Optional[str]:\n return pulumi.get(self, \"ip\")","def ip(self):\n if not self._ip:\n if 'ip' in self.config:\n ip = self.config['ip']\n else:\n ip = self.protocol.transport.get_extra_info('sockname')[0]\n ip = ip_address(ip)\n if ip.version == 4:\n self._ip = ip\n else: # pragma: no cover\n response = urlopen('http://ipv4.icanhazip.com/')\n ip = response.read().strip().decode()\n ip = ip_address(ip)\n self._ip = ip\n return self._ip","def get_IP_address(request):\n # Catchs the case when the user is on a proxy\n ip = request.META.get('HTTP_X_FORWARDED_FOR', '')\n if ip == '' or ip.lower() in ('unkown', ):\n ip = request.META.get('REMOTE_ADDR', '') # User is not on a proxy\n if ip == '' or ip.lower() in ('unkown', ):\n ip = request.META.get('HTTP_X_REAL_IP')\n return ip","def getManageIp(self):\n dev = self.device()\n if dev: return dev.getManageIp()\n return \"\"","def ip_address(self):\n expression = expressions.WPA_IP\n name = expressions.IP_ADDRESS_NAME\n command = self.status_command\n return self._match(expression,\n name,\n command)","def get_ip(request):\n ip1 = request.META.get('REMOTE_ADDR', '')\n ip2 = request.META.get('HTTP_X_FORWARDED_FOR', '').split(\",\")[0].strip()\n ip = ip1 or ip2 or '0.0.0.0'\n return ip","def external_IP(self):\r\n return self._external_ip","def get_global_ip_ipv6():\n network_info_providers = [\n 'http://v6.ipv6-test.com/api/myip.php',\n 'http://v6.ident.me/',\n ]\n random.shuffle(network_info_providers)\n for url in network_info_providers:\n try:\n return requests.get(url).text.lstrip().rstrip()\n except Exception:\n continue\n else:\n log.info('cannot find global ipv6 ip')\n return \"\"","def _get_local_ip():\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n try:\n sock.connect(('10.255.255.255', 1))\n ip = sock.getsockname()[0]\n except Exception:\n ip = '127.0.0.1'\n finally:\n sock.close()\n\n return ip","def internalIP(self):\r\n return self._internalIP","def get_host_ip_addr():\n return nova_conf.my_ip","def get_remote_ip(request):\n \n return utilities.get_remote_ip(request)","def get_ip():\n\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n try:\n # doesn't even have to be reachable\n s.connect(('10.255.255.255', 1))\n return s.getsockname()[0]\n except:\n return '127.0.0.1'\n finally:\n s.close()","def get_ip():\n if not request.headers.getlist(\"X-Forwarded-For\"):\n return str(request.remote_addr)\n else:\n return str(request.headers.getlist(\"X-Forwarded-For\")[0])","def _LeasedIP(self):\n check_command = 'ip addr show {interface} | grep \"inet \"'.format(\n interface=self.interface)\n try:\n # grep exit with return code 0 when we have retrieved an IP.\n out = self._device.CheckOutput(check_command)\n except device_types.CalledProcessError:\n return False\n # ex: inet 192.168.159.78/20 brd 192.168.159.255 scope global wlan0\n return out.split()[1].split('/')[0]","def __lookup_public_ip(self):\n\n response = requests.get('https://api.ipify.org?format=json', timeout=self.timeout)\n\n if response.status_code == 200:\n ip_data = response.json()\n if 'ip' not in ip_data.keys():\n return 'Unable to determine IP'\n else:\n return ip_data['ip']\n else:\n return 'Unable to determine IP'","def _get_ip():\n cmd_netstat = ['netstat', '-nr']\n p1 = subprocess.Popen(cmd_netstat, stdout=subprocess.PIPE)\n cmd_grep = ['grep', '^0\\.0\\.0\\.0']\n p2 = subprocess.Popen(cmd_grep, stdin=p1.stdout, stdout=subprocess.PIPE)\n cmd_awk = ['awk', '{ print $2 }']\n p3 = subprocess.Popen(cmd_awk, stdin=p2.stdout, stdout=subprocess.PIPE)\n galaxy_ip = p3.stdout.read()\n log.debug('Host IP determined to be %s', galaxy_ip)\n return galaxy_ip","def get_ip():\n return os.getenv(\"HOST_IP\", \"127.0.0.1\")","def getIP():\n try:\n page = urlopen(\"http://www.whatismyip.com/automation/n09230945.asp\")\n IP = page.read()\n page.close()\n return IP\n except:\n return \"Could not retrieve the IP address.\"","def get_ip(request):\n ip = request.META.get(\"HTTP_X_FORWARDED_FOR\", None)\n if ip:\n # X_FORWARDED_FOR returns client1, proxy1, proxy2,...\n ip = ip.split(\", \")[0]\n else:\n ip = request.META.get(\"REMOTE_ADDR\", \"\")\n return ip","def get_ip(request):\n x_forwarded_for = request.META.get('HTTP_X_FORWARDED_FOR')\n if x_forwarded_for:\n ip = x_forwarded_for.split(',')[0]\n else:\n ip = request.META.get('REMOTE_ADDR')\n return ip","def get_ips():\r\n local_ips = []\r\n public_ips = []\r\n \r\n # list of iface names, 'lo0', 'eth0', etc.\r\n for iface in netifaces.interfaces():\r\n # list of ipv4 addrinfo dicts\r\n ipv4s = netifaces.ifaddresses(iface).get(netifaces.AF_INET, [])\r\n for entry in ipv4s:\r\n addr = entry.get('addr')\r\n #print(\"addr: \" + addr)\r\n if not addr:\r\n continue\r\n if not (iface.startswith('lo') or addr.startswith('127.')):\r\n public_ips.append(addr)\r\n else:\r\n local_ips.append(addr) \r\n return public_ips","def getip(self):\n if configIpAddress == \"none\":\n strngtoXmit = 'M-SEARCH * HTTP/1.1' + '\\r\\n' + \\\n 'HOST: 239.255.255.250:1900' + '\\r\\n' + \\\n 'MAN: \"ssdp:discover\"' + '\\r\\n' + \\\n 'MX: 2' + '\\r\\n' + \\\n 'ST: urn:schemas-upnp-org:device:MediaRenderer:1' + '\\r\\n' + '\\r\\n'\n\n bytestoXmit = strngtoXmit.encode()\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n sock.settimeout(3)\n gotstr = 'notyet'\n found = False\n ipaddress = None\n sock.sendto(bytestoXmit, ('239.255.255.250', 1900))\n try:\n gotbytes, addressport = sock.recvfrom(512)\n gotstr = gotbytes.decode()\n except:\n sock.sendto(bytestoXmit, ('239.255.255.250', 1900))\n if re.search('LG', gotstr):\n ipaddress, _ = addressport\n found = True\n self._state = STATE_PLAYING\n else:\n gotstr = 'notyet'\n sock.close()\n if not found:\n print(\"LG TV not found\")\n ipaddress = None\n self._state = STATE_OFF\n lgtv[\"ipaddress\"] = ipaddress\n else:\n lgtv[\"ipaddress\"] = configIpAddress\n if self.isOnline():\n self._state = STATE_PLAYING\n else:\n self._state = STATE_OFF","def get_ip_address_filter(self):\n return self.mycam.devicemgmt.GetIPAddressFilter()","def internal_IP(self):\r\n return self._internal_ip","def get_ip() -> str:\n for ip in socket.gethostbyname_ex(socket.gethostname())[2]:\n if not ip.startswith(\"127.\"):\n return ip\n for s in [socket.socket(socket.AF_INET, socket.SOCK_DGRAM)]:\n s.connect((\"8.8.8.8\", 53))\n ip, port = s.getsockname()\n s.close()\n if not ip.startswith(\"127.\"):\n return ip\n raise ConnectionError(\"Can not get a suitable IP\")","def inetVisibleIP(self):\n def handle(results):\n ips = [ result[1][0] for result in results if result[0] ]\n self.log.debug(\"other nodes think our ip is %s\" % str(ips))\n return ips\n\n ds = []\n for neighbor in self.bootstrappableNeighbors():\n ds.append(self.protocol.stun(neighbor))\n d = defer.gatherResults(ds)\n d.addCallback(handle)\n d.addErrback(self.onError)\n return d","def ip(self) -> str:\n return self._ip","def getnetwork(ipaddr):\n return '192.168.1.0/24'","def get_fw_ip_and_port(user, passw, logger, server_url):\n ip, port = get_ip_and_port('firewall', get_paos(user, passw, server_url, logger))\n return f\"{ip}:{port}\"","def get_local_ip():\n try:\n ip_task = os.popen(\"ifconfig | grep -Eo 'inet (addr:)?(Adresse:)?([0-9]*\\.){3}[0-9]*' | grep -Eo '([0-9]*\\.){3}[0-9]*' | grep -v '127.0.0.1'\")\n local_ip = ip_task.read().strip()\n ip_task.close()\n if '\\n' in local_ip:\n local_ip = local_ip.split('\\n')[0]\n print ' >> got local ip:', local_ip\n return local_ip\n except:\n return '0.0.0.0'","def get_local_ip(self):\n # Get the local IP address used to communicate with the GNS3\n # server. Not the GNS3 server's address, but rather the local\n # machine's address that we use to send messages to the GNS3\n # server. If that address isn't 127.0.0.1 (localhost), use it.\n server_local_ip = self.server.get_local_ip()\n if server_local_ip != '127.0.0.1':\n return server_local_ip\n else:\n # Otherwise, find the first interface on the first cloud node (if it exists)\n try:\n first_cloud_node = next(node for node in self.nodes() if node['node_type'] == 'cloud')\n interface = first_cloud_node['properties']['ports_mapping'][0]['interface']\n\n # If the interface is virtual, find and record its\n # mate's first IP address, which is the address we can\n # send to.\n\n ip_proc = subprocess.Popen(['ip', 'link', 'show', interface], stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)\n first_field = ip_proc.stdout.read().decode().split()[1].split('@')\n if first_field[0] == interface:\n paired_interface = first_field[1].split(':')[0]\n return ni.ifaddresses(paired_interface)[ni.AF_INET][0]['addr']\n except (StopIteration, ValueError):\n # StopIteration if there are no cloud nodes\n # ValueError if there are no IP addresses on the paired interface\n pass\n\n return None","def get_remote_ip(request):\n return request.META.get(\"HTTP_REMOTE_ADDR\", request.META.get(\"REMOTE_ADDR\", \"\"))","def get_local_ip(self, system):\n if system == \"Linux\":\n # This is a bit ugly but it works\n ips = check_output(['hostname', '--all-ip-addresses']).decode(\"utf-8\")\n return ips.split(\" \")[0]\n else:\n return socket.gethostbyname(socket.gethostname())","def local_ip():\n sys_name = system()\n if sys_name == 'Darwin':\n # OSX\n route = Command('route')\n ifconfig = Command('ifconfig')\n\n iface = [\n line.strip()\n for line in route('-n', 'get', 'default')\n if line.strip().startswith('interface')\n ][0].split(':')[1].strip()\n return [\n line.strip()\n for line in ifconfig(iface)\n if line.strip().startswith('inet ')\n ][0].split(' ')[1]\n elif sys_name == 'Linux':\n try:\n ip = Command('ip')\n iface = [\n line.strip()\n for line in ip('route')\n if line.strip().startswith('default ')\n ][0].split(' ')[4]\n except CommandNotFound:\n route = Command('route')\n iface = [\n line.strip()\n for line in route('-n')\n if line.startswith('0.0.0.0')\n ][0].split(' ').pop()\n\n try:\n # try with IP\n ip = Command('ip')\n return [\n line.strip()\n for line in ip('addr', 'show', iface)\n if line.strip().startswith('inet ')\n ][0].split(' ')[1].split('/')[0]\n except CommandNotFound:\n pass\n\n # fallback to ifconfig\n ifconfig = Command('ifconfig')\n return [\n line.strip()\n for line in ifconfig(iface)\n if line.strip().startswith('inet ')\n ][0].split(' ')[1]\n\n return None","def wan_address(self):\n if self._community.dispersy:\n host = self._community.dispersy.wan_address[0]\n\n if host == \"0.0.0.0\":\n host = self._community.dispersy.lan_address[0]\n\n else:\n host = \"0.0.0.0\"\n\n _, port = self._socket.getsockname()\n return (host, port)","def get_public_ip(session, return_type=None, **kwargs):\n path = '/api/settings/public_ips.json'\n\n return session.get_api(path=path, return_type=return_type, **kwargs)","def server_ip(self) -> str:\n return pulumi.get(self, \"server_ip\")","def get_ip_address(self):\n return self.__ip_address","def address(self):\n \n return self.__ip","def get_bot_ip(bot, update):\n sender = update.message.from_user\n if sender.id == Bot.OWNER_ID:\n msg_to_send = \"\"\n try:\n ip_string = check_output([\"curl\", \"ipinfo.io/ip\"],\n universal_newlines=True,\n timeout=5)\n msg_to_send = msgIpAddress + ip_string\n except CalledProcessError:\n msg_to_send = strings.errUnknown\n except TimeoutExpired:\n msg_to_send = strings.errTimeout\n update.message.reply_text(msg_to_send)","def get_user_ip(user, machine):\n iraw = xexec(['fab', '-H', machine, 'user_ip:'+user])\n uip = iraw.split('=>')[-1].strip()\n return uip","def get_akamai_ips_connection(self):\n return self.m_connection.akamai_ips"],"string":"[\n \"def GetWirelessIP(self):\\n ip = self.wifi.GetIP()\\n return ip\",\n \"def get_public_ip():\\n public_ip = get('https://api.ipify.org').text\\n return public_ip\",\n \"def publicIP(self):\\n return self.query('https://plex.tv/:/ip')\",\n \"def checkIP(self):\\n\\t\\tself.get(\\\"https://ifconfig.me/\\\")\\n\\t\\treturn self.findId(\\\"ip_address\\\").text\",\n \"def get_device_ip(connection):\\n ifconfig = connection.adb(\\\"shell ifconfig\\\", disable_fixers=True, timeout=1)\\n ips = re.findall(r\\\"wlan0.*\\\\n.*inet addr:(\\\\d+\\\\.\\\\d+\\\\.\\\\d+\\\\.\\\\d+)\\\", ifconfig)\\n if ips:\\n return unicode(ips[0])\\n else:\\n return None\",\n \"def get_IP(): \\n \\n return socket.gethostbyname(socket.gethostname())\",\n \"def get_wifi(self):\\n return self._wifi\",\n \"def get_ip(self):\",\n \"def GetWiredIP(self):\\n ip = self.wired.GetIP()\\n return ip\",\n \"def get_my_ip():\\r\\n try:\\r\\n return [x[4] for x in conf.route.routes if x[2] != '0.0.0.0'][0]\\r\\n except IndexError:\\r\\n return '127.0.0.1'\",\n \"def getIP():\\n data = _get_page(\\\"http://myip.cz\\\")\\n data = data.split(\\\"Your IP Address is: <b>\\\")[-1].split(\\\"</b>\\\")[0]\\n return data.strip()\",\n \"def getMyIP():\\r\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\r\\n s.connect(('8.8.8.8', 1)) # connect() for UDP doesn't send packets\\r\\n return s.getsockname()[0]\",\n \"def get_local_host_ip(self) -> str:\",\n \"def ip():\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n s.connect((\\\"8.8.8.8\\\", 80))\\n ip = s.getsockname()[0]\\n s.close()\\n return ip\",\n \"def get_IP():\\n\\n return socket.gethostbyname(socket.gethostname())\",\n \"def getLocalIpAddress() :\\n \\n if (platform.system() == 'Linux') :\\n cmd = \\\"ifconfig wlan0 | grep 'inet addr:' | cut -d: -f2 | awk '{print $1}'\\\"\\n return subprocess.check_output(cmd, shell=True) \\n else : # Darwin\\n return socket.gethostbyname(socket.gethostname())\",\n \"def get_device_ip():\\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n sock.connect((\\\"8.8.8.8\\\", 80))\\n ip = sock.getsockname()[0]\\n sock.close()\\n return ip\",\n \"def ip_address(self) -> str:\\n return self._device.ip if self.is_connected else None\",\n \"def ip(self) -> str:\\n return pulumi.get(self, \\\"ip\\\")\",\n \"def get_IPaddress():\\n config = get_ifconfig()\\n return config[0]\",\n \"def get_local_ip():\\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n sock.connect((\\\"8.8.8.8\\\", 80))\\n local_ip = sock.getsockname()[0]\\n sock.close()\\n\\n return local_ip\",\n \"def get_ip():\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n try:\\n # doesn't even have to be reachable\\n s.connect(('10.255.255.255', 1))\\n ip = s.getsockname()[0]\\n except Exception:\\n ip = '127.0.0.1'\\n finally:\\n s.close()\\n return ip\",\n \"def get_ip_address(self):\\n return self.adb.get_ip_address()\",\n \"def get_self_ip():\\n\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n s.connect(('8.8.8.8', 80))\\n ip = s.getsockname()\\n s.close()\\n return ip[0]\",\n \"def obtain_public_ip():\\n from urllib2 import urlopen\\n my_ip = urlopen('http://ip.42.pl/raw').read()\\n logger.debug('The public ip is: %s' % my_ip)\\n return str(my_ip)\",\n \"def get_ip():\\n with hide(\\\"everything\\\"):\\n ip_addresses = run('hostname -I').split(' ')\\n return ip_addresses[0]\",\n \"async def getWanIpAsync():\\n\\tresp = await request(\\\"ip4only.me\\\",80,b\\\"/api/\\\")\\n\\tif resp:\\n\\t\\tspl = resp.split(b\\\",\\\")\\n\\t\\tif len(spl) > 1:\\n\\t\\t\\treturn spl[1].decode(\\\"utf-8\\\")\\n\\treturn None\",\n \"def ip(self):\\n return os.environ.get('REMOTE_ADDR')\",\n \"def fetchWANIP():\\n logging.info(\\\"Trying to fetch WAN IP\\\")\\n _wanIf = config.get(\\\"interface\\\", \\\"wan\\\")\\n _wanip = None\\n try:\\n bus = dbus.SystemBus()\\n proxy = bus.get_object(\\\"org.freedesktop.NetworkManager\\\", \\\"/org/freedesktop/NetworkManager\\\")\\n manager = dbus.Interface(proxy, \\\"org.freedesktop.NetworkManager\\\")\\n devices = manager.GetDevices()\\n for device in devices:\\n devProxy = bus.get_object(\\\"org.freedesktop.NetworkManager\\\", device)\\n devConfIface = dbus.Interface(devProxy, \\\"org.freedesktop.DBus.Properties\\\")\\n devConf = devConfIface.GetAll(\\\"org.freedesktop.NetworkManager.Device\\\")\\n if devConf['Interface'] == _wanIf:\\n actConProxy = bus.get_object(\\\"org.freedesktop.NetworkManager\\\", devConf[\\\"ActiveConnection\\\"])\\n actConIface = dbus.Interface(actConProxy, \\\"org.freedesktop.DBus.Properties\\\")\\n actConConf = actConIface.GetAll(\\\"org.freedesktop.NetworkManager.Connection.Active\\\")\\n actConIP4Proxy = bus.get_object(\\\"org.freedesktop.NetworkManager\\\", actConConf['Ip4Config'])\\n actConIP4Iface = dbus.Interface(actConIP4Proxy, \\\"org.freedesktop.DBus.Properties\\\")\\n actConIP4Conf = actConIP4Iface.GetAll(\\\"org.freedesktop.NetworkManager.IP4Config\\\")\\n _wanip = actConIP4Conf[\\\"AddressData\\\"][0][\\\"address\\\"]\\n for dnsEntry in actConIP4Conf[\\\"NameserverData\\\"]:\\n wandns.append(dnsEntry[\\\"address\\\"])\\n logging.info(f\\\"WAN DNS server fetched for {_wanIf} - {dnsEntry['address']}\\\")\\n logging.info(f\\\"WAN IP fetched for {_wanIf} - {_wanip}\\\")\\n except Exception as e:\\n logging.error(\\\"Trying to fetch WAN IP error\\\")\\n logging.error(e)\\n # return WAN IP\\n return _wanip\",\n \"def sitepublicip(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._sitepublicip\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def siteip(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._siteip\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def _get_my_ip():\\n try:\\n csock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n csock.connect(('8.8.8.8', 80))\\n (addr, port) = csock.getsockname()\\n csock.close()\\n return addr\\n except socket.error:\\n return \\\"127.0.0.1\\\"\",\n \"def _get_user_ip(request):\\n x_forwarded_for = request.META.get(\\\"HTTP_X_FORWARDED_FOR\\\")\\n if x_forwarded_for:\\n ip = x_forwarded_for.split(\\\",\\\")[0]\\n else:\\n ip = request.META.get(\\\"REMOTE_ADDR\\\")\\n return ip\",\n \"def GetExternalIp():\\n h = httplib2.Http(tempfile.gettempdir(), timeout=10)\\n url = 'http://whatismyip.akamai.com'\\n resp, content = h.request(url, 'GET')\\n if resp.status == 200:\\n return content\\n for provider in (UltraDNSAuth(), MyResolverInfo()):\\n answer = provider.GetClientIp()\\n if answer:\\n return answer\",\n \"def localip(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._localip\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def get_tun_ip(ip_addr, username):\\n cmd = \\\"ifconfig tun0 | grep 'inet addr:'| grep -v '127.0.0.1' | cut -d: -f2 | awk '{ print $1}'\\\" \\n tun_ip = remote_fetch(ip_addr, username, cmd)[0].strip()\\n return tun_ip\",\n \"def getPublicIp():\\n global PUBLIC_IP\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n try:\\n # doesn't even have to be reachable\\n s.connect(('10.255.255.255', 1))\\n PUBLIC_IP = s.getsockname()[0]\\n except Exception:\\n PUBLIC_IP = '127.0.0.1'\\n finally:\\n s.close()\\n return PUBLIC_IP\",\n \"def joinwifi():\\n station = network.WLAN(network.STA_IF) # initiate a station mode\\n\\n if not station.isconnected():\\n print('connecting to network:', ssid())\\n station.active(True)\\n station.connect(ssid(), password())\\n \\n\\n while not station.isconnected():\\n pass\\n\\n # deactivating access point mode\\n ap = network.WLAN(network.AP_IF)\\n ap.active(False)\\n\\n ip = station.ifconfig()[0]\\n print('connected as:', ip)\\n\\n return ip\",\n \"def ip_info():\\n return str(getIP())\",\n \"def ipAddress():\\n \\n sk = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n sk.connect((\\\"8.8.8.8\\\", 80))\\n ip = (sk.getsockname()[0])\\n sk.close()\\n return str(ip)\",\n \"def getIp(self):\\n raise NotImplementedError\",\n \"def get_ip():\\n return request.environ['HTTP_REMOTE_ADDR']\",\n \"def _get_user_ip(request):\\n x_forwarded_for = request.META.get('HTTP_X_FORWARDED_FOR')\\n if x_forwarded_for:\\n ip = x_forwarded_for.split(',')[0]\\n else:\\n ip = request.META.get('REMOTE_ADDR')\\n return ip\",\n \"def get_global_ip():\\n network_info_providers = [\\n 'http://api.ipify.org/',\\n 'http://myip.dnsomatic.com',\\n 'http://inet-ip.info/ip',\\n 'http://v4.ident.me/',\\n ]\\n random.shuffle(network_info_providers)\\n for url in network_info_providers:\\n try:\\n return requests.get(url).text.lstrip().rstrip()\\n except Exception:\\n continue\\n else:\\n log.info('cannot find global ip')\\n return \\\"\\\"\",\n \"def get_ip_string():\\n return netifaces.ifaddresses('br0')[netifaces.AF_INET][0]['addr']\",\n \"def ip(self):\\n return self._ip\",\n \"def ip(self):\\n return self._ip\",\n \"def getLocalhostIP():\\n return socket.getaddrinfo('localhost', 0)[0][4][0]\",\n \"def getLocalIP():\\r\\n try:\\r\\n csock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\r\\n csock.connect(('8.8.8.8', 80))\\r\\n (addr, port) = csock.getsockname()\\r\\n csock.close()\\r\\n return addr\\r\\n except socket.error:\\r\\n return \\\"127.0.0.1\\\"\",\n \"def get_local_ip():\\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n try:\\n # doesn't even have to be reachable\\n sock.connect(('8.8.8.8', 1))\\n ip = sock.getsockname()[0]\\n except:\\n ip = '127.0.0.1'\\n finally:\\n sock.close()\\n return ip\",\n \"def get_externalip(self):\\n\\n myip = \\\"\\\"\\n for i in range(5):\\n myip = self.fetch(random.choice(self.server_list))\\n if myip != \\\"\\\":\\n return myip\\n else:\\n continue\\n return \\\"\\\"\",\n \"def ip_address(self) -> str:\\n return pulumi.get(self, \\\"ip_address\\\")\",\n \"def get_global_ip() -> str:\\n return urllib.request.urlopen(\\\"https://icanhazip.com\\\").read().decode().strip()\",\n \"def IP(self):\\r\\n return self._ip\",\n \"def get_local_ip():\\n\\n return os.environ[LOCAL_IP_KEY]\",\n \"def get_public_ip(self):\\n return self.public_ip\",\n \"def api_myip():\\n return request.remote_addr, 200, {'Content-Type': 'text/plain'}\",\n \"def ip(self) -> Optional[str]:\\n return pulumi.get(self, \\\"ip\\\")\",\n \"def ip(self):\\n if not self._ip:\\n if 'ip' in self.config:\\n ip = self.config['ip']\\n else:\\n ip = self.protocol.transport.get_extra_info('sockname')[0]\\n ip = ip_address(ip)\\n if ip.version == 4:\\n self._ip = ip\\n else: # pragma: no cover\\n response = urlopen('http://ipv4.icanhazip.com/')\\n ip = response.read().strip().decode()\\n ip = ip_address(ip)\\n self._ip = ip\\n return self._ip\",\n \"def get_IP_address(request):\\n # Catchs the case when the user is on a proxy\\n ip = request.META.get('HTTP_X_FORWARDED_FOR', '')\\n if ip == '' or ip.lower() in ('unkown', ):\\n ip = request.META.get('REMOTE_ADDR', '') # User is not on a proxy\\n if ip == '' or ip.lower() in ('unkown', ):\\n ip = request.META.get('HTTP_X_REAL_IP')\\n return ip\",\n \"def getManageIp(self):\\n dev = self.device()\\n if dev: return dev.getManageIp()\\n return \\\"\\\"\",\n \"def ip_address(self):\\n expression = expressions.WPA_IP\\n name = expressions.IP_ADDRESS_NAME\\n command = self.status_command\\n return self._match(expression,\\n name,\\n command)\",\n \"def get_ip(request):\\n ip1 = request.META.get('REMOTE_ADDR', '')\\n ip2 = request.META.get('HTTP_X_FORWARDED_FOR', '').split(\\\",\\\")[0].strip()\\n ip = ip1 or ip2 or '0.0.0.0'\\n return ip\",\n \"def external_IP(self):\\r\\n return self._external_ip\",\n \"def get_global_ip_ipv6():\\n network_info_providers = [\\n 'http://v6.ipv6-test.com/api/myip.php',\\n 'http://v6.ident.me/',\\n ]\\n random.shuffle(network_info_providers)\\n for url in network_info_providers:\\n try:\\n return requests.get(url).text.lstrip().rstrip()\\n except Exception:\\n continue\\n else:\\n log.info('cannot find global ipv6 ip')\\n return \\\"\\\"\",\n \"def _get_local_ip():\\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n try:\\n sock.connect(('10.255.255.255', 1))\\n ip = sock.getsockname()[0]\\n except Exception:\\n ip = '127.0.0.1'\\n finally:\\n sock.close()\\n\\n return ip\",\n \"def internalIP(self):\\r\\n return self._internalIP\",\n \"def get_host_ip_addr():\\n return nova_conf.my_ip\",\n \"def get_remote_ip(request):\\n \\n return utilities.get_remote_ip(request)\",\n \"def get_ip():\\n\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n try:\\n # doesn't even have to be reachable\\n s.connect(('10.255.255.255', 1))\\n return s.getsockname()[0]\\n except:\\n return '127.0.0.1'\\n finally:\\n s.close()\",\n \"def get_ip():\\n if not request.headers.getlist(\\\"X-Forwarded-For\\\"):\\n return str(request.remote_addr)\\n else:\\n return str(request.headers.getlist(\\\"X-Forwarded-For\\\")[0])\",\n \"def _LeasedIP(self):\\n check_command = 'ip addr show {interface} | grep \\\"inet \\\"'.format(\\n interface=self.interface)\\n try:\\n # grep exit with return code 0 when we have retrieved an IP.\\n out = self._device.CheckOutput(check_command)\\n except device_types.CalledProcessError:\\n return False\\n # ex: inet 192.168.159.78/20 brd 192.168.159.255 scope global wlan0\\n return out.split()[1].split('/')[0]\",\n \"def __lookup_public_ip(self):\\n\\n response = requests.get('https://api.ipify.org?format=json', timeout=self.timeout)\\n\\n if response.status_code == 200:\\n ip_data = response.json()\\n if 'ip' not in ip_data.keys():\\n return 'Unable to determine IP'\\n else:\\n return ip_data['ip']\\n else:\\n return 'Unable to determine IP'\",\n \"def _get_ip():\\n cmd_netstat = ['netstat', '-nr']\\n p1 = subprocess.Popen(cmd_netstat, stdout=subprocess.PIPE)\\n cmd_grep = ['grep', '^0\\\\.0\\\\.0\\\\.0']\\n p2 = subprocess.Popen(cmd_grep, stdin=p1.stdout, stdout=subprocess.PIPE)\\n cmd_awk = ['awk', '{ print $2 }']\\n p3 = subprocess.Popen(cmd_awk, stdin=p2.stdout, stdout=subprocess.PIPE)\\n galaxy_ip = p3.stdout.read()\\n log.debug('Host IP determined to be %s', galaxy_ip)\\n return galaxy_ip\",\n \"def get_ip():\\n return os.getenv(\\\"HOST_IP\\\", \\\"127.0.0.1\\\")\",\n \"def getIP():\\n try:\\n page = urlopen(\\\"http://www.whatismyip.com/automation/n09230945.asp\\\")\\n IP = page.read()\\n page.close()\\n return IP\\n except:\\n return \\\"Could not retrieve the IP address.\\\"\",\n \"def get_ip(request):\\n ip = request.META.get(\\\"HTTP_X_FORWARDED_FOR\\\", None)\\n if ip:\\n # X_FORWARDED_FOR returns client1, proxy1, proxy2,...\\n ip = ip.split(\\\", \\\")[0]\\n else:\\n ip = request.META.get(\\\"REMOTE_ADDR\\\", \\\"\\\")\\n return ip\",\n \"def get_ip(request):\\n x_forwarded_for = request.META.get('HTTP_X_FORWARDED_FOR')\\n if x_forwarded_for:\\n ip = x_forwarded_for.split(',')[0]\\n else:\\n ip = request.META.get('REMOTE_ADDR')\\n return ip\",\n \"def get_ips():\\r\\n local_ips = []\\r\\n public_ips = []\\r\\n \\r\\n # list of iface names, 'lo0', 'eth0', etc.\\r\\n for iface in netifaces.interfaces():\\r\\n # list of ipv4 addrinfo dicts\\r\\n ipv4s = netifaces.ifaddresses(iface).get(netifaces.AF_INET, [])\\r\\n for entry in ipv4s:\\r\\n addr = entry.get('addr')\\r\\n #print(\\\"addr: \\\" + addr)\\r\\n if not addr:\\r\\n continue\\r\\n if not (iface.startswith('lo') or addr.startswith('127.')):\\r\\n public_ips.append(addr)\\r\\n else:\\r\\n local_ips.append(addr) \\r\\n return public_ips\",\n \"def getip(self):\\n if configIpAddress == \\\"none\\\":\\n strngtoXmit = 'M-SEARCH * HTTP/1.1' + '\\\\r\\\\n' + \\\\\\n 'HOST: 239.255.255.250:1900' + '\\\\r\\\\n' + \\\\\\n 'MAN: \\\"ssdp:discover\\\"' + '\\\\r\\\\n' + \\\\\\n 'MX: 2' + '\\\\r\\\\n' + \\\\\\n 'ST: urn:schemas-upnp-org:device:MediaRenderer:1' + '\\\\r\\\\n' + '\\\\r\\\\n'\\n\\n bytestoXmit = strngtoXmit.encode()\\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n sock.settimeout(3)\\n gotstr = 'notyet'\\n found = False\\n ipaddress = None\\n sock.sendto(bytestoXmit, ('239.255.255.250', 1900))\\n try:\\n gotbytes, addressport = sock.recvfrom(512)\\n gotstr = gotbytes.decode()\\n except:\\n sock.sendto(bytestoXmit, ('239.255.255.250', 1900))\\n if re.search('LG', gotstr):\\n ipaddress, _ = addressport\\n found = True\\n self._state = STATE_PLAYING\\n else:\\n gotstr = 'notyet'\\n sock.close()\\n if not found:\\n print(\\\"LG TV not found\\\")\\n ipaddress = None\\n self._state = STATE_OFF\\n lgtv[\\\"ipaddress\\\"] = ipaddress\\n else:\\n lgtv[\\\"ipaddress\\\"] = configIpAddress\\n if self.isOnline():\\n self._state = STATE_PLAYING\\n else:\\n self._state = STATE_OFF\",\n \"def get_ip_address_filter(self):\\n return self.mycam.devicemgmt.GetIPAddressFilter()\",\n \"def internal_IP(self):\\r\\n return self._internal_ip\",\n \"def get_ip() -> str:\\n for ip in socket.gethostbyname_ex(socket.gethostname())[2]:\\n if not ip.startswith(\\\"127.\\\"):\\n return ip\\n for s in [socket.socket(socket.AF_INET, socket.SOCK_DGRAM)]:\\n s.connect((\\\"8.8.8.8\\\", 53))\\n ip, port = s.getsockname()\\n s.close()\\n if not ip.startswith(\\\"127.\\\"):\\n return ip\\n raise ConnectionError(\\\"Can not get a suitable IP\\\")\",\n \"def inetVisibleIP(self):\\n def handle(results):\\n ips = [ result[1][0] for result in results if result[0] ]\\n self.log.debug(\\\"other nodes think our ip is %s\\\" % str(ips))\\n return ips\\n\\n ds = []\\n for neighbor in self.bootstrappableNeighbors():\\n ds.append(self.protocol.stun(neighbor))\\n d = defer.gatherResults(ds)\\n d.addCallback(handle)\\n d.addErrback(self.onError)\\n return d\",\n \"def ip(self) -> str:\\n return self._ip\",\n \"def getnetwork(ipaddr):\\n return '192.168.1.0/24'\",\n \"def get_fw_ip_and_port(user, passw, logger, server_url):\\n ip, port = get_ip_and_port('firewall', get_paos(user, passw, server_url, logger))\\n return f\\\"{ip}:{port}\\\"\",\n \"def get_local_ip():\\n try:\\n ip_task = os.popen(\\\"ifconfig | grep -Eo 'inet (addr:)?(Adresse:)?([0-9]*\\\\.){3}[0-9]*' | grep -Eo '([0-9]*\\\\.){3}[0-9]*' | grep -v '127.0.0.1'\\\")\\n local_ip = ip_task.read().strip()\\n ip_task.close()\\n if '\\\\n' in local_ip:\\n local_ip = local_ip.split('\\\\n')[0]\\n print ' >> got local ip:', local_ip\\n return local_ip\\n except:\\n return '0.0.0.0'\",\n \"def get_local_ip(self):\\n # Get the local IP address used to communicate with the GNS3\\n # server. Not the GNS3 server's address, but rather the local\\n # machine's address that we use to send messages to the GNS3\\n # server. If that address isn't 127.0.0.1 (localhost), use it.\\n server_local_ip = self.server.get_local_ip()\\n if server_local_ip != '127.0.0.1':\\n return server_local_ip\\n else:\\n # Otherwise, find the first interface on the first cloud node (if it exists)\\n try:\\n first_cloud_node = next(node for node in self.nodes() if node['node_type'] == 'cloud')\\n interface = first_cloud_node['properties']['ports_mapping'][0]['interface']\\n\\n # If the interface is virtual, find and record its\\n # mate's first IP address, which is the address we can\\n # send to.\\n\\n ip_proc = subprocess.Popen(['ip', 'link', 'show', interface], stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)\\n first_field = ip_proc.stdout.read().decode().split()[1].split('@')\\n if first_field[0] == interface:\\n paired_interface = first_field[1].split(':')[0]\\n return ni.ifaddresses(paired_interface)[ni.AF_INET][0]['addr']\\n except (StopIteration, ValueError):\\n # StopIteration if there are no cloud nodes\\n # ValueError if there are no IP addresses on the paired interface\\n pass\\n\\n return None\",\n \"def get_remote_ip(request):\\n return request.META.get(\\\"HTTP_REMOTE_ADDR\\\", request.META.get(\\\"REMOTE_ADDR\\\", \\\"\\\"))\",\n \"def get_local_ip(self, system):\\n if system == \\\"Linux\\\":\\n # This is a bit ugly but it works\\n ips = check_output(['hostname', '--all-ip-addresses']).decode(\\\"utf-8\\\")\\n return ips.split(\\\" \\\")[0]\\n else:\\n return socket.gethostbyname(socket.gethostname())\",\n \"def local_ip():\\n sys_name = system()\\n if sys_name == 'Darwin':\\n # OSX\\n route = Command('route')\\n ifconfig = Command('ifconfig')\\n\\n iface = [\\n line.strip()\\n for line in route('-n', 'get', 'default')\\n if line.strip().startswith('interface')\\n ][0].split(':')[1].strip()\\n return [\\n line.strip()\\n for line in ifconfig(iface)\\n if line.strip().startswith('inet ')\\n ][0].split(' ')[1]\\n elif sys_name == 'Linux':\\n try:\\n ip = Command('ip')\\n iface = [\\n line.strip()\\n for line in ip('route')\\n if line.strip().startswith('default ')\\n ][0].split(' ')[4]\\n except CommandNotFound:\\n route = Command('route')\\n iface = [\\n line.strip()\\n for line in route('-n')\\n if line.startswith('0.0.0.0')\\n ][0].split(' ').pop()\\n\\n try:\\n # try with IP\\n ip = Command('ip')\\n return [\\n line.strip()\\n for line in ip('addr', 'show', iface)\\n if line.strip().startswith('inet ')\\n ][0].split(' ')[1].split('/')[0]\\n except CommandNotFound:\\n pass\\n\\n # fallback to ifconfig\\n ifconfig = Command('ifconfig')\\n return [\\n line.strip()\\n for line in ifconfig(iface)\\n if line.strip().startswith('inet ')\\n ][0].split(' ')[1]\\n\\n return None\",\n \"def wan_address(self):\\n if self._community.dispersy:\\n host = self._community.dispersy.wan_address[0]\\n\\n if host == \\\"0.0.0.0\\\":\\n host = self._community.dispersy.lan_address[0]\\n\\n else:\\n host = \\\"0.0.0.0\\\"\\n\\n _, port = self._socket.getsockname()\\n return (host, port)\",\n \"def get_public_ip(session, return_type=None, **kwargs):\\n path = '/api/settings/public_ips.json'\\n\\n return session.get_api(path=path, return_type=return_type, **kwargs)\",\n \"def server_ip(self) -> str:\\n return pulumi.get(self, \\\"server_ip\\\")\",\n \"def get_ip_address(self):\\n return self.__ip_address\",\n \"def address(self):\\n \\n return self.__ip\",\n \"def get_bot_ip(bot, update):\\n sender = update.message.from_user\\n if sender.id == Bot.OWNER_ID:\\n msg_to_send = \\\"\\\"\\n try:\\n ip_string = check_output([\\\"curl\\\", \\\"ipinfo.io/ip\\\"],\\n universal_newlines=True,\\n timeout=5)\\n msg_to_send = msgIpAddress + ip_string\\n except CalledProcessError:\\n msg_to_send = strings.errUnknown\\n except TimeoutExpired:\\n msg_to_send = strings.errTimeout\\n update.message.reply_text(msg_to_send)\",\n \"def get_user_ip(user, machine):\\n iraw = xexec(['fab', '-H', machine, 'user_ip:'+user])\\n uip = iraw.split('=>')[-1].strip()\\n return uip\",\n \"def get_akamai_ips_connection(self):\\n return self.m_connection.akamai_ips\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7699801","0.67521274","0.67227936","0.6681065","0.6656468","0.6654231","0.6638662","0.66331196","0.66173965","0.6582812","0.6568506","0.65680355","0.6561505","0.6549181","0.65410495","0.6517311","0.6516988","0.65079516","0.65009683","0.64814854","0.6435455","0.6424083","0.641246","0.640804","0.6404245","0.63830626","0.63740337","0.6359676","0.63553995","0.6341458","0.63354945","0.6331173","0.6328054","0.632116","0.63102025","0.62812376","0.6280812","0.62677145","0.62531924","0.62465376","0.62436503","0.62379086","0.6220265","0.62129337","0.6210052","0.62044156","0.62044156","0.62029696","0.6202384","0.619392","0.6192742","0.6191428","0.6189371","0.6185151","0.618059","0.6177132","0.6175252","0.61578536","0.61539894","0.61495966","0.6148825","0.6141112","0.61332816","0.6132275","0.61235327","0.6115991","0.61136025","0.6099054","0.6094435","0.60699624","0.6059678","0.6053166","0.6049029","0.60293055","0.6026797","0.6025524","0.60084057","0.6004299","0.5990078","0.59881496","0.5969277","0.59643763","0.59610134","0.5948817","0.5945123","0.5943572","0.5934192","0.59253573","0.5901065","0.5898248","0.58927006","0.58919793","0.58908254","0.5887864","0.5885732","0.58783096","0.58778375","0.58744234","0.5858952","0.5852931"],"string":"[\n \"0.7699801\",\n \"0.67521274\",\n \"0.67227936\",\n \"0.6681065\",\n \"0.6656468\",\n \"0.6654231\",\n \"0.6638662\",\n \"0.66331196\",\n \"0.66173965\",\n \"0.6582812\",\n \"0.6568506\",\n \"0.65680355\",\n \"0.6561505\",\n \"0.6549181\",\n \"0.65410495\",\n \"0.6517311\",\n \"0.6516988\",\n \"0.65079516\",\n \"0.65009683\",\n \"0.64814854\",\n \"0.6435455\",\n \"0.6424083\",\n \"0.641246\",\n \"0.640804\",\n \"0.6404245\",\n \"0.63830626\",\n \"0.63740337\",\n \"0.6359676\",\n \"0.63553995\",\n \"0.6341458\",\n \"0.63354945\",\n \"0.6331173\",\n \"0.6328054\",\n \"0.632116\",\n \"0.63102025\",\n \"0.62812376\",\n \"0.6280812\",\n \"0.62677145\",\n \"0.62531924\",\n \"0.62465376\",\n \"0.62436503\",\n \"0.62379086\",\n \"0.6220265\",\n \"0.62129337\",\n \"0.6210052\",\n \"0.62044156\",\n \"0.62044156\",\n \"0.62029696\",\n \"0.6202384\",\n \"0.619392\",\n \"0.6192742\",\n \"0.6191428\",\n \"0.6189371\",\n \"0.6185151\",\n \"0.618059\",\n \"0.6177132\",\n \"0.6175252\",\n \"0.61578536\",\n \"0.61539894\",\n \"0.61495966\",\n \"0.6148825\",\n \"0.6141112\",\n \"0.61332816\",\n \"0.6132275\",\n \"0.61235327\",\n \"0.6115991\",\n \"0.61136025\",\n \"0.6099054\",\n \"0.6094435\",\n \"0.60699624\",\n \"0.6059678\",\n \"0.6053166\",\n \"0.6049029\",\n \"0.60293055\",\n \"0.6026797\",\n \"0.6025524\",\n \"0.60084057\",\n \"0.6004299\",\n \"0.5990078\",\n \"0.59881496\",\n \"0.5969277\",\n \"0.59643763\",\n \"0.59610134\",\n \"0.5948817\",\n \"0.5945123\",\n \"0.5943572\",\n \"0.5934192\",\n \"0.59253573\",\n \"0.5901065\",\n \"0.5898248\",\n \"0.58927006\",\n \"0.58919793\",\n \"0.58908254\",\n \"0.5887864\",\n \"0.5885732\",\n \"0.58783096\",\n \"0.58778375\",\n \"0.58744234\",\n \"0.5858952\",\n \"0.5852931\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65245605"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":93885,"cells":{"query":{"kind":"string","value":"Returns the list of the users that are currently on the VLC/mmWave"},"document":{"kind":"string","value":"def get_vlc_users(wifi_list):\n vlc_users = [user for user in users if user[\"vlc_ip\"] not in wifi_list]\n return vlc_users"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_users(self):\n return self.mycam.devicemgmt.GetUsers()","def user_list(self, mapp, url_of_liveserver):\n return mapp.getjson(url_of_liveserver)['result'].keys()","def all_users():\n\treturn [unicode(name[:-4]).lower() for name in os.listdir(os.path.join(WORLD_DIR, 'players'))]","def get_users(self):\n # remove some user media fields that we can't submit back\n def clean_media(entry):\n entry.pop(\"mediaid\", None)\n entry.pop(\"userid\", None)\n entry.pop(\"description\", None)\n return entry\n zabbix_users = self.conn.user.get(selectMedias=\"extend\", selectUsrgrps=\"extend\")\n zabbix_users = {user[\"alias\"].lower(): User(\n id=user[\"userid\"],\n name=user[\"name\"],\n surname=user[\"surname\"],\n alias=user[\"alias\"],\n groups=set(g[\"usrgrpid\"] for g in user[\"usrgrps\"]),\n media=[clean_media(entry) for entry in user[\"medias\"]],\n ) for user in zabbix_users}\n return zabbix_users","def get_wifi_users():\n response = requests.get(url=f\"http://{init_param['ryu_ip']}:8080/\\\nstats/flow/{init_param['br-int_dpid']}\")\n rules = response.json()[init_param['br-int_dpid']]\n wifi_users = []\n for rule in rules:\n try:\n vlc_ip = rule[\"match\"][\"nw_dst\"]\n except KeyError:\n continue\n else:\n wifi_users.append(vlc_ip)\n return wifi_users","def users(self, site = None):\r\n uids = self.user_ids()\r\n if uids:\r\n users = Account._byID(uids, True, return_dict = False)\r\n return [self.ajax_user(u) for u in users]\r\n else:\r\n return ()","def get_usernames(self) -> list:\n db_list = list(self.cursor.execute('SELECT * FROM sqlite_master'))\n users = [db_list[i][1] for i in range(0, len(db_list), 2)]\n return users","def users(self):\n return self.get_data(\"users\")","def _users_list(self):\n result = self.slack.api_call(\"users.list\", presence=0)\n\n if not result.get(\"ok\"):\n logging.error(result['error'])\n return None\n\n return result['members']","def getUsers(self):\n return [u[0] for u in pwd.getpwall()\n if (u[5].startswith('/home/') and u[6].endswith('sh'))]","def current_users(self):\n return self.query('getstreamnum').get('stream_num')","def list_users(self):\n return self.get_admin(\"users\")","def get_all_users():","def view_list_users(self, user):\r\n return user.realm._users.keys()","def get_watched(self, username): # WORKS\n self.cur.execute(\"SELECT video_ID FROM watched WHERE username = \\\"{}\\\"\".format(username))\n watched_video_IDs = []\n for ID in self.cur.fetchall():\n watched_video_IDs.append(ID[0])\n return watched_video_IDs","def refresh_userlist(self):\n if self._userlist is not None:\n self._userlist.delete(0, Tix.END)\n if self._channel in self._frame.client.channels:\n ops = [ ]\n voices = [ ]\n users = [ ]\n l = self._frame.client.channels[self._channel].nicknames\n for name in l:\n ni = l[name]\n mode = l[name].mode\n if 'o' in mode:\n ops.append(\"@\" + ni.nickname)\n elif 'v' in mode:\n voices.append(\"+\" + ni.nickname)\n else:\n users.append(ni.nickname)\n l = (sorted(ops, key=unicode.lower)\n + sorted(voices, key=unicode.lower)\n + sorted(users, key=unicode.lower))\n for name in l:\n self._userlist.insert(Tix.END, name)\n self._userlistlabel.configure(\n text=\"%d Users, %d OPs\" % (len(l), len(ops)))","def user_list(self):\n self.cur.execute(\"SELECT username FROM users\")\n users = []\n for username in self.cur.fetchall():\n users.append(username[0])\n return users","def get_users(self):\r\n\t\tlogger.debug(\"Fetch users\")\r\n\t\t\r\n\t\treturn login.get_users()","def list_users():\n\ttry:\n\t\tusers_call = sc.api_call(\"users.list\")\n\t\tusers = []\n\t\tif users_call.get('ok'):\n\t\t\treturn users_call['members']\n\texcept:\n\t\tprint(\"users error\")\n\treturn None","def get_users():\n users = functions.users()\n return users","def list_users(self):\n raise NotImplementedError","def list_users(item):\n users = User.load_all_users(item)\n for user in users:\n print(user.username)","def getUsers(self) -> List[bbUser.bbUser]:\n return list(self.users.values())","def _get_users_list(self):\n return self.users['user_id'].tolist()","def get_connected_user():\n usernames = clients.keys()\n data = json.dumps(usernames)\n emit('on_client_list_received', data)","def users():\n retlist = []\n rawlist = cext.users()\n for item in rawlist:\n user, hostname, tstamp = item\n user = py2_strencode(user)\n nt = _common.suser(user, None, hostname, tstamp, None)\n retlist.append(nt)\n return retlist","def list_users(self, stream_name:str, version:int=1)->List[str]:\n stream_path = self._get_storage_path(stream_name=stream_name, version=version)\n all_users = self._ls_dir(stream_name=stream_name, version=version)\n user_ids = []\n for usr in all_users:\n user_ids.append(usr.replace(stream_path,\"\").replace(\"user=\",\"\").replace(\"study=\"+self.study_name, \"\"))\n return user_ids","def get_current_user_lists():\n return TaskList.get_user_lists(users.get_current_user())","def getConnectedUsers(self):\n\n\t\treturn self.connectedUsers","def get_current_users(self):\n active_sessions = Session.objects.filter(expire_date__gte=timezone.now())\n user_id_list = []\n for session in active_sessions:\n data = session.get_decoded()\n user_id_list.append(data.get('_auth_user_id', None))\n # Query all logged in users based on id list\n return self.filter(id__in=user_id_list)","def get_users(self):\n fields = ['name', ]\n return self.get_data(\"myUsers\", fields)","def getInterestedUsers():","def listusers():\n allusers = []\n with open('/etc/passwd', 'r') as pw:\n for l in pw.readlines():\n allusers.append(l.split(':')[0])\n users = [ d for d in os.listdir(\"/home\") if d in allusers ]\n return(users)","def users(self) -> List[str]:\n return self._users","def display_users(cls):\n return cls.user_list","def get_online_users(self):\n self.data = {}\n iq = self.xmpp.plugin['xep_0050'].send_command(\n config['domain'],\n ONLINE_USERS)\n sessionid = iq['command']['sessionid']\n\n form = self.xmpp.plugin['xep_0004'].make_form(ftype='submit')\n field = form.add_field(\n ftype='hidden',\n type='hidden',\n var='FORM_TYPE',\n value=ADMIN)\n field['type'] = 'hidden'\n form.add_field(var='max_items', value='100')\n\n result = self.xmpp.plugin['xep_0050'].send_command(\n config['domain'],\n ONLINE_USERS,\n sessionid=sessionid,\n payload=form)\n\n field = result['command']['form']['fields']['onlineuserjids']\n\n if not field['value']: # no user online, abort\n return\n\n for item in field['value'].split('\\n'):\n self.get_user_statistics(item)\n Gdk.threads_enter()\n for jid in self.data:\n data = self.data[jid]\n row = self.store.append(None, [\n jid, int(data['rostersize']),\n len(data['onlineresources']),\n '', '', False\n ])\n resources = self.data[jid]['onlineresources']\n for index, resource in enumerate(resources):\n self.store.append(row,\n [\n '%s/%s' % (jid, resource),\n int(data['rostersize']),\n len(data['onlineresources']),\n data['ipaddresses'][index][1],\n data['ipaddresses'][index][0],\n False\n ]\n )\n countries = []\n for addr in data['ipaddresses']:\n countries.append(addr[1])\n countries = Counter(countries)\n self.store.set_value(row, 3, max(countries.items(), key=lambda tup: tup[1])[0])\n Gdk.threads_leave()","def list_users(self):\n _url = \"http://\" + self.host_ip + \":35357/v2.0/users\"\n _body = None\n _headers = {'Content-type': 'application/json',\n 'x-auth-token': self.cloud_admin_info['token_project']}\n\n response = self.request(\"GET\", _url, _headers, _body)\n if response is None:\n LOG_OBJ.error(\" no response from Server\")\n return response\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\n \"get user list Failed with status %s \" %\n response.status)\n return response.status\n\n output = json.loads(response.data)\n LOG_OBJ.info(\"users List : %s\")\n return output[\"users\"]","def listUsers(self):\n return self._client.listUsers()","def get_active_users(text_channel) -> List[discord.Member]:\n\n active_users = []\n for m in text_channel.members:\n if m.status.name in [\"online\", \"dnd\"] and m.bot == False:\n active_users.append(m)\n\n return active_users","def _list_users(self):\n users = fileIO.load_json(\"users.json\")\n print(\"The list of users is as follows:\")\n for i in users:\n print(users[i][\"name\"])\n self._list_user_settings(users)","def user_list(server_object, client, address, command_args):\n\n\tmsg = \"\"\n\n\t#: Create a formatted string of all the users.\n\tfor usr in server_object.usrs.values():\n\t\tmsg += usr + '\\n'\n\n\tclient.send(msg.encode())","def get_users_list(self, session):\n\n users = session.query(User.chat_id).filter(User.is_admin==False).all()\n return users","def get_user_info_list(self):\n\t\treturn Job(SDK.PrlSrv_GetUserInfoList(self.handle)[0])","def get_users(self):\n cmd = \"\"\" SELECT %s FROM %s; \"\"\" %(COL_WALLETS_NAME,\n TABLE_WALLETS)\n self.__dbcursor.execute(cmd)\n return [row[0] for row in self.__dbcursor.fetchall()]","def user_list(ctx):\n data = ctx.obj.get_all_users()\n output_json_data(data)","def users(self):\n return self._users","def lookup_users(self):\n return self.slack_users","def get_users(msg: telebot.types.Message):\n users = User.select()\n m = ''\n for user in users:\n menu_caption = \"In PVP game\" if user.state == states.USER_IN_PVP_GAME else \"In AI game\" if user.state == states.USER_IN_AI_GAME else \"In menu\"\n m += f'[{user.first_name}](tg://user?id={user.user_id}) - {menu_caption}\\n'\n\n bot.send_message(\n msg.from_user.id,\n m,\n parse_mode='Markdown'\n )","def get_users_list_full(self, session):\n\n users = session.query(\n User.chat_id,\n User.is_banned,\n User.username,\n User.first_name,\n User.last_name,\n User.time_registered\n ).filter(User.is_admin==False).all()\n return users","def get_user_list():\n users_tuple = db_session.query(Chat.chatID).all()\n users_list = [user for user, in users_tuple]\n return users_list","def getAllUsers(self):\r\n return [(ind, user) for ind, user in enumerate(self.users)]","def get_blocked_usernames_list():\n return []","def user_ids(self):\n return list(self.get_users())","def show_users(self):\n\n u = User(self.settings)\n users_list = u.find_users()\n\n # transform the results in a \"jsonifiable\"-form\n json_results = []\n for user in users_list:\n json_results.append(user.to_json())\n\n # return\n return json_results","def list_users_in_pool():\n files = []\n USERS_DIR = os.path.join(UPLOAD_DIRECTORY, \"users\")\n for filename in os.listdir(USERS_DIR):\n path = os.path.join(USERS_DIR, filename)\n if os.path.isdir(path):\n files.append(filename)\n return jsonify(files)","def users(message):\n user_list = []\n for userid, user in iteritems(message._client.users):\n user_list.append(user[\"name\"])\n message.reply(Strings['USERS_FOUND'].format(len(user_list)))","def get_users(self):\n users = []\n page = 1\n while not len(users) % 100:\n users += self._get('/users?{0}'.format(urllib.urlencode({'per_page': 100, 'page': page})))\n if not users:\n break\n page += 1\n return users","def local_user_ids(steam):\n if steam is None:\n return None\n # The userdata directory, at the top level, just contains a single\n # subdirectory for every user which has logged into this system (and\n # therefore that Steam has data for)\n return os.listdir(steam.userdata_directory)","def online_users(room):\n threshold = datetime.now() - timedelta(seconds=10)\n authorizations = models.Authorization.gql(\"WHERE room = :room AND last_checked_in >= :threshold\", room=room, threshold=threshold).fetch(1000)\n return [x.user for x in authorizations]","def users(self):\n # TODO:\n # Ideally, this should have returned users as it advertises. Instead\n # it returns user IDs. We're introducing the user_ids property below\n # to take the place of this property prior to switching this it\n # over to fixing it so clients from this point forward have a property\n # that returns value consistent with its name. Plan to do a scan of\n # existing systems before switching this over so we don't break things.\n return self.get_users()","def get_user_list():\n\tudb = UserPageDB()\n\ttry:\n\t\tusers = udb.user_list()\n\t\treturn UserList([_transform_user(u) for u in users])\n\tfinally:\n\t\tudb.close()","def current_user_playlists(self, limit: int = 20, offset: int = 0):\n return self._get('me/playlists', limit=limit, offset=offset)","def list_users(BrokerId=None, MaxResults=None, NextToken=None):\n pass","def get_users():\n return db.fetch_users()","def listUsers(self):\n return tuple(User.create({'name':name},self._modelDataManager) for name in self.pm_getUserManager().listUsers())","def get_users(self):\n return self.get_all_dbusers()","def users():\n access_token = session['access_token']\n return \"%s\" % list_users(access_token)","async def list_users(self) -> List[int]:\n return [\n # row[0]\n # async for row in self.conn.execute(\n # \"select userid from tg_users\",\n # )\n ]","def get_users(users_list):\n formatted_list = \",\".join(users_list)\n built_get = \"%s&steamids=%s\" % (GETPLAYERSUMM_URL, formatted_list)\n try:\n req = requests.get(built_get)\n if req.status_code == 200:\n response = json.loads(req.text)[\"response\"][\"players\"]\n players = {}\n for player in response:\n players[player[\"steamid\"]] = player\n return players\n except requests.exceptions.RequestException as req_exc:\n print(req_exc)\n\n return []","def _list(room_name):\n members = redis.smembers(room_name)\n \n if str(members) == 'set()':\n text = '```Users in list: none```'\n return text\n\n text = 'Users in list: %s ' % ','.join(members)\n \n return text","def users(self):\n from sagas.ofbiz.entities import OfEntity as e, oc\n rs=e().allUserLogin()\n for r in rs:\n print(r['userLoginId'])","def fetch_users(self):\n users = super(type(self), self).fetch_users()\n return list(filter(self._check_active, users))","def _get_userlist_by_userright(self, userright):\n params = {\n \"action\": \"query\",\n \"list\": \"allusers\",\n \"format\": \"json\",\n \"augroup\": userright,\n \"aulimit\": \"500\",\n }\n r = self.session.get(ENWIKI_API, params=params)\n data = r.json()\n return [u[\"name\"] for u in data[\"query\"][\"allusers\"]]","def get_users():\n table_response = USER_FAVORITES_TABLE.scan()\n return table_response['Items']","def users(message):\n message.reply(Strings['USERS_FOUND'].format(len(hf.get_users())))","def get_user_list(self, connection):\n http = get_web_service(connection)\n try:\n req = http.request('GET', connection[\"url\"] + '/users/?