONF_ROOM_OFF: dyn_const.CONF_ROOM_OFF,\\n CONF_CHANNEL_COVER: dyn_const.CONF_CHANNEL_COVER,\\n CONF_DEVICE_CLASS: dyn_const.CONF_DEVICE_CLASS,\\n CONF_OPEN_PRESET: dyn_const.CONF_OPEN_PRESET,\\n CONF_CLOSE_PRESET: dyn_const.CONF_CLOSE_PRESET,\\n CONF_STOP_PRESET: dyn_const.CONF_STOP_PRESET,\\n CONF_DURATION: dyn_const.CONF_DURATION,\\n CONF_TILT_TIME: dyn_const.CONF_TILT_TIME,\\n }\\n result = convert_with_map(config, my_map)\\n if CONF_CHANNEL in config:\\n result[dyn_const.CONF_CHANNEL] = {\\n channel: convert_channel(channel_conf)\\n for (channel, channel_conf) in config[CONF_CHANNEL].items()\\n }\\n if CONF_PRESET in config:\\n result[dyn_const.CONF_PRESET] = {\\n preset: convert_preset(preset_conf)\\n for (preset, preset_conf) in config[CONF_PRESET].items()\\n }\\n if CONF_TEMPLATE in config:\\n result[dyn_const.CONF_TEMPLATE] = TEMPLATE_MAP[config[CONF_TEMPLATE]]\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8820ca0f214d75bd13acc80a805e32ba\",\n \"score\": \"0.4770118\",\n \"text\": \"def setup_directories(self, config):\\n # self.input_dir = os.path.join(config['run_directory'], 'HYDRO_IN')\\n # self.output_dir = os.path.join(config['run_directory'], 'HYDRO_OUTPUT')\\n self.input_dir = os.path.join(\\\"..\\\", \\\"HYDRO_IN\\\")\\n self.output_dir = os.path.join(\\\"..\\\", \\\"HYDRO_OUTPUT\\\")\\n if os.path.exists(self.input_dir) is False:\\n os.mkdir(self.input_dir, 0o755)\\n if os.path.exists(self.output_dir) is False:\\n os.mkdir(self.output_dir, 0o755)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"583e222f8bd286553c858d1efc3be5bd\",\n \"score\": \"0.4767908\",\n \"text\": \"def convert_config(\\n config: dict[str, Any] | MappingProxyType[str, Any]\\n) -> dict[str, Any]:\\n my_map = {\\n CONF_NAME: dyn_const.CONF_NAME,\\n CONF_HOST: dyn_const.CONF_HOST,\\n CONF_PORT: dyn_const.CONF_PORT,\\n CONF_AUTO_DISCOVER: dyn_const.CONF_AUTO_DISCOVER,\\n CONF_POLL_TIMER: dyn_const.CONF_POLL_TIMER,\\n }\\n result = convert_with_map(config, my_map)\\n if CONF_AREA in config:\\n result[dyn_const.CONF_AREA] = {\\n area: convert_area(area_conf)\\n for (area, area_conf) in config[CONF_AREA].items()\\n }\\n if CONF_DEFAULT in config:\\n result[dyn_const.CONF_DEFAULT] = convert_default(config[CONF_DEFAULT])\\n if CONF_ACTIVE in config:\\n result[dyn_const.CONF_ACTIVE] = ACTIVE_MAP[config[CONF_ACTIVE]]\\n if CONF_PRESET in config:\\n result[dyn_const.CONF_PRESET] = {\\n preset: convert_preset(preset_conf)\\n for (preset, preset_conf) in config[CONF_PRESET].items()\\n }\\n if CONF_TEMPLATE in config:\\n result[dyn_const.CONF_TEMPLATE] = {\\n TEMPLATE_MAP[template]: convert_template(template_conf)\\n for (template, template_conf) in config[CONF_TEMPLATE].items()\\n }\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d61a5423a7361acd85da28d777ddbf9\",\n \"score\": \"0.47649974\",\n \"text\": \"def __configure(self):\\n\\n # load the core configuration\\n self.context = zope.configuration.xmlconfig.file(self.confFile,\\n execute=False)\\n\\n # load extensions and plugins\\n for epath in p6.app.extension.extPaths():\\n for extconf in p6.app.extension.extConfs(epath):\\n print extconf\\n p6.app.extension.loadExtension(extconf, self.context)\\n\\n # perform the actions specified by the configuration files\\n self.context.execute_actions()\\n\\n # expand any metadata page groups\\n newpages = []\\n for page in self.pages:\\n if getattr(page, 'expand', False):\\n newpages = newpages + [n for n in page(None)]\\n else:\\n newpages.append(page)\\n\\n self.pages = newpages\\n del newpages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"209e4332bddf24f6df92200144c582c9\",\n \"score\": \"0.47578472\",\n \"text\": \"def convert_preset(config: dict[str, Any]) -> dict[str, Any]:\\n my_map = {\\n CONF_NAME: dyn_const.CONF_NAME,\\n CONF_FADE: dyn_const.CONF_FADE,\\n CONF_LEVEL: dyn_const.CONF_LEVEL,\\n }\\n return convert_with_map(config, my_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9380a7021e055edbfc46110674f09a0\",\n \"score\": \"0.47564927\",\n \"text\": \"def customize_process(group_tree, groupvars, group_hostnames, hostvars, specific_vars):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fceee2e3bd77da8acbbc1503cdb5530e\",\n \"score\": \"0.47562543\",\n \"text\": \"def _propagate_to_ctg(meta, new_cycle_acls):\\n print \\\"Propagating roles to cycle task groups\\\"\\n new_ctg_acls = _propagate_to_children(\\n meta,\\n new_cycle_acls,\\n \\\"CycleTaskGroup\\\",\\n \\\"cycle_id\\\",\\n \\\"Cycle\\\",\\n )\\n\\n _propagate_to_cycle_tasks(meta, new_ctg_acls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94e539f5279a0065475a320f1cbdcb7a\",\n \"score\": \"0.47546867\",\n \"text\": \"def from_yaml_tree_transform(self, node, tag, ctx):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1551c75fe2645763cd4a9141653b99cc\",\n \"score\": \"0.47540516\",\n \"text\": \"def run_rendering(self):\\n \\n if not isinstance(self.config, list):\\n # If the config was not a list, just convert this one element into a list\\n self.config = [self.config]\\n\\n for i, c in enumerate(self.config):\\n # For each conversion\\n if not 'data' in c:\\n # Check that the yaml resume file is specified\\n error(\\\"Configuration file has not defined 'data' with resume yaml file\\\")\\n else:\\n with open(c['data']) as resume_file:\\n self.resume = yaml.load(resume_file)\\n\\n for output in c['outputs']:\\n fmt = output['format']\\n # Check that we have a plugin whose classname starts with this format\\n assert any([x.startswith(fmt) for x in Plugin.registered])\\n template_file = output['template']\\n filebasename,filetype = os.path.splitext(template_file)\\n if filetype[1:] not in self.allowed_filetypes:\\n error(\\\"File type/extension %s is not one of following: %s\\\" % (filetype,' '.join(self.allowed_filetypes)))\\n output_filename = output['output']\\n # Instantiate the required conversion plugin\\n print (\\\"Creating %s ...\\\" % output_filename, end='')\\n text = Plugin.registered['%sResume' % fmt](template_file, self.resume, self.skip)\\n text.render(output_filename)\\n print (\\\" done\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e423b542d0f9c5ad93a7d5aa81f56bc\",\n \"score\": \"0.47520763\",\n \"text\": \"def load_config():\\n config = Config(\\\"config.yaml\\\")\\n # print(config)\\n class_setting = dict()\\n for item in config[\\\"dynamic\\\"][\\\"inherit\\\"].values():\\n cls_path = item[\\\"path\\\"]\\n del item[\\\"path\\\"]\\n class_setting[cls_path] = dict(item)\\n\\n return class_setting\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1afebd696a1ae7c310b440158b0c15a\",\n \"score\": \"0.4749547\",\n \"text\": \"def handle_config(cls, config, base_config):\\n if 'include' not in config:\\n config['include'] = []\\n elif isinstance(config['include'], str):\\n config['include'] = [config['include']]\\n for p, path in enumerate(config['include']):\\n if not os.path.isabs(path):\\n config['include'][p] = os.path.abspath(os.path.join(base_config['base_dir'], path))\\n load_includes(config['include'])\\n\\n defaults = {'runner': 'local', 'interface': 'memmap', 'custom': False}\\n for key, default in defaults.items():\\n if key not in config:\\n config[key] = default\\n\\n if not isinstance(config['runner'], MutableMapping):\\n config['runner'] = {'class': config['runner']}\\n Runner[config['runner']['class']].handle_subconfig(config['runner'], base_config)\\n if not isinstance(config['interface'], MutableMapping):\\n config['interface'] = {'class': config['interface']}\\n RunnerInterface[config['interface']['class']].handle_config(config['interface'], base_config)\\n Worker.handle_config(config, base_config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"949dc7b05142d1855fc7580314eb17c1\",\n \"score\": \"0.47479874\",\n \"text\": \"def normalize(conf):\\n\\n def expand(section_name):\\n section = conf.get(section_name)\\n if isinstance(section, string_types):\\n conf[section_name] = {\\n 'search': [section],\\n 'register': section,\\n 'publish': section\\n }\\n elif section:\\n search = section.get('search')\\n if search and not isinstance(search, list):\\n section['search'] = [search]\\n\\n expand('registry')\\n expand('ca')\\n\\n registry = conf.get('registry')\\n registry['search'] = map(lambda item: item.rstrip('/'), registry.get('search', []))\\n registry['register'] = registry.get('register', '').rstrip('/')\\n registry['publish'] = registry.get('publish', '').rstrip('/')\\n\\n conf['tmp'] = path.abspath(conf['tmp'])\\n return conf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8ad7d8cdbba28fac64b55cef09e59d6\",\n \"score\": \"0.47457963\",\n \"text\": \"def post_run_javac(self):\\n\\n\\tpar = {}\\n\\tfor x in self.inputs:\\n\\t\\tpar[x.parent.id] = x.parent\\n\\n\\tinner = {}\\n\\tfor k in par.values():\\n\\t\\tpath = k.abspath(self.env)\\n\\t\\tlst = os.listdir(path)\\n\\n\\t\\tfor u in lst:\\n\\t\\t\\tif u.find('$') >= 0:\\n\\t\\t\\t\\tinner_class_node = k.find_or_declare(u)\\n\\t\\t\\t\\tinner[inner_class_node.id] = inner_class_node\\n\\n\\tto_add = set(inner.keys()) - set([x.id for x in self.outputs])\\n\\tfor x in to_add:\\n\\t\\tself.outputs.append(inner[x])\\n\\n\\treturn Task.Task.post_run(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b4816f12318d09a78b0efb53530460f\",\n \"score\": \"0.4735967\",\n \"text\": \"def configure_device():\\n config = {}\\n\\n profile = device_config[\\\"sys\\\"][\\\"device\\\"].get(\\\"profile\\\", ATTR_SWITCH)\\n\\n for cover_id in range(covers):\\n topic, payload = get_cover(cover_id, profile)\\n config[topic] = payload\\n\\n for sensor, description in cover_sensors.items():\\n topic, payload = get_sensor(\\n sensor, description, profile=profile, cover_id=cover_id\\n )\\n config[topic] = payload\\n\\n for relay_id in range(relays):\\n consumption_types = [\\n item.lower()\\n for item in device_config[\\\"sys\\\"][\\\"ui_data\\\"].get(\\\"consumption_types\\\", [])\\n ]\\n relay_type = get_consumption_type(consumption_types, relay_id)\\n\\n topic, payload = get_switch(relay_id, relay_type, profile)\\n config[topic] = payload\\n\\n topic, payload = get_light(relay_id, relay_type, profile)\\n config[topic] = payload\\n\\n for sensor, description in relay_sensors.items():\\n topic, payload = get_sensor(\\n sensor, description, profile=profile, relay_id=relay_id\\n )\\n config[topic] = payload\\n\\n for binary_sensor, description in relay_binary_sensors.items():\\n topic, payload = get_binary_sensor(\\n binary_sensor, description, relay_id, profile=profile\\n )\\n config[topic] = payload\\n\\n for input_id in range(inputs):\\n input_type = device_config[f\\\"input:{input_id}\\\"][\\\"type\\\"]\\n\\n for event in input_events:\\n topic, payload = get_input(input_id, input_type, event)\\n config[topic] = payload\\n\\n for binary_sensor, description in input_binary_sensors.items():\\n topic, payload = get_binary_sensor(\\n binary_sensor,\\n description,\\n input_id,\\n is_input=True,\\n input_type=input_type,\\n )\\n config[topic] = payload\\n\\n for button, descripton in buttons.items():\\n topic, payload = get_button(button, descripton)\\n config[topic] = payload\\n\\n for sensor, description in sensors.items():\\n topic, payload = get_sensor(sensor, description)\\n config[topic] = payload\\n\\n for binary_sensor, description in binary_sensors.items():\\n topic, payload = get_binary_sensor(binary_sensor, description)\\n config[topic] = payload\\n\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53d6aaf01ff66b084ff75781667b1f44\",\n \"score\": \"0.4717589\",\n \"text\": \"def __init__(self,parser):\\n ## TODO: Initialization without parser (e.g. script example)\\n self.parser = parser\\n \\n ## set some vars\\n ################################################################\\n ## separators\\n self.separator = \\\"## -----------------------------------------------------------------------------\\\"\\n self.verboseseparator = \\\"## --Verbose--------------------------------------------------------------------\\\"\\n self.debugSeparator = \\\"## --Debug----------------------------------------------------------------------\\\"\\n \\n ## get options and args\\n ################################################################\\n (self.options, self.args) = parser.parse_args()\\n\\n ## Setup Logs\\n ################################################################\\n self.messages = {}\\n \\n ## main section inside configuration files\\n ################################################################\\n self.main_section = self.options.section\\n ## TODO: set in cfg-file or options or 'main'\\n self.sysclass_section = \\\"sysclass\\\"\\n\\n ## parse config files\\n ################################################################\\n self.config = self.options.config\\n ## find path\\n if self.config:\\n config_files = []\\n ## File/Path destination of the configuration\\n if not os.path.exists(self.config):\\n msg = str(self.config) + \\\" is not a file or path in current directory. Trying out script location.\\\"\\n self.add_message(msg, level='WARNING')\\n if self.config.startswith('./'):\\n self.config = os.path.dirname(os.path.abspath(sys.argv[0])) + '/' + self.config.lstrip('./')\\n if not os.path.exists(self.config):\\n msg = \\\"Sorry, no configuration file found! Use --no-config if you want to disable configfile usage\\\"\\n self.add_message(msg, level='ERROR')\\n raise Exception(msg)\\n\\n if os.path.isfile(self.config):\\n config_files = [self.config]\\n elif os.path.isdir(self.config):\\n for f in os.listdir(self.config):\\n if f.endswith(\\\".conf\\\"):\\n config_files.append(self.config.rstrip('/') + '/' + f)\\n else:\\n msg = \\\"Sorry, \\\" + str(self.config) + \\\" is neither a directory, nor a file!\\\"\\n self.add_message(msg, level='ERROR')\\n raise Exception(msg)\\n ## List of configuration files to be parsed\\n self.config_files = config_files\\n\\n ## ConfigParser object with parsed configuration\\n self.configuration = ConfigParser.ConfigParser()\\n self.configuration.read(self.config_files)\\n else:\\n self.add_message(\\\"Configfile functionality disabled!\\\", level='SYSINFO')\\n\\n \\n ## Set debugging and the verbosity\\n ################################################################\\n ## Get verbosity option\\n# if options.verbosity:\\n if ((self.options.verbosity) or\\n (self.config and\\n self.configuration.has_option(self.sysclass_section,'verbose') and\\n self.configuration.get(self.sysclass_section,'verbose') == 'True')):\\n self.verbose = True\\n else:\\n self.verbose = False\\n \\n \\n ## Get debugging option\\n# if options.debug:\\n if ((self.options.debug) or\\n (self.config and\\n self.configuration.has_option(self.sysclass_section,'debug') and\\n self.configuration.get(self.sysclass_section,'debug') == 'True')):\\n self.debug = True\\n self.verbose = True\\n else:\\n self.debug = False\\n if self.verbose:\\n self.BeVerbose(\\\"Getting Options:\\\\nVerbose:\\\\t\\\" + str(self.verbose) + \\\"\\\\nDebug:\\\\t\\\" + str(self.debug))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a27ef88bf7c868d002a1ecf0b90fd0c\",\n \"score\": \"0.4716479\",\n \"text\": \"def test_configuration(dumbalgo):\\n nested_algo = {\\\"DumbAlgo\\\": dict(value=6, scoring=5)}\\n algo = dumbalgo(8, value=1)\\n config = algo.configuration\\n assert config == {\\n \\\"dumbalgo\\\": {\\n \\\"seed\\\": None,\\n \\\"value\\\": 1,\\n \\\"scoring\\\": 0,\\n \\\"judgement\\\": None,\\n \\\"suspend\\\": False,\\n \\\"done\\\": False,\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5edd682a6eff9d939ae70cedd2f1a73\",\n \"score\": \"0.47078672\",\n \"text\": \"def _propagate_to_cte(meta, new_ct_acls):\\n print \\\"Propagating roles to cycle task entries\\\"\\n return _propagate_to_children(\\n meta,\\n new_ct_acls,\\n \\\"CycleTaskEntry\\\",\\n \\\"cycle_task_group_object_task_id\\\",\\n \\\"CycleTaskGroupObjectTask\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6449199d42453275cb4ca5819151be5\",\n \"score\": \"0.4705307\",\n \"text\": \"def _get_conv_configs(dtype_configs):\\n conv_configs = []\\n observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT\\n for convs in [_Conv1dMetadata, _Conv2dMetadata, _Conv3dMetadata]:\\n\\n # (1) Single conv modules/functions\\n # -----------------------------------\\n # conv module\\n conv_configs.append(\\n BackendPatternConfig(convs.root).set_observation_type(\\n observation_type) # noqa: E131\\n .set_dtype_configs(dtype_configs).set_root_module(\\n convs.root).set_reference_quantized_module(\\n convs.reference).set_qat_module(convs.qat))\\n # conv qat module\\n conv_configs.append(\\n BackendPatternConfig(convs.qat).set_observation_type(\\n observation_type) # noqa: E131\\n .set_dtype_configs(dtype_configs).set_root_module(\\n convs.root).set_reference_quantized_module(convs.reference))\\n # functional conv\\n conv_configs.append(\\n BackendPatternConfig(convs.func).set_observation_type(\\n observation_type) # noqa: E131\\n .set_dtype_configs(dtype_configs)._set_input_type_to_index({\\n 'weight':\\n 1,\\n 'bias':\\n 2\\n }))\\n\\n # (2) Conv + relu\\n # -----------------\\n # 2.1 conv module + relu fusion configs\\n # conv relu fusion, conv module + relu module\\n conv_configs.append(\\n BackendPatternConfig(\\n (torch.nn.ReLU,\\n convs.root)).set_dtype_configs(dtype_configs) # noqa: E131\\n .set_fuser_method(\\n reverse_sequential_wrapper2(\\n convs.fused_conv_relu)).set_fused_module(\\n convs.fused_conv_relu))\\n # conv relu fusion, conv module + functional relu\\n conv_configs.append(\\n BackendPatternConfig(\\n (F.relu,\\n convs.root)).set_dtype_configs(dtype_configs) # noqa: E131\\n .set_fuser_method(\\n reverse_sequential_wrapper2(\\n convs.fused_conv_relu)).set_fused_module(\\n convs.fused_conv_relu))\\n # 2.2 conv module + relu fused module configs\\n # conv relu, fused module\\n conv_configs.append(\\n BackendPatternConfig(convs.fused_conv_relu).set_observation_type(\\n observation_type) # noqa: E131\\n .set_dtype_configs(dtype_configs).set_root_module(\\n convs.root).set_reference_quantized_module(\\n convs.reference).set_qat_module(convs.relu_qat))\\n # conv relu, qat fused module\\n conv_configs.append(\\n BackendPatternConfig(convs.relu_qat).set_observation_type(\\n observation_type) # noqa: E131\\n .set_dtype_configs(dtype_configs).set_root_module(\\n convs.root).set_reference_quantized_module(convs.reference))\\n # 2.3 functional conv + relu configs\\n # conv relu, functional conv + relu module\\n conv_configs.append(\\n BackendPatternConfig(\\n (torch.nn.ReLU, convs.func)).set_observation_type(\\n observation_type) # noqa: E131\\n .set_dtype_configs(dtype_configs))\\n # conv relu, functional conv + functional relu\\n conv_configs.append(\\n BackendPatternConfig((F.relu, convs.func)).set_observation_type(\\n observation_type) # noqa: E131\\n .set_dtype_configs(dtype_configs))\\n\\n # fused conv relu\\n conv_configs.append(\\n BackendPatternConfig(convs.fused_conv_relu).set_dtype_configs(\\n dtype_configs) # noqa: E131\\n .set_qat_module(convs.relu_qat))\\n\\n conv_configs.append(\\n BackendPatternConfig(convs.relu_qat).set_dtype_configs(\\n dtype_configs) # noqa: E131\\n .set_root_module(convs.root).set_reference_quantized_module(\\n convs.reference))\\n\\n # (3) Conv + batchnorm (+ relu)\\n # -------------------------------\\n # 3.1 conv bn fusion configs\\n # conv + bn fusion\\n conv_configs.append(\\n BackendPatternConfig(\\n (convs.bn,\\n convs.root)).set_dtype_configs(dtype_configs) # noqa: E131\\n .set_fuser_method(reverse2(fuse_conv_bn)).set_fused_module(\\n convs.fused_conv_bn))\\n # conv + bn + relu module fusion\\n conv_configs.append(\\n BackendPatternConfig(\\n (nn.ReLU,\\n (convs.bn,\\n convs.root))).set_dtype_configs(dtype_configs) # noqa: E131\\n .set_fuser_method(reverse3(fuse_conv_bn_relu)).set_fused_module(\\n convs.fused_conv_bn_relu))\\n # conv + bn + relu functional fusion\\n conv_configs.append(\\n BackendPatternConfig(\\n (F.relu,\\n (convs.bn,\\n convs.root))).set_dtype_configs(dtype_configs) # noqa: E131\\n .set_root_module(convs.root).set_fuser_method(\\n reverse3(fuse_conv_bn_relu)).set_fused_module(\\n convs.fused_conv_bn_relu))\\n # TODO: we can add fusion for torch.relu as well\\n\\n # 3.2 conv + bn (+ relu) fused module configs\\n # fused conv bn\\n conv_configs.append(\\n BackendPatternConfig(convs.fused_conv_bn).set_dtype_configs(\\n dtype_configs) # noqa: E131\\n .set_qat_module(convs.bn_qat))\\n\\n # fused conv bn relu\\n conv_configs.append(\\n BackendPatternConfig(convs.fused_conv_bn_relu).set_dtype_configs(\\n dtype_configs) # noqa: E131\\n .set_qat_module(convs.bn_relu_qat))\\n\\n # conv bn, qat fused module\\n conv_configs.append(\\n BackendPatternConfig(convs.bn_qat).set_observation_type(\\n observation_type) # noqa: E131\\n .set_dtype_configs(dtype_configs).set_root_module(\\n convs.root).set_reference_quantized_module(convs.reference))\\n # conv bn relu, qat fused module\\n conv_configs.append(\\n BackendPatternConfig(convs.bn_relu_qat).set_observation_type(\\n observation_type) # noqa: E131\\n .set_dtype_configs(dtype_configs).set_root_module(\\n convs.root).set_reference_quantized_module(convs.reference))\\n\\n # (4) conv transpose and its fusion\\n # 4.1 conv transpose config\\n conv_configs.append(\\n BackendPatternConfig(convs.transpose).set_dtype_configs(\\n dtype_configs) # noqa: E131\\n .set_root_module(convs.transpose).set_reference_quantized_module(\\n convs.transpose_reference))\\n\\n # 4.2 conv transpose + bn fusion\\n conv_configs.append(\\n BackendPatternConfig(\\n (convs.bn, convs.transpose)).set_dtype_configs(\\n dtype_configs) # noqa: E131\\n .set_fuser_method(reverse2(fuse_convtranspose_bn)).set_root_module(\\n convs.transpose).set_reference_quantized_module(\\n convs.transpose_reference))\\n\\n return conv_configs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1caa3b30e9e06bff1c9c8184508bb5ff\",\n \"score\": \"0.47014508\",\n \"text\": \"def _state_replaced(self, want, have):\\n\\n commands = []\\n\\n # Drill each iteration of want n have and then based on dest and afi tyoe comparison take config call\\n for w in want:\\n for addr_want in w.get(\\\"address_families\\\"):\\n for route_want in addr_want.get(\\\"routes\\\"):\\n check = False\\n for h in have:\\n if h.get(\\\"address_families\\\"):\\n for addr_have in h.get(\\\"address_families\\\"):\\n for route_have in addr_have.get(\\\"routes\\\"):\\n if (\\n route_want.get(\\\"dest\\\")\\n == route_have.get(\\\"dest\\\")\\n and addr_want[\\\"afi\\\"]\\n == addr_have[\\\"afi\\\"]\\n ):\\n check = True\\n have_set = set()\\n new_hops = []\\n for each in route_want.get(\\n \\\"next_hops\\\"\\n ):\\n want_set = set()\\n new_dict_to_set(\\n each, [], want_set, 0\\n )\\n new_hops.append(want_set)\\n new_dict_to_set(\\n addr_have, [], have_set, 0\\n )\\n # Check if the have dict next_hops value is diff from want dict next_hops\\n have_dict = filter_dict_having_none_value(\\n route_want.get(\\\"next_hops\\\")[0],\\n route_have.get(\\\"next_hops\\\")[0],\\n )\\n # update the have_dict with forward_router_address\\n have_dict.update(\\n {\\n \\\"forward_router_address\\\": route_have.get(\\n \\\"next_hops\\\"\\n )[\\n 0\\n ].get(\\n \\\"forward_router_address\\\"\\n )\\n }\\n )\\n # updating the have_dict with next_hops val that's not None\\n new_have_dict = {}\\n for k, v in have_dict.items():\\n if v is not None:\\n new_have_dict.update({k: v})\\n\\n # Set the new config from the user provided want config\\n cmd = self._set_config(\\n w,\\n h,\\n addr_want,\\n route_want,\\n route_have,\\n new_hops,\\n have_set,\\n )\\n\\n if cmd:\\n # since inplace update isn't allowed for static routes, preconfigured\\n # static routes needs to be deleted before the new want static routes changes\\n # are applied\\n clear_route_have = copy.deepcopy(\\n route_have\\n )\\n # inplace update is allowed in case of ipv6 static routes, so not deleting it\\n # before applying the want changes\\n if \\\":\\\" not in route_want.get(\\n \\\"dest\\\"\\n ):\\n commands.extend(\\n self._clear_config(\\n {},\\n h,\\n {},\\n addr_have,\\n {},\\n clear_route_have,\\n )\\n )\\n commands.extend(cmd)\\n if check:\\n break\\n if check:\\n break\\n if not check:\\n # For configuring any non-existing want config\\n new_hops = []\\n for each in route_want.get(\\\"next_hops\\\"):\\n want_set = set()\\n new_dict_to_set(each, [], want_set, 0)\\n new_hops.append(want_set)\\n commands.extend(\\n self._set_config(\\n w,\\n {},\\n addr_want,\\n route_want,\\n {},\\n new_hops,\\n set(),\\n )\\n )\\n commands = [each for each in commands if \\\"no\\\" in each] + [\\n each for each in commands if \\\"no\\\" not in each\\n ]\\n\\n return commands\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e02a8d145f05b26953933f17729d4b01\",\n \"score\": \"0.46912372\",\n \"text\": \"def start(self):\\n\\n # tenants is the main structure to be converted into zuul main.yaml\\n tenants = {}\\n # projects_list is the list of projects used to check for conflicts\\n projects_list = {}\\n tenant_resources_cache = {}\\n\\n auth_rules = {}\\n\\n for tenant_name, tenant_conf in self.main_resources.get(\\n \\\"resources\\\", {}).get(\\\"tenants\\\", {}).items():\\n\\n # When a tenant_resources already exists\\n # It is only set if we loaded tenant resources from\\n # a config repo copy (eg. config-check)\\n if self.tenant_resources:\\n # We only proceed when the args gateway_url\\n # match the tenant url\\n if tenant_conf['url'] != '%s/manage' % (\\n self.gateway_url.rstrip('/')):\\n continue\\n\\n self.log.debug(\\n \\\"--[ Processing %s - %s\\\" % (tenant_name, tenant_conf))\\n\\n # First we look for the tenant resources\\n if tenant_name != self.default_tenant_name and \\\\\\n tenant_conf[\\\"url\\\"] != self.main_resources[\\\"public-url\\\"]:\\n # check for v2\\n tenant_url = tenant_conf['url'].rstrip('/')\\n if not tenant_url.endswith('/v2'):\\n tenant_url = os.path.join(tenant_url, \\\"v2\\\")\\n url = os.path.join(tenant_url, 'resources')\\n if self.tenant_resources:\\n self.log.debug(\\\"%s: loading resources from workspace\\\",\\n tenant_name)\\n tenant_resources = self.tenant_resources\\n else:\\n self.log.debug(\\\"%s: loading resources %s\\\",\\n tenant_name, url)\\n tenant_resources = get_resources(url)\\n else:\\n tenant_resources = self.main_resources\\n # Fallback to default_tenant_name tenant default connection\\n if not tenant_conf.get(\\\"default-connection\\\"):\\n tenant_conf[\\\"default-connection\\\"] = self.main_resources[\\n \\\"resources\\\"][\\\"tenants\\\"][self.default_tenant_name][\\n \\\"default-connection\\\"]\\n\\n tenant_resources_cache[tenant_name] = tenant_resources\\n\\n if not self.utests:\\n # Then we pull tenant config repository for legacy zuul\\n # flat files\\n path = self.fetch_git_repo(\\n tenant_name, tenant_resources[\\\"config-repo\\\"],\\n self.cache_dir)\\n tenants_dir = os.path.join(path, 'zuul')\\n if not os.path.isdir(tenants_dir):\\n continue\\n tenants_conf_files = self.discover_yaml_files(tenants_dir)\\n # And we load flat files\\n self.merge_tenant_from_files(\\n tenants, auth_rules, tenants_conf_files,\\n tenant_name, projects_list)\\n\\n # We load project from the resources\\n default_conn = tenant_conf[\\\"default-connection\\\"]\\n self.merge_tenant_from_resources(\\n tenants, tenant_resources, tenant_name, projects_list,\\n self.main_resources, default_conn, tenant_conf)\\n\\n for tenant_name, tenant_conf in self.main_resources.get(\\n \\\"resources\\\", {}).get(\\\"tenants\\\", {}).items():\\n\\n tenant_resources = tenant_resources_cache.get(tenant_name)\\n if not tenant_resources:\\n continue\\n\\n default_conn = tenant_conf[\\\"default-connection\\\"]\\n\\n # Finally we add Repos not listed in sr with an include: [] to Zuul\\n skip_missing_resources = False\\n if tenant_conf[\\\"url\\\"] == self.main_resources[\\\"public-url\\\"]:\\n if tenant_name != self.default_tenant_name:\\n # We only add local missing resources to the\\n # default_tenant_name tenant\\n skip_missing_resources = True\\n # Check default_conn is a registered connection\\n if default_conn not in self.main_resources[\\n 'resources']['connections']:\\n # We cannot add repos to Zuul if no valid connection for\\n # that tenant\\n self.log.debug(\\n \\\"Skip adding missing repos. The tenant has an invalid\\\"\\n \\\" default connection: %s\\\" % default_conn)\\n continue\\n if not skip_missing_resources:\\n self.add_missing_repos(\\n tenants, tenant_resources, tenant_name, projects_list,\\n self.main_resources, default_conn)\\n\\n self.log.debug(\\\"]-- Finish processing %s\\\" % tenant_name)\\n\\n final_tenant_data = self.final_tenant_merge(tenants)\\n auth_rules_data = self.merge_auth_rules(auth_rules)\\n final_data = auth_rules_data + final_tenant_data\\n return yaml.safe_dump(final_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e24f6f8a3a25a8ed0cab7fc4b0ca09e8\",\n \"score\": \"0.46880388\",\n \"text\": \"def configure_common_directories(cls, tc_config_files):\\n if cls.config_directory is None:\\n # Get config directory from properties\\n config_directory = cls.get_configured_value('TOOLIUM_CONFIG_DIRECTORY',\\n tc_config_files.config_directory, 'conf')\\n prop_filenames = cls.get_configured_value('TOOLIUM_CONFIG_PROPERTIES_FILENAMES',\\n tc_config_files.config_properties_filenames, 'properties.cfg')\\n cls.config_directory = cls._find_parent_directory(config_directory, prop_filenames.split(';')[0])\\n\\n # Get output directory from properties and create it\\n cls.output_directory = cls.get_configured_value('TOOLIUM_OUTPUT_DIRECTORY',\\n tc_config_files.output_directory, 'output')\\n if not os.path.isabs(cls.output_directory):\\n # If output directory is relative, we use the same path as config directory\\n cls.output_directory = os.path.join(os.path.dirname(cls.config_directory), cls.output_directory)\\n makedirs_safe(cls.output_directory)\\n\\n # Get visual baseline directory from properties\\n default_baseline = os.path.join(cls.output_directory, 'visualtests', 'baseline')\\n cls.visual_baseline_directory = cls.get_configured_value('TOOLIUM_VISUAL_BASELINE_DIRECTORY',\\n tc_config_files.visual_baseline_directory,\\n default_baseline)\\n if not os.path.isabs(cls.visual_baseline_directory):\\n # If baseline directory is relative, we use the same path as config directory\\n cls.visual_baseline_directory = os.path.join(os.path.dirname(cls.config_directory),\\n cls.visual_baseline_directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e9d755a2d1fb2e7bdd6e7c0b88c579d\",\n \"score\": \"0.468542\",\n \"text\": \"def translate_cfg(self,cfg_str):\\n yml = {\\\"name\\\": None,\\\"parameters\\\": {}, \\\"cells\\\": [] }\\n # Regex\\n re_params = re.compile('^\\\\s*\\\\$param{\\\\s*\\\"([a-zA-Z0-9_]+)\\\"\\\\s*}\\\\s*=\\\\s*([^;]+);?\\\\s*$')\\n re_name = re.compile('^\\\\s*Name\\\\s*=\\\\s([a-zA-Z0-9_]+)\\\\s*$')\\n re_cell = re.compile('^\\\\s*([a-zA-Z0-9_]+)\\\\s+([RWrw])\\\\s+([^;\\\\s]+)\\\\s+([^;\\\\s]+)\\\\s*$')\\n for line in cfg_str.split('\\\\n'): \\n p = re_params.search(line)\\n n = re_name.search(line)\\n c = re_cell.search(line)\\n # Check to see which one matched\\n if p is not None:\\n rhs = self.sub_perl_params(p.group(2))\\n rhs = self.attempt_int_conv(rhs)\\n yml['parameters'][p.group(1)] = rhs\\n elif n is not None:\\n yml['name'] = n.group(1)\\n elif c is not None:\\n width = self.sub_perl_params(c.group(3))\\n width = self.attempt_int_conv(width)\\n mult = self.sub_perl_params(c.group(4))\\n mult = self.attempt_int_conv(mult)\\n yml['cells'].append(self.return_cell(c.group(1),c.group(2),width,mult))\\n return yaml.dump(yml)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d75bf4d4785b4f76cc377da141cea37c\",\n \"score\": \"0.46834138\",\n \"text\": \"def _parse_train_cfg(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1587bdd915b1bef0bf99a5e0ae053992\",\n \"score\": \"0.46775225\",\n \"text\": \"def prepare_config(config: ConfigType, env_metadata: EnvMetaDataType) -> ConfigType:\\n config = config_utils.make_config_mutable(config_utils.unset_struct(config))\\n key = \\\"type_to_select\\\"\\n if key in config.agent.encoder:\\n encoder_type_to_select = config.agent.encoder[key]\\n # config.agent.encoder = config.agent.encoder[encoder_type_to_select]\\n else:\\n encoder_type_to_select = config.agent.encoder.type\\n if encoder_type_to_select in [\\\"identity\\\"]:\\n # if the encoder is an identity encoder infer the shape of the input dim.\\n config.agent.encoder_feature_dim = env_metadata[\\\"env_obs_space\\\"].shape[0]\\n\\n key = \\\"ordered_task_list\\\"\\n if key in env_metadata and env_metadata[key]:\\n config.env.ordered_task_list = deepcopy(env_metadata[key])\\n config = config_utils.make_config_immutable(config)\\n\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8af68d96081823c8bd0aabe0c6eda2f\",\n \"score\": \"0.46763518\",\n \"text\": \"def c3k_sysbi_automation_config():\\n path = 'apollo.scripts.entsw.automation.auto_control' # Standard path required; link must point to specific module (use run_tests2 >=v2.9.0).\\n apollo_config = lib.get_station_configuration()\\n uag_production_line = apollo_config.add_production_line(name='UAG_C3K')\\n pcbst = uag_production_line.add_area(name='SYSBI')\\n station = pcbst.add_test_station(name='Station')\\n # Configuration Data\\n station.add_configuration_data(key='AUTOMATION', value=dict(enabled=True))\\n\\n ssh_dictionary = dict(host='localhost', user='gen-apollo', password='Ad@pCr01!', timeout='30')\\n\\n auto_container = station.add_super_container(name='AUTO')\\n auto_container.add_connection(\\n name='PC',\\n host=\\\"10.1.1.12\\\" if '185' in lib.get_hostname() else \\\"10.1.1.11\\\",\\n port=2027,\\n protocol='telnet',\\n )\\n auto_container.add_connection(name='LOCAL', protocol='ssh', **ssh_dictionary)\\n\\n auto_container.assign_pre_sequence(sequence_definition='{}.pre_control'.format(path))\\n auto_container.add_pid_map(pid='73-*',\\n sequence_definition='{}.main_control'.format(path),\\n )\\n\\n for rack in range(1, 3):\\n for slot in range(1, 13):\\n cell = auto_container.add_container(name='UUT{:02}_{:02}'.format(rack, slot))\\n cell.add_connection(\\n name='uutTN',\\n host=\\\"10.1.1.11\\\",\\n port=2003 + (rack - 1) * 12 + (slot - 1),\\n protocol='telnet',\\n )\\n cell.add_connection(name='serverSSH', protocol='ssh', **ssh_dictionary)\\n cell.assign_pre_sequence(\\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_all_pre_sequences.pre_pcbft_gen2'\\n )\\n cell.add_pid_map(\\n pid='WS-C3*',\\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_pcbft_run.standard_switch_sysbi'\\n )\\n\\n for rack in range(3, 5):\\n for slot in range(1, 13):\\n cell = auto_container.add_container(name='UUT{:02}_{:02}'.format(rack, slot))\\n cell.add_connection(\\n name='uutTN',\\n host=\\\"10.1.1.8\\\",\\n port=2003 + (rack - 3) * 12 + (slot - 1),\\n protocol='telnet',\\n )\\n cell.add_connection(name='serverSSH', protocol='ssh', **ssh_dictionary)\\n cell.assign_pre_sequence(\\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_all_pre_sequences.pre_pcbft_gen2'\\n )\\n cell.add_pid_map(\\n pid='WS-C3*',\\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_pcbft_run.standard_switch_sysbi'\\n )\\n\\n for rack in range(5, 7):\\n for slot in range(1, 13):\\n cell = auto_container.add_container(name='UUT{:02}_{:02}'.format(rack, slot))\\n cell.add_connection(\\n name='uutTN',\\n host=\\\"10.1.1.9\\\",\\n port=2003 + (rack - 5) * 12 + (slot - 1),\\n protocol='telnet',\\n )\\n cell.add_connection(name='serverSSH', protocol='ssh', **ssh_dictionary)\\n cell.assign_pre_sequence(\\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_all_pre_sequences.pre_pcbft_gen2'\\n )\\n cell.add_pid_map(\\n pid='WS-C3*',\\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_pcbft_run.standard_switch_sysbi'\\n )\\n\\n for rack in range(7, 9):\\n for slot in range(1, 13):\\n cell = auto_container.add_container(name='UUT{:02}_{:02}'.format(rack, slot))\\n cell.add_connection(\\n name='uutTN',\\n host=\\\"10.1.1.10\\\",\\n port=2003 + (rack - 7) * 12 + (slot - 1),\\n protocol='telnet',\\n )\\n cell.add_connection(name='serverSSH', protocol='ssh', **ssh_dictionary)\\n cell.assign_pre_sequence(\\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_all_pre_sequences.pre_pcbft_gen2'\\n )\\n cell.add_pid_map(\\n pid='WS-C3*',\\n sequence_definition='apollo.scripts.entsw.cat3.area_sequences.c3k_pcbft_run.standard_switch_sysbi'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daa76f69532dd141363f6cc0d3f11375\",\n \"score\": \"0.46760038\",\n \"text\": \"def get_config():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23a827c5a16d63ae28cc7fccb9852083\",\n \"score\": \"0.46759373\",\n \"text\": \"def test_generic_preprocess(self):\\n init_test_data()\\n with tempfile.TemporaryDirectory() as tmpdirname:\\n scratch_dir = Path(tmpdirname)\\n print('scratch dir :', tmpdirname)\\n args = [(f'masking_config.masking_config_{mod}.visualisation_path', str(scratch_dir / f'vis_{mod}')) for mod in\\n ['anat', 'func']]\\n config, workflow_uid = prepare_config(json_config_path=JSON_CONFIG_PATH, scratch_dir=scratch_dir,\\n additional_args=args)\\n prepare_experiment_result_dataframe(config=config, workflow_uid=workflow_uid)\\n config_path = os.path.expanduser(os.path.join(scratch_dir, 'config.json'))\\n\\n generic(BIDS_BASE,\\n '/usr/share/mouse-brain-atlases/dsurqec_200micron.nii',\\n registration_mask='/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',\\n functional_match={'acquisition': ['EPIlowcov'], },\\n structural_match={'acquisition': ['TurboRARElowcov'], },\\n out_base='{}/preprocessing'.format(scratch_dir),\\n workflow_name='generic',\\n keep_work=False,\\n subjects=['0000'],\\n masking_config_path=config_path,\\n )\\n assert not os.path.exists(os.path.join(scratch_dir, 'preprocessing/crashdump'))\\n for f in ['anat/sub-0000_ses-ofM_acq-TurboRARElowcov_T2w.nii.gz',\\n 'func/sub-0000_ses-ofM_task-JogB_acq-EPIlowcov_run-0_bold.nii.gz']:\\n out_path = os.path.exists(os.path.join(scratch_dir, 'preprocessing/generic/sub-0000/ses-ofM', f))\\n assert out_path, f'Test failed, path {out_path} does not exist.'\\n print('finished test_generic_preprocess successfully.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35cfbe6673f488b4f1197f563d23a2de\",\n \"score\": \"0.46735027\",\n \"text\": \"def get_configurations_by_preset(config, model, fq_to_hw_confs, hardware_config):\\n\\n def _apply_preset_rule(preset_, fq_name, param_type, confs, to_skip=None):\\n if param_type == 'weights':\\n if preset_ == 'accuracy':\\n return confs[-1]\\n return confs[0]\\n if not to_skip or fq_name not in [fq for _, fqs in to_skip for fq in fqs]:\\n if preset_ == 'performance':\\n return confs[0]\\n return confs[-1]\\n return confs\\n\\n def _intersect_and_apply_preset(preset_, fq_to_hw_confs_, fqs_to_unify_):\\n\\n def _unify_and_apply_preset(preset_, cur_conf, fqs_to_unify_):\\n def _test_shapes(shapes):\\n return any([s[0] != shapes[0][0] or len(s) == 1 or s[1] != shapes[0][1] for s in shapes])\\n\\n for bridges, fqs in fqs_to_unify_:\\n res_conf = []\\n with_concat = 'Concat' in [get_node_by_name(model, bridge).type for bridge in bridges]\\n fq_input_shapes = [get_input_shape(get_node_by_name(model, fq), 0) for fq in fqs]\\n unclear_layout = _test_shapes(fq_input_shapes)\\n bridge_layers = [get_node_by_name(model, bridge) for bridge in bridges]\\n bridge_input_shapes = [get_input_shape(layer, i) for layer in bridge_layers for i in layer.in_ports()]\\n broadcasting = _test_shapes(bridge_input_shapes)\\n for fq in fqs:\\n for key in cur_conf[fq]:\\n if key in ACTIVATION_QUANTIZATION_MODES:\\n if with_concat or unclear_layout or broadcasting:\\n if not isinstance(cur_conf[fq]['activations'], list):\\n cur_conf[fq]['activations'] = [cur_conf[fq]['activations']]\\n\\n configuration = [c for c in cur_conf[fq][key] if c['granularity'] == 'pertensor']\\n else:\\n configuration = cur_conf[fq][key]\\n res_conf = intersect_configs(res_conf, configuration, primary_bitwidth) if res_conf \\\\\\n else configuration\\n if not res_conf:\\n raise Exception('Fake quantize nodes {} cannot be unified'.format(fqs))\\n for fq in fqs:\\n for key in cur_conf[fq]:\\n if key in ACTIVATION_QUANTIZATION_MODES:\\n cur_conf[fq][key] = _apply_preset_rule(preset_, fq, key, res_conf)\\n return cur_conf\\n\\n primary_bitwidth = hardware_config[1]['primary_bitwidth']\\n res = {}\\n for key, value in fq_to_hw_confs_.items():\\n conf = dict()\\n for i_type in QUANTIZATION_MODES:\\n if i_type in value:\\n res_conf = []\\n for _, configuration in value[i_type]:\\n res_conf = intersect_configs(res_conf, configuration, primary_bitwidth) if res_conf \\\\\\n else configuration\\n if not res_conf:\\n raise Exception('Fake quantize node {} does not have a suitable configuration'\\n ' for layers {}'.format(key, [layer for layer, _ in value[i_type]]))\\n conf[i_type] = _apply_preset_rule(preset_, key, i_type, res_conf, fqs_to_unify_)\\n res[key] = conf\\n res = _unify_and_apply_preset(preset_, res, fqs_to_unify_)\\n return res\\n\\n available_presets = ['accuracy', 'mixed', 'performance']\\n preset = config.preset\\n if preset not in available_presets:\\n raise Exception('Unsupported preset value: {}.'\\n ' Supported values are {}'.format(preset, available_presets))\\n\\n fqs_to_unify = find_fqs_to_unify(model, config)\\n result = _intersect_and_apply_preset(preset, fq_to_hw_confs, fqs_to_unify)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a5811e1ec6fd0a011b53eb410840557\",\n \"score\": \"0.46715492\",\n \"text\": \"def do_aws_cf_configure():\\n\\n # TODO(cmaloney): Move to Config class introduced in https://github.com/dcos/dcos/pull/623\\n config = Config(CONFIG_PATH)\\n\\n # This process is usually ran from a docker container where default boto3 credential\\n # method may fail and as such, we allow passing these creds explicitly\\n if 'aws_template_storage_access_key_id' in config:\\n os.environ['AWS_ACCESS_KEY_ID'] = config['aws_template_storage_access_key_id']\\n if 'aws_template_storage_secret_access_key' in config:\\n os.environ['AWS_SECRET_ACCESS_KEY'] = config['aws_template_storage_secret_access_key']\\n if 'aws_template_storage_region_name' in config:\\n os.environ['AWS_DEFAULT_REGION'] = config['aws_template_storage_region_name']\\n\\n gen_config = config.as_gen_format()\\n\\n extra_sources = [\\n gen.build_deploy.aws.aws_base_source,\\n aws_advanced_source,\\n gen.build_deploy.aws.groups['master'][1]]\\n\\n sources, targets, _ = gen.get_dcosconfig_source_target_and_templates(gen_config, [], extra_sources)\\n targets.append(get_aws_advanced_target())\\n resolver = gen.internals.resolve_configuration(sources, targets)\\n # TODO(cmaloney): kill this function and make the API return the structured\\n # results api as was always intended rather than the flattened / lossy other\\n # format. This will be an API incompatible change. The messages format was\\n # specifically so that there wouldn't be this sort of API incompatibility.\\n messages = normalize_config_validation(resolver.status_dict)\\n if messages:\\n print_messages(messages)\\n return 1\\n\\n # TODO(cmaloney): This is really hacky but a lot simpler than merging all the config flows into\\n # one currently.\\n # Get out the calculated arguments and manually move critical calculated ones to the gen_config\\n # object.\\n # NOTE: the copying across, as well as validation is guaranteed to succeed because we've already\\n # done a validation run.\\n full_config = {k: v.value for k, v in resolver.arguments.items()}\\n\\n # Calculate the config ID and values that depend on it.\\n config_id = gen.get_config_id(full_config)\\n reproducible_artifact_path = 'config_id/{}'.format(config_id)\\n cloudformation_s3_url = '{}/config_id/{}'.format(full_config['bootstrap_url'], config_id)\\n cloudformation_s3_url_full = '{}/cloudformation'.format(cloudformation_s3_url)\\n\\n # TODO(cmaloney): Switch to using the targets\\n gen_config['bootstrap_url'] = full_config['bootstrap_url']\\n gen_config['provider'] = full_config['provider']\\n gen_config['bootstrap_id'] = full_config['bootstrap_id']\\n gen_config['package_ids'] = full_config['package_ids']\\n gen_config['cloudformation_s3_url_full'] = cloudformation_s3_url_full\\n\\n # Convert the bootstrap_Variant string we have back to a bootstrap_id as used internally by all\\n # the tooling (never has empty string, uses None to say \\\"no variant\\\")\\n bootstrap_variant = full_config['bootstrap_variant'] if full_config['bootstrap_variant'] else None\\n\\n artifacts = list()\\n for built_resource in list(gen.build_deploy.aws.do_create(\\n tag='dcos_generate_config.sh --aws-cloudformation',\\n build_name='Custom',\\n reproducible_artifact_path=reproducible_artifact_path,\\n variant_arguments={bootstrap_variant: gen_config},\\n commit=full_config['dcos_image_commit'],\\n all_completes=None)):\\n artifacts += release.built_resource_to_artifacts(built_resource)\\n\\n artifacts += list(release.make_bootstrap_artifacts(\\n full_config['bootstrap_id'],\\n json.loads(full_config['package_ids']),\\n bootstrap_variant,\\n 'artifacts',\\n ))\\n\\n for package_id in json.loads(full_config['package_ids']):\\n package_filename = release.make_package_filename(package_id)\\n artifacts.append({\\n 'reproducible_path': package_filename,\\n 'local_path': 'artifacts/' + package_filename,\\n })\\n\\n # Upload all the artifacts to the config-id path and then print out what\\n # the path that should be used is, as well as saving a local json file for\\n # easy machine access / processing.\\n repository = release.Repository(\\n full_config['aws_template_storage_bucket_path'],\\n None,\\n 'config_id/' + config_id)\\n\\n storage_commands = repository.make_commands({'core_artifacts': [], 'channel_artifacts': artifacts})\\n\\n cf_dir = GENCONF_DIR + '/cloudformation'\\n log.warning(\\\"Writing local copies to {}\\\".format(cf_dir))\\n storage_provider = release.storage.local.LocalStorageProvider(cf_dir)\\n release.apply_storage_commands({'local': storage_provider}, storage_commands)\\n\\n log.warning(\\n \\\"Generated templates locally available at %s\\\",\\n cf_dir + \\\"/\\\" + reproducible_artifact_path)\\n # TODO(cmaloney): Print where the user can find the files locally\\n\\n if full_config['aws_template_upload'] == 'false':\\n return 0\\n\\n storage_provider = release.storage.aws.S3StorageProvider(\\n bucket=full_config['aws_template_storage_bucket'],\\n object_prefix=None,\\n download_url=cloudformation_s3_url,\\n region_name=full_config['aws_template_storage_region_name'],\\n access_key_id=full_config['aws_template_storage_access_key_id'],\\n secret_access_key=full_config['aws_template_storage_secret_access_key'])\\n\\n log.warning(\\\"Uploading to AWS\\\")\\n release.apply_storage_commands({'aws': storage_provider}, storage_commands)\\n log.warning(\\\"AWS CloudFormation templates now available at: {}\\\".format(cloudformation_s3_url))\\n\\n # TODO(cmaloney): Print where the user can find the files in AWS\\n # TODO(cmaloney): Dump out a JSON with machine paths to make scripting easier.\\n return 0\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994096,"cells":{"query_id":{"kind":"string","value":"a29a5b68ad9385db44275aaac25299f2"},"query":{"kind":"string","value":"calculate all lazily calculated properties of explainer"},"positive_passages":{"kind":"list like","value":[{"docid":"98cda06ab8b2983211f12fad2566a05b","score":"0.58270127","text":"def calculate_properties(self, include_interactions=True):\n _ = self.pred_probas\n super().calculate_properties(include_interactions=include_interactions)","title":""}],"string":"[\n {\n \"docid\": \"98cda06ab8b2983211f12fad2566a05b\",\n \"score\": \"0.58270127\",\n \"text\": \"def calculate_properties(self, include_interactions=True):\\n _ = self.pred_probas\\n super().calculate_properties(include_interactions=include_interactions)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"db812ffcc271376b51b84567bde6244c","score":"0.59954566","text":"def get_descent(self):","title":""},{"docid":"db812ffcc271376b51b84567bde6244c","score":"0.59954566","text":"def get_descent(self):","title":""},{"docid":"b28db5e20f7c8f8092ca30609d273dd9","score":"0.57683766","text":"def analytical_properties(self):\n return [ method[4:] for method in dir(self) if (method[0:4]=='get_') ]","title":""},{"docid":"4f452603007b9a151974899464a467bf","score":"0.5758349","text":"def explain(self) -> global___Explain:","title":""},{"docid":"dc9d4c7095c957f61f1943918c256ac6","score":"0.5672616","text":"def calculate_attributes():\n\n pass","title":""},{"docid":"b21d0ab366b0127b71fff049ed4d8c9b","score":"0.5619407","text":"def def_advanced_properties(self): \n\n raise NotImplementedError('def_advanced_properties has to be implemented')","title":""},{"docid":"cfa74ecf76709ca612bed25272d7c559","score":"0.5605686","text":"def explaination(self):","title":""},{"docid":"34645bcb1ee630640a76cf3d9775086d","score":"0.5546397","text":"def summarize(self):\n ...","title":""},{"docid":"dd5d900f4fde61d402f4b31a7812a5f2","score":"0.5544888","text":"def GetExtraDescent(self):","title":""},{"docid":"8758a04afa7c27e3cc29b8a5177443b6","score":"0.553566","text":"def computed_properties(self):\n raise NotImplementedError(\n \"Subclasses must define the list of computed properties.\"\n )","title":""},{"docid":"840912db0a24bc05ebc77c1e1745677a","score":"0.5525588","text":"def AdvancedProperties(adv_prop_definition): \n def advanced_prop_wrapper(*args):\n prop_dict = adv_prop_definition(*args)\n #print \"in decorator: \",properties\n host = args[0]\n if not host._run_from_web: # property run locally\n host._wvs.advanced_vars =prop_dict\n host.reducer.prop_man.set_input_parameters(**prop_dict)\n return prop_dict\n\n return advanced_prop_wrapper","title":""},{"docid":"c5b7fe63ba6abd1f698c374ca78bcb40","score":"0.542923","text":"def calculate_properties(self, mol):\r\n properties = []\r\n properties.append(mol.GetNumAtoms())\r\n properties.append(desc.CalcCrippenDescriptors(mol)[0])\r\n properties.append(desc.CalcTPSA(mol))\r\n properties.append(desc.CalcNumRotatableBonds(mol))\r\n properties.append(desc.CalcFractionCSP3(mol))\r\n return properties","title":""},{"docid":"f2281018e9f694427ceaa1a8fc2bf8a9","score":"0.5423781","text":"def prop(self):","title":""},{"docid":"87e9d0c9832fa25ccb56add91be55878","score":"0.5414532","text":"def _meta_iteration(self):\n assert (\n self._fin_hts is not None\n ), \"Must call BMLHOptimizer.Aggregate_all before performing optimization.\"\n return self._fin_hts","title":""},{"docid":"2aa3e986a49ded653e413c352977fe9d","score":"0.5389935","text":"def parse_all(self):\n return dict(\n (key, expand_results(getattr(self, key, self.empty_result)))\n for key, attr in self.__class__.__dict__.items()\n if hasattr(attr, '_attached') and type(attr).__name__ == 'cached_property'\n )","title":""},{"docid":"aa3c2df5bbc6923b3bda5d52d816df0e","score":"0.53672475","text":"def get_calculated(self):\n self.initialize_if_necessary()\n return self._calculated_propertynames","title":""},{"docid":"7c020af9159eefa082fd379f2ca805ab","score":"0.5330502","text":"def calculate_properties(self, include_interactions=True):\n _ = (self.preds, self.pred_percentiles,\n self.shap_base_value, self.shap_values,\n self.mean_abs_shap)\n if not self.y_missing:\n _ = self.permutation_importances\n if self.onehot_cols:\n _ = (self.mean_abs_shap_cats, self.X_cats,\n self.shap_values_cats)\n if self.interactions_should_work and include_interactions:\n _ = self.shap_interaction_values\n if self.onehot_cols:\n _ = self.shap_interaction_values_cats","title":""},{"docid":"383f0efa551142aa469042b543c69d64","score":"0.5319493","text":"def derive_related_properties():\n logger.info('Deriving related properties ...')\n data = statistics.get_json_data('properties')\n\n related = {}\n\n for pid in data:\n if 'r' in data[pid] and data[pid]['r']:\n related[pid] = data[pid]['r']\n\n statistics.update_json_data('properties/related', related)\n statistics.update_split_json_data('properties/related', related, 10)","title":""},{"docid":"fbab870c74e1662c36568a56b4b40fe2","score":"0.5308144","text":"def ContextProperties(self) -> _n_1_t_0:","title":""},{"docid":"22f32729ae0f34692c7167b09f4a57f2","score":"0.530135","text":"def pred(self, index_set=None):\n return [ self.simple_reflection(i) for i in self.descents(index_set) ]","title":""},{"docid":"dd90c2a2a93a85c909ffd2d18650a897","score":"0.5282453","text":"def get_properties(self):\n return super(PatternAnalyzer, self).get_properties() + (self.pattern,)","title":""},{"docid":"acfc3d024910ff732ab395426b3742b5","score":"0.52702093","text":"def _compute_experiment_statistics(self):","title":""},{"docid":"2ab3d678901b63572098516bbb48f2bb","score":"0.5239263","text":"def summary(self):","title":""},{"docid":"2ab3d678901b63572098516bbb48f2bb","score":"0.5239263","text":"def summary(self):","title":""},{"docid":"1f6cdfab0381137ff8dd32d761e54fbd","score":"0.52306867","text":"def _lazyproperties(self):\n def islazyproperty(obj):\n return isinstance(obj, lazyproperty)\n\n return [i[0] for i in inspect.getmembers(self.__class__,\n predicate=islazyproperty)]","title":""},{"docid":"f991943d87adfe236e2177f3c65e7fcd","score":"0.51917374","text":"def get_class_properties(table, graph, entities):\n ne = table.get_NE_cols()\n for column in ne.columns:\n column_candidates = column.candidates\n\n column_property_candidates = {}\n\n for cell in tqdm(column.cells):\n for candidate in cell.candidates:\n #for some reason if the candidate is null\n if candidate is None:\n continue\n \n\n #this one find the properties suggested to the belonging class\n #properties of the instance\n #these ones we add to the graph and then connect to the candidate classes\n properties = get_all_instance_properties(entities[candidate].uri)\n if properties is not None:\n for p in properties:\n prop = p[\"property\"][\"value\"]\n\n if prop.find(\"http://dbpedia.org/ontology\") == -1:\n continue\n\n exclusion = [\n \"http://dbpedia.org/ontology/abstract\",\n \"http://dbpedia.org/ontology/thumbnail\",\n \"http://dbpedia.org/ontology/wikiPageID\",\n \"http://dbpedia.org/ontology/wikiPageRevisionID\",\n \"http://dbpedia.org/ontology/wikiPageExternalLink\"\n ]\n\n if prop in exclusion:\n continue\n \n prop_id = create_entity(entities, graph, prop, \"property\")\n if prop_id not in column_property_candidates:\n column_property_candidates[prop_id] = 0\n column_property_candidates[prop_id] += 1\n\n #TODO: modify this to include only the parents of\n #related cell instances?\n for column_candidate in column_candidates:\n #check if cell and column candidates are related\n if graph.has_edge(candidate, column_candidate):\n graph.add_edge(column_candidate, prop_id)\n \n #adding the property labels\n entities[prop_id].add_value(p[\"callret-1\"][\"value\"])\n \n #now filter the column_property_space to select the top 50%\n if len(column_property_candidates) > 0:\n cmax = column_property_candidates[max(column_property_candidates, key=column_property_candidates.get)]\n selected_property_candidates = filter(lambda k: column_property_candidates[k] > cmax*0.5, \n column_property_candidates)\n \n #keep the winning properties in the graph and remove the rest\n for prop in column_property_candidates:\n if prop not in selected_property_candidates:\n delete_entity(entities, graph, prop)","title":""},{"docid":"0a15aa303f049f3a9330c3f447344bba","score":"0.5190388","text":"def properties(self):\n o = self.oorig\n getters = [name for name in dir(o)\n if name.startswith('get_') and callable(getattr(o, name))]\n getters.sort()\n d = {}\n for name in getters:\n func = getattr(o, name)\n if self.is_alias(func):\n continue\n try:\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n val = func()\n except Exception:\n continue\n else:\n d[name[4:]] = val\n return d","title":""},{"docid":"af66631559ea394a9a18d31c63ebba5f","score":"0.51859057","text":"def properties(raw):\n prop_list = []\n dfs(prop_list, [], raw)\n return prop_list","title":""},{"docid":"ea6cb88959f5b731e33f8efe7b31349d","score":"0.51762813","text":"def pprint_getters(self):\n lines = []\n for name, val in sorted(self.properties().items()):\n if getattr(val, 'shape', ()) != () and len(val) > 6:\n s = str(val[:6]) + '...'\n else:\n s = str(val)\n s = s.replace('\\n', ' ')\n if len(s) > 50:\n s = s[:50] + '...'\n name = self.aliased_name(name)\n lines.append(' %s = %s' % (name, s))\n return lines","title":""},{"docid":"18c4f7f3e315858370b95b546e616e02","score":"0.51607627","text":"def explain(self):\n\n return None","title":""},{"docid":"7f8f7e74968f88b15546cb6553725eb1","score":"0.5153185","text":"def get_ascent(self):","title":""},{"docid":"7f8f7e74968f88b15546cb6553725eb1","score":"0.5153185","text":"def get_ascent(self):","title":""},{"docid":"1bac82955f75219b07c52a9beb8097bd","score":"0.5128885","text":"def calc_deficit(self):","title":""},{"docid":"a8f7fcd0e2ac7ae370f9be1f4718b188","score":"0.51115346","text":"def _computeInfo(self):\n return {\"answer\": 42} #### Calculated by the Deep Thought supercomputer in 7.5M years","title":""},{"docid":"e034ea853a7336efb3b79086dd954790","score":"0.5109337","text":"def describe(self):","title":""},{"docid":"e034ea853a7336efb3b79086dd954790","score":"0.5109337","text":"def describe(self):","title":""},{"docid":"da2141ccb73a26e572108b5ac86afae2","score":"0.5107499","text":"def compute(self):\n \n return _computeMethdos(self)","title":""},{"docid":"a33b3166d66683afdcbc4b672bfed5d6","score":"0.51025355","text":"def propertyValues():","title":""},{"docid":"a33b3166d66683afdcbc4b672bfed5d6","score":"0.51025355","text":"def propertyValues():","title":""},{"docid":"8dfda7e5724cdd96650404785283f903","score":"0.50917476","text":"def succ(self, index_set=None):\n return [ self.simple_reflection(i) for i in self.descents(index_set=index_set, positive=True) ]","title":""},{"docid":"2525fcfc0b0231f2f79f226b0a59ac6e","score":"0.5081335","text":"def property_summary(self):\n summary = {\"size\": self.size(),\n \"depth\": self.depth(),\n \"width\": self.width(),\n \"bits\": self.num_cbits(),\n \"factors\": self.num_tensor_factors(),\n \"operations\": self.count_ops()}\n return summary","title":""},{"docid":"782d5f770ef5fe5216e3aea443318dcc","score":"0.5073165","text":"def properties():\n N, M, Psi, GT = load_dataset()\n print('# of sources: {}'.format(N))\n print('# of object: {}'.format(M))\n obs_n = 0\n V = [0 for obj in range(M)]\n for obj in range(M):\n obs_n += len(Psi[obj])\n V[obj] = len(set([val for s, val in Psi[obj]]))\n print('# of observations: {}'.format(obs_n))\n print('average # of values per object: {:1.3f}'.format(np.average(V)))\n print('min # of values per object: {:1.3f}'.format(min(V)))\n print('max # of values per object: {:1.3f}'.format(max(V)))","title":""},{"docid":"1267c35e738c29b613b02c75bee56ec3","score":"0.50695896","text":"def generate_summary(self):","title":""},{"docid":"181076b93364a9d6440edad08e9cb99f","score":"0.50634605","text":"def _get_full_objs_decorator(func):\n addr = get_config()[\"mongo_rest_interface_addr\"]\n get_obj = lambda t: json.loads(preprocess_resp(get_book_by(\"author_or_title\", addr, t)))[0]\n get_overall_sentiment = lambda o: o[\"sentiment\"][\"overall\"][0]\n\n def __inner(base_title, scores, *args, **kwargs):\n resp = func(base_title, scores, *args, **kwargs)\n head_obj, *tail_objs = resp[\"resp\"]\n\n base_obj = get_obj(head_obj[\"title\"])\n\n for kvs in tail_objs:\n kvs[\"title\"] = get_obj(kvs[\"title\"])\n\n kvs[\"title\"][\"sentiment\"][\"overall\"] = [get_overall_sentiment(base_obj),\n get_overall_sentiment(kvs[\"title\"])]\n _, *top_matches = resp[\"resp\"]\n return top_matches\n return __inner","title":""},{"docid":"c7080ea558116200ad07f2645fdfb8cb","score":"0.50614005","text":"def permalist(self):","title":""},{"docid":"b59b5e8f1ebb0721ccf3e0b0a397fef7","score":"0.5055935","text":"def compute_properties(self):\n # Calculate the area\n self._area = self.calc_area()\n self._Iz, self._Iy = self.calc_inertia()","title":""},{"docid":"93351b779816729dc368f80431863bf5","score":"0.5048545","text":"def properties(self):\n if self.bound and \"properties\" in self.__stale:\n self.refresh()\n return super(Node, self).properties","title":""},{"docid":"ecea282618a86c234828d9b527ecd9a2","score":"0.5042679","text":"def interleaved_computed_per_page_values_and_summaries(self):\n return self._interleaved_computed_per_page_values_and_summaries","title":""},{"docid":"810328cc57897b797bbda6d27df0618c","score":"0.5038257","text":"def update_derived_property_records():\n derive_property_classification()\n derive_related_properties()\n derive_url_patterns()\n derive_property_usage()\n derive_property_datatypes()","title":""},{"docid":"0a34dc84f791777729a301eca2318c93","score":"0.50336385","text":"def analyse(self):","title":""},{"docid":"c71458250acae4505869fe52aca6c916","score":"0.50191516","text":"def compute(self):","title":""},{"docid":"c71458250acae4505869fe52aca6c916","score":"0.50191516","text":"def compute(self):","title":""},{"docid":"349e548a728b283e55ee3e8a5a65e401","score":"0.5018438","text":"def _optimize(self):\n return {}","title":""},{"docid":"012ed6daab996ded5cfb53db1b748881","score":"0.5005248","text":"def explain(self):\n for i in self.m:\n for j in i:\n if j.visited==1:\n txt='{:2d}'.format(j.data)\n print(txt,end=' ')\n else:\n print(' ',end=' ')\n print('')","title":""},{"docid":"9ab95ae5087e0d81a419496cc5fb8442","score":"0.49948457","text":"async def describe(self):\n\n self._ready.clear()\n\n entries = await self._pool.fetch(self._script)\n\n details = collections.defaultdict(dict)\n primaries = collections.defaultdict(list)\n for entry in map(dict, entries):\n table = entry.pop('table')\n field = entry.pop('field')\n details[table][field] = entry\n if entry['main']:\n primaries[table].append(field)\n\n self._details = dict(details)\n self._primaries = dict(primaries)\n\n self._ready.set()","title":""},{"docid":"b000a125aca09181facb8eb5f61294a7","score":"0.4990571","text":"def calc(self):\r\n for word in self:\r\n word.calc()","title":""},{"docid":"b000a125aca09181facb8eb5f61294a7","score":"0.4990571","text":"def calc(self):\r\n for word in self:\r\n word.calc()","title":""},{"docid":"0e3f52a806b31ffe11af809df856b59c","score":"0.49786073","text":"def test_get_efficiency(self):\n pass","title":""},{"docid":"a3118bd450580db71e9a2cdb5d56885e","score":"0.4973626","text":"def _get_calculations(self) -> dict:\n calculations = {}\n for element in dir(self):\n # BLACKMAGIC : _repr_html_ must be skipped\n \"\"\"\n `_repr_html_` is an element of the directory of the PostProc object which\n causes the search of calculated attributes to overflow, looping indefinitely\n and never reaching the actual elements containing those said attributes.\n It will be skipped until the issue has been properly identified and fixed.\n You can uncomment the block below to observe how the directory loops after\n reaching that element - acknowledging you are not skipping it.\n \"\"\"\n if element not in PROBLEMATIC_ATTRIBUTE_NAMES:\n try:\n # print(\n # \"directory element : \",\n # e,\n # \", calculations list : \",\n # calculations,\n # )\n for k in getattr(getattr(self, element), \"_calculates\"):\n calculations[k] = element\n except AttributeError:\n pass\n\n return calculations","title":""},{"docid":"950711e8283b79c14f79e0728cd0d77d","score":"0.49721813","text":"def compute_info(self):\n pass","title":""},{"docid":"2de725265a7c822bf42ac8fad2e3611c","score":"0.49636874","text":"def propertyItems():","title":""},{"docid":"2de725265a7c822bf42ac8fad2e3611c","score":"0.49636874","text":"def propertyItems():","title":""},{"docid":"59cf8734cf8658986cb62dbc2e56bf0a","score":"0.495714","text":"def calculate(self):\r\n attributes = {\r\n \"defense\": 0,\r\n \"influence\": 0,\r\n \"lands\": 0,\r\n \"law\": 0,\r\n \"population\": 0,\r\n \"power\": 0,\r\n \"wealth\": 0,\r\n }\r\n\r\n for attr in attributes:\r\n attributes[attr] += getattr(self.baseattributes, attr)\r\n attributes[attr] += getattr(self.realm, attr)\r\n attributes[attr] += getattr(self.destinycharacters, attr)\r\n for event in self.event_set.all():\r\n attributes[attr] += getattr(event, attr)\r\n setattr(self, attr, attributes[attr])","title":""},{"docid":"f588bb121d172b4d455b060d487aecf8","score":"0.4955306","text":"def get_properties():\n raise NotImplementedError","title":""},{"docid":"43f457912db7e4b79f0efcee47cfb141","score":"0.4943038","text":"def get_prop():","title":""},{"docid":"3785277201b4bec926b9e6edec2be9eb","score":"0.49346378","text":"def get_properties(self):\n raise NotImplementedError(\"not implemented\")","title":""},{"docid":"14a577e71092f0402aee7cd48674b59c","score":"0.49253562","text":"def get_properties(self):\n return None","title":""},{"docid":"9d027674503c723afb7808d4c92be92b","score":"0.49251354","text":"def properties(self):\n if self.bound and \"properties\" in self.__stale:\n self.refresh()\n return super(Rel, self).properties","title":""},{"docid":"901a041f66daf6cfe787c18c1c6939dd","score":"0.4925062","text":"def node_properties(self):\n for n in self.nodes:\n yield dict(n)","title":""},{"docid":"3ae7ea9f7af562970e90b832e81e037a","score":"0.49059898","text":"def calculate(self):\n pass","title":""},{"docid":"e14ee427d2ef763dd991be61c8317536","score":"0.49059656","text":"def compute_all_properties(cls):\n all_properties = {}\n\n props = []\n # Get all of the properties of the class including inherited ones\n for m in dir(cls):\n p = getattr(cls, m)\n if isinstance(p, Property):\n props.append((m, p))\n\n props = sorted(props, key=lambda p: p[1].instance_idx)\n for prop_name, prop_value in props:\n value_name = prop_value.name\n if value_name:\n all_properties[value_name] = prop_name\n prop_name = value_name\n else:\n all_properties[prop_name] = prop_name\n\n Graph.guard_reserved_words(prop_name, cls)\n return all_properties","title":""},{"docid":"b55d73e0f5d867bd9189d23c4c07ddd1","score":"0.49003336","text":"def properties(cls):\n matrix = []\n for name, prop in readme_md.getmembers(cls, inspect.isdatadescriptor):\n row = readme_md.Row(cls, name).get_columns()\n matrix.append(row)\n return mdown.table((\"`%s` properties\" % cls.__name__, \"`__doc__`\"), matrix)","title":""},{"docid":"ebc7802b4072d3156b6ef46985067d6d","score":"0.48835608","text":"def dict(self):\n measures={}\n for prop in self.properties:\n measures[prop]=getattr(self,prop)\n return measures","title":""},{"docid":"d76b65f5f8de1db3c7f17f2356fa2343","score":"0.48829767","text":"def _get_properties(edges_response):\n es = Elasticsearch(args.elastic_hosts)\n ic = IndicesClient(es)\n \"\"\" edges_response looks like ...\n {\"responses\": [{\"status\": 200,\n \"hits\": {\"hits\": [], \"total\": 69644, \"max_score\": 0.0},\n \"_shards\": {\"successful\": 5, \"failed\": 0, \"total\": 5},\n \"took\": 1,\n \"aggregations\": {\"Labels\":\n {\"buckets\": [{\"key\": \"Gene\", \"doc_count\": 39892},\n {\"key\": \"Pubmed\", \"doc_count\": 18044},\n {\"key\": \"Individual\", \"doc_count\": 11036},\n {\"key\": \"GeneFamily\", \"doc_count\": 589},\n {\"key\": \"Cohort\", \"doc_count\": 40},\n {\"key\": \"Project\", \"doc_count\": 33},\n {\"key\": \"GeneDatabase\", \"doc_count\": 10}]\n }\n \"\"\"\n if (\n \"responses\" not in edges_response or\n 'aggregations' not in edges_response['responses'][0]\n ):\n raise tornado.gen.Return(\n None\n )\n print(edges_response)\n edges = edges_response[\"responses\"][0]['aggregations']['Labels']['buckets'] # NOQA\n edge_names = []\n for edge in edges:\n edge_names.append(edge['key'])\n raise tornado.gen.Return(\n ic.get_mapping(index=self.index,\n doc_type=edge_names, request_timeout=60)\n )","title":""},{"docid":"f5aa11c2100c2bec77e5dd3c2b12dff1","score":"0.48822555","text":"def _automap_properties(self, protected_methods=True):\n for prop_name, prop in self._properties.items():\n # search for object methods\n getter_name = 'get_'\n setter_name = 'set_'\n if protected_methods:\n getter_name = '_'+getter_name\n setter_name = '_'+setter_name\n\n try:\n prop.getter = self.__getattribute__(getter_name+prop_name)\n except AttributeError:\n # not found\n pass\n\n try:\n prop.setter = self.__getattribute__(setter_name+prop_name)\n except AttributeError:\n pass","title":""},{"docid":"f984008e01be4d6409bc4de00b4d274a","score":"0.48814657","text":"def summary(self):\n# print \"***********AT BASE CLASS summary()***********\"\n return {}","title":""},{"docid":"34b37622a4a2329d14f6410744bf9fd4","score":"0.4873339","text":"def get_properties(self):\n return self.property_desc.keys()","title":""},{"docid":"319398ae65d544fd7bfe2ed4a8402cb9","score":"0.48716626","text":"def get_properties(self) -> Dict:\n _dict = {}\n\n for _key in [\n k for k in vars(self).keys()\n if k not in AnalyzedResult._properties\n ]:\n _dict[_key] = getattr(self, _key)\n return _dict","title":""},{"docid":"2790abc1f36414395152a9aa25f3ba84","score":"0.48583788","text":"def _propertyMap():","title":""},{"docid":"6439a3e9734749a82b0ac08f65985663","score":"0.48567018","text":"def _calculate(self) -> None:","title":""},{"docid":"d8e18f224adf606a41b30b201be1fdd2","score":"0.4851939","text":"def item_view_object(context, request):\n properties = item_links(context, request)\n if not asbool(request.params.get('skip_calculated')):\n calculated = calculate_properties(context, request, properties)\n properties.update(calculated)\n return properties","title":""},{"docid":"0ef69ea85d9aee3b6c413fa287f57eec","score":"0.48498613","text":"def calculate_properties(self, atoms, properties):\n # TODO: Check atoms.\n\n if \"rdf\" in properties:\n self.results[\"rdf\"] = self.data_handler.target_calculator.\\\n get_radial_distribution_function(atoms)\n if \"tpcf\" in properties:\n self.results[\"tpcf\"] = self.data_handler.target_calculator.\\\n get_three_particle_correlation_function(atoms)\n if \"static_structure_factor\" in properties:\n self.results[\"static_structure_factor\"] = self.data_handler.\\\n target_calculator.get_static_structure_factor(atoms)\n if \"ion_ion_energy\" in properties:\n self.results[\"ion_ion_energy\"] = self.\\\n last_energy_contributions[\"e_ewald\"]","title":""},{"docid":"f0f72abc460055d57a106eb4be922eaf","score":"0.48431128","text":"def explore(self):","title":""},{"docid":"4f7f831411a4294950e812c639385da4","score":"0.48429444","text":"def _compute_experiment_statistics(self):\n pass","title":""},{"docid":"2e87eb70fc8d2a953382d8bb6dd98989","score":"0.48422936","text":"def preheat_oven(self):\n pass","title":""},{"docid":"c849196a34453b0930cebb5d3564793d","score":"0.48355392","text":"def calculate_stats(self):\n\n pass","title":""},{"docid":"88266901cb4a33d83f9e9a65f5c55a52","score":"0.4831468","text":"def _explain(self):\n self._explain_simple_consensus()","title":""},{"docid":"f20fb9d6150b6535b9da1b58a9e5b8b6","score":"0.4828499","text":"def def_main_properties(self): \n raise NotImplementedError('def_main_properties has to be implemented')","title":""},{"docid":"8e3afe11f8cd519164755df422ceb676","score":"0.48283434","text":"def __init__(self):\n self.intermediate = {}\n self.result = []","title":""},{"docid":"61df8d1f0db70b4e34eacc623a692525","score":"0.48221806","text":"def _compute_descriptor(self, data):","title":""},{"docid":"f45fc55020fddb60c60bb46b7ff04420","score":"0.48158824","text":"def transformer_properties(self):\n for key, transformer in self._transformer.items():\n yield (key, transformer.mean_, transformer.stdev_)","title":""},{"docid":"d035c637678eba36787becfe27c6bdec","score":"0.48091656","text":"def compat_explain(cur):\n res = cur.explain()\n if 'nscannedObjects' in res:\n res['executionStats'] = dict(\n nReturned=res.pop('n'),\n totalKeysExamined=res.pop('nscanned'),\n totalDocsExamined=res.pop('nscannedObjects'),\n executionTimeMillis=res.pop('millis'),\n )\n return res","title":""},{"docid":"014770ad1416e17d09ef96cec7ec50aa","score":"0.4799041","text":"def get_score(self, real_data, synthetic_data, metadata, progress_bar=None):\n if self._single_table_property is None:\n raise NotImplementedError()\n\n all_details = []\n for table_name in metadata['tables']:\n property_instance = self._single_table_property()\n self._properties[table_name] = property_instance\n self._properties[table_name].get_score(\n real_data[table_name],\n synthetic_data[table_name],\n metadata['tables'][table_name],\n progress_bar\n )\n all_details.append(property_instance._details)\n\n self.is_computed = True\n all_details = pd.concat(all_details)\n return all_details['Score'].mean()","title":""},{"docid":"bb42787d8b3c67a7c80f111cca104ed1","score":"0.47980973","text":"def propertyMap():","title":""},{"docid":"bb42787d8b3c67a7c80f111cca104ed1","score":"0.47980973","text":"def propertyMap():","title":""},{"docid":"729b9b655b13377f742b5ea80b286184","score":"0.4794479","text":"def properties(self):\n return self._properties","title":""},{"docid":"729b9b655b13377f742b5ea80b286184","score":"0.4794479","text":"def properties(self):\n return self._properties","title":""},{"docid":"5bb0858e5c8ae1c4a429c71c7fd66867","score":"0.47892186","text":"def read_relation_property(self):\n relation_property_head = {x: [] for x in range(len(self.relations))}\n relation_property_tail = {x: [] for x in range(len(self.relations))}\n\n for t in self.triplets['train']:\n relation_property_head[t.r].append(t.h)\n relation_property_tail[t.r].append(t.t)\n\n self.relation_property = {x: (len(set(relation_property_tail[x]))) / ( \\\n len(set(relation_property_head[x])) + len(set(relation_property_tail[x]))) \\\n for x in relation_property_head.keys()}\n\n return self.relation_property","title":""},{"docid":"abcda2d6d03dd8ed602c4b9044ef929c","score":"0.4787436","text":"def derive_property_usage():\n logger.info('Deriving property usage ...')\n data = statistics.get_json_data('properties')\n\n usage = defaultdict(dict)\n keys = ['i', 's', 'q', 'e', 'qs', 'pc']\n\n for pid in data:\n for key in keys:\n if key in data[pid] and data[pid][key]:\n usage[pid][key] = data[pid][key]\n\n statistics.update_json_data('properties/usage', usage)","title":""},{"docid":"8fa31df26635e049f6793e66f1e61f4b","score":"0.47843242","text":"def expand_properties(self, fmt):\n pass","title":""}],"string":"[\n {\n \"docid\": \"db812ffcc271376b51b84567bde6244c\",\n \"score\": \"0.59954566\",\n \"text\": \"def get_descent(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db812ffcc271376b51b84567bde6244c\",\n \"score\": \"0.59954566\",\n \"text\": \"def get_descent(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b28db5e20f7c8f8092ca30609d273dd9\",\n \"score\": \"0.57683766\",\n \"text\": \"def analytical_properties(self):\\n return [ method[4:] for method in dir(self) if (method[0:4]=='get_') ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f452603007b9a151974899464a467bf\",\n \"score\": \"0.5758349\",\n \"text\": \"def explain(self) -> global___Explain:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc9d4c7095c957f61f1943918c256ac6\",\n \"score\": \"0.5672616\",\n \"text\": \"def calculate_attributes():\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21d0ab366b0127b71fff049ed4d8c9b\",\n \"score\": \"0.5619407\",\n \"text\": \"def def_advanced_properties(self): \\n\\n raise NotImplementedError('def_advanced_properties has to be implemented')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfa74ecf76709ca612bed25272d7c559\",\n \"score\": \"0.5605686\",\n \"text\": \"def explaination(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34645bcb1ee630640a76cf3d9775086d\",\n \"score\": \"0.5546397\",\n \"text\": \"def summarize(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd5d900f4fde61d402f4b31a7812a5f2\",\n \"score\": \"0.5544888\",\n \"text\": \"def GetExtraDescent(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8758a04afa7c27e3cc29b8a5177443b6\",\n \"score\": \"0.553566\",\n \"text\": \"def computed_properties(self):\\n raise NotImplementedError(\\n \\\"Subclasses must define the list of computed properties.\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"840912db0a24bc05ebc77c1e1745677a\",\n \"score\": \"0.5525588\",\n \"text\": \"def AdvancedProperties(adv_prop_definition): \\n def advanced_prop_wrapper(*args):\\n prop_dict = adv_prop_definition(*args)\\n #print \\\"in decorator: \\\",properties\\n host = args[0]\\n if not host._run_from_web: # property run locally\\n host._wvs.advanced_vars =prop_dict\\n host.reducer.prop_man.set_input_parameters(**prop_dict)\\n return prop_dict\\n\\n return advanced_prop_wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5b7fe63ba6abd1f698c374ca78bcb40\",\n \"score\": \"0.542923\",\n \"text\": \"def calculate_properties(self, mol):\\r\\n properties = []\\r\\n properties.append(mol.GetNumAtoms())\\r\\n properties.append(desc.CalcCrippenDescriptors(mol)[0])\\r\\n properties.append(desc.CalcTPSA(mol))\\r\\n properties.append(desc.CalcNumRotatableBonds(mol))\\r\\n properties.append(desc.CalcFractionCSP3(mol))\\r\\n return properties\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2281018e9f694427ceaa1a8fc2bf8a9\",\n \"score\": \"0.5423781\",\n \"text\": \"def prop(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87e9d0c9832fa25ccb56add91be55878\",\n \"score\": \"0.5414532\",\n \"text\": \"def _meta_iteration(self):\\n assert (\\n self._fin_hts is not None\\n ), \\\"Must call BMLHOptimizer.Aggregate_all before performing optimization.\\\"\\n return self._fin_hts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aa3e986a49ded653e413c352977fe9d\",\n \"score\": \"0.5389935\",\n \"text\": \"def parse_all(self):\\n return dict(\\n (key, expand_results(getattr(self, key, self.empty_result)))\\n for key, attr in self.__class__.__dict__.items()\\n if hasattr(attr, '_attached') and type(attr).__name__ == 'cached_property'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa3c2df5bbc6923b3bda5d52d816df0e\",\n \"score\": \"0.53672475\",\n \"text\": \"def get_calculated(self):\\n self.initialize_if_necessary()\\n return self._calculated_propertynames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c020af9159eefa082fd379f2ca805ab\",\n \"score\": \"0.5330502\",\n \"text\": \"def calculate_properties(self, include_interactions=True):\\n _ = (self.preds, self.pred_percentiles,\\n self.shap_base_value, self.shap_values,\\n self.mean_abs_shap)\\n if not self.y_missing:\\n _ = self.permutation_importances\\n if self.onehot_cols:\\n _ = (self.mean_abs_shap_cats, self.X_cats,\\n self.shap_values_cats)\\n if self.interactions_should_work and include_interactions:\\n _ = self.shap_interaction_values\\n if self.onehot_cols:\\n _ = self.shap_interaction_values_cats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"383f0efa551142aa469042b543c69d64\",\n \"score\": \"0.5319493\",\n \"text\": \"def derive_related_properties():\\n logger.info('Deriving related properties ...')\\n data = statistics.get_json_data('properties')\\n\\n related = {}\\n\\n for pid in data:\\n if 'r' in data[pid] and data[pid]['r']:\\n related[pid] = data[pid]['r']\\n\\n statistics.update_json_data('properties/related', related)\\n statistics.update_split_json_data('properties/related', related, 10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbab870c74e1662c36568a56b4b40fe2\",\n \"score\": \"0.5308144\",\n \"text\": \"def ContextProperties(self) -> _n_1_t_0:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f32729ae0f34692c7167b09f4a57f2\",\n \"score\": \"0.530135\",\n \"text\": \"def pred(self, index_set=None):\\n return [ self.simple_reflection(i) for i in self.descents(index_set) ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd90c2a2a93a85c909ffd2d18650a897\",\n \"score\": \"0.5282453\",\n \"text\": \"def get_properties(self):\\n return super(PatternAnalyzer, self).get_properties() + (self.pattern,)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acfc3d024910ff732ab395426b3742b5\",\n \"score\": \"0.52702093\",\n \"text\": \"def _compute_experiment_statistics(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ab3d678901b63572098516bbb48f2bb\",\n \"score\": \"0.5239263\",\n \"text\": \"def summary(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ab3d678901b63572098516bbb48f2bb\",\n \"score\": \"0.5239263\",\n \"text\": \"def summary(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f6cdfab0381137ff8dd32d761e54fbd\",\n \"score\": \"0.52306867\",\n \"text\": \"def _lazyproperties(self):\\n def islazyproperty(obj):\\n return isinstance(obj, lazyproperty)\\n\\n return [i[0] for i in inspect.getmembers(self.__class__,\\n predicate=islazyproperty)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f991943d87adfe236e2177f3c65e7fcd\",\n \"score\": \"0.51917374\",\n \"text\": \"def get_class_properties(table, graph, entities):\\n ne = table.get_NE_cols()\\n for column in ne.columns:\\n column_candidates = column.candidates\\n\\n column_property_candidates = {}\\n\\n for cell in tqdm(column.cells):\\n for candidate in cell.candidates:\\n #for some reason if the candidate is null\\n if candidate is None:\\n continue\\n \\n\\n #this one find the properties suggested to the belonging class\\n #properties of the instance\\n #these ones we add to the graph and then connect to the candidate classes\\n properties = get_all_instance_properties(entities[candidate].uri)\\n if properties is not None:\\n for p in properties:\\n prop = p[\\\"property\\\"][\\\"value\\\"]\\n\\n if prop.find(\\\"http://dbpedia.org/ontology\\\") == -1:\\n continue\\n\\n exclusion = [\\n \\\"http://dbpedia.org/ontology/abstract\\\",\\n \\\"http://dbpedia.org/ontology/thumbnail\\\",\\n \\\"http://dbpedia.org/ontology/wikiPageID\\\",\\n \\\"http://dbpedia.org/ontology/wikiPageRevisionID\\\",\\n \\\"http://dbpedia.org/ontology/wikiPageExternalLink\\\"\\n ]\\n\\n if prop in exclusion:\\n continue\\n \\n prop_id = create_entity(entities, graph, prop, \\\"property\\\")\\n if prop_id not in column_property_candidates:\\n column_property_candidates[prop_id] = 0\\n column_property_candidates[prop_id] += 1\\n\\n #TODO: modify this to include only the parents of\\n #related cell instances?\\n for column_candidate in column_candidates:\\n #check if cell and column candidates are related\\n if graph.has_edge(candidate, column_candidate):\\n graph.add_edge(column_candidate, prop_id)\\n \\n #adding the property labels\\n entities[prop_id].add_value(p[\\\"callret-1\\\"][\\\"value\\\"])\\n \\n #now filter the column_property_space to select the top 50%\\n if len(column_property_candidates) > 0:\\n cmax = column_property_candidates[max(column_property_candidates, key=column_property_candidates.get)]\\n selected_property_candidates = filter(lambda k: column_property_candidates[k] > cmax*0.5, \\n column_property_candidates)\\n \\n #keep the winning properties in the graph and remove the rest\\n for prop in column_property_candidates:\\n if prop not in selected_property_candidates:\\n delete_entity(entities, graph, prop)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a15aa303f049f3a9330c3f447344bba\",\n \"score\": \"0.5190388\",\n \"text\": \"def properties(self):\\n o = self.oorig\\n getters = [name for name in dir(o)\\n if name.startswith('get_') and callable(getattr(o, name))]\\n getters.sort()\\n d = {}\\n for name in getters:\\n func = getattr(o, name)\\n if self.is_alias(func):\\n continue\\n try:\\n with warnings.catch_warnings():\\n warnings.simplefilter('ignore')\\n val = func()\\n except Exception:\\n continue\\n else:\\n d[name[4:]] = val\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af66631559ea394a9a18d31c63ebba5f\",\n \"score\": \"0.51859057\",\n \"text\": \"def properties(raw):\\n prop_list = []\\n dfs(prop_list, [], raw)\\n return prop_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea6cb88959f5b731e33f8efe7b31349d\",\n \"score\": \"0.51762813\",\n \"text\": \"def pprint_getters(self):\\n lines = []\\n for name, val in sorted(self.properties().items()):\\n if getattr(val, 'shape', ()) != () and len(val) > 6:\\n s = str(val[:6]) + '...'\\n else:\\n s = str(val)\\n s = s.replace('\\\\n', ' ')\\n if len(s) > 50:\\n s = s[:50] + '...'