_format=json')\n data = json.loads(req.data.decode('utf-8'))\n # print(json.dumps(data, indent=4, sort_keys=True))\n return data\n except urllib3.exceptions.HTTPError as e:\n print(\"Connection error\")\n print(e)","def show_users():\n users_list = []\n all_users = storage.all('User')\n for obj in all_users.values():\n users_list.append(obj.to_dict())\n return jsonify(users_list)","def get_users_for(self, server, channame):\n skey = server.lower()\n ckey = irc.strings.lower(channame)\n users = []\n if skey in self.serverchans and ckey in self.serverchans[skey]:\n users = self.serverchans[skey][ckey].users.keys()\n return users","def _load_users(self) -> List[Dict]:\n try:\n api_call = self.web_client.api_call('users.list')\n if api_call.get('ok'):\n return api_call.get('members')\n except Exception:\n LOGGER.exception('Cannot get users')\n raise","def getResponsibleUsers():","def list_users(self, user=None):\n from expfactory.database.models import Participant\n\n participants = Participant.query.all()\n users = []\n for user in participants:\n users.append(self.print_user(user))\n return users","def list_users(self, instance, limit=None, marker=None):\n return instance.list_users(limit=limit, marker=marker)","def get_watchlists(user_id):\n # user = User.query.get(user_id)\n\n watchlists = Watchlist.query.filter(Watchlist.user_id == user_id).all()\n\n return watchlists","def list( self, mess, args):\n user = self.get_sender_username(mess)\n args = args.replace(' ', '_')\n if user in self.users:\n user_list = 'All these users are subscribed - \\n'\n user_list += '\\n'.join(['%s :: %s' %(u, self.users[u]) for u in sorted(self.users)])\n if self.invited.keys():\n user_list += '\\n The following users are invited - \\n'\n user_list += '\\n'.join(self.invited.keys())\n self.log.info( '%s checks list of users.' % user)\n return user_list","def get_users(self):\n return self.execute(TABELLE['users']['select']['all'])","def get_user_list(self):\n self.user_list = db.get_user_list()\n for each in self.user_list:\n print each[1] # username\n while(True):\n selection = raw_input(\"Enter username to use\")\n if selection in self.user_list:\n return selection","def get_users(self):\n return get_users(self['__store'].db, self)","def users(self) -> pulumi.Output[Sequence['outputs.BrokerUser']]:\n return pulumi.get(self, \"users\")","def getUsersData(self):\n users_data = self.get_api_results(\n \"/api/user/list?fields=id,username,name,phone,email,url,about,role,joined,lastactive,avatar,company,position,location&api_key={0}&format=json\")\n for user in users_data:\n if user[\"name\"] not in self.users:\n self.add_user(user)\n else:\n self.merge_user(user)\n return users_data","def list_users():\n check_admin()\n results = User.query.order_by(-User.id)\n return render_template('user_list.html', users=results)","def list_playlists(self, user=None):\n if user:\n playlists = self.sp.user_playlists(user)['items']\n else:\n playlists = self.sp.user_playlists(self.user)['items']\n pl_names = [pl['name'] for pl in playlists]\n pl_id = [pl['id'] for pl in playlists]\n pl_own = [pl['owner']['id'] for pl in playlists]\n return pl_names, pl_id, pl_own","def getUsers(self):\n logger.debug(\"Func: getUsers\")\n\n return sorted(self._usersDict.keys())","def user_list():\n users = User.objects.all()\n return {\"users\": users}","def get_all_users():\n token = request.headers.get('token')\n\n # Token Validation\n token_valid, response = is_token_valid(token)\n if not token_valid:\n return response\n token_username = response\n\n # Privilege handling\n if token_username != 'admin':\n return jsonify({'message': \"You aren't allowed to access this\"}), 404\n\n return jsonify(list(Users.values())), 200","def getLoadedUsersByVoIPUsername(self,voip_usernames):\n loaded_users=map(user_main.getUserPool().getUserByVoIPUsername,voip_usernames)\n return loaded_users","def list_user(self, username):\n return self.get_admin(u\"users/{}\".format(username))","def getMyUsers(self):\n my_users = None\n if self.current_user.is_superuser or self.current_user.has_perm(\"manage_input_templates\"):\n my_users = UserCompany.objects.filter(\n company__pk=self.client_session.companyId).all()\n else:\n my_users = UserCompany.objects.filter(pk=self.client_session.userCompanyId).all()\n\n ids = []\n if my_users.count() > 0:\n ids = [str(d.id) for d in my_users]\n return \",\".join(ids)","def user_list():\n if session['user_admin'] == False:\n abort(403)\n\n # Retrieve all Users\n sqa_sess = sqa_session()\n users = sqa_sess.query(User).all()\n\n return render_template('admin/user_list.html', users=users)","def get_existing_users():\n # also check PublicKeys a user with no servers/networks exist\n from synnefo.userdata.models import PublicKeyPair\n from synnefo.db.models import VirtualMachine, Network\n\n keypairusernames = PublicKeyPair.objects.filter().values_list('user',\n flat=True)\n serverusernames = VirtualMachine.objects.filter().values_list('userid',\n flat=True)\n networkusernames = Network.objects.filter().values_list('userid',\n flat=True)\n\n return set(list(keypairusernames) + list(serverusernames) +\n list(networkusernames))","def get_users_admins_list(self, session):\n\n users = session.query(User.chat_id).all()\n return users"],"string":"[\n \"def get_users(self):\\n return self.mycam.devicemgmt.GetUsers()\",\n \"def user_list(self, mapp, url_of_liveserver):\\n return mapp.getjson(url_of_liveserver)['result'].keys()\",\n \"def all_users():\\n\\treturn [unicode(name[:-4]).lower() for name in os.listdir(os.path.join(WORLD_DIR, 'players'))]\",\n \"def get_users(self):\\n # remove some user media fields that we can't submit back\\n def clean_media(entry):\\n entry.pop(\\\"mediaid\\\", None)\\n entry.pop(\\\"userid\\\", None)\\n entry.pop(\\\"description\\\", None)\\n return entry\\n zabbix_users = self.conn.user.get(selectMedias=\\\"extend\\\", selectUsrgrps=\\\"extend\\\")\\n zabbix_users = {user[\\\"alias\\\"].lower(): User(\\n id=user[\\\"userid\\\"],\\n name=user[\\\"name\\\"],\\n surname=user[\\\"surname\\\"],\\n alias=user[\\\"alias\\\"],\\n groups=set(g[\\\"usrgrpid\\\"] for g in user[\\\"usrgrps\\\"]),\\n media=[clean_media(entry) for entry in user[\\\"medias\\\"]],\\n ) for user in zabbix_users}\\n return zabbix_users\",\n \"def get_wifi_users():\\n response = requests.get(url=f\\\"http://{init_param['ryu_ip']}:8080/\\\\\\nstats/flow/{init_param['br-int_dpid']}\\\")\\n rules = response.json()[init_param['br-int_dpid']]\\n wifi_users = []\\n for rule in rules:\\n try:\\n vlc_ip = rule[\\\"match\\\"][\\\"nw_dst\\\"]\\n except KeyError:\\n continue\\n else:\\n wifi_users.append(vlc_ip)\\n return wifi_users\",\n \"def users(self, site = None):\\r\\n uids = self.user_ids()\\r\\n if uids:\\r\\n users = Account._byID(uids, True, return_dict = False)\\r\\n return [self.ajax_user(u) for u in users]\\r\\n else:\\r\\n return ()\",\n \"def get_usernames(self) -> list:\\n db_list = list(self.cursor.execute('SELECT * FROM sqlite_master'))\\n users = [db_list[i][1] for i in range(0, len(db_list), 2)]\\n return users\",\n \"def users(self):\\n return self.get_data(\\\"users\\\")\",\n \"def _users_list(self):\\n result = self.slack.api_call(\\\"users.list\\\", presence=0)\\n\\n if not result.get(\\\"ok\\\"):\\n logging.error(result['error'])\\n return None\\n\\n return result['members']\",\n \"def getUsers(self):\\n return [u[0] for u in pwd.getpwall()\\n if (u[5].startswith('/home/') and u[6].endswith('sh'))]\",\n \"def current_users(self):\\n return self.query('getstreamnum').get('stream_num')\",\n \"def list_users(self):\\n return self.get_admin(\\\"users\\\")\",\n \"def get_all_users():\",\n \"def view_list_users(self, user):\\r\\n return user.realm._users.keys()\",\n \"def get_watched(self, username): # WORKS\\n self.cur.execute(\\\"SELECT video_ID FROM watched WHERE username = \\\\\\\"{}\\\\\\\"\\\".format(username))\\n watched_video_IDs = []\\n for ID in self.cur.fetchall():\\n watched_video_IDs.append(ID[0])\\n return watched_video_IDs\",\n \"def refresh_userlist(self):\\n if self._userlist is not None:\\n self._userlist.delete(0, Tix.END)\\n if self._channel in self._frame.client.channels:\\n ops = [ ]\\n voices = [ ]\\n users = [ ]\\n l = self._frame.client.channels[self._channel].nicknames\\n for name in l:\\n ni = l[name]\\n mode = l[name].mode\\n if 'o' in mode:\\n ops.append(\\\"@\\\" + ni.nickname)\\n elif 'v' in mode:\\n voices.append(\\\"+\\\" + ni.nickname)\\n else:\\n users.append(ni.nickname)\\n l = (sorted(ops, key=unicode.lower)\\n + sorted(voices, key=unicode.lower)\\n + sorted(users, key=unicode.lower))\\n for name in l:\\n self._userlist.insert(Tix.END, name)\\n self._userlistlabel.configure(\\n text=\\\"%d Users, %d OPs\\\" % (len(l), len(ops)))\",\n \"def user_list(self):\\n self.cur.execute(\\\"SELECT username FROM users\\\")\\n users = []\\n for username in self.cur.fetchall():\\n users.append(username[0])\\n return users\",\n \"def get_users(self):\\r\\n\\t\\tlogger.debug(\\\"Fetch users\\\")\\r\\n\\t\\t\\r\\n\\t\\treturn login.get_users()\",\n \"def list_users():\\n\\ttry:\\n\\t\\tusers_call = sc.api_call(\\\"users.list\\\")\\n\\t\\tusers = []\\n\\t\\tif users_call.get('ok'):\\n\\t\\t\\treturn users_call['members']\\n\\texcept:\\n\\t\\tprint(\\\"users error\\\")\\n\\treturn None\",\n \"def get_users():\\n users = functions.users()\\n return users\",\n \"def list_users(self):\\n raise NotImplementedError\",\n \"def list_users(item):\\n users = User.load_all_users(item)\\n for user in users:\\n print(user.username)\",\n \"def getUsers(self) -> List[bbUser.bbUser]:\\n return list(self.users.values())\",\n \"def _get_users_list(self):\\n return self.users['user_id'].tolist()\",\n \"def get_connected_user():\\n usernames = clients.keys()\\n data = json.dumps(usernames)\\n emit('on_client_list_received', data)\",\n \"def users():\\n retlist = []\\n rawlist = cext.users()\\n for item in rawlist:\\n user, hostname, tstamp = item\\n user = py2_strencode(user)\\n nt = _common.suser(user, None, hostname, tstamp, None)\\n retlist.append(nt)\\n return retlist\",\n \"def list_users(self, stream_name:str, version:int=1)->List[str]:\\n stream_path = self._get_storage_path(stream_name=stream_name, version=version)\\n all_users = self._ls_dir(stream_name=stream_name, version=version)\\n user_ids = []\\n for usr in all_users:\\n user_ids.append(usr.replace(stream_path,\\\"\\\").replace(\\\"user=\\\",\\\"\\\").replace(\\\"study=\\\"+self.study_name, \\\"\\\"))\\n return user_ids\",\n \"def get_current_user_lists():\\n return TaskList.get_user_lists(users.get_current_user())\",\n \"def getConnectedUsers(self):\\n\\n\\t\\treturn self.connectedUsers\",\n \"def get_current_users(self):\\n active_sessions = Session.objects.filter(expire_date__gte=timezone.now())\\n user_id_list = []\\n for session in active_sessions:\\n data = session.get_decoded()\\n user_id_list.append(data.get('_auth_user_id', None))\\n # Query all logged in users based on id list\\n return self.filter(id__in=user_id_list)\",\n \"def get_users(self):\\n fields = ['name', ]\\n return self.get_data(\\\"myUsers\\\", fields)\",\n \"def getInterestedUsers():\",\n \"def listusers():\\n allusers = []\\n with open('/etc/passwd', 'r') as pw:\\n for l in pw.readlines():\\n allusers.append(l.split(':')[0])\\n users = [ d for d in os.listdir(\\\"/home\\\") if d in allusers ]\\n return(users)\",\n \"def users(self) -> List[str]:\\n return self._users\",\n \"def display_users(cls):\\n return cls.user_list\",\n \"def get_online_users(self):\\n self.data = {}\\n iq = self.xmpp.plugin['xep_0050'].send_command(\\n config['domain'],\\n ONLINE_USERS)\\n sessionid = iq['command']['sessionid']\\n\\n form = self.xmpp.plugin['xep_0004'].make_form(ftype='submit')\\n field = form.add_field(\\n ftype='hidden',\\n type='hidden',\\n var='FORM_TYPE',\\n value=ADMIN)\\n field['type'] = 'hidden'\\n form.add_field(var='max_items', value='100')\\n\\n result = self.xmpp.plugin['xep_0050'].send_command(\\n config['domain'],\\n ONLINE_USERS,\\n sessionid=sessionid,\\n payload=form)\\n\\n field = result['command']['form']['fields']['onlineuserjids']\\n\\n if not field['value']: # no user online, abort\\n return\\n\\n for item in field['value'].split('\\\\n'):\\n self.get_user_statistics(item)\\n Gdk.threads_enter()\\n for jid in self.data:\\n data = self.data[jid]\\n row = self.store.append(None, [\\n jid, int(data['rostersize']),\\n len(data['onlineresources']),\\n '', '', False\\n ])\\n resources = self.data[jid]['onlineresources']\\n for index, resource in enumerate(resources):\\n self.store.append(row,\\n [\\n '%s/%s' % (jid, resource),\\n int(data['rostersize']),\\n len(data['onlineresources']),\\n data['ipaddresses'][index][1],\\n data['ipaddresses'][index][0],\\n False\\n ]\\n )\\n countries = []\\n for addr in data['ipaddresses']:\\n countries.append(addr[1])\\n countries = Counter(countries)\\n self.store.set_value(row, 3, max(countries.items(), key=lambda tup: tup[1])[0])\\n Gdk.threads_leave()\",\n \"def list_users(self):\\n _url = \\\"http://\\\" + self.host_ip + \\\":35357/v2.0/users\\\"\\n _body = None\\n _headers = {'Content-type': 'application/json',\\n 'x-auth-token': self.cloud_admin_info['token_project']}\\n\\n response = self.request(\\\"GET\\\", _url, _headers, _body)\\n if response is None:\\n LOG_OBJ.error(\\\" no response from Server\\\")\\n return response\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\n \\\"get user list Failed with status %s \\\" %\\n response.status)\\n return response.status\\n\\n output = json.loads(response.data)\\n LOG_OBJ.info(\\\"users List : %s\\\")\\n return output[\\\"users\\\"]\",\n \"def listUsers(self):\\n return self._client.listUsers()\",\n \"def get_active_users(text_channel) -> List[discord.Member]:\\n\\n active_users = []\\n for m in text_channel.members:\\n if m.status.name in [\\\"online\\\", \\\"dnd\\\"] and m.bot == False:\\n active_users.append(m)\\n\\n return active_users\",\n \"def _list_users(self):\\n users = fileIO.load_json(\\\"users.json\\\")\\n print(\\\"The list of users is as follows:\\\")\\n for i in users:\\n print(users[i][\\\"name\\\"])\\n self._list_user_settings(users)\",\n \"def user_list(server_object, client, address, command_args):\\n\\n\\tmsg = \\\"\\\"\\n\\n\\t#: Create a formatted string of all the users.\\n\\tfor usr in server_object.usrs.values():\\n\\t\\tmsg += usr + '\\\\n'\\n\\n\\tclient.send(msg.encode())\",\n \"def get_users_list(self, session):\\n\\n users = session.query(User.chat_id).filter(User.is_admin==False).all()\\n return users\",\n \"def get_user_info_list(self):\\n\\t\\treturn Job(SDK.PrlSrv_GetUserInfoList(self.handle)[0])\",\n \"def get_users(self):\\n cmd = \\\"\\\"\\\" SELECT %s FROM %s; \\\"\\\"\\\" %(COL_WALLETS_NAME,\\n TABLE_WALLETS)\\n self.__dbcursor.execute(cmd)\\n return [row[0] for row in self.__dbcursor.fetchall()]\",\n \"def user_list(ctx):\\n data = ctx.obj.get_all_users()\\n output_json_data(data)\",\n \"def users(self):\\n return self._users\",\n \"def lookup_users(self):\\n return self.slack_users\",\n \"def get_users(msg: telebot.types.Message):\\n users = User.select()\\n m = ''\\n for user in users:\\n menu_caption = \\\"In PVP game\\\" if user.state == states.USER_IN_PVP_GAME else \\\"In AI game\\\" if user.state == states.USER_IN_AI_GAME else \\\"In menu\\\"\\n m += f'[{user.first_name}](tg://user?id={user.user_id}) - {menu_caption}\\\\n'\\n\\n bot.send_message(\\n msg.from_user.id,\\n m,\\n parse_mode='Markdown'\\n )\",\n \"def get_users_list_full(self, session):\\n\\n users = session.query(\\n User.chat_id,\\n User.is_banned,\\n User.username,\\n User.first_name,\\n User.last_name,\\n User.time_registered\\n ).filter(User.is_admin==False).all()\\n return users\",\n \"def get_user_list():\\n users_tuple = db_session.query(Chat.chatID).all()\\n users_list = [user for user, in users_tuple]\\n return users_list\",\n \"def getAllUsers(self):\\r\\n return [(ind, user) for ind, user in enumerate(self.users)]\",\n \"def get_blocked_usernames_list():\\n return []\",\n \"def user_ids(self):\\n return list(self.get_users())\",\n \"def show_users(self):\\n\\n u = User(self.settings)\\n users_list = u.find_users()\\n\\n # transform the results in a \\\"jsonifiable\\\"-form\\n json_results = []\\n for user in users_list:\\n json_results.append(user.to_json())\\n\\n # return\\n return json_results\",\n \"def list_users_in_pool():\\n files = []\\n USERS_DIR = os.path.join(UPLOAD_DIRECTORY, \\\"users\\\")\\n for filename in os.listdir(USERS_DIR):\\n path = os.path.join(USERS_DIR, filename)\\n if os.path.isdir(path):\\n files.append(filename)\\n return jsonify(files)\",\n \"def users(message):\\n user_list = []\\n for userid, user in iteritems(message._client.users):\\n user_list.append(user[\\\"name\\\"])\\n message.reply(Strings['USERS_FOUND'].format(len(user_list)))\",\n \"def get_users(self):\\n users = []\\n page = 1\\n while not len(users) % 100:\\n users += self._get('/users?{0}'.format(urllib.urlencode({'per_page': 100, 'page': page})))\\n if not users:\\n break\\n page += 1\\n return users\",\n \"def local_user_ids(steam):\\n if steam is None:\\n return None\\n # The userdata directory, at the top level, just contains a single\\n # subdirectory for every user which has logged into this system (and\\n # therefore that Steam has data for)\\n return os.listdir(steam.userdata_directory)\",\n \"def online_users(room):\\n threshold = datetime.now() - timedelta(seconds=10)\\n authorizations = models.Authorization.gql(\\\"WHERE room = :room AND last_checked_in >= :threshold\\\", room=room, threshold=threshold).fetch(1000)\\n return [x.user for x in authorizations]\",\n \"def users(self):\\n # TODO:\\n # Ideally, this should have returned users as it advertises. Instead\\n # it returns user IDs. We're introducing the user_ids property below\\n # to take the place of this property prior to switching this it\\n # over to fixing it so clients from this point forward have a property\\n # that returns value consistent with its name. Plan to do a scan of\\n # existing systems before switching this over so we don't break things.\\n return self.get_users()\",\n \"def get_user_list():\\n\\tudb = UserPageDB()\\n\\ttry:\\n\\t\\tusers = udb.user_list()\\n\\t\\treturn UserList([_transform_user(u) for u in users])\\n\\tfinally:\\n\\t\\tudb.close()\",\n \"def current_user_playlists(self, limit: int = 20, offset: int = 0):\\n return self._get('me/playlists', limit=limit, offset=offset)\",\n \"def list_users(BrokerId=None, MaxResults=None, NextToken=None):\\n pass\",\n \"def get_users():\\n return db.fetch_users()\",\n \"def listUsers(self):\\n return tuple(User.create({'name':name},self._modelDataManager) for name in self.pm_getUserManager().listUsers())\",\n \"def get_users(self):\\n return self.get_all_dbusers()\",\n \"def users():\\n access_token = session['access_token']\\n return \\\"%s\\\" % list_users(access_token)\",\n \"async def list_users(self) -> List[int]:\\n return [\\n # row[0]\\n # async for row in self.conn.execute(\\n # \\\"select userid from tg_users\\\",\\n # )\\n ]\",\n \"def get_users(users_list):\\n formatted_list = \\\",\\\".join(users_list)\\n built_get = \\\"%s&steamids=%s\\\" % (GETPLAYERSUMM_URL, formatted_list)\\n try:\\n req = requests.get(built_get)\\n if req.status_code == 200:\\n response = json.loads(req.text)[\\\"response\\\"][\\\"players\\\"]\\n players = {}\\n for player in response:\\n players[player[\\\"steamid\\\"]] = player\\n return players\\n except requests.exceptions.RequestException as req_exc:\\n print(req_exc)\\n\\n return []\",\n \"def _list(room_name):\\n members = redis.smembers(room_name)\\n \\n if str(members) == 'set()':\\n text = '```Users in list: none```'\\n return text\\n\\n text = 'Users in list: %s ' % ','.join(members)\\n \\n return text\",\n \"def users(self):\\n from sagas.ofbiz.entities import OfEntity as e, oc\\n rs=e().allUserLogin()\\n for r in rs:\\n print(r['userLoginId'])\",\n \"def fetch_users(self):\\n users = super(type(self), self).fetch_users()\\n return list(filter(self._check_active, users))\",\n \"def _get_userlist_by_userright(self, userright):\\n params = {\\n \\\"action\\\": \\\"query\\\",\\n \\\"list\\\": \\\"allusers\\\",\\n \\\"format\\\": \\\"json\\\",\\n \\\"augroup\\\": userright,\\n \\\"aulimit\\\": \\\"500\\\",\\n }\\n r = self.session.get(ENWIKI_API, params=params)\\n data = r.json()\\n return [u[\\\"name\\\"] for u in data[\\\"query\\\"][\\\"allusers\\\"]]\",\n \"def get_users():\\n table_response = USER_FAVORITES_TABLE.scan()\\n return table_response['Items']\",\n \"def users(message):\\n message.reply(Strings['USERS_FOUND'].format(len(hf.get_users())))\",\n \"def get_user_list(self, connection):\\n http = get_web_service(connection)\\n try:\\n req = http.request('GET', connection[\\\"url\\\"] + '/users/?_format=json')\\n data = json.loads(req.data.decode('utf-8'))\\n # print(json.dumps(data, indent=4, sort_keys=True))\\n return data\\n except urllib3.exceptions.HTTPError as e:\\n print(\\\"Connection error\\\")\\n print(e)\",\n \"def show_users():\\n users_list = []\\n all_users = storage.all('User')\\n for obj in all_users.values():\\n users_list.append(obj.to_dict())\\n return jsonify(users_list)\",\n \"def get_users_for(self, server, channame):\\n skey = server.lower()\\n ckey = irc.strings.lower(channame)\\n users = []\\n if skey in self.serverchans and ckey in self.serverchans[skey]:\\n users = self.serverchans[skey][ckey].users.keys()\\n return users\",\n \"def _load_users(self) -> List[Dict]:\\n try:\\n api_call = self.web_client.api_call('users.list')\\n if api_call.get('ok'):\\n return api_call.get('members')\\n except Exception:\\n LOGGER.exception('Cannot get users')\\n raise\",\n \"def getResponsibleUsers():\",\n \"def list_users(self, user=None):\\n from expfactory.database.models import Participant\\n\\n participants = Participant.query.all()\\n users = []\\n for user in participants:\\n users.append(self.print_user(user))\\n return users\",\n \"def list_users(self, instance, limit=None, marker=None):\\n return instance.list_users(limit=limit, marker=marker)\",\n \"def get_watchlists(user_id):\\n # user = User.query.get(user_id)\\n\\n watchlists = Watchlist.query.filter(Watchlist.user_id == user_id).all()\\n\\n return watchlists\",\n \"def list( self, mess, args):\\n user = self.get_sender_username(mess)\\n args = args.replace(' ', '_')\\n if user in self.users:\\n user_list = 'All these users are subscribed - \\\\n'\\n user_list += '\\\\n'.join(['%s :: %s' %(u, self.users[u]) for u in sorted(self.users)])\\n if self.invited.keys():\\n user_list += '\\\\n The following users are invited - \\\\n'\\n user_list += '\\\\n'.join(self.invited.keys())\\n self.log.info( '%s checks list of users.' % user)\\n return user_list\",\n \"def get_users(self):\\n return self.execute(TABELLE['users']['select']['all'])\",\n \"def get_user_list(self):\\n self.user_list = db.get_user_list()\\n for each in self.user_list:\\n print each[1] # username\\n while(True):\\n selection = raw_input(\\\"Enter username to use\\\")\\n if selection in self.user_list:\\n return selection\",\n \"def get_users(self):\\n return get_users(self['__store'].db, self)\",\n \"def users(self) -> pulumi.Output[Sequence['outputs.BrokerUser']]:\\n return pulumi.get(self, \\\"users\\\")\",\n \"def getUsersData(self):\\n users_data = self.get_api_results(\\n \\\"/api/user/list?fields=id,username,name,phone,email,url,about,role,joined,lastactive,avatar,company,position,location&api_key={0}&format=json\\\")\\n for user in users_data:\\n if user[\\\"name\\\"] not in self.users:\\n self.add_user(user)\\n else:\\n self.merge_user(user)\\n return users_data\",\n \"def list_users():\\n check_admin()\\n results = User.query.order_by(-User.id)\\n return render_template('user_list.html', users=results)\",\n \"def list_playlists(self, user=None):\\n if user:\\n playlists = self.sp.user_playlists(user)['items']\\n else:\\n playlists = self.sp.user_playlists(self.user)['items']\\n pl_names = [pl['name'] for pl in playlists]\\n pl_id = [pl['id'] for pl in playlists]\\n pl_own = [pl['owner']['id'] for pl in playlists]\\n return pl_names, pl_id, pl_own\",\n \"def getUsers(self):\\n logger.debug(\\\"Func: getUsers\\\")\\n\\n return sorted(self._usersDict.keys())\",\n \"def user_list():\\n users = User.objects.all()\\n return {\\\"users\\\": users}\",\n \"def get_all_users():\\n token = request.headers.get('token')\\n\\n # Token Validation\\n token_valid, response = is_token_valid(token)\\n if not token_valid:\\n return response\\n token_username = response\\n\\n # Privilege handling\\n if token_username != 'admin':\\n return jsonify({'message': \\\"You aren't allowed to access this\\\"}), 404\\n\\n return jsonify(list(Users.values())), 200\",\n \"def getLoadedUsersByVoIPUsername(self,voip_usernames):\\n loaded_users=map(user_main.getUserPool().getUserByVoIPUsername,voip_usernames)\\n return loaded_users\",\n \"def list_user(self, username):\\n return self.get_admin(u\\\"users/{}\\\".format(username))\",\n \"def getMyUsers(self):\\n my_users = None\\n if self.current_user.is_superuser or self.current_user.has_perm(\\\"manage_input_templates\\\"):\\n my_users = UserCompany.objects.filter(\\n company__pk=self.client_session.companyId).all()\\n else:\\n my_users = UserCompany.objects.filter(pk=self.client_session.userCompanyId).all()\\n\\n ids = []\\n if my_users.count() > 0:\\n ids = [str(d.id) for d in my_users]\\n return \\\",\\\".join(ids)\",\n \"def user_list():\\n if session['user_admin'] == False:\\n abort(403)\\n\\n # Retrieve all Users\\n sqa_sess = sqa_session()\\n users = sqa_sess.query(User).all()\\n\\n return render_template('admin/user_list.html', users=users)\",\n \"def get_existing_users():\\n # also check PublicKeys a user with no servers/networks exist\\n from synnefo.userdata.models import PublicKeyPair\\n from synnefo.db.models import VirtualMachine, Network\\n\\n keypairusernames = PublicKeyPair.objects.filter().values_list('user',\\n flat=True)\\n serverusernames = VirtualMachine.objects.filter().values_list('userid',\\n flat=True)\\n networkusernames = Network.objects.filter().values_list('userid',\\n flat=True)\\n\\n return set(list(keypairusernames) + list(serverusernames) +\\n list(networkusernames))\",\n \"def get_users_admins_list(self, session):\\n\\n users = session.query(User.chat_id).all()\\n return users\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74427754","0.6795005","0.672411","0.67044204","0.6702518","0.66885984","0.66357285","0.6614727","0.65836966","0.65774393","0.6577427","0.65748775","0.6572361","0.65383404","0.653604","0.6530479","0.65299314","0.6490531","0.6474435","0.6440596","0.6431235","0.6414904","0.6408844","0.639892","0.6378118","0.6372368","0.63712376","0.6340847","0.63323903","0.63203466","0.6318945","0.62953794","0.6290887","0.62902933","0.6288058","0.62851787","0.62488294","0.6243327","0.6237835","0.6233573","0.62297726","0.6222678","0.6209221","0.6199433","0.6196114","0.6194866","0.6165968","0.6161444","0.6151531","0.6146306","0.61449075","0.6136857","0.6111469","0.61020976","0.60996884","0.60958093","0.6090473","0.6090168","0.60861623","0.6083475","0.60757136","0.6061405","0.6058187","0.6052075","0.60516703","0.6047405","0.6045696","0.60370487","0.6035126","0.60313475","0.6015825","0.5982958","0.5971674","0.5969997","0.5954665","0.5950403","0.59431577","0.59398854","0.59389627","0.59323454","0.5930548","0.592898","0.59242404","0.5922421","0.59220505","0.59193367","0.5910448","0.5883489","0.5880809","0.58806837","0.58564943","0.5856302","0.5855557","0.5853933","0.5852481","0.58516586","0.58506846","0.5845898","0.58386445","0.5833036"],"string":"[\n \"0.74427754\",\n \"0.6795005\",\n \"0.672411\",\n \"0.67044204\",\n \"0.6702518\",\n \"0.66885984\",\n \"0.66357285\",\n \"0.6614727\",\n \"0.65836966\",\n \"0.65774393\",\n \"0.6577427\",\n \"0.65748775\",\n \"0.6572361\",\n \"0.65383404\",\n \"0.653604\",\n \"0.6530479\",\n \"0.65299314\",\n \"0.6490531\",\n \"0.6474435\",\n \"0.6440596\",\n \"0.6431235\",\n \"0.6414904\",\n \"0.6408844\",\n \"0.639892\",\n \"0.6378118\",\n \"0.6372368\",\n \"0.63712376\",\n \"0.6340847\",\n \"0.63323903\",\n \"0.63203466\",\n \"0.6318945\",\n \"0.62953794\",\n \"0.6290887\",\n \"0.62902933\",\n \"0.6288058\",\n \"0.62851787\",\n \"0.62488294\",\n \"0.6243327\",\n \"0.6237835\",\n \"0.6233573\",\n \"0.62297726\",\n \"0.6222678\",\n \"0.6209221\",\n \"0.6199433\",\n \"0.6196114\",\n \"0.6194866\",\n \"0.6165968\",\n \"0.6161444\",\n \"0.6151531\",\n \"0.6146306\",\n \"0.61449075\",\n \"0.6136857\",\n \"0.6111469\",\n \"0.61020976\",\n \"0.60996884\",\n \"0.60958093\",\n \"0.6090473\",\n \"0.6090168\",\n \"0.60861623\",\n \"0.6083475\",\n \"0.60757136\",\n \"0.6061405\",\n \"0.6058187\",\n \"0.6052075\",\n \"0.60516703\",\n \"0.6047405\",\n \"0.6045696\",\n \"0.60370487\",\n \"0.6035126\",\n \"0.60313475\",\n \"0.6015825\",\n \"0.5982958\",\n \"0.5971674\",\n \"0.5969997\",\n \"0.5954665\",\n \"0.5950403\",\n \"0.59431577\",\n \"0.59398854\",\n \"0.59389627\",\n \"0.59323454\",\n \"0.5930548\",\n \"0.592898\",\n \"0.59242404\",\n \"0.5922421\",\n \"0.59220505\",\n \"0.59193367\",\n \"0.5910448\",\n \"0.5883489\",\n \"0.5880809\",\n \"0.58806837\",\n \"0.58564943\",\n \"0.5856302\",\n \"0.5855557\",\n \"0.5853933\",\n \"0.5852481\",\n \"0.58516586\",\n \"0.58506846\",\n \"0.5845898\",\n \"0.58386445\",\n \"0.5833036\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75741893"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":93886,"cells":{"query":{"kind":"string","value":"Converts time as an option into a datetime.datetime. Returns None if not specified."},"document":{"kind":"string","value":"def parse_time_option(value):\n if not value:\n return None\n try:\n return _EPOCH + datetime.timedelta(seconds=int(value))\n except ValueError:\n pass\n for fmt in ('%Y-%m-%d',):\n try:\n return datetime.datetime.strptime(value, fmt)\n except ValueError:\n pass\n raise ValueError('Failed to parse %s' % value)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def datetime_from_time(time: datetime.time, date: datetime.date = datetime.date.today()):\n if type(time) == datetime.time:\n return datetime.datetime.combine(date, time)\n else:\n return time","def parse_time(time_input, *, force_datetime=False, allow_undefined=False, **kwargs):\n\n if allow_undefined and time_input in [None, '..']:\n return None\n\n if isinstance(time_input, dt.date):\n if force_datetime and not isinstance(time_input, dt.datetime):\n return date_to_datetime(time_input)\n\n if kwargs.get('ignoretz') and isinstance(time_input, dt.datetime):\n return time_input.replace(tzinfo=None)\n\n return time_input\n\n time = dateutil.parser.parse(time_input, **kwargs)\n if force_datetime or len(time_input) > 10: # This check is not very accurate but it works for iso format\n return time\n return time.date()","def get_datetime_from_time(value: datetime | time) -> datetime:\n if isinstance(value, time):\n value = datetime.combine(dt_util.now().date(), value, dt_util.DEFAULT_TIME_ZONE)\n if isinstance(value, datetime):\n value = value.replace(tzinfo=dt_util.DEFAULT_TIME_ZONE)\n if value > dt_util.now():\n raise ValidationError(\"Time cannot be in the future.\")\n return value","def parse_time_into(option_name, default=None):\r\n return parse_amount_into(parse_time, option_name, default=default)","def parse_time(time: Union[str, datetime]) -> datetime:\n if isinstance(time, str):\n try:\n from ciso8601 import parse_datetime # pylint: disable=wrong-import-position # noqa: F401\n return parse_datetime(time)\n except (ImportError, ValueError): # pragma: no cover\n return dateutil.parser.parse(time)\n\n return time","def adapt_timefield_value(self, value):\n if value is None:\n return None\n \n # Expression values are adapted by the database.\n if hasattr(value, 'resolve_expression'):\n return value\n # SQL Server doesn't support microseconds\n if isinstance(value, string_types):\n return datetime.datetime(*(time.strptime(value, '%H:%M:%S')[:6]))\n if timezone.is_aware(value):\n raise ValueError(\"DBMaker backend does not support timezone-aware times.\")\n return datetime.time(value.hour, value.minute, value.second)","def from_isodatetime(date_time: str) -> Optional[Union[datetime.datetime,\n datetime.timedelta,\n datetime.time]]:\n if not date_time:\n return None\n utc = datetime.timezone(datetime.timedelta(0))\n if date_time[:2] == 'PT':\n match = duration_re.match(date_time)\n if not match:\n raise ValueError(date_time)\n hours, minutes, seconds = match.group(\n 'hours'), match.group('minutes'), match.group('seconds')\n secs: float = 0\n if hours is not None:\n secs += int(match.group('hours')) * 3600\n if minutes is not None:\n secs += int(match.group('minutes')) * 60\n if seconds is not None:\n secs += float(match.group('seconds'))\n return datetime.timedelta(seconds=secs)\n if 'T' in date_time:\n try:\n return datetime.datetime.strptime(\n date_time, \"%Y-%m-%dT%H:%M:%S.%fZ\").replace(tzinfo=utc)\n except ValueError:\n pass\n try:\n return datetime.datetime.strptime(date_time, \"%Y-%m-%dT%H:%M:%SZ\").replace(tzinfo=utc)\n except ValueError:\n return datetime.datetime.strptime(date_time, \"%Y-%m-%dT%H:%MZ\").replace(tzinfo=utc)\n if not 'Z' in date_time:\n try:\n return datetime.datetime.strptime(date_time, \"%Y-%m-%d\")\n except ValueError:\n return datetime.datetime.strptime(date_time, \"%d/%m/%Y\")\n return datetime.datetime.strptime(date_time, \"%H:%M:%SZ\").replace(tzinfo=utc).time()","def make_datetime(value):\n if value:\n return value\n return None","def get_time(time):\n regtime = re.compile(r'^([0-1][0-9]|[2][0-3]):([0-5][0-9])$')\n if not regtime.match(time):\n return None\n time_group = regtime.match(time).groups()\n time_final = datetime.time(int(time_group[0]), int(time_group[1]))\n return time_final","def _parse_time(time_string: str, source: str = \"input\") -> Optional[datetime.datetime]:\n if not time_string:\n return None\n\n format_string = \"%Y-%m-%d\" if source == \"input\" else \"%Y-%m-%dT%H:%M:%SZ\"\n try:\n return datetime.datetime.strptime(time_string, format_string)\n except ValueError:\n raise AnalyzerError(\"Incorrect date format\")","def date_to_datetime(date, time=None):\n if time is None:\n time = dt.datetime.min.time()\n return dt.datetime.combine(date, time)","def __handleDateAttribute(self, timeString):\n try:\n if len(str(timeString)) == 13:\n return datetime.datetime.fromtimestamp(timeString / 1000)\n else:\n return datetime.datetime.fromtimestamp(timeString)\n except ValueError:\n return None\n except TypeError:\n return None","def datetime_or_none(dt_str: str | None) -> dt.datetime | None:\n\n if not dt_str:\n return None\n\n str_format = \"%Y-%m-%dT%H:%M:%S\"\n dt_val = dt.datetime.strptime(dt_str, str_format)\n\n return adjust_icms_v1_datetime(dt_val)","def _parse_datetime(value):\n return parse(value).replace(tzinfo=pytz.utc) if value else None","def _strptime(cls, raw: typing.Optional[str]) -> typing.Optional[datetime.datetime]:\n if not raw:\n return None\n return datetime.datetime.strptime(raw, cls._TSFMT)","def convert_datetime(t):\r\n try:\r\n if isinstance(t, str):\r\n converted = datetime.strptime(t, '%d-%m-%Y')\r\n # to get time in seconds:\r\n t = int(time.mktime(converted.timetuple()))\r\n return t\r\n except Exception as e:\r\n print(e)\r\n return None","def time_to_datetime(time):\n split_time = time.split(':')\n hour = int(split_time[0])\n minutes = int(split_time[1])\n now = dt.datetime.now()\n time_as_datetime = dt.datetime(now.year, now.month, now.day,\n hour=hour, minute=minutes)\n\n # Need to change the day to tommorow if time has already passed\n if time_as_datetime < now:\n day = now.day + 1\n time_as_datetime = dt.datetime(now.year, now.month, day,\n hour=hour, minute=minutes)\n\n return time_as_datetime","def get_datetime(time):\n year = int(time[0:4])\n month = int(time[5:7])\n day = int(time[8:10])\n hour = int(time[11:13])\n minute = int(time[14:16])\n second = int(time[17:19])\n return datetime(year, month, day, hour, minute, second)","def ensure_datetime(ob: AnyDatetime) -> datetime.datetime:\n if isinstance(ob, datetime.datetime):\n return ob\n date = cast(datetime.date, ob)\n time = cast(datetime.time, ob)\n if isinstance(ob, datetime.date):\n time = datetime.time()\n if isinstance(ob, datetime.time):\n date = datetime.date(1900, 1, 1)\n return datetime.datetime.combine(date, time)","def make_datetime_from_dicom_date(date: str, time: str = None) -> Optional[datetime]:\n try:\n return datetime(\n year=int(date[:4]),\n month=int(date[4:6]),\n day=int(date[6:8]),\n hour=int(time[:2]),\n minute=int(time[2:4]),\n second=int(time[4:6])\n )\n except (ValueError, TypeError):\n try:\n return datetime(\n year=int(date[:4]),\n month=int(date[4:6]),\n day=int(date[6:8]),\n )\n except (ValueError, TypeError):\n return None","def convert_time(cls, time_str):\n if cls.date_ignore_pattern:\n time_str = re.sub(cls.date_ignore_pattern, '', time_str)\n return datetime.strptime(time_str, cls.date_format)","def datetime_from_string(time):\n try:\n if type(time) == datetime.datetime:\n return time\n else:\n try:\n return datetime.datetime.strptime(time, '%Y-%m-%d %H:%M:%S')\n except ValueError:\n return datetime.datetime.strptime(time, '%Y-%m-%d %H:%M:%S.%f')\n except ValueError:\n return time\n except TypeError:\n return time","def _time_to_datetime(value):\r\n assert isinstance(value, datetime.time)\r\n return datetime.datetime(1970, 1, 1,\r\n value.hour, value.minute, value.second,\r\n value.microsecond)","def parse_datetime(val):\n try: return maya.parse(val).datetime()\n except: return val","def force_to_datetime(val):\n if not val:\n return val\n elif isinstance(val, datetime.datetime):\n return val\n elif isinstance(val, datetime.date):\n return datetime.datetime.combine(val, datetime.time())\n elif isinstance(val, str):\n return string_to_datetime(val)\n else:\n raise ValueError(\"object must be date or datetime!\")","def _check_and_convert_time(time_input, assign_default_time=False):\n\n try:\n if isinstance(time_input, str): # input time_input as string\n if time_input.replace(\n \".\", \"\", 1\n ).isdigit(): # input time_input as numeric string\n time_input = (\n float(time_input)\n if \".\" in time_input\n else int(time_input) / 1000.0\n )\n else: # input time_input as datetime string\n time_input = dateutil.parser.parse(time_input).timestamp()\n elif isinstance(\n time_input, int\n ): # input time_input as epoch timestamps in milliseconds\n time_input = time_input / 1000.0\n elif isinstance(time_input, datetime):\n time_input = time_input.timestamp()\n\n datetime.fromtimestamp(time_input) # check current time_input is valid\n except Exception:\n if assign_default_time:\n logging.info(\n \"Cannot convert time_input into timestamps: {}\".format(time_input)\n )\n time_input = time.time()\n else:\n raise ValueError(\n \"Cannot convert time_input into timestamps: {}\".format(time_input)\n )\n\n return time_input","def convert_to_time(value):\n if isinstance(value, datetime.time):\n return value\n elif isinstance(value, str):\n return datetime.time.fromisoformat(value)\n else:\n return datetime.time(value)","def datetime(self):\n\n time = self.time()\n if time is None or time < 0 or time >= 24:\n time = 0\n\n try:\n d = datetime(self.year(), self.month(), self.day()) + timedelta(hours=time)\n return d\n except:\n return","def parse_date(option):\n date_opt = option\n date = Time(datetime.utcnow()).datetime\n\n if date_opt is not None:\n date = Time(date_opt, scale=\"utc\").datetime\n\n date = datetime(date.year, date.month, date.day)\n\n return {\"date\": date}","def _parse_datetime(self, data):\n d = data.find('./itdDate').attrib\n t = data.find('./itdTime').attrib\n\n # -1 means nope, there is no time known\n if d['weekday'] == '-1' or d['day'] == '-1' or t['minute'] == '-1':\n return None\n\n # convert time – the EFA API likes to talk about 24:00, so we have to correct that.\n result = datetime(int(d['year']), int(d['month']), int(d['day']), min(int(t['hour']), 23), int(t['minute']))\n if int(t['hour']) == 24:\n result += timedelta(hours=1)\n return result","def _astropy_time(time):\n return time if isinstance(time, astropy.time.Time) else astropy.time.Time(parse_time(time))","def try_datetime(timestamp: Any, **kwargs) -> Optional[datetime]:\n if isinstance(timestamp, datetime):\n return timestamp\n if not timestamp or not str(timestamp).strip():\n return None\n\n try:\n # Suppress UnknownTimezoneWarning\n with catch_warnings():\n simplefilter('ignore', category=UnknownTimezoneWarning)\n return parse_date(timestamp, **kwargs)\n except (AttributeError, TypeError, ValueError) as e:\n logger.debug(f'Could not parse timestamp: {timestamp}: \"{str(e)}\"')\n return None","def _get_time(self, _time: Optional[float] = None) -> float:\n if _time is None:\n return time.time()\n\n return _time","def __parse_time(self, time_obj):\n if time_obj:\n resp = ''\n if isinstance(time_obj, int) or isinstance(time_obj, str):\n resp = time_obj\n elif isinstance(time_obj, datetime.datetime):\n resp = calendar.timegm(time_obj.timetuple())\n else:\n raise Exception(\"Unknown __parse_time format for {0}\".format(time_obj))\n return str(resp)\n return None","def convert_datetime(date, time):\n return datetime.datetime.strptime(date + \" \" + time, '%Y-%m-%d %H:%M:%S')","def datetime(self):\n time = self.time()\n if time is None or time < 0 or time >= 24:\n time = 0\n\n try:\n d = datetime(self.year(), self.month(), self.day()) + \\\n timedelta(hours=time)\n return d\n except:\n return","def _time_to_date(parsed_time):\n if not parsed_time:\n return parsed_time\n return datetime.fromtimestamp(calendar.timegm(parsed_time), tz=timezone.utc)","def _change_time_format(time_string):\n datetime_object = parser.isoparse(time_string)\n return datetime_object","def apply_timezone_datetime(_local_tz: str, _time: datetime.time):\n return pytz.timezone(_local_tz).localize(\n datetime.datetime.combine(datetime.datetime.now().date(), _time)\n )","def extract_date_from_iso_time(t: str) -> typing.Optional[datetime.date]:\n if not t:\n return None\n\n date, _ = t.split('T')\n return datetime.date.fromisoformat(date)","def to_datetime(value: str | None) -> datetime.datetime | None:\n if value is None:\n return None\n if value.endswith(\"Z\"):\n # Parse and set the timezone as UTC\n o = datetime.datetime.fromisoformat(value[:-1]).replace(\n tzinfo=datetime.timezone.utc\n )\n else:\n o = datetime.datetime.fromisoformat(value)\n if o.tzinfo:\n # Convert any aware datetime to UTC\n return o.astimezone(datetime.timezone.utc)\n return o","def datetime_from_iso(iso_time):\n # \"start_time\": \"2015-05-23T10:00:00-0700\",\n # \"end_time\": \"2015-05-23T20:00:00-0700\",\n if iso_time is None:\n return iso_time\n\n try:\n return datetime.strptime(iso_time.rsplit(\"-\", 1)[0], \"%Y-%m-%dT%H:%M:%S\")\n except ValueError:\n try:\n return datetime.strptime(iso_time, \"%Y-%m-%dT%H:%M:%S\") # no UTC offset\n except ValueError:\n try:\n return datetime.strptime(iso_time, \"%Y-%m-%d\") # assume no UTC offset\n except ValueError:\n try:\n return datetime.strptime(iso_time, \"%Y-%m\") # assume no UTC offset\n except ValueError:\n try:\n return datetime.strptime(iso_time, \"%Y\") # assume no UTC offset\n except:\n logging.warning(\"what! iso_time: %s\", iso_time)","def make_datetime_obj(date, time):\n\n conv_date = datetime.strptime(date, \"%Y-%m-%d\").date()\n conv_time = datetime.strptime(time, \"%H:%M\").time()\n\n return datetime.combine(conv_date, conv_time)","def get_time(option_set):\n return option_set & TIME_MASK","def from_isodatetime(date_time):\n if not date_time:\n return None\n if date_time[:2]=='PT':\n if 'M' in date_time:\n dt = datetime.datetime.strptime(date_time, \"PT%HH%MM%SS\")\n else:\n dt = datetime.datetime.strptime(date_time, \"PT%H:%M:%S\")\n secs = (dt.hour*60+dt.minute)*60 + dt.second\n return datetime.timedelta(seconds=secs)\n if 'T' in date_time:\n try:\n return datetime.datetime.strptime(date_time, \"%Y-%m-%dT%H:%M:%S.%fZ\")\n except ValueError:\n pass\n try:\n return datetime.datetime.strptime(date_time, \"%Y-%m-%dT%H:%M:%SZ\")\n except ValueError:\n return datetime.datetime.strptime(date_time, \"%Y-%m-%dT%H:%MZ\") \n if not 'Z' in date_time:\n try:\n return datetime.datetime.strptime(date_time, \"%Y-%m-%d\")\n except ValueError:\n return datetime.datetime.strptime(date_time, \"%d/%m/%Y\")\n return datetime.datetime.strptime(date_time, \"%H:%M:%SZ\").time()","def parse_time(value: str) -> datetime:\n\n try:\n return datetime.strptime(value, \"%Y-%m-%dT%H:%M:%SZ\")\n except ValueError:\n return datetime.min","def try_parse_datetime(log_line: str) -> Optional[time.struct_time]:\n match = TIMESTAMP_RE.search(log_line)\n if not match:\n return None\n\n timestamp_str = match.group(0)\n try:\n timed, weekdayed, time_struct = OrgDate().parse_datetime(timestamp_str)\n return time_struct\n except AttributeError:\n # 'NoneType' object has no attribute 'group'\n return None","def _get_datetime(dt_value):\n result = None\n if result is None:\n result = _get_datetime_from_format(dt_value, \"%Y-%m-%d %H:%M:%S.%f %z\")\n if result is None:\n result = _get_datetime_from_format(dt_value, \"%Y-%m-%d %H:%M:%S.%f\")\n if result is None:\n result = _get_datetime_from_format(dt_value, \"%Y-%m-%d %H:%M:%S\")\n if result is None:\n raise RuntimeError(\n \"Failed to convert '{}' into datetime object\".format(dt_value))\n return result","def make_datetime():\n date = input(\"Please give a date as month/day/year, (month ex jan feb): \")\n time = input(\"Please give a time in hour:minute (AM/PM): \")\n the_datetime = date + time\n try:\n our_datetime = datetime.datetime.strptime(the_datetime, \"%b/%d/%Y%I:%M %p\")\n return our_datetime\n except ValueError:\n return make_datetime()","def _datetime(year, month, day, hour, minute, second):\n try:\n return datetime.datetime(\n year, month, day, hour, minute, second, tzinfo=datetime.timezone.utc\n )\n except ValueError:\n invalid_datetime = (\n f\"{year:04d}-{month:02d}-{day:02d} \"\n f\"{hour:02d}:{minute:02d}:{second:02d}\"\n )\n raise ftputil.error.ParserError(\n \"invalid datetime {0!r}\".format(invalid_datetime)\n )","def etime(self):\n try:\n return self['datetime_2']\n except TypeError:\n return None","def create_time(given_time: Any | None) -> str | None:\n if not given_time:\n return None\n if datetime_time := arg_to_datetime(given_time):\n return datetime_time.strftime(DATE_FORMAT)\n else:\n raise DemistoException(\"Time parameter supplied in invalid, make sure to supply a valid argument\")","def parse_time_str(self, time_str):\n try:\n return datetime.strptime(self.force_hour_two_digits(time_str), TIME_FORMAT).time()\n except ValueError:\n return None","def datetime_object(__date):\n if isinstance(__date, datetime.datetime):\n return datetime.datetime(__date.year, __date.month, __date.day, __date.hour, __date.minute, __date.second)\n return None","def convert_date_to_datetime(date):\n return datetime.combine(date, dtime()) if date else None","def __parse_init_time(args):\n if args.init_time is None:\n return\n try:\n if args.init_time.isdigit():\n args.init_time=int(args.init_time)\n else:\n args.init_time=datetime.strptime(args.init_time, __DATE_FORMAT)\n except Exception as ex:\n error_exit(str(ex))","def get_time_now(as_string: bool = False) -> Union[str, datetime]:\n now = arrow.utcnow()\n return now.iso_format() if as_string else now.datetime # type: ignore","def ensure_datetime(value: Union[Date, DateTime, str], **kwargs: int) -> DateTime:\n ## Check the type of the value and act accordingly.\n if isinstance(value, DateTime):\n ## It is a datetime instance. Nothing to be done. Just return with replacement:\n return value.replace(**kwargs) # type: ignore\n elif isinstance(value, Date):\n ## It is a date instance. Set to morning and return with replacement:\n return DateTime.combine(value, DateTime.min.time()).replace(**kwargs) # type: ignore\n elif isinstance(value, str):\n ## We have a string. Attempt to parse and return with replacement:\n try:\n return parse(value).replace(**kwargs) # type: ignore\n except ParserError:\n raise ValueError(\"Can not parse value into a date/time object: {}\".format(value))\n\n ## We have a problem here: Don't know how to convert other\n ## object. Raise a value error:\n raise ValueError(\"Don't know how to convert value to date/time object: {}\".format(value))","def _get_current_time_if_none(given_time):\n\t\treturn given_time or time.time()","def TimeOrDate(context, datetime, long_format = False):\n localLongTimeFormat = context.portal_properties.site_properties.localLongTimeFormat\n localTimeFormat = context.portal_properties.site_properties.localTimeFormat\n localTimeOnlyFormat = context.portal_properties.site_properties.localTimeOnlyFormat\n\n if hasattr(datetime, 'Date'):\n if (datetime.Date() > DateTime().Date()) or long_format:\n dt = datetime.asdatetime().strftime(localLongTimeFormat)\n elif (datetime.Date() < DateTime().Date()):\n dt = datetime.asdatetime().strftime(\"%d %b %Y\")\n elif datetime.Date() == DateTime().Date():\n dt = datetime.asdatetime().strftime(localTimeOnlyFormat)\n else:\n dt = datetime.asdatetime().strftime(localTimeFormat)\n dt = dt.replace(\"PM\", \"pm\").replace(\"AM\", \"am\")\n if len(dt) > 10:\n dt = dt.replace(\"12:00 am\", \"\")\n if dt == \"12:00 am\":\n dt = datetime.asdatetime().strftime(localTimeFormat)\n else:\n dt = datetime\n return dt","def extract_time(bs_soup):\n sub_item = bs_soup.find(\"div\", class_=TIME_CLASS).text\n \n if sub_item:\n return datetime.strptime(sub_item, \"%d %b %Y, %H:%M\")\n return None","def datetime(when = None):\n if when is None:\n return xmlrpclib.DateTime(time.gmtime())\n else:\n return xmlrpclib.DateTime(time.gmtime(when))","def _coerce_datetime(\n value: Any, annotation: Type[Union[datetime.date, datetime.datetime]]\n ) -> Union[datetime.datetime, datetime.date]:\n if isinstance(value, datetime.date) and annotation == datetime.datetime:\n value = datetime.datetime(value.year, value.month, value.day)\n elif isinstance(value, datetime.datetime) and annotation == datetime.date:\n value = value.date()\n elif isinstance(value, (int, float)):\n value = annotation.fromtimestamp(value)\n else:\n value = dateutil.parser.parse(value)\n\n return value","def time_convert(time):\n try:\n time_data = str(time)\n if time_data:\n try:\n time_data = datetime.strptime(time_data, '%Y%m%d')\n except Exception:\n time_data = datetime.strptime(time_data, '%Y%m%d%H%M%S')\n time_data = time_data.strftime('%Y-%m-%d')\n return time_data\n except Exception:\n return False","def str_to_time(my_time):\n time_format = \"%H:%M\"\n try:\n my_time = datetime.strptime(my_time, time_format)\n except:\n my_time = datetime.now()\n\n return my_time","def omniscient_datetime(*args):\n d = original_datetime(*args)\n if settings.USE_TZ:\n d = timezone.make_aware(d, timezone.utc)\n return d","def quote_datetime(self, value):\n if value:\n if isinstance(value, type_check):\n self._quote_datetime = parse(value)\n elif isinstance(value, datetime.datetime):\n self._quote_datetime = value","def unmarshall_time(tyme):\r\n return datetime.datetime(day=tyme['day'],\r\n month=tyme['month'],\r\n year=tyme['year'],\r\n hour=tyme['hour'],\r\n minute=tyme['minute'],\r\n second=tyme['second'],\r\n microsecond=tyme['microsecond'])","def dt(day: date, time: str) -> datetime:\n t = parser.parse_time(time)\n return datetime.combine(day, t)","def stime(self):\n try:\n return self['datetime_1']\n except TypeError:\n return None","def _get_datetime_or_error(self) -> dt:\n return dt(\n *(self.date[key] for key in ['year', 'month', 'day'] if key in self.date)\n )","def _parse_datetime(value, type=datetime.datetime):\r\n assert type in (datetime.datetime, datetime.date, datetime.time)\r\n if not isinstance(value, basestring):\r\n return value\r\n #TODO: use current locale and timezone info to parse to value\r\n formats = {\r\n datetime.datetime: '%Y-%m-%d %H:%M:%S',\r\n datetime.date: '%Y-%m-%d',\r\n datetime.time: '%H:%M:%S'\r\n }\r\n format = formats[type]\r\n try:\r\n value = datetime.datetime.strptime(value, format)\r\n except ValueError, e:\r\n raise ValidationError(e)\r\n try:\r\n return getattr(value, type.__name__)()\r\n except:\r\n return value","def orm2datetime(value, tformat=ORM_DATETIME_FORMAT, default=None):\n if not value:\n return default\n return datetime.strptime(value, tformat)","def test_parse_none_time_of_day(self):\n\n res = sf_c.parse_time_of_day(None)\n self.assertIs(res, None)","def get_date_time(day,time):\n try:time = mapping[time]\n except KeyError:\n if not time:\n time = datetime.datetime.now()\n else:\n time = try_parsing_date(time)\n try:date = mapping[day]\n except KeyError:date = datetime.date.today()\n\n return datetime.datetime.combine(date, time.time())","async def init_dt(message: discord.Message, time: str, timezone: str):\n timezone = reverse_gmt(timezone)\n\n try:\n dt = pendulum.parse(time, tz=timezone)\n except ValueError:\n await client.say(message, \"Time format not recognized.\")\n return None, None\n\n return dt, timezone","def normalise_dt(dt: Union[str, datetime]) -> datetime:\n if isinstance(dt, str):\n dt = parse_time(dt)\n if dt.tzinfo is not None:\n dt = dt.astimezone(tzutc()).replace(tzinfo=None)\n return dt","def extract_time(maybe_time_str: str) -> Optional[str]:\n match = TIMESTAMP_RE.search(maybe_time_str)\n if match is not None:\n return match.group()\n return None","def sqlalch_datetime(dt):\n if isinstance(dt, str):\n return datetime.strptime(dt, \"%Y-%m-%d %H:%M:%S.%f\").replace(tzinfo=UTC)\n if dt.tzinfo is not None and dt.tzinfo.utcoffset(dt) is not None:\n return dt.astimezone(UTC)\n return dt.replace(tzinfo=UTC)","def parse_tql_time(self, data, pid, label):\n field = self.parse_tql_field(data, pid, label)\n if field:\n hour, minute, second = [int(i) for i in field.split(':')[0:3]]\n field = datetime.time(hour, minute, second)\n return field","def datetime(self):\n return datetime(*tuple(self))","def rfc3339nano_to_datetime(my_time, time_dash='_'):\n my_time = my_time.replace('_', ':') # Replace undercores with colons\n my_time = re.sub(r\"\\.\\d*\", \"\", my_time) # Strip nanoseconds\n return datetime.datetime.strptime(my_time, f\"%Y-%m-%dT%H:%M:%S%z\") # Parse string to datetime","def datetimeify(t):\n if type(t) in [datetime, Timestamp]:\n return t\n fmts = ['%Y-%m-%d %H:%M:%S', '%Y-%m-%d', '%Y %m %d %H %M %S',]\n for fmt in fmts:\n try:\n return datetime.strptime(t, fmt)\n except ValueError:\n pass\n raise ValueError(\"time data '{:s}' not a recognized format\".format(t))","def time(self, value: typing.Union[str, _datetime.datetime, _datetime.date]):\n if isinstance(value, _datetime.datetime):\n value = value.strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n elif isinstance(value, _datetime.date):\n value = value.strftime(\"%Y-%m-%dT00:00:00Z\")\n self._properties[\"time\"] = value","def init_value(self, value, strict: bool = True):\n if isinstance(value, str):\n value = datetime.datetime.fromisoformat(value)\n elif isinstance(value, float):\n value = datetime.datetime.fromtimestamp(value)\n return super().init_value(value, strict)","def get_time(self):\n return self.time_param","def __call__(self, value):\n try:\n return to_utc_datetime(value)\n except Exception as e:\n raise argparse.ArgumentTypeError(\n \"Start time and end time filters must be in the ISO 8601 UTC format: YYYY-MM-DDThh:mm:ssZ \"\n f\"(e.g. 1984-09-15T19:20:30Z or 1984-09-15). {e}\"\n )","def test_as_datetime(self):\n self.assertEqual(\n time_display.as_datetime(\n datetime(2020, 7, 31, 23, 59, 30, 357921),\n with_usec=True),\n '2020-07-31 23:59:30.357921')","async def test_process_set_custom_time(self):\n xknx = XKNX()\n self.datetime = DateTime(\n xknx,\n \"TestDateTime\",\n group_address=\"1/2/3\",\n broadcast_type=\"TIME\",\n localtime=False,\n )\n assert self.datetime.remote_value.value is None\n\n test_time = time.strptime(\"9:13:14\", \"%H:%M:%S\")\n await self.datetime.set(test_time)\n telegram = xknx.telegrams.get_nowait()\n assert telegram == Telegram(\n destination_address=GroupAddress(\"1/2/3\"),\n payload=GroupValueWrite(DPTArray((0x9, 0xD, 0xE))),\n )\n await self.datetime.process(telegram)\n assert self.datetime.remote_value.value == test_time","def datetime(value):\n if value in (None, \"\"):\n return \"\"\n\n if isinstance(value, str):\n value = dt_parse(value)\n\n if value.hour == 0 and value.minute == 0 and value.second == 0:\n arg = \"DATE_FORMAT\"\n else:\n arg = \"DATETIME_FORMAT\"\n try:\n return formats.date_format(value, arg)\n except AttributeError:\n raise\n try:\n return format(value, arg)\n except AttributeError:\n return \"\"","def mocked_time():\n return datetime.datetime(2017, 10, 27, 22, 54, 56, 566179)","def _get_datetime(s):\n\n # It would be nice to be able to define a single format string \n # for use with datetime.strptime, but as seen in the examples, \n # Kindle clipping files can express datetimes in slightly \n # different ways. Therefore, we first have to normalize the components \n # of the Kindle datetime string into a consistent form. \n # The normalized form has no commas and always specifies seconds:\n # \"April 22 2018 12:33:10 PM\"\n \n # Use the DATETIME_REGEX regex which contains these groups:\n # 1: Month (en_US spelling of months)\n # 2: Day\n # 3: Year\n # 4: Hour\n # 5: Minute\n # 6: Optional seconds\n # 7: AM/PM (en_US spelling)\n \n month = None\n day = None\n year = None\n hour = None\n minute = None\n seconds = '00' # the Kindle datetime may not specify any seconds\n period = 'AM'\n \n for match in datetime_regex.finditer(s):\n month = match.group(1)\n day = match.group(2).zfill(2) #zero padded, two digits\n year = match.group(3)\n hour = match.group(4).zfill(2) #zero padded, two digits\n minute = match.group(5)\n if match.group(6) is not None:\n seconds = match.group(6)\n period = match.group(7)\n break \n\n\n normalized_string = \"%s %s %s %s:%s:%s %s\" % (month, day, year, hour, minute, seconds, period) \n dt = datetime.datetime.strptime(normalized_string, \"%B %d %Y %I:%M:%S %p\")\n return dt","async def parse_datetime(self, time_str: str, name=None, aware=False, today=False, days_offset=0) -> dt.datetime:\n return await self.AD.sched.parse_datetime(time_str, name, aware, today=today, days_offset=days_offset)","def convert_to_datetime(config):\n try:\n current_time = datetime.datetime.now()\n now, tz = is_dst(datetime.datetime.now())\n startTimeHour, startTimeMinute = config['startTime'].split(',')\n stopTimeHour, stopTimeMinute = config['stopTime'].split(',')\n start_up_time = datetime.time(int(startTimeHour), int(startTimeMinute))\n stop_time = datetime.time(int(stopTimeHour), int(stopTimeMinute))\n return start_up_time, stop_time, now, tz\n except Exception as error:\n logger.info(\"Converting config values to time objects failed with the following error : {}\".format(error))","def time_trans(datetime_str):\n\t\tif re.compile(\"(\\d+)-(\\d+)-(\\d+) (\\d+):(\\d+):(\\d+)\").match(datetime_str):\n\t\t\treturn datetime.strptime(datetime_str, \"%Y-%m-%d %H:%M:%S\")","def parse_time(dt: str) -> datetime:\n return datetime.strptime(dt, \"%Y-%m-%dT%H:%M:%SZ\")","def CalculateDateTime(time):\n time = str(time)\n date = time.split(' ')[0]\n splitted_date = date.split('-')\n day = splitted_date[2]\n month = splitted_date[1]\n year = splitted_date[0]\n time = time.split(' ')[1]\n hour = time.split(':')[0]\n minute = time.split(':')[1]\n updated_hour = int(hour) + 1\n if updated_hour > 12:\n updated_hour = updated_hour - 12\n fulldate = day + \" \" + month + \" \" + year + \" | \" + str(updated_hour) + \":\" + minute\n return fulldate\n else:\n fulldate = day + \" - \" + month + \" - \" + year + \" | \" + str(updated_hour) + \":\" + minute\n return fulldate","def datetime(value):\n if value in (None, ''):\n return ''\n\n if isinstance(value, str):\n value = dt_parse(value)\n\n if value.hour == 0 and value.minute == 0 and value.second == 0:\n arg = \"DATE_FORMAT\"\n else:\n arg = \"DATETIME_FORMAT\"\n try:\n return formats.date_format(value, arg)\n except AttributeError:\n raise\n try:\n return format(value, arg)\n except AttributeError:\n return ''","def get_datetime(hours):\n return datetime.datetime.utcfromtimestamp(hours * 60 * 60)"],"string":"[\n \"def datetime_from_time(time: datetime.time, date: datetime.date = datetime.date.today()):\\n if type(time) == datetime.time:\\n return datetime.datetime.combine(date, time)\\n else:\\n return time\",\n \"def parse_time(time_input, *, force_datetime=False, allow_undefined=False, **kwargs):\\n\\n if allow_undefined and time_input in [None, '..']:\\n return None\\n\\n if isinstance(time_input, dt.date):\\n if force_datetime and not isinstance(time_input, dt.datetime):\\n return date_to_datetime(time_input)\\n\\n if kwargs.get('ignoretz') and isinstance(time_input, dt.datetime):\\n return time_input.replace(tzinfo=None)\\n\\n return time_input\\n\\n time = dateutil.parser.parse(time_input, **kwargs)\\n if force_datetime or len(time_input) > 10: # This check is not very accurate but it works for iso format\\n return time\\n return time.date()\",\n \"def get_datetime_from_time(value: datetime | time) -> datetime:\\n if isinstance(value, time):\\n value = datetime.combine(dt_util.now().date(), value, dt_util.DEFAULT_TIME_ZONE)\\n if isinstance(value, datetime):\\n value = value.replace(tzinfo=dt_util.DEFAULT_TIME_ZONE)\\n if value > dt_util.now():\\n raise ValidationError(\\\"Time cannot be in the future.\\\")\\n return value\",\n \"def parse_time_into(option_name, default=None):\\r\\n return parse_amount_into(parse_time, option_name, default=default)\",\n \"def parse_time(time: Union[str, datetime]) -> datetime:\\n if isinstance(time, str):\\n try:\\n from ciso8601 import parse_datetime # pylint: disable=wrong-import-position # noqa: F401\\n return parse_datetime(time)\\n except (ImportError, ValueError): # pragma: no cover\\n return dateutil.parser.parse(time)\\n\\n return time\",\n \"def adapt_timefield_value(self, value):\\n if value is None:\\n return None\\n \\n # Expression values are adapted by the database.\\n if hasattr(value, 'resolve_expression'):\\n return value\\n # SQL Server doesn't support microseconds\\n if isinstance(value, string_types):\\n return datetime.datetime(*(time.strptime(value, '%H:%M:%S')[:6]))\\n if timezone.is_aware(value):\\n raise ValueError(\\\"DBMaker backend does not support timezone-aware times.\\\")\\n return datetime.time(value.hour, value.minute, value.second)\",\n \"def from_isodatetime(date_time: str) -> Optional[Union[datetime.datetime,\\n datetime.timedelta,\\n datetime.time]]:\\n if not date_time:\\n return None\\n utc = datetime.timezone(datetime.timedelta(0))\\n if date_time[:2] == 'PT':\\n match = duration_re.match(date_time)\\n if not match:\\n raise ValueError(date_time)\\n hours, minutes, seconds = match.group(\\n 'hours'), match.group('minutes'), match.group('seconds')\\n secs: float = 0\\n if hours is not None:\\n secs += int(match.group('hours')) * 3600\\n if minutes is not None:\\n secs += int(match.group('minutes')) * 60\\n if seconds is not None:\\n secs += float(match.group('seconds'))\\n return datetime.timedelta(seconds=secs)\\n if 'T' in date_time:\\n try:\\n return datetime.datetime.strptime(\\n date_time, \\\"%Y-%m-%dT%H:%M:%S.%fZ\\\").replace(tzinfo=utc)\\n except ValueError:\\n pass\\n try:\\n return datetime.datetime.strptime(date_time, \\\"%Y-%m-%dT%H:%M:%SZ\\\").replace(tzinfo=utc)\\n except ValueError:\\n return datetime.datetime.strptime(date_time, \\\"%Y-%m-%dT%H:%MZ\\\").replace(tzinfo=utc)\\n if not 'Z' in date_time:\\n try:\\n return datetime.datetime.strptime(date_time, \\\"%Y-%m-%d\\\")\\n except ValueError:\\n return datetime.datetime.strptime(date_time, \\\"%d/%m/%Y\\\")\\n return datetime.datetime.strptime(date_time, \\\"%H:%M:%SZ\\\").replace(tzinfo=utc).time()\",\n \"def make_datetime(value):\\n if value:\\n return value\\n return None\",\n \"def get_time(time):\\n regtime = re.compile(r'^([0-1][0-9]|[2][0-3]):([0-5][0-9])$')\\n if not regtime.match(time):\\n return None\\n time_group = regtime.match(time).groups()\\n time_final = datetime.time(int(time_group[0]), int(time_group[1]))\\n return time_final\",\n \"def _parse_time(time_string: str, source: str = \\\"input\\\") -> Optional[datetime.datetime]:\\n if not time_string:\\n return None\\n\\n format_string = \\\"%Y-%m-%d\\\" if source == \\\"input\\\" else \\\"%Y-%m-%dT%H:%M:%SZ\\\"\\n try:\\n return datetime.datetime.strptime(time_string, format_string)\\n except ValueError:\\n raise AnalyzerError(\\\"Incorrect date format\\\")\",\n \"def date_to_datetime(date, time=None):\\n if time is None:\\n time = dt.datetime.min.time()\\n return dt.datetime.combine(date, time)\",\n \"def __handleDateAttribute(self, timeString):\\n try:\\n if len(str(timeString)) == 13:\\n return datetime.datetime.fromtimestamp(timeString / 1000)\\n else:\\n return datetime.datetime.fromtimestamp(timeString)\\n except ValueError:\\n return None\\n except TypeError:\\n return None\",\n \"def datetime_or_none(dt_str: str | None) -> dt.datetime | None:\\n\\n if not dt_str:\\n return None\\n\\n str_format = \\\"%Y-%m-%dT%H:%M:%S\\\"\\n dt_val = dt.datetime.strptime(dt_str, str_format)\\n\\n return adjust_icms_v1_datetime(dt_val)\",\n \"def _parse_datetime(value):\\n return parse(value).replace(tzinfo=pytz.utc) if value else None\",\n \"def _strptime(cls, raw: typing.Optional[str]) -> typing.Optional[datetime.datetime]:\\n if not raw:\\n return None\\n return datetime.datetime.strptime(raw, cls._TSFMT)\",\n \"def convert_datetime(t):\\r\\n try:\\r\\n if isinstance(t, str):\\r\\n converted = datetime.strptime(t, '%d-%m-%Y')\\r\\n # to get time in seconds:\\r\\n t = int(time.mktime(converted.timetuple()))\\r\\n return t\\r\\n except Exception as e:\\r\\n print(e)\\r\\n return None\",\n \"def time_to_datetime(time):\\n split_time = time.split(':')\\n hour = int(split_time[0])\\n minutes = int(split_time[1])\\n now = dt.datetime.now()\\n time_as_datetime = dt.datetime(now.year, now.month, now.day,\\n hour=hour, minute=minutes)\\n\\n # Need to change the day to tommorow if time has already passed\\n if time_as_datetime < now:\\n day = now.day + 1\\n time_as_datetime = dt.datetime(now.year, now.month, day,\\n hour=hour, minute=minutes)\\n\\n return time_as_datetime\",\n \"def get_datetime(time):\\n year = int(time[0:4])\\n month = int(time[5:7])\\n day = int(time[8:10])\\n hour = int(time[11:13])\\n minute = int(time[14:16])\\n second = int(time[17:19])\\n return datetime(year, month, day, hour, minute, second)\",\n \"def ensure_datetime(ob: AnyDatetime) -> datetime.datetime:\\n if isinstance(ob, datetime.datetime):\\n return ob\\n date = cast(datetime.date, ob)\\n time = cast(datetime.time, ob)\\n if isinstance(ob, datetime.date):\\n time = datetime.time()\\n if isinstance(ob, datetime.time):\\n date = datetime.date(1900, 1, 1)\\n return datetime.datetime.combine(date, time)\",\n \"def make_datetime_from_dicom_date(date: str, time: str = None) -> Optional[datetime]:\\n try:\\n return datetime(\\n year=int(date[:4]),\\n month=int(date[4:6]),\\n day=int(date[6:8]),\\n hour=int(time[:2]),\\n minute=int(time[2:4]),\\n second=int(time[4:6])\\n )\\n except (ValueError, TypeError):\\n try:\\n return datetime(\\n year=int(date[:4]),\\n month=int(date[4:6]),\\n day=int(date[6:8]),\\n )\\n except (ValueError, TypeError):\\n return None\",\n \"def convert_time(cls, time_str):\\n if cls.date_ignore_pattern:\\n time_str = re.sub(cls.date_ignore_pattern, '', time_str)\\n return datetime.strptime(time_str, cls.date_format)\",\n \"def datetime_from_string(time):\\n try:\\n if type(time) == datetime.datetime:\\n return time\\n else:\\n try:\\n return datetime.datetime.strptime(time, '%Y-%m-%d %H:%M:%S')\\n except ValueError:\\n return datetime.datetime.strptime(time, '%Y-%m-%d %H:%M:%S.%f')\\n except ValueError:\\n return time\\n except TypeError:\\n return time\",\n \"def _time_to_datetime(value):\\r\\n assert isinstance(value, datetime.time)\\r\\n return datetime.datetime(1970, 1, 1,\\r\\n value.hour, value.minute, value.second,\\r\\n value.microsecond)\",\n \"def parse_datetime(val):\\n try: return maya.parse(val).datetime()\\n except: return val\",\n \"def force_to_datetime(val):\\n if not val:\\n return val\\n elif isinstance(val, datetime.datetime):\\n return val\\n elif isinstance(val, datetime.date):\\n return datetime.datetime.combine(val, datetime.time())\\n elif isinstance(val, str):\\n return string_to_datetime(val)\\n else:\\n raise ValueError(\\\"object must be date or datetime!\\\")\",\n \"def _check_and_convert_time(time_input, assign_default_time=False):\\n\\n try:\\n if isinstance(time_input, str): # input time_input as string\\n if time_input.replace(\\n \\\".\\\", \\\"\\\", 1\\n ).isdigit(): # input time_input as numeric string\\n time_input = (\\n float(time_input)\\n if \\\".\\\" in time_input\\n else int(time_input) / 1000.0\\n )\\n else: # input time_input as datetime string\\n time_input = dateutil.parser.parse(time_input).timestamp()\\n elif isinstance(\\n time_input, int\\n ): # input time_input as epoch timestamps in milliseconds\\n time_input = time_input / 1000.0\\n elif isinstance(time_input, datetime):\\n time_input = time_input.timestamp()\\n\\n datetime.fromtimestamp(time_input) # check current time_input is valid\\n except Exception:\\n if assign_default_time:\\n logging.info(\\n \\\"Cannot convert time_input into timestamps: {}\\\".format(time_input)\\n )\\n time_input = time.time()\\n else:\\n raise ValueError(\\n \\\"Cannot convert time_input into timestamps: {}\\\".format(time_input)\\n )\\n\\n return time_input\",\n \"def convert_to_time(value):\\n if isinstance(value, datetime.time):\\n return value\\n elif isinstance(value, str):\\n return datetime.time.fromisoformat(value)\\n else:\\n return datetime.time(value)\",\n \"def datetime(self):\\n\\n time = self.time()\\n if time is None or time < 0 or time >= 24:\\n time = 0\\n\\n try:\\n d = datetime(self.year(), self.month(), self.day()) + timedelta(hours=time)\\n return d\\n except:\\n return\",\n \"def parse_date(option):\\n date_opt = option\\n date = Time(datetime.utcnow()).datetime\\n\\n if date_opt is not None:\\n date = Time(date_opt, scale=\\\"utc\\\").datetime\\n\\n date = datetime(date.year, date.month, date.day)\\n\\n return {\\\"date\\\": date}\",\n \"def _parse_datetime(self, data):\\n d = data.find('./itdDate').attrib\\n t = data.find('./itdTime').attrib\\n\\n # -1 means nope, there is no time known\\n if d['weekday'] == '-1' or d['day'] == '-1' or t['minute'] == '-1':\\n return None\\n\\n # convert time – the EFA API likes to talk about 24:00, so we have to correct that.\\n result = datetime(int(d['year']), int(d['month']), int(d['day']), min(int(t['hour']), 23), int(t['minute']))\\n if int(t['hour']) == 24:\\n result += timedelta(hours=1)\\n return result\",\n \"def _astropy_time(time):\\n return time if isinstance(time, astropy.time.Time) else astropy.time.Time(parse_time(time))\",\n \"def try_datetime(timestamp: Any, **kwargs) -> Optional[datetime]:\\n if isinstance(timestamp, datetime):\\n return timestamp\\n if not timestamp or not str(timestamp).strip():\\n return None\\n\\n try:\\n # Suppress UnknownTimezoneWarning\\n with catch_warnings():\\n simplefilter('ignore', category=UnknownTimezoneWarning)\\n return parse_date(timestamp, **kwargs)\\n except (AttributeError, TypeError, ValueError) as e:\\n logger.debug(f'Could not parse timestamp: {timestamp}: \\\"{str(e)}\\\"')\\n return None\",\n \"def _get_time(self, _time: Optional[float] = None) -> float:\\n if _time is None:\\n return time.time()\\n\\n return _time\",\n \"def __parse_time(self, time_obj):\\n if time_obj:\\n resp = ''\\n if isinstance(time_obj, int) or isinstance(time_obj, str):\\n resp = time_obj\\n elif isinstance(time_obj, datetime.datetime):\\n resp = calendar.timegm(time_obj.timetuple())\\n else:\\n raise Exception(\\\"Unknown __parse_time format for {0}\\\".format(time_obj))\\n return str(resp)\\n return None\",\n \"def convert_datetime(date, time):\\n return datetime.datetime.strptime(date + \\\" \\\" + time, '%Y-%m-%d %H:%M:%S')\",\n \"def datetime(self):\\n time = self.time()\\n if time is None or time < 0 or time >= 24:\\n time = 0\\n\\n try:\\n d = datetime(self.year(), self.month(), self.day()) + \\\\\\n timedelta(hours=time)\\n return d\\n except:\\n return\",\n \"def _time_to_date(parsed_time):\\n if not parsed_time:\\n return parsed_time\\n return datetime.fromtimestamp(calendar.timegm(parsed_time), tz=timezone.utc)\",\n \"def _change_time_format(time_string):\\n datetime_object = parser.isoparse(time_string)\\n return datetime_object\",\n \"def apply_timezone_datetime(_local_tz: str, _time: datetime.time):\\n return pytz.timezone(_local_tz).localize(\\n datetime.datetime.combine(datetime.datetime.now().date(), _time)\\n )\",\n \"def extract_date_from_iso_time(t: str) -> typing.Optional[datetime.date]:\\n if not t:\\n return None\\n\\n date, _ = t.split('T')\\n return datetime.date.fromisoformat(date)\",\n \"def to_datetime(value: str | None) -> datetime.datetime | None:\\n if value is None:\\n return None\\n if value.endswith(\\\"Z\\\"):\\n # Parse and set the timezone as UTC\\n o = datetime.datetime.fromisoformat(value[:-1]).replace(\\n tzinfo=datetime.timezone.utc\\n )\\n else:\\n o = datetime.datetime.fromisoformat(value)\\n if o.tzinfo:\\n # Convert any aware datetime to UTC\\n return o.astimezone(datetime.timezone.utc)\\n return o\",\n \"def datetime_from_iso(iso_time):\\n # \\\"start_time\\\": \\\"2015-05-23T10:00:00-0700\\\",\\n # \\\"end_time\\\": \\\"2015-05-23T20:00:00-0700\\\",\\n if iso_time is None:\\n return iso_time\\n\\n try:\\n return datetime.strptime(iso_time.rsplit(\\\"-\\\", 1)[0], \\\"%Y-%m-%dT%H:%M:%S\\\")\\n except ValueError:\\n try:\\n return datetime.strptime(iso_time, \\\"%Y-%m-%dT%H:%M:%S\\\") # no UTC offset\\n except ValueError:\\n try:\\n return datetime.strptime(iso_time, \\\"%Y-%m-%d\\\") # assume no UTC offset\\n except ValueError:\\n try:\\n return datetime.strptime(iso_time, \\\"%Y-%m\\\") # assume no UTC offset\\n except ValueError:\\n try:\\n return datetime.strptime(iso_time, \\\"%Y\\\") # assume no UTC offset\\n except:\\n logging.warning(\\\"what! iso_time: %s\\\", iso_time)\",\n \"def make_datetime_obj(date, time):\\n\\n conv_date = datetime.strptime(date, \\\"%Y-%m-%d\\\").date()\\n conv_time = datetime.strptime(time, \\\"%H:%M\\\").time()\\n\\n return datetime.combine(conv_date, conv_time)\",\n \"def get_time(option_set):\\n return option_set & TIME_MASK\",\n \"def from_isodatetime(date_time):\\n if not date_time:\\n return None\\n if date_time[:2]=='PT':\\n if 'M' in date_time:\\n dt = datetime.datetime.strptime(date_time, \\\"PT%HH%MM%SS\\\")\\n else:\\n dt = datetime.datetime.strptime(date_time, \\\"PT%H:%M:%S\\\")\\n secs = (dt.hour*60+dt.minute)*60 + dt.second\\n return datetime.timedelta(seconds=secs)\\n if 'T' in date_time:\\n try:\\n return datetime.datetime.strptime(date_time, \\\"%Y-%m-%dT%H:%M:%S.%fZ\\\")\\n except ValueError:\\n pass\\n try:\\n return datetime.datetime.strptime(date_time, \\\"%Y-%m-%dT%H:%M:%SZ\\\")\\n except ValueError:\\n return datetime.datetime.strptime(date_time, \\\"%Y-%m-%dT%H:%MZ\\\") \\n if not 'Z' in date_time:\\n try:\\n return datetime.datetime.strptime(date_time, \\\"%Y-%m-%d\\\")\\n except ValueError:\\n return datetime.datetime.strptime(date_time, \\\"%d/%m/%Y\\\")\\n return datetime.datetime.strptime(date_time, \\\"%H:%M:%SZ\\\").time()\",\n \"def parse_time(value: str) -> datetime:\\n\\n try:\\n return datetime.strptime(value, \\\"%Y-%m-%dT%H:%M:%SZ\\\")\\n except ValueError:\\n return datetime.min\",\n \"def try_parse_datetime(log_line: str) -> Optional[time.struct_time]:\\n match = TIMESTAMP_RE.search(log_line)\\n if not match:\\n return None\\n\\n timestamp_str = match.group(0)\\n try:\\n timed, weekdayed, time_struct = OrgDate().parse_datetime(timestamp_str)\\n return time_struct\\n except AttributeError:\\n # 'NoneType' object has no attribute 'group'\\n return None\",\n \"def _get_datetime(dt_value):\\n result = None\\n if result is None:\\n result = _get_datetime_from_format(dt_value, \\\"%Y-%m-%d %H:%M:%S.%f %z\\\")\\n if result is None:\\n result = _get_datetime_from_format(dt_value, \\\"%Y-%m-%d %H:%M:%S.%f\\\")\\n if result is None:\\n result = _get_datetime_from_format(dt_value, \\\"%Y-%m-%d %H:%M:%S\\\")\\n if result is None:\\n raise RuntimeError(\\n \\\"Failed to convert '{}' into datetime object\\\".format(dt_value))\\n return result\",\n \"def make_datetime():\\n date = input(\\\"Please give a date as month/day/year, (month ex jan feb): \\\")\\n time = input(\\\"Please give a time in hour:minute (AM/PM): \\\")\\n the_datetime = date + time\\n try:\\n our_datetime = datetime.datetime.strptime(the_datetime, \\\"%b/%d/%Y%I:%M %p\\\")\\n return our_datetime\\n except ValueError:\\n return make_datetime()\",\n \"def _datetime(year, month, day, hour, minute, second):\\n try:\\n return datetime.datetime(\\n year, month, day, hour, minute, second, tzinfo=datetime.timezone.utc\\n )\\n except ValueError:\\n invalid_datetime = (\\n f\\\"{year:04d}-{month:02d}-{day:02d} \\\"\\n f\\\"{hour:02d}:{minute:02d}:{second:02d}\\\"\\n )\\n raise ftputil.error.ParserError(\\n \\\"invalid datetime {0!r}\\\".format(invalid_datetime)\\n )\",\n \"def etime(self):\\n try:\\n return self['datetime_2']\\n except TypeError:\\n return None\",\n \"def create_time(given_time: Any | None) -> str | None:\\n if not given_time:\\n return None\\n if datetime_time := arg_to_datetime(given_time):\\n return datetime_time.strftime(DATE_FORMAT)\\n else:\\n raise DemistoException(\\\"Time parameter supplied in invalid, make sure to supply a valid argument\\\")\",\n \"def parse_time_str(self, time_str):\\n try:\\n return datetime.strptime(self.force_hour_two_digits(time_str), TIME_FORMAT).time()\\n except ValueError:\\n return None\",\n \"def datetime_object(__date):\\n if isinstance(__date, datetime.datetime):\\n return datetime.datetime(__date.year, __date.month, __date.day, __date.hour, __date.minute, __date.second)\\n return None\",\n \"def convert_date_to_datetime(date):\\n return datetime.combine(date, dtime()) if date else None\",\n \"def __parse_init_time(args):\\n if args.init_time is None:\\n return\\n try:\\n if args.init_time.isdigit():\\n args.init_time=int(args.init_time)\\n else:\\n args.init_time=datetime.strptime(args.init_time, __DATE_FORMAT)\\n except Exception as ex:\\n error_exit(str(ex))\",\n \"def get_time_now(as_string: bool = False) -> Union[str, datetime]:\\n now = arrow.utcnow()\\n return now.iso_format() if as_string else now.datetime # type: ignore\",\n \"def ensure_datetime(value: Union[Date, DateTime, str], **kwargs: int) -> DateTime:\\n ## Check the type of the value and act accordingly.\\n if isinstance(value, DateTime):\\n ## It is a datetime instance. Nothing to be done. Just return with replacement:\\n return value.replace(**kwargs) # type: ignore\\n elif isinstance(value, Date):\\n ## It is a date instance. Set to morning and return with replacement:\\n return DateTime.combine(value, DateTime.min.time()).replace(**kwargs) # type: ignore\\n elif isinstance(value, str):\\n ## We have a string. Attempt to parse and return with replacement:\\n try:\\n return parse(value).replace(**kwargs) # type: ignore\\n except ParserError:\\n raise ValueError(\\\"Can not parse value into a date/time object: {}\\\".format(value))\\n\\n ## We have a problem here: Don't know how to convert other\\n ## object. Raise a value error:\\n raise ValueError(\\\"Don't know how to convert value to date/time object: {}\\\".format(value))\",\n \"def _get_current_time_if_none(given_time):\\n\\t\\treturn given_time or time.time()\",\n \"def TimeOrDate(context, datetime, long_format = False):\\n localLongTimeFormat = context.portal_properties.site_properties.localLongTimeFormat\\n localTimeFormat = context.portal_properties.site_properties.localTimeFormat\\n localTimeOnlyFormat = context.portal_properties.site_properties.localTimeOnlyFormat\\n\\n if hasattr(datetime, 'Date'):\\n if (datetime.Date() > DateTime().Date()) or long_format:\\n dt = datetime.asdatetime().strftime(localLongTimeFormat)\\n elif (datetime.Date() < DateTime().Date()):\\n dt = datetime.asdatetime().strftime(\\\"%d %b %Y\\\")\\n elif datetime.Date() == DateTime().Date():\\n dt = datetime.asdatetime().strftime(localTimeOnlyFormat)\\n else:\\n dt = datetime.asdatetime().strftime(localTimeFormat)\\n dt = dt.replace(\\\"PM\\\", \\\"pm\\\").replace(\\\"AM\\\", \\\"am\\\")\\n if len(dt) > 10:\\n dt = dt.replace(\\\"12:00 am\\\", \\\"\\\")\\n if dt == \\\"12:00 am\\\":\\n dt = datetime.asdatetime().strftime(localTimeFormat)\\n else:\\n dt = datetime\\n return dt\",\n \"def extract_time(bs_soup):\\n sub_item = bs_soup.find(\\\"div\\\", class_=TIME_CLASS).text\\n \\n if sub_item:\\n return datetime.strptime(sub_item, \\\"%d %b %Y, %H:%M\\\")\\n return None\",\n \"def datetime(when = None):\\n if when is None:\\n return xmlrpclib.DateTime(time.gmtime())\\n else:\\n return xmlrpclib.DateTime(time.gmtime(when))\",\n \"def _coerce_datetime(\\n value: Any, annotation: Type[Union[datetime.date, datetime.datetime]]\\n ) -> Union[datetime.datetime, datetime.date]:\\n if isinstance(value, datetime.date) and annotation == datetime.datetime:\\n value = datetime.datetime(value.year, value.month, value.day)\\n elif isinstance(value, datetime.datetime) and annotation == datetime.date:\\n value = value.date()\\n elif isinstance(value, (int, float)):\\n value = annotation.fromtimestamp(value)\\n else:\\n value = dateutil.parser.parse(value)\\n\\n return value\",\n \"def time_convert(time):\\n try:\\n time_data = str(time)\\n if time_data:\\n try:\\n time_data = datetime.strptime(time_data, '%Y%m%d')\\n except Exception:\\n time_data = datetime.strptime(time_data, '%Y%m%d%H%M%S')\\n time_data = time_data.strftime('%Y-%m-%d')\\n return time_data\\n except Exception:\\n return False\",\n \"def str_to_time(my_time):\\n time_format = \\\"%H:%M\\\"\\n try:\\n my_time = datetime.strptime(my_time, time_format)\\n except:\\n my_time = datetime.now()\\n\\n return my_time\",\n \"def omniscient_datetime(*args):\\n d = original_datetime(*args)\\n if settings.USE_TZ:\\n d = timezone.make_aware(d, timezone.utc)\\n return d\",\n \"def quote_datetime(self, value):\\n if value:\\n if isinstance(value, type_check):\\n self._quote_datetime = parse(value)\\n elif isinstance(value, datetime.datetime):\\n self._quote_datetime = value\",\n \"def unmarshall_time(tyme):\\r\\n return datetime.datetime(day=tyme['day'],\\r\\n month=tyme['month'],\\r\\n year=tyme['year'],\\r\\n hour=tyme['hour'],\\r\\n minute=tyme['minute'],\\r\\n second=tyme['second'],\\r\\n microsecond=tyme['microsecond'])\",\n \"def dt(day: date, time: str) -> datetime:\\n t = parser.parse_time(time)\\n return datetime.combine(day, t)\",\n \"def stime(self):\\n try:\\n return self['datetime_1']\\n except TypeError:\\n return None\",\n \"def _get_datetime_or_error(self) -> dt:\\n return dt(\\n *(self.date[key] for key in ['year', 'month', 'day'] if key in self.date)\\n )\",\n \"def _parse_datetime(value, type=datetime.datetime):\\r\\n assert type in (datetime.datetime, datetime.date, datetime.time)\\r\\n if not isinstance(value, basestring):\\r\\n return value\\r\\n #TODO: use current locale and timezone info to parse to value\\r\\n formats = {\\r\\n datetime.datetime: '%Y-%m-%d %H:%M:%S',\\r\\n datetime.date: '%Y-%m-%d',\\r\\n datetime.time: '%H:%M:%S'\\r\\n }\\r\\n format = formats[type]\\r\\n try:\\r\\n value = datetime.datetime.strptime(value, format)\\r\\n except ValueError, e:\\r\\n raise ValidationError(e)\\r\\n try:\\r\\n return getattr(value, type.__name__)()\\r\\n except:\\r\\n return value\",\n \"def orm2datetime(value, tformat=ORM_DATETIME_FORMAT, default=None):\\n if not value:\\n return default\\n return datetime.strptime(value, tformat)\",\n \"def test_parse_none_time_of_day(self):\\n\\n res = sf_c.parse_time_of_day(None)\\n self.assertIs(res, None)\",\n \"def get_date_time(day,time):\\n try:time = mapping[time]\\n except KeyError:\\n if not time:\\n time = datetime.datetime.now()\\n else:\\n time = try_parsing_date(time)\\n try:date = mapping[day]\\n except KeyError:date = datetime.date.today()\\n\\n return datetime.datetime.combine(date, time.time())\",\n \"async def init_dt(message: discord.Message, time: str, timezone: str):\\n timezone = reverse_gmt(timezone)\\n\\n try:\\n dt = pendulum.parse(time, tz=timezone)\\n except ValueError:\\n await client.say(message, \\\"Time format not recognized.\\\")\\n return None, None\\n\\n return dt, timezone\",\n \"def normalise_dt(dt: Union[str, datetime]) -> datetime:\\n if isinstance(dt, str):\\n dt = parse_time(dt)\\n if dt.tzinfo is not None:\\n dt = dt.astimezone(tzutc()).replace(tzinfo=None)\\n return dt\",\n \"def extract_time(maybe_time_str: str) -> Optional[str]:\\n match = TIMESTAMP_RE.search(maybe_time_str)\\n if match is not None:\\n return match.group()\\n return None\",\n \"def sqlalch_datetime(dt):\\n if isinstance(dt, str):\\n return datetime.strptime(dt, \\\"%Y-%m-%d %H:%M:%S.%f\\\").replace(tzinfo=UTC)\\n if dt.tzinfo is not None and dt.tzinfo.utcoffset(dt) is not None:\\n return dt.astimezone(UTC)\\n return dt.replace(tzinfo=UTC)\",\n \"def parse_tql_time(self, data, pid, label):\\n field = self.parse_tql_field(data, pid, label)\\n if field:\\n hour, minute, second = [int(i) for i in field.split(':')[0:3]]\\n field = datetime.time(hour, minute, second)\\n return field\",\n \"def datetime(self):\\n return datetime(*tuple(self))\",\n \"def rfc3339nano_to_datetime(my_time, time_dash='_'):\\n my_time = my_time.replace('_', ':') # Replace undercores with colons\\n my_time = re.sub(r\\\"\\\\.\\\\d*\\\", \\\"\\\", my_time) # Strip nanoseconds\\n return datetime.datetime.strptime(my_time, f\\\"%Y-%m-%dT%H:%M:%S%z\\\") # Parse string to datetime\",\n \"def datetimeify(t):\\n if type(t) in [datetime, Timestamp]:\\n return t\\n fmts = ['%Y-%m-%d %H:%M:%S', '%Y-%m-%d', '%Y %m %d %H %M %S',]\\n for fmt in fmts:\\n try:\\n return datetime.strptime(t, fmt)\\n except ValueError:\\n pass\\n raise ValueError(\\\"time data '{:s}' not a recognized format\\\".format(t))\",\n \"def time(self, value: typing.Union[str, _datetime.datetime, _datetime.date]):\\n if isinstance(value, _datetime.datetime):\\n value = value.strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\")\\n elif isinstance(value, _datetime.date):\\n value = value.strftime(\\\"%Y-%m-%dT00:00:00Z\\\")\\n self._properties[\\\"time\\\"] = value\",\n \"def init_value(self, value, strict: bool = True):\\n if isinstance(value, str):\\n value = datetime.datetime.fromisoformat(value)\\n elif isinstance(value, float):\\n value = datetime.datetime.fromtimestamp(value)\\n return super().init_value(value, strict)\",\n \"def get_time(self):\\n return self.time_param\",\n \"def __call__(self, value):\\n try:\\n return to_utc_datetime(value)\\n except Exception as e:\\n raise argparse.ArgumentTypeError(\\n \\\"Start time and end time filters must be in the ISO 8601 UTC format: YYYY-MM-DDThh:mm:ssZ \\\"\\n f\\\"(e.g. 1984-09-15T19:20:30Z or 1984-09-15). {e}\\\"\\n )\",\n \"def test_as_datetime(self):\\n self.assertEqual(\\n time_display.as_datetime(\\n datetime(2020, 7, 31, 23, 59, 30, 357921),\\n with_usec=True),\\n '2020-07-31 23:59:30.357921')\",\n \"async def test_process_set_custom_time(self):\\n xknx = XKNX()\\n self.datetime = DateTime(\\n xknx,\\n \\\"TestDateTime\\\",\\n group_address=\\\"1/2/3\\\",\\n broadcast_type=\\\"TIME\\\",\\n localtime=False,\\n )\\n assert self.datetime.remote_value.value is None\\n\\n test_time = time.strptime(\\\"9:13:14\\\", \\\"%H:%M:%S\\\")\\n await self.datetime.set(test_time)\\n telegram = xknx.telegrams.get_nowait()\\n assert telegram == Telegram(\\n destination_address=GroupAddress(\\\"1/2/3\\\"),\\n payload=GroupValueWrite(DPTArray((0x9, 0xD, 0xE))),\\n )\\n await self.datetime.process(telegram)\\n assert self.datetime.remote_value.value == test_time\",\n \"def datetime(value):\\n if value in (None, \\\"\\\"):\\n return \\\"\\\"\\n\\n if isinstance(value, str):\\n value = dt_parse(value)\\n\\n if value.hour == 0 and value.minute == 0 and value.second == 0:\\n arg = \\\"DATE_FORMAT\\\"\\n else:\\n arg = \\\"DATETIME_FORMAT\\\"\\n try:\\n return formats.date_format(value, arg)\\n except AttributeError:\\n raise\\n try:\\n return format(value, arg)\\n except AttributeError:\\n return \\\"\\\"\",\n \"def mocked_time():\\n return datetime.datetime(2017, 10, 27, 22, 54, 56, 566179)\",\n \"def _get_datetime(s):\\n\\n # It would be nice to be able to define a single format string \\n # for use with datetime.strptime, but as seen in the examples, \\n # Kindle clipping files can express datetimes in slightly \\n # different ways. Therefore, we first have to normalize the components \\n # of the Kindle datetime string into a consistent form. \\n # The normalized form has no commas and always specifies seconds:\\n # \\\"April 22 2018 12:33:10 PM\\\"\\n \\n # Use the DATETIME_REGEX regex which contains these groups:\\n # 1: Month (en_US spelling of months)\\n # 2: Day\\n # 3: Year\\n # 4: Hour\\n # 5: Minute\\n # 6: Optional seconds\\n # 7: AM/PM (en_US spelling)\\n \\n month = None\\n day = None\\n year = None\\n hour = None\\n minute = None\\n seconds = '00' # the Kindle datetime may not specify any seconds\\n period = 'AM'\\n \\n for match in datetime_regex.finditer(s):\\n month = match.group(1)\\n day = match.group(2).zfill(2) #zero padded, two digits\\n year = match.group(3)\\n hour = match.group(4).zfill(2) #zero padded, two digits\\n minute = match.group(5)\\n if match.group(6) is not None:\\n seconds = match.group(6)\\n period = match.group(7)\\n break \\n\\n\\n normalized_string = \\\"%s %s %s %s:%s:%s %s\\\" % (month, day, year, hour, minute, seconds, period) \\n dt = datetime.datetime.strptime(normalized_string, \\\"%B %d %Y %I:%M:%S %p\\\")\\n return dt\",\n \"async def parse_datetime(self, time_str: str, name=None, aware=False, today=False, days_offset=0) -> dt.datetime:\\n return await self.AD.sched.parse_datetime(time_str, name, aware, today=today, days_offset=days_offset)\",\n \"def convert_to_datetime(config):\\n try:\\n current_time = datetime.datetime.now()\\n now, tz = is_dst(datetime.datetime.now())\\n startTimeHour, startTimeMinute = config['startTime'].split(',')\\n stopTimeHour, stopTimeMinute = config['stopTime'].split(',')\\n start_up_time = datetime.time(int(startTimeHour), int(startTimeMinute))\\n stop_time = datetime.time(int(stopTimeHour), int(stopTimeMinute))\\n return start_up_time, stop_time, now, tz\\n except Exception as error:\\n logger.info(\\\"Converting config values to time objects failed with the following error : {}\\\".format(error))\",\n \"def time_trans(datetime_str):\\n\\t\\tif re.compile(\\\"(\\\\d+)-(\\\\d+)-(\\\\d+) (\\\\d+):(\\\\d+):(\\\\d+)\\\").match(datetime_str):\\n\\t\\t\\treturn datetime.strptime(datetime_str, \\\"%Y-%m-%d %H:%M:%S\\\")\",\n \"def parse_time(dt: str) -> datetime:\\n return datetime.strptime(dt, \\\"%Y-%m-%dT%H:%M:%SZ\\\")\",\n \"def CalculateDateTime(time):\\n time = str(time)\\n date = time.split(' ')[0]\\n splitted_date = date.split('-')\\n day = splitted_date[2]\\n month = splitted_date[1]\\n year = splitted_date[0]\\n time = time.split(' ')[1]\\n hour = time.split(':')[0]\\n minute = time.split(':')[1]\\n updated_hour = int(hour) + 1\\n if updated_hour > 12:\\n updated_hour = updated_hour - 12\\n fulldate = day + \\\" \\\" + month + \\\" \\\" + year + \\\" | \\\" + str(updated_hour) + \\\":\\\" + minute\\n return fulldate\\n else:\\n fulldate = day + \\\" - \\\" + month + \\\" - \\\" + year + \\\" | \\\" + str(updated_hour) + \\\":\\\" + minute\\n return fulldate\",\n \"def datetime(value):\\n if value in (None, ''):\\n return ''\\n\\n if isinstance(value, str):\\n value = dt_parse(value)\\n\\n if value.hour == 0 and value.minute == 0 and value.second == 0:\\n arg = \\\"DATE_FORMAT\\\"\\n else:\\n arg = \\\"DATETIME_FORMAT\\\"\\n try:\\n return formats.date_format(value, arg)\\n except AttributeError:\\n raise\\n try:\\n return format(value, arg)\\n except AttributeError:\\n return ''\",\n \"def get_datetime(hours):\\n return datetime.datetime.utcfromtimestamp(hours * 60 * 60)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68546826","0.683096","0.67861164","0.6604809","0.64384985","0.6394784","0.6331635","0.62863314","0.62711996","0.6268744","0.62352127","0.620883","0.6158795","0.6090492","0.60857975","0.60848105","0.6053297","0.60125333","0.59862024","0.5982028","0.5981134","0.59732383","0.5970754","0.59687775","0.59684664","0.59120667","0.5895636","0.5879655","0.5875099","0.58564293","0.5835596","0.5830636","0.58238435","0.5814608","0.58101183","0.58051646","0.5713252","0.56840646","0.5651159","0.5650837","0.5636569","0.5635012","0.56226957","0.55990094","0.55854344","0.5574819","0.55677146","0.55676013","0.5567062","0.5558969","0.554772","0.55466586","0.55153924","0.55117655","0.5506262","0.5503731","0.5502341","0.5499861","0.548596","0.54842174","0.5482393","0.5471086","0.5466877","0.54628396","0.5428204","0.54251415","0.54049605","0.5403244","0.5389719","0.5373378","0.5372232","0.5367579","0.5363136","0.53451556","0.5339003","0.53360695","0.5335259","0.5323317","0.53164005","0.53049743","0.52994776","0.5296937","0.5291231","0.528704","0.5266763","0.5262502","0.5258401","0.525581","0.52502084","0.52338606","0.52298003","0.52189153","0.5212948","0.5210156","0.5203381","0.5202081","0.52002835","0.51956797","0.5195505"],"string":"[\n \"0.68546826\",\n \"0.683096\",\n \"0.67861164\",\n \"0.6604809\",\n \"0.64384985\",\n \"0.6394784\",\n \"0.6331635\",\n \"0.62863314\",\n \"0.62711996\",\n \"0.6268744\",\n \"0.62352127\",\n \"0.620883\",\n \"0.6158795\",\n \"0.6090492\",\n \"0.60857975\",\n \"0.60848105\",\n \"0.6053297\",\n \"0.60125333\",\n \"0.59862024\",\n \"0.5982028\",\n \"0.5981134\",\n \"0.59732383\",\n \"0.5970754\",\n \"0.59687775\",\n \"0.59684664\",\n \"0.59120667\",\n \"0.5895636\",\n \"0.5879655\",\n \"0.5875099\",\n \"0.58564293\",\n \"0.5835596\",\n \"0.5830636\",\n \"0.58238435\",\n \"0.5814608\",\n \"0.58101183\",\n \"0.58051646\",\n \"0.5713252\",\n \"0.56840646\",\n \"0.5651159\",\n \"0.5650837\",\n \"0.5636569\",\n \"0.5635012\",\n \"0.56226957\",\n \"0.55990094\",\n \"0.55854344\",\n \"0.5574819\",\n \"0.55677146\",\n \"0.55676013\",\n \"0.5567062\",\n \"0.5558969\",\n \"0.554772\",\n \"0.55466586\",\n \"0.55153924\",\n \"0.55117655\",\n \"0.5506262\",\n \"0.5503731\",\n \"0.5502341\",\n \"0.5499861\",\n \"0.548596\",\n \"0.54842174\",\n \"0.5482393\",\n \"0.5471086\",\n \"0.5466877\",\n \"0.54628396\",\n \"0.5428204\",\n \"0.54251415\",\n \"0.54049605\",\n \"0.5403244\",\n \"0.5389719\",\n \"0.5373378\",\n \"0.5372232\",\n \"0.5367579\",\n \"0.5363136\",\n \"0.53451556\",\n \"0.5339003\",\n \"0.53360695\",\n \"0.5335259\",\n \"0.5323317\",\n \"0.53164005\",\n \"0.53049743\",\n \"0.52994776\",\n \"0.5296937\",\n \"0.5291231\",\n \"0.528704\",\n \"0.5266763\",\n \"0.5262502\",\n \"0.5258401\",\n \"0.525581\",\n \"0.52502084\",\n \"0.52338606\",\n \"0.52298003\",\n \"0.52189153\",\n \"0.5212948\",\n \"0.5210156\",\n \"0.5203381\",\n \"0.5202081\",\n \"0.52002835\",\n \"0.51956797\",\n \"0.5195505\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67915386"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":93887,"cells":{"query":{"kind":"string","value":"Fetches data from swarming and returns it."},"document":{"kind":"string","value":"def fetch_data(swarming, start, end, state, tags):\n # Split the work in days. That's a lot of requests to do.\n queue = Queue.Queue()\n threads = []\n def run(start, cmd):\n data = json.loads(subprocess.check_output(cmd))\n queue.put((start, int(data['count'])))\n\n day = start\n while day != end:\n data = [\n ('start', int((day - _EPOCH).total_seconds())),\n ('end', int((day + datetime.timedelta(days=1)-_EPOCH).total_seconds())),\n ('state', state),\n ]\n for tag in tags:\n data.append(('tags', tag))\n cmd = [\n sys.executable, os.path.join(CLIENT_DIR, 'swarming.py'),\n 'query', '-S', swarming, 'tasks/count?' + urllib.urlencode(data),\n ]\n thread = threading.Thread(target=run, args=(day.strftime('%Y-%m-%d'), cmd))\n thread.daemon = True\n thread.start()\n threads.append(thread)\n while len(threads) > 100:\n # Throttle a bit.\n for i, thread in enumerate(threads):\n if not thread.is_alive():\n thread.join()\n threads.pop(i)\n sys.stdout.write('.')\n sys.stdout.flush()\n break\n day = day + datetime.timedelta(days=1)\n\n while threads:\n # Throttle a bit.\n for i, thread in enumerate(threads):\n if not thread.is_alive():\n thread.join()\n threads.pop(i)\n sys.stdout.write('.')\n sys.stdout.flush()\n break\n print('')\n data = []\n while True:\n try:\n data.append(queue.get_nowait())\n except Queue.Empty:\n break\n return dict(data)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _fetch_data(self):\n pass","def run(self):\n results = self.fetch()\n return results","def fetch_data(self):","async def fetch_data(self) -> T:","def data():\n return volumes_fetchers.get_json_data()","def get_data():\n return","async def _fetch_data(self) -> T:\n raise NotImplementedError","def fetch_data():\n data.fetch_data()\n data.start_updating()","def fetch_data(self):\n\n data_dict = {\n 'price': self.get_current_price(),\n }\n\n return self.save_data(data_dict)","def get_data():\n pass","def fetch_data(data_url):\n return requests.get(data_url).content","async def async_get_data(self):\n stage = await self.async_get_stage()\n data = {\n \"data\": {\"stage\": stage},\n }\n return data","def getTheData(self, dev):\n if self.debugLevel >= 2 and self.debug:\n self.debugLog(u\"getTheData FrontViewAPI method called.\")\n\n # dev.updateStateOnServer('deviceIsOnline', value=True, uiValue=\"Download\")\n try:\n url = 'http://' + dev.pluginProps['sourceXML'] + '/FrontView'\n r = requests.get(url,timeout=5)\n result = r.json()\n if self.debugLevel >= 2 and self.debug:\n self.debugLog(u\"Result:\" + unicode(result))\n self.WaitInterval = 1\n dev.updateStateOnServer('deviceIsOnline', value=True, uiValue=\"Online\")\n dev.setErrorStateOnServer(None)\n # dev.updateStateOnServer('deviceTimestamp', value=t.time())\n return result\n\n except Exception as error:\n\n indigo.server.log(u\"Error connecting to Device:\" + dev.name)\n self.WaitInterval = 60\n if self.debugLevel >= 2 and self.debug:\n self.debugLog(u\"Device is offline. No data to return. \")\n dev.updateStateOnServer('deviceIsOnline', value=False, uiValue=\"Offline\")\n # dev.updateStateOnServer('deviceTimestamp', value=t.time())\n dev.setErrorStateOnServer(u'Offline')\n result = \"\"\n return result","def fetch(self) -> None:\n pass","def fetch(self):\n pass","def fetch(self):\n pass","def get_data():\n pass","def get_data():\n pass","def get_data():\n pass","async def _fetch_data(self) -> JobInfo:\n return await self.api.get_job()","def do_fetch(self):\n pass","def _fetch(cls, *args, **kwargs):\n apikey = htpc.settings.get('plexpy_apikey')\n\n if apikey is None:\n raise\n\n url = '%sapi/v2?apikey=%s&%s' % (cls._build_url(), apikey, urlencode(kwargs))\n\n try:\n r = requests.get(url, verify=False)\n r.raise_for_status()\n # Lets just copy the headers for now.\n cherrypy.response.headers['Content-Type'] = r.headers.get('Content-Type', 'application/json;charset=UTF-8')\n resp = r.json()\n if resp.get('response', {}).get('result') == 'success':\n return resp['response']['data']\n except:\n log.exception('Failed to get %s' % url)\n return","def get_rest_data_source(uri):\n def players_from_rest():\n response = requests.get(uri)\n response.raise_for_status()\n return parse_players_json(response.text)\n return players_from_rest","async def fetch_async(self) -> FleetInstance:\n\n payload = await self._version.fetch_async(\n method=\"GET\",\n uri=self._uri,\n )\n\n return FleetInstance(\n self._version,\n payload,\n sid=self._solution[\"sid\"],\n )","def get_data(self):\n data_i = sqlite3.connect('data::memory:', check_same_thread=False)\n data_cursor = data_i.cursor()\n data_cursor.execute('SELECT * FROM localdata')\n item = data_cursor.fetchall()\n data_i.commit()\n data_i.close()\n return self.generate_data_list(item)","def getData(self, local_cache):","def AcquiredData (self) :\n\t\treturn self.run(\"AcquiredData\")","def fetch():\n req_data= request.get_json()\n \n ## ddb uses text files, using this as to eat my own dogfoor and improve\n ## no service sql client. No daemon, low cpu.\n\n\n e=load_db()\n try:\n res=e.query(req_data['query'])\n \n serialized = jsonpickle.encode( res,\n unpicklable=False,\n make_refs=False)\n return serialized\n except Exception as ex:\n return \"{0} -> '{1}'\".format(ex,req_data['query'])","def refresh(self):\r\n data = super(Share, self)._fetch()\r\n self.data_set = data[self.symbol]\r\n return data[self.symbol]","def fetch(self):\n raise NotImplementedError()","def get(self):\n return self.get_data()","async def fetch_data(self):\n url = URL_HASSIO_VERSION.format(self.upstream)\n try:\n _LOGGER.info(\"Fetch update data from %s\", url)\n with async_timeout.timeout(10, loop=self.loop):\n async with self.websession.get(url) as request:\n data = await request.json(content_type=None)\n\n except (aiohttp.ClientError, asyncio.TimeoutError, KeyError) as err:\n _LOGGER.warning(\"Can't fetch versions from %s -> %s\", url, err)\n return\n\n except json.JSONDecodeError as err:\n _LOGGER.warning(\"Can't parse versions from %s -> %s\", url, err)\n return\n\n # data valid?