\\n name = self.aliased_name(name)\\n lines.append(' %s = %s' % (name, s))\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c4f7f3e315858370b95b546e616e02\",\n \"score\": \"0.51607627\",\n \"text\": \"def explain(self):\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f8f7e74968f88b15546cb6553725eb1\",\n \"score\": \"0.5153185\",\n \"text\": \"def get_ascent(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f8f7e74968f88b15546cb6553725eb1\",\n \"score\": \"0.5153185\",\n \"text\": \"def get_ascent(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bac82955f75219b07c52a9beb8097bd\",\n \"score\": \"0.5128885\",\n \"text\": \"def calc_deficit(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8f7fcd0e2ac7ae370f9be1f4718b188\",\n \"score\": \"0.51115346\",\n \"text\": \"def _computeInfo(self):\\n return {\\\"answer\\\": 42} #### Calculated by the Deep Thought supercomputer in 7.5M years\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e034ea853a7336efb3b79086dd954790\",\n \"score\": \"0.5109337\",\n \"text\": \"def describe(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e034ea853a7336efb3b79086dd954790\",\n \"score\": \"0.5109337\",\n \"text\": \"def describe(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da2141ccb73a26e572108b5ac86afae2\",\n \"score\": \"0.5107499\",\n \"text\": \"def compute(self):\\n \\n return _computeMethdos(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a33b3166d66683afdcbc4b672bfed5d6\",\n \"score\": \"0.51025355\",\n \"text\": \"def propertyValues():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a33b3166d66683afdcbc4b672bfed5d6\",\n \"score\": \"0.51025355\",\n \"text\": \"def propertyValues():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dfda7e5724cdd96650404785283f903\",\n \"score\": \"0.50917476\",\n \"text\": \"def succ(self, index_set=None):\\n return [ self.simple_reflection(i) for i in self.descents(index_set=index_set, positive=True) ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2525fcfc0b0231f2f79f226b0a59ac6e\",\n \"score\": \"0.5081335\",\n \"text\": \"def property_summary(self):\\n summary = {\\\"size\\\": self.size(),\\n \\\"depth\\\": self.depth(),\\n \\\"width\\\": self.width(),\\n \\\"bits\\\": self.num_cbits(),\\n \\\"factors\\\": self.num_tensor_factors(),\\n \\\"operations\\\": self.count_ops()}\\n return summary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"782d5f770ef5fe5216e3aea443318dcc\",\n \"score\": \"0.5073165\",\n \"text\": \"def properties():\\n N, M, Psi, GT = load_dataset()\\n print('# of sources: {}'.format(N))\\n print('# of object: {}'.format(M))\\n obs_n = 0\\n V = [0 for obj in range(M)]\\n for obj in range(M):\\n obs_n += len(Psi[obj])\\n V[obj] = len(set([val for s, val in Psi[obj]]))\\n print('# of observations: {}'.format(obs_n))\\n print('average # of values per object: {:1.3f}'.format(np.average(V)))\\n print('min # of values per object: {:1.3f}'.format(min(V)))\\n print('max # of values per object: {:1.3f}'.format(max(V)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1267c35e738c29b613b02c75bee56ec3\",\n \"score\": \"0.50695896\",\n \"text\": \"def generate_summary(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"181076b93364a9d6440edad08e9cb99f\",\n \"score\": \"0.50634605\",\n \"text\": \"def _get_full_objs_decorator(func):\\n addr = get_config()[\\\"mongo_rest_interface_addr\\\"]\\n get_obj = lambda t: json.loads(preprocess_resp(get_book_by(\\\"author_or_title\\\", addr, t)))[0]\\n get_overall_sentiment = lambda o: o[\\\"sentiment\\\"][\\\"overall\\\"][0]\\n\\n def __inner(base_title, scores, *args, **kwargs):\\n resp = func(base_title, scores, *args, **kwargs)\\n head_obj, *tail_objs = resp[\\\"resp\\\"]\\n\\n base_obj = get_obj(head_obj[\\\"title\\\"])\\n\\n for kvs in tail_objs:\\n kvs[\\\"title\\\"] = get_obj(kvs[\\\"title\\\"])\\n\\n kvs[\\\"title\\\"][\\\"sentiment\\\"][\\\"overall\\\"] = [get_overall_sentiment(base_obj),\\n get_overall_sentiment(kvs[\\\"title\\\"])]\\n _, *top_matches = resp[\\\"resp\\\"]\\n return top_matches\\n return __inner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7080ea558116200ad07f2645fdfb8cb\",\n \"score\": \"0.50614005\",\n \"text\": \"def permalist(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b59b5e8f1ebb0721ccf3e0b0a397fef7\",\n \"score\": \"0.5055935\",\n \"text\": \"def compute_properties(self):\\n # Calculate the area\\n self._area = self.calc_area()\\n self._Iz, self._Iy = self.calc_inertia()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93351b779816729dc368f80431863bf5\",\n \"score\": \"0.5048545\",\n \"text\": \"def properties(self):\\n if self.bound and \\\"properties\\\" in self.__stale:\\n self.refresh()\\n return super(Node, self).properties\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecea282618a86c234828d9b527ecd9a2\",\n \"score\": \"0.5042679\",\n \"text\": \"def interleaved_computed_per_page_values_and_summaries(self):\\n return self._interleaved_computed_per_page_values_and_summaries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"810328cc57897b797bbda6d27df0618c\",\n \"score\": \"0.5038257\",\n \"text\": \"def update_derived_property_records():\\n derive_property_classification()\\n derive_related_properties()\\n derive_url_patterns()\\n derive_property_usage()\\n derive_property_datatypes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a34dc84f791777729a301eca2318c93\",\n \"score\": \"0.50336385\",\n \"text\": \"def analyse(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c71458250acae4505869fe52aca6c916\",\n \"score\": \"0.50191516\",\n \"text\": \"def compute(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c71458250acae4505869fe52aca6c916\",\n \"score\": \"0.50191516\",\n \"text\": \"def compute(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"349e548a728b283e55ee3e8a5a65e401\",\n \"score\": \"0.5018438\",\n \"text\": \"def _optimize(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"012ed6daab996ded5cfb53db1b748881\",\n \"score\": \"0.5005248\",\n \"text\": \"def explain(self):\\n for i in self.m:\\n for j in i:\\n if j.visited==1:\\n txt='{:2d}'.format(j.data)\\n print(txt,end=' ')\\n else:\\n print(' ',end=' ')\\n print('')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ab95ae5087e0d81a419496cc5fb8442\",\n \"score\": \"0.49948457\",\n \"text\": \"async def describe(self):\\n\\n self._ready.clear()\\n\\n entries = await self._pool.fetch(self._script)\\n\\n details = collections.defaultdict(dict)\\n primaries = collections.defaultdict(list)\\n for entry in map(dict, entries):\\n table = entry.pop('table')\\n field = entry.pop('field')\\n details[table][field] = entry\\n if entry['main']:\\n primaries[table].append(field)\\n\\n self._details = dict(details)\\n self._primaries = dict(primaries)\\n\\n self._ready.set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b000a125aca09181facb8eb5f61294a7\",\n \"score\": \"0.4990571\",\n \"text\": \"def calc(self):\\r\\n for word in self:\\r\\n word.calc()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b000a125aca09181facb8eb5f61294a7\",\n \"score\": \"0.4990571\",\n \"text\": \"def calc(self):\\r\\n for word in self:\\r\\n word.calc()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e3f52a806b31ffe11af809df856b59c\",\n \"score\": \"0.49786073\",\n \"text\": \"def test_get_efficiency(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3118bd450580db71e9a2cdb5d56885e\",\n \"score\": \"0.4973626\",\n \"text\": \"def _get_calculations(self) -> dict:\\n calculations = {}\\n for element in dir(self):\\n # BLACKMAGIC : _repr_html_ must be skipped\\n \\\"\\\"\\\"\\n `_repr_html_` is an element of the directory of the PostProc object which\\n causes the search of calculated attributes to overflow, looping indefinitely\\n and never reaching the actual elements containing those said attributes.\\n It will be skipped until the issue has been properly identified and fixed.\\n You can uncomment the block below to observe how the directory loops after\\n reaching that element - acknowledging you are not skipping it.\\n \\\"\\\"\\\"\\n if element not in PROBLEMATIC_ATTRIBUTE_NAMES:\\n try:\\n # print(\\n # \\\"directory element : \\\",\\n # e,\\n # \\\", calculations list : \\\",\\n # calculations,\\n # )\\n for k in getattr(getattr(self, element), \\\"_calculates\\\"):\\n calculations[k] = element\\n except AttributeError:\\n pass\\n\\n return calculations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"950711e8283b79c14f79e0728cd0d77d\",\n \"score\": \"0.49721813\",\n \"text\": \"def compute_info(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2de725265a7c822bf42ac8fad2e3611c\",\n \"score\": \"0.49636874\",\n \"text\": \"def propertyItems():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2de725265a7c822bf42ac8fad2e3611c\",\n \"score\": \"0.49636874\",\n \"text\": \"def propertyItems():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59cf8734cf8658986cb62dbc2e56bf0a\",\n \"score\": \"0.495714\",\n \"text\": \"def calculate(self):\\r\\n attributes = {\\r\\n \\\"defense\\\": 0,\\r\\n \\\"influence\\\": 0,\\r\\n \\\"lands\\\": 0,\\r\\n \\\"law\\\": 0,\\r\\n \\\"population\\\": 0,\\r\\n \\\"power\\\": 0,\\r\\n \\\"wealth\\\": 0,\\r\\n }\\r\\n\\r\\n for attr in attributes:\\r\\n attributes[attr] += getattr(self.baseattributes, attr)\\r\\n attributes[attr] += getattr(self.realm, attr)\\r\\n attributes[attr] += getattr(self.destinycharacters, attr)\\r\\n for event in self.event_set.all():\\r\\n attributes[attr] += getattr(event, attr)\\r\\n setattr(self, attr, attributes[attr])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f588bb121d172b4d455b060d487aecf8\",\n \"score\": \"0.4955306\",\n \"text\": \"def get_properties():\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f457912db7e4b79f0efcee47cfb141\",\n \"score\": \"0.4943038\",\n \"text\": \"def get_prop():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3785277201b4bec926b9e6edec2be9eb\",\n \"score\": \"0.49346378\",\n \"text\": \"def get_properties(self):\\n raise NotImplementedError(\\\"not implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14a577e71092f0402aee7cd48674b59c\",\n \"score\": \"0.49253562\",\n \"text\": \"def get_properties(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d027674503c723afb7808d4c92be92b\",\n \"score\": \"0.49251354\",\n \"text\": \"def properties(self):\\n if self.bound and \\\"properties\\\" in self.__stale:\\n self.refresh()\\n return super(Rel, self).properties\",\n \"title\": \"\"\n },\n {\n \"docid\": \"901a041f66daf6cfe787c18c1c6939dd\",\n \"score\": \"0.4925062\",\n \"text\": \"def node_properties(self):\\n for n in self.nodes:\\n yield dict(n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae7ea9f7af562970e90b832e81e037a\",\n \"score\": \"0.49059898\",\n \"text\": \"def calculate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e14ee427d2ef763dd991be61c8317536\",\n \"score\": \"0.49059656\",\n \"text\": \"def compute_all_properties(cls):\\n all_properties = {}\\n\\n props = []\\n # Get all of the properties of the class including inherited ones\\n for m in dir(cls):\\n p = getattr(cls, m)\\n if isinstance(p, Property):\\n props.append((m, p))\\n\\n props = sorted(props, key=lambda p: p[1].instance_idx)\\n for prop_name, prop_value in props:\\n value_name = prop_value.name\\n if value_name:\\n all_properties[value_name] = prop_name\\n prop_name = value_name\\n else:\\n all_properties[prop_name] = prop_name\\n\\n Graph.guard_reserved_words(prop_name, cls)\\n return all_properties\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b55d73e0f5d867bd9189d23c4c07ddd1\",\n \"score\": \"0.49003336\",\n \"text\": \"def properties(cls):\\n matrix = []\\n for name, prop in readme_md.getmembers(cls, inspect.isdatadescriptor):\\n row = readme_md.Row(cls, name).get_columns()\\n matrix.append(row)\\n return mdown.table((\\\"`%s` properties\\\" % cls.__name__, \\\"`__doc__`\\\"), matrix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebc7802b4072d3156b6ef46985067d6d\",\n \"score\": \"0.48835608\",\n \"text\": \"def dict(self):\\n measures={}\\n for prop in self.properties:\\n measures[prop]=getattr(self,prop)\\n return measures\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76b65f5f8de1db3c7f17f2356fa2343\",\n \"score\": \"0.48829767\",\n \"text\": \"def _get_properties(edges_response):\\n es = Elasticsearch(args.elastic_hosts)\\n ic = IndicesClient(es)\\n \\\"\\\"\\\" edges_response looks like ...\\n {\\\"responses\\\": [{\\\"status\\\": 200,\\n \\\"hits\\\": {\\\"hits\\\": [], \\\"total\\\": 69644, \\\"max_score\\\": 0.0},\\n \\\"_shards\\\": {\\\"successful\\\": 5, \\\"failed\\\": 0, \\\"total\\\": 5},\\n \\\"took\\\": 1,\\n \\\"aggregations\\\": {\\\"Labels\\\":\\n {\\\"buckets\\\": [{\\\"key\\\": \\\"Gene\\\", \\\"doc_count\\\": 39892},\\n {\\\"key\\\": \\\"Pubmed\\\", \\\"doc_count\\\": 18044},\\n {\\\"key\\\": \\\"Individual\\\", \\\"doc_count\\\": 11036},\\n {\\\"key\\\": \\\"GeneFamily\\\", \\\"doc_count\\\": 589},\\n {\\\"key\\\": \\\"Cohort\\\", \\\"doc_count\\\": 40},\\n {\\\"key\\\": \\\"Project\\\", \\\"doc_count\\\": 33},\\n {\\\"key\\\": \\\"GeneDatabase\\\", \\\"doc_count\\\": 10}]\\n }\\n \\\"\\\"\\\"\\n if (\\n \\\"responses\\\" not in edges_response or\\n 'aggregations' not in edges_response['responses'][0]\\n ):\\n raise tornado.gen.Return(\\n None\\n )\\n print(edges_response)\\n edges = edges_response[\\\"responses\\\"][0]['aggregations']['Labels']['buckets'] # NOQA\\n edge_names = []\\n for edge in edges:\\n edge_names.append(edge['key'])\\n raise tornado.gen.Return(\\n ic.get_mapping(index=self.index,\\n doc_type=edge_names, request_timeout=60)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5aa11c2100c2bec77e5dd3c2b12dff1\",\n \"score\": \"0.48822555\",\n \"text\": \"def _automap_properties(self, protected_methods=True):\\n for prop_name, prop in self._properties.items():\\n # search for object methods\\n getter_name = 'get_'\\n setter_name = 'set_'\\n if protected_methods:\\n getter_name = '_'+getter_name\\n setter_name = '_'+setter_name\\n\\n try:\\n prop.getter = self.__getattribute__(getter_name+prop_name)\\n except AttributeError:\\n # not found\\n pass\\n\\n try:\\n prop.setter = self.__getattribute__(setter_name+prop_name)\\n except AttributeError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f984008e01be4d6409bc4de00b4d274a\",\n \"score\": \"0.48814657\",\n \"text\": \"def summary(self):\\n# print \\\"***********AT BASE CLASS summary()***********\\\"\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34b37622a4a2329d14f6410744bf9fd4\",\n \"score\": \"0.4873339\",\n \"text\": \"def get_properties(self):\\n return self.property_desc.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"319398ae65d544fd7bfe2ed4a8402cb9\",\n \"score\": \"0.48716626\",\n \"text\": \"def get_properties(self) -> Dict:\\n _dict = {}\\n\\n for _key in [\\n k for k in vars(self).keys()\\n if k not in AnalyzedResult._properties\\n ]:\\n _dict[_key] = getattr(self, _key)\\n return _dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2790abc1f36414395152a9aa25f3ba84\",\n \"score\": \"0.48583788\",\n \"text\": \"def _propertyMap():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6439a3e9734749a82b0ac08f65985663\",\n \"score\": \"0.48567018\",\n \"text\": \"def _calculate(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8e18f224adf606a41b30b201be1fdd2\",\n \"score\": \"0.4851939\",\n \"text\": \"def item_view_object(context, request):\\n properties = item_links(context, request)\\n if not asbool(request.params.get('skip_calculated')):\\n calculated = calculate_properties(context, request, properties)\\n properties.update(calculated)\\n return properties\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef69ea85d9aee3b6c413fa287f57eec\",\n \"score\": \"0.48498613\",\n \"text\": \"def calculate_properties(self, atoms, properties):\\n # TODO: Check atoms.\\n\\n if \\\"rdf\\\" in properties:\\n self.results[\\\"rdf\\\"] = self.data_handler.target_calculator.\\\\\\n get_radial_distribution_function(atoms)\\n if \\\"tpcf\\\" in properties:\\n self.results[\\\"tpcf\\\"] = self.data_handler.target_calculator.\\\\\\n get_three_particle_correlation_function(atoms)\\n if \\\"static_structure_factor\\\" in properties:\\n self.results[\\\"static_structure_factor\\\"] = self.data_handler.\\\\\\n target_calculator.get_static_structure_factor(atoms)\\n if \\\"ion_ion_energy\\\" in properties:\\n self.results[\\\"ion_ion_energy\\\"] = self.\\\\\\n last_energy_contributions[\\\"e_ewald\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0f72abc460055d57a106eb4be922eaf\",\n \"score\": \"0.48431128\",\n \"text\": \"def explore(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f7f831411a4294950e812c639385da4\",\n \"score\": \"0.48429444\",\n \"text\": \"def _compute_experiment_statistics(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e87eb70fc8d2a953382d8bb6dd98989\",\n \"score\": \"0.48422936\",\n \"text\": \"def preheat_oven(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c849196a34453b0930cebb5d3564793d\",\n \"score\": \"0.48355392\",\n \"text\": \"def calculate_stats(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88266901cb4a33d83f9e9a65f5c55a52\",\n \"score\": \"0.4831468\",\n \"text\": \"def _explain(self):\\n self._explain_simple_consensus()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f20fb9d6150b6535b9da1b58a9e5b8b6\",\n \"score\": \"0.4828499\",\n \"text\": \"def def_main_properties(self): \\n raise NotImplementedError('def_main_properties has to be implemented')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e3afe11f8cd519164755df422ceb676\",\n \"score\": \"0.48283434\",\n \"text\": \"def __init__(self):\\n self.intermediate = {}\\n self.result = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61df8d1f0db70b4e34eacc623a692525\",\n \"score\": \"0.48221806\",\n \"text\": \"def _compute_descriptor(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f45fc55020fddb60c60bb46b7ff04420\",\n \"score\": \"0.48158824\",\n \"text\": \"def transformer_properties(self):\\n for key, transformer in self._transformer.items():\\n yield (key, transformer.mean_, transformer.stdev_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d035c637678eba36787becfe27c6bdec\",\n \"score\": \"0.48091656\",\n \"text\": \"def compat_explain(cur):\\n res = cur.explain()\\n if 'nscannedObjects' in res:\\n res['executionStats'] = dict(\\n nReturned=res.pop('n'),\\n totalKeysExamined=res.pop('nscanned'),\\n totalDocsExamined=res.pop('nscannedObjects'),\\n executionTimeMillis=res.pop('millis'),\\n )\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"014770ad1416e17d09ef96cec7ec50aa\",\n \"score\": \"0.4799041\",\n \"text\": \"def get_score(self, real_data, synthetic_data, metadata, progress_bar=None):\\n if self._single_table_property is None:\\n raise NotImplementedError()\\n\\n all_details = []\\n for table_name in metadata['tables']:\\n property_instance = self._single_table_property()\\n self._properties[table_name] = property_instance\\n self._properties[table_name].get_score(\\n real_data[table_name],\\n synthetic_data[table_name],\\n metadata['tables'][table_name],\\n progress_bar\\n )\\n all_details.append(property_instance._details)\\n\\n self.is_computed = True\\n all_details = pd.concat(all_details)\\n return all_details['Score'].mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb42787d8b3c67a7c80f111cca104ed1\",\n \"score\": \"0.47980973\",\n \"text\": \"def propertyMap():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb42787d8b3c67a7c80f111cca104ed1\",\n \"score\": \"0.47980973\",\n \"text\": \"def propertyMap():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729b9b655b13377f742b5ea80b286184\",\n \"score\": \"0.4794479\",\n \"text\": \"def properties(self):\\n return self._properties\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729b9b655b13377f742b5ea80b286184\",\n \"score\": \"0.4794479\",\n \"text\": \"def properties(self):\\n return self._properties\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb0858e5c8ae1c4a429c71c7fd66867\",\n \"score\": \"0.47892186\",\n \"text\": \"def read_relation_property(self):\\n relation_property_head = {x: [] for x in range(len(self.relations))}\\n relation_property_tail = {x: [] for x in range(len(self.relations))}\\n\\n for t in self.triplets['train']:\\n relation_property_head[t.r].append(t.h)\\n relation_property_tail[t.r].append(t.t)\\n\\n self.relation_property = {x: (len(set(relation_property_tail[x]))) / ( \\\\\\n len(set(relation_property_head[x])) + len(set(relation_property_tail[x]))) \\\\\\n for x in relation_property_head.keys()}\\n\\n return self.relation_property\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abcda2d6d03dd8ed602c4b9044ef929c\",\n \"score\": \"0.4787436\",\n \"text\": \"def derive_property_usage():\\n logger.info('Deriving property usage ...')\\n data = statistics.get_json_data('properties')\\n\\n usage = defaultdict(dict)\\n keys = ['i', 's', 'q', 'e', 'qs', 'pc']\\n\\n for pid in data:\\n for key in keys:\\n if key in data[pid] and data[pid][key]:\\n usage[pid][key] = data[pid][key]\\n\\n statistics.update_json_data('properties/usage', usage)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fa31df26635e049f6793e66f1e61f4b\",\n \"score\": \"0.47843242\",\n \"text\": \"def expand_properties(self, fmt):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994097,"cells":{"query_id":{"kind":"string","value":"3281e3c9560e50b7f3b70816c2c11ccd"},"query":{"kind":"string","value":"this function responsible for the initial skip grams"},"positive_passages":{"kind":"list like","value":[{"docid":"fee1eac4d6717fd2cddf61b9d2eb07ec","score":"0.59567994","text":"def initial_skip_grams(full_sentence_info, k_skip, n_gram):\n if n_gram == 1:\n return [[full_sentence_info[0]]]\n grams = []\n for j in range(min(k_skip + 1, len(full_sentence_info) - 1)):\n kmj_skip_nm1_grams = initial_skip_grams(full_sentence_info[j + 1:], k_skip - j, n_gram - 1)\n if kmj_skip_nm1_grams is not None:\n for gram in kmj_skip_nm1_grams:\n grams.append([full_sentence_info[0]] + gram)\n return grams","title":""}],"string":"[\n {\n \"docid\": \"fee1eac4d6717fd2cddf61b9d2eb07ec\",\n \"score\": \"0.59567994\",\n \"text\": \"def initial_skip_grams(full_sentence_info, k_skip, n_gram):\\n if n_gram == 1:\\n return [[full_sentence_info[0]]]\\n grams = []\\n for j in range(min(k_skip + 1, len(full_sentence_info) - 1)):\\n kmj_skip_nm1_grams = initial_skip_grams(full_sentence_info[j + 1:], k_skip - j, n_gram - 1)\\n if kmj_skip_nm1_grams is not None:\\n for gram in kmj_skip_nm1_grams:\\n grams.append([full_sentence_info[0]] + gram)\\n return grams\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d5df531ee98e066ac7f749a019d9ea17","score":"0.6951125","text":"def Skip(self, skip=True):","title":""},{"docid":"62f28dd39a3b4451ebad8dc5424e80ef","score":"0.6750491","text":"def skip(self):\n self.next()","title":""},{"docid":"4092f1a261f3ddab45595c1952c32e68","score":"0.67421526","text":"def skip(self, count: int, /) -> None:\n ...","title":""},{"docid":"01b7e6105f79494336eddb5d0a432a00","score":"0.67299765","text":"def skip(self, n):\n pass","title":""},{"docid":"874a9fbd87e6a04065af897211780ef7","score":"0.6707515","text":"def skip(self) -> bool:","title":""},{"docid":"bf962400dbab3e823e4704729366621c","score":"0.64574397","text":"def test_skip(self):\n pass","title":""},{"docid":"bf962400dbab3e823e4704729366621c","score":"0.64574397","text":"def test_skip(self):\n pass","title":""},{"docid":"bf962400dbab3e823e4704729366621c","score":"0.64574397","text":"def test_skip(self):\n pass","title":""},{"docid":"81d61bf2b408b4dd00ddcf998699d6a2","score":"0.6378706","text":"def skip(*args,**kwarg): \n pass","title":""},{"docid":"1eff94be5aead165408f70bbbd4da62d","score":"0.63501203","text":"def GetSkipped(self):","title":""},{"docid":"a1ec771adf0f58b306ce03d0dbb25613","score":"0.63429886","text":"def prescan(self): # -> None:\n ...","title":""},{"docid":"5393b5337bdf77fa2e3b4d089cea1c79","score":"0.6274985","text":"def skip ( self ) :\n return self.__skip","title":""},{"docid":"5393b5337bdf77fa2e3b4d089cea1c79","score":"0.6274985","text":"def skip ( self ) :\n return self.__skip","title":""},{"docid":"8229e6239cef6b0d057ec6fdee078eb2","score":"0.62682694","text":"def SkipSize(self) -> int:","title":""},{"docid":"9a38f1b34b6c8cb9b4f79ff282aaded7","score":"0.62615716","text":"def logSkippedPars(self):\n logSkippedPars(self)","title":""},{"docid":"4d5c16c75adb9a7eeca2276119cf34f5","score":"0.62334174","text":"def skip(t, n):\n pu(t)\n fd(t, n)\n pd(t)","title":""},{"docid":"0b56d06d54a8f66add86db58117a49c7","score":"0.61965966","text":"def test_skip(self):","title":""},{"docid":"0b56d06d54a8f66add86db58117a49c7","score":"0.61965966","text":"def test_skip(self):","title":""},{"docid":"47caa2d1cf73d795799dd027a6aa209d","score":"0.61688685","text":"def skip(self):\n self.currentiv += 1\n self.next()","title":""},{"docid":"9742035f1406b108a015656208d04b17","score":"0.61307335","text":"def skip(self, amount: int):\n raise NotImplementedError","title":""},{"docid":"e7dd27f54c5f6ae1deb3b787d0183f62","score":"0.6098544","text":"def unskip(self, step):\n self.skip = [i for i in self.skip if step != i]","title":""},{"docid":"fdd0a53894f2abeeaac78d599775a394","score":"0.6093332","text":"def make_skip():\n return { \"kind\": \"Skip\" }","title":""},{"docid":"7f961f1c83bcedb420dfff15924da229","score":"0.6080094","text":"def __init__(self, apply=1.0):\n self.skip = apply","title":""},{"docid":"1ee882c65921e5428eeb6aa4a53f1cdc","score":"0.6065739","text":"def skip(self, n=1):\r\n for x in range(n):\r\n next(self)","title":""},{"docid":"56aa8181063c52f56d0d0d4e9730c126","score":"0.6059741","text":"def skip(self) -> None:\n\n self._skipped.append(\n {\n \"line\": self.item[\"line\"],\n \"column\": self.item[\"line\"],\n \"kind\": self.kind,\n \"name\": self.name,\n }\n )","title":""},{"docid":"47e74ce399d2cd23f20ea7cbf5aa2c18","score":"0.60528964","text":"def _skip_sampling(self, total, skip_ids):\n rand_id = np.random.randint(total - len(skip_ids))\n return rand_id if rand_id < min(skip_ids) else rand_id + len(skip_ids)","title":""},{"docid":"3cbb136836f2356532a94bfb50e5a937","score":"0.60461617","text":"def _skip(self, dataset):\n determ = self._deterministic\n return dataset.map(\n self._add_param,\n num_parallel_calls=tf.data.AUTOTUNE,\n deterministic=determ)","title":""},{"docid":"76bfb511fc368d0073c9673538ac45a5","score":"0.60394377","text":"def _skipped_inc(self, item):\n\t\tself.skipped += 1","title":""},{"docid":"fa383704ac626f135aeb789f5c21612d","score":"0.60279024","text":"def skip(self, n: int):\n # makes use of knowing the number of nodes\n # does some math, skips extraneous full loops\n for x in range(n % self.numnodes):\n # updates the correct amount of times\n self.go_next()","title":""},{"docid":"893f6501f28c883999a4fc19cf3ee265","score":"0.6023453","text":"def skip(self, params):\n return False","title":""},{"docid":"29e39bb1f76561be0bb867058512838e","score":"0.5973915","text":"def skip(self, amount):\n for i in range(0, amount):\n self.next()\n time.sleep(0.5) # FIXME: Direct access to playlist","title":""},{"docid":"a7178b5e5f0fc0cd8c02c9f7aa3eedf3","score":"0.59552014","text":"def n_skip_steps(self) -> int:\n return self._n_skip_steps","title":""},{"docid":"48eb3a3b0fdce736c2abd7a127390807","score":"0.5947714","text":"def skip(self):\n \n self.playing_i = (self.playing_i + 1) % len(self.players)\n self.playing = self.players[self.playing_i]","title":""},{"docid":"213e9341e561f3661095c316faa0cf57","score":"0.59147024","text":"def skip_forward(self, j):\n pass","title":""},{"docid":"c8b17e822015c4decb0672e0a9801ebd","score":"0.59073013","text":"def addSkip(self, test):\n pass","title":""},{"docid":"b2885727e7a68fbc46afd96577d55917","score":"0.586508","text":"def skipToken():\n raise SkipToken","title":""},{"docid":"a915c56a4f2b8c202d712020443e471f","score":"0.5860903","text":"def on_skip(self, record):","title":""},{"docid":"69f3bbe77fe5972dd27fd8ee98cdb9a3","score":"0.58512914","text":"def _heuristic_skip_inspection(self):\n self._ninsps = self._ninsps + 1 if hasattr(self, '_ninsps') else 1\n if self.mode in ['tile', 'only_tile'] and self._ninsps < 3:\n return True\n return False","title":""},{"docid":"f363a9888452ff5429121b28d26ae811","score":"0.5842535","text":"def skip(self, count):\n self.lexer.skip(count)","title":""},{"docid":"bafb62b5bde62b0195b50ecefaf25457","score":"0.5828772","text":"def skip_gd_pair():\n skip = [[10.9697, 4.4073],\n [15.3189, 2.0326],\n [23.7452, 24.5140],\n [28.4283, 20.9148],\n [35.1739, 1.1985],\n [40.0218, -0.6527],\n [41.2998, -1.2216],\n [41.9780, 0.6380],\n [123.3026, 54.2806],\n [126.6732, 45.7450],\n [131.5022, 7.0747],\n [150.3681, 50.4663],\n [150.3362, 55.8989], # FOS lens\n [158.2551, 47.2532],\n [164.0395, 55.2669],\n [170.1281, 54.7426],\n [176.7206, 16.7400],\n [188.5052, 6.5367],\n [190.7380, 25.7174],\n [193.7286, 8.7812],\n [196.9841, 4.3710],\n [198.7737, 47.9047],\n [201.3239, 37.6164],\n [211.2581, 44.8000],\n [222.7320, 47.0272],\n [238.3773, 22.5040],\n [243.2571, 8.1350],\n [253.7555, 26.0882],\n [357.0800, 0.9549],\n [116.9959, 43.3015],\n [184.6687, 50.2621],\n [166.63912, -18.35661], # FOS lens\n [166.6396, -18.3567], # FOS lens\n [216.9947, -1.3601],\n [9.9763, -27.4229], # 2QZ pair\n [341.6578, -29.4963], # 2QZ pair\n ]\n # Table\n sa = np.array(skip)\n stbl = Table()\n stbl['RA'] = sa[:,0]\n stbl['DEC'] = sa[:,1]\n # Return\n return stbl","title":""},{"docid":"e41e2fcc3ec4b95c491d85eba4313b05","score":"0.58279467","text":"def _must_skip(self):\n if self.bananas_record[\"integradoERP10\"] != \"0\":\n return True\n return","title":""},{"docid":"b08980845210b010348007e2d8c591a9","score":"0.58222044","text":"def skip_next(self) -> None:\n \n self.spotipyObject.next_track()","title":""},{"docid":"c978322e212ef48e058f6b408ef6ff02","score":"0.57943505","text":"def get_skipgrams(self):\n # List to hold skipgrams and associated metadata\n skipgram_data = []\n # List of indexes (of original texts list from dataframe) where we are throwing the doc out due to short length\n self.purged_docs = []\n # List holding the number of tokens in each document\n self.doc_lengths = []\n # ID to count document IDS\n doc_id_counter = 0\n print(\"\\n---------- Getting Skipgrams ----------\")\n for i, t in tqdm(enumerate(self.idx_data)):\n pairs, _ = skipgrams(t,\n vocabulary_size = self.vocab_size,\n window_size = self.window_size,\n shuffle=True,\n negative_samples=0)\n \n if len(pairs) > 2:\n for pair in pairs:\n temp_data = pair\n # Appends doc ID\n temp_data.append(doc_id_counter)\n # Appends document index (index of original texts list from dataframe)\n temp_data.append(i)\n skipgram_data.append(temp_data)\n self.doc_lengths.append(len(t))\n doc_id_counter+=1\n else:\n # We purge docs with less than 2 pairs\n self.purged_docs.append(i)\n\n self.skipgrams_df = pd.DataFrame(skipgram_data)","title":""},{"docid":"95fbef75ca01e8646532379961971f91","score":"0.57853013","text":"def set_skip(self,value=True):\n\n self.skip = value","title":""},{"docid":"824cde9ce4286701d1695c911d6d6d2d","score":"0.5774947","text":"def skip_gram_features(full_sentence_info_list, k_skip, n_gram):\n # Open a file\n shutil.rmtree('../dataset/output') # removes a directory with sub directories and files\n os.mkdir('../dataset/output') # creates a new directory\n output_file = open('../dataset/output/jurry.skip.txt', 'a') # open a file in append mode\n\n grams = []\n for full_sentence_info in full_sentence_info_list: # iterates ove the values (sentence_info(s)) list\n if n_gram == 0 or len(full_sentence_info) == 0:\n return None\n for i in range(len(full_sentence_info) - n_gram + 1):\n grams.extend(initial_skip_grams(full_sentence_info[i:], k_skip, n_gram))\n for gram in grams: # for printing the skip_gram\n for gra in gram:\n output_file.write(gra.token + '_')\n output_file.write('\\n')\n output_file.close() # closes the file","title":""},{"docid":"bf056b6d221093852ab970a0f5b03ff9","score":"0.5765171","text":"def add_skip(self, test, reason):\n self.stream.writeln('SKIP', YELLOW)\n self.write_details(reason, test.parents_count, YELLOW)","title":""},{"docid":"9e74a5ce8df99c05bb42830bcb68f87e","score":"0.5762922","text":"def _choose_skip_length(self):\n max_length = min((self._skip_frame_range[1] - self._skip_frame_range[0]), \\\n (self._full_dataset_view // self._skip_increase_interval))\n self._skip_length = 0 if max_length == 0 else randint(0, max_length)","title":""},{"docid":"f092d2b433791add56819844a214f61f","score":"0.57521266","text":"def _pre_water_erosion_steps(self):\n pass","title":""},{"docid":"4dd7f286612575fcd1c2e89b4903ca28","score":"0.5745903","text":"def test_skipping(self):\n raise NotImplementedError()","title":""},{"docid":"802c6738fae7e2655838098cdb45f268","score":"0.5745476","text":"def skipRemaining(self):\n return False","title":""},{"docid":"158139a207689b98aad9ad126e782cb2","score":"0.5741946","text":"def test_skips(self):\n assert not hasattr(self.t, \"test_code_cell_1\")","title":""},{"docid":"d9fdae50ec89b1df68dc406866bd15bc","score":"0.5739221","text":"def skip_token():\n raise SkipToken","title":""},{"docid":"20496c1dc81a3ddda96b33ed8137a9bb","score":"0.57227665","text":"def _skipgram_generator(self, doc):\n return it.izip(*[it.islice(seq, i, None) for i, seq in enumerate(it.tee(doc, self.skipgram_len))])","title":""},{"docid":"04959898e940422e797a11ab1559f5da","score":"0.57183963","text":"def _get_skip_shapes(self, in_shape):\n in_shape = utils.convert_shape_to_np_array(in_shape)\n sh = in_shape\n# ch_axis = self.channel_axis\n# if ch_axis < 0:\n# ch_axis = len(in_shape) + self.channel_axis\n \n skip_out_shapes = list()\n# skip_in_shapes = list()\n for block in self.block_list:\n bdef = self.block_definitions[block]\n out_num = self._get_out_num(sh[self.channel_axis], block)\n # get indices of skip_save (possibly more than one)\n skip_concat_idx = [i for i, layer in enumerate(bdef) if layer=='skip_concat']\n# skip_save_idx = [i for i, layer in enumerate(bdef) if layer=='skip_save']\n if not skip_concat_idx:# and not skip_save_idx:\n # just determine block out shape\n sh = self._get_layer_sequence_output_shape(sh, bdef, out_num)\n# elif skip_save_idx:\n# # sequence until skip concat\n# seq = bdef[:skip_save_idx[0]] \n# sh = self._get_layer_sequence_output_shape(sh, seq, out_num)\n# skip_in_shapes.append(sh)\n# # will only enter for-loop if len(skips)>1\n# for i in range(len(skip_save_idx)-1):\n# seq = bdef[skip_save_idx[i]:skip_save_idx[i+1]]\n# sh = self._get_layer_sequence_output_shape(sh, seq, out_num)\n# skip_in_shapes.append(sh)\n# # sequence after skip concat\n# seq = bdef[skip_save_idx[-1]:] \n# sh = self._get_layer_sequence_output_shape(sh, seq, out_num)\n elif skip_concat_idx:\n # sequence until skip concat\n seq = bdef[:skip_concat_idx[0]] \n sh = self._get_layer_sequence_output_shape(sh, seq, out_num)\n skip_out_shapes.append(sh)\n # will only enter for-loop if len(skips)>1\n for i in range(len(skip_concat_idx)-1):\n seq = bdef[skip_concat_idx[i]:skip_concat_idx[i+1]]\n sh = self._get_layer_sequence_output_shape(sh, seq, out_num)\n skip_out_shapes.append(sh)\n # sequence after skip concat\n seq = bdef[skip_concat_idx[-1]:] \n sh = self._get_layer_sequence_output_shape(sh, seq, out_num)\n# if len(skip_out_shapes) != len(skip_in_shapes):\n# raise RuntimeError(\"There are \" + str(len(skip_out_shapes)) + \n# \"skip_concats and \" + str(len(skip_in_shapes)) +\n# \"skip_saves. This needs to match.\")\n# skip_in_shapes = skip_in_shapes[::-1]\n# skip_shapes = list()\n# for i in range(len(skip_out_shapes)):\n# skip = skip_out_shapes[i]\n# skip[self.im_axis] = skip_in_shapes[i][self.im_axis]\n# skip[self.channel_axis] = skip_in_shapes[i][self.channel_axis]\n# skip_shapes.append(skip)\n# list([skip_in_shapes[self.im_axis]] + \n# [skip_out_shapes[spatial_axes])\n# skip_shapes.insert(ch_axis, skip_in_shapes[self.im_axis])\n if self.input_output_skip:\n skip_out_shapes.append(self._get_net_output_shape(in_shape))\n return skip_out_shapes","title":""},{"docid":"8e1865c975c18feca094e819d44fd346","score":"0.57174057","text":"def handle_skipped(self, song):\n self.update_item(song['beets_item'], 'skip_count', increment=1)\n self._log.info('skipped {0}', displayable_path(song['path']))","title":""},{"docid":"e2093ffbce03e3375de0639135b58baf","score":"0.5697211","text":"def set_skip(self, n):\n\n self.skip = n","title":""},{"docid":"b9b012ec3d25a5c0fbebb83b0b3ff7e8","score":"0.5680758","text":"def skip(self):\n\n\t\tif not len(self.args.skip):\n\t\t\tself._show_skip()\n\t\telse:\n\t\t\t# This could signify STDIN contains json or xml to intrepret as ytids???\n\t\t\tif self.args.json:\n\t\t\t\traise NotImplementedError(\"--json not meaningful when adding skipped videos\")\n\t\t\tif self.args.xml:\n\t\t\t\traise NotImplementedError(\"--xml not meaningful when adding skipped videos\")\n\n\t\t\tytids = list(set(self.args.skip))\n\t\t\tytids = ['-' + _[1:] for _ in ytids if _[0] == '='] + [_ for _ in ytids if _[0] != '=']\n\n\t\t\t# Split into videos and playlists\n\t\t\tv_ytids = [_ for _ in ytids if len(_) == 11]\n\t\t\tpl_ytids= [_ for _ in ytids if len(_) != 11]\n\n\n\t\t\tself.db.begin()\n\t\t\tprint(\"Marking videos to skip (%d):\" % len(v_ytids))\n\t\t\tfor ytid in v_ytids:\n\t\t\t\tprint(\"\\t%s\" % ytid)\n\t\t\t\trow = self.db.v.select_one(\"rowid\", \"`ytid`=?\", [ytid])\n\t\t\t\tself.db.v.update({\"rowid\": row['rowid']}, {\"skip\": True})\n\n\t\t\t\t# Delete any sleep times for this video, this will not error if no rows present\n\t\t\t\tself.db.v_sleep.delete({'ytid': ytid})\n\n\t\t\t\t# Hook: \"skip-video\"\n\t\t\t\tif not self.args.nohook:\n\t\t\t\t\trun_hook(self.db, 'skip-video', ytid=ytid)\n\n\t\t\tprint(\"Marking playlists to skip (%d):\" % len(pl_ytids))\n\t\t\tfor ytid in pl_ytids:\n\t\t\t\tprint(\"\\t%s\" % ytid)\n\t\t\t\trow = self.db.pl.select_one(\"rowid\", \"`ytid`=?\", [ytid])\n\t\t\t\tself.db.pl.update({\"rowid\": row['rowid']}, {\"skip\": True})\n\n\t\t\t\t# Hook: \"skip-playlist\"\n\t\t\t\tif not self.args.nohook:\n\t\t\t\t\trun_hook(self.db, 'skip-playlist', plid=ytid)\n\n\t\t\tself.db.commit()","title":""},{"docid":"f4c4d77c1f083a16dbbab434b181654f","score":"0.56698376","text":"def should_skip(self):\n\n return self.skip","title":""},{"docid":"238e9ed6b6678061ec99f39c0de60f01","score":"0.56594795","text":"def skipped(self, bot_name, deferred):\n pass","title":""},{"docid":"3c9ecbf61a6a952a25c8c475be00faff","score":"0.56442344","text":"def WasSkipped(self):","title":""},{"docid":"3c9ecbf61a6a952a25c8c475be00faff","score":"0.564423","text":"def WasSkipped(self):","title":""},{"docid":"506802fdcc6d4796fd68623a4e99320a","score":"0.56310844","text":"def skip(self, amount):\n return self.__class__(self.graph, self._labels, self._conditions,\n self._order_by, amount, self._limit)","title":""},{"docid":"3731700f48639672eb4cecb7d643f599","score":"0.5629662","text":"def skipFrame(self, frames, cap):\n totalFrames = self.getFPS(cap)\n skip = totalFrames // frames # 30 / 3 = 10 1 ..1 ..11 ..21 ..22 .. 30 *** 2 \n return skip","title":""},{"docid":"7b51737de404de773e564718c366b0af","score":"0.56170434","text":"def clear_empties(self):\n keep = self.galpairs > 0\n self.sep, self.galpairs, self.ranpairs, self.est = \\\n self.sep[keep], self.galpairs[keep], self.ranpairs[keep],\\\n self.est[keep]\n self.nbin = len(self.sep)\n return self.nbin","title":""},{"docid":"0994fe2efd4a0254320d86ac5ee20df1","score":"0.5602012","text":"def skip_tiles(self, tiles=None, tiles_batches=None):\n logger.debug(\"determine which tiles to skip...\")\n # only check for existing output in \"continue\" mode\n if self.config.mode == \"continue\":\n yield from tiles_exist(\n config=self.config,\n process_tiles=tiles,\n process_tiles_batches=tiles_batches,\n )\n # otherwise don't skip tiles\n else:\n if tiles_batches:\n for batch in tiles_batches:\n for tile in batch:\n yield (tile, False)\n else:\n for tile in tiles:\n yield (tile, False)","title":""},{"docid":"a1ee24891f27db18e2367aaa02a8b033","score":"0.55959487","text":"def test_with_skip(self):\n # self.assertEqual(3, add(1, 2))\n # self.assertNotEqual(3, add(2, 2))","title":""},{"docid":"a1ee24891f27db18e2367aaa02a8b033","score":"0.55959487","text":"def test_with_skip(self):\n # self.assertEqual(3, add(1, 2))\n # self.assertNotEqual(3, add(2, 2))","title":""},{"docid":"cd7941b90a877a2fcfb21b13cc439e83","score":"0.5585231","text":"def __manage_rows_skipping(self, kwargs, skipchunks, skiprows, nrows):\n if skipchunks is not None:\n skiprows = skipchunks * kwargs.get('chunksize', self.chunksize)\n if skiprows is not None:\n kwargs['skiprows'] = skiprows\n if self.columns is not None:\n col = self.columns[0]\n kwargs['skiprows'] += len(col) if isinstance(col, tuple) else 1\n kwargs['header'] = None\n kwargs['names'] = self.columns\n if nrows is not None:\n kwargs['nrows'] = nrows","title":""},{"docid":"e54b0be2ffffbc065e23cbaa20b9c66b","score":"0.5567402","text":"def skipgrams(pages, max_context, l):\n for words in pages:\n for index, current in enumerate(words):\n if current != 0 and not subsample(pages.counter[pages.decode(current)]/pages.total_count, l):\n continue\n context = np.random.randint(1, max_context)\n for target in words[max(0, index - context): index]:\n yield current, target\n for target in words[index + 1: index + context + 1]:\n yield current, target","title":""},{"docid":"668064206217007e075b18f3c1b189e3","score":"0.5560861","text":"def skip(self, n):\n self.pos += n","title":""},{"docid":"5bd8b2e558abb903e98cf61f3826d16c","score":"0.5554391","text":"def SkipGramOptionsStart(builder):\n return Start(builder)","title":""},{"docid":"69d5d3b895903d2d77448322bb2e94c3","score":"0.55486065","text":"def _check_skip(self, planred, name):\n # Update the attributes\n self._skipexred = {k: exists(v) for k, v in self.summpaths.items()}\n self._skipexplan = {k: exists(v) for k, v in self.planpaths.items()}\n _name = name if isinstance(name, str) else f\"lv{name}\"\n\n # Check if the step is skipped\n if planred.lower().startswith(\"r\"):\n if self._skipexred[name]:\n if self.verbose >= 1:\n print(f\"SKIP: Summary exists for {_name} at {self.summpaths[name]}\")\n return self._read_summ(name)\n\n elif planred.lower().startswith(\"p\"):\n if self._skipexplan[name]:\n if self.verbose >= 1:\n print(f\"SKIP: Planer exists for {_name} at {self.planpaths[name]}\")\n return self._read_plan(name)","title":""},{"docid":"06c84841a1e02e383768daceabeb89c2","score":"0.5548301","text":"def skip_arm(self):\n self._next_arm()\n self.list_next = self.list_next[1:]","title":""},{"docid":"3170d44e5013cade5703f785793b55cf","score":"0.55476904","text":"def skip(self, size):\n self.__pntr += size","title":""},{"docid":"ebdb3fb5d1a62726faaefd7b47bde15f","score":"0.55332303","text":"def skipped_sim_steps(self) -> int:\n skipped_steps = 0\n if self.enable_step_throttling:\n while skipped_steps < self.n_skip_steps:\n skipped_steps = self.obj_handle.get_sim_step() - self.prev_sim_step\n time.sleep(1e-5)\n self.prev_sim_step = self.obj_handle.get_sim_step()\n if skipped_steps > self.n_skip_steps:\n print(\n 'WARN: Skipped {} steps, Default skip limit {} Steps'.format(\n skipped_steps,\n self.n_skip_steps\n )\n )\n else:\n skipped_steps = self.obj_handle.get_sim_step() - self.prev_sim_step\n self.prev_sim_step = self.obj_handle.get_sim_step()\n\n self._skipped_sim_steps = skipped_steps\n\n return self._skipped_sim_steps","title":""},{"docid":"48e6df9d636f87999f0bd9e9443112dc","score":"0.5514506","text":"def _get_skip_info(self, helix_size, base_list):\n skip_info = [0]*helix_size\n for base in base_list:\n skip_info[base.p] = base.num_deletions\n return skip_info","title":""},{"docid":"81c0aa508c97312e471d31ed729be6dc","score":"0.550138","text":"def skip(self, n):\n self.incr_index(n)\n return self","title":""},{"docid":"5390c985177e4691846d25d779343346","score":"0.5497097","text":"def n_skip_steps(self, value: int):\n self._n_skip_steps = value\n self._world_handle.set_num_step_skips(value)\n return","title":""},{"docid":"623e22884d3b7bca211c253434d07507","score":"0.5491756","text":"def _default_skip(insym, keras_layer, _): # pylint: disable=unused-argument\n return insym","title":""},{"docid":"e1f34aecfc6267b4b9b2f04715bd1bf6","score":"0.54896474","text":"def skip(self, n=None):\n\n try:\n for _ in zip(xrange(random.randint(0, 4000) if n is None else n), self._normal_words()): pass\n except NameError:\n # why the fuck would you do this to your own language?\n for _ in zip(range(random.randint(0, 4000) if n is None else n), self._normal_words()): pass","title":""},{"docid":"693ba86404a34d63d32ee32b1e3d2268","score":"0.54856104","text":"def skip_chromosome(self):\n self.load_chromosome(collect_data=False)","title":""},{"docid":"603f64f6be66da0012c4852fc34ad685","score":"0.547532","text":"def log_skipped( self, text ):\r\n text = \"[*] SKIPPED: \" + text\r\n self._log(text, YELLOW, True)","title":""},{"docid":"e8d13299b9914bb5bd106565adbaae3b","score":"0.5467124","text":"def skip(self, player) -> None:\n if self.round:\n new_round = self.round.skip(player)\n if new_round:\n self.round.chat.update_chat(f\"Round {self.round_count} has been skipped\")\n self.round_ended()\n return True\n return False\n else:\n raise Exception(\"No round started yet!\")","title":""},{"docid":"2a54f079b4611b6887f763b5869f4840","score":"0.54654855","text":"def test_no_samples(self):\n data = self.collagen[:0]\n for proc in PREPROCESSORS:\n d2 = proc(data)","title":""},{"docid":"73764e5b7d5ce467a862396c9e4b9342","score":"0.54648125","text":"def _EXKK(self, opcode):\n self.skip_keys[self.get_n(opcode)](self.get_x(opcode))","title":""},{"docid":"b1cfe6e4ef869641c05bb90b39a08222","score":"0.54574573","text":"def can_skip(self, nf):\n return False","title":""},{"docid":"11c4792fcecc5bc680fde22db6ee7f0a","score":"0.54529834","text":"def get_skipped_plays(self):\n return self.games_to_skip","title":""},{"docid":"8260734187621fe7fd0d554b02ce4f8f","score":"0.5448306","text":"def skip_line(self, amount=1):\n for i in xrange(amount):\n self.f.readline()","title":""},{"docid":"fcdd3a0245a8b9800e20d16bf5fb652e","score":"0.54468286","text":"def skipPlayer(self, event):\n if self.screen.selected is not None:\n self.screen.selected.skipped = True\n self.stopRunning()","title":""},{"docid":"bae95ca5c4662be826556605e53fdbcc","score":"0.5437433","text":"def eliminated(people, skip):\n assert people > 0\n assert skip >= 0\n circle = Queue()\n for position in range(1, people + 1):\n circle.enqueue(position)\n positions = []\n while circle:\n for _skipped in range(skip):\n circle.enqueue(circle.dequeue())\n positions.append(circle.dequeue())\n return positions","title":""},{"docid":"045b1f74e1d7dacddb2dfcc7366466a3","score":"0.5437125","text":"def skip(func):\n def inner(self, *args, **kwargs):\n \"\"\"Inner.\"\"\"\n if SKIP:\n return\n return func(self, *args, **kwargs)\n return inner","title":""},{"docid":"e02c2549536f28188cf2d62b21c8f987","score":"0.54360056","text":"def _finish_early(self):\n self._logger.debug(\"Confidence threshold met; skipping remaining sources\")\n with self._queues.lock:\n for source in self.sources:\n source.skip()\n self.finished = True","title":""},{"docid":"e02c2549536f28188cf2d62b21c8f987","score":"0.54360056","text":"def _finish_early(self):\n self._logger.debug(\"Confidence threshold met; skipping remaining sources\")\n with self._queues.lock:\n for source in self.sources:\n source.skip()\n self.finished = True","title":""},{"docid":"4b6687a9206845986cfab72161ad54c9","score":"0.5432385","text":"def skip(self):\n self.skips += 1\n if self.skips > len(self.players) -2:\n return True\n \n return False","title":""},{"docid":"01fe9601de6545768257ff631ccee65c","score":"0.5428907","text":"def handle_skips(blocks):\n skip_tags = ['skip_this', 'skip_other', 'skip_below', 'skip_above']\n\n def check_skip_syntax(b, h=skip_tags):\n l = [k for k in b['kwargs'].keys() if k.startswith('skip_')]\n n = [k for k in l if not k in h]\n if n:\n app.die('Not understood skip statment', unknown=n, allowed=h)\n\n def skip(b):\n b['kwargs']['skip_this'] = True\n\n for b in blocks:\n check_skip_syntax(b)\n if b['kwargs'].get('skip_other'):\n for c in blocks:\n skip(c)\n b['kwargs'].pop('skip_this')\n return True\n if b['kwargs'].get('skip_below'):\n s = False\n for c in blocks:\n if c == b:\n s = True\n continue\n if s:\n skip(c)\n return True\n\n if b['kwargs'].get('skip_above'):\n for c in blocks:\n if c == b:\n return True\n skip(c)\n\n if b['kwargs'].get('skip_this'):\n return True","title":""},{"docid":"c17a9534907a21d6a6a78963e537dfa0","score":"0.5425112","text":"def begin_sampling(self):\n pass","title":""},{"docid":"756c407cbb9841cd268370ad1aa1647e","score":"0.5415831","text":"def addSkip(self, test, reason):\n super(MyTestResult, self).addSkip(test, reason)\n self.addResult(test, STATES.SKIP, reason)","title":""},{"docid":"bcd54a1fcf8b84f842e7a3a9fa6b7218","score":"0.5412402","text":"def skip_chunk(self, stream, chunk_size):\n pass","title":""},{"docid":"ae9ee695fc68d43756065c289763ad89","score":"0.5409749","text":"def skip(self, n):\n stream = self._stream\n\n def inner():\n for _ in range(n):\n next(stream)\n for x in stream:\n yield x\n self._stream = inner()\n return self","title":""},{"docid":"2af106a36165790c562616841c97f2a0","score":"0.54060584","text":"def gen_lines_prepass(self) -> None:\n pass","title":""}],"string":"[\n {\n \"docid\": \"d5df531ee98e066ac7f749a019d9ea17\",\n \"score\": \"0.6951125\",\n \"text\": \"def Skip(self, skip=True):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62f28dd39a3b4451ebad8dc5424e80ef\",\n \"score\": \"0.6750491\",\n \"text\": \"def skip(self):\\n self.next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4092f1a261f3ddab45595c1952c32e68\",\n \"score\": \"0.67421526\",\n \"text\": \"def skip(self, count: int, /) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01b7e6105f79494336eddb5d0a432a00\",\n \"score\": \"0.67299765\",\n \"text\": \"def skip(self, n):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874a9fbd87e6a04065af897211780ef7\",\n \"score\": \"0.6707515\",\n \"text\": \"def skip(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf962400dbab3e823e4704729366621c\",\n \"score\": \"0.64574397\",\n \"text\": \"def test_skip(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf962400dbab3e823e4704729366621c\",\n \"score\": \"0.64574397\",\n \"text\": \"def test_skip(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf962400dbab3e823e4704729366621c\",\n \"score\": \"0.64574397\",\n \"text\": \"def test_skip(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d61bf2b408b4dd00ddcf998699d6a2\",\n \"score\": \"0.6378706\",\n \"text\": \"def skip(*args,**kwarg): \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eff94be5aead165408f70bbbd4da62d\",\n \"score\": \"0.63501203\",\n \"text\": \"def GetSkipped(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1ec771adf0f58b306ce03d0dbb25613\",\n \"score\": \"0.63429886\",\n \"text\": \"def prescan(self): # -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5393b5337bdf77fa2e3b4d089cea1c79\",\n \"score\": \"0.6274985\",\n \"text\": \"def skip ( self ) :\\n return self.__skip\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5393b5337bdf77fa2e3b4d089cea1c79\",\n \"score\": \"0.6274985\",\n \"text\": \"def skip ( self ) :\\n return self.__skip\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8229e6239cef6b0d057ec6fdee078eb2\",\n \"score\": \"0.62682694\",\n \"text\": \"def SkipSize(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a38f1b34b6c8cb9b4f79ff282aaded7\",\n \"score\": \"0.62615716\",\n \"text\": \"def logSkippedPars(self):\\n logSkippedPars(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d5c16c75adb9a7eeca2276119cf34f5\",\n \"score\": \"0.62334174\",\n \"text\": \"def skip(t, n):\\n pu(t)\\n fd(t, n)\\n pd(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b56d06d54a8f66add86db58117a49c7\",\n \"score\": \"0.61965966\",\n \"text\": \"def test_skip(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b56d06d54a8f66add86db58117a49c7\",\n \"score\": \"0.61965966\",\n \"text\": \"def test_skip(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47caa2d1cf73d795799dd027a6aa209d\",\n \"score\": \"0.61688685\",\n \"text\": \"def skip(self):\\n self.currentiv += 1\\n self.next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9742035f1406b108a015656208d04b17\",\n \"score\": \"0.61307335\",\n \"text\": \"def skip(self, amount: int):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7dd27f54c5f6ae1deb3b787d0183f62\",\n \"score\": \"0.6098544\",\n \"text\": \"def unskip(self, step):\\n self.skip = [i for i in self.skip if step != i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdd0a53894f2abeeaac78d599775a394\",\n \"score\": \"0.6093332\",\n \"text\": \"def make_skip():\\n return { \\\"kind\\\": \\\"Skip\\\" }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f961f1c83bcedb420dfff15924da229\",\n \"score\": \"0.6080094\",\n \"text\": \"def __init__(self, apply=1.0):\\n self.skip = apply\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ee882c65921e5428eeb6aa4a53f1cdc\",\n \"score\": \"0.6065739\",\n \"text\": \"def skip(self, n=1):\\r\\n for x in range(n):\\r\\n next(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56aa8181063c52f56d0d0d4e9730c126\",\n \"score\": \"0.6059741\",\n \"text\": \"def skip(self) -> None:\\n\\n self._skipped.append(\\n {\\n \\\"line\\\": self.item[\\\"line\\\"],\\n \\\"column\\\": self.item[\\\"line\\\"],\\n \\\"kind\\\": self.kind,\\n \\\"name\\\": self.name,\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47e74ce399d2cd23f20ea7cbf5aa2c18\",\n \"score\": \"0.60528964\",\n \"text\": \"def _skip_sampling(self, total, skip_ids):\\n rand_id = np.random.randint(total - len(skip_ids))\\n return rand_id if rand_id < min(skip_ids) else rand_id + len(skip_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cbb136836f2356532a94bfb50e5a937\",\n \"score\": \"0.60461617\",\n \"text\": \"def _skip(self, dataset):\\n determ = self._deterministic\\n return dataset.map(\\n self._add_param,\\n num_parallel_calls=tf.data.AUTOTUNE,\\n deterministic=determ)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76bfb511fc368d0073c9673538ac45a5\",\n \"score\": \"0.60394377\",\n \"text\": \"def _skipped_inc(self, item):\\n\\t\\tself.skipped += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa383704ac626f135aeb789f5c21612d\",\n \"score\": \"0.60279024\",\n \"text\": \"def skip(self, n: int):\\n # makes use of knowing the number of nodes\\n # does some math, skips extraneous full loops\\n for x in range(n % self.numnodes):\\n # updates the correct amount of times\\n self.go_next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"893f6501f28c883999a4fc19cf3ee265\",\n \"score\": \"0.6023453\",\n \"text\": \"def skip(self, params):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29e39bb1f76561be0bb867058512838e\",\n \"score\": \"0.5973915\",\n \"text\": \"def skip(self, amount):\\n for i in range(0, amount):\\n self.next()\\n time.sleep(0.5) # FIXME: Direct access to playlist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7178b5e5f0fc0cd8c02c9f7aa3eedf3\",\n \"score\": \"0.59552014\",\n \"text\": \"def n_skip_steps(self) -> int:\\n return self._n_skip_steps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48eb3a3b0fdce736c2abd7a127390807\",\n \"score\": \"0.5947714\",\n \"text\": \"def skip(self):\\n \\n self.playing_i = (self.playing_i + 1) % len(self.players)\\n self.playing = self.players[self.playing_i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"213e9341e561f3661095c316faa0cf57\",\n \"score\": \"0.59147024\",\n \"text\": \"def skip_forward(self, j):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8b17e822015c4decb0672e0a9801ebd\",\n \"score\": \"0.59073013\",\n \"text\": \"def addSkip(self, test):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2885727e7a68fbc46afd96577d55917\",\n \"score\": \"0.586508\",\n \"text\": \"def skipToken():\\n raise SkipToken\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a915c56a4f2b8c202d712020443e471f\",\n \"score\": \"0.5860903\",\n \"text\": \"def on_skip(self, record):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69f3bbe77fe5972dd27fd8ee98cdb9a3\",\n \"score\": \"0.58512914\",\n \"text\": \"def _heuristic_skip_inspection(self):\\n self._ninsps = self._ninsps + 1 if hasattr(self, '_ninsps') else 1\\n if self.mode in ['tile', 'only_tile'] and self._ninsps < 3:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f363a9888452ff5429121b28d26ae811\",\n \"score\": \"0.5842535\",\n \"text\": \"def skip(self, count):\\n self.lexer.skip(count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bafb62b5bde62b0195b50ecefaf25457\",\n \"score\": \"0.5828772\",\n \"text\": \"def skip_gd_pair():\\n skip = [[10.9697, 4.4073],\\n [15.3189, 2.0326],\\n [23.7452, 24.5140],\\n [28.4283, 20.9148],\\n [35.1739, 1.1985],\\n [40.0218, -0.6527],\\n [41.2998, -1.2216],\\n [41.9780, 0.6380],\\n [123.3026, 54.2806],\\n [126.6732, 45.7450],\\n [131.5022, 7.0747],\\n [150.3681, 50.4663],\\n [150.3362, 55.8989], # FOS lens\\n [158.2551, 47.2532],\\n [164.0395, 55.2669],\\n [170.1281, 54.7426],\\n [176.7206, 16.7400],\\n [188.5052, 6.5367],\\n [190.7380, 25.7174],\\n [193.7286, 8.7812],\\n [196.9841, 4.3710],\\n [198.7737, 47.9047],\\n [201.3239, 37.6164],\\n [211.2581, 44.8000],\\n [222.7320, 47.0272],\\n [238.3773, 22.5040],\\n [243.2571, 8.1350],\\n [253.7555, 26.0882],\\n [357.0800, 0.9549],\\n [116.9959, 43.3015],\\n [184.6687, 50.2621],\\n [166.63912, -18.35661], # FOS lens\\n [166.6396, -18.3567], # FOS lens\\n [216.9947, -1.3601],\\n [9.9763, -27.4229], # 2QZ pair\\n [341.6578, -29.4963], # 2QZ pair\\n ]\\n # Table\\n sa = np.array(skip)\\n stbl = Table()\\n stbl['RA'] = sa[:,0]\\n stbl['DEC'] = sa[:,1]\\n # Return\\n return stbl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e41e2fcc3ec4b95c491d85eba4313b05\",\n \"score\": \"0.58279467\",\n \"text\": \"def _must_skip(self):\\n if self.bananas_record[\\\"integradoERP10\\\"] != \\\"0\\\":\\n return True\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b08980845210b010348007e2d8c591a9\",\n \"score\": \"0.58222044\",\n \"text\": \"def skip_next(self) -> None:\\n \\n self.spotipyObject.next_track()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c978322e212ef48e058f6b408ef6ff02\",\n \"score\": \"0.57943505\",\n \"text\": \"def get_skipgrams(self):\\n # List to hold skipgrams and associated metadata\\n skipgram_data = []\\n # List of indexes (of original texts list from dataframe) where we are throwing the doc out due to short length\\n self.purged_docs = []\\n # List holding the number of tokens in each document\\n self.doc_lengths = []\\n # ID to count document IDS\\n doc_id_counter = 0\\n print(\\\"\\\\n---------- Getting Skipgrams ----------\\\")\\n for i, t in tqdm(enumerate(self.idx_data)):\\n pairs, _ = skipgrams(t,\\n vocabulary_size = self.vocab_size,\\n window_size = self.window_size,\\n shuffle=True,\\n negative_samples=0)\\n \\n if len(pairs) > 2:\\n for pair in pairs:\\n temp_data = pair\\n # Appends doc ID\\n temp_data.append(doc_id_counter)\\n # Appends document index (index of original texts list from dataframe)\\n temp_data.append(i)\\n skipgram_data.append(temp_data)\\n self.doc_lengths.append(len(t))\\n doc_id_counter+=1\\n else:\\n # We purge docs with less than 2 pairs\\n self.purged_docs.append(i)\\n\\n self.skipgrams_df = pd.DataFrame(skipgram_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95fbef75ca01e8646532379961971f91\",\n \"score\": \"0.57853013\",\n \"text\": \"def set_skip(self,value=True):\\n\\n self.skip = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"824cde9ce4286701d1695c911d6d6d2d\",\n \"score\": \"0.5774947\",\n \"text\": \"def skip_gram_features(full_sentence_info_list, k_skip, n_gram):\\n # Open a file\\n shutil.rmtree('../dataset/output') # removes a directory with sub directories and files\\n os.mkdir('../dataset/output') # creates a new directory\\n output_file = open('../dataset/output/jurry.skip.txt', 'a') # open a file in append mode\\n\\n grams = []\\n for full_sentence_info in full_sentence_info_list: # iterates ove the values (sentence_info(s)) list\\n if n_gram == 0 or len(full_sentence_info) == 0:\\n return None\\n for i in range(len(full_sentence_info) - n_gram + 1):\\n grams.extend(initial_skip_grams(full_sentence_info[i:], k_skip, n_gram))\\n for gram in grams: # for printing the skip_gram\\n for gra in gram:\\n output_file.write(gra.token + '_')\\n output_file.write('\\\\n')\\n output_file.close() # closes the file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf056b6d221093852ab970a0f5b03ff9\",\n \"score\": \"0.5765171\",\n \"text\": \"def add_skip(self, test, reason):\\n self.stream.writeln('SKIP', YELLOW)\\n self.write_details(reason, test.parents_count, YELLOW)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e74a5ce8df99c05bb42830bcb68f87e\",\n \"score\": \"0.5762922\",\n \"text\": \"def _choose_skip_length(self):\\n max_length = min((self._skip_frame_range[1] - self._skip_frame_range[0]), \\\\\\n (self._full_dataset_view // self._skip_increase_interval))\\n self._skip_length = 0 if max_length == 0 else randint(0, max_length)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f092d2b433791add56819844a214f61f\",\n \"score\": \"0.57521266\",\n \"text\": \"def _pre_water_erosion_steps(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dd7f286612575fcd1c2e89b4903ca28\",\n \"score\": \"0.5745903\",\n \"text\": \"def test_skipping(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"802c6738fae7e2655838098cdb45f268\",\n \"score\": \"0.5745476\",\n \"text\": \"def skipRemaining(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"158139a207689b98aad9ad126e782cb2\",\n \"score\": \"0.5741946\",\n \"text\": \"def test_skips(self):\\n assert not hasattr(self.t, \\\"test_code_cell_1\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9fdae50ec89b1df68dc406866bd15bc\",\n \"score\": \"0.5739221\",\n \"text\": \"def skip_token():\\n raise SkipToken\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20496c1dc81a3ddda96b33ed8137a9bb\",\n \"score\": \"0.57227665\",\n \"text\": \"def _skipgram_generator(self, doc):\\n return it.izip(*[it.islice(seq, i, None) for i, seq in enumerate(it.tee(doc, self.skipgram_len))])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04959898e940422e797a11ab1559f5da\",\n \"score\": \"0.57183963\",\n \"text\": \"def _get_skip_shapes(self, in_shape):\\n in_shape = utils.convert_shape_to_np_array(in_shape)\\n sh = in_shape\\n# ch_axis = self.channel_axis\\n# if ch_axis < 0:\\n# ch_axis = len(in_shape) + self.channel_axis\\n \\n skip_out_shapes = list()\\n# skip_in_shapes = list()\\n for block in self.block_list:\\n bdef = self.block_definitions[block]\\n out_num = self._get_out_num(sh[self.channel_axis], block)\\n # get indices of skip_save (possibly more than one)\\n skip_concat_idx = [i for i, layer in enumerate(bdef) if layer=='skip_concat']\\n# skip_save_idx = [i for i, layer in enumerate(bdef) if layer=='skip_save']\\n if not skip_concat_idx:# and not skip_save_idx:\\n # just determine block out shape\\n sh = self._get_layer_sequence_output_shape(sh, bdef, out_num)\\n# elif skip_save_idx:\\n# # sequence until skip concat\\n# seq = bdef[:skip_save_idx[0]] \\n# sh = self._get_layer_sequence_output_shape(sh, seq, out_num)\\n# skip_in_shapes.append(sh)\\n# # will only enter for-loop if len(skips)>1\\n# for i in range(len(skip_save_idx)-1):\\n# seq = bdef[skip_save_idx[i]:skip_save_idx[i+1]]\\n# sh = self._get_layer_sequence_output_shape(sh, seq, out_num)\\n# skip_in_shapes.append(sh)\\n# # sequence after skip concat\\n# seq = bdef[skip_save_idx[-1]:] \\n# sh = self._get_layer_sequence_output_shape(sh, seq, out_num)\\n elif skip_concat_idx:\\n # sequence until skip concat\\n seq = bdef[:skip_concat_idx[0]] \\n sh = self._get_layer_sequence_output_shape(sh, seq, out_num)\\n skip_out_shapes.append(sh)\\n # will only enter for-loop if len(skips)>1\\n for i in range(len(skip_concat_idx)-1):\\n seq = bdef[skip_concat_idx[i]:skip_concat_idx[i+1]]\\n sh = self._get_layer_sequence_output_shape(sh, seq, out_num)\\n skip_out_shapes.append(sh)\\n # sequence after skip concat\\n seq = bdef[skip_concat_idx[-1]:] \\n sh = self._get_layer_sequence_output_shape(sh, seq, out_num)\\n# if len(skip_out_shapes) != len(skip_in_shapes):\\n# raise RuntimeError(\\\"There are \\\" + str(len(skip_out_shapes)) + \\n# \\\"skip_concats and \\\" + str(len(skip_in_shapes)) +\\n# \\\"skip_saves. This needs to match.\\\")\\n# skip_in_shapes = skip_in_shapes[::-1]\\n# skip_shapes = list()\\n# for i in range(len(skip_out_shapes)):\\n# skip = skip_out_shapes[i]\\n# skip[self.im_axis] = skip_in_shapes[i][self.im_axis]\\n# skip[self.channel_axis] = skip_in_shapes[i][self.channel_axis]\\n# skip_shapes.append(skip)\\n# list([skip_in_shapes[self.im_axis]] + \\n# [skip_out_shapes[spatial_axes])\\n# skip_shapes.insert(ch_axis, skip_in_shapes[self.im_axis])\\n if self.input_output_skip:\\n skip_out_shapes.append(self._get_net_output_shape(in_shape))\\n return skip_out_shapes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e1865c975c18feca094e819d44fd346\",\n \"score\": \"0.57174057\",\n \"text\": \"def handle_skipped(self, song):\\n self.update_item(song['beets_item'], 'skip_count', increment=1)\\n self._log.info('skipped {0}', displayable_path(song['path']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2093ffbce03e3375de0639135b58baf\",\n \"score\": \"0.5697211\",\n \"text\": \"def set_skip(self, n):\\n\\n self.skip = n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9b012ec3d25a5c0fbebb83b0b3ff7e8\",\n \"score\": \"0.5680758\",\n \"text\": \"def skip(self):\\n\\n\\t\\tif not len(self.args.skip):\\n\\t\\t\\tself._show_skip()\\n\\t\\telse:\\n\\t\\t\\t# This could signify STDIN contains json or xml to intrepret as ytids???\\n\\t\\t\\tif self.args.json:\\n\\t\\t\\t\\traise NotImplementedError(\\\"--json not meaningful when adding skipped videos\\\")\\n\\t\\t\\tif self.args.xml:\\n\\t\\t\\t\\traise NotImplementedError(\\\"--xml not meaningful when adding skipped videos\\\")\\n\\n\\t\\t\\tytids = list(set(self.args.skip))\\n\\t\\t\\tytids = ['-' + _[1:] for _ in ytids if _[0] == '='] + [_ for _ in ytids if _[0] != '=']\\n\\n\\t\\t\\t# Split into videos and playlists\\n\\t\\t\\tv_ytids = [_ for _ in ytids if len(_) == 11]\\n\\t\\t\\tpl_ytids= [_ for _ in ytids if len(_) != 11]\\n\\n\\n\\t\\t\\tself.db.begin()\\n\\t\\t\\tprint(\\\"Marking videos to skip (%d):\\\" % len(v_ytids))\\n\\t\\t\\tfor ytid in v_ytids:\\n\\t\\t\\t\\tprint(\\\"\\\\t%s\\\" % ytid)\\n\\t\\t\\t\\trow = self.db.v.select_one(\\\"rowid\\\", \\\"`ytid`=?\\\", [ytid])\\n\\t\\t\\t\\tself.db.v.update({\\\"rowid\\\": row['rowid']}, {\\\"skip\\\": True})\\n\\n\\t\\t\\t\\t# Delete any sleep times for this video, this will not error if no rows present\\n\\t\\t\\t\\tself.db.v_sleep.delete({'ytid': ytid})\\n\\n\\t\\t\\t\\t# Hook: \\\"skip-video\\\"\\n\\t\\t\\t\\tif not self.args.nohook:\\n\\t\\t\\t\\t\\trun_hook(self.db, 'skip-video', ytid=ytid)\\n\\n\\t\\t\\tprint(\\\"Marking playlists to skip (%d):\\\" % len(pl_ytids))\\n\\t\\t\\tfor ytid in pl_ytids:\\n\\t\\t\\t\\tprint(\\\"\\\\t%s\\\" % ytid)\\n\\t\\t\\t\\trow = self.db.pl.select_one(\\\"rowid\\\", \\\"`ytid`=?\\\", [ytid])\\n\\t\\t\\t\\tself.db.pl.update({\\\"rowid\\\": row['rowid']}, {\\\"skip\\\": True})\\n\\n\\t\\t\\t\\t# Hook: \\\"skip-playlist\\\"\\n\\t\\t\\t\\tif not self.args.nohook:\\n\\t\\t\\t\\t\\trun_hook(self.db, 'skip-playlist', plid=ytid)\\n\\n\\t\\t\\tself.db.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c4d77c1f083a16dbbab434b181654f\",\n \"score\": \"0.56698376\",\n \"text\": \"def should_skip(self):\\n\\n return self.skip\",\n \"title\": \"\"\n },\n {\n \"docid\": \"238e9ed6b6678061ec99f39c0de60f01\",\n \"score\": \"0.56594795\",\n \"text\": \"def skipped(self, bot_name, deferred):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c9ecbf61a6a952a25c8c475be00faff\",\n \"score\": \"0.56442344\",\n \"text\": \"def WasSkipped(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c9ecbf61a6a952a25c8c475be00faff\",\n \"score\": \"0.564423\",\n \"text\": \"def WasSkipped(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"506802fdcc6d4796fd68623a4e99320a\",\n \"score\": \"0.56310844\",\n \"text\": \"def skip(self, amount):\\n return self.__class__(self.graph, self._labels, self._conditions,\\n self._order_by, amount, self._limit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3731700f48639672eb4cecb7d643f599\",\n \"score\": \"0.5629662\",\n \"text\": \"def skipFrame(self, frames, cap):\\n totalFrames = self.getFPS(cap)\\n skip = totalFrames // frames # 30 / 3 = 10 1 ..1 ..11 ..21 ..22 .. 30 *** 2 \\n return skip\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b51737de404de773e564718c366b0af\",\n \"score\": \"0.56170434\",\n \"text\": \"def clear_empties(self):\\n keep = self.galpairs > 0\\n self.sep, self.galpairs, self.ranpairs, self.est = \\\\\\n self.sep[keep], self.galpairs[keep], self.ranpairs[keep],\\\\\\n self.est[keep]\\n self.nbin = len(self.sep)\\n return self.nbin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0994fe2efd4a0254320d86ac5ee20df1\",\n \"score\": \"0.5602012\",\n \"text\": \"def skip_tiles(self, tiles=None, tiles_batches=None):\\n logger.debug(\\\"determine which tiles to skip...\\\")\\n # only check for existing output in \\\"continue\\\" mode\\n if self.config.mode == \\\"continue\\\":\\n yield from tiles_exist(\\n config=self.config,\\n process_tiles=tiles,\\n process_tiles_batches=tiles_batches,\\n )\\n # otherwise don't skip tiles\\n else:\\n if tiles_batches:\\n for batch in tiles_batches:\\n for tile in batch:\\n yield (tile, False)\\n else:\\n for tile in tiles:\\n yield (tile, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1ee24891f27db18e2367aaa02a8b033\",\n \"score\": \"0.55959487\",\n \"text\": \"def test_with_skip(self):\\n # self.assertEqual(3, add(1, 2))\\n # self.assertNotEqual(3, add(2, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1ee24891f27db18e2367aaa02a8b033\",\n \"score\": \"0.55959487\",\n \"text\": \"def test_with_skip(self):\\n # self.assertEqual(3, add(1, 2))\\n # self.assertNotEqual(3, add(2, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd7941b90a877a2fcfb21b13cc439e83\",\n \"score\": \"0.5585231\",\n \"text\": \"def __manage_rows_skipping(self, kwargs, skipchunks, skiprows, nrows):\\n if skipchunks is not None:\\n skiprows = skipchunks * kwargs.get('chunksize', self.chunksize)\\n if skiprows is not None:\\n kwargs['skiprows'] = skiprows\\n if self.columns is not None:\\n col = self.columns[0]\\n kwargs['skiprows'] += len(col) if isinstance(col, tuple) else 1\\n kwargs['header'] = None\\n kwargs['names'] = self.columns\\n if nrows is not None:\\n kwargs['nrows'] = nrows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54b0be2ffffbc065e23cbaa20b9c66b\",\n \"score\": \"0.5567402\",\n \"text\": \"def skipgrams(pages, max_context, l):\\n for words in pages:\\n for index, current in enumerate(words):\\n if current != 0 and not subsample(pages.counter[pages.decode(current)]/pages.total_count, l):\\n continue\\n context = np.random.randint(1, max_context)\\n for target in words[max(0, index - context): index]:\\n yield current, target\\n for target in words[index + 1: index + context + 1]:\\n yield current, target\",\n \"title\": \"\"\n },\n {\n \"docid\": \"668064206217007e075b18f3c1b189e3\",\n \"score\": \"0.5560861\",\n \"text\": \"def skip(self, n):\\n self.pos += n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bd8b2e558abb903e98cf61f3826d16c\",\n \"score\": \"0.5554391\",\n \"text\": \"def SkipGramOptionsStart(builder):\\n return Start(builder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69d5d3b895903d2d77448322bb2e94c3\",\n \"score\": \"0.55486065\",\n \"text\": \"def _check_skip(self, planred, name):\\n # Update the attributes\\n self._skipexred = {k: exists(v) for k, v in self.summpaths.items()}\\n self._skipexplan = {k: exists(v) for k, v in self.planpaths.items()}\\n _name = name if isinstance(name, str) else f\\\"lv{name}\\\"\\n\\n # Check if the step is skipped\\n if planred.lower().startswith(\\\"r\\\"):\\n if self._skipexred[name]:\\n if self.verbose >= 1:\\n print(f\\\"SKIP: Summary exists for {_name} at {self.summpaths[name]}\\\")\\n return self._read_summ(name)\\n\\n elif planred.lower().startswith(\\\"p\\\"):\\n if self._skipexplan[name]:\\n if self.verbose >= 1:\\n print(f\\\"SKIP: Planer exists for {_name} at {self.planpaths[name]}\\\")\\n return self._read_plan(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06c84841a1e02e383768daceabeb89c2\",\n \"score\": \"0.5548301\",\n \"text\": \"def skip_arm(self):\\n self._next_arm()\\n self.list_next = self.list_next[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3170d44e5013cade5703f785793b55cf\",\n \"score\": \"0.55476904\",\n \"text\": \"def skip(self, size):\\n self.__pntr += size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebdb3fb5d1a62726faaefd7b47bde15f\",\n \"score\": \"0.55332303\",\n \"text\": \"def skipped_sim_steps(self) -> int:\\n skipped_steps = 0\\n if self.enable_step_throttling:\\n while skipped_steps < self.n_skip_steps:\\n skipped_steps = self.obj_handle.get_sim_step() - self.prev_sim_step\\n time.sleep(1e-5)\\n self.prev_sim_step = self.obj_handle.get_sim_step()\\n if skipped_steps > self.n_skip_steps:\\n print(\\n 'WARN: Skipped {} steps, Default skip limit {} Steps'.format(\\n skipped_steps,\\n self.n_skip_steps\\n )\\n )\\n else:\\n skipped_steps = self.obj_handle.get_sim_step() - self.prev_sim_step\\n self.prev_sim_step = self.obj_handle.get_sim_step()\\n\\n self._skipped_sim_steps = skipped_steps\\n\\n return self._skipped_sim_steps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48e6df9d636f87999f0bd9e9443112dc\",\n \"score\": \"0.5514506\",\n \"text\": \"def _get_skip_info(self, helix_size, base_list):\\n skip_info = [0]*helix_size\\n for base in base_list:\\n skip_info[base.p] = base.num_deletions\\n return skip_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81c0aa508c97312e471d31ed729be6dc\",\n \"score\": \"0.550138\",\n \"text\": \"def skip(self, n):\\n self.incr_index(n)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5390c985177e4691846d25d779343346\",\n \"score\": \"0.5497097\",\n \"text\": \"def n_skip_steps(self, value: int):\\n self._n_skip_steps = value\\n self._world_handle.set_num_step_skips(value)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"623e22884d3b7bca211c253434d07507\",\n \"score\": \"0.5491756\",\n \"text\": \"def _default_skip(insym, keras_layer, _): # pylint: disable=unused-argument\\n return insym\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1f34aecfc6267b4b9b2f04715bd1bf6\",\n \"score\": \"0.54896474\",\n \"text\": \"def skip(self, n=None):\\n\\n try:\\n for _ in zip(xrange(random.randint(0, 4000) if n is None else n), self._normal_words()): pass\\n except NameError:\\n # why the fuck would you do this to your own language?\\n for _ in zip(range(random.randint(0, 4000) if n is None else n), self._normal_words()): pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"693ba86404a34d63d32ee32b1e3d2268\",\n \"score\": \"0.54856104\",\n \"text\": \"def skip_chromosome(self):\\n self.load_chromosome(collect_data=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"603f64f6be66da0012c4852fc34ad685\",\n \"score\": \"0.547532\",\n \"text\": \"def log_skipped( self, text ):\\r\\n text = \\\"[*] SKIPPED: \\\" + text\\r\\n self._log(text, YELLOW, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8d13299b9914bb5bd106565adbaae3b\",\n \"score\": \"0.5467124\",\n \"text\": \"def skip(self, player) -> None:\\n if self.round:\\n new_round = self.round.skip(player)\\n if new_round:\\n self.round.chat.update_chat(f\\\"Round {self.round_count} has been skipped\\\")\\n self.round_ended()\\n return True\\n return False\\n else:\\n raise Exception(\\\"No round started yet!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a54f079b4611b6887f763b5869f4840\",\n \"score\": \"0.54654855\",\n \"text\": \"def test_no_samples(self):\\n data = self.collagen[:0]\\n for proc in PREPROCESSORS:\\n d2 = proc(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73764e5b7d5ce467a862396c9e4b9342\",\n \"score\": \"0.54648125\",\n \"text\": \"def _EXKK(self, opcode):\\n self.skip_keys[self.get_n(opcode)](self.get_x(opcode))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1cfe6e4ef869641c05bb90b39a08222\",\n \"score\": \"0.54574573\",\n \"text\": \"def can_skip(self, nf):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11c4792fcecc5bc680fde22db6ee7f0a\",\n \"score\": \"0.54529834\",\n \"text\": \"def get_skipped_plays(self):\\n return self.games_to_skip\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8260734187621fe7fd0d554b02ce4f8f\",\n \"score\": \"0.5448306\",\n \"text\": \"def skip_line(self, amount=1):\\n for i in xrange(amount):\\n self.f.readline()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcdd3a0245a8b9800e20d16bf5fb652e\",\n \"score\": \"0.54468286\",\n \"text\": \"def skipPlayer(self, event):\\n if self.screen.selected is not None:\\n self.screen.selected.skipped = True\\n self.stopRunning()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bae95ca5c4662be826556605e53fdbcc\",\n \"score\": \"0.5437433\",\n \"text\": \"def eliminated(people, skip):\\n assert people > 0\\n assert skip >= 0\\n circle = Queue()\\n for position in range(1, people + 1):\\n circle.enqueue(position)\\n positions = []\\n while circle:\\n for _skipped in range(skip):\\n circle.enqueue(circle.dequeue())\\n positions.append(circle.dequeue())\\n return positions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"045b1f74e1d7dacddb2dfcc7366466a3\",\n \"score\": \"0.5437125\",\n \"text\": \"def skip(func):\\n def inner(self, *args, **kwargs):\\n \\\"\\\"\\\"Inner.\\\"\\\"\\\"\\n if SKIP:\\n return\\n return func(self, *args, **kwargs)\\n return inner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e02c2549536f28188cf2d62b21c8f987\",\n \"score\": \"0.54360056\",\n \"text\": \"def _finish_early(self):\\n self._logger.debug(\\\"Confidence threshold met; skipping remaining sources\\\")\\n with self._queues.lock:\\n for source in self.sources:\\n source.skip()\\n self.finished = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e02c2549536f28188cf2d62b21c8f987\",\n \"score\": \"0.54360056\",\n \"text\": \"def _finish_early(self):\\n self._logger.debug(\\\"Confidence threshold met; skipping remaining sources\\\")\\n with self._queues.lock:\\n for source in self.sources:\\n source.skip()\\n self.finished = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b6687a9206845986cfab72161ad54c9\",\n \"score\": \"0.5432385\",\n \"text\": \"def skip(self):\\n self.skips += 1\\n if self.skips > len(self.players) -2:\\n return True\\n \\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01fe9601de6545768257ff631ccee65c\",\n \"score\": \"0.5428907\",\n \"text\": \"def handle_skips(blocks):\\n skip_tags = ['skip_this', 'skip_other', 'skip_below', 'skip_above']\\n\\n def check_skip_syntax(b, h=skip_tags):\\n l = [k for k in b['kwargs'].keys() if k.startswith('skip_')]\\n n = [k for k in l if not k in h]\\n if n:\\n app.die('Not understood skip statment', unknown=n, allowed=h)\\n\\n def skip(b):\\n b['kwargs']['skip_this'] = True\\n\\n for b in blocks:\\n check_skip_syntax(b)\\n if b['kwargs'].get('skip_other'):\\n for c in blocks:\\n skip(c)\\n b['kwargs'].pop('skip_this')\\n return True\\n if b['kwargs'].get('skip_below'):\\n s = False\\n for c in blocks:\\n if c == b:\\n s = True\\n continue\\n if s:\\n skip(c)\\n return True\\n\\n if b['kwargs'].get('skip_above'):\\n for c in blocks:\\n if c == b:\\n return True\\n skip(c)\\n\\n if b['kwargs'].get('skip_this'):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17a9534907a21d6a6a78963e537dfa0\",\n \"score\": \"0.5425112\",\n \"text\": \"def begin_sampling(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"756c407cbb9841cd268370ad1aa1647e\",\n \"score\": \"0.5415831\",\n \"text\": \"def addSkip(self, test, reason):\\n super(MyTestResult, self).addSkip(test, reason)\\n self.addResult(test, STATES.SKIP, reason)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcd54a1fcf8b84f842e7a3a9fa6b7218\",\n \"score\": \"0.5412402\",\n \"text\": \"def skip_chunk(self, stream, chunk_size):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae9ee695fc68d43756065c289763ad89\",\n \"score\": \"0.5409749\",\n \"text\": \"def skip(self, n):\\n stream = self._stream\\n\\n def inner():\\n for _ in range(n):\\n next(stream)\\n for x in stream:\\n yield x\\n self._stream = inner()\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2af106a36165790c562616841c97f2a0\",\n \"score\": \"0.54060584\",\n \"text\": \"def gen_lines_prepass(self) -> None:\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994098,"cells":{"query_id":{"kind":"string","value":"294970926228fce0b623e8b5c3e22575"},"query":{"kind":"string","value":"Loads a network from a CSV file."},"positive_passages":{"kind":"list like","value":[{"docid":"88a79f5ad83f58d5f4dffa69cdbc21e0","score":"0.61747426","text":"def network_from_csv(filename):\n from openpnm.network import Network\n fname = _parse_filename(filename=filename, ext='csv')\n\n a = read_table(filepath_or_buffer=fname,\n sep=',',\n skipinitialspace=True,\n index_col=False,\n true_values=['T', 't', 'True', 'true', 'TRUE'],\n false_values=['F', 'f', 'False', 'false', 'FALSE'])\n\n # First parse through all the items and re-merge columns\n dct = {}\n keys = sorted(list(a.keys()))\n for item in keys:\n m = re.search(r'\\[.\\]', item) # The dot '.' is a wildcard\n if m: # m is None if pattern not found, otherwise merge cols\n pname = re.split(r'\\[.\\]', item)[0] # Get base propname\n # Find all other keys with same base propname\n merge_keys = [k for k in a.keys() if k.startswith(pname)]\n # Rerieve and remove arrays with same base propname\n merge_cols = [a.pop(k) for k in merge_keys]\n # Merge arrays into multi-column array and store in DataFrame\n dct[pname] = np.vstack(merge_cols).T\n # Remove key from list of keys\n for k in keys:\n if k.startswith(pname):\n keys.pop(keys.index(k))\n else:\n dct[item] = np.array(a.pop(item))\n\n # Now scan through 'pore.coords' and 'throat.conns' to get Np and Nt,\n # then remove the nans\n try:\n Np = np.where(np.isnan(dct['pore.coords'][:, 0]))[0][0]\n except IndexError:\n Np = dct['pore.coords'][:, 0].shape[0]\n try:\n Nt = np.where(np.isnan(dct['throat.conns'][:, 0]))[0][0]\n except IndexError:\n Nt = dct['throat.conns'][:, 0].shape[0]\n for k, v in dct.items():\n if k.startswith('pore.'):\n dct[k] = v[:Np, ...]\n if k.startswith('throat.'):\n dct[k] = v[:Nt, ...]\n\n network = Network()\n network.update(dct)\n return network","title":""}],"string":"[\n {\n \"docid\": \"88a79f5ad83f58d5f4dffa69cdbc21e0\",\n \"score\": \"0.61747426\",\n \"text\": \"def network_from_csv(filename):\\n from openpnm.network import Network\\n fname = _parse_filename(filename=filename, ext='csv')\\n\\n a = read_table(filepath_or_buffer=fname,\\n sep=',',\\n skipinitialspace=True,\\n index_col=False,\\n true_values=['T', 't', 'True', 'true', 'TRUE'],\\n false_values=['F', 'f', 'False', 'false', 'FALSE'])\\n\\n # First parse through all the items and re-merge columns\\n dct = {}\\n keys = sorted(list(a.keys()))\\n for item in keys:\\n m = re.search(r'\\\\[.\\\\]', item) # The dot '.' is a wildcard\\n if m: # m is None if pattern not found, otherwise merge cols\\n pname = re.split(r'\\\\[.\\\\]', item)[0] # Get base propname\\n # Find all other keys with same base propname\\n merge_keys = [k for k in a.keys() if k.startswith(pname)]\\n # Rerieve and remove arrays with same base propname\\n merge_cols = [a.pop(k) for k in merge_keys]\\n # Merge arrays into multi-column array and store in DataFrame\\n dct[pname] = np.vstack(merge_cols).T\\n # Remove key from list of keys\\n for k in keys:\\n if k.startswith(pname):\\n keys.pop(keys.index(k))\\n else:\\n dct[item] = np.array(a.pop(item))\\n\\n # Now scan through 'pore.coords' and 'throat.conns' to get Np and Nt,\\n # then remove the nans\\n try:\\n Np = np.where(np.isnan(dct['pore.coords'][:, 0]))[0][0]\\n except IndexError:\\n Np = dct['pore.coords'][:, 0].shape[0]\\n try:\\n Nt = np.where(np.isnan(dct['throat.conns'][:, 0]))[0][0]\\n except IndexError:\\n Nt = dct['throat.conns'][:, 0].shape[0]\\n for k, v in dct.items():\\n if k.startswith('pore.'):\\n dct[k] = v[:Np, ...]\\n if k.startswith('throat.'):\\n dct[k] = v[:Nt, ...]\\n\\n network = Network()\\n network.update(dct)\\n return network\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"da41cb35073df863beef61c4f11e5bb6","score":"0.70894367","text":"def from_csv(self, file: str):\n with open(file) as f:\n for line in f:\n words = line.split(',')\n line_type = words[0]\n if line_type == 'c':\n self._add_node(int(words[1]), int(words[2]))\n elif line_type == 'e':\n self.add_edge(int(words[1]), int(words[2]))","title":""},{"docid":"5d5420377b332b7df83d2febe0b0acb0","score":"0.6935833","text":"def from_csv(self, file: str):\n with open(file) as f:\n for line in f:\n words = line.split(',')\n src = int(words[0])\n dest = int(words[1])\n self.add_edge(src, dest)","title":""},{"docid":"016b9e346ff6c9bf6f94b7cabaa1c281","score":"0.68656546","text":"def load(file_path=\"./graph.csv\"):\n assert path.isfile(file_path), \"'{}' is not a file\".format(file_path)\n log.debug(\"loading graph from: {}\".format(file_path))\n graph = None\n\n with open(file_path, 'r', newline='', encoding='utf-8') as file:\n reader = csv.reader(file, quotechar='\"')\n for i, row in enumerate(reader):\n if not graph:\n graph = ActorsGraph(row)\n else:\n for j, movie_ids in enumerate(row):\n for movie_id in movie_ids.split(\",\"):\n graph.add_edge_by_indices(i - 1, j, movie_id)\n\n assert graph, \"graph has not been created, check '{}' file\".format(file_path)\n return graph","title":""},{"docid":"646ca8b8548ac2e59a675c298e6f8dd5","score":"0.66369027","text":"def load_filename(self, filename):\n graph = {}\n with open(filename, 'rb') as csvfile:\n reader = csv.reader(csvfile)\n nodes = map(eval, next(reader)[1:])\n for line in reader:\n base = eval(line.pop(0))\n graph[base] = dict((n1, n2)\n for n1, n2 in zip(nodes, map(float, line))\n if n2 > 0)\n self.load(graph)","title":""},{"docid":"2e1bd8ca7edd2fc331237792c15e1e17","score":"0.65834135","text":"def from_csv(self, csvfile, header=None, start=0, size=None, delimiter=','):\n with open(csvfile) as f:\n reader = csv.reader(f, delimiter=delimiter)\n i = 0\n if header:\n next(reader, None)\n i = 1\n while i < start:\n i += 1\n next(reader, None)\n i = 0\n for row in reader:\n pred = cleanse(row[1])\n sub = cleanse(row[0])\n ob = cleanse(row[2])\n if not (sub.replace('_','').isalnum() and ob.replace('_','').isalnum()):\n continue\n if len(row) > 3:\n node_attributes = [json.loads(row[3]), json.loads(row[4])]\n edge_attributes = json.loads(row[5])\n else:\n node_attributes = []\n edge_attributes = {}\n self.store('{}({},{})'.format(pred, sub, ob), node_attributes=node_attributes, edge_attributes=edge_attributes)\n i += 1\n if size and i > size:\n break","title":""},{"docid":"3647cf0b83315f0b8e469a5703d8da49","score":"0.65201735","text":"def load_neos(neo_csv_path):\n # We are making new list of neo collections:\n # open the csv file, skipping the first line of headers,\n # then each row from csv file read into\n # new Near Earth Object, then add this row to collection.\n # Return collection.\n\n neo_collection = []\n with open(neo_csv_path, 'r') as infile:\n reader = csv.reader(infile)\n next(reader)\n for elem in reader:\n neo = NearEarthObject(elem[3], elem[4], elem[15], elem[7])\n neo_collection.append(neo)\n return neo_collection","title":""},{"docid":"0a029943560567f6f2f07cce925f9f88","score":"0.64970046","text":"def load_network(self, directory):\n with open(directory + '/structure', \"r\") as f:\n line = f.readline()\n input_name, input_size = line.split(' ')\n self.add_input(int(input_size))\n i = 0\n\n while 1:\n line = f.readline()\n if len(line) <= 0:\n break\n i += 1\n layer_name, layer_size, layer_activation = line.split(' ')\n layer_activation = layer_activation[:-1] # get rid of endline\n self.add_fully_connected(int(layer_size), layer_activation)\n self.layers[-1].set_weights(np.loadtxt(directory + '/weights' + str(i) + '.csv', delimiter=','))","title":""},{"docid":"bac7d6321e9e8b10f896797c4fd9c98f","score":"0.6475423","text":"def load_csv_into_graph(file_name: str) -> Graph:\n user_map = Graph()\n row_num = 0\n width_of_map = 0\n\n with open(file_name) as csv_file:\n reader = csv.reader(csv_file)\n for row in reader:\n width_of_map = len(row)\n for i in range(len(row)):\n if row[i] == \"B\":\n user_map.add_vertex((i, row_num), \"blocked\")\n elif row[i] == \"S\":\n user_map.add_vertex((i, row_num), \"start\")\n elif row[i] == \"E\":\n user_map.add_vertex((i, row_num), \"end\")\n else:\n user_map.add_vertex((i, row_num))\n row_num += 1\n height_of_map = row_num\n # Knowing the dimensions of the graph, connect the graph\n for y in range(height_of_map):\n for x in range(width_of_map):\n if x == width_of_map - 1 and y == height_of_map - 1:\n pass\n elif x == width_of_map - 1:\n user_map.add_edge((x, y), (x, y + 1))\n elif y == height_of_map - 1:\n user_map.add_edge((x, y), (x + 1, y))\n else:\n user_map.add_edge((x, y), (x, y + 1))\n user_map.add_edge((x, y), (x + 1, y))\n\n return user_map","title":""},{"docid":"ce4ed47a0c620e3fe34bc893bccb7f41","score":"0.64610356","text":"def load_neos(neo_csv_path):\n neos = []\n with open(neo_csv_path, 'r') as f:\n reader = csv.DictReader(f)\n for row in reader:\n neo = NearEarthObject(\n row['pdes'],\n row['name'],\n row['diameter'],\n row['pha'],\n [])\n neos.append(neo)\n return neos","title":""},{"docid":"5372ee685555bced9ea62c81fe36d312","score":"0.6425544","text":"def __init__(self, filename='social_network.csv'):\n with open(filename, 'r') as csvfile:\n rows = np.array(list(csv.reader(csvfile)))\n\n #flatten all name tuples, cast to set to \n #get unique names and cast to list for ordering\n self.names = list(set(rows.flatten()))\n\n #network adjacency matrix\n n = len(self.names)\n self.A = np.zeros((n , n))\n\n #populate network\n for c in rows:\n #get cooresponding indicies of names in name list\n i = self.get_name_index(c[0])\n j = self.get_name_index(c[1])\n #make each bi-directional connection\n self.A[i, j] = 1\n self.A[j, i] = 1\n\n #resistance of the network A\n self.R = effective_resistance(self.A)","title":""},{"docid":"c183cf4faf90d2f5eb4d654d82d819f4","score":"0.64249206","text":"def load_neos(neo_csv_path='data/neos.csv'):\r\n neos = [] # create list of neo objects\r\n with open(neo_csv_path) as f:\r\n reader = csv.reader(f)\r\n next(reader)\r\n # parse row of csv into pdes(design.) ,name,diameter, and hazardous\r\n for row in reader:\r\n if row[3] is not None or row[3] != '':\r\n if row[7] == 'Y':\r\n H = True\r\n else:\r\n H = False\r\n # create Neo obj with designation,name,diameter,hazardous\r\n neo_obj = NearEarthObject(row[3], row[4], row[15], H)\r\n neos.append(neo_obj) # append obj to the list\r\n return neos","title":""},{"docid":"efcd830aa775344932897d3c26e6176b","score":"0.6359306","text":"def loadGraph(graph_name):\n with open('./dataset/{}_node.csv'.format(graph_name), 'r',\n encoding='utf-8') as fp:\n reader = csv.reader(fp)\n nodes = list(int(_[0]) for _ in reader)\n with open('./dataset/{}_edge.csv'.format(graph_name), 'r',\n encoding='utf-8') as fp:\n reader = csv.reader(fp)\n edges = list((int(_[0]), int(_[1])) for _ in reader if _[0] != _[1])\n G = nx.Graph()\n G.add_nodes_from(nodes)\n G.add_edges_from(edges)\n return G","title":""},{"docid":"ac479056f306df2d3483ce461ed954f6","score":"0.63494384","text":"def load_diagram_from_csv_file(self, filepath):\n\n bpmn_csv_import.BpmnDiagramGraphCSVImport.load_diagram_from_csv(filepath, self)","title":""},{"docid":"fe8ecfbf3b66364c2850f3d67acda4c8","score":"0.62989837","text":"def load_data():\n import time\n\n print(\"Loading data and create road network (about 30 sec)...\")\n start_time = time.clock()\n\n road_network = RoadNetwork()\n\n vertices = pd.read_csv(\"./data/vertices.csv\")\n edges = pd.read_csv(\"./data/edges.csv\")\n\n print(\"Loading vertices...\")\n for index, row in vertices.iterrows():\n road_network.add_vertex(int(row['v_id']), row['lat'], row['lon'])\n\n print(\"Loading edges...\")\n for index, row in edges.iterrows():\n road_network.add_edge(int(row['e_id']), int(row['start_vid']), int(row['end_vid']), row['length'])\n\n print(\"Done. Elapsed time is %f seconds\" % (time.clock() - start_time))\n\n return road_network","title":""},{"docid":"fd086123b7299e8b8145e6d6fddd883b","score":"0.6272067","text":"def load_neos(neo_csv_path):\n with open(neo_csv_path, \"r\") as infile:\n\n neos = csv.DictReader(infile)\n\n neos = [\n NearEarthObject(\n pdes=neo[\"pdes\"],\n name=neo[\"name\"],\n diameter=neo[\"diameter\"],\n pha=neo[\"pha\"]\n )\n for neo in neos\n ]\n\n return neos","title":""},{"docid":"df5270252e8af5368e07d84da320a1f4","score":"0.6266226","text":"def load_network(network_file):\n handler = h.MiTabHandler()\n handler.parse(network_file)\n return handler.network","title":""},{"docid":"1e747687e4d7d45913fa1d8f2634ea33","score":"0.62265074","text":"def _load(self):\n self._data = load_network_from_file(self.path, fmt=self.ext)","title":""},{"docid":"f612bc245d36f71694ab05f863b289a7","score":"0.62200767","text":"def read_from_csv(self, filename):\n \n self.graph = []\n\n # Read the file into a string separated by newlines for each row\n try:\n with open(filename,'r') as readfile:\n lines = readfile.read().splitlines()\n except IOError, e:\n print \"I/O error: %s\" % e\n return\n\n # Read lines with csv.reader\n csvreader = csv.reader(lines)\n\n # Load the rows into self.graph matrix\n try:\n for row in csvreader:\n self.graph.append([])\n for i in range(csvreader.line_num):\n self.graph[csvreader.line_num-1].append(int(row[i]))\n except csv.Error, e:\n print \"CSV error: %s\" % e\n return\n except IndexError, e:\n print \"Index error: %s\" % e\n return\n \n # Make matrix symmetric\n # While increasing size, we do not have to check for\n # out of range when getting data from the graph\n for i in range(len(self.graph)):\n for j in range(i+1,len(self.graph)):\n self.graph[i].append(self.graph[j][i])","title":""},{"docid":"a7b106ad047eafa594e2894d56642b56","score":"0.6205403","text":"def load_prefecture_network(path):\n data = pd.read_csv(path, header=0, index_col=0)\n return data","title":""},{"docid":"3b48a841d24bdccc9d08635c8b51aa72","score":"0.61859536","text":"def load_dataset(net, filename): \n \n data = N.recfromcsv(filename, delimiter=\",\", names=True, autostrip=True, case_sensitive=True)\n\n m, n = data.size, len(data.dtype.names)\n numberdata = N.zeros((m, n), order='F')\n \n for node, d in net.node.iteritems():\n states = d['states_ind']\n numberdata[:,net.graph['nilut'][node]] = [states[str(s)] for s in data[node]]\n \n return numberdata","title":""},{"docid":"dec56d138351081b72fe9541a8a78881","score":"0.6177733","text":"def multinet_from_csv(\n file_name,\n filter_func=util.default_filter,\n weight_func=util.default_weight,\n layer_func=util.default_layer,\n ow='ORIGIN', dw='DEST',\n create_using=None,\n **csv_reader_argv):\n\n index_dict = {}\n if create_using is None:\n create_using = mn.DiMultinet()\n mg = create_using\n\n if type(weight_func) == str:\n weight_func = util.weight_from_string(weight_func)\n\n if type(layer_func) == str:\n layer_func = util.layer_from_string(layer_func)\n\n with open(file_name) as netfile:\n if not csv_reader_argv:\n netreader = csv.reader(netfile,\n delimiter=',',\n quotechar='\\\"',\n quoting=csv.QUOTE_NONNUMERIC)\n else:\n netreader = csv.reader(netfile,**csv_reader_argv)\n\n index_line = next(netreader)\n\n index = 0\n for item in index_line:\n index_dict[item] = index\n index += 1\n\n origin_index = index_dict[ow]\n dest_index = index_dict[dw]\n\n for line in netreader:\n if not filter_func(index_dict,line):\n continue\n\n origin = str(line[origin_index])\n dest = str(line[dest_index])\n\n layer = layer_func(index_dict,line)\n weight = weight_func(index_dict,line)\n\n mg.add_node(origin)\n mg.add_node(dest)\n\n mg.aggregate_edge(origin,dest,layer,weight)\n\n return mg","title":""},{"docid":"cba4544819e8f41ea873371efe1fcc71","score":"0.61474216","text":"def load(self, csvFilePath, metadataFilePath):\n\t\tf = open(csvFilePath, 'rb')\n\t\treader = csv.reader(f)\n\t\tl = list(reader)\n\n\t\t# extract attributes and data points\n\t\tself.attributes = map(lambda x: x.strip(), l[0])\n\t\tself.data = l[1:10]\n\n\t\tself.classAttr = self.attributes[-1].strip()\n\n\t\tf = open(metadataFilePath)\n\t\tfor line in f:\n\t\t\ttype = line[line.index(':')+1:].strip()\n\n\t\t\t# save attribute type\n\t\t\tif type == 'nominal':\n\t\t\t\tself.attrTypes.append(self.NOMINAL)\n\t\t\telif type == 'numeric':\n\t\t\t\tself.attrTypes.append(self.NUMERIC)\n\t\t\telse:\n\t\t\t\tself.attrTypes.append(self.OTHERS)\n\n\t\t# convert values to correct type\n\t\tfor i, sample in enumerate(self.data):\n\t\t\tself.numData += 1\n\t\t\tfor j, type in enumerate(self.attrTypes):\n\t\t\t\tval = self.data[i][j]\n\t\t\t\tif type == self.NUMERIC and val != '?':\n\t\t\t\t\tself.data[i][j] = float(val)","title":""},{"docid":"92f98edb63b7406938b60dffac8da187","score":"0.612518","text":"def load_network(filename):\n temp = pickle.load(filename)\n n = Network(None, None, None, None, None, None, None)\n n.__setstate__(temp)\n return n","title":""},{"docid":"a7f005dd7f8132267f72b11a4cbf66e2","score":"0.6119918","text":"def loadnetwork(filename):\n with open(filename, 'rb') as networkin:\n self = pickle.load(networkin)\n \n return self","title":""},{"docid":"b2576bf23e2ffe4e7d4f5afeb42a1fdc","score":"0.61056966","text":"def from_CSV(cls, path):\n with open(path) as file:\n tiles = list(csv.reader(file))\n\n return cls.from_list(tiles)","title":""},{"docid":"c763d849205884dd187de2654fa93c00","score":"0.60922307","text":"def load_gangs(self, filename):\n\n with open(filename, 'r') as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n gang = Gang(int(row[\"gang number\"]),\n (float(row[\"x location\"]), float(row[\"y\"])),\n int(row[\"gang members\"]))\n self.gang_info[int(row[\"gang number\"])] = gang","title":""},{"docid":"0dd346d3371c6ccbb172b88c6cd1b4b7","score":"0.6080333","text":"def load_network(path, filename):\n network_path = '{}/networks/{}.graphml'.format(path, filename)\n h = open(network_path, 'r')\n try:\n multithreading.lock_file_handle(h)\n return igraph.Graph.Read(network_path)\n finally:\n multithreading.unlock_file_handle(h)\n h.close()","title":""},{"docid":"ed93b8dde00643a09fc9bf65891af5c5","score":"0.60763484","text":"def from_csv(self, filepath, delim = ','):\n\n tdo = textio.read_csv(filepath, True, delim)\n\n self.from_tdo(tdo)\n\n return","title":""},{"docid":"2fb46335b50f5ba95cd2d73a56df4acb","score":"0.60590553","text":"def load_networks(filename, datadir=None):\n fpath = utils.uploaded_filepath(filename, FT_NETWORKS, datadir=datadir)\n\n return fortios_xutils.load_network_graph(fpath)","title":""},{"docid":"13c7634a7ab150373f2ea161731959d6","score":"0.605758","text":"def load_neos(neo_csv_path):\n\n df = pd.read_csv(neo_csv_path, low_memory=False)\n df = df[['pdes', 'name', 'pha', 'diameter']]\n df.pdes = df.pdes.astype('str')\n neos = [NearEarthObject(**value) for value in df.T.to_dict().values()]\n return neos","title":""},{"docid":"5d030bde2bbc9d187d7b7ed526954ae9","score":"0.6051468","text":"def loadcsv(self, fn):\n csvfile = fn\n with open(csvfile) as f:\n reader = csv.reader(f, delimiter=\",\")\n #print reader.next(), \" ommitted\"\n for row in reader:\n row0 = row[0]\n row8 = row[8]\n row3 = row[3]\n row4 = row[4]\n row5 = row[5]\n row6 = row[6]\n row7 = row[7]\n self.name[row8] = row0\n\n self.path[row8] = row3\n if \"Size\" not in row4:\n row4 = int(row4)\n self.size[row8] = row4\n #self.pctsize[row[8]] = float(row[4) / float(self.tsize)\n # size in KB\n self.modified[row8] = row5\n self.accessed[row8] = row6\n self.created[row8] = row7\n\n return","title":""},{"docid":"571f9546034a448ed91689f40134e88c","score":"0.59965175","text":"def load(self, filename):\n self.ids = []\n self.nodes = {}\n self.neigh = {}\n f = open(filename)\n for line in f:\n if not line.strip(): break\n i, l = map(str.strip, line.split(':'))\n if i in self.nodes:\n raise ValueError\n self.nodes[i] = Node(i, l)\n self.neigh[i] = []\n self.ids.append(i)\n\n for line in f:\n fr, l, to = map(str.strip, line.split(':'))\n if not l: l = None\n if fr not in self.nodes or to not in self.nodes:\n raise ValueError\n self.nodes[fr].add_neighbour(self.nodes[to], l)\n if l == None: l = ''\n self.neigh[fr].append((to, l))","title":""},{"docid":"be2c1a8defb1ce0e566b3528ac8244d0","score":"0.59776723","text":"def load_from_file_csv(cls):\n csvfile = '{}.csv'.format(cls.__name__)\n try:\n with open(csvfile, 'r', newline='') as csvf:\n if cls.__name__ == 'Square':\n fields = [\"id\", \"size\", \"x\", \"y\"]\n else:\n fields = [\"id\", \"width\", \"height\", \"x\", \"y\"]\n doc = csv.DictReader(csvf, fieldnames=fields)\n ls_of_dics = [{k: int(v) for k, v in dic.items()}\n for dic in doc]\n return [cls.create(**dics) for dics in ls_of_dics]\n except IOError:\n return []","title":""},{"docid":"8b1ea210fdc0d6869d1498b833db686a","score":"0.597655","text":"def load_network(path):\n with open(path) as f:\n content = f.read()\n\n edge_str = content.split('\\n')\n network = list()\n for edge in edge_str:\n network.append(edge.split())\n\n return network","title":""},{"docid":"adb3630915556b4c9cd421e6919b9b09","score":"0.595501","text":"def from_file(cls):\n with open('data.csv') as file:\n data_reader = csv.reader(file)\n for row in data_reader:\n first, last, mail = row\n cls(first, last, mail, True)","title":""},{"docid":"4d467ad4992d1021521dee67f9665b5b","score":"0.59509164","text":"def load_csv(csv):\n t = so(\"csv\",\"random\",\"fgdb\")\n TableToTable_conversion(\n in_rows=csv, \n out_path=os.path.dirname(t), \n out_name=os.path.basename(t)\n )\n return t","title":""},{"docid":"50050f90f10435fb78d38077b88f50c6","score":"0.5927157","text":"def load_network(self, fileName):\n\t\tif '.json' not in modelName:\n\t\t\tself.model = model_from_json(open(modelName+'_arch.json', 'r').read())\n\t\telse:\n\t\t\tself.model = model_from_json(open(modelName, 'r').read())","title":""},{"docid":"7f5f10673e74e62904d8dc62089bfcd1","score":"0.5914837","text":"def load_neos(neo_csv_path):\n\n result = []\n with open(neo_csv_path, 'r') as file:\n reader = DictReader(file)\n for x in reader:\n result.append(NearEarthObject(designation=x['pdes'],\n name=x['name'],\n hazardous=x['pha'],\n diameter=x['diameter']))\n\n return result","title":""},{"docid":"6edae08debda8804f99096dd97d582e2","score":"0.5914653","text":"def load_from_file_csv(cls):\n filename = \"{}.csv\".format(cls.__name__)\n\n if os.path.exists(filename):\n with open(filename, encoding=\"utf-8\") as f:\n content = f.read()\n objs = cls.from_json_string(content)\n newlist = []\n for obj in objs:\n new = cls.create(**obj)\n newlist.append(new)\n return newlist\n else:\n return []","title":""},{"docid":"977642f6f45ec5ff12797222e8dc7241","score":"0.58989906","text":"def import_network_from_file(file=None, base_url=DEFAULT_BASE_URL):\n if file is None:\n file = os.path.abspath('data/galFiltered.sif')\n else:\n file = os.path.abspath(file)\n res = commands.commands_post('network load file file=' + file, base_url=base_url)\n # TODO: Fix R documentation to match what's really returned\n\n # should not be necessary, but is because \"network load file\" doesn't actually set the current network\n # until after it's done. So, without the sleep(), setting the current network will be superceded by\n # \"network load file\"'s own network. This is race condition that can be solved by \"network load file\"\n # not returning until it's actually done.\n # TODO: Fix this race condition\n time.sleep(CATCHUP_NETWORK_SECS)\n\n return res","title":""},{"docid":"46f33d0cf7544970cce1d77957613291","score":"0.5894413","text":"def load_from_file_csv(cls):\n filename = cls.__name__ + \".csv\"\n # Unless the file does not exist, opens the file and reads each row.\n # The length of the row specifies the type of object. If this is not\n # a valid value, raises a TypeError. Otherwise, values are paired\n # with their correct attribute name in a dictionary, then used to\n # create those instances. A list of instances is returned upon success\n try:\n with open(filename, \"r\") as fcsv:\n c_reader = csv.reader(fcsv, delimiter=\",\")\n retlist = []\n for row in c_reader:\n if len(row) == 5:\n ats = [\"id\", \"width\", \"height\", \"x\", \"y\"]\n elif len(row) == 4:\n ats = [\"id\", \"size\", \"x\", \"y\"]\n else:\n raise TypeError\n values_dic = {}\n for i in range(len(row)):\n if type(row[i]) is not int:\n row[i] = int(row[i])\n values_dic[ats[i]] = row[i]\n obj = cls.create(**values_dic)\n retlist.append(obj)\n return retlist\n except IOError:\n raise IOError","title":""},{"docid":"3938bce4fa5e66c2764752243c9ca8bb","score":"0.58855146","text":"def load_dataset(self,\n csv_file,\n root_dir=None,\n augment=False,\n shuffle=True,\n batch_size=1,\n train=False):\n\n ds = dataset.TreeDataset(csv_file=csv_file,\n root_dir=root_dir,\n transforms=self.transforms(augment=augment),\n label_dict=self.label_dict,\n preload_images=self.config[\"train\"][\"preload_images\"])\n\n data_loader = torch.utils.data.DataLoader(\n ds,\n batch_size=batch_size,\n shuffle=shuffle,\n collate_fn=utilities.collate_fn,\n num_workers=self.config[\"workers\"],\n )\n\n return data_loader","title":""},{"docid":"23b08b15a197bb24f43fe9eb1f031099","score":"0.58812714","text":"def load(cls, device):\n fpath = Path(COLLISION_DIR, 'collisions.csv')\n inputs, labels = [], []\n with open(fpath, 'r') as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n for line in reader:\n input = [float(s) for s in line[:-1]]\n inputs.append(input)\n\n label = int(line[-1])\n assert label in [0, 1]\n labels.append(label)\n\n inputs = torch.tensor(inputs).to(device)\n labels = torch.tensor(labels).long().to(device)\n return cls(inputs, labels)","title":""},{"docid":"785315180eb18f154bca3be42a832bf3","score":"0.58764255","text":"def load_net(self, path):\n\n with open(path, 'rb') as nn_file:\n net = pickle.load(nn_file)\n\n if type(net) == dict:\n loaded = list()\n if 'model' in net.keys():\n self.model = net['model']\n loaded.append('model')\n if 'meta_params' in net.keys():\n self.meta_params = net['meta_params']\n loaded.append('meta_params')\n if 'data_sets' in net.keys():\n self.data_sets = net['data_sets']\n loaded.append('data_sets')\n if 'costs' in net.keys():\n self.costs = net['costs']\n loaded.append('costs')\n if 'cost_counter' in net.keys():\n self.cost_counter = net['cost_counter']\n loaded.append('cost_counter')\n print('Loaded from {0}: {1}'.format(path, ', '.join(loaded)))","title":""},{"docid":"5920150abf03965e2c05acb19006904b","score":"0.5874864","text":"def from_csv(csv_file, units=DEFAULT_UNIT):\n\n if isinstance(csv_file, str):\n if not csv_file.endswith('.csv'):\n raise ValueError(\n 'Filetype must be .csv to create a DistanceMatrix.')\n\n with open(csv_file, 'r') as open_file:\n return DistanceMatrix.from_csv(open_file)\n\n lines = [line.strip().split(',') for line in csv_file.readlines()]\n\n # Strip trailing empty strings if they exist\n try:\n [line.remove('') for line in lines]\n except ValueError:\n pass\n\n highest_line_length = 0\n matrix = []\n for line in lines:\n if len(line) == highest_line_length + 1:\n if all(is_numeric(item) for item in line):\n matrix.append([float(item) for item in line])\n highest_line_length += 1\n else:\n raise ValueError(\n 'Data lines in input file should be in order of ascending length.')\n\n return DistanceMatrix(matrix, units)","title":""},{"docid":"7139a8d85797a92497b30657ec0db74d","score":"0.58667594","text":"def load_graph(file):\n g = nx.DiGraph()\n mode = \"N\"\n for l in file:\n l = l.strip()\n if mode == \"N\":\n if l == \"// Nodes\":\n mode = \"LN\"\n elif mode == \"LN\":\n if l == \"// Edges\":\n mode = \"LE\"\n else: # LOAD NODES\n nparts = l.split(\" \", 2)\n g.add_node(int(nparts[0]),\n {'unixtime': int(nparts[1]), 'line': nparts[2]})\n pass\n elif mode == \"LE\" and len(l) > 0: # LOAD EDGES\n eparts = [int(x) for x in l.split(\" \", 1)]\n g.add_edge(eparts[0], eparts[1])\n return g","title":""},{"docid":"ecd30f289351eeca88b0cfe2a13c6164","score":"0.58567697","text":"def loadNetworkFromFile(filename, mode = 'pickle'):\n if mode == 'pickle':\n import pickle\n fp = open(filename)\n network = pickle.load(fp)\n fp.close()\n return network\n elif mode in ['plain', 'conx']:\n fp = open(filename, \"r\")\n line = fp.readline()\n network = None\n while line:\n if line.startswith(\"layer,\"):\n # layer, name, size\n temp, name, sizeStr = line.split(\",\")\n name = name.strip()\n size = int(sizeStr)\n network.addLayer(name, size)\n line = fp.readline()\n weights = [float(f) for f in line.split()]\n for i in range(network[name].size):\n network[name].weight[i] = weights[i]\n elif line.startswith(\"connection,\"):\n # connection, fromLayer, toLayer\n temp, nameFrom, nameTo = line.split(\",\")\n nameFrom, nameTo = nameFrom.strip(), nameTo.strip()\n network.connect(nameFrom, nameTo)\n for i in range(network[nameFrom].size):\n line = fp.readline()\n weights = [float(f) for f in line.split()]\n for j in range(network[nameTo].size):\n network[nameFrom, nameTo].weight[i][j] = weights[j]\n elif line.startswith(\"parameter,\"):\n temp, exp = line.split(\",\")\n exec(exp) # network is the neural network object\n elif line.startswith(\"network,\"):\n temp, netType = line.split(\",\")\n netType = netType.strip().lower()\n if netType == \"cascornetwork\":\n from pyrobot.brain.cascor import CascorNetwork\n network = CascorNetwork()\n elif netType == \"network\":\n network = Network()\n elif netType == \"srn\":\n network = SRN()\n else:\n raise AttributeError, \"unknown network type: '%s'\" % netType\n line = fp.readline()\n return network","title":""},{"docid":"0e064e8bc5fd29283f229fd615b2651c","score":"0.58296126","text":"def load_from_file(file_path: str):\n routes = []\n\n with open(file_path, \"r\") as routes_file:\n csv_reader = csv.reader(routes_file, delimiter=\",\")\n next(csv_reader, None) # Dump header\n\n for row in csv_reader:\n routes.append(MMTRoute.parse_from_row(row))\n\n return MMTRouteDataController(routes)","title":""},{"docid":"6d49a58993ee4b5c0b0a55e3db38a612","score":"0.58203024","text":"def load_from_file_csv(cls):\n file_name = cls.__name__ + '.csv'\n list_objs = []\n with open(file_name, 'r') as fp:\n csv_r = csv.DictReader(fp)\n for i in csv_r:\n for k, v in i:\n i[k] = int(v)\n list_objs.append(cls.create(**i))","title":""},{"docid":"c3fe6e022dfe252490f00c97af2196a2","score":"0.57661384","text":"def load_categories(self, path: str):\n with open(path, 'r') as f:\n csv_reader = csv.reader(f, delimiter=';')\n # skip header \n next(csv_reader)\n \n for row in csv_reader:\n if row[2] == 'NULL':\n p = None\n else:\n p = self.categories[int(row[2])]\n \n self.categories[int(row[0])] = Category(int(row[0]), row[1], p)","title":""},{"docid":"11b4f4cc3188c0afa49e3406dc74e365","score":"0.5759813","text":"def loadData(labelName = \"G3\"):\n print(\"\\nLoading data…\")\n trainPath, testPath = TRAIN_PATH, TEST_PATH\n print(\"\\nParse local csv\")\n train = pd.read_csv(filepath_or_buffer = trainPath,\n names = CSV_COLUMN_NAMES,\n header = 0)\n print(\"\\nGet train information\")\n trainFeatures, trainLabel = train, train.pop(labelName)\n\n # Parse local csv\n test = pd.read_csv(filepath_or_buffer = testPath,\n names = CSV_COLUMN_NAMES,\n header = 0)\n # Get test information\n testFeatures, testLabel = test, test.pop(labelName)\n\n return (trainFeatures, trainLabel),(testFeatures, testLabel)","title":""},{"docid":"8e834572a37f9f14237a1d951ada26a9","score":"0.5752725","text":"def load_train_data(csv_path=_TRAIN_DATA_PATH):\n train_df = pd.read_csv(open(csv_path, 'r', encoding=\"iso-8859-1\"), quotechar='\"')\n\n return train_df","title":""},{"docid":"1315fb2c3dea930f843b178f8f07d96e","score":"0.57414705","text":"def loadCSV(self, path):\n dtframe = pd.read_csv(path)\n self.loadDataframe(dtframe)\n \n #####","title":""},{"docid":"5f2b242c33300281a9e6db95f09d55d6","score":"0.5735814","text":"def load_graph_from_file(filename):\n with open(filename) as UK_cities:\n dict_uk_cities = json.load(UK_cities)\n return nx.Graph(dict_uk_cities)","title":""},{"docid":"2f086418c793a6cedcce66864bd38c0f","score":"0.57288903","text":"def importCSV(self):\n filepath = QFileDialog.getOpenFileName(self, 'Load CSV file',\"\",\"Comma-separated values (*.csv)\")\n if filepath !=\"\":\n urllib.urlretrieve('file:/// %s' % filepath,'Schedule.csv')\n self.tableWidget.clear()\n self.loadSchedule()","title":""},{"docid":"2ed0c3b6f8c016dd5881c8f5ce09f2f3","score":"0.5704464","text":"def load_from_file_csv(cls):\n csv_file = cls.__name__ + \".csv\"\n lst = list()\n try:\n with open(csv_file, \"r\") as fh:\n reader = csv.DictReader(fh)\n res = list(reader)\n for d in res:\n for item in d.keys():\n d[item] = int(d[item])\n temp = cls.create(**d)\n lst.append(temp)\n return lst\n except:\n return list()","title":""},{"docid":"2321149ca9513bf6e89e5c5e2d9f5867","score":"0.570376","text":"def load_from_file(self, filename):\n\n with open(filename, newline='') as csvfile:\n reader = csv.reader(csvfile)\n self.column_names = next(reader) # Take note of the header names and save them seperate from the data\n for row in reader: # Now save the data row by row\n self.data.append(row)\n\n self.convert_to_numeric() # Convert values to floats (that can be converted)\n\n return self","title":""},{"docid":"863c4a47d27a8f567de055ec086191ee","score":"0.5689223","text":"def do_load(self, line):\n with open(self.rosterFilePath, 'r') as csvFile:\n rosterReader = csv.DictReader(csvFile)\n for row in rosterReader:\n completedProblems = row['completedProblems'] if 'completedProblems' in row else 0\n currentBalloons = row['currentBalloons'] if 'currentBalloons' in row else 0\n contestant = Contestant(row['Room #'], row['Team #'],\n row['Check-In'], row['HackerRank'].lower(), \n row['Partner 1 Name'], row['Partner 2 Name'], \n completedProblems, currentBalloons)\n try: \n Contestant.add_to_cache(contestant)\n except Exception as e:\n continue","title":""},{"docid":"6670fea3c870c995ac2a9826a1bb35d5","score":"0.56890976","text":"def import_csv(filename, csv_map=None, encoding=\"latin1\"):\n if not os.path.exists(filename):\n raise FileNotFoundError\n\n with open(filename, mode=\"r\", encoding=encoding, errors=\"ignore\") as csv_file:\n Part._import_csv_content(csv_file, csv_map)","title":""},{"docid":"7e7e83b5d5a4445c0518663ba841302a","score":"0.5683459","text":"def __init__(self, csv_file):\n self.server_list = SLFile.convert_csv_to_dict_list(csv_file)","title":""},{"docid":"5266981ae08f217e9facbdbd41d1217b","score":"0.56708896","text":"def load_csv(filename, label_col, num_test):\n\n X_train = []\n X_test = []\n num = 0\n with open(filename,'rt') as f:\n reader = csv.reader(f, delimiter=',')\n for row in reader:\n row1 = [float(item) for item in row if item != '\\0']\n last_ele = row1.pop(label_col)\n X_train.append(row1)\n X_test.append(int(last_ele))\n num+=1\n if num > num_test:\n break\n \n f.close()\n return X_train, X_test","title":""},{"docid":"83af557eee48511af436f09dd9bb4771","score":"0.5633198","text":"def run(self) -> None:\n with open(self.import_csv_path) as csv_file:\n csv_reader = csv.DictReader(csv_file)\n for row in csv_reader:\n aso_node = graph.SpaceObject.find_one(norad_id=row['aso_id'])\n if aso_node is None:\n continue\n is_compliant = row['is_compliant'] == 'True'\n compliance_nodes = aso_node.compliance.all()\n if compliance_nodes:\n compliance_node = compliance_nodes[0]\n compliance_node.is_compliant = is_compliant\n compliance_node.save()\n else:\n compliance_node = graph.Compliance(\n is_compliant=is_compliant\n )\n compliance_node.save()\n aso_node.compliance.connect(compliance_node)\n compliance_node.from_data_source.connect(\n self.data_source_node\n )","title":""},{"docid":"9dee2a459d3b62f52f82e800bfef054c","score":"0.5628896","text":"def load_prod_table(csv_fname):\n left_col, right_col = 0, 3\n return load_csv(csv_fname, left_col, right_col)","title":""},{"docid":"21e4c283554e332e93fdceb92a314174","score":"0.5624825","text":"def load_from_file_csv(cls):\n empty_list = []\n complete_name = cls.__name__+'.csv'\n try:\n with open(complete_name, 'r') as f:\n instance = cls.from_json_string(f.read())\n instance_list = [cls.create(**line) for line in instance]\n return instance_list\n except:\n return (empty_list)","title":""},{"docid":"4cee894ea39247d1992edca7294f7f63","score":"0.5606623","text":"def load_destination_data(input_data, header_lines): # Move to own controller file??\n with open(input_data) as places:\n data = csv.reader(places, delimiter=',')\n num_col = len(next(data)) # count number of columns in the csv file\n places.seek(0) # Return csv reader back to start of file\n for i in range(0, header_lines): # skip specified number of header lines\n next(data, None)\n\n for place in data: # Add all vertices to the graph.\n address = place[1]\n vertex = model.graph.Vertex(address)\n graph_instance.add_vertex(vertex, address)\n\n places.seek(0) # Return csv reader back to start of file.\n for i in range(0, header_lines): # skip specified number of header lines\n next(data, None)\n\n index_counter = 0\n for place in data: # Add edges to the graph.\n # name = place[0]\n # key = place[1]\n i = 2\n while place[i] != '':\n if place[i] != '':\n vertex_a = graph_instance.get_vertex(graph_instance.vertex_key_list[index_counter])\n vertex_b = graph_instance.get_vertex(graph_instance.vertex_key_list[i - 2])\n distance = float(place[i])\n graph_instance.add_undirected_edge(vertex_a, vertex_b, distance)\n i += 1\n if i > num_col - 1:\n break\n\n index_counter += 1","title":""},{"docid":"6c3dd2dc89274e5a9e68b3aa3bcc636c","score":"0.5574019","text":"def from_csv(file_path: str) -> \"CorrelationsData\":\n return CorrelationsData(data=pd.read_csv(file_path))","title":""},{"docid":"39d2c32b2ce6bd037e9faf39ad6f051c","score":"0.557101","text":"def __init__(self, filename='social_network.csv'):\n with open(filename) as file:\n data = file.read().split(\"\\n\")\n \n pair = []\n names = set()\n for line in data[:-1]:\n line = line.split(\",\")\n names.add(line[0])\n names.add(line[1])\n pair.append(line)\n names = sorted(list(names))\n \n n = len(names)\n M = np.zeros((n,n))\n for k in pair:\n i, j = names.index(k[0]),names.index(k[1])\n M[i,j] = 1\n M[j,i] = 1\n \n self.names = names\n self.M = M\n self.R = effective_resistance(M)","title":""},{"docid":"59582d82e29df5023dd641dcaeb763f3","score":"0.5569314","text":"def import_cities(self, path):\n labeled_cities = pd.read_csv(path)\n for i in range(100):\n x = labeled_cities.iloc[i,1]\n y = labeled_cities.iloc[i,2]\n self.tab_villes.append([x, y])","title":""},{"docid":"5bdeec59eda957d65b3ce4892c5bafd5","score":"0.55657744","text":"def loadNeuralNetwork(filename):\n extn=filename.rsplit('.',1)[-1]\n if extn=='t7':\n return cv2.dnn.readNetFromTorch(filename)\n if extn=='xml':\n return cv2.dnn.readNetFromONNX(filename)\n raise IOError('Unknown file format for \"'+filename+'\"')","title":""},{"docid":"bcf035ab6fa7768376fb6508a13f29d2","score":"0.5562248","text":"def load_label(csv_file):\n with open(csv_file, 'r') as label_file:\n labels = label_file.read()\n\n labels = labels.split(\",\")\n labels = [int(l) for l in labels]\n # Labels is a list of int, representing gesture for each frame\n return labels","title":""},{"docid":"c2024c38a2bbbd2bdaf989c985dbfb66","score":"0.55456424","text":"def loadships(shipcsv):\n shipdict = shipreader(shipcsv)\n availableShips = craftassign(shipdict)\n return availableShips","title":""},{"docid":"23712c0c4eccc53633e99ce25acdf01b","score":"0.55411035","text":"def load_csv(self, filename, sep=\",\"):\n\n return self.session.read.csv(\n filename,\n inferSchema=True,\n sep=sep,\n header=True\n )","title":""},{"docid":"fa889fb82f390b4988022fef57c072ad","score":"0.5538708","text":"def load_model_file(data_file):\n # Open the file with the data, read it, and split it into lines\n with open(data_file, 'r') as f:\n data = f.read()\n \n lines = data.splitlines()\n \n # Define the interfaces info \n int_info_begin_index = 2\n int_info_end_index = lines.index(\"NODES_TABLE,,,,\") - 1\n interface_lines = lines[int_info_begin_index:int_info_end_index]\n interface_set = set([]) \n node_list = []\n for interface_line in interface_lines:\n node_name, remote_node_name, name, cost, capacity = \\\n interface_line.split(',')\n interface_set.add(Interface(name, int(cost), int(capacity), Node(node_name),\n Node(remote_node_name)))\n node_list.append(Node(node_name))\n node_list.append(Node(remote_node_name))\n model = Model(interface_set, set(node_list))\n \n # Define the nodes info\n nodes_info_begin_index = int_info_end_index + 3\n nodes_info_end_index = lines.index(\"DEMANDS_TABLE,,,,\") -1\n node_lines = lines[nodes_info_begin_index:nodes_info_end_index]\n node_names = set([node.name for node in node_list])\n for node_line in node_lines:\n node_info = node_line.split(',')\n node_name = node_info[0]\n try:\n node_lat = int(node_info[1])\n except ValueError:\n node_lat = 0\n try:\n node_lon = int(node_info[2])\n except ValueError:\n node_lon = 0 \n if node_name not in node_names: # Pick up orphan nodes\n new_node = Node(node_name)\n model.add_node(new_node)\n new_node.lat = node_lat\n new_node.lon = node_lon\n else:\n model.get_node_object(node_name).lat = node_lat\n model.get_node_object(node_name).lon = node_lon\n \n # Define the demands info\n demands_info_begin_index = nodes_info_end_index + 3\n demands_lines = lines[demands_info_begin_index:]\n for demand_line in demands_lines:\n demand_info = demand_line.split(',')\n source = demand_info[0]\n dest = demand_info[1]\n traffic = int(demand_info[2])\n name = demand_info[3] \n if name == '':\n demand_name = 'none'\n else: \n demand_name = name \n model.add_demand(source, dest, traffic, demand_name)\n \n return model","title":""},{"docid":"d1413decf89a10a4c5c57ba490db4023","score":"0.553635","text":"def openCsv():\n csvFile = 'BDO_app/modules/nodes/cityNodes.csv'\n return csvFile","title":""},{"docid":"b216637a354afa4a13ebf51db370cad7","score":"0.55337936","text":"def load_dataset(csv_path):\n\n # read in and shuffle data\n df = pd.read_csv(csv_path).sample(frac=1)\n # drop indicator columns for destinations\n cols = [c for c in df.columns if c[:11] !=\n 'destination' and c[:14] != 'wind_direction']\n df = df[cols]\n # print(len(cols))\n # print(df.columns.values.tolist())\n\n inputs = df.drop('time_taxi_out', axis=1).values\n labels = df['time_taxi_out'].values\n return inputs, labels","title":""},{"docid":"a9f7cf9a98d1a0de583fde1596254f16","score":"0.5517911","text":"def import_csv(self, csvfile, columns=None, delimiter=' ', quotechar='|'):\n if isinstance(csvfile, str):\n csvfile = open(csvfile, 'rb')\n import csv\n R = csv.reader(csvfile, delimiter=delimiter, quotechar=quotechar)\n if columns is None:\n columns = R.next()\n d = []\n for x in R:\n z = {}\n for i in range(len(x)):\n y = x[i]\n if y != '':\n if y.isdigit():\n y = eval(y)\n else:\n v = y.split('.')\n if len(v) == 2 and v[0].isdigit() and v[1].isdigit():\n y = eval(y)\n z[columns[i]] = y\n d.append(z)\n self.insert(d)","title":""},{"docid":"08cf2abbd97ca76ef69d719d91aa8ba4","score":"0.5512902","text":"def load_csv(filename):\n with open(filename, 'rb') as csvfile:\n dialect = csv.Sniffer().sniff(csvfile.read(1024))\n csvfile.seek(0)\n csv_data = csv.reader(csvfile, dialect)\n data = []\n for row in csv_data:\n data.append(row)\n return data","title":""},{"docid":"091fb5851a65435b2b84c66fca0ef03e","score":"0.5506476","text":"def load_network_from_file(filename, fmt=None):\n # if no format has been given, get it from the file name\n if fmt is None:\n _, fmt = os.path.splitext(filename)\n fmt = fmt[1:]\n # decide which function to call to load the data\n if fmt == 'pickle':\n return _load_network_from_pickle(filename)\n elif fmt == 'json':\n return _load_network_from_json(filename)\n else:\n raise ValueError('Only pickle or json formats are supported.')","title":""},{"docid":"baae61805c40247c078a14b9e8911484","score":"0.5500594","text":"def load_from_file_csv(cls):\n obj_list = []\n file_name = cls.__name__ + \".csv\"\n if cls.__name__ == \"Rectangle\":\n field_names = ['id', 'width', 'height', 'x', 'y']\n elif cls.__name__ == \"Square\":\n field_names = ['id', 'size', 'x', 'y']\n try:\n with open(file_name, mode=\"r\", encoding=\"utf-8\") as f:\n reader = csv.DictReader(f, fieldnames=field_names)\n for row in reader:\n dic = {}\n for key, value in dict(row).items():\n dic[key] = int(value)\n obj_list.append(cls.create(**dic))\n except:\n return []\n return obj_list","title":""},{"docid":"2cb05094f5ed7ab5dce913655e0d3284","score":"0.5500581","text":"def load_data(self, fname):\n with open(fname) as f:\n data = csv.reader(f, delimiter=',')\n data = [row for row in data]\n tx = [map(float, x[1:len(x)]) for x in data[6:]]\n ty = [x[0] for x in data[6:]]\n for i, label in enumerate(ty):\n if label == 'BRICKFACE':\n ty[i] = 0\n elif label == 'SKY':\n ty[i] = 1\n elif label == 'FOLIAGE':\n ty[i] = 2\n elif label == 'CEMENT':\n ty[i] = 3\n elif label == 'WINDOW':\n ty[i] = 4\n elif label == 'PATH':\n ty[i] = 5\n elif label == 'GRASS':\n ty[i] = 6\n else:\n print 'error'\n ds = ClassificationDataSet(19, 1, nb_classes=7)\n for x, y in zip(tx, ty):\n ds.addSample(x, y)\n ds._convertToOneOfMany()\n return ds","title":""},{"docid":"65c9e9aa8b116c154d60fa2f1185c5cb","score":"0.5496548","text":"def load_map(self, filename):\n self._graph = Graph()\n self._location = {}\n self._streetnames = {}\n\n with open(filename, 'r') as f:\n for line in f:\n elements = line.split(\",\")\n if(elements[0] == \"V\"):\n self._graph.add_vertex(int(elements[1]))\n self._location[int(elements[1])] = (self.process_coord(elements[2]),\n self.process_coord(elements[3]))\n elif (elements[0] == \"E\"):\n self._graph.add_edge((int(elements[1]), int(elements[2])))\n self._streetnames[(int(elements[1]), int(elements[2]))] = elements[3]","title":""},{"docid":"55fc32b5964a413c53f0ad9b59103fa7","score":"0.54956716","text":"def __csv_to_nc__(file_name):\n load_file = pd.read_csv(file_name, header=0, index_col=0)\n load_file.index = pd.to_datetime(load_file.index)\n \n # convert to xarray.Dataset and drop unused local time column\n ds = load_file.to_xarray().drop(cest_col)\n var_names = [var for var in ds.data_vars]\n \n # convert again to datetime, won't be recognized otherwise\n ds[utc_col] = pd.to_datetime(ds[utc_col])\n \n # set unit variable for each variable; all MW, exept for some shares\n for var in var_names:\n unit = 'MW' if any(label in var for label in hasMWlbl) else 'share'\n ds[var].attrs.update(OrderedDict([('units', unit)]))\n \n # write to file\n ds.to_netcdf(load_path)","title":""},{"docid":"9532a98922cf16fe2a04b9a441503d75","score":"0.54931366","text":"def load_network(name):\r\n net = Network([0])\r\n net.sizes = np.load(\"pretrained_networks/{0}/sizes.npy\".format(name))\r\n net.weights = np.load(\"pretrained_networks/{0}/weights.npy\".format(name))\r\n net.biases = np.load(\"pretrained_networks/{0}/biases.npy\".format(name))\r\n net.num_layers = len(net.sizes)\r\n return net","title":""},{"docid":"51260f127265c158937fe23200daf337","score":"0.54910463","text":"def load_from_file_csv(cls):\n if not os.path.isfile(cls.__name__ + \".csv\"):\n return []\n with open(cls.__name__ + \".csv\", encoding=\"utf-8\") as f:\n csv_r = list(csv.DictReader(f))\n a = []\n for i in csv_r:\n i = {x: int(y) for x, y in i.items()}\n a[len(a):] = [cls.create(**i)]\n return a","title":""},{"docid":"2d8de737e6445a95ccc14101df892172","score":"0.5490962","text":"def load_mac_addresses(self, path=None, file_name=None):\n self._path = path\n if self._path is None:\n self._path = os.getcwd() \n\n self._file_name = file_name\n if self._file_name is None:\n # TODO: get list of files here\n pass\n\n with open(file_name, \"r\") as csv_file:\n first_line = True\n for line in csv_file:\n if first_line:\n first_line = False\n continue\n line_split = line.split(\",\")\n self._base[line_split[0]] = [x for x in line_split[1:]]","title":""},{"docid":"7fa30ee8e99aabaffb424d8ed94b2af8","score":"0.54839677","text":"def load_Network(Filename):\n print(\"Loading Network map from file \", Filename)\n g = SmartDigraph()\n hash_table = {}\n counter = 1\n \n lines = open(Filename, 'r').readlines()\n \n for i in lines:\n for j in range(2):\n line = i.split()\n if line[j] not in hash_table:\n hash_table[line[j]] = counter\n counter += 1\n \n for i in hash_table:\n g.addNode(SmartNode(hash_table[i]))\n \n for i in lines:\n line = i.split()\n start_node = g.getNode(str(hash_table[line[0]]))\n end_node = g.getNode(str(hash_table[line[1]]))\n lead_time = int(line[2])\n\n edge = WeightedEdge(start_node, end_node,lead_time) # creates the edge\n g.addEdge(edge) # adds the edge to the smartdigrahp\n\n return g, hash_table","title":""},{"docid":"fe96f1f9ec2a76632bc7aa390d75c146","score":"0.5463261","text":"def read_travels_csv_file(filename):\n\n fileIN=open(filename, 'r')\n\n # Read the first line\n line=fileIN.readline()\n\n # Try to guess the seperator\n separator=None\n for sep in [';', ',', '\\t']:\n if line.strip().split(sep) == ns_header:\n separator=sep\n break\n else:\n error(\"I cannot recognise the specified file '{}', sorry.\".format(filename))\n error(\"Try exporting the file to csv with a semicolon (;) as field separator\")\n exit(-1)\n \n # Store all trips\n travels=list()\n\n # Read all the lines in the file\n # NOTE: entries in the file are not guaranteed to be ordered on time/date\n line=fileIN.readline()\n while line:\n # Remove trailing newline and split the line on the separator we found\n spline=line.split(separator)\n\n # If the line does not contain as many fields as the header, something is wrong\n if len(spline) != len(header):\n error(\"Error, invalid line detected:\\n{}\".format(line))\n\n # Unpack the split line, and make a Trip object out of it\n travels.append(Trip(*spline))\n line=fileIN.readline()\n\n # Close the file\n fileIN.close()\n return travels","title":""},{"docid":"d10a81011185d1988e810e451bdb083a","score":"0.54531014","text":"def ReadCsv(url = 'http://developer.mbta.com/lib/rthr/red.csv'):\n fp = urllib2.urlopen(url)\n reader = csv.reader(fp)\n\n tss = []\n for t in reader:\n if t[5] != 'Kendall/MIT': continue \n if t[3] != 'Braintree': continue\n\n ts = TrainSpotting(t)\n tss.append(ts)\n\n fp.close()\n return tss","title":""},{"docid":"f3b08ec7a8f359d6a664bc1f93639e58","score":"0.54518944","text":"def load_geolocations():\n\n with open(\"data/geolocation.csv\", \"rb\") as f:\n \n reader = csv.reader(f, delimiter=\",\")\n\n for row in reader: \n\n street_addr = row[0]\n city = row[1]\n state = row[2]\n latitude = row[3]\n longitude = row[4]\n elevation = row[5]\n timezone=row[6]\n\n geolocation = Geolocation(street_addr=street_addr, city=city, state=state, latitude=latitude, \n longitude=longitude, elevation=elevation, timezone=timezone)\n\n db.session.add(geolocation)\n db.session.commit()\n\n print \"Geolocation committed\"","title":""},{"docid":"618114906e9262687fdcc448797a0f5d","score":"0.544634","text":"def load_data(self, train_csv=None, test_csv=None):\n\n if train_csv is not None and test_csv is not None:\n self.train_data_vector = pandas.read_csv(train_csv)\n self.test_data_vector = pandas.read_csv(test_csv)","title":""},{"docid":"bbe6c47983b0f7f789aff6770cd7c3f6","score":"0.5436664","text":"def load_csv_from_path(self, csv_path):\n if not os.path.isabs(csv_path):\n csv_path = os.path.join(self.data_path, csv_path)\n\n # Deal with multiple possible extensions\n path_base, path_ext = os.path.splitext(csv_path)\n if path_ext == '.mp4':\n csv_path = path_base + '.csv'\n elif path_ext == '':\n csv_path = csv_path + '.csv'\n # Load the bounding box and format the timestamp\n box_data = np.loadtxt(csv_path, delimiter=',',\n dtype=float, skiprows=1,\n converters={0: lambda x: self.parse_time(x)})\n\n return box_data","title":""},{"docid":"713d828d81ebb66a31ccd76f47794e01","score":"0.54298455","text":"def read_csv(request):\n graph_file = request.param\n dataset_path = graph_file.get_path()\n M = utils.read_csv_for_nx(dataset_path)\n M_cu = utils.read_csv_file(dataset_path)\n\n return M_cu, M, graph_file","title":""},{"docid":"6574459bcb87e4653e9df4eba672a4f3","score":"0.54286075","text":"def loadFromFile(self, path):\n with open (path, \"rt\") as f:\n for line in f.readlines():\n card, cardNum = [int(x) for x in line.split(',')]\n self.add((card, cardNum))","title":""},{"docid":"5b4b36c3f160721581ea4ecedce5eb90","score":"0.54274607","text":"def load_csv(input):\n\n data_countries = pd.read_csv(input)\n return data_countries","title":""},{"docid":"50727d13cfa04bb896088b1610e1b853","score":"0.541671","text":"def load(self, hyperlinks):\n \tif Path(hyperlinks).is_file():\n \t\tself.g = nx.DiGraph()\n\t \twith open(hyperlinks, \"rb\") as ifile:\n\t \t\tfor line in ifile.readlines():\n\t \t\t\tdoc, links = line.split(\":\")\n\t \t\t\tdoc = int(doc)\n\t \t\t\tfor link in links.split(\";\"):\n\t \t\t\t\tself.g.add_edge(doc, int(link))\n\t \tlog.debug(\"%d documents et %d hyperliens chargés\", len(self.g), self.g.size())\n\t else:\n\t \tlog.error(\"Erreur lors de l'ouverture du fichier %s\", hyperlinks)","title":""},{"docid":"bef65f244899491ffdbcae4b70dbbffa","score":"0.5415988","text":"def loadCsvFile(self,myFile):\n # open file\n myFile.open(mode='rb')\n # read the field names\n head = myFile.readline().decode()\n self.allFieldNames = head.split(',')\n for index in range(len(self.allFieldNames)):\n self.allFieldNames[index] = self.allFieldNames[index].strip()\n # load the table data (from the csv file) into a numpy nd array\n data = np.loadtxt(myFile,dtype='float',delimiter=',',ndmin=2,skiprows=1)\n # close file\n myFile.close()\n return data","title":""},{"docid":"bef65f244899491ffdbcae4b70dbbffa","score":"0.5415988","text":"def loadCsvFile(self,myFile):\n # open file\n myFile.open(mode='rb')\n # read the field names\n head = myFile.readline().decode()\n self.allFieldNames = head.split(',')\n for index in range(len(self.allFieldNames)):\n self.allFieldNames[index] = self.allFieldNames[index].strip()\n # load the table data (from the csv file) into a numpy nd array\n data = np.loadtxt(myFile,dtype='float',delimiter=',',ndmin=2,skiprows=1)\n # close file\n myFile.close()\n return data","title":""},{"docid":"f41675bf47059717aa58eca84e153c21","score":"0.5413548","text":"def load_dataset(self):\n print(\"Loading Dataset\")\n csv_retrieve_dataset(self.TRAINING_DATA_PATH,self.TRAINING_DATA_URL,self.COLUMN_NAMES)","title":""},{"docid":"13ff2357baf8a0d38bca6ebddc1d9c39","score":"0.5409063","text":"def load_csv(file_name):\n if sys.version_info[0] < 3:\n lines = []\n infile = open(file_name, 'rb')\n else:\n lines = []\n infile = open(file_name, 'r', newline='')\n\n ant_dict = {}\n with infile as f:\n csvreader = csv.reader(f)\n for c, lines in enumerate(csvreader):\n if c == 0:\n continue\n ant_id = lines[0]\n if ant_id not in ant_dict:\n ant_dict[ant_id] = {}\n ant_dict[ant_id]['frames'] = []\n ant_dict[ant_id]['coordiantes'] = []\n if lines[-1]:\n ant_dict[ant_id]['frames'].append(\n '{:05d}'.format(int(lines[1])))\n ant_dict[ant_id]['coordiantes'].append([lines[2], lines[3]])\n\n return ant_dict","title":""},{"docid":"be721ab29de43bf1abc58ec0148e2fae","score":"0.5408601","text":"def import_tofrom_csv(file_name, id_col_name, to_node_col, from_node_col):\n use_cols = [\n str(id_col_name), str(to_node_col), str(from_node_col)\n ]\n\n df = pd.read_csv(file_name, usecols=use_cols, dtype=str)\n network_df, indx_df = df_transform(\n df, id_col_name, to_node_col, from_node_col\n )\n return network_df, indx_df","title":""}],"string":"[\n {\n \"docid\": \"da41cb35073df863beef61c4f11e5bb6\",\n \"score\": \"0.70894367\",\n \"text\": \"def from_csv(self, file: str):\\n with open(file) as f:\\n for line in f:\\n words = line.split(',')\\n line_type = words[0]\\n if line_type == 'c':\\n self._add_node(int(words[1]), int(words[2]))\\n elif line_type == 'e':\\n self.add_edge(int(words[1]), int(words[2]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d5420377b332b7df83d2febe0b0acb0\",\n \"score\": \"0.6935833\",\n \"text\": \"def from_csv(self, file: str):\\n with open(file) as f:\\n for line in f:\\n words = line.split(',')\\n src = int(words[0])\\n dest = int(words[1])\\n self.add_edge(src, dest)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"016b9e346ff6c9bf6f94b7cabaa1c281\",\n \"score\": \"0.68656546\",\n \"text\": \"def load(file_path=\\\"./graph.csv\\\"):\\n assert path.isfile(file_path), \\\"'{}' is not a file\\\".format(file_path)\\n log.debug(\\\"loading graph from: {}\\\".format(file_path))\\n graph = None\\n\\n with open(file_path, 'r', newline='', encoding='utf-8') as file:\\n reader = csv.reader(file, quotechar='\\\"')\\n for i, row in enumerate(reader):\\n if not graph:\\n graph = ActorsGraph(row)\\n else:\\n for j, movie_ids in enumerate(row):\\n for movie_id in movie_ids.split(\\\",\\\"):\\n graph.add_edge_by_indices(i - 1, j, movie_id)\\n\\n assert graph, \\\"graph has not been created, check '{}' file\\\".format(file_path)\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"646ca8b8548ac2e59a675c298e6f8dd5\",\n \"score\": \"0.66369027\",\n \"text\": \"def load_filename(self, filename):\\n graph = {}\\n with open(filename, 'rb') as csvfile:\\n reader = csv.reader(csvfile)\\n nodes = map(eval, next(reader)[1:])\\n for line in reader:\\n base = eval(line.pop(0))\\n graph[base] = dict((n1, n2)\\n for n1, n2 in zip(nodes, map(float, line))\\n if n2 > 0)\\n self.load(graph)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e1bd8ca7edd2fc331237792c15e1e17\",\n \"score\": \"0.65834135\",\n \"text\": \"def from_csv(self, csvfile, header=None, start=0, size=None, delimiter=','):\\n with open(csvfile) as f:\\n reader = csv.reader(f, delimiter=delimiter)\\n i = 0\\n if header:\\n next(reader, None)\\n i = 1\\n while i < start:\\n i += 1\\n next(reader, None)\\n i = 0\\n for row in reader:\\n pred = cleanse(row[1])\\n sub = cleanse(row[0])\\n ob = cleanse(row[2])\\n if not (sub.replace('_','').isalnum() and ob.replace('_','').isalnum()):\\n continue\\n if len(row) > 3:\\n node_attributes = [json.loads(row[3]), json.loads(row[4])]\\n edge_attributes = json.loads(row[5])\\n else:\\n node_attributes = []\\n edge_attributes = {}\\n self.store('{}({},{})'.format(pred, sub, ob), node_attributes=node_attributes, edge_attributes=edge_attributes)\\n i += 1\\n if size and i > size:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3647cf0b83315f0b8e469a5703d8da49\",\n \"score\": \"0.65201735\",\n \"text\": \"def load_neos(neo_csv_path):\\n # We are making new list of neo collections:\\n # open the csv file, skipping the first line of headers,\\n # then each row from csv file read into\\n # new Near Earth Object, then add this row to collection.\\n # Return collection.\\n\\n neo_collection = []\\n with open(neo_csv_path, 'r') as infile:\\n reader = csv.reader(infile)\\n next(reader)\\n for elem in reader:\\n neo = NearEarthObject(elem[3], elem[4], elem[15], elem[7])\\n neo_collection.append(neo)\\n return neo_collection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a029943560567f6f2f07cce925f9f88\",\n \"score\": \"0.64970046\",\n \"text\": \"def load_network(self, directory):\\n with open(directory + '/structure', \\\"r\\\") as f:\\n line = f.readline()\\n input_name, input_size = line.split(' ')\\n self.add_input(int(input_size))\\n i = 0\\n\\n while 1:\\n line = f.readline()\\n if len(line) <= 0:\\n break\\n i += 1\\n layer_name, layer_size, layer_activation = line.split(' ')\\n layer_activation = layer_activation[:-1] # get rid of endline\\n self.add_fully_connected(int(layer_size), layer_activation)\\n self.layers[-1].set_weights(np.loadtxt(directory + '/weights' + str(i) + '.csv', delimiter=','))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bac7d6321e9e8b10f896797c4fd9c98f\",\n \"score\": \"0.6475423\",\n \"text\": \"def load_csv_into_graph(file_name: str) -> Graph:\\n user_map = Graph()\\n row_num = 0\\n width_of_map = 0\\n\\n with open(file_name) as csv_file:\\n reader = csv.reader(csv_file)\\n for row in reader:\\n width_of_map = len(row)\\n for i in range(len(row)):\\n if row[i] == \\\"B\\\":\\n user_map.add_vertex((i, row_num), \\\"blocked\\\")\\n elif row[i] == \\\"S\\\":\\n user_map.add_vertex((i, row_num), \\\"start\\\")\\n elif row[i] == \\\"E\\\":\\n user_map.add_vertex((i, row_num), \\\"end\\\")\\n else:\\n user_map.add_vertex((i, row_num))\\n row_num += 1\\n height_of_map = row_num\\n # Knowing the dimensions of the graph, connect the graph\\n for y in range(height_of_map):\\n for x in range(width_of_map):\\n if x == width_of_map - 1 and y == height_of_map - 1:\\n pass\\n elif x == width_of_map - 1:\\n user_map.add_edge((x, y), (x, y + 1))\\n elif y == height_of_map - 1:\\n user_map.add_edge((x, y), (x + 1, y))\\n else:\\n user_map.add_edge((x, y), (x, y + 1))\\n user_map.add_edge((x, y), (x + 1, y))\\n\\n return user_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce4ed47a0c620e3fe34bc893bccb7f41\",\n \"score\": \"0.64610356\",\n \"text\": \"def load_neos(neo_csv_path):\\n neos = []\\n with open(neo_csv_path, 'r') as f:\\n reader = csv.DictReader(f)\\n for row in reader:\\n neo = NearEarthObject(\\n row['pdes'],\\n row['name'],\\n row['diameter'],\\n row['pha'],\\n [])\\n neos.append(neo)\\n return neos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5372ee685555bced9ea62c81fe36d312\",\n \"score\": \"0.6425544\",\n \"text\": \"def __init__(self, filename='social_network.csv'):\\n with open(filename, 'r') as csvfile:\\n rows = np.array(list(csv.reader(csvfile)))\\n\\n #flatten all name tuples, cast to set to \\n #get unique names and cast to list for ordering\\n self.names = list(set(rows.flatten()))\\n\\n #network adjacency matrix\\n n = len(self.names)\\n self.A = np.zeros((n , n))\\n\\n #populate network\\n for c in rows:\\n #get cooresponding indicies of names in name list\\n i = self.get_name_index(c[0])\\n j = self.get_name_index(c[1])\\n #make each bi-directional connection\\n self.A[i, j] = 1\\n self.A[j, i] = 1\\n\\n #resistance of the network A\\n self.R = effective_resistance(self.A)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c183cf4faf90d2f5eb4d654d82d819f4\",\n \"score\": \"0.64249206\",\n \"text\": \"def load_neos(neo_csv_path='data/neos.csv'):\\r\\n neos = [] # create list of neo objects\\r\\n with open(neo_csv_path) as f:\\r\\n reader = csv.reader(f)\\r\\n next(reader)\\r\\n # parse row of csv into pdes(design.) ,name,diameter, and hazardous\\r\\n for row in reader:\\r\\n if row[3] is not None or row[3] != '':\\r\\n if row[7] == 'Y':\\r\\n H = True\\r\\n else:\\r\\n H = False\\r\\n # create Neo obj with designation,name,diameter,hazardous\\r\\n neo_obj = NearEarthObject(row[3], row[4], row[15], H)\\r\\n neos.append(neo_obj) # append obj to the list\\r\\n return neos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efcd830aa775344932897d3c26e6176b\",\n \"score\": \"0.6359306\",\n \"text\": \"def loadGraph(graph_name):\\n with open('./dataset/{}_node.csv'.format(graph_name), 'r',\\n encoding='utf-8') as fp:\\n reader = csv.reader(fp)\\n nodes = list(int(_[0]) for _ in reader)\\n with open('./dataset/{}_edge.csv'.format(graph_name), 'r',\\n encoding='utf-8') as fp:\\n reader = csv.reader(fp)\\n edges = list((int(_[0]), int(_[1])) for _ in reader if _[0] != _[1])\\n G = nx.Graph()\\n G.add_nodes_from(nodes)\\n G.add_edges_from(edges)\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac479056f306df2d3483ce461ed954f6\",\n \"score\": \"0.63494384\",\n \"text\": \"def load_diagram_from_csv_file(self, filepath):\\n\\n bpmn_csv_import.BpmnDiagramGraphCSVImport.load_diagram_from_csv(filepath, self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe8ecfbf3b66364c2850f3d67acda4c8\",\n \"score\": \"0.62989837\",\n \"text\": \"def load_data():\\n import time\\n\\n print(\\\"Loading data and create road network (about 30 sec)...\\\")\\n start_time = time.clock()\\n\\n road_network = RoadNetwork()\\n\\n vertices = pd.read_csv(\\\"./data/vertices.csv\\\")\\n edges = pd.read_csv(\\\"./data/edges.csv\\\")\\n\\n print(\\\"Loading vertices...\\\")\\n for index, row in vertices.iterrows():\\n road_network.add_vertex(int(row['v_id']), row['lat'], row['lon'])\\n\\n print(\\\"Loading edges...\\\")\\n for index, row in edges.iterrows():\\n road_network.add_edge(int(row['e_id']), int(row['start_vid']), int(row['end_vid']), row['length'])\\n\\n print(\\\"Done. Elapsed time is %f seconds\\\" % (time.clock() - start_time))\\n\\n return road_network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd086123b7299e8b8145e6d6fddd883b\",\n \"score\": \"0.6272067\",\n \"text\": \"def load_neos(neo_csv_path):\\n with open(neo_csv_path, \\\"r\\\") as infile:\\n\\n neos = csv.DictReader(infile)\\n\\n neos = [\\n NearEarthObject(\\n pdes=neo[\\\"pdes\\\"],\\n name=neo[\\\"name\\\"],\\n diameter=neo[\\\"diameter\\\"],\\n pha=neo[\\\"pha\\\"]\\n )\\n for neo in neos\\n ]\\n\\n return neos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df5270252e8af5368e07d84da320a1f4\",\n \"score\": \"0.6266226\",\n \"text\": \"def load_network(network_file):\\n handler = h.MiTabHandler()\\n handler.parse(network_file)\\n return handler.network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e747687e4d7d45913fa1d8f2634ea33\",\n \"score\": \"0.62265074\",\n \"text\": \"def _load(self):\\n self._data = load_network_from_file(self.path, fmt=self.ext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f612bc245d36f71694ab05f863b289a7\",\n \"score\": \"0.62200767\",\n \"text\": \"def read_from_csv(self, filename):\\n \\n self.graph = []\\n\\n # Read the file into a string separated by newlines for each row\\n try:\\n with open(filename,'r') as readfile:\\n lines = readfile.read().splitlines()\\n except IOError, e:\\n print \\\"I/O error: %s\\\" % e\\n return\\n\\n # Read lines with csv.reader\\n csvreader = csv.reader(lines)\\n\\n # Load the rows into self.graph matrix\\n try:\\n for row in csvreader:\\n self.graph.append([])\\n for i in range(csvreader.line_num):\\n self.graph[csvreader.line_num-1].append(int(row[i]))\\n except csv.Error, e:\\n print \\\"CSV error: %s\\\" % e\\n return\\n except IndexError, e:\\n print \\\"Index error: %s\\\" % e\\n return\\n \\n # Make matrix symmetric\\n # While increasing size, we do not have to check for\\n # out of range when getting data from the graph\\n for i in range(len(self.graph)):\\n for j in range(i+1,len(self.graph)):\\n self.graph[i].append(self.graph[j][i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7b106ad047eafa594e2894d56642b56\",\n \"score\": \"0.6205403\",\n \"text\": \"def load_prefecture_network(path):\\n data = pd.read_csv(path, header=0, index_col=0)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b48a841d24bdccc9d08635c8b51aa72\",\n \"score\": \"0.61859536\",\n \"text\": \"def load_dataset(net, filename): \\n \\n data = N.recfromcsv(filename, delimiter=\\\",\\\", names=True, autostrip=True, case_sensitive=True)\\n\\n m, n = data.size, len(data.dtype.names)\\n numberdata = N.zeros((m, n), order='F')\\n \\n for node, d in net.node.iteritems():\\n states = d['states_ind']\\n numberdata[:,net.graph['nilut'][node]] = [states[str(s)] for s in data[node]]\\n \\n return numberdata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dec56d138351081b72fe9541a8a78881\",\n \"score\": \"0.6177733\",\n \"text\": \"def multinet_from_csv(\\n file_name,\\n filter_func=util.default_filter,\\n weight_func=util.default_weight,\\n layer_func=util.default_layer,\\n ow='ORIGIN', dw='DEST',\\n create_using=None,\\n **csv_reader_argv):\\n\\n index_dict = {}\\n if create_using is None:\\n create_using = mn.DiMultinet()\\n mg = create_using\\n\\n if type(weight_func) == str:\\n weight_func = util.weight_from_string(weight_func)\\n\\n if type(layer_func) == str:\\n layer_func = util.layer_from_string(layer_func)\\n\\n with open(file_name) as netfile:\\n if not csv_reader_argv:\\n netreader = csv.reader(netfile,\\n delimiter=',',\\n quotechar='\\\\\\\"',\\n quoting=csv.QUOTE_NONNUMERIC)\\n else:\\n netreader = csv.reader(netfile,**csv_reader_argv)\\n\\n index_line = next(netreader)\\n\\n index = 0\\n for item in index_line:\\n index_dict[item] = index\\n index += 1\\n\\n origin_index = index_dict[ow]\\n dest_index = index_dict[dw]\\n\\n for line in netreader:\\n if not filter_func(index_dict,line):\\n continue\\n\\n origin = str(line[origin_index])\\n dest = str(line[dest_index])\\n\\n layer = layer_func(index_dict,line)\\n weight = weight_func(index_dict,line)\\n\\n mg.add_node(origin)\\n mg.add_node(dest)\\n\\n mg.aggregate_edge(origin,dest,layer,weight)\\n\\n return mg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cba4544819e8f41ea873371efe1fcc71\",\n \"score\": \"0.61474216\",\n \"text\": \"def load(self, csvFilePath, metadataFilePath):\\n\\t\\tf = open(csvFilePath, 'rb')\\n\\t\\treader = csv.reader(f)\\n\\t\\tl = list(reader)\\n\\n\\t\\t# extract attributes and data points\\n\\t\\tself.attributes = map(lambda x: x.strip(), l[0])\\n\\t\\tself.data = l[1:10]\\n\\n\\t\\tself.classAttr = self.attributes[-1].strip()\\n\\n\\t\\tf = open(metadataFilePath)\\n\\t\\tfor line in f:\\n\\t\\t\\ttype = line[line.index(':')+1:].strip()\\n\\n\\t\\t\\t# save attribute type\\n\\t\\t\\tif type == 'nominal':\\n\\t\\t\\t\\tself.attrTypes.append(self.NOMINAL)\\n\\t\\t\\telif type == 'numeric':\\n\\t\\t\\t\\tself.attrTypes.append(self.NUMERIC)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.attrTypes.append(self.OTHERS)\\n\\n\\t\\t# convert values to correct type\\n\\t\\tfor i, sample in enumerate(self.data):\\n\\t\\t\\tself.numData += 1\\n\\t\\t\\tfor j, type in enumerate(self.attrTypes):\\n\\t\\t\\t\\tval = self.data[i][j]\\n\\t\\t\\t\\tif type == self.NUMERIC and val != '?':\\n\\t\\t\\t\\t\\tself.data[i][j] = float(val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92f98edb63b7406938b60dffac8da187\",\n \"score\": \"0.612518\",\n \"text\": \"def load_network(filename):\\n temp = pickle.load(filename)\\n n = Network(None, None, None, None, None, None, None)\\n n.__setstate__(temp)\\n return n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7f005dd7f8132267f72b11a4cbf66e2\",\n \"score\": \"0.6119918\",\n \"text\": \"def loadnetwork(filename):\\n with open(filename, 'rb') as networkin:\\n self = pickle.load(networkin)\\n \\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2576bf23e2ffe4e7d4f5afeb42a1fdc\",\n \"score\": \"0.61056966\",\n \"text\": \"def from_CSV(cls, path):\\n with open(path) as file:\\n tiles = list(csv.reader(file))\\n\\n return cls.from_list(tiles)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c763d849205884dd187de2654fa93c00\",\n \"score\": \"0.60922307\",\n \"text\": \"def load_gangs(self, filename):\\n\\n with open(filename, 'r') as csvfile:\\n reader = csv.DictReader(csvfile)\\n for row in reader:\\n gang = Gang(int(row[\\\"gang number\\\"]),\\n (float(row[\\\"x location\\\"]), float(row[\\\"y\\\"])),\\n int(row[\\\"gang members\\\"]))\\n self.gang_info[int(row[\\\"gang number\\\"])] = gang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dd346d3371c6ccbb172b88c6cd1b4b7\",\n \"score\": \"0.6080333\",\n \"text\": \"def load_network(path, filename):\\n network_path = '{}/networks/{}.graphml'.format(path, filename)\\n h = open(network_path, 'r')\\n try:\\n multithreading.lock_file_handle(h)\\n return igraph.Graph.Read(network_path)\\n finally:\\n multithreading.unlock_file_handle(h)\\n h.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed93b8dde00643a09fc9bf65891af5c5\",\n \"score\": \"0.60763484\",\n \"text\": \"def from_csv(self, filepath, delim = ','):\\n\\n tdo = textio.read_csv(filepath, True, delim)\\n\\n self.from_tdo(tdo)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fb46335b50f5ba95cd2d73a56df4acb\",\n \"score\": \"0.60590553\",\n \"text\": \"def load_networks(filename, datadir=None):\\n fpath = utils.uploaded_filepath(filename, FT_NETWORKS, datadir=datadir)\\n\\n return fortios_xutils.load_network_graph(fpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13c7634a7ab150373f2ea161731959d6\",\n \"score\": \"0.605758\",\n \"text\": \"def load_neos(neo_csv_path):\\n\\n df = pd.read_csv(neo_csv_path, low_memory=False)\\n df = df[['pdes', 'name', 'pha', 'diameter']]\\n df.pdes = df.pdes.astype('str')\\n neos = [NearEarthObject(**value) for value in df.T.to_dict().values()]\\n return neos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d030bde2bbc9d187d7b7ed526954ae9\",\n \"score\": \"0.6051468\",\n \"text\": \"def loadcsv(self, fn):\\n csvfile = fn\\n with open(csvfile) as f:\\n reader = csv.reader(f, delimiter=\\\",\\\")\\n #print reader.next(), \\\" ommitted\\\"\\n for row in reader:\\n row0 = row[0]\\n row8 = row[8]\\n row3 = row[3]\\n row4 = row[4]\\n row5 = row[5]\\n row6 = row[6]\\n row7 = row[7]\\n self.name[row8] = row0\\n\\n self.path[row8] = row3\\n if \\\"Size\\\" not in row4:\\n row4 = int(row4)\\n self.size[row8] = row4\\n #self.pctsize[row[8]] = float(row[4) / float(self.tsize)\\n # size in KB\\n self.modified[row8] = row5\\n self.accessed[row8] = row6\\n self.created[row8] = row7\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"571f9546034a448ed91689f40134e88c\",\n \"score\": \"0.59965175\",\n \"text\": \"def load(self, filename):\\n self.ids = []\\n self.nodes = {}\\n self.neigh = {}\\n f = open(filename)\\n for line in f:\\n if not line.strip(): break\\n i, l = map(str.strip, line.split(':'))\\n if i in self.nodes:\\n raise ValueError\\n self.nodes[i] = Node(i, l)\\n self.neigh[i] = []\\n self.ids.append(i)\\n\\n for line in f:\\n fr, l, to = map(str.strip, line.split(':'))\\n if not l: l = None\\n if fr not in self.nodes or to not in self.nodes:\\n raise ValueError\\n self.nodes[fr].add_neighbour(self.nodes[to], l)\\n if l == None: l = ''\\n self.neigh[fr].append((to, l))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be2c1a8defb1ce0e566b3528ac8244d0\",\n \"score\": \"0.59776723\",\n \"text\": \"def load_from_file_csv(cls):\\n csvfile = '{}.csv'.format(cls.__name__)\\n try:\\n with open(csvfile, 'r', newline='') as csvf:\\n if cls.__name__ == 'Square':\\n fields = [\\\"id\\\", \\\"size\\\", \\\"x\\\", \\\"y\\\"]\\n else:\\n fields = [\\\"id\\\", \\\"width\\\", \\\"height\\\", \\\"x\\\", \\\"y\\\"]\\n doc = csv.DictReader(csvf, fieldnames=fields)\\n ls_of_dics = [{k: int(v) for k, v in dic.items()}\\n for dic in doc]\\n return [cls.create(**dics) for dics in ls_of_dics]\\n except IOError:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b1ea210fdc0d6869d1498b833db686a\",\n \"score\": \"0.597655\",\n \"text\": \"def load_network(path):\\n with open(path) as f:\\n content = f.read()\\n\\n edge_str = content.split('\\\\n')\\n network = list()\\n for edge in edge_str:\\n network.append(edge.split())\\n\\n return network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adb3630915556b4c9cd421e6919b9b09\",\n \"score\": \"0.595501\",\n \"text\": \"def from_file(cls):\\n with open('data.csv') as file:\\n data_reader = csv.reader(file)\\n for row in data_reader:\\n first, last, mail = row\\n cls(first, last, mail, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d467ad4992d1021521dee67f9665b5b\",\n \"score\": \"0.59509164\",\n \"text\": \"def load_csv(csv):\\n t = so(\\\"csv\\\",\\\"random\\\",\\\"fgdb\\\")\\n TableToTable_conversion(\\n in_rows=csv, \\n out_path=os.path.dirname(t), \\n out_name=os.path.basename(t)\\n )\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50050f90f10435fb78d38077b88f50c6\",\n \"score\": \"0.5927157\",\n \"text\": \"def load_network(self, fileName):\\n\\t\\tif '.json' not in modelName:\\n\\t\\t\\tself.model = model_from_json(open(modelName+'_arch.json', 'r').read())\\n\\t\\telse:\\n\\t\\t\\tself.model = model_from_json(open(modelName, 'r').read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f5f10673e74e62904d8dc62089bfcd1\",\n \"score\": \"0.5914837\",\n \"text\": \"def load_neos(neo_csv_path):\\n\\n result = []\\n with open(neo_csv_path, 'r') as file:\\n reader = DictReader(file)\\n for x in reader:\\n result.append(NearEarthObject(designation=x['pdes'],\\n name=x['name'],\\n hazardous=x['pha'],\\n diameter=x['diameter']))\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6edae08debda8804f99096dd97d582e2\",\n \"score\": \"0.5914653\",\n \"text\": \"def load_from_file_csv(cls):\\n filename = \\\"{}.csv\\\".format(cls.__name__)\\n\\n if os.path.exists(filename):\\n with open(filename, encoding=\\\"utf-8\\\") as f:\\n content = f.read()\\n objs = cls.from_json_string(content)\\n newlist = []\\n for obj in objs:\\n new = cls.create(**obj)\\n newlist.append(new)\\n return newlist\\n else:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"977642f6f45ec5ff12797222e8dc7241\",\n \"score\": \"0.58989906\",\n \"text\": \"def import_network_from_file(file=None, base_url=DEFAULT_BASE_URL):\\n if file is None:\\n file = os.path.abspath('data/galFiltered.sif')\\n else:\\n file = os.path.abspath(file)\\n res = commands.commands_post('network load file file=' + file, base_url=base_url)\\n # TODO: Fix R documentation to match what's really returned\\n\\n # should not be necessary, but is because \\\"network load file\\\" doesn't actually set the current network\\n # until after it's done. So, without the sleep(), setting the current network will be superceded by\\n # \\\"network load file\\\"'s own network. This is race condition that can be solved by \\\"network load file\\\"\\n # not returning until it's actually done.\\n # TODO: Fix this race condition\\n time.sleep(CATCHUP_NETWORK_SECS)\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46f33d0cf7544970cce1d77957613291\",\n \"score\": \"0.5894413\",\n \"text\": \"def load_from_file_csv(cls):\\n filename = cls.__name__ + \\\".csv\\\"\\n # Unless the file does not exist, opens the file and reads each row.\\n # The length of the row specifies the type of object. If this is not\\n # a valid value, raises a TypeError. Otherwise, values are paired\\n # with their correct attribute name in a dictionary, then used to\\n # create those instances. A list of instances is returned upon success\\n try:\\n with open(filename, \\\"r\\\") as fcsv:\\n c_reader = csv.reader(fcsv, delimiter=\\\",\\\")\\n retlist = []\\n for row in c_reader:\\n if len(row) == 5:\\n ats = [\\\"id\\\", \\\"width\\\", \\\"height\\\", \\\"x\\\", \\\"y\\\"]\\n elif len(row) == 4:\\n ats = [\\\"id\\\", \\\"size\\\", \\\"x\\\", \\\"y\\\"]\\n else:\\n raise TypeError\\n values_dic = {}\\n for i in range(len(row)):\\n if type(row[i]) is not int:\\n row[i] = int(row[i])\\n values_dic[ats[i]] = row[i]\\n obj = cls.create(**values_dic)\\n retlist.append(obj)\\n return retlist\\n except IOError:\\n raise IOError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3938bce4fa5e66c2764752243c9ca8bb\",\n \"score\": \"0.58855146\",\n \"text\": \"def load_dataset(self,\\n csv_file,\\n root_dir=None,\\n augment=False,\\n shuffle=True,\\n batch_size=1,\\n train=False):\\n\\n ds = dataset.TreeDataset(csv_file=csv_file,\\n root_dir=root_dir,\\n transforms=self.transforms(augment=augment),\\n label_dict=self.label_dict,\\n preload_images=self.config[\\\"train\\\"][\\\"preload_images\\\"])\\n\\n data_loader = torch.utils.data.DataLoader(\\n ds,\\n batch_size=batch_size,\\n shuffle=shuffle,\\n collate_fn=utilities.collate_fn,\\n num_workers=self.config[\\\"workers\\\"],\\n )\\n\\n return data_loader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23b08b15a197bb24f43fe9eb1f031099\",\n \"score\": \"0.58812714\",\n \"text\": \"def load(cls, device):\\n fpath = Path(COLLISION_DIR, 'collisions.csv')\\n inputs, labels = [], []\\n with open(fpath, 'r') as csvfile:\\n reader = csv.reader(csvfile, delimiter=',')\\n for line in reader:\\n input = [float(s) for s in line[:-1]]\\n inputs.append(input)\\n\\n label = int(line[-1])\\n assert label in [0, 1]\\n labels.append(label)\\n\\n inputs = torch.tensor(inputs).to(device)\\n labels = torch.tensor(labels).long().to(device)\\n return cls(inputs, labels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"785315180eb18f154bca3be42a832bf3\",\n \"score\": \"0.58764255\",\n \"text\": \"def load_net(self, path):\\n\\n with open(path, 'rb') as nn_file:\\n net = pickle.load(nn_file)\\n\\n if type(net) == dict:\\n loaded = list()\\n if 'model' in net.keys():\\n self.model = net['model']\\n loaded.append('model')\\n if 'meta_params' in net.keys():\\n self.meta_params = net['meta_params']\\n loaded.append('meta_params')\\n if 'data_sets' in net.keys():\\n self.data_sets = net['data_sets']\\n loaded.append('data_sets')\\n if 'costs' in net.keys():\\n self.costs = net['costs']\\n loaded.append('costs')\\n if 'cost_counter' in net.keys():\\n self.cost_counter = net['cost_counter']\\n loaded.append('cost_counter')\\n print('Loaded from {0}: {1}'.format(path, ', '.join(loaded)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5920150abf03965e2c05acb19006904b\",\n \"score\": \"0.5874864\",\n \"text\": \"def from_csv(csv_file, units=DEFAULT_UNIT):\\n\\n if isinstance(csv_file, str):\\n if not csv_file.endswith('.csv'):\\n raise ValueError(\\n 'Filetype must be .csv to create a DistanceMatrix.')\\n\\n with open(csv_file, 'r') as open_file:\\n return DistanceMatrix.from_csv(open_file)\\n\\n lines = [line.strip().split(',') for line in csv_file.readlines()]\\n\\n # Strip trailing empty strings if they exist\\n try:\\n [line.remove('') for line in lines]\\n except ValueError:\\n pass\\n\\n highest_line_length = 0\\n matrix = []\\n for line in lines:\\n if len(line) == highest_line_length + 1:\\n if all(is_numeric(item) for item in line):\\n matrix.append([float(item) for item in line])\\n highest_line_length += 1\\n else:\\n raise ValueError(\\n 'Data lines in input file should be in order of ascending length.')\\n\\n return DistanceMatrix(matrix, units)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7139a8d85797a92497b30657ec0db74d\",\n \"score\": \"0.58667594\",\n \"text\": \"def load_graph(file):\\n g = nx.DiGraph()\\n mode = \\\"N\\\"\\n for l in file:\\n l = l.strip()\\n if mode == \\\"N\\\":\\n if l == \\\"// Nodes\\\":\\n mode = \\\"LN\\\"\\n elif mode == \\\"LN\\\":\\n if l == \\\"// Edges\\\":\\n mode = \\\"LE\\\"\\n else: # LOAD NODES\\n nparts = l.split(\\\" \\\", 2)\\n g.add_node(int(nparts[0]),\\n {'unixtime': int(nparts[1]), 'line': nparts[2]})\\n pass\\n elif mode == \\\"LE\\\" and len(l) > 0: # LOAD EDGES\\n eparts = [int(x) for x in l.split(\\\" \\\", 1)]\\n g.add_edge(eparts[0], eparts[1])\\n return g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd30f289351eeca88b0cfe2a13c6164\",\n \"score\": \"0.58567697\",\n \"text\": \"def loadNetworkFromFile(filename, mode = 'pickle'):\\n if mode == 'pickle':\\n import pickle\\n fp = open(filename)\\n network = pickle.load(fp)\\n fp.close()\\n return network\\n elif mode in ['plain', 'conx']:\\n fp = open(filename, \\\"r\\\")\\n line = fp.readline()\\n network = None\\n while line:\\n if line.startswith(\\\"layer,\\\"):\\n # layer, name, size\\n temp, name, sizeStr = line.split(\\\",\\\")\\n name = name.strip()\\n size = int(sizeStr)\\n network.addLayer(name, size)\\n line = fp.readline()\\n weights = [float(f) for f in line.split()]\\n for i in range(network[name].size):\\n network[name].weight[i] = weights[i]\\n elif line.startswith(\\\"connection,\\\"):\\n # connection, fromLayer, toLayer\\n temp, nameFrom, nameTo = line.split(\\\",\\\")\\n nameFrom, nameTo = nameFrom.strip(), nameTo.strip()\\n network.connect(nameFrom, nameTo)\\n for i in range(network[nameFrom].size):\\n line = fp.readline()\\n weights = [float(f) for f in line.split()]\\n for j in range(network[nameTo].size):\\n network[nameFrom, nameTo].weight[i][j] = weights[j]\\n elif line.startswith(\\\"parameter,\\\"):\\n temp, exp = line.split(\\\",\\\")\\n exec(exp) # network is the neural network object\\n elif line.startswith(\\\"network,\\\"):\\n temp, netType = line.split(\\\",\\\")\\n netType = netType.strip().lower()\\n if netType == \\\"cascornetwork\\\":\\n from pyrobot.brain.cascor import CascorNetwork\\n network = CascorNetwork()\\n elif netType == \\\"network\\\":\\n network = Network()\\n elif netType == \\\"srn\\\":\\n network = SRN()\\n else:\\n raise AttributeError, \\\"unknown network type: '%s'\\\" % netType\\n line = fp.readline()\\n return network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e064e8bc5fd29283f229fd615b2651c\",\n \"score\": \"0.58296126\",\n \"text\": \"def load_from_file(file_path: str):\\n routes = []\\n\\n with open(file_path, \\\"r\\\") as routes_file:\\n csv_reader = csv.reader(routes_file, delimiter=\\\",\\\")\\n next(csv_reader, None) # Dump header\\n\\n for row in csv_reader:\\n routes.append(MMTRoute.parse_from_row(row))\\n\\n return MMTRouteDataController(routes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d49a58993ee4b5c0b0a55e3db38a612\",\n \"score\": \"0.58203024\",\n \"text\": \"def load_from_file_csv(cls):\\n file_name = cls.__name__ + '.csv'\\n list_objs = []\\n with open(file_name, 'r') as fp:\\n csv_r = csv.DictReader(fp)\\n for i in csv_r:\\n for k, v in i:\\n i[k] = int(v)\\n list_objs.append(cls.create(**i))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3fe6e022dfe252490f00c97af2196a2\",\n \"score\": \"0.57661384\",\n \"text\": \"def load_categories(self, path: str):\\n with open(path, 'r') as f:\\n csv_reader = csv.reader(f, delimiter=';')\\n # skip header \\n next(csv_reader)\\n \\n for row in csv_reader:\\n if row[2] == 'NULL':\\n p = None\\n else:\\n p = self.categories[int(row[2])]\\n \\n self.categories[int(row[0])] = Category(int(row[0]), row[1], p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11b4f4cc3188c0afa49e3406dc74e365\",\n \"score\": \"0.5759813\",\n \"text\": \"def loadData(labelName = \\\"G3\\\"):\\n print(\\\"\\\\nLoading data…\\\")\\n trainPath, testPath = TRAIN_PATH, TEST_PATH\\n print(\\\"\\\\nParse local csv\\\")\\n train = pd.read_csv(filepath_or_buffer = trainPath,\\n names = CSV_COLUMN_NAMES,\\n header = 0)\\n print(\\\"\\\\nGet train information\\\")\\n trainFeatures, trainLabel = train, train.pop(labelName)\\n\\n # Parse local csv\\n test = pd.read_csv(filepath_or_buffer = testPath,\\n names = CSV_COLUMN_NAMES,\\n header = 0)\\n # Get test information\\n testFeatures, testLabel = test, test.pop(labelName)\\n\\n return (trainFeatures, trainLabel),(testFeatures, testLabel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e834572a37f9f14237a1d951ada26a9\",\n \"score\": \"0.5752725\",\n \"text\": \"def load_train_data(csv_path=_TRAIN_DATA_PATH):\\n train_df = pd.read_csv(open(csv_path, 'r', encoding=\\\"iso-8859-1\\\"), quotechar='\\\"')\\n\\n return train_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1315fb2c3dea930f843b178f8f07d96e\",\n \"score\": \"0.57414705\",\n \"text\": \"def loadCSV(self, path):\\n dtframe = pd.read_csv(path)\\n self.loadDataframe(dtframe)\\n \\n #####\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f2b242c33300281a9e6db95f09d55d6\",\n \"score\": \"0.5735814\",\n \"text\": \"def load_graph_from_file(filename):\\n with open(filename) as UK_cities:\\n dict_uk_cities = json.load(UK_cities)\\n return nx.Graph(dict_uk_cities)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f086418c793a6cedcce66864bd38c0f\",\n \"score\": \"0.57288903\",\n \"text\": \"def importCSV(self):\\n filepath = QFileDialog.getOpenFileName(self, 'Load CSV file',\\\"\\\",\\\"Comma-separated values (*.csv)\\\")\\n if filepath !=\\\"\\\":\\n urllib.urlretrieve('file:/// %s' % filepath,'Schedule.csv')\\n self.tableWidget.clear()\\n self.loadSchedule()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ed0c3b6f8c016dd5881c8f5ce09f2f3\",\n \"score\": \"0.5704464\",\n \"text\": \"def load_from_file_csv(cls):\\n csv_file = cls.__name__ + \\\".csv\\\"\\n lst = list()\\n try:\\n with open(csv_file, \\\"r\\\") as fh:\\n reader = csv.DictReader(fh)\\n res = list(reader)\\n for d in res:\\n for item in d.keys():\\n d[item] = int(d[item])\\n temp = cls.create(**d)\\n lst.append(temp)\\n return lst\\n except:\\n return list()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2321149ca9513bf6e89e5c5e2d9f5867\",\n \"score\": \"0.570376\",\n \"text\": \"def load_from_file(self, filename):\\n\\n with open(filename, newline='') as csvfile:\\n reader = csv.reader(csvfile)\\n self.column_names = next(reader) # Take note of the header names and save them seperate from the data\\n for row in reader: # Now save the data row by row\\n self.data.append(row)\\n\\n self.convert_to_numeric() # Convert values to floats (that can be converted)\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"863c4a47d27a8f567de055ec086191ee\",\n \"score\": \"0.5689223\",\n \"text\": \"def do_load(self, line):\\n with open(self.rosterFilePath, 'r') as csvFile:\\n rosterReader = csv.DictReader(csvFile)\\n for row in rosterReader:\\n completedProblems = row['completedProblems'] if 'completedProblems' in row else 0\\n currentBalloons = row['currentBalloons'] if 'currentBalloons' in row else 0\\n contestant = Contestant(row['Room #'], row['Team #'],\\n row['Check-In'], row['HackerRank'].lower(), \\n row['Partner 1 Name'], row['Partner 2 Name'], \\n completedProblems, currentBalloons)\\n try: \\n Contestant.add_to_cache(contestant)\\n except Exception as e:\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6670fea3c870c995ac2a9826a1bb35d5\",\n \"score\": \"0.56890976\",\n \"text\": \"def import_csv(filename, csv_map=None, encoding=\\\"latin1\\\"):\\n if not os.path.exists(filename):\\n raise FileNotFoundError\\n\\n with open(filename, mode=\\\"r\\\", encoding=encoding, errors=\\\"ignore\\\") as csv_file:\\n Part._import_csv_content(csv_file, csv_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e7e83b5d5a4445c0518663ba841302a\",\n \"score\": \"0.5683459\",\n \"text\": \"def __init__(self, csv_file):\\n self.server_list = SLFile.convert_csv_to_dict_list(csv_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5266981ae08f217e9facbdbd41d1217b\",\n \"score\": \"0.56708896\",\n \"text\": \"def load_csv(filename, label_col, num_test):\\n\\n X_train = []\\n X_test = []\\n num = 0\\n with open(filename,'rt') as f:\\n reader = csv.reader(f, delimiter=',')\\n for row in reader:\\n row1 = [float(item) for item in row if item != '\\\\0']\\n last_ele = row1.pop(label_col)\\n X_train.append(row1)\\n X_test.append(int(last_ele))\\n num+=1\\n if num > num_test:\\n break\\n \\n f.close()\\n return X_train, X_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83af557eee48511af436f09dd9bb4771\",\n \"score\": \"0.5633198\",\n \"text\": \"def run(self) -> None:\\n with open(self.import_csv_path) as csv_file:\\n csv_reader = csv.DictReader(csv_file)\\n for row in csv_reader:\\n aso_node = graph.SpaceObject.find_one(norad_id=row['aso_id'])\\n if aso_node is None:\\n continue\\n is_compliant = row['is_compliant'] == 'True'\\n compliance_nodes = aso_node.compliance.all()\\n if compliance_nodes:\\n compliance_node = compliance_nodes[0]\\n compliance_node.is_compliant = is_compliant\\n compliance_node.save()\\n else:\\n compliance_node = graph.Compliance(\\n is_compliant=is_compliant\\n )\\n compliance_node.save()\\n aso_node.compliance.connect(compliance_node)\\n compliance_node.from_data_source.connect(\\n self.data_source_node\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dee2a459d3b62f52f82e800bfef054c\",\n \"score\": \"0.5628896\",\n \"text\": \"def load_prod_table(csv_fname):\\n left_col, right_col = 0, 3\\n return load_csv(csv_fname, left_col, right_col)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21e4c283554e332e93fdceb92a314174\",\n \"score\": \"0.5624825\",\n \"text\": \"def load_from_file_csv(cls):\\n empty_list = []\\n complete_name = cls.