\n if not data:\n _LOGGER.warning(\"Invalid data from %s\", url)\n return\n\n # update versions\n self._data[ATTR_HOMEASSISTANT] = data.get('homeassistant')\n self._data[ATTR_HASSIO] = data.get('hassio')\n self.save()","def get_sdata(self):\n payload = self.get('data_request?id=sdata&output_format=json')\n return payload","def GetData(self):\r\n if self.Error == False:\r\n Extra = {}\r\n try:\r\n result = {}\r\n temp = self.ScrapeMainWebpage()\r\n if temp != None:\r\n result.update(temp)\r\n temp = self.ScrapeParameters1Webpage()\r\n if temp != None:\r\n result.update(temp)\r\n temp = self.ScrapeParameters2Webpage()\r\n if temp != None:\r\n result.update(temp)\r\n temp = self.ScrapeStatusWebpage()\r\n if temp != None:\r\n result.update(temp)\r\n sqlArray = {}\r\n sqlArray[self.deviceDescr] = {}\r\n sqlArray[self.deviceDescr][self.devNumber] = {}\r\n sqlArray[self.deviceDescr][self.devNumber][\"General\"] = result\r\n sqlArray[self.deviceDescr][self.devNumber][\"_ExtractInfo\"] = {}\r\n sqlArray[self.deviceDescr][self.devNumber][\"_ExtractInfo\"][\"ExtractTime\"] = time.time()\r\n sqlArray[\"ReadError\"] = False \r\n return sqlArray\r\n \r\n except Exception as e: \r\n self.log.printError(\"ERROR in Retreiving Seatel VSAT Data,%s Module Error\" % sys._getframe().f_code.co_name) \r\n self.log.printError( str(e))\r\n self.Error = True\r\n Extra[\"ReadError\"] = True\r\n return Extra\r\n else:\r\n self.log.printWarning(\"%s skipped due to previous failure\" % sys._getframe().f_code.co_name)\r\n return None","async def get_data(self) -> list[dict]:\n tasks = [self.miners[miner].get_api_data() for miner in self.miners]\n results = await asyncio.gather(*tasks)\n return results","def _load (self):\n\n cursor = self._exec (self.select)\n rows = cursor.fetchall ()\n if not rows:\n return None\n\n expires, pickled_data = rows[0]\n data = pickle.loads (pickled_data)\n return data, expires","def _get_data(api):\n url = API_BASE_URL + api\n logging.info(\"Querying URL: %s\", url)\n response = requests.get(url)\n count = 0\n retry_count = 50\n if response.status_code == 404:\n logging.error(\"Received 404\")\n return None\n while response.status_code != 200:\n count += 1\n if count == retry_count:\n break\n logging.error(\"Received status code %s, expected 200\",\n response.status_code)\n time.sleep(2)\n try:\n response = requests.get(url)\n except:\n pass\n if response.status_code != 200:\n # Send emoty dataframe. Remove None\n return None\n rdd = SPARK_CONTEXT.parallelize([response.json()])\n data_frame = SPARK_SESSION.read.json(rdd)\n return data_frame","def _get_raw_data(self, url, series):\n url = self._get_url(url, series)\n try:\n response = self.http.request(url, headers=self._reqheaders)\n except httplib2.ServerNotFoundError as e:\n raise TVDBConnectError(e.message), None, sys.exc_info()[2]\n rep = response[0]\n log.debug(\n 'http-status:%s,content:%s', \n rep['status'], \n rep['content-type']\n )\n if int(rep['status']) >= 400:\n raise TVDBConnectError(\n 'Failed to get \"%s\" from thetvdb. errno:%s' % (\n series, rep['status']),\n rep['status']\n ) \n return response[1]","def get_data(self, out_format: str='json'):\n if self.data:\n return self.data\n self.data_ready = self.check_available()\n if not self.data_ready:\n raise DataNotReady(\"The run {} has not yet finished, data not available yet.\".format(self))\n resp = self.ph.conn.request(\n 'GET', self.ph.URLS['getdata'].format(self.run_token), dict(api_key=self.ph.api_key, format=out_format))\n data = resp.data.decode('utf-8')\n self.data = self.parse_json_data(data)\n return self.data","def get_data(self):\n return self._result","def loadData(catalog):\n return controller.loadData(catalog)","def loadData(catalog):\n return controller.loadData(catalog)","def _fetch_data(self, rrdObject, startTime, endTime):\n\n #print rrdObject\n\n if not path.exists(rrdObject):\n raise Exception(\"File not exists: %s\" % rrdObject)\n\n #print \"%s - %s\" % (startTime, endTime)\n\n rrd_data = None\n\n try:\n rrd_data = rrdtool.fetch(str(rrdObject), \"AVERAGE\", \"--start\", str(startTime), \"--end\", str(endTime))\n except Exception as err:\n LOG.error(\"fetch exc %s | %s\", err, rrdObject)\n\n return RrdData(info=rrd_data[0], additional=rrd_data[1], series=rrd_data[2])","def _retrieveCachedData(self):","def get_data(self):\n pass","def get_data(self):\n pass","def get(self,\n datastore,\n ):\n return self._invoke('get',\n {\n 'datastore': datastore,\n })","def get_data(self):","def _fetch(self):\n self._data = self._get(self.url)\n\n if self._data['released_errata'] is not None:\n self._released_errata = Erratum(errata_id=self._data[\n 'released_errata']['id'])\n\n for errata_dict in self._data['all_errata']:\n errata = Erratum(errata_id=errata_dict['id'])\n self._all_errata.append(errata)\n\n self._signed_rpms = self._data.get('rpms_signed')\n\n for et_file in self._data['files']:\n self._files.append(et_file['path'])","async def stations_data():\n with open(\"/data/station_data.json\") as j:\n data = json.load(j)\n return data","def fetch(self):\n # type: () -> List[List[Any]]\n if self._request.next_uri is None:\n self._finished = True\n return []\n response = self._request.get(self._request.next_uri)\n status = self._request.process(response)\n if status.columns:\n self._columns = status.columns\n self._stats.update(status.stats)\n if status.next_uri is None:\n self._finished = True\n return status.rows","def getData(self, taskId:int):\n return self.pool.getData(taskId)","async def fetch_async(self) -> \"FleetInstance\":\n return await self._proxy.fetch_async()","def _fetch_resource(self):\n\n qs = self._build_qs() # Build the query string\n url = self._build_url(qs) # Build the full url\n fp = self._api_call(url) # Fetch the data as a file pointer\n\n # Parse the list of dicts in to a dict generator\n return csv.DictReader(fp)","def data_pull_success():\n return jsonify(result=Statistics.accessible_data())","def fetch(self):\r\n if not self._fetched:\r\n self._fetched = True\r\n self.data = query_cache.get(self.iden) or []","def pull(self):\n data = api.get(endpoint=self.endpoint, resource_id=self.id)\n self.__init__(**data)","def get_data(self):\n return self._results","def get_data(link):\n data = re.get(link)\n jsondata = data.json()\n for weatherstation in jsondata['weatherStations']:\n FetchandStore.sensordict.update({weatherstation[\"id\"]:weatherstation[\"sensorValues\"]})\n for sensorvalue in weatherstation[\"sensorValues\"]:\n FetchandStore.sensors.append({\"id\": sensorvalue[\"roadStationId\"], \"name\": sensorvalue[\"oldName\"],\n \"value\": sensorvalue[\"sensorValue\"], \"unit\": sensorvalue[\"sensorUnit\"],\n \"datetime\": sensorvalue[\"measuredTime\"]})\n return FetchandStore.sensors","def get_data(self, url):\n return self.get(url).get('data', [])","def get(self, name):\n assert name, \"Must input a valid dataset name.\"\n return self.manager.get_data(name)","def get_data(self):\r\n pass","async def get_next_launches(self):\n response = {}\n\n if self._key:\n request_url = f\"{BASE_URL}?key={self._key}\"\n else:\n request_url = BASE_URL\n\n async with await self._session.get(request_url) as resp:\n response = await resp.text()\n\n if response is not None:\n try:\n return json.loads(response)\n except json.decoder.JSONDecodeError as error:\n raise ValueError(f\"Error decoding data from rocketlaunch.live ({error}).\")\n except Exception as error:\n raise ValueError(f\"Unknown error in rocketlaunch.live data ({error})\")\n else:\n raise ConnectionError(\"Error getting data from rocketlaunch.live.\")","def _FetchBuilderData(builder_url):\n data = None\n try:\n url = urllib2.urlopen(builder_url)\n except urllib2.URLError, e:\n print ('urllib2.urlopen error %s, waterfall status page down.[%s]' % (\n builder_url, str(e)))\n return None\n if url is not None:\n try:\n data = url.read()\n except IOError, e:\n print 'urllib2 file object read error %s, [%s].' % (builder_url, str(e))\n return data","def _get_data(self, url: str)->dict:\n data = None\n resp = self._get(url)\n if resp:\n data = resp.json()['data']\n return data","def fetch(self, object_ids):\n return libplasma.fetch(self.conn, object_ids)","def get_data(force=False):\n gen_data(force)\n\n return pickle.load(open(DATA_FILE))","def get_data(self):\n if self.data is None:\n connection = self._open_db()\n cursor = connection.cursor()\n cursor.execute('SELECT data FROM sessions WHERE id = ?;',\n (self.sid,))\n data = cursor.fetchone()\n if data is not None:\n data = data[0]\n if data is not None:\n self.data = pickle.loads(base64.decodestring(data))\n return self.data","def get_data(stage=0):\n return get_files(stage)[1]","def fetch_temp_data(url):\n res = requests.get(url)\n return res.json()","def _get_data(self):\n raise NotImplementedError()","def _get(self):\n self.lib.get()\n blob = self.get_cached_blob()\n if not blob:\n blob = self.knex.get(self.nested_url(), url_options=self.inherited_url_options)\n self.load_blob(blob)\n self.cache_blob(blob)\n else:\n self.load_blob(blob)","def getStockData():\n pass","def get_velib_data():\n api_url = \"https://api.jcdecaux.com/vls/v1/\"\n query_string = \"stations?contract=Paris&apiKey=\"\n api_key = \"ec29d3b17e5162e1459aaad45cddfe74fe832379\"\n my_url = api_url + query_string + api_key\n\n urlobj = URL.urlopen(my_url)\n data = json.load(urlobj)\n# data = urlobj.read()\n# help(data)\n return data","def get(self):\r\n return self.data","def get_data_from_web():\n pass","def _fetch_data(self, samples):\n pass","def fetch_data_from_db(sensorName):\n connection = sqlite3.connect('sensordata.db')\n cursor = connection.cursor()\n cursor.execute(\"SELECT * FROM sensordata WHERE name = :name\", {'name': sensorName})\n observedsensor = cursor.fetchall()\n return observedsensor","def fetch(self):\n for req in self.requests:\n req[6] = self._collect\n\n self.process()\n\n out = self._responses\n self._responses = []\n\n if len(out) == 1:\n return out[0]\n return out","def lastest_datapoint(instance_name: str):\n if request.method == \"GET\":\n with lock:\n if not instances.get(instance_name):\n return jsonify(message=\"Instance Does Not Exist\"), 404\n return jsonify(asdict(instances.get(instance_name).latest()))\n return Response(status=200)","def Results(self):\n return self.data","def Results(self):\n return self.data","def get_data(self):\n self._send_command(self._adapter.get_data())","def collect(self):\n import ray\n return ray.get([shard.get_data.remote() for shard in self.shard_list])","def data(self):\n self._get_latest_content()\n return self._data.get('data', {})","def get_fresh_data(get_team):\n\n \"\"\" Create the URL for today \"\"\"\n now = datetime.datetime.now()\n url = \"http://gd2.mlb.com/gdcross/components/game/mlb/year_\" + '{}'.format(now.year) + \"/month_\" + '{:02d}'.format(now.month) + \"/day_\" + '{:02d}'.format(now.day) + \"/miniscoreboard.json\"\n\n \"\"\" Grab the first response and write it to a file, we'll update it once the game starts \"\"\"\n data_write_file = status_dir + '{}'.format(now.year) + '{:02d}'.format(now.month) + '{:02d}'.format(now.day) + \".json\"\n\n \"\"\" Get the json data if the file doesn't exist, or if it's over three minutes old \"\"\"\n if not os.path.isfile(data_write_file) or time.time() - os.path.getmtime(data_write_file) > 60:\n response = urllib.urlopen(url)\n full_data = json.loads(response.read())\n with open(data_write_file, 'w') as outfile:\n json.dump(full_data, outfile, sort_keys=True, indent=2, ensure_ascii=False)\n\n \"\"\" Use the data from the status file \"\"\"\n with open(data_write_file, 'r') as json_data:\n full_data = json.load(json_data)\n\n \"\"\" This will return false if there is no game today, else will return json data for just our team \"\"\"\n my_game = False\n for game in full_data['data']['games']['game']:\n if get_team in game['home_file_code'] or get_team in game['away_file_code']:\n my_game = game\n \n return my_game","def fetch_list(self):\n\t\treturn self.fetch(self.list_url % ART_SERVER_HOST)","async def _async_update_data(self) -> dict | None:\n\n async with self.api_lock:\n switchbot_data = await self.hass.async_add_executor_job(self._update_data)\n\n if not switchbot_data:\n raise UpdateFailed(\"Unable to fetch switchbot services data\")\n\n return switchbot_data","def get(self):\n\n \n\n try:\n db = getDatabase()\n connection = db.connect()\n\n connection.get(self)\n\n except Exception as e:\n raise e\n finally:\n db.dispose()","def get(self, data):\n ret = self._rest_call({}, 'GET')\n return json.loads(ret[2])","async def fetch_data(self, url: str) -> dict:\n async with self.bot.http_session.get(url) as r:\n return await r.json()","def get_dataset():\n\n return db.store.all()","def download():\n toydata = requests.get(DATA_URL).json()\n return toydata","def get(self, context, id_):\n try:\n db_resource_data = self.db_api.get_resource(\n context, id_)\n _resource_data = _make_response(db_resource_data,\n meta_data=False)\n\n except exception.NotFound as e:\n raise e\n\n except Exception as e:\n msg = (\"Error retrieving the 'resource':%s. Reason: %s\"\n % (id_, e.message))\n LOG.exception(msg)\n raise exception.RetrieveException(e.message)\n\n LOG.info(\"Resource manager data is %s \" %\n logging.mask_password(_resource_data))\n return _resource_data","def get(self):\n return self.data","def data(self):\r\n if self.metadata is None:\r\n self.refresh()\r\n return self.metadata","def pull(self):\n data = api.get(endpoint=self.endpoint, resource_id=self.slug)\n self.__init__(**data)","def get_data(self, calc_throughput=False):\n n_timestamp = datetime.datetime.now()\n delta = n_timestamp - self.timestamp\n\n try:\n ret = ds_client().call('bridge\\n')\n except socket.error:\n return self.data\n\n self.timestamp = n_timestamp\n\n for brdata in ret:\n name = brdata[u'name']\n req = 'bridge ' + name.encode() + ' stats\\n'\n res = ds_client().call(req)[0]\n\n if calc_throughput == True:\n try:\n lps = calc_pps(res[u'flow-lookup-count'],\n self.data[brdata[u'name']][u'flow-lookup-count'],\n delta)\n mps = calc_pps(res[u'flow-matched-count'],\n self.data[brdata[u'name']][u'flow-matched-count'],\n delta)\n res.update({u'lookup_per_sec':lps, u'match_per_sec':mps})\n except KeyError:\n res.update({u'lookup_per_sec':0, u'match_per_sec':0})\n try:\n c_hit = (res[u'flowcache-hit']\n - self.data[brdata[u'name']][u'flowcache-hit'])\n lookup = (res[u'flow-lookup-count']\n - self.data[brdata[u'name']][u'flow-lookup-count'])\n c_rate = c_hit / lookup\n res.update({u'cache_hitrate':str(c_rate)})\n except (KeyError, ZeroDivisionError):\n res.update({u'cache_hitrate':'-'})\n self.data[res[u'name']] = res\n return self.data","def fetch_weather():\n\n # Fetch the current weather.\n response = requests.get(f'https://api.openweathermap.org/data/2.5/weather?q=Manchester,UK&units=metric&APPID={WEATHER_API_KEY}')\n\n # Return the data.\n return response.json()","def load_data():\n try:\n loader.download()\n load_table_data()\n status = 'loaded'\n except Exception as ex:\n log.log_traceback(ex)\n status = 'failed'\n return flask.jsonify({'status': status})"],"string":"[\n \"def _fetch_data(self):\\n pass\",\n \"def run(self):\\n results = self.fetch()\\n return results\",\n \"def fetch_data(self):\",\n \"async def fetch_data(self) -> T:\",\n \"def data():\\n return volumes_fetchers.get_json_data()\",\n \"def get_data():\\n return\",\n \"async def _fetch_data(self) -> T:\\n raise NotImplementedError\",\n \"def fetch_data():\\n data.fetch_data()\\n data.start_updating()\",\n \"def fetch_data(self):\\n\\n data_dict = {\\n 'price': self.get_current_price(),\\n }\\n\\n return self.save_data(data_dict)\",\n \"def get_data():\\n pass\",\n \"def fetch_data(data_url):\\n return requests.get(data_url).content\",\n \"async def async_get_data(self):\\n stage = await self.async_get_stage()\\n data = {\\n \\\"data\\\": {\\\"stage\\\": stage},\\n }\\n return data\",\n \"def getTheData(self, dev):\\n if self.debugLevel >= 2 and self.debug:\\n self.debugLog(u\\\"getTheData FrontViewAPI method called.\\\")\\n\\n # dev.updateStateOnServer('deviceIsOnline', value=True, uiValue=\\\"Download\\\")\\n try:\\n url = 'http://' + dev.pluginProps['sourceXML'] + '/FrontView'\\n r = requests.get(url,timeout=5)\\n result = r.json()\\n if self.debugLevel >= 2 and self.debug:\\n self.debugLog(u\\\"Result:\\\" + unicode(result))\\n self.WaitInterval = 1\\n dev.updateStateOnServer('deviceIsOnline', value=True, uiValue=\\\"Online\\\")\\n dev.setErrorStateOnServer(None)\\n # dev.updateStateOnServer('deviceTimestamp', value=t.time())\\n return result\\n\\n except Exception as error:\\n\\n indigo.server.log(u\\\"Error connecting to Device:\\\" + dev.name)\\n self.WaitInterval = 60\\n if self.debugLevel >= 2 and self.debug:\\n self.debugLog(u\\\"Device is offline. No data to return. \\\")\\n dev.updateStateOnServer('deviceIsOnline', value=False, uiValue=\\\"Offline\\\")\\n # dev.updateStateOnServer('deviceTimestamp', value=t.time())\\n dev.setErrorStateOnServer(u'Offline')\\n result = \\\"\\\"\\n return result\",\n \"def fetch(self) -> None:\\n pass\",\n \"def fetch(self):\\n pass\",\n \"def fetch(self):\\n pass\",\n \"def get_data():\\n pass\",\n \"def get_data():\\n pass\",\n \"def get_data():\\n pass\",\n \"async def _fetch_data(self) -> JobInfo:\\n return await self.api.get_job()\",\n \"def do_fetch(self):\\n pass\",\n \"def _fetch(cls, *args, **kwargs):\\n apikey = htpc.settings.get('plexpy_apikey')\\n\\n if apikey is None:\\n raise\\n\\n url = '%sapi/v2?apikey=%s&%s' % (cls._build_url(), apikey, urlencode(kwargs))\\n\\n try:\\n r = requests.get(url, verify=False)\\n r.raise_for_status()\\n # Lets just copy the headers for now.\\n cherrypy.response.headers['Content-Type'] = r.headers.get('Content-Type', 'application/json;charset=UTF-8')\\n resp = r.json()\\n if resp.get('response', {}).get('result') == 'success':\\n return resp['response']['data']\\n except:\\n log.exception('Failed to get %s' % url)\\n return\",\n \"def get_rest_data_source(uri):\\n def players_from_rest():\\n response = requests.get(uri)\\n response.raise_for_status()\\n return parse_players_json(response.text)\\n return players_from_rest\",\n \"async def fetch_async(self) -> FleetInstance:\\n\\n payload = await self._version.fetch_async(\\n method=\\\"GET\\\",\\n uri=self._uri,\\n )\\n\\n return FleetInstance(\\n self._version,\\n payload,\\n sid=self._solution[\\\"sid\\\"],\\n )\",\n \"def get_data(self):\\n data_i = sqlite3.connect('data::memory:', check_same_thread=False)\\n data_cursor = data_i.cursor()\\n data_cursor.execute('SELECT * FROM localdata')\\n item = data_cursor.fetchall()\\n data_i.commit()\\n data_i.close()\\n return self.generate_data_list(item)\",\n \"def getData(self, local_cache):\",\n \"def AcquiredData (self) :\\n\\t\\treturn self.run(\\\"AcquiredData\\\")\",\n \"def fetch():\\n req_data= request.get_json()\\n \\n ## ddb uses text files, using this as to eat my own dogfoor and improve\\n ## no service sql client. No daemon, low cpu.\\n\\n\\n e=load_db()\\n try:\\n res=e.query(req_data['query'])\\n \\n serialized = jsonpickle.encode( res,\\n unpicklable=False,\\n make_refs=False)\\n return serialized\\n except Exception as ex:\\n return \\\"{0} -> '{1}'\\\".format(ex,req_data['query'])\",\n \"def refresh(self):\\r\\n data = super(Share, self)._fetch()\\r\\n self.data_set = data[self.symbol]\\r\\n return data[self.symbol]\",\n \"def fetch(self):\\n raise NotImplementedError()\",\n \"def get(self):\\n return self.get_data()\",\n \"async def fetch_data(self):\\n url = URL_HASSIO_VERSION.format(self.upstream)\\n try:\\n _LOGGER.info(\\\"Fetch update data from %s\\\", url)\\n with async_timeout.timeout(10, loop=self.loop):\\n async with self.websession.get(url) as request:\\n data = await request.json(content_type=None)\\n\\n except (aiohttp.ClientError, asyncio.TimeoutError, KeyError) as err:\\n _LOGGER.warning(\\\"Can't fetch versions from %s -> %s\\\", url, err)\\n return\\n\\n except json.JSONDecodeError as err:\\n _LOGGER.warning(\\\"Can't parse versions from %s -> %s\\\", url, err)\\n return\\n\\n # data valid?\\n if not data:\\n _LOGGER.warning(\\\"Invalid data from %s\\\", url)\\n return\\n\\n # update versions\\n self._data[ATTR_HOMEASSISTANT] = data.get('homeassistant')\\n self._data[ATTR_HASSIO] = data.get('hassio')\\n self.save()\",\n \"def get_sdata(self):\\n payload = self.get('data_request?id=sdata&output_format=json')\\n return payload\",\n \"def GetData(self):\\r\\n if self.Error == False:\\r\\n Extra = {}\\r\\n try:\\r\\n result = {}\\r\\n temp = self.ScrapeMainWebpage()\\r\\n if temp != None:\\r\\n result.update(temp)\\r\\n temp = self.ScrapeParameters1Webpage()\\r\\n if temp != None:\\r\\n result.update(temp)\\r\\n temp = self.ScrapeParameters2Webpage()\\r\\n if temp != None:\\r\\n result.update(temp)\\r\\n temp = self.ScrapeStatusWebpage()\\r\\n if temp != None:\\r\\n result.update(temp)\\r\\n sqlArray = {}\\r\\n sqlArray[self.deviceDescr] = {}\\r\\n sqlArray[self.deviceDescr][self.devNumber] = {}\\r\\n sqlArray[self.deviceDescr][self.devNumber][\\\"General\\\"] = result\\r\\n sqlArray[self.deviceDescr][self.devNumber][\\\"_ExtractInfo\\\"] = {}\\r\\n sqlArray[self.deviceDescr][self.devNumber][\\\"_ExtractInfo\\\"][\\\"ExtractTime\\\"] = time.time()\\r\\n sqlArray[\\\"ReadError\\\"] = False \\r\\n return sqlArray\\r\\n \\r\\n except Exception as e: \\r\\n self.log.printError(\\\"ERROR in Retreiving Seatel VSAT Data,%s Module Error\\\" % sys._getframe().f_code.co_name) \\r\\n self.log.printError( str(e))\\r\\n self.Error = True\\r\\n Extra[\\\"ReadError\\\"] = True\\r\\n return Extra\\r\\n else:\\r\\n self.log.printWarning(\\\"%s skipped due to previous failure\\\" % sys._getframe().f_code.co_name)\\r\\n return None\",\n \"async def get_data(self) -> list[dict]:\\n tasks = [self.miners[miner].get_api_data() for miner in self.miners]\\n results = await asyncio.gather(*tasks)\\n return results\",\n \"def _load (self):\\n\\n cursor = self._exec (self.select)\\n rows = cursor.fetchall ()\\n if not rows:\\n return None\\n\\n expires, pickled_data = rows[0]\\n data = pickle.loads (pickled_data)\\n return data, expires\",\n \"def _get_data(api):\\n url = API_BASE_URL + api\\n logging.info(\\\"Querying URL: %s\\\", url)\\n response = requests.get(url)\\n count = 0\\n retry_count = 50\\n if response.status_code == 404:\\n logging.error(\\\"Received 404\\\")\\n return None\\n while response.status_code != 200:\\n count += 1\\n if count == retry_count:\\n break\\n logging.error(\\\"Received status code %s, expected 200\\\",\\n response.status_code)\\n time.sleep(2)\\n try:\\n response = requests.get(url)\\n except:\\n pass\\n if response.status_code != 200:\\n # Send emoty dataframe. Remove None\\n return None\\n rdd = SPARK_CONTEXT.parallelize([response.json()])\\n data_frame = SPARK_SESSION.read.json(rdd)\\n return data_frame\",\n \"def _get_raw_data(self, url, series):\\n url = self._get_url(url, series)\\n try:\\n response = self.http.request(url, headers=self._reqheaders)\\n except httplib2.ServerNotFoundError as e:\\n raise TVDBConnectError(e.message), None, sys.exc_info()[2]\\n rep = response[0]\\n log.debug(\\n 'http-status:%s,content:%s', \\n rep['status'], \\n rep['content-type']\\n )\\n if int(rep['status']) >= 400:\\n raise TVDBConnectError(\\n 'Failed to get \\\"%s\\\" from thetvdb. errno:%s' % (\\n series, rep['status']),\\n rep['status']\\n ) \\n return response[1]\",\n \"def get_data(self, out_format: str='json'):\\n if self.data:\\n return self.data\\n self.data_ready = self.check_available()\\n if not self.data_ready:\\n raise DataNotReady(\\\"The run {} has not yet finished, data not available yet.\\\".format(self))\\n resp = self.ph.conn.request(\\n 'GET', self.ph.URLS['getdata'].format(self.run_token), dict(api_key=self.ph.api_key, format=out_format))\\n data = resp.data.decode('utf-8')\\n self.data = self.parse_json_data(data)\\n return self.data\",\n \"def get_data(self):\\n return self._result\",\n \"def loadData(catalog):\\n return controller.loadData(catalog)\",\n \"def loadData(catalog):\\n return controller.loadData(catalog)\",\n \"def _fetch_data(self, rrdObject, startTime, endTime):\\n\\n #print rrdObject\\n\\n if not path.exists(rrdObject):\\n raise Exception(\\\"File not exists: %s\\\" % rrdObject)\\n\\n #print \\\"%s - %s\\\" % (startTime, endTime)\\n\\n rrd_data = None\\n\\n try:\\n rrd_data = rrdtool.fetch(str(rrdObject), \\\"AVERAGE\\\", \\\"--start\\\", str(startTime), \\\"--end\\\", str(endTime))\\n except Exception as err:\\n LOG.error(\\\"fetch exc %s | %s\\\", err, rrdObject)\\n\\n return RrdData(info=rrd_data[0], additional=rrd_data[1], series=rrd_data[2])\",\n \"def _retrieveCachedData(self):\",\n \"def get_data(self):\\n pass\",\n \"def get_data(self):\\n pass\",\n \"def get(self,\\n datastore,\\n ):\\n return self._invoke('get',\\n {\\n 'datastore': datastore,\\n })\",\n \"def get_data(self):\",\n \"def _fetch(self):\\n self._data = self._get(self.url)\\n\\n if self._data['released_errata'] is not None:\\n self._released_errata = Erratum(errata_id=self._data[\\n 'released_errata']['id'])\\n\\n for errata_dict in self._data['all_errata']:\\n errata = Erratum(errata_id=errata_dict['id'])\\n self._all_errata.append(errata)\\n\\n self._signed_rpms = self._data.get('rpms_signed')\\n\\n for et_file in self._data['files']:\\n self._files.append(et_file['path'])\",\n \"async def stations_data():\\n with open(\\\"/data/station_data.json\\\") as j:\\n data = json.load(j)\\n return data\",\n \"def fetch(self):\\n # type: () -> List[List[Any]]\\n if self._request.next_uri is None:\\n self._finished = True\\n return []\\n response = self._request.get(self._request.next_uri)\\n status = self._request.process(response)\\n if status.columns:\\n self._columns = status.columns\\n self._stats.update(status.stats)\\n if status.next_uri is None:\\n self._finished = True\\n return status.rows\",\n \"def getData(self, taskId:int):\\n return self.pool.getData(taskId)\",\n \"async def fetch_async(self) -> \\\"FleetInstance\\\":\\n return await self._proxy.fetch_async()\",\n \"def _fetch_resource(self):\\n\\n qs = self._build_qs() # Build the query string\\n url = self._build_url(qs) # Build the full url\\n fp = self._api_call(url) # Fetch the data as a file pointer\\n\\n # Parse the list of dicts in to a dict generator\\n return csv.DictReader(fp)\",\n \"def data_pull_success():\\n return jsonify(result=Statistics.accessible_data())\",\n \"def fetch(self):\\r\\n if not self._fetched:\\r\\n self._fetched = True\\r\\n self.data = query_cache.get(self.iden) or []\",\n \"def pull(self):\\n data = api.get(endpoint=self.endpoint, resource_id=self.id)\\n self.__init__(**data)\",\n \"def get_data(self):\\n return self._results\",\n \"def get_data(link):\\n data = re.get(link)\\n jsondata = data.json()\\n for weatherstation in jsondata['weatherStations']:\\n FetchandStore.sensordict.update({weatherstation[\\\"id\\\"]:weatherstation[\\\"sensorValues\\\"]})\\n for sensorvalue in weatherstation[\\\"sensorValues\\\"]:\\n FetchandStore.sensors.append({\\\"id\\\": sensorvalue[\\\"roadStationId\\\"], \\\"name\\\": sensorvalue[\\\"oldName\\\"],\\n \\\"value\\\": sensorvalue[\\\"sensorValue\\\"], \\\"unit\\\": sensorvalue[\\\"sensorUnit\\\"],\\n \\\"datetime\\\": sensorvalue[\\\"measuredTime\\\"]})\\n return FetchandStore.sensors\",\n \"def get_data(self, url):\\n return self.get(url).get('data', [])\",\n \"def get(self, name):\\n assert name, \\\"Must input a valid dataset name.\\\"\\n return self.manager.get_data(name)\",\n \"def get_data(self):\\r\\n pass\",\n \"async def get_next_launches(self):\\n response = {}\\n\\n if self._key:\\n request_url = f\\\"{BASE_URL}?key={self._key}\\\"\\n else:\\n request_url = BASE_URL\\n\\n async with await self._session.get(request_url) as resp:\\n response = await resp.text()\\n\\n if response is not None:\\n try:\\n return json.loads(response)\\n except json.decoder.JSONDecodeError as error:\\n raise ValueError(f\\\"Error decoding data from rocketlaunch.live ({error}).\\\")\\n except Exception as error:\\n raise ValueError(f\\\"Unknown error in rocketlaunch.live data ({error})\\\")\\n else:\\n raise ConnectionError(\\\"Error getting data from rocketlaunch.live.\\\")\",\n \"def _FetchBuilderData(builder_url):\\n data = None\\n try:\\n url = urllib2.urlopen(builder_url)\\n except urllib2.URLError, e:\\n print ('urllib2.urlopen error %s, waterfall status page down.[%s]' % (\\n builder_url, str(e)))\\n return None\\n if url is not None:\\n try:\\n data = url.read()\\n except IOError, e:\\n print 'urllib2 file object read error %s, [%s].' % (builder_url, str(e))\\n return data\",\n \"def _get_data(self, url: str)->dict:\\n data = None\\n resp = self._get(url)\\n if resp:\\n data = resp.json()['data']\\n return data\",\n \"def fetch(self, object_ids):\\n return libplasma.fetch(self.conn, object_ids)\",\n \"def get_data(force=False):\\n gen_data(force)\\n\\n return pickle.load(open(DATA_FILE))\",\n \"def get_data(self):\\n if self.data is None:\\n connection = self._open_db()\\n cursor = connection.cursor()\\n cursor.execute('SELECT data FROM sessions WHERE id = ?;',\\n (self.sid,))\\n data = cursor.fetchone()\\n if data is not None:\\n data = data[0]\\n if data is not None:\\n self.data = pickle.loads(base64.decodestring(data))\\n return self.data\",\n \"def get_data(stage=0):\\n return get_files(stage)[1]\",\n \"def fetch_temp_data(url):\\n res = requests.get(url)\\n return res.json()\",\n \"def _get_data(self):\\n raise NotImplementedError()\",\n \"def _get(self):\\n self.lib.get()\\n blob = self.get_cached_blob()\\n if not blob:\\n blob = self.knex.get(self.nested_url(), url_options=self.inherited_url_options)\\n self.load_blob(blob)\\n self.cache_blob(blob)\\n else:\\n self.load_blob(blob)\",\n \"def getStockData():\\n pass\",\n \"def get_velib_data():\\n api_url = \\\"https://api.jcdecaux.com/vls/v1/\\\"\\n query_string = \\\"stations?contract=Paris&apiKey=\\\"\\n api_key = \\\"ec29d3b17e5162e1459aaad45cddfe74fe832379\\\"\\n my_url = api_url + query_string + api_key\\n\\n urlobj = URL.urlopen(my_url)\\n data = json.load(urlobj)\\n# data = urlobj.read()\\n# help(data)\\n return data\",\n \"def get(self):\\r\\n return self.data\",\n \"def get_data_from_web():\\n pass\",\n \"def _fetch_data(self, samples):\\n pass\",\n \"def fetch_data_from_db(sensorName):\\n connection = sqlite3.connect('sensordata.db')\\n cursor = connection.cursor()\\n cursor.execute(\\\"SELECT * FROM sensordata WHERE name = :name\\\", {'name': sensorName})\\n observedsensor = cursor.fetchall()\\n return observedsensor\",\n \"def fetch(self):\\n for req in self.requests:\\n req[6] = self._collect\\n\\n self.process()\\n\\n out = self._responses\\n self._responses = []\\n\\n if len(out) == 1:\\n return out[0]\\n return out\",\n \"def lastest_datapoint(instance_name: str):\\n if request.method == \\\"GET\\\":\\n with lock:\\n if not instances.get(instance_name):\\n return jsonify(message=\\\"Instance Does Not Exist\\\"), 404\\n return jsonify(asdict(instances.get(instance_name).latest()))\\n return Response(status=200)\",\n \"def Results(self):\\n return self.data\",\n \"def Results(self):\\n return self.data\",\n \"def get_data(self):\\n self._send_command(self._adapter.get_data())\",\n \"def collect(self):\\n import ray\\n return ray.get([shard.get_data.remote() for shard in self.shard_list])\",\n \"def data(self):\\n self._get_latest_content()\\n return self._data.get('data', {})\",\n \"def get_fresh_data(get_team):\\n\\n \\\"\\\"\\\" Create the URL for today \\\"\\\"\\\"\\n now = datetime.datetime.now()\\n url = \\\"http://gd2.mlb.com/gdcross/components/game/mlb/year_\\\" + '{}'.format(now.year) + \\\"/month_\\\" + '{:02d}'.format(now.month) + \\\"/day_\\\" + '{:02d}'.format(now.day) + \\\"/miniscoreboard.json\\\"\\n\\n \\\"\\\"\\\" Grab the first response and write it to a file, we'll update it once the game starts \\\"\\\"\\\"\\n data_write_file = status_dir + '{}'.format(now.year) + '{:02d}'.format(now.month) + '{:02d}'.format(now.day) + \\\".json\\\"\\n\\n \\\"\\\"\\\" Get the json data if the file doesn't exist, or if it's over three minutes old \\\"\\\"\\\"\\n if not os.path.isfile(data_write_file) or time.time() - os.path.getmtime(data_write_file) > 60:\\n response = urllib.urlopen(url)\\n full_data = json.loads(response.read())\\n with open(data_write_file, 'w') as outfile:\\n json.dump(full_data, outfile, sort_keys=True, indent=2, ensure_ascii=False)\\n\\n \\\"\\\"\\\" Use the data from the status file \\\"\\\"\\\"\\n with open(data_write_file, 'r') as json_data:\\n full_data = json.load(json_data)\\n\\n \\\"\\\"\\\" This will return false if there is no game today, else will return json data for just our team \\\"\\\"\\\"\\n my_game = False\\n for game in full_data['data']['games']['game']:\\n if get_team in game['home_file_code'] or get_team in game['away_file_code']:\\n my_game = game\\n \\n return my_game\",\n \"def fetch_list(self):\\n\\t\\treturn self.fetch(self.list_url % ART_SERVER_HOST)\",\n \"async def _async_update_data(self) -> dict | None:\\n\\n async with self.api_lock:\\n switchbot_data = await self.hass.async_add_executor_job(self._update_data)\\n\\n if not switchbot_data:\\n raise UpdateFailed(\\\"Unable to fetch switchbot services data\\\")\\n\\n return switchbot_data\",\n \"def get(self):\\n\\n \\n\\n try:\\n db = getDatabase()\\n connection = db.connect()\\n\\n connection.get(self)\\n\\n except Exception as e:\\n raise e\\n finally:\\n db.dispose()\",\n \"def get(self, data):\\n ret = self._rest_call({}, 'GET')\\n return json.loads(ret[2])\",\n \"async def fetch_data(self, url: str) -> dict:\\n async with self.bot.http_session.get(url) as r:\\n return await r.json()\",\n \"def get_dataset():\\n\\n return db.store.all()\",\n \"def download():\\n toydata = requests.get(DATA_URL).json()\\n return toydata\",\n \"def get(self, context, id_):\\n try:\\n db_resource_data = self.db_api.get_resource(\\n context, id_)\\n _resource_data = _make_response(db_resource_data,\\n meta_data=False)\\n\\n except exception.NotFound as e:\\n raise e\\n\\n except Exception as e:\\n msg = (\\\"Error retrieving the 'resource':%s. Reason: %s\\\"\\n % (id_, e.message))\\n LOG.exception(msg)\\n raise exception.RetrieveException(e.message)\\n\\n LOG.info(\\\"Resource manager data is %s \\\" %\\n logging.mask_password(_resource_data))\\n return _resource_data\",\n \"def get(self):\\n return self.data\",\n \"def data(self):\\r\\n if self.metadata is None:\\r\\n self.refresh()\\r\\n return self.metadata\",\n \"def pull(self):\\n data = api.get(endpoint=self.endpoint, resource_id=self.slug)\\n self.__init__(**data)\",\n \"def get_data(self, calc_throughput=False):\\n n_timestamp = datetime.datetime.now()\\n delta = n_timestamp - self.timestamp\\n\\n try:\\n ret = ds_client().call('bridge\\\\n')\\n except socket.error:\\n return self.data\\n\\n self.timestamp = n_timestamp\\n\\n for brdata in ret:\\n name = brdata[u'name']\\n req = 'bridge ' + name.encode() + ' stats\\\\n'\\n res = ds_client().call(req)[0]\\n\\n if calc_throughput == True:\\n try:\\n lps = calc_pps(res[u'flow-lookup-count'],\\n self.data[brdata[u'name']][u'flow-lookup-count'],\\n delta)\\n mps = calc_pps(res[u'flow-matched-count'],\\n self.data[brdata[u'name']][u'flow-matched-count'],\\n delta)\\n res.update({u'lookup_per_sec':lps, u'match_per_sec':mps})\\n except KeyError:\\n res.update({u'lookup_per_sec':0, u'match_per_sec':0})\\n try:\\n c_hit = (res[u'flowcache-hit']\\n - self.data[brdata[u'name']][u'flowcache-hit'])\\n lookup = (res[u'flow-lookup-count']\\n - self.data[brdata[u'name']][u'flow-lookup-count'])\\n c_rate = c_hit / lookup\\n res.update({u'cache_hitrate':str(c_rate)})\\n except (KeyError, ZeroDivisionError):\\n res.update({u'cache_hitrate':'-'})\\n self.data[res[u'name']] = res\\n return self.data\",\n \"def fetch_weather():\\n\\n # Fetch the current weather.\\n response = requests.get(f'https://api.openweathermap.org/data/2.5/weather?q=Manchester,UK&units=metric&APPID={WEATHER_API_KEY}')\\n\\n # Return the data.\\n return response.json()\",\n \"def load_data():\\n try:\\n loader.download()\\n load_table_data()\\n status = 'loaded'\\n except Exception as ex:\\n log.log_traceback(ex)\\n status = 'failed'\\n return flask.jsonify({'status': status})\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.656309","0.6497311","0.64277226","0.6388809","0.6219082","0.61422515","0.61238706","0.6111634","0.6094759","0.607809","0.60757875","0.60490596","0.5996118","0.598899","0.59853625","0.59853625","0.5912132","0.5912132","0.5912132","0.5903248","0.5899217","0.5856119","0.58066005","0.58013403","0.5784876","0.5763161","0.5731849","0.5726598","0.5713088","0.57114464","0.57098013","0.5705788","0.5692526","0.5666539","0.5649513","0.56432945","0.5599284","0.55894816","0.55831105","0.55712336","0.5568318","0.5568318","0.5567938","0.55655056","0.5560199","0.5560199","0.5549584","0.55405927","0.55341464","0.553326","0.55239147","0.5509345","0.5502191","0.54998773","0.54829025","0.54619735","0.54605246","0.54596126","0.5457247","0.5433068","0.5425854","0.5415754","0.54145616","0.5412003","0.5404393","0.5401983","0.54006684","0.53970516","0.5396939","0.5393815","0.5384798","0.5384685","0.5380318","0.53798103","0.5360204","0.5347525","0.5347285","0.5345698","0.5338688","0.5326406","0.5324142","0.5324142","0.5321097","0.5316413","0.5310629","0.53090006","0.53078526","0.5294797","0.52916455","0.5283484","0.52820677","0.52815485","0.52801037","0.52787054","0.5275045","0.527503","0.52738327","0.5268437","0.52646434","0.52398306"],"string":"[\n \"0.656309\",\n \"0.6497311\",\n \"0.64277226\",\n \"0.6388809\",\n \"0.6219082\",\n \"0.61422515\",\n \"0.61238706\",\n \"0.6111634\",\n \"0.6094759\",\n \"0.607809\",\n \"0.60757875\",\n \"0.60490596\",\n \"0.5996118\",\n \"0.598899\",\n \"0.59853625\",\n \"0.59853625\",\n \"0.5912132\",\n \"0.5912132\",\n \"0.5912132\",\n \"0.5903248\",\n \"0.5899217\",\n \"0.5856119\",\n \"0.58066005\",\n \"0.58013403\",\n \"0.5784876\",\n \"0.5763161\",\n \"0.5731849\",\n \"0.5726598\",\n \"0.5713088\",\n \"0.57114464\",\n \"0.57098013\",\n \"0.5705788\",\n \"0.5692526\",\n \"0.5666539\",\n \"0.5649513\",\n \"0.56432945\",\n \"0.5599284\",\n \"0.55894816\",\n \"0.55831105\",\n \"0.55712336\",\n \"0.5568318\",\n \"0.5568318\",\n \"0.5567938\",\n \"0.55655056\",\n \"0.5560199\",\n \"0.5560199\",\n \"0.5549584\",\n \"0.55405927\",\n \"0.55341464\",\n \"0.553326\",\n \"0.55239147\",\n \"0.5509345\",\n \"0.5502191\",\n \"0.54998773\",\n \"0.54829025\",\n \"0.54619735\",\n \"0.54605246\",\n \"0.54596126\",\n \"0.5457247\",\n \"0.5433068\",\n \"0.5425854\",\n \"0.5415754\",\n \"0.54145616\",\n \"0.5412003\",\n \"0.5404393\",\n \"0.5401983\",\n \"0.54006684\",\n \"0.53970516\",\n \"0.5396939\",\n \"0.5393815\",\n \"0.5384798\",\n \"0.5384685\",\n \"0.5380318\",\n \"0.53798103\",\n \"0.5360204\",\n \"0.5347525\",\n \"0.5347285\",\n \"0.5345698\",\n \"0.5338688\",\n \"0.5326406\",\n \"0.5324142\",\n \"0.5324142\",\n \"0.5321097\",\n \"0.5316413\",\n \"0.5310629\",\n \"0.53090006\",\n \"0.53078526\",\n \"0.5294797\",\n \"0.52916455\",\n \"0.5283484\",\n \"0.52820677\",\n \"0.52815485\",\n \"0.52801037\",\n \"0.52787054\",\n \"0.5275045\",\n \"0.527503\",\n \"0.52738327\",\n \"0.5268437\",\n \"0.52646434\",\n \"0.52398306\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5962772"},"document_rank":{"kind":"string","value":"16"}}},{"rowIdx":93888,"cells":{"query":{"kind":"string","value":"Setup the aREST binary sensor."},"document":{"kind":"string","value":"def setup_platform(hass, config, add_devices, discovery_info=None):\n resource = config.get(CONF_RESOURCE)\n pin = config.get(CONF_PIN)\n\n if None in (resource, pin):\n _LOGGER.error('Not all required config keys present: %s',\n ', '.join((CONF_RESOURCE, CONF_PIN)))\n return False\n\n try:\n response = requests.get(resource, timeout=10).json()\n except requests.exceptions.MissingSchema:\n _LOGGER.error('Missing resource or schema in configuration. '\n 'Add http:// to your URL.')\n return False\n except requests.exceptions.ConnectionError:\n _LOGGER.error('No route to device at %s. '\n 'Please check the IP address in the configuration file.',\n resource)\n return False\n\n arest = ArestData(resource, pin)\n\n add_devices([ArestBinarySensor(arest,\n resource,\n config.get('name', response['name']),\n pin)])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def setUp(self):\n self.ser = Serial()\n self.device_obj = ZBSensor(self.ser)","def setUp(self):\n self.sensor = Sensor('127.1.1.3', 9000)","def __init__(self):\n self._read_calibration_data()\n self.configure_sensor(\n TemperatureOversamplings.x08,\n PressureOversamplings.x16,\n HumidityOversamplings.x08,\n IIRFilterCoefficients.FC_003,\n 250,\n 250)","def setup(self):\n self.log.debug('RFSwitch - in RFSwitch setup()')\n # Add resource setup code here\n print(\"Calling RFSwitch:setup\")","def _setup_sensor ( self ):\n self.spectral = Spectral ( np.array([500, 610, 780, 1580.] ),\n np.array([590, 680, 890, 1750.] ) )","def _initialize(self):\n self.flush()\n print(\"Initializing sensor...\")\n try:\n self.get_sample()\n print('Initialization successful')\n except:\n print('Initialization failed. Please disconnect and reconnect sensor.')","def _setup_sensor ( self ):\n self.spectral = Spectral ( np.array([450, 520, 630, 770., 1550, 2090.] ),\n np.array([ 520, 600, 690, 900., 1750., 2350.] ) )","def setUp(self):\n import protolibs.ics_servers as ics_servers\n from point import Point\n from configobj import ConfigObj\n\n # Get config file\n configfile = '/'.join(['sims', 'rtutank', 'config'])\n config=ConfigObj(infile=configfile, unrepr=True)\n self.config = config\n #Set global variable devconfig here \n devconfig=config['vdevs']['slave'] \n\n ##--Set up points\n points={}\n for p in devconfig['points']:\n points.update( { p['name'] : Point(**p) } ) \n #The ** treats the p dictionary as the arguments to the Point class\n self.server = ics_servers.ModbusRTU(devconfig['icsifaces'][0], points.values())\n self.server.start()","def setup_sensors(self):\n super(EddRoach2ProductController, self).setup_sensors()\n self._firmware_server_sensor = Sensor.string(\n \"firmware-server\",\n description=\"The address of the firmware server started by this product\",\n default=\"\",\n initial_status=Sensor.UNKNOWN)\n self.add_sensor(self._firmware_server_sensor)\n self._parent.mass_inform(Message.inform('interface-changed'))","def _setup(self) -> None:\n self._api = get_api(\n self._password,\n self._host,\n self._username,\n self._port,\n self._ssl,\n )\n\n self._info = self._api.get_info()\n self.device_name = self._info.get(\"DeviceName\", DEFAULT_NAME)\n self.model = self._info.get(\"ModelName\")\n self.firmware_version = self._info.get(\"Firmwareversion\")\n\n for model in MODELS_V2:\n if self.model.startswith(model):\n self._method_version = 2","async def init(self):\n logger.info(\"Init device: %s\", self._serial)\n self._callback(STATUS_INIT)\n\n self._init_binaries()\n self._init_apks()\n await self._init_forwards()\n\n await adb.shell(self._serial, \"/data/local/tmp/atx-agent server --stop\")\n await adb.shell(self._serial, \"/data/local/tmp/atx-agent server --nouia -d\")","def __init__(self) -> None:\n self.sensor = serial.Serial(config.DEVICE)\n super().__init__()","def __init__(self, parent, endpoint):\n Wemo_Endpoint.__init__(self, parent, endpoint)\n self.device_type = self._Parent._DeviceTypes.get('wemo_binary_sensor')\n self.FEATURES.update({\n FEATURE_BRIGHTNESS: False,\n FEATURE_PERCENT: False,\n FEATURE_NUMBER_OF_STEPS: False\n })","def setup_fan():\n global dev_fan\n dev_fan = iot_fan.Fan(config.option('pin_name', 'Fan'))\n fan_init()","def __init__(self, sensor, temperature_resolution, humidity_resolution):\n self.sensor = sensor\n self.sensor.turnHeaterOn() \n time.sleep(1.0) # Burn off condensed stuff.\n self.sensor.turnHeaterOff() \n self.update()\n # Main Program\n #print \"------------\"\n #print \"Manfacturer ID=0x%X\"% self.sensor.readManufacturerID() \n #print \"Device ID=0x%X\"% self.sensor.readDeviceID() \n #print \"Serial Number ID=0x%X\"% self.sensor.readSerialNumber() \n \n # change temperature resolution\n self.sensor.setTemperatureResolution(temperature_resolution)\n self.sensor.setHumidityResolution(humidity_resolution)","def __init__(self, com_str: str):\n self.com_str = com_str\n self.ser_arduino = Serial(self.com_str, 9600)\n logging.info(\"Arduino detected\")\n\n # Launch mediator\n self.arduino_mediator = ArduinoMediator(self.ser_arduino)\n self.arduino_mediator.start()\n logging.info(\"Arduino Mediator started\")\n\n handler = CsgoRequestHandler.create(\n self.arduino_mediator, self.data_store\n )\n self.server = HTTPServer((\"localhost\", 3000), handler)","def setUp(self):\n # Direct connection used to match the property values\n self.sockobj = socket(AF_INET, SOCK_STREAM)\n self.sockobj.settimeout(socket_timeout)\n # Connect to the selected server\n self.sockobj.connect(server) \n self.pyclient = PySimpleClient()\n self.cmd_num = 0\n for servo_type in app_nr.values():\n self.__dict__[servo_type] = self.pyclient.getComponent(\"MINORSERVO/\" + servo_type)","def setUp(self):\n self.server_address = \"http://localhost:3030/$/\"\n self.request_address = \"http://localhost:3030/ds\"\n self.api = \"http://localhost:4032/\"\n self.version = \"0.2\"","def init():\n\n global registry, fsk_router, ook_router\n\n radio.init()\n OpenThings.init(Devices.CRYPT_PID)\n\n fsk_router = Registry.Router(\"fsk\")\n\n #OOK receive not yet written\n #It will be used to be able to learn codes from Energenie legacy hand remotes\n ##ook_router = Registry.Router(\"ook\")\n\n registry = Registry.DeviceRegistry()\n registry.set_fsk_router(fsk_router)\n ##registry.set_ook_router(ook_router\n\n path = os.path.join(sys.path[0], registry.DEFAULT_FILENAME)\n if os.path.isfile(path):\n registry.load_from(path)\n print(\"loaded registry from file\")\n registry.list()\n fsk_router.list()\n\n # Default discovery mode, unless changed by app\n ##discovery_none()\n ##discovery_auto()\n ##discovery_ask(ask)\n discovery_autojoin()\n ##discovery_askjoin(ask)","def setup(self):\n settings: ClusterQRM_RF_Settings = self.settings\n if self.is_connected:\n # Load settings\n port_settings: ClusterRF_OutputPort_Settings = settings.ports[\"o1\"]\n self.ports[\"o1\"].channel = port_settings.channel\n self._port_channel_map[\"o1\"] = self.ports[\"o1\"].channel\n self.ports[\"o1\"].attenuation = port_settings.attenuation\n self.ports[\"o1\"].lo_enabled = port_settings.lo_enabled\n self.ports[\"o1\"].lo_frequency = port_settings.lo_frequency\n self.ports[\"o1\"].gain = port_settings.gain\n self.ports[\"o1\"].hardware_mod_en = port_settings.hardware_mod_en\n\n self.ports[\"o1\"].nco_freq = 0\n self.ports[\"o1\"].nco_phase_offs = 0\n\n port_settings: QbloxInputPort_Settings = settings.ports[\"i1\"]\n self.ports[\"i1\"].channel = port_settings.channel\n self._port_channel_map[\"i1\"] = self.ports[\"i1\"].channel\n self.ports[\"i1\"].hardware_demod_en = port_settings.hardware_demod_en\n self.ports[\"i1\"].acquisition_hold_off = port_settings.acquisition_hold_off\n self.ports[\"i1\"].acquisition_duration = port_settings.acquisition_duration\n\n self._channel_port_map = {v: k for k, v in self._port_channel_map.items()}\n self.channels = list(self._channel_port_map.keys())\n else:\n raise Exception(\"The instrument cannot be set up, there is no connection\")","def on_start(self):\n self.id = str(self.locust).split('object at')[1].strip().replace('>','')\n\n # get data from common config\n self.read_locust_config()\n\n # get data from tempest config\n self.keystone_user = self.get_tempest_config_value('identity','username')\n self.keystone_tenant = self.get_tempest_config_value('identity','tenant_name')\n self.keystone_pw = self.get_tempest_config_value('identity','password')\n self.keystone_uri = self.get_tempest_config_value('identity','uri')\n\n self.output(\"Prepare to be rannsaka'd...\")","async def async_setup(self) -> None:\n if not self.aiolivisi.livisi_connection_data:\n livisi_connection_data = {\n \"ip_address\": self.config_entry.data[CONF_HOST],\n \"password\": self.config_entry.data[CONF_PASSWORD],\n }\n\n await self.aiolivisi.async_set_token(\n livisi_connection_data=livisi_connection_data\n )\n controller_data = await self.aiolivisi.async_get_controller()\n if controller_data[\"controllerType\"] == \"Avatar\":\n self.port = AVATAR_PORT\n self.is_avatar = True\n else:\n self.port = CLASSIC_PORT\n self.is_avatar = False\n self.serial_number = controller_data[\"serialNumber\"]\n self.controller_type = controller_data[\"controllerType\"]","def init_edra(self) -> None:\n ...","def setup(self):\n self.ae = None","def __init__(self):\n self._read_calibration_data()\n self.set_oversamplings_and_mode(\n HumidityOversampling.x08,\n TemperatureOversampling.x08,\n PressureOversampling.x16,\n SensorMode.Normal)\n self.set_config(\n InactiveDuration.ms1000,\n FilterCoefficient.fc04)","def _InitializeBase(self):\n imuen = rospy.get_param(\"~imuenable\", \"True\")\n if imuen:\n message = 'Startimu\\r'\n else:\n message = 'Startnoimu\\r'\n\n rospy.loginfo(\"Initializing Base \" + message)\n self._WriteSerial(message)\n \n lincorrection = rospy.get_param(\"~linear_correction\", 1.0)\n angcorrection = rospy.get_param(\"~angular_correction\", 0.984)\n message = 'ascale %d %d\\r' % self._GetBaseAndExponent(angcorrection)\n rospy.loginfo(\"Sending correction value: \" + message)\n self._WriteSerial(message)\n message = 'lscale %d %d\\r' % self._GetBaseAndExponent(lincorrection)\n rospy.loginfo(\"Sending correction value: \" + message)\n self._WriteSerial(message)","def __setup(self):\n\n # Create empty config json file if not there.\n if not self.config_file:\n self.config_file = \"./default.config\"\n if not os.path.isfile(self.config_file):\n with open(self.config_file, 'w') as f:\n json.dump({}, f)\n\n # Recycle and replace values in the config file\n with open(self.config_file, 'r') as json_file:\n data = json.load(json_file)\n\n # Records the `server`, `license_key`, and `user_email` if they don't exist.\n # Server\n if (self.server not in data.keys() or data[self.server] == {}):\n data[self.server] = {}\n\n # License key\n if (self.license_key not in data[self.server].keys() or data[self.server][self.license_key] == {}):\n data[self.server][self.license_key] = {}\n\n # User email\n if self.user.email not in data[self.server][self.license_key].keys():\n data[self.server][self.license_key][self.user.email] = {}\n\n # Next, if a `device_token` doesn't exist for this server and\n # license, call the `Organization.registerLicense()` endpoint to\n # obtain a device token. On the other hand, if the device token\n # already exists, it takes the existing token to prevent\n # redundantly registering the same license.\n\n # Device token\n if (\"device_token\" not in data[self.server][self.license_key].keys()\n or data[self.server][self.license_key][\"device_token\"] == ''):\n out = self.organization.registerLicense(self.device_name)\n if 'Token' not in out:\n self.__record(data=data) # Save current state\n raise PermissionError(\n \"Registration error. Make sure your license key is valid for the selected server.\")\n\n self.device_token = out['Token']\n data[self.server][self.license_key][\"device_token\"] = self.device_token\n\n elif (self.device_token == '' and data[self.server][self.license_key][\"device_token\"] != ''):\n self.device_token = data[self.server][self.license_key][\"device_token\"]\n\n # Next, if a `user_token` does not exist for the current user on\n # this license and server, it tries to log in (`User.login()`) the\n # user first using the device token. If cannot be logged in, it\n # needs to create a new user (`User.create()`) before logging in.\n # The user information and credentials are already handled in the\n # `User` class, so it only needs to pass in the `device_token`.\n\n # User token\n if (\"user_token\" not in data[self.server][self.license_key][self.user.email].keys()\n or data[self.server][self.license_key][self.user.email][\"user_token\"] == ''):\n res = self.user.login(self.device_token)\n\n if res == \"INVALID_USER\":\n res = self.user.create(self.device_token)\n if res == 'INTERNAL_ERROR':\n self.__record(data=data)\n raise PermissionError(\"Cannot create new user. Check your license permissions.\")\n\n self.user_id = res\n res = self.user.login(self.device_token)\n\n elif res == \"INVALID_PASSWORD\":\n self.__record(data=data)\n raise PermissionError(\"Incorrect login password.\")\n\n self.user_token = self.user.user_token\n if self.user_token != '':\n data[self.server][self.license_key][self.user.email][\"user_token\"] = self.user_token\n else:\n self.user_token = data[self.server][self.license_key][self.user.email][\"user_token\"]\n self.user.user_token = self.user_token\n\n # Record updated data into the config file.\n self.__record(data=data)","def setup_method(self):\n self.hass = get_test_home_assistant()\n\n self.config = {\n ip.DOMAIN: {\n \"platform\": \"microsoft_face_identify\",\n \"source\": {\"entity_id\": \"camera.demo_camera\", \"name\": \"test local\"},\n \"group\": \"Test Group1\",\n },\n \"camera\": {\"platform\": \"demo\"},\n mf.DOMAIN: {\"api_key\": \"12345678abcdef6\"},\n }\n\n self.endpoint_url = f\"https://westus.{mf.FACE_API_URL}\"","def setUp(self):\n import protolibs.ics_servers as ics_servers\n from point import Point\n from configobj import ConfigObj\n\n # Get config file\n configfile = '/'.join(['sims', 'tcptank', 'config'])\n config=ConfigObj(infile=configfile, unrepr=True)\n self.config = config\n #Set global variable devconfig here \n devconfig=config['vdevs']['slave'] \n\n ##--Set up points\n points={}\n for p in devconfig['points']:\n points.update( { p['name'] : Point(**p) } ) \n #The ** treats the p dictionary as the arguments to the Point class\n self.server = ics_servers.ModbusTCP( devconfig['icsifaces'][0], points.values() )\n self.server.start()","def __init__(self):\n config = self.read_config()\n self.deployment = config['deployment']\n self.deployment_config = config[self.deployment]\n logger.info(f'Initializing storage client with the {self.deployment} deployment config {pformat(self.deployment_config)}')\n\n # get the MLOS config from the user else default it from the deployment config file\n # self.mlos_config = config['MLOS']\n # logger.info(f'Initializing storage client with the MLOS config {pformat(self.mlos_config)}')\n\n # setup the mount path\n if self.deployment == \"LOCAL\":\n self.mount_dir = self.setup_mount()\n logger.info(f'Mount directory setup completed: {self.mount_dir}')","def setUp(self):\n self.api = \"http://localhost:4031/\"\n self.version = \"0.2\"\n self.app = init_api()","def setup(request):\n print \"SETUP ENDPOINT\"\n routes = None\n if request.method == 'POST':\n content = request.content.read()\n routes = json.loads(content)\n print \"ROUTES ARE: \", str(routes)\n node.routes = routes\n return \"OK\"\n else:\n print \"Not a POST request.\"","def __init__(self, comport, logging = False):\n super(Bin_API, self).__init__(comport, logging)","def __init__(self):\n # FIXME: IS this needed?\n super(ArduinoStation, self).__init__()\n\n self.serial_port_pattern = '/dev/ttyACM{port_num}'\n self.serial_port_num = None\n self.baudrate = 9600\n self.ser = self._setup_serial_connection()\n\n\n # Sensor 1 (DHT11) has 2 readings, Sensor 2 has 1\n ## FIXME: Should look for key pairs in list and submit when no more unique readings are coming through\n if config.SCB_CONFIGURATION == 'standard':\n self.lines_per_observation = 3\n else:\n self.lines_per_observation = 7 # Allows for up to 5 DS18B20 along w/ DHT-11.","def __init__(self):\n self.server_name = 'Binary Light Device'\n self.device = None","def _custom_endpoint_init(self, node_config, *argv):\n config = {}\n selector = node_config.get('template', None)\n if not selector:\n selector = argv[0]\n _LOGGER.debug(\" selector: %s\", selector)\n config = {\n \"config_report\": [\n [0x0001, 0x0020, 60, 3600, 5], \n [0x0001, 0x0021, 60, 3600, 5]\n ],\n \"in_cluster\": [0x0000, 0x0001, 0x0500, ],\n \"out_cluster\": [0x0500],\n \"type\": \"binary_sensor\",\n }\n self.add_input_cluster(0x0500)\n self.add_output_cluster(0x0500)","def setUpClass(cls):\n super().setUpClass()\n super().set_raw_dump_file('v0x05', 'ofpt_port_status')\n super().set_raw_dump_object(_new_portstatus)\n super().set_minimum_size(80)","def __init__(self, arest, resource, name, pin):\n self.arest = arest\n self._resource = resource\n self._name = name\n self._pin = pin\n self.update()\n\n if self._pin is not None:\n request = requests.get('{}/mode/{}/i'.format\n (self._resource, self._pin), timeout=10)\n if request.status_code is not 200:\n _LOGGER.error(\"Can't set mode. Is device offline?\")","def __init__( self, dev, port ):\n super( Grove_Light_Sensor, self ).__init__( dev, port )","async def _hw_init(self):\n await self._write_async(b\":XR\\r\") # Broadcast: initialize + execute\n # Note: no need to consume reply here because there is none (since we are using broadcast)","def __init__(self, nsx_appliance=None):\n super(ControllerUpgrade, self).__init__()\n self.log = logger.setup_logging(self.__class__.__name__)\n self.schema_class = 'controller_upgrade_schema.ControllerUpgradeSchema'\n self.set_content_type('application/xml')\n self.set_accept_type('application/xml')\n self.auth_type = \"vsm\"\n self.client_type = \"vsm\"\n if nsx_appliance != None:\n self.set_connection(nsx_appliance.get_connection())\n self.start_endpoint = '/vdn/controller/cluster/upgrade'\n self.capability_endpoint = 'vdn/controller/upgrade-available'\n self.create_endpoint = self.start_endpoint\n self.cluster_status_endpoint = self.start_endpoint\n self.id = None","def setup(self):\n\n default_config = self.read()\n\n self.write(default_config)","def setUpClass(self):\n self.content_type = \"application/json\"\n self.product_payload = {\"name\": \"Olive Oil\"}","def __init__(self, touch_sensor, port=\"A\"):\r\n # ---------------------------------------------------------------------\r\n # Done: 4. Read the following, ASKING QUESTIONS AS NEEDED.\r\n # Once you understand the code, change this _TODO_ to DONE.\r\n # ---------------------------------------------------------------------\r\n\r\n # The arm motor should always use a 100% duty cycle for its speed.\r\n self.speed = 100\r\n\r\n self.arm_touch_sensor = touch_sensor\r\n self.arm_motor = rose_ev3.Motor(port, motor_type=\"medium\")\r\n self.is_calibrated = False","def setup_sensors(self, configs):\n self.__sensors = self.setup_components(configs, 'scale_client.sensors')","def __init__(self, api, coordinator, name, dev_id, binary_sensor, model):\n super().__init__(\n api, coordinator, name, dev_id, False, binary_sensor, None, model\n )\n\n self._attributes = {}","def do_load_environment(self, *arg):\n print(\"Loading sensors\")\n self.environment = ArduinoSerialMonitor(auto_detect=False)\n self.do_enable_sensor('environment', delay=1)","def initialize(self):\n watch_tv = self.args['watch_tv']\n cleaning = self.args['cleaning']\n self.sensor_living = self.get_app('globals').sensor_living # type: Sensor\n self.sensor_bedroom = self.get_app('globals').sensor_bedroom # type: Sensor\n self.sensor_spare = self.get_app('globals').sensor_spare # type: Sensor\n self.listen_state(self.watching_tv, watch_tv, new=\"on\")\n self.listen_state(self.stop_watching, watch_tv, new=\"off\")\n self.listen_state(self.clean_on, cleaning, new='on')\n self.listen_state(self.clean_off, cleaning, new='off')","def setUp(self):\n self._s1ap_wrapper = s1ap_wrapper.TestWrapper()","def setUp(self):\n self._s1ap_wrapper = s1ap_wrapper.TestWrapper()","def setUp(self):\n sumo_bin = sumolib.checkBinary('sumo')\n traci.start([sumo_bin, \"-n\", self.network_path, \"-r\", self.routes_path])\n traci.simulationStep()","def setUpClass(cls):\n cls.nhf = nhflux.NhfluxStream.readBinary(SIMPLE_HEXZ_NHFLUX)","def TurbiniaSetUp(\n self, project: str, turbinia_auth: bool,\n turbinia_recipe: Union[str, None], turbinia_zone: str, turbinia_api: str,\n incident_id: str, sketch_id: int) -> None:\n self.project = project\n self.turbinia_auth = turbinia_auth\n self.turbinia_api = turbinia_api\n self.turbinia_recipe = turbinia_recipe\n self.turbinia_zone = turbinia_zone\n self.incident_id = incident_id\n self.sketch_id = sketch_id\n self.client_config = turbinia_api_lib.Configuration(host=self.turbinia_api)\n self.client = self.InitializeTurbiniaApiClient(self.credentials)\n self.requests_api_instance = turbinia_requests_api.TurbiniaRequestsApi(\n self.client)\n # We need to get the output path from the Turbinia server.\n api_instance = turbinia_configuration_api.TurbiniaConfigurationApi(\n self.client)\n try:\n api_response = api_instance.read_config()\n self.output_path = api_response.get('OUTPUT_DIR')\n except turbinia_api_lib.ApiException as exception:\n self.ModuleError(exception.body, critical=True)","def __init__(self, envirophat, use_leds):\n self.envirophat = envirophat\n self.use_leds = use_leds\n # sensors readings\n self.light = None\n self.light_red = None\n self.light_green = None\n self.light_blue = None\n self.accelerometer_x = None\n self.accelerometer_y = None\n self.accelerometer_z = None\n self.magnetometer_x = None\n self.magnetometer_y = None\n self.magnetometer_z = None\n self.temperature = None\n self.pressure = None\n self.voltage_0 = None\n self.voltage_1 = None\n self.voltage_2 = None\n self.voltage_3 = None","def _swift_saio_setup(self):\n self._swift_storage_setup()\n self._swift_proxy_setup()","def __init__(self, address=None, auth=(), config=None):\n self.log = logging.getLogger(f\"{__name__}.API\")\n if config:\n address, auth = apiconfig(config)\n if not address:\n raise Error(\"no address specified\")\n self.url = f\"https://{address}:8443/JSSResource\"\n self.session = requests.Session()\n self.session.headers.update({'Accept': 'application/xml'})\n if auth:\n self.session.auth = tuple(auth)","def init(self):\n self.AOMBoxConnection = pyArdDAC.ARD_DAC(HOST=self.AOMBox_IP, PORT=8888, DEBUG=False)#connects to arduino in High frequency Na AOM box\n #channel number should be defined in subclass\n self.INTEGER_MIN = 0\n self.INTEGER_MAX = 65535\n self.VOLTAGE_MIN = 0.0\n self.VOLTAGE_MAX = 5.0\n self.initialised=True\n return \"%s init successful\" % self.hardwareActionName","def __init__(self):\n self.config = get_config()\n self.log = get_logger(self)\n\n self.factory = SugarServerFactory(\"wss://*:5505\")\n self.factory.protocol = SugarServerProtocol\n\n self.console_factory = SugarConsoleServerFactory(\"wss://localhost:5507\")\n self.console_factory.protocol = SugarConsoleServerProtocol\n\n self.api = APIService(self.config)","def async_setup(self, conf):\n if conf.get(VERIFICATION_CODE_KEY):\n return\n if conf.get(CONF_ACCESS_TOKEN_CACHE_FILE) is None:\n conf[\n CONF_ACCESS_TOKEN_CACHE_FILE\n ] = f\".{conf[CONF_USERNAME]}{DEFAULT_AUGUST_CONFIG_FILE}\"\n self._config = conf\n\n self._api = Api(\n timeout=self._config.get(CONF_TIMEOUT), http_session=self._api_http_session,\n )\n\n self._authenticator = Authenticator(\n self._api,\n self._config[CONF_LOGIN_METHOD],\n self._config[CONF_USERNAME],\n self._config[CONF_PASSWORD],\n install_id=self._config.get(CONF_INSTALL_ID),\n access_token_cache_file=self._hass.config.path(\n self._config[CONF_ACCESS_TOKEN_CACHE_FILE]\n ),\n )","def setUpClass(cls):\n cls.nhf = nhflux.NhfluxStreamVariant.readBinary(SIMPLE_HEXZ_NHFLUX_VARIANT)","def _setup(self) -> None:\n\t\treturn","def setup(self, rc):\n pass","def setup(self):\n self.fname = None\n self.remote = self.camera.get('remote', None)\n self.picture_directory = self.camera.get('directory', Bawt.DEFAULT_DIRECTORY)\n self.resolution = self.camera.get('resolution', Bawt.DEFAULT_RESOLUTION)\n LOG.info(\"Picture directory set to: %s\" % self.picture_directory)\n LOG.info(\"Resolution set to %s\" % self.resolution)\n self.timelapse = self.camera.get('timelapse', None)\n self._is_initialized = False","def __init__(self, node, mac, sensor_id):\n super().__init__(node, mac)\n self.sensor_id = sensor_id\n self.sensor_type = SENSORS[sensor_id]\n self.node_callbacks = (AVAILABLE_SENSOR_ID, sensor_id)","def initService(self):","def initialize(self):\n self.ha_url = self.args.get(\"ha_url\", None)\n self.use_current_brightness = self.args.get(\"use_current_brightness\", False)\n self.condition = self.args.get(\"condition\")\n self.lights = self.args[\"lights\"]\n self.listen_state(self.change_lights_color, self.args[\"media_player\"], attribute = self.args.get(\"photo_attribute\", \"entity_picture\"))","def __init__(self):\n self.raw_wires = PyWires.WireNetwork();\n self.__initialize_wires();","def __init__(self, parent, endpoint):\n Wemo_Endpoint.__init__(self, parent, endpoint)\n self.device_type = self._Parent._DeviceTypes.get('wemo_light')\n self.FEATURES.update({\n FEATURE_BRIGHTNESS: True,\n FEATURE_PERCENT: True,\n FEATURE_NUMBER_OF_STEPS: 100\n })","def setUp(self):\n\t\tself.model = models.resnet18()\n\t\tself.watcher = ww.WeightWatcher(model=self.model, log_level=logging.WARNING)","def config(self):\n self._resource_manager = self._api._ixnetwork.ResourceManager\n self._ixn_vport = self._api._vport\n self._delete_vports()\n self._create_vports()\n self._create_capture()\n self._set_location()\n self._set_layer1()","def ready(self):\n # self.init_es_templete()\n self.init_uwsgi_log()","def __init__(self, db_info_obj):\n EODataDownSensor.__init__(self, db_info_obj)\n self.sensor_name = \"Sentinel1ASF\"\n self.db_tab_name = \"EDDSentinel1ASF\"\n self.base_api_url = \"https://api.daac.asf.alaska.edu/services/search/param\"\n\n self.use_roi = False\n self.intersect_vec_file = ''\n self.intersect_vec_lyr = ''\n self.subset_vec_file = ''\n self.subset_vec_lyr = ''\n self.mask_outputs = False\n self.mask_vec_file = ''\n self.mask_vec_lyr = ''\n self.std_vis_img_stch = None","async def _setup(self):","def __init__(self):\n GPIO.setwarnings(False)\n GPIO.cleanup() # Reset the high and low levels of the GPIO port\n #The following code defines the GPIO used to control the L298N chip. This definition is different for different Raspberry Pi driver boards.\n self.Motor_A_EN = 17\n self.Motor_B_EN = 4\n self.Motor_A_Pin1 = 27\n self.Motor_A_Pin2 = 18\n self.Motor_B_Pin1 = 21\n self.Motor_B_Pin2 = 26\n self.setup()","async def init_device(self):\n await Device.init_device(self)\n # PROTECTED REGION ID(AsyncTabata.init_device) ENABLED START #\n self.logger = logging.getLogger(__name__)\n self._lock = threading.Lock()\n self._dev_factory = DevFactory()\n self._prepare = 10\n self._work = 20\n self._rest = 10\n self._cycles = 8\n self._tabatas = 1\n self._running_state = RunningState.PREPARE\n self.subscribed = False\n self.set_state(DevState.OFF)\n # The below commented commands are not really needed\n # since in GreenMode.Asyncio mode the monitor\n # lock is disabled by default.\n # util = tango.Util.instance()\n # util.set_serial_model(tango.SerialModel.NO_SYNC)\n # PROTECTED REGION END # // AsyncTabata.init_device","def do_load_weather(self, *arg):\n try:\n port = self.config['weather']['aag_cloud']['serial_port']\n except KeyError:\n port = '/dev/ttyUSB0'\n\n print(\"Loading AAG Cloud Sensor on {}\".format(port))\n self.weather = AAGCloudSensor(serial_address=port, use_mongo=True)\n self.do_enable_sensor('weather')","def __init__(self, resource, base_uri, auth):\n super(ValidationResource, self).__init__(base_uri,auth)\n\n self.endpoint += \"/%s\" % resource","def __init__(self, rest=AuthenticatingRestProxy(RestRequests())):\n self._rest = rest","def write_rest(self, value):\n # PROTECTED REGION ID(AsyncTabata.rest_write) ENABLED START #\n if value < 1:\n raise Exception(\"only positive value!\")\n\n if self.get_state() == DevState.ON:\n raise Exception(\"cannot change values when device is running!\")\n\n with self._lock:\n self._rest = value\n # PROTECTED REGION END # // AsyncTabata.rest_write","def _setup(self):","def _setup(self):","def initialize(self):\n self.ros.enable()\n self.phone_link.enable()","def __init__(self):\n\n self.config = load_config()\n self.set_env_var()","def init(self):\n self.reset()\n\n self.__interface.send_command('POWER_SETTING')\n self.__interface.send_data(0x37)\n self.__interface.send_data(0x00)\n\n self.__interface.send_command('PANEL_SETTING')\n self.__interface.send_data(0xCF)\n self.__interface.send_data(0x08)\n\n self.__interface.send_command('BOOSTER_SOFT_START')\n self.__interface.send_data(0xc7)\n self.__interface.send_data(0xcc)\n self.__interface.send_data(0x28)\n\n self.__interface.send_command('POWER_ON')\n self.wait_until_idle()\n\n self.__interface.send_command('PLL_CONTROL')\n self.__interface.send_data(0x3c)\n\n self.__interface.send_command('TEMPERATURE_CALIBRATION')\n self.__interface.send_data(0x00)\n\n self.__interface.send_command('VCOM_AND_DATA_INTERVAL_SETTING')\n self.__interface.send_data(0x77)\n\n self.__interface.send_command('TCON_SETTING')\n self.__interface.send_data(0x22)\n\n self.__interface.send_command('TCON_RESOLUTION')\n self.__interface.send_data(0x02) #source 640\n self.__interface.send_data(0x80)\n self.__interface.send_data(0x01) #gate 384\n self.__interface.send_data(0x80)\n\n self.__interface.send_command('VCM_DC_SETTING')\n self.__interface.send_data(0x1E) #decide by LUT file\n\n self.__interface.send_command(0xe5, False) #FLASH MODE\n self.__interface.send_data(0x03)","def setup(self):\n\n self._enable_torque(self._reg.TORQUE_ENABLE)\n self.change_operating_mode(self._reg.MODE_EXT_POSI)\n # set to max velocity\n self.change_veloity(self._default_velocity)","def __init__(self):\n super(Nasbench201, self).__init__()\n self.args.data_path = FileOps.download_dataset(self.args.data_path)\n self.nasbench201_api = API('self.args.data_path')","def setup():\n\trfm.setup()\n\t\"\"\"Read the interrupt status1 register\"\"\"\n\tItStatus1 = rfm.read_register(0x03)\n\tItStatus2 = rfm.read_register(0x04)\n\n\t\"\"\"Set RF Parameters\"\"\"\n\t# Set the center frequency to 915MHz\n\trfm.write_register(0x75, 0x75) # Write 0x75 to the Frequency Band Select register\n\trfm.write_register(0x76, 0xBB) # Write 0xBB to the Nominal Carrier Frequency1 register\n\trfm.write_register(0x77, 0x80) # Write 0x80 to the Nominal Carrier Frequency0 register\n\t# Set the desired TX data rate (9.6kbps)\n\trfm.write_register(0x6E, 0x4E) # Write 0x4E to the TXDataRate 1 register\n\trfm.write_register(0x6F, 0xA5) # Write 0xA5 to the TXDataRate 0 register\n\trfm.write_register(0x70, 0x2C) # Write 0x2C to the Modulation Mode Control 1 register\n\t# Set the desired TX deviation (+=45kHz)\n\trfm.write_register(0x72, 0x48) # Write 0x48 to the Frequency Deviation Register\n\n\t\"\"\"Set Packet Configuration\"\"\"\n\t# Set packet structure and modulation type\n\trfm.write_register(0x34, 0x09) # Write 0x09 to the Preamble length register\n\t# Disable header bytes; set variable packet length (the length of the packet is defined by the\n\t# received packet length field of the packet); set the synch word to two bytes long\n\trfm.write_register(0x33, 0x02) # Write 0x02 to the Header Control 2 register\n\t# Set the sync word pattern to 0x2DD4\n\trfm.write_register(0x36, 0x2D) # Write 0x2D to the Sync Word 3 register\n\trfm.write_register(0x37, 0xD4) # Write 0xD4 to the Sync Word 2 register\n\t# Enable the TX packet handler and CRC-16 (IBM) check\n\trfm.write_register(0x30, 0x0D) # Write 0x0D to the Data Access Control register\n\t# Enable FIFO mode and GFSK modulation\n\trfm.write_register(0x71, 0x63) # Write 0x63 to the Modulation Mode Control 2 Register \n\n\t\"\"\"Select modulation\"\"\"\n\t# Set VCO and PLL\n\trfm.write_register(0x54, 0x7F) # Write 0x7F to the VCO Current Trimming register\n\trfm.write_register(0x59, 0x40) # Write 0x40 to the Divider Current Trimming register","def startup( self ):\n # ---- Setup UPNPC ----\n if self.config.neuron.use_upnpc:\n bittensor.logging.success(prefix = 'Set upnpc', sufix = '<green>ON</green>')\n try:\n self.external_port = net.upnpc_create_port_map( port = self.axon.port )\n except net.UPNPCException as upnpc_exception:\n logger.critical('Failed to hole-punch with upnpc')\n raise RuntimeError('Failed to hole-punch with upnpc')\n else:\n bittensor.logging.success(prefix = 'Set upnpc', sufix = '<red>OFF</red>')\n self.external_port = self.config.axon.port\n\n # ---- Get external ip ----\n try:\n self.external_ip = net.get_external_ip()\n bittensor.logging.success(prefix = 'External IP', sufix = '<blue>{}</blue>'.format(self.external_ip))\n except net.ExternalIPNotFound as external_port_exception:\n raise RuntimeError('Unable to attain your external ip. Check your internet connection. error:{}', external_port_exception)\n\n # ---- Setup tensorboard ----\n if self.config.neuron.use_tensorboard == True:\n self._tensorboard_program = program.TensorBoard()\n self._tensorboard_program.configure(argv=[None, '--logdir', self.config.neuron.full_path, '--load_fast=true'])\n self._tensorbaord_url = self._tensorboard_program.launch()\n bittensor.logging.success(prefix = 'Set tensorboard', sufix = '<blue>http://localhost:6006/</blue>')\n else: bittensor.logging.success(prefix = 'Set tensorboard', sufix = '<red>OFF</red>')\n\n # ---- Setup Wallet. ----\n if not self.wallet.has_coldkeypub:\n self.wallet.create_new_coldkey( n_words = 12, use_password = True )\n if not self.wallet.has_coldkeypub:\n raise RuntimeError('Miner must have access to a decrypted coldkeypub')\n if not self.wallet.has_hotkey:\n self.wallet.create_new_hotkey( n_words = 12, use_password = False )\n if not self.wallet.has_hotkey:\n raise RuntimeError('Miner must have access to a decrypted hotkey')\n\n # ---- Subscribe to chain ----\n subscribe_success = self.subtensor.subscribe(\n wallet = self.wallet,\n ip = self.external_ip,\n port = self.external_port,\n modality = bittensor.proto.Modality.TEXT,\n wait_for_finalization = True,\n timeout = 4 * bittensor.__blocktime__,\n )\n if not subscribe_success:\n raise RuntimeError('Failed to subscribe neuron.')\n\n # ---- Starting axon ----\n self.axon.start()","def init(self, userdata, conn):\r\n pass","def setUpClass(cls):\n cls._config = imageroller.test.write_config(\n \"config\", CONFIG_SERVER_VALID_MINIMAL, CONFIG_DATA)\n cls._auth = imageroller.test.write_config(\n \"auth\", AUTH_VALID, AUTH_DATA)","def initialize(self, sensors = None, readings = None, clients = None):\n\n self.sensors = sensors\n self.readings = readings\n self.clients = clients","def _setup(self):\n self.log_object.write_log(\n \"MESH0001\", None, {\"mailbox\": self.mailbox, \"environment\": self.environment}\n )\n\n common_params = MeshCommon.get_ssm_params(f\"/{self.environment}/mesh\")\n mailbox_params = MeshCommon.get_ssm_params(\n f\"/{self.environment}/mesh/mailboxes/{self.mailbox}\"\n )\n self.params = {**common_params, **mailbox_params}\n # self._write_certs_to_files()\n\n # maybe_verify = bool(\n # self.mailbox_params.get(\"MESH_VERIFY_SSL\", \"True\") == \"True\"\n # )\n\n # if not maybe_verify:\n # requests.urllib3.disable_warnings(InsecureRequestWarning)\n\n # # rewrite MeshClient\n # self.mesh_client = ExtendedMeshClient(\n # common_params[\"MESH_URL\"],\n # self.mailbox,\n # mailbox_params[\"MAILBOX_PASSWORD\"],\n # shared_key=common_params[\"MESH_SHARED_KEY\"].encode(\"utf8\"),\n # cert=(self.client_cert_file.name, self.client_key_file.name),\n # verify=self.ca_cert_file.name if maybe_verify else None,\n # max_chunk_size=MeshCommon.DEFAULT_CHUNK_SIZE,\n # )","def setUpClass(cls):\n\n cls.client = TestClient(fastapi_app.app)\n log.info('Completed initialization for FastAPI based REST API tests')","def configure( self, sensors, actuators, archiver=None ):\n self.__sensors = sensors\n self.__actuators = actuators\n self.__archiver = archiver\n self.__configured = True","async def setup(self):\n load_base_templates()\n uris = URI.gather()\n for uri, resource in uris.items():\n methods = resource.methods\n if \"get\" not in methods:\n methods[\"get\"] = None\n\n for method in methods.keys():\n self.app.add_routes([\n getattr(aioweb, method)(uri, resource.process)\n ])\n self.app.add_routes([aioweb.get(\"/hello\", hello)])\n\n # TMP code\n max_age = 3600 * 24 * 365 # 1 year\n setup(self.app, PonyStorage(max_age=max_age))\n self.preparing_task = asyncio.create_task(self.prepare_web())","def _set_config(self):\n\n self.config.data_path = \"http://{0}:{1}/db/data\".format(\n self.config.host,\n self.config.port)\n\n self.config.node_path = \"/\".join([self.config.data_path, \"node\"])\n self.config.headers = dict([])\n self.config.headers[\"get\"] = {\"Accept\": \"application/json\"}\n self.config.headers[\"put\"] = {\"Content-Type\": \"application/json\"}","def initial_setup_baxter():\n \n #print 'Initializing node...'\n #rospy.init_node('baxter_or')\n baxter.enable()\n baxter.calibrateLeftGripper()","def __init__(self, reactor, commPort):\r\n Endpoint.__init__(self, reactor, Loader(), commPort)\r\n\r\n self._dbFile = '/opt/rce/data/rosenvbridge.db' # TODO: Hardcoded?\r","def __init__(self, hass, config):\n super().__init__()\n self._parent = hass\n self.ezvizService = hass.data[PY_EZVIZ_GATEWAY]\n\n self.deviceSerial = self.ezvizService.deviceSerial\n\n self._interval_snapshots = 30\n self._last_image = \"\"\n self._last_snapshot_time = 0","def initialize(self):\n self.log.info(\"Initialize raspPinball hardware.\")\n\n self.config = self.machine.config['rasppinball']\n self.machine.config_validator.validate_config(\"rasppinball\", self.config)\n print(\"***************************\")\n print(self.config)\n #self.machine_type = (\n # self.machine.config['hardware']['driverboards'].lower())\n\n self._connect_to_hardware()\n\n\n # keypad\n self._kp = Keypad()\n self.old_key = \"\"\n self.key = \"\"\n # leds\n self.init_strips()"],"string":"[\n \"def setUp(self):\\n self.ser = Serial()\\n self.device_obj = ZBSensor(self.ser)\",\n \"def setUp(self):\\n self.sensor = Sensor('127.1.1.3', 9000)\",\n \"def __init__(self):\\n self._read_calibration_data()\\n self.configure_sensor(\\n TemperatureOversamplings.x08,\\n PressureOversamplings.x16,\\n HumidityOversamplings.x08,\\n IIRFilterCoefficients.FC_003,\\n 250,\\n 250)\",\n \"def setup(self):\\n self.log.debug('RFSwitch - in RFSwitch setup()')\\n # Add resource setup code here\\n print(\\\"Calling RFSwitch:setup\\\")\",\n \"def _setup_sensor ( self ):\\n self.spectral = Spectral ( np.array([500, 610, 780, 1580.] ),\\n np.array([590, 680, 890, 1750.] ) )\",\n \"def _initialize(self):\\n self.flush()\\n print(\\\"Initializing sensor...\\\")\\n try:\\n self.get_sample()\\n print('Initialization successful')\\n except:\\n print('Initialization failed. Please disconnect and reconnect sensor.')\",\n \"def _setup_sensor ( self ):\\n self.spectral = Spectral ( np.array([450, 520, 630, 770., 1550, 2090.] ),\\n np.array([ 520, 600, 690, 900., 1750., 2350.] ) )\",\n \"def setUp(self):\\n import protolibs.ics_servers as ics_servers\\n from point import Point\\n from configobj import ConfigObj\\n\\n # Get config file\\n configfile = '/'.join(['sims', 'rtutank', 'config'])\\n config=ConfigObj(infile=configfile, unrepr=True)\\n self.config = config\\n #Set global variable devconfig here \\n devconfig=config['vdevs']['slave'] \\n\\n ##--Set up points\\n points={}\\n for p in devconfig['points']:\\n points.update( { p['name'] : Point(**p) } ) \\n #The ** treats the p dictionary as the arguments to the Point class\\n self.server = ics_servers.ModbusRTU(devconfig['icsifaces'][0], points.values())\\n self.server.start()\",\n \"def setup_sensors(self):\\n super(EddRoach2ProductController, self).setup_sensors()\\n self._firmware_server_sensor = Sensor.string(\\n \\\"firmware-server\\\",\\n description=\\\"The address of the firmware server started by this product\\\",\\n default=\\\"\\\",\\n initial_status=Sensor.UNKNOWN)\\n self.add_sensor(self._firmware_server_sensor)\\n self._parent.mass_inform(Message.inform('interface-changed'))\",\n \"def _setup(self) -> None:\\n self._api = get_api(\\n self._password,\\n self._host,\\n self._username,\\n self._port,\\n self._ssl,\\n )\\n\\n self._info = self._api.get_info()\\n self.device_name = self._info.get(\\\"DeviceName\\\", DEFAULT_NAME)\\n self.model = self._info.get(\\\"ModelName\\\")\\n self.firmware_version = self._info.get(\\\"Firmwareversion\\\")\\n\\n for model in MODELS_V2:\\n if self.model.startswith(model):\\n self._method_version = 2\",\n \"async def init(self):\\n logger.info(\\\"Init device: %s\\\", self._serial)\\n self._callback(STATUS_INIT)\\n\\n self._init_binaries()\\n self._init_apks()\\n await self._init_forwards()\\n\\n await adb.shell(self._serial, \\\"/data/local/tmp/atx-agent server --stop\\\")\\n await adb.shell(self._serial, \\\"/data/local/tmp/atx-agent server --nouia -d\\\")\",\n \"def __init__(self) -> None:\\n self.sensor = serial.Serial(config.DEVICE)\\n super().__init__()\",\n \"def __init__(self, parent, endpoint):\\n Wemo_Endpoint.__init__(self, parent, endpoint)\\n self.device_type = self._Parent._DeviceTypes.get('wemo_binary_sensor')\\n self.FEATURES.update({\\n FEATURE_BRIGHTNESS: False,\\n FEATURE_PERCENT: False,\\n FEATURE_NUMBER_OF_STEPS: False\\n })\",\n \"def setup_fan():\\n global dev_fan\\n dev_fan = iot_fan.Fan(config.option('pin_name', 'Fan'))\\n fan_init()\",\n \"def __init__(self, sensor, temperature_resolution, humidity_resolution):\\n self.sensor = sensor\\n self.sensor.turnHeaterOn() \\n time.sleep(1.0) # Burn off condensed stuff.\\n self.sensor.turnHeaterOff() \\n self.update()\\n # Main Program\\n #print \\\"------------\\\"\\n #print \\\"Manfacturer ID=0x%X\\\"% self.sensor.readManufacturerID() \\n #print \\\"Device ID=0x%X\\\"% self.sensor.readDeviceID() \\n #print \\\"Serial Number ID=0x%X\\\"% self.sensor.readSerialNumber() \\n \\n # change temperature resolution\\n self.sensor.setTemperatureResolution(temperature_resolution)\\n self.sensor.setHumidityResolution(humidity_resolution)\",\n \"def __init__(self, com_str: str):\\n self.com_str = com_str\\n self.ser_arduino = Serial(self.com_str, 9600)\\n logging.info(\\\"Arduino detected\\\")\\n\\n # Launch mediator\\n self.arduino_mediator = ArduinoMediator(self.ser_arduino)\\n self.arduino_mediator.start()\\n logging.info(\\\"Arduino Mediator started\\\")\\n\\n handler = CsgoRequestHandler.create(\\n self.arduino_mediator, self.data_store\\n )\\n self.server = HTTPServer((\\\"localhost\\\", 3000), handler)\",\n \"def setUp(self):\\n # Direct connection used to match the property values\\n self.sockobj = socket(AF_INET, SOCK_STREAM)\\n self.sockobj.settimeout(socket_timeout)\\n # Connect to the selected server\\n self.sockobj.connect(server) \\n self.pyclient = PySimpleClient()\\n self.cmd_num = 0\\n for servo_type in app_nr.values():\\n self.__dict__[servo_type] = self.pyclient.getComponent(\\\"MINORSERVO/\\\" + servo_type)\",\n \"def setUp(self):\\n self.server_address = \\\"http://localhost:3030/$/\\\"\\n self.request_address = \\\"http://localhost:3030/ds\\\"\\n self.api = \\\"http://localhost:4032/\\\"\\n self.version = \\\"0.2\\\"\",\n \"def init():\\n\\n global registry, fsk_router, ook_router\\n\\n radio.init()\\n OpenThings.init(Devices.CRYPT_PID)\\n\\n fsk_router = Registry.Router(\\\"fsk\\\")\\n\\n #OOK receive not yet written\\n #It will be used to be able to learn codes from Energenie legacy hand remotes\\n ##ook_router = Registry.Router(\\\"ook\\\")\\n\\n registry = Registry.DeviceRegistry()\\n registry.set_fsk_router(fsk_router)\\n ##registry.set_ook_router(ook_router\\n\\n path = os.path.join(sys.path[0], registry.DEFAULT_FILENAME)\\n if os.path.isfile(path):\\n registry.load_from(path)\\n print(\\\"loaded registry from file\\\")\\n registry.list()\\n fsk_router.list()\\n\\n # Default discovery mode, unless changed by app\\n ##discovery_none()\\n ##discovery_auto()\\n ##discovery_ask(ask)\\n discovery_autojoin()\\n ##discovery_askjoin(ask)\",\n \"def setup(self):\\n settings: ClusterQRM_RF_Settings = self.settings\\n if self.is_connected:\\n # Load settings\\n port_settings: ClusterRF_OutputPort_Settings = settings.ports[\\\"o1\\\"]\\n self.ports[\\\"o1\\\"].channel = port_settings.channel\\n self._port_channel_map[\\\"o1\\\"] = self.ports[\\\"o1\\\"].channel\\n self.ports[\\\"o1\\\"].attenuation = port_settings.attenuation\\n self.ports[\\\"o1\\\"].lo_enabled = port_settings.lo_enabled\\n self.ports[\\\"o1\\\"].lo_frequency = port_settings.lo_frequency\\n self.ports[\\\"o1\\\"].gain = port_settings.gain\\n self.ports[\\\"o1\\\"].hardware_mod_en = port_settings.hardware_mod_en\\n\\n self.ports[\\\"o1\\\"].nco_freq = 0\\n self.ports[\\\"o1\\\"].nco_phase_offs = 0\\n\\n port_settings: QbloxInputPort_Settings = settings.ports[\\\"i1\\\"]\\n self.ports[\\\"i1\\\"].channel = port_settings.channel\\n self._port_channel_map[\\\"i1\\\"] = self.ports[\\\"i1\\\"].channel\\n self.ports[\\\"i1\\\"].hardware_demod_en = port_settings.hardware_demod_en\\n self.ports[\\\"i1\\\"].acquisition_hold_off = port_settings.acquisition_hold_off\\n self.ports[\\\"i1\\\"].acquisition_duration = port_settings.acquisition_duration\\n\\n self._channel_port_map = {v: k for k, v in self._port_channel_map.items()}\\n self.channels = list(self._channel_port_map.keys())\\n else:\\n raise Exception(\\\"The instrument cannot be set up, there is no connection\\\")\",\n \"def on_start(self):\\n self.id = str(self.locust).split('object at')[1].strip().replace('>','')\\n\\n # get data from common config\\n self.read_locust_config()\\n\\n # get data from tempest config\\n self.keystone_user = self.get_tempest_config_value('identity','username')\\n self.keystone_tenant = self.get_tempest_config_value('identity','tenant_name')\\n self.keystone_pw = self.get_tempest_config_value('identity','password')\\n self.keystone_uri = self.get_tempest_config_value('identity','uri')\\n\\n self.output(\\\"Prepare to be rannsaka'd...\\\")\",\n \"async def async_setup(self) -> None:\\n if not self.aiolivisi.livisi_connection_data:\\n livisi_connection_data = {\\n \\\"ip_address\\\": self.config_entry.data[CONF_HOST],\\n \\\"password\\\": self.config_entry.data[CONF_PASSWORD],\\n }\\n\\n await self.aiolivisi.async_set_token(\\n livisi_connection_data=livisi_connection_data\\n )\\n controller_data = await self.aiolivisi.async_get_controller()\\n if controller_data[\\\"controllerType\\\"] == \\\"Avatar\\\":\\n self.port = AVATAR_PORT\\n self.is_avatar = True\\n else:\\n self.port = CLASSIC_PORT\\n self.is_avatar = False\\n self.serial_number = controller_data[\\\"serialNumber\\\"]\\n self.controller_type = controller_data[\\\"controllerType\\\"]\",\n \"def init_edra(self) -> None:\\n ...\",\n \"def setup(self):\\n self.ae = None\",\n \"def __init__(self):\\n self._read_calibration_data()\\n self.set_oversamplings_and_mode(\\n HumidityOversampling.x08,\\n TemperatureOversampling.x08,\\n PressureOversampling.x16,\\n SensorMode.Normal)\\n self.set_config(\\n InactiveDuration.ms1000,\\n FilterCoefficient.fc04)\",\n \"def _InitializeBase(self):\\n imuen = rospy.get_param(\\\"~imuenable\\\", \\\"True\\\")\\n if imuen:\\n message = 'Startimu\\\\r'\\n else:\\n message = 'Startnoimu\\\\r'\\n\\n rospy.loginfo(\\\"Initializing Base \\\" + message)\\n self._WriteSerial(message)\\n \\n lincorrection = rospy.get_param(\\\"~linear_correction\\\", 1.0)\\n angcorrection = rospy.get_param(\\\"~angular_correction\\\", 0.984)\\n message = 'ascale %d %d\\\\r' % self._GetBaseAndExponent(angcorrection)\\n rospy.loginfo(\\\"Sending correction value: \\\" + message)\\n self._WriteSerial(message)\\n message = 'lscale %d %d\\\\r' % self._GetBaseAndExponent(lincorrection)\\n rospy.loginfo(\\\"Sending correction value: \\\" + message)\\n self._WriteSerial(message)\",\n \"def __setup(self):\\n\\n # Create empty config json file if not there.\\n if not self.config_file:\\n self.config_file = \\\"./default.config\\\"\\n if not os.path.isfile(self.config_file):\\n with open(self.config_file, 'w') as f:\\n json.dump({}, f)\\n\\n # Recycle and replace values in the config file\\n with open(self.config_file, 'r') as json_file:\\n data = json.load(json_file)\\n\\n # Records the `server`, `license_key`, and `user_email` if they don't exist.\\n # Server\\n if (self.server not in data.keys() or data[self.server] == {}):\\n data[self.server] = {}\\n\\n # License key\\n if (self.license_key not in data[self.server].keys() or data[self.server][self.license_key] == {}):\\n data[self.server][self.license_key] = {}\\n\\n # User email\\n if self.user.email not in data[self.server][self.license_key].keys():\\n data[self.server][self.license_key][self.user.email] = {}\\n\\n # Next, if a `device_token` doesn't exist for this server and\\n # license, call the `Organization.registerLicense()` endpoint to\\n # obtain a device token. On the other hand, if the device token\\n # already exists, it takes the existing token to prevent\\n # redundantly registering the same license.\\n\\n # Device token\\n if (\\\"device_token\\\" not in data[self.server][self.license_key].keys()\\n or data[self.server][self.license_key][\\\"device_token\\\"] == ''):\\n out = self.organization.registerLicense(self.device_name)\\n if 'Token' not in out:\\n self.__record(data=data) # Save current state\\n raise PermissionError(\\n \\\"Registration error. Make sure your license key is valid for the selected server.\\\")\\n\\n self.device_token = out['Token']\\n data[self.server][self.license_key][\\\"device_token\\\"] = self.device_token\\n\\n elif (self.device_token == '' and data[self.server][self.license_key][\\\"device_token\\\"] != ''):\\n self.device_token = data[self.server][self.license_key][\\\"device_token\\\"]\\n\\n # Next, if a `user_token` does not exist for the current user on\\n # this license and server, it tries to log in (`User.login()`) the\\n # user first using the device token. If cannot be logged in, it\\n # needs to create a new user (`User.create()`) before logging in.\\n # The user information and credentials are already handled in the\\n # `User` class, so it only needs to pass in the `device_token`.\\n\\n # User token\\n if (\\\"user_token\\\" not in data[self.server][self.license_key][self.user.email].keys()\\n or data[self.server][self.license_key][self.user.email][\\\"user_token\\\"] == ''):\\n res = self.user.login(self.device_token)\\n\\n if res == \\\"INVALID_USER\\\":\\n res = self.user.create(self.device_token)\\n if res == 'INTERNAL_ERROR':\\n self.__record(data=data)\\n raise PermissionError(\\\"Cannot create new user. Check your license permissions.\\\")\\n\\n self.user_id = res\\n res = self.user.login(self.device_token)\\n\\n elif res == \\\"INVALID_PASSWORD\\\":\\n self.__record(data=data)\\n raise PermissionError(\\\"Incorrect login password.\\\")\\n\\n self.user_token = self.user.user_token\\n if self.user_token != '':\\n data[self.server][self.license_key][self.user.email][\\\"user_token\\\"] = self.user_token\\n else:\\n self.user_token = data[self.server][self.license_key][self.user.email][\\\"user_token\\\"]\\n self.user.user_token = self.user_token\\n\\n # Record updated data into the config file.\\n self.__record(data=data)\",\n \"def setup_method(self):\\n self.hass = get_test_home_assistant()\\n\\n self.config = {\\n ip.DOMAIN: {\\n \\\"platform\\\": \\\"microsoft_face_identify\\\",\\n \\\"source\\\": {\\\"entity_id\\\": \\\"camera.demo_camera\\\", \\\"name\\\": \\\"test local\\\"},\\n \\\"group\\\": \\\"Test Group1\\\",\\n },\\n \\\"camera\\\": {\\\"platform\\\": \\\"demo\\\"},\\n mf.DOMAIN: {\\\"api_key\\\": \\\"12345678abcdef6\\\"},\\n }\\n\\n self.endpoint_url = f\\\"https://westus.{mf.FACE_API_URL}\\\"\",\n \"def setUp(self):\\n import protolibs.ics_servers as ics_servers\\n from point import Point\\n from configobj import ConfigObj\\n\\n # Get config file\\n configfile = '/'.join(['sims', 'tcptank', 'config'])\\n config=ConfigObj(infile=configfile, unrepr=True)\\n self.config = config\\n #Set global variable devconfig here \\n devconfig=config['vdevs']['slave'] \\n\\n ##--Set up points\\n points={}\\n for p in devconfig['points']:\\n points.update( { p['name'] : Point(**p) } ) \\n #The ** treats the p dictionary as the arguments to the Point class\\n self.server = ics_servers.ModbusTCP( devconfig['icsifaces'][0], points.values() )\\n self.server.start()\",\n \"def __init__(self):\\n config = self.read_config()\\n self.deployment = config['deployment']\\n self.deployment_config = config[self.deployment]\\n logger.info(f'Initializing storage client with the {self.deployment} deployment config {pformat(self.deployment_config)}')\\n\\n # get the MLOS config from the user else default it from the deployment config file\\n # self.mlos_config = config['MLOS']\\n # logger.info(f'Initializing storage client with the MLOS config {pformat(self.mlos_config)}')\\n\\n # setup the mount path\\n if self.deployment == \\\"LOCAL\\\":\\n self.mount_dir = self.setup_mount()\\n logger.info(f'Mount directory setup completed: {self.mount_dir}')\",\n \"def setUp(self):\\n self.api = \\\"http://localhost:4031/\\\"\\n self.version = \\\"0.2\\\"\\n self.app = init_api()\",\n \"def setup(request):\\n print \\\"SETUP ENDPOINT\\\"\\n routes = None\\n if request.method == 'POST':\\n content = request.content.read()\\n routes = json.loads(content)\\n print \\\"ROUTES ARE: \\\", str(routes)\\n node.routes = routes\\n return \\\"OK\\\"\\n else:\\n print \\\"Not a POST request.\\\"\",\n \"def __init__(self, comport, logging = False):\\n super(Bin_API, self).__init__(comport, logging)\",\n \"def __init__(self):\\n # FIXME: IS this needed?\\n super(ArduinoStation, self).__init__()\\n\\n self.serial_port_pattern = '/dev/ttyACM{port_num}'\\n self.serial_port_num = None\\n self.baudrate = 9600\\n self.ser = self._setup_serial_connection()\\n\\n\\n # Sensor 1 (DHT11) has 2 readings, Sensor 2 has 1\\n ## FIXME: Should look for key pairs in list and submit when no more unique readings are coming through\\n if config.SCB_CONFIGURATION == 'standard':\\n self.lines_per_observation = 3\\n else:\\n self.lines_per_observation = 7 # Allows for up to 5 DS18B20 along w/ DHT-11.\",\n \"def __init__(self):\\n self.server_name = 'Binary Light Device'\\n self.device = None\",\n \"def _custom_endpoint_init(self, node_config, *argv):\\n config = {}\\n selector = node_config.get('template', None)\\n if not selector:\\n selector = argv[0]\\n _LOGGER.debug(\\\" selector: %s\\\", selector)\\n config = {\\n \\\"config_report\\\": [\\n [0x0001, 0x0020, 60, 3600, 5], \\n [0x0001, 0x0021, 60, 3600, 5]\\n ],\\n \\\"in_cluster\\\": [0x0000, 0x0001, 0x0500, ],\\n \\\"out_cluster\\\": [0x0500],\\n \\\"type\\\": \\\"binary_sensor\\\",\\n }\\n self.add_input_cluster(0x0500)\\n self.add_output_cluster(0x0500)\",\n \"def setUpClass(cls):\\n super().setUpClass()\\n super().set_raw_dump_file('v0x05', 'ofpt_port_status')\\n super().set_raw_dump_object(_new_portstatus)\\n super().set_minimum_size(80)\",\n \"def __init__(self, arest, resource, name, pin):\\n self.arest = arest\\n self._resource = resource\\n self._name = name\\n self._pin = pin\\n self.update()\\n\\n if self._pin is not None:\\n request = requests.get('{}/mode/{}/i'.format\\n (self._resource, self._pin), timeout=10)\\n if request.status_code is not 200:\\n _LOGGER.error(\\\"Can't set mode. Is device offline?\\\")\",\n \"def __init__( self, dev, port ):\\n super( Grove_Light_Sensor, self ).__init__( dev, port )\",\n \"async def _hw_init(self):\\n await self._write_async(b\\\":XR\\\\r\\\") # Broadcast: initialize + execute\\n # Note: no need to consume reply here because there is none (since we are using broadcast)\",\n \"def __init__(self, nsx_appliance=None):\\n super(ControllerUpgrade, self).__init__()\\n self.log = logger.setup_logging(self.__class__.__name__)\\n self.schema_class = 'controller_upgrade_schema.ControllerUpgradeSchema'\\n self.set_content_type('application/xml')\\n self.set_accept_type('application/xml')\\n self.auth_type = \\\"vsm\\\"\\n self.client_type = \\\"vsm\\\"\\n if nsx_appliance != None:\\n self.set_connection(nsx_appliance.get_connection())\\n self.start_endpoint = '/vdn/controller/cluster/upgrade'\\n self.capability_endpoint = 'vdn/controller/upgrade-available'\\n self.create_endpoint = self.start_endpoint\\n self.cluster_status_endpoint = self.start_endpoint\\n self.id = None\",\n \"def setup(self):\\n\\n default_config = self.read()\\n\\n self.write(default_config)\",\n \"def setUpClass(self):\\n self.content_type = \\\"application/json\\\"\\n self.product_payload = {\\\"name\\\": \\\"Olive Oil\\\"}\",\n \"def __init__(self, touch_sensor, port=\\\"A\\\"):\\r\\n # ---------------------------------------------------------------------\\r\\n # Done: 4. Read the following, ASKING QUESTIONS AS NEEDED.\\r\\n # Once you understand the code, change this _TODO_ to DONE.\\r\\n # ---------------------------------------------------------------------\\r\\n\\r\\n # The arm motor should always use a 100% duty cycle for its speed.\\r\\n self.speed = 100\\r\\n\\r\\n self.arm_touch_sensor = touch_sensor\\r\\n self.arm_motor = rose_ev3.Motor(port, motor_type=\\\"medium\\\")\\r\\n self.is_calibrated = False\",\n \"def setup_sensors(self, configs):\\n self.__sensors = self.setup_components(configs, 'scale_client.sensors')\",\n \"def __init__(self, api, coordinator, name, dev_id, binary_sensor, model):\\n super().__init__(\\n api, coordinator, name, dev_id, False, binary_sensor, None, model\\n )\\n\\n self._attributes = {}\",\n \"def do_load_environment(self, *arg):\\n print(\\\"Loading sensors\\\")\\n self.environment = ArduinoSerialMonitor(auto_detect=False)\\n self.do_enable_sensor('environment', delay=1)\",\n \"def initialize(self):\\n watch_tv = self.args['watch_tv']\\n cleaning = self.args['cleaning']\\n self.sensor_living = self.get_app('globals').sensor_living # type: Sensor\\n self.sensor_bedroom = self.get_app('globals').sensor_bedroom # type: Sensor\\n self.sensor_spare = self.get_app('globals').sensor_spare # type: Sensor\\n self.listen_state(self.watching_tv, watch_tv, new=\\\"on\\\")\\n self.listen_state(self.stop_watching, watch_tv, new=\\\"off\\\")\\n self.listen_state(self.clean_on, cleaning, new='on')\\n self.listen_state(self.clean_off, cleaning, new='off')\",\n \"def setUp(self):\\n self._s1ap_wrapper = s1ap_wrapper.TestWrapper()\",\n \"def setUp(self):\\n self._s1ap_wrapper = s1ap_wrapper.TestWrapper()\",\n \"def setUp(self):\\n sumo_bin = sumolib.checkBinary('sumo')\\n traci.start([sumo_bin, \\\"-n\\\", self.network_path, \\\"-r\\\", self.routes_path])\\n traci.simulationStep()\",\n \"def setUpClass(cls):\\n cls.nhf = nhflux.NhfluxStream.readBinary(SIMPLE_HEXZ_NHFLUX)\",\n \"def TurbiniaSetUp(\\n self, project: str, turbinia_auth: bool,\\n turbinia_recipe: Union[str, None], turbinia_zone: str, turbinia_api: str,\\n incident_id: str, sketch_id: int) -> None:\\n self.project = project\\n self.turbinia_auth = turbinia_auth\\n self.turbinia_api = turbinia_api\\n self.turbinia_recipe = turbinia_recipe\\n self.turbinia_zone = turbinia_zone\\n self.incident_id = incident_id\\n self.sketch_id = sketch_id\\n self.client_config = turbinia_api_lib.Configuration(host=self.turbinia_api)\\n self.client = self.InitializeTurbiniaApiClient(self.credentials)\\n self.requests_api_instance = turbinia_requests_api.TurbiniaRequestsApi(\\n self.client)\\n # We need to get the output path from the Turbinia server.\\n api_instance = turbinia_configuration_api.TurbiniaConfigurationApi(\\n self.client)\\n try:\\n api_response = api_instance.read_config()\\n self.output_path = api_response.get('OUTPUT_DIR')\\n except turbinia_api_lib.ApiException as exception:\\n self.ModuleError(exception.body, critical=True)\",\n \"def __init__(self, envirophat, use_leds):\\n self.envirophat = envirophat\\n self.use_leds = use_leds\\n # sensors readings\\n self.light = None\\n self.light_red = None\\n self.light_green = None\\n self.light_blue = None\\n self.accelerometer_x = None\\n self.accelerometer_y = None\\n self.accelerometer_z = None\\n self.magnetometer_x = None\\n self.magnetometer_y = None\\n self.magnetometer_z = None\\n self.temperature = None\\n self.pressure = None\\n self.voltage_0 = None\\n self.voltage_1 = None\\n self.voltage_2 = None\\n self.voltage_3 = None\",\n \"def _swift_saio_setup(self):\\n self._swift_storage_setup()\\n self._swift_proxy_setup()\",\n \"def __init__(self, address=None, auth=(), config=None):\\n self.log = logging.getLogger(f\\\"{__name__}.API\\\")\\n if config:\\n address, auth = apiconfig(config)\\n if not address:\\n raise Error(\\\"no address specified\\\")\\n self.url = f\\\"https://{address}:8443/JSSResource\\\"\\n self.session = requests.Session()\\n self.session.headers.update({'Accept': 'application/xml'})\\n if auth:\\n self.session.auth = tuple(auth)\",\n \"def init(self):\\n self.AOMBoxConnection = pyArdDAC.ARD_DAC(HOST=self.AOMBox_IP, PORT=8888, DEBUG=False)#connects to arduino in High frequency Na AOM box\\n #channel number should be defined in subclass\\n self.INTEGER_MIN = 0\\n self.INTEGER_MAX = 65535\\n self.VOLTAGE_MIN = 0.0\\n self.VOLTAGE_MAX = 5.0\\n self.initialised=True\\n return \\\"%s init successful\\\" % self.hardwareActionName\",\n \"def __init__(self):\\n self.config = get_config()\\n self.log = get_logger(self)\\n\\n self.factory = SugarServerFactory(\\\"wss://*:5505\\\")\\n self.factory.protocol = SugarServerProtocol\\n\\n self.console_factory = SugarConsoleServerFactory(\\\"wss://localhost:5507\\\")\\n self.console_factory.protocol = SugarConsoleServerProtocol\\n\\n self.api = APIService(self.config)\",\n \"def async_setup(self, conf):\\n if conf.get(VERIFICATION_CODE_KEY):\\n return\\n if conf.get(CONF_ACCESS_TOKEN_CACHE_FILE) is None:\\n conf[\\n CONF_ACCESS_TOKEN_CACHE_FILE\\n ] = f\\\".{conf[CONF_USERNAME]}{DEFAULT_AUGUST_CONFIG_FILE}\\\"\\n self._config = conf\\n\\n self._api = Api(\\n timeout=self._config.get(CONF_TIMEOUT), http_session=self._api_http_session,\\n )\\n\\n self._authenticator = Authenticator(\\n self._api,\\n self._config[CONF_LOGIN_METHOD],\\n self._config[CONF_USERNAME],\\n self._config[CONF_PASSWORD],\\n install_id=self._config.get(CONF_INSTALL_ID),\\n access_token_cache_file=self._hass.config.path(\\n self._config[CONF_ACCESS_TOKEN_CACHE_FILE]\\n ),\\n )\",\n \"def setUpClass(cls):\\n cls.nhf = nhflux.NhfluxStreamVariant.readBinary(SIMPLE_HEXZ_NHFLUX_VARIANT)\",\n \"def _setup(self) -> None:\\n\\t\\treturn\",\n \"def setup(self, rc):\\n pass\",\n \"def setup(self):\\n self.fname = None\\n self.remote = self.camera.get('remote', None)\\n self.picture_directory = self.camera.get('directory', Bawt.DEFAULT_DIRECTORY)\\n self.resolution = self.camera.get('resolution', Bawt.DEFAULT_RESOLUTION)\\n LOG.info(\\\"Picture directory set to: %s\\\" % self.picture_directory)\\n LOG.info(\\\"Resolution set to %s\\\" % self.resolution)\\n self.timelapse = self.camera.get('timelapse', None)\\n self._is_initialized = False\",\n \"def __init__(self, node, mac, sensor_id):\\n super().__init__(node, mac)\\n self.sensor_id = sensor_id\\n self.sensor_type = SENSORS[sensor_id]\\n self.node_callbacks = (AVAILABLE_SENSOR_ID, sensor_id)\",\n \"def initService(self):\",\n \"def initialize(self):\\n self.ha_url = self.args.get(\\\"ha_url\\\", None)\\n self.use_current_brightness = self.args.get(\\\"use_current_brightness\\\", False)\\n self.condition = self.args.get(\\\"condition\\\")\\n self.lights = self.args[\\\"lights\\\"]\\n self.listen_state(self.change_lights_color, self.args[\\\"media_player\\\"], attribute = self.args.get(\\\"photo_attribute\\\", \\\"entity_picture\\\"))\",\n \"def __init__(self):\\n self.raw_wires = PyWires.WireNetwork();\\n self.