__name__+'.csv'\\n try:\\n with open(complete_name, 'r') as f:\\n instance = cls.from_json_string(f.read())\\n instance_list = [cls.create(**line) for line in instance]\\n return instance_list\\n except:\\n return (empty_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cee894ea39247d1992edca7294f7f63\",\n \"score\": \"0.5606623\",\n \"text\": \"def load_destination_data(input_data, header_lines): # Move to own controller file??\\n with open(input_data) as places:\\n data = csv.reader(places, delimiter=',')\\n num_col = len(next(data)) # count number of columns in the csv file\\n places.seek(0) # Return csv reader back to start of file\\n for i in range(0, header_lines): # skip specified number of header lines\\n next(data, None)\\n\\n for place in data: # Add all vertices to the graph.\\n address = place[1]\\n vertex = model.graph.Vertex(address)\\n graph_instance.add_vertex(vertex, address)\\n\\n places.seek(0) # Return csv reader back to start of file.\\n for i in range(0, header_lines): # skip specified number of header lines\\n next(data, None)\\n\\n index_counter = 0\\n for place in data: # Add edges to the graph.\\n # name = place[0]\\n # key = place[1]\\n i = 2\\n while place[i] != '':\\n if place[i] != '':\\n vertex_a = graph_instance.get_vertex(graph_instance.vertex_key_list[index_counter])\\n vertex_b = graph_instance.get_vertex(graph_instance.vertex_key_list[i - 2])\\n distance = float(place[i])\\n graph_instance.add_undirected_edge(vertex_a, vertex_b, distance)\\n i += 1\\n if i > num_col - 1:\\n break\\n\\n index_counter += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c3dd2dc89274e5a9e68b3aa3bcc636c\",\n \"score\": \"0.5574019\",\n \"text\": \"def from_csv(file_path: str) -> \\\"CorrelationsData\\\":\\n return CorrelationsData(data=pd.read_csv(file_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39d2c32b2ce6bd037e9faf39ad6f051c\",\n \"score\": \"0.557101\",\n \"text\": \"def __init__(self, filename='social_network.csv'):\\n with open(filename) as file:\\n data = file.read().split(\\\"\\\\n\\\")\\n \\n pair = []\\n names = set()\\n for line in data[:-1]:\\n line = line.split(\\\",\\\")\\n names.add(line[0])\\n names.add(line[1])\\n pair.append(line)\\n names = sorted(list(names))\\n \\n n = len(names)\\n M = np.zeros((n,n))\\n for k in pair:\\n i, j = names.index(k[0]),names.index(k[1])\\n M[i,j] = 1\\n M[j,i] = 1\\n \\n self.names = names\\n self.M = M\\n self.R = effective_resistance(M)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59582d82e29df5023dd641dcaeb763f3\",\n \"score\": \"0.5569314\",\n \"text\": \"def import_cities(self, path):\\n labeled_cities = pd.read_csv(path)\\n for i in range(100):\\n x = labeled_cities.iloc[i,1]\\n y = labeled_cities.iloc[i,2]\\n self.tab_villes.append([x, y])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bdeec59eda957d65b3ce4892c5bafd5\",\n \"score\": \"0.55657744\",\n \"text\": \"def loadNeuralNetwork(filename):\\n extn=filename.rsplit('.',1)[-1]\\n if extn=='t7':\\n return cv2.dnn.readNetFromTorch(filename)\\n if extn=='xml':\\n return cv2.dnn.readNetFromONNX(filename)\\n raise IOError('Unknown file format for \\\"'+filename+'\\\"')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcf035ab6fa7768376fb6508a13f29d2\",\n \"score\": \"0.5562248\",\n \"text\": \"def load_label(csv_file):\\n with open(csv_file, 'r') as label_file:\\n labels = label_file.read()\\n\\n labels = labels.split(\\\",\\\")\\n labels = [int(l) for l in labels]\\n # Labels is a list of int, representing gesture for each frame\\n return labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2024c38a2bbbd2bdaf989c985dbfb66\",\n \"score\": \"0.55456424\",\n \"text\": \"def loadships(shipcsv):\\n shipdict = shipreader(shipcsv)\\n availableShips = craftassign(shipdict)\\n return availableShips\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23712c0c4eccc53633e99ce25acdf01b\",\n \"score\": \"0.55411035\",\n \"text\": \"def load_csv(self, filename, sep=\\\",\\\"):\\n\\n return self.session.read.csv(\\n filename,\\n inferSchema=True,\\n sep=sep,\\n header=True\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa889fb82f390b4988022fef57c072ad\",\n \"score\": \"0.5538708\",\n \"text\": \"def load_model_file(data_file):\\n # Open the file with the data, read it, and split it into lines\\n with open(data_file, 'r') as f:\\n data = f.read()\\n \\n lines = data.splitlines()\\n \\n # Define the interfaces info \\n int_info_begin_index = 2\\n int_info_end_index = lines.index(\\\"NODES_TABLE,,,,\\\") - 1\\n interface_lines = lines[int_info_begin_index:int_info_end_index]\\n interface_set = set([]) \\n node_list = []\\n for interface_line in interface_lines:\\n node_name, remote_node_name, name, cost, capacity = \\\\\\n interface_line.split(',')\\n interface_set.add(Interface(name, int(cost), int(capacity), Node(node_name),\\n Node(remote_node_name)))\\n node_list.append(Node(node_name))\\n node_list.append(Node(remote_node_name))\\n model = Model(interface_set, set(node_list))\\n \\n # Define the nodes info\\n nodes_info_begin_index = int_info_end_index + 3\\n nodes_info_end_index = lines.index(\\\"DEMANDS_TABLE,,,,\\\") -1\\n node_lines = lines[nodes_info_begin_index:nodes_info_end_index]\\n node_names = set([node.name for node in node_list])\\n for node_line in node_lines:\\n node_info = node_line.split(',')\\n node_name = node_info[0]\\n try:\\n node_lat = int(node_info[1])\\n except ValueError:\\n node_lat = 0\\n try:\\n node_lon = int(node_info[2])\\n except ValueError:\\n node_lon = 0 \\n if node_name not in node_names: # Pick up orphan nodes\\n new_node = Node(node_name)\\n model.add_node(new_node)\\n new_node.lat = node_lat\\n new_node.lon = node_lon\\n else:\\n model.get_node_object(node_name).lat = node_lat\\n model.get_node_object(node_name).lon = node_lon\\n \\n # Define the demands info\\n demands_info_begin_index = nodes_info_end_index + 3\\n demands_lines = lines[demands_info_begin_index:]\\n for demand_line in demands_lines:\\n demand_info = demand_line.split(',')\\n source = demand_info[0]\\n dest = demand_info[1]\\n traffic = int(demand_info[2])\\n name = demand_info[3] \\n if name == '':\\n demand_name = 'none'\\n else: \\n demand_name = name \\n model.add_demand(source, dest, traffic, demand_name)\\n \\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1413decf89a10a4c5c57ba490db4023\",\n \"score\": \"0.553635\",\n \"text\": \"def openCsv():\\n csvFile = 'BDO_app/modules/nodes/cityNodes.csv'\\n return csvFile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b216637a354afa4a13ebf51db370cad7\",\n \"score\": \"0.55337936\",\n \"text\": \"def load_dataset(csv_path):\\n\\n # read in and shuffle data\\n df = pd.read_csv(csv_path).sample(frac=1)\\n # drop indicator columns for destinations\\n cols = [c for c in df.columns if c[:11] !=\\n 'destination' and c[:14] != 'wind_direction']\\n df = df[cols]\\n # print(len(cols))\\n # print(df.columns.values.tolist())\\n\\n inputs = df.drop('time_taxi_out', axis=1).values\\n labels = df['time_taxi_out'].values\\n return inputs, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9f7cf9a98d1a0de583fde1596254f16\",\n \"score\": \"0.5517911\",\n \"text\": \"def import_csv(self, csvfile, columns=None, delimiter=' ', quotechar='|'):\\n if isinstance(csvfile, str):\\n csvfile = open(csvfile, 'rb')\\n import csv\\n R = csv.reader(csvfile, delimiter=delimiter, quotechar=quotechar)\\n if columns is None:\\n columns = R.next()\\n d = []\\n for x in R:\\n z = {}\\n for i in range(len(x)):\\n y = x[i]\\n if y != '':\\n if y.isdigit():\\n y = eval(y)\\n else:\\n v = y.split('.')\\n if len(v) == 2 and v[0].isdigit() and v[1].isdigit():\\n y = eval(y)\\n z[columns[i]] = y\\n d.append(z)\\n self.insert(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08cf2abbd97ca76ef69d719d91aa8ba4\",\n \"score\": \"0.5512902\",\n \"text\": \"def load_csv(filename):\\n with open(filename, 'rb') as csvfile:\\n dialect = csv.Sniffer().sniff(csvfile.read(1024))\\n csvfile.seek(0)\\n csv_data = csv.reader(csvfile, dialect)\\n data = []\\n for row in csv_data:\\n data.append(row)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"091fb5851a65435b2b84c66fca0ef03e\",\n \"score\": \"0.5506476\",\n \"text\": \"def load_network_from_file(filename, fmt=None):\\n # if no format has been given, get it from the file name\\n if fmt is None:\\n _, fmt = os.path.splitext(filename)\\n fmt = fmt[1:]\\n # decide which function to call to load the data\\n if fmt == 'pickle':\\n return _load_network_from_pickle(filename)\\n elif fmt == 'json':\\n return _load_network_from_json(filename)\\n else:\\n raise ValueError('Only pickle or json formats are supported.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baae61805c40247c078a14b9e8911484\",\n \"score\": \"0.5500594\",\n \"text\": \"def load_from_file_csv(cls):\\n obj_list = []\\n file_name = cls.__name__ + \\\".csv\\\"\\n if cls.__name__ == \\\"Rectangle\\\":\\n field_names = ['id', 'width', 'height', 'x', 'y']\\n elif cls.__name__ == \\\"Square\\\":\\n field_names = ['id', 'size', 'x', 'y']\\n try:\\n with open(file_name, mode=\\\"r\\\", encoding=\\\"utf-8\\\") as f:\\n reader = csv.DictReader(f, fieldnames=field_names)\\n for row in reader:\\n dic = {}\\n for key, value in dict(row).items():\\n dic[key] = int(value)\\n obj_list.append(cls.create(**dic))\\n except:\\n return []\\n return obj_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cb05094f5ed7ab5dce913655e0d3284\",\n \"score\": \"0.5500581\",\n \"text\": \"def load_data(self, fname):\\n with open(fname) as f:\\n data = csv.reader(f, delimiter=',')\\n data = [row for row in data]\\n tx = [map(float, x[1:len(x)]) for x in data[6:]]\\n ty = [x[0] for x in data[6:]]\\n for i, label in enumerate(ty):\\n if label == 'BRICKFACE':\\n ty[i] = 0\\n elif label == 'SKY':\\n ty[i] = 1\\n elif label == 'FOLIAGE':\\n ty[i] = 2\\n elif label == 'CEMENT':\\n ty[i] = 3\\n elif label == 'WINDOW':\\n ty[i] = 4\\n elif label == 'PATH':\\n ty[i] = 5\\n elif label == 'GRASS':\\n ty[i] = 6\\n else:\\n print 'error'\\n ds = ClassificationDataSet(19, 1, nb_classes=7)\\n for x, y in zip(tx, ty):\\n ds.addSample(x, y)\\n ds._convertToOneOfMany()\\n return ds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65c9e9aa8b116c154d60fa2f1185c5cb\",\n \"score\": \"0.5496548\",\n \"text\": \"def load_map(self, filename):\\n self._graph = Graph()\\n self._location = {}\\n self._streetnames = {}\\n\\n with open(filename, 'r') as f:\\n for line in f:\\n elements = line.split(\\\",\\\")\\n if(elements[0] == \\\"V\\\"):\\n self._graph.add_vertex(int(elements[1]))\\n self._location[int(elements[1])] = (self.process_coord(elements[2]),\\n self.process_coord(elements[3]))\\n elif (elements[0] == \\\"E\\\"):\\n self._graph.add_edge((int(elements[1]), int(elements[2])))\\n self._streetnames[(int(elements[1]), int(elements[2]))] = elements[3]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55fc32b5964a413c53f0ad9b59103fa7\",\n \"score\": \"0.54956716\",\n \"text\": \"def __csv_to_nc__(file_name):\\n load_file = pd.read_csv(file_name, header=0, index_col=0)\\n load_file.index = pd.to_datetime(load_file.index)\\n \\n # convert to xarray.Dataset and drop unused local time column\\n ds = load_file.to_xarray().drop(cest_col)\\n var_names = [var for var in ds.data_vars]\\n \\n # convert again to datetime, won't be recognized otherwise\\n ds[utc_col] = pd.to_datetime(ds[utc_col])\\n \\n # set unit variable for each variable; all MW, exept for some shares\\n for var in var_names:\\n unit = 'MW' if any(label in var for label in hasMWlbl) else 'share'\\n ds[var].attrs.update(OrderedDict([('units', unit)]))\\n \\n # write to file\\n ds.to_netcdf(load_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9532a98922cf16fe2a04b9a441503d75\",\n \"score\": \"0.54931366\",\n \"text\": \"def load_network(name):\\r\\n net = Network([0])\\r\\n net.sizes = np.load(\\\"pretrained_networks/{0}/sizes.npy\\\".format(name))\\r\\n net.weights = np.load(\\\"pretrained_networks/{0}/weights.npy\\\".format(name))\\r\\n net.biases = np.load(\\\"pretrained_networks/{0}/biases.npy\\\".format(name))\\r\\n net.num_layers = len(net.sizes)\\r\\n return net\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51260f127265c158937fe23200daf337\",\n \"score\": \"0.54910463\",\n \"text\": \"def load_from_file_csv(cls):\\n if not os.path.isfile(cls.__name__ + \\\".csv\\\"):\\n return []\\n with open(cls.__name__ + \\\".csv\\\", encoding=\\\"utf-8\\\") as f:\\n csv_r = list(csv.DictReader(f))\\n a = []\\n for i in csv_r:\\n i = {x: int(y) for x, y in i.items()}\\n a[len(a):] = [cls.create(**i)]\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d8de737e6445a95ccc14101df892172\",\n \"score\": \"0.5490962\",\n \"text\": \"def load_mac_addresses(self, path=None, file_name=None):\\n self._path = path\\n if self._path is None:\\n self._path = os.getcwd() \\n\\n self._file_name = file_name\\n if self._file_name is None:\\n # TODO: get list of files here\\n pass\\n\\n with open(file_name, \\\"r\\\") as csv_file:\\n first_line = True\\n for line in csv_file:\\n if first_line:\\n first_line = False\\n continue\\n line_split = line.split(\\\",\\\")\\n self._base[line_split[0]] = [x for x in line_split[1:]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fa30ee8e99aabaffb424d8ed94b2af8\",\n \"score\": \"0.54839677\",\n \"text\": \"def load_Network(Filename):\\n print(\\\"Loading Network map from file \\\", Filename)\\n g = SmartDigraph()\\n hash_table = {}\\n counter = 1\\n \\n lines = open(Filename, 'r').readlines()\\n \\n for i in lines:\\n for j in range(2):\\n line = i.split()\\n if line[j] not in hash_table:\\n hash_table[line[j]] = counter\\n counter += 1\\n \\n for i in hash_table:\\n g.addNode(SmartNode(hash_table[i]))\\n \\n for i in lines:\\n line = i.split()\\n start_node = g.getNode(str(hash_table[line[0]]))\\n end_node = g.getNode(str(hash_table[line[1]]))\\n lead_time = int(line[2])\\n\\n edge = WeightedEdge(start_node, end_node,lead_time) # creates the edge\\n g.addEdge(edge) # adds the edge to the smartdigrahp\\n\\n return g, hash_table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe96f1f9ec2a76632bc7aa390d75c146\",\n \"score\": \"0.5463261\",\n \"text\": \"def read_travels_csv_file(filename):\\n\\n fileIN=open(filename, 'r')\\n\\n # Read the first line\\n line=fileIN.readline()\\n\\n # Try to guess the seperator\\n separator=None\\n for sep in [';', ',', '\\\\t']:\\n if line.strip().split(sep) == ns_header:\\n separator=sep\\n break\\n else:\\n error(\\\"I cannot recognise the specified file '{}', sorry.\\\".format(filename))\\n error(\\\"Try exporting the file to csv with a semicolon (;) as field separator\\\")\\n exit(-1)\\n \\n # Store all trips\\n travels=list()\\n\\n # Read all the lines in the file\\n # NOTE: entries in the file are not guaranteed to be ordered on time/date\\n line=fileIN.readline()\\n while line:\\n # Remove trailing newline and split the line on the separator we found\\n spline=line.split(separator)\\n\\n # If the line does not contain as many fields as the header, something is wrong\\n if len(spline) != len(header):\\n error(\\\"Error, invalid line detected:\\\\n{}\\\".format(line))\\n\\n # Unpack the split line, and make a Trip object out of it\\n travels.append(Trip(*spline))\\n line=fileIN.readline()\\n\\n # Close the file\\n fileIN.close()\\n return travels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d10a81011185d1988e810e451bdb083a\",\n \"score\": \"0.54531014\",\n \"text\": \"def ReadCsv(url = 'http://developer.mbta.com/lib/rthr/red.csv'):\\n fp = urllib2.urlopen(url)\\n reader = csv.reader(fp)\\n\\n tss = []\\n for t in reader:\\n if t[5] != 'Kendall/MIT': continue \\n if t[3] != 'Braintree': continue\\n\\n ts = TrainSpotting(t)\\n tss.append(ts)\\n\\n fp.close()\\n return tss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b08ec7a8f359d6a664bc1f93639e58\",\n \"score\": \"0.54518944\",\n \"text\": \"def load_geolocations():\\n\\n with open(\\\"data/geolocation.csv\\\", \\\"rb\\\") as f:\\n \\n reader = csv.reader(f, delimiter=\\\",\\\")\\n\\n for row in reader: \\n\\n street_addr = row[0]\\n city = row[1]\\n state = row[2]\\n latitude = row[3]\\n longitude = row[4]\\n elevation = row[5]\\n timezone=row[6]\\n\\n geolocation = Geolocation(street_addr=street_addr, city=city, state=state, latitude=latitude, \\n longitude=longitude, elevation=elevation, timezone=timezone)\\n\\n db.session.add(geolocation)\\n db.session.commit()\\n\\n print \\\"Geolocation committed\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"618114906e9262687fdcc448797a0f5d\",\n \"score\": \"0.544634\",\n \"text\": \"def load_data(self, train_csv=None, test_csv=None):\\n\\n if train_csv is not None and test_csv is not None:\\n self.train_data_vector = pandas.read_csv(train_csv)\\n self.test_data_vector = pandas.read_csv(test_csv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe6c47983b0f7f789aff6770cd7c3f6\",\n \"score\": \"0.5436664\",\n \"text\": \"def load_csv_from_path(self, csv_path):\\n if not os.path.isabs(csv_path):\\n csv_path = os.path.join(self.data_path, csv_path)\\n\\n # Deal with multiple possible extensions\\n path_base, path_ext = os.path.splitext(csv_path)\\n if path_ext == '.mp4':\\n csv_path = path_base + '.csv'\\n elif path_ext == '':\\n csv_path = csv_path + '.csv'\\n # Load the bounding box and format the timestamp\\n box_data = np.loadtxt(csv_path, delimiter=',',\\n dtype=float, skiprows=1,\\n converters={0: lambda x: self.parse_time(x)})\\n\\n return box_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"713d828d81ebb66a31ccd76f47794e01\",\n \"score\": \"0.54298455\",\n \"text\": \"def read_csv(request):\\n graph_file = request.param\\n dataset_path = graph_file.get_path()\\n M = utils.read_csv_for_nx(dataset_path)\\n M_cu = utils.read_csv_file(dataset_path)\\n\\n return M_cu, M, graph_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6574459bcb87e4653e9df4eba672a4f3\",\n \"score\": \"0.54286075\",\n \"text\": \"def loadFromFile(self, path):\\n with open (path, \\\"rt\\\") as f:\\n for line in f.readlines():\\n card, cardNum = [int(x) for x in line.split(',')]\\n self.add((card, cardNum))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b4b36c3f160721581ea4ecedce5eb90\",\n \"score\": \"0.54274607\",\n \"text\": \"def load_csv(input):\\n\\n data_countries = pd.read_csv(input)\\n return data_countries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50727d13cfa04bb896088b1610e1b853\",\n \"score\": \"0.541671\",\n \"text\": \"def load(self, hyperlinks):\\n \\tif Path(hyperlinks).is_file():\\n \\t\\tself.g = nx.DiGraph()\\n\\t \\twith open(hyperlinks, \\\"rb\\\") as ifile:\\n\\t \\t\\tfor line in ifile.readlines():\\n\\t \\t\\t\\tdoc, links = line.split(\\\":\\\")\\n\\t \\t\\t\\tdoc = int(doc)\\n\\t \\t\\t\\tfor link in links.split(\\\";\\\"):\\n\\t \\t\\t\\t\\tself.g.add_edge(doc, int(link))\\n\\t \\tlog.debug(\\\"%d documents et %d hyperliens chargés\\\", len(self.g), self.g.size())\\n\\t else:\\n\\t \\tlog.error(\\\"Erreur lors de l'ouverture du fichier %s\\\", hyperlinks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bef65f244899491ffdbcae4b70dbbffa\",\n \"score\": \"0.5415988\",\n \"text\": \"def loadCsvFile(self,myFile):\\n # open file\\n myFile.open(mode='rb')\\n # read the field names\\n head = myFile.readline().decode()\\n self.allFieldNames = head.split(',')\\n for index in range(len(self.allFieldNames)):\\n self.allFieldNames[index] = self.allFieldNames[index].strip()\\n # load the table data (from the csv file) into a numpy nd array\\n data = np.loadtxt(myFile,dtype='float',delimiter=',',ndmin=2,skiprows=1)\\n # close file\\n myFile.close()\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bef65f244899491ffdbcae4b70dbbffa\",\n \"score\": \"0.5415988\",\n \"text\": \"def loadCsvFile(self,myFile):\\n # open file\\n myFile.open(mode='rb')\\n # read the field names\\n head = myFile.readline().decode()\\n self.allFieldNames = head.split(',')\\n for index in range(len(self.allFieldNames)):\\n self.allFieldNames[index] = self.allFieldNames[index].strip()\\n # load the table data (from the csv file) into a numpy nd array\\n data = np.loadtxt(myFile,dtype='float',delimiter=',',ndmin=2,skiprows=1)\\n # close file\\n myFile.close()\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f41675bf47059717aa58eca84e153c21\",\n \"score\": \"0.5413548\",\n \"text\": \"def load_dataset(self):\\n print(\\\"Loading Dataset\\\")\\n csv_retrieve_dataset(self.TRAINING_DATA_PATH,self.TRAINING_DATA_URL,self.COLUMN_NAMES)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13ff2357baf8a0d38bca6ebddc1d9c39\",\n \"score\": \"0.5409063\",\n \"text\": \"def load_csv(file_name):\\n if sys.version_info[0] < 3:\\n lines = []\\n infile = open(file_name, 'rb')\\n else:\\n lines = []\\n infile = open(file_name, 'r', newline='')\\n\\n ant_dict = {}\\n with infile as f:\\n csvreader = csv.reader(f)\\n for c, lines in enumerate(csvreader):\\n if c == 0:\\n continue\\n ant_id = lines[0]\\n if ant_id not in ant_dict:\\n ant_dict[ant_id] = {}\\n ant_dict[ant_id]['frames'] = []\\n ant_dict[ant_id]['coordiantes'] = []\\n if lines[-1]:\\n ant_dict[ant_id]['frames'].append(\\n '{:05d}'.format(int(lines[1])))\\n ant_dict[ant_id]['coordiantes'].append([lines[2], lines[3]])\\n\\n return ant_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be721ab29de43bf1abc58ec0148e2fae\",\n \"score\": \"0.5408601\",\n \"text\": \"def import_tofrom_csv(file_name, id_col_name, to_node_col, from_node_col):\\n use_cols = [\\n str(id_col_name), str(to_node_col), str(from_node_col)\\n ]\\n\\n df = pd.read_csv(file_name, usecols=use_cols, dtype=str)\\n network_df, indx_df = df_transform(\\n df, id_col_name, to_node_col, from_node_col\\n )\\n return network_df, indx_df\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994099,"cells":{"query_id":{"kind":"string","value":"d85d71c32c197fa3a34b069127d452a1"},"query":{"kind":"string","value":"Reads reference data from the original \"new_reference.dat\" file from ff_generator Currently torsion and out_bends do not support reading a value in Offdiagonal values of the Hessian are not supported Must be called after setup"},"positive_passages":{"kind":"list like","value":[{"docid":"0cb73e703624784c0f6107acfd7b7d63","score":"0.60091716","text":"def read_ref_from_refdat(self, filename, conv_angle=np.pi/180.0, conv_energy = 143.88, conv_length=1.0, read_fit_flag=True):\n assert self._setup\n #\n f = open(filename, \"r\")\n line = f.readline()\n if line[:3] != \"###\":\n raise IOError, \"This does not look like a traditional reference file\"\n line = f.readline()\n stop = False\n while not stop:\n sline = line.split()\n if sline[1] == \"stretch\":\n ind = map(string.atoi, sline[2:4])\n ref_val = string.atof(sline[4])*conv_length\n ref_2nd = string.atof(sline[5])*conv_energy\n weight = 0.0\n if len(sline) > 6:\n if sline[6] == \"fit\": weight = 1.0\n i, iglob = self.map_ric(\"str\", ind)\n if i >=0:\n self.ref_val_str[i] = ref_val\n self.ref_hes[iglob, iglob] = ref_2nd\n print \"read RIC stretch %20s ref values %10.5f %10.5f\" % (ind, ref_val, ref_2nd) \n if read_fit_flag:\n self.wgt_val_str[i] = weight\n self.wgt_hes[iglob, iglob] = weight\n if weight >0: print \" RIC is fitted\"\n else:\n print \"RIC stretch %s is not defiend\" % ind\n elif sline[1] == \"bend\":\n ind = map(string.atoi, sline[2:5])\n ref_val = string.atof(sline[5])*conv_angle\n ref_2nd = string.atof(sline[6])*conv_energy\n weight = 0.0\n if len(sline) > 7:\n if sline[7] == \"fit\": weight = 1.0\n i, iglob = self.map_ric(\"ibe\", ind)\n if i >=0:\n self.ref_val_ibe[i] = ref_val\n self.ref_hes[iglob, iglob] = ref_2nd\n print \"read RIC bend %20s ref values %10.5f %10.5f\" % (ind, ref_val, ref_2nd) \n if read_fit_flag:\n self.wgt_val_ibe[i] = weight\n self.wgt_hes[iglob, iglob] = weight\n if weight >0: print \" RIC is fitted\"\n else:\n print \"RIC bend %s is not defiend\" % ind\n elif sline[1] == \"wag\":\n ind = map(string.atoi, sline[2:6])\n ref_2nd = string.atof(sline[6])*conv_energy\n weight = 0.0\n if len(sline) > 7:\n if sline[7] == \"fit\": weight = 1.0\n i, iglob = self.map_ric(\"obe\", ind)\n if i >=0:\n self.ref_hes[iglob, iglob] = ref_2nd\n print \"read RIC wag %20s ref values %10.5f\" % (ind, ref_2nd) \n if read_fit_flag:\n self.wgt_hes[iglob, iglob] = weight\n if weight >0: print \" RIC is fitted\"\n else:\n print \"RIC wag %s is not defiend\" % ind\n elif sline[1] == \"torsion\":\n for i in xrange(2, len(sline)):\n if sline[i].count(\".\") == 1: break\n ind = map(string.atoi, sline[2:i])\n na = len(ind)\n ind = ind[na/2-1:na/2+1]\n weight = 0.0\n if len(sline) > i+1:\n if sline[i+1] == \"fit\": weight = 1.0\n ref_2nd = string.atof(sline[i])*conv_energy\n i, iglob = self.map_ric(\"tor\", ind)\n if i >=0:\n self.ref_hes[iglob, iglob] = ref_2nd\n print \"read RIC torsion %20s ref values %10.5f\" % (ind, ref_2nd) \n if read_fit_flag:\n self.wgt_hes[iglob, iglob] = weight\n if weight >0: print \" RIC is fitted\"\n else:\n print \"RIC torsion %s is not defiend\" % ind\n else:\n raise IOError, \"Unknown RIC type in reference file\"\n line = f.readline()\n if len(line)==0: stop=True\n # in the refernece_dat file the Hessian elements start with a line with \"###\" -> stop here\n if line[:3] == \"###\": stop=True\n f.close()\n return","title":""}],"string":"[\n {\n \"docid\": \"0cb73e703624784c0f6107acfd7b7d63\",\n \"score\": \"0.60091716\",\n \"text\": \"def read_ref_from_refdat(self, filename, conv_angle=np.pi/180.0, conv_energy = 143.88, conv_length=1.0, read_fit_flag=True):\\n assert self._setup\\n #\\n f = open(filename, \\\"r\\\")\\n line = f.readline()\\n if line[:3] != \\\"###\\\":\\n raise IOError, \\\"This does not look like a traditional reference file\\\"\\n line = f.readline()\\n stop = False\\n while not stop:\\n sline = line.split()\\n if sline[1] == \\\"stretch\\\":\\n ind = map(string.atoi, sline[2:4])\\n ref_val = string.atof(sline[4])*conv_length\\n ref_2nd = string.atof(sline[5])*conv_energy\\n weight = 0.0\\n if len(sline) > 6:\\n if sline[6] == \\\"fit\\\": weight = 1.0\\n i, iglob = self.map_ric(\\\"str\\\", ind)\\n if i >=0:\\n self.ref_val_str[i] = ref_val\\n self.ref_hes[iglob, iglob] = ref_2nd\\n print \\\"read RIC stretch %20s ref values %10.5f %10.5f\\\" % (ind, ref_val, ref_2nd) \\n if read_fit_flag:\\n self.wgt_val_str[i] = weight\\n self.wgt_hes[iglob, iglob] = weight\\n if weight >0: print \\\" RIC is fitted\\\"\\n else:\\n print \\\"RIC stretch %s is not defiend\\\" % ind\\n elif sline[1] == \\\"bend\\\":\\n ind = map(string.atoi, sline[2:5])\\n ref_val = string.atof(sline[5])*conv_angle\\n ref_2nd = string.atof(sline[6])*conv_energy\\n weight = 0.0\\n if len(sline) > 7:\\n if sline[7] == \\\"fit\\\": weight = 1.0\\n i, iglob = self.map_ric(\\\"ibe\\\", ind)\\n if i >=0:\\n self.ref_val_ibe[i] = ref_val\\n self.ref_hes[iglob, iglob] = ref_2nd\\n print \\\"read RIC bend %20s ref values %10.5f %10.5f\\\" % (ind, ref_val, ref_2nd) \\n if read_fit_flag:\\n self.wgt_val_ibe[i] = weight\\n self.wgt_hes[iglob, iglob] = weight\\n if weight >0: print \\\" RIC is fitted\\\"\\n else:\\n print \\\"RIC bend %s is not defiend\\\" % ind\\n elif sline[1] == \\\"wag\\\":\\n ind = map(string.atoi, sline[2:6])\\n ref_2nd = string.atof(sline[6])*conv_energy\\n weight = 0.0\\n if len(sline) > 7:\\n if sline[7] == \\\"fit\\\": weight = 1.0\\n i, iglob = self.map_ric(\\\"obe\\\", ind)\\n if i >=0:\\n self.ref_hes[iglob, iglob] = ref_2nd\\n print \\\"read RIC wag %20s ref values %10.5f\\\" % (ind, ref_2nd) \\n if read_fit_flag:\\n self.wgt_hes[iglob, iglob] = weight\\n if weight >0: print \\\" RIC is fitted\\\"\\n else:\\n print \\\"RIC wag %s is not defiend\\\" % ind\\n elif sline[1] == \\\"torsion\\\":\\n for i in xrange(2, len(sline)):\\n if sline[i].count(\\\".\\\") == 1: break\\n ind = map(string.atoi, sline[2:i])\\n na = len(ind)\\n ind = ind[na/2-1:na/2+1]\\n weight = 0.0\\n if len(sline) > i+1:\\n if sline[i+1] == \\\"fit\\\": weight = 1.0\\n ref_2nd = string.atof(sline[i])*conv_energy\\n i, iglob = self.map_ric(\\\"tor\\\", ind)\\n if i >=0:\\n self.ref_hes[iglob, iglob] = ref_2nd\\n print \\\"read RIC torsion %20s ref values %10.5f\\\" % (ind, ref_2nd) \\n if read_fit_flag:\\n self.wgt_hes[iglob, iglob] = weight\\n if weight >0: print \\\" RIC is fitted\\\"\\n else:\\n print \\\"RIC torsion %s is not defiend\\\" % ind\\n else:\\n raise IOError, \\\"Unknown RIC type in reference file\\\"\\n line = f.readline()\\n if len(line)==0: stop=True\\n # in the refernece_dat file the Hessian elements start with a line with \\\"###\\\" -> stop here\\n if line[:3] == \\\"###\\\": stop=True\\n f.close()\\n return\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b5be3e4f061018379de1fd59914de34a","score":"0.61490226","text":"def Load_reference(self):\n if self.reffile is None:\n raise ValueError('No reference file specified')\n if os.path.isfile(self.reffile):\n hdu = fits.open(self.reffile)\n else:\n raise ValueError('{} Does not exist. Create the file with tess_reference.py'.format(self.reffile)) \n\n data = hdu[0].data\n cut = Cutout2D(data,(1024+44,1024),2048)\n self.reference = cut.data\n return","title":""},{"docid":"85c4202012909b3b69aa844c8363b49b","score":"0.5946181","text":"def regenerate_reference_data():\n generate_data(2,\n 2,\n maxn=4,\n maxrdm=4,\n daga=[0],\n undaga=[1],\n dagb=[0],\n undagb=[0])","title":""},{"docid":"d97ee0d4a5d6029e1576f30cdbf488d1","score":"0.5614137","text":"def traindata(output, resol,study, reference, foci, max_distance,w,feature):\n\n if checkConfig():\n pass\n else:\n print('Please run refhic config first.')\n print('Good bye!')\n sys.exit()\n\n dataParams={'resol':resol,'feature':feature,'w':w}\n reference = referenceMeta(reference)\n\n chromTest = {'chr15','chr16','chr17',15,16,17,'15','16','17'}\n chromVal = {'chr11','chr12',11,12,'11','12'}\n _mask = np.zeros(2 * (w * 2 + 1) ** 2 + 2 * (2 * w + 1) + 4)\n featureMask = feature.split(',')\n if '0' in featureMask:\n _mask[:] = 1\n if '1' in featureMask:\n _mask[:(2 * w + 1) ** 2] = 1\n if '2' in featureMask:\n _mask[(2 * w + 1) ** 2:2 * (2 * w + 1) ** 2] = 1\n if '3' in featureMask:\n _mask[2 * (2 * w + 1) ** 2:2 * (2 * w + 1) ** 2 + 2 * (2 * w + 1)] = 1\n if '4' in featureMask:\n _mask[2 * (2 * w + 1) ** 2 + 2 * (2 * w + 1)] = 1\n if '5' in featureMask:\n _mask[2 * (2 * w + 1) ** 2 + 2 * (2 * w + 1) + 1] = 1\n if '6' in featureMask:\n _mask[2 * (2 * w + 1) ** 2 + 2 * (2 * w + 1) + 2] = 1\n if '7' in featureMask:\n _mask[2 * (2 * w + 1) ** 2 + 2 * (2 * w + 1) + 3] = 1\n featureMask = np.ma.make_mask(_mask)\n\n\n\n studyBcools = [bcool(file_path+'::/resolutions/'+str(resol)) for file_path in study.split(',')]\n referencedBcools = [bcool(file_path+'::/resolutions/'+str(resol)) for file_path in reference['file'].to_list()]\n _foci = pd.read_csv(foci, header=None, sep='\\t')\n\n # read labels\n labels = {}\n\n for row in _foci.itertuples():\n chr1 = row[1]\n pos1 = row[2]\n chr2 = row[4]\n pos2 = row[5]\n label = list(row[7:])\n if chr1 == chr2 and abs(pos2 - pos1) < max_distance:\n if chr1 not in labels:\n labels[chr1] = {'contact':[],'label':[]}\n labels[chr1]['contact'].append((pos1,pos2))\n labels[chr1]['label'].append(label)\n\n\n dataParams['classes'] = len(label)\n dataParams['featureDim'] = np.sum(featureMask)\n print('This training data contains ', dataParams['featureDim'], ' features, and ', dataParams['classes'], ' targets per item')\n\n\n Xs = {} # for extra\n label={}\n X = {}\n for chrom in labels:\n if chrom not in X:\n label[chrom] = []\n for i in range(len(labels[chrom]['label'])):\n label[chrom].append(labels[chrom]['label'][i])\n\n for b in studyBcools:\n if chrom not in X:\n X[chrom] = [[] for _ in range(len(labels[chrom]['contact']))]\n bmatrix = b.bchr(chrom, max_distance=max_distance)\n for i in range(len(labels[chrom]['contact'])):\n x,y = labels[chrom]['contact'][i]\n\n mat,meta = bmatrix.square(x,y,w,'b')\n X[chrom][i].append(np.concatenate((mat.flatten(), meta)))\n\n for b in referencedBcools:\n if chrom not in Xs:\n Xs[chrom] = [[] for _ in range(len(labels[chrom]['contact']))]\n bmatrix = b.bchr(chrom, max_distance=max_distance)\n for i in range(len(labels[chrom]['contact'])):\n x,y = labels[chrom]['contact'][i]\n mat,meta = bmatrix.square(x,y,w,'b')\n Xs[chrom][i].append(np.concatenate((mat.flatten(), meta)))\n\n\n X_train = []\n Xs_train = []\n y_label_train = []\n X_test = []\n Xs_test = []\n y_label_test = []\n X_val = []\n Xs_val = []\n y_label_val = []\n for chrom in X:\n for i in range(len(X[chrom])):\n x=np.asarray(X[chrom][i])[:, featureMask]\n xs=np.asarray(Xs[chrom][i])[:, featureMask]\n if chrom in chromTest:\n X_test.append(x)\n Xs_test.append(xs)\n y_label_test.append(label[chrom][i])\n elif chrom in chromVal:\n X_val.append(x)\n Xs_val.append(xs)\n y_label_val.append(label[chrom][i])\n else:\n X_train.append(x)\n Xs_train.append(xs)\n y_label_train.append(label[chrom][i])\n del X,Xs\n with open(output+'.pkl', 'wb') as handle:\n pickle.dump((dataParams,X_train, Xs_train, y_label_train,\n X_test, Xs_test, y_label_test,\n X_val, Xs_val, y_label_val), handle, protocol=pickle.HIGHEST_PROTOCOL)","title":""},{"docid":"e35be5e75be2c84b29336d64029e8491","score":"0.5596661","text":"def gen_reference(in_img,fwhm=5,newpath=None):\n import nibabel as nb \n import numpy as np\n import os \n from nibabel.processing import smooth_image\n newpath = Path(newpath or \".\")\n ss=check_img(in_img)\n if ss == 0: \n ref_data=nb.load(in_img).get_fdata()\n else: \n nii = nb.load(in_img).get_fdata()\n ref_data=np.mean(nii,axis=3)\n new_file = nb.Nifti1Image(dataobj=ref_data,header=nb.load(in_img).header,\n affine=nb.load(in_img).affine)\n\n new_file = smooth_image(new_file,fwhm=fwhm)\n out_file = fname_presuffix('aslref', suffix=\"_reference.nii.gz\", newpath=str(newpath.absolute()))\n new_file.to_filename(out_file)\n return out_file","title":""},{"docid":"0b631b81068cd0edffa6ac9a4bb507dd","score":"0.54430836","text":"def read_foreign(self):\n ffile = pd.read_csv(INPUTPATH + 'BEA/international-by-industry.csv')\n ffile.set_index('indcode', inplace=True)\n self.foreign = copy.deepcopy(ffile)","title":""},{"docid":"130fc29d15da1c20f1afe1be162365a7","score":"0.5427231","text":"def read_ric(self, filename):\n f = open(filename, \"r\")\n line = f.readline()\n if line[:3] != \"###\":\n raise IOError, \"This does not look like a traditional reference file\"\n line = f.readline()\n stop = False\n while not stop:\n sline = line.split()\n if sline[1] == \"stretch\":\n ind = map(string.atoi, sline[2:4])\n self.add_stretch(ind)\n elif sline[1] == \"bend\":\n ind = map(string.atoi, sline[2:5])\n self.add_in_bend(ind)\n elif sline[1] == \"wag\":\n ind = map(string.atoi, sline[2:6])\n self.add_out_bend(ind)\n elif sline[1] == \"torsion\":\n for i in xrange(2, len(sline)):\n if sline[i].count(\".\") == 1: break\n ind = map(string.atoi, sline[2:i])\n self.add_torsion(ind)\n else:\n raise IOError, \"Unknown RIC type in reference file\"\n line = f.readline()\n if len(line)==0: stop=True\n # in the refernece_dat file the Hessian elements start with a line with \"###\" -> stop here\n if line[:3] == \"###\": stop=True\n f.close()\n # HACK\n # assumption ... non peridic .. fix\n self.add_eckart_rots()\n return","title":""},{"docid":"344d3c97a83e1549ea6cc41eeb6a6007","score":"0.54000765","text":"def read_reference(self):\n ref = general.load_csv(self.reference_filename, drop_gene_names=False)\n self.gene_symbol_dict = {name: wbgene for name, wbgene in zip(ref[self.ref_gene_col], ref[self.ref_wbgene_col])\n if not pd.isna(name)}\n self.sequence_dict = {name: wbgene for name, wbgene in zip(ref[self.ref_seq_col], ref[self.ref_wbgene_col])\n if not pd.isna(name)}\n\n split_other_id = ref[self.ref_other_col].str.split(pat=\";\")\n for namelst, wbgene in zip(split_other_id, ref[self.ref_wbgene_col]):\n if isinstance(namelst, list):\n for name in namelst:\n self.other_id_dict[name] = wbgene","title":""},{"docid":"eeb386a58ffe5e49d2eb27ee88412acf","score":"0.53429496","text":"def setup(self):\n # now let us do all the extra stuff to map rics and allocate python arrays for the reference\n self.nstretch = len(self.pd.ric._stretches)\n self.first_str = 0\n self.nin_bend = len(self.pd.ric._in_bends)\n self.first_ibe = self.first_str+self.nstretch\n self.nout_bend = len(self.pd.ric._out_bends)\n self.first_obe = self.first_ibe+self.nin_bend\n self.nlin_bend = len(self.pd.ric._lin_bends)\n self.first_lbe = self.first_obe+self.nout_bend\n self.ntorsion = len(self.pd.ric._torsions)\n self.first_tor = self.first_lbe+self.nlin_bend\n # keep a list of central atoms for all torsions\n self.tor_cent = []\n for t in self.pd.ric._torsions:\n na = len(t)\n self.tor_cent.append(t[na/2-1:na/2+1])\n # Allocate a reference structure/hessian array\n self.ref_val_str = np.zeros([self.nstretch],dtype=\"float64\")\n self.wgt_val_str = np.zeros([self.nstretch],dtype=\"float64\")\n self.ref_val_ibe = np.zeros([self.nin_bend],dtype=\"float64\")\n self.wgt_val_ibe = np.zeros([self.nin_bend],dtype=\"float64\")\n self.ref_val_obe = np.zeros([self.nout_bend],dtype=\"float64\")\n self.wgt_val_obe = np.zeros([self.nout_bend],dtype=\"float64\")\n self.ref_val_lbe = np.zeros([self.nlin_bend],dtype=\"float64\")\n self.wgt_val_lbe = np.zeros([self.nlin_bend],dtype=\"float64\")\n self.ref_val_tor = np.zeros([self.ntorsion],dtype=\"float64\")\n self.wgt_val_tor = np.zeros([self.ntorsion],dtype=\"float64\")\n self.ref_hes = np.zeros([self.pd.ric.nric, self.pd.ric.nric], dtype=\"float64\")\n self.wgt_hes = np.zeros([self.pd.ric.nric, self.pd.ric.nric], dtype=\"float64\")\n # set some defaults\n self.fact_str = 1.0\n self.fact_ibe = 1.0\n self.fact_obe = 1.0\n self.fact_lbe = 1.0\n self.fact_tor = 1.0\n self.cycle = 0\n self.fdiagnostics = open('ric_fit_%s.punch' % self.tag, 'w')\n # We use here the original convention to compare Hessian elements in milidyn*A\n self.fact_hes = 1.0/143.88\n # generate referece data \n self.generate_reference(self.pd.get_cell())\n self.riclist = []\n self.riclist.append(self.pd.ric._stretches)\n self.riclist.append(self.pd.ric._in_bends)\n self.riclist.append(self.pd.ric._out_bends)\n self.riclist.append(self.pd.ric._lin_bends)\n self.riclist.append(self.pd.ric._torsions)\n self.firstrics = []\n self.firstrics.append(self.first_str)\n self.firstrics.append(self.first_ibe)\n self.firstrics.append(self.first_obe)\n self.firstrics.append(self.first_lbe)\n self.firstrics.append(self.first_tor)\n return","title":""},{"docid":"403e1c93bb6687b44869880662d2189e","score":"0.5340942","text":"def reference_offset(data):\n offset = data[:, :, 0, 0] # Reference point: person 0, joint 0 -> NTURGB+D\n data -= offset[:, :, None, None]\n return data","title":""},{"docid":"bd078f174079b22353fd75b6d7e6bfbe","score":"0.52440065","text":"def read_body_inc(f,verbose,jj,glueball_corr) :\n for ibin in range(0, numbin):\n for iopA in range(0,numop) :\n for iblockA in range(0, nblock) :\n for iopB in range(0,numop) : \n for iblockB in range(0, nblock) :\n for t in range(0, Ntmax) :\n \n try:\n bbb = f.read(8)\n except IOError as e:\n print (\"I/O error({0}): {1}\".format(e.errno, e.strerror))\n except:\n print (\"Unexpected error:\", sys.exc_info()[0])\n raise\n\n vvv = struct.unpack('d',bbb)\n if verbose :\n print t, vvv[0]\n \n glueball_corr[jj, iblockA,iblockB,t, iopA,iopB, ibin] = vvv[0]\n\n\n\n f.close()","title":""},{"docid":"7da5a38e4c47d3f065ff7d0c7d1d8744","score":"0.5213994","text":"def read_QM_in( network_architecture, scaling_factors, filename, stepnumber, options ): \n #get path of QM.in\n pathQMNN=options['QMoutpath']\n #create QM_in object (imported from QM_transformer) that holds all information from the QM.in-file \n raw_data = QM_in( filename, stepnumber, pathQMNN )\n #get descriptors\n descriptors = raw_data.descriptors#_coulomb\n descriptors = np.array(descriptors)\n states = raw_data.states\n nmstates = raw_data.nmstates\n n_atoms = raw_data.n_atoms\n nstates = raw_data.nstates\n spin = raw_data.spin\n #charges = raw_data.charges\n istates = raw_data.istates\n n_singlets = raw_data.n_singlets\n n_triplets = raw_data.n_triplets\n input_dim = len( descriptors )\n\n if input_dim != network_architecture[0][0]:\n print( 'Descriptor dimension of input and training data not matching.' )\n exit()\n\n #scale descriptors \n descriptors = ( descriptors - scaling_factors[0] ) / scaling_factors[1]\n\n #load descriptors in theano shared variables\n descriptors = theano.shared( np.asarray(descriptors,dtype=theano.config.floatX), borrow=True ) \n xyz_coords = []\n xyz_coords.append(raw_data.geoms)\n\n return descriptors, xyz_coords, states, n_atoms, nstates, nmstates, istates, spin, n_singlets, n_triplets","title":""},{"docid":"bf953df72149f789baabb51a18f2951f","score":"0.52125144","text":"def PullReadIrefCalibrationFile(self):\n if os.path.isfile(self.gc_step1_filename):\n cmdstring = 'del ' + self.gc_step1_filename\n os.system(cmdstring) \n cmdstring = 'adb pull /factory_setting/' + self.gc_step1_filename\n os.system(cmdstring)\n self.r_iref_values = np.zeros(self.NumLEDs)\n self.g_iref_values = np.zeros(self.NumLEDs)\n self.b_iref_values = np.zeros(self.NumLEDs)\n with open(self.gc_step1_filename, 'r') as f:\n for i in range(self.NumLEDs):\n linestring = f.readline()\n strlist = str.split(linestring, ' ')\n self.r_iref_values[i] = int(strlist[0])\n self.g_iref_values[i] = int(strlist[1])\n self.b_iref_values[i] = int(strlist[2])\n cmdstring = 'del ' + self.gc_step1_filename\n os.system(cmdstring)\n self.WriteIrefRegisters()","title":""},{"docid":"a8d7072f845d6fcf170c24d9163675ea","score":"0.5206732","text":"def get_full_data(cur_params):\n path_prefix = './'\n if cur_params['monkey']=='D':\n data = sio.loadmat(path_prefix+'drakeFeb.mat')\n else:\n data = sio.loadmat(path_prefix+'cousFeb.mat')\n\n # Built augmented data\n emg2 = preprocess_array(data['D'][0, 0]['EMG'])\n time_axis, time_inds1, time_inds2 = get_time_axis(data['D'][0, 0]['KIN'])\n y_data1 = emg2[time_axis]\n p = y_data1.shape[-1]\n\n # Build inputs\n m = 2\n u_data1 = create_input_array(y_data1.shape)\n\n # Augmented data\n # For regularizing the network -- it must fit actual and augmented data\n period = int(np.round(np.diff(time_inds2).mean()))\n y_cat1 = augmented_data(emg2, time_inds1, period=period, tiles=10)\n y_cat1 = y_cat1[::25]\n y_cat2 = augmented_data(emg2, time_inds2, period=period, tiles=10)\n y_cat2 = y_cat2[::25]\n\n u_cat1 = create_input_array(y_cat1.shape)\n u_cat2 = create_input_array(y_cat2.shape)\n\n sequence_length = [y_data1.shape[0], y_cat1.shape[0], y_cat2.shape[0]]\n y_data = np.zeros((np.max(sequence_length), 4*3, p))\n u_data = np.zeros((np.max(sequence_length), 4*3, m))\n\n y_data[:sequence_length[0], 0:4, :] = y_data1\n y_data[:sequence_length[1], 4:8, :] = y_cat1\n y_data[:sequence_length[2], 8:12, :] = y_cat2\n\n u_data[:sequence_length[0], 0:4, :] = u_data1\n u_data[:sequence_length[1], 4:8, :] = u_cat1\n u_data[:sequence_length[2], 8:12, :] = u_cat2\n return u_data, y_data","title":""},{"docid":"6f5c1b50de304bd33d22a134c7667f31","score":"0.51948804","text":"def load_physician_referral_data(infilename):\n tmpdir = tempfile.mkdtemp()\n try:\n archive = zipfile.ZipFile(infilename, 'r')\n archive.extract('physician-shared-patient-patterns-2014-days30.txt', tmpdir)\n filename = os.path.join(tmpdir, \"physician-shared-patient-patterns-2014-days30.txt\")\n archive.close()\n context = snap.TTableContext()\n schema = snap.Schema()\n ## schema.Add(snap.TStrTAttrPr(\"NPI_1\", snap.atInt))\n ## schema.Add(snap.TStrTAttrPr(\"NPI_2\", snap.atInt))\n # the above 2 lines worked with SNAP 4.0.0 on VLSCI \n # but now using SNAP 4.1.0\n # on hpc.ics.usi.ch find that all ids are -1 so graph wrong.\n # Cannot work out why so changed to string not int to try to fix it:\n schema.Add(snap.TStrTAttrPr(\"NPI_1\", snap.atStr))\n schema.Add(snap.TStrTAttrPr(\"NPI_2\", snap.atStr))\n ## schema.Add(snap.TStrTAttrPr(\"count\", snap.atInt))\n ## schema.Add(snap.TStrTAttrPr(\"unique_bene\", snap.atInt))\n ## schema.Add(snap.TStrTAttrPr(\"same_day_count\", snap.atInt))\n # The above 3 lines also worked fine with SNAP 4.0.0 before but\n # now fail on SNAP 4.1.0 (seems to be due to spaces in CSV fields,\n # not inexplicable like first two which have no spaces) but not using\n # them at the moment anyway so easier to just make (unused) strings:\n schema.Add(snap.TStrTAttrPr(\"count\", snap.atStr))\n schema.Add(snap.TStrTAttrPr(\"unique_bene\", snap.atStr))\n schema.Add(snap.TStrTAttrPr(\"same_day_count\", snap.atStr))\n table = snap.TTable.LoadSS(schema, filename, context, \",\", snap.TBool(False))\n G = snap.ToGraph(snap.PNGraph, table, \"NPI_1\", \"NPI_2\", snap.aaFirst) \n finally:\n cleanup_tmpdir(tmpdir)\n\n return G","title":""},{"docid":"ae262497a0c062583959b4412d45d2c7","score":"0.519233","text":"def test_ref_degenerate():\n info = read_info(ctf_fname)\n # exclude ref by default\n projs = compute_proj_hfc(info)\n meg_names = [\n info[\"ch_names\"][pick]\n for pick in pick_types(info, meg=True, ref_meg=False, exclude=[])\n ]\n assert len(projs) == 3\n assert projs[0][\"desc\"] == \"HFC: l=1 m=-1\"\n assert projs[1][\"desc\"] == \"HFC: l=1 m=0\"\n assert projs[2][\"desc\"] == \"HFC: l=1 m=1\"\n assert projs[0][\"data\"][\"col_names\"] == meg_names\n meg_ref_names = [\n info[\"ch_names\"][pick]\n for pick in pick_types(info, meg=True, ref_meg=True, exclude=[])\n ]\n projs = compute_proj_hfc(info, picks=(\"meg\", \"ref_meg\"))\n assert projs[0][\"data\"][\"col_names\"] == meg_ref_names\n\n # Degenerate\n info = read_info(fif_fname)\n compute_proj_hfc(info) # smoke test\n with pytest.raises(ValueError, match=\"Only.*could be interpreted as MEG\"):\n compute_proj_hfc(info, picks=[0, 330]) # one MEG, one EEG\n info[\"chs\"][0][\"loc\"][:] = np.nan # first MEG proj\n with pytest.raises(ValueError, match=\"non-finite projectors\"):\n compute_proj_hfc(info)\n info_eeg = pick_info(info, pick_types(info, meg=False, eeg=True))\n with pytest.raises(ValueError, match=r\"picks \\(\\'meg\\'\\) could not be\"):\n compute_proj_hfc(info_eeg)","title":""},{"docid":"9e85d0b782e7b6bd3b291741b4d15c0b","score":"0.5188064","text":"def read_infit(self): \n from astropy.io import ascii\n self.fdat = ascii.read(self.cdir+self.bdir+self.ifit)\n self.ldat = ascii.read(self.cdir+self.bdir+self.loop)\n self.tdat = join(self.fdat,self.ldat,keys=['mod_nam'])\n\n self.comp_tsig()\n self.read_model()\n #remove some bad coronal loops\n\n#use model id for abbicus\n self.tdat['mod_id'] = [int(i.split('_')[0].replace('model','')) for i in self.tdat['mod_nam']]","title":""},{"docid":"839e5bfbf6679cdb08e2931f1be010eb","score":"0.5169292","text":"def test_fqe_data_manipulation():\n test = fqe_data.FqeData(1, 1, 2)\n ref = numpy.random.rand(2, 2) + 1.j * numpy.random.rand(2, 2)\n test.set_wfn(strategy='from_data', raw_data=ref)\n assert numpy.allclose(test.beta_inversion(), ref[:, (1, 0)])\n test.conj()\n assert numpy.allclose(test.coeff, numpy.conj(ref))","title":""},{"docid":"d6c7802d7a5b79dfd6337fe00870cef3","score":"0.5151748","text":"def read_op2_dynamics(self):\n key = self._get_key()\n if self._ibytes == 4:\n header_str = struct.Struct(self._endian + b'iii')\n hbytes = 12\n else:\n header_str = struct.Struct(self._endian + b'qqq')\n hbytes = 24\n\n eot = 0\n print('self._intstr = %r' % self._intstr)\n data = np.zeros(0, dtype=self._intstr)\n while not eot:\n while key > 0:\n self._fileh.read(4) # reclen\n header = header_str.unpack(self._fileh.read(hbytes))\n if header == (7107, 71, 138):\n if key < self._rowsCutoff:\n bytes = (key-3)*self._ibytes\n ndata = struct.unpack(self._intstru % (key-3),\n self._fileh.read(bytes))\n else:\n ndata = np.fromfile(self._fileh,\n self._intstr, key-3)\n data = np.hstack((data, ndata))\n else:\n self._fileh.seek((key-3)*self._ibytes, 1)\n self._fileh.read(4) # endrec\n key = self._get_key()\n self._skip_key(2)\n eot, key = self._read_op2_end_of_table()\n\n if np.any(data):\n L = len(data)\n mult5 = L == 5*(L // 5)\n mult6 = L == 6*(L // 6)\n err1 = ('Could not determine if TLOADs are 5 or 6 rows! '\n 'Both work. Routine needs updating.')\n err2 = ('Could not determine if TLOADs are 5 or 6 rows! '\n 'Neither work. Routine needs updating.')\n if mult5:\n mindelta5 = np.min(np.diff(data[0::5]))\n if mult6:\n mindelta6 = np.min(np.diff(data[0::6]))\n if mult5:\n if mult6:\n # L is multiple of both 5 and 6:\n if mindelta5 > 0:\n if mindelta6 > 0:\n raise ValueError(err1)\n rows = 5\n else:\n if mindelta6 > 0:\n rows = 6\n else:\n raise ValueError(err2)\n else:\n if mindelta5 > 0:\n rows = 5\n else:\n raise ValueError(err2)\n elif mult6:\n if mindelta6 > 0:\n rows = 6\n else:\n raise ValueError(err2)\n else:\n raise ValueError(err2)\n data = np.reshape(data, (rows, -1), order='F')\n return data","title":""},{"docid":"0df54f7668b50397167a12fb6fff7471","score":"0.5142007","text":"def read_body(f,verbose,jj,glueball_corr) :\n for ibin in range(0, numbin):\n for iopA in range(0,numop) :\n for iblockA in range(0, nblock) :\n for iopB in range(0,numop) : \n for iblockB in range(0, nblock) :\n for t in range(0, Ntmax) :\n \n try:\n bbb = f.read(8)\n except IOError as e:\n print (\"I/O error({0}): {1}\".format(e.errno, e.strerror))\n except:\n print (\"Unexpected error:\", sys.exc_info()[0])\n raise\n\n vvv = struct.unpack('d',bbb)\n if verbose :\n print (t, vvv[0])\n\n \n glueball_corr[jj, iblockA,iblockB,t, iopA,iopB, ibin] = vvv[0]\n\n\n\n f.close()","title":""},{"docid":"02d2d25a183dbde04caaec41861f8752","score":"0.5140673","text":"def test_io_forward(tmp_path):\n # do extensive tests with MEEG + grad\n n_channels, n_src = 366, 108\n fwd = read_forward_solution(fname_meeg_grad)\n assert isinstance(fwd, Forward)\n fwd = read_forward_solution(fname_meeg_grad)\n fwd = convert_forward_solution(fwd, surf_ori=True)\n leadfield = fwd[\"sol\"][\"data\"]\n assert_equal(leadfield.shape, (n_channels, n_src))\n assert_equal(len(fwd[\"sol\"][\"row_names\"]), n_channels)\n fname_temp = tmp_path / \"test-fwd.fif\"\n with pytest.warns(RuntimeWarning, match=\"stored on disk\"):\n write_forward_solution(fname_temp, fwd, overwrite=True)\n\n fwd = read_forward_solution(fname_meeg_grad)\n fwd = convert_forward_solution(fwd, surf_ori=True)\n fwd_read = read_forward_solution(fname_temp)\n fwd_read = convert_forward_solution(fwd_read, surf_ori=True)\n leadfield = fwd_read[\"sol\"][\"data\"]\n assert_equal(leadfield.shape, (n_channels, n_src))\n assert_equal(len(fwd_read[\"sol\"][\"row_names\"]), n_channels)\n assert_equal(len(fwd_read[\"info\"][\"chs\"]), n_channels)\n assert \"dev_head_t\" in fwd_read[\"info\"]\n assert \"mri_head_t\" in fwd_read\n assert_array_almost_equal(fwd[\"sol\"][\"data\"], fwd_read[\"sol\"][\"data\"])\n\n fwd = read_forward_solution(fname_meeg)\n fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=True, use_cps=False)\n with pytest.warns(RuntimeWarning, match=\"stored on disk\"):\n write_forward_solution(fname_temp, fwd, overwrite=True)\n fwd_read = read_forward_solution(fname_temp)\n fwd_read = convert_forward_solution(\n fwd_read, surf_ori=True, force_fixed=True, use_cps=False\n )\n assert repr(fwd_read)\n assert isinstance(fwd_read, Forward)\n assert is_fixed_orient(fwd_read)\n assert_forward_allclose(fwd, fwd_read)\n\n fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=True, use_cps=True)\n leadfield = fwd[\"sol\"][\"data\"]\n assert_equal(leadfield.shape, (n_channels, 1494 / 3))\n assert_equal(len(fwd[\"sol\"][\"row_names\"]), n_channels)\n assert_equal(len(fwd[\"info\"][\"chs\"]), n_channels)\n assert \"dev_head_t\" in fwd[\"info\"]\n assert \"mri_head_t\" in fwd\n assert fwd[\"surf_ori\"]\n with pytest.warns(RuntimeWarning, match=\"stored on disk\"):\n write_forward_solution(fname_temp, fwd, overwrite=True)\n fwd_read = read_forward_solution(fname_temp)\n fwd_read = convert_forward_solution(\n fwd_read, surf_ori=True, force_fixed=True, use_cps=True\n )\n assert repr(fwd_read)\n assert isinstance(fwd_read, Forward)\n assert is_fixed_orient(fwd_read)\n assert_forward_allclose(fwd, fwd_read)\n\n fwd = read_forward_solution(fname_meeg_grad)\n fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=True, use_cps=True)\n leadfield = fwd[\"sol\"][\"data\"]\n assert_equal(leadfield.shape, (n_channels, n_src / 3))\n assert_equal(len(fwd[\"sol\"][\"row_names\"]), n_channels)\n assert_equal(len(fwd[\"info\"][\"chs\"]), n_channels)\n assert \"dev_head_t\" in fwd[\"info\"]\n assert \"mri_head_t\" in fwd\n assert fwd[\"surf_ori\"]\n with pytest.warns(RuntimeWarning, match=\"stored on disk\"):\n write_forward_solution(fname_temp, fwd, overwrite=True)\n fwd_read = read_forward_solution(fname_temp)\n fwd_read = convert_forward_solution(\n fwd_read, surf_ori=True, force_fixed=True, use_cps=True\n )\n assert repr(fwd_read)\n assert isinstance(fwd_read, Forward)\n assert is_fixed_orient(fwd_read)\n assert_forward_allclose(fwd, fwd_read)\n\n # test warnings on bad filenames\n fwd = read_forward_solution(fname_meeg_grad)\n fwd_badname = tmp_path / \"test-bad-name.fif.gz\"\n with pytest.warns(RuntimeWarning, match=\"end with\"):\n write_forward_solution(fwd_badname, fwd)\n with pytest.warns(RuntimeWarning, match=\"end with\"):\n read_forward_solution(fwd_badname)\n\n fwd = read_forward_solution(fname_meeg)\n write_forward_solution(fname_temp, fwd, overwrite=True)\n fwd_read = read_forward_solution(fname_temp)\n assert_forward_allclose(fwd, fwd_read)","title":""},{"docid":"136b7409a35513fc514c65435d29a833","score":"0.50885725","text":"def get_ref(in_file):\n with open(\"./input_data/\" + in_file) as in_handle:\n for title, seq in SimpleFastaParser(in_handle):\n ref_seq = seq\n break\n\n return ref_seq","title":""},{"docid":"909e8ffcbc7f64e1cde32b68b3f5f296","score":"0.507998","text":"def compute_Fnat_fast(self,ref_pairs=None,cutoff=5):\n # read the izone file\n if ref_pairs is None:\n residue_pairs_ref = self.compute_residue_pairs_ref(cutoff,save_file=False)\n\n elif not os.path.isfile(ref_pairs):\n self.compute_residue_pairs_ref(cutoff,save_file=True,filename=ref_pairs)\n f = open(ref_pairs,'rb')\n residue_pairs_ref = pickle.load(f)\n f.close()\n else:\n f = open(ref_pairs,'rb')\n residue_pairs_ref = pickle.load(f)\n f.close()\n\n # create a dict of the ecoy data\n if isinstance(self.decoy,str) and os.path.isfile(self.decoy):\n with open(self.decoy,'r') as f:\n data_decoy = f.readlines()\n decoy_name = os.path.basename(self.decoy)\n elif isinstance(self.decoy,np.ndarray):\n data_decoy = [l.decode('utf-8') for l in self.decoy]\n decoy_name = 'decoy'\n else:\n raise ValueError('Decoy not found')\n\n # read the decoy data\n atom_decoy, xyz_decoy = [],[]\n residue_xyz = {}\n residue_name = {}\n\n # go through all the lines\n # that starts with ATOM\n for line in data_decoy:\n\n if line.startswith('ATOM'):\n\n # chain ID\n chainID = line[21]\n if chainID == ' ':\n chainID = line[72]\n\n # atom info\n resSeq = int(line[22:26])\n resName = line[17:20].strip()\n name = line[12:16].strip()\n\n # position\n x,y,z = float(line[30:38]), float(line[38:46]), float(line[46:54])\n\n # dict entry\n key = (chainID,resSeq,resName)\n\n # create the dict entry if necessary\n if key not in residue_xyz.keys():\n residue_xyz[key] = []\n residue_name[key] = []\n\n # we exclude the Hydrogens from the search\n if name[0] != 'H':\n residue_xyz[key].append([x,y,z])\n residue_name[key].append(name)\n\n # loop over the residue pairs of the\n # and increment common if an atom pair is close enough\n nCommon,nTotal = 0,0\n for resA,resB_list in residue_pairs_ref.items():\n if resA in residue_xyz:\n xyzA = residue_xyz[resA]\n for resB in resB_list:\n if resB in residue_xyz.keys():\n xyzB = residue_xyz[resB]\n dist_min = np.min(np.array([ np.sqrt(np.sum((np.array(p1)-np.array(p2))**2)) for p1 in xyzA for p2 in xyzB ]))\n if dist_min<=cutoff:\n nCommon += 1\n nTotal += 1\n else:\n msg = '\\t FNAT Warning could not find residue: ', resA, ' in: ',decoy_name\n _printif(msg,self.verbose)\n\n # normalize\n return nCommon/nTotal","title":""},{"docid":"35d63de85a9d0930b49815b0247f47dc","score":"0.50795496","text":"def read_data(self):\n import os\n import numpy as np\n\n if isinstance(self.filename, str):\n f = open(self.filename)\n nitpi2f = None\n itpi2f = None\n i2f = []\n ni2f = []\n\n for n,line in enumerate(f):\n if 'k-points in 1st BZ' in line:\n #initiate temporary array\n self.nitpi2f = np.array(ni2f)\n i2f = []\n\n if 'KPOINTS_INTER' in line:\n # save the first array\n self.nitpi2f = np.array(ni2f)\n\n if 't-inv' in line:\n # read the mapping indeces\n i2f.append(int(line.split()[4])-1)\n ni2f.append((int(line.split()[4])-1,float(line.split()[0]),float(line.split()[1]),float(line.split()[2]),float(line.split()[3])))\n\n self.itpi2f = np.array(i2f)\n #self.nitpi2f = np.array(ni2f)","title":""},{"docid":"3969afca6b74e3d99bf20fa077569dee","score":"0.5076639","text":"def loadub(self, file):\r\n ub=None\r\n pos=None\r\n cons_list=None\r\n latt=None\r\n azi_ref=None\r\n azi_hkl=None\r\n \r\n if isinstance(file,int):\r\n file=self.listub(ret=True)[file]\r\n \r\n if str(file).endswith('.txt'):\r\n with open (file,'r') as f:\r\n print(\"reading .txt file\\n\",file)\r\n \r\n lines=f.read().split('\\n')\r\n #print(lines)\r\n \r\n #go through the file looking for markers\r\n for i,line in enumerate(lines):\r\n #line=line.strip().split()\r\n #print(line,line[0])\r\n #read the ub matrix rows\r\n if(line=='ub'):\r\n ub=np.zeros((3,3))\r\n #print(\"ub marker\")\r\n for j in range(3):\r\n ubrow=lines[i+j+1].strip().split()\r\n ubrow=[float(x) for x in ubrow]\r\n ub[j,:]=np.array(ubrow)\r\n \r\n #read position\r\n elif(line=='sixc'):\r\n #print(\"pos marker\")\r\n pos=lines[i+1].strip().split()\r\n pos=[float(x) for x in pos] #uses position order\r\n \r\n #read contraints\r\n elif(line=='con'):\r\n cons={}\r\n cons_list=[]\r\n #print(\"con marker\")\r\n for j in range(3):\r\n if (i+j+1)<len(lines):\r\n conline=lines[i+j+1].strip().split()\r\n if len(conline)==1:\r\n cons[conline[0]]=True\r\n elif len(conline)==2:\r\n if (conline[1]=='None' or conline[1]==None):\r\n cons[conline[0]]=True\r\n else:\r\n cons[conline[0]]=float(conline[1])\r\n\r\n for key in cons:\r\n key_varified = self.varifykey(key)\r\n cons_list.append(key_varified)\r\n cons_list.append(cons[key])\r\n \r\n elif(line=='en'):\r\n en=float(lines[i+1].strip())\r\n \r\n elif(line=='lattice'):\r\n latt_name=lines[i+1].strip()\r\n latt=[float(x) for x in lines[i+2].strip().split()]\r\n \r\n elif(line=='azi_ref'):\r\n azi_ref=lines[i+1].strip().split()\r\n azi_ref=[float(x) for x in azi_ref] #uses position order\r\n \r\n elif(line=='azi_hkl'):\r\n azi_hkl=lines[i+1].strip().split()\r\n azi_hkl=[float(x) for x in azi_hkl]\r\n \r\n \r\n #print(cons_list,cons)\r\n \r\n print(\"Setting:\\n\")\r\n if not(ub is None):\r\n print(\"ub\")\r\n self.ubcalc.set_ub(ub)\r\n self.hklcalc.ubcalc.UB=ub\r\n print(self.ubcalc.UB)\r\n print()\r\n if not(pos is None):\r\n print(\"sixc\")\r\n self.pos(self.scannables['sixc'],self.pos_to_sixc(pos))\r\n if not(cons_list is None):\r\n print(\"con\")\r\n self.con(*cons_list)\r\n if not(en is None):\r\n print('en')\r\n self.pos(self.scannables['en'],en)\r\n if not(latt is None):\r\n self.setlat(latt_name,*latt)\r\n print(\"lattice\")\r\n print(latt_name,latt)\r\n print()\r\n \r\n try:\r\n if self.ubcalc.crystal is not None:\r\n self.ubcalc.U = self.ubcalc.UB @ np.linalg.inv(self.ubcalc.crystal.B)\r\n except Exception as e:\r\n traceback.print_exc()\r\n print(e)\r\n print('Failed to set U matrix')\r\n \r\n elif not(self.ubcalc.UB is None):\r\n pass\r\n #get real vectors from UB\r\n \r\n if not (azi_ref is None):\r\n self.setnphi(azi_ref)\r\n print('nphi')\r\n print(azi_ref)\r\n print()\r\n if not (azi_hkl is None):\r\n self.setnhkl(azi_hkl)\r\n print('nhkl')\r\n print(azi_hkl)\r\n print()\r\n \r\n self.hklcalc= HklCalculation(self.ubcalc, self.cons)\r\n self.update_pos()\r\n self.set_reciprocal_vectors()\r\n vectors.set_vector('reference azimuthal',self.setnphi())\r\n \r\n #if it is a nexus file, create a suitable .txt and read that\r\n elif str(file).endswith('.nxs'):\r\n from i16sim.util.hdf5_to_i16sim import interpret\r\n interpret(file)\r\n self.loadub(str(file)+'.i16sim.txt')\r\n \r\n else:\r\n raise Exception('File not recognised')","title":""},{"docid":"f7c06e2b12fc1af265c18813538817b8","score":"0.5075727","text":"def gen_reference(out_data_list, bus_num=2, file=axis_check_file):\r\n with open(file, \"w+\") as f:\r\n for l in out_data_list:\r\n l_tmp = l.copy()\r\n while len(l_tmp) > bus_num:\r\n f.write(\"0_\")\r\n for b in range(bus_num):\r\n trans_part = l_tmp.pop(0)\r\n f.write(\"{0:08x}{1:08x}\".format(int_to_short(trans_part.imag, 32), int_to_short(trans_part.real, 32)))\r\n f.write(\"\\n\")\r\n else:\r\n f.write(\"1_\")\r\n for b in range(bus_num):\r\n trans_part = l_tmp.pop(0)\r\n f.write(\"{0:08x}{1:08x}\".format(int_to_short(trans_part.imag, 32), int_to_short(trans_part.real, 32)))\r\n f.write(\"\\n\")","title":""},{"docid":"3d1d579f393da01bae1c4ad8282cdd3e","score":"0.5053391","text":"def _otherreference(self):\n\n print('Loading otherreference.tsv')\n ipath = os.path.join(self._ipath, 'otherreference.tsv.bz2')\n opath = os.path.join(self._ipath, 'otherreference.pkl.bz2')\n if os.path.exists(opath):\n return pd.read_pickle(opath)\n otherreference = pd.read_csv(ipath, sep='\\t', quoting=3, dtype=str,\n lineterminator='\\n').drop(\n columns=['uuid', 'sequence'])\n otherreference.dropna(axis='index', how='any', inplace=True)\n otherreference = otherreference.groupby('patent_id').aggregate(\n lambda x: len(list(x)))\n otherreference.rename(columns={'text': 'otherreference'}, inplace=True)\n otherreference.index.rename('pid', inplace=True)\n otherreference.to_pickle(opath)\n return otherreference","title":""},{"docid":"28ed0f1a9ad5fa47fc21f3e0c4e95ded","score":"0.5052405","text":"def read_datafile(self):\n if self.datafile is None:\n\n default_files = ['ai1dat.dat','ai1mod.dat','ai1fit.dat',\n 'inmodel.dat','inregulm.dat']\n dlst = [i for i in os.listdir(self.working_directory) if \\\n (i[-4:] == '.dat') and (i not in default_files)]\n if len(dlst) == 1:\n self.datafile = dlst[0]\n else:\n print(\"please define datafile\")\n return\n\n # define path to file\n datafpath = os.path.join(self.working_directory,self.datafile)\n self.mode = open(datafpath).readline().strip().split()[0]\n data = np.loadtxt(datafpath,skiprows = 2)\n self.freq = 1./data[:,0]\n \n if self.mode == 'I': \n zr = np.vstack([data[:,i] for i in range(len(data[0])) if (i-1)%4 == 0])\n ze = np.vstack([data[:,i] for i in range(len(data[0])) if (i-2)%4 == 0])\n zi = -np.vstack([data[:,i] for i in range(len(data[0])) if (i-3)%4 == 0])\n z = zr + 1j*zi\n self.z = z.T.reshape(len(z[0]),2,2)\n self.zerr = ze.T.reshape(len(z[0]),2,2)\n\n \n # make a frequency array that has the same shape as z\n freq2 = np.zeros(np.shape(self.z))\n for i in range(len(freq2)):\n freq2[i,:,:] = 1./data[:,0][i] \n \n# calculate resistivity\n self.resistivity = 0.2*np.abs(self.z)**2/freq2\n \n q = np.zeros(np.shape(self.resistivity))\n# q[(zr<0)&(zi<0)] = np.pi\n# q[(zr<0)&(zi>0)] = -np.pi\n phase = np.zeros([len(self.z),2,2])\n res = np.zeros([len(self.z),2,2])\n self.resistivity_err = np.zeros([len(self.z),2,2])\n self.phase_err = np.zeros([len(self.z),2,2]) \n \n \n self.q = q\n for iz in range(len(self.z)):\n for i in range(2):\n for j in range(2):\n phase[iz,i,j] = np.rad2deg(cmath.phase(self.z[iz,i,j]))\n res[iz,i,j]= 0.2*np.abs(self.z[iz,i,j])**2/self.freq[iz]\n r_err, phi_err = MTcc.zerror2r_phi_error(\n np.real(self.z[iz,i,j]), \n self.zerr[iz,i,j], \n np.imag(self.z[iz,i,j]), \n self.zerr[iz,i,j])\n\n \n\n self.resistivity_err[iz,i,j] = \\\n 0.4*np.abs(self.z[iz,i,j])/\\\n self.freq[iz]*r_err\n self.phase_err[iz,i,j] = phi_err\n\n phase[phase<-180] += 360\n self.phase = phase\n self.resistivity = res\n \n elif self.mode == 'R':\n res = np.vstack([data[:,i] for i in range(len(data[0])) if (i-1)%4 == 0])\n self.resistivity = res.T.reshape(len(res[0]),2,2)\n res_err = np.vstack([data[:,i] for i in range(len(data[0])) if (i-2)%4 == 0])\n self.resistivity_err = res_err.T.reshape(len(res_err[0]),2,2)\n \n phs = np.vstack([data[:,i] for i in range(len(data[0])) if (i-3)%4 == 0])\n self.phase = phs.T.reshape(len(phs[0]),2,2)\n phs_err = np.vstack([data[:,i] for i in range(len(data[0])) if (i-4)%4 == 0])\n self.phase_err = phs_err.T.reshape(len(phs_err[0]),2,2)","title":""},{"docid":"35877cecfc58cbbc07d9dc94f46bc419","score":"0.5046248","text":"def run_reference() :\n db = get_db(0)\n db.put_int('adjoint', 0)\n db.put_int(\"store_angular_flux\", 1)\n \n mat = get_material()\n\n #--------------------------------------------------------------------------#\n # MODELS\n #--------------------------------------------------------------------------#\n phi = []\n numcells = []\n keff = []\n pin_power = []\n angularmoment = []\n nModels = len(cases)-1\n names = cases[nDLPcases:nModels]\n print names\n number = 0\n if nGroups == 238 :\n thermal_cutoff_group = 203\n elif nGroups == 44 :\n thermal_cutoff_group = 27\n for n in names :\n if n == '1-pin' :\n db.put_str(\"outer_solver\", \"GS\")\n db.put_str(\"bc_west\", \"periodic\")\n db.put_str(\"bc_east\", \"periodic\")\n else :\n db.put_str(\"outer_solver\", \"GMRES\")\n db.put_str(\"bc_west\", \"reflect\")\n db.put_str(\"bc_east\", \"reflect\") \n print n\n x = []\n mesh = get_mesh(number) \n number += 1 \n solver = Eigen1D(db, mat, mesh)\n solver.solve()\n if n == '10-pin' :\n keff = solver.state().eigenvalue()\n rates = ReactionRates(mat, mesh, solver.state())\n pin_power = rates.region_power(\"PINS\") \n ng = solver.material().number_groups()\n nc = solver.mesh().number_cells()\n no = solver.quadrature().number_octants()/2\n na = solver.quadrature().number_angles_octant()\n phig = np.zeros((ng,nc))\n x.append(mesh.dx(0) *0.5)\n for i in range(1,nc) :\n x.append(x[i-1]+mesh.dx(i-1))\n numcells.append(x)\n for g in range(0, ng) :\n for i in range(0, nc) :\n phig[g][i] = solver.state().phi(g)[i]\n np.savetxt(dir_name+'/refdata/'+n+'_mg_flux', phig)\n phi.append(phig)\n\n nOrder = 4\n angmom = np.zeros((nOrder,ng,nc))\n P = OrthogonalBasisParameters()\n P.order = nOrder\n P.size = na\n P.x.resize(P.size, 0.0)\n P.qw.resize(P.size, 0.0)\n P.lower_bound = 0.0\n P.upper_bound = 1.0\n P.orthonormal = False\n x = np.linspace(P.lower_bound, P.upper_bound, P.size)\n for g in range(ng) :\n for i in range(nc) :\n for o in range(no) :\n psi = np.zeros((na))\n for a in range(na) :\n psi[a] = solver.state().psi(g,o,a)[i]\n for a in range(na) :\n P.x[a] = solver.quadrature().mu(o,a)\n P.qw[a] = solver.quadrature().weight(a)\n ang = OrthogonalBasis.Create(\"jacobi\", P)\n angular = np.zeros(nOrder+1)\n y = vec_dbl(psi.tolist())\n z = vec_dbl(angular.tolist())\n ang.transform(y, z)\n for order in range(nOrder) : \n angmom[order][g][i] += z[order]\n for order in range(nOrder) :\n np.savetxt(dir_name+'/refdata/'+n+'_mg_moment'+str(order), angmom[order])\n angularmoment.append(angmom)\n \n #--------------------------------------------------------------------------#\n # PLOTS AND DATA\n #--------------------------------------------------------------------------#\n\n io = IO_HDF5('cross_section_libraries/'+str(test_problem)+'_'+str(nGroups)+'_library.h5')\n db = io.read_input()\n EG = db.get_vec_dbl('neutron_energy_bounds')\n energyrange = np.zeros((len(EG)-1))\n deltaE = np.zeros((len(EG)-1))\n for i in range(len(energyrange)):\n energyrange[i] = (EG[i]+EG[i+1])/2\n deltaE[i] = abs(EG[i+1] - EG[i])\n \n Eave = []\n Save = []\n for order in range(nOrder) :\n E = []\n S = []\n for c in range(len(names)) :\n E.append(np.mean(angularmoment[c][order], axis=0))\n S.append(np.mean(angularmoment[c][order], axis=1))\n Eave.append(E)\n Save.append(S)\n\n A = []\n # Data for Phi plots \n for a in range(len(phi)): \n y, A0 = common.barchart(EG, np.mean(phi[a], axis=1))\n A.append(A0)\n C = []\n for order in range(nOrder) :\n CC = []\n for core in range(len(names)) :\n y, B00 = common.barchart(EG, Save[order][core])\n CC.append(B00)\n C.append(CC)\n\n groups = range(0, nGroups)\n fontP = FontProperties()\n fontP.set_size('medium')\n plt.figure(0, figsize=(8, 6.5))\n plt.semilogy(groups, np.mean(phi[0], axis=1), 'k-', \\\n groups, np.mean(phi[1], axis=1), 'b-', \\\n groups, np.mean(phi[2], axis=1), 'g-', \\\n groups, np.mean(phi[3], axis=1), 'r-' )\n plt.xlabel('$g$')\n plt.ylabel('$\\phi_g$')\n leg = ['10-pin', 'UO$_2$', 'MOX', '1-pin']\n plt.legend(leg, loc = 0, prop=fontP)\n plt.grid(True)\n plt.tight_layout()\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'group_spectra.pdf')\n\n # Spectra Plot\n leg = ['10-pin', 'UO$_2$', 'MOX']\n plt.figure(1, figsize=(8, 6.5))\n plt.loglog(y, A[0], 'k-', \\\n y, A[1], 'r-', \\\n y, A[2], 'g-' )\n plt.xlabel('energy in $eV$')\n plt.ylabel('$\\phi_g$')\n plt.legend(leg, loc = 0, prop=fontP)\n plt.grid(True) \n plt.tight_layout()\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'group_spectra_energy.pdf') \n \n # Spatially Averaged Moment Plots\n # Moment 0 Plot\n plt.figure(2, figsize=(8, 6.5))\n plt.loglog(y, C[0][0], 'k-', \\\n y, C[0][1], 'r-', \\\n y, C[0][2], 'g-' )\n plt.xlabel('energy in $eV$')\n plt.ylabel('${moment0}_g$')\n plt.legend(leg, loc = 0, prop=fontP)\n plt.grid(True) \n plt.tight_layout()\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'monent0_spectra_energy.pdf')\n \n # Moment 1 Plot\n plt.figure(3, figsize=(8, 6.5))\n plt.loglog(y, C[1][0], 'k-', \\\n y, C[1][1], 'r-', \\\n y, C[1][2], 'g-' )\n plt.xlabel('energy in $eV$')\n plt.ylabel('${moment1}_g$')\n plt.legend(leg, loc = 0, prop=fontP)\n plt.grid(True)\n plt.tight_layout()\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'monent1_spectra_energy.pdf')\n \n # Moment 2 Plot\n plt.figure(4, figsize=(8, 6.5))\n plt.loglog(y, C[2][0], 'k-', \\\n y, C[2][1], 'r-', \\\n y, C[2][2], 'g-' )\n plt.xlabel('energy in $eV$')\n plt.ylabel('${moment2}_g$')\n plt.legend(leg, loc = 0, prop=fontP)\n plt.grid(True)\n plt.tight_layout()\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'monent2_spectra_energy.pdf')\n \n # Energy Averaged Plots\n x = numcells[0] \n \n leg = ['Fast Phi', 'Thermal Phi']\n plt.figure(5, figsize=(8, 6.5))\n plt.plot(x, np.mean(phi[0][:thermal_cutoff_group][0:len(x)],axis=0), 'k-', \\\n x, np.mean(phi[0][thermal_cutoff_group:][0:len(x)],axis=0), 'g-' )\n plt.xlabel('position in cm')\n plt.ylabel('$\\phi$')\n plt.legend(leg, loc = 0, prop=fontP)\n plt.grid(True)\n plt.tight_layout()\n plt.savefig(dir_name+'/reference_figures/spectra_full_energyaveraged.pdf')\n \n # Moment 0 Plot\n \n leg = ['$0^{th}$ moment', '$1^{st}$ moment', '$2^{nd}$ moment']\n plt.figure(6, figsize=(8, 6.5))\n plt.plot(x, Eave[0][0], 'k-', \\\n x, Eave[1][0], 'r-', \\\n x, Eave[2][0], 'g-' )\n plt.xlabel('position in cm')\n plt.ylabel('${moment0}_g$')\n plt.legend(leg, loc = 0, prop=fontP)\n plt.grid(True)\n plt.tight_layout()\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'monent_spectra_full.pdf')\n \n # Moment 1 Plot\n x = numcells[1]\n n = len(x)\n plt.figure(7, figsize=(8, 6.5))\n plt.plot(x, Eave[0][1], 'k-', \\\n x, Eave[1][1], 'r-', \\\n x, Eave[2][1], 'g-' )\n plt.xlabel('position in cm')\n plt.ylabel('${moment1}_g$')\n plt.legend(leg, loc = 0, prop=fontP)\n plt.grid(True)\n plt.tight_layout()\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'monent_spectra_uo2.pdf')\n \n # Moment 2 Plot\n x = numcells[2]\n n = len(x)\n plt.figure(8, figsize=(8, 6.5))\n plt.plot(x, Eave[0][2], 'k-', \\\n x, Eave[1][2], 'r-', \\\n x, Eave[2][2], 'g-' )\n plt.xlabel('position in cm')\n plt.ylabel('${moment2}_g$')\n plt.legend(leg, loc = 0, prop=fontP)\n plt.grid(True)\n plt.tight_layout()\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'monent_spectra_mox.pdf') \n \n data = {}\n data['pin_power'] = pin_power\n data['phi_full'] = np.mean(phi[0],axis=1)\n data['phi_uo2'] = np.mean(phi[1],axis=1)\n data['phi_mox'] = np.mean(phi[2],axis=1)\n data['keff'] = keff\n pickle.dump(data, open(dir_name+'/refdata/reference_data.p', 'wb'))","title":""},{"docid":"1275eed1d2c98c29c58b5664e015cfc3","score":"0.5036114","text":"def previous_load_data(path=\"./data/cora/\", dataset=\"cora\" ,n_train = 140):\n print('Loading {} dataset...'.format(dataset))\n\n idx_features_labels = np.genfromtxt(\"{}{}.content\".format(path, dataset), dtype=np.dtype(str))\n features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)\n labels = encode_onehot(idx_features_labels[:, -1])\n\n # build graph\n idx = np.array(idx_features_labels[:, 0], dtype=np.int32)\n idx_map = {j: i for i, j in enumerate(idx)}\n edges_unordered = np.genfromtxt(\"{}{}.cites\".format(path, dataset), dtype=np.int32)\n edges = np.array(list(map(idx_map.get, edges_unordered.flatten())), dtype=np.int32).reshape(edges_unordered.shape)\n adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(labels.shape[0], labels.shape[0]), dtype=np.float32)\n\n # build symmetric adjacency matrix\n adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)\n # print(adj)\n # print(features)\n\n features = normalize_features(features)\n adj = normalize_adj(adj + sp.eye(adj.shape[0]))\n\n #idx_train = range(140)\n idx_train = range(n_train)\n idx_val = range(200, 500)\n idx_test = range(500, 1500)\n\n adj = torch.FloatTensor(np.array(adj.todense()))\n features = torch.FloatTensor(np.array(features.todense()))\n labels = torch.LongTensor(np.where(labels)[1])\n\n idx_train = torch.LongTensor(idx_train)\n idx_val = torch.LongTensor(idx_val)\n idx_test = torch.LongTensor(idx_test)\n\n return adj, features, labels, idx_train, idx_val, idx_test","title":""},{"docid":"3ac936c39581019a922df685d063ee38","score":"0.5005946","text":"def within_reference(bampath, outpath,n, param):\n\n\tmin_isize, max_isize = param.lib_min, param.lib_max\n\n\tcorrelated_check = CorrelatedSample(n)\n\tbamfile = pysam.Samfile(bampath, 'rb')\n\toutfile = pysam.Samfile(outpath, 'wb', template=bamfile)\n\n\ti = 0\n\treads_rev = 0\n\treads_fwd = 0\n\tread_read1 = 0\n\tread_read2 = 0\n\tprinted_fwd = set()\n\n\tfor read, mate_pos in proper_read_isize(bamfile, min_isize, max_isize, param):\n\t\tread_pos = read.pos\n\n\t\t# remove unactive samples (read1 is more than n bp away)\n\t\tcorrelated_check.remove_inactive(read_pos)\n\n\t\tif not read.is_reverse:\n\t\t\tif correlated_check.is_correlated(mate_pos):\n\t\t\t\t#print 'correlated'\n\t\t\t\tcontinue\n\t\t\t\t# read is forward\n\t\t\telse:\n\t\t\t\t# add to sample neighborhood\n\t\t\t\tcorrelated_check.add_sample(read_pos, mate_pos)\n\t\t\t\t# print to filtered bamfile\n\t\t\t\toutfile.write(read)\n\t\t\t\tprinted_fwd.add(read.qname)\n\t\t\t\treads_fwd += 1\n\t\t\t\tif read.is_read1:\n\t\t\t\t\tread_read1 += 1\n\t\t\t\telif not read.is_read1:\n\t\t\t\t\tread_read2 += 1\n\n\t\t# read is reverse, both reads parsed\n\t\telse:\n\t\t\tif read.qname in printed_fwd:\n\t\t\t\toutfile.write(read)\n\t\t\t\tprinted_fwd.remove(read.qname)\n\t\t\t\treads_rev += 1\n\n\t\t\t\tif read.is_read1:\n\t\t\t\t\tread_read1 += 1\n\t\t\t\telif not read.is_read1:\n\t\t\t\t\tread_read2 += 1\n\t\t\t#print 'not!'\n\t\ti += 1\n\t\tif i % 10000 == 1:\n\t\t\tprint 'processing coordinate', read.pos, 'on ref:', read.tid\n\t\t\tprint reads_fwd,reads_rev,read_read1,read_read2\n\tprint reads_fwd,reads_rev,read_read1,read_read2\n\tpysam.index(outpath)\n\toutfile.close()","title":""},{"docid":"9411e2a1cc4d5bca1be6b8ab8011a7b5","score":"0.4994594","text":"def example_gen_fixe_ref_patch(vol_names, vol_size, fid=None):\n ref_name = 'FTD096K7'\n ref_patch = 0\n dir_path = os.path.dirname(vol_names[0])\n part_names = [os.path.basename(file).split('_')[0] for file in vol_names]\n patch_indices = [(os.path.basename(file).split('_')[1]).split('.')[0] for file in vol_names]\n part_names = list(set(part_names) -set([ref_name]))\n patch_indices = list(set(patch_indices)-set([ref_patch]))\n print('part_name is ', part_names)\n while(True):\n name_idx = np.random.permutation(len(part_names))[0]\n patch_idx = np.random.permutation(len(patch_indices))[0]\n file1 = os.path.join(dir_path, part_names[name_idx] + '_' + str(ref_patch) + '.nii.gz')\n file2 = os.path.join(dir_path, ref_name + '_' + str(ref_patch) + '.nii.gz')\n print(file1)\n print(file2)\n if(fid is not None):\n fid.write(file1 + '\\n')\n fid.write(file2 + '\\n')\n fid.flush()\n X1 = nib.load(file1).get_data()\n X2 = nib.load(file2).get_data()\n X1, X2 = augment(X1, X2, vol_size)\n X1 = X1/(pow(2,16)-1.0)\n X2 = X2/(pow(2,16)-1.0)\n X1 = X1[np.newaxis, :,:,:, np.newaxis]\n X2 = X2[np.newaxis, :,:,:, np.newaxis]\n return_vals = (X1, X2, part_names[name_idx], ref_name)\n yield tuple(return_vals)","title":""},{"docid":"6cce94b63d87ad7d17954326f77b8d2b","score":"0.49934655","text":"def transform_target_data(audiofile,analyze=0):\n target_file = DATA_PATH + audiofile[:-4] + \"REF.txt\"\n data = read_array(target_file,',')\n \n if analyze:\n pylab.subplot(211)\n pylab.plot(data[:,1])\n pylab.subplot(212)\n pylab.plot(data[:,2])\n \n pylab.figure()\n pylab.subplot(211)\n pylab.hist(data[:,1],bins=25)\n pylab.xlim(40,65)\n pylab.subplot(212)\n pylab.hist(data[:,2],bins=13)\n pylab.xlim(0,12)\n \n # get the unique element (nr. of different notes) of the piece\n midi_data = data[:,1]\n unique = list(set(midi_data))\n target = np.zeros((len(midi_data), len(unique)))\n target_chromas = np.zeros((len(midi_data), 13))\n for n in range( len(midi_data) ):\n ind = unique.index( midi_data[n] )\n target[n,ind] = 1\n target_chromas[n,data[n,2]+1] = 1\n \n if analyze:\n print \"pitch classes:\",len(unique)\n pylab.show()\n exit(0)\n \n savefile = OUTPUT_DIR + audiofile[:-4] + \"_STFT.dat\"\n data = shelve.open(savefile)\n lenge = data[\"features\"].shape[0]\n data[\"targets\"] = target[:lenge]\n data[\"target_midi\"] = midi_data[:lenge]\n data[\"target_chromas\"] = target_chromas[:lenge]\n data[\"pitch_classes\"] = unique\n data.close()","title":""},{"docid":"4da9c11c649e92c83f1c50dbdfeff2ae","score":"0.4990033","text":"def testUpdateDataFile(self):\n self.lfh.write(\"\\nStarting %s %s\\n\" % (self.__class__.__name__,\n sys._getframe().f_code.co_name))\n try:\n # Create a initial data file --\n #\n myDataList=[]\n ofh = open(\"test-output-1.cif\", \"w\")\n curContainer=DataContainer(\"myblock\")\n aCat=DataCategory(\"pdbx_seqtool_mapping_ref\")\n aCat.appendAttribute(\"ordinal\")\n aCat.appendAttribute(\"entity_id\")\n aCat.appendAttribute(\"auth_mon_id\")\n aCat.appendAttribute(\"auth_mon_num\")\n aCat.appendAttribute(\"pdb_chain_id\")\n aCat.appendAttribute(\"ref_mon_id\")\n aCat.appendAttribute(\"ref_mon_num\") \n aCat.append((1,2,3,4,5,6,7))\n aCat.append((1,2,3,4,5,6,7))\n aCat.append((1,2,3,4,5,6,7))\n aCat.append((1,2,3,4,5,6,7))\n curContainer.append(aCat)\n myDataList.append(curContainer)\n pdbxW=PdbxWriter(ofh)\n pdbxW.write(myDataList)\n ofh.close()\n #\n # Read and update the data -\n # \n myDataList=[]\n ifh = open(\"test-output-1.cif\", \"r\")\n pRd=PdbxReader(ifh)\n pRd.read(myDataList)\n ifh.close()\n #\n myBlock=myDataList[0]\n myBlock.printIt()\n myCat=myBlock.getObj('pdbx_seqtool_mapping_ref')\n myCat.printIt()\n for iRow in xrange(0,myCat.getRowCount()):\n myCat.setValue('some value', 'ref_mon_id',iRow)\n myCat.setValue(100, 'ref_mon_num',iRow)\n ofh = open(\"test-output-2.cif\", \"w\") \n pdbxW=PdbxWriter(ofh)\n pdbxW.write(myDataList)\n ofh.close() \n \n except:\n traceback.print_exc(file=sys.stderr)\n self.fail()","title":""},{"docid":"43f0ff94674c0493e95c1e1d5bd8e539","score":"0.49866006","text":"def load_sequential_aras_last(isPerson):\n #by Adele\n #print (\"module_path: \" + module_path )\n #/by Adele\n \n with open(\"E:\\Lessons_tutorials\\Behavioural user profile articles\\Datasets\\Aras\\House A\\CSV_Summery\\Sequential\\Day\\last\\Whole_data.csv\") as csv_file:\n \n data_file = csv.reader(csv_file)\n #target_names = np.array(temp[2:]) # the name of labels \n n_samples = 2068#sum(1 for row in data_file) \n n_features = 41\n data = np.empty((n_samples, n_features))\n target = np.empty((n_samples,), dtype=np.int)\n temp_data = np.empty(n_features+1)# copy whole data to it, then select all col except 20\n \n idx_IN_columns = [] \n for i, ir in enumerate(data_file):\n #print(i)\n temp_data = np.asarray(ir[:42], dtype=np.int)\n #print(temp_data)\n if isPerson == True:\n n_features = 40\n data = np.empty((n_samples, n_features))\n \n # in version 2 the activity col is removed too because it is not important, sensor data are required.