__initialize_wires();\",\n \"def __init__(self, parent, endpoint):\\n Wemo_Endpoint.__init__(self, parent, endpoint)\\n self.device_type = self._Parent._DeviceTypes.get('wemo_light')\\n self.FEATURES.update({\\n FEATURE_BRIGHTNESS: True,\\n FEATURE_PERCENT: True,\\n FEATURE_NUMBER_OF_STEPS: 100\\n })\",\n \"def setUp(self):\\n\\t\\tself.model = models.resnet18()\\n\\t\\tself.watcher = ww.WeightWatcher(model=self.model, log_level=logging.WARNING)\",\n \"def config(self):\\n self._resource_manager = self._api._ixnetwork.ResourceManager\\n self._ixn_vport = self._api._vport\\n self._delete_vports()\\n self._create_vports()\\n self._create_capture()\\n self._set_location()\\n self._set_layer1()\",\n \"def ready(self):\\n # self.init_es_templete()\\n self.init_uwsgi_log()\",\n \"def __init__(self, db_info_obj):\\n EODataDownSensor.__init__(self, db_info_obj)\\n self.sensor_name = \\\"Sentinel1ASF\\\"\\n self.db_tab_name = \\\"EDDSentinel1ASF\\\"\\n self.base_api_url = \\\"https://api.daac.asf.alaska.edu/services/search/param\\\"\\n\\n self.use_roi = False\\n self.intersect_vec_file = ''\\n self.intersect_vec_lyr = ''\\n self.subset_vec_file = ''\\n self.subset_vec_lyr = ''\\n self.mask_outputs = False\\n self.mask_vec_file = ''\\n self.mask_vec_lyr = ''\\n self.std_vis_img_stch = None\",\n \"async def _setup(self):\",\n \"def __init__(self):\\n GPIO.setwarnings(False)\\n GPIO.cleanup() # Reset the high and low levels of the GPIO port\\n #The following code defines the GPIO used to control the L298N chip. This definition is different for different Raspberry Pi driver boards.\\n self.Motor_A_EN = 17\\n self.Motor_B_EN = 4\\n self.Motor_A_Pin1 = 27\\n self.Motor_A_Pin2 = 18\\n self.Motor_B_Pin1 = 21\\n self.Motor_B_Pin2 = 26\\n self.setup()\",\n \"async def init_device(self):\\n await Device.init_device(self)\\n # PROTECTED REGION ID(AsyncTabata.init_device) ENABLED START #\\n self.logger = logging.getLogger(__name__)\\n self._lock = threading.Lock()\\n self._dev_factory = DevFactory()\\n self._prepare = 10\\n self._work = 20\\n self._rest = 10\\n self._cycles = 8\\n self._tabatas = 1\\n self._running_state = RunningState.PREPARE\\n self.subscribed = False\\n self.set_state(DevState.OFF)\\n # The below commented commands are not really needed\\n # since in GreenMode.Asyncio mode the monitor\\n # lock is disabled by default.\\n # util = tango.Util.instance()\\n # util.set_serial_model(tango.SerialModel.NO_SYNC)\\n # PROTECTED REGION END # // AsyncTabata.init_device\",\n \"def do_load_weather(self, *arg):\\n try:\\n port = self.config['weather']['aag_cloud']['serial_port']\\n except KeyError:\\n port = '/dev/ttyUSB0'\\n\\n print(\\\"Loading AAG Cloud Sensor on {}\\\".format(port))\\n self.weather = AAGCloudSensor(serial_address=port, use_mongo=True)\\n self.do_enable_sensor('weather')\",\n \"def __init__(self, resource, base_uri, auth):\\n super(ValidationResource, self).__init__(base_uri,auth)\\n\\n self.endpoint += \\\"/%s\\\" % resource\",\n \"def __init__(self, rest=AuthenticatingRestProxy(RestRequests())):\\n self._rest = rest\",\n \"def write_rest(self, value):\\n # PROTECTED REGION ID(AsyncTabata.rest_write) ENABLED START #\\n if value < 1:\\n raise Exception(\\\"only positive value!\\\")\\n\\n if self.get_state() == DevState.ON:\\n raise Exception(\\\"cannot change values when device is running!\\\")\\n\\n with self._lock:\\n self._rest = value\\n # PROTECTED REGION END # // AsyncTabata.rest_write\",\n \"def _setup(self):\",\n \"def _setup(self):\",\n \"def initialize(self):\\n self.ros.enable()\\n self.phone_link.enable()\",\n \"def __init__(self):\\n\\n self.config = load_config()\\n self.set_env_var()\",\n \"def init(self):\\n self.reset()\\n\\n self.__interface.send_command('POWER_SETTING')\\n self.__interface.send_data(0x37)\\n self.__interface.send_data(0x00)\\n\\n self.__interface.send_command('PANEL_SETTING')\\n self.__interface.send_data(0xCF)\\n self.__interface.send_data(0x08)\\n\\n self.__interface.send_command('BOOSTER_SOFT_START')\\n self.__interface.send_data(0xc7)\\n self.__interface.send_data(0xcc)\\n self.__interface.send_data(0x28)\\n\\n self.__interface.send_command('POWER_ON')\\n self.wait_until_idle()\\n\\n self.__interface.send_command('PLL_CONTROL')\\n self.__interface.send_data(0x3c)\\n\\n self.__interface.send_command('TEMPERATURE_CALIBRATION')\\n self.__interface.send_data(0x00)\\n\\n self.__interface.send_command('VCOM_AND_DATA_INTERVAL_SETTING')\\n self.__interface.send_data(0x77)\\n\\n self.__interface.send_command('TCON_SETTING')\\n self.__interface.send_data(0x22)\\n\\n self.__interface.send_command('TCON_RESOLUTION')\\n self.__interface.send_data(0x02) #source 640\\n self.__interface.send_data(0x80)\\n self.__interface.send_data(0x01) #gate 384\\n self.__interface.send_data(0x80)\\n\\n self.__interface.send_command('VCM_DC_SETTING')\\n self.__interface.send_data(0x1E) #decide by LUT file\\n\\n self.__interface.send_command(0xe5, False) #FLASH MODE\\n self.__interface.send_data(0x03)\",\n \"def setup(self):\\n\\n self._enable_torque(self._reg.TORQUE_ENABLE)\\n self.change_operating_mode(self._reg.MODE_EXT_POSI)\\n # set to max velocity\\n self.change_veloity(self._default_velocity)\",\n \"def __init__(self):\\n super(Nasbench201, self).__init__()\\n self.args.data_path = FileOps.download_dataset(self.args.data_path)\\n self.nasbench201_api = API('self.args.data_path')\",\n \"def setup():\\n\\trfm.setup()\\n\\t\\\"\\\"\\\"Read the interrupt status1 register\\\"\\\"\\\"\\n\\tItStatus1 = rfm.read_register(0x03)\\n\\tItStatus2 = rfm.read_register(0x04)\\n\\n\\t\\\"\\\"\\\"Set RF Parameters\\\"\\\"\\\"\\n\\t# Set the center frequency to 915MHz\\n\\trfm.write_register(0x75, 0x75) # Write 0x75 to the Frequency Band Select register\\n\\trfm.write_register(0x76, 0xBB) # Write 0xBB to the Nominal Carrier Frequency1 register\\n\\trfm.write_register(0x77, 0x80) # Write 0x80 to the Nominal Carrier Frequency0 register\\n\\t# Set the desired TX data rate (9.6kbps)\\n\\trfm.write_register(0x6E, 0x4E) # Write 0x4E to the TXDataRate 1 register\\n\\trfm.write_register(0x6F, 0xA5) # Write 0xA5 to the TXDataRate 0 register\\n\\trfm.write_register(0x70, 0x2C) # Write 0x2C to the Modulation Mode Control 1 register\\n\\t# Set the desired TX deviation (+=45kHz)\\n\\trfm.write_register(0x72, 0x48) # Write 0x48 to the Frequency Deviation Register\\n\\n\\t\\\"\\\"\\\"Set Packet Configuration\\\"\\\"\\\"\\n\\t# Set packet structure and modulation type\\n\\trfm.write_register(0x34, 0x09) # Write 0x09 to the Preamble length register\\n\\t# Disable header bytes; set variable packet length (the length of the packet is defined by the\\n\\t# received packet length field of the packet); set the synch word to two bytes long\\n\\trfm.write_register(0x33, 0x02) # Write 0x02 to the Header Control 2 register\\n\\t# Set the sync word pattern to 0x2DD4\\n\\trfm.write_register(0x36, 0x2D) # Write 0x2D to the Sync Word 3 register\\n\\trfm.write_register(0x37, 0xD4) # Write 0xD4 to the Sync Word 2 register\\n\\t# Enable the TX packet handler and CRC-16 (IBM) check\\n\\trfm.write_register(0x30, 0x0D) # Write 0x0D to the Data Access Control register\\n\\t# Enable FIFO mode and GFSK modulation\\n\\trfm.write_register(0x71, 0x63) # Write 0x63 to the Modulation Mode Control 2 Register \\n\\n\\t\\\"\\\"\\\"Select modulation\\\"\\\"\\\"\\n\\t# Set VCO and PLL\\n\\trfm.write_register(0x54, 0x7F) # Write 0x7F to the VCO Current Trimming register\\n\\trfm.write_register(0x59, 0x40) # Write 0x40 to the Divider Current Trimming register\",\n \"def startup( self ):\\n # ---- Setup UPNPC ----\\n if self.config.neuron.use_upnpc:\\n bittensor.logging.success(prefix = 'Set upnpc', sufix = '<green>ON</green>')\\n try:\\n self.external_port = net.upnpc_create_port_map( port = self.axon.port )\\n except net.UPNPCException as upnpc_exception:\\n logger.critical('Failed to hole-punch with upnpc')\\n raise RuntimeError('Failed to hole-punch with upnpc')\\n else:\\n bittensor.logging.success(prefix = 'Set upnpc', sufix = '<red>OFF</red>')\\n self.external_port = self.config.axon.port\\n\\n # ---- Get external ip ----\\n try:\\n self.external_ip = net.get_external_ip()\\n bittensor.logging.success(prefix = 'External IP', sufix = '<blue>{}</blue>'.format(self.external_ip))\\n except net.ExternalIPNotFound as external_port_exception:\\n raise RuntimeError('Unable to attain your external ip. Check your internet connection. error:{}', external_port_exception)\\n\\n # ---- Setup tensorboard ----\\n if self.config.neuron.use_tensorboard == True:\\n self._tensorboard_program = program.TensorBoard()\\n self._tensorboard_program.configure(argv=[None, '--logdir', self.config.neuron.full_path, '--load_fast=true'])\\n self._tensorbaord_url = self._tensorboard_program.launch()\\n bittensor.logging.success(prefix = 'Set tensorboard', sufix = '<blue>http://localhost:6006/</blue>')\\n else: bittensor.logging.success(prefix = 'Set tensorboard', sufix = '<red>OFF</red>')\\n\\n # ---- Setup Wallet. ----\\n if not self.wallet.has_coldkeypub:\\n self.wallet.create_new_coldkey( n_words = 12, use_password = True )\\n if not self.wallet.has_coldkeypub:\\n raise RuntimeError('Miner must have access to a decrypted coldkeypub')\\n if not self.wallet.has_hotkey:\\n self.wallet.create_new_hotkey( n_words = 12, use_password = False )\\n if not self.wallet.has_hotkey:\\n raise RuntimeError('Miner must have access to a decrypted hotkey')\\n\\n # ---- Subscribe to chain ----\\n subscribe_success = self.subtensor.subscribe(\\n wallet = self.wallet,\\n ip = self.external_ip,\\n port = self.external_port,\\n modality = bittensor.proto.Modality.TEXT,\\n wait_for_finalization = True,\\n timeout = 4 * bittensor.__blocktime__,\\n )\\n if not subscribe_success:\\n raise RuntimeError('Failed to subscribe neuron.')\\n\\n # ---- Starting axon ----\\n self.axon.start()\",\n \"def init(self, userdata, conn):\\r\\n pass\",\n \"def setUpClass(cls):\\n cls._config = imageroller.test.write_config(\\n \\\"config\\\", CONFIG_SERVER_VALID_MINIMAL, CONFIG_DATA)\\n cls._auth = imageroller.test.write_config(\\n \\\"auth\\\", AUTH_VALID, AUTH_DATA)\",\n \"def initialize(self, sensors = None, readings = None, clients = None):\\n\\n self.sensors = sensors\\n self.readings = readings\\n self.clients = clients\",\n \"def _setup(self):\\n self.log_object.write_log(\\n \\\"MESH0001\\\", None, {\\\"mailbox\\\": self.mailbox, \\\"environment\\\": self.environment}\\n )\\n\\n common_params = MeshCommon.get_ssm_params(f\\\"/{self.environment}/mesh\\\")\\n mailbox_params = MeshCommon.get_ssm_params(\\n f\\\"/{self.environment}/mesh/mailboxes/{self.mailbox}\\\"\\n )\\n self.params = {**common_params, **mailbox_params}\\n # self._write_certs_to_files()\\n\\n # maybe_verify = bool(\\n # self.mailbox_params.get(\\\"MESH_VERIFY_SSL\\\", \\\"True\\\") == \\\"True\\\"\\n # )\\n\\n # if not maybe_verify:\\n # requests.urllib3.disable_warnings(InsecureRequestWarning)\\n\\n # # rewrite MeshClient\\n # self.mesh_client = ExtendedMeshClient(\\n # common_params[\\\"MESH_URL\\\"],\\n # self.mailbox,\\n # mailbox_params[\\\"MAILBOX_PASSWORD\\\"],\\n # shared_key=common_params[\\\"MESH_SHARED_KEY\\\"].encode(\\\"utf8\\\"),\\n # cert=(self.client_cert_file.name, self.client_key_file.name),\\n # verify=self.ca_cert_file.name if maybe_verify else None,\\n # max_chunk_size=MeshCommon.DEFAULT_CHUNK_SIZE,\\n # )\",\n \"def setUpClass(cls):\\n\\n cls.client = TestClient(fastapi_app.app)\\n log.info('Completed initialization for FastAPI based REST API tests')\",\n \"def configure( self, sensors, actuators, archiver=None ):\\n self.__sensors = sensors\\n self.__actuators = actuators\\n self.__archiver = archiver\\n self.__configured = True\",\n \"async def setup(self):\\n load_base_templates()\\n uris = URI.gather()\\n for uri, resource in uris.items():\\n methods = resource.methods\\n if \\\"get\\\" not in methods:\\n methods[\\\"get\\\"] = None\\n\\n for method in methods.keys():\\n self.app.add_routes([\\n getattr(aioweb, method)(uri, resource.process)\\n ])\\n self.app.add_routes([aioweb.get(\\\"/hello\\\", hello)])\\n\\n # TMP code\\n max_age = 3600 * 24 * 365 # 1 year\\n setup(self.app, PonyStorage(max_age=max_age))\\n self.preparing_task = asyncio.create_task(self.prepare_web())\",\n \"def _set_config(self):\\n\\n self.config.data_path = \\\"http://{0}:{1}/db/data\\\".format(\\n self.config.host,\\n self.config.port)\\n\\n self.config.node_path = \\\"/\\\".join([self.config.data_path, \\\"node\\\"])\\n self.config.headers = dict([])\\n self.config.headers[\\\"get\\\"] = {\\\"Accept\\\": \\\"application/json\\\"}\\n self.config.headers[\\\"put\\\"] = {\\\"Content-Type\\\": \\\"application/json\\\"}\",\n \"def initial_setup_baxter():\\n \\n #print 'Initializing node...'\\n #rospy.init_node('baxter_or')\\n baxter.enable()\\n baxter.calibrateLeftGripper()\",\n \"def __init__(self, reactor, commPort):\\r\\n Endpoint.__init__(self, reactor, Loader(), commPort)\\r\\n\\r\\n self._dbFile = '/opt/rce/data/rosenvbridge.db' # TODO: Hardcoded?\\r\",\n \"def __init__(self, hass, config):\\n super().__init__()\\n self._parent = hass\\n self.ezvizService = hass.data[PY_EZVIZ_GATEWAY]\\n\\n self.deviceSerial = self.ezvizService.deviceSerial\\n\\n self._interval_snapshots = 30\\n self._last_image = \\\"\\\"\\n self._last_snapshot_time = 0\",\n \"def initialize(self):\\n self.log.info(\\\"Initialize raspPinball hardware.\\\")\\n\\n self.config = self.machine.config['rasppinball']\\n self.machine.config_validator.validate_config(\\\"rasppinball\\\", self.config)\\n print(\\\"***************************\\\")\\n print(self.config)\\n #self.machine_type = (\\n # self.machine.config['hardware']['driverboards'].lower())\\n\\n self._connect_to_hardware()\\n\\n\\n # keypad\\n self._kp = Keypad()\\n self.old_key = \\\"\\\"\\n self.key = \\\"\\\"\\n # leds\\n self.init_strips()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6209206","0.6132519","0.57827735","0.57115394","0.56619436","0.5657306","0.56566656","0.5559778","0.5507198","0.55019826","0.5441228","0.5435611","0.5408168","0.53973293","0.538949","0.53633904","0.5356179","0.5353374","0.5349762","0.5343878","0.5338553","0.5325432","0.5281565","0.527382","0.5269924","0.5267352","0.5261668","0.5246937","0.52465904","0.5242274","0.5230292","0.5225945","0.5225133","0.52176696","0.5217437","0.5214908","0.52136284","0.5210604","0.5209985","0.5189983","0.51879704","0.51817304","0.5177679","0.516793","0.5165162","0.5152784","0.5147645","0.51424474","0.5136638","0.5136638","0.5135466","0.5121893","0.5116854","0.5116602","0.51124895","0.51104766","0.51093","0.5106885","0.5106534","0.5103445","0.50923294","0.5092244","0.5090007","0.5086328","0.5075018","0.50746214","0.50737655","0.50731504","0.50628823","0.5062259","0.50615776","0.5052035","0.50481355","0.5041044","0.50404537","0.5036046","0.5034362","0.50330347","0.50324285","0.50305957","0.50305957","0.5027923","0.50267714","0.50255936","0.5024808","0.5023678","0.50212914","0.500789","0.5005591","0.5001644","0.4998901","0.49985206","0.49937972","0.4992001","0.49873313","0.4985715","0.49785158","0.497297","0.4970856","0.4970233"],"string":"[\n \"0.6209206\",\n \"0.6132519\",\n \"0.57827735\",\n \"0.57115394\",\n \"0.56619436\",\n \"0.5657306\",\n \"0.56566656\",\n \"0.5559778\",\n \"0.5507198\",\n \"0.55019826\",\n \"0.5441228\",\n \"0.5435611\",\n \"0.5408168\",\n \"0.53973293\",\n \"0.538949\",\n \"0.53633904\",\n \"0.5356179\",\n \"0.5353374\",\n \"0.5349762\",\n \"0.5343878\",\n \"0.5338553\",\n \"0.5325432\",\n \"0.5281565\",\n \"0.527382\",\n \"0.5269924\",\n \"0.5267352\",\n \"0.5261668\",\n \"0.5246937\",\n \"0.52465904\",\n \"0.5242274\",\n \"0.5230292\",\n \"0.5225945\",\n \"0.5225133\",\n \"0.52176696\",\n \"0.5217437\",\n \"0.5214908\",\n \"0.52136284\",\n \"0.5210604\",\n \"0.5209985\",\n \"0.5189983\",\n \"0.51879704\",\n \"0.51817304\",\n \"0.5177679\",\n \"0.516793\",\n \"0.5165162\",\n \"0.5152784\",\n \"0.5147645\",\n \"0.51424474\",\n \"0.5136638\",\n \"0.5136638\",\n \"0.5135466\",\n \"0.5121893\",\n \"0.5116854\",\n \"0.5116602\",\n \"0.51124895\",\n \"0.51104766\",\n \"0.51093\",\n \"0.5106885\",\n \"0.5106534\",\n \"0.5103445\",\n \"0.50923294\",\n \"0.5092244\",\n \"0.5090007\",\n \"0.5086328\",\n \"0.5075018\",\n \"0.50746214\",\n \"0.50737655\",\n \"0.50731504\",\n \"0.50628823\",\n \"0.5062259\",\n \"0.50615776\",\n \"0.5052035\",\n \"0.50481355\",\n \"0.5041044\",\n \"0.50404537\",\n \"0.5036046\",\n \"0.5034362\",\n \"0.50330347\",\n \"0.50324285\",\n \"0.50305957\",\n \"0.50305957\",\n \"0.5027923\",\n \"0.50267714\",\n \"0.50255936\",\n \"0.5024808\",\n \"0.5023678\",\n \"0.50212914\",\n \"0.500789\",\n \"0.5005591\",\n \"0.5001644\",\n \"0.4998901\",\n \"0.49985206\",\n \"0.49937972\",\n \"0.4992001\",\n \"0.49873313\",\n \"0.4985715\",\n \"0.49785158\",\n \"0.497297\",\n \"0.4970856\",\n \"0.4970233\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.55974704"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":93889,"cells":{"query":{"kind":"string","value":"Initialize the aREST device."},"document":{"kind":"string","value":"def __init__(self, arest, resource, name, pin):\n self.arest = arest\n self._resource = resource\n self._name = name\n self._pin = pin\n self.update()\n\n if self._pin is not None:\n request = requests.get('{}/mode/{}/i'.format\n (self._resource, self._pin), timeout=10)\n if request.status_code is not 200:\n _LOGGER.error(\"Can't set mode. Is device offline?\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["async def init(self):\n logger.info(\"Init device: %s\", self._serial)\n self._callback(STATUS_INIT)\n\n self._init_binaries()\n self._init_apks()\n await self._init_forwards()\n\n await adb.shell(self._serial, \"/data/local/tmp/atx-agent server --stop\")\n await adb.shell(self._serial, \"/data/local/tmp/atx-agent server --nouia -d\")","def initialise(self):\n self.device.initialise()\n return \"OK\"","def init_rest(app_):\n\n rest_api = Api(app_)\n rest_api.add_resource(views.rest_resources.AppListResource,\n ActiveConfig.REST_URL_APPS_LIST,\n ActiveConfig.REST_URL_APPS_LIST + '/')\n rest_api.add_resource(views.rest_resources.AppResource,\n ActiveConfig.REST_URL_APPS_ITEM,\n ActiveConfig.REST_URL_APPS,\n ActiveConfig.REST_URL_APPS + '/')","def __init__(self, rest=AuthenticatingRestProxy(RestRequests())):\n self._rest = rest","async def init_device(self):\n await Device.init_device(self)\n # PROTECTED REGION ID(AsyncTabata.init_device) ENABLED START #\n self.logger = logging.getLogger(__name__)\n self._lock = threading.Lock()\n self._dev_factory = DevFactory()\n self._prepare = 10\n self._work = 20\n self._rest = 10\n self._cycles = 8\n self._tabatas = 1\n self._running_state = RunningState.PREPARE\n self.subscribed = False\n self.set_state(DevState.OFF)\n # The below commented commands are not really needed\n # since in GreenMode.Asyncio mode the monitor\n # lock is disabled by default.\n # util = tango.Util.instance()\n # util.set_serial_model(tango.SerialModel.NO_SYNC)\n # PROTECTED REGION END # // AsyncTabata.init_device","def initialize(self):\n if self.real:\n self.agent.connect(self)\n else:\n self.connect() # Connect python client to VREP\n self.agent.connect(self)","def __init__(self):\n config = self.read_config()\n self.deployment = config['deployment']\n self.deployment_config = config[self.deployment]\n logger.info(f'Initializing storage client with the {self.deployment} deployment config {pformat(self.deployment_config)}')\n\n # get the MLOS config from the user else default it from the deployment config file\n # self.mlos_config = config['MLOS']\n # logger.info(f'Initializing storage client with the MLOS config {pformat(self.mlos_config)}')\n\n # setup the mount path\n if self.deployment == \"LOCAL\":\n self.mount_dir = self.setup_mount()\n logger.info(f'Mount directory setup completed: {self.mount_dir}')","def initialize(self, *a, **kw):\n webapp2.RequestHandler.initialize(self, *a, **kw)","def init():\n\n global registry, fsk_router, ook_router\n\n radio.init()\n OpenThings.init(Devices.CRYPT_PID)\n\n fsk_router = Registry.Router(\"fsk\")\n\n #OOK receive not yet written\n #It will be used to be able to learn codes from Energenie legacy hand remotes\n ##ook_router = Registry.Router(\"ook\")\n\n registry = Registry.DeviceRegistry()\n registry.set_fsk_router(fsk_router)\n ##registry.set_ook_router(ook_router\n\n path = os.path.join(sys.path[0], registry.DEFAULT_FILENAME)\n if os.path.isfile(path):\n registry.load_from(path)\n print(\"loaded registry from file\")\n registry.list()\n fsk_router.list()\n\n # Default discovery mode, unless changed by app\n ##discovery_none()\n ##discovery_auto()\n ##discovery_ask(ask)\n discovery_autojoin()\n ##discovery_askjoin(ask)","def init():\n safe_call(backend.get().af_init())","def _initialize(self):\n self.flush()\n print(\"Initializing sensor...\")\n try:\n self.get_sample()\n print('Initialization successful')\n except:\n print('Initialization failed. Please disconnect and reconnect sensor.')","def doInitializeDevice(self):\n super().doInitializeDevice()","def _initialize(self):\n pass","def _initialize(self):\n pass","def _initialize(self):\n pass","def __init__(self):\n self._url_base = None\n self._keystone = None\n self._auth_token = None\n self._auth_lock = threading.Lock()\n self._failed_auth = False","def request_initialization(self) -> global___Snippet.SimpleRequestInitialization:","def request_initialization(self) -> global___Snippet.SimpleRequestInitialization:","def request_initialization(self) -> global___Snippet.SimpleRequestInitialization:","def request_initialization(self) -> global___Snippet.SimpleRequestInitialization:","def _initialize(self):\n self.send_init_command()","def initService(self):","async def init(self) -> None:","async def init(self) -> None:","def __init__(self, bb_reader,\r\n service_name=None,\r\n cache_refresh=None,\r\n port=None):\r\n self._v1_endpoints = [Endpoint('v1', self._version_1,\r\n show_help=self._show_help),\r\n ]\r\n\r\n self.logger = get_logger()\r\n\r\n self.logger.debug(\"Initializing OrgMgmtREST object\")\r\n super().__init__(port=port, service_name=service_name)\r\n self.logger.debug(\"Register endpoints\")\r\n self.register_multiple_endpoints(self._v1_endpoints)\r\n\r\n self._bbreader = bb_reader\r\n self._cache_refresh = cache_refresh","def RoutingInterfaceInitialize(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\n raise NotImplementedError()","def initialize(self):\n self.ros.enable()\n self.phone_link.enable()","def __init__(self, app=None, prefix='', decorators=None, response=None):\n self.app = None\n self.blueprint = None\n self.blueprint_setup = None\n self.prefix = prefix\n self.resources = []\n self.endpoints = set()\n self.decorators = decorators if decorators else []\n self.responder = response if response else JSONResponse()\n\n if app is not None:\n self.app = app\n self.init_app(app)","def init(self, userdata, conn):\r\n pass","def __init__(self, realm):\r\n Resource.__init__(self)\r\n\r\n verifyObject(IMasterRealm, realm)\r\n self._realm = realm","def __init__(self, **auth_kwargs):\n self.session = None\n self.neutron = None\n self.nova = None\n self.glance = None\n self.cinder = None\n self.swift = None\n self.keystone = None\n self.auth_kwargs = auth_kwargs","def __init__(self):\n self.config = get_config()\n self.log = get_logger(self)\n\n self.factory = SugarServerFactory(\"wss://*:5505\")\n self.factory.protocol = SugarServerProtocol\n\n self.console_factory = SugarConsoleServerFactory(\"wss://localhost:5507\")\n self.console_factory.protocol = SugarConsoleServerProtocol\n\n self.api = APIService(self.config)","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def __init__(self):\n\n\t\tself.Helpers = Helpers(\"TassAI\", False)\n\n\t\tself.qs = 16\n\t\tself.context = InferenceContext([self.Helpers.confs[\"iotJumpWay\"][\"MQTT\"][\"TassAI\"][\"runas\"], self.Helpers.confs[\"iotJumpWay\"][\"MQTT\"][\"TassAI\"][\"runas\"], self.Helpers.confs[\"iotJumpWay\"][\"MQTT\"][\"TassAI\"][\"runas\"]], \"\", \"\", \"\")\n\n\t\tself.Helpers.logger.info(\"TassAI Helper Class initialization complete.\")","def __init__(self, address=None, auth=(), config=None):\n self.log = logging.getLogger(f\"{__name__}.API\")\n if config:\n address, auth = apiconfig(config)\n if not address:\n raise Error(\"no address specified\")\n self.url = f\"https://{address}:8443/JSSResource\"\n self.session = requests.Session()\n self.session.headers.update({'Accept': 'application/xml'})\n if auth:\n self.session.auth = tuple(auth)","def init(self):\n self._service_store = ServiceStore(self.driver, self.network)\n self._emulator = NetworkEmulator(self.store, self.driver)","def __init__(self):\n self.device_id = None\n self.devices = []\n self.onvif_config = {}","def __init__(self):\r\n self.postgres = PostgreSQL()\r\n self.couch_query = Queries()\r\n self.unit_conversion = UnitConversion()\r\n self.calc = calculate_alarm_trigger.CalculateAlarmTrigger()\r\n super(DeviceOverview, self).__init__()","def initialize(self, sensors = None, readings = None, clients = None):\n\n self.sensors = sensors\n self.readings = readings\n self.clients = clients","def __init__(\n self,\n email: Optional[str] = None,\n password: Optional[str] = None,\n # loop: Optional[asyncio.AbstractEventLoop] = None,\n # session: Optional[aiohttp.ClientSession] = None,\n auth_token: Optional[str] = None,\n api_timeout: int = API_TIMEOUT,\n ) -> None:\n\n # random device id\n self._device_id: str = str(uuid1())\n\n self.sac = SureAPIClient(\n email=email, password=password, auth_token=auth_token, api_timeout=api_timeout\n )\n\n # api token management\n self._auth_token: Optional[str] = None\n if auth_token and token_seems_valid(auth_token):\n self._auth_token = auth_token\n else: # if token := find_token():\n self._auth_token = find_token()\n\n # storage for received api data\n self._resource: Dict[str, Any] = {}\n # storage for etags\n self._etags: Dict[str, str] = {}\n\n logger.debug(\"initialization completed | vars(): %s\", vars())","async def _init(self, **kwargs):","def request_initialization(self) -> global___Snippet.StreamingRequestInitialization:","def request_initialization(self) -> global___Snippet.StreamingRequestInitialization:","def initialize(self) -> None:\n pass","def initialize(self):\r\n pass","def initialize(self):\r\n pass","def initialize():\n environment = Environment()\n environment.setup()","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def initialize(context):","def init():","def initialize(self):\n\t\tpass","async def initialize(self):","def initialize(self, *args, **kwargs):\n pass","def __init__(self):\n\n self.META_AE_IP = 'HTTP_X_APPENGINE_USER_IP'\n self.FORWARDED_FOR = 'HTTP_X_FORWARDED_FOR'\n\n print('{} - Initialized'.format(__name__))","def __init__(self):\n self.server_name = 'Binary Light Device'\n self.device = None","def init_application(app, config):\n app.config.from_object(config)\n\n api = Api(app)\n api.add_resource(Build, config.WSPATH)\n api.add_resource(Request, config.WSPATH + '/<request_id>')\n api.add_resource(Result, config.WSPATH + '/<request_id>/result')\n api.add_resource(Image, config.WSPATH + '/<request_id>/result/image')\n api.add_resource(Output, config.WSPATH + '/<request_id>/result/output/<int:output_id>')\n api.add_resource(Log, config.WSPATH + '/<request_id>/result/log')\n\n AgroLogHandler(app).init()\n app.logger.info(\"Flask Application initialized\")","def init(self) -> None:\n ...","def __init__(self, wm) -> None:\n conf_dict = wm.context.config.arango_storage._to_dict()\n\n log.debug(conf_dict)\n client = ArangoClient(hosts=conf_dict['hosts'])\n db = client.db(conf_dict['database'],\n username=conf_dict['username'],\n password=conf_dict['password'])\n\n self.db = db\n self.client = client","def __init__(self):\n super(LoopbackTransport, self).__init__([_JSON_RPC_SERVER_PATH])","def __init__(self):\n self.sp, self.user = self.init_auth_client()\n self.logger = logging.getLogger(__name__)","def initialize(self):\n UseCaseBase.initialize(self)\n verdict, msg = Global.SUCCESS, \"\"\n\n # Export all ACS Global parameters as an JSON object into ACS_GLOBAL_PARAMETERS\n # environment variable so that user can access them from inside the\n # script that is being executed\n device_cfg_dict = self._global_conf.benchConfig.get_dict()\n dc_l = len(self._global_conf.deviceConfig)\n for x in range(1, dc_l + 1):\n device_cfg_dict['PHONE%s' % x] = self._global_conf.deviceConfig['PHONE%s' % x]\n device_cfg_dict['PHONE1']['serialNumber'] = self._device.retrieve_serial_number()\n os.environ['ACS_GLOBAL_PARAMETERS'] = json.dumps(device_cfg_dict)\n\n return verdict, msg","def __init__(self, rest_auth_key, app_id=None, version='v1'):\n self.app_id = app_id\n self.rest_auth_key = rest_auth_key\n assert isinstance(version, basestring), 'version is not valid'\n self.api_url = BASE_URL + version","def init(self):\n # Initialize runtime and MDK:\n self.runtime = fakeRuntime()\n self.runtime.getEnvVarsService().set(\"DATAWIRE_TOKEN\", \"somevalue\")\n self.runtime.dependencies.registerService(\"failurepolicy_factory\",\n RecordingFailurePolicyFactory())\n self.mdk = MDKImpl(self.runtime)\n self.mdk.start()\n self.disco = self.mdk._disco\n # Create a session:\n self.session = self.mdk.session()","def _setup(self) -> None:\n self._api = get_api(\n self._password,\n self._host,\n self._username,\n self._port,\n self._ssl,\n )\n\n self._info = self._api.get_info()\n self.device_name = self._info.get(\"DeviceName\", DEFAULT_NAME)\n self.model = self._info.get(\"ModelName\")\n self.firmware_version = self._info.get(\"Firmwareversion\")\n\n for model in MODELS_V2:\n if self.model.startswith(model):\n self._method_version = 2","def init_edra(self) -> None:\n ...","def __init__(self):\n self.authurl = Config().auth\n self.baseurl = Config().api\n self.s = Session()\n self.s.headers = {'Accept': 'application/json'}\n data = {\"grant_type\": \"client_credentials\", \"scope\": \"/read-public\", \"client_id\": Config().client_id,\n \"client_secret\": Config().client_secret}\n r = self.s.request(method=\"post\", url=self.authurl, data=data)\n self.s.headers = {'Accept': 'application/json', \"Access token\": r.json()[\"access_token\"]}","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def initialize(self):\n if not self._ready:\n self._real_initialize()\n self._ready = True","def initialize( self, logger, loop, netconf_ip, netconf_port, statistics,\n xml_to_json_translator):\n self.init_stream_handler(logger, loop, \n netconf_ip, netconf_port, statistics, xml_to_json_translator)","def initialize(self, *args, **kwargs):\n self.initialized = True","def __init__(self, address):\r\n self.loop = asyncio.new_event_loop()\r\n self.device_address = address\r\n\r\n self.__api_address = \"http://\" + address if not address.startswith('http://') else address\r\n _LOGGER.debug(\"Api address: %s\", self.__api_address)\r\n self.model_raw = None\r\n self.model = None\r\n self.working_mode_raw = None\r\n self.working_mode = None\r\n self.host_name = None\r\n self.main_status = None\r\n\r\n self.wifi_sta = None\r\n self.system = None\r\n self.cloud = None\r\n self.mqtt = None\r\n self.firmware = None\r\n self.relays = None\r\n self.rollers = None","def __init__(self):\n self.ju = ju.JSONUtil()\n self.apiC = api.API()"],"string":"[\n \"async def init(self):\\n logger.info(\\\"Init device: %s\\\", self._serial)\\n self._callback(STATUS_INIT)\\n\\n self._init_binaries()\\n self._init_apks()\\n await self._init_forwards()\\n\\n await adb.shell(self._serial, \\\"/data/local/tmp/atx-agent server --stop\\\")\\n await adb.shell(self._serial, \\\"/data/local/tmp/atx-agent server --nouia -d\\\")\",\n \"def initialise(self):\\n self.device.initialise()\\n return \\\"OK\\\"\",\n \"def init_rest(app_):\\n\\n rest_api = Api(app_)\\n rest_api.add_resource(views.rest_resources.AppListResource,\\n ActiveConfig.REST_URL_APPS_LIST,\\n ActiveConfig.REST_URL_APPS_LIST + '/')\\n rest_api.add_resource(views.rest_resources.AppResource,\\n ActiveConfig.REST_URL_APPS_ITEM,\\n ActiveConfig.REST_URL_APPS,\\n ActiveConfig.REST_URL_APPS + '/')\",\n \"def __init__(self, rest=AuthenticatingRestProxy(RestRequests())):\\n self._rest = rest\",\n \"async def init_device(self):\\n await Device.init_device(self)\\n # PROTECTED REGION ID(AsyncTabata.init_device) ENABLED START #\\n self.logger = logging.getLogger(__name__)\\n self._lock = threading.Lock()\\n self._dev_factory = DevFactory()\\n self._prepare = 10\\n self._work = 20\\n self._rest = 10\\n self._cycles = 8\\n self._tabatas = 1\\n self._running_state = RunningState.PREPARE\\n self.subscribed = False\\n self.set_state(DevState.OFF)\\n # The below commented commands are not really needed\\n # since in GreenMode.Asyncio mode the monitor\\n # lock is disabled by default.\\n # util = tango.Util.instance()\\n # util.set_serial_model(tango.SerialModel.NO_SYNC)\\n # PROTECTED REGION END # // AsyncTabata.init_device\",\n \"def initialize(self):\\n if self.real:\\n self.agent.connect(self)\\n else:\\n self.connect() # Connect python client to VREP\\n self.agent.connect(self)\",\n \"def __init__(self):\\n config = self.read_config()\\n self.deployment = config['deployment']\\n self.deployment_config = config[self.deployment]\\n logger.info(f'Initializing storage client with the {self.deployment} deployment config {pformat(self.deployment_config)}')\\n\\n # get the MLOS config from the user else default it from the deployment config file\\n # self.mlos_config = config['MLOS']\\n # logger.info(f'Initializing storage client with the MLOS config {pformat(self.mlos_config)}')\\n\\n # setup the mount path\\n if self.deployment == \\\"LOCAL\\\":\\n self.mount_dir = self.setup_mount()\\n logger.info(f'Mount directory setup completed: {self.mount_dir}')\",\n \"def initialize(self, *a, **kw):\\n webapp2.RequestHandler.initialize(self, *a, **kw)\",\n \"def init():\\n\\n global registry, fsk_router, ook_router\\n\\n radio.init()\\n OpenThings.init(Devices.CRYPT_PID)\\n\\n fsk_router = Registry.Router(\\\"fsk\\\")\\n\\n #OOK receive not yet written\\n #It will be used to be able to learn codes from Energenie legacy hand remotes\\n ##ook_router = Registry.Router(\\\"ook\\\")\\n\\n registry = Registry.DeviceRegistry()\\n registry.set_fsk_router(fsk_router)\\n ##registry.set_ook_router(ook_router\\n\\n path = os.path.join(sys.path[0], registry.DEFAULT_FILENAME)\\n if os.path.isfile(path):\\n registry.load_from(path)\\n print(\\\"loaded registry from file\\\")\\n registry.list()\\n fsk_router.list()\\n\\n # Default discovery mode, unless changed by app\\n ##discovery_none()\\n ##discovery_auto()\\n ##discovery_ask(ask)\\n discovery_autojoin()\\n ##discovery_askjoin(ask)\",\n \"def init():\\n safe_call(backend.get().af_init())\",\n \"def _initialize(self):\\n self.flush()\\n print(\\\"Initializing sensor...\\\")\\n try:\\n self.get_sample()\\n print('Initialization successful')\\n except:\\n print('Initialization failed. Please disconnect and reconnect sensor.')\",\n \"def doInitializeDevice(self):\\n super().doInitializeDevice()\",\n \"def _initialize(self):\\n pass\",\n \"def _initialize(self):\\n pass\",\n \"def _initialize(self):\\n pass\",\n \"def __init__(self):\\n self._url_base = None\\n self._keystone = None\\n self._auth_token = None\\n self._auth_lock = threading.Lock()\\n self._failed_auth = False\",\n \"def request_initialization(self) -> global___Snippet.SimpleRequestInitialization:\",\n \"def request_initialization(self) -> global___Snippet.SimpleRequestInitialization:\",\n \"def request_initialization(self) -> global___Snippet.SimpleRequestInitialization:\",\n \"def request_initialization(self) -> global___Snippet.SimpleRequestInitialization:\",\n \"def _initialize(self):\\n self.send_init_command()\",\n \"def initService(self):\",\n \"async def init(self) -> None:\",\n \"async def init(self) -> None:\",\n \"def __init__(self, bb_reader,\\r\\n service_name=None,\\r\\n cache_refresh=None,\\r\\n port=None):\\r\\n self._v1_endpoints = [Endpoint('v1', self._version_1,\\r\\n show_help=self._show_help),\\r\\n ]\\r\\n\\r\\n self.logger = get_logger()\\r\\n\\r\\n self.logger.debug(\\\"Initializing OrgMgmtREST object\\\")\\r\\n super().__init__(port=port, service_name=service_name)\\r\\n self.logger.debug(\\\"Register endpoints\\\")\\r\\n self.register_multiple_endpoints(self._v1_endpoints)\\r\\n\\r\\n self._bbreader = bb_reader\\r\\n self._cache_refresh = cache_refresh\",\n \"def RoutingInterfaceInitialize(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\\n raise NotImplementedError()\",\n \"def initialize(self):\\n self.ros.enable()\\n self.phone_link.enable()\",\n \"def __init__(self, app=None, prefix='', decorators=None, response=None):\\n self.app = None\\n self.blueprint = None\\n self.blueprint_setup = None\\n self.prefix = prefix\\n self.resources = []\\n self.endpoints = set()\\n self.decorators = decorators if decorators else []\\n self.responder = response if response else JSONResponse()\\n\\n if app is not None:\\n self.app = app\\n self.init_app(app)\",\n \"def init(self, userdata, conn):\\r\\n pass\",\n \"def __init__(self, realm):\\r\\n Resource.__init__(self)\\r\\n\\r\\n verifyObject(IMasterRealm, realm)\\r\\n self._realm = realm\",\n \"def __init__(self, **auth_kwargs):\\n self.session = None\\n self.neutron = None\\n self.nova = None\\n self.glance = None\\n self.cinder = None\\n self.swift = None\\n self.keystone = None\\n self.auth_kwargs = auth_kwargs\",\n \"def __init__(self):\\n self.config = get_config()\\n self.log = get_logger(self)\\n\\n self.factory = SugarServerFactory(\\\"wss://*:5505\\\")\\n self.factory.protocol = SugarServerProtocol\\n\\n self.console_factory = SugarConsoleServerFactory(\\\"wss://localhost:5507\\\")\\n self.console_factory.protocol = SugarConsoleServerProtocol\\n\\n self.api = APIService(self.config)\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def __init__(self):\\n\\n\\t\\tself.Helpers = Helpers(\\\"TassAI\\\", False)\\n\\n\\t\\tself.qs = 16\\n\\t\\tself.context = InferenceContext([self.Helpers.confs[\\\"iotJumpWay\\\"][\\\"MQTT\\\"][\\\"TassAI\\\"][\\\"runas\\\"], self.Helpers.confs[\\\"iotJumpWay\\\"][\\\"MQTT\\\"][\\\"TassAI\\\"][\\\"runas\\\"], self.Helpers.confs[\\\"iotJumpWay\\\"][\\\"MQTT\\\"][\\\"TassAI\\\"][\\\"runas\\\"]], \\\"\\\", \\\"\\\", \\\"\\\")\\n\\n\\t\\tself.Helpers.logger.info(\\\"TassAI Helper Class initialization complete.\\\")\",\n \"def __init__(self, address=None, auth=(), config=None):\\n self.log = logging.getLogger(f\\\"{__name__}.API\\\")\\n if config:\\n address, auth = apiconfig(config)\\n if not address:\\n raise Error(\\\"no address specified\\\")\\n self.url = f\\\"https://{address}:8443/JSSResource\\\"\\n self.session = requests.Session()\\n self.session.headers.update({'Accept': 'application/xml'})\\n if auth:\\n self.session.auth = tuple(auth)\",\n \"def init(self):\\n self._service_store = ServiceStore(self.driver, self.network)\\n self._emulator = NetworkEmulator(self.store, self.driver)\",\n \"def __init__(self):\\n self.device_id = None\\n self.devices = []\\n self.onvif_config = {}\",\n \"def __init__(self):\\r\\n self.postgres = PostgreSQL()\\r\\n self.couch_query = Queries()\\r\\n self.unit_conversion = UnitConversion()\\r\\n self.calc = calculate_alarm_trigger.CalculateAlarmTrigger()\\r\\n super(DeviceOverview, self).__init__()\",\n \"def initialize(self, sensors = None, readings = None, clients = None):\\n\\n self.sensors = sensors\\n self.readings = readings\\n self.clients = clients\",\n \"def __init__(\\n self,\\n email: Optional[str] = None,\\n password: Optional[str] = None,\\n # loop: Optional[asyncio.AbstractEventLoop] = None,\\n # session: Optional[aiohttp.ClientSession] = None,\\n auth_token: Optional[str] = None,\\n api_timeout: int = API_TIMEOUT,\\n ) -> None:\\n\\n # random device id\\n self._device_id: str = str(uuid1())\\n\\n self.sac = SureAPIClient(\\n email=email, password=password, auth_token=auth_token, api_timeout=api_timeout\\n )\\n\\n # api token management\\n self._auth_token: Optional[str] = None\\n if auth_token and token_seems_valid(auth_token):\\n self._auth_token = auth_token\\n else: # if token := find_token():\\n self._auth_token = find_token()\\n\\n # storage for received api data\\n self._resource: Dict[str, Any] = {}\\n # storage for etags\\n self._etags: Dict[str, str] = {}\\n\\n logger.debug(\\\"initialization completed | vars(): %s\\\", vars())\",\n \"async def _init(self, **kwargs):\",\n \"def request_initialization(self) -> global___Snippet.StreamingRequestInitialization:\",\n \"def request_initialization(self) -> global___Snippet.StreamingRequestInitialization:\",\n \"def initialize(self) -> None:\\n pass\",\n \"def initialize(self):\\r\\n pass\",\n \"def initialize(self):\\r\\n pass\",\n \"def initialize():\\n environment = Environment()\\n environment.setup()\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def initialize(context):\",\n \"def init():\",\n \"def initialize(self):\\n\\t\\tpass\",\n \"async def initialize(self):\",\n \"def initialize(self, *args, **kwargs):\\n pass\",\n \"def __init__(self):\\n\\n self.META_AE_IP = 'HTTP_X_APPENGINE_USER_IP'\\n self.FORWARDED_FOR = 'HTTP_X_FORWARDED_FOR'\\n\\n print('{} - Initialized'.format(__name__))\",\n \"def __init__(self):\\n self.server_name = 'Binary Light Device'\\n self.device = None\",\n \"def init_application(app, config):\\n app.config.from_object(config)\\n\\n api = Api(app)\\n api.add_resource(Build, config.WSPATH)\\n api.add_resource(Request, config.WSPATH + '/<request_id>')\\n api.add_resource(Result, config.WSPATH + '/<request_id>/result')\\n api.add_resource(Image, config.WSPATH + '/<request_id>/result/image')\\n api.add_resource(Output, config.WSPATH + '/<request_id>/result/output/<int:output_id>')\\n api.add_resource(Log, config.WSPATH + '/<request_id>/result/log')\\n\\n AgroLogHandler(app).init()\\n app.logger.info(\\\"Flask Application initialized\\\")\",\n \"def init(self) -> None:\\n ...\",\n \"def __init__(self, wm) -> None:\\n conf_dict = wm.context.config.arango_storage._to_dict()\\n\\n log.debug(conf_dict)\\n client = ArangoClient(hosts=conf_dict['hosts'])\\n db = client.db(conf_dict['database'],\\n username=conf_dict['username'],\\n password=conf_dict['password'])\\n\\n self.db = db\\n self.client = client\",\n \"def __init__(self):\\n super(LoopbackTransport, self).__init__([_JSON_RPC_SERVER_PATH])\",\n \"def __init__(self):\\n self.sp, self.user = self.init_auth_client()\\n self.logger = logging.getLogger(__name__)\",\n \"def initialize(self):\\n UseCaseBase.initialize(self)\\n verdict, msg = Global.SUCCESS, \\\"\\\"\\n\\n # Export all ACS Global parameters as an JSON object into ACS_GLOBAL_PARAMETERS\\n # environment variable so that user can access them from inside the\\n # script that is being executed\\n device_cfg_dict = self._global_conf.benchConfig.get_dict()\\n dc_l = len(self._global_conf.deviceConfig)\\n for x in range(1, dc_l + 1):\\n device_cfg_dict['PHONE%s' % x] = self._global_conf.deviceConfig['PHONE%s' % x]\\n device_cfg_dict['PHONE1']['serialNumber'] = self._device.retrieve_serial_number()\\n os.environ['ACS_GLOBAL_PARAMETERS'] = json.dumps(device_cfg_dict)\\n\\n return verdict, msg\",\n \"def __init__(self, rest_auth_key, app_id=None, version='v1'):\\n self.app_id = app_id\\n self.rest_auth_key = rest_auth_key\\n assert isinstance(version, basestring), 'version is not valid'\\n self.api_url = BASE_URL + version\",\n \"def init(self):\\n # Initialize runtime and MDK:\\n self.runtime = fakeRuntime()\\n self.runtime.getEnvVarsService().set(\\\"DATAWIRE_TOKEN\\\", \\\"somevalue\\\")\\n self.runtime.dependencies.registerService(\\\"failurepolicy_factory\\\",\\n RecordingFailurePolicyFactory())\\n self.mdk = MDKImpl(self.runtime)\\n self.mdk.start()\\n self.disco = self.mdk._disco\\n # Create a session:\\n self.session = self.mdk.session()\",\n \"def _setup(self) -> None:\\n self._api = get_api(\\n self._password,\\n self._host,\\n self._username,\\n self._port,\\n self._ssl,\\n )\\n\\n self._info = self._api.get_info()\\n self.device_name = self._info.get(\\\"DeviceName\\\", DEFAULT_NAME)\\n self.model = self._info.get(\\\"ModelName\\\")\\n self.firmware_version = self._info.get(\\\"Firmwareversion\\\")\\n\\n for model in MODELS_V2:\\n if self.model.startswith(model):\\n self._method_version = 2\",\n \"def init_edra(self) -> None:\\n ...\",\n \"def __init__(self):\\n self.authurl = Config().auth\\n self.baseurl = Config().api\\n self.s = Session()\\n self.s.headers = {'Accept': 'application/json'}\\n data = {\\\"grant_type\\\": \\\"client_credentials\\\", \\\"scope\\\": \\\"/read-public\\\", \\\"client_id\\\": Config().client_id,\\n \\\"client_secret\\\": Config().client_secret}\\n r = self.s.request(method=\\\"post\\\", url=self.authurl, data=data)\\n self.s.headers = {'Accept': 'application/json', \\\"Access token\\\": r.json()[\\\"access_token\\\"]}\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def initialize(self):\\n if not self._ready:\\n self._real_initialize()\\n self._ready = True\",\n \"def initialize( self, logger, loop, netconf_ip, netconf_port, statistics,\\n xml_to_json_translator):\\n self.init_stream_handler(logger, loop, \\n netconf_ip, netconf_port, statistics, xml_to_json_translator)\",\n \"def initialize(self, *args, **kwargs):\\n self.initialized = True\",\n \"def __init__(self, address):\\r\\n self.loop = asyncio.new_event_loop()\\r\\n self.device_address = address\\r\\n\\r\\n self.__api_address = \\\"http://\\\" + address if not address.startswith('http://') else address\\r\\n _LOGGER.debug(\\\"Api address: %s\\\", self.__api_address)\\r\\n self.model_raw = None\\r\\n self.model = None\\r\\n self.working_mode_raw = None\\r\\n self.working_mode = None\\r\\n self.host_name = None\\r\\n self.main_status = None\\r\\n\\r\\n self.wifi_sta = None\\r\\n self.system = None\\r\\n self.cloud = None\\r\\n self.mqtt = None\\r\\n self.firmware = None\\r\\n self.relays = None\\r\\n self.rollers = None\",\n \"def __init__(self):\\n self.ju = ju.JSONUtil()\\n self.apiC = api.API()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6444274","0.6330388","0.6299139","0.62126595","0.61097986","0.6039827","0.5993592","0.59869677","0.59306645","0.5883426","0.5880507","0.58715373","0.5868116","0.5868116","0.5868116","0.5859604","0.5851872","0.5851872","0.5851872","0.5851872","0.58076674","0.5774516","0.5750498","0.5750498","0.5735375","0.57294667","0.57293147","0.5717514","0.5716957","0.57081777","0.56964666","0.5690365","0.5687375","0.5687375","0.5687375","0.5687375","0.5687375","0.5683396","0.5680819","0.5680688","0.5680286","0.567368","0.56655276","0.5662114","0.565422","0.5650672","0.5650672","0.5637466","0.5637383","0.5637383","0.5634098","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.5633667","0.56314707","0.56276274","0.5627331","0.56178564","0.56158316","0.56148475","0.5612929","0.5591689","0.55908084","0.55865085","0.5584313","0.55779177","0.55778235","0.5574733","0.5571902","0.5569542","0.5558746","0.5557369","0.5557369","0.5557369","0.5557369","0.5557369","0.5557369","0.5557369","0.5557369","0.5550747","0.554571","0.5543015","0.55378544","0.5526387"],"string":"[\n \"0.6444274\",\n \"0.6330388\",\n \"0.6299139\",\n \"0.62126595\",\n \"0.61097986\",\n \"0.6039827\",\n \"0.5993592\",\n \"0.59869677\",\n \"0.59306645\",\n \"0.5883426\",\n \"0.5880507\",\n \"0.58715373\",\n \"0.5868116\",\n \"0.5868116\",\n \"0.5868116\",\n \"0.5859604\",\n \"0.5851872\",\n \"0.5851872\",\n \"0.5851872\",\n \"0.5851872\",\n \"0.58076674\",\n \"0.5774516\",\n \"0.5750498\",\n \"0.5750498\",\n \"0.5735375\",\n \"0.57294667\",\n \"0.57293147\",\n \"0.5717514\",\n \"0.5716957\",\n \"0.57081777\",\n \"0.56964666\",\n \"0.5690365\",\n \"0.5687375\",\n \"0.5687375\",\n \"0.5687375\",\n \"0.5687375\",\n \"0.5687375\",\n \"0.5683396\",\n \"0.5680819\",\n \"0.5680688\",\n \"0.5680286\",\n \"0.567368\",\n \"0.56655276\",\n \"0.5662114\",\n \"0.565422\",\n \"0.5650672\",\n \"0.5650672\",\n \"0.5637466\",\n \"0.5637383\",\n \"0.5637383\",\n \"0.5634098\",\n \"0.5633667\",\n \"0.5633667\",\n \"0.5633667\",\n \"0.5633667\",\n \"0.5633667\",\n \"0.5633667\",\n \"0.5633667\",\n 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name(self):\n return self._name"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def name(self):\n return f\"{DEFAULT_NAME}_{BINARY_SENSOR}\"","def get_sensor_name(self):\n return self.data[1]","def name(self):\n return self._sensor.name","def name(self):\n return f\"{self.sensor_type['name']} ({self._mac[-5:]})\"","def name(self):\n return self.device.device_data[self.device_id]['name'] + \\\n f' {self._sensor_type}'","def name(self):\n return f\"{self._name} {SENSOR_TYPES[self.sensor][0]}\"","def name(self) -> str:\n return f\"{self.platform_name} {self._sensor_name}\"","def name(self):\n return f\"{self._name}_{self._sensor}\"","def name(self):\n return f\"{self._name} {self._sensor_name}\"","def get_sensor_name(self):\n\n return self._sensor_results_list[0].get_sensor_model()","def name(self):\n return f\"{self._tc_object.name} {SENSOR_TYPES[self.type][0]}\"","def name(self):\n return f\"{habitica.DOMAIN}_{self._name}_{self._sensor_name}\"","def name(self):\n return f\"{get_device_name(self._data, 0, self._name)}\"","def friendly_name(self):\n return self._sensor_name","def name(self):\n return f'{self._vehicle.name} {self.wan_name} Signal'","def name(self):\n return self._device.device_data[self._uuid]['name']","def name(self):\n return self.device.name()","def name(self):\n return self.device.device_data[self.device_id]['name']","def name(self):\n return self._device.name","def name(self):\n return self._device.name","def name(self):\n return self._device.name","def find_iio_device_name(self):\n self.iio_device_dir()\n self.console.runcmd(f\"cat name\", expected=\"\\r\\n\")\n iio_device_name = self.console.output()\n return iio_device_name","def name(self) -> str:\n return self.camera_info[\"device_name\"]","def data_name(self):\n return \"Bispectrum\"","def name(self):\n return self.devname","def symbolize_sensorname_sysfs(name):\n return name.split(\"_\")[1] + \"_temp\"","def name(self) -> str:\n return self._device.name or self._device.mac","def name(self) -> str:\n station_name = self._get_station_name()\n return f\"{station_name} {self._fuel_type}\"","def name(self):\n return f\"{super().name} Battery\"","def name(self):\n return f\"{self.device_name} {self.device_variable}\"","def getName(self):\n return _libsbml.FluxBound_getName(self)","def sensor(self, _mask, _target, args) -> str:\n return self.get_sensor(args[\"<sensor_name>\"])","def topology_read_bus_name(self):\n result = ctypes.c_char_p(self.dss_obj.TopologyS(ctypes.c_int32(2), ctypes.c_int32(0)))\n return result.value.decode('ascii')","def get_binary_name():\n return os.path.basename(inspect.stack()[-1][1])[:16]","def name(self):\n if self._connection.location_names:\n return '{} {} {}'.format(self._device.location2, self._device.location, self._device.name)\n else:\n return self._device.name","def name(self):\n return f\"PoolSense {BINARY_SENSORS[self.info_type]['name']}\"","def vehicleName(self):\n return Cbytestring2Python(\n self.Rf2Scor.mVehicles[self.