\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]#all of features except feature 40 (Person_ID\n target[i] = np.asarray(ir[40], dtype=np.int)# Person ID is the class\n else:\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]#all of features except feature 41 (Activity_ID\n target[i] = np.asarray(ir[41], dtype=np.int)# Activity ID is the class\n \n \n extractedData = temp_data[idx_IN_columns]\n #print(extractedData)\n data[i] = np.asarray(extractedData , dtype=np.int)\n #print(target[i])\n \n \n #print(data)\n #print(target)\n return Bunch(data=data, target=target)\n # target_names=target_names)\n #feature_names=['sepal length (cm)', 'sepal width (cm)',\n # 'petal length (cm)', 'petal width (cm)'])","title":""},{"docid":"761dc5c8f94d8c330bea3536353931b3","score":"0.49820733","text":"def flirt2aff_files(matfile, in_fname, ref_fname):\n mat = np.loadtxt(matfile)\n in_img = nib.load(in_fname)\n ref_img = nib.load(ref_fname)\n return flirt2aff(mat, in_img, ref_img)","title":""},{"docid":"e5f27708ef324c06df58754c1e355448","score":"0.49668267","text":"def create_reference(dataset: tl.TimeSeriesData, timestep, batch_size):\n # creates reference for a certain timestep\n history_horizon = dataset.history_horizon\n forecast_horizon = dataset.forecast_horizon\n num_encoder_features = len(dataset.encoder_features)\n num_decoder_features = len(dataset.decoder_features)\n seed(1) # seed random number generator\n\n features1_references_np = np.zeros(shape=(batch_size, history_horizon, num_encoder_features))\n features2_references_np = np.zeros(shape=(batch_size, forecast_horizon, num_decoder_features))\n\n inputs1_np = dataset[timestep][0].cpu().numpy()\n inputs2_np = dataset[timestep][1].cpu().numpy()\n\n for x in range(num_encoder_features): # iterate through encoder features\n feature_x = inputs1_np[:, x]\n mu = 0\n sigma = abs(np.std(feature_x)) # 0.3 is chosen arbitrarily # hier np.std nehmen\n for j in range(batch_size):\n noise_feature1 = np.random.default_rng().normal(mu, sigma, history_horizon) # create white noise series\n features1_references_np[j, :, x] = noise_feature1 + feature_x\n\n for x in range(num_decoder_features): # iterate through decoder features\n feature_x = inputs2_np[:, x]\n mu = 0\n sigma = abs(np.std(feature_x)) # 0.3 is chosen arbitrarily\n for j in range(batch_size):\n noise_feature2 = np.random.default_rng().normal(mu, sigma, forecast_horizon)\n features2_references_np[j, :, x] = noise_feature2 + feature_x\n\n return torch.Tensor(features1_references_np).to(DEVICE), torch.Tensor(features2_references_np).to(DEVICE)","title":""},{"docid":"8e444fc4c930b1f0719f8de41744aa07","score":"0.49487188","text":"def load_FRG(fpath):\n\n def readFRGFile(fpath):\n data = {}\n\n with open(fpath, \"r\") as f:\n ReadingHeader = True # check when header has been fully parsed\n BothDirections = False # sweep forward + reverse\n\n while ReadingHeader:\n line = f.readline()\n lineparts = line.split(\":\")\n\n if \"Data\" in lineparts[0]:\n ReadingHeader = False\n f.readline()\n f.readline()\n else:\n try:\n data[lineparts[0]] = float(lineparts[1])\n except:\n data[lineparts[0]] = lineparts[1][1:].replace(\"\\n\", \"\")\n\n vforward = []\n iforward = []\n timeforward = []\n if data[\"sweepDir\"] == \"Forward + Reverse\":\n BothDirections = True\n vreverse = []\n ireverse = []\n timereverse = []\n\n for line in f:\n lineparts = f.readline().split(\"\\t\")\n if len(lineparts) == 1:\n break\n vforward.append(lineparts[0])\n iforward.append(lineparts[1])\n timeforward.append(lineparts[2])\n if BothDirections:\n vreverse.append(lineparts[0])\n ireverse.append(lineparts[1])\n timereverse.append(lineparts[2])\n\n data[\"V\"] = np.array(vforward).astype(float)\n data[\"I\"] = np.array(iforward).astype(float)\n data[\"J\"] = data[\"I\"] / data[\"area_cm2\"]\n data[\"delay\"] = np.array(timeforward).astype(float)\n\n if BothDirections:\n data2 = data.copy()\n data2[\"sampleName\"] = data[\"sampleName\"] + \"_Reverse\"\n data[\"sampleName\"] = data[\"sampleName\"] + \"_Forward\"\n data2[\"V\"] = np.array(vreverse).astype(float)\n data2[\"I\"] = np.array(ireverse).astype(float)\n data2[\"J\"] = data2[\"I\"] / data2[\"area_cm2\"]\n data2[\"delay\"] = np.array(timereverse).astype(float)\n output = [data, data2]\n else:\n output = data\n\n return output\n\n fids = [os.path.join(fpath, x) for x in os.listdir(fpath)]\n\n alldata = {}\n for f in fids:\n output = readFRGFile(f)\n if type(output) == list:\n for each in output:\n alldata[each[\"sampleName\"]] = each\n else:\n alldata[output[\"sampleName\"]] = output\n return alldata","title":""},{"docid":"60482db3aa202b94337a3464dd780a34","score":"0.49388656","text":"def test_reference_data():\n inputs = [\n [3735928559, 195939070, 229505742, 305419896],\n [3668361503, 4165561550, 1661411377, 3634257570],\n [164546577, 4166754639, 1765190214, 1303880213],\n [446610472, 3941463886, 522937693, 1882353782],\n [1864922766, 1719732118, 3882010307, 1776744564],\n [4141682960, 3310988675, 553637289, 902896340],\n [1134851934, 2352871630, 3699409824, 2648159817],\n [1240956131, 3107113773, 1283198141, 1924506131],\n [2669565031, 579818610, 3042504477, 2774880435],\n [2766103236, 2883057919, 4029656435, 862374500],\n ]\n outputs = [\n [3914649087, 576849849, 3593928901, 2229911004],\n [2240804226, 3691353228, 1365957195, 2654016646],\n [3562296087, 3191708229, 1147942216, 3726991905],\n [1403443605, 3591372999, 1291086759, 441919183],\n [1086200464, 2191331643, 560336446, 3658716651],\n [3249937430, 2346751812, 847844327, 2996632307],\n [2584285912, 4034195531, 3523502488, 169742686],\n [959045797, 3875435559, 1886309314, 359682705],\n [3978441347, 432478529, 3223635119, 138903045],\n [296367413, 4262059219, 13109864, 3283683422],\n ]\n outputs64 = [\n [2477551240072187391, 9577394838764454085],\n [15854241394484835714, 11398914698975566411],\n [13708282465491374871, 16007308345579681096],\n [15424829579845884309, 1898028439751125927],\n [9411697742461147792, 15714068361935982142],\n [10079222287618677782, 12870437757549876199],\n [17326737873898640088, 729039288628699544],\n [16644868984619524261, 1544825456798124994],\n [1857481142255628931, 596584038813451439],\n [18305404959516669237, 14103312907920476776],\n ]\n for seed, expected, expected64 in zip(inputs, outputs, outputs64):\n expected = np.array(expected, dtype=np.uint32)\n ss = SeedSequence(seed)\n state = ss.generate_state(len(expected))\n assert_array_equal(state, expected)\n state64 = ss.generate_state(len(expected64), dtype=np.uint64)\n assert_array_equal(state64, expected64)","title":""},{"docid":"7ed377464398ed9f38ced6709e53852f","score":"0.49329084","text":"def loadReference(self, data, ref, otherref='', percentile=50):\n if ref == 'Mean':\n return data.raw.mean(0)\n elif ref == 'Percentile':\n return np.percentile(data.raw, percentile, axis=0)\n elif ref == 'Other':\n if otherref == '':\n raise RuntimeError('Specify a reference spectrum file')\n return correction.load_reference(data.wavenumber,\n matfilename=otherref)\n else:\n return correction.load_reference(data.wavenumber, what=ref.lower())","title":""},{"docid":"2dbc92c4c161a4af001bf3fb02ba2a8a","score":"0.49298394","text":"def call_methylated_sites_pe(inputf, sample, reference_fasta,\n unmethylated_control = None,\n sig_cutoff=.01,num_procs = 1,\n num_upstr_bases=0,num_downstr_bases=2,\n generate_mpileup_file=True,\n compress_output=True,\n bgzip=False,\n path_to_bgzip=\"\",\n path_to_tabix=\"\",\n buffer_line_number=100000,\n min_mapq=30,\n min_cov=1,binom_test=True,\n path_to_samtools=\"\",\n remove_chr_prefix=True,\n add_snp_info=False,\n sort_mem=\"500M\",\n path_to_files=\"\",min_base_quality=1,\n keep_temp_files=False):\n\n #Flip the strand of read 2 and create a new bam file\n ##input: sample+\"_processed_reads_no_clonal.bam\"\n ##output: sample+\"_processed_reads_no_clonal_flipped.bam\"\n\n print_checkpoint(\"Begin flipping the strand of read 2\")\n flip_read2_strand(input_file = inputf,\n output_file = inputf+\".read2flipped.bam\",\n path_to_samtools=path_to_samtools)\n\n \n #Call methylated sites\n call_methylated_sites(inputf = inputf+\".read2flipped.bam\",\n sample = sample,\n reference_fasta = reference_fasta,\n unmethylated_control = unmethylated_control,\n sig_cutoff = sig_cutoff,\n num_procs = num_procs,\n num_upstr_bases=num_upstr_bases,\n num_downstr_bases=num_downstr_bases,\n generate_mpileup_file=generate_mpileup_file,\n compress_output=compress_output,\n bgzip=bgzip,\n path_to_bgzip=path_to_bgzip,\n path_to_tabix=path_to_tabix,\n buffer_line_number = buffer_line_number,\n min_mapq=min_mapq,\n min_cov = min_cov,\n binom_test = binom_test,\n path_to_samtools = path_to_samtools,\n sort_mem=sort_mem,\n path_to_files = path_to_files,\n min_base_quality = min_base_quality,\n remove_chr_prefix = remove_chr_prefix,\n add_snp_info = add_snp_info,\n keep_temp_files = keep_temp_files)\n\n #Remove intermediate bam file\n try:\n subprocess.check_call(shlex.split(\"rm -f \"+inputf+\".read2flipped.bam\"+\n \" \"+inputf+\".read2flipped.bam.bai\"))\n except:\n pass","title":""},{"docid":"479c09ea885cbee278474efa3a423fcf","score":"0.4925192","text":"def createReference(self, data, params, outfile):\n params.scDo = False\n params.srDo = False\n wns = []\n ys = []\n try:\n for fi in range(len(data.filenames)):\n self.batchProgress.emit(fi, len(data.filenames))\n if not self.loadFile(data, fi):\n continue\n\n wn = data.wavenumber\n y = data.raw\n\n if params.scRef == 'Percentile':\n y = np.percentile(y, params.scRefPercentile, axis=0)[None, :]\n else:\n y = y.mean(0)[None, :]\n\n y = self.callACandSC(data, params, wn, y)\n wn, y = self.callSGFandSRandBC(params, wn, y)\n if params.normDo:\n y = normalization.normalize_spectra(\n params.normMethod, y, wn,\n wavenum=params.normWavenum)\n wns.append(wn)\n ys.append(y[0])\n\n # Do all images have the same wavenumbers?\n if all(np.array_equal(v, wns[0]) for v in wns):\n ys = np.median(ys, axis=0)\n else:\n w1 = min(v.min() for v in wns)\n w2 = max(v.max() for v in wns)\n maxres = max((len(v) - 1) / (v.max() - v.min()) for v in wns)\n wn = np.linspace(w1, w2, num=maxres * (w2 - w1) + 1)\n interpol = interp1d(np.concatenate(wns), np.concatenate(ys))\n ys = interpol(wn)\n\n ab = np.hstack((wn[:, None], ys[:, None]))\n scipy.io.savemat(outfile, {'AB': ab } )\n self.batchDone.emit(True)\n return\n\n except InterruptedError:\n self.stopped.emit()\n except Exception as e:\n traceback.print_exc()\n self.failed.emit(repr(e), traceback.format_exc())\n self.batchDone.emit(False)","title":""},{"docid":"4b411971d81d0cbb79813e36b3eb97f3","score":"0.49233758","text":"def transform_target_data_old(audiofile,analyze=0):\n target_file = DATA_PATH + audiofile[:-4] + \"REF.txt\"\n data = read_array(target_file)\n \n # remove time axes and convert to midi\n data = data[:,1]\n midi_data = audiotools.freq_to_midi(data)\n midi_data = np.round(midi_data)\n \n if analyze:\n pylab.plot(midi_data)\n \n # get the unique element (nr. of different notes) of the piece\n unique = list(set(midi_data))\n target = np.zeros((len(midi_data), len(unique)))\n for n in range( len(midi_data) ):\n ind = unique.index( midi_data[n] )\n target[n,ind] = 1\n \n if analyze:\n print \"classes:\",len(unique)\n pylab.figure()\n pylab.psd(target.flatten())\n pylab.show()\n exit(0)\n \n savefile = OUTPUT_DIR + audiofile[:-4] + \"_STFT.dat\"\n data = shelve.open(savefile)\n lenge = data[\"features\"].shape[0]\n data[\"targets\"] = target[:lenge]\n data[\"target_midi\"] = midi_data[:lenge]\n data[\"pitch_classes\"] = unique\n data.close()","title":""},{"docid":"3b2f592a92f23cc012b64b330d477b0f","score":"0.49093008","text":"def reference_parser(args, genome_chroms):\n global referenceFiles\n\n referenceFiles = os.path.join(args.dir, \"refAnnotation_\" + args.output_prefix + \".genePred\")\n print(\"**** Parsing Reference Transcriptome....\", file=sys.stdout)\n\n # don't check for exisitng reference file, because need to create a new one for exon and cds centric runs\n # if os.path.exists(referenceFiles):\n # print(\"{0} already exists. Using it.\".format(referenceFiles), file=sys.stdout)\n # else:\n ## gtf to genePred\n if not args.genename:\n subprocess.call([GTF2GENEPRED_PROG, args.annotation, referenceFiles, '-genePredExt', '-allErrors', '-ignoreGroupsWithoutExons'])\n else:\n subprocess.call([GTF2GENEPRED_PROG, args.annotation, referenceFiles, '-genePredExt', '-allErrors', '-ignoreGroupsWithoutExons', '-geneNameAsName2'])\n\n ## parse reference annotation\n # 1. ignore all miRNAs (< 200 bp)\n # 2. separately store single exon and multi-exon references\n refs_1exon_by_chr = defaultdict(lambda: IntervalTree()) #\n refs_exons_by_chr = defaultdict(lambda: IntervalTree())\n # store donors as the exon end (1-based) and acceptor as the exon start (0-based)\n # will convert the sets to sorted list later\n junctions_by_chr = defaultdict(lambda: {'donors': set(), 'acceptors': set(), 'da_pairs': set()})\n # dict of gene name --> set of junctions (don't need to record chromosome)\n junctions_by_gene = defaultdict(lambda: set())\n # dict of gene name --> list of known begins and ends (begin always < end, regardless of strand)\n known_5_3_by_gene = defaultdict(lambda: {'begin':set(), 'end': set()})\n\n ## dictionary of record.id (e.g., gencode enst) to genePred record objects\n ## need this for later computation of 5' and 3' overhangs for protein classification\n refDict = {}\n\n for r in genePredReader(referenceFiles):\n refDict[r.id] = r\n if r.length < args.min_ref_len and not args.is_fusion: continue # ignore miRNAs\n if r.exonCount == 1:\n refs_1exon_by_chr[r.chrom].insert(r.txStart, r.txEnd, r)\n known_5_3_by_gene[r.gene]['begin'].add(r.txStart)\n known_5_3_by_gene[r.gene]['end'].add(r.txEnd)\n else:\n refs_exons_by_chr[r.chrom].insert(r.txStart, r.txEnd, r)\n # only store junctions for multi-exon transcripts\n for d, a in r.junctions:\n junctions_by_chr[r.chrom]['donors'].add(d)\n junctions_by_chr[r.chrom]['acceptors'].add(a)\n junctions_by_chr[r.chrom]['da_pairs'].add((d,a))\n junctions_by_gene[r.gene].add((d,a))\n known_5_3_by_gene[r.gene]['begin'].add(r.txStart)\n known_5_3_by_gene[r.gene]['end'].add(r.txEnd)\n\n # check that all genes' chromosomes are in the genome file\n ref_chroms = set(refs_1exon_by_chr.keys()).union(list(refs_exons_by_chr.keys()))\n if genome_chroms is not None:\n diff = ref_chroms.difference(genome_chroms)\n if len(diff) > 0:\n print(\"WARNING: ref annotation contains chromosomes not in genome: {0}\\n\".format(\",\".join(diff)), file=sys.stderr)\n\n # convert the content of junctions_by_chr to sorted list\n for k in junctions_by_chr:\n junctions_by_chr[k]['donors'] = list(junctions_by_chr[k]['donors'])\n junctions_by_chr[k]['donors'].sort()\n junctions_by_chr[k]['acceptors'] = list(junctions_by_chr[k]['acceptors'])\n junctions_by_chr[k]['acceptors'].sort()\n junctions_by_chr[k]['da_pairs'] = list(junctions_by_chr[k]['da_pairs'])\n junctions_by_chr[k]['da_pairs'].sort()\n\n return dict(refs_1exon_by_chr), dict(refs_exons_by_chr), dict(junctions_by_chr), dict(junctions_by_gene), dict(known_5_3_by_gene), refDict","title":""},{"docid":"995dd4b23722e85a8cc71ce8fbe7d0b4","score":"0.49028236","text":"def __init__(self, filename='as_R.UXD', echo=False,\r\n mode='pf', bgmode='manual', monitoring = False,\r\n sep = \"; (Data for Range number\",dfc=None):\r\n if mode == 'pf':\r\n print('')\r\n print('---------------------------')\r\n print('POLE FIGURE AQUISITION MODE')\r\n print('---------------------------\\n')\r\n elif mode =='df':\r\n print('')\r\n print('---------------------------')\r\n print('DEFOCUS DATA AQUSITION MODE')\r\n print('---------------------------\\n')\r\n\r\n blocks = make_blocks(filename=filename, sep = sep)\r\n self.blocks = blocks\r\n blocks = block_in(blocks)\r\n header = blocks[0]\r\n self.data_block = blocks[1:len(blocks)]\r\n\r\n # Trim the data_block neatly.\r\n for i in range(len(self.data_block)):\r\n self.data_block[i]=self.data_block[i][0:len(self.data_block[i])-1]\r\n\r\n print('** Total number of data blocks: ', len(self.data_block))\r\n\r\n self.th2s = []\r\n set_2thet = set()\r\n # print '%5s %5s %5s %5s'%('2th','khi','time','size')\r\n for i in range(len(self.data_block)):\r\n cb = self.data_block[i] #cb : current block\r\n info = self.block_info(block=cb, echo=echo)\r\n\r\n _2theta, _khi, _steptime, _stepsize = list(map(float,info[:4]))\r\n # print '%5.3f %5.1f %5.2i %5.2i'%(_2theta, _khi, _steptime, _stepsize)\r\n set_2thet.add(round(float(info[0]), 3))\r\n\r\n _2th_ = round(float(info[0]),3)\r\n if _2th_ in self.th2s: pass\r\n else: self.th2s.append(_2th_)\r\n\r\n # print '-----------'\r\n # raise IOError, 'debug'\r\n\r\n\r\n\r\n # th2 = set_2thet.copy()\r\n th2 = self.th2s\r\n self.listh2= self.th2s\r\n\r\n #print th2\"\r\n #if raw_input()=='q': raise IOError\"\r\n\r\n # print 'Kinds of _2theta are printed out'\r\n # for i in range(len(th2)):\r\n # print th2[i], '',\r\n # print\r\n\r\n set_2thet=list(set_2thet)\r\n set_2thet=np.array(set_2thet,dtype='float')\r\n set_2thet=np.sort(set_2thet)\r\n for i in range(len(set_2thet)):\r\n print('%2.2i'%i,set_2thet[i])\r\n\r\n # raise IOError, 'Debug: 2set_2thet'\r\n\r\n # while True:\r\n # try:\r\n # print set_2thet.pop(),' ',\r\n # except: break\r\n # print\r\n\r\n\r\n # self.listh2 = []\r\n # while True:\r\n # try: self.listh2.append(th2.pop())\r\n # except: break\r\n\r\n self.pfs = []\r\n for i in range(len(self.listh2)):\r\n self.pfs.append(self.lookfor(th2=self.listh2[i]))\r\n\r\n ###################################\r\n # polefigures and backgrounds #\r\n ###################################\r\n self.polefigures=[]\r\n self.backgrounds=[]\r\n # bg_determine_condition='digits'\r\n bg_determine_condition='short'\r\n\r\n for i in range(len(self.pfs)):\r\n if self.bg_or_pf(self.pfs[i], condition = bg_determine_condition)=='pf':\r\n self.polefigures.append(self.pfs[i])\r\n elif self.bg_or_pf(self.pfs[i], condition = bg_determine_condition)=='bg':\r\n self.backgrounds.append(self.pfs[i])\r\n\r\n print('\\n')\r\n for i in range(len(self.polefigures)):\r\n print('PF #',i+1)\r\n _2th, _st, _sz, d_alpha, d_khi, _khis = self.pf_info(self.polefigures[i])\r\n print('peak at Bragg the 2theta of ', round(_2th,3))\r\n print('delta alpha = ', d_alpha,' delta khi = ', d_khi, end=' ')\r\n print(' step time :', _st, end=' ')\r\n print(' step size :', _sz)\r\n\r\n\r\n print('\\n')\r\n for i in range(len(self.backgrounds)):\r\n print('BG #', i+1)\r\n _2th, _st, _sz, d_alpha, d_khis, _khis = self.pf_info(self.backgrounds[i])\r\n print('peak at Bragg the 2theta of ', round(_2th,3))\r\n if d_alpha !='unknown': print('delta alpha = ', d_alpha, end=' ')\r\n print(' delta khi = ', d_khi, end=' ')\r\n print(' step time :', _st, end=' ')\r\n print(' step size :', _sz)\r\n\r\n input(' Press Enter >>> ')\r\n\r\n self.__pf_selection__()\r\n\r\n input(\"Press enter if you'd like to proceed >> \")\r\n\r\n if os.name=='nt': os.system('cls')\r\n elif os.name=='posix': os.system('clear')\r\n\r\n print(\"\\n\\n***************************************************************\")\r\n print(\"d_alpha is given to backgrounds, that's most probably because\")\r\n print(\"the backgrounds are measured only at along a certain phi angle\")\r\n print(\"In other words, it's been partially measured.\")\r\n print(\"***************************************************************\")\r\n\r\n \"Combines a certain set of pole figure and backgrounds\"\r\n\r\n #----------------------------------------------------------\r\n # Recommends sets of polefigure and its background measure\r\n #----------------------------------------------------------\r\n self.__pf_bg_sets__(bgmode = bgmode)\r\n #access the combination set by self.combi_pf_bg\r\n\r\n \"\"\"\r\n Note that if bgmode is None, background is not subtracted\r\n in the final end. This Ipf = Ipf - dI is not performed in the\r\n next big loop below\r\n \"\"\"\r\n\r\n #----------------------------------------------------------\r\n # Core of this scripts\r\n # Outer-Loop over polefifures\r\n # Inner-Loop over its corresponding two backgrounds\r\n #----------------------------------------------------------\r\n\r\n INTENSITIES =[]\r\n\r\n if bgmode==None:\r\n for i in range(len(self.polefigures)):\r\n INTENSITIES.append([])\r\n for j in range(len(self.polefigures[i])):\r\n INTENSITIES[i].append([])\r\n C_pf = self.polefigures[i][j]\r\n Ipf = th2count(block = C_pf )[0] #intensity\r\n info_temp = self.block_info(C_pf, echo=False)\r\n #normalization by the step time\r\n for k in range(len(Ipf)):\r\n Ipf[k] = Ipf[k]/float(info_temp[2])\r\n INTENSITIES[i][j].append(Ipf[k])\r\n\r\n #monitoring upon polefigure\r\n if monitoring ==True:\r\n if os.name=='posix': os.system('clear')\r\n elif os.name=='nt': os.system('cls')\r\n print(\" You chose not to subtract the backgrounds\")\r\n print(\" Now you have %i polefigure(s) \"%(len(self.polefigures)))\r\n print(\" Do you like to plot pole figures ? \")\r\n if input( 'yes/no') == 'yes':\r\n for i in range(len(self.polefigures)):\r\n __pf_plot__(intensity = polefigures[i], ifig=6+i)\r\n else : pass\r\n else: pass\r\n\r\n elif bgmode!=None:\r\n for i in range(len(self.polefigures)):\r\n INTENSITIES.append([])\r\n \"On each polefigure set\"\r\n iask = True\r\n for j in range(len(self.polefigures[i])):\r\n INTENSITIES[i].append([])\r\n \" On each chi \"\r\n R_bg = self.backgrounds[self.combi_pf_bg[i][0]][j]\r\n L_bg = self.backgrounds[self.combi_pf_bg[i][1]][j]\r\n C_pf = self.polefigures[i][j]\r\n Ipf = th2count(block = C_pf)[0]\r\n Ibgl = th2count(block = R_bg)[0]\r\n Ibgr = th2count(block = L_bg)[0]\r\n info_temp = self.block_info(C_pf, echo=False)\r\n info_temp = list(map(float,info_temp[0:4]))\r\n pf_2th = info_temp[0]\r\n pf_khi = info_temp[1]\r\n pf_steptime = info_temp[2]\r\n pf_stepsize = info_temp[3]\r\n info_temp = self.block_info(L_bg, echo=False)\r\n info_temp = list(map(float,info_temp[0:4]))\r\n L_bg_2th = info_temp[0]\r\n L_bg_khi = info_temp[1]\r\n L_bg_steptime = info_temp[2]\r\n L_bg_stepsize = info_temp[3]\r\n info_temp = self.block_info(R_bg, echo=False)\r\n info_temp = list(map(float,info_temp[0:4]))\r\n R_bg_2th = info_temp[0]\r\n R_bg_khi = info_temp[1]\r\n R_bg_steptime = info_temp[2]\r\n R_bg_stepsize = info_temp[3]\r\n \"Normalize the intensity by its steptime\"\r\n for k in range(len(Ipf)): Ipf[k] =Ipf[k]/float(pf_steptime)\r\n for k in range(len(Ibgl)): Ibgl[k]=Ibgl[k]/float(L_bg_steptime)\r\n for k in range(len(Ibgr)): Ibgr[k]=Ibgr[k]/float(L_bg_steptime)\r\n\r\n if bgmode!=None:\r\n bglr_len = [len(Ibgl),len(Ibgr)]\r\n if any(bglr_len[k] !=len(Ipf) for k in range(2)):\r\n print('** Partial background measured **')\r\n pass\r\n\r\n elif bgmode==None: print('** No Background subtraction **')\r\n\r\n for k in range(len(Ipf)):\r\n \"\"\"\r\n If Ibgl and Ibgr were measured at a certain phi,\r\n at different phi then\r\n that certain phi is assumed to be that of the only measured\r\n \"\"\"\r\n try: cibgl = Ibgl[k]\r\n except IndexError: cibgl = Ibgl[0]\r\n try: cibgr = Ibgr[k]\r\n except IndexError: cibgr = Ibgr[0]\r\n slope = (cibgr - cibgl)/(R_bg_2th-L_bg_2th)\r\n dI = slope * (pf_2th - L_bg_2th) + cibgl\r\n Ipf[k] = Ipf[k] - dI\r\n if Ipf[k] < 0:\r\n if iask==True:\r\n print('Caution) Negative value from prior BG subtraction: ', end=' ')\r\n print('value = ', Ipf[k])\r\n print('Do you want to keep on going(yes)?, or', end=' ')\r\n print(\"Don't ask this to the end(ignore)\")\r\n ans = input('Type answer (yes/ignore) >> ')\r\n if len(ans)==0: pass\r\n elif ans =='yes': pass\r\n elif ans =='ignore': iask = False; pass\r\n elif ans =='no':\r\n print(\"\\n******************************************\")\r\n print(\"There's no 'no' answer here\")\r\n print(\"Negative intensity is physically non-sense\")\r\n print(\"The code raises an error\")\r\n print(\"******************************************\")\r\n input()\r\n raise IOError\r\n else: raise IOError\r\n Ipf[k] = 1\r\n elif iask == False:\r\n print(\"If negative intensity will be returned to be 1.\")\r\n Ipf[k] = 1\r\n INTENSITIES[i][j].append(Ipf[k])\r\n\r\n\r\n self.INTENSITIES = INTENSITIES\r\n if mode =='pf':\r\n ## find defocus files\r\n dff=[]\r\n if type(dfc)==type(None):\r\n dff = glob.glob('*.dfc')\r\n else:\r\n dff = dfc\r\n\r\n if len(dff)==0:\r\n print('You do not have any *.dfc file')\r\n else:\r\n print('*********************************************')\r\n print('%15s\\n'%('Available FILES and its _2THETA'))\r\n print('%3s %15s %6s %6s'%('ID','FILENAME','PEAK_AT','COMMENT'))\r\n for i in range(len(dff)):\r\n ff = open(dff[i],'r')\r\n lines = ff.readlines()\r\n try: float(lines[3].split('=')[1])\r\n except:\r\n print('Could not get %s file rightly.\\n'%(dff[i]))\r\n print('**************************************')\r\n print(\"Contents of the file is shown as below\\n\")\r\n if os.name =='posix': os.system('cat %s'%(dff[i]))\r\n elif os.name =='nt': os.system('type %s'%(dff[i]))\r\n print('\\n Please type enter to proceed'); input()\r\n pass\r\n else:\r\n _2th = float(lines[3].split('=')[1])\r\n comment = lines[2]\r\n ff.close()\r\n print('%3i %15s %5.3f %s'%(i, dff[i], _2th, comment))\r\n\r\n for i in range(len(self.INTENSITIES)):\r\n print(' Type the defocus correction file id (from 0)')\r\n print(' minus value (e.g. -1) will turn down ', end=' ')\r\n print('the defoucssing correction')\r\n id_dfc = input(' >>> ')\r\n id_dfc = int(id_dfc)\r\n if id_dfc<0: pass\r\n else: self.__dfc_correct__(filename=dff[id_dfc], pf_id=i)\r\n\r\n #--------------------------------------\r\n # Normalize the intensity so that max\r\n # intensity in the all pole figures in\r\n # the given file equals to 9999 (modified from 999 to 9999)\r\n #--------------------------------------\r\n self.__normalize___()\r\n\r\n #--------------------------------------\r\n # WRITING ACTIVITY TO POLE FIGURE FILE\r\n #--------------------------------------\r\n # if os.name=='nt': os.system('cls')\r\n # elif os.name=='posix': os.system('clear')\r\n\r\n print(\"############################\")\r\n print(\" WRITING ACTIVITY \")\r\n print(\"############################\\n\")\r\n print(\" available formats: epf(0), list(1), No file writing(-1)\")\r\n fileformat = input(\"Type format flag(0,1,-1)(default=0) >> \")\r\n if len(fileformat)==0: fileformat='epf'\r\n else:\r\n if fileformat =='0': fileformat='epf'\r\n elif fileformat =='1': fileformat='list'\r\n elif fileformat =='-1': fileformat=None\r\n else: print('Wrong fileformat input'); raise IOError\r\n if fileformat == None: pass\r\n else:\r\n print(\" Type the file name\")\r\n filename = input(\" >> \")\r\n self.write(filename=filename, mode=fileformat)\r\n\r\n elif mode=='df': #defocus correction curve making mode\r\n #--------------------------------\r\n # DEFOCUS CORRECTION CURVE FILE\r\n #--------------------------------\r\n print(\"\\n\\n ****************************************\")\r\n print(\" * Defocus correction curve file maker *\")\r\n print(\" ****************************************\\n\")\r\n for i in range(len(self.polefigures)):\r\n self.defc(pfid=i, filename='dfc_'+str(i)+'.dfc', mode='avg')","title":""},{"docid":"a911d1d4ca26448aa9c288c526fb16a4","score":"0.48980546","text":"def load_sequential_aras_frequency_occur(isPerson):\n #by Adele\n #print (\"module_path: \" + module_path )\n #/by Adele\n \n with open(r\"E:\\Lessons_tutorials\\Behavioural user profile articles\\Datasets\\Aras\\House A\\CSV_Summery\\Sequential\\Day\\frequency_occur\\Whole_data.csv\") as csv_file:\n \n data_file = csv.reader(csv_file)\n #target_names = np.array(temp[2:]) # the name of labels \n n_samples = 2068#sum(1 for row in data_file) \n n_features = 41\n data = np.empty((n_samples, n_features))\n target = np.empty((n_samples,), dtype=np.int)\n temp_data = np.empty(n_features+1)# copy whole data to it, then select all col except 20\n \n idx_IN_columns = [] \n for i, ir in enumerate(data_file):\n #print(i)\n temp_data = np.asarray(ir[:42], dtype=np.int)\n #print(temp_data)\n if isPerson == True:\n n_features = 40\n data = np.empty((n_samples, n_features))\n \n # in version 2 the activity col is removed too because it is not important, sensor data are required.\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]#all of features except feature 40 (Person_ID\n target[i] = np.asarray(ir[40], dtype=np.int)# Person ID is the class\n else:\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]#all of features except feature 41 (Activity_ID\n target[i] = np.asarray(ir[41], dtype=np.int)# Activity ID is the class\n \n \n extractedData = temp_data[idx_IN_columns]\n #print(extractedData)\n data[i] = np.asarray(extractedData , dtype=np.int)\n print(target[i])\n \n return Bunch(data=data, target=target)\n # target_names=target_names)\n #feature_names=['sepal length (cm)', 'sepal width (cm)',\n # 'petal length (cm)', 'petal width (cm)'])","title":""},{"docid":"632db86e8061ec1ef3f2d5c2ce481b9e","score":"0.48885354","text":"def __init__(self, reference_file):\n self.reference_results = np.load(open(reference_file, 'rb'))","title":""},{"docid":"13f52f070f653d54098e1beda0a9fcbc","score":"0.487911","text":"def testReferenceReturned(self):\n h5f = h5py.File(self.h5_fname, \"r+\")\n\n # n-D\n a0 = h5f[\"my_array\"]\n self.aw.setArrayData(a0, copy=False)\n a1 = self.aw.getData(copy=False)\n self.assertIs(a0, a1)\n\n # 1D\n b0 = h5f[\"my_1D_array\"]\n self.aw.setArrayData(b0, copy=False)\n b1 = self.aw.getData(copy=False)\n self.assertIs(b0, b1)\n\n h5f.close()","title":""},{"docid":"a71e49456ea0c7eb48c48f245608213c","score":"0.48657942","text":"def load_ref_hist_mappings(self):\n\n # DEBUG DEBUG DEBUG\n assert len(self.ref_hist_mappings) < 1, \\\n \"ERROR Should not be RE-loading \" \\\n \"reference histogram mappings!\"\n # DEBUG DEBUG DEBUG end\n\n self.logger.info(\"Loading reference histogram mappings \" \\\n \"from file `%s'\" % \\\n self.ref_hist_mappings_file_name)\n\n mappings_lines = None\n try:\n mappings_file = file(self.ref_hist_mappings_file_name, \"r\")\n mappings_lines = mappings_file.readlines()\n mappings_file.close()\n except IOError:\n msg = \"ERROR: Could not open reference histogram mapping \"\\\n \"file `%s'\" % self.ref_hist_mappings_file_name\n self.logger.fatal(msg)\n raise Error(msg)\n\n ##########\n\n # The format we expect is: two white-space separated pieces\n # per line. The first the dataset name for which the reference\n # should be used, the second one the name of the reference\n # histogram in the database.\n\n for mapping in mappings_lines:\n # Skip comment lines.\n if not mapping.startswith(\"#\"):\n mapping = mapping.strip()\n if len(mapping) > 0:\n mapping_pieces = mapping.split()\n if len(mapping_pieces) != 2:\n msg = \"ERROR: The reference histogram mapping \" \\\n \"file contains a line I don't \" \\\n \"understand:\\n %s\" % mapping\n self.logger.fatal(msg)\n raise Error(msg)\n dataset_name = mapping_pieces[0].strip()\n ref_hist_name = mapping_pieces[1].strip()\n # We don't want people to accidentally specify\n # multiple mappings for the same dataset. Just\n # don't accept those cases.\n if dataset_name in self.ref_hist_mappings:\n msg = \"ERROR: The reference histogram mapping \" \\\n \"file contains multiple mappings for \" \\\n \"dataset `%s'.\"\n self.logger.fatal(msg)\n raise Error(msg)\n\n # All is well that ends well.\n self.ref_hist_mappings[dataset_name] = ref_hist_name\n\n ##########\n\n self.logger.info(\" Successfully loaded %d mapping(s)\" % \\\n len(self.ref_hist_mappings))\n max_len = max([len(i) for i in self.ref_hist_mappings.keys()])\n for (map_from, map_to) in self.ref_hist_mappings.items():\n self.logger.info(\" %-*s -> %s\" % \\\n (max_len, map_from, map_to))\n\n # End of load_ref_hist_mappings.","title":""},{"docid":"0d12cfe474accc1198daa4cb54faa3c2","score":"0.4860401","text":"def _readData(self):\n fh = pf.open(self.values['input'])\n self.data = fh[self.values['ext']].data\n self.hdu = fh[self.values['ext']].header\n self.log.info('Read data from {0:>s} extension {1:d}'.format(self.values['input'], self.values['ext']))\n self.log.debug('Read data dimensions are {0:d} times {1:d}'.format(*self.data.shape))\n\n if 'rue' in self.hdu['OVERSCA'] and self.data.shape[0] < 2500:\n #a single quadrant; over and prescans were simulated, need to be removed...\n self.log.info('Trying to remove pre- and overscan regions from the given in input data...')\n self.log.info('Quadrant is {0:d}'.format(self.hdu['QUADRANT']))\n\n if self.hdu['QUADRANT'] in (0, 2):\n self.data = self.data[:, self.hdu['PRESCANX']:-self.hdu['OVRSCANX']].copy()\n else:\n self.data = self.data[:, self.hdu['OVRSCANX']:-self.hdu['PRESCANX']].copy()","title":""},{"docid":"5c5ac5c5bf1264493e432f358fae4a86","score":"0.48594093","text":"def calc_eff_mark2(vanad,backgr,norm_ref=\"bm3_counts\",bottom = 0, top = 127,\n low_frame=0,high_frame=967,eff_sig=10):\n\n import stat,datetime,time,sys\n starttime = time.time()\n omega = vanad[\"mom\"][0] # for reference\n takeoff = vanad[\"mtth\"][0]\n # TODO: intelligent determination of Wombat wavelength\n #crystal = AddCifMetadata.pick_hkl(omega-takeoff/2.0,\"335\") #post April 2009 used 335 only\n #\n # Get important information from the basic files\n #\n # Get file times from timestamps as older NeXuS files had bad values here\n #\n #wl = AddCifMetadata.calc_wavelength(crystal,takeoff)\n vtime = os.stat(vanad.location)[stat.ST_CTIME]\n vtime = datetime.datetime.fromtimestamp(vtime)\n vtime = vtime.strftime(\"%Y-%m-%dT%H:%M:%S%z\")\n btime = os.stat(backgr.location)[stat.ST_CTIME]\n btime = datetime.datetime.fromtimestamp(btime)\n btime = btime.strftime(\"%Y-%m-%dT%H:%M:%S%z\")\n # This step required to insert our metadata hooks into the dataset object\n AddCifMetadata.add_metadata_methods(vanad)\n AddCifMetadata.add_metadata_methods(backgr)\n # Fail early\n print 'Using %s and %s' % (str(vanad.location),str(backgr.location))\n # Subtract the background\n if norm_ref is not None:\n norm_target = reduction.applyNormalization(vanad,norm_ref,-1)\n # store for checking later\n #check_val = backgr[8,64,64]\n nn = reduction.applyNormalization(backgr,norm_ref,norm_target)\n # \n print 'Normalising background to %f' % norm_target\n pure_vanad = (vanad - backgr).get_reduced() #remove the annoying 2nd dimension\n # drop any frames that have been requested\n if low_frame != 0 or high_frame < len(pure_vanad):\n pure_vanad = pure_vanad[low_frame:high_frame]\n print 'Only using part of supplied data: %d to %d, new length %d' % (low_frame,high_frame,len(pure_vanad))\n stth = pure_vanad.stth #store for later\n # pure_vanad.copy_cif_metadata(vanad)\n #print 'Check: %f, %f -> %f' % (vanad[8,64,64],check_val,pure_vanad[8,64,64])\n nosteps = pure_vanad.shape[0]\n # generate a rough correction\n simple_vanad = pure_vanad.intg(axis=0) # sum over the detector step axis\n # calculate typical variability across the detector\n eff_array = array.zeros_like(simple_vanad)\n eff_array[simple_vanad > 10] = simple_vanad\n eff_array = eff_array*eff_array.size/eff_array.sum()\n eff_array[simple_vanad > 0] = 1.0/eff_array\n # apply this temporary correction to last frame which we expect to have the most\n # peaks, as no V peaks will be at low enough angle to fall off the\n # detector during scanning. If this assumption is incorrect, a more\n # rigourous routine could do this twice, for the first and last frames\n frame_last = (pure_vanad.storage[nosteps-1]*eff_array).intg(axis=0) #sum vertically\n print 'Final frame max, min values after correction: %f %f' % (max(frame_last),min(frame_last))# find the peaks, get a background too\n peak_list,back_lev = peak_find(frame_last,sig_val=eff_sig)\n # Prepare return information\n peak_pos = [(stth[nosteps-1]+a*0.125,b*0.125) for (a,b) in peak_list]\n info_list = \"List of peaks found and purged:\\n Position Purge range\"\n for pos,fwhm in peak_pos:\n info_list += \"%8.2f %8.2f\\n\" % (pos,fwhm)\n # Remove these peaks from all frames.\n # degrees. The step size is...\n step_size = (stth[nosteps-1]-stth[0])/(nosteps-1)\n print 'Found step size of %f' % step_size\n # Remove all peaks from the pure data\n purged = peak_scrub(pure_vanad,peak_list,step_size,start_at_end=True)\n # Get gain based on pixels above quarter background\n eff_array,eff_error,non_zero_contribs,fudge_map = nonzero_gain(purged,back_lev/(pure_vanad.shape[1]*4))\n final_map = Dataset(eff_array)\n final_map.var = eff_error\n return final_map,non_zero_contribs,fudge_map,frame_last #last frame, for reference\n #return {\"_[local]_efficiency_data\":eff_array.transpose(),\n # \"_[local]_efficiency_variance\":eff_error.transpose(),\n # \"contributors\":non_zero_contribs,\n # \"_[local]_efficiency_raw_data\":os.path.basename(str(vanad.location)),\n # \"_[local]_efficiency_raw_timestamp\":vtime,\n # \"_[local]_efficiency_background_data\":os.path.basename(str(backgr.location)),\n # \"_[local]_efficiency_background_timestamp\":btime,\n # \"_[local]_efficiency_determination_material\":\"Vanadium\",\n # \"_[local]_efficiency_determination_method\":\"From flood field produced by 9mm V rod\",\n # \"_[local]_efficiency_monochr_omega\":omega,\n # \"_pd_proc_info_data_reduction\":\n # \"Vanadium peaks at the following positions were purged:\\n Pos Range\\n \" + info_list\n # },non_zero_contribs","title":""},{"docid":"adae34f92e6a88cade15a7c5ee1c0fb7","score":"0.48560834","text":"def get_ref_af_y ( af_file ):\n af = cst.AirfoilShape.from_txt_file(af_file)\n mod_af = cst.AirfoilShape.from_cst_parameters(af.cst().cst_lower, af.te_lower, af.cst().cst_upper, af.te_upper)\n return mod_af.yco","title":""},{"docid":"f73e849bb7ef8116c29328de15473063","score":"0.48479775","text":"def __read_reference_solutions():\n exact = dict()\n for varid in ('psi', 'chi', 'vrt', 'div', 'uchi', 'vchi', 'upsi', 'vpsi',\n 'chigradu', 'chigradv', 'uwnd', 'vwnd'):\n try:\n filename = os.path.join(test_data_path(),\n '{!s}.ref.npy'.format(varid))\n exact[varid] = np.load(filename).squeeze()\n except IOError:\n raise IOError('required data file not found')\n return exact","title":""},{"docid":"5bc9d8b60b8900b00e743c653a866b9e","score":"0.48293543","text":"def read(self, infname):\n if self.npts > 0:\n print ('*** receiver function data is already stored!')\n return False\n inarr \t\t = np.loadtxt(infname, dtype=np.float64)\n self.npts = inarr.shape[0] \n self.to = inarr[:,0]\n self.rfo = inarr[:,1]\n try:\n self.stdrfo = inarr[:,2]\n except IndexError:\n self.stdrfo = np.ones(self.npts, dtype = np.float64)*0.1\n self.fs = 1./(self.to[1] - self.to[0])\n return True","title":""},{"docid":"050a5f5b3caf7cad312ee340ab96a563","score":"0.48207533","text":"def ff_data_augmentation(patch, ref):\n patch = patch.clone()\n temp = (torch.rand(len(ref)) + torch.rand(len(ref)) * (-1)).numpy()\n patch = torch.FloatTensor(patch.numpy() + ref * temp)\n return patch","title":""},{"docid":"08571ea5d7dabe236c466967511b90cb","score":"0.48010716","text":"def test_reindex_against_reference(dials_regression: Path, tmp_path):\n data_dir = os.path.join(dials_regression, \"indexing_test_data\", \"i04_weak_data\")\n pickle_path = os.path.join(data_dir, \"indexed.pickle\")\n experiments_path = os.path.join(data_dir, \"experiments.json\")\n\n commands = [\n shutil.which(\"dials.reindex\"),\n pickle_path,\n experiments_path,\n \"change_of_basis_op=a,b,c\",\n \"space_group=P4\",\n \"output.reflections=P4.refl\",\n \"output.experiments=P4.expt\",\n ]\n\n result = subprocess.run(commands, cwd=tmp_path, capture_output=True)\n assert not result.returncode and not result.stderr\n assert (tmp_path / \"P4.refl\").is_file()\n assert (tmp_path / \"P4.expt\").is_file()\n new_experiments = load.experiment_list(tmp_path / \"P4.expt\", check_format=False)\n assert new_experiments[0].crystal.get_space_group().type().hall_symbol() == \" P 4\"\n\n # Now have something in P4, get another dataset in a different indexing scheme\n\n cb_op = sgtbx.change_of_basis_op(\"a,-b,-c\")\n commands = [\n shutil.which(\"dials.reindex\"),\n \"P4.refl\",\n \"P4.expt\",\n f\"change_of_basis_op={cb_op}\",\n \"output.experiments=P4_reindexed.expt\",\n \"output.reflections=P4_reindexed.refl\",\n ]\n result = subprocess.run(commands, cwd=tmp_path, capture_output=True)\n assert not result.returncode and not result.stderr\n\n # now run reference reindexing\n commands = [\n shutil.which(\"dials.reindex\"),\n \"P4.refl\",\n \"P4.expt\",\n \"reference.experiments=P4_reindexed.expt\",\n \"reference.reflections=P4_reindexed.refl\",\n ]\n result = subprocess.run(commands, cwd=tmp_path, capture_output=True)\n assert not result.returncode and not result.stderr\n\n # expect reindexed_reflections to be same as P4_reindexed, not P4_reflections\n reindexed_reflections = flex.reflection_table.from_file(tmp_path / \"reindexed.refl\")\n P4_reindexed = flex.reflection_table.from_file(tmp_path / \"P4_reindexed.refl\")\n P4_reflections = flex.reflection_table.from_file(tmp_path / \"P4.refl\")\n\n h1, k1, l1 = reindexed_reflections[\"miller_index\"].as_vec3_double().parts()\n h2, k2, l2 = P4_reindexed[\"miller_index\"].as_vec3_double().parts()\n h3, k3, l3 = P4_reflections[\"miller_index\"].as_vec3_double().parts()\n\n # hkl1 and hkl2 should be same, as should have been reindexed by against the\n # reference, with the program determining a reindexing operator of a,-b,-c\n assert list(h1) == pytest.approx(list(h2))\n assert list(l1) == pytest.approx(list(l2))\n assert list(k1) == pytest.approx(list(k2))\n # h1 and h3 should be same, but not l and k, as these dataset should differ\n # by an a twinning operator of a,-b,-c\n assert list(h1) == pytest.approx(list(h3))\n assert list(l1) != pytest.approx(list(l3))\n assert list(k1) != pytest.approx(list(k3))","title":""},{"docid":"fcd9ae449be4d8904036adbd60cda4b1","score":"0.48007515","text":"def load_sequential_aras_occur(isPerson):\n #by Adele\n #print (\"module_path: \" + module_path )\n #/by Adele\n \n with open(\"E:\\Lessons_tutorials\\Behavioural user profile articles\\Datasets\\Aras\\House A\\CSV_Summery\\Sequential\\Day\\occur\\Whole_data.csv\") as csv_file:\n \n data_file = csv.reader(csv_file)\n #target_names = np.array(temp[2:]) # the name of labels \n n_samples = 2068#sum(1 for row in data_file) \n n_features = 41\n data = np.empty((n_samples, n_features))\n target = np.empty((n_samples,), dtype=np.int)\n temp_data = np.empty(n_features+1)# copy whole data to it, then select all col except 20\n \n idx_IN_columns = [] \n for i, ir in enumerate(data_file):\n #print(i)\n temp_data = np.asarray(ir[:42], dtype=np.int)\n #print(temp_data)\n if isPerson == True:\n n_features = 40\n data = np.empty((n_samples, n_features))\n \n # in version 2 the activity col is removed too because it is not important, sensor data are required.\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]#all of features except feature 40 (Person_ID\n target[i] = np.asarray(ir[40], dtype=np.int)# Person ID is the class\n else:\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]#all of features except feature 41 (Activity_ID\n target[i] = np.asarray(ir[41], dtype=np.int)# Activity ID is the class\n \n \n extractedData = temp_data[idx_IN_columns]\n #print(extractedData)\n data[i] = np.asarray(extractedData , dtype=np.int)\n print(target[i])\n \n return Bunch(data=data, target=target)\n # target_names=target_names)\n #feature_names=['sepal length (cm)', 'sepal width (cm)',\n # 'petal length (cm)', 'petal width (cm)'])","title":""},{"docid":"d7d1e5810484b203cd09c731323929bc","score":"0.47900528","text":"def read_python_FFTfield(self):\n dcr=np.load(self.infile)\n Ng=dcr.shape[1]\n# print('ngrid='+str(Ng))\n if (Ng != self.Ngrid):\n print('Ngrid set='+str(self.Ngrid))\n print('Ngrid read='+str(Ng))\n exit()\n # Set NYQUIST to be real\n return dcr","title":""},{"docid":"a993f7f5ad1271955e95a4737bd90ead","score":"0.47891465","text":"def read():\r\n print \"Reading S. cerevisiae FunCat annotated sequence data set (D1 FC seq) ...\"\r\n dir = dirname(dirname(abspath(__file__))) + sep + 'datasets' + \\\r\n sep + 'S_cerevisiae_FC' + sep + 'seq_yeast_FUN' + sep\r\n train_data = dir + 'seq_yeast_FUN.train.arff'\r\n valid_data = dir + 'seq_yeast_FUN.valid.arff'\r\n test_data = dir + 'seq_yeast_FUN.test.arff'\r\n print \" Reading S. cerevisiae FunCat annotated sequence (D1 FC seq) TRAIN set ...\"\r\n train, idx2attr, idx2class = transform_data(\r\n train_data, include_meta=True)\r\n print \" ... Finished.\"\r\n print \" Reading S. cerevisiae FunCat annotated sequence (D1 FC seq) VALIDATION set ...\"\r\n valid = transform_data(valid_data)\r\n print \" ... Finished.\"\r\n print \" Reading S. cerevisiae FunCat annotated sequence (D1 FC seq) TEST set ...\"\r\n test = transform_data(test_data)\r\n print \" ... Finished.\"\r\n print \" Joining S. cerevisiae FunCat annotated sequence (D1 FC seq) TEST and VALIDATION set ...\"\r\n tv_data = _join(train, valid)\r\n print \" ... Finished.\"\r\n print \"... Finished\"\r\n return tv_data, test, idx2attr, idx2class","title":""},{"docid":"812d5fbad9eb70a716ab4b58f2a23cae","score":"0.4787292","text":"def getRef(fp, n_ref):\n\n refs = {}\n reads = {}\n cnt = 0\n for record in fasta.fasta_iter(fp):\n name = record['name']\n seq = record['sequence']\n if 'ReFeReNcE' in name:\n refs[name] = seq\n cnt += 1 \n if cnt >= n_ref:\n break\n\n if cnt < n_ref:\n print 'not enough ReFeReNcE seqs'\n sys.exit(1)\n\n template = refs.values()[0].upper() #use the first refSeq as template\n profile = []\n length = len(template)\n for i in range(length):\n if template[i] == 'N' or not template[i].isalpha():\n profile.append(0)\n else:\n profile.append(1)\n\n return name, template, profile #return the ref seq and its mask profile","title":""},{"docid":"96d98dc265fa87cdefdbc6da9e3416d9","score":"0.4785966","text":"def map_isoforms_to_reference_transcripts(self):\n m5out = self.output_analysis_fn + \".blasr.out.m5\"\n cmd = 'blasr %s %s --bestn 1 -m 5 --out %s' % \\\n (self.isoseq_output_fa, self.reference_transcripts_fn, m5out)\n execute(cmd)\n return [r for r in BLASRM5Reader(m5out)]","title":""},{"docid":"585aa88fd1cce6de7ba92541b433678e","score":"0.47792578","text":"def load_data(scenario):\n data_file = 'measurements_' + str(scenario) + '.data'\n ref_file = 'reference_' + str(scenario) + '.data'\n \n data = np.loadtxt(data_file,skiprows = 0)\n reference = np.loadtxt(ref_file,skiprows = 0)\n \n return (data,reference)","title":""},{"docid":"22f9d5c4b308ce33d72cf660f99e4c55","score":"0.47790813","text":"def ref_delta_g_20():\n return np.load('tests/ref-data/spc2-fes1d-delta-gs-20.npy')","title":""},{"docid":"c4c33a627258edf7ae2a7db14f1deb3d","score":"0.4776031","text":"def LoadFRG(fpath):\n\tdef readFRGFile(fpath):\n\t\tdata = {}\n\n\t\twith open(fpath, 'r') as f:\n\t\t\tReadingHeader = True \t#check when header has been fully parsed\n\t\t\tBothDirections = False\t#sweep forward + reverse\n\n\t\t\twhile ReadingHeader:\t\n\t\t\t\tline = f.readline()\n\t\t\t\tlineparts = line.split(':')\n\n\t\t\t\tif 'Data' in lineparts[0]:\n\t\t\t\t\tReadingHeader = False\n\t\t\t\t\tf.readline()\n\t\t\t\t\tf.readline()\n\t\t\t\telse:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tdata[lineparts[0]] = float(lineparts[1])\n\t\t\t\t\texcept:\n\t\t\t\t\t\tdata[lineparts[0]] = lineparts[1][1:].replace('\\n', '')\n\n\n\t\t\tvforward = []\n\t\t\tiforward = []\n\t\t\ttimeforward = []\t\t\t\n\t\t\tif data['sweepDir'] == 'Forward + Reverse':\n\t\t\t\tBothDirections = True\n\t\t\t\tvreverse = []\n\t\t\t\tireverse = []\n\t\t\t\ttimereverse = []\n\n\t\t\tfor line in f:\n\t\t\t\tlineparts = f.readline().split('\\t')\n\t\t\t\tif len(lineparts) == 1:\n\t\t\t\t\tbreak\n\t\t\t\tvforward.append(lineparts[0])\t\n\t\t\t\tiforward.append(lineparts[1])\n\t\t\t\ttimeforward.append(lineparts[2])\n\t\t\t\tif BothDirections:\n\t\t\t\t\tvreverse.append(lineparts[0])\t\n\t\t\t\t\tireverse.append(lineparts[1])\n\t\t\t\t\ttimereverse.append(lineparts[2])\n\n\t\t\tdata['V'] = np.array(vforward).astype(float)\n\t\t\tdata['I'] = np.array(iforward).astype(float)\n\t\t\tdata['J'] = data['I']/data['area_cm2']\n\t\t\tdata['delay'] = np.array(timeforward).astype(float)\n\n\t\t\tif BothDirections:\n\t\t\t\tdata2 = data.copy()\n\t\t\t\tdata2['sampleName'] = data['sampleName'] + '_Reverse'\n\t\t\t\tdata['sampleName'] = data['sampleName'] + '_Forward'\n\t\t\t\tdata2['V'] = np.array(vreverse).astype(float)\n\t\t\t\tdata2['I'] = np.array(ireverse).astype(float)\n\t\t\t\tdata2['J'] = data2['I']/data2['area_cm2']\n\t\t\t\tdata2['delay'] = np.array(timereverse).astype(float)\n\t\t\t\toutput = [data, data2]\n\t\t\telse:\n\t\t\t\toutput = data\n\n\t\treturn output\n\n\n\n\tfids = [os.path.join(fpath, x) for x in os.listdir(fpath)]\n\t\n\talldata = {}\n\tfor f in fids:\n\t\toutput = readFRGFile(f)\n\t\tif type(output) == list:\n\t\t\tfor each in output:\n\t\t\t\talldata[each['sampleName']] = each\n\t\telse:\n\t\t\talldata[output['sampleName']] = output\n\treturn alldata","title":""},{"docid":"97e8bf36e1576f80ac6529d3e15e972c","score":"0.4774368","text":"def read_dat(filename):\n with open(filename, 'r') as dat_file:\n # reading in the header block\n is_header = True\n header_block = []\n while is_header:\n tmp_line = dat_file.readline()\n if tmp_line.find('#') == 0:\n header_block.append(tmp_line)\n eq_pos = tmp_line.find('=')\n if tmp_line.find(\"De\") != -1:\n De = float(tmp_line[eq_pos+1:])\n elif tmp_line.find(\"time\") != -1:\n time = float(tmp_line[eq_pos+1:])\n elif tmp_line.find(\"viscRat\") != -1:\n visc_rat = float(tmp_line[eq_pos+1:])\n elif tmp_line.find(\"volRat\") != -1:\n vol_rat = float(tmp_line[eq_pos+1:])\n elif tmp_line.find(\"deformRate\") != -1:\n deformRate = float(tmp_line[eq_pos+1:])\n elif tmp_line.find(\"tnum\") != -1:\n _tnum = float(tmp_line[eq_pos+1:])\n else:\n is_header = False\n # get the next two Tecplot lines and then go back one line\n header_block.append(tmp_line)\n last_pos = dat_file.tell()\n tmp_line = dat_file.readline()\n header_block.append(tmp_line)\n dat_file.seek(last_pos)\n\n try:\n reader = csv.reader(dat_file, delimiter=' ')\n type_line = next(reader)\n nvert = int(type_line[1][2:])\n nface = int(type_line[2][2:])\n all_data = [] # position + all other data\n f2v = [] # connectivity\n\n count = 0\n while count < nvert:\n lst = next(reader)\n all_data.append(lst)\n count += 1\n all_data = np.array(all_data, dtype=float)\n\n count = 0\n while count < nface:\n lst = next(reader)[0:3] # should just be 3 values\n f2v.append([int(i) for i in lst])\n count += 1\n f2v = np.array(f2v, dtype=int)\n\n except csv.Error as e:\n sys.exit('file %s, line %d: %s' % (filename, reader.line_num, e))\n\n try:\n params = {\"visc_rat\": visc_rat, \"vol_rat\": vol_rat, \"De\": De, \"deformRate\": deformRate,\n \"time\": time, \"nvert\": nvert, \"nface\": nface, \"header_block\": header_block}\n return (all_data, f2v, params)\n except NameError as e:\n print(\"One of the required variables was not instantiated: {}\".format(e))","title":""},{"docid":"80db1bee8c85d44e696e1e75742402f6","score":"0.47732306","text":"def _read_calibration(self) -> None:\n coeff = self._read(_BME680_BME680_COEFF_ADDR1, 25)\n coeff += self._read(_BME680_BME680_COEFF_ADDR2, 16)\n\n coeff = list(struct.unpack(\"<hbBHhbBhhbbHhhBBBHbbbBbHhbb\", bytes(coeff[1:39])))\n # print(\"\\n\\n\",coeff)\n coeff = [float(i) for i in coeff]\n self._temp_calibration = [coeff[x] for x in [23, 0, 1]]\n self._pressure_calibration = [\n coeff[x] for x in [3, 4, 5, 7, 8, 10, 9, 12, 13, 14]\n ]\n self._humidity_calibration = [coeff[x] for x in [17, 16, 18, 19, 20, 21, 22]]\n self._gas_calibration = [coeff[x] for x in [25, 24, 26]]\n\n # flip around H1 & H2\n self._humidity_calibration[1] *= 16\n self._humidity_calibration[1] += self._humidity_calibration[0] % 16\n self._humidity_calibration[0] /= 16\n\n self._heat_range = (self._read_byte(0x02) & 0x30) / 16\n self._heat_val = self._read_byte(0x00)\n self._sw_err = (self._read_byte(0x04) & 0xF0) / 16","title":""},{"docid":"7fb3407c0fe262c2529ac9875b47230a","score":"0.47712365","text":"def read_body_Ainc(f,verbose,glueball_corr) :\n for ibin in range(0, numbin):\n for iopA in range(0,numop) :\n for iblockA in range(0, nblock) :\n for iopB in range(0,numop) : \n for iblockB in range(0, nblock) :\n for t in range(0, Ntmax) :\n \n try:\n bbb = f.read(8)\n except IOError as e:\n print (\"I/O error({0}): {1}\".format(e.errno, e.strerror))\n except:\n print (\"Unexpected error:\", sys.exc_info()[0])\n raise\n\n vvv = struct.unpack('d',bbb)\n if verbose :\n print (t, vvv[0])\n \n glueball_corr[iblockA,iblockB,t, iopA,iopB, ibin] = \\\n glueball_corr[iblockA,iblockB,t, iopA,iopB, ibin] + vvv[0]\n\n\n\n f.close()","title":""},{"docid":"f6cfbf516b9e176d0a45a4a8d03bccd5","score":"0.47711852","text":"def test_read_data():\n # generate data and save to a tempfile\n Time = np.linspace(0, 1800, num=1800)\n AltTime = Time\n y2 = 0.5 - 0.5 * np.tanh((Time-600)/100) # TGA result\n y = y2/y2[0]\n dy = 0.005-0.005 * np.tanh(Time/100 - 6)**2 # exact derivative of former\n ny = y + 0.1*np.random.randn(len(Time)) # noisy\n outdata = np.stack((Time, AltTime, y, dy, y2, ny), axis=1)\n # delete=False is needed so that the tempfile can be written to in windows\n # create working directory\n fp = open(\"output.csv\", mode=\"w+t\")\n # delete=False so we can close() the file and delete later\n\n np.savetxt(fp.name, outdata,\n delimiter=\",\",\n header=\"Time,AltTime,y,dy,y2,ny\",\n comments='')\n fp.close()\n fname = \"output.csv\"\n # line with only the required fields\n line = {\"fname\": fname,\n \"dep_col_name\": \"y\"}\n # populate rest of the fields\n line = _set_data_line_defaults(line, header=0)\n read_Time, read_y = read_data(**line)\n\n # Test normalization, gradient calculation and ind_col_name functionality\n line['normalize'] = True\n line['dep_col_name'] = 'y2'\n read_Time, norm_y2 = read_data(**line)\n\n # Test normalization, gradient calculation and ind_col_name functionality\n line['gradient'] = True\n line['normalize'] = True\n line['ind_col_name'] = 'AltTime'\n line['dep_col_name'] = 'y2'\n read_AltTime, calc_dy = read_data(**line)\n\n # delete file now that we don't need it anymore\n os.remove(fname)\n\n assert np.allclose(read_y, y)\n assert np.allclose(norm_y2, y)\n assert np.allclose(read_Time, Time)\n assert np.allclose(read_Time, read_AltTime)\n # rtol = 1e-5 fails\n assert np.allclose(dy, calc_dy, rtol=1e-4)","title":""},{"docid":"b4fd7366f1fcf4fd0bdce957f61c242d","score":"0.47710222","text":"def genreg(masterphotref_fistar,\n outfile,\n xycols=(1,2)):\n\n # get the x and y coordinate columns from the source list (fistar)\n srcxy = np.genfromtxt(masterphotref_fistar,\n usecols=xycols,\n dtype='f8,f8',\n names=['x','y'])\n\n xsize = 2048.\n ysize = 2048.\n\n sel = (\n (srcxy['x']>xsize+1) | (srcxy['x']<0) |\n (srcxy['y']>ysize+1) | (srcxy['y']<0)\n )\n # in *rare* instances (i.e., this happened for one star, in one ccd, out\n # of the entire TESS cycle1-4 reduction), srcxy can be outside the bounds.\n # in such cases raise a warning and just \"mask\" those xy values to 1, since\n # less than ~10 sources will not affect the photref frame generation\n if len(srcxy[sel]) > 0:\n N_off_silicon = len(srcxy[sel])\n print(f\"WARNING! Got {N_off_silicon} sources in genreg from \"\n f\"{masterphotref_fistar}. Masking...\")\n if N_off_silicon > 10:\n msg = 'This should never happen'\n raise AssertionError(msg)\n srcxy[sel] = np.ones_like(srcxy[sel])\n\n # set up the grid (this weirdness is transcribed directly from Chelsea's\n # regslct.py) TODO: figure out WTF this does\n\n BX = 30; BY = 30\n mx = np.zeros(BX*BY)-1\n my = np.zeros(BX*BY)-1\n ma = np.zeros(BX*BY)\n bx = (srcxy['x']*BX/xsize).astype(int)\n by = (srcxy['y']*BY/ysize).astype(int)\n mx[by*bx+bx] = srcxy['x']\n my[by*bx+bx] = srcxy['y']\n\n outf = open(outfile,'wb')\n\n for i in range(int(BX*BY)):\n outf.write((\"%8.0f %8.0f %8.0f\\n\" % (mx[i],my[i],20)).encode(\"utf-8\"))\n\n outf.close()","title":""},{"docid":"52f7bffc915699a950907ba7a5cf945d","score":"0.4768592","text":"def get_abi_ref(dataset, check=False, dtype=None):\n ref = dataset.Rad * dataset.kappa0\n if check:\n DQF = dataset.DQF\n ref[DQF < 0] = np.nan\n ref[DQF > 1] = np.nan\n if dtype is None:\n return ref\n else:\n return ref.astype(dtype)","title":""},{"docid":"20f60d1ead558d432600bdd9b5112cf3","score":"0.4765774","text":"def __extract_cascadedata(self):\n \n return fromfile(self.fpath, dtype = int32)[:128*128*16*8].reshape(8,16,128,128)","title":""},{"docid":"f600ce327e7bdaf8e39fa1145e1f617d","score":"0.4762291","text":"def get_altseq(ref_seq, ref_allele, var_allele, var_pos, strand, features_info, cds_info=None):\n if cds_info is None:\n cds_info = []\n alt_seq = \"\"\n if len(cds_info) == 2:\n start_coding_index = cds_info[0] - 1 # it should be index not pos\n stop_coding_index = cds_info[1] # get end position of the last cds\n else:\n start_coding_index = 0\n total_len = 0\n for x in features_info:\n total_len += x[1] - x[0] + 1\n stop_coding_index = total_len # the features are sorted by end therefroe the end pos of the last item is the last coding nc\n\n if strand == '-': # ge the correct orientation, because exons are oredered based on their position\n ref_seq = ref_seq[\n ::-1] # in order to calculate from the first base of the first feature (sorted by genomic coordinates)\n ref_allele = ref_allele.complement() # the reverse will be done on return\n var_allele = var_allele.complement() # the reverse will be done on return\n\n ref_seq = ref_seq[\n start_coding_index:stop_coding_index] # just keep the coding regions (mostly effective in case of protein-coding genes)\n nc_index = 0\n if len(ref_allele) == len(var_allele) or ref_allele[0] == var_allele[0]:\n for feature in features_info: # for every exon, cds or stop codon\n if var_pos in range(feature[0], feature[\n 1] + 1): # get index of the var relative to the position of the overlapping feature in the coding region\n var_index_in_cds = nc_index + (var_pos - feature[0])\n # modify the coding reference sequence accoding to the var_allele\n c = len(ref_allele)\n alt_seq = ref_seq[0:var_index_in_cds] + var_allele + ref_seq[\n var_index_in_cds + c::] # variant and ref strand??\n if strand == '-':\n return ref_seq[::-1], alt_seq[::-1]\n else:\n return ref_seq, alt_seq\n\n nc_index += (feature[1] - feature[0] + 1)\n\n return ref_seq, alt_seq","title":""},{"docid":"6d2969e33e747376b22064309480b8c2","score":"0.47605693","text":"def load_bias(self):\n if not os.path.exists(self.data_file):\n return\n \n with h5.File(self.data_file, 'r') as hfile:\n if \"bias\" in hfile.keys():\n data = np.array(hfile[\"bias\"])\n self.bias.bias_array = data\n self.log(\"Bias loaded from {}\".format(self.data_file))\n\n # Subtract of the an overall constant\n self.bias.bias_array -= self.bias.bias_array[0]","title":""},{"docid":"d344b939282e1ed8a445e60f6ccfbb8f","score":"0.4756925","text":"def test_fqe_data_set_wfn_data():\n test = fqe_data.FqeData(1, 1, 2)\n ref = numpy.random.rand(2, 2) + 1.j * numpy.random.rand(2, 2)\n test.set_wfn(strategy='from_data', raw_data=ref)\n assert numpy.allclose(test.coeff, ref)","title":""},{"docid":"1663928531405a5099c5bb945235365a","score":"0.4753721","text":"def initcafg(self):\n\n #all available redshift for UVB \n avail_z=np.arange(0,10,0.05)\n\n #pick closest in redshift\n zindex=np.argmin(abs(avail_z-self.redshift))\n nameuvb='fg_uvb_dec11/fg_uvb_dec11_z_{}.dat'.format(avail_z[zindex])\n try:\n fgtab=np.loadtxt(nameuvb,dtype={'names': ('nu','f'),'formats':('f10','f10')})\n except:\n print('Could not read UVB file {}'.format(nameuvb))\n exit()\n\n #from Ry to Hz\n self.source['lgnu']=np.log10(fgtab['nu'])+np.log10(3.2898419499e15)\n\n #from (10^-21 erg s^-1 cm^-2 Hz^-1 sr^-1) to Log of erg/s/cm2/Hz\n self.source['lgfpiJ']=np.log10(fgtab['f'])-21+np.log10(4*np.pi)\n\n #now reverse freq to have high freq first and low freq last (as in HM12)\n #This is nothing special, but it is needed for how the writing procedure\n #fixes the boundary of the SED\n self.source['lgnu'] = self.source['lgnu'][::-1]\n self.source['lgfpiJ'] = self.source['lgfpiJ'][::-1]","title":""},{"docid":"e0b3d05b5728a40d66074126cceee64d","score":"0.47522852","text":"def dtopo_tt3_v2(indir,outfile):\n from numpy import mod,zeros\n\n fid_data = indir+'/claw.data'\n\n fid=open(fid_data,'r')\n for i in range(15):\n line=fid.readline()\n\n line=fid.readline().split()\n t0 =float(line[0])\n\n for i in range(2):\n line=fid.readline()\n\n line=fid.readline().split()\n mt =int(line[0])\n\n line=fid.readline().split()\n tf =float(line[0])\n\n fid.close()\n\n dt =(tf-t0)/mt\n\n dir = indir\n\n infile=\"fort.qxxxx\"\n\n infile1=dir+\"/fort.q0000\"\n fid= open(infile1,'r')\n fout=open(outfile,'w')\n\n for i in range(2):\n line=fid.readline()\n\n line=fid.readline().split()\n mx = int(line[0])\n line=fid.readline().split()\n my = int(line[0])\n line=fid.readline().split()\n xlow = float(line[0])\n line=fid.readline().split()\n ylow = float(line[0])\n line=fid.readline().split()\n dx = float(line[0])\n line=fid.readline().split()\n dy = float(line[0])\n\n line=fid.readline()\n\n fout.write(\"%10d \\n\" % mx)\n fout.write(\"%10d \\n\" % my)\n fout.write(\"%10d \\n\" % mt)\n fout.write(\"%20.10e \\n\" % xlow)\n fout.write(\"%20.10e \\n\" % ylow)\n fout.write(\"%20.10e \\n\" % (t0+dt))\n fout.write(\"%20.10e \\n\" % dx)\n fout.write(\"%20.10e \\n\" % dy)\n fout.write(\"%20.10e \\n\" % dt)\n\n h_orig=zeros(mx*my)\n k=0\n for j in range(my):\n for i in range(mx):\n line=fid.readline().split()\n h_orig[k]=float(line[0])\n k+=1\n line=fid.readline()\n\n fid.close()\n\n infile1=infile\n dz=zeros(mx*my)\n\n for k in range(1,mt+1):\n for ipos in range(10,6,-1):\n idigit=mod(k,10)\n infile1=infile1[0:ipos-1]+str(idigit)+infile1[ipos:10]\n k=k/10\n\n fid1= open(dir+'/'+infile1,'r')\n print fid1\n\n for i in range(9):\n line=fid1.readline().split()\n \n k=0 \n for j in range(my):\n for i in range(mx):\n line1=fid1.readline().split()\n hnew=float(line1[0])\n dz[i+(my-j-1)*mx]=hnew-h_orig[k]\n k+=1\n line=fid1.readline()\n \n for j in range(my):\n for i in range(mx): \n k=i+j*mx\n \n fout.write(\"%20.10e \" % dz[k])\n\n fout.write(\"\\n\")\n\n fid1.close()\n\n fout.close()","title":""},{"docid":"64b52f94534221feaa42782df1147aff","score":"0.4752102","text":"def load_sequential_aras_first(isPerson):\n #by Adele\n #print (\"module_path: \" + module_path )\n #/by Adele\n \n with open(\"E:\\Lessons_tutorials\\Behavioural user profile articles\\Datasets\\Aras\\House A\\CSV_Summery\\Sequential\\Day\\Whole_data.csv\") as csv_file:\n \n data_file = csv.reader(csv_file)\n #target_names = np.array(temp[2:]) # the name of labels \n n_samples = 2068#sum(1 for row in data_file) \n n_features = 41\n data = np.empty((n_samples, n_features))\n target = np.empty((n_samples,), dtype=np.int)\n temp_data = np.empty(n_features+1)# copy whole data to it, then select all col except 20\n \n idx_IN_columns = [] \n for i, ir in enumerate(data_file):\n #print(i)\n temp_data = np.asarray(ir[:42], dtype=np.int)\n #print(temp_data)\n if isPerson == True:\n n_features = 40\n data = np.empty((n_samples, n_features))\n \n # in version 2 the activity col is removed too because it is not important, sensor data are required.\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]#all of features except feature 40 (Person_ID\n target[i] = np.asarray(ir[40], dtype=np.int)# Person ID is the class\n else:\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]#all of features except feature 41 (Activity_ID\n target[i] = np.asarray(ir[41], dtype=np.int)# Activity ID is the class\n \n \n extractedData = temp_data[idx_IN_columns]\n #print(extractedData)\n data[i] = np.asarray(extractedData , dtype=np.int)\n #print(target[i])\n \n \n #print(data)\n #print(target)\n return Bunch(data=data, target=target)\n # target_names=target_names)\n #feature_names=['sepal length (cm)', 'sepal width (cm)',\n # 'petal length (cm)', 'petal width (cm)'])","title":""},{"docid":"54b64006b59b81b898b6cc71ccf6d48d","score":"0.47514185","text":"def load_original_data():\n print(\"Loading original data, only happens the first time\")\n\n # Load dataframes\n drug_chemical_info_with_fp = pd.read_csv(\"Data/DrugComb/drug_chemical_info_with_fingerprints.csv\")\n drugcomb_scored = pd.read_csv(\"Data/DrugComb/drugcombs_scored.csv\")\n drug_protein_link = pd.read_csv(\"Data/DrugComb/drug_protein_links.tsv\", sep=\"\\t\")\n protein_protein_interactions = pd.read_csv(\"Data/DrugComb/protein_protein_links.txt\", sep=' ')\n\n ####################################################################################################################\n # Build node feature matrix\n ####################################################################################################################\n drug_chemical_info_with_fp['has_fp'] = drug_chemical_info_with_fp['fp0'].apply(lambda fp: fp != -1)\n drug_chemical_info_with_fp['is_drug'] = 1\n drug_chemical_info_with_fp = drug_chemical_info_with_fp.rename(columns={'drugName': 'name'})\n\n print(\"Number of drugs\", len(drug_chemical_info_with_fp))\n\n # Retrieve all protein names\n all_proteins = set(protein_protein_interactions['protein1']). \\\n union(set(protein_protein_interactions['protein2'])).union(set(drug_protein_link['protein']))\n protein_nodes = pd.DataFrame(all_proteins, columns=['name'])\n protein_nodes['is_drug'] = 0\n protein_nodes['has_fp'] = False\n\n print(\"Number of proteins\", len(protein_nodes))\n\n nodes = pd.concat((drug_chemical_info_with_fp, protein_nodes), ignore_index=True, sort=False)\n nodes = nodes.fillna(-1)\n x = nodes.drop(['cIds', 'drugNameOfficial', 'molecularWeight', 'smilesString', 'name'], axis=1)\n x = x.to_numpy().astype(np.int)\n\n ####################################################################################################################\n # Build edge index\n ####################################################################################################################\n\n # Dictionaries to retrieve indices\n ##################################################\n cid_to_idx_dict = {nodes.at[i, 'cIds']: i for i in range(len(nodes))}\n name_to_idx_dict = {nodes.at[i, 'name']: i for i in range(len(nodes))}\n\n # PPI\n ##################################################\n protein_protein_interactions['idx_prot1'] = protein_protein_interactions['protein1'].apply(\n lambda s: name_to_idx_dict[s])\n protein_protein_interactions['idx_prot2'] = protein_protein_interactions['protein2'].apply(\n lambda s: name_to_idx_dict[s])\n\n edge_index_ppi = protein_protein_interactions[['idx_prot1', 'idx_prot2']].to_numpy().T\n edge_attr_ppi = np.zeros((edge_index_ppi.shape[1], 4))\n\n print(\"Number of Prot-Prot Interactions\", edge_index_ppi.shape[1])\n\n # Drug Protein interaction\n ##################################################\n drug_protein_link['idx_chemical'] = drug_protein_link['chemical'].\\\n apply(lambda s: cid_to_idx_dict[s] if s in cid_to_idx_dict.keys() else -1)\n drug_protein_link['idx_prot'] = drug_protein_link['protein'].apply(lambda s: name_to_idx_dict[s])\n\n edge_index_dpi = drug_protein_link[drug_protein_link['idx_chemical'] != -1][['idx_chemical', 'idx_prot']].\\\n to_numpy().T\n edge_attr_dpi = np.zeros((edge_index_dpi.shape[1], 4))\n\n print(\"Number of Drug-Prot Interactions\", edge_index_dpi.shape[1])\n\n # Drug Synergy scores\n ##################################################\n drugcomb_scored['idx_Drug1'] = drugcomb_scored['Drug1'].apply(\n lambda s: name_to_idx_dict[s] if s in name_to_idx_dict.keys() else -1)\n drugcomb_scored['idx_Drug2'] = drugcomb_scored['Drug2'].apply(\n lambda s: name_to_idx_dict[s] if s in name_to_idx_dict.keys() else -1)\n\n # Remove measures that have been performed several times.\n drug_drug_edges = drugcomb_scored[drugcomb_scored[['idx_Drug1', 'idx_Drug2']].duplicated() == False]\n # Remove measures for which there is no information about one of the drugs\n drug_drug_edges = drug_drug_edges[drug_drug_edges['idx_Drug1'] != -1]\n drug_drug_edges = drug_drug_edges[drug_drug_edges['idx_Drug2'] != -1]\n\n edge_index_ddi = drug_drug_edges[['idx_Drug1', 'idx_Drug2']].to_numpy().T\n edge_attr_ddi = drug_drug_edges[['ZIP', 'Bliss', 'Loewe', 'HSA']].to_numpy()\n\n print(\"Number of Drug-Drug Interactions\", edge_index_ddi.shape[1])\n\n # Aggregate different edges\n ##################################################\n edge_index = np.concatenate((edge_index_ppi, edge_index_dpi, edge_index_ddi), axis=1)\n edge_attr = np.concatenate((edge_attr_ppi, edge_attr_dpi, edge_attr_ddi), axis=0)\n # Edges are directed, we need to feed them both ways\n edge_index = np.concatenate((edge_index, edge_index[::-1, :]), axis=1)\n edge_attr = np.concatenate((edge_attr, edge_attr), axis=0)\n\n data = Data(x=torch.tensor(x, dtype=torch.float),\n edge_index=torch.tensor(edge_index, dtype=torch.long),\n edge_attr=torch.tensor(edge_attr, dtype=torch.float))\n\n # Add drug-drug interaction informations as attribute to data, useful for training\n data.number_of_drugs = len(drug_chemical_info_with_fp)\n data.edge_index_ddi = torch.tensor(edge_index_ddi)\n data.edge_attr_ddi = torch.tensor(edge_attr_ddi)\n\n return data","title":""},{"docid":"37da8b2ee508383391bce66308f27560","score":"0.4750281","text":"def TInReference(self, groundTruth, index, infer=False):\n i = self.readToRead[index]\n return groundTruth.MapPBISubreadToReference(\n i.tpbi.movie, i.tpbi.holeNumber,\n i.abs_tstart, i.abs_tend, infer)","title":""},{"docid":"fd50618a92d363c291877803066875cb","score":"0.47472703","text":"def test_missing_references(self):\n buff = StringIO(\"\"\"0 @I1@ INDI\n1 FAMS @FmissingS@\n1 FAMC @FmissingC@\n0 @F1@ FAM\n1 WIFE @ImissingW@\n1 HUSB @ImissingH@\n1 CHIL @ImissingC@\"\"\")\n (ind, fam, warns) = parse_file(buff)\n self.assertTrue(len([x for x in warns if x.story == \"US26\"]) == 5)\n self.assertTrue(ind['@I1@'].spouse_family_ids == [])\n self.assertTrue(ind['@I1@'].spouse_families == [])\n self.assertTrue(ind['@I1@'].child_family_ids == [])\n self.assertTrue(ind['@I1@'].child_families == [])\n self.assertTrue(fam['@F1@'].husband_id == None)\n self.assertTrue(fam['@F1@'].husband == None)\n self.assertTrue(fam['@F1@'].wife_id == None)\n self.assertTrue(fam['@F1@'].wife == None)\n self.assertTrue(fam['@F1@'].children_id_list == [])\n self.assertTrue(fam['@F1@'].children_list == [])","title":""},{"docid":"7fb5c516623d59d8620c3411eaa43fe6","score":"0.47471806","text":"def read_debt(self):\n debtfile = pd.read_csv(INPUTPATH + 'debt_financing.csv')\n debtfile.set_index('indcode', inplace=True)\n self.Deltas = copy.deepcopy(debtfile)","title":""},{"docid":"dca9f1418a7412c5e3b895affd6eef9d","score":"0.4747172","text":"def load_data_single_mod(gen,model,scenario,Tanglob_idx=False,Tref_all=True,Tref_start='1870-01-01',Tref_end='1900-01-01',usr_time_res=\"ann\",var='tas'):\n # the dictionaries are NOT ordered properly + some other adjustments -> will need to be careful with my old scripts\n\n # see e-mail from Verena on 20191112 for additional infos how could read in several files at once with xarr\n # additionally: she transforms dataset into dataarray to make indexing easier -> for consistency reason with earlier\n # version of emulator (& thus to be able to reuse my scripts), I do not do this (fow now).\n \n\t# right now I keep reloading constants fields for each run I add -> does not really make sense. \n # Maybe add boolean to decide instead. however they are small & I have to read them in at some point anyways\n # -> maybe path of least resistence is to not care about it\n\tprint('start with model',model)\n\n\t# vars which used to be part of the inputs but did not really make sense as I employ the same ones all the time anyways (could be changed later if needed)\n\t#var='tas'\n\ttemp_res = usr_time_res # if not, reading the var file needs to be changed as time var is not named in the same way anymore\n\tspatial_res = 'g05'\n\n\n # load in the constants files\n\tdir_data = '/net/so4/landclim/snath/data/'\n\tfile_ls = 'interim_invariant_lsmask_regrid_g05.nc' # ERA-interim mask regridded by Richard from 73x144 to 72x144\n\tfile_srex = 'srex-region-masks_20120709.srex_mask_SREX_masks_all.50deg.time-invariant.nc'\n\tfile_srex_shape = 'referenceRegions.shp'\n\n\n\t#df_all_regs = geopandas.read_file(dir_data+file_srex_shape)\n\tsrex_names = ['ALA','CGI','WNA','CNA','ENA','CAM','AMZ','NEB','WSA','SSA','NEU','CEU','MED','SAH','WAF','EAF','SAF',\n 'NAS','WAS','CAS','TIB','EAS','SAS','SEA','NAU','SAU'] # SREX names ordered according to SREX mask I am \n # employing\n\t#df_srex = df_all_regs.loc[df_all_regs['LAB'].isin(srex_names)] # alternative indexing: search in column LAB for names\n\tsrex_raw = xr.open_mfdataset(dir_data+file_srex, combine='by_coords',decode_times=False) # srex_raw nrs from 1-26\n\t#df_srex=srex_raw \n\t#srex_raw[\"time\"]=pd.to_datetime(srex_raw.time.values)\n\tfrac_l = xr.open_mfdataset(dir_data+file_ls, combine='by_coords',decode_times=False) #land-sea mask of ERA-interim bilinearily interpolated \n\tlons, lats = np.meshgrid(frac_l.longitude.values,frac_l.latitude.values) # the lon, lat grid (just to derive weights) \n\t#frac_1[\"time\"]=pd.to_datetime(frac_1.time.values)\n\tfrac_l = frac_l.where(frac_l.latitude>-60,0) # remove Antarctica from frac_l field (ie set frac l to 0)\n\n\tidx_l=np.squeeze(frac_l.lsm.values)>0.0 # idex land #-> everything >0 I consider land\n \n\n\twgt = norm_cos_wgt(lats) # area weights of each grid point\n\twgt_l = wgt[idx_l] # area weights for land grid points\n\tlon_pc, lat_pc = mpu.infer_interval_breaks(frac_l.longitude, frac_l.latitude) # the lon / lat for the plotting with pcolormesh\n\tsrex=(np.squeeze(srex_raw.srex_mask.values)-1)[idx_l] # srex indices on land\n\n \n\ty={}\n\tT_ref = np.zeros([36,72])\n\trun_nrs={}\n\tif Tanglob_idx == True:\n\t\tTan_wgt_globmean = {}\n\tif gen == 5:\n\t\tdir_var='/net/so4/landclim/snath/DA/data/%s/regridded/'%model\n\t\trun_names_list=sorted(glob.glob(dir_var+var+'_'+temp_res+'_'+model+'_'+scenario+'_'+'r*i1p1'+'_'+spatial_res+'.nc'))\n # ATTENTION: are ordered but does not work for models with runs above digit 9 \n\t\t#index_tr = [i for i, s in enumerate(run_names_list) if 'r1i1p1' in s][0] # find training run \n\t\t#print(run_names_list)\n\t\t#run_names_list.insert(0, run_names_list.pop(index_tr)) # move training run to begin of list\n\n # exception for cmip5 GISS-E2-H_rcp85_r2i1p1 ie the strange run I excluded from ESD paper\n\t\tif '/net/atmos/data/cmip5-ng/tas/tas_%s_GISS-E2-H_rcp85_r2i1p1_g025.nc'%usr_time_res in run_names_list:\n\t\t\trun_names_list.remove('/net/atmos/data/cmip5-ng/tas/tas_%s_GISS-E2-H_rcp85_r2i1p1_g025.nc'%usr_time_res )\n \n # loop over all runs to obtain the absolute values \n\t\tprint(run_names_list)\n\t\tfor run_name in run_names_list:\n\n\t\t\tdata = xr.open_mfdataset(run_name,decode_times=False)\n\t\t\tif usr_time_res==\"ann\":\n\t\t\t\tdata=data.rename({'year':'time'})\n\t\t\t\tdata=data.rename({'longitude':'lon'})\n\t\t\t\tdata=data.rename({'latitude':'lat'}) \n\t\t\tdata[\"time\"]=cf_units.num2date(data.time.values, 'days since 1800-01-01 00:00:00', cf_units.CALENDAR_STANDARD)\n\t\t\tdata=data.sel(time=slice('1870-01-01', '2101-01-01'))#.roll(lon=72)\n # rename so it is consisten with cmip6 \n # roll so that it is on same grid as others (no longer Pacific centered) \n\n\t\t\t#print(data.time.values)\n\t\t\tdata = data.assign_coords(lon= (((data.lon + 180) % 360) - 180)) # assign_coords so same labels as others\n\t\t\trun=int(data.attrs['source_ensemble'].split('r')[1].split('i')[0]) # extract ens member\n\t\t\trun_nrs[run_name]=run\n \n\t\t\ty[run] = data.tas.values # still absolute values + still contains sea pixels\n\t\t\tT_ref += data.tas.sel(time=slice(Tref_start, Tref_end)).mean(dim='time').values*1.0/len(run_names_list) # sum up all ref climates\n \n \n\t\t\tif run==1 and Tref_all != True:\n\t\t\t\tT_ref_1=data.tas.sel(time=slice(Tref_start, Tref_end)).mean(dim='time').values\n\t\t\t\tprint('create ref for ',run_name)\n \n\t\t\tif Tanglob_idx == True:\n\t\t\t\tTan_wgt_globmean[run] = np.asarray([np.average(y[run][t],weights=wgt) for t in np.arange(y[run].shape[0])]) #area weighted but abs values \n \n \n\tif gen == 6:\n\t\tdir_var = '/net/so4/landclim/snath/DA/data/%s/regridded/'%model\n\t\trun_names_list=sorted(glob.glob(dir_var+var+'_'+temp_res+'_'+model+'_'+scenario+'_'+'r*i1p1f*'+'_'+spatial_res+'.nc'))\n # ATTENTION: are ordered but does not work for models with runs above digit 9\n # idea is: every ssp one needs a corresponding hist one (vice versa not the case)\n\t\t#index_tr = [i for i, s in enumerate(run_names_list) if 'r1i1p1' in s][0] # find training run \n\t\t#run_names_list.insert(0, run_names_list.pop(index_tr)) # move training run to begin of list\n\n\t\tif model == 'EC-Earth3': # temporary fix added on 20191118 because these runs are not available as historical ones\n\t\t\trun_names_list.remove('/net/cfc/cmip6/Next_Generation/tas/%s/g025/tas_%s_EC-Earth3_ssp585_r1i1p1f1_g025.nc'%(usr_time_res,usr_time_res))\n\t\t\t#run_names_list.remove('/net/cfc/cmip6/Next_Generation/tas/ann/g025/tas_ann_EC-Earth3_ssp585_r6i1p1f1_g025.nc') #6 removed because historical seems faulty (ie only ghg driven) on 20191119\n\t\t\t#run_names_list.remove('/net/cfc/cmip6/Next_Generation/tas/ann/g025/tas_ann_EC-Earth3_ssp585_r11i1p1f1_g025.nc')\n\t\t\t#run_names_list.remove('/net/cfc/cmip6/Next_Generation/tas/ann/g025/tas_ann_EC-Earth3_ssp585_r13i1p1f1_g025.nc')\n\t\t\t#run_names_list.remove('/net/cfc/cmip6/Next_Generation/tas/ann/g025/tas_ann_EC-Earth3_ssp585_r15i1p1f1_g025.nc')\n\t\t\tprint('ATTENTION: ssp realizations 1 because no historical available on 20191122 (open issue on ESGF).')\n\t\tprint(run_names_list)\n\t\tfor run_name in run_names_list:\n\t\t\t#run_name_ssp = run_name\n\t\t\t#run_name_hist = run_name.replace(scenario,'historical') \n\t\t\trun_name_hist = run_name \n\t\t\trun_name_ssp = dir_var+var+'_%s_'%(usr_time_res)+model+'_ssp370_r1i1p1f1_g05.nc' \n\t\t\tif model=='HadGEM3-GC31-LL':\n\t\t\t\trun_name_ssp = dir_var+var+'_%s_'%(usr_time_res)+model+'_ssp245_r1i1p1f3_g05.nc' \n\t\t\tif model=='CNRM-CM6-1':\n\t\t\t\trun_name_ssp = dir_var+var+'_%s_'%(usr_time_res) +model+'_ssp370_r1i1p1f2_g05.nc' \n\t\t\tif model=='GISS-E2-1-G':\n\t\t\t\trun_name_ssp = dir_var+var+'_%s_'%(usr_time_res) +model+'_ssp370_r6i1p1f2_g05.nc' \n\n\t\t\tdata = xr.open_mfdataset([run_name_hist,run_name_ssp],concat_dim='time').sel(time=slice('1944-01-01', '2019-01-01'))\n\t\t\t#data = data.assign_coords(lon= (((data.lon + 180) % 360) - 180)) # assign_coords so same labels as others\n\t\t\trun = int(run_name_hist.split('_')[4].split('r')[1].split('i')[0])#data.attrs['realization_index']\n\t\t\trun_nrs[run_name]=run\n \n\t\t\ty[run] = data[var].values # still absolute values + still contains sea pixels\n\t\t\tT_ref += data[var].sel(time=slice(Tref_start, Tref_end)).mean(dim='time').values*1.0/len(run_names_list) # sum up all ref climates\n \n\t\t\tif run==1 and Tref_all != True:\n\t\t\t\tT_ref_1=data[var].sel(time=slice(Tref_start, Tref_end)).mean(dim='time').values\n\t\t\t\tprint('create ref for ',run_name) \n\n\t\t\tif model=='CAMS-CSM1-0': # temporary (?) fix added on 20191119 because CAMS-CSM1-0 data are currently only available till 2099 instead of 2100\n\t\t\t\ty[run]=y[run][:-1]\n \n\t\t\tif Tanglob_idx == True:\n\t\t\t\tTan_wgt_globmean[run] = np.asarray([np.average(y[run][t],weights=wgt) for t in np.arange(y[run].shape[0])]) #area weighted but abs values \n\n # obtain the anomalies\n\tfor run_name in run_names_list:\n\t\trun = run_nrs[run_name]\n\t\tif Tref_all == True:\n\t\t\ty[run]=(y[run]-T_ref)\n\t\t\tif Tanglob_idx == True:\n\t\t\t\tTan_wgt_globmean[run]=Tan_wgt_globmean[run]-np.average(T_ref,weights=wgt)\n\t\telse:\n\t\t\ty[run]=y[run]\n\t\t\tif Tanglob_idx == True:\n\t\t\t\tTan_wgt_globmean[run]=Tan_wgt_globmean[run]-np.average(T_ref_1,weights=wgt) \n\ttime=data[\"time\"]\n\tif Tanglob_idx == False: \n\t\treturn y,time,srex,srex_names,lon_pc,lat_pc,idx_l,wgt_l\n\telse:\n\t\treturn y,time,srex,srex_names,lon_pc,lat_pc,idx_l,wgt_l,Tan_wgt_globmean","title":""},{"docid":"6b94b9a51e916bbd19da92f1e67c74cd","score":"0.47448468","text":"def test_missing_reference_data():\n td = TorsionDriveData.from_qdata(\n dihedral=(6, 10, 11, 8), qdata_file=get_data(\"biphenyl_qdata.txt\")\n )\n del td.reference_data[0]\n with pytest.raises(MissingReferenceData):\n td.validate_angles()","title":""},{"docid":"4b3ee2bbebe912e9550c038bebd0a40c","score":"0.47353658","text":"def get_forces_data(outcar_filename=\"OUTCAR\", convergence=None, warn=False):\n with open(outcar_filename, \"r\") as f:\n outcar = f.read()\n\n number_of_ions_re = re.compile(r\"NIONS = \\s+([\\d]+)\")\n try:\n number_of_ions = int(number_of_ions_re.findall(outcar)[0])\n except IndexError as exc:\n raise ValueError(\"Unable to read NIONS.\") from exc\n except:\n raise\n if not convergence:\n convergence_re = re.compile(r\"EDIFFG = -([\\d\\.E-]+)\")\n try:\n convergence = float(convergence_re.findall(outcar)[0])\n except IndexError as exc:\n raise ValueError(\"Unable to read EDIFFG.\") from exc\n except:\n raise\n\n # find force output block positions\n forces_block_start = []\n forces_header_re = re.compile(\"\\sPOSITION\\s+TOTAL-FORCE \\(eV/Angst\\)\")\n for i, line in enumerate(outcar.split(\"\\n\")):\n if forces_header_re.search(line):\n forces_block_start.append(i)\n if not forces_block_start:\n raise ValueError(\"No FORCES blocks found.\")\n # Ideally we want the most recent complete block of forces data.\n most_recent_block_start = forces_block_start[-1] + 2\n forces_lines = outcar.split(\"\\n\")[\n most_recent_block_start : most_recent_block_start + number_of_ions\n ]\n if not forces_block_is_well_formed(forces_lines):\n # If the most recent forces block is ill-formed, try to parse the previous block.\n if warn:\n print(\n \"The last FORCES block is not well-formed. Trying to parse the preceeding block.\"\n )\n next_most_recent_block_start = forces_block_start[-2] + 2\n forces_lines = outcar.split(\"\\n\")[\n next_most_recent_block_start : next_most_recent_block_start + number_of_ions\n ]\n if not forces_block_is_well_formed(forces_lines):\n # If the last two forces blocks are ill-formed, we assume the input file is mis-formatted.\n raise Exception(\n \"The last two FORCES blocks are not well-formed. Your input might be mis-formatted.\"\n )\n forces = []\n for line in forces_lines:\n forces.append([float(s) for s in line.split()[-3:]])\n forces_data = ForcesData(forces=forces, convergence=convergence)\n return forces_data","title":""},{"docid":"3ad1cdc5e35c2eb3930375ee3ac5e721","score":"0.4734519","text":"def read_data(self, force_distances=False):\n import pandas as pd\n from io import StringIO\n\n fin = open(self._input_file)\n l = fin.readline()\n bytes_read = len(l)\n p = l.split()\n self.N = int(p[0])\n if len(p) > 1:\n self.fit_measure_ = float(p[1])\n\n l = [fin.readline() for _ in range(self.N)]\n bytes_read += sum(len(x) for x in l)\n\n self.sequences = pd.read_table(\n StringIO(u\"\".join(l)),\n nrows=self.N,\n sep=\"\\t\",\n header=None,\n engine=\"python\")\n\n assert self.N == len(self.sequences)\n\n log.info(\"Read %d sequences.\", self.N)\n\n if self.sequences.shape[1] > 1:\n log.info(\"Setting embedding from input data\")\n self.embedding = self.sequences.set_index(0)\n self.embedding.columns = self.coord_dims\n self.embedding.index.name = \"Sequence\"\n\n if (not hasattr(self, \"embedding\")) or force_distances:\n log.info(\"Memory usage %gMB\" % (util.memory_usage()))\n log.info(\"Reading distances.\")\n self.read_distances(fin)\n log.info(\"Memory usage %gMB\" % (util.memory_usage()))","title":""},{"docid":"b5573c69ea9117cb6778fc2d0b4eaa0d","score":"0.47334468","text":"def _fix_georeferencing(self):\n logger.info(\"Fixing georeferencing information in %s\", self.ri_path)\n\n # Set the coordinate reference system for rasterio methods\n with xr.open_dataset(self.ri_path, engine=\"netcdf4\") as data:\n # data = xr.open_dataset(self.ri_path)\n data = set_georeferencing(\"ri\", data)\n\n # Change longitude range and rebuild this coordinate dataset\n lons = data[\"longitude\"].data - 360\n data = data.assign_coords({\"longitude\": lons})\n data[\"longitude\"].attrs = {\n \"units\": \"degrees_east\",\n \"long_name\": \"Longitude\",\n \"actual_range\": [\n data[\"longitude\"].data.min(),\n data[\"longitude\"].data.max()\n ]\n }\n\n # The default nan value (-9.96921e+36) throws overflow warnings\n navalue = -9999\n data[\"precip\"].data[np.isnan(data[\"precip\"].data)] = navalue\n data[\"precip\"].encoding[\"missing_value\"] = -9999\n data[\"precip\"].encoding[\"_FillValue\"] = -9999\n\n # Overwrite download path\n os.remove(self.ri_path)\n data.to_netcdf(self.ri_path)\n\n logger.info(\"Georeferencing information adjusted, overwriting %s\",\n self.ri_path)","title":""},{"docid":"6171afdc51971ba49a671845fc2ecbd8","score":"0.473311","text":"def read_short_dat(filename):\n with open(filename, 'r') as dat_file:\n # reading in the header block\n is_header = True\n header_block = []\n while is_header:\n tmp_line = dat_file.readline()\n if tmp_line.find('#') == 0:\n header_block.append(tmp_line)\n else:\n is_header = False\n # get the next two Tecplot lines and then go back one line\n header_block.append(tmp_line)\n last_pos = dat_file.tell()\n tmp_line = dat_file.readline()\n header_block.append(tmp_line)\n dat_file.seek(last_pos)\n\n try:\n reader = csv.reader(dat_file, delimiter=' ')\n type_line = next(reader)\n nvert = int(type_line[1][2:])\n nface = int(type_line[2][2:])\n x_data = [] # position\n f2v = [] # connectivity\n\n count = 0\n while count < nvert:\n lst = next(reader)[0:3]\n x_data.append(lst)\n count += 1\n x_data = np.array(x_data, dtype=float)\n\n count = 0\n while count < nface:\n lst = next(reader)[0:3] # should just be 3 values\n f2v.append([int(i) for i in lst])\n count += 1\n f2v = np.array(f2v, dtype=int)\n\n except csv.Error as e:\n sys.exit('file %s, line %d: %s' % (filename, reader.line_num, e))\n\n try:\n params = {\"header_block\": header_block}\n return (x_data, f2v, params)\n except NameError as e:\n print(\"One of the required variables was not instantiated: {}\".format(e))","title":""},{"docid":"cfc0aa800cc7109504f36afac087b470","score":"0.47310045","text":"def testNewreadoutlier(self):\n imageids, imsizes, phasecenters, masks, models, paramdic, newformat=self.imset.newreadoutlier(self.newoutlierfile)\n # the reference outlier file contains each field's paramaters per line\n print \"Using \", self.newoutlierfile\n f = open(self.newoutlierreffile,'r')\n lines=f.readlines()\n f.close()\n cnt = 0\n for elm in lines:\n if len(elm.split())!=0 and elm.split()[0]!='#' :\n cnt +=1\n print \"N fields=\",cnt\n #print \"imsizes=\",imsizes,\" phasecenters=\",phasecenters\n print \"len(imsizes)=\",len(imsizes), \" len(imageids)=\",len(imageids)\n if len(imsizes) == len(phasecenters) == len(imageids) == len(masks) == len(models) == cnt:\n self.res=True\n else:\n self.res=False\n self.assertTrue(self.res)","title":""},{"docid":"e4740d61cc550ea7366ccd24ebe7e7a5","score":"0.47260442","text":"def _readData(self):\n raw = numpy.genfromtxt(self.filename, delimiter=[8]*10)\n\n # component 0\n dataStart = self._headerTotalLines\n acc0 = self._extract(raw, dataStart, self._dataNumLines)\n dataStart += self._dataNumLines\n if self._dataNumValues == 3:\n vel0 = self._extract(raw, dataStart, self._dataNumLines)\n dataStart += self._dataNumLines\n disp0 = self._extract(raw, dataStart, self._dataNumLines)\n dataStart += self._dataNumLines\n\n # component 1\n dataStart = self._headerTotalLines + \\\n 1*(self._headerTotalLines + self._dataNumValues*self._dataNumLines)\n acc1 = self._extract(raw, dataStart, self._dataNumLines)\n dataStart += self._dataNumLines\n if self._dataNumValues == 3:\n vel1 = self._extract(raw, dataStart, self._dataNumLines)\n dataStart += self._dataNumLines\n disp1 = self._extract(raw, dataStart, self._dataNumLines)\n dataStart += self._dataNumLines\n\n # component 2\n dataStart = self._headerTotalLines + \\\n 2*(self._headerTotalLines + self._dataNumValues*self._dataNumLines)\n acc2 = self._extract(raw, dataStart, self._dataNumLines)\n dataStart += self._dataNumLines\n if self._dataNumValues == 3:\n vel2 = self._extract(raw, dataStart, self._dataNumLines)\n dataStart += self._dataNumLines\n disp2 = self._extract(raw, dataStart, self._dataNumLines)\n dataStart += self._dataNumLines\n\n # Assemble components\n self.acc = numpy.array( (acc0, acc1, acc2) ).transpose()\n self.acc *= self.header['toSI']\n if self._dataNumValues == 3:\n self.vel = numpy.array( (vel0, vel1, vel2) ).transpose()\n self.vel *= self.header['toSI']\n self.disp = numpy.array( (disp0, disp1, disp2) ).transpose()\n self.disp *= self.header['toSI']\n\n # Rotate to E,N,U\n R = numpy.zeros((3, 3), dtype=numpy.float64)\n for i,az in enumerate(self.azimuth.values()):\n if az <= 360.0:\n azR = az / 180.0 * math.pi\n R[i,:] = (math.sin(azR), math.cos(azR), 0.0)\n else:\n R[i,:] = (0.0, 0.0, 1.0)\n self.acc = numpy.dot(self.acc, R)\n if not self.vel is None:\n self.vel = numpy.dot(self.vel, R)\n if not self.disp is None:\n self.disp = numpy.dot(self.disp, R)\n\n return","title":""},{"docid":"1b439b80bb993bbdb8e1b5c1f2bf243b","score":"0.47248137","text":"def make_reference(self, reference_probes):\n self.refs = pd.DataFrame(reference_probes, columns=['sequence'])\n self.refs['name'] = [naming['reference'](i) for i, _ in self.refs.iterrows()]\n self.refs['color'] = 'tomato'\n self.refs['type'] = 'stellaris (sense) reference'\n self.refs = self.refs[benchling_headers]","title":""},{"docid":"82005183b11a6e328c7802733a263954","score":"0.47230077","text":"def backward(self, top, propagate_down, bottom):\n\n batch=1\n index = 0\n level = 6\n dis = 7\n SepSize = batch * level\n self.ref= np.zeros(bottom[0].num,dtype=np.float32)\n\n for k in range(dis):\n for i in range(SepSize*k,SepSize*(k+1)-batch):\n for j in range(SepSize*k + int((i-SepSize*k)/batch+1)*batch,SepSize*(k+1)):\n\n\t\t if self.loss[index]>0:\n self.ref[i] += -1\n self.ref[j] += +1\n index +=1\n\n for k in range(dis):\n\t\tindex = k*SepSize\n\t\tself.ref[index] = (bottom[0].data[index] - bottom[1].data[index])*bottom[0].num /100\n\n for k in range(dis):\n\t\tif (bottom[0].data[(k+1)*SepSize-1] < 0):\n\t\t\tself.ref[(k+1)*SepSize-1] += bottom[0].data[(k+1)*SepSize-1]*bottom[0].num /100\n\n\tbottom[0].diff[...]= np.reshape(self.ref,(bottom[0].num,1))/bottom[0].num","title":""},{"docid":"2d2c72529e014e69538255bd85bd9ad3","score":"0.47196406","text":"def fix_nonref_positions(in_file, ref_file):\n ignore_chrs = [\".\"]\n ref2bit = twobit.TwoBitFile(open(ref_file))\n out_file = in_file.replace(\"-raw.vcf\", \".vcf\")\n\n with open(in_file) as in_handle:\n with open(out_file, \"w\") as out_handle:\n for line in in_handle:\n if line.startswith(\"#\"):\n out_handle.write(line)\n else:\n parts = line.rstrip(\"\\r\\n\").split(\"\\t\")\n pos = int(parts[1])\n # handle chr/non-chr naming\n if parts[0] not in ref2bit.keys():\n parts[0] = parts[0].replace(\"chr\", \"\")\n ref_base = None\n if parts[0] not in ignore_chrs:\n try:\n ref_base = ref2bit[parts[0]].get(pos-1, pos).upper()\n except Exception, msg:\n # off the end of the chromosome\n if str(msg).startswith(\"end before start\"):\n print msg\n else:\n print parts\n raise\n parts = fix_vcf_line(parts, ref_base)\n if parts is not None:\n out_handle.write(\"\\t\".join(parts) + \"\\n\")\n return out_file","title":""},{"docid":"c48fb2692c4c5e73cfff883b7a41c001","score":"0.47189966","text":"def load_data(self):\n fn = \"../data/redclump_sample_A_updatedvalues_only.txt\"\n print \"Reading %s ...\" % fn\n data = np.genfromtxt(fn)\n names = [\"ID\", \"distance\", \"Radius_gal\", \"Phi_gal\", \"z\", \"Teff\",\n \"logg\", \"[Fe/H]\", \"[alpha/Fe]\", \"age\", \"mass\"]\n print data[2]\n print data.shape\n print \"Read %s\" % fn\n ages = data[:, 9].flatten()\n sigma = 0.25 / np.log10(np.e) # MAGIC\n ivars = np.zeros_like(ages) + 1. / (sigma * sigma)\n self.set_data(ages, ivars)","title":""},{"docid":"ddf1ec23b0976a68cd2917aa6f6e2aec","score":"0.4716494","text":"def load(self):\n data = np.load(self.path)\n self.target = data['target']\n self.indices = list(data['indices'])\n self.ref = list(data['ref'])","title":""},{"docid":"9190505865acffe7522fcfd3d4fab6fc","score":"0.47135812","text":"def get_reference_focus_data(self, reference_trajectory):\n\n # Reorder image.\n foc_X = self.foc_X.reshape((self.N), order=\"F\")\n foc_Y = self.foc_Y.reshape((self.N), order=\"F\")\n foc_Z = self.foc_Z.reshape((self.N), order=\"F\")\n image = np.asanyarray([foc_X, foc_Y, foc_Z])\n\n # Get local_look and local_squint for the distributed target.\n self.local_look_ref_traj, self.local_squint_ref_traj = Utils.get_angles_antenna_distributed_target(reference_trajectory, image)\n\n N = image.shape[1]\n NT = len(reference_trajectory.flight_x)\n\n # Compute distances between sensor and target.\n self.distances_ref_traj = np.sqrt( (np.full((N, NT), reference_trajectory.flight_x[:]) - np.full((N, NT), foc_X[:, None]) )**2 + \n (np.full((N, NT), reference_trajectory.flight_y[:]) - np.full((N, NT), foc_Y[:, None]) )**2 + \n (np.full((N, NT), reference_trajectory.flight_z[:]) - np.full((N, NT), foc_Z[:, None]) )**2)","title":""},{"docid":"e5e87521336ac960999949ce42b6fb32","score":"0.47075123","text":"def read(self) -> None:\n (\n self.iteration,\n self.s,\n self.y,\n self.rho,\n self.r0,\n self.f0,\n self.e0,\n self.task,\n ) = self.load()\n self.load_restart = True","title":""},{"docid":"863e2f9fb817febc0c63b3c844366811","score":"0.47070032","text":"def readCCout():\n\n inputName = 'ccinput.dat'\n ccextension = '_cc-fit.out'\n \n data = np.loadtxt(inputName+ccextension, skiprows = 13)\n \n return data","title":""},{"docid":"dd0dc9a1497547a6da69c2a041d31210","score":"0.47039145","text":"def get_ref_step2(self):\n wrong_ref = self.check_for_wrong_ref() or self.wrong_ref_pp()\n\n # if self.ref: # probabaly should be calculated once\n # if self.author != \"תנך\" and self.ref.index.title in library.get_indexes_in_category('Tanakh'):\n # include_dependant = True\n # wrong_ref = True\n # elif self.author != \"תלמוד בבלי\" and self.ref.index.title in library.get_indexes_in_category('Bavli'):\n # include_dependant = False\n # wrong_ref = True\n\n if self.ref:\n if wrong_ref: # or (self.raw_ref and not self.ref and not self.raw_ref.is_sham):\n new_ref = None\n include_dependant = True\n if self.author == \"משנה תורה\":\n include_dependant = False\n look_here = self.get_index_options(include_dependant=include_dependant)\n if look_here:\n new_ref = self.get_new_ref_w_look_here(look_here)\n else: # couldn't find a indexs for this author\n parser.missing_authors.add(self.author)\n # if look_here and (self.index or wrong_ref):\n # # than try to get the true title from the cat from look_here\n # look_here_shared_title_word = '({})'.format(\n # '|'.join(list(set.intersection(*[set(x.title.split()) for x in look_here]))))\n # alt_ref_titles = map(lambda x: x['text'], self.ref.index.schema['titles'])\n # found_index = [ind for ind in look_here for tanakh_book in alt_ref_titles if\n # tanakh_book in re.sub(look_here_shared_title_word, '', ind.title).strip()]\n # if found_index:\n # if len(set(found_index)) > 1: # assert len(found_index) == 0\n # print \"more than one index option\" # todo: problem with אלשיך דברים and with books I, II\n # print found_index[0].title, found_index[-1].title\n # self.index = found_index[0]\n # try:\n # new_ref = Ref('{} {}'.format(self.index.title, re.sub(self.ref.index.title, '', self.ref.normal()).strip()))\n # print \"deleting wrong: {} found new Index: {} new ref: {}\".format(self.ref, self.index, new_ref)\n # self.ref = new_ref\n # except exceptions.InputError as e:\n # print \"inputError for this string {}, extracted from this rawref {}\".format('{} {}'.format(self.index.title, re.sub(self.ref.index.title, '', self.ref.normal()).strip()), self.raw_ref)\n # tanakh is wrong but can't find the index ex: רש\"ר הירש\n if self.index and not self.ref:\n ind = self.index\n #todo: look into the original catching see why it is not catching kalla. also see how to copy the intresting parts to here\n self.raw_ref.book = ind.title\n split_raw_text = re.sub('[\\(\\)]', '', self.raw_ref.rawText).split()\n self.raw_ref.section_level = split_raw_text\n\n pass\n if not self.index and not new_ref:# todo: it was an or why? turend into an and lets see if/what it breaks.\n self.change_look_here(author = 'תרגום יונתן', new_author = 'תרגום', category = 'Writings')\n if not self.index:\n # print \"deleting wrong: {} couldn't find an index\".format(self.ref)\n self.ref = None\n else: # not wrong_ref. and found a ref, might just be a correct ref. todo: how to test it is correct?\n pass\n # try to get the ref from the index via the titles found (without an index - ex. ילקוט שמעוני, דניאל תתרסה)\n # it is look at nodes names when we are not testing giving wrong Ref?\n elif hasattr(self, \"opt_titles\") and self.opt_titles:\n indexs = self.extract_cat(include_dependant=True)\n if indexs:\n indexs.extend(self.indexs)\n # if self.author == 'משנה תורה':\n # self.reduce_indexs_with_matcher()\n node_name = ''\n for opt_title in self.opt_titles:\n try:\n new_index = library.get_index(opt_title)\n if not indexs or new_index.is_complex(): # or library.get_title_node_dict('he')[opt_title] != library.get_title_node_dict('en')[new_index.title]\n self.index = new_index\n possible_nodes = new_index.all_titles('he')\n node_guess = intersect_list_string(possible_nodes, self.raw_ref.rawText)\n if not self.ref and node_guess:\n try:\n r = Ref(node_guess)\n if not self.check_for_wrong_ref(r):\n self.ref = r\n except InputError:\n self.ref = ''\n print('the node guess is not a Ref')\n elif node_guess:\n # then we should check witch is the better option\n # maybe using the length of matching words in regards to the titles\n ref_opt1 = self.ref.he_normal()\n ref_opt2 = node_guess\n sets = strings2sets(ref_opt1,ref_opt2, self.raw_ref.rawText)\n if abs(len(set[1])-len(set[2])) < abs(len(set[0])-len(set[2])):\n self.ref = Ref(node_guess)\n elif new_index not in indexs:\n intersected = self.intersected_indexes(new_index, indexs)\n if intersected:\n node_name = Ref(intersected[0].title).he_normal() # TODO: find cases with list longer than one and figure it out.\n self.index = intersected\n except (exceptions.BookNameError, TypeError):\n # print \"excepted {}\".format(opt_title)\n pass\n if node_name:\n try:\n new_string = re.sub(opt_title, node_name, self.text)\n new_string = re.sub('[()]', '', new_string) # because we are going to use Ref rather than library.get_refs_in_string() that needes the parentheses\n self.ref = Ref(new_string)\n except exceptions.InputError:\n try:\n self.ref = Ref(','.join(new_string.split(',')[0:2]))\n except exceptions.InputError:\n # try again without the name of the node, but with the index recognizing that the node is in that index\n try:\n new_string = re.sub(opt_title, \"\", new_string)\n self.ref = Ref(new_string)\n except exceptions.InputError:\n print(\"we tried\")\n self.ref = None\n elif (self.indexs or self.index) and self.raw_ref and not self.raw_ref.is_sham:\n self.ref_opt = []\n if not self.indexs:\n self.indexs = [self.index]\n for ind in self.indexs:\n new_index = ind if isinstance(ind, Index) else library.get_index(ind)\n self.index = new_index\n possible_nodes = new_index.all_titles('he')\n ns_titles_and_refs = []\n if hasattr(new_index, 'alt_structs'):\n ns=[]\n ns_titles_and_refs = dict()\n [ns.extend(alt['nodes']) for alt in new_index.alt_structs.values()]\n if any('titles' in nsone for nsone in ns): # 'titles' in ns[0].keys():\n ns_titles_and_refs = dict([(x['titles'][1]['text'], x['wholeRef']) for x\n in ns if ('wholeRef' in x and 'titles' in x)])\n if any('sharedTitle' in nsone for nsone in ns): # todo: old code: 'sharedTitle' in ns[0].keys():\n ns_titles_and_refs.update(dict([(Term().load_by_title(x['sharedTitle']).get_primary_title('he')\n, x['wholeRef']) for x in ns if 'sharedTitle' in x]))\n possible_nodes.extend(ns_titles_and_refs)\n node_guess = intersect_list_string(possible_nodes, self.raw_ref.rawText)\n if not self.ref and node_guess:\n try:\n if ns_titles_and_refs:\n try:\n r = Ref(ns_titles_and_refs.get(node_guess, ''))\n r.normal()\n except (InputError, AttributeError):\n r = Ref(node_guess)\n else:\n r = Ref(node_guess)\n if not self.check_for_wrong_ref(r):\n self.ref_opt.append(r)\n except InputError:\n # self.ref_opt.append('')\n print('the node guess is not a Ref')\n if self.ref_opt:\n if len(self.ref_opt) == 1:\n r = self.ref_opt[0]\n #self.ref = r\n if not r.sections:\n sections = ' '.join([sect for sect in re.sub('[(,)]', ' ', self.raw_ref.rawText).split() if is_hebrew_number(sect)]) #todo: maybe bad huristic and should wait to do this on the PM level?\n if sections:\n try:\n self.ref = Ref(r.he_normal() + ' ' + sections)\n except InputError as e:\n pass\n return\n self.ref = r\n else: # more than one option, we need to choose the better one.\n he_options = [r.he_normal().replace(\"ן\", \"ם\") for r in self.ref_opt]\n try:\n better = intersect_list_string(he_options, re.sub('[()]', '', self.raw_ref.rawText), ref_opt = True)\n r = Ref(better)\n if not r.sections:\n sections = ' '.join(\n [sect for sect in re.sub('[(,)]', ' ', self.raw_ref.rawText).split() if\n is_hebrew_number(\n sect)]) # todo: maybe bad huristic and should wait to do this on the PM level?\n if sections:\n try:\n self.ref = Ref(r.he_normal() + ' ' + sections)\n except (AttributeError, InputError):\n pass\n return\n self.ref = r\n except InputError:\n print(\"more than one option to guess from\")\n\n\n # elif node_guess:\n # # then we should check witch is the better option\n # # maybe using the length of matching words in regards to the titles\n # ref_opt1 = self.ref.he_normal()\n # ref_opt2 = node_guess\n # # sets = strings2sets(ref_opt1, ref_opt2, self.raw_ref.rawText)\n # if abs(len(set[1]) - len(set[2])) < abs(len(set[0]) - len(set[2])):\n # self.ref = Ref(node_guess)","title":""},{"docid":"8c9cf859a5192a7066c7d24f18e992df","score":"0.47011754","text":"def test_readwriteread():\n uv_in = UVCal()\n uv_out = UVCal()\n testfile = os.path.join(DATA_PATH, 'zen.2457698.40355.xx.fitsA')\n write_file = os.path.join(DATA_PATH, 'test/outtest_omnical.fits')\n uv_in.read_calfits(testfile)\n uv_in.write_calfits(write_file, clobber=True)\n uv_out.read_calfits(write_file)\n nt.assert_equal(uv_in, uv_out)\n\n # test without freq_range parameter\n uv_in.freq_range = None\n uv_in.write_calfits(write_file, clobber=True)\n uv_out.read_calfits(write_file)\n nt.assert_equal(uv_in, uv_out)","title":""},{"docid":"ddf3d1076918f601a7b704a10561fda6","score":"0.46959445","text":"def gfalternate_updatedict(cf,ds_tower,ds_alt):\n if \"alternate\" not in dir(ds_tower): return\n section = \"Drivers\"\n series_list = cf[section].keys()\n for series in series_list:\n if \"GapFillFromAlternate\" not in cf[section][series].keys(): continue\n # name of alternate output series in ds\n output_list = cf[section][series][\"GapFillFromAlternate\"].keys()\n # loop over the outputs listed in the control file\n for output in output_list:\n if output not in ds_tower.alternate.keys(): ds_tower.alternate[output] = {}\n ds_tower.alternate[output][\"label_tower\"] = series\n # source name\n ds_tower.alternate[output][\"source\"] = cf[section][series][\"GapFillFromAlternate\"][output][\"source\"]\n # site name\n ds_tower.alternate[output][\"site_name\"] = ds_tower.globalattributes[\"site_name\"]\n # alternate data file name\n # first, look in the [Files] section for a generic file name\n file_list = cf[\"Files\"].keys()\n lower_file_list = [item.lower() for item in file_list]\n if ds_tower.alternate[output][\"source\"].lower() in lower_file_list:\n # found a generic file name\n i = lower_file_list.index(ds_tower.alternate[output][\"source\"].lower())\n ds_tower.alternate[output][\"file_name\"] = cf[\"Files\"][file_list[i]]\n else:\n # no generic file name found, look for a file name in the variable section\n ds_tower.alternate[output][\"file_name\"] = cf[section][series][\"GapFillFromAlternate\"][output][\"file_name\"]\n # if the file has not already been read, do it now\n if ds_tower.alternate[output][\"file_name\"] not in ds_alt:\n ds_alt[ds_tower.alternate[output][\"file_name\"]] = qcio.nc_read_series(ds_tower.alternate[output][\"file_name\"])\n # get the type of fit\n ds_tower.alternate[output][\"fit_type\"] = \"OLS\"\n if \"fit\" in cf[section][series][\"GapFillFromAlternate\"][output]:\n if cf[section][series][\"GapFillFromAlternate\"][output][\"fit\"].lower() in [\"ols\",\"ols_thru0\",\"mrev\",\"replace\",\"rma\",\"odr\"]:\n ds_tower.alternate[output][\"fit_type\"] = cf[section][series][\"GapFillFromAlternate\"][output][\"fit\"]\n else:\n log.info(\"gfAlternate: unrecognised fit option for series \"+output)\n # force the fit through the origin\n #ds_tower.alternate[output][\"thru0\"] = \"no\"\n #if \"thru0\" in cf[section][series][\"GapFillFromAlternate\"][output]:\n #if cf[section][series][\"GapFillFromAlternate\"][output][\"thru0\"].lower() in [\"yes\",\"true\"]:\n #ds_tower.alternate[output][\"thru0\"] = \"yes\"\n #else:\n #log.info(\"gfAlternate: unrecognised thru0 option for series \"+output)\n # correct for lag?\n ds_tower.alternate[output][\"lag\"] = \"yes\"\n if \"lag\" in cf[section][series][\"GapFillFromAlternate\"][output]:\n if cf[section][series][\"GapFillFromAlternate\"][output][\"lag\"].lower() in [\"no\",\"false\"]:\n ds_tower.alternate[output][\"lag\"] = \"no\"\n else:\n log.info(\"gfAlternate: unrecognised lag option for series \"+output)\n # alternate data variable name if different from name used in control file\n if \"alternate_name\" in cf[section][series][\"GapFillFromAlternate\"][output]:\n ds_tower.alternate[output][\"alternate_name\"] = cf[section][series][\"GapFillFromAlternate\"][output][\"alternate_name\"]\n else:\n ds_tower.alternate[output][\"alternate_name\"] = series\n # results of best fit for plotting later on\n if \"results\" not in ds_tower.alternate[output].keys():\n ds_tower.alternate[output][\"results\"] = {\"startdate\":[],\"enddate\":[],\"No. points\":[],\"r\":[],\n \"Bias\":[],\"RMSE\":[],\"Frac Bias\":[],\"NMSE\":[],\n \"Avg (tower)\":[],\"Avg (alternate)\":[],\n \"Var (tower)\":[],\"Var (alternate)\":[],\"Var ratio\":[],\n \"Lag (uncorrected)\":[],\"Lag (corrected)\":[],\n \"Slope\":[],\"Offset\":[]}\n # create an empty series in ds if the alternate output series doesn't exist yet\n if output not in ds_tower.series.keys():\n data,flag,attr = qcutils.MakeEmptySeries(ds_tower,output)\n qcutils.CreateSeries(ds_tower,output,data,Flag=flag,Attr=attr)","title":""},{"docid":"a1b7d82507b0ec6bd0d3e84537d53d50","score":"0.46922886","text":"def load_chain(outfile):\n chain = np.genfromtxt(outfile)\n return reshape_chain(chain)","title":""}],"string":"[\n {\n \"docid\": \"b5be3e4f061018379de1fd59914de34a\",\n \"score\": \"0.61490226\",\n \"text\": \"def Load_reference(self):\\n if self.reffile is None:\\n raise ValueError('No reference file specified')\\n if os.path.isfile(self.reffile):\\n hdu = fits.open(self.reffile)\\n else:\\n raise ValueError('{} Does not exist. Create the file with tess_reference.py'.format(self.reffile)) \\n\\n data = hdu[0].data\\n cut = Cutout2D(data,(1024+44,1024),2048)\\n self.reference = cut.data\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85c4202012909b3b69aa844c8363b49b\",\n \"score\": \"0.5946181\",\n \"text\": \"def regenerate_reference_data():\\n generate_data(2,\\n 2,\\n maxn=4,\\n maxrdm=4,\\n daga=[0],\\n undaga=[1],\\n dagb=[0],\\n undagb=[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97ee0d4a5d6029e1576f30cdbf488d1\",\n \"score\": \"0.5614137\",\n \"text\": \"def traindata(output, resol,study, reference, foci, max_distance,w,feature):\\n\\n if checkConfig():\\n pass\\n else:\\n print('Please run refhic config first.')\\n print('Good bye!')\\n sys.exit()\\n\\n dataParams={'resol':resol,'feature':feature,'w':w}\\n reference = referenceMeta(reference)\\n\\n chromTest = {'chr15','chr16','chr17',15,16,17,'15','16','17'}\\n chromVal = {'chr11','chr12',11,12,'11','12'}\\n _mask = np.zeros(2 * (w * 2 + 1) ** 2 + 2 * (2 * w + 1) + 4)\\n featureMask = feature.split(',')\\n if '0' in featureMask:\\n _mask[:] = 1\\n if '1' in featureMask:\\n _mask[:(2 * w + 1) ** 2] = 1\\n if '2' in featureMask:\\n _mask[(2 * w + 1) ** 2:2 * (2 * w + 1) ** 2] = 1\\n if '3' in featureMask:\\n _mask[2 * (2 * w + 1) ** 2:2 * (2 * w + 1) ** 2 + 2 * (2 * w + 1)] = 1\\n if '4' in featureMask:\\n _mask[2 * (2 * w + 1) ** 2 + 2 * (2 * w + 1)] = 1\\n if '5' in featureMask:\\n _mask[2 * (2 * w + 1) ** 2 + 2 * (2 * w + 1) + 1] = 1\\n if '6' in featureMask:\\n _mask[2 * (2 * w + 1) ** 2 + 2 * (2 * w + 1) + 2] = 1\\n if '7' in featureMask:\\n _mask[2 * (2 * w + 1) ** 2 + 2 * (2 * w + 1) + 3] = 1\\n featureMask = np.ma.make_mask(_mask)\\n\\n\\n\\n studyBcools = [bcool(file_path+'::/resolutions/'+str(resol)) for file_path in study.split(',')]\\n referencedBcools = [bcool(file_path+'::/resolutions/'+str(resol)) for file_path in reference['file'].to_list()]\\n _foci = pd.read_csv(foci, header=None, sep='\\\\t')\\n\\n # read labels\\n labels = {}\\n\\n for row in _foci.itertuples():\\n chr1 = row[1]\\n pos1 = row[2]\\n chr2 = row[4]\\n pos2 = row[5]\\n label = list(row[7:])\\n if chr1 == chr2 and abs(pos2 - pos1) < max_distance:\\n if chr1 not in labels:\\n labels[chr1] = {'contact':[],'label':[]}\\n labels[chr1]['contact'].append((pos1,pos2))\\n labels[chr1]['label'].append(label)\\n\\n\\n dataParams['classes'] = len(label)\\n dataParams['featureDim'] = np.sum(featureMask)\\n print('This training data contains ', dataParams['featureDim'], ' features, and ', dataParams['classes'], ' targets per item')\\n\\n\\n Xs = {} # for extra\\n label={}\\n X = {}\\n for chrom in labels:\\n if chrom not in X:\\n label[chrom] = []\\n for i in range(len(labels[chrom]['label'])):\\n label[chrom].append(labels[chrom]['label'][i])\\n\\n for b in studyBcools:\\n if chrom not in X:\\n X[chrom] = [[] for _ in range(len(labels[chrom]['contact']))]\\n bmatrix = b.bchr(chrom, max_distance=max_distance)\\n for i in range(len(labels[chrom]['contact'])):\\n x,y = labels[chrom]['contact'][i]\\n\\n mat,meta = bmatrix.square(x,y,w,'b')\\n X[chrom][i].append(np.concatenate((mat.flatten(), meta)))\\n\\n for b in referencedBcools:\\n if chrom not in Xs:\\n Xs[chrom] = [[] for _ in range(len(labels[chrom]['contact']))]\\n bmatrix = b.bchr(chrom, max_distance=max_distance)\\n for i in range(len(labels[chrom]['contact'])):\\n x,y = labels[chrom]['contact'][i]\\n mat,meta = bmatrix.square(x,y,w,'b')\\n Xs[chrom][i].append(np.concatenate((mat.flatten(), meta)))\\n\\n\\n X_train = []\\n Xs_train = []\\n y_label_train = []\\n X_test = []\\n Xs_test = []\\n y_label_test = []\\n X_val = []\\n Xs_val = []\\n y_label_val = []\\n for chrom in X:\\n for i in range(len(X[chrom])):\\n x=np.asarray(X[chrom][i])[:, featureMask]\\n xs=np.asarray(Xs[chrom][i])[:, featureMask]\\n if chrom in chromTest:\\n X_test.append(x)\\n Xs_test.append(xs)\\n y_label_test.append(label[chrom][i])\\n elif chrom in chromVal:\\n X_val.append(x)\\n Xs_val.append(xs)\\n y_label_val.append(label[chrom][i])\\n else:\\n X_train.append(x)\\n Xs_train.append(xs)\\n y_label_train.append(label[chrom][i])\\n del X,Xs\\n with open(output+'.pkl', 'wb') as handle:\\n pickle.dump((dataParams,X_train, Xs_train, y_label_train,\\n X_test, Xs_test, y_label_test,\\n X_val, Xs_val, y_label_val), handle, protocol=pickle.HIGHEST_PROTOCOL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e35be5e75be2c84b29336d64029e8491\",\n \"score\": \"0.5596661\",\n \"text\": \"def gen_reference(in_img,fwhm=5,newpath=None):\\n import nibabel as nb \\n import numpy as np\\n import os \\n from nibabel.processing import smooth_image\\n newpath = Path(newpath or \\\".\\\")\\n ss=check_img(in_img)\\n if ss == 0: \\n ref_data=nb.load(in_img).get_fdata()\\n else: \\n nii = nb.load(in_img).get_fdata()\\n ref_data=np.mean(nii,axis=3)\\n new_file = nb.Nifti1Image(dataobj=ref_data,header=nb.load(in_img).header,\\n affine=nb.load(in_img).affine)\\n\\n new_file = smooth_image(new_file,fwhm=fwhm)\\n out_file = fname_presuffix('aslref', suffix=\\\"_reference.nii.gz\\\", newpath=str(newpath.absolute()))\\n new_file.to_filename(out_file)\\n return out_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b631b81068cd0edffa6ac9a4bb507dd\",\n \"score\": \"0.54430836\",\n \"text\": \"def read_foreign(self):\\n ffile = pd.read_csv(INPUTPATH + 'BEA/international-by-industry.csv')\\n ffile.set_index('indcode', inplace=True)\\n self.foreign = copy.deepcopy(ffile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"130fc29d15da1c20f1afe1be162365a7\",\n \"score\": \"0.5427231\",\n \"text\": \"def read_ric(self, filename):\\n f = open(filename, \\\"r\\\")\\n line = f.readline()\\n if line[:3] != \\\"###\\\":\\n raise IOError, \\\"This does not look like a traditional reference file\\\"\\n line = f.readline()\\n stop = False\\n while not stop:\\n sline = line.split()\\n if sline[1] == \\\"stretch\\\":\\n ind = map(string.atoi, sline[2:4])\\n self.add_stretch(ind)\\n elif sline[1] == \\\"bend\\\":\\n ind = map(string.atoi, sline[2:5])\\n self.add_in_bend(ind)\\n elif sline[1] == \\\"wag\\\":\\n ind = map(string.atoi, sline[2:6])\\n self.add_out_bend(ind)\\n elif sline[1] == \\\"torsion\\\":\\n for i in xrange(2, len(sline)):\\n if sline[i].count(\\\".\\\") == 1: break\\n ind = map(string.atoi, sline[2:i])\\n self.add_torsion(ind)\\n else:\\n raise IOError, \\\"Unknown RIC type in reference file\\\"\\n line = f.readline()\\n if len(line)==0: stop=True\\n # in the refernece_dat file the Hessian elements start with a line with \\\"###\\\" -> stop here\\n if line[:3] == \\\"###\\\": stop=True\\n f.close()\\n # HACK\\n # assumption ... non peridic .. fix\\n self.add_eckart_rots()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"344d3c97a83e1549ea6cc41eeb6a6007\",\n \"score\": \"0.54000765\",\n \"text\": \"def read_reference(self):\\n ref = general.load_csv(self.reference_filename, drop_gene_names=False)\\n self.gene_symbol_dict = {name: wbgene for name, wbgene in zip(ref[self.ref_gene_col], ref[self.ref_wbgene_col])\\n if not pd.isna(name)}\\n self.sequence_dict = {name: wbgene for name, wbgene in zip(ref[self.ref_seq_col], ref[self.ref_wbgene_col])\\n if not pd.isna(name)}\\n\\n split_other_id = ref[self.ref_other_col].str.split(pat=\\\";\\\")\\n for namelst, wbgene in zip(split_other_id, ref[self.ref_wbgene_col]):\\n if isinstance(namelst, list):\\n for name in namelst:\\n self.other_id_dict[name] = wbgene\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeb386a58ffe5e49d2eb27ee88412acf\",\n \"score\": \"0.53429496\",\n \"text\": \"def setup(self):\\n # now let us do all the extra stuff to map rics and allocate python arrays for the reference\\n self.nstretch = len(self.pd.ric._stretches)\\n self.first_str = 0\\n self.nin_bend = len(self.pd.ric._in_bends)\\n self.first_ibe = self.first_str+self.nstretch\\n self.nout_bend = len(self.pd.ric._out_bends)\\n self.first_obe = self.first_ibe+self.nin_bend\\n self.nlin_bend = len(self.pd.ric._lin_bends)\\n self.first_lbe = self.first_obe+self.nout_bend\\n self.ntorsion = len(self.pd.ric._torsions)\\n self.first_tor = self.first_lbe+self.nlin_bend\\n # keep a list of central atoms for all torsions\\n self.tor_cent = []\\n for t in self.pd.ric._torsions:\\n na = len(t)\\n self.tor_cent.append(t[na/2-1:na/2+1])\\n # Allocate a reference structure/hessian array\\n self.ref_val_str = np.zeros([self.nstretch],dtype=\\\"float64\\\")\\n self.wgt_val_str = np.zeros([self.nstretch],dtype=\\\"float64\\\")\\n self.ref_val_ibe = np.zeros([self.nin_bend],dtype=\\\"float64\\\")\\n self.wgt_val_ibe = np.zeros([self.nin_bend],dtype=\\\"float64\\\")\\n self.ref_val_obe = np.zeros([self.nout_bend],dtype=\\\"float64\\\")\\n self.wgt_val_obe = np.zeros([self.nout_bend],dtype=\\\"float64\\\")\\n self.ref_val_lbe = np.zeros([self.nlin_bend],dtype=\\\"float64\\\")\\n self.wgt_val_lbe = np.zeros([self.nlin_bend],dtype=\\\"float64\\\")\\n self.ref_val_tor = np.zeros([self.ntorsion],dtype=\\\"float64\\\")\\n self.wgt_val_tor = np.zeros([self.ntorsion],dtype=\\\"float64\\\")\\n self.ref_hes = np.zeros([self.pd.ric.nric, self.pd.ric.nric], dtype=\\\"float64\\\")\\n self.wgt_hes = np.zeros([self.pd.ric.nric, self.pd.ric.nric], dtype=\\\"float64\\\")\\n # set some defaults\\n self.fact_str = 1.0\\n self.fact_ibe = 1.0\\n self.fact_obe = 1.0\\n self.fact_lbe = 1.0\\n self.fact_tor = 1.0\\n self.cycle = 0\\n self.fdiagnostics = open('ric_fit_%s.punch' % self.tag, 'w')\\n # We use here the original convention to compare Hessian elements in milidyn*A\\n self.fact_hes = 1.0/143.88\\n # generate referece data \\n self.generate_reference(self.pd.get_cell())\\n self.riclist = []\\n self.riclist.append(self.pd.ric._stretches)\\n self.riclist.append(self.pd.ric._in_bends)\\n self.riclist.append(self.pd.ric._out_bends)\\n self.riclist.append(self.pd.ric._lin_bends)\\n self.riclist.append(self.pd.ric._torsions)\\n self.firstrics = []\\n self.firstrics.append(self.first_str)\\n self.firstrics.append(self.first_ibe)\\n self.firstrics.append(self.first_obe)\\n self.firstrics.append(self.first_lbe)\\n self.firstrics.append(self.first_tor)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403e1c93bb6687b44869880662d2189e\",\n \"score\": \"0.5340942\",\n \"text\": \"def reference_offset(data):\\n offset = data[:, :, 0, 0] # Reference point: person 0, joint 0 -> NTURGB+D\\n data -= offset[:, :, None, None]\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd078f174079b22353fd75b6d7e6bfbe\",\n \"score\": \"0.52440065\",\n \"text\": \"def read_body_inc(f,verbose,jj,glueball_corr) :\\n for ibin in range(0, numbin):\\n for iopA in range(0,numop) :\\n for iblockA in range(0, nblock) :\\n for iopB in range(0,numop) : \\n for iblockB in range(0, nblock) :\\n for t in range(0, Ntmax) :\\n \\n try:\\n bbb = f.read(8)\\n except IOError as e:\\n print (\\\"I/O error({0}): {1}\\\".format(e.errno, e.strerror))\\n except:\\n print (\\\"Unexpected error:\\\", sys.exc_info()[0])\\n raise\\n\\n vvv = struct.unpack('d',bbb)\\n if verbose :\\n print t, vvv[0]\\n \\n glueball_corr[jj, iblockA,iblockB,t, iopA,iopB, ibin] = vvv[0]\\n\\n\\n\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7da5a38e4c47d3f065ff7d0c7d1d8744\",\n \"score\": \"0.5213994\",\n \"text\": \"def read_QM_in( network_architecture, scaling_factors, filename, stepnumber, options ): \\n #get path of QM.in\\n pathQMNN=options['QMoutpath']\\n #create QM_in object (imported from QM_transformer) that holds all information from the QM.in-file \\n raw_data = QM_in( filename, stepnumber, pathQMNN )\\n #get descriptors\\n descriptors = raw_data.descriptors#_coulomb\\n descriptors = np.array(descriptors)\\n states = raw_data.states\\n nmstates = raw_data.nmstates\\n n_atoms = raw_data.n_atoms\\n nstates = raw_data.nstates\\n spin = raw_data.spin\\n #charges = raw_data.charges\\n istates = raw_data.istates\\n n_singlets = raw_data.n_singlets\\n n_triplets = raw_data.n_triplets\\n input_dim = len( descriptors )\\n\\n if input_dim != network_architecture[0][0]:\\n print( 'Descriptor dimension of input and training data not matching.' )\\n exit()\\n\\n #scale descriptors \\n descriptors = ( descriptors - scaling_factors[0] ) / scaling_factors[1]\\n\\n #load descriptors in theano shared variables\\n descriptors = theano.shared( np.asarray(descriptors,dtype=theano.config.floatX), borrow=True ) \\n xyz_coords = []\\n xyz_coords.append(raw_data.geoms)\\n\\n return descriptors, xyz_coords, states, n_atoms, nstates, nmstates, istates, spin, n_singlets, n_triplets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf953df72149f789baabb51a18f2951f\",\n \"score\": \"0.52125144\",\n \"text\": \"def PullReadIrefCalibrationFile(self):\\n if os.path.isfile(self.gc_step1_filename):\\n cmdstring = 'del ' + self.gc_step1_filename\\n os.system(cmdstring) \\n cmdstring = 'adb pull /factory_setting/' + self.gc_step1_filename\\n os.system(cmdstring)\\n self.r_iref_values = np.zeros(self.NumLEDs)\\n self.g_iref_values = np.zeros(self.NumLEDs)\\n self.b_iref_values = np.zeros(self.NumLEDs)\\n with open(self.gc_step1_filename, 'r') as f:\\n for i in range(self.NumLEDs):\\n linestring = f.readline()\\n strlist = str.split(linestring, ' ')\\n self.r_iref_values[i] = int(strlist[0])\\n self.g_iref_values[i] = int(strlist[1])\\n self.b_iref_values[i] = int(strlist[2])\\n cmdstring = 'del ' + self.gc_step1_filename\\n os.system(cmdstring)\\n self.WriteIrefRegisters()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8d7072f845d6fcf170c24d9163675ea\",\n \"score\": \"0.5206732\",\n \"text\": \"def get_full_data(cur_params):\\n path_prefix = './'\\n if cur_params['monkey']=='D':\\n data = sio.loadmat(path_prefix+'drakeFeb.mat')\\n else:\\n data = sio.loadmat(path_prefix+'cousFeb.mat')\\n\\n # Built augmented data\\n emg2 = preprocess_array(data['D'][0, 0]['EMG'])\\n time_axis, time_inds1, time_inds2 = get_time_axis(data['D'][0, 0]['KIN'])\\n y_data1 = emg2[time_axis]\\n p = y_data1.shape[-1]\\n\\n # Build inputs\\n m = 2\\n u_data1 = create_input_array(y_data1.shape)\\n\\n # Augmented data\\n # For regularizing the network -- it must fit actual and augmented data\\n period = int(np.round(np.diff(time_inds2).mean()))\\n y_cat1 = augmented_data(emg2, time_inds1, period=period, tiles=10)\\n y_cat1 = y_cat1[::25]\\n y_cat2 = augmented_data(emg2, time_inds2, period=period, tiles=10)\\n y_cat2 = y_cat2[::25]\\n\\n u_cat1 = create_input_array(y_cat1.shape)\\n u_cat2 = create_input_array(y_cat2.shape)\\n\\n sequence_length = [y_data1.shape[0], y_cat1.shape[0], y_cat2.shape[0]]\\n y_data = np.zeros((np.max(sequence_length), 4*3, p))\\n u_data = np.zeros((np.max(sequence_length), 4*3, m))\\n\\n y_data[:sequence_length[0], 0:4, :] = y_data1\\n y_data[:sequence_length[1], 4:8, :] = y_cat1\\n y_data[:sequence_length[2], 8:12, :] = y_cat2\\n\\n u_data[:sequence_length[0], 0:4, :] = u_data1\\n u_data[:sequence_length[1], 4:8, :] = u_cat1\\n u_data[:sequence_length[2], 8:12, :] = u_cat2\\n return u_data, y_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f5c1b50de304bd33d22a134c7667f31\",\n \"score\": \"0.51948804\",\n \"text\": \"def load_physician_referral_data(infilename):\\n tmpdir = tempfile.mkdtemp()\\n try:\\n archive = zipfile.ZipFile(infilename, 'r')\\n archive.extract('physician-shared-patient-patterns-2014-days30.txt', tmpdir)\\n filename = os.path.join(tmpdir, \\\"physician-shared-patient-patterns-2014-days30.txt\\\")\\n archive.close()\\n context = snap.TTableContext()\\n schema = snap.Schema()\\n ## schema.Add(snap.TStrTAttrPr(\\\"NPI_1\\\", snap.atInt))\\n ## schema.Add(snap.TStrTAttrPr(\\\"NPI_2\\\", snap.atInt))\\n # the above 2 lines worked with SNAP 4.0.0 on VLSCI \\n # but now using SNAP 4.1.0\\n # on hpc.ics.usi.ch find that all ids are -1 so graph wrong.\\n # Cannot work out why so changed to string not int to try to fix it:\\n schema.Add(snap.TStrTAttrPr(\\\"NPI_1\\\", snap.atStr))\\n schema.Add(snap.TStrTAttrPr(\\\"NPI_2\\\", snap.atStr))\\n ## schema.Add(snap.TStrTAttrPr(\\\"count\\\", snap.atInt))\\n ## schema.Add(snap.TStrTAttrPr(\\\"unique_bene\\\", snap.atInt))\\n ## schema.Add(snap.TStrTAttrPr(\\\"same_day_count\\\", snap.atInt))\\n # The above 3 lines also worked fine with SNAP 4.0.0 before but\\n # now fail on SNAP 4.1.0 (seems to be due to spaces in CSV fields,\\n # not inexplicable like first two which have no spaces) but not using\\n # them at the moment anyway so easier to just make (unused) strings:\\n schema.Add(snap.TStrTAttrPr(\\\"count\\\", snap.atStr))\\n schema.Add(snap.TStrTAttrPr(\\\"unique_bene\\\", snap.atStr))\\n schema.Add(snap.TStrTAttrPr(\\\"same_day_count\\\", snap.atStr))\\n table = snap.TTable.LoadSS(schema, filename, context, \\\",\\\", snap.TBool(False))\\n G = snap.ToGraph(snap.PNGraph, table, \\\"NPI_1\\\", \\\"NPI_2\\\", snap.aaFirst) \\n finally:\\n cleanup_tmpdir(tmpdir)\\n\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae262497a0c062583959b4412d45d2c7\",\n \"score\": \"0.519233\",\n \"text\": \"def test_ref_degenerate():\\n info = read_info(ctf_fname)\\n # exclude ref by default\\n projs = compute_proj_hfc(info)\\n meg_names = [\\n info[\\\"ch_names\\\"][pick]\\n for pick in pick_types(info, meg=True, ref_meg=False, exclude=[])\\n ]\\n assert len(projs) == 3\\n assert projs[0][\\\"desc\\\"] == \\\"HFC: l=1 m=-1\\\"\\n assert projs[1][\\\"desc\\\"] == \\\"HFC: l=1 m=0\\\"\\n assert projs[2][\\\"desc\\\"] == \\\"HFC: l=1 m=1\\\"\\n assert projs[0][\\\"data\\\"][\\\"col_names\\\"] == meg_names\\n meg_ref_names = [\\n info[\\\"ch_names\\\"][pick]\\n for pick in pick_types(info, meg=True, ref_meg=True, exclude=[])\\n ]\\n projs = compute_proj_hfc(info, picks=(\\\"meg\\\", \\\"ref_meg\\\"))\\n assert projs[0][\\\"data\\\"][\\\"col_names\\\"] == meg_ref_names\\n\\n # Degenerate\\n info = read_info(fif_fname)\\n compute_proj_hfc(info) # smoke test\\n with pytest.raises(ValueError, match=\\\"Only.*could be interpreted as MEG\\\"):\\n compute_proj_hfc(info, picks=[0, 330]) # one MEG, one EEG\\n info[\\\"chs\\\"][0][\\\"loc\\\"][:] = np.nan # first MEG proj\\n with pytest.raises(ValueError, match=\\\"non-finite projectors\\\"):\\n compute_proj_hfc(info)\\n info_eeg = pick_info(info, pick_types(info, meg=False, eeg=True))\\n with pytest.raises(ValueError, match=r\\\"picks \\\\(\\\\'meg\\\\'\\\\) could not be\\\"):\\n compute_proj_hfc(info_eeg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e85d0b782e7b6bd3b291741b4d15c0b\",\n \"score\": \"0.5188064\",\n \"text\": \"def read_infit(self): \\n from astropy.io import ascii\\n self.fdat = ascii.read(self.cdir+self.bdir+self.ifit)\\n self.ldat = ascii.read(self.cdir+self.bdir+self.loop)\\n self.tdat = join(self.fdat,self.ldat,keys=['mod_nam'])\\n\\n self.comp_tsig()\\n self.read_model()\\n #remove some bad coronal loops\\n\\n#use model id for abbicus\\n self.tdat['mod_id'] = [int(i.split('_')[0].replace('model','')) for i in self.tdat['mod_nam']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"839e5bfbf6679cdb08e2931f1be010eb\",\n \"score\": \"0.5169292\",\n \"text\": \"def test_fqe_data_manipulation():\\n test = fqe_data.FqeData(1, 1, 2)\\n ref = numpy.random.rand(2, 2) + 1.j * numpy.random.rand(2, 2)\\n test.set_wfn(strategy='from_data', raw_data=ref)\\n assert numpy.allclose(test.beta_inversion(), ref[:, (1, 0)])\\n test.conj()\\n assert numpy.allclose(test.coeff, numpy.conj(ref))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6c7802d7a5b79dfd6337fe00870cef3\",\n \"score\": \"0.5151748\",\n \"text\": \"def read_op2_dynamics(self):\\n key = self._get_key()\\n if self._ibytes == 4:\\n header_str = struct.Struct(self._endian + b'iii')\\n hbytes = 12\\n else:\\n header_str = struct.Struct(self._endian + b'qqq')\\n hbytes = 24\\n\\n eot = 0\\n print('self._intstr = %r' % self._intstr)\\n data = np.zeros(0, dtype=self._intstr)\\n while not eot:\\n while key > 0:\\n self._fileh.read(4) # reclen\\n header = header_str.unpack(self._fileh.read(hbytes))\\n if header == (7107, 71, 138):\\n if key < self._rowsCutoff:\\n bytes = (key-3)*self._ibytes\\n ndata = struct.unpack(self._intstru % (key-3),\\n self._fileh.read(bytes))\\n else:\\n ndata = np.fromfile(self._fileh,\\n self._intstr, key-3)\\n data = np.hstack((data, ndata))\\n else:\\n self._fileh.seek((key-3)*self._ibytes, 1)\\n self._fileh.read(4) # endrec\\n key = self._get_key()\\n self._skip_key(2)\\n eot, key = self._read_op2_end_of_table()\\n\\n if np.any(data):\\n L = len(data)\\n mult5 = L == 5*(L // 5)\\n mult6 = L == 6*(L // 6)\\n err1 = ('Could not determine if TLOADs are 5 or 6 rows! '\\n 'Both work. Routine needs updating.')\\n err2 = ('Could not determine if TLOADs are 5 or 6 rows! '\\n 'Neither work. Routine needs updating.')\\n if mult5:\\n mindelta5 = np.min(np.diff(data[0::5]))\\n if mult6:\\n mindelta6 = np.min(np.diff(data[0::6]))\\n if mult5:\\n if mult6:\\n # L is multiple of both 5 and 6:\\n if mindelta5 > 0:\\n if mindelta6 > 0:\\n raise ValueError(err1)\\n rows = 5\\n else:\\n if mindelta6 > 0:\\n rows = 6\\n else:\\n raise ValueError(err2)\\n else:\\n if mindelta5 > 0:\\n rows = 5\\n else:\\n raise ValueError(err2)\\n elif mult6:\\n if mindelta6 > 0:\\n rows = 6\\n else:\\n raise ValueError(err2)\\n else:\\n raise ValueError(err2)\\n data = np.reshape(data, (rows, -1), order='F')\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0df54f7668b50397167a12fb6fff7471\",\n \"score\": \"0.5142007\",\n \"text\": \"def read_body(f,verbose,jj,glueball_corr) :\\n for ibin in range(0, numbin):\\n for iopA in range(0,numop) :\\n for iblockA in range(0, nblock) :\\n for iopB in range(0,numop) : \\n for iblockB in range(0, nblock) :\\n for t in range(0, Ntmax) :\\n \\n try:\\n bbb = f.read(8)\\n except IOError as e:\\n print (\\\"I/O error({0}): {1}\\\".format(e.errno, e.strerror))\\n except:\\n print (\\\"Unexpected error:\\\", sys.exc_info()[0])\\n raise\\n\\n vvv = struct.unpack('d',bbb)\\n if verbose :\\n print (t, vvv[0])\\n\\n \\n glueball_corr[jj, iblockA,iblockB,t, iopA,iopB, ibin] = vvv[0]\\n\\n\\n\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02d2d25a183dbde04caaec41861f8752\",\n \"score\": \"0.5140673\",\n \"text\": \"def test_io_forward(tmp_path):\\n # do extensive tests with MEEG + grad\\n n_channels, n_src = 366, 108\\n fwd = read_forward_solution(fname_meeg_grad)\\n assert isinstance(fwd, Forward)\\n fwd = read_forward_solution(fname_meeg_grad)\\n fwd = convert_forward_solution(fwd, surf_ori=True)\\n leadfield = fwd[\\\"sol\\\"][\\\"data\\\"]\\n assert_equal(leadfield.shape, (n_channels, n_src))\\n assert_equal(len(fwd[\\\"sol\\\"][\\\"row_names\\\"]), n_channels)\\n fname_temp = tmp_path / \\\"test-fwd.fif\\\"\\n with pytest.warns(RuntimeWarning, match=\\\"stored on disk\\\"):\\n write_forward_solution(fname_temp, fwd, overwrite=True)\\n\\n fwd = read_forward_solution(fname_meeg_grad)\\n fwd = convert_forward_solution(fwd, surf_ori=True)\\n fwd_read = read_forward_solution(fname_temp)\\n fwd_read = convert_forward_solution(fwd_read, surf_ori=True)\\n leadfield = fwd_read[\\\"sol\\\"][\\\"data\\\"]\\n assert_equal(leadfield.shape, (n_channels, n_src))\\n assert_equal(len(fwd_read[\\\"sol\\\"][\\\"row_names\\\"]), n_channels)\\n assert_equal(len(fwd_read[\\\"info\\\"][\\\"chs\\\"]), n_channels)\\n assert \\\"dev_head_t\\\" in fwd_read[\\\"info\\\"]\\n assert \\\"mri_head_t\\\" in fwd_read\\n assert_array_almost_equal(fwd[\\\"sol\\\"][\\\"data\\\"], fwd_read[\\\"sol\\\"][\\\"data\\\"])\\n\\n fwd = read_forward_solution(fname_meeg)\\n fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=True, use_cps=False)\\n with pytest.warns(RuntimeWarning, match=\\\"stored on disk\\\"):\\n write_forward_solution(fname_temp, fwd, overwrite=True)\\n fwd_read = read_forward_solution(fname_temp)\\n fwd_read = convert_forward_solution(\\n fwd_read, surf_ori=True, force_fixed=True, use_cps=False\\n )\\n assert repr(fwd_read)\\n assert isinstance(fwd_read, Forward)\\n assert is_fixed_orient(fwd_read)\\n assert_forward_allclose(fwd, fwd_read)\\n\\n fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=True, use_cps=True)\\n leadfield = fwd[\\\"sol\\\"][\\\"data\\\"]\\n assert_equal(leadfield.shape, (n_channels, 1494 / 3))\\n assert_equal(len(fwd[\\\"sol\\\"][\\\"row_names\\\"]), n_channels)\\n assert_equal(len(fwd[\\\"info\\\"][\\\"chs\\\"]), n_channels)\\n assert \\\"dev_head_t\\\" in fwd[\\\"info\\\"]\\n assert \\\"mri_head_t\\\" in fwd\\n assert fwd[\\\"surf_ori\\\"]\\n with pytest.warns(RuntimeWarning, match=\\\"stored on disk\\\"):\\n write_forward_solution(fname_temp, fwd, overwrite=True)\\n fwd_read = read_forward_solution(fname_temp)\\n fwd_read = convert_forward_solution(\\n fwd_read, surf_ori=True, force_fixed=True, use_cps=True\\n )\\n assert repr(fwd_read)\\n assert isinstance(fwd_read, Forward)\\n assert is_fixed_orient(fwd_read)\\n assert_forward_allclose(fwd, fwd_read)\\n\\n fwd = read_forward_solution(fname_meeg_grad)\\n fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=True, use_cps=True)\\n leadfield = fwd[\\\"sol\\\"][\\\"data\\\"]\\n assert_equal(leadfield.shape, (n_channels, n_src / 3))\\n assert_equal(len(fwd[\\\"sol\\\"][\\\"row_names\\\"]), n_channels)\\n assert_equal(len(fwd[\\\"info\\\"][\\\"chs\\\"]), n_channels)\\n assert \\\"dev_head_t\\\" in fwd[\\\"info\\\"]\\n assert \\\"mri_head_t\\\" in fwd\\n assert fwd[\\\"surf_ori\\\"]\\n with pytest.warns(RuntimeWarning, match=\\\"stored on disk\\\"):\\n write_forward_solution(fname_temp, fwd, overwrite=True)\\n fwd_read = read_forward_solution(fname_temp)\\n fwd_read = convert_forward_solution(\\n fwd_read, surf_ori=True, force_fixed=True, use_cps=True\\n )\\n assert repr(fwd_read)\\n assert isinstance(fwd_read, Forward)\\n assert is_fixed_orient(fwd_read)\\n assert_forward_allclose(fwd, fwd_read)\\n\\n # test warnings on bad filenames\\n fwd = read_forward_solution(fname_meeg_grad)\\n fwd_badname = tmp_path / \\\"test-bad-name.fif.gz\\\"\\n with pytest.warns(RuntimeWarning, match=\\\"end with\\\"):\\n write_forward_solution(fwd_badname, fwd)\\n with pytest.warns(RuntimeWarning, match=\\\"end with\\\"):\\n read_forward_solution(fwd_badname)\\n\\n fwd = read_forward_solution(fname_meeg)\\n write_forward_solution(fname_temp, fwd, overwrite=True)\\n fwd_read = read_forward_solution(fname_temp)\\n assert_forward_allclose(fwd, fwd_read)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"136b7409a35513fc514c65435d29a833\",\n \"score\": \"0.50885725\",\n \"text\": \"def get_ref(in_file):\\n with open(\\\"./input_data/\\\" + in_file) as in_handle:\\n for title, seq in SimpleFastaParser(in_handle):\\n ref_seq = seq\\n break\\n\\n return ref_seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"909e8ffcbc7f64e1cde32b68b3f5f296\",\n \"score\": \"0.507998\",\n \"text\": \"def compute_Fnat_fast(self,ref_pairs=None,cutoff=5):\\n # read the izone file\\n if ref_pairs is None:\\n residue_pairs_ref = self.compute_residue_pairs_ref(cutoff,save_file=False)\\n\\n elif not os.path.isfile(ref_pairs):\\n self.compute_residue_pairs_ref(cutoff,save_file=True,filename=ref_pairs)\\n f = open(ref_pairs,'rb')\\n residue_pairs_ref = pickle.load(f)\\n f.close()\\n else:\\n f = open(ref_pairs,'rb')\\n residue_pairs_ref = pickle.load(f)\\n f.close()\\n\\n # create a dict of the ecoy data\\n if isinstance(self.decoy,str) and os.path.isfile(self.decoy):\\n with open(self.decoy,'r') as f:\\n data_decoy = f.readlines()\\n decoy_name = os.path.basename(self.decoy)\\n elif isinstance(self.decoy,np.ndarray):\\n data_decoy = [l.decode('utf-8') for l in self.decoy]\\n decoy_name = 'decoy'\\n else:\\n raise ValueError('Decoy not found')\\n\\n # read the decoy data\\n atom_decoy, xyz_decoy = [],[]\\n residue_xyz = {}\\n residue_name = {}\\n\\n # go through all the lines\\n # that starts with ATOM\\n for line in data_decoy:\\n\\n if line.startswith('ATOM'):\\n\\n # chain ID\\n chainID = line[21]\\n if chainID == ' ':\\n chainID = line[72]\\n\\n # atom info\\n resSeq = int(line[22:26])\\n resName = line[17:20].strip()\\n name = line[12:16].strip()\\n\\n # position\\n x,y,z = float(line[30:38]), float(line[38:46]), float(line[46:54])\\n\\n # dict entry\\n key = (chainID,resSeq,resName)\\n\\n # create the dict entry if necessary\\n if key not in residue_xyz.keys():\\n residue_xyz[key] = []\\n residue_name[key] = []\\n\\n # we exclude the Hydrogens from the search\\n if name[0] != 'H':\\n residue_xyz[key].append([x,y,z])\\n residue_name[key].append(name)\\n\\n # loop over the residue pairs of the\\n # and increment common if an atom pair is close enough\\n nCommon,nTotal = 0,0\\n for resA,resB_list in residue_pairs_ref.items():\\n if resA in residue_xyz:\\n xyzA = residue_xyz[resA]\\n for resB in resB_list:\\n if resB in residue_xyz.keys():\\n xyzB = residue_xyz[resB]\\n dist_min = np.min(np.array([ np.sqrt(np.sum((np.array(p1)-np.array(p2))**2)) for p1 in xyzA for p2 in xyzB ]))\\n if dist_min<=cutoff:\\n nCommon += 1\\n nTotal += 1\\n else:\\n msg = '\\\\t FNAT Warning could not find residue: ', resA, ' in: ',decoy_name\\n _printif(msg,self.verbose)\\n\\n # normalize\\n return nCommon/nTotal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35d63de85a9d0930b49815b0247f47dc\",\n \"score\": \"0.50795496\",\n \"text\": \"def read_data(self):\\n import os\\n import numpy as np\\n\\n if isinstance(self.filename, str):\\n f = open(self.filename)\\n nitpi2f = None\\n itpi2f = None\\n i2f = []\\n ni2f = []\\n\\n for n,line in enumerate(f):\\n if 'k-points in 1st BZ' in line:\\n #initiate temporary array\\n self.nitpi2f = np.array(ni2f)\\n i2f = []\\n\\n if 'KPOINTS_INTER' in line:\\n # save the first array\\n self.nitpi2f = np.array(ni2f)\\n\\n if 't-inv' in line:\\n # read the mapping indeces\\n i2f.append(int(line.split()[4])-1)\\n ni2f.append((int(line.split()[4])-1,float(line.split()[0]),float(line.split()[1]),float(line.split()[2]),float(line.split()[3])))\\n\\n self.itpi2f = np.array(i2f)\\n #self.nitpi2f = np.array(ni2f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3969afca6b74e3d99bf20fa077569dee\",\n \"score\": \"0.5076639\",\n \"text\": \"def loadub(self, file):\\r\\n ub=None\\r\\n pos=None\\r\\n cons_list=None\\r\\n latt=None\\r\\n azi_ref=None\\r\\n azi_hkl=None\\r\\n \\r\\n if isinstance(file,int):\\r\\n file=self.listub(ret=True)[file]\\r\\n \\r\\n if str(file).endswith('.txt'):\\r\\n with open (file,'r') as f:\\r\\n print(\\\"reading .txt file\\\\n\\\",file)\\r\\n \\r\\n lines=f.read().split('\\\\n')\\r\\n #print(lines)\\r\\n \\r\\n #go through the file looking for markers\\r\\n for i,line in enumerate(lines):\\r\\n #line=line.strip().split()\\r\\n #print(line,line[0])\\r\\n #read the ub matrix rows\\r\\n if(line=='ub'):\\r\\n ub=np.zeros((3,3))\\r\\n #print(\\\"ub marker\\\")\\r\\n for j in range(3):\\r\\n ubrow=lines[i+j+1].strip().split()\\r\\n ubrow=[float(x) for x in ubrow]\\r\\n ub[j,:]=np.array(ubrow)\\r\\n \\r\\n #read position\\r\\n elif(line=='sixc'):\\r\\n #print(\\\"pos marker\\\")\\r\\n pos=lines[i+1].strip().split()\\r\\n pos=[float(x) for x in pos] #uses position order\\r\\n \\r\\n #read contraints\\r\\n elif(line=='con'):\\r\\n cons={}\\r\\n cons_list=[]\\r\\n #print(\\\"con marker\\\")\\r\\n for j in range(3):\\r\\n if (i+j+1)<len(lines):\\r\\n conline=lines[i+j+1].strip().split()\\r\\n if len(conline)==1:\\r\\n cons[conline[0]]=True\\r\\n elif len(conline)==2:\\r\\n if (conline[1]=='None' or conline[1]==None):\\r\\n cons[conline[0]]=True\\r\\n else:\\r\\n cons[conline[0]]=float(conline[1])\\r\\n\\r\\n for key in cons:\\r\\n key_varified = self.varifykey(key)\\r\\n cons_list.append(key_varified)\\r\\n cons_list.append(cons[key])\\r\\n \\r\\n elif(line=='en'):\\r\\n en=float(lines[i+1].strip())\\r\\n \\r\\n elif(line=='lattice'):\\r\\n latt_name=lines[i+1].strip()\\r\\n latt=[float(x) for x in lines[i+2].strip().split()]\\r\\n \\r\\n elif(line=='azi_ref'):\\r\\n azi_ref=lines[i+1].strip().split()\\r\\n azi_ref=[float(x) for x in azi_ref] #uses position order\\r\\n \\r\\n elif(line=='azi_hkl'):\\r\\n azi_hkl=lines[i+1].strip().split()\\r\\n azi_hkl=[float(x) for x in azi_hkl]\\r\\n \\r\\n \\r\\n #print(cons_list,cons)\\r\\n \\r\\n print(\\\"Setting:\\\\n\\\")\\r\\n if not(ub is None):\\r\\n print(\\\"ub\\\")\\r\\n self.ubcalc.set_ub(ub)\\r\\n self.hklcalc.ubcalc.UB=ub\\r\\n print(self.ubcalc.UB)\\r\\n print()\\r\\n if not(pos is None):\\r\\n print(\\\"sixc\\\")\\r\\n self.pos(self.scannables['sixc'],self.pos_to_sixc(pos))\\r\\n if not(cons_list is None):\\r\\n print(\\\"con\\\")\\r\\n self.con(*cons_list)\\r\\n if not(en is None):\\r\\n print('en')\\r\\n self.pos(self.scannables['en'],en)\\r\\n if not(latt is None):\\r\\n self.setlat(latt_name,*latt)\\r\\n print(\\\"lattice\\\")\\r\\n print(latt_name,latt)\\r\\n print()\\r\\n \\r\\n try:\\r\\n if self.ubcalc.crystal is not None:\\r\\n self.ubcalc.U = self.ubcalc.UB @ np.linalg.inv(self.ubcalc.crystal.B)\\r\\n except Exception as e:\\r\\n traceback.print_exc()\\r\\n print(e)\\r\\n print('Failed to set U matrix')\\r\\n \\r\\n elif not(self.ubcalc.UB is None):\\r\\n pass\\r\\n #get real vectors from UB\\r\\n \\r\\n if not (azi_ref is None):\\r\\n self.setnphi(azi_ref)\\r\\n print('nphi')\\r\\n print(azi_ref)\\r\\n print()\\r\\n if not (azi_hkl is None):\\r\\n self.setnhkl(azi_hkl)\\r\\n print('nhkl')\\r\\n print(azi_hkl)\\r\\n print()\\r\\n \\r\\n self.hklcalc= HklCalculation(self.ubcalc, self.cons)\\r\\n self.update_pos()\\r\\n self.set_reciprocal_vectors()\\r\\n vectors.set_vector('reference azimuthal',self.setnphi())\\r\\n \\r\\n #if it is a nexus file, create a suitable .txt and read that\\r\\n elif str(file).endswith('.nxs'):\\r\\n from i16sim.util.hdf5_to_i16sim import interpret\\r\\n interpret(file)\\r\\n self.loadub(str(file)+'.i16sim.txt')\\r\\n \\r\\n else:\\r\\n raise Exception('File not recognised')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7c06e2b12fc1af265c18813538817b8\",\n \"score\": \"0.5075727\",\n \"text\": \"def gen_reference(out_data_list, bus_num=2, file=axis_check_file):\\r\\n with open(file, \\\"w+\\\") as f:\\r\\n for l in out_data_list:\\r\\n l_tmp = l.copy()\\r\\n while len(l_tmp) > bus_num:\\r\\n f.write(\\\"0_\\\")\\r\\n for b in range(bus_num):\\r\\n trans_part = l_tmp.pop(0)\\r\\n f.write(\\\"{0:08x}{1:08x}\\\".format(int_to_short(trans_part.imag, 32), int_to_short(trans_part.real, 32)))\\r\\n f.write(\\\"\\\\n\\\")\\r\\n else:\\r\\n f.write(\\\"1_\\\")\\r\\n for b in range(bus_num):\\r\\n trans_part = l_tmp.pop(0)\\r\\n f.write(\\\"{0:08x}{1:08x}\\\".format(int_to_short(trans_part.imag, 32), int_to_short(trans_part.real, 32)))\\r\\n f.write(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d1d579f393da01bae1c4ad8282cdd3e\",\n \"score\": \"0.5053391\",\n \"text\": \"def _otherreference(self):\\n\\n print('Loading otherreference.tsv')\\n ipath = os.path.join(self._ipath, 'otherreference.tsv.bz2')\\n opath = os.path.join(self._ipath, 'otherreference.pkl.bz2')\\n if os.path.exists(opath):\\n return pd.read_pickle(opath)\\n otherreference = pd.read_csv(ipath, sep='\\\\t', quoting=3, dtype=str,\\n lineterminator='\\\\n').drop(\\n columns=['uuid', 'sequence'])\\n otherreference.dropna(axis='index', how='any', inplace=True)\\n otherreference = otherreference.groupby('patent_id').aggregate(\\n lambda x: len(list(x)))\\n otherreference.rename(columns={'text': 'otherreference'}, inplace=True)\\n otherreference.index.rename('pid', inplace=True)\\n otherreference.to_pickle(opath)\\n return otherreference\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28ed0f1a9ad5fa47fc21f3e0c4e95ded\",\n \"score\": \"0.5052405\",\n \"text\": \"def read_datafile(self):\\n if self.datafile is None:\\n\\n default_files = ['ai1dat.dat','ai1mod.dat','ai1fit.dat',\\n 'inmodel.dat','inregulm.dat']\\n dlst = [i for i in os.listdir(self.working_directory) if \\\\\\n (i[-4:] == '.dat') and (i not in default_files)]\\n if len(dlst) == 1:\\n self.datafile = dlst[0]\\n else:\\n print(\\\"please define datafile\\\")\\n return\\n\\n # define path to file\\n datafpath = os.path.join(self.working_directory,self.datafile)\\n self.mode = open(datafpath).readline().strip().split()[0]\\n data = np.loadtxt(datafpath,skiprows = 2)\\n self.freq = 1./data[:,0]\\n \\n if self.mode == 'I': \\n zr = np.vstack([data[:,i] for i in range(len(data[0])) if (i-1)%4 == 0])\\n ze = np.vstack([data[:,i] for i in range(len(data[0])) if (i-2)%4 == 0])\\n zi = -np.vstack([data[:,i] for i in range(len(data[0])) if (i-3)%4 == 0])\\n z = zr + 1j*zi\\n self.z = z.T.reshape(len(z[0]),2,2)\\n self.zerr = ze.T.reshape(len(z[0]),2,2)\\n\\n \\n # make a frequency array that has the same shape as z\\n freq2 = np.zeros(np.shape(self.z))\\n for i in range(len(freq2)):\\n freq2[i,:,:] = 1./data[:,0][i] \\n \\n# calculate resistivity\\n self.resistivity = 0.2*np.abs(self.z)**2/freq2\\n \\n q = np.zeros(np.shape(self.resistivity))\\n# q[(zr<0)&(zi<0)] = np.pi\\n# q[(zr<0)&(zi>0)] = -np.pi\\n phase = np.zeros([len(self.z),2,2])\\n res = np.zeros([len(self.z),2,2])\\n self.resistivity_err = np.zeros([len(self.z),2,2])\\n self.phase_err = np.zeros([len(self.z),2,2]) \\n \\n \\n self.q = q\\n for iz in range(len(self.z)):\\n for i in range(2):\\n for j in range(2):\\n phase[iz,i,j] = np.rad2deg(cmath.phase(self.z[iz,i,j]))\\n res[iz,i,j]= 0.2*np.abs(self.z[iz,i,j])**2/self.freq[iz]\\n r_err, phi_err = MTcc.zerror2r_phi_error(\\n np.real(self.z[iz,i,j]), \\n self.zerr[iz,i,j], \\n np.imag(self.z[iz,i,j]), \\n self.zerr[iz,i,j])\\n\\n \\n\\n self.resistivity_err[iz,i,j] = \\\\\\n 0.4*np.abs(self.z[iz,i,j])/\\\\\\n self.freq[iz]*r_err\\n self.phase_err[iz,i,j] = phi_err\\n\\n phase[phase<-180] += 360\\n self.phase = phase\\n self.resistivity = res\\n \\n elif self.mode == 'R':\\n res = np.vstack([data[:,i] for i in range(len(data[0])) if (i-1)%4 == 0])\\n self.resistivity = res.T.reshape(len(res[0]),2,2)\\n res_err = np.vstack([data[:,i] for i in range(len(data[0])) if (i-2)%4 == 0])\\n self.resistivity_err = res_err.T.reshape(len(res_err[0]),2,2)\\n \\n phs = np.vstack([data[:,i] for i in range(len(data[0])) if (i-3)%4 == 0])\\n self.phase = phs.T.reshape(len(phs[0]),2,2)\\n phs_err = np.vstack([data[:,i] for i in range(len(data[0])) if (i-4)%4 == 0])\\n self.phase_err = phs_err.T.reshape(len(phs_err[0]),2,2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35877cecfc58cbbc07d9dc94f46bc419\",\n \"score\": \"0.5046248\",\n \"text\": \"def run_reference() :\\n db = get_db(0)\\n db.put_int('adjoint', 0)\\n db.put_int(\\\"store_angular_flux\\\", 1)\\n \\n mat = get_material()\\n\\n #--------------------------------------------------------------------------#\\n # MODELS\\n #--------------------------------------------------------------------------#\\n phi = []\\n numcells = []\\n keff = []\\n pin_power = []\\n angularmoment = []\\n nModels = len(cases)-1\\n names = cases[nDLPcases:nModels]\\n print names\\n number = 0\\n if nGroups == 238 :\\n thermal_cutoff_group = 203\\n elif nGroups == 44 :\\n thermal_cutoff_group = 27\\n for n in names :\\n if n == '1-pin' :\\n db.put_str(\\\"outer_solver\\\", \\\"GS\\\")\\n db.put_str(\\\"bc_west\\\", \\\"periodic\\\")\\n db.put_str(\\\"bc_east\\\", \\\"periodic\\\")\\n else :\\n db.put_str(\\\"outer_solver\\\", \\\"GMRES\\\")\\n db.put_str(\\\"bc_west\\\", \\\"reflect\\\")\\n db.put_str(\\\"bc_east\\\", \\\"reflect\\\") \\n print n\\n x = []\\n mesh = get_mesh(number) \\n number += 1 \\n solver = Eigen1D(db, mat, mesh)\\n solver.solve()\\n if n == '10-pin' :\\n keff = solver.state().eigenvalue()\\n rates = ReactionRates(mat, mesh, solver.state())\\n pin_power = rates.region_power(\\\"PINS\\\") \\n ng = solver.material().number_groups()\\n nc = solver.mesh().number_cells()\\n no = solver.quadrature().number_octants()/2\\n na = solver.quadrature().number_angles_octant()\\n phig = np.zeros((ng,nc))\\n x.append(mesh.dx(0) *0.5)\\n for i in range(1,nc) :\\n x.append(x[i-1]+mesh.dx(i-1))\\n numcells.append(x)\\n for g in range(0, ng) :\\n for i in range(0, nc) :\\n phig[g][i] = solver.state().phi(g)[i]\\n np.savetxt(dir_name+'/refdata/'+n+'_mg_flux', phig)\\n phi.append(phig)\\n\\n nOrder = 4\\n angmom = np.zeros((nOrder,ng,nc))\\n P = OrthogonalBasisParameters()\\n P.order = nOrder\\n P.size = na\\n P.x.resize(P.size, 0.0)\\n P.qw.resize(P.size, 0.0)\\n P.lower_bound = 0.0\\n P.upper_bound = 1.0\\n P.orthonormal = False\\n x = np.linspace(P.lower_bound, P.upper_bound, P.size)\\n for g in range(ng) :\\n for i in range(nc) :\\n for o in range(no) :\\n psi = np.zeros((na))\\n for a in range(na) :\\n psi[a] = solver.state().psi(g,o,a)[i]\\n for a in range(na) :\\n P.x[a] = solver.quadrature().mu(o,a)\\n P.qw[a] = solver.quadrature().weight(a)\\n ang = OrthogonalBasis.Create(\\\"jacobi\\\", P)\\n angular = np.zeros(nOrder+1)\\n y = vec_dbl(psi.tolist())\\n z = vec_dbl(angular.tolist())\\n ang.transform(y, z)\\n for order in range(nOrder) : \\n angmom[order][g][i] += z[order]\\n for order in range(nOrder) :\\n np.savetxt(dir_name+'/refdata/'+n+'_mg_moment'+str(order), angmom[order])\\n angularmoment.append(angmom)\\n \\n #--------------------------------------------------------------------------#\\n # PLOTS AND DATA\\n #--------------------------------------------------------------------------#\\n\\n io = IO_HDF5('cross_section_libraries/'+str(test_problem)+'_'+str(nGroups)+'_library.h5')\\n db = io.read_input()\\n EG = db.get_vec_dbl('neutron_energy_bounds')\\n energyrange = np.zeros((len(EG)-1))\\n deltaE = np.zeros((len(EG)-1))\\n for i in range(len(energyrange)):\\n energyrange[i] = (EG[i]+EG[i+1])/2\\n deltaE[i] = abs(EG[i+1] - EG[i])\\n \\n Eave = []\\n Save = []\\n for order in range(nOrder) :\\n E = []\\n S = []\\n for c in range(len(names)) :\\n E.append(np.mean(angularmoment[c][order], axis=0))\\n S.append(np.mean(angularmoment[c][order], axis=1))\\n Eave.append(E)\\n Save.append(S)\\n\\n A = []\\n # Data for Phi plots \\n for a in range(len(phi)): \\n y, A0 = common.barchart(EG, np.mean(phi[a], axis=1))\\n A.append(A0)\\n C = []\\n for order in range(nOrder) :\\n CC = []\\n for core in range(len(names)) :\\n y, B00 = common.barchart(EG, Save[order][core])\\n CC.append(B00)\\n C.append(CC)\\n\\n groups = range(0, nGroups)\\n fontP = FontProperties()\\n fontP.set_size('medium')\\n plt.figure(0, figsize=(8, 6.5))\\n plt.semilogy(groups, np.mean(phi[0], axis=1), 'k-', \\\\\\n groups, np.mean(phi[1], axis=1), 'b-', \\\\\\n groups, np.mean(phi[2], axis=1), 'g-', \\\\\\n groups, np.mean(phi[3], axis=1), 'r-' )\\n plt.xlabel('$g$')\\n plt.ylabel('$\\\\phi_g$')\\n leg = ['10-pin', 'UO$_2$', 'MOX', '1-pin']\\n plt.legend(leg, loc = 0, prop=fontP)\\n plt.grid(True)\\n plt.tight_layout()\\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'group_spectra.pdf')\\n\\n # Spectra Plot\\n leg = ['10-pin', 'UO$_2$', 'MOX']\\n plt.figure(1, figsize=(8, 6.5))\\n plt.loglog(y, A[0], 'k-', \\\\\\n y, A[1], 'r-', \\\\\\n y, A[2], 'g-' )\\n plt.xlabel('energy in $eV$')\\n plt.ylabel('$\\\\phi_g$')\\n plt.legend(leg, loc = 0, prop=fontP)\\n plt.grid(True) \\n plt.tight_layout()\\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'group_spectra_energy.pdf') \\n \\n # Spatially Averaged Moment Plots\\n # Moment 0 Plot\\n plt.figure(2, figsize=(8, 6.5))\\n plt.loglog(y, C[0][0], 'k-', \\\\\\n y, C[0][1], 'r-', \\\\\\n y, C[0][2], 'g-' )\\n plt.xlabel('energy in $eV$')\\n plt.ylabel('${moment0}_g$')\\n plt.legend(leg, loc = 0, prop=fontP)\\n plt.grid(True) \\n plt.tight_layout()\\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'monent0_spectra_energy.pdf')\\n \\n # Moment 1 Plot\\n plt.figure(3, figsize=(8, 6.5))\\n plt.loglog(y, C[1][0], 'k-', \\\\\\n y, C[1][1], 'r-', \\\\\\n y, C[1][2], 'g-' )\\n plt.xlabel('energy in $eV$')\\n plt.ylabel('${moment1}_g$')\\n plt.legend(leg, loc = 0, prop=fontP)\\n plt.grid(True)\\n plt.tight_layout()\\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'monent1_spectra_energy.pdf')\\n \\n # Moment 2 Plot\\n plt.figure(4, figsize=(8, 6.5))\\n plt.loglog(y, C[2][0], 'k-', \\\\\\n y, C[2][1], 'r-', \\\\\\n y, C[2][2], 'g-' )\\n plt.xlabel('energy in $eV$')\\n plt.ylabel('${moment2}_g$')\\n plt.legend(leg, loc = 0, prop=fontP)\\n plt.grid(True)\\n plt.tight_layout()\\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'monent2_spectra_energy.pdf')\\n \\n # Energy Averaged Plots\\n x = numcells[0] \\n \\n leg = ['Fast Phi', 'Thermal Phi']\\n plt.figure(5, figsize=(8, 6.5))\\n plt.plot(x, np.mean(phi[0][:thermal_cutoff_group][0:len(x)],axis=0), 'k-', \\\\\\n x, np.mean(phi[0][thermal_cutoff_group:][0:len(x)],axis=0), 'g-' )\\n plt.xlabel('position in cm')\\n plt.ylabel('$\\\\phi$')\\n plt.legend(leg, loc = 0, prop=fontP)\\n plt.grid(True)\\n plt.tight_layout()\\n plt.savefig(dir_name+'/reference_figures/spectra_full_energyaveraged.pdf')\\n \\n # Moment 0 Plot\\n \\n leg = ['$0^{th}$ moment', '$1^{st}$ moment', '$2^{nd}$ moment']\\n plt.figure(6, figsize=(8, 6.5))\\n plt.plot(x, Eave[0][0], 'k-', \\\\\\n x, Eave[1][0], 'r-', \\\\\\n x, Eave[2][0], 'g-' )\\n plt.xlabel('position in cm')\\n plt.ylabel('${moment0}_g$')\\n plt.legend(leg, loc = 0, prop=fontP)\\n plt.grid(True)\\n plt.tight_layout()\\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'monent_spectra_full.pdf')\\n \\n # Moment 1 Plot\\n x = numcells[1]\\n n = len(x)\\n plt.figure(7, figsize=(8, 6.5))\\n plt.plot(x, Eave[0][1], 'k-', \\\\\\n x, Eave[1][1], 'r-', \\\\\\n x, Eave[2][1], 'g-' )\\n plt.xlabel('position in cm')\\n plt.ylabel('${moment1}_g$')\\n plt.legend(leg, loc = 0, prop=fontP)\\n plt.grid(True)\\n plt.tight_layout()\\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'monent_spectra_uo2.pdf')\\n \\n # Moment 2 Plot\\n x = numcells[2]\\n n = len(x)\\n plt.figure(8, figsize=(8, 6.5))\\n plt.plot(x, Eave[0][2], 'k-', \\\\\\n x, Eave[1][2], 'r-', \\\\\\n x, Eave[2][2], 'g-' )\\n plt.xlabel('position in cm')\\n plt.ylabel('${moment2}_g$')\\n plt.legend(leg, loc = 0, prop=fontP)\\n plt.grid(True)\\n plt.tight_layout()\\n plt.savefig(dir_name+'/reference_figures/'+str(nGroups)+'monent_spectra_mox.pdf') \\n \\n data = {}\\n data['pin_power'] = pin_power\\n data['phi_full'] = np.mean(phi[0],axis=1)\\n data['phi_uo2'] = np.mean(phi[1],axis=1)\\n data['phi_mox'] = np.mean(phi[2],axis=1)\\n data['keff'] = keff\\n pickle.dump(data, open(dir_name+'/refdata/reference_data.p', 'wb'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1275eed1d2c98c29c58b5664e015cfc3\",\n \"score\": \"0.5036114\",\n \"text\": \"def previous_load_data(path=\\\"./data/cora/\\\", dataset=\\\"cora\\\" ,n_train = 140):\\n print('Loading {} dataset...'.format(dataset))\\n\\n idx_features_labels = np.genfromtxt(\\\"{}{}.content\\\".format(path, dataset), dtype=np.dtype(str))\\n features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)\\n labels = encode_onehot(idx_features_labels[:, -1])\\n\\n # build graph\\n idx = np.array(idx_features_labels[:, 0], dtype=np.int32)\\n idx_map = {j: i for i, j in enumerate(idx)}\\n edges_unordered = np.genfromtxt(\\\"{}{}.cites\\\".format(path, dataset), dtype=np.int32)\\n edges = np.array(list(map(idx_map.get, edges_unordered.flatten())), dtype=np.int32).reshape(edges_unordered.shape)\\n adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(labels.shape[0], labels.shape[0]), dtype=np.float32)\\n\\n # build symmetric adjacency matrix\\n adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)\\n # print(adj)\\n # print(features)\\n\\n features = normalize_features(features)\\n adj = normalize_adj(adj + sp.eye(adj.shape[0]))\\n\\n #idx_train = range(140)\\n idx_train = range(n_train)\\n idx_val = range(200, 500)\\n idx_test = range(500, 1500)\\n\\n adj = torch.FloatTensor(np.array(adj.todense()))\\n features = torch.FloatTensor(np.array(features.todense()))\\n labels = torch.LongTensor(np.where(labels)[1])\\n\\n idx_train = torch.LongTensor(idx_train)\\n idx_val = torch.LongTensor(idx_val)\\n idx_test = torch.LongTensor(idx_test)\\n\\n return adj, features, labels, idx_train, idx_val, idx_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ac936c39581019a922df685d063ee38\",\n \"score\": \"0.5005946\",\n \"text\": \"def within_reference(bampath, outpath,n, param):\\n\\n\\tmin_isize, max_isize = param.lib_min, param.lib_max\\n\\n\\tcorrelated_check = CorrelatedSample(n)\\n\\tbamfile = pysam.Samfile(bampath, 'rb')\\n\\toutfile = pysam.Samfile(outpath, 'wb', template=bamfile)\\n\\n\\ti = 0\\n\\treads_rev = 0\\n\\treads_fwd = 0\\n\\tread_read1 = 0\\n\\tread_read2 = 0\\n\\tprinted_fwd = set()\\n\\n\\tfor read, mate_pos in proper_read_isize(bamfile, min_isize, max_isize, param):\\n\\t\\tread_pos = read.pos\\n\\n\\t\\t# remove unactive samples (read1 is more than n bp away)\\n\\t\\tcorrelated_check.remove_inactive(read_pos)\\n\\n\\t\\tif not read.is_reverse:\\n\\t\\t\\tif correlated_check.is_correlated(mate_pos):\\n\\t\\t\\t\\t#print 'correlated'\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t# read is forward\\n\\t\\t\\telse:\\n\\t\\t\\t\\t# add to sample neighborhood\\n\\t\\t\\t\\tcorrelated_check.add_sample(read_pos, mate_pos)\\n\\t\\t\\t\\t# print to filtered bamfile\\n\\t\\t\\t\\toutfile.write(read)\\n\\t\\t\\t\\tprinted_fwd.add(read.qname)\\n\\t\\t\\t\\treads_fwd += 1\\n\\t\\t\\t\\tif read.is_read1:\\n\\t\\t\\t\\t\\tread_read1 += 1\\n\\t\\t\\t\\telif not read.is_read1:\\n\\t\\t\\t\\t\\tread_read2 += 1\\n\\n\\t\\t# read is reverse, both reads parsed\\n\\t\\telse:\\n\\t\\t\\tif read.qname in printed_fwd:\\n\\t\\t\\t\\toutfile.write(read)\\n\\t\\t\\t\\tprinted_fwd.remove(read.qname)\\n\\t\\t\\t\\treads_rev += 1\\n\\n\\t\\t\\t\\tif read.is_read1:\\n\\t\\t\\t\\t\\tread_read1 += 1\\n\\t\\t\\t\\telif not read.is_read1:\\n\\t\\t\\t\\t\\tread_read2 += 1\\n\\t\\t\\t#print 'not!'\\n\\t\\ti += 1\\n\\t\\tif i % 10000 == 1:\\n\\t\\t\\tprint 'processing coordinate', read.pos, 'on ref:', read.tid\\n\\t\\t\\tprint reads_fwd,reads_rev,read_read1,read_read2\\n\\tprint reads_fwd,reads_rev,read_read1,read_read2\\n\\tpysam.index(outpath)\\n\\toutfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9411e2a1cc4d5bca1be6b8ab8011a7b5\",\n \"score\": \"0.4994594\",\n \"text\": \"def example_gen_fixe_ref_patch(vol_names, vol_size, fid=None):\\n ref_name = 'FTD096K7'\\n ref_patch = 0\\n dir_path = os.path.dirname(vol_names[0])\\n part_names = [os.path.basename(file).split('_')[0] for file in vol_names]\\n patch_indices = [(os.path.basename(file).split('_')[1]).split('.')[0] for file in vol_names]\\n part_names = list(set(part_names) -set([ref_name]))\\n patch_indices = list(set(patch_indices)-set([ref_patch]))\\n print('part_name is ', part_names)\\n while(True):\\n name_idx = np.random.permutation(len(part_names))[0]\\n patch_idx = np.random.permutation(len(patch_indices))[0]\\n file1 = os.path.join(dir_path, part_names[name_idx] + '_' + str(ref_patch) + '.nii.gz')\\n file2 = os.path.join(dir_path, ref_name + '_' + str(ref_patch) + '.nii.gz')\\n print(file1)\\n print(file2)\\n if(fid is not None):\\n fid.write(file1 + '\\\\n')\\n fid.write(file2 + '\\\\n')\\n fid.flush()\\n X1 = nib.load(file1).get_data()\\n X2 = nib.load(file2).get_data()\\n X1, X2 = augment(X1, X2, vol_size)\\n X1 = X1/(pow(2,16)-1.0)\\n X2 = X2/(pow(2,16)-1.0)\\n X1 = X1[np.newaxis, :,:,:, np.newaxis]\\n X2 = X2[np.newaxis, :,:,:, np.newaxis]\\n return_vals = (X1, X2, part_names[name_idx], ref_name)\\n yield tuple(return_vals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cce94b63d87ad7d17954326f77b8d2b\",\n \"score\": \"0.49934655\",\n \"text\": \"def transform_target_data(audiofile,analyze=0):\\n target_file = DATA_PATH + audiofile[:-4] + \\\"REF.txt\\\"\\n data = read_array(target_file,',')\\n \\n if analyze:\\n pylab.subplot(211)\\n pylab.plot(data[:,1])\\n pylab.subplot(212)\\n pylab.plot(data[:,2])\\n \\n pylab.figure()\\n pylab.subplot(211)\\n pylab.hist(data[:,1],bins=25)\\n pylab.xlim(40,65)\\n pylab.subplot(212)\\n pylab.hist(data[:,2],bins=13)\\n pylab.xlim(0,12)\\n \\n # get the unique element (nr. of different notes) of the piece\\n midi_data = data[:,1]\\n unique = list(set(midi_data))\\n target = np.zeros((len(midi_data), len(unique)))\\n target_chromas = np.zeros((len(midi_data), 13))\\n for n in range( len(midi_data) ):\\n ind = unique.index( midi_data[n] )\\n target[n,ind] = 1\\n target_chromas[n,data[n,2]+1] = 1\\n \\n if analyze:\\n print \\\"pitch classes:\\\",len(unique)\\n pylab.show()\\n exit(0)\\n \\n savefile = OUTPUT_DIR + audiofile[:-4] + \\\"_STFT.dat\\\"\\n data = shelve.open(savefile)\\n lenge = data[\\\"features\\\"].shape[0]\\n data[\\\"targets\\\"] = target[:lenge]\\n data[\\\"target_midi\\\"] = midi_data[:lenge]\\n data[\\\"target_chromas\\\"] = target_chromas[:lenge]\\n data[\\\"pitch_classes\\\"] = unique\\n data.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4da9c11c649e92c83f1c50dbdfeff2ae\",\n \"score\": \"0.4990033\",\n \"text\": \"def testUpdateDataFile(self):\\n self.lfh.write(\\\"\\\\nStarting %s %s\\\\n\\\" % (self.__class__.__name__,\\n sys._getframe().f_code.co_name))\\n try:\\n # Create a initial data file --\\n #\\n myDataList=[]\\n ofh = open(\\\"test-output-1.cif\\\", \\\"w\\\")\\n curContainer=DataContainer(\\\"myblock\\\")\\n aCat=DataCategory(\\\"pdbx_seqtool_mapping_ref\\\")\\n aCat.appendAttribute(\\\"ordinal\\\")\\n aCat.appendAttribute(\\\"entity_id\\\")\\n aCat.appendAttribute(\\\"auth_mon_id\\\")\\n aCat.appendAttribute(\\\"auth_mon_num\\\")\\n aCat.appendAttribute(\\\"pdb_chain_id\\\")\\n aCat.appendAttribute(\\\"ref_mon_id\\\")\\n aCat.appendAttribute(\\\"ref_mon_num\\\") \\n aCat.append((1,2,3,4,5,6,7))\\n aCat.append((1,2,3,4,5,6,7))\\n aCat.append((1,2,3,4,5,6,7))\\n aCat.append((1,2,3,4,5,6,7))\\n curContainer.append(aCat)\\n myDataList.append(curContainer)\\n pdbxW=PdbxWriter(ofh)\\n pdbxW.write(myDataList)\\n ofh.close()\\n #\\n # Read and update the data -\\n # \\n myDataList=[]\\n ifh = open(\\\"test-output-1.cif\\\", \\\"r\\\")\\n pRd=PdbxReader(ifh)\\n pRd.read(myDataList)\\n ifh.close()\\n #\\n myBlock=myDataList[0]\\n myBlock.printIt()\\n myCat=myBlock.getObj('pdbx_seqtool_mapping_ref')\\n myCat.printIt()\\n for iRow in xrange(0,myCat.getRowCount()):\\n myCat.setValue('some value', 'ref_mon_id',iRow)\\n myCat.setValue(100, 'ref_mon_num',iRow)\\n ofh = open(\\\"test-output-2.cif\\\", \\\"w\\\") \\n pdbxW=PdbxWriter(ofh)\\n pdbxW.write(myDataList)\\n ofh.close() \\n \\n except:\\n traceback.print_exc(file=sys.stderr)\\n self.fail()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f0ff94674c0493e95c1e1d5bd8e539\",\n \"score\": \"0.49866006\",\n \"text\": \"def load_sequential_aras_last(isPerson):\\n #by Adele\\n #print (\\\"module_path: \\\" + module_path )\\n #/by Adele\\n \\n with open(\\\"E:\\\\Lessons_tutorials\\\\Behavioural user profile articles\\\\Datasets\\\\Aras\\\\House A\\\\CSV_Summery\\\\Sequential\\\\Day\\\\last\\\\Whole_data.csv\\\") as csv_file:\\n \\n data_file = csv.reader(csv_file)\\n #target_names = np.array(temp[2:]) # the name of labels \\n n_samples = 2068#sum(1 for row in data_file) \\n n_features = 41\\n data = np.empty((n_samples, n_features))\\n target = np.empty((n_samples,), dtype=np.int)\\n temp_data = np.empty(n_features+1)# copy whole data to it, then select all col except 20\\n \\n idx_IN_columns = [] \\n for i, ir in enumerate(data_file):\\n #print(i)\\n temp_data = np.asarray(ir[:42], dtype=np.int)\\n #print(temp_data)\\n if isPerson == True:\\n n_features = 40\\n data = np.empty((n_samples, n_features))\\n \\n # in version 2 the activity col is removed too because it is not important, sensor data are required.\\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]#all of features except feature 40 (Person_ID\\n target[i] = np.asarray(ir[40], dtype=np.int)# Person ID is the class\\n else:\\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]#all of features except feature 41 (Activity_ID\\n target[i] = np.asarray(ir[41], dtype=np.int)# Activity ID is the class\\n \\n \\n extractedData = temp_data[idx_IN_columns]\\n #print(extractedData)\\n data[i] = np.asarray(extractedData , dtype=np.int)\\n #print(target[i])\\n \\n \\n #print(data)\\n #print(target)\\n return Bunch(data=data, target=target)\\n # target_names=target_names)\\n #feature_names=['sepal length (cm)', 'sepal width (cm)',\\n # 'petal length (cm)', 'petal width (cm)'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"761dc5c8f94d8c330bea3536353931b3\",\n \"score\": \"0.49820733\",\n \"text\": \"def flirt2aff_files(matfile, in_fname, ref_fname):\\n mat = np.loadtxt(matfile)\\n in_img = nib.load(in_fname)\\n ref_img = nib.load(ref_fname)\\n return flirt2aff(mat, in_img, ref_img)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5f27708ef324c06df58754c1e355448\",\n \"score\": \"0.49668267\",\n \"text\": \"def create_reference(dataset: tl.TimeSeriesData, timestep, batch_size):\\n # creates reference for a certain timestep\\n history_horizon = dataset.history_horizon\\n forecast_horizon = dataset.forecast_horizon\\n num_encoder_features = len(dataset.encoder_features)\\n num_decoder_features = len(dataset.decoder_features)\\n seed(1) # seed random number generator\\n\\n features1_references_np = np.zeros(shape=(batch_size, history_horizon, num_encoder_features))\\n features2_references_np = np.zeros(shape=(batch_size, forecast_horizon, num_decoder_features))\\n\\n inputs1_np = dataset[timestep][0].cpu().numpy()\\n inputs2_np = dataset[timestep][1].cpu().numpy()\\n\\n for x in range(num_encoder_features): # iterate through encoder features\\n feature_x = inputs1_np[:, x]\\n mu = 0\\n sigma = abs(np.std(feature_x)) # 0.3 is chosen arbitrarily # hier np.std nehmen\\n for j in range(batch_size):\\n noise_feature1 = np.random.default_rng().normal(mu, sigma, history_horizon) # create white noise series\\n features1_references_np[j, :, x] = noise_feature1 + feature_x\\n\\n for x in range(num_decoder_features): # iterate through decoder features\\n feature_x = inputs2_np[:, x]\\n mu = 0\\n sigma = abs(np.std(feature_x)) # 0.3 is chosen arbitrarily\\n for j in range(batch_size):\\n noise_feature2 = np.random.default_rng().normal(mu, sigma, forecast_horizon)\\n features2_references_np[j, :, x] = noise_feature2 + feature_x\\n\\n return torch.Tensor(features1_references_np).to(DEVICE), torch.Tensor(features2_references_np).to(DEVICE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e444fc4c930b1f0719f8de41744aa07\",\n \"score\": \"0.49487188\",\n \"text\": \"def load_FRG(fpath):\\n\\n def readFRGFile(fpath):\\n data = {}\\n\\n with open(fpath, \\\"r\\\") as f:\\n ReadingHeader = True # check when header has been fully parsed\\n BothDirections = False # sweep forward + reverse\\n\\n while ReadingHeader:\\n line = f.readline()\\n lineparts = line.split(\\\":\\\")\\n\\n if \\\"Data\\\" in lineparts[0]:\\n ReadingHeader = False\\n f.readline()\\n f.readline()\\n else:\\n try:\\n data[lineparts[0]] = float(lineparts[1])\\n except:\\n data[lineparts[0]] = lineparts[1][1:].replace(\\\"\\\\n\\\", \\\"\\\")\\n\\n vforward = []\\n iforward = []\\n timeforward = []\\n if data[\\\"sweepDir\\\"] == \\\"Forward + Reverse\\\":\\n BothDirections = True\\n vreverse = []\\n ireverse = []\\n timereverse = []\\n\\n for line in f:\\n lineparts = f.readline().split(\\\"\\\\t\\\")\\n if len(lineparts) == 1:\\n break\\n vforward.append(lineparts[0])\\n iforward.append(lineparts[1])\\n timeforward.append(lineparts[2])\\n if BothDirections:\\n vreverse.append(lineparts[0])\\n ireverse.append(lineparts[1])\\n timereverse.append(lineparts[2])\\n\\n data[\\\"V\\\"] = np.array(vforward).astype(float)\\n data[\\\"I\\\"] = np.array(iforward).astype(float)\\n data[\\\"J\\\"] = data[\\\"I\\\"] / data[\\\"area_cm2\\\"]\\n data[\\\"delay\\\"] = np.array(timeforward).astype(float)\\n\\n if BothDirections:\\n data2 = data.copy()\\n data2[\\\"sampleName\\\"] = data[\\\"sampleName\\\"] + \\\"_Reverse\\\"\\n data[\\\"sampleName\\\"] = data[\\\"sampleName\\\"] + \\\"_Forward\\\"\\n data2[\\\"V\\\"] = np.array(vreverse).astype(float)\\n data2[\\\"I\\\"] = np.array(ireverse).astype(float)\\n data2[\\\"J\\\"] = data2[\\\"I\\\"] / data2[\\\"area_cm2\\\"]\\n data2[\\\"delay\\\"] = np.array(timereverse).astype(float)\\n output = [data, data2]\\n else:\\n output = data\\n\\n return output\\n\\n fids = [os.path.join(fpath, x) for x in os.listdir(fpath)]\\n\\n alldata = {}\\n for f in fids:\\n output = readFRGFile(f)\\n if type(output) == list:\\n for each in output:\\n alldata[each[\\\"sampleName\\\"]] = each\\n else:\\n alldata[output[\\\"sampleName\\\"]] = output\\n return alldata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60482db3aa202b94337a3464dd780a34\",\n \"score\": \"0.49388656\",\n \"text\": \"def test_reference_data():\\n inputs = [\\n [3735928559, 195939070, 229505742, 305419896],\\n [3668361503, 4165561550, 1661411377, 3634257570],\\n [164546577, 4166754639, 1765190214, 1303880213],\\n [446610472, 3941463886, 522937693, 1882353782],\\n [1864922766, 1719732118, 3882010307, 1776744564],\\n [4141682960, 3310988675, 553637289, 902896340],\\n [1134851934, 2352871630, 3699409824, 2648159817],\\n [1240956131, 3107113773, 1283198141, 1924506131],\\n [2669565031, 579818610, 3042504477, 2774880435],\\n [2766103236, 2883057919, 4029656435, 862374500],\\n ]\\n outputs = [\\n [3914649087, 576849849, 3593928901, 2229911004],\\n [2240804226, 3691353228, 1365957195, 2654016646],\\n [3562296087, 3191708229, 1147942216, 3726991905],\\n [1403443605, 3591372999, 1291086759, 441919183],\\n [1086200464, 2191331643, 560336446, 3658716651],\\n [3249937430, 2346751812, 847844327, 2996632307],\\n [2584285912, 4034195531, 3523502488, 169742686],\\n [959045797, 3875435559, 1886309314, 359682705],\\n [3978441347, 432478529, 3223635119, 138903045],\\n [296367413, 4262059219, 13109864, 3283683422],\\n ]\\n outputs64 = [\\n [2477551240072187391, 9577394838764454085],\\n [15854241394484835714, 11398914698975566411],\\n [13708282465491374871, 16007308345579681096],\\n [15424829579845884309, 1898028439751125927],\\n [9411697742461147792, 15714068361935982142],\\n [10079222287618677782, 12870437757549876199],\\n [17326737873898640088, 729039288628699544],\\n [16644868984619524261, 1544825456798124994],\\n [1857481142255628931, 596584038813451439],\\n [18305404959516669237, 14103312907920476776],\\n ]\\n for seed, expected, expected64 in zip(inputs, outputs, outputs64):\\n expected = np.array(expected, dtype=np.uint32)\\n ss = SeedSequence(seed)\\n state = ss.generate_state(len(expected))\\n assert_array_equal(state, expected)\\n state64 = ss.generate_state(len(expected64), dtype=np.uint64)\\n assert_array_equal(state64, expected64)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ed377464398ed9f38ced6709e53852f\",\n \"score\": \"0.49329084\",\n \"text\": \"def loadReference(self, data, ref, otherref='', percentile=50):\\n if ref == 'Mean':\\n return data.raw.mean(0)\\n elif ref == 'Percentile':\\n return np.percentile(data.raw, percentile, axis=0)\\n elif ref == 'Other':\\n if otherref == '':\\n raise RuntimeError('Specify a reference spectrum file')\\n return correction.load_reference(data.wavenumber,\\n matfilename=otherref)\\n else:\\n return correction.load_reference(data.wavenumber, what=ref.lower())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dbc92c4c161a4af001bf3fb02ba2a8a\",\n \"score\": \"0.49298394\",\n \"text\": \"def call_methylated_sites_pe(inputf, sample, reference_fasta,\\n unmethylated_control = None,\\n sig_cutoff=.01,num_procs = 1,\\n num_upstr_bases=0,num_downstr_bases=2,\\n generate_mpileup_file=True,\\n compress_output=True,\\n bgzip=False,\\n path_to_bgzip=\\\"\\\",\\n path_to_tabix=\\\"\\\",\\n buffer_line_number=100000,\\n min_mapq=30,\\n min_cov=1,binom_test=True,\\n path_to_samtools=\\\"\\\",\\n remove_chr_prefix=True,\\n add_snp_info=False,\\n sort_mem=\\\"500M\\\",\\n path_to_files=\\\"\\\",min_base_quality=1,\\n keep_temp_files=False):\\n\\n #Flip the strand of read 2 and create a new bam file\\n ##input: sample+\\\"_processed_reads_no_clonal.bam\\\"\\n ##output: sample+\\\"_processed_reads_no_clonal_flipped.bam\\\"\\n\\n print_checkpoint(\\\"Begin flipping the strand of read 2\\\")\\n flip_read2_strand(input_file = inputf,\\n output_file = inputf+\\\".read2flipped.bam\\\",\\n path_to_samtools=path_to_samtools)\\n\\n \\n #Call methylated sites\\n call_methylated_sites(inputf = inputf+\\\".read2flipped.bam\\\",\\n sample = sample,\\n reference_fasta = reference_fasta,\\n unmethylated_control = unmethylated_control,\\n sig_cutoff = sig_cutoff,\\n num_procs = num_procs,\\n num_upstr_bases=num_upstr_bases,\\n num_downstr_bases=num_downstr_bases,\\n generate_mpileup_file=generate_mpileup_file,\\n compress_output=compress_output,\\n bgzip=bgzip,\\n path_to_bgzip=path_to_bgzip,\\n path_to_tabix=path_to_tabix,\\n buffer_line_number = buffer_line_number,\\n min_mapq=min_mapq,\\n min_cov = min_cov,\\n binom_test = binom_test,\\n path_to_samtools = path_to_samtools,\\n sort_mem=sort_mem,\\n path_to_files = path_to_files,\\n min_base_quality = min_base_quality,\\n remove_chr_prefix = remove_chr_prefix,\\n add_snp_info = add_snp_info,\\n keep_temp_files = keep_temp_files)\\n\\n #Remove intermediate bam file\\n try:\\n subprocess.check_call(shlex.split(\\\"rm -f \\\"+inputf+\\\".read2flipped.bam\\\"+\\n \\\" \\\"+inputf+\\\".read2flipped.bam.bai\\\"))\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"479c09ea885cbee278474efa3a423fcf\",\n \"score\": \"0.4925192\",\n \"text\": \"def createReference(self, data, params, outfile):\\n params.scDo = False\\n params.srDo = False\\n wns = []\\n ys = []\\n try:\\n for fi in range(len(data.filenames)):\\n self.batchProgress.emit(fi, len(data.filenames))\\n if not self.loadFile(data, fi):\\n continue\\n\\n wn = data.wavenumber\\n y = data.raw\\n\\n if params.scRef == 'Percentile':\\n y = np.percentile(y, params.scRefPercentile, axis=0)[None, :]\\n else:\\n y = y.mean(0)[None, :]\\n\\n y = self.callACandSC(data, params, wn, y)\\n wn, y = self.callSGFandSRandBC(params, wn, y)\\n if params.normDo:\\n y = normalization.normalize_spectra(\\n params.normMethod, y, wn,\\n wavenum=params.normWavenum)\\n wns.append(wn)\\n ys.append(y[0])\\n\\n # Do all images have the same wavenumbers?