__playersDriverNum()].mVehicleName)","def driverName(self):\n return Cbytestring2Python(\n self.Rf2Scor.mVehicles[self.__playersDriverNum()].mDriverName)","def device_class(self):\n return BINARY_SENSOR_DEVICE_CLASS","def get_name(self):\n return self.nvPublic.get_name()","def name(self):\n # self._name = \"wyzeapi_\"+self._device_mac+\"_\"+ self._name\n return self._device.nickname","def get_humidity_name(self, sensor):\n if sensor >= self.num_humidities or sensor < 0:\n raise I2CException('Illegal sensor index {} specified'.format(sensor))\n\n return PSCU.HUMIDITY_SENSOR_NAMES[sensor]","def name(self):\n return self._device.description_pretty()","def name(self):\n if ( self._typeSensor == _production):\n name = \"myEnedis.%s.production\" %(self._myDataSensorEnedis.get_PDL_ID())\n else:\n name = \"myEnedis.%s\" %(self._myDataSensorEnedis.get_PDL_ID())\n return name","def name(self) -> str:\n return self.dev.label","def _get_sensor_name(self, zone=0, sensor_type=None):\n zone = int(zone)\n if zone == 0:\n return self._name + \" Last heartbeat\"\n else:\n zone_name = self._get_zone_name(zone)\n if sensor_type:\n return (\n self._name\n + (\" \" + zone_name + \" \" if zone_name else \" \")\n + sensor_type\n )\n else:\n _LOGGER.error(\n \"Hub: Get Sensor Name: Not allowed to create an entity_id without type, unless zone == 0.\"\n )\n return None","def get_sensor_type(self):\n return self.data[1][:-1]","def getName(self):\n return signal_base_get_name(self.obj)","def get_temperature_name(self, sensor):\n if sensor >= self.num_temperatures or sensor < 0:\n raise I2CException('Illegal sensor index {} specified'.format(sensor))\n\n return PSCU.TEMP_SENSOR_NAMES[sensor]","def get_internal_name(self) -> str:\n self.serial.write(b\"A!\")\n internal_name = self.__extract_string(self.__read_response(1)[0], b\"!N\")\n\n return internal_name","def name(self):\n return f\"{get_device_name(self._data, self._device.id)} Light\"","def get_device_name(self):\n name = self._device[\"name\"]\n if not name or name == \"--\":\n name = self._mac\n\n return name","def device_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"device_name\")","def device_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"device_name\")","def name(self):\n return self._get_device_class_name()","def name(self):\n if not self.has_name():\n return \"\"\n name_length = self.unpack_word(0x2)\n unpacked_string = self.unpack_string(0x14, name_length)\n if self.has_ascii_name():\n return unpacked_string.decode(\"windows-1252\")\n return unpacked_string.decode(\"utf-16le\")","def getDeviceName(self):\n name = str(nvmlDeviceGetName(self.handle))\n return name","def core_device_thing_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"core_device_thing_name\")","def name(self):\n return self.device.get_station_name(self.station_number)","def name(self):\n return f\"{get_device_name(self._data, self._actuator.id)} Floor Temp Offset\"","def name(self) -> str:\n return self.observation.name","def name(self):\n return self.robot.name + ' ' + SWITCH_TYPES[self.type][0]","def sensor(self , sensor_index):\n sensor = obd_sensors.SENSORS[sensor_index]\n try:\n r = self.get_sensor_value(sensor)\n except \"NORESPONSE\":\n r = \"NORESPONSE\"\n return (sensor.name,r, sensor.unit)","def get_name(self):\n name = getname(self)\n return TPM2B_NAME(name)","def get_name(self):\n name = getname(self)\n return TPM2B_NAME(name)","def name(self):\n return signal_base_get_name(self.obj)","def name(self):\n return self.raw.get(\"name\")","def hub_name(self):\n return self._props[\"persistent_identifiers\"].get(self._hub_name_prop)","def topology_read_branch_name(self):\n result = ctypes.c_char_p(self.dss_obj.TopologyS(ctypes.c_int32(0), ctypes.c_int32(0)))\n return result.value.decode('ascii')","def name(self):\n return f'{self._vehicle.name} Status'","def sensor_value(self) -> Optional[str]:\n raise NotImplementedError()","def read_name(self):\n return self.node.read_name()","def read_name(self):\n return self.node.read_name()","def read_name(self):\n return self.node.read_name()","def read_name(self):\n return self.node.read_name()","def get_name(self) -> str:\n return self.message[42:74].decode().rstrip(\"\\x00\")","def name(self):\n return self._fname","def name(self):\n return self._fname","def unique_id(self) -> str:\n return '_'.join(['wavin', self._controller_id, str(self._name), 'battery'])","def monitors_read_name(self) -> str:\n result = ctypes.c_char_p(self.dss_obj.MonitorsS(ctypes.c_int32(1), ctypes.c_int32(0)))\n return result.value.decode('ascii')","def getName():","def getName():","def getName():","def getName():","def getName():","def getName():","def name(self):\n return str()","def name(self) -> str:\n return self.data['name']","def monitors_file_name(self) -> str:\n result = ctypes.c_char_p(self.dss_obj.MonitorsS(ctypes.c_int32(0), ctypes.c_int32(0)))\n return result.value.decode('ascii')","def get_name(self):\n raise DeviceException(DeviceException.FEATURE_NOT_IMPLEMENTED)","def get_name_from_status(status):\n return status.stream_id + \"_\" + status.tune.strip(\".npy\")","def sysfs_dm_name_file(self):\n if not self.sysfs_dm_dir:\n return None\n return os.path.join(self.sysfs_dm_dir, 'name')","def unique_id(self) -> str:\n return f\"{self._device.unique_id}_battery\"","def raw_sensor_strings(self):\n try:\n with open(self.sensorpath, \"r\") as f:\n data = f.readlines()\n except IOError:\n raise NoSensorFoundError(self.type_name, self.id)\n\n if data[0].strip()[-3:] != \"YES\":\n raise SensorNotReadyError(self)\n\n return data","def get_name():","def name(self):\n return self._imu.IMUName()","def get_name() -> str:","def get_sensor_type_name(sensor_type_id):\n query = db.session.query(\n TypeClass.sensor_type,\n ).filter(TypeClass.id == sensor_type_id)\n sensor_name = db.session.execute(query).fetchone()\n if isinstance(sensor_name, Iterable):\n sensor_name = sensor_name[0]\n return sensor_name","def get_soc_name():\n return get_soc_spec(\"SOC_VERSION\")","def firmware(self) -> str:\n return self._device_info[\"Firmware\"]","def getName(self):\n return _libsbml.SBase_getName(self)"],"string":"[\n \"def name(self):\\n return f\\\"{DEFAULT_NAME}_{BINARY_SENSOR}\\\"\",\n \"def get_sensor_name(self):\\n return self.data[1]\",\n \"def name(self):\\n return self._sensor.name\",\n \"def name(self):\\n return f\\\"{self.sensor_type['name']} ({self._mac[-5:]})\\\"\",\n \"def name(self):\\n return self.device.device_data[self.device_id]['name'] + \\\\\\n f' {self._sensor_type}'\",\n \"def name(self):\\n return f\\\"{self._name} {SENSOR_TYPES[self.sensor][0]}\\\"\",\n \"def name(self) -> str:\\n return f\\\"{self.platform_name} {self._sensor_name}\\\"\",\n \"def name(self):\\n return f\\\"{self._name}_{self._sensor}\\\"\",\n \"def name(self):\\n return f\\\"{self._name} {self._sensor_name}\\\"\",\n \"def get_sensor_name(self):\\n\\n return self._sensor_results_list[0].get_sensor_model()\",\n \"def name(self):\\n return f\\\"{self._tc_object.name} {SENSOR_TYPES[self.type][0]}\\\"\",\n \"def name(self):\\n return f\\\"{habitica.DOMAIN}_{self._name}_{self._sensor_name}\\\"\",\n \"def name(self):\\n return f\\\"{get_device_name(self._data, 0, self._name)}\\\"\",\n \"def friendly_name(self):\\n return self._sensor_name\",\n \"def name(self):\\n return f'{self._vehicle.name} {self.wan_name} Signal'\",\n \"def name(self):\\n return self._device.device_data[self._uuid]['name']\",\n \"def name(self):\\n return self.device.name()\",\n \"def name(self):\\n return self.device.device_data[self.device_id]['name']\",\n \"def name(self):\\n return self._device.name\",\n \"def name(self):\\n return self._device.name\",\n \"def name(self):\\n return self._device.name\",\n \"def find_iio_device_name(self):\\n self.iio_device_dir()\\n self.console.runcmd(f\\\"cat name\\\", expected=\\\"\\\\r\\\\n\\\")\\n iio_device_name = self.console.output()\\n return iio_device_name\",\n \"def name(self) -> str:\\n return self.camera_info[\\\"device_name\\\"]\",\n \"def data_name(self):\\n return \\\"Bispectrum\\\"\",\n \"def name(self):\\n return self.devname\",\n \"def symbolize_sensorname_sysfs(name):\\n return name.split(\\\"_\\\")[1] + \\\"_temp\\\"\",\n \"def name(self) -> str:\\n return self._device.name or self._device.mac\",\n \"def name(self) -> str:\\n station_name = self._get_station_name()\\n return f\\\"{station_name} {self._fuel_type}\\\"\",\n \"def name(self):\\n return f\\\"{super().name} Battery\\\"\",\n \"def name(self):\\n return f\\\"{self.device_name} {self.device_variable}\\\"\",\n \"def getName(self):\\n return _libsbml.FluxBound_getName(self)\",\n \"def sensor(self, _mask, _target, args) -> str:\\n return self.get_sensor(args[\\\"<sensor_name>\\\"])\",\n \"def topology_read_bus_name(self):\\n result = ctypes.c_char_p(self.dss_obj.TopologyS(ctypes.c_int32(2), ctypes.c_int32(0)))\\n return result.value.decode('ascii')\",\n \"def get_binary_name():\\n return os.path.basename(inspect.stack()[-1][1])[:16]\",\n \"def name(self):\\n if self._connection.location_names:\\n return '{} {} {}'.format(self._device.location2, self._device.location, self._device.name)\\n else:\\n return self._device.name\",\n \"def name(self):\\n return f\\\"PoolSense {BINARY_SENSORS[self.info_type]['name']}\\\"\",\n \"def vehicleName(self):\\n return Cbytestring2Python(\\n self.Rf2Scor.mVehicles[self.__playersDriverNum()].mVehicleName)\",\n \"def driverName(self):\\n return Cbytestring2Python(\\n self.Rf2Scor.mVehicles[self.__playersDriverNum()].mDriverName)\",\n \"def device_class(self):\\n return BINARY_SENSOR_DEVICE_CLASS\",\n \"def get_name(self):\\n return self.nvPublic.get_name()\",\n \"def name(self):\\n # self._name = \\\"wyzeapi_\\\"+self._device_mac+\\\"_\\\"+ self._name\\n return self._device.nickname\",\n \"def get_humidity_name(self, sensor):\\n if sensor >= self.num_humidities or sensor < 0:\\n raise I2CException('Illegal sensor index {} specified'.format(sensor))\\n\\n return PSCU.HUMIDITY_SENSOR_NAMES[sensor]\",\n \"def name(self):\\n return self._device.description_pretty()\",\n \"def name(self):\\n if ( self._typeSensor == _production):\\n name = \\\"myEnedis.%s.production\\\" %(self._myDataSensorEnedis.get_PDL_ID())\\n else:\\n name = \\\"myEnedis.%s\\\" %(self._myDataSensorEnedis.get_PDL_ID())\\n return name\",\n \"def name(self) -> str:\\n return self.dev.label\",\n \"def _get_sensor_name(self, zone=0, sensor_type=None):\\n zone = int(zone)\\n if zone == 0:\\n return self._name + \\\" Last heartbeat\\\"\\n else:\\n zone_name = self._get_zone_name(zone)\\n if sensor_type:\\n return (\\n self._name\\n + (\\\" \\\" + zone_name + \\\" \\\" if zone_name else \\\" \\\")\\n + sensor_type\\n )\\n else:\\n _LOGGER.error(\\n \\\"Hub: Get Sensor Name: Not allowed to create an entity_id without type, unless zone == 0.\\\"\\n )\\n return None\",\n \"def get_sensor_type(self):\\n return self.data[1][:-1]\",\n \"def getName(self):\\n return signal_base_get_name(self.obj)\",\n \"def get_temperature_name(self, sensor):\\n if sensor >= self.num_temperatures or sensor < 0:\\n raise I2CException('Illegal sensor index {} specified'.format(sensor))\\n\\n return PSCU.TEMP_SENSOR_NAMES[sensor]\",\n \"def get_internal_name(self) -> str:\\n self.serial.write(b\\\"A!\\\")\\n internal_name = self.__extract_string(self.__read_response(1)[0], b\\\"!N\\\")\\n\\n return internal_name\",\n \"def name(self):\\n return f\\\"{get_device_name(self._data, self._device.id)} Light\\\"\",\n \"def get_device_name(self):\\n name = self._device[\\\"name\\\"]\\n if not name or name == \\\"--\\\":\\n name = self._mac\\n\\n return name\",\n \"def device_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"device_name\\\")\",\n \"def device_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"device_name\\\")\",\n \"def name(self):\\n return self._get_device_class_name()\",\n \"def name(self):\\n if not self.has_name():\\n return \\\"\\\"\\n name_length = self.unpack_word(0x2)\\n unpacked_string = self.unpack_string(0x14, name_length)\\n if self.has_ascii_name():\\n return unpacked_string.decode(\\\"windows-1252\\\")\\n return unpacked_string.decode(\\\"utf-16le\\\")\",\n \"def getDeviceName(self):\\n name = str(nvmlDeviceGetName(self.handle))\\n return name\",\n \"def core_device_thing_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"core_device_thing_name\\\")\",\n \"def name(self):\\n return self.device.get_station_name(self.station_number)\",\n \"def name(self):\\n return f\\\"{get_device_name(self._data, self._actuator.id)} Floor Temp Offset\\\"\",\n \"def name(self) -> str:\\n return self.observation.name\",\n \"def name(self):\\n return self.robot.name + ' ' + SWITCH_TYPES[self.type][0]\",\n \"def sensor(self , sensor_index):\\n sensor = obd_sensors.SENSORS[sensor_index]\\n try:\\n r = self.get_sensor_value(sensor)\\n except \\\"NORESPONSE\\\":\\n r = \\\"NORESPONSE\\\"\\n return (sensor.name,r, sensor.unit)\",\n \"def get_name(self):\\n name = getname(self)\\n return TPM2B_NAME(name)\",\n \"def get_name(self):\\n name = getname(self)\\n return TPM2B_NAME(name)\",\n \"def name(self):\\n return signal_base_get_name(self.obj)\",\n \"def name(self):\\n return self.raw.get(\\\"name\\\")\",\n \"def hub_name(self):\\n return self._props[\\\"persistent_identifiers\\\"].get(self._hub_name_prop)\",\n \"def topology_read_branch_name(self):\\n result = ctypes.c_char_p(self.dss_obj.TopologyS(ctypes.c_int32(0), ctypes.c_int32(0)))\\n return result.value.decode('ascii')\",\n \"def name(self):\\n return f'{self._vehicle.name} Status'\",\n \"def sensor_value(self) -> Optional[str]:\\n raise NotImplementedError()\",\n \"def read_name(self):\\n return self.node.read_name()\",\n \"def read_name(self):\\n return self.node.read_name()\",\n \"def read_name(self):\\n return self.node.read_name()\",\n \"def read_name(self):\\n return self.node.read_name()\",\n \"def get_name(self) -> str:\\n return self.message[42:74].decode().rstrip(\\\"\\\\x00\\\")\",\n \"def name(self):\\n return self._fname\",\n \"def name(self):\\n return self._fname\",\n \"def unique_id(self) -> str:\\n return '_'.join(['wavin', self._controller_id, str(self._name), 'battery'])\",\n \"def monitors_read_name(self) -> str:\\n result = ctypes.c_char_p(self.dss_obj.MonitorsS(ctypes.c_int32(1), ctypes.c_int32(0)))\\n return result.value.decode('ascii')\",\n \"def getName():\",\n \"def getName():\",\n \"def getName():\",\n \"def getName():\",\n \"def getName():\",\n \"def getName():\",\n \"def name(self):\\n return str()\",\n \"def name(self) -> str:\\n return self.data['name']\",\n \"def monitors_file_name(self) -> str:\\n result = ctypes.c_char_p(self.dss_obj.MonitorsS(ctypes.c_int32(0), ctypes.c_int32(0)))\\n return result.value.decode('ascii')\",\n \"def get_name(self):\\n raise DeviceException(DeviceException.FEATURE_NOT_IMPLEMENTED)\",\n \"def get_name_from_status(status):\\n return status.stream_id + \\\"_\\\" + status.tune.strip(\\\".npy\\\")\",\n \"def sysfs_dm_name_file(self):\\n if not self.sysfs_dm_dir:\\n return None\\n return os.path.join(self.sysfs_dm_dir, 'name')\",\n \"def unique_id(self) -> str:\\n return f\\\"{self._device.unique_id}_battery\\\"\",\n \"def raw_sensor_strings(self):\\n try:\\n with open(self.sensorpath, \\\"r\\\") as f:\\n data = f.readlines()\\n except IOError:\\n raise NoSensorFoundError(self.type_name, self.id)\\n\\n if data[0].strip()[-3:] != \\\"YES\\\":\\n raise SensorNotReadyError(self)\\n\\n return data\",\n \"def get_name():\",\n \"def name(self):\\n return self._imu.IMUName()\",\n \"def get_name() -> str:\",\n \"def get_sensor_type_name(sensor_type_id):\\n query = db.session.query(\\n TypeClass.sensor_type,\\n ).filter(TypeClass.id == sensor_type_id)\\n sensor_name = db.session.execute(query).fetchone()\\n if isinstance(sensor_name, Iterable):\\n sensor_name = sensor_name[0]\\n return sensor_name\",\n \"def get_soc_name():\\n return get_soc_spec(\\\"SOC_VERSION\\\")\",\n \"def firmware(self) -> str:\\n return self._device_info[\\\"Firmware\\\"]\",\n \"def getName(self):\\n return _libsbml.SBase_getName(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.82069147","0.8016458","0.7923548","0.76958257","0.7668517","0.7534579","0.72059137","0.7146881","0.7066772","0.6925692","0.6923594","0.67905974","0.67600256","0.67595863","0.66872895","0.6645599","0.6642735","0.66148776","0.6601633","0.6601633","0.6601633","0.65078527","0.6485433","0.6467763","0.64300233","0.6418223","0.63564795","0.63535976","0.634989","0.6312314","0.62281036","0.6227928","0.6226296","0.6215336","0.62106955","0.619323","0.6192957","0.61736006","0.617228","0.61626714","0.6161593","0.6160629","0.6150049","0.6146415","0.6144629","0.61286265","0.607514","0.60736436","0.60731685","0.60702634","0.6058256","0.60564584","0.60458565","0.60458565","0.60310596","0.6030087","0.60196215","0.60087967","0.5998081","0.59865487","0.59671533","0.5946134","0.5940502","0.5938853","0.5938853","0.5938829","0.59236777","0.59188896","0.58996356","0.5897636","0.5890073","0.5885399","0.5885399","0.5885399","0.5885399","0.58783007","0.58647233","0.58647233","0.5863007","0.5853714","0.5841905","0.5841905","0.5841905","0.5841905","0.5841905","0.5841905","0.5828875","0.58246815","0.58204216","0.58200717","0.5814388","0.58085454","0.58042663","0.5802017","0.5797528","0.5794689","0.5788975","0.57868993","0.5785756","0.5785613","0.5775064"],"string":"[\n \"0.82069147\",\n \"0.8016458\",\n \"0.7923548\",\n \"0.76958257\",\n \"0.7668517\",\n \"0.7534579\",\n \"0.72059137\",\n \"0.7146881\",\n \"0.7066772\",\n \"0.6925692\",\n \"0.6923594\",\n \"0.67905974\",\n \"0.67600256\",\n \"0.67595863\",\n \"0.66872895\",\n \"0.6645599\",\n \"0.6642735\",\n \"0.66148776\",\n \"0.6601633\",\n \"0.6601633\",\n \"0.6601633\",\n \"0.65078527\",\n \"0.6485433\",\n \"0.6467763\",\n \"0.64300233\",\n \"0.6418223\",\n \"0.63564795\",\n \"0.63535976\",\n \"0.634989\",\n \"0.6312314\",\n \"0.62281036\",\n \"0.6227928\",\n \"0.6226296\",\n \"0.6215336\",\n \"0.62106955\",\n \"0.619323\",\n \"0.6192957\",\n \"0.61736006\",\n \"0.617228\",\n \"0.61626714\",\n \"0.6161593\",\n \"0.6160629\",\n \"0.6150049\",\n \"0.6146415\",\n \"0.6144629\",\n \"0.61286265\",\n \"0.607514\",\n \"0.60736436\",\n \"0.60731685\",\n \"0.60702634\",\n \"0.6058256\",\n \"0.60564584\",\n \"0.60458565\",\n \"0.60458565\",\n \"0.60310596\",\n \"0.6030087\",\n \"0.60196215\",\n \"0.60087967\",\n \"0.5998081\",\n \"0.59865487\",\n \"0.59671533\",\n \"0.5946134\",\n \"0.5940502\",\n \"0.5938853\",\n \"0.5938853\",\n \"0.5938829\",\n \"0.59236777\",\n \"0.59188896\",\n \"0.58996356\",\n \"0.5897636\",\n \"0.5890073\",\n \"0.5885399\",\n \"0.5885399\",\n \"0.5885399\",\n \"0.5885399\",\n \"0.58783007\",\n \"0.58647233\",\n \"0.58647233\",\n \"0.5863007\",\n \"0.5853714\",\n \"0.5841905\",\n \"0.5841905\",\n \"0.5841905\",\n \"0.5841905\",\n \"0.5841905\",\n \"0.5841905\",\n \"0.5828875\",\n \"0.58246815\",\n \"0.58204216\",\n \"0.58200717\",\n \"0.5814388\",\n \"0.58085454\",\n \"0.58042663\",\n \"0.5802017\",\n \"0.5797528\",\n \"0.5794689\",\n \"0.5788975\",\n \"0.57868993\",\n \"0.5785756\",\n \"0.5785613\",\n \"0.5775064\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":93891,"cells":{"query":{"kind":"string","value":"Return true if the binary sensor is on."},"document":{"kind":"string","value":"def is_on(self):\n return bool(self.arest.data.get('state'))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def is_on(self):\n return self._sensor_state()","def is_on(self):\n return getattr(self.coordinator.data[0], self._sensor) is True","def is_on(self):\n data = self.sensor_data\n if data and data[\"model\"] == \"SML\" and data[\"changed\"]:\n return data[\"state\"] == STATE_ON\n return False","def is_on(self) -> bool:\n return self._device.is_on","def is_on(self):\n return self._device.is_on","def is_on(self):\n return self._device.is_on","def is_on(self):\n return self._device.is_on","def check_sensor(self):\n return True if (self._read_register_1ubyte(self.BME680_ID) == 0x61) else False","def is_on(self):\n if self._power_state == HYSEN_POWERON :\n return True\n else:\n return False","def is_on(self) -> bool:\n return self.entity_description.get_ufp_value(self.device) is True","def is_on(self) -> bool:\n return self.entity_description.get_ufp_value(self.device) is True","def is_on(self) -> bool:\n return self.tuya_device.status.get(DPCODE_SWITCH, False)","def is_on(self):\n return self._brightness != 0","def is_on(self):\n return self._device.state","def is_on(pin):\n return GPIO.input(pin) == GPIO.HIGH","def is_on(self) -> bool:\n raise NotImplementedError(\"Device subclass needs to implement this.\")","def is_on(self):\n if self._sensor_type != DEVICE_TYPE_DOORBELL:\n return self._camera_data[\"event_on\"]\n return self._camera_data[\"event_ring_on\"]","def is_on(self):\n return getattr(self._node, STICK_API[USB_MOTION_ID][ATTR_STATE])","def is_on(self) -> bool:\n return self._device.fan_on","def is_on(self):\n return (\n self._device.batterylevel != SHCBatteryDevice.BatteryLevelService.State.OK\n )","def is_on(self):\r\n self._state = self.iobus.read_pin(self.targetpin)\r\n return self._state","def is_on(self) -> bool:\n return self._state == STATE_ON","def is_on(self):\n return self._brightness > 0 or self._white_value > 0","def is_on(self):\n return self._brightness_pct != 0","def is_on(self):\n return self._program.get(\"enabled\") is True","def is_on(self) -> bool:\n return bool(self._state)","def is_on(self):\n return self._state == STATE_ON","def is_on(self):\n return self._state == STATE_ON","def is_on(self):\n return self._state == STATE_ON","def is_on(self):\n return self._state == STATE_ON","def is_on(self):\n return self._state == STATE_ON","def is_on(self):\n return True if self._device.device_data[self._uuid]['streaming_state'] == \\\n 'streaming-enabled' or \\\n self._device.device_data[self._uuid]['streaming_state'] == \\\n 'online-disabled' else False","def is_on(self) -> bool:\n return self._client.get_circ_pump()","def is_on(self):\n if self._switch_type == \"record_motion\":\n return self._camera_data[\"recording_mode\"] == TYPE_RECORD_MOTION\n elif self._switch_type == \"record_always\":\n return self._camera_data[\"recording_mode\"] == TYPE_RECORD_ALWAYS\n elif self._switch_type == \"record_smart\":\n return self._camera_data[\"recording_mode\"] == TYPE_RECORD_SMARTDETECT\n elif self._switch_type == \"ir_mode\":\n return self._camera_data[\"ir_mode\"] == self._ir_on_cmd\n elif self._switch_type == \"hdr_mode\":\n return self._camera_data[\"hdr_mode\"] is True\n elif self._switch_type == \"high_fps\":\n return self._camera_data[\"video_mode\"] == TYPE_HIGH_FPS_ON\n else:\n return self._camera_data[\"status_light\"] == \"True\"","def is_on(self):\n return bool(self._state)","def is_on(self):\n return bool(self._state)","def is_connected(self) -> bool:\n return self.arduino is not None","def is_on(self) -> bool:\n if self._state == STATE_UNKNOWN:\n return False\n return bool(self._state)","def is_on(self) -> bool:\n return self._is_on","def is_on(self):\n return self.wink.state()","def is_on(self):\n return self._on","def is_on(self) -> bool:\n return self._state","def is_on(self):\n pass","def is_on(self):\n return self._poe_data.ports[self._port]['state'] == STATE_ON","def is_on(self):\n return self._mower_status in [\n STATUS_EXECUTING_START, STATUS_OK_CHARGING,\n STATUS_OK_CUTTING, STATUS_OK_LEAVING, STATUS_OK_SEARCHING, STATUS_OK_CUTTING_MANUAL]","def Get(self):\n return bool(self.Bus.Read_uInt8(self.Address, 0x40+self.Pin))","def _is_device_active(self):\n return self.power_mode == STATE_ON","def ison(self):\n return bool(self.pin.state) if self.pinishigh else not bool(self.pin.state)","def is_on(self) -> bool:\n return self.coordinator.data.get_metric(METRIC_KEY_MODE) == MODE_ON","def is_measuring(self):\n # 5.4.4 of the datasheet indicates the relevant bit\n return bool(self.read_register(STATUS_REGISTER) & 0b1000)","def output(self):\n match = re.search('STS1=(\\d+)', self.ask('OC;'))\n status = int(match.groups(0)[0])\n isOn = status & 16\n if isOn:\n isOn = True\n else:\n isOn = False\n self.notify('output', isOn)\n return isOn","def is_on(self):\n ret_val = self._get_switch_state()\n if ret_val is None:\n return False\n if isinstance(ret_val, bool):\n return ret_val\n if ret_val == STATE_ON:\n return True\n state = STATE_LOOKUP.get(ret_val, STATE_OFF)\n return state == STATE_ON","def is_on(self):\n return bool(self.enabled)","def is_on(self) -> bool:\n return self._state == \"yes\"","def is_on(self):\n return self._device.car_state.get(self._key)","def is_on(self):\n return False","def is_on(self):\n return not self.ready","def is_on(self):\n camera = self.coordinator.data[self._camera_id]\n if self._switch_type == \"record_motion\":\n enabled = True if camera[\"recording_mode\"] == TYPE_RECORD_MOTION else False\n elif self._switch_type == \"record_always\":\n enabled = True if camera[\"recording_mode\"] == TYPE_RECORD_ALLWAYS else False\n else:\n enabled = True if camera[\"ir_mode\"] == self._ir_on_cmd else False\n return enabled","def is_on(self) -> bool | None:\n return self._state","def is_on(self):\n return self._client.get_power()","def _get_new_data_status(self):\n readValue = self._read_register_1ubyte(self.BME680_EAS_STATUS_0)\n\n return True if ((readValue & 0b10000000) == 0b10000000) else False","def is_host_on(self):\n status = False\n cmd = \"/usr/local/bin/wedge_power.sh status\"\n data = run_shell_cmd(cmd)\n Logger.info(\"[FSCD Testing] Executing cmd= [{}]\".format(cmd))\n Logger.info(\"[FSCD Testing] Received data= [{}]\".format(data))\n if \"on\" in data:\n status = True\n Logger.info(\"[FSCD Testing] userver power status {}\".format(status))\n return status","def get_state(self, channel=None):\n return bool(self.getBinaryData(\"SENSOR\", channel))","def is_on(self):\n return self._light_on","def is_on(self):\n return self._is_on","def is_on(self):\n return self._is_on","def is_on(self):\n return self._is_on","def is_on(self):\n return self._is_on","def is_on(self):\n return self._is_on","def is_on(self):\n return self._is_on","def is_on(self):\n return self._is_on","def is_on(self):\n return self._is_on","def is_on(self):\n return self._is_on","def is_on(self):\n return self._is_on","def is_on(self):\n return self._is_on","def is_on(self):\n return self.car.data[DATA_PLUGGED_IN]","def get_wind_sensor_presence(self) -> bool:\n self.serial.write(b\"v!\")\n wind_sensor = self.__extract_int(self.__read_response(1)[0], b\"!v\")\n\n return wind_sensor == 1","def _get_measuring_status(self):\n readValue = self._read_register_1ubyte(self.BME680_EAS_STATUS_0)\n\n return True if ((readValue & 0b00100000) == 0b00100000) else False","def is_on(self):\n return bool(getattr(self.resource, self.variable))","def is_on(self):\n return self.car.data[DATA_CHARGING]","def is_on(self):\n return self._data[\"enabled\"]","def state(self):\n # None will return False\n return bool(self.switch.value)","def light_is_on(self):\r\n return self._light == \"ON\"","def is_on(self):\n return self._device.state == SHCShutterContact.ShutterContactService.State.OPEN","def on(self):\n status = self.dev.ctrl_transfer(0x40, 0x01, 0x0001, 0xa0, [])\n if status == 0:\n self.ev.set()\n return (status == 0)","def is_on(self):\n return self._controller.is_on","def read_bool(self):\n return self.read_uint32() == 1","def is_power_onoff(self):\n return self['application'] == 'ccd201_pon_app'","def connected(self):\n return bool(self.serial)","def is_connected(self):\n return self.hw_connected","def is_on(self) -> bool:\n return self._current_speed != SPEED_OFF","def is_on(self):\n return self._get_state() == ServerState.ON","def light_is_on(self):\n return self._light == \"ON\"","def is_on(self):\n request = requests.get(self._resource+\"/state\", timeout=10)\n \n try:\n if int(float(request.text)) > 0:\n self._state = True\n else:\n self._state = False\n \n except:\n self._state = None\n \n return self._state","def on_off_bool(value):\n return value == 'on'","def detect():\n try:\n s = serial.Serial(port = 0, baudrate = 19200, parity = 'O', timeout=1)\n except Exception, e:\n log = logging.getLogger('root')\n log.exception(e)\n return False\n else:\n return True\n finally:\n s.close()","def is_on(self) -> bool:\n if self._on_off_cluster_handler.on_off is None:\n return False\n return self._on_off_cluster_handler.on_off","def is_on(self):\n return self._state","def is_on(self):\n return self._state","def is_on(self):\n return self._state"],"string":"[\n \"def is_on(self):\\n return self._sensor_state()\",\n \"def is_on(self):\\n return getattr(self.coordinator.data[0], self._sensor) is True\",\n \"def is_on(self):\\n data = self.sensor_data\\n if data and data[\\\"model\\\"] == \\\"SML\\\" and data[\\\"changed\\\"]:\\n return data[\\\"state\\\"] == STATE_ON\\n return False\",\n \"def is_on(self) -> bool:\\n return self._device.is_on\",\n \"def is_on(self):\\n return self._device.is_on\",\n \"def is_on(self):\\n return self._device.is_on\",\n \"def is_on(self):\\n return self._device.is_on\",\n \"def check_sensor(self):\\n return True if (self._read_register_1ubyte(self.BME680_ID) == 0x61) else False\",\n \"def is_on(self):\\n if self._power_state == HYSEN_POWERON :\\n return True\\n else:\\n return False\",\n \"def is_on(self) -> bool:\\n return self.entity_description.get_ufp_value(self.device) is True\",\n \"def is_on(self) -> bool:\\n return self.entity_description.get_ufp_value(self.device) is True\",\n \"def is_on(self) -> bool:\\n return self.tuya_device.status.get(DPCODE_SWITCH, False)\",\n \"def is_on(self):\\n return self._brightness != 0\",\n \"def is_on(self):\\n return self._device.state\",\n \"def is_on(pin):\\n return GPIO.input(pin) == GPIO.HIGH\",\n \"def is_on(self) -> bool:\\n raise NotImplementedError(\\\"Device subclass needs to implement this.\\\")\",\n \"def is_on(self):\\n if self._sensor_type != DEVICE_TYPE_DOORBELL:\\n return self._camera_data[\\\"event_on\\\"]\\n return self._camera_data[\\\"event_ring_on\\\"]\",\n \"def is_on(self):\\n return getattr(self._node, STICK_API[USB_MOTION_ID][ATTR_STATE])\",\n \"def is_on(self) -> bool:\\n return self._device.fan_on\",\n \"def is_on(self):\\n return (\\n self._device.batterylevel != SHCBatteryDevice.BatteryLevelService.State.OK\\n )\",\n \"def is_on(self):\\r\\n self._state = self.iobus.read_pin(self.targetpin)\\r\\n return self._state\",\n \"def is_on(self) -> bool:\\n return self._state == STATE_ON\",\n \"def is_on(self):\\n return self._brightness > 0 or self._white_value > 0\",\n \"def is_on(self):\\n return self._brightness_pct != 0\",\n \"def is_on(self):\\n return self._program.get(\\\"enabled\\\") is True\",\n \"def is_on(self) -> bool:\\n return bool(self._state)\",\n \"def is_on(self):\\n return self._state == STATE_ON\",\n \"def is_on(self):\\n return self._state == STATE_ON\",\n \"def is_on(self):\\n return self._state == STATE_ON\",\n \"def is_on(self):\\n return self._state == STATE_ON\",\n \"def is_on(self):\\n return self._state == STATE_ON\",\n \"def is_on(self):\\n return True if self._device.device_data[self._uuid]['streaming_state'] == \\\\\\n 'streaming-enabled' or \\\\\\n self._device.device_data[self._uuid]['streaming_state'] == \\\\\\n 'online-disabled' else False\",\n \"def is_on(self) -> bool:\\n return self._client.get_circ_pump()\",\n \"def is_on(self):\\n if self._switch_type == \\\"record_motion\\\":\\n return self._camera_data[\\\"recording_mode\\\"] == TYPE_RECORD_MOTION\\n elif self._switch_type == \\\"record_always\\\":\\n return self._camera_data[\\\"recording_mode\\\"] == TYPE_RECORD_ALWAYS\\n elif self._switch_type == \\\"record_smart\\\":\\n return self._camera_data[\\\"recording_mode\\\"] == TYPE_RECORD_SMARTDETECT\\n elif self._switch_type == \\\"ir_mode\\\":\\n return self._camera_data[\\\"ir_mode\\\"] == self._ir_on_cmd\\n elif self._switch_type == \\\"hdr_mode\\\":\\n return self._camera_data[\\\"hdr_mode\\\"] is True\\n elif self._switch_type == \\\"high_fps\\\":\\n return self._camera_data[\\\"video_mode\\\"] == TYPE_HIGH_FPS_ON\\n else:\\n return self._camera_data[\\\"status_light\\\"] == \\\"True\\\"\",\n \"def is_on(self):\\n return bool(self._state)\",\n \"def is_on(self):\\n return bool(self._state)\",\n \"def is_connected(self) -> bool:\\n return self.arduino is not None\",\n \"def is_on(self) -> bool:\\n if self._state == STATE_UNKNOWN:\\n return False\\n return bool(self._state)\",\n \"def is_on(self) -> bool:\\n return self._is_on\",\n \"def is_on(self):\\n return self.wink.state()\",\n \"def is_on(self):\\n return self._on\",\n \"def is_on(self) -> bool:\\n return self._state\",\n \"def is_on(self):\\n pass\",\n \"def is_on(self):\\n return self._poe_data.ports[self._port]['state'] == STATE_ON\",\n \"def is_on(self):\\n return self._mower_status in [\\n STATUS_EXECUTING_START, STATUS_OK_CHARGING,\\n STATUS_OK_CUTTING, STATUS_OK_LEAVING, STATUS_OK_SEARCHING, STATUS_OK_CUTTING_MANUAL]\",\n \"def Get(self):\\n return bool(self.Bus.Read_uInt8(self.Address, 0x40+self.Pin))\",\n \"def _is_device_active(self):\\n return self.power_mode == STATE_ON\",\n \"def ison(self):\\n return bool(self.pin.state) if self.pinishigh else not bool(self.pin.state)\",\n \"def is_on(self) -> bool:\\n return self.coordinator.data.get_metric(METRIC_KEY_MODE) == MODE_ON\",\n \"def is_measuring(self):\\n # 5.4.4 of the datasheet indicates the relevant bit\\n return bool(self.read_register(STATUS_REGISTER) & 0b1000)\",\n \"def output(self):\\n match = re.search('STS1=(\\\\d+)', self.ask('OC;'))\\n status = int(match.groups(0)[0])\\n isOn = status & 16\\n if isOn:\\n isOn = True\\n else:\\n isOn = False\\n self.notify('output', isOn)\\n return isOn\",\n \"def is_on(self):\\n ret_val = self._get_switch_state()\\n if ret_val is None:\\n return False\\n if isinstance(ret_val, bool):\\n return ret_val\\n if ret_val == STATE_ON:\\n return True\\n state = STATE_LOOKUP.get(ret_val, STATE_OFF)\\n return state == STATE_ON\",\n \"def is_on(self):\\n return bool(self.enabled)\",\n \"def is_on(self) -> bool:\\n return self._state == \\\"yes\\\"\",\n \"def is_on(self):\\n return self._device.car_state.get(self._key)\",\n \"def is_on(self):\\n return False\",\n \"def is_on(self):\\n return not self.ready\",\n \"def is_on(self):\\n camera = self.coordinator.data[self._camera_id]\\n if self._switch_type == \\\"record_motion\\\":\\n enabled = True if camera[\\\"recording_mode\\\"] == TYPE_RECORD_MOTION else False\\n elif self._switch_type == \\\"record_always\\\":\\n enabled = True if camera[\\\"recording_mode\\\"] == TYPE_RECORD_ALLWAYS else False\\n else:\\n enabled = True if camera[\\\"ir_mode\\\"] == self._ir_on_cmd else False\\n return enabled\",\n \"def is_on(self) -> bool | None:\\n return self._state\",\n \"def is_on(self):\\n return self._client.get_power()\",\n \"def _get_new_data_status(self):\\n readValue = self._read_register_1ubyte(self.BME680_EAS_STATUS_0)\\n\\n return True if ((readValue & 0b10000000) == 0b10000000) else False\",\n \"def is_host_on(self):\\n status = False\\n cmd = \\\"/usr/local/bin/wedge_power.sh status\\\"\\n data = run_shell_cmd(cmd)\\n Logger.info(\\\"[FSCD Testing] Executing cmd= [{}]\\\".format(cmd))\\n Logger.info(\\\"[FSCD Testing] Received data= [{}]\\\".format(data))\\n if \\\"on\\\" in data:\\n status = True\\n Logger.info(\\\"[FSCD Testing] userver power status {}\\\".format(status))\\n return status\",\n \"def get_state(self, channel=None):\\n return bool(self.getBinaryData(\\\"SENSOR\\\", channel))\",\n \"def is_on(self):\\n return self._light_on\",\n \"def is_on(self):\\n return self._is_on\",\n \"def is_on(self):\\n return self._is_on\",\n \"def is_on(self):\\n return self._is_on\",\n \"def is_on(self):\\n return self._is_on\",\n \"def is_on(self):\\n return self._is_on\",\n \"def is_on(self):\\n return self._is_on\",\n \"def is_on(self):\\n return self._is_on\",\n \"def is_on(self):\\n return self._is_on\",\n \"def is_on(self):\\n return self._is_on\",\n \"def is_on(self):\\n return self._is_on\",\n \"def is_on(self):\\n return self._is_on\",\n \"def is_on(self):\\n return self.car.data[DATA_PLUGGED_IN]\",\n \"def get_wind_sensor_presence(self) -> bool:\\n self.serial.write(b\\\"v!\\\")\\n wind_sensor = self.__extract_int(self.__read_response(1)[0], b\\\"!v\\\")\\n\\n return wind_sensor == 1\",\n \"def _get_measuring_status(self):\\n readValue = self._read_register_1ubyte(self.BME680_EAS_STATUS_0)\\n\\n return True if ((readValue & 0b00100000) == 0b00100000) else False\",\n \"def is_on(self):\\n return bool(getattr(self.resource, self.variable))\",\n \"def is_on(self):\\n return self.car.data[DATA_CHARGING]\",\n \"def is_on(self):\\n return self._data[\\\"enabled\\\"]\",\n \"def state(self):\\n # None will return False\\n return bool(self.switch.value)\",\n \"def light_is_on(self):\\r\\n return self._light == \\\"ON\\\"\",\n \"def is_on(self):\\n return self._device.state == SHCShutterContact.ShutterContactService.State.OPEN\",\n \"def on(self):\\n status = self.dev.ctrl_transfer(0x40, 0x01, 0x0001, 0xa0, [])\\n if status == 0:\\n self.ev.set()\\n return (status == 0)\",\n \"def is_on(self):\\n return self._controller.is_on\",\n \"def read_bool(self):\\n return self.read_uint32() == 1\",\n \"def is_power_onoff(self):\\n return self['application'] == 'ccd201_pon_app'\",\n \"def connected(self):\\n return bool(self.serial)\",\n \"def is_connected(self):\\n return self.hw_connected\",\n \"def is_on(self) -> bool:\\n return self._current_speed != SPEED_OFF\",\n \"def is_on(self):\\n return self._get_state() == ServerState.ON\",\n \"def light_is_on(self):\\n return self._light == \\\"ON\\\"\",\n \"def is_on(self):\\n request = requests.get(self._resource+\\\"/state\\\", timeout=10)\\n \\n try:\\n if int(float(request.text)) > 0:\\n self._state = True\\n else:\\n self._state = False\\n \\n except:\\n self._state = None\\n \\n return self._state\",\n \"def on_off_bool(value):\\n return value == 'on'\",\n \"def detect():\\n try:\\n s = serial.Serial(port = 0, baudrate = 19200, parity = 'O', timeout=1)\\n except Exception, e:\\n log = logging.getLogger('root')\\n log.exception(e)\\n return False\\n else:\\n return True\\n finally:\\n s.close()\",\n \"def is_on(self) -> bool:\\n if self._on_off_cluster_handler.on_off is None:\\n return False\\n return self._on_off_cluster_handler.on_off\",\n \"def is_on(self):\\n return self._state\",\n \"def is_on(self):\\n return self._state\",\n \"def is_on(self):\\n return self._state\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7853332","0.76352257","0.7587365","0.7494062","0.74632245","0.74632245","0.74632245","0.7458867","0.7458476","0.73671085","0.73671085","0.73581773","0.73362136","0.7291605","0.7263485","0.7212699","0.7204223","0.718982","0.7121019","0.7081601","0.7058469","0.70583594","0.70360994","0.70336795","0.7003756","0.6999645","0.6971765","0.6971765","0.6971765","0.6971765","0.6971765","0.69712484","0.6959092","0.6937267","0.69365174","0.69365174","0.69292957","0.69259715","0.6925788","0.69198596","0.6903057","0.6901576","0.6899449","0.6888077","0.6882666","0.6873506","0.68672734","0.6862132","0.6860423","0.6837339","0.6829147","0.68283594","0.68037236","0.6802309","0.6796092","0.6794376","0.6791251","0.6784271","0.67832214","0.6762821","0.6753162","0.67429584","0.67395395","0.67373925","0.6736537","0.6736537","0.6736537","0.6736537","0.6736537","0.6736537","0.6736537","0.6736537","0.6736537","0.6736537","0.6736537","0.67353916","0.6728229","0.67043024","0.6703118","0.67027867","0.6691013","0.6674911","0.6672736","0.6668119","0.6659299","0.66524196","0.6649296","0.6634886","0.66182977","0.65988976","0.6586687","0.6568786","0.6566521","0.6564582","0.65635085","0.65633947","0.6558757","0.6551998","0.6551998","0.6551998"],"string":"[\n \"0.7853332\",\n \"0.76352257\",\n \"0.7587365\",\n \"0.7494062\",\n \"0.74632245\",\n \"0.74632245\",\n \"0.74632245\",\n \"0.7458867\",\n \"0.7458476\",\n \"0.73671085\",\n \"0.73671085\",\n \"0.73581773\",\n \"0.73362136\",\n \"0.7291605\",\n \"0.7263485\",\n \"0.7212699\",\n \"0.7204223\",\n \"0.718982\",\n \"0.7121019\",\n \"0.7081601\",\n \"0.7058469\",\n \"0.70583594\",\n \"0.70360994\",\n \"0.70336795\",\n \"0.7003756\",\n \"0.6999645\",\n \"0.6971765\",\n \"0.6971765\",\n \"0.6971765\",\n \"0.6971765\",\n \"0.6971765\",\n \"0.69712484\",\n \"0.6959092\",\n \"0.6937267\",\n \"0.69365174\",\n \"0.69365174\",\n \"0.69292957\",\n \"0.69259715\",\n \"0.6925788\",\n \"0.69198596\",\n \"0.6903057\",\n \"0.6901576\",\n \"0.6899449\",\n \"0.6888077\",\n \"0.6882666\",\n \"0.6873506\",\n \"0.68672734\",\n \"0.6862132\",\n \"0.6860423\",\n \"0.6837339\",\n \"0.6829147\",\n \"0.68283594\",\n \"0.68037236\",\n \"0.6802309\",\n \"0.6796092\",\n \"0.6794376\",\n \"0.6791251\",\n \"0.6784271\",\n \"0.67832214\",\n \"0.6762821\",\n \"0.6753162\",\n \"0.67429584\",\n \"0.67395395\",\n \"0.67373925\",\n \"0.6736537\",\n \"0.6736537\",\n \"0.6736537\",\n \"0.6736537\",\n \"0.6736537\",\n \"0.6736537\",\n \"0.6736537\",\n \"0.6736537\",\n \"0.6736537\",\n \"0.6736537\",\n \"0.6736537\",\n \"0.67353916\",\n \"0.6728229\",\n \"0.67043024\",\n \"0.6703118\",\n \"0.67027867\",\n \"0.6691013\",\n \"0.6674911\",\n \"0.6672736\",\n \"0.6668119\",\n \"0.6659299\",\n \"0.66524196\",\n \"0.6649296\",\n \"0.6634886\",\n \"0.66182977\",\n \"0.65988976\",\n \"0.6586687\",\n \"0.6568786\",\n \"0.6566521\",\n \"0.6564582\",\n \"0.65635085\",\n \"0.65633947\",\n \"0.6558757\",\n \"0.6551998\",\n \"0.6551998\",\n \"0.6551998\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7081065"},"document_rank":{"kind":"string","value":"20"}}},{"rowIdx":93892,"cells":{"query":{"kind":"string","value":"Get the latest data from aREST API."},"document":{"kind":"string","value":"def update(self):\n self.arest.update()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_latest_data():\n try:\n print '\\nRequesting new data.....\\n'\n response = get(\"https://api.myjson.com/bins/2csub\")\n if response.status_code is 200:\n print '\\nSuccess (200) in downloading data\\n'\n current_json = response.json()\n set_backup_data(current_json)\n else: \n current_json = get_backup_data()\n except ConnectionError:\n current_json = get_backup_data()\n return current_json","def get(self, latest=None, start_date=None, end_date=None):\r\n params = base.get_params(None, locals())\r\n request = http.Request('GET', self.get_url(), params)\r\n\r\n return request, parsers.parse_json","def main():\n print get_latest_data()","def getLatestData(self):\n jsonText = self.session.get(self.jsonURL).text\n\n # Somehow, the output I am getting has some garbage at the beginning.\n # So, skipping all text before first instance of \"{\".\n jsonText = jsonText[jsonText.find(\"{\"):]\n latestData = json.loads(jsonText)\n return latestData","def get(self, limit=None, offset=None):\r\n\r\n params = base.get_params(None, locals())\r\n\r\n return http.Request('GET', self.get_url(), params), parsers.parse_json","def get(self, limit=None, offset=None):\r\n\r\n params = base.get_params(None, locals())\r\n\r\n return http.Request('GET', self.get_url(), params), parsers.parse_json","def get(self):\r\n return http.Request('GET', self.get_url()), parsers.parse_json","def get(self):\r\n return http.Request('GET', self.get_url()), parsers.parse_json","def get_all(self):\n result_get = GetRest(function = self.function).performRequest()\n return result_get","def last_update(self):\r\n request = http.Request('GET', '/metadata/last_update.json')\r\n return request, parsers.parse_json","def GET(self, *args):\n all_news= self.get_all_news()\n all_news.sort( key=lambda n : n['date'], reverse=True)\n if len(args):\n n_last=int(args[0])\n all_news = all_news[:n_last]\n\n return json.dumps(all_news)","def fetchRest(self, *args, **kwargs):\n return KorAPClient.fetchRest(self, *args, **kwargs)","def get(self, start=None, limit=None):\r\n params = base.get_params(None, locals())\r\n return http.Request('GET', self.get_url(), params), parsers.parse_json","def refresh(request):\n \n t, created = LastUpdate.objects.get_or_create(id=1)\n if created:\n t.save()\n t = t.unix_time()\n else:\n t = t.unix_time()\n latest = int(t) - 2\n return JsonResponse({'latest':int(latest)})","def latest_release_get():\n try:\n return json_response.success({'version': version.latest_version()})\n except version.Error as e:\n return json_response.error(str(e)), 200","def getAll(self):\n result_get = GetRest(function = self.function).performRequest()\n return result_get","def get(self, limit):\r\n params = base.get_params(None, locals())\r\n\r\n request = http.Request('GET', self.get_url(), params)\r\n return request, parsers.parse_json","def get(self, data):\n ret = self._rest_call({}, 'GET')\n return json.loads(ret[2])","async def latest(self, *args, **kwargs):\n\n return await self._makeApiCall(self.funcinfo[\"latest\"], *args, **kwargs)","def apicall():\r\n# try:\r\n print request.get_json()\r\n test_json = request.get_json()\r\n logger.info(\"input json object loaded\")\r\n logger.info(test_json)\r\n k=MetaData(test_json)\r\n int_res=k.getData()\r\n print '------------------------------'\r\n print int_res\r\n return jsonify(int_res)","def get_api_results(self, endpoint):\n endpoint = self.sched_url + endpoint.format(self.API_KEY)\n if self._verbose:\n print(\"Fetch data from: {} \".format(endpoint))\n try:\n resp = requests.get(url=endpoint)\n data = resp.json()\n return data\n except Exception as e:\n print(e)\n return False","def read_rest(self):\n # PROTECTED REGION ID(AsyncTabata.rest_read) ENABLED START #\n return self._rest\n # PROTECTED REGION END # // AsyncTabata.rest_read","def last(self):\n data = self._http_get(\"last\")\n return data.json()","def refresh(self):\n self.dto = self.res.get()\n log.debug(f\"Refreshed {self.url}\")","def lastest_datapoint(instance_name: str):\n if request.method == \"GET\":\n with lock:\n if not instances.get(instance_name):\n return jsonify(message=\"Instance Does Not Exist\"), 404\n return jsonify(asdict(instances.get(instance_name).latest()))\n return Response(status=200)","def get_latest_results(session):\n LOG.debug(\"Exporting latest results for LIMS integration\")\n\n # restrict access to LIMS IP and localhost\n if request.remote_addr not in [os.environ['LIMS_IP'], '127.0.0.1']:\n abort(403)\n\n latest_results = datastore.fetch_rows_from_table(session, (\"shipping\", \"latest_results\"))\n\n return Response((row[0] + '\\n' for row in latest_results), mimetype=\"application/x-ndjson\")","def test_smoker_latest_get(self):\n pass","def get(self, since=None, page=None, per_page=None):\r\n params = base.get_params(('since', 'page', 'per_page'), locals())\r\n\r\n return http.Request('GET', self.get_url(), params), parsers.parse_json","def api_get(self, *args, **kwargs):\n return self.api_get_with_response(*args, **kwargs)[0]","def get_recent_cars(request): \n \n try:\n parsed_data = get_json_data(request)\n ret_list = []\n cars = Car.objects.all()\n sorted_cars = sorted(cars, key=lambda x: x.last_edit, reverse=True)\n start = int(parsed_data[\"start\"])\n end = int(parsed_data[\"end\"])\n end = min(end, len(sorted_cars) - 1)\n car_list = sorted_cars[start : end + 1]\n ret = Response(SUCCESS, error_code[SUCCESS])\n for car in car_list:\n car_serial = CarSerializer(car)\n ret_list.append(car_serial.serialize())\n ret.set_ret(\"data\", ret_list) \n except IndexError as e:\n ret = Response(OVERFLOW, error_code[OVERFLOW])\n except ValueError as e:\n ret = Response(INPUT_FORMAT, error_code[INPUT_FORMAT])\n except:\n ret = Response(UNKNOWN_ERROR, error_code[UNKNOWN_ERROR])\n return HttpResponse(ret.serialize(f))","def get_json(self, url, *, timeout, headers):","def __request(self,endpoint):\n apiRequest = requests.get(\"%s/%s\" % (self.baseurl,endpoint), \n auth=requests.auth.HTTPBasicAuth(self.api_id, self.api_secret))\n try:\n json = apiRequest.json()\n return json\n except JSONDecodeError:\n print(\"Failed to download or failed to parse JSON.\")\n print(apiRequest)\n return None","def get_data(last):\n Table = \"ServerRoom\"\n filter = \"\"\n if last == \"lastone\":\n data = request_meteodata(\"SELECT * from `ServerRoom` ORDER BY id DESC LIMIT 1 \")\n if len(data) == 0:\n return [SensorData(datetime.datetime.now(), 0, 0)]\n res = []\n for d in data:\n res.append(SensorData(d[1], d[2], d[3]))\n return res\n if last != \"All\":\n limit = datetime.datetime.now().astimezone(utz)\n if last == \"24hours\":\n limit -= datetime.timedelta(hours=24)\n else:\n limit = limit.replace(hour=0, minute=0, second=0, microsecond=0)\n if last == \"3days\":\n limit -= datetime.timedelta(days=3)\n elif last == \"7days\":\n limit -= datetime.timedelta(days=7)\n elif last == \"month\":\n limit = limit.replace(day=1)\n elif last == \"30days\":\n limit -= datetime.timedelta(days=30)\n elif last == \"year\":\n limit = limit.replace(day=1, month=1)\n filter = \" WHERE `date` > '\" + str(limit) + \"'\"\n order = \" ORDER BY `date` ASC\"\n req = \"SELECT * FROM `\" + Table + \"`\" + filter + order\n data = request_meteodata(req)\n if len(data) == 0:\n print(\"no data: get all\")\n req = \"SELECT * FROM `\" + Table + \"`\" + order\n data = request_meteodata(req)\n res = []\n for d in data:\n res.append(SensorData(d[1], d[2], d[3]))\n return res","def pull(self):\n data = api.get(endpoint=self.endpoint, resource_id=self.id)\n self.__init__(**data)","def pull(self):\n data = api.get(endpoint=self.endpoint, resource_id=self.slug)\n self.__init__(**data)","def _get(self, endpoint):\n res = self._request(\"get\", endpoint)\n if not res.content:\n return {}\n try:\n res = res.json()\n except ValueError:\n raise ValueError(\"Cannot parse {} as JSON\".format(res))\n if \"error\" in res:\n raise AirthingsError(res[\"error\"])\n return res","def get(self):\n if 'wipe' in self.request.path_url:\n InboundRequest.wipe()\n self.response.write('Db wiped')\n return\n\n # log out last 10 requests\n data = InboundRequest.get_latest()\n\n resultList = []\n for item in data:\n headers = ''\n header_list = item.request_headers.split('\\r\\n')\n\n # try format if json\n body = item.request_body\n try:\n json_body = json.loads(item.request_body)\n body = json.dumps(json_body, indent=2, separators=(',', ': '))\n except:\n #logging.info('not in json')\n pass\n\n resultfields = {'ts': item.time_stamp, 'host_url': item.request_host_url, 'path': item.request_path,\n 'query_params': item.request_query_params, 'headers': header_list, 'body': body}\n resultList.append(resultfields)\n\n template_values = {\n 'number_returned': len(data),\n 'results': resultList\n }\n self.render_template('index.html', template_values)","async def fetch_data(self) -> T:","def getData(tme=currentTime):\n # attempts request 10 times\n for attempt in range(10):\n try:\n # make a request to the url and return it in json format\n url = \"https://api.darksky.net/forecast/%s/%s,%s,%s?exclude=minutely,hourly,daily,alerts,flags\" % (API_KEY, LAT, LNG, tme)\n return get(url).json()\n except:\n # Wait .05 seconds and try again\n sleep(.05)\n pass","def get():\n return jsonify({'events': 'Events API'}), 200","def fetch(self,url=URL):\n\t\tlog.info('downloading latest PHE case data')\n#\t\tself.data=lookup_json(url)\n\t\tself.fetch_csv() #JSON discontinued; switched back to CSV\n\t\tself.edition=self.latest_samples\n\t\tlog.info(f'Last samples from {self.edition}')","def download_data(self, format = 'srt'):\n resp, content = httplib2.Http(\".cache\").request(self.url, \"GET\")\n suburl = json.loads(content)['url']\n resp, content = httplib2.Http(\".cache\").request(suburl, \"GET\")\n\n return content","def fetch_data(self):","def get():","def get():","def get_rest_list(request):\n if request.method == \"GET\":\n rest_list = Package.objects.order_by('-location')\n serializer = PackageSerializer(rest_list, many=True)\n return JsonResponse(serializer.data, safe=False)","def _api_call(self, **kwargs):\n params = {\n 'format': 'json',\n }\n params.update(kwargs)\n r = requests.get(self.api_base_url, params=params)\n return r.json()","def get(self, count=None, offset=None):\r\n params = base.get_params(None, locals())\r\n request = http.Request('GET', self.get_url(), params)\r\n\r\n return request, parsers.parse_json","async def get(self):\n\n pass","def get_api_info_first_and_last_r(\n self,\n\n headers: t.Dict[str, str] = None,\n body: JSONEncodable = None,\n fields_data: t.Dict[str, str] = None,\n **kwargs\n ):\n r = self._do_call(\n method='GET',\n url=f'{self.API_BASE_URL}/info/first-and-last',\n headers=headers,\n body=body,\n fields=fields_data,\n **kwargs\n )\n return r","def fetch_timestamp(self):\r\n return self.__public_request('GET', '/api/v1/timestamp')","def last(self, count=None):\r\n url = '{0}/{1}'.format(self.get_pull_url(), 'last')\r\n params = base.get_params(('count',), locals())\r\n\r\n return http.Request('GET', url, params), parsers.parse_json","def next_update(self):\r\n request = http.Request('GET', '/metadata/next_update.json')\r\n return request, parsers.parse_json","def _fetch(self):\n self._data = self._get(self.url)\n\n if self._data['released_errata'] is not None:\n self._released_errata = Erratum(errata_id=self._data[\n 'released_errata']['id'])\n\n for errata_dict in self._data['all_errata']:\n errata = Erratum(errata_id=errata_dict['id'])\n self._all_errata.append(errata)\n\n self._signed_rpms = self._data.get('rpms_signed')\n\n for et_file in self._data['files']:\n self._files.append(et_file['path'])","def retrieve_last(self, namespace=None, apikey=None, fields=None,\n attributes=None, limit=1):\n self.__validate_retrieve_last(fields, limit)\n request = {\n 'limit': limit,\n 'fields': fields,\n 'attributes': attributes\n }\n self.__update_request(request, namespace, apikey)\n return self._send_request('/retrieve-last/', Chunk(request))","def __apiRequest(self, url, parms={}):\n authparms = self.__addAuthParms(parms);\n request = self.http.request('GET', url, fields=authparms)\n if request.status != 200:\n raise ApiCommunicationError('Failed to retrieve data from Marvel, HTTP Status {}'.format(request.status))\n else:\n return json.loads( request.data.decode('utf-8') )","def get(self, sort='newest', page=None, per_page=None):\r\n params = base.get_params(('sort', 'page', 'per_page'), locals())\r\n request = http.Request('GET', self.get_url(), params)\r\n\r\n return request, parsers.parse_json","async def async_get(\n self, endpoint: str | None = None, entry: str | None = None\n ) -> Any:\n url = f\"{self.url}/api/\"\n if endpoint:\n url = self.endpoints[endpoint]\n if entry:\n url = f\"{url}{entry}\"\n with async_timeout.timeout(10):\n resp = await self.session.get(\n url=url,\n headers=self.headers,\n raise_for_status=True,\n )\n\n return await resp.json()","def latest(self, base='USD'):\n try:\n resp = self.client.get(self.ENDPOINT_LATEST, params={'base': base})\n resp.raise_for_status()\n except requests.exceptions.RequestException as e:\n raise OpenExchangeRatesClientException(e)\n return resp.json(parse_int=decimal.Decimal,\n parse_float=decimal.Decimal)","def get(self, start=None, limit=None):\r\n params = base.get_params(None, locals())\r\n request = http.Request('GET', self.get_url(), params)\r\n\r\n return request, parsers.parse_json","def do_GET(self):\n print(\"do_GET: got: \" + str(self.path))\n path_split = self.path.split(\"/\", 2) # ['', 'hello', 'nikolay/uuuuer']\n username = path_split[-1] # nikolay/uuuuer\n print(\"do_GET: Let's open db_conn\")\n connection = db_open()\n birth_date = db_select(username, connection)\n db_close(connection)\n\n days_until_bday = get_days_to_bday(birth_date)\n json_obj = http_construct_json(username, days_until_bday)\n\n self.http_send_reply(json_obj)\n print(\"do_GET: DONE! \\n\")","def fetch_data():\n \n # Logging\n print(\"Fetching new data...\")\n\n # Get current datetime, convert it to timestamp of 10 minutes before. \n timestamp = datetime.now() - timedelta(minutes=10)\n \n # Try to fetch record with latest publishedAt. \n try:\n # If found, we use it's timestamp to search for videos published after this\n timestamp = VideoData.objects.latest('publishedAt').publishedAt\n print(f\"Got existing timestamp {timestamp}\")\n except VideoData.DoesNotExist:\n # If not found, we use the default timestamp i.e 10 minute delta from current time\n print(f\"Using default timestamp {timestamp}\")\n\n timestamp = fromDateTimeToIso(timestamp)\n \n # Index of which API Key is being used\n curr_key_index = 0\n \n is_data_fetched = False\n total_api_call_fails = 0\n\n # A dummy response, in case all API request fail\n response = {'items':[]}\n\n # Loop to try out all keys until all have been exhausted\n while is_data_fetched == False:\n is_data_fetched = True\n\n print(f\"Using key ({curr_key_index}) = {settings.YOUTUBE_API_KEY[curr_key_index]}\")\n\n try:\n service = build(\n 'youtube',\n 'v3',\n developerKey=settings.YOUTUBE_API_KEY[curr_key_index], \n cache_discovery=False\n )\n collection = service.search().list(\n maxResults=25,\n part=['id','snippet'],\n q='cricket',\n type='video',\n order='date',\n publishedAfter=timestamp\n )\n\n response = collection.execute()\n except HttpError as e:\n # Exception when either the key is wrong or API limit is exhausted\n print(f\"!!!!DEBUG LOG!!!! {e}\")\n curr_key_index = curr_key_index + 1 \n total_api_call_fails = total_api_call_fails + 1\n\n # If total keys is less than current index, take mod\n curr_key_index = curr_key_index % len(settings.YOUTUBE_API_KEY)\n is_data_fetched = False \n \n # If all the keys have been tried, break the loop\n if total_api_call_fails == len(settings.YOUTUBE_API_KEY):\n print(\"API call limit exhausted for all keys\")\n break\n\n print(f\"Fetched {len(response['items'])} items\")\n\n #If list contains items, populate database\n if len(response['items']) > 0:\n count = populate_data(response['items'])\n print(f\"Successfully inserted {count} data\")\n\n return False","def get(self, api_url, timeout=30):\n return self._request('GET', api_url, timeout=timeout)","def get(self):\n\t\treturn self.connection.get(self.base_uri)","async def current(self) -> dict:\n return await self._request(\n \"get\", \"https://www.asthmaforecast.com/api/forecast/current/asthma\"\n )","def get(self):\r\n request = http.Request('GET', self.get_url())\r\n\r\n return request, parsers.parse_json","def get(self):\r\n request = http.Request('GET', self.get_url())\r\n\r\n return request, parsers.parse_json","def get(self):\r\n request = http.Request('GET', self.get_url())\r\n\r\n return request, parsers.parse_json","def get(self):\r\n request = http.Request('GET', self.get_url())\r\n\r\n return request, parsers.parse_json","def get(self, count=None, max_timestamp=None, min_timestamp=None,\r\n min_id=None, max_id=None):\r\n params = base.get_params(\r\n ('count', 'max_timestamp', 'min_timestamp', 'min_id', 'max_id'),\r\n locals())\r\n request = http.Request('GET', self.get_url(), params)\r\n\r\n return request, parsers.parse_json","def get(self, part, id):\r\n params = base.get_params(None, locals())\r\n\r\n request = http.Request('GET', self.get_url(), params)\r\n return request, parsers.parse_json","def get_data(self):\n has_next_page = True\n page = 1\n while has_next_page:\n print(f'Getting page {page}')\n response = self.get_articles(\n page=page,\n size=200,\n order_by='extracted_at',\n order_type='asc'\n )\n pagination = response.get('pagination')\n has_next_page = pagination.get('has_next')\n self.save_articles(response.get('articles'))\n page += 1\n time.sleep(2.5)","def get(self, type=None, count=None, offset=None):\r\n params = base.get_params(None, locals())\r\n request = http.Request('GET', self.get_url(), params)\r\n\r\n return request, parsers.parse_json","def get_all_data():\n return jsonify(service.get_all_data())","def get(self):\n self.finish(json.dumps(self.build_response_dict()))","def get(self):\n try:\n query_parameters = request.args\n logging.debug(f\" query params - {query_parameters}\")\n top, skip, expand_code, selects = parse_args(query_parameters)\n ds_list = Datastreams.return_page_with_expand(\n top, skip, expand_code, selects\n )\n response = jsonify(ds_list)\n response.status_code = 200\n except Exception as e:\n logging.warning(e)\n response = jsonify({\"message\": \"error\"})\n response.status_code = 400\n\n finally:\n return response","def get(self):\r\n return get_all()","def get_information():\n session = Session()\n session.headers.update(headers)\n try:\n response = session.get(url, params=parameters)\n data = json.loads(response.text)\n with open('data.json', 'w') as file:\n json.dump(data, file, ensure_ascii=False, indent=4)\n return data\n except (ConnectionError, Timeout, TooManyRedirects) as e:\n print(e)","def refresh(self) -> object:\n requestor = Requestor(local_api_key=self._api_key)\n url = self.instance_url()\n response, api_key = requestor.request(method=RequestMethod.GET, url=url, params=self._retrieve_params)\n self.refresh_from(values=response, api_key=api_key)\n return self","def fetch_temp_data(url):\n res = requests.get(url)\n return res.json()","def api():\n from gluon.contrib.hypermedia import Collection\n rules = {\n '<tablename>': {'GET':{},'POST':{},'PUT':{},'DELETE':{}},\n }\n return Collection(db).process(request,response,rules)","def api():\n from gluon.contrib.hypermedia import Collection\n rules = {\n '<tablename>': {'GET':{},'POST':{},'PUT':{},'DELETE':{}},\n }\n return Collection(db).process(request,response,rules)","def api():\n from gluon.contrib.hypermedia import Collection\n rules = {\n '<tablename>': {'GET':{},'POST':{},'PUT':{},'DELETE':{}},\n }\n return Collection(db).process(request,response,rules)","def get_last_data(request):\n\n return_list = []\n if request.POST:\n try:\n conn = psycopg2.connect(dbname=DB_NAME,\n user=DB_USER,\n password=DB_PASSWORD,\n host=DB_HOST)\n cur = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n\n cur.execute(\"SELECT sn, time, icsmain, icsz1, icsz2, icsz3, icsboiler, t1, t2, t3, t4, t5, t6, t7,\"\n \"rt1, rt2, rt3, endswitch, weather FROM devicedata \"\n \"WHERE sn='BRANDON_SN_US' \"\n \"ORDER BY id DESC LIMIT 5\")\n\n get_device_data = cur.fetchall()\n\n cur.close()\n conn.close()\n\n for info in get_device_data:\n item_dict = {\"sn\": info[0], \"time\": str(info[1]), \"icsmain\": info[2], \"icsz1\": info[3], \"icsz2\": info[4],\n \"icsz3\": info[5], \"icsboiler\": info[6], \"t1\": info[7], \"t2\": info[8], \"t3\": info[9],\n \"t4\": info[10], \"t5\": info[11], \"t6\": info[12], \"t7\": info[13], \"rt1\": info[14], \"rt2\": info[15],\n \"rt3\": info[16], \"endswitch\": info[17]}\n return_list.append(item_dict)\n except:\n pass\n\n return JsonResponse(return_list, safe=False)","def _get_data_in_api(url: str) -> list:\n\n try:\n resp = requests.request('GET', url, timeout=10)\n resp.raise_for_status\n\n return Froxy._data_filter(resp.text)\n\n except (\n requests.ConnectionError,\n requests.ConnectTimeout,\n requests.HTTPError,\n requests.ReadTimeout\n ) as err:\n sys.exit(err)","async def get_api_data(self, referer: str, params: list) -> list:\n return await asyncio.ensure_future(self.get_response(referer, params))","def get(self):\n self.get_or_post(method='GET')","def refresh_from_api(self):\n self.populate_from_api(self.get_from_api())","def get_data():\r\n try:\r\n BME280_TEMPERATURE.labels(device_name=device_name).set(bme280.temperature)\r\n except Exception as e:\r\n logger.error(\"Failed to update temperature. Exception: {}\".format(e))\r\n try:\r\n BME280_RELATIVE_HUMIDITY.labels(device_name=device_name).set(bme280.relative_humidity)\r\n except Exception as e:\r\n logger.error(\"Failed to update relative humidity. Exception: {}\".format(e))\r\n try:\r\n BME280_PRESSURE.labels(device_name=device_name).set(bme280.pressure)\r\n except Exception as e:\r\n logger.error(\"Failed to update pressure. Exception: {}\".format(e))\r\n try:\r\n BME280_ALTITUDE.labels(device_name=device_name).set(bme280.altitude)\r\n except Exception as e:\r\n logger.error(\"Failed to update altitude. Exception: {}\".format(e))\r\n\r\n return Response(generate_latest(), mimetype=CONTENT_TYPE_LATEST)","def get(self, data):\n pass","def get_json(self, url, params, timeout=5, retries=3, data=None):\n return self.request('GET', url, params, timeout=timeout, retries=retries, data=data)","def download():\n toydata = requests.get(DATA_URL).json()\n return toydata","def refresh_details(self) -> 'outputs.RefreshDetailsResponse':\n return pulumi.get(self, \"refresh_details\")","def _fetch_data(self):\n pass","def get(self):\n\n try:\n args = api_parameters.parse_args()\n limit = args.get(\"limit\")\n controller = self.controller()\n schema = self.schema(many=True)\n raw_data = controller.get_list(**args)\n\n if limit:\n items = raw_data.items\n items = schema.dump(items)\n data = ResponseHandler.get_section_paginate(raw_data, items)\n\n else:\n data = schema.dump(raw_data)\n\n return ResponseHandler.render_response(data=data)\n\n except Exception as ex:\n\n return ResponseHandler.render_response(status=ERR, message=traceback.format_exc())","def post_get_recent(requst, limit):\n if requst.method == 'GET':\n recent_posts = Post.objects.order_by('-created_at')[:int(limit)]\n TopicNestedSerializer.Meta.depth = 1\n PostNestedSerializer.Meta.depth = 1\n serializer = PostNestedSerializer(recent_posts, many=True)\n return Response(serializer.data)","def get_info(self):\n url = self._url_for_op('info')\n data= None # This will be a GET request since data is None\n response = self._get_raw_response(self._get_json_headers,\n self._get_json_response, url, data)\n response = json.loads(response)\n self.api_info = response['results']\n return self.api_info","def get_feed():\n return jsonify(dict({\n \"result\": mongo.get_hpfeed(),\n \"code\": 200\n }))","def rest_api(self):\n self.__db_init('rest')\n api = self.__api_init()\n self.c.execute(\"SELECT MAX(id) FROM tweets\")\n db_max_id = self.c.fetchone()[0] \n try: \n most_recent = api.search(q=self.keyword, result_type='recent')[0].id\n except tweepy.TweepError as e:\n print(str(e.message[0]['message']) + \n ' Update api.ini with your proper credentials:')\n print(os.path.abspath(_path_finder('userconfig','api.ini')))\n sys.exit(-1)\n flag = 0\n while ( flag == 0 ):\n try:\n batch = 5000\n flag = batch\n for search_res in tweepy.Cursor(api.search, q=self.keyword,\n count=100, result_type=\"recent\", \n since_id=db_max_id, \n max_id=most_recent).items(batch):\n flag -= 1\n print(search_res.id, search_res.created_at)\n self.c.execute('''INSERT OR IGNORE INTO tweets (id, date) \n VALUES (?, ?)''', \n (search_res.id, search_res.created_at))\n except tweepy.TweepError as e:\n print('I caught an error:', e.message)\n flag = 0\n finally:\n self.c.execute(\"SELECT last_insert_rowid() from tweets\")\n rid = self.c.fetchone()[0]\n if rid:\n self.c.execute('''SELECT id FROM tweets WHERE\n rowid={0}'''.format(rid))\n rid = self.c.fetchone()[0]\n most_recent = rid - 1\n data = api.rate_limit_status()\n print(data['resources']['search'])\n self.conn.commit()\n self.conn.close()\n print('REST database file has been created/updated:') \n print(os.path.abspath(_path_finder(\n 'keydata','{0}_rest.db'.format(self.keyword))))","def pull(self, url, params=None, data=None, auth=None, method=\"GET\"):\n try:\n __method_name = inspect.currentframe().f_code.co_name\n if method == \"POST\":\n res = self.session.post(\n url=url,\n auth=auth,\n params=params,\n data=data,\n timeout=consts.API_TIMEOUT,\n )\n else:\n res = self.session.get(\n url=url,\n auth=auth,\n params=params,\n data=data,\n timeout=consts.API_TIMEOUT,\n )\n res.raise_for_status()\n if res and res.status_code in [200, 201]:\n self.applogger.debug(\n '{}(method={}) : {} : API call: Response received successfully. url=\"{}\" params=\"{}\"'.format(\n consts.LOGS_STARTS_WITH,\n __method_name,\n self.function_name,\n url,\n params,\n )\n )\n return res.json()\n else:\n self.applogger.error(\n \"{}(method={}) : {} : API call: Unknown status code or empty \"\n 'response: url=\"{}\" status_code=\"{}\" response=\"{}\"'.format(\n consts.LOGS_STARTS_WITH,\n __method_name,\n self.function_name,\n url,\n res.status_code,\n res.text,\n )\n )\n raise Exception(\"Received unknown status code or empty response.\")\n except requests.exceptions.HTTPError as ex:\n if res.status_code == 404:\n self.applogger.debug(\n '{}(method={}) : {} : API call: Got {} Status Code : url=\"{}\"'\n ' response=\"{}\"'.format(\n consts.LOGS_STARTS_WITH,\n __method_name,\n self.function_name,\n url,\n ex.response.status_code,\n ex.response.text,\n )\n )\n return {}\n else:\n self.applogger.error(\n '{}(method={}) : {} : API call: Unsuccessful response: url=\"{}\" status_code=\"{}\"'\n ' response=\"{}\"'.format(\n consts.LOGS_STARTS_WITH,\n __method_name,\n self.function_name,\n url,\n ex.response.status_code,\n ex.response.text,\n )\n )\n raise Exception(\"HTTP Error Occurred while getting response from api.\")\n except Exception as ex:\n self.applogger.error(\n '{}(method={}) : {} : API call: Unexpected error while API call url=\"{}\" error=\"{}\"'.format(\n consts.LOGS_STARTS_WITH,\n __method_name,\n self.function_name,\n url,\n str(ex),\n )\n )\n raise Exception(\"Error Occurred while getting response from api.\")","def top_task_element(request):\n if request.method == \"GET\":\n task = Task.objects.filter(status=\"Open\").order_by(\"-timestamp\").first()\n serializer = TaskSerializer(task)\n return Response(serializer.data)"],"string":"[\n \"def get_latest_data():\\n try:\\n print '\\\\nRequesting new data.....\\\\n'\\n response = get(\\\"https://api.myjson.com/bins/2csub\\\")\\n if response.status_code is 200:\\n print '\\\\nSuccess (200) in downloading data\\\\n'\\n current_json = response.json()\\n set_backup_data(current_json)\\n else: \\n current_json = get_backup_data()\\n except ConnectionError:\\n current_json = get_backup_data()\\n return current_json\",\n \"def get(self, latest=None, start_date=None, end_date=None):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def main():\\n print get_latest_data()\",\n \"def getLatestData(self):\\n jsonText = self.session.get(self.jsonURL).text\\n\\n # Somehow, the output I am getting has some garbage at the beginning.\\n # So, skipping all text before first instance of \\\"{\\\".\\n jsonText = jsonText[jsonText.find(\\\"{\\\"):]\\n latestData = json.loads(jsonText)\\n return latestData\",\n \"def get(self, limit=None, offset=None):\\r\\n\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n return http.Request('GET', self.get_url(), params), parsers.parse_json\",\n \"def get(self, limit=None, offset=None):\\r\\n\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n return http.Request('GET', self.get_url(), params), parsers.parse_json\",\n \"def get(self):\\r\\n return http.Request('GET', self.get_url()), parsers.parse_json\",\n \"def get(self):\\r\\n return http.Request('GET', self.get_url()), parsers.parse_json\",\n \"def get_all(self):\\n result_get = GetRest(function = self.function).performRequest()\\n return result_get\",\n \"def last_update(self):\\r\\n request = http.Request('GET', '/metadata/last_update.json')\\r\\n return request, parsers.parse_json\",\n \"def GET(self, *args):\\n all_news= self.get_all_news()\\n all_news.sort( key=lambda n : n['date'], reverse=True)\\n if len(args):\\n n_last=int(args[0])\\n all_news = all_news[:n_last]\\n\\n return json.dumps(all_news)\",\n \"def fetchRest(self, *args, **kwargs):\\n return KorAPClient.fetchRest(self, *args, **kwargs)\",\n \"def get(self, start=None, limit=None):\\r\\n params = base.get_params(None, locals())\\r\\n return http.Request('GET', self.get_url(), params), parsers.parse_json\",\n \"def refresh(request):\\n \\n t, created = LastUpdate.objects.get_or_create(id=1)\\n if created:\\n t.save()\\n t = t.unix_time()\\n else:\\n t = t.unix_time()\\n latest = int(t) - 2\\n return JsonResponse({'latest':int(latest)})\",\n \"def latest_release_get():\\n try:\\n return json_response.success({'version': version.latest_version()})\\n except version.Error as e:\\n return json_response.error(str(e)), 200\",\n \"def getAll(self):\\n result_get = GetRest(function = self.function).performRequest()\\n return result_get\",\n \"def get(self, limit):\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def get(self, data):\\n ret = self._rest_call({}, 'GET')\\n return json.loads(ret[2])\",\n \"async def latest(self, *args, **kwargs):\\n\\n return await self._makeApiCall(self.funcinfo[\\\"latest\\\"], *args, **kwargs)\",\n \"def apicall():\\r\\n# try:\\r\\n print request.get_json()\\r\\n test_json = request.get_json()\\r\\n logger.info(\\\"input json object loaded\\\")\\r\\n logger.info(test_json)\\r\\n k=MetaData(test_json)\\r\\n int_res=k.getData()\\r\\n print '------------------------------'\\r\\n print int_res\\r\\n return jsonify(int_res)\",\n \"def get_api_results(self, endpoint):\\n endpoint = self.sched_url + endpoint.format(self.API_KEY)\\n if self._verbose:\\n print(\\\"Fetch data from: {} \\\".format(endpoint))\\n try:\\n resp = requests.get(url=endpoint)\\n data = resp.json()\\n return data\\n except Exception as e:\\n print(e)\\n return False\",\n \"def read_rest(self):\\n # PROTECTED REGION ID(AsyncTabata.rest_read) ENABLED START #\\n return self._rest\\n # PROTECTED REGION END # // AsyncTabata.rest_read\",\n \"def last(self):\\n data = self._http_get(\\\"last\\\")\\n return data.json()\",\n \"def refresh(self):\\n self.dto = self.res.get()\\n log.debug(f\\\"Refreshed {self.url}\\\")\",\n \"def lastest_datapoint(instance_name: str):\\n if request.method == \\\"GET\\\":\\n with lock:\\n if not instances.get(instance_name):\\n return jsonify(message=\\\"Instance Does Not Exist\\\"), 404\\n return jsonify(asdict(instances.get(instance_name).latest()))\\n return Response(status=200)\",\n \"def get_latest_results(session):\\n LOG.debug(\\\"Exporting latest results for LIMS integration\\\")\\n\\n # restrict access to LIMS IP and localhost\\n if request.remote_addr not in [os.environ['LIMS_IP'], '127.0.0.1']:\\n abort(403)\\n\\n latest_results = datastore.fetch_rows_from_table(session, (\\\"shipping\\\", \\\"latest_results\\\"))\\n\\n return Response((row[0] + '\\\\n' for row in latest_results), mimetype=\\\"application/x-ndjson\\\")\",\n \"def test_smoker_latest_get(self):\\n pass\",\n \"def get(self, since=None, page=None, per_page=None):\\r\\n params = base.get_params(('since', 'page', 'per_page'), locals())\\r\\n\\r\\n return http.Request('GET', self.get_url(), params), parsers.parse_json\",\n \"def api_get(self, *args, **kwargs):\\n return self.api_get_with_response(*args, **kwargs)[0]\",\n \"def get_recent_cars(request): \\n \\n try:\\n parsed_data = get_json_data(request)\\n ret_list = []\\n cars = Car.objects.all()\\n sorted_cars = sorted(cars, key=lambda x: x.last_edit, reverse=True)\\n start = int(parsed_data[\\\"start\\\"])\\n end = int(parsed_data[\\\"end\\\"])\\n end = min(end, len(sorted_cars) - 1)\\n car_list = sorted_cars[start : end + 1]\\n ret = Response(SUCCESS, error_code[SUCCESS])\\n for car in car_list:\\n car_serial = CarSerializer(car)\\n ret_list.append(car_serial.serialize())\\n ret.set_ret(\\\"data\\\", ret_list) \\n except IndexError as e:\\n ret = Response(OVERFLOW, error_code[OVERFLOW])\\n except ValueError as e:\\n ret = Response(INPUT_FORMAT, error_code[INPUT_FORMAT])\\n except:\\n ret = Response(UNKNOWN_ERROR, error_code[UNKNOWN_ERROR])\\n return HttpResponse(ret.serialize(f))\",\n \"def get_json(self, url, *, timeout, headers):\",\n \"def __request(self,endpoint):\\n apiRequest = requests.get(\\\"%s/%s\\\" % (self.baseurl,endpoint), \\n auth=requests.auth.HTTPBasicAuth(self.api_id, self.api_secret))\\n try:\\n json = apiRequest.json()\\n return json\\n except JSONDecodeError:\\n print(\\\"Failed to download or failed to parse JSON.\\\")\\n print(apiRequest)\\n return None\",\n \"def get_data(last):\\n Table = \\\"ServerRoom\\\"\\n filter = \\\"\\\"\\n if last == \\\"lastone\\\":\\n data = request_meteodata(\\\"SELECT * from `ServerRoom` ORDER BY id DESC LIMIT 1 \\\")\\n if len(data) == 0:\\n return [SensorData(datetime.datetime.now(), 0, 0)]\\n res = []\\n for d in data:\\n res.append(SensorData(d[1], d[2], d[3]))\\n return res\\n if last != \\\"All\\\":\\n limit = datetime.datetime.now().astimezone(utz)\\n if last == \\\"24hours\\\":\\n limit -= datetime.timedelta(hours=24)\\n else:\\n limit = limit.replace(hour=0, minute=0, second=0, microsecond=0)\\n if last == \\\"3days\\\":\\n limit -= datetime.timedelta(days=3)\\n elif last == \\\"7days\\\":\\n limit -= datetime.timedelta(days=7)\\n elif last == \\\"month\\\":\\n limit = limit.replace(day=1)\\n elif last == \\\"30days\\\":\\n limit -= datetime.timedelta(days=30)\\n elif last == \\\"year\\\":\\n limit = limit.replace(day=1, month=1)\\n filter = \\\" WHERE `date` > '\\\" + str(limit) + \\\"'\\\"\\n order = \\\" ORDER BY `date` ASC\\\"\\n req = \\\"SELECT * FROM `\\\" + Table + \\\"`\\\" + filter + order\\n data = request_meteodata(req)\\n if len(data) == 0:\\n print(\\\"no data: get all\\\")\\n req = \\\"SELECT * FROM `\\\" + Table + \\\"`\\\" + order\\n data = request_meteodata(req)\\n res = []\\n for d in data:\\n res.append(SensorData(d[1], d[2], d[3]))\\n return res\",\n \"def pull(self):\\n data = api.get(endpoint=self.endpoint, resource_id=self.id)\\n self.__init__(**data)\",\n \"def pull(self):\\n data = api.get(endpoint=self.endpoint, resource_id=self.slug)\\n self.__init__(**data)\",\n \"def _get(self, endpoint):\\n res = self._request(\\\"get\\\", endpoint)\\n if not res.content:\\n return {}\\n try:\\n res = res.json()\\n except ValueError:\\n raise ValueError(\\\"Cannot parse {} as JSON\\\".format(res))\\n if \\\"error\\\" in res:\\n raise AirthingsError(res[\\\"error\\\"])\\n return res\",\n \"def get(self):\\n if 'wipe' in self.request.path_url:\\n InboundRequest.wipe()\\n self.response.write('Db wiped')\\n return\\n\\n # log out last 10 requests\\n data = InboundRequest.get_latest()\\n\\n resultList = []\\n for item in data:\\n headers = ''\\n header_list = item.request_headers.split('\\\\r\\\\n')\\n\\n # try format if json\\n body = item.request_body\\n try:\\n json_body = json.loads(item.request_body)\\n body = json.dumps(json_body, indent=2, separators=(',', ': '))\\n except:\\n #logging.info('not in json')\\n pass\\n\\n resultfields = {'ts': item.time_stamp, 'host_url': item.request_host_url, 'path': item.request_path,\\n 'query_params': item.request_query_params, 'headers': header_list, 'body': body}\\n resultList.append(resultfields)\\n\\n template_values = {\\n 'number_returned': len(data),\\n 'results': resultList\\n }\\n self.render_template('index.html', template_values)\",\n \"async def fetch_data(self) -> T:\",\n \"def getData(tme=currentTime):\\n # attempts request 10 times\\n for attempt in range(10):\\n try:\\n # make a request to the url and return it in json format\\n url = \\\"https://api.darksky.net/forecast/%s/%s,%s,%s?exclude=minutely,hourly,daily,alerts,flags\\\" % (API_KEY, LAT, LNG, tme)\\n return get(url).json()\\n except:\\n # Wait .05 seconds and try again\\n sleep(.05)\\n pass\",\n \"def get():\\n return jsonify({'events': 'Events API'}), 200\",\n \"def fetch(self,url=URL):\\n\\t\\tlog.info('downloading latest PHE case data')\\n#\\t\\tself.data=lookup_json(url)\\n\\t\\tself.fetch_csv() #JSON discontinued; switched back to CSV\\n\\t\\tself.edition=self.latest_samples\\n\\t\\tlog.info(f'Last samples from {self.edition}')\",\n \"def download_data(self, format = 'srt'):\\n resp, content = httplib2.Http(\\\".cache\\\").request(self.url, \\\"GET\\\")\\n suburl = json.loads(content)['url']\\n resp, content = httplib2.Http(\\\".cache\\\").request(suburl, \\\"GET\\\")\\n\\n return content\",\n \"def fetch_data(self):\",\n \"def get():\",\n \"def get():\",\n \"def get_rest_list(request):\\n if request.method == \\\"GET\\\":\\n rest_list = Package.objects.order_by('-location')\\n serializer = PackageSerializer(rest_list, many=True)\\n return JsonResponse(serializer.data, safe=False)\",\n \"def _api_call(self, **kwargs):\\n params = {\\n 'format': 'json',\\n }\\n params.update(kwargs)\\n r = requests.get(self.api_base_url, params=params)\\n return r.json()\",\n \"def get(self, count=None, offset=None):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"async def get(self):\\n\\n pass\",\n \"def get_api_info_first_and_last_r(\\n self,\\n\\n headers: t.Dict[str, str] = None,\\n body: JSONEncodable = None,\\n fields_data: t.Dict[str, str] = None,\\n **kwargs\\n ):\\n r = self._do_call(\\n method='GET',\\n url=f'{self.API_BASE_URL}/info/first-and-last',\\n headers=headers,\\n body=body,\\n fields=fields_data,\\n **kwargs\\n )\\n return r\",\n \"def fetch_timestamp(self):\\r\\n return self.__public_request('GET', '/api/v1/timestamp')\",\n \"def last(self, count=None):\\r\\n url = '{0}/{1}'.format(self.get_pull_url(), 'last')\\r\\n params = base.get_params(('count',), locals())\\r\\n\\r\\n return http.Request('GET', url, params), parsers.parse_json\",\n \"def next_update(self):\\r\\n request = http.Request('GET', '/metadata/next_update.json')\\r\\n return request, parsers.parse_json\",\n \"def _fetch(self):\\n self._data = self._get(self.url)\\n\\n if self._data['released_errata'] is not None:\\n self._released_errata = Erratum(errata_id=self._data[\\n 'released_errata']['id'])\\n\\n for errata_dict in self._data['all_errata']:\\n errata = Erratum(errata_id=errata_dict['id'])\\n self._all_errata.append(errata)\\n\\n self._signed_rpms = self._data.get('rpms_signed')\\n\\n for et_file in self._data['files']:\\n self._files.append(et_file['path'])\",\n \"def retrieve_last(self, namespace=None, apikey=None, fields=None,\\n attributes=None, limit=1):\\n self.__validate_retrieve_last(fields, limit)\\n request = {\\n 'limit': limit,\\n 'fields': fields,\\n 'attributes': attributes\\n }\\n self.__update_request(request, namespace, apikey)\\n return self._send_request('/retrieve-last/', Chunk(request))\",\n \"def __apiRequest(self, url, parms={}):\\n authparms = self.__addAuthParms(parms);\\n request = self.http.request('GET', url, fields=authparms)\\n if request.status != 200:\\n raise ApiCommunicationError('Failed to retrieve data from Marvel, HTTP Status {}'.format(request.status))\\n else:\\n return json.loads( request.data.decode('utf-8') )\",\n \"def get(self, sort='newest', page=None, per_page=None):\\r\\n params = base.get_params(('sort', 'page', 'per_page'), locals())\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"async def async_get(\\n self, endpoint: str | None = None, entry: str | None = None\\n ) -> Any:\\n url = f\\\"{self.url}/api/\\\"\\n if endpoint:\\n url = self.endpoints[endpoint]\\n if entry:\\n url = f\\\"{url}{entry}\\\"\\n with async_timeout.timeout(10):\\n resp = await self.session.get(\\n url=url,\\n headers=self.headers,\\n raise_for_status=True,\\n )\\n\\n return await resp.json()\",\n \"def latest(self, base='USD'):\\n try:\\n resp = self.client.get(self.ENDPOINT_LATEST, params={'base': base})\\n resp.raise_for_status()\\n except requests.exceptions.RequestException as e:\\n raise OpenExchangeRatesClientException(e)\\n return resp.json(parse_int=decimal.Decimal,\\n parse_float=decimal.Decimal)\",\n \"def get(self, start=None, limit=None):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def do_GET(self):\\n print(\\\"do_GET: got: \\\" + str(self.path))\\n path_split = self.path.split(\\\"/\\\", 2) # ['', 'hello', 'nikolay/uuuuer']\\n username = path_split[-1] # nikolay/uuuuer\\n print(\\\"do_GET: Let's open db_conn\\\")\\n connection = db_open()\\n birth_date = db_select(username, connection)\\n db_close(connection)\\n\\n days_until_bday = get_days_to_bday(birth_date)\\n json_obj = http_construct_json(username, days_until_bday)\\n\\n self.http_send_reply(json_obj)\\n print(\\\"do_GET: DONE! \\\\n\\\")\",\n \"def fetch_data():\\n \\n # Logging\\n print(\\\"Fetching new data...\\\")\\n\\n # Get current datetime, convert it to timestamp of 10 minutes before. \\n timestamp = datetime.now() - timedelta(minutes=10)\\n \\n # Try to fetch record with latest publishedAt. \\n try:\\n # If found, we use it's timestamp to search for videos published after this\\n timestamp = VideoData.objects.latest('publishedAt').publishedAt\\n print(f\\\"Got existing timestamp {timestamp}\\\")\\n except VideoData.DoesNotExist:\\n # If not found, we use the default timestamp i.e 10 minute delta from current time\\n print(f\\\"Using default timestamp {timestamp}\\\")\\n\\n timestamp = fromDateTimeToIso(timestamp)\\n \\n # Index of which API Key is being used\\n curr_key_index = 0\\n \\n is_data_fetched = False\\n total_api_call_fails = 0\\n\\n # A dummy response, in case all API request fail\\n response = {'items':[]}\\n\\n # Loop to try out all keys until all have been exhausted\\n while is_data_fetched == False:\\n is_data_fetched = True\\n\\n print(f\\\"Using key ({curr_key_index}) = {settings.YOUTUBE_API_KEY[curr_key_index]}\\\")\\n\\n try:\\n service = build(\\n 'youtube',\\n 'v3',\\n developerKey=settings.YOUTUBE_API_KEY[curr_key_index], \\n cache_discovery=False\\n )\\n collection = service.search().list(\\n maxResults=25,\\n part=['id','snippet'],\\n q='cricket',\\n type='video',\\n order='date',\\n publishedAfter=timestamp\\n )\\n\\n response = collection.execute()\\n except HttpError as e:\\n # Exception when either the key is wrong or API limit is exhausted\\n print(f\\\"!!!!DEBUG LOG!!!! {e}\\\")\\n curr_key_index = curr_key_index + 1 \\n total_api_call_fails = total_api_call_fails + 1\\n\\n # If total keys is less than current index, take mod\\n curr_key_index = curr_key_index % len(settings.YOUTUBE_API_KEY)\\n is_data_fetched = False \\n \\n # If all the keys have been tried, break the loop\\n if total_api_call_fails == len(settings.YOUTUBE_API_KEY):\\n print(\\\"API call limit exhausted for all keys\\\")\\n break\\n\\n print(f\\\"Fetched {len(response['items'])} items\\\")\\n\\n #If list contains items, populate database\\n if len(response['items']) > 0:\\n count = populate_data(response['items'])\\n print(f\\\"Successfully inserted {count} data\\\")\\n\\n return False\",\n \"def get(self, api_url, timeout=30):\\n return self._request('GET', api_url, timeout=timeout)\",\n \"def get(self):\\n\\t\\treturn self.connection.get(self.base_uri)\",\n \"async def current(self) -> dict:\\n return await self._request(\\n \\\"get\\\", \\\"https://www.asthmaforecast.com/api/forecast/current/asthma\\\"\\n )\",\n \"def get(self):\\r\\n request = http.Request('GET', self.get_url())\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def get(self):\\r\\n request = http.Request('GET', self.get_url())\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def get(self):\\r\\n request = http.Request('GET', self.get_url())\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def get(self):\\r\\n request = http.Request('GET', self.get_url())\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def get(self, count=None, max_timestamp=None, min_timestamp=None,\\r\\n min_id=None, max_id=None):\\r\\n params = base.get_params(\\r\\n ('count', 'max_timestamp', 'min_timestamp', 'min_id', 'max_id'),\\r\\n locals())\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def get(self, part, id):\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def get_data(self):\\n has_next_page = True\\n page = 1\\n while has_next_page:\\n print(f'Getting page {page}')\\n response = self.get_articles(\\n page=page,\\n size=200,\\n order_by='extracted_at',\\n order_type='asc'\\n )\\n pagination = response.get('pagination')\\n has_next_page = pagination.get('has_next')\\n self.save_articles(response.get('articles'))\\n page += 1\\n time.sleep(2.5)\",\n \"def get(self, type=None, count=None, offset=None):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def get_all_data():\\n return jsonify(service.get_all_data())\",\n \"def get(self):\\n self.finish(json.dumps(self.build_response_dict()))\",\n \"def get(self):\\n try:\\n query_parameters = request.args\\n logging.debug(f\\\" query params - {query_parameters}\\\")\\n top, skip, expand_code, selects = parse_args(query_parameters)\\n ds_list = Datastreams.return_page_with_expand(\\n top, skip, expand_code, selects\\n )\\n response = jsonify(ds_list)\\n response.status_code = 200\\n except Exception as e:\\n logging.warning(e)\\n response = jsonify({\\\"message\\\": \\\"error\\\"})\\n response.status_code = 400\\n\\n finally:\\n return response\",\n \"def get(self):\\r\\n return get_all()\",\n \"def get_information():\\n session = Session()\\n session.headers.update(headers)\\n try:\\n response = session.get(url, params=parameters)\\n data = json.loads(response.text)\\n with open('data.json', 'w') as file:\\n json.dump(data, file, ensure_ascii=False, indent=4)\\n return data\\n except (ConnectionError, Timeout, TooManyRedirects) as e:\\n print(e)\",\n \"def refresh(self) -> object:\\n requestor = Requestor(local_api_key=self._api_key)\\n url = self.instance_url()\\n response, api_key = requestor.request(method=RequestMethod.GET, url=url, params=self._retrieve_params)\\n self.refresh_from(values=response, api_key=api_key)\\n return self\",\n \"def fetch_temp_data(url):\\n res = requests.get(url)\\n return res.json()\",\n \"def api():\\n from gluon.contrib.hypermedia import Collection\\n rules = {\\n '<tablename>': {'GET':{},'POST':{},'PUT':{},'DELETE':{}},\\n }\\n return Collection(db).process(request,response,rules)\",\n \"def api():\\n from gluon.contrib.hypermedia import Collection\\n rules = {\\n '<tablename>': {'GET':{},'POST':{},'PUT':{},'DELETE':{}},\\n }\\n return Collection(db).process(request,response,rules)\",\n \"def api():\\n from gluon.contrib.hypermedia import Collection\\n rules = {\\n '<tablename>': {'GET':{},'POST':{},'PUT':{},'DELETE':{}},\\n }\\n return Collection(db).process(request,response,rules)\",\n \"def get_last_data(request):\\n\\n return_list = []\\n if request.POST:\\n try:\\n conn = psycopg2.connect(dbname=DB_NAME,\\n user=DB_USER,\\n password=DB_PASSWORD,\\n host=DB_HOST)\\n cur = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\\n\\n cur.execute(\\\"SELECT sn, time, icsmain, icsz1, icsz2, icsz3, icsboiler, t1, t2, t3, t4, t5, t6, t7,\\\"\\n \\\"rt1, rt2, rt3, endswitch, weather FROM devicedata \\\"\\n \\\"WHERE sn='BRANDON_SN_US' \\\"\\n \\\"ORDER BY id DESC LIMIT 5\\\")\\n\\n get_device_data = cur.fetchall()\\n\\n cur.close()\\n conn.close()\\n\\n for info in get_device_data:\\n item_dict = {\\\"sn\\\": info[0], \\\"time\\\": str(info[1]), \\\"icsmain\\\": info[2], \\\"icsz1\\\": info[3], \\\"icsz2\\\": info[4],\\n \\\"icsz3\\\": info[5], \\\"icsboiler\\\": info[6], \\\"t1\\\": info[7], \\\"t2\\\": info[8], \\\"t3\\\": info[9],\\n \\\"t4\\\": info[10], \\\"t5\\\": info[11], \\\"t6\\\": info[12], \\\"t7\\\": info[13], \\\"rt1\\\": info[14], \\\"rt2\\\": info[15],\\n \\\"rt3\\\": info[16], \\\"endswitch\\\": info[17]}\\n return_list.append(item_dict)\\n except:\\n pass\\n\\n return JsonResponse(return_list, safe=False)\",\n \"def _get_data_in_api(url: str) -> list:\\n\\n try:\\n resp = requests.request('GET', url, timeout=10)\\n resp.raise_for_status\\n\\n return Froxy._data_filter(resp.text)\\n\\n except (\\n requests.ConnectionError,\\n requests.ConnectTimeout,\\n requests.HTTPError,\\n requests.ReadTimeout\\n ) as err:\\n sys.exit(err)\",\n \"async def get_api_data(self, referer: str, params: list) -> list:\\n return await asyncio.ensure_future(self.get_response(referer, params))\",\n \"def get(self):\\n self.get_or_post(method='GET')\",\n \"def refresh_from_api(self):\\n self.populate_from_api(self.get_from_api())\",\n \"def get_data():\\r\\n try:\\r\\n BME280_TEMPERATURE.labels(device_name=device_name).set(bme280.temperature)\\r\\n except Exception as e:\\r\\n logger.error(\\\"Failed to update temperature. Exception: {}\\\".format(e))\\r\\n try:\\r\\n BME280_RELATIVE_HUMIDITY.labels(device_name=device_name).set(bme280.relative_humidity)\\r\\n except Exception as e:\\r\\n logger.error(\\\"Failed to update relative humidity. Exception: {}\\\".format(e))\\r\\n try:\\r\\n BME280_PRESSURE.labels(device_name=device_name).set(bme280.pressure)\\r\\n except Exception as e:\\r\\n logger.error(\\\"Failed to update pressure. Exception: {}\\\".format(e))\\r\\n try:\\r\\n BME280_ALTITUDE.labels(device_name=device_name).set(bme280.altitude)\\r\\n except Exception as e:\\r\\n logger.error(\\\"Failed to update altitude. Exception: {}\\\".format(e))\\r\\n\\r\\n return Response(generate_latest(), mimetype=CONTENT_TYPE_LATEST)\",\n \"def get(self, data):\\n pass\",\n \"def get_json(self, url, params, timeout=5, retries=3, data=None):\\n return self.request('GET', url, params, timeout=timeout, retries=retries, data=data)\",\n \"def download():\\n toydata = requests.get(DATA_URL).json()\\n return toydata\",\n \"def refresh_details(self) -> 'outputs.RefreshDetailsResponse':\\n return pulumi.get(self, \\\"refresh_details\\\")\",\n \"def _fetch_data(self):\\n pass\",\n \"def get(self):\\n\\n try:\\n args = api_parameters.parse_args()\\n limit = args.get(\\\"limit\\\")\\n controller = self.controller()\\n schema = self.schema(many=True)\\n raw_data = controller.get_list(**args)\\n\\n if limit:\\n items = raw_data.items\\n items = schema.dump(items)\\n data = ResponseHandler.get_section_paginate(raw_data, items)\\n\\n else:\\n data = schema.dump(raw_data)\\n\\n return ResponseHandler.render_response(data=data)\\n\\n except Exception as ex:\\n\\n return ResponseHandler.render_response(status=ERR, message=traceback.format_exc())\",\n \"def post_get_recent(requst, limit):\\n if requst.method == 'GET':\\n recent_posts = Post.objects.order_by('-created_at')[:int(limit)]\\n TopicNestedSerializer.Meta.depth = 1\\n PostNestedSerializer.Meta.depth = 1\\n serializer = PostNestedSerializer(recent_posts, many=True)\\n return Response(serializer.data)\",\n \"def get_info(self):\\n url = self._url_for_op('info')\\n data= None # This will be a GET request since data is None\\n response = self._get_raw_response(self._get_json_headers,\\n self._get_json_response, url, data)\\n response = json.loads(response)\\n self.api_info = response['results']\\n return self.api_info\",\n \"def get_feed():\\n return jsonify(dict({\\n \\\"result\\\": mongo.get_hpfeed(),\\n \\\"code\\\": 200\\n }))\",\n \"def rest_api(self):\\n self.__db_init('rest')\\n api = self.__api_init()\\n self.c.execute(\\\"SELECT MAX(id) FROM tweets\\\")\\n db_max_id = self.c.fetchone()[0] \\n try: \\n most_recent = api.search(q=self.keyword, result_type='recent')[0].id\\n except tweepy.TweepError as e:\\n print(str(e.message[0]['message']) + \\n ' Update api.ini with your proper credentials:')\\n print(os.path.abspath(_path_finder('userconfig','api.ini')))\\n sys.exit(-1)\\n flag = 0\\n while ( flag == 0 ):\\n try:\\n batch = 5000\\n flag = batch\\n for search_res in tweepy.Cursor(api.search, q=self.keyword,\\n count=100, result_type=\\\"recent\\\", \\n since_id=db_max_id, \\n max_id=most_recent).items(batch):\\n flag -= 1\\n print(search_res.id, search_res.created_at)\\n self.c.execute('''INSERT OR IGNORE INTO tweets (id, date) \\n VALUES (?, ?)''', \\n (search_res.id, search_res.created_at))\\n except tweepy.TweepError as e:\\n print('I caught an error:', e.message)\\n flag = 0\\n finally:\\n self.c.execute(\\\"SELECT last_insert_rowid() from tweets\\\")\\n rid = self.c.fetchone()[0]\\n if rid:\\n self.c.execute('''SELECT id FROM tweets WHERE\\n rowid={0}'''.format(rid))\\n rid = self.c.fetchone()[0]\\n most_recent = rid - 1\\n data = api.rate_limit_status()\\n print(data['resources']['search'])\\n self.conn.commit()\\n self.conn.close()\\n print('REST database file has been created/updated:') \\n print(os.path.abspath(_path_finder(\\n 'keydata','{0}_rest.db'.format(self.keyword))))\",\n \"def pull(self, url, params=None, data=None, auth=None, method=\\\"GET\\\"):\\n try:\\n __method_name = inspect.currentframe().f_code.co_name\\n if method == \\\"POST\\\":\\n res = self.session.post(\\n url=url,\\n auth=auth,\\n params=params,\\n data=data,\\n timeout=consts.API_TIMEOUT,\\n )\\n else:\\n res = self.session.get(\\n url=url,\\n auth=auth,\\n params=params,\\n data=data,\\n timeout=consts.API_TIMEOUT,\\n )\\n res.raise_for_status()\\n if res and res.status_code in [200, 201]:\\n self.applogger.debug(\\n '{}(method={}) : {} : API call: Response received successfully. url=\\\"{}\\\" params=\\\"{}\\\"'.format(\\n consts.LOGS_STARTS_WITH,\\n __method_name,\\n self.function_name,\\n url,\\n params,\\n )\\n )\\n return res.json()\\n else:\\n self.applogger.error(\\n \\\"{}(method={}) : {} : API call: Unknown status code or empty \\\"\\n 'response: url=\\\"{}\\\" status_code=\\\"{}\\\" response=\\\"{}\\\"'.format(\\n consts.LOGS_STARTS_WITH,\\n __method_name,\\n self.function_name,\\n url,\\n res.status_code,\\n res.text,\\n )\\n )\\n raise Exception(\\\"Received unknown status code or empty response.\\\")\\n except requests.exceptions.HTTPError as ex:\\n if res.status_code == 404:\\n self.applogger.debug(\\n '{}(method={}) : {} : API call: Got {} Status Code : url=\\\"{}\\\"'\\n ' response=\\\"{}\\\"'.format(\\n consts.LOGS_STARTS_WITH,\\n __method_name,\\n self.function_name,\\n url,\\n ex.response.status_code,\\n ex.response.text,\\n )\\n )\\n return {}\\n else:\\n self.applogger.error(\\n '{}(method={}) : {} : API call: Unsuccessful response: url=\\\"{}\\\" status_code=\\\"{}\\\"'\\n ' response=\\\"{}\\\"'.format(\\n consts.LOGS_STARTS_WITH,\\n __method_name,\\n self.function_name,\\n url,\\n ex.response.status_code,\\n ex.response.text,\\n )\\n )\\n raise Exception(\\\"HTTP Error Occurred while getting response from api.\\\")\\n except Exception as ex:\\n self.applogger.error(\\n '{}(method={}) : {} : API call: Unexpected error while API call url=\\\"{}\\\" error=\\\"{}\\\"'.format(\\n consts.LOGS_STARTS_WITH,\\n __method_name,\\n self.function_name,\\n url,\\n str(ex),\\n )\\n )\\n raise Exception(\\\"Error Occurred while getting response from api.\\\")\",\n \"def top_task_element(request):\\n if request.method == \\\"GET\\\":\\n task = Task.objects.filter(status=\\\"Open\\\").order_by(\\\"-timestamp\\\").first()\\n serializer = TaskSerializer(task)\\n return Response(serializer.data)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69037545","0.6617101","0.648815","0.63266337","0.63005227","0.63005227","0.6281676","0.6281676","0.6278101","0.6271919","0.6258911","0.62241006","0.6204899","0.61852705","0.6169884","0.6165374","0.61476934","0.6141144","0.61288834","0.60983145","0.6092812","0.60351354","0.6004257","0.59997827","0.59933275","0.5978843","0.59552234","0.59176487","0.59174883","0.5871819","0.5862947","0.58607084","0.58511114","0.58503944","0.5837288","0.5835993","0.5833447","0.5823832","0.5810524","0.57920986","0.5788289","0.5781512","0.5777799","0.57637084","0.57637084","0.57634926","0.5758406","0.5754495","0.5752107","0.5751578","0.5751319","0.5745261","0.57430464","0.5731993","0.5730507","0.57270736","0.57229584","0.57222587","0.57075304","0.5703438","0.5689737","0.56885433","0.568347","0.5679574","0.5678103","0.5676261","0.5676261","0.5676261","0.5676261","0.56675994","0.56671464","0.5657654","0.56558496","0.5655849","0.56531334","0.565052","0.56473905","0.56401443","0.5639371","0.5633754","0.5631619","0.5631619","0.5631619","0.562502","0.5622941","0.56223947","0.5621852","0.5620851","0.5619791","0.56194764","0.5615162","0.56147254","0.5607262","0.5605767","0.56025225","0.55927104","0.5588471","0.55723554","0.55715543","0.5567491","0.5562154"],"string":"[\n \"0.69037545\",\n \"0.6617101\",\n \"0.648815\",\n \"0.63266337\",\n \"0.63005227\",\n \"0.63005227\",\n \"0.6281676\",\n \"0.6281676\",\n \"0.6278101\",\n \"0.6271919\",\n \"0.6258911\",\n \"0.62241006\",\n \"0.6204899\",\n \"0.61852705\",\n \"0.6169884\",\n \"0.6165374\",\n \"0.61476934\",\n \"0.6141144\",\n \"0.61288834\",\n \"0.60983145\",\n \"0.6092812\",\n \"0.60351354\",\n \"0.6004257\",\n \"0.59997827\",\n \"0.59933275\",\n \"0.5978843\",\n \"0.59552234\",\n \"0.59176487\",\n \"0.59174883\",\n \"0.5871819\",\n \"0.5862947\",\n \"0.58607084\",\n \"0.58511114\",\n \"0.58503944\",\n \"0.5837288\",\n \"0.5835993\",\n \"0.5833447\",\n \"0.5823832\",\n \"0.5810524\",\n \"0.57920986\",\n \"0.5788289\",\n \"0.5781512\",\n \"0.5777799\",\n \"0.57637084\",\n \"0.57637084\",\n \"0.57634926\",\n \"0.5758406\",\n \"0.5754495\",\n \"0.5752107\",\n \"0.5751578\",\n \"0.5751319\",\n \"0.5745261\",\n \"0.57430464\",\n \"0.5731993\",\n \"0.5730507\",\n \"0.57270736\",\n \"0.57229584\",\n \"0.57222587\",\n \"0.57075304\",\n \"0.5703438\",\n \"0.5689737\",\n \"0.56885433\",\n \"0.568347\",\n \"0.5679574\",\n \"0.5678103\",\n \"0.5676261\",\n \"0.5676261\",\n \"0.5676261\",\n \"0.5676261\",\n \"0.56675994\",\n \"0.56671464\",\n \"0.5657654\",\n \"0.56558496\",\n \"0.5655849\",\n \"0.56531334\",\n \"0.565052\",\n \"0.56473905\",\n \"0.56401443\",\n \"0.5639371\",\n \"0.5633754\",\n \"0.5631619\",\n \"0.5631619\",\n \"0.5631619\",\n \"0.562502\",\n \"0.5622941\",\n \"0.56223947\",\n \"0.5621852\",\n \"0.5620851\",\n \"0.5619791\",\n \"0.56194764\",\n \"0.5615162\",\n \"0.56147254\",\n \"0.5607262\",\n \"0.5605767\",\n \"0.56025225\",\n \"0.55927104\",\n \"0.5588471\",\n \"0.55723554\",\n \"0.55715543\",\n \"0.5567491\",\n \"0.5562154\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":93893,"cells":{"query":{"kind":"string","value":"Initialize the aREST data object."},"document":{"kind":"string","value":"def __init__(self, resource, pin):\n self._resource = resource\n self._pin = pin\n self.data = {}"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self):\n\t\tsuper().__init__()\n\t\t\n\t\t# Typically a list of data here\n\t\t# Typically a dict of header keys and values here","def __init__(self, data):\n self.data = data\n return","def __init__(self, data=None):\n self.data = data","def __init__(self):\n self.data = json.loads(resource_string(__name__, 'data/oz_postcodes.json'))","def __init__(self, data):\n\t\tassert isinstance(data, str), \"Data location must be provided in type 'str'!\"\n\t\t\n\t\t# load the location provided\n\t\tdata = json.loads(open(data).read())\n\n\t\t# check for correct format\n\t\tassert isinstance(data, list), \"Data must be of type 'list'!\"\n\n\t\tfor element in data:\n\t\t\tassert isinstance(element, dict), \"Each element of data must be of type 'dict'!\"\n\n\t\tself.data = data","def __init__(self, data):\n self.data = data","def __init__(self, data):\n self.data = data","def __init__(self, data):\n self.data = data","def __init__(self, data):\n self.data = data","def __init__( self\n , _o_data\n ):\n self.o_data = _o_data","def __init__(self, rest=AuthenticatingRestProxy(RestRequests())):\n self._rest = rest","def __init__(self,data):\n\n self.data = data","def __init__(self):\n\n # initialise the empty mappings dictionary\n self.data = {\n 'loan_id': None,\n 'product': None,