\\n if all(np.array_equal(v, wns[0]) for v in wns):\\n ys = np.median(ys, axis=0)\\n else:\\n w1 = min(v.min() for v in wns)\\n w2 = max(v.max() for v in wns)\\n maxres = max((len(v) - 1) / (v.max() - v.min()) for v in wns)\\n wn = np.linspace(w1, w2, num=maxres * (w2 - w1) + 1)\\n interpol = interp1d(np.concatenate(wns), np.concatenate(ys))\\n ys = interpol(wn)\\n\\n ab = np.hstack((wn[:, None], ys[:, None]))\\n scipy.io.savemat(outfile, {'AB': ab } )\\n self.batchDone.emit(True)\\n return\\n\\n except InterruptedError:\\n self.stopped.emit()\\n except Exception as e:\\n traceback.print_exc()\\n self.failed.emit(repr(e), traceback.format_exc())\\n self.batchDone.emit(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b411971d81d0cbb79813e36b3eb97f3\",\n \"score\": \"0.49233758\",\n \"text\": \"def transform_target_data_old(audiofile,analyze=0):\\n target_file = DATA_PATH + audiofile[:-4] + \\\"REF.txt\\\"\\n data = read_array(target_file)\\n \\n # remove time axes and convert to midi\\n data = data[:,1]\\n midi_data = audiotools.freq_to_midi(data)\\n midi_data = np.round(midi_data)\\n \\n if analyze:\\n pylab.plot(midi_data)\\n \\n # get the unique element (nr. of different notes) of the piece\\n unique = list(set(midi_data))\\n target = np.zeros((len(midi_data), len(unique)))\\n for n in range( len(midi_data) ):\\n ind = unique.index( midi_data[n] )\\n target[n,ind] = 1\\n \\n if analyze:\\n print \\\"classes:\\\",len(unique)\\n pylab.figure()\\n pylab.psd(target.flatten())\\n pylab.show()\\n exit(0)\\n \\n savefile = OUTPUT_DIR + audiofile[:-4] + \\\"_STFT.dat\\\"\\n data = shelve.open(savefile)\\n lenge = data[\\\"features\\\"].shape[0]\\n data[\\\"targets\\\"] = target[:lenge]\\n data[\\\"target_midi\\\"] = midi_data[:lenge]\\n data[\\\"pitch_classes\\\"] = unique\\n data.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b2f592a92f23cc012b64b330d477b0f\",\n \"score\": \"0.49093008\",\n \"text\": \"def reference_parser(args, genome_chroms):\\n global referenceFiles\\n\\n referenceFiles = os.path.join(args.dir, \\\"refAnnotation_\\\" + args.output_prefix + \\\".genePred\\\")\\n print(\\\"**** Parsing Reference Transcriptome....\\\", file=sys.stdout)\\n\\n # don't check for exisitng reference file, because need to create a new one for exon and cds centric runs\\n # if os.path.exists(referenceFiles):\\n # print(\\\"{0} already exists. Using it.\\\".format(referenceFiles), file=sys.stdout)\\n # else:\\n ## gtf to genePred\\n if not args.genename:\\n subprocess.call([GTF2GENEPRED_PROG, args.annotation, referenceFiles, '-genePredExt', '-allErrors', '-ignoreGroupsWithoutExons'])\\n else:\\n subprocess.call([GTF2GENEPRED_PROG, args.annotation, referenceFiles, '-genePredExt', '-allErrors', '-ignoreGroupsWithoutExons', '-geneNameAsName2'])\\n\\n ## parse reference annotation\\n # 1. ignore all miRNAs (< 200 bp)\\n # 2. separately store single exon and multi-exon references\\n refs_1exon_by_chr = defaultdict(lambda: IntervalTree()) #\\n refs_exons_by_chr = defaultdict(lambda: IntervalTree())\\n # store donors as the exon end (1-based) and acceptor as the exon start (0-based)\\n # will convert the sets to sorted list later\\n junctions_by_chr = defaultdict(lambda: {'donors': set(), 'acceptors': set(), 'da_pairs': set()})\\n # dict of gene name --> set of junctions (don't need to record chromosome)\\n junctions_by_gene = defaultdict(lambda: set())\\n # dict of gene name --> list of known begins and ends (begin always < end, regardless of strand)\\n known_5_3_by_gene = defaultdict(lambda: {'begin':set(), 'end': set()})\\n\\n ## dictionary of record.id (e.g., gencode enst) to genePred record objects\\n ## need this for later computation of 5' and 3' overhangs for protein classification\\n refDict = {}\\n\\n for r in genePredReader(referenceFiles):\\n refDict[r.id] = r\\n if r.length < args.min_ref_len and not args.is_fusion: continue # ignore miRNAs\\n if r.exonCount == 1:\\n refs_1exon_by_chr[r.chrom].insert(r.txStart, r.txEnd, r)\\n known_5_3_by_gene[r.gene]['begin'].add(r.txStart)\\n known_5_3_by_gene[r.gene]['end'].add(r.txEnd)\\n else:\\n refs_exons_by_chr[r.chrom].insert(r.txStart, r.txEnd, r)\\n # only store junctions for multi-exon transcripts\\n for d, a in r.junctions:\\n junctions_by_chr[r.chrom]['donors'].add(d)\\n junctions_by_chr[r.chrom]['acceptors'].add(a)\\n junctions_by_chr[r.chrom]['da_pairs'].add((d,a))\\n junctions_by_gene[r.gene].add((d,a))\\n known_5_3_by_gene[r.gene]['begin'].add(r.txStart)\\n known_5_3_by_gene[r.gene]['end'].add(r.txEnd)\\n\\n # check that all genes' chromosomes are in the genome file\\n ref_chroms = set(refs_1exon_by_chr.keys()).union(list(refs_exons_by_chr.keys()))\\n if genome_chroms is not None:\\n diff = ref_chroms.difference(genome_chroms)\\n if len(diff) > 0:\\n print(\\\"WARNING: ref annotation contains chromosomes not in genome: {0}\\\\n\\\".format(\\\",\\\".join(diff)), file=sys.stderr)\\n\\n # convert the content of junctions_by_chr to sorted list\\n for k in junctions_by_chr:\\n junctions_by_chr[k]['donors'] = list(junctions_by_chr[k]['donors'])\\n junctions_by_chr[k]['donors'].sort()\\n junctions_by_chr[k]['acceptors'] = list(junctions_by_chr[k]['acceptors'])\\n junctions_by_chr[k]['acceptors'].sort()\\n junctions_by_chr[k]['da_pairs'] = list(junctions_by_chr[k]['da_pairs'])\\n junctions_by_chr[k]['da_pairs'].sort()\\n\\n return dict(refs_1exon_by_chr), dict(refs_exons_by_chr), dict(junctions_by_chr), dict(junctions_by_gene), dict(known_5_3_by_gene), refDict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"995dd4b23722e85a8cc71ce8fbe7d0b4\",\n \"score\": \"0.49028236\",\n \"text\": \"def __init__(self, filename='as_R.UXD', echo=False,\\r\\n mode='pf', bgmode='manual', monitoring = False,\\r\\n sep = \\\"; (Data for Range number\\\",dfc=None):\\r\\n if mode == 'pf':\\r\\n print('')\\r\\n print('---------------------------')\\r\\n print('POLE FIGURE AQUISITION MODE')\\r\\n print('---------------------------\\\\n')\\r\\n elif mode =='df':\\r\\n print('')\\r\\n print('---------------------------')\\r\\n print('DEFOCUS DATA AQUSITION MODE')\\r\\n print('---------------------------\\\\n')\\r\\n\\r\\n blocks = make_blocks(filename=filename, sep = sep)\\r\\n self.blocks = blocks\\r\\n blocks = block_in(blocks)\\r\\n header = blocks[0]\\r\\n self.data_block = blocks[1:len(blocks)]\\r\\n\\r\\n # Trim the data_block neatly.\\r\\n for i in range(len(self.data_block)):\\r\\n self.data_block[i]=self.data_block[i][0:len(self.data_block[i])-1]\\r\\n\\r\\n print('** Total number of data blocks: ', len(self.data_block))\\r\\n\\r\\n self.th2s = []\\r\\n set_2thet = set()\\r\\n # print '%5s %5s %5s %5s'%('2th','khi','time','size')\\r\\n for i in range(len(self.data_block)):\\r\\n cb = self.data_block[i] #cb : current block\\r\\n info = self.block_info(block=cb, echo=echo)\\r\\n\\r\\n _2theta, _khi, _steptime, _stepsize = list(map(float,info[:4]))\\r\\n # print '%5.3f %5.1f %5.2i %5.2i'%(_2theta, _khi, _steptime, _stepsize)\\r\\n set_2thet.add(round(float(info[0]), 3))\\r\\n\\r\\n _2th_ = round(float(info[0]),3)\\r\\n if _2th_ in self.th2s: pass\\r\\n else: self.th2s.append(_2th_)\\r\\n\\r\\n # print '-----------'\\r\\n # raise IOError, 'debug'\\r\\n\\r\\n\\r\\n\\r\\n # th2 = set_2thet.copy()\\r\\n th2 = self.th2s\\r\\n self.listh2= self.th2s\\r\\n\\r\\n #print th2\\\"\\r\\n #if raw_input()=='q': raise IOError\\\"\\r\\n\\r\\n # print 'Kinds of _2theta are printed out'\\r\\n # for i in range(len(th2)):\\r\\n # print th2[i], '',\\r\\n # print\\r\\n\\r\\n set_2thet=list(set_2thet)\\r\\n set_2thet=np.array(set_2thet,dtype='float')\\r\\n set_2thet=np.sort(set_2thet)\\r\\n for i in range(len(set_2thet)):\\r\\n print('%2.2i'%i,set_2thet[i])\\r\\n\\r\\n # raise IOError, 'Debug: 2set_2thet'\\r\\n\\r\\n # while True:\\r\\n # try:\\r\\n # print set_2thet.pop(),' ',\\r\\n # except: break\\r\\n # print\\r\\n\\r\\n\\r\\n # self.listh2 = []\\r\\n # while True:\\r\\n # try: self.listh2.append(th2.pop())\\r\\n # except: break\\r\\n\\r\\n self.pfs = []\\r\\n for i in range(len(self.listh2)):\\r\\n self.pfs.append(self.lookfor(th2=self.listh2[i]))\\r\\n\\r\\n ###################################\\r\\n # polefigures and backgrounds #\\r\\n ###################################\\r\\n self.polefigures=[]\\r\\n self.backgrounds=[]\\r\\n # bg_determine_condition='digits'\\r\\n bg_determine_condition='short'\\r\\n\\r\\n for i in range(len(self.pfs)):\\r\\n if self.bg_or_pf(self.pfs[i], condition = bg_determine_condition)=='pf':\\r\\n self.polefigures.append(self.pfs[i])\\r\\n elif self.bg_or_pf(self.pfs[i], condition = bg_determine_condition)=='bg':\\r\\n self.backgrounds.append(self.pfs[i])\\r\\n\\r\\n print('\\\\n')\\r\\n for i in range(len(self.polefigures)):\\r\\n print('PF #',i+1)\\r\\n _2th, _st, _sz, d_alpha, d_khi, _khis = self.pf_info(self.polefigures[i])\\r\\n print('peak at Bragg the 2theta of ', round(_2th,3))\\r\\n print('delta alpha = ', d_alpha,' delta khi = ', d_khi, end=' ')\\r\\n print(' step time :', _st, end=' ')\\r\\n print(' step size :', _sz)\\r\\n\\r\\n\\r\\n print('\\\\n')\\r\\n for i in range(len(self.backgrounds)):\\r\\n print('BG #', i+1)\\r\\n _2th, _st, _sz, d_alpha, d_khis, _khis = self.pf_info(self.backgrounds[i])\\r\\n print('peak at Bragg the 2theta of ', round(_2th,3))\\r\\n if d_alpha !='unknown': print('delta alpha = ', d_alpha, end=' ')\\r\\n print(' delta khi = ', d_khi, end=' ')\\r\\n print(' step time :', _st, end=' ')\\r\\n print(' step size :', _sz)\\r\\n\\r\\n input(' Press Enter >>> ')\\r\\n\\r\\n self.__pf_selection__()\\r\\n\\r\\n input(\\\"Press enter if you'd like to proceed >> \\\")\\r\\n\\r\\n if os.name=='nt': os.system('cls')\\r\\n elif os.name=='posix': os.system('clear')\\r\\n\\r\\n print(\\\"\\\\n\\\\n***************************************************************\\\")\\r\\n print(\\\"d_alpha is given to backgrounds, that's most probably because\\\")\\r\\n print(\\\"the backgrounds are measured only at along a certain phi angle\\\")\\r\\n print(\\\"In other words, it's been partially measured.\\\")\\r\\n print(\\\"***************************************************************\\\")\\r\\n\\r\\n \\\"Combines a certain set of pole figure and backgrounds\\\"\\r\\n\\r\\n #----------------------------------------------------------\\r\\n # Recommends sets of polefigure and its background measure\\r\\n #----------------------------------------------------------\\r\\n self.__pf_bg_sets__(bgmode = bgmode)\\r\\n #access the combination set by self.combi_pf_bg\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Note that if bgmode is None, background is not subtracted\\r\\n in the final end. This Ipf = Ipf - dI is not performed in the\\r\\n next big loop below\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n #----------------------------------------------------------\\r\\n # Core of this scripts\\r\\n # Outer-Loop over polefifures\\r\\n # Inner-Loop over its corresponding two backgrounds\\r\\n #----------------------------------------------------------\\r\\n\\r\\n INTENSITIES =[]\\r\\n\\r\\n if bgmode==None:\\r\\n for i in range(len(self.polefigures)):\\r\\n INTENSITIES.append([])\\r\\n for j in range(len(self.polefigures[i])):\\r\\n INTENSITIES[i].append([])\\r\\n C_pf = self.polefigures[i][j]\\r\\n Ipf = th2count(block = C_pf )[0] #intensity\\r\\n info_temp = self.block_info(C_pf, echo=False)\\r\\n #normalization by the step time\\r\\n for k in range(len(Ipf)):\\r\\n Ipf[k] = Ipf[k]/float(info_temp[2])\\r\\n INTENSITIES[i][j].append(Ipf[k])\\r\\n\\r\\n #monitoring upon polefigure\\r\\n if monitoring ==True:\\r\\n if os.name=='posix': os.system('clear')\\r\\n elif os.name=='nt': os.system('cls')\\r\\n print(\\\" You chose not to subtract the backgrounds\\\")\\r\\n print(\\\" Now you have %i polefigure(s) \\\"%(len(self.polefigures)))\\r\\n print(\\\" Do you like to plot pole figures ? \\\")\\r\\n if input( 'yes/no') == 'yes':\\r\\n for i in range(len(self.polefigures)):\\r\\n __pf_plot__(intensity = polefigures[i], ifig=6+i)\\r\\n else : pass\\r\\n else: pass\\r\\n\\r\\n elif bgmode!=None:\\r\\n for i in range(len(self.polefigures)):\\r\\n INTENSITIES.append([])\\r\\n \\\"On each polefigure set\\\"\\r\\n iask = True\\r\\n for j in range(len(self.polefigures[i])):\\r\\n INTENSITIES[i].append([])\\r\\n \\\" On each chi \\\"\\r\\n R_bg = self.backgrounds[self.combi_pf_bg[i][0]][j]\\r\\n L_bg = self.backgrounds[self.combi_pf_bg[i][1]][j]\\r\\n C_pf = self.polefigures[i][j]\\r\\n Ipf = th2count(block = C_pf)[0]\\r\\n Ibgl = th2count(block = R_bg)[0]\\r\\n Ibgr = th2count(block = L_bg)[0]\\r\\n info_temp = self.block_info(C_pf, echo=False)\\r\\n info_temp = list(map(float,info_temp[0:4]))\\r\\n pf_2th = info_temp[0]\\r\\n pf_khi = info_temp[1]\\r\\n pf_steptime = info_temp[2]\\r\\n pf_stepsize = info_temp[3]\\r\\n info_temp = self.block_info(L_bg, echo=False)\\r\\n info_temp = list(map(float,info_temp[0:4]))\\r\\n L_bg_2th = info_temp[0]\\r\\n L_bg_khi = info_temp[1]\\r\\n L_bg_steptime = info_temp[2]\\r\\n L_bg_stepsize = info_temp[3]\\r\\n info_temp = self.block_info(R_bg, echo=False)\\r\\n info_temp = list(map(float,info_temp[0:4]))\\r\\n R_bg_2th = info_temp[0]\\r\\n R_bg_khi = info_temp[1]\\r\\n R_bg_steptime = info_temp[2]\\r\\n R_bg_stepsize = info_temp[3]\\r\\n \\\"Normalize the intensity by its steptime\\\"\\r\\n for k in range(len(Ipf)): Ipf[k] =Ipf[k]/float(pf_steptime)\\r\\n for k in range(len(Ibgl)): Ibgl[k]=Ibgl[k]/float(L_bg_steptime)\\r\\n for k in range(len(Ibgr)): Ibgr[k]=Ibgr[k]/float(L_bg_steptime)\\r\\n\\r\\n if bgmode!=None:\\r\\n bglr_len = [len(Ibgl),len(Ibgr)]\\r\\n if any(bglr_len[k] !=len(Ipf) for k in range(2)):\\r\\n print('** Partial background measured **')\\r\\n pass\\r\\n\\r\\n elif bgmode==None: print('** No Background subtraction **')\\r\\n\\r\\n for k in range(len(Ipf)):\\r\\n \\\"\\\"\\\"\\r\\n If Ibgl and Ibgr were measured at a certain phi,\\r\\n at different phi then\\r\\n that certain phi is assumed to be that of the only measured\\r\\n \\\"\\\"\\\"\\r\\n try: cibgl = Ibgl[k]\\r\\n except IndexError: cibgl = Ibgl[0]\\r\\n try: cibgr = Ibgr[k]\\r\\n except IndexError: cibgr = Ibgr[0]\\r\\n slope = (cibgr - cibgl)/(R_bg_2th-L_bg_2th)\\r\\n dI = slope * (pf_2th - L_bg_2th) + cibgl\\r\\n Ipf[k] = Ipf[k] - dI\\r\\n if Ipf[k] < 0:\\r\\n if iask==True:\\r\\n print('Caution) Negative value from prior BG subtraction: ', end=' ')\\r\\n print('value = ', Ipf[k])\\r\\n print('Do you want to keep on going(yes)?, or', end=' ')\\r\\n print(\\\"Don't ask this to the end(ignore)\\\")\\r\\n ans = input('Type answer (yes/ignore) >> ')\\r\\n if len(ans)==0: pass\\r\\n elif ans =='yes': pass\\r\\n elif ans =='ignore': iask = False; pass\\r\\n elif ans =='no':\\r\\n print(\\\"\\\\n******************************************\\\")\\r\\n print(\\\"There's no 'no' answer here\\\")\\r\\n print(\\\"Negative intensity is physically non-sense\\\")\\r\\n print(\\\"The code raises an error\\\")\\r\\n print(\\\"******************************************\\\")\\r\\n input()\\r\\n raise IOError\\r\\n else: raise IOError\\r\\n Ipf[k] = 1\\r\\n elif iask == False:\\r\\n print(\\\"If negative intensity will be returned to be 1.\\\")\\r\\n Ipf[k] = 1\\r\\n INTENSITIES[i][j].append(Ipf[k])\\r\\n\\r\\n\\r\\n self.INTENSITIES = INTENSITIES\\r\\n if mode =='pf':\\r\\n ## find defocus files\\r\\n dff=[]\\r\\n if type(dfc)==type(None):\\r\\n dff = glob.glob('*.dfc')\\r\\n else:\\r\\n dff = dfc\\r\\n\\r\\n if len(dff)==0:\\r\\n print('You do not have any *.dfc file')\\r\\n else:\\r\\n print('*********************************************')\\r\\n print('%15s\\\\n'%('Available FILES and its _2THETA'))\\r\\n print('%3s %15s %6s %6s'%('ID','FILENAME','PEAK_AT','COMMENT'))\\r\\n for i in range(len(dff)):\\r\\n ff = open(dff[i],'r')\\r\\n lines = ff.readlines()\\r\\n try: float(lines[3].split('=')[1])\\r\\n except:\\r\\n print('Could not get %s file rightly.\\\\n'%(dff[i]))\\r\\n print('**************************************')\\r\\n print(\\\"Contents of the file is shown as below\\\\n\\\")\\r\\n if os.name =='posix': os.system('cat %s'%(dff[i]))\\r\\n elif os.name =='nt': os.system('type %s'%(dff[i]))\\r\\n print('\\\\n Please type enter to proceed'); input()\\r\\n pass\\r\\n else:\\r\\n _2th = float(lines[3].split('=')[1])\\r\\n comment = lines[2]\\r\\n ff.close()\\r\\n print('%3i %15s %5.3f %s'%(i, dff[i], _2th, comment))\\r\\n\\r\\n for i in range(len(self.INTENSITIES)):\\r\\n print(' Type the defocus correction file id (from 0)')\\r\\n print(' minus value (e.g. -1) will turn down ', end=' ')\\r\\n print('the defoucssing correction')\\r\\n id_dfc = input(' >>> ')\\r\\n id_dfc = int(id_dfc)\\r\\n if id_dfc<0: pass\\r\\n else: self.__dfc_correct__(filename=dff[id_dfc], pf_id=i)\\r\\n\\r\\n #--------------------------------------\\r\\n # Normalize the intensity so that max\\r\\n # intensity in the all pole figures in\\r\\n # the given file equals to 9999 (modified from 999 to 9999)\\r\\n #--------------------------------------\\r\\n self.__normalize___()\\r\\n\\r\\n #--------------------------------------\\r\\n # WRITING ACTIVITY TO POLE FIGURE FILE\\r\\n #--------------------------------------\\r\\n # if os.name=='nt': os.system('cls')\\r\\n # elif os.name=='posix': os.system('clear')\\r\\n\\r\\n print(\\\"############################\\\")\\r\\n print(\\\" WRITING ACTIVITY \\\")\\r\\n print(\\\"############################\\\\n\\\")\\r\\n print(\\\" available formats: epf(0), list(1), No file writing(-1)\\\")\\r\\n fileformat = input(\\\"Type format flag(0,1,-1)(default=0) >> \\\")\\r\\n if len(fileformat)==0: fileformat='epf'\\r\\n else:\\r\\n if fileformat =='0': fileformat='epf'\\r\\n elif fileformat =='1': fileformat='list'\\r\\n elif fileformat =='-1': fileformat=None\\r\\n else: print('Wrong fileformat input'); raise IOError\\r\\n if fileformat == None: pass\\r\\n else:\\r\\n print(\\\" Type the file name\\\")\\r\\n filename = input(\\\" >> \\\")\\r\\n self.write(filename=filename, mode=fileformat)\\r\\n\\r\\n elif mode=='df': #defocus correction curve making mode\\r\\n #--------------------------------\\r\\n # DEFOCUS CORRECTION CURVE FILE\\r\\n #--------------------------------\\r\\n print(\\\"\\\\n\\\\n ****************************************\\\")\\r\\n print(\\\" * Defocus correction curve file maker *\\\")\\r\\n print(\\\" ****************************************\\\\n\\\")\\r\\n for i in range(len(self.polefigures)):\\r\\n self.defc(pfid=i, filename='dfc_'+str(i)+'.dfc', mode='avg')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a911d1d4ca26448aa9c288c526fb16a4\",\n \"score\": \"0.48980546\",\n \"text\": \"def load_sequential_aras_frequency_occur(isPerson):\\n #by Adele\\n #print (\\\"module_path: \\\" + module_path )\\n #/by Adele\\n \\n with open(r\\\"E:\\\\Lessons_tutorials\\\\Behavioural user profile articles\\\\Datasets\\\\Aras\\\\House A\\\\CSV_Summery\\\\Sequential\\\\Day\\\\frequency_occur\\\\Whole_data.csv\\\") as csv_file:\\n \\n data_file = csv.reader(csv_file)\\n #target_names = np.array(temp[2:]) # the name of labels \\n n_samples = 2068#sum(1 for row in data_file) \\n n_features = 41\\n data = np.empty((n_samples, n_features))\\n target = np.empty((n_samples,), dtype=np.int)\\n temp_data = np.empty(n_features+1)# copy whole data to it, then select all col except 20\\n \\n idx_IN_columns = [] \\n for i, ir in enumerate(data_file):\\n #print(i)\\n temp_data = np.asarray(ir[:42], dtype=np.int)\\n #print(temp_data)\\n if isPerson == True:\\n n_features = 40\\n data = np.empty((n_samples, n_features))\\n \\n # in version 2 the activity col is removed too because it is not important, sensor data are required.\\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]#all of features except feature 40 (Person_ID\\n target[i] = np.asarray(ir[40], dtype=np.int)# Person ID is the class\\n else:\\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]#all of features except feature 41 (Activity_ID\\n target[i] = np.asarray(ir[41], dtype=np.int)# Activity ID is the class\\n \\n \\n extractedData = temp_data[idx_IN_columns]\\n #print(extractedData)\\n data[i] = np.asarray(extractedData , dtype=np.int)\\n print(target[i])\\n \\n return Bunch(data=data, target=target)\\n # target_names=target_names)\\n #feature_names=['sepal length (cm)', 'sepal width (cm)',\\n # 'petal length (cm)', 'petal width (cm)'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"632db86e8061ec1ef3f2d5c2ce481b9e\",\n \"score\": \"0.48885354\",\n \"text\": \"def __init__(self, reference_file):\\n self.reference_results = np.load(open(reference_file, 'rb'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13f52f070f653d54098e1beda0a9fcbc\",\n \"score\": \"0.487911\",\n \"text\": \"def testReferenceReturned(self):\\n h5f = h5py.File(self.h5_fname, \\\"r+\\\")\\n\\n # n-D\\n a0 = h5f[\\\"my_array\\\"]\\n self.aw.setArrayData(a0, copy=False)\\n a1 = self.aw.getData(copy=False)\\n self.assertIs(a0, a1)\\n\\n # 1D\\n b0 = h5f[\\\"my_1D_array\\\"]\\n self.aw.setArrayData(b0, copy=False)\\n b1 = self.aw.getData(copy=False)\\n self.assertIs(b0, b1)\\n\\n h5f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a71e49456ea0c7eb48c48f245608213c\",\n \"score\": \"0.48657942\",\n \"text\": \"def load_ref_hist_mappings(self):\\n\\n # DEBUG DEBUG DEBUG\\n assert len(self.ref_hist_mappings) < 1, \\\\\\n \\\"ERROR Should not be RE-loading \\\" \\\\\\n \\\"reference histogram mappings!\\\"\\n # DEBUG DEBUG DEBUG end\\n\\n self.logger.info(\\\"Loading reference histogram mappings \\\" \\\\\\n \\\"from file `%s'\\\" % \\\\\\n self.ref_hist_mappings_file_name)\\n\\n mappings_lines = None\\n try:\\n mappings_file = file(self.ref_hist_mappings_file_name, \\\"r\\\")\\n mappings_lines = mappings_file.readlines()\\n mappings_file.close()\\n except IOError:\\n msg = \\\"ERROR: Could not open reference histogram mapping \\\"\\\\\\n \\\"file `%s'\\\" % self.ref_hist_mappings_file_name\\n self.logger.fatal(msg)\\n raise Error(msg)\\n\\n ##########\\n\\n # The format we expect is: two white-space separated pieces\\n # per line. The first the dataset name for which the reference\\n # should be used, the second one the name of the reference\\n # histogram in the database.\\n\\n for mapping in mappings_lines:\\n # Skip comment lines.\\n if not mapping.startswith(\\\"#\\\"):\\n mapping = mapping.strip()\\n if len(mapping) > 0:\\n mapping_pieces = mapping.split()\\n if len(mapping_pieces) != 2:\\n msg = \\\"ERROR: The reference histogram mapping \\\" \\\\\\n \\\"file contains a line I don't \\\" \\\\\\n \\\"understand:\\\\n %s\\\" % mapping\\n self.logger.fatal(msg)\\n raise Error(msg)\\n dataset_name = mapping_pieces[0].strip()\\n ref_hist_name = mapping_pieces[1].strip()\\n # We don't want people to accidentally specify\\n # multiple mappings for the same dataset. Just\\n # don't accept those cases.\\n if dataset_name in self.ref_hist_mappings:\\n msg = \\\"ERROR: The reference histogram mapping \\\" \\\\\\n \\\"file contains multiple mappings for \\\" \\\\\\n \\\"dataset `%s'.\\\"\\n self.logger.fatal(msg)\\n raise Error(msg)\\n\\n # All is well that ends well.\\n self.ref_hist_mappings[dataset_name] = ref_hist_name\\n\\n ##########\\n\\n self.logger.info(\\\" Successfully loaded %d mapping(s)\\\" % \\\\\\n len(self.ref_hist_mappings))\\n max_len = max([len(i) for i in self.ref_hist_mappings.keys()])\\n for (map_from, map_to) in self.ref_hist_mappings.items():\\n self.logger.info(\\\" %-*s -> %s\\\" % \\\\\\n (max_len, map_from, map_to))\\n\\n # End of load_ref_hist_mappings.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d12cfe474accc1198daa4cb54faa3c2\",\n \"score\": \"0.4860401\",\n \"text\": \"def _readData(self):\\n fh = pf.open(self.values['input'])\\n self.data = fh[self.values['ext']].data\\n self.hdu = fh[self.values['ext']].header\\n self.log.info('Read data from {0:>s} extension {1:d}'.format(self.values['input'], self.values['ext']))\\n self.log.debug('Read data dimensions are {0:d} times {1:d}'.format(*self.data.shape))\\n\\n if 'rue' in self.hdu['OVERSCA'] and self.data.shape[0] < 2500:\\n #a single quadrant; over and prescans were simulated, need to be removed...\\n self.log.info('Trying to remove pre- and overscan regions from the given in input data...')\\n self.log.info('Quadrant is {0:d}'.format(self.hdu['QUADRANT']))\\n\\n if self.hdu['QUADRANT'] in (0, 2):\\n self.data = self.data[:, self.hdu['PRESCANX']:-self.hdu['OVRSCANX']].copy()\\n else:\\n self.data = self.data[:, self.hdu['OVRSCANX']:-self.hdu['PRESCANX']].copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c5ac5c5bf1264493e432f358fae4a86\",\n \"score\": \"0.48594093\",\n \"text\": \"def calc_eff_mark2(vanad,backgr,norm_ref=\\\"bm3_counts\\\",bottom = 0, top = 127,\\n low_frame=0,high_frame=967,eff_sig=10):\\n\\n import stat,datetime,time,sys\\n starttime = time.time()\\n omega = vanad[\\\"mom\\\"][0] # for reference\\n takeoff = vanad[\\\"mtth\\\"][0]\\n # TODO: intelligent determination of Wombat wavelength\\n #crystal = AddCifMetadata.pick_hkl(omega-takeoff/2.0,\\\"335\\\") #post April 2009 used 335 only\\n #\\n # Get important information from the basic files\\n #\\n # Get file times from timestamps as older NeXuS files had bad values here\\n #\\n #wl = AddCifMetadata.calc_wavelength(crystal,takeoff)\\n vtime = os.stat(vanad.location)[stat.ST_CTIME]\\n vtime = datetime.datetime.fromtimestamp(vtime)\\n vtime = vtime.strftime(\\\"%Y-%m-%dT%H:%M:%S%z\\\")\\n btime = os.stat(backgr.location)[stat.ST_CTIME]\\n btime = datetime.datetime.fromtimestamp(btime)\\n btime = btime.strftime(\\\"%Y-%m-%dT%H:%M:%S%z\\\")\\n # This step required to insert our metadata hooks into the dataset object\\n AddCifMetadata.add_metadata_methods(vanad)\\n AddCifMetadata.add_metadata_methods(backgr)\\n # Fail early\\n print 'Using %s and %s' % (str(vanad.location),str(backgr.location))\\n # Subtract the background\\n if norm_ref is not None:\\n norm_target = reduction.applyNormalization(vanad,norm_ref,-1)\\n # store for checking later\\n #check_val = backgr[8,64,64]\\n nn = reduction.applyNormalization(backgr,norm_ref,norm_target)\\n # \\n print 'Normalising background to %f' % norm_target\\n pure_vanad = (vanad - backgr).get_reduced() #remove the annoying 2nd dimension\\n # drop any frames that have been requested\\n if low_frame != 0 or high_frame < len(pure_vanad):\\n pure_vanad = pure_vanad[low_frame:high_frame]\\n print 'Only using part of supplied data: %d to %d, new length %d' % (low_frame,high_frame,len(pure_vanad))\\n stth = pure_vanad.stth #store for later\\n # pure_vanad.copy_cif_metadata(vanad)\\n #print 'Check: %f, %f -> %f' % (vanad[8,64,64],check_val,pure_vanad[8,64,64])\\n nosteps = pure_vanad.shape[0]\\n # generate a rough correction\\n simple_vanad = pure_vanad.intg(axis=0) # sum over the detector step axis\\n # calculate typical variability across the detector\\n eff_array = array.zeros_like(simple_vanad)\\n eff_array[simple_vanad > 10] = simple_vanad\\n eff_array = eff_array*eff_array.size/eff_array.sum()\\n eff_array[simple_vanad > 0] = 1.0/eff_array\\n # apply this temporary correction to last frame which we expect to have the most\\n # peaks, as no V peaks will be at low enough angle to fall off the\\n # detector during scanning. If this assumption is incorrect, a more\\n # rigourous routine could do this twice, for the first and last frames\\n frame_last = (pure_vanad.storage[nosteps-1]*eff_array).intg(axis=0) #sum vertically\\n print 'Final frame max, min values after correction: %f %f' % (max(frame_last),min(frame_last))# find the peaks, get a background too\\n peak_list,back_lev = peak_find(frame_last,sig_val=eff_sig)\\n # Prepare return information\\n peak_pos = [(stth[nosteps-1]+a*0.125,b*0.125) for (a,b) in peak_list]\\n info_list = \\\"List of peaks found and purged:\\\\n Position Purge range\\\"\\n for pos,fwhm in peak_pos:\\n info_list += \\\"%8.2f %8.2f\\\\n\\\" % (pos,fwhm)\\n # Remove these peaks from all frames.\\n # degrees. The step size is...\\n step_size = (stth[nosteps-1]-stth[0])/(nosteps-1)\\n print 'Found step size of %f' % step_size\\n # Remove all peaks from the pure data\\n purged = peak_scrub(pure_vanad,peak_list,step_size,start_at_end=True)\\n # Get gain based on pixels above quarter background\\n eff_array,eff_error,non_zero_contribs,fudge_map = nonzero_gain(purged,back_lev/(pure_vanad.shape[1]*4))\\n final_map = Dataset(eff_array)\\n final_map.var = eff_error\\n return final_map,non_zero_contribs,fudge_map,frame_last #last frame, for reference\\n #return {\\\"_[local]_efficiency_data\\\":eff_array.transpose(),\\n # \\\"_[local]_efficiency_variance\\\":eff_error.transpose(),\\n # \\\"contributors\\\":non_zero_contribs,\\n # \\\"_[local]_efficiency_raw_data\\\":os.path.basename(str(vanad.location)),\\n # \\\"_[local]_efficiency_raw_timestamp\\\":vtime,\\n # \\\"_[local]_efficiency_background_data\\\":os.path.basename(str(backgr.location)),\\n # \\\"_[local]_efficiency_background_timestamp\\\":btime,\\n # \\\"_[local]_efficiency_determination_material\\\":\\\"Vanadium\\\",\\n # \\\"_[local]_efficiency_determination_method\\\":\\\"From flood field produced by 9mm V rod\\\",\\n # \\\"_[local]_efficiency_monochr_omega\\\":omega,\\n # \\\"_pd_proc_info_data_reduction\\\":\\n # \\\"Vanadium peaks at the following positions were purged:\\\\n Pos Range\\\\n \\\" + info_list\\n # },non_zero_contribs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adae34f92e6a88cade15a7c5ee1c0fb7\",\n \"score\": \"0.48560834\",\n \"text\": \"def get_ref_af_y ( af_file ):\\n af = cst.AirfoilShape.from_txt_file(af_file)\\n mod_af = cst.AirfoilShape.from_cst_parameters(af.cst().cst_lower, af.te_lower, af.cst().cst_upper, af.te_upper)\\n return mod_af.yco\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73e849bb7ef8116c29328de15473063\",\n \"score\": \"0.48479775\",\n \"text\": \"def __read_reference_solutions():\\n exact = dict()\\n for varid in ('psi', 'chi', 'vrt', 'div', 'uchi', 'vchi', 'upsi', 'vpsi',\\n 'chigradu', 'chigradv', 'uwnd', 'vwnd'):\\n try:\\n filename = os.path.join(test_data_path(),\\n '{!s}.ref.npy'.format(varid))\\n exact[varid] = np.load(filename).squeeze()\\n except IOError:\\n raise IOError('required data file not found')\\n return exact\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bc9d8b60b8900b00e743c653a866b9e\",\n \"score\": \"0.48293543\",\n \"text\": \"def read(self, infname):\\n if self.npts > 0:\\n print ('*** receiver function data is already stored!')\\n return False\\n inarr \\t\\t = np.loadtxt(infname, dtype=np.float64)\\n self.npts = inarr.shape[0] \\n self.to = inarr[:,0]\\n self.rfo = inarr[:,1]\\n try:\\n self.stdrfo = inarr[:,2]\\n except IndexError:\\n self.stdrfo = np.ones(self.npts, dtype = np.float64)*0.1\\n self.fs = 1./(self.to[1] - self.to[0])\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"050a5f5b3caf7cad312ee340ab96a563\",\n \"score\": \"0.48207533\",\n \"text\": \"def ff_data_augmentation(patch, ref):\\n patch = patch.clone()\\n temp = (torch.rand(len(ref)) + torch.rand(len(ref)) * (-1)).numpy()\\n patch = torch.FloatTensor(patch.numpy() + ref * temp)\\n return patch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08571ea5d7dabe236c466967511b90cb\",\n \"score\": \"0.48010716\",\n \"text\": \"def test_reindex_against_reference(dials_regression: Path, tmp_path):\\n data_dir = os.path.join(dials_regression, \\\"indexing_test_data\\\", \\\"i04_weak_data\\\")\\n pickle_path = os.path.join(data_dir, \\\"indexed.pickle\\\")\\n experiments_path = os.path.join(data_dir, \\\"experiments.json\\\")\\n\\n commands = [\\n shutil.which(\\\"dials.reindex\\\"),\\n pickle_path,\\n experiments_path,\\n \\\"change_of_basis_op=a,b,c\\\",\\n \\\"space_group=P4\\\",\\n \\\"output.reflections=P4.refl\\\",\\n \\\"output.experiments=P4.expt\\\",\\n ]\\n\\n result = subprocess.run(commands, cwd=tmp_path, capture_output=True)\\n assert not result.returncode and not result.stderr\\n assert (tmp_path / \\\"P4.refl\\\").is_file()\\n assert (tmp_path / \\\"P4.expt\\\").is_file()\\n new_experiments = load.experiment_list(tmp_path / \\\"P4.expt\\\", check_format=False)\\n assert new_experiments[0].crystal.get_space_group().type().hall_symbol() == \\\" P 4\\\"\\n\\n # Now have something in P4, get another dataset in a different indexing scheme\\n\\n cb_op = sgtbx.change_of_basis_op(\\\"a,-b,-c\\\")\\n commands = [\\n shutil.which(\\\"dials.reindex\\\"),\\n \\\"P4.refl\\\",\\n \\\"P4.expt\\\",\\n f\\\"change_of_basis_op={cb_op}\\\",\\n \\\"output.experiments=P4_reindexed.expt\\\",\\n \\\"output.reflections=P4_reindexed.refl\\\",\\n ]\\n result = subprocess.run(commands, cwd=tmp_path, capture_output=True)\\n assert not result.returncode and not result.stderr\\n\\n # now run reference reindexing\\n commands = [\\n shutil.which(\\\"dials.reindex\\\"),\\n \\\"P4.refl\\\",\\n \\\"P4.expt\\\",\\n \\\"reference.experiments=P4_reindexed.expt\\\",\\n \\\"reference.reflections=P4_reindexed.refl\\\",\\n ]\\n result = subprocess.run(commands, cwd=tmp_path, capture_output=True)\\n assert not result.returncode and not result.stderr\\n\\n # expect reindexed_reflections to be same as P4_reindexed, not P4_reflections\\n reindexed_reflections = flex.reflection_table.from_file(tmp_path / \\\"reindexed.refl\\\")\\n P4_reindexed = flex.reflection_table.from_file(tmp_path / \\\"P4_reindexed.refl\\\")\\n P4_reflections = flex.reflection_table.from_file(tmp_path / \\\"P4.refl\\\")\\n\\n h1, k1, l1 = reindexed_reflections[\\\"miller_index\\\"].as_vec3_double().parts()\\n h2, k2, l2 = P4_reindexed[\\\"miller_index\\\"].as_vec3_double().parts()\\n h3, k3, l3 = P4_reflections[\\\"miller_index\\\"].as_vec3_double().parts()\\n\\n # hkl1 and hkl2 should be same, as should have been reindexed by against the\\n # reference, with the program determining a reindexing operator of a,-b,-c\\n assert list(h1) == pytest.approx(list(h2))\\n assert list(l1) == pytest.approx(list(l2))\\n assert list(k1) == pytest.approx(list(k2))\\n # h1 and h3 should be same, but not l and k, as these dataset should differ\\n # by an a twinning operator of a,-b,-c\\n assert list(h1) == pytest.approx(list(h3))\\n assert list(l1) != pytest.approx(list(l3))\\n assert list(k1) != pytest.approx(list(k3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcd9ae449be4d8904036adbd60cda4b1\",\n \"score\": \"0.48007515\",\n \"text\": \"def load_sequential_aras_occur(isPerson):\\n #by Adele\\n #print (\\\"module_path: \\\" + module_path )\\n #/by Adele\\n \\n with open(\\\"E:\\\\Lessons_tutorials\\\\Behavioural user profile articles\\\\Datasets\\\\Aras\\\\House A\\\\CSV_Summery\\\\Sequential\\\\Day\\\\occur\\\\Whole_data.csv\\\") as csv_file:\\n \\n data_file = csv.reader(csv_file)\\n #target_names = np.array(temp[2:]) # the name of labels \\n n_samples = 2068#sum(1 for row in data_file) \\n n_features = 41\\n data = np.empty((n_samples, n_features))\\n target = np.empty((n_samples,), dtype=np.int)\\n temp_data = np.empty(n_features+1)# copy whole data to it, then select all col except 20\\n \\n idx_IN_columns = [] \\n for i, ir in enumerate(data_file):\\n #print(i)\\n temp_data = np.asarray(ir[:42], dtype=np.int)\\n #print(temp_data)\\n if isPerson == True:\\n n_features = 40\\n data = np.empty((n_samples, n_features))\\n \\n # in version 2 the activity col is removed too because it is not important, sensor data are required.\\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]#all of features except feature 40 (Person_ID\\n target[i] = np.asarray(ir[40], dtype=np.int)# Person ID is the class\\n else:\\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]#all of features except feature 41 (Activity_ID\\n target[i] = np.asarray(ir[41], dtype=np.int)# Activity ID is the class\\n \\n \\n extractedData = temp_data[idx_IN_columns]\\n #print(extractedData)\\n data[i] = np.asarray(extractedData , dtype=np.int)\\n print(target[i])\\n \\n return Bunch(data=data, target=target)\\n # target_names=target_names)\\n #feature_names=['sepal length (cm)', 'sepal width (cm)',\\n # 'petal length (cm)', 'petal width (cm)'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d1e5810484b203cd09c731323929bc\",\n \"score\": \"0.47900528\",\n \"text\": \"def read_python_FFTfield(self):\\n dcr=np.load(self.infile)\\n Ng=dcr.shape[1]\\n# print('ngrid='+str(Ng))\\n if (Ng != self.Ngrid):\\n print('Ngrid set='+str(self.Ngrid))\\n print('Ngrid read='+str(Ng))\\n exit()\\n # Set NYQUIST to be real\\n return dcr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a993f7f5ad1271955e95a4737bd90ead\",\n \"score\": \"0.47891465\",\n \"text\": \"def read():\\r\\n print \\\"Reading S. cerevisiae FunCat annotated sequence data set (D1 FC seq) ...\\\"\\r\\n dir = dirname(dirname(abspath(__file__))) + sep + 'datasets' + \\\\\\r\\n sep + 'S_cerevisiae_FC' + sep + 'seq_yeast_FUN' + sep\\r\\n train_data = dir + 'seq_yeast_FUN.train.arff'\\r\\n valid_data = dir + 'seq_yeast_FUN.valid.arff'\\r\\n test_data = dir + 'seq_yeast_FUN.test.arff'\\r\\n print \\\" Reading S. cerevisiae FunCat annotated sequence (D1 FC seq) TRAIN set ...\\\"\\r\\n train, idx2attr, idx2class = transform_data(\\r\\n train_data, include_meta=True)\\r\\n print \\\" ... Finished.\\\"\\r\\n print \\\" Reading S. cerevisiae FunCat annotated sequence (D1 FC seq) VALIDATION set ...\\\"\\r\\n valid = transform_data(valid_data)\\r\\n print \\\" ... Finished.\\\"\\r\\n print \\\" Reading S. cerevisiae FunCat annotated sequence (D1 FC seq) TEST set ...\\\"\\r\\n test = transform_data(test_data)\\r\\n print \\\" ... Finished.\\\"\\r\\n print \\\" Joining S. cerevisiae FunCat annotated sequence (D1 FC seq) TEST and VALIDATION set ...\\\"\\r\\n tv_data = _join(train, valid)\\r\\n print \\\" ... Finished.\\\"\\r\\n print \\\"... Finished\\\"\\r\\n return tv_data, test, idx2attr, idx2class\",\n \"title\": \"\"\n },\n {\n \"docid\": \"812d5fbad9eb70a716ab4b58f2a23cae\",\n \"score\": \"0.4787292\",\n \"text\": \"def getRef(fp, n_ref):\\n\\n refs = {}\\n reads = {}\\n cnt = 0\\n for record in fasta.fasta_iter(fp):\\n name = record['name']\\n seq = record['sequence']\\n if 'ReFeReNcE' in name:\\n refs[name] = seq\\n cnt += 1 \\n if cnt >= n_ref:\\n break\\n\\n if cnt < n_ref:\\n print 'not enough ReFeReNcE seqs'\\n sys.exit(1)\\n\\n template = refs.values()[0].upper() #use the first refSeq as template\\n profile = []\\n length = len(template)\\n for i in range(length):\\n if template[i] == 'N' or not template[i].isalpha():\\n profile.append(0)\\n else:\\n profile.append(1)\\n\\n return name, template, profile #return the ref seq and its mask profile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96d98dc265fa87cdefdbc6da9e3416d9\",\n \"score\": \"0.4785966\",\n \"text\": \"def map_isoforms_to_reference_transcripts(self):\\n m5out = self.output_analysis_fn + \\\".blasr.out.m5\\\"\\n cmd = 'blasr %s %s --bestn 1 -m 5 --out %s' % \\\\\\n (self.isoseq_output_fa, self.reference_transcripts_fn, m5out)\\n execute(cmd)\\n return [r for r in BLASRM5Reader(m5out)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"585aa88fd1cce6de7ba92541b433678e\",\n \"score\": \"0.47792578\",\n \"text\": \"def load_data(scenario):\\n data_file = 'measurements_' + str(scenario) + '.data'\\n ref_file = 'reference_' + str(scenario) + '.data'\\n \\n data = np.loadtxt(data_file,skiprows = 0)\\n reference = np.loadtxt(ref_file,skiprows = 0)\\n \\n return (data,reference)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f9d5c4b308ce33d72cf660f99e4c55\",\n \"score\": \"0.47790813\",\n \"text\": \"def ref_delta_g_20():\\n return np.load('tests/ref-data/spc2-fes1d-delta-gs-20.npy')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4c33a627258edf7ae2a7db14f1deb3d\",\n \"score\": \"0.4776031\",\n \"text\": \"def LoadFRG(fpath):\\n\\tdef readFRGFile(fpath):\\n\\t\\tdata = {}\\n\\n\\t\\twith open(fpath, 'r') as f:\\n\\t\\t\\tReadingHeader = True \\t#check when header has been fully parsed\\n\\t\\t\\tBothDirections = False\\t#sweep forward + reverse\\n\\n\\t\\t\\twhile ReadingHeader:\\t\\n\\t\\t\\t\\tline = f.readline()\\n\\t\\t\\t\\tlineparts = line.split(':')\\n\\n\\t\\t\\t\\tif 'Data' in lineparts[0]:\\n\\t\\t\\t\\t\\tReadingHeader = False\\n\\t\\t\\t\\t\\tf.readline()\\n\\t\\t\\t\\t\\tf.readline()\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tdata[lineparts[0]] = float(lineparts[1])\\n\\t\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\t\\tdata[lineparts[0]] = lineparts[1][1:].replace('\\\\n', '')\\n\\n\\n\\t\\t\\tvforward = []\\n\\t\\t\\tiforward = []\\n\\t\\t\\ttimeforward = []\\t\\t\\t\\n\\t\\t\\tif data['sweepDir'] == 'Forward + Reverse':\\n\\t\\t\\t\\tBothDirections = True\\n\\t\\t\\t\\tvreverse = []\\n\\t\\t\\t\\tireverse = []\\n\\t\\t\\t\\ttimereverse = []\\n\\n\\t\\t\\tfor line in f:\\n\\t\\t\\t\\tlineparts = f.readline().split('\\\\t')\\n\\t\\t\\t\\tif len(lineparts) == 1:\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\tvforward.append(lineparts[0])\\t\\n\\t\\t\\t\\tiforward.append(lineparts[1])\\n\\t\\t\\t\\ttimeforward.append(lineparts[2])\\n\\t\\t\\t\\tif BothDirections:\\n\\t\\t\\t\\t\\tvreverse.append(lineparts[0])\\t\\n\\t\\t\\t\\t\\tireverse.append(lineparts[1])\\n\\t\\t\\t\\t\\ttimereverse.append(lineparts[2])\\n\\n\\t\\t\\tdata['V'] = np.array(vforward).astype(float)\\n\\t\\t\\tdata['I'] = np.array(iforward).astype(float)\\n\\t\\t\\tdata['J'] = data['I']/data['area_cm2']\\n\\t\\t\\tdata['delay'] = np.array(timeforward).astype(float)\\n\\n\\t\\t\\tif BothDirections:\\n\\t\\t\\t\\tdata2 = data.copy()\\n\\t\\t\\t\\tdata2['sampleName'] = data['sampleName'] + '_Reverse'\\n\\t\\t\\t\\tdata['sampleName'] = data['sampleName'] + '_Forward'\\n\\t\\t\\t\\tdata2['V'] = np.array(vreverse).astype(float)\\n\\t\\t\\t\\tdata2['I'] = np.array(ireverse).astype(float)\\n\\t\\t\\t\\tdata2['J'] = data2['I']/data2['area_cm2']\\n\\t\\t\\t\\tdata2['delay'] = np.array(timereverse).astype(float)\\n\\t\\t\\t\\toutput = [data, data2]\\n\\t\\t\\telse:\\n\\t\\t\\t\\toutput = data\\n\\n\\t\\treturn output\\n\\n\\n\\n\\tfids = [os.path.join(fpath, x) for x in os.listdir(fpath)]\\n\\t\\n\\talldata = {}\\n\\tfor f in fids:\\n\\t\\toutput = readFRGFile(f)\\n\\t\\tif type(output) == list:\\n\\t\\t\\tfor each in output:\\n\\t\\t\\t\\talldata[each['sampleName']] = each\\n\\t\\telse:\\n\\t\\t\\talldata[output['sampleName']] = output\\n\\treturn alldata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97e8bf36e1576f80ac6529d3e15e972c\",\n \"score\": \"0.4774368\",\n \"text\": \"def read_dat(filename):\\n with open(filename, 'r') as dat_file:\\n # reading in the header block\\n is_header = True\\n header_block = []\\n while is_header:\\n tmp_line = dat_file.readline()\\n if tmp_line.find('#') == 0:\\n header_block.append(tmp_line)\\n eq_pos = tmp_line.find('=')\\n if tmp_line.find(\\\"De\\\") != -1:\\n De = float(tmp_line[eq_pos+1:])\\n elif tmp_line.find(\\\"time\\\") != -1:\\n time = float(tmp_line[eq_pos+1:])\\n elif tmp_line.find(\\\"viscRat\\\") != -1:\\n visc_rat = float(tmp_line[eq_pos+1:])\\n elif tmp_line.find(\\\"volRat\\\") != -1:\\n vol_rat = float(tmp_line[eq_pos+1:])\\n elif tmp_line.find(\\\"deformRate\\\") != -1:\\n deformRate = float(tmp_line[eq_pos+1:])\\n elif tmp_line.find(\\\"tnum\\\") != -1:\\n _tnum = float(tmp_line[eq_pos+1:])\\n else:\\n is_header = False\\n # get the next two Tecplot lines and then go back one line\\n header_block.append(tmp_line)\\n last_pos = dat_file.tell()\\n tmp_line = dat_file.readline()\\n header_block.append(tmp_line)\\n dat_file.seek(last_pos)\\n\\n try:\\n reader = csv.reader(dat_file, delimiter=' ')\\n type_line = next(reader)\\n nvert = int(type_line[1][2:])\\n nface = int(type_line[2][2:])\\n all_data = [] # position + all other data\\n f2v = [] # connectivity\\n\\n count = 0\\n while count < nvert:\\n lst = next(reader)\\n all_data.append(lst)\\n count += 1\\n all_data = np.array(all_data, dtype=float)\\n\\n count = 0\\n while count < nface:\\n lst = next(reader)[0:3] # should just be 3 values\\n f2v.append([int(i) for i in lst])\\n count += 1\\n f2v = np.array(f2v, dtype=int)\\n\\n except csv.Error as e:\\n sys.exit('file %s, line %d: %s' % (filename, reader.line_num, e))\\n\\n try:\\n params = {\\\"visc_rat\\\": visc_rat, \\\"vol_rat\\\": vol_rat, \\\"De\\\": De, \\\"deformRate\\\": deformRate,\\n \\\"time\\\": time, \\\"nvert\\\": nvert, \\\"nface\\\": nface, \\\"header_block\\\": header_block}\\n return (all_data, f2v, params)\\n except NameError as e:\\n print(\\\"One of the required variables was not instantiated: {}\\\".format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80db1bee8c85d44e696e1e75742402f6\",\n \"score\": \"0.47732306\",\n \"text\": \"def _read_calibration(self) -> None:\\n coeff = self._read(_BME680_BME680_COEFF_ADDR1, 25)\\n coeff += self._read(_BME680_BME680_COEFF_ADDR2, 16)\\n\\n coeff = list(struct.unpack(\\\"<hbBHhbBhhbbHhhBBBHbbbBbHhbb\\\", bytes(coeff[1:39])))\\n # print(\\\"\\\\n\\\\n\\\",coeff)\\n coeff = [float(i) for i in coeff]\\n self._temp_calibration = [coeff[x] for x in [23, 0, 1]]\\n self._pressure_calibration = [\\n coeff[x] for x in [3, 4, 5, 7, 8, 10, 9, 12, 13, 14]\\n ]\\n self._humidity_calibration = [coeff[x] for x in [17, 16, 18, 19, 20, 21, 22]]\\n self._gas_calibration = [coeff[x] for x in [25, 24, 26]]\\n\\n # flip around H1 & H2\\n self._humidity_calibration[1] *= 16\\n self._humidity_calibration[1] += self._humidity_calibration[0] % 16\\n self._humidity_calibration[0] /= 16\\n\\n self._heat_range = (self._read_byte(0x02) & 0x30) / 16\\n self._heat_val = self._read_byte(0x00)\\n self._sw_err = (self._read_byte(0x04) & 0xF0) / 16\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb3407c0fe262c2529ac9875b47230a\",\n \"score\": \"0.47712365\",\n \"text\": \"def read_body_Ainc(f,verbose,glueball_corr) :\\n for ibin in range(0, numbin):\\n for iopA in range(0,numop) :\\n for iblockA in range(0, nblock) :\\n for iopB in range(0,numop) : \\n for iblockB in range(0, nblock) :\\n for t in range(0, Ntmax) :\\n \\n try:\\n bbb = f.read(8)\\n except IOError as e:\\n print (\\\"I/O error({0}): {1}\\\".format(e.errno, e.strerror))\\n except:\\n print (\\\"Unexpected error:\\\", sys.exc_info()[0])\\n raise\\n\\n vvv = struct.unpack('d',bbb)\\n if verbose :\\n print (t, vvv[0])\\n \\n glueball_corr[iblockA,iblockB,t, iopA,iopB, ibin] = \\\\\\n glueball_corr[iblockA,iblockB,t, iopA,iopB, ibin] + vvv[0]\\n\\n\\n\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6cfbf516b9e176d0a45a4a8d03bccd5\",\n \"score\": \"0.47711852\",\n \"text\": \"def test_read_data():\\n # generate data and save to a tempfile\\n Time = np.linspace(0, 1800, num=1800)\\n AltTime = Time\\n y2 = 0.5 - 0.5 * np.tanh((Time-600)/100) # TGA result\\n y = y2/y2[0]\\n dy = 0.005-0.005 * np.tanh(Time/100 - 6)**2 # exact derivative of former\\n ny = y + 0.1*np.random.randn(len(Time)) # noisy\\n outdata = np.stack((Time, AltTime, y, dy, y2, ny), axis=1)\\n # delete=False is needed so that the tempfile can be written to in windows\\n # create working directory\\n fp = open(\\\"output.csv\\\", mode=\\\"w+t\\\")\\n # delete=False so we can close() the file and delete later\\n\\n np.savetxt(fp.name, outdata,\\n delimiter=\\\",\\\",\\n header=\\\"Time,AltTime,y,dy,y2,ny\\\",\\n comments='')\\n fp.close()\\n fname = \\\"output.csv\\\"\\n # line with only the required fields\\n line = {\\\"fname\\\": fname,\\n \\\"dep_col_name\\\": \\\"y\\\"}\\n # populate rest of the fields\\n line = _set_data_line_defaults(line, header=0)\\n read_Time, read_y = read_data(**line)\\n\\n # Test normalization, gradient calculation and ind_col_name functionality\\n line['normalize'] = True\\n line['dep_col_name'] = 'y2'\\n read_Time, norm_y2 = read_data(**line)\\n\\n # Test normalization, gradient calculation and ind_col_name functionality\\n line['gradient'] = True\\n line['normalize'] = True\\n line['ind_col_name'] = 'AltTime'\\n line['dep_col_name'] = 'y2'\\n read_AltTime, calc_dy = read_data(**line)\\n\\n # delete file now that we don't need it anymore\\n os.remove(fname)\\n\\n assert np.allclose(read_y, y)\\n assert np.allclose(norm_y2, y)\\n assert np.allclose(read_Time, Time)\\n assert np.allclose(read_Time, read_AltTime)\\n # rtol = 1e-5 fails\\n assert np.allclose(dy, calc_dy, rtol=1e-4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4fd7366f1fcf4fd0bdce957f61c242d\",\n \"score\": \"0.47710222\",\n \"text\": \"def genreg(masterphotref_fistar,\\n outfile,\\n xycols=(1,2)):\\n\\n # get the x and y coordinate columns from the source list (fistar)\\n srcxy = np.genfromtxt(masterphotref_fistar,\\n usecols=xycols,\\n dtype='f8,f8',\\n names=['x','y'])\\n\\n xsize = 2048.\\n ysize = 2048.\\n\\n sel = (\\n (srcxy['x']>xsize+1) | (srcxy['x']<0) |\\n (srcxy['y']>ysize+1) | (srcxy['y']<0)\\n )\\n # in *rare* instances (i.e., this happened for one star, in one ccd, out\\n # of the entire TESS cycle1-4 reduction), srcxy can be outside the bounds.\\n # in such cases raise a warning and just \\\"mask\\\" those xy values to 1, since\\n # less than ~10 sources will not affect the photref frame generation\\n if len(srcxy[sel]) > 0:\\n N_off_silicon = len(srcxy[sel])\\n print(f\\\"WARNING! Got {N_off_silicon} sources in genreg from \\\"\\n f\\\"{masterphotref_fistar}. Masking...\\\")\\n if N_off_silicon > 10:\\n msg = 'This should never happen'\\n raise AssertionError(msg)\\n srcxy[sel] = np.ones_like(srcxy[sel])\\n\\n # set up the grid (this weirdness is transcribed directly from Chelsea's\\n # regslct.py) TODO: figure out WTF this does\\n\\n BX = 30; BY = 30\\n mx = np.zeros(BX*BY)-1\\n my = np.zeros(BX*BY)-1\\n ma = np.zeros(BX*BY)\\n bx = (srcxy['x']*BX/xsize).astype(int)\\n by = (srcxy['y']*BY/ysize).astype(int)\\n mx[by*bx+bx] = srcxy['x']\\n my[by*bx+bx] = srcxy['y']\\n\\n outf = open(outfile,'wb')\\n\\n for i in range(int(BX*BY)):\\n outf.write((\\\"%8.0f %8.0f %8.0f\\\\n\\\" % (mx[i],my[i],20)).encode(\\\"utf-8\\\"))\\n\\n outf.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52f7bffc915699a950907ba7a5cf945d\",\n \"score\": \"0.4768592\",\n \"text\": \"def get_abi_ref(dataset, check=False, dtype=None):\\n ref = dataset.Rad * dataset.kappa0\\n if check:\\n DQF = dataset.DQF\\n ref[DQF < 0] = np.nan\\n ref[DQF > 1] = np.nan\\n if dtype is None:\\n return ref\\n else:\\n return ref.astype(dtype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f60d1ead558d432600bdd9b5112cf3\",\n \"score\": \"0.4765774\",\n \"text\": \"def __extract_cascadedata(self):\\n \\n return fromfile(self.fpath, dtype = int32)[:128*128*16*8].reshape(8,16,128,128)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f600ce327e7bdaf8e39fa1145e1f617d\",\n \"score\": \"0.4762291\",\n \"text\": \"def get_altseq(ref_seq, ref_allele, var_allele, var_pos, strand, features_info, cds_info=None):\\n if cds_info is None:\\n cds_info = []\\n alt_seq = \\\"\\\"\\n if len(cds_info) == 2:\\n start_coding_index = cds_info[0] - 1 # it should be index not pos\\n stop_coding_index = cds_info[1] # get end position of the last cds\\n else:\\n start_coding_index = 0\\n total_len = 0\\n for x in features_info:\\n total_len += x[1] - x[0] + 1\\n stop_coding_index = total_len # the features are sorted by end therefroe the end pos of the last item is the last coding nc\\n\\n if strand == '-': # ge the correct orientation, because exons are oredered based on their position\\n ref_seq = ref_seq[\\n ::-1] # in order to calculate from the first base of the first feature (sorted by genomic coordinates)\\n ref_allele = ref_allele.complement() # the reverse will be done on return\\n var_allele = var_allele.complement() # the reverse will be done on return\\n\\n ref_seq = ref_seq[\\n start_coding_index:stop_coding_index] # just keep the coding regions (mostly effective in case of protein-coding genes)\\n nc_index = 0\\n if len(ref_allele) == len(var_allele) or ref_allele[0] == var_allele[0]:\\n for feature in features_info: # for every exon, cds or stop codon\\n if var_pos in range(feature[0], feature[\\n 1] + 1): # get index of the var relative to the position of the overlapping feature in the coding region\\n var_index_in_cds = nc_index + (var_pos - feature[0])\\n # modify the coding reference sequence accoding to the var_allele\\n c = len(ref_allele)\\n alt_seq = ref_seq[0:var_index_in_cds] + var_allele + ref_seq[\\n var_index_in_cds + c::] # variant and ref strand??\\n if strand == '-':\\n return ref_seq[::-1], alt_seq[::-1]\\n else:\\n return ref_seq, alt_seq\\n\\n nc_index += (feature[1] - feature[0] + 1)\\n\\n return ref_seq, alt_seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d2969e33e747376b22064309480b8c2\",\n \"score\": \"0.47605693\",\n \"text\": \"def load_bias(self):\\n if not os.path.exists(self.data_file):\\n return\\n \\n with h5.File(self.data_file, 'r') as hfile:\\n if \\\"bias\\\" in hfile.keys():\\n data = np.array(hfile[\\\"bias\\\"])\\n self.bias.bias_array = data\\n self.log(\\\"Bias loaded from {}\\\".format(self.data_file))\\n\\n # Subtract of the an overall constant\\n self.bias.bias_array -= self.bias.bias_array[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d344b939282e1ed8a445e60f6ccfbb8f\",\n \"score\": \"0.4756925\",\n \"text\": \"def test_fqe_data_set_wfn_data():\\n test = fqe_data.FqeData(1, 1, 2)\\n ref = numpy.random.rand(2, 2) + 1.j * numpy.random.rand(2, 2)\\n test.set_wfn(strategy='from_data', raw_data=ref)\\n assert numpy.allclose(test.coeff, ref)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1663928531405a5099c5bb945235365a\",\n \"score\": \"0.4753721\",\n \"text\": \"def initcafg(self):\\n\\n #all available redshift for UVB \\n avail_z=np.arange(0,10,0.05)\\n\\n #pick closest in redshift\\n zindex=np.argmin(abs(avail_z-self.redshift))\\n nameuvb='fg_uvb_dec11/fg_uvb_dec11_z_{}.dat'.format(avail_z[zindex])\\n try:\\n fgtab=np.loadtxt(nameuvb,dtype={'names': ('nu','f'),'formats':('f10','f10')})\\n except:\\n print('Could not read UVB file {}'.format(nameuvb))\\n exit()\\n\\n #from Ry to Hz\\n self.source['lgnu']=np.log10(fgtab['nu'])+np.log10(3.2898419499e15)\\n\\n #from (10^-21 erg s^-1 cm^-2 Hz^-1 sr^-1) to Log of erg/s/cm2/Hz\\n self.source['lgfpiJ']=np.log10(fgtab['f'])-21+np.log10(4*np.pi)\\n\\n #now reverse freq to have high freq first and low freq last (as in HM12)\\n #This is nothing special, but it is needed for how the writing procedure\\n #fixes the boundary of the SED\\n self.source['lgnu'] = self.source['lgnu'][::-1]\\n self.source['lgfpiJ'] = self.source['lgfpiJ'][::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0b3d05b5728a40d66074126cceee64d\",\n \"score\": \"0.47522852\",\n \"text\": \"def dtopo_tt3_v2(indir,outfile):\\n from numpy import mod,zeros\\n\\n fid_data = indir+'/claw.data'\\n\\n fid=open(fid_data,'r')\\n for i in range(15):\\n line=fid.readline()\\n\\n line=fid.readline().split()\\n t0 =float(line[0])\\n\\n for i in range(2):\\n line=fid.readline()\\n\\n line=fid.readline().split()\\n mt =int(line[0])\\n\\n line=fid.readline().split()\\n tf =float(line[0])\\n\\n fid.close()\\n\\n dt =(tf-t0)/mt\\n\\n dir = indir\\n\\n infile=\\\"fort.qxxxx\\\"\\n\\n infile1=dir+\\\"/fort.q0000\\\"\\n fid= open(infile1,'r')\\n fout=open(outfile,'w')\\n\\n for i in range(2):\\n line=fid.readline()\\n\\n line=fid.readline().split()\\n mx = int(line[0])\\n line=fid.readline().split()\\n my = int(line[0])\\n line=fid.readline().split()\\n xlow = float(line[0])\\n line=fid.readline().split()\\n ylow = float(line[0])\\n line=fid.readline().split()\\n dx = float(line[0])\\n line=fid.readline().split()\\n dy = float(line[0])\\n\\n line=fid.readline()\\n\\n fout.write(\\\"%10d \\\\n\\\" % mx)\\n fout.write(\\\"%10d \\\\n\\\" % my)\\n fout.write(\\\"%10d \\\\n\\\" % mt)\\n fout.write(\\\"%20.10e \\\\n\\\" % xlow)\\n fout.write(\\\"%20.10e \\\\n\\\" % ylow)\\n fout.write(\\\"%20.10e \\\\n\\\" % (t0+dt))\\n fout.write(\\\"%20.10e \\\\n\\\" % dx)\\n fout.write(\\\"%20.10e \\\\n\\\" % dy)\\n fout.write(\\\"%20.10e \\\\n\\\" % dt)\\n\\n h_orig=zeros(mx*my)\\n k=0\\n for j in range(my):\\n for i in range(mx):\\n line=fid.readline().split()\\n h_orig[k]=float(line[0])\\n k+=1\\n line=fid.readline()\\n\\n fid.close()\\n\\n infile1=infile\\n dz=zeros(mx*my)\\n\\n for k in range(1,mt+1):\\n for ipos in range(10,6,-1):\\n idigit=mod(k,10)\\n infile1=infile1[0:ipos-1]+str(idigit)+infile1[ipos:10]\\n k=k/10\\n\\n fid1= open(dir+'/'+infile1,'r')\\n print fid1\\n\\n for i in range(9):\\n line=fid1.readline().split()\\n \\n k=0 \\n for j in range(my):\\n for i in range(mx):\\n line1=fid1.readline().split()\\n hnew=float(line1[0])\\n dz[i+(my-j-1)*mx]=hnew-h_orig[k]\\n k+=1\\n line=fid1.readline()\\n \\n for j in range(my):\\n for i in range(mx): \\n k=i+j*mx\\n \\n fout.write(\\\"%20.10e \\\" % dz[k])\\n\\n fout.write(\\\"\\\\n\\\")\\n\\n fid1.close()\\n\\n fout.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64b52f94534221feaa42782df1147aff\",\n \"score\": \"0.4752102\",\n \"text\": \"def load_sequential_aras_first(isPerson):\\n #by Adele\\n #print (\\\"module_path: \\\" + module_path )\\n #/by Adele\\n \\n with open(\\\"E:\\\\Lessons_tutorials\\\\Behavioural user profile articles\\\\Datasets\\\\Aras\\\\House A\\\\CSV_Summery\\\\Sequential\\\\Day\\\\Whole_data.csv\\\") as csv_file:\\n \\n data_file = csv.reader(csv_file)\\n #target_names = np.array(temp[2:]) # the name of labels \\n n_samples = 2068#sum(1 for row in data_file) \\n n_features = 41\\n data = np.empty((n_samples, n_features))\\n target = np.empty((n_samples,), dtype=np.int)\\n temp_data = np.empty(n_features+1)# copy whole data to it, then select all col except 20\\n \\n idx_IN_columns = [] \\n for i, ir in enumerate(data_file):\\n #print(i)\\n temp_data = np.asarray(ir[:42], dtype=np.int)\\n #print(temp_data)\\n if isPerson == True:\\n n_features = 40\\n data = np.empty((n_samples, n_features))\\n \\n # in version 2 the activity col is removed too because it is not important, sensor data are required.\\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]#all of features except feature 40 (Person_ID\\n target[i] = np.asarray(ir[40], dtype=np.int)# Person ID is the class\\n else:\\n idx_IN_columns = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]#all of features except feature 41 (Activity_ID\\n target[i] = np.asarray(ir[41], dtype=np.int)# Activity ID is the class\\n \\n \\n extractedData = temp_data[idx_IN_columns]\\n #print(extractedData)\\n data[i] = np.asarray(extractedData , dtype=np.int)\\n #print(target[i])\\n \\n \\n #print(data)\\n #print(target)\\n return Bunch(data=data, target=target)\\n # target_names=target_names)\\n #feature_names=['sepal length (cm)', 'sepal width (cm)',\\n # 'petal length (cm)', 'petal width (cm)'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54b64006b59b81b898b6cc71ccf6d48d\",\n \"score\": \"0.47514185\",\n \"text\": \"def load_original_data():\\n print(\\\"Loading original data, only happens the first time\\\")\\n\\n # Load dataframes\\n drug_chemical_info_with_fp = pd.read_csv(\\\"Data/DrugComb/drug_chemical_info_with_fingerprints.csv\\\")\\n drugcomb_scored = pd.read_csv(\\\"Data/DrugComb/drugcombs_scored.csv\\\")\\n drug_protein_link = pd.read_csv(\\\"Data/DrugComb/drug_protein_links.tsv\\\", sep=\\\"\\\\t\\\")\\n protein_protein_interactions = pd.read_csv(\\\"Data/DrugComb/protein_protein_links.txt\\\", sep=' ')\\n\\n ####################################################################################################################\\n # Build node feature matrix\\n ####################################################################################################################\\n drug_chemical_info_with_fp['has_fp'] = drug_chemical_info_with_fp['fp0'].apply(lambda fp: fp != -1)\\n drug_chemical_info_with_fp['is_drug'] = 1\\n drug_chemical_info_with_fp = drug_chemical_info_with_fp.rename(columns={'drugName': 'name'})\\n\\n print(\\\"Number of drugs\\\", len(drug_chemical_info_with_fp))\\n\\n # Retrieve all protein names\\n all_proteins = set(protein_protein_interactions['protein1']). \\\\\\n union(set(protein_protein_interactions['protein2'])).union(set(drug_protein_link['protein']))\\n protein_nodes = pd.DataFrame(all_proteins, columns=['name'])\\n protein_nodes['is_drug'] = 0\\n protein_nodes['has_fp'] = False\\n\\n print(\\\"Number of proteins\\\", len(protein_nodes))\\n\\n nodes = pd.concat((drug_chemical_info_with_fp, protein_nodes), ignore_index=True, sort=False)\\n nodes = nodes.fillna(-1)\\n x = nodes.drop(['cIds', 'drugNameOfficial', 'molecularWeight', 'smilesString', 'name'], axis=1)\\n x = x.to_numpy().astype(np.int)\\n\\n ####################################################################################################################\\n # Build edge index\\n ####################################################################################################################\\n\\n # Dictionaries to retrieve indices\\n ##################################################\\n cid_to_idx_dict = {nodes.at[i, 'cIds']: i for i in range(len(nodes))}\\n name_to_idx_dict = {nodes.at[i, 'name']: i for i in range(len(nodes))}\\n\\n # PPI\\n ##################################################\\n protein_protein_interactions['idx_prot1'] = protein_protein_interactions['protein1'].apply(\\n lambda s: name_to_idx_dict[s])\\n protein_protein_interactions['idx_prot2'] = protein_protein_interactions['protein2'].apply(\\n lambda s: name_to_idx_dict[s])\\n\\n edge_index_ppi = protein_protein_interactions[['idx_prot1', 'idx_prot2']].to_numpy().T\\n edge_attr_ppi = np.zeros((edge_index_ppi.shape[1], 4))\\n\\n print(\\\"Number of Prot-Prot Interactions\\\", edge_index_ppi.shape[1])\\n\\n # Drug Protein interaction\\n ##################################################\\n drug_protein_link['idx_chemical'] = drug_protein_link['chemical'].\\\\\\n apply(lambda s: cid_to_idx_dict[s] if s in cid_to_idx_dict.keys() else -1)\\n drug_protein_link['idx_prot'] = drug_protein_link['protein'].apply(lambda s: name_to_idx_dict[s])\\n\\n edge_index_dpi = drug_protein_link[drug_protein_link['idx_chemical'] != -1][['idx_chemical', 'idx_prot']].\\\\\\n to_numpy().T\\n edge_attr_dpi = np.zeros((edge_index_dpi.shape[1], 4))\\n\\n print(\\\"Number of Drug-Prot Interactions\\\", edge_index_dpi.shape[1])\\n\\n # Drug Synergy scores\\n ##################################################\\n drugcomb_scored['idx_Drug1'] = drugcomb_scored['Drug1'].apply(\\n lambda s: name_to_idx_dict[s] if s in name_to_idx_dict.keys() else -1)\\n drugcomb_scored['idx_Drug2'] = drugcomb_scored['Drug2'].apply(\\n lambda s: name_to_idx_dict[s] if s in name_to_idx_dict.keys() else -1)\\n\\n # Remove measures that have been performed several times.\\n drug_drug_edges = drugcomb_scored[drugcomb_scored[['idx_Drug1', 'idx_Drug2']].duplicated() == False]\\n # Remove measures for which there is no information about one of the drugs\\n drug_drug_edges = drug_drug_edges[drug_drug_edges['idx_Drug1'] != -1]\\n drug_drug_edges = drug_drug_edges[drug_drug_edges['idx_Drug2'] != -1]\\n\\n edge_index_ddi = drug_drug_edges[['idx_Drug1', 'idx_Drug2']].to_numpy().T\\n edge_attr_ddi = drug_drug_edges[['ZIP', 'Bliss', 'Loewe', 'HSA']].to_numpy()\\n\\n print(\\\"Number of Drug-Drug Interactions\\\", edge_index_ddi.shape[1])\\n\\n # Aggregate different edges\\n ##################################################\\n edge_index = np.concatenate((edge_index_ppi, edge_index_dpi, edge_index_ddi), axis=1)\\n edge_attr = np.concatenate((edge_attr_ppi, edge_attr_dpi, edge_attr_ddi), axis=0)\\n # Edges are directed, we need to feed them both ways\\n edge_index = np.concatenate((edge_index, edge_index[::-1, :]), axis=1)\\n edge_attr = np.concatenate((edge_attr, edge_attr), axis=0)\\n\\n data = Data(x=torch.tensor(x, dtype=torch.float),\\n edge_index=torch.tensor(edge_index, dtype=torch.long),\\n edge_attr=torch.tensor(edge_attr, dtype=torch.float))\\n\\n # Add drug-drug interaction informations as attribute to data, useful for training\\n data.number_of_drugs = len(drug_chemical_info_with_fp)\\n data.edge_index_ddi = torch.tensor(edge_index_ddi)\\n data.edge_attr_ddi = torch.tensor(edge_attr_ddi)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37da8b2ee508383391bce66308f27560\",\n \"score\": \"0.4750281\",\n \"text\": \"def TInReference(self, groundTruth, index, infer=False):\\n i = self.readToRead[index]\\n return groundTruth.MapPBISubreadToReference(\\n i.tpbi.movie, i.tpbi.holeNumber,\\n i.abs_tstart, i.abs_tend, infer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd50618a92d363c291877803066875cb\",\n \"score\": \"0.47472703\",\n \"text\": \"def test_missing_references(self):\\n buff = StringIO(\\\"\\\"\\\"0 @I1@ INDI\\n1 FAMS @FmissingS@\\n1 FAMC @FmissingC@\\n0 @F1@ FAM\\n1 WIFE @ImissingW@\\n1 HUSB @ImissingH@\\n1 CHIL @ImissingC@\\\"\\\"\\\")\\n (ind, fam, warns) = parse_file(buff)\\n self.assertTrue(len([x for x in warns if x.story == \\\"US26\\\"]) == 5)\\n self.assertTrue(ind['@I1@'].spouse_family_ids == [])\\n self.assertTrue(ind['@I1@'].spouse_families == [])\\n self.assertTrue(ind['@I1@'].child_family_ids == [])\\n self.assertTrue(ind['@I1@'].child_families == [])\\n self.assertTrue(fam['@F1@'].husband_id == None)\\n self.assertTrue(fam['@F1@'].husband == None)\\n self.assertTrue(fam['@F1@'].wife_id == None)\\n self.assertTrue(fam['@F1@'].wife == None)\\n self.assertTrue(fam['@F1@'].children_id_list == [])\\n self.assertTrue(fam['@F1@'].children_list == [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb5c516623d59d8620c3411eaa43fe6\",\n \"score\": \"0.47471806\",\n \"text\": \"def read_debt(self):\\n debtfile = pd.read_csv(INPUTPATH + 'debt_financing.csv')\\n debtfile.set_index('indcode', inplace=True)\\n self.Deltas = copy.deepcopy(debtfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dca9f1418a7412c5e3b895affd6eef9d\",\n \"score\": \"0.4747172\",\n \"text\": \"def load_data_single_mod(gen,model,scenario,Tanglob_idx=False,Tref_all=True,Tref_start='1870-01-01',Tref_end='1900-01-01',usr_time_res=\\\"ann\\\",var='tas'):\\n # the dictionaries are NOT ordered properly + some other adjustments -> will need to be careful with my old scripts\\n\\n # see e-mail from Verena on 20191112 for additional infos how could read in several files at once with xarr\\n # additionally: she transforms dataset into dataarray to make indexing easier -> for consistency reason with earlier\\n # version of emulator (& thus to be able to reuse my scripts), I do not do this (fow now).\\n \\n\\t# right now I keep reloading constants fields for each run I add -> does not really make sense. \\n # Maybe add boolean to decide instead. however they are small & I have to read them in at some point anyways\\n # -> maybe path of least resistence is to not care about it\\n\\tprint('start with model',model)\\n\\n\\t# vars which used to be part of the inputs but did not really make sense as I employ the same ones all the time anyways (could be changed later if needed)\\n\\t#var='tas'\\n\\ttemp_res = usr_time_res # if not, reading the var file needs to be changed as time var is not named in the same way anymore\\n\\tspatial_res = 'g05'\\n\\n\\n # load in the constants files\\n\\tdir_data = '/net/so4/landclim/snath/data/'\\n\\tfile_ls = 'interim_invariant_lsmask_regrid_g05.nc' # ERA-interim mask regridded by Richard from 73x144 to 72x144\\n\\tfile_srex = 'srex-region-masks_20120709.srex_mask_SREX_masks_all.50deg.time-invariant.nc'\\n\\tfile_srex_shape = 'referenceRegions.shp'\\n\\n\\n\\t#df_all_regs = geopandas.read_file(dir_data+file_srex_shape)\\n\\tsrex_names = ['ALA','CGI','WNA','CNA','ENA','CAM','AMZ','NEB','WSA','SSA','NEU','CEU','MED','SAH','WAF','EAF','SAF',\\n 'NAS','WAS','CAS','TIB','EAS','SAS','SEA','NAU','SAU'] # SREX names ordered according to SREX mask I am \\n # employing\\n\\t#df_srex = df_all_regs.loc[df_all_regs['LAB'].isin(srex_names)] # alternative indexing: search in column LAB for names\\n\\tsrex_raw = xr.open_mfdataset(dir_data+file_srex, combine='by_coords',decode_times=False) # srex_raw nrs from 1-26\\n\\t#df_srex=srex_raw \\n\\t#srex_raw[\\\"time\\\"]=pd.to_datetime(srex_raw.time.values)\\n\\tfrac_l = xr.open_mfdataset(dir_data+file_ls, combine='by_coords',decode_times=False) #land-sea mask of ERA-interim bilinearily interpolated \\n\\tlons, lats = np.meshgrid(frac_l.longitude.values,frac_l.latitude.values) # the lon, lat grid (just to derive weights) \\n\\t#frac_1[\\\"time\\\"]=pd.to_datetime(frac_1.time.values)\\n\\tfrac_l = frac_l.where(frac_l.latitude>-60,0) # remove Antarctica from frac_l field (ie set frac l to 0)\\n\\n\\tidx_l=np.squeeze(frac_l.lsm.values)>0.0 # idex land #-> everything >0 I consider land\\n \\n\\n\\twgt = norm_cos_wgt(lats) # area weights of each grid point\\n\\twgt_l = wgt[idx_l] # area weights for land grid points\\n\\tlon_pc, lat_pc = mpu.infer_interval_breaks(frac_l.longitude, frac_l.latitude) # the lon / lat for the plotting with pcolormesh\\n\\tsrex=(np.squeeze(srex_raw.srex_mask.values)-1)[idx_l] # srex indices on land\\n\\n \\n\\ty={}\\n\\tT_ref = np.zeros([36,72])\\n\\trun_nrs={}\\n\\tif Tanglob_idx == True:\\n\\t\\tTan_wgt_globmean = {}\\n\\tif gen == 5:\\n\\t\\tdir_var='/net/so4/landclim/snath/DA/data/%s/regridded/'%model\\n\\t\\trun_names_list=sorted(glob.glob(dir_var+var+'_'+temp_res+'_'+model+'_'+scenario+'_'+'r*i1p1'+'_'+spatial_res+'.nc'))\\n # ATTENTION: are ordered but does not work for models with runs above digit 9 \\n\\t\\t#index_tr = [i for i, s in enumerate(run_names_list) if 'r1i1p1' in s][0] # find training run \\n\\t\\t#print(run_names_list)\\n\\t\\t#run_names_list.insert(0, run_names_list.pop(index_tr)) # move training run to begin of list\\n\\n # exception for cmip5 GISS-E2-H_rcp85_r2i1p1 ie the strange run I excluded from ESD paper\\n\\t\\tif '/net/atmos/data/cmip5-ng/tas/tas_%s_GISS-E2-H_rcp85_r2i1p1_g025.nc'%usr_time_res in run_names_list:\\n\\t\\t\\trun_names_list.remove('/net/atmos/data/cmip5-ng/tas/tas_%s_GISS-E2-H_rcp85_r2i1p1_g025.nc'%usr_time_res )\\n \\n # loop over all runs to obtain the absolute values \\n\\t\\tprint(run_names_list)\\n\\t\\tfor run_name in run_names_list:\\n\\n\\t\\t\\tdata = xr.open_mfdataset(run_name,decode_times=False)\\n\\t\\t\\tif usr_time_res==\\\"ann\\\":\\n\\t\\t\\t\\tdata=data.rename({'year':'time'})\\n\\t\\t\\t\\tdata=data.rename({'longitude':'lon'})\\n\\t\\t\\t\\tdata=data.rename({'latitude':'lat'}) \\n\\t\\t\\tdata[\\\"time\\\"]=cf_units.num2date(data.time.values, 'days since 1800-01-01 00:00:00', cf_units.CALENDAR_STANDARD)\\n\\t\\t\\tdata=data.sel(time=slice('1870-01-01', '2101-01-01'))#.roll(lon=72)\\n # rename so it is consisten with cmip6 \\n # roll so that it is on same grid as others (no longer Pacific centered) \\n\\n\\t\\t\\t#print(data.time.values)\\n\\t\\t\\tdata = data.assign_coords(lon= (((data.lon + 180) % 360) - 180)) # assign_coords so same labels as others\\n\\t\\t\\trun=int(data.attrs['source_ensemble'].split('r')[1].split('i')[0]) # extract ens member\\n\\t\\t\\trun_nrs[run_name]=run\\n \\n\\t\\t\\ty[run] = data.tas.values # still absolute values + still contains sea pixels\\n\\t\\t\\tT_ref += data.tas.sel(time=slice(Tref_start, Tref_end)).mean(dim='time').values*1.0/len(run_names_list) # sum up all ref climates\\n \\n \\n\\t\\t\\tif run==1 and Tref_all != True:\\n\\t\\t\\t\\tT_ref_1=data.tas.sel(time=slice(Tref_start, Tref_end)).mean(dim='time').values\\n\\t\\t\\t\\tprint('create ref for ',run_name)\\n \\n\\t\\t\\tif Tanglob_idx == True:\\n\\t\\t\\t\\tTan_wgt_globmean[run] = np.asarray([np.average(y[run][t],weights=wgt) for t in np.arange(y[run].shape[0])]) #area weighted but abs values \\n \\n \\n\\tif gen == 6:\\n\\t\\tdir_var = '/net/so4/landclim/snath/DA/data/%s/regridded/'%model\\n\\t\\trun_names_list=sorted(glob.glob(dir_var+var+'_'+temp_res+'_'+model+'_'+scenario+'_'+'r*i1p1f*'+'_'+spatial_res+'.nc'))\\n # ATTENTION: are ordered but does not work for models with runs above digit 9\\n # idea is: every ssp one needs a corresponding hist one (vice versa not the case)\\n\\t\\t#index_tr = [i for i, s in enumerate(run_names_list) if 'r1i1p1' in s][0] # find training run \\n\\t\\t#run_names_list.insert(0, run_names_list.pop(index_tr)) # move training run to begin of list\\n\\n\\t\\tif model == 'EC-Earth3': # temporary fix added on 20191118 because these runs are not available as historical ones\\n\\t\\t\\trun_names_list.remove('/net/cfc/cmip6/Next_Generation/tas/%s/g025/tas_%s_EC-Earth3_ssp585_r1i1p1f1_g025.nc'%(usr_time_res,usr_time_res))\\n\\t\\t\\t#run_names_list.remove('/net/cfc/cmip6/Next_Generation/tas/ann/g025/tas_ann_EC-Earth3_ssp585_r6i1p1f1_g025.nc') #6 removed because historical seems faulty (ie only ghg driven) on 20191119\\n\\t\\t\\t#run_names_list.remove('/net/cfc/cmip6/Next_Generation/tas/ann/g025/tas_ann_EC-Earth3_ssp585_r11i1p1f1_g025.nc')\\n\\t\\t\\t#run_names_list.remove('/net/cfc/cmip6/Next_Generation/tas/ann/g025/tas_ann_EC-Earth3_ssp585_r13i1p1f1_g025.nc')\\n\\t\\t\\t#run_names_list.remove('/net/cfc/cmip6/Next_Generation/tas/ann/g025/tas_ann_EC-Earth3_ssp585_r15i1p1f1_g025.nc')\\n\\t\\t\\tprint('ATTENTION: ssp realizations 1 because no historical available on 20191122 (open issue on ESGF).')\\n\\t\\tprint(run_names_list)\\n\\t\\tfor run_name in run_names_list:\\n\\t\\t\\t#run_name_ssp = run_name\\n\\t\\t\\t#run_name_hist = run_name.replace(scenario,'historical') \\n\\t\\t\\trun_name_hist = run_name \\n\\t\\t\\trun_name_ssp = dir_var+var+'_%s_'%(usr_time_res)+model+'_ssp370_r1i1p1f1_g05.nc' \\n\\t\\t\\tif model=='HadGEM3-GC31-LL':\\n\\t\\t\\t\\trun_name_ssp = dir_var+var+'_%s_'%(usr_time_res)+model+'_ssp245_r1i1p1f3_g05.nc' \\n\\t\\t\\tif model=='CNRM-CM6-1':\\n\\t\\t\\t\\trun_name_ssp = dir_var+var+'_%s_'%(usr_time_res) +model+'_ssp370_r1i1p1f2_g05.nc' \\n\\t\\t\\tif model=='GISS-E2-1-G':\\n\\t\\t\\t\\trun_name_ssp = dir_var+var+'_%s_'%(usr_time_res) +model+'_ssp370_r6i1p1f2_g05.nc' \\n\\n\\t\\t\\tdata = xr.open_mfdataset([run_name_hist,run_name_ssp],concat_dim='time').sel(time=slice('1944-01-01', '2019-01-01'))\\n\\t\\t\\t#data = data.assign_coords(lon= (((data.lon + 180) % 360) - 180)) # assign_coords so same labels as others\\n\\t\\t\\trun = int(run_name_hist.split('_')[4].split('r')[1].split('i')[0])#data.attrs['realization_index']\\n\\t\\t\\trun_nrs[run_name]=run\\n \\n\\t\\t\\ty[run] = data[var].values # still absolute values + still contains sea pixels\\n\\t\\t\\tT_ref += data[var].sel(time=slice(Tref_start, Tref_end)).mean(dim='time').values*1.0/len(run_names_list) # sum up all ref climates\\n \\n\\t\\t\\tif run==1 and Tref_all != True:\\n\\t\\t\\t\\tT_ref_1=data[var].sel(time=slice(Tref_start, Tref_end)).mean(dim='time').values\\n\\t\\t\\t\\tprint('create ref for ',run_name) \\n\\n\\t\\t\\tif model=='CAMS-CSM1-0': # temporary (?) fix added on 20191119 because CAMS-CSM1-0 data are currently only available till 2099 instead of 2100\\n\\t\\t\\t\\ty[run]=y[run][:-1]\\n \\n\\t\\t\\tif Tanglob_idx == True:\\n\\t\\t\\t\\tTan_wgt_globmean[run] = np.asarray([np.average(y[run][t],weights=wgt) for t in np.arange(y[run].shape[0])]) #area weighted but abs values \\n\\n # obtain the anomalies\\n\\tfor run_name in run_names_list:\\n\\t\\trun = run_nrs[run_name]\\n\\t\\tif Tref_all == True:\\n\\t\\t\\ty[run]=(y[run]-T_ref)\\n\\t\\t\\tif Tanglob_idx == True:\\n\\t\\t\\t\\tTan_wgt_globmean[run]=Tan_wgt_globmean[run]-np.average(T_ref,weights=wgt)\\n\\t\\telse:\\n\\t\\t\\ty[run]=y[run]\\n\\t\\t\\tif Tanglob_idx == True:\\n\\t\\t\\t\\tTan_wgt_globmean[run]=Tan_wgt_globmean[run]-np.average(T_ref_1,weights=wgt) \\n\\ttime=data[\\\"time\\\"]\\n\\tif Tanglob_idx == False: \\n\\t\\treturn y,time,srex,srex_names,lon_pc,lat_pc,idx_l,wgt_l\\n\\telse:\\n\\t\\treturn y,time,srex,srex_names,lon_pc,lat_pc,idx_l,wgt_l,Tan_wgt_globmean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b94b9a51e916bbd19da92f1e67c74cd\",\n \"score\": \"0.47448468\",\n \"text\": \"def test_missing_reference_data():\\n td = TorsionDriveData.from_qdata(\\n dihedral=(6, 10, 11, 8), qdata_file=get_data(\\\"biphenyl_qdata.txt\\\")\\n )\\n del td.reference_data[0]\\n with pytest.raises(MissingReferenceData):\\n td.validate_angles()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b3ee2bbebe912e9550c038bebd0a40c\",\n \"score\": \"0.47353658\",\n \"text\": \"def get_forces_data(outcar_filename=\\\"OUTCAR\\\", convergence=None, warn=False):\\n with open(outcar_filename, \\\"r\\\") as f:\\n outcar = f.read()\\n\\n number_of_ions_re = re.compile(r\\\"NIONS = \\\\s+([\\\\d]+)\\\")\\n try:\\n number_of_ions = int(number_of_ions_re.findall(outcar)[0])\\n except IndexError as exc:\\n raise ValueError(\\\"Unable to read NIONS.\\\") from exc\\n except:\\n raise\\n if not convergence:\\n convergence_re = re.compile(r\\\"EDIFFG = -([\\\\d\\\\.E-]+)\\\")\\n try:\\n convergence = float(convergence_re.findall(outcar)[0])\\n except IndexError as exc:\\n raise ValueError(\\\"Unable to read EDIFFG.\\\") from exc\\n except:\\n raise\\n\\n # find force output block positions\\n forces_block_start = []\\n forces_header_re = re.compile(\\\"\\\\sPOSITION\\\\s+TOTAL-FORCE \\\\(eV/Angst\\\\)\\\")\\n for i, line in enumerate(outcar.split(\\\"\\\\n\\\")):\\n if forces_header_re.search(line):\\n forces_block_start.append(i)\\n if not forces_block_start:\\n raise ValueError(\\\"No FORCES blocks found.\\\")\\n # Ideally we want the most recent complete block of forces data.\\n most_recent_block_start = forces_block_start[-1] + 2\\n forces_lines = outcar.split(\\\"\\\\n\\\")[\\n most_recent_block_start : most_recent_block_start + number_of_ions\\n ]\\n if not forces_block_is_well_formed(forces_lines):\\n # If the most recent forces block is ill-formed, try to parse the previous block.\\n if warn:\\n print(\\n \\\"The last FORCES block is not well-formed. Trying to parse the preceeding block.\\\"\\n )\\n next_most_recent_block_start = forces_block_start[-2] + 2\\n forces_lines = outcar.split(\\\"\\\\n\\\")[\\n next_most_recent_block_start : next_most_recent_block_start + number_of_ions\\n ]\\n if not forces_block_is_well_formed(forces_lines):\\n # If the last two forces blocks are ill-formed, we assume the input file is mis-formatted.\\n raise Exception(\\n \\\"The last two FORCES blocks are not well-formed. Your input might be mis-formatted.\\\"\\n )\\n forces = []\\n for line in forces_lines:\\n forces.append([float(s) for s in line.split()[-3:]])\\n forces_data = ForcesData(forces=forces, convergence=convergence)\\n return forces_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ad1cdc5e35c2eb3930375ee3ac5e721\",\n \"score\": \"0.4734519\",\n \"text\": \"def read_data(self, force_distances=False):\\n import pandas as pd\\n from io import StringIO\\n\\n fin = open(self._input_file)\\n l = fin.readline()\\n bytes_read = len(l)\\n p = l.split()\\n self.N = int(p[0])\\n if len(p) > 1:\\n self.fit_measure_ = float(p[1])\\n\\n l = [fin.readline() for _ in range(self.N)]\\n bytes_read += sum(len(x) for x in l)\\n\\n self.sequences = pd.read_table(\\n StringIO(u\\\"\\\".join(l)),\\n nrows=self.N,\\n sep=\\\"\\\\t\\\",\\n header=None,\\n engine=\\\"python\\\")\\n\\n assert self.N == len(self.sequences)\\n\\n log.info(\\\"Read %d sequences.\\\", self.N)\\n\\n if self.sequences.shape[1] > 1:\\n log.info(\\\"Setting embedding from input data\\\")\\n self.embedding = self.sequences.set_index(0)\\n self.embedding.columns = self.coord_dims\\n self.embedding.index.name = \\\"Sequence\\\"\\n\\n if (not hasattr(self, \\\"embedding\\\")) or force_distances:\\n log.info(\\\"Memory usage %gMB\\\" % (util.memory_usage()))\\n log.info(\\\"Reading distances.\\\")\\n self.read_distances(fin)\\n log.info(\\\"Memory usage %gMB\\\" % (util.memory_usage()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5573c69ea9117cb6778fc2d0b4eaa0d\",\n \"score\": \"0.47334468\",\n \"text\": \"def _fix_georeferencing(self):\\n logger.info(\\\"Fixing georeferencing information in %s\\\", self.ri_path)\\n\\n # Set the coordinate reference system for rasterio methods\\n with xr.open_dataset(self.ri_path, engine=\\\"netcdf4\\\") as data:\\n # data = xr.open_dataset(self.ri_path)\\n data = set_georeferencing(\\\"ri\\\", data)\\n\\n # Change longitude range and rebuild this coordinate dataset\\n lons = data[\\\"longitude\\\"].data - 360\\n data = data.assign_coords({\\\"longitude\\\": lons})\\n data[\\\"longitude\\\"].attrs = {\\n \\\"units\\\": \\\"degrees_east\\\",\\n \\\"long_name\\\": \\\"Longitude\\\",\\n \\\"actual_range\\\": [\\n data[\\\"longitude\\\"].data.min(),\\n data[\\\"longitude\\\"].data.max()\\n ]\\n }\\n\\n # The default nan value (-9.96921e+36) throws overflow warnings\\n navalue = -9999\\n data[\\\"precip\\\"].data[np.isnan(data[\\\"precip\\\"].data)] = navalue\\n data[\\\"precip\\\"].encoding[\\\"missing_value\\\"] = -9999\\n data[\\\"precip\\\"].encoding[\\\"_FillValue\\\"] = -9999\\n\\n # Overwrite download path\\n os.remove(self.ri_path)\\n data.to_netcdf(self.ri_path)\\n\\n logger.info(\\\"Georeferencing information adjusted, overwriting %s\\\",\\n self.ri_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6171afdc51971ba49a671845fc2ecbd8\",\n \"score\": \"0.473311\",\n \"text\": \"def read_short_dat(filename):\\n with open(filename, 'r') as dat_file:\\n # reading in the header block\\n is_header = True\\n header_block = []\\n while is_header:\\n tmp_line = dat_file.readline()\\n if tmp_line.find('#') == 0:\\n header_block.append(tmp_line)\\n else:\\n is_header = False\\n # get the next two Tecplot lines and then go back one line\\n header_block.append(tmp_line)\\n last_pos = dat_file.tell()\\n tmp_line = dat_file.readline()\\n header_block.append(tmp_line)\\n dat_file.seek(last_pos)\\n\\n try:\\n reader = csv.reader(dat_file, delimiter=' ')\\n type_line = next(reader)\\n nvert = int(type_line[1][2:])\\n nface = int(type_line[2][2:])\\n x_data = [] # position\\n f2v = [] # connectivity\\n\\n count = 0\\n while count < nvert:\\n lst = next(reader)[0:3]\\n x_data.append(lst)\\n count += 1\\n x_data = np.array(x_data, dtype=float)\\n\\n count = 0\\n while count < nface:\\n lst = next(reader)[0:3] # should just be 3 values\\n f2v.append([int(i) for i in lst])\\n count += 1\\n f2v = np.array(f2v, dtype=int)\\n\\n except csv.Error as e:\\n sys.exit('file %s, line %d: %s' % (filename, reader.line_num, e))\\n\\n try:\\n params = {\\\"header_block\\\": header_block}\\n return (x_data, f2v, params)\\n except NameError as e:\\n print(\\\"One of the required variables was not instantiated: {}\\\".format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfc0aa800cc7109504f36afac087b470\",\n \"score\": \"0.47310045\",\n \"text\": \"def testNewreadoutlier(self):\\n imageids, imsizes, phasecenters, masks, models, paramdic, newformat=self.imset.newreadoutlier(self.newoutlierfile)\\n # the reference outlier file contains each field's paramaters per line\\n print \\\"Using \\\", self.newoutlierfile\\n f = open(self.newoutlierreffile,'r')\\n lines=f.readlines()\\n f.close()\\n cnt = 0\\n for elm in lines:\\n if len(elm.split())!=0 and elm.split()[0]!='#' :\\n cnt +=1\\n print \\\"N fields=\\\",cnt\\n #print \\\"imsizes=\\\",imsizes,\\\" phasecenters=\\\",phasecenters\\n print \\\"len(imsizes)=\\\",len(imsizes), \\\" len(imageids)=\\\",len(imageids)\\n if len(imsizes) == len(phasecenters) == len(imageids) == len(masks) == len(models) == cnt:\\n self.res=True\\n else:\\n self.res=False\\n self.assertTrue(self.res)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4740d61cc550ea7366ccd24ebe7e7a5\",\n \"score\": \"0.47260442\",\n \"text\": \"def _readData(self):\\n raw = numpy.genfromtxt(self.filename, delimiter=[8]*10)\\n\\n # component 0\\n dataStart = self._headerTotalLines\\n acc0 = self._extract(raw, dataStart, self._dataNumLines)\\n dataStart += self._dataNumLines\\n if self._dataNumValues == 3:\\n vel0 = self._extract(raw, dataStart, self._dataNumLines)\\n dataStart += self._dataNumLines\\n disp0 = self._extract(raw, dataStart, self._dataNumLines)\\n dataStart += self._dataNumLines\\n\\n # component 1\\n dataStart = self._headerTotalLines + \\\\\\n 1*(self._headerTotalLines + self._dataNumValues*self._dataNumLines)\\n acc1 = self._extract(raw, dataStart, self._dataNumLines)\\n dataStart += self._dataNumLines\\n if self._dataNumValues == 3:\\n vel1 = self._extract(raw, dataStart, self._dataNumLines)\\n dataStart += self._dataNumLines\\n disp1 = self._extract(raw, dataStart, self._dataNumLines)\\n dataStart += self._dataNumLines\\n\\n # component 2\\n dataStart = self._headerTotalLines + \\\\\\n 2*(self._headerTotalLines + self._dataNumValues*self._dataNumLines)\\n acc2 = self._extract(raw, dataStart, self._dataNumLines)\\n dataStart += self._dataNumLines\\n if self._dataNumValues == 3:\\n vel2 = self._extract(raw, dataStart, self._dataNumLines)\\n dataStart += self._dataNumLines\\n disp2 = self._extract(raw, dataStart, self._dataNumLines)\\n dataStart += self._dataNumLines\\n\\n # Assemble components\\n self.acc = numpy.array( (acc0, acc1, acc2) ).transpose()\\n self.acc *= self.header['toSI']\\n if self._dataNumValues == 3:\\n self.vel = numpy.array( (vel0, vel1, vel2) ).transpose()\\n self.vel *= self.header['toSI']\\n self.disp = numpy.array( (disp0, disp1, disp2) ).transpose()\\n self.disp *= self.header['toSI']\\n\\n # Rotate to E,N,U\\n R = numpy.zeros((3, 3), dtype=numpy.float64)\\n for i,az in enumerate(self.azimuth.values()):\\n if az <= 360.0:\\n azR = az / 180.0 * math.pi\\n R[i,:] = (math.sin(azR), math.cos(azR), 0.0)\\n else:\\n R[i,:] = (0.0, 0.0, 1.0)\\n self.acc = numpy.dot(self.acc, R)\\n if not self.vel is None:\\n self.vel = numpy.dot(self.vel, R)\\n if not self.disp is None:\\n self.disp = numpy.dot(self.disp, R)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b439b80bb993bbdb8e1b5c1f2bf243b\",\n \"score\": \"0.47248137\",\n \"text\": \"def make_reference(self, reference_probes):\\n self.refs = pd.DataFrame(reference_probes, columns=['sequence'])\\n self.refs['name'] = [naming['reference'](i) for i, _ in self.refs.iterrows()]\\n self.refs['color'] = 'tomato'\\n self.refs['type'] = 'stellaris (sense) reference'\\n self.refs = self.refs[benchling_headers]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82005183b11a6e328c7802733a263954\",\n \"score\": \"0.47230077\",\n \"text\": \"def backward(self, top, propagate_down, bottom):\\n\\n batch=1\\n index = 0\\n level = 6\\n dis = 7\\n SepSize = batch * level\\n self.ref= np.zeros(bottom[0].num,dtype=np.float32)\\n\\n for k in range(dis):\\n for i in range(SepSize*k,SepSize*(k+1)-batch):\\n for j in range(SepSize*k + int((i-SepSize*k)/batch+1)*batch,SepSize*(k+1)):\\n\\n\\t\\t if self.loss[index]>0:\\n self.ref[i] += -1\\n self.ref[j] += +1\\n index +=1\\n\\n for k in range(dis):\\n\\t\\tindex = k*SepSize\\n\\t\\tself.ref[index] = (bottom[0].data[index] - bottom[1].data[index])*bottom[0].num /100\\n\\n for k in range(dis):\\n\\t\\tif (bottom[0].data[(k+1)*SepSize-1] < 0):\\n\\t\\t\\tself.ref[(k+1)*SepSize-1] += bottom[0].data[(k+1)*SepSize-1]*bottom[0].num /100\\n\\n\\tbottom[0].diff[...]= np.reshape(self.ref,(bottom[0].num,1))/bottom[0].num\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d2c72529e014e69538255bd85bd9ad3\",\n \"score\": \"0.47196406\",\n \"text\": \"def fix_nonref_positions(in_file, ref_file):\\n ignore_chrs = [\\\".\\\"]\\n ref2bit = twobit.TwoBitFile(open(ref_file))\\n out_file = in_file.replace(\\\"-raw.vcf\\\", \\\".vcf\\\")\\n\\n with open(in_file) as in_handle:\\n with open(out_file, \\\"w\\\") as out_handle:\\n for line in in_handle:\\n if line.startswith(\\\"#\\\"):\\n out_handle.write(line)\\n else:\\n parts = line.rstrip(\\\"\\\\r\\\\n\\\").split(\\\"\\\\t\\\")\\n pos = int(parts[1])\\n # handle chr/non-chr naming\\n if parts[0] not in ref2bit.keys():\\n parts[0] = parts[0].replace(\\\"chr\\\", \\\"\\\")\\n ref_base = None\\n if parts[0] not in ignore_chrs:\\n try:\\n ref_base = ref2bit[parts[0]].get(pos-1, pos).upper()\\n except Exception, msg:\\n # off the end of the chromosome\\n if str(msg).startswith(\\\"end before start\\\"):\\n print msg\\n else:\\n print parts\\n raise\\n parts = fix_vcf_line(parts, ref_base)\\n if parts is not None:\\n out_handle.write(\\\"\\\\t\\\".join(parts) + \\\"\\\\n\\\")\\n return out_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c48fb2692c4c5e73cfff883b7a41c001\",\n \"score\": \"0.47189966\",\n \"text\": \"def load_data(self):\\n fn = \\\"../data/redclump_sample_A_updatedvalues_only.txt\\\"\\n print \\\"Reading %s ...\\\" % fn\\n data = np.genfromtxt(fn)\\n names = [\\\"ID\\\", \\\"distance\\\", \\\"Radius_gal\\\", \\\"Phi_gal\\\", \\\"z\\\", \\\"Teff\\\",\\n \\\"logg\\\", \\\"[Fe/H]\\\", \\\"[alpha/Fe]\\\", \\\"age\\\", \\\"mass\\\"]\\n print data[2]\\n print data.shape\\n print \\\"Read %s\\\" % fn\\n ages = data[:, 9].flatten()\\n sigma = 0.25 / np.log10(np.e) # MAGIC\\n ivars = np.zeros_like(ages) + 1. / (sigma * sigma)\\n self.set_data(ages, ivars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddf1ec23b0976a68cd2917aa6f6e2aec\",\n \"score\": \"0.4716494\",\n \"text\": \"def load(self):\\n data = np.load(self.path)\\n self.target = data['target']\\n self.indices = list(data['indices'])\\n self.ref = list(data['ref'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9190505865acffe7522fcfd3d4fab6fc\",\n \"score\": \"0.47135812\",\n \"text\": \"def get_reference_focus_data(self, reference_trajectory):\\n\\n # Reorder image.\\n foc_X = self.foc_X.reshape((self.N), order=\\\"F\\\")\\n foc_Y = self.foc_Y.reshape((self.N), order=\\\"F\\\")\\n foc_Z = self.foc_Z.reshape((self.N), order=\\\"F\\\")\\n image = np.asanyarray([foc_X, foc_Y, foc_Z])\\n\\n # Get local_look and local_squint for the distributed target.\\n self.local_look_ref_traj, self.local_squint_ref_traj = Utils.get_angles_antenna_distributed_target(reference_trajectory, image)\\n\\n N = image.shape[1]\\n NT = len(reference_trajectory.flight_x)\\n\\n # Compute distances between sensor and target.\\n self.distances_ref_traj = np.sqrt( (np.full((N, NT), reference_trajectory.flight_x[:]) - np.full((N, NT), foc_X[:, None]) )**2 + \\n (np.full((N, NT), reference_trajectory.flight_y[:]) - np.full((N, NT), foc_Y[:, None]) )**2 + \\n (np.full((N, NT), reference_trajectory.flight_z[:]) - np.full((N, NT), foc_Z[:, None]) )**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e87521336ac960999949ce42b6fb32\",\n \"score\": \"0.47075123\",\n \"text\": \"def read(self) -> None:\\n (\\n self.iteration,\\n self.s,\\n self.y,\\n self.rho,\\n self.r0,\\n self.f0,\\n self.e0,\\n self.task,\\n ) = self.load()\\n self.load_restart = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"863e2f9fb817febc0c63b3c844366811\",\n \"score\": \"0.47070032\",\n \"text\": \"def readCCout():\\n\\n inputName = 'ccinput.dat'\\n ccextension = '_cc-fit.out'\\n \\n data = np.loadtxt(inputName+ccextension, skiprows = 13)\\n \\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd0dc9a1497547a6da69c2a041d31210\",\n \"score\": \"0.47039145\",\n \"text\": \"def get_ref_step2(self):\\n wrong_ref = self.check_for_wrong_ref() or self.wrong_ref_pp()\\n\\n # if self.ref: # probabaly should be calculated once\\n # if self.author != \\\"תנך\\\" and self.ref.index.title in library.get_indexes_in_category('Tanakh'):\\n # include_dependant = True\\n # wrong_ref = True\\n # elif self.author != \\\"תלמוד בבלי\\\" and self.ref.index.title in library.get_indexes_in_category('Bavli'):\\n # include_dependant = False\\n # wrong_ref = True\\n\\n if self.ref:\\n if wrong_ref: # or (self.raw_ref and not self.ref and not self.raw_ref.is_sham):\\n new_ref = None\\n include_dependant = True\\n if self.author == \\\"משנה תורה\\\":\\n include_dependant = False\\n look_here = self.get_index_options(include_dependant=include_dependant)\\n if look_here:\\n new_ref = self.get_new_ref_w_look_here(look_here)\\n else: # couldn't find a indexs for this author\\n parser.missing_authors.add(self.author)\\n # if look_here and (self.index or wrong_ref):\\n # # than try to get the true title from the cat from look_here\\n # look_here_shared_title_word = '({})'.format(\\n # '|'.join(list(set.intersection(*[set(x.title.split()) for x in look_here]))))\\n # alt_ref_titles = map(lambda x: x['text'], self.ref.index.schema['titles'])\\n # found_index = [ind for ind in look_here for tanakh_book in alt_ref_titles if\\n # tanakh_book in re.sub(look_here_shared_title_word, '', ind.title).strip()]\\n # if found_index:\\n # if len(set(found_index)) > 1: # assert len(found_index) == 0\\n # print \\\"more than one index option\\\" # todo: problem with אלשיך דברים and with books I, II\\n # print found_index[0].title, found_index[-1].title\\n # self.index = found_index[0]\\n # try:\\n # new_ref = Ref('{} {}'.format(self.index.title, re.sub(self.ref.index.title, '', self.ref.normal()).strip()))\\n # print \\\"deleting wrong: {} found new Index: {} new ref: {}\\\".format(self.ref, self.index, new_ref)\\n # self.ref = new_ref\\n # except exceptions.InputError as e:\\n # print \\\"inputError for this string {}, extracted from this rawref {}\\\".format('{} {}'.format(self.index.title, re.sub(self.ref.index.title, '', self.ref.normal()).strip()), self.raw_ref)\\n # tanakh is wrong but can't find the index ex: רש\\\"ר הירש\\n if self.index and not self.ref:\\n ind = self.index\\n #todo: look into the original catching see why it is not catching kalla. also see how to copy the intresting parts to here\\n self.raw_ref.book = ind.title\\n split_raw_text = re.sub('[\\\\(\\\\)]', '', self.raw_ref.rawText).split()\\n self.raw_ref.section_level = split_raw_text\\n\\n pass\\n if not self.index and not new_ref:# todo: it was an or why? turend into an and lets see if/what it breaks.\\n self.change_look_here(author = 'תרגום יונתן', new_author = 'תרגום', category = 'Writings')\\n if not self.index:\\n # print \\\"deleting wrong: {} couldn't find an index\\\".format(self.ref)\\n self.ref = None\\n else: # not wrong_ref. and found a ref, might just be a correct ref. todo: how to test it is correct?\\n pass\\n # try to get the ref from the index via the titles found (without an index - ex. ילקוט שמעוני, דניאל תתרסה)\\n # it is look at nodes names when we are not testing giving wrong Ref?\\n elif hasattr(self, \\\"opt_titles\\\") and self.opt_titles:\\n indexs = self.extract_cat(include_dependant=True)\\n if indexs:\\n indexs.extend(self.indexs)\\n # if self.author == 'משנה תורה':\\n # self.reduce_indexs_with_matcher()\\n node_name = ''\\n for opt_title in self.opt_titles:\\n try:\\n new_index = library.get_index(opt_title)\\n if not indexs or new_index.is_complex(): # or library.get_title_node_dict('he')[opt_title] != library.get_title_node_dict('en')[new_index.title]\\n self.index = new_index\\n possible_nodes = new_index.all_titles('he')\\n node_guess = intersect_list_string(possible_nodes, self.raw_ref.rawText)\\n if not self.ref and node_guess:\\n try:\\n r = Ref(node_guess)\\n if not self.check_for_wrong_ref(r):\\n self.ref = r\\n except InputError:\\n self.ref = ''\\n print('the node guess is not a Ref')\\n elif node_guess:\\n # then we should check witch is the better option\\n # maybe using the length of matching words in regards to the titles\\n ref_opt1 = self.ref.he_normal()\\n ref_opt2 = node_guess\\n sets = strings2sets(ref_opt1,ref_opt2, self.raw_ref.rawText)\\n if abs(len(set[1])-len(set[2])) < abs(len(set[0])-len(set[2])):\\n self.ref = Ref(node_guess)\\n elif new_index not in indexs:\\n intersected = self.intersected_indexes(new_index, indexs)\\n if intersected:\\n node_name = Ref(intersected[0].title).he_normal() # TODO: find cases with list longer than one and figure it out.\\n self.index = intersected\\n except (exceptions.BookNameError, TypeError):\\n # print \\\"excepted {}\\\".format(opt_title)\\n pass\\n if node_name:\\n try:\\n new_string = re.sub(opt_title, node_name, self.text)\\n new_string = re.sub('[()]', '', new_string) # because we are going to use Ref rather than library.get_refs_in_string() that needes the parentheses\\n self.ref = Ref(new_string)\\n except exceptions.InputError:\\n try:\\n self.ref = Ref(','.join(new_string.split(',')[0:2]))\\n except exceptions.InputError:\\n # try again without the name of the node, but with the index recognizing that the node is in that index\\n try:\\n new_string = re.sub(opt_title, \\\"\\\", new_string)\\n self.ref = Ref(new_string)\\n except exceptions.InputError:\\n print(\\\"we tried\\\")\\n self.ref = None\\n elif (self.indexs or self.index) and self.raw_ref and not self.raw_ref.is_sham:\\n self.ref_opt = []\\n if not self.indexs:\\n self.indexs = [self.index]\\n for ind in self.indexs:\\n new_index = ind if isinstance(ind, Index) else library.get_index(ind)\\n self.index = new_index\\n possible_nodes = new_index.all_titles('he')\\n ns_titles_and_refs = []\\n if hasattr(new_index, 'alt_structs'):\\n ns=[]\\n ns_titles_and_refs = dict()\\n [ns.extend(alt['nodes']) for alt in new_index.alt_structs.values()]\\n if any('titles' in nsone for nsone in ns): # 'titles' in ns[0].keys():\\n ns_titles_and_refs = dict([(x['titles'][1]['text'], x['wholeRef']) for x\\n in ns if ('wholeRef' in x and 'titles' in x)])\\n if any('sharedTitle' in nsone for nsone in ns): # todo: old code: 'sharedTitle' in ns[0].keys():\\n ns_titles_and_refs.update(dict([(Term().load_by_title(x['sharedTitle']).get_primary_title('he')\\n, x['wholeRef']) for x in ns if 'sharedTitle' in x]))\\n possible_nodes.extend(ns_titles_and_refs)\\n node_guess = intersect_list_string(possible_nodes, self.raw_ref.rawText)\\n if not self.ref and node_guess:\\n try:\\n if ns_titles_and_refs:\\n try:\\n r = Ref(ns_titles_and_refs.get(node_guess, ''))\\n r.normal()\\n except (InputError, AttributeError):\\n r = Ref(node_guess)\\n else:\\n r = Ref(node_guess)\\n if not self.check_for_wrong_ref(r):\\n self.ref_opt.append(r)\\n except InputError:\\n # self.ref_opt.append('')\\n print('the node guess is not a Ref')\\n if self.ref_opt:\\n if len(self.ref_opt) == 1:\\n r = self.ref_opt[0]\\n #self.ref = r\\n if not r.sections:\\n sections = ' '.join([sect for sect in re.sub('[(,)]', ' ', self.raw_ref.rawText).split() if is_hebrew_number(sect)]) #todo: maybe bad huristic and should wait to do this on the PM level?\\n if sections:\\n try:\\n self.ref = Ref(r.he_normal() + ' ' + sections)\\n except InputError as e:\\n pass\\n return\\n self.ref = r\\n else: # more than one option, we need to choose the better one.\\n he_options = [r.he_normal().replace(\\\"ן\\\", \\\"ם\\\") for r in self.ref_opt]\\n try:\\n better = intersect_list_string(he_options, re.sub('[()]', '', self.raw_ref.rawText), ref_opt = True)\\n r = Ref(better)\\n if not r.sections:\\n sections = ' '.join(\\n [sect for sect in re.sub('[(,)]', ' ', self.raw_ref.rawText).split() if\\n is_hebrew_number(\\n sect)]) # todo: maybe bad huristic and should wait to do this on the PM level?\\n if sections:\\n try:\\n self.ref = Ref(r.he_normal() + ' ' + sections)\\n except (AttributeError, InputError):\\n pass\\n return\\n self.ref = r\\n except InputError:\\n print(\\\"more than one option to guess from\\\")\\n\\n\\n # elif node_guess:\\n # # then we should check witch is the better option\\n # # maybe using the length of matching words in regards to the titles\\n # ref_opt1 = self.ref.he_normal()\\n # ref_opt2 = node_guess\\n # # sets = strings2sets(ref_opt1, ref_opt2, self.raw_ref.rawText)\\n # if abs(len(set[1]) - len(set[2])) < abs(len(set[0]) - len(set[2])):\\n # self.ref = Ref(node_guess)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c9cf859a5192a7066c7d24f18e992df\",\n \"score\": \"0.47011754\",\n \"text\": \"def test_readwriteread():\\n uv_in = UVCal()\\n uv_out = UVCal()\\n testfile = os.path.join(DATA_PATH, 'zen.2457698.40355.xx.fitsA')\\n write_file = os.path.join(DATA_PATH, 'test/outtest_omnical.fits')\\n uv_in.read_calfits(testfile)\\n uv_in.write_calfits(write_file, clobber=True)\\n uv_out.read_calfits(write_file)\\n nt.assert_equal(uv_in, uv_out)\\n\\n # test without freq_range parameter\\n uv_in.freq_range = None\\n uv_in.write_calfits(write_file, clobber=True)\\n uv_out.read_calfits(write_file)\\n nt.assert_equal(uv_in, uv_out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddf3d1076918f601a7b704a10561fda6\",\n \"score\": \"0.46959445\",\n \"text\": \"def gfalternate_updatedict(cf,ds_tower,ds_alt):\\n if \\\"alternate\\\" not in dir(ds_tower): return\\n section = \\\"Drivers\\\"\\n series_list = cf[section].keys()\\n for series in series_list:\\n if \\\"GapFillFromAlternate\\\" not in cf[section][series].keys(): continue\\n # name of alternate output series in ds\\n output_list = cf[section][series][\\\"GapFillFromAlternate\\\"].keys()\\n # loop over the outputs listed in the control file\\n for output in output_list:\\n if output not in ds_tower.alternate.keys(): ds_tower.alternate[output] = {}\\n ds_tower.alternate[output][\\\"label_tower\\\"] = series\\n # source name\\n ds_tower.alternate[output][\\\"source\\\"] = cf[section][series][\\\"GapFillFromAlternate\\\"][output][\\\"source\\\"]\\n # site name\\n ds_tower.alternate[output][\\\"site_name\\\"] = ds_tower.globalattributes[\\\"site_name\\\"]\\n # alternate data file name\\n # first, look in the [Files] section for a generic file name\\n file_list = cf[\\\"Files\\\"].keys()\\n lower_file_list = [item.lower() for item in file_list]\\n if ds_tower.alternate[output][\\\"source\\\"].lower() in lower_file_list:\\n # found a generic file name\\n i = lower_file_list.index(ds_tower.alternate[output][\\\"source\\\"].lower())\\n ds_tower.alternate[output][\\\"file_name\\\"] = cf[\\\"Files\\\"][file_list[i]]\\n else:\\n # no generic file name found, look for a file name in the variable section\\n ds_tower.alternate[output][\\\"file_name\\\"] = cf[section][series][\\\"GapFillFromAlternate\\\"][output][\\\"file_name\\\"]\\n # if the file has not already been read, do it now\\n if ds_tower.alternate[output][\\\"file_name\\\"] not in ds_alt:\\n ds_alt[ds_tower.alternate[output][\\\"file_name\\\"]] = qcio.nc_read_series(ds_tower.alternate[output][\\\"file_name\\\"])\\n # get the type of fit\\n ds_tower.alternate[output][\\\"fit_type\\\"] = \\\"OLS\\\"\\n if \\\"fit\\\" in cf[section][series][\\\"GapFillFromAlternate\\\"][output]:\\n if cf[section][series][\\\"GapFillFromAlternate\\\"][output][\\\"fit\\\"].lower() in [\\\"ols\\\",\\\"ols_thru0\\\",\\\"mrev\\\",\\\"replace\\\",\\\"rma\\\",\\\"odr\\\"]:\\n ds_tower.alternate[output][\\\"fit_type\\\"] = cf[section][series][\\\"GapFillFromAlternate\\\"][output][\\\"fit\\\"]\\n else:\\n log.info(\\\"gfAlternate: unrecognised fit option for series \\\"+output)\\n # force the fit through the origin\\n #ds_tower.alternate[output][\\\"thru0\\\"] = \\\"no\\\"\\n #if \\\"thru0\\\" in cf[section][series][\\\"GapFillFromAlternate\\\"][output]:\\n #if cf[section][series][\\\"GapFillFromAlternate\\\"][output][\\\"thru0\\\"].lower() in [\\\"yes\\\",\\\"true\\\"]:\\n #ds_tower.alternate[output][\\\"thru0\\\"] = \\\"yes\\\"\\n #else:\\n #log.info(\\\"gfAlternate: unrecognised thru0 option for series \\\"+output)\\n # correct for lag?\\n ds_tower.alternate[output][\\\"lag\\\"] = \\\"yes\\\"\\n if \\\"lag\\\" in cf[section][series][\\\"GapFillFromAlternate\\\"][output]:\\n if cf[section][series][\\\"GapFillFromAlternate\\\"][output][\\\"lag\\\"].lower() in [\\\"no\\\",\\\"false\\\"]:\\n ds_tower.alternate[output][\\\"lag\\\"] = \\\"no\\\"\\n else:\\n log.info(\\\"gfAlternate: unrecognised lag option for series \\\"+output)\\n # alternate data variable name if different from name used in control file\\n if \\\"alternate_name\\\" in cf[section][series][\\\"GapFillFromAlternate\\\"][output]:\\n ds_tower.alternate[output][\\\"alternate_name\\\"] = cf[section][series][\\\"GapFillFromAlternate\\\"][output][\\\"alternate_name\\\"]\\n else:\\n ds_tower.alternate[output][\\\"alternate_name\\\"] = series\\n # results of best fit for plotting later on\\n if \\\"results\\\" not in ds_tower.alternate[output].keys():\\n ds_tower.alternate[output][\\\"results\\\"] = {\\\"startdate\\\":[],\\\"enddate\\\":[],\\\"No. points\\\":[],\\\"r\\\":[],\\n \\\"Bias\\\":[],\\\"RMSE\\\":[],\\\"Frac Bias\\\":[],\\\"NMSE\\\":[],\\n \\\"Avg (tower)\\\":[],\\\"Avg (alternate)\\\":[],\\n \\\"Var (tower)\\\":[],\\\"Var (alternate)\\\":[],\\\"Var ratio\\\":[],\\n \\\"Lag (uncorrected)\\\":[],\\\"Lag (corrected)\\\":[],\\n \\\"Slope\\\":[],\\\"Offset\\\":[]}\\n # create an empty series in ds if the alternate output series doesn't exist yet\\n if output not in ds_tower.series.keys():\\n data,flag,attr = qcutils.MakeEmptySeries(ds_tower,output)\\n qcutils.CreateSeries(ds_tower,output,data,Flag=flag,Attr=attr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1b7d82507b0ec6bd0d3e84537d53d50\",\n \"score\": \"0.46922886\",\n \"text\": \"def load_chain(outfile):\\n chain = np.genfromtxt(outfile)\\n return reshape_chain(chain)\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":9940,"numItemsPerPage":100,"numTotalItems":994482,"offset":994000,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3ODUzNzk1OCwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc3ODU0MTU1OCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.wc6mJddHtEBie3DNY9O4gAOI8rEDELnBc0seqQTNG1C8anc8oxddz40hZy32s0qpbPBa1SnUQhrJjAORuyE-BQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":994482,"numBytesParquet":23972089656}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
c73d7cd464aed602cee2f59592fdf968 | Looks for allocations made within the current element and declares them all at once. | [
{
"docid": "d93c02f04f3fc52c8ae6861292124c81",
"score": "0.0",
"text": "def _writeAllocations( self, element, implicitsOnly=False ):\n\t\t# FIXME: assert(element.dataflow) fails sometimes\n\t\tif element and element.dataflow:\n\t\t\t# NOTE: Implicits can sometimes be declared twice\n\t\t\tdeclared = {}\... | [
{
"docid": "2dd29c606647048c35bc68cae3a29e72",
"score": "0.60576046",
"text": "def allocate(self):\n self.allocatables.sort(key=lambda a: len(a.constraint), reverse=True)\n allocatable = self.allocatables.pop()\n for cn in allocatable.constraint:\n if self.slots[cn] == -1:\n self.sl... |
4dd26934f63cb73e0462327ec2c1167f | SetMaskImage(itkConnectedComponentImageFilterIUC3ISS3 self, itkImageUC3 _arg) | [
{
"docid": "1a128a54e47001e0f8c40bd4b686b2b3",
"score": "0.90249586",
"text": "def SetMaskImage(self, _arg: 'itkImageUC3') -> \"void\":\n return _itkConnectedComponentImageFilterPython.itkConnectedComponentImageFilterIUC3ISS3_SetMaskImage(self, _arg)",
"title": ""
}
] | [
{
"docid": "b21a7e333812da8cfb63704eebc66c7e",
"score": "0.90715206",
"text": "def SetMaskImage(self, _arg: 'itkImageUC3') -> \"void\":\n return _itkConnectedComponentImageFilterPython.itkConnectedComponentImageFilterIUC3IUL3_SetMaskImage(self, _arg)",
"title": ""
},
{
"docid": "d2cbe... |
3dbbe677964b2d559e3a5f88e09f0174 | The default IPv4 gateway for the user plane interface. This should match one of the interfaces configured on your Azure Stack Edge device. | [
{
"docid": "24aeb0ea7fe7434db5953e53bc3c1f16",
"score": "0.73977745",
"text": "def user_plane_access_ipv4_gateway(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"user_plane_access_ipv4_gateway\")",
"title": ""
}
] | [
{
"docid": "94daa44747249a6620cb9ce4735d3dc6",
"score": "0.74860233",
"text": "def user_plane_access_ipv4_gateway(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_plane_access_ipv4_gateway\")",
"title": ""
},
{
"docid": "94daa44747249a6620cb9ce4735d3dc6",
"sc... |
7d793c2aaa9fc460d44b0b5ee6dc5fd8 | Calculates the accuracy of prediction methods for the grouped evaluation problem. Only counts those predictions that have all groups correct in a predicted groupings. Ignores any partial correctness. | [
{
"docid": "f34a4b76e309f215ed7a616666afd354",
"score": "0.7314673",
"text": "def calculateAccuracy(self, results, dataSet):\n\t\tmatch = 0\n\t\tfor key in dataSet.keys():\n\t\t\tpredictedSenseGroups = results[key]\n\t\t\t# Examples are in order so split into group size to find groups \n\t\t\torderedExa... | [
{
"docid": "0d4bfdaa5c68857e9cc98002b314587a",
"score": "0.7493893",
"text": "def accuracy(self):\n # Initialize key variables\n correct = {}\n prediction = 0\n cls_count = {}\n accuracy = {}\n\n # Analyze all the data\n for cls in self.pca_object.classes... |
1a7d7436bc50e8f0af440046d0fba746 | Returns the monospaced terminal display width of char. | [
{
"docid": "380e4c610a6f1598c0941077b73c4267",
"score": "0.7959377",
"text": "def GetCharacterDisplayWidth(char):\n if not isinstance(char, unicode):\n # Non-unicode chars have width 1. Don't use this function on control chars.\n return 1\n\n # Normalize to avoid special cases.\n char = unicode... | [
{
"docid": "da032b13152348ef195466b6f5f197a3",
"score": "0.7047037",
"text": "def DisplayWidth(self, buf):\n if not isinstance(buf, basestring):\n # Handle non-string objects like Colorizer().\n return len(buf)\n width = 0\n i = 0\n while i < len(buf):\n if self._csi and buf[i... |
14ae8c9a0de3273ad2cc6908a0638b97 | full path of file in same dir as this module | [
{
"docid": "7241ece943ecf2933e26465a63cbc2b5",
"score": "0.0",
"text": "def output_file(filename):\n return os.path.join(base_dir(), \"output\", filename)",
"title": ""
}
] | [
{
"docid": "7e4bdee811f8cd82d52a6ce59bc3cac9",
"score": "0.8570931",
"text": "def getFilepath(self):\n return os.path.join(self.getModuleDir(), *(self.name().split('/')))",
"title": ""
},
{
"docid": "db89b52e56bf59ce28b7546606fd8c1e",
"score": "0.7867944",
"text": "def module_... |
e8d87379e0b817bb4911cc5b1c034938 | Function called when the value must be an integer. | [
{
"docid": "a719345e77eb61920c2033337574f159",
"score": "0.0",
"text": "def checking_value(value):\n if user_language[0] == \"fr\":\n prb_text = \"La durée doit être un nombre entier.\"\n ok_text = \"La durée a bien été modifiée.\"\n\n else: #user_language[0] == \"en\":\n prb_... | [
{
"docid": "77a2a671d460d73c24d10626bc3aea3a",
"score": "0.78474224",
"text": "def isInteger(self):",
"title": ""
},
{
"docid": "77a2a671d460d73c24d10626bc3aea3a",
"score": "0.78474224",
"text": "def isInteger(self):",
"title": ""
},
{
"docid": "77a2a671d460d73c24d10626bc... |
81d4e11178365569ca0b96520a3dfa26 | Since this view will only be used by admin, just get the whole list of attendees | [
{
"docid": "83ae6a1f32ba50996ae2b9b33fd5ce4c",
"score": "0.60177976",
"text": "def attendee_collection(self):\n return CourseAttendeeCollection(self.request.session)",
"title": ""
}
] | [
{
"docid": "c00c53b34d7111218c8a2117bdcc1180",
"score": "0.65361494",
"text": "def list(self):\n return self._list(\"/tenants\", \"tenants\")",
"title": ""
},
{
"docid": "b13fbed2332f686985f5e76691651a63",
"score": "0.60792476",
"text": "def get_admins(self):\n response... |
102470a2cbd8a8384dfb0744ab966f09 | Returns ``True`` if the current node does not have any siblings. | [
{
"docid": "00c7acd158b6d7a7e0a1deead49129f5",
"score": "0.0",
"text": "def is_single_child(context: TreeContext) -> bool:\n return len(context[-2]._children) == 1",
"title": ""
}
] | [
{
"docid": "2b2207a21da8511908fb1ad60ade9e26",
"score": "0.7273685",
"text": "def isSiblingOf(self, node):\n return (self in node.siblings())",
"title": ""
},
{
"docid": "d2491c3b0f2a6d54ff005364b5d1a2dd",
"score": "0.7044521",
"text": "def is_root(self):\n return not s... |
04fa42307f6d0531171fdaf923a417ac | add column with percent of each race in geographical region | [
{
"docid": "90c97e2ddff3407a870a2e2fd54867e3",
"score": "0.717701",
"text": "def compute_percent_each_race(self, df):\n percent_black = round(\n (df.loc[:, 'Estimate!!Total:!!Not Hispanic or Latino:!!Black or African American alone']\n / df.loc[:, 'Estimate!!Total:']) * 100,... | [
{
"docid": "d6e97377f59a15631938157378aa7106",
"score": "0.6053051",
"text": "def percentage(municipalities, year, parties, input):\n\n for row in range(len(input)):\n if row != 0:\n # check if the municipality existed in 2017\n if input.loc[row, 'RegioNaam'] in municipal... |
3c9f776ede826eb32d3f3263d1126199 | Get the single matching Country's ISO31662 code from a partial name. | [
{
"docid": "7330db2871d6aa4b548ed4c6dda5f2dc",
"score": "0.70137024",
"text": "def getISO3166Code(self, countryName):\n logger.debug(f\"Getting country code for {countryName}\")\n # workaround a common problem: \"United States\" is the Mapbox name for US, but the algorithm\n # below... | [
{
"docid": "ab0b971edb8cd06feb6bb4c5ab5e70da",
"score": "0.7358333",
"text": "def get_country_code_alpha_3(name):\n for co in pc.countries:\n if name == 'XXX':\n return 'XXX'\n if name in co.alpha_2:\n return co.alpha_3\n return 'XXX'",
"title": ""
},
{
... |
2507fb0156b112837b29dfc6d8f88fb9 | Return dict of key/value pairs vs. list of key/values dicts. | [
{
"docid": "894e77ddacf4f1ae3f5e7a164877e7a2",
"score": "0.0",
"text": "def get_tag_dict_for_resource(self, arn: str) -> Dict[str, str]:\n result = {}\n if self.has_tags(arn):\n for key, val in self.tags[arn].items():\n result[key] = val\n return result # ... | [
{
"docid": "cd12463d85324a2d7adbeaab7ab1b347",
"score": "0.6692818",
"text": "def get_dicts(self) -> List[Dict]:\n return deepcopy(self.value.dicts)",
"title": ""
},
{
"docid": "af9a8f1e84754210a207676b1019ed6c",
"score": "0.63920045",
"text": "def list_of_dicts_gen_singleval(... |
2441abb23bc7ca6c80f06ed0f1e59133 | Subclassed by implementor to react to the ball being completely over automatically invoked by end_ball(). At this point the ball is over | [
{
"docid": "a35f042629cafca6e894e2ee30de33ac",
"score": "0.6899138",
"text": "def ball_ended(self):\n self.log(\"Skel: BALL ENDED\")\n\n # turn off the flippers\n self.enable_flippers(False)\n # self.enable_alphanumeric_flippers(False)\n if(self.use_ballsearch_mode):\n... | [
{
"docid": "3e6fea8386528e150f2c5d464b07144a",
"score": "0.70871973",
"text": "def updateBall(self):\n \n self._ball.moveBall(self._paddle.collides(self._ball),\\\n self.collisionWithBricks())\n self._offScreen = self._ball.checkOffScreen()",
"title": ... |
a53f33dc7e4342805ba6d0e3d6122bd4 | Create the config and optionally a htdigest file | [
{
"docid": "bbb350e8c0d33e081ef6e2150de58605",
"score": "0.70987415",
"text": "def trac_config_create(adminuser, adminpass, realm, digestfile, trac_ini):\n config_str = ''\n config_str += BASE_CONFIG\n \n htdigest_create(digestfile, adminuser, realm, adminpass)\n # adding appropriate conf... | [
{
"docid": "a74ae68dbc7a73070c813da713290bcf",
"score": "0.67188954",
"text": "def generate_config(config):\n config['HASHBENCH'] = {'location_john': './john/run/john',\n 'location_hashcat': './hashcat/hashcat'}\n with open(\"hashbench.conf\", \"w\") as configuration_file... |
76f1e124c63d081a7ac8be64e178f2f8 | Loads the grid profile required for the run. | [
{
"docid": "d74062a4691924661853becce801b7e2",
"score": "0.67323035",
"text": "def load_grid_profile(\n auto_generated_files_directory: str, logger: Logger, scenario: Scenario\n) -> Optional[pd.DataFrame]:\n\n grid_profile: Optional[pd.DataFrame] = None\n if scenario.grid:\n try:\n ... | [
{
"docid": "a9fef0df39a0ea62e69b7433b2580ad4",
"score": "0.6204795",
"text": "def test_profile_loading(self):\n self.click('empty_profile')\n self.find('no_plugins_enabled')\n\n self.click('owasp_top_10')\n self.find('audit_plugins_enabled')\n\n self.click('empty_profi... |
6deaeac49a5e00cf85b672ee7e6c9014 | Test serialization/deserialization for CreatePeerBodyStorage | [
{
"docid": "0eb0102d88ceb9abf14e0f30a62bd4bc",
"score": "0.8516777",
"text": "def test_create_peer_body_storage_serialization(self):\n\n # Construct dict forms of any model objects needed in order to build this model.\n\n storage_object_model = {} # StorageObject\n storage_object_mo... | [
{
"docid": "4a37b93496932e4910b45d05b5ebc111",
"score": "0.7728197",
"text": "def test_peer_response_storage_serialization(self):\n\n # Construct dict forms of any model objects needed in order to build this model.\n\n storage_object_model = {} # StorageObject\n storage_object_model... |
db63fbbf0ad44a9f2cdc9d0ff9e6bcac | return a directory path | [
{
"docid": "05b8149e481899b172e6077f2c01d5a0",
"score": "0.6978848",
"text": "def get_dir(path):\n return Dirpath.get_instance(path)",
"title": ""
}
] | [
{
"docid": "3deae8a2787265d785ffa279f672b4d5",
"score": "0.8039122",
"text": "def dirPath(self):\n\t\treturn os.path.dirname( self.path ) + '/'",
"title": ""
},
{
"docid": "21032099a19e8ee44262d34373fb2be1",
"score": "0.7886094",
"text": "def get_directory_path(self):\n return... |
6155a505416f858b45399811e7f617f1 | Instantiate RequestFactory and User objects to pass POST requests to the TripCreate view. | [
{
"docid": "15690371abf4a887ae5e3b44df8eb149",
"score": "0.5762043",
"text": "def setUp(self):\n self.factory = RequestFactory()\n self.user = User.objects.create(username='Abdullah',\n email='abd@gmail.com',\n p... | [
{
"docid": "59a348e0c16bfb59d077cf3914961971",
"score": "0.6906684",
"text": "def setUp(self):\n self.factory = RequestFactory()\n self.user = User.objects.create(username='Abdullah',\n email='abd@gmail.com',\n p... |
c691eca7ff52e62e7604da056a14a9d9 | Load a jpeg file and return a numpy array | [
{
"docid": "b68780fd1872766229f304d6227d324d",
"score": "0.84246373",
"text": "def load_jpeg(path_to_image, jpeg_file):\n jpeg_img = Image.open(os.path.join(path_to_image, jpeg_file))\n return np.array(jpeg_img)",
"title": ""
}
] | [
{
"docid": "d65110329748b38ae8ef93b25a9cd423",
"score": "0.81133324",
"text": "def load_image(filename):\n im = Image.open(filename)\n return np.array(im)",
"title": ""
},
{
"docid": "ff4b677912360981dede603dbda45afa",
"score": "0.7762997",
"text": "def load_image(path: str) ->... |
28746434125195c7f3b81818344debe7 | change the image for better generalization Add rotation Add translation | [
{
"docid": "53e7a6f6e35b21cffcef47162f10178b",
"score": "0.0",
"text": "def data_oversampling_pipe(image, random_factor=False):\n if random_factor:\n flip = 1 if random.randint(0, 1) == 1 else -1\n r = random.random() * flip\n else:\n r = 1\n # rotation\n img = rotate_im... | [
{
"docid": "61b489a5f85b9c63ab37d3f7fd126d12",
"score": "0.7310402",
"text": "def update_rotation_img(self, context):\n \n cobj = get_object(context, self.lightname)\n world = context.scene.world\n mapping = world.node_tree.nodes['Mapping']\n mapping2 = world.node_tree.nodes['Mapping.001'... |
689d8416058a63d1dcb4dbdd8f2aebc1 | Testing method / function web_api_server_url. | [
{
"docid": "fefd3ea03d8f2ac053ca194123e0231a",
"score": "0.82735455",
"text": "def test_web_api_server_url(self):\n # Test\n result = self.config.web_api_server_url()\n self.assertEqual(\n result, 'http://127.0.0.12:50002/pattoo/api/v1/web/graphql')",
"title": ""
}
... | [
{
"docid": "5d74342a9b7d8ad0bdf95cb6885454b8",
"score": "0.7382157",
"text": "def test_agent_api_server_url(self):\n # Initialize key values\n expected = 'http://127.0.0.11:50001/pattoo/api/v1/agent/receive/123'\n agent_id = 123\n\n # Test\n result = self.config.agent_... |
6bf40afc0ad695debed41ec389d22c30 | Creates matrix from carla transform. | [
{
"docid": "379c79bcf651de3bc7d9d15fab44eb67",
"score": "0.57375145",
"text": "def get_matrix(transform):\n\n rotation = transform.rotation\n location = transform.location\n c_y = np.cos(np.radians(rotation.yaw))\n s_y = np.sin(np.radians(rotation.yaw))\n c_r = np.cos(... | [
{
"docid": "1c170c6319c966afb4509a550815648f",
"score": "0.69329536",
"text": "def makeMatrix(self, transformation):\n m = mathutils.Matrix()\n (m[0][0], m[0][1], m[0][2], m[0][3], m[1][0], m[1][1], m[1][2], m[1][3], \n m[2][0], m[2][1], m[2][2], m[2][3], m[3][0], m[3][1], m[3][2], m[3][3]) = ... |
cdd4fef4ac17a7d633fd81329cd1d45b | load spectra from raw file | [
{
"docid": "5e40777ede40d2fcc25c75608bbbccf4",
"score": "0.61535674",
"text": "def load_spec_file(self, filename, load_to_ram=False):\r\n self.type = 'spec'\r\n\r\n self.f = netCDF4.Dataset(filename, 'r')\r\n print('keys ', self.f.variables.keys())\r\n\r\n self.timestamps = s... | [
{
"docid": "71fb37fa0e0483864f730fc4934bbd94",
"score": "0.73631144",
"text": "def loadSpectra(self, path):\n if type(path) is not str:\n raise TypeError(\"path argument is not a string.\")\n foundBackground = False\n doGetWaveLength = False\n foundFiles = []\n ... |
d28270e8adc9d043239caf4a1c9fb088 | Gets player ready to take a turn | [
{
"docid": "d99491b0ec7ec3919a427f9d9d069418",
"score": "0.68041337",
"text": "def takeTurn(self):\n # PUTTING IN A TEMPORARY FIX\n for player in self.world.getPlayerList():\n if player.showhandbutton['state'] == NORMAL:\n print('Not ' + self.name + \"'s turn yet\... | [
{
"docid": "d1e68be4f905ec1aa1a1a1ce3372e8e0",
"score": "0.69901085",
"text": "def play_turn(self):\n pass",
"title": ""
},
{
"docid": "f7614474157def4ed5a856906b2f3919",
"score": "0.683937",
"text": "def passes_turn(self) -> Player:\n self.players.current_player.my_tur... |
d90f519778af27fa3f96cd02ff606899 | Get adresse from coordinates | [
{
"docid": "d45fdc976395b175215ebb8584f0f811",
"score": "0.72789454",
"text": "def get_adress(x, y):\n url = str((\"http://api-adresse.data.gouv.fr/reverse/?lon=\" + str(x) + \"&lat=\" + str(y)))\n headers = {\"Content-Type\": \"application/json\"}\n r = requests.get(url, headers=headers, data=... | [
{
"docid": "46e5221e9cd0da12a88540f87d3dd5e7",
"score": "0.7197132",
"text": "def get_address(coordinates):\n locator = Nominatim(user_agent=\"myGeocoder\")\n location = locator.reverse(coordinates)\n locate = location.address.split(',')\n len_location = len(locate)\n address = locate[0] ... |
0b92e07bb768414209e0068aaf7f76b2 | Internal helper. Returns the salted/hashed password using the argon2id13 algorithm. The return value is base64encoded. | [
{
"docid": "b155daf89bcf2e57ce6d536af0ce830c",
"score": "0.6997533",
"text": "def _hash_argon2id13_secret(password, salt, iterations, memory):\n rawhash = hash_secret(\n secret=password,\n salt=base64.b64decode(salt),\n time_cost=iterations,\n memory_cost=memory,\n ... | [
{
"docid": "fcbd677c75f3a83a7652200df41e7426",
"score": "0.7048091",
"text": "def gen_base64_passwd(length):\n return base64.b64encode(gen_passwd(length))",
"title": ""
},
{
"docid": "cbb6a3aea458d4eb72a3574953c347c5",
"score": "0.68947244",
"text": "def _get_hashed_pw(self, pw: s... |
5616d56f5b345b2ed8fc300ef20417a9 | Accuracy as a function of the height of class' the lowest common ancestor. This is only applicable to 2way ImageNet episodes. Given the class set of each episode, we find the corresponding 2 leaves of the ImageNet graph and compute the lowest common ancestor of those leaves. Its height is computed as the maximum over t... | [
{
"docid": "e611c854613c936284707eee19c54e54",
"score": "0.532225",
"text": "def get_height_to_accuracy(class_ids, logits, targets):\n height_to_accuracy = collections.defaultdict(list)\n for episode_num, episode_class_ids in enumerate(class_ids):\n if len(episode_class_ids) != 2:\n raise Valu... | [
{
"docid": "2b98e5a25740fc9c70b1d02b642706e3",
"score": "0.57431614",
"text": "def optimum(self):\n return self.accuracy(self.classify)",
"title": ""
},
{
"docid": "9764555874146dc2da06586bad0d1fb3",
"score": "0.57204765",
"text": "def get_best_accuracy(self) -> float:\n ... |
5ff149adf70930497f1654028eafdbf5 | Load up and normalize the data | [
{
"docid": "21359bbcda5ebf7e576f3ce55127b9da",
"score": "0.0",
"text": "def load_data(infile_counts, infile_seqs, infile_total_reads):\n\n # Load up the counts mapping to each gene\n df_counts = pd.read_csv(infile_counts, sep = \",\", index_col=0)\n df_counts[\"name\"] = df_counts.index\n # ... | [
{
"docid": "9b51362af4fa34926dd3979d119cd9ce",
"score": "0.69728583",
"text": "def load_data(self):",
"title": ""
},
{
"docid": "4c5734a2335ef562a6894d5a7730397c",
"score": "0.6786813",
"text": "def load_data(self):\n self.dataset, self.info = DataLoader()\n self._prepr... |
8c7e27b74fb3c9a67ed9ef8289982f49 | Convert a string into a slug, such as what is used for entity ids. | [
{
"docid": "6e4d7635c0970f0453063ba140efb493",
"score": "0.6548574",
"text": "def slugify(value, separator=\"_\"):\n return slugify_util(value, separator=separator)",
"title": ""
}
] | [
{
"docid": "ea58bb1b6b657f83082acb78fd78913c",
"score": "0.84102273",
"text": "def to_slug(string):\n return string.replace(' ', '-').lower()",
"title": ""
},
{
"docid": "1eb8f3ccb75563fd56cdd52615dfeb18",
"score": "0.82447875",
"text": "def create_slug(string):\n output = ... |
e89be0eb7eb2b4e6fb6a949ba47ad99e | Check the given kind is of our type. | [
{
"docid": "640d7d9074cbae98b60f03e272a6fc59",
"score": "0.7642859",
"text": "def match(cls, kind: 'dsl.Any') -> bool:\n return isinstance(kind, cls)",
"title": ""
}
] | [
{
"docid": "c11b585ca9647e51a22fc6a9c1ce9840",
"score": "0.7114784",
"text": "def verify_type(self, obj):\n return isinstance(obj, self.type_)",
"title": ""
},
{
"docid": "fe57552b29789df749ffecff55d4755a",
"score": "0.69722956",
"text": "def CheckType(self, *args, **kwargs):\... |
3890b2352dbe58ac483e3d71788032f3 | reset the iterator to the starting position | [
{
"docid": "83ce75be55bd351ece23d309076c9b98",
"score": "0.6484919",
"text": "def rewind(self):\n # virtual bool rewind() = 0;\n if self._index < 0:\n return False\n self._index = 0\n return True",
"title": ""
}
] | [
{
"docid": "ee8d71f0cc8808c5655c458dd3186ec5",
"score": "0.7686198",
"text": "def reset_curr_iter(self):\n self.curr_iter = 0",
"title": ""
},
{
"docid": "cc422a19bfebbcf07f078fba564b26b2",
"score": "0.7562216",
"text": "def reset(self):\n self.iterable = self._iterate(... |
01b117fec804d3ca4610422e1eefe55a | Launch djv_view. If path is specified and is a file, automatically load sequence. If path is a directory, start in that directory. If path is not specified, use shot directory. | [
{
"docid": "40c4bac34994153aece947f111717519",
"score": "0.7444207",
"text": "def viewer(path=None):\n\t# Get starting directory\n\tstartupDir = os.environ['IC_SHOTPATH']\n\tpathIsFile = False\n\tif path is not None:\n\t\tif os.path.isfile(path):\n\t\t\tstartupDir = os.path.dirname(path)\n\t\t\tpathIsFi... | [
{
"docid": "1ec470864416f9fe138c2513231647fc",
"score": "0.6450935",
"text": "def run(self, path, frameRanges, views, options):\n ...",
"title": ""
},
{
"docid": "55571b58c4c6de83a726f97a9e9b8494",
"score": "0.58631945",
"text": "def run_frame(path):\n\n if not os.path.exis... |
2633d44d1e5945a959b683e89ab3b53b | Transformation from abc representation to alphabeta representation | [
{
"docid": "32adbf73832c443e858889e2a3816596",
"score": "0.0",
"text": "def t_23(quantities):\r\n return np.matmul(SynchronousMotor._t23, quantities)",
"title": ""
}
] | [
{
"docid": "d0a85869f5b35dd44b3379dd290b2997",
"score": "0.7189133",
"text": "def aa_alphabet():\n abc = ['G', 'A', 'S', 'P', 'V', 'T', 'C', 'I', 'L', 'N', 'D', 'K', 'Q', 'E', 'M', 'H', 'F', 'R', 'Y', 'W']\n return abc",
"title": ""
},
{
"docid": "db0ccd77914199a84a06ac22acc30abf",
... |
596a49e2f1dddcc2a18c5e3d2fc6d484 | r"""Converts `hparams` value into a list. | [
{
"docid": "1b0dd68ab6f3b90b382f12a234e76abd",
"score": "0.7053691",
"text": "def _to_list(value: Union[Dict[str, Any], List, Tuple, int], name=None,\n list_length=None):\n if not isinstance(value, (list, tuple)):\n if list_length is not None:\n value = [value] * list_le... | [
{
"docid": "b272f8df421d7be9da3b018912810091",
"score": "0.6540132",
"text": "def params_to_lists(params):\n if type(params) != list: raise TypeError('Incorrect data type: function params_to_list needs a list of dictionaries.')\n listofparams = [params[0]['yoffset']] # yoffset should be the same f... |
e6f61daa42d943e628e298f7eedecfb5 | Provides operations to call the getSkypeForBusinessParticipantActivityCounts method. | [
{
"docid": "ec1fbabf6807349ca4bbe6b52ace4c0d",
"score": "0.5942781",
"text": "def get_skype_for_business_participant_activity_counts_with_period(self,period: Optional[str] = None) -> GetSkypeForBusinessParticipantActivityCountsWithPeriodRequestBuilder:\n if not period:\n raise TypeErro... | [
{
"docid": "e3de64b0950a51dd41c9c6e704e9d55e",
"score": "0.7065211",
"text": "def get_skype_for_business_participant_activity_user_counts(\n self,\n period, # type: str\n **kwargs # type: Any\n ):\n # type: (...) -> \"models.MicrosoftGraphReport\"\n cls = kwargs.p... |
02e9133c95aad92f77f0ce8497ed635f | fetches information on an order made by the user | [
{
"docid": "141cf78b75f4717d3bc7b295de24de6c",
"score": "0.558745",
"text": "async def fetch_order(self, id: str, symbol: Optional[str] = None, params={}):\n await self.load_markets()\n request = {\n 'id': id,\n }\n response = await self.privateGetOrdersGet(self.ex... | [
{
"docid": "18a3fdbfa52c87f6e8bea5267f659b91",
"score": "0.7255066",
"text": "def get_order(order_id): # pragma: no cover",
"title": ""
},
{
"docid": "86c184f294a199dc763577326206317c",
"score": "0.69069827",
"text": "def on_retrieve_order(self, order_id, context=None):\n pas... |
f519c5e185687bcd8c9aae2adddd9e02 | Check if the current session was created using an appropriate authentication type (e.g. "login" or "apikey"), the argument is a whitelist (string or list object) of acceptable authentication types. | [
{
"docid": "cf4ff46866f52ea92df38ab4a2b3fd55",
"score": "0.6155261",
"text": "def authentication_type(auth_type):\n\n def func(method):\n @functools.wraps(method)\n def wrapper(self, *args, **kwargs):\n if self.session is not None:\n if isinstance(auth_type, ba... | [
{
"docid": "9c43e85be1716e5081dd5b8cada10ced",
"score": "0.5811439",
"text": "def is_authenticated(self, request):\n authenticated = False\n for auth in self.auth_types:\n authenticated = auth.is_authenticated(request)\n if authenticated:\n selected_aut... |
be0059908e9c39cd679d8c2756867624 | Takes an index into a flattened 3D array and its side length. Returns the coordinate in the cube. | [
{
"docid": "73d42ac28eabe7bc138e1e9edd434b73",
"score": "0.50769484",
"text": "def index_to_coord(index, sl):\n coord = []\n two_d_slice_size = sl * sl\n coord.append(index // two_d_slice_size)\n remaining = index % two_d_slice_size\n coord.append(remaining // sl)\n coord.append(remain... | [
{
"docid": "494eda5982266fe514015723e0cd5ba2",
"score": "0.58937633",
"text": "def calculateCubeIdx3D(cube_size, vol_size, strides):\n x_idx, y_idx, z_idx = calculateIdx3D(vol_size, cube_size, strides)\n pos_idx_flat = np.zeros(x_idx.shape[0] * y_idx.shape[0] * z_idx.shape[0])\n flat_idx = 0\n ... |
b7fea5fe355cde8ec14ccdd6653e0c46 | Get item shortcut through both dicts. | [
{
"docid": "06d3cd8a0386c261b7d67402f6c54233",
"score": "0.0",
"text": "def __getitem__(self, command: TelnetCommand) -> ExpectedResponse:\n return self._queue[command.group][command]",
"title": ""
}
] | [
{
"docid": "472f524275ca6c4f4f4d33910ff44203",
"score": "0.597796",
"text": "def _get_item(dic: dict, keys: list) -> dict:\n\tfor key in keys:\n\t\tdic = dic[key]\n\n\treturn dic",
"title": ""
},
{
"docid": "880a504ea925a5235047cca96ec8d9f3",
"score": "0.5780727",
"text": "def __geti... |
3aae4b28f301e8cdc8fba6f89a073908 | Calculate the sum of the number of truths tracked with ambiguous ID over all timestamps eg. A truth that has one track with correct ID, and one with unknown ID assigned to it would be counted. | [
{
"docid": "2e51093d3e27675f9349a80364b93a60",
"score": "0.0",
"text": "def _ja_sum(self, manager, timestamps):\n return sum(self._ja_t(manager, timestamp) for timestamp in timestamps)",
"title": ""
}
] | [
{
"docid": "c3442804961ac918c979fed29342d916",
"score": "0.57795316",
"text": "def _nu_j(self, manager, truth):\n\n # Starting at the beginning of the truth find the track associated at that timestamp with\n # the longest length, increase the track count by one and move time to the end of ... |
b6314c694f141ca98b11b20cdc9c471f | Produce any customization definitions (types, fields, message destinations, etc) that should be installed by `resilientcircuits customize` | [
{
"docid": "e8cb9bf1c39847a06d843046e12286ea",
"score": "0.5533729",
"text": "def customization_data(client=None):\n\n # This import data contains:\n # Function inputs:\n # artifact_value\n # Message Destinations:\n # bluecoat_site_review\n # Functions:\n # bluecoa... | [
{
"docid": "0c3174bb75572433d27a14a7034f28bf",
"score": "0.64324254",
"text": "def apply_customization(self, args):\n pass",
"title": ""
},
{
"docid": "29cce1b4c52c4beb66fdf4d95ee4eda4",
"score": "0.56733376",
"text": "def customizing(self, p_customizing: str):",
"title": ""
... |
a27a6263310b83f334a5c20a33dfbdea | Try to find a handler for the specified action. | [
{
"docid": "f10ca768c00b50727e194b3f09686196",
"score": "0.58375084",
"text": "def get_action(self, action):\n if not hasattr(self, action):\n return None\n\n x = getattr(self, action)\n if not hasattr(x, 'chat_action'):\n return None\n\n return x",
... | [
{
"docid": "507c1d6f9f4e70d5e40e29ca55edcb2f",
"score": "0.79287875",
"text": "def get_handler_for_action(action: str) -> Union[HandlerFuncType, None]:\n return _registry.get(action)",
"title": ""
},
{
"docid": "9011100e1235f2fbb8172ea80d5b24a7",
"score": "0.7242842",
"text": "def... |
3b6c297c202100584aa08d25d0940cb9 | Uses GATK HaplotypeCaller to identify SNPs and Indels and writes a gVCF. Calls persample genotyper to genotype gVCF. | [
{
"docid": "8c96fdfc9302cbcaa2784a0bd993bae6",
"score": "0.6022498",
"text": "def haplotype_caller(job, shared_ids, input_args):\n work_dir = job.fileStore.getLocalTempDir()\n input_files = ['ref.fa', 'ref.fa.fai', 'ref.dict', 'toil.bam', 'toil.bam.bai']\n read_from_filestore_hc(job, work_dir, ... | [
{
"docid": "af8695a76a8c8ada581f2704922dd564",
"score": "0.61019504",
"text": "def genotype_gvcf(job, shared_ids, input_args):\n\n work_dir = job.fileStore.getLocalTempDir()\n input_files = ['%s.raw.BOTH%s.gvcf' % (input_args['uuid'],\n input_args['suffix'... |
c565d045277814380a3e3596a51179dd | Given the name of a senator, compares his/her voting record to the voting records of all senators whose names are in sen_set, computing a dot product for each, and then returns the average dot product. | [
{
"docid": "5fa5a3767610b7c38df1320b6ce2deb1",
"score": "0.7502001",
"text": "def find_average_similarity(sen, sen_set, voting_dict):\n sum = 0\n for s in sen_set:\n sum += policy_compare(sen, s, voting_dict)\n avg = sum / len(sen_set)\n return avg",
"title": ""
}
] | [
{
"docid": "0856858641cf367033ed245076e621b2",
"score": "0.6240421",
"text": "def distance_sen_dem_rep(votes, senator_name):\n # Get democrats and republicans lists\n democrat_names_list = list(votes[votes[\"party\"] == \"D\"].name)\n republican_names_list = list(votes[votes[\"party\"] == \"R\"... |
cfbeb513e67f6e0c0a154e6f1fb50c54 | Get a 8 bit signed integer | [
{
"docid": "d32642cc31fd09a40962ab0b8d4a519a",
"score": "0.64202243",
"text": "def get_s8(self):\n val = self._data[self._idx]\n self._idx += 1\n return val",
"title": ""
}
] | [
{
"docid": "873e8c65023e1951d2bc1b4b6b02b33a",
"score": "0.7569413",
"text": "def int8(self) -> int:\n raise NotImplementedError(\"int8 not implemented\")",
"title": ""
},
{
"docid": "f767d287d3deabfa858f95dfa2f88309",
"score": "0.71418214",
"text": "def uint8(self) -> int:\n ... |
142f468e0a664f8f5b2526e1efbf8876 | Requests the service to restart after a specified delay, in seconds | [
{
"docid": "611a590f87f39170cdf624474b8a1340",
"score": "0.7568714",
"text": "def restart():\n info = request.get_json() or {}\n delay_secs = int(info.get('delay', 0))\n\n t = threading.Timer(delay_secs, update_trigger_file)\n t.start()\n\n return jsonify(SUCCESS)",
"title": ""
}
] | [
{
"docid": "ac9fe64f3c901f266ac194c718e1cf1d",
"score": "0.68044424",
"text": "def restart():\n restart_service(SERVICE)\n status()",
"title": ""
},
{
"docid": "3178091b7c5a01776e80f1a9c9a4af66",
"score": "0.67897755",
"text": "def restart(service, timeout=120):\n service.re... |
977574b9ad89fd6fc241218b438a7c4b | The function creates a segment tree in O(n) time | [
{
"docid": "b60935cd3960c70ff30657d3664a4acd",
"score": "0.7451623",
"text": "def segment_tree_creation(arr):\n n = len(arr)\n\n # for n leaf nodes, there are maximum double of 2**max_height-1 nodes, adding extra 0th elem for ease of calculations\n height = ceil(log2(n))\n m = 2 * (2 ** heig... | [
{
"docid": "65e049501dc3abc075cf63ac3bc25d92",
"score": "0.6716978",
"text": "def __init__(self, nums):\n n = len(nums)\n if n == 0: return\n max_size = 2 * pow(2, int(math.ceil(math.log(n, 2)))) - 1\n self.seg_tree = [0 for i in xrange(max_size)]\n self.nums = nums[:]... |
cf84055bc026711dba27ebb401e4cb2a | Regrid to 2deg (180lon90lat) horizontal resolution Input | [
{
"docid": "9ccee3ab882e0228bda38a0e6f71da74",
"score": "0.0",
"text": "def Regrid2deg(d, var):\n tgrid = grid.create_uniform_grid(-89, 89, 2.0, 0.0, 358., 2.0)\n drg = d.regridder.horizontal(var, tgrid, tool=\"xesmf\", method=\"conservative_normed\",periodic=True)[var]\n \n print(\"Complete... | [
{
"docid": "f815fa237589ae2cb01084b4ca4ace69",
"score": "0.58806723",
"text": "def setup_grid():\n i0t,imt,j0t,jmt = (0000 ,8640, 0, 4320)\n incr = 360.0/imt\n jR = np.arange(j0t, jmt)\n iR = np.arange(i0t, imt)\n latvec = ( 90 - jR*incr - incr/2)[::-1]\n lonvec = (-180 + iR*in... |
f72c141c9093906b6ba328d63eff93cc | Gets all liked YouTube videos and saves artist and track info for any songs | [
{
"docid": "68f3cd4e4e039c39a125a5c8ac98ea53",
"score": "0.7771531",
"text": "def get_liked_videos(self):\n max_results = 50\n request = self.youtube_client.videos().list(\n part=\"snippet,contentDetails,statistics\",\n maxResults=max_results,\n myRating=\"... | [
{
"docid": "0630591a3e4269d584fd7dc8e64b81a9",
"score": "0.82091135",
"text": "def get_liked_videos(self):\n #Requisita o token atualizado para recuperar os likeds videos\n youtube_token=self.get_token_youtube()\n query = \"https://www.googleapis.com/youtube/v3/videos?part=snippet%2... |
f70b95f2a81e1ba3b0a84c67134f2d8b | Retrieves the exam schedule link from McGill's Exam website. | [
{
"docid": "19386bba2c459029b87044113e1f7109",
"score": "0.641286",
"text": "async def exam(self, ctx):\n await ctx.trigger_typing()\n\n r = requests.get(MCGILL_EXAM_URL)\n soup = BeautifulSoup(r.content, \"html.parser\")\n r.close()\n link = soup.find(\"a\", href=re.c... | [
{
"docid": "78e21614f6c76a4272bedf073cc94e46",
"score": "0.5595076",
"text": "def scrape_schedules():",
"title": ""
},
{
"docid": "f8d0628902171ac3ac65d518d92c14ad",
"score": "0.53754807",
"text": "def get_schedule_url (user_id, year=2011, semester=Semester.Winter):\n\n semester_i... |
16b874686501b17be9428203cbca25c6 | String should be returned in reversed order | [
{
"docid": "9764c1c5237dd6493396e36ee89a3b24",
"score": "0.0",
"text": "def test_reverse_three_chars(self):\n diversifier = Diversifier('abc')\n self.assertEqual('cba', diversifier.reverse())",
"title": ""
}
] | [
{
"docid": "b354bd93c532860e00bdc75a57a7cd7d",
"score": "0.74614257",
"text": "def task_10_get_reverse_string(text: str):\n return text[::-1]",
"title": ""
},
{
"docid": "f0b5b1a3f3eaba1b8d9634b792123c3a",
"score": "0.7371981",
"text": "def reverse(self): \n letters = list(... |
82258dd61b1d65083fa88155a6715429 | builder = self.get_builder() builder.right_join( "report_groups as rg", | [
{
"docid": "0a76de558e94f35a9b0bb1e7f4901da3",
"score": "0.6038224",
"text": "def can_compile_right_join_clause_with_lambda(self):\n return \"\"\"SELECT * FROM \"users\" RIGHT JOIN \"report_groups\" AS \"rg\" ON \"bgt\".\"fund\" = \"rg\".\"fund\" OR \"bgt\" IS NULL\"\"\"",
"title": ""
}
] | [
{
"docid": "ccc08f56946948335d1efe68dd64a809",
"score": "0.6065083",
"text": "def can_compile_join_clause(self):\n return \"\"\"SELECT * FROM \"users\" INNER JOIN \"report_groups\" AS \"rg\" ON \"bgt\".\"fund\" = \"rg\".\"fund\" AND \"bgt\".\"dept\" = \"rg\".\"dept\" AND \"bgt\".\"acct\" = \"rg\"... |
88ea6a4368326b09e886361e32fdef8d | utility function to normalize a tensor. Arguments | [
{
"docid": "bb0fecb9a020b414f099d2bdb96e8270",
"score": "0.63248867",
"text": "def normalize(x):\n return x / (K.sqrt(K.mean(K.square(x))) + K.epsilon())",
"title": ""
}
] | [
{
"docid": "6464ad47b7d44ed5d05c06acd8bd6fae",
"score": "0.77866507",
"text": "def tensor_normalize(tensor):\n _tensor = tensor.detach().clone()\n _tensor_each_sum = _tensor.sum(dim=1)\n _tensor /= _tensor_each_sum.unsqueeze(1)\n\n _tensor[torch.isnan(_tensor)] = 0.0\n _tensor = 2*_tensor... |
5504b1209d1a26576a35bdcbaafc8d6e | Main text parsing function | [
{
"docid": "cc3440f37b4917c9629a8c5e29edb25b",
"score": "0.0",
"text": "def parse(\n data: str,\n raw: bool = False,\n quiet: bool = False\n) -> List[Dict]:\n jc.utils.compatibility(__name__, info.compatible, quiet)\n jc.utils.input_type_check(data)\n\n raw_output: List = []\n outpu... | [
{
"docid": "cdaedad6e486837c12364bf2b5dfa2fe",
"score": "0.8498604",
"text": "def parse(self, text):",
"title": ""
},
{
"docid": "e2d89aedac93ac6c6e41cbc1a364b65d",
"score": "0.74919677",
"text": "def parse(self, text) -> Generator:",
"title": ""
},
{
"docid": "e2d70c25ed... |
9f3c663242af5a3bcfda75eb6d31d72f | Get next pos according to input position. | [
{
"docid": "b126f20ee600d1229fde2642ed1ebe60",
"score": "0.0",
"text": "def _parse_pos(self, pos):\n elements = pos.split(':')\n try:\n idx = int(elements[-1])\n except ValueError:\n log.error(\"Invalid index. The index in pos should be digit but get pos:%s\", ... | [
{
"docid": "398dfb63dd007099fb0067135088b9b6",
"score": "0.76904935",
"text": "def getNextPos(self):\n\t\ti=0\n\t\tfor p in self.positions:\n\t\t\tif p[1]>self.pos[1]: return p\n\t\t\tif len(self.positions)<i+2: return self.pos\n\t\t\ti+=1\n\t\traise Exception('getNextPos is not expected to come this fa... |
64d5b66935cc2b6d65ce1d359380ed1d | use lists to check if two strings are anagrams | [
{
"docid": "5be43e10ece0a00d0d8d210511210b03",
"score": "0.74138683",
"text": "def anagram_list(s1, s2):\n # largely copied from exercise\n c1 = [0] * 26\n c2 = [0] * 26\n\n for i in range(len(s1)):\n pos = ord(s1[i]) - ord('a') # gets index\n c1[pos] = c1[pos] + 1\n\n # jus... | [
{
"docid": "6cf901e881211c3bcf9daf5f308acaef",
"score": "0.8629491",
"text": "def anagrams_lst(str1: str, str2: str) -> bool:\n return sorted(list(str1)) == sorted(list(str2))",
"title": ""
},
{
"docid": "eed012ad0a439017fbd2fde094cdb8f1",
"score": "0.8194693",
"text": "def anagra... |
855b796ec640af7d550f4c3b672d38b6 | Checks RapidMiner moved potential label to right of attribute list | [
{
"docid": "ede05908fcdb8de37184d37b3047135d",
"score": "0.63274306",
"text": "def check_label(attr, rem_labels):\n attr_len = attr.__len__()\n for item in rem_labels:\n idx_position = attr.index(item)\n if (idx_position / attr_len) > 0.5:\n # valid label present\n ... | [
{
"docid": "ca5b20e23f3723ffef1524b16fed64f4",
"score": "0.55617946",
"text": "def find_ignore_label(self):\n for key, val in self.index2rel.items():\n if val == self.ign_label:\n self.label2ignore = key\n assert self.label2ignore != -1",
"title": ""
},
{
... |
188861d9c40850d37e5c9ef4dd7d3ad7 | Adds vmcheckerupdatedb speciffic options to an already populated family of options. | [
{
"docid": "2f1fb51907ca0215f88a861da4623f2e",
"score": "0.0",
"text": "def add_update_db_optparse(cmdline):\n group = optparse.OptionGroup(cmdline, 'update_db.py')\n group.add_option('-f', '--force', action='store_true', dest='force',\n default=False, help='Force updating all ... | [
{
"docid": "fc76c15d97e63233f5d826ce76ad1e94",
"score": "0.62515515",
"text": "def _update_options(self):\n # set verbosity level (also of self.logger, in case of a custom one)\n if self.options[\"verbose\"] <= 0:\n logging.getLogger(\"pyffi\").setLevel(logging.WARNING)\n ... |
dc5c4d99d8041ad7be98aaba4665c596 | Flag all ec2s that have been running for longer than 1 week (WARN) or 2 weeks (FAIL) if any contain any strings from `flag_names` in their names, or if they have no name. | [
{
"docid": "ab45cc8da72d33d0c824df675203a820",
"score": "0.61171246",
"text": "def check_long_running_ec2s(connection, **kwargs):\n check = CheckResult(connection, 'check_long_running_ec2s')\n if Stage.is_stage_prod() is False:\n check.summary = check.description = 'This check only runs on ... | [
{
"docid": "9d8ee2ccefb74207e8b9fc29c0cf0d40",
"score": "0.53161347",
"text": "def flag_ignored_mousedays(self):\n dirname_in = self.binary_dir + 'HCM_data/qc/flagged/'\n dirname_in2 = self.binary_dir + 'HCM_data/qc/flagged_msgs/'\n flagged = []\n reason = []\n for str... |
95f40e89781ba8bb8c7616c89317f79f | Return the diagnostic results. | [
{
"docid": "8e36f61f8339717c88e9778af469e118",
"score": "0.0",
"text": "def get_results(self):\n return copy.deepcopy(self._results)",
"title": ""
}
] | [
{
"docid": "fb824862fc25c212b60aa8351eb76e37",
"score": "0.688687",
"text": "def GatherResults(self):\n\t\tResults = self.ProbeManager.GetResult()\n\n\t\t#ordino i risultati in base alla run prima di restituirli\n\t\tResults.sort(key=lambda ele: (ele[1]))\n\t\treturn Results",
"title": ""
},
{
... |
17dc4805118f3b1847d262679905a2bc | Assert that the script got called. | [
{
"docid": "0e5b8876a3b927aef357a79a905d60df",
"score": "0.8074525",
"text": "def assertScriptCalled(self):\n # Wait a bit as the events are processed in a different thread.\n with open(os.path.join(self.tempdir, 'result')) as s:\n self.assertEqual('script called', s.read())",
... | [
{
"docid": "4e01602e6d1b691fccc1b30b02bf62c1",
"score": "0.69822",
"text": "def assertScriptNotCalled(self):\n # Wait a bit as so the event can get propagated.\n self.assertFalse(os.path.exists(os.path.join(self.tempdir, 'result')))",
"title": ""
},
{
"docid": "d7d99651f3a9b5ad... |
e428f622cad9393b7b8625fa67d8623d | this is a custom list converter that convert a list of tuple to a list of all the second element of each tuple | [
{
"docid": "e2143046c2cb436c639d40f5c2a4e1cf",
"score": "0.0",
"text": "def to_list(entry,index):\n out = []\n for e in entry:\n out.append(e[index])\n return out",
"title": ""
}
] | [
{
"docid": "2e74caf3afc4b7843f8e24a0eb594860",
"score": "0.7229407",
"text": "def convert_list_to_tuple(self):\n\n # Change self.list to a tuple\n\n pass",
"title": ""
},
{
"docid": "bd3cabcb8d5aa05ef113aba444bc2842",
"score": "0.7141554",
"text": "def listToTuple ( l )... |
3058d15b600c04c5550977cf6df2908e | coins is a list of the values of the coins in your possession | [
{
"docid": "bc85cbe1656ecb56861d4ce0ad7b503a",
"score": "0.6165489",
"text": "def makeChange(coins, total):\n\n if total <= 0:\n return 0\n\n if coins is None or coins == []:\n return -1\n\n coins = sorted(coins, reverse=True)\n cont_coins = 0\n\n for coin in coins:\n ... | [
{
"docid": "9d89ece5b00e635df5659787b3410db5",
"score": "0.7158293",
"text": "async def coins(self, ctx: BBContext):\n pass",
"title": ""
},
{
"docid": "8eabe22d656c02c186b671b60b5e40f6",
"score": "0.7005791",
"text": "def _get_coins(self, num_coins):\n min_requestable_... |
d0a0ee151504cb81fe704c753ca268ba | Gets the gnomAD v3 info TableResource | [
{
"docid": "2543294f5392e3763945bfe15143f4e2",
"score": "0.5697084",
"text": "def get_info(split: bool = True) -> VersionedTableResource:\n\n return VersionedTableResource(\n CURRENT_RELEASE,\n {\n release: TableResource(\n path=\"{}/gnomad_genomes_v{}_info{}.h... | [
{
"docid": "6ae4ff619386eee68b71478e4f997011",
"score": "0.58526546",
"text": "def getTable(self):\n return self.__table",
"title": ""
},
{
"docid": "ffc49ff7bb41356e4c94a330d93f4d77",
"score": "0.58430845",
"text": "def get_table(table_name: str):\n try:\n response ... |
8175c1e21206db487ee07a2ae912ec8d | Test Ansible module loader. | [
{
"docid": "d76070268e34b061f597a49663e7c50a",
"score": "0.7000035",
"text": "def test_resolver_module_loader(resolver):\n with patch(\"salt.modules.ansiblegate.importlib\", MagicMock()), patch(\n \"salt.modules.ansiblegate.importlib.import_module\", lambda x: x\n ):\n assert resolve... | [
{
"docid": "0c1959200c05694f42caeea52eceb129",
"score": "0.65836585",
"text": "def test_ansible_module_help(resolver):\n\n class Module(object):\n \"\"\"\n An ansible module mock.\n \"\"\"\n\n __name__ = \"foo\"\n DOCUMENTATION = \"\"\"\n---\none:\n text here\n--... |
5c3f6103716dfaeaae300f15ddebf9cd | _get_clients_for_channel(channel_name) > [client] Given a channel_name, this function returns the clients connected to the nodes that handle the channel. The length of the list is 1 for local channels and user channels. For a global channel the length is equal to the total number of Beaconpush nodes. channel_name the c... | [
{
"docid": "4aef8017db638ef12f44a1d8cc41612a",
"score": "0.8403511",
"text": "def _get_clients_for_channel(self, channel_name):\r\n if not channel_name or channel_name[0] == \"*\":\r\n # Channel name is empty or a global channel\r\n return self._get_clients()\r\n else... | [
{
"docid": "1e245e92f4a76ac8102fae4cb68f3d5c",
"score": "0.62264466",
"text": "def get_users_in_channel(self, channel_name):\r\n\r\n clients = self._get_clients_for_channel(channel_name)\r\n greenlets = [client.getUsersInChannel(channel_name) for client in clients]\r\n self._join_al... |
69907cdd1fb10ed77ccb10841edc39a5 | unloadSfx(self) Unloads any sound effects that may have been loaded. | [
{
"docid": "fb6339aaa3f1d5f4c73877a25ef8cc05",
"score": "0.8124208",
"text": "def unloadSfx(self):\n if self.cogDropSound != None:\n self.cogDropSound = None\n self.cogLandSound = None\n self.cogSettleSound = None\n self.openSfx = None\n \n ... | [
{
"docid": "eabb169063d4295eba34b9a8e1a66cb8",
"score": "0.7055466",
"text": "def unload(self, *args, **kwargs):\n \n if self.fs:\n self.fs.sfunload(self.sfid, update_midi_preset=0)",
"title": ""
},
{
"docid": "000e57bdd28eed6537fdefc2043e4290",
"score": "0.60565... |
48f5d542884a82f9b65b9c9cb4555eaa | Force reloading the data from the file. All data in the inmemory dictionary is discarded. This method is called automatically by the constructor, normally you don't need to call it. | [
{
"docid": "1b837d3a0ca06c72d167d2ed4f31d5bb",
"score": "0.0",
"text": "def load(self):\n self._check_open()\n try:\n data = json.load(self.file, **self.load_args)\n except ValueError:\n data = {}\n if not isinstance(data, dict):\n raise Value... | [
{
"docid": "c91f69b5199796c9f13475ff65faf51e",
"score": "0.7423884",
"text": "def reload_file(self):\n self.__load_file()",
"title": ""
},
{
"docid": "37495bab71e58f0c4711c4a8f445cfd9",
"score": "0.7374094",
"text": "def Reload(self):\n self.LoadFromFile(self.infile_name)",... |
926a0f55b8770271cc5a1ea38bafe2bb | Create a editor account | [
{
"docid": "cd846220b2f381b50036779188cc176d",
"score": "0.0",
"text": "def createsuperuser(username, password):\r\n u = User(username=username, password=password,confirmed=True)\r\n u.role = Role.query.filter_by(name='Admin').first()\r\n db.session.add(u)\r\n db.session.comm... | [
{
"docid": "2fbceed6628b4873f1d3e2e219f3fe5a",
"score": "0.7483424",
"text": "def createeditor(username, password):\r\n u = User(username=username, password=password)\r\n u.role = Role.query.filter_by(name='Editor').first()\r\n db.session.add(u)\r\n db.session.commit()\r\n ... |
16a93f50d67d8c2e598a35f05f8402a2 | Describes all the unique elements in a column | [
{
"docid": "8e864e7c847448d8b7f7d6017dfaab5f",
"score": "0.7731794",
"text": "def describe_unique(df, colname, filter_unnecessary=True):\n print(\"Column name : \", colname)\n unique_elems = pd.unique(df[colname])\n types_of_data = [type(x) for x in unique_elems]\n if filter_unnecessary:\n ... | [
{
"docid": "41599ce2227a6e0260c76034890cd48a",
"score": "0.7366619",
"text": "def unique_values(df):\r\n \r\n print(\"Unique values:\")\r\n for col in df.columns:\r\n print(col + \":\", df[col].unique())",
"title": ""
},
{
"docid": "93e91793f8b371a425e0d984c1727f30",
"sco... |
fdc72e8ee05e22ce74abf4fb861fafb1 | Plot the simulation results and save the plots | [
{
"docid": "7821ee5577b36c3b849205fa9f7f2495",
"score": "0.0",
"text": "def plot_results_parameters(matrix_averages_infected, matrix_averages_death, matrix_averages_recovery, n_nodes):\n\n\tfig = plt.figure(figsize = (14, 8))\n\t\n\t######## Infections subplots ########\n\tax1 = fig.add_subplot(131)\n\t... | [
{
"docid": "4ff24f98596cd6e1e5d3ff8afc6c8ff7",
"score": "0.769103",
"text": "def plot_results():\r\n fig_format = 'png'\r\n plt.figure()\r\n plt.plot(return_history)\r\n plt.xlabel('Iteration')\r\n plt.ylabel('Return')\r\n plt.title('Return (Discounted Cumulative Reward) Convergence')\... |
8d4471b9ada82bb05125e270d96b5f67 | Load config from file, overwriting current contents. | [
{
"docid": "413045953b29f344c28ecd358539afb4",
"score": "0.0",
"text": "def load(self):\r\n try:\r\n self.loading = True\r\n if os.path.exists(self.filename):\r\n text = open(self.filename).read()\r\n obj = json_decode(text)\r\n f... | [
{
"docid": "13792ba4b1af4d88d4b7e93afd2d1326",
"score": "0.75971365",
"text": "def load_config(self, fname):\n\n self.config = config.Config(fname).config",
"title": ""
},
{
"docid": "d68380d81829c94c3d3adc0387fc7d70",
"score": "0.755855",
"text": "def load(self):\n Deb... |
68ae881d624711688d61b991f3ae080d | Tests two busy times, one in the middle of the start day and one on a different day. | [
{
"docid": "6a335bd9b281b3021e5ca2b79521bb38",
"score": "0.6556361",
"text": "def get_free_times_7_test():\n begin_date = arrow.get().replace(\n tzinfo=tz.tzlocal(), hour=9, minute=0, second=0, microsecond=0, day=16,\n month=11, year=2015)\n end_date = arrow.get().replace(\n t... | [
{
"docid": "dedcc10e201d066c2c9eb74db9004b9a",
"score": "0.71987724",
"text": "def really_between_times(instance, begin_time, end_time):\r\n\r\n\t# Case where the user searches for busy times throughout the whole day(24h)\r\n\tbegin = arrow.get(begin_time).format(\"HH:mm\")\r\n\tend = arrow.get(end_ti... |
3f64b6653a03e4f175305cf6bc764495 | Produces classifier predictions on the set | [
{
"docid": "e69a02c969fd0e25c28c13db3edcc456",
"score": "0.0",
"text": "def predict(self, X):\n step_1f = self.layer_1.forward(X)\n step_2f = self.layer_2.forward(step_1f)\n step_3f = self.layer_3.forward(step_2f)\n probs = softmax(step_3f)\n pred = np.array(list(map(l... | [
{
"docid": "0ba7dfaf59c269fbf03499fc467b096d",
"score": "0.7867263",
"text": "def predict(self, inputset):\n return self.clf.predict(inputset)",
"title": ""
},
{
"docid": "a051de44eecda307d96be7e662c2b897",
"score": "0.76698387",
"text": "def predict(self):\n self.predi... |
e8bcd69370b79d6f78a35d37f89c5612 | Get a list of substitutions by quasirandom sampling from a Sobol sequence. | [
{
"docid": "b7fe19d9cc9206ed92268ad8aec491cb",
"score": "0.49346414",
"text": "def sobol(range_map: Dict[str, ranges.Range], trials: int) -> List[Dict[str, str]]:\n ordered_range_map = collections.OrderedDict(range_map)\n sobol_values = sobol_seq.i4_sobol_generate(len(range_map), trials)\n\n de... | [
{
"docid": "5b26bd916d5dca4a8cadd3b66c7eaeac",
"score": "0.5697027",
"text": "def pseudosample(x):\n#\n BXs=[]\n for k in range(len(x)):\n ind=random.randint(0,len(x)-1)\n BXs.append(x[ind])\n return BXs",
"title": ""
},
{
"docid": "ec87410c0f6f07aed4874be7e5d77bf0",
... |
b447f7bdab8614e6d27385d8724d2074 | Returns a slice of the dataframe | [
{
"docid": "5f62b1d87254e787540d0f413f5893c5",
"score": "0.6506718",
"text": "def get_data_chunck(self, df=None, iloc_start=None, iloc_end=None, chunck_size=1000):\n # Set iloc_start and iloc_end if not defined\n if not iloc_start:\n iloc_start = 0\n if not iloc_end:\n ... | [
{
"docid": "d72bb54cad3c68f028e087e559ff977f",
"score": "0.76382184",
"text": "def slice(self, offset: int, length: int) -> \"DataFrame\":\n return wrap_df(self._df.slice(offset, length))",
"title": ""
},
{
"docid": "6b983d995a0ae43a049731a50c24c1cb",
"score": "0.703219",
"tex... |
ce47eb18c87a2c70cffbf3ac3f075263 | Get user list and their time left | [
{
"docid": "27e2da0aa664e50e2ed56a8c1dfd9d42",
"score": "0.6999063",
"text": "def getUserList(self):\n \"\"\"Sets allowed days for the user\n server expects only the days that are allowed, sorted in ascending order\"\"\"\n # result\n result = 0\n message = \"\"\n ... | [
{
"docid": "1d6786e0b68ad28cca0eaedcc1105e35",
"score": "0.6761791",
"text": "def user_list(self) -> None:\n users_list = []\n\n for user in state.get_users()[\"records\"]:\n users_list.append(\n [\n str(user[\"id\"]),\n user[... |
640019edd0a391d862b878cb752234ba | Insert new course data | [
{
"docid": "9a04d325b5531af61b6049bc93c6890e",
"score": "0.7448042",
"text": "def insert_course(self, course):\n largest_id = max([int(i) for i in self.data.keys()])\n new_course_id = str(largest_id + 1)\n self.data[new_course_id] = course\n return self.data",
"title": ""... | [
{
"docid": "026398ace73e1ad5dbfa7bb033766ef4",
"score": "0.7772229",
"text": "def add_course(self, info):\n\n\t\twith self.connection.cursor() as cur:\n\t\t\tcur.execute(\"\"\"INSERT INTO courses VALUES (%s, %s, %s, %s)\"\"\", info)\n\n\t\tself.commit()",
"title": ""
},
{
"docid": "123aede24... |
565d4025c023804111e8665eb2b2f138 | Sets the campaign_id of this Context132. | [
{
"docid": "eaf13e8218bd8433db01dd35c98a3556",
"score": "0.8459122",
"text": "def campaign_id(self, campaign_id):\n\n self._campaign_id = campaign_id",
"title": ""
}
] | [
{
"docid": "a618fa22da5ee9e1f5b3ee60371da942",
"score": "0.8029553",
"text": "def campaign_id(self, value):\n\n self._campaign_id.set(value)",
"title": ""
},
{
"docid": "80ed485b0d0c85fc62a94f2903a5d87d",
"score": "0.7275301",
"text": "def setCampaign(self, campaign):\n ... |
48738414c52251aace9e1552c271cc99 | Finds the nearest region to given coordinates. | [
{
"docid": "c8906b8ca44ffb522de320e48c3f128e",
"score": "0.7113429",
"text": "def FindNearestRegion(self, lat, lon):\n self._Refresh()\n return self._cities.FindNearestNeighbor(lat, lon).parent",
"title": ""
}
] | [
{
"docid": "cc5224e323d654e0d4732c87fd7ff737",
"score": "0.6737705",
"text": "def nearest(coordinate, coordinate_list, limit):",
"title": ""
},
{
"docid": "11396b53fc89921c28fd992f59f2363d",
"score": "0.6671556",
"text": "def get_nearest_valid_coordinates(self, coordinates):\n ... |
63dca1f63f881be5338907f45eeddff6 | Creates a SQL context | [
{
"docid": "cf9d4d144a78107634dc376e8e35f83b",
"score": "0.6197181",
"text": "def __init__(self, url=None, connection=None, schema=None):\r\n\r\n if not url and not connection:\r\n raise AttributeError(\"Either url or connection should be provided\" \\\r\n ... | [
{
"docid": "22361e6b7fd296a7df81b6110d25b91b",
"score": "0.68279225",
"text": "def _make_context():\n return {'app': app, 'db': db}",
"title": ""
},
{
"docid": "4c9b643c0b896ecd18fb03e4b3fd641b",
"score": "0.6694832",
"text": "def sql_context(self):\n if not self._sql_conte... |
09a4bd3f156469c3b8e7375097f7f947 | Gets is_grade attribute of instance Returns | [
{
"docid": "56b57c1cb0e5c0ace8d84a7e09e28fdb",
"score": "0.7083966",
"text": "def get_is_graded(self):\n return self.is_graded",
"title": ""
}
] | [
{
"docid": "ebc51373a9a05f5f317713d900e8baf8",
"score": "0.743497",
"text": "def get_grade(self):\n return self.grade",
"title": ""
},
{
"docid": "dfb54dfff723a785b613028952041e0a",
"score": "0.6901927",
"text": "def grade(self):\n return self._grade",
"title": ""
... |
f293cb81966d4d558331b83cb872dc75 | Ensure that all entries are returned. | [
{
"docid": "57a0df6352d166edd9eb96351fc2fc59",
"score": "0.6007018",
"text": "def test_view_everything_returns_all_records(self):\n data = self.create_test_dates()['test_log_entry_data']\n\n records = self.dbm.view_everything()\n records = [dict(entry) for entry in records]\n\n ... | [
{
"docid": "b5acbfbedb9e1524417d1e5e14283e4f",
"score": "0.7266865",
"text": "def all_entries(self) -> requests.models.Response:",
"title": ""
},
{
"docid": "151603cea417ba3dabcb63e9d0ba8d72",
"score": "0.7057368",
"text": "def getAllEntries(self):\n try:\n self.dat... |
d5d1ba5f2a340e997fd179d7fbbeb007 | The rsquared value (a.k.a. coefficient of determination) | [
{
"docid": "ac093f765728612363ed6781c28d505b",
"score": "0.0",
"text": "def r_squared(A, b):\n ssr = sum_of_squared_residuals(A, b)\n sst = total_sum_of_squares(A, b)\n return 1.0 - ssr / sst",
"title": ""
}
] | [
{
"docid": "72fac2c5ab96a955bb8597aa092c64df",
"score": "0.7222706",
"text": "def rsq(self):\n return metrics.rsq(self.model, self.x, self.y)",
"title": ""
},
{
"docid": "47d63c0cfd2b0557ff484c91495f6744",
"score": "0.70095164",
"text": "def get_res_rsquare(self, res_type: str... |
2e27c39c5690eadd8bfa2d8d5fe35f2e | Given a username, sign in the user. | [
{
"docid": "adaa9822983a2910836001f830cdc2cc",
"score": "0.77780646",
"text": "def sign_in(self, username):\n if self.url == 'about:blank':\n # We need a page loaded in order to set an authentication cookie.\n self.visit('/')\n # This is duplicated from User.sign_in t... | [
{
"docid": "386a8dc8087d28e7989fe62d2c220f31",
"score": "0.7601162",
"text": "def login(self, username, password):\n self._set_username(username=username)\n self._set_password(password=password)\n self._sign_in()",
"title": ""
},
{
"docid": "c5fae1aed819e10ecaea99aa83104... |
404671f607e8e6fefe302d3dceab2092 | Sets the access control policy on the specified resource. Replaces any existing policy. | [
{
"docid": "9cd543d8f92bc3bf059f6ebd6f1d2a6b",
"score": "0.50599295",
"text": "def SetIamPolicy(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')",
"title":... | [
{
"docid": "43c26021a7a50ef9c9a7379a2cefa46d",
"score": "0.7376781",
"text": "def set_resource_policy(self, resource, policy):\n\n @lockutils.synchronized(self.lock_name)\n def _set_resource_policy():\n policy_id = self.resource_2_qos_policies.get(resource)\n old_poli... |
3ac87c776c8aadc9761f4a9bee3738c6 | Generates a link allowing the data in a given panda dataframe to be downloaded | [
{
"docid": "5f40d00bea1eb279ad6117ffc5b495d6",
"score": "0.7543758",
"text": "def get_table_download_link(df):\n csv = df.to_csv(index=False)\n b64 = base64.b64encode(\n csv.encode()\n ).decode() # some strings <-> bytes conversions necessary here\n ... | [
{
"docid": "652badd8b29f86288a249339bc14ca84",
"score": "0.7853004",
"text": "def get_table_download_link(dataframe: pd.DataFrame):\n val = to_excel(dataframe)\n b64 = base64.b64encode(val)\n # TODO: Create a random number as filename, check generate_xlsx\n return ('<p style=\"text-align:cen... |
13bc9786d72c182ae5556b7203767daf | Convert topology object into an ns2 Tcl script that deploys that topology into ns2. | [
{
"docid": "602ccfd7732aa6a1f9642f34693d5188",
"score": "0.55723596",
"text": "def to_ns2(topology, path, stacks=True):\n try:\n from mako.template import Template\n except ImportError:\n raise ImportError('Cannot import mako.template module. '\n 'Make sure m... | [
{
"docid": "2b5d43ca9342feb75ca534ffe1a11c9d",
"score": "0.54831195",
"text": "def topology(args):\n info(\"*** Creating network\\n\")\n topology_data = load_topology() \n \n add_hmds()\n add_base_stations(topology_data)\n sdn_controller = get_sdn_controller()\n\n info(\"*** Configu... |
4cca531af85e252640b38a122ef3ef97 | Creates a new archive_file (hdf5) with groups for gpt and distgen. Calls .archive method of GPT and Distgen objects, into these groups. | [
{
"docid": "5cf03ca819bee0c064fc670222362c11",
"score": "0.76831263",
"text": "def archive_gpt_with_distgen(gpt_object,\n distgen_object,\n archive_file=None,\n gpt_group ='gpt',\n distgen_gro... | [
{
"docid": "095f84ec623087ed69bf071f02b382e4",
"score": "0.604668",
"text": "def write_hdf5(out_file, data):\n gmu_proc_file = h5py.File(out_file, 'w')\n print(gmu_proc_file)\n for g in data:\n group = gmu_proc_file.create_group(g)\n for datum in data[g]:\n try:\n ... |
92a56c7f57a736b69d2267ad2bc5e9d5 | Authenticates a challenge message and responds to the requestor. | [
{
"docid": "2b049151ea4c4fa28d8780963a4016be",
"score": "0.8119253",
"text": "def authenticate_challenge(self, message: Dict[Any, Any]):\n\n challenge = message[\"challenge\"]\n log.info(\"Authenticating a challenge from the server\")\n\n try:\n signed_challenge = sign_ch... | [
{
"docid": "e7ab498cb458cdcb5097216cbfed59d8",
"score": "0.7524756",
"text": "def process_challenge(self, message: str) -> None:\n\t\tvalue = message.get('val', None)\n\n\t\tif value != self.challenge_ans:\n\t\t\tlogger.error(\"Authentication failed\")\n\t\t\treturn False\n\t\t\n\n\t\tself.state = STATE... |
6a373c6e7a1bd455aa1ed75f652c7e26 | Tests the method which let us set the URL to work from for the case that the given URL is not a supported URL. | [
{
"docid": "20831f84b83e2b3651fbe1de70c2a13f",
"score": "0.0",
"text": "def test_set_url_base_ends_with_slash(self) -> None:\n\n given = \"http://example.org/\"\n expected = \"http://example.org\"\n\n self.query_tool.url_base = given\n actual = self.query_tool.url_base\n\n ... | [
{
"docid": "6be824e2daffbb7a6dd84fc0d7cbc843",
"score": "0.7581493",
"text": "def set_broken_url():",
"title": ""
},
{
"docid": "e02dcdf95f707f20fc80126e7cd69e75",
"score": "0.7048977",
"text": "def _set_url(self, url):\n ...",
"title": ""
},
{
"docid": "1079ed216e... |
7732897d672fe036c1d2b58e44dd262c | Make a random matrix with elements in range (lowhigh) | [
{
"docid": "e27867629904731d44dbbc8c2a9a695c",
"score": "0.76756394",
"text": "def makeRandom(cls, m, n, low=0, high=10):\n\n obj = Matrix(m, n)\n for x in range(m):\n obj.rows[x] = [random.randrange(low, high) for i in range(obj.csize)]\n\n return obj",
"title": ""
... | [
{
"docid": "b5e8a62cd84fd0814bfb38c042749abe",
"score": "0.7580365",
"text": "def randmatrix(m, n, lower=-0.5, upper=0.5):\n return np.array([random.uniform(lower, upper) for i in range(m*n)]).reshape(m, n)",
"title": ""
},
{
"docid": "4154e7b54e17a3fc806a6f9e15ecbed0",
"score": "0.74... |
68d870d5d23b867b8cde3c54962e3557 | Creates the temporary html_to_pdf.html file that will be located in the rssparser/data/ folder. With pdfkit library converts this html file to the pdf file specified in topdf arg. And removes the temporary html file. | [
{
"docid": "38fd8232ae791da34cff94ddf52be21a",
"score": "0.7395331",
"text": "def to_pdf(items, path_to_save_pdf):\n to_html(items, 'rssparser/data/html_to_pdf.html')\n pdfkit.from_file('rssparser/data/html_to_pdf.html', path_to_save_pdf)\n os.remove('rssparser/data/html_to_pdf.html')",
"ti... | [
{
"docid": "0294fe5f674f0b44652a9c10498ec7d8",
"score": "0.73789454",
"text": "def write_pdf(self, html):\n try:\n f = tempfile.NamedTemporaryFile(delete=False, suffix='.html')\n f.write(html.encode('utf_8', 'xmlcharrefreplace'))\n f.close()\n except Except... |
bc1345310a64780e09c789847d11b327 | Classifies the given inputs (images) by comparing the processing\ results of the images to the target classes targets. | [
{
"docid": "6840b3a9df26313e808670b73b4439d1",
"score": "0.79305094",
"text": "def classify(self, inputs, targets):\n assert inputs.shape[0] == targets.shape[0], \\\n 'Different number of inputs and targets provided. inputs.shape[0] ' \\\n 'and targets.shape[0] must be ident... | [
{
"docid": "8a295023de0f3cc752c3de5964aeb74a",
"score": "0.6684749",
"text": "def postprocess(results, filenames, batch_size):\n if len(results) != 1:\n raise Exception(\"expected 1 result, got {}\".format(len(results)))\n\n batched_result = results[0].batch_classes\n if len(batched_result) != bat... |
3209a5dac48fd4a7f382b7f2a4c9652b | Test eq as arrayarray for failing with different data positions in array 2 Array code d. | [
{
"docid": "ced6c869f5ee002089761d83cdfd7e8a",
"score": "0.79888207",
"text": "def test_eq_numpos_array_array_c2(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1, sel... | [
{
"docid": "71d41f5200c765aa5f06fb50a38ef57b",
"score": "0.8023773",
"text": "def test_eq_numpos_array_array_c1(self):\n\t\tfor testpos in range(self.testarraylen):\n\t\t\twith self.subTest(msg='Failed with posistion', testpos = testpos):\n\n\t\t\t\texpected = all([x == y for x,y in zip(self.data1fail, ... |
a813f98ec27f13401901346feec8a484 | Recursively turn the TC logic into TC configuration | [
{
"docid": "10a45d1c09ea052b511d7a5891329964",
"score": "0.0",
"text": "def make(self):\n tcg = TCCommandGenerator()\n print(\"Make ClassifierFilter\")\n CmdExecutor.run_and_print(tcg.add_classifier_filter(self))",
"title": ""
}
] | [
{
"docid": "ef40f7ed175823942ced5d3c9a0f4d46",
"score": "0.5959538",
"text": "def process_config(lunch_config):\n def add(domain_name, tree_root, product):\n tree_key = inner_tree.InnerTreeKey(tree_root, product)\n if tree_key in trees:\n tree = trees[tree_key]\n else:... |
a29a5b68ad9385db44275aaac25299f2 | calculate all lazily calculated properties of explainer | [
{
"docid": "98cda06ab8b2983211f12fad2566a05b",
"score": "0.58270127",
"text": "def calculate_properties(self, include_interactions=True):\n _ = self.pred_probas\n super().calculate_properties(include_interactions=include_interactions)",
"title": ""
}
] | [
{
"docid": "db812ffcc271376b51b84567bde6244c",
"score": "0.59954566",
"text": "def get_descent(self):",
"title": ""
},
{
"docid": "db812ffcc271376b51b84567bde6244c",
"score": "0.59954566",
"text": "def get_descent(self):",
"title": ""
},
{
"docid": "b28db5e20f7c8f8092ca30... |
3281e3c9560e50b7f3b70816c2c11ccd | this function responsible for the initial skip grams | [
{
"docid": "fee1eac4d6717fd2cddf61b9d2eb07ec",
"score": "0.59567994",
"text": "def initial_skip_grams(full_sentence_info, k_skip, n_gram):\n if n_gram == 1:\n return [[full_sentence_info[0]]]\n grams = []\n for j in range(min(k_skip + 1, len(full_sentence_info) - 1)):\n kmj_skip_n... | [
{
"docid": "d5df531ee98e066ac7f749a019d9ea17",
"score": "0.6951125",
"text": "def Skip(self, skip=True):",
"title": ""
},
{
"docid": "62f28dd39a3b4451ebad8dc5424e80ef",
"score": "0.6750491",
"text": "def skip(self):\n self.next()",
"title": ""
},
{
"docid": "4092f1... |
294970926228fce0b623e8b5c3e22575 | Loads a network from a CSV file. | [
{
"docid": "88a79f5ad83f58d5f4dffa69cdbc21e0",
"score": "0.61747426",
"text": "def network_from_csv(filename):\n from openpnm.network import Network\n fname = _parse_filename(filename=filename, ext='csv')\n\n a = read_table(filepath_or_buffer=fname,\n sep=',',\n ... | [
{
"docid": "da41cb35073df863beef61c4f11e5bb6",
"score": "0.70894367",
"text": "def from_csv(self, file: str):\n with open(file) as f:\n for line in f:\n words = line.split(',')\n line_type = words[0]\n if line_type == 'c':\n ... |
d85d71c32c197fa3a34b069127d452a1 | Reads reference data from the original "new_reference.dat" file from ff_generator Currently torsion and out_bends do not support reading a value in Offdiagonal values of the Hessian are not supported Must be called after setup | [
{
"docid": "0cb73e703624784c0f6107acfd7b7d63",
"score": "0.60091716",
"text": "def read_ref_from_refdat(self, filename, conv_angle=np.pi/180.0, conv_energy = 143.88, conv_length=1.0, read_fit_flag=True):\n assert self._setup\n #\n f = open(filename, \"r\")\n line = f.readline... | [
{
"docid": "b5be3e4f061018379de1fd59914de34a",
"score": "0.61490226",
"text": "def Load_reference(self):\n if self.reffile is None:\n raise ValueError('No reference file specified')\n if os.path.isfile(self.reffile):\n hdu = fits.open(self.reffile)\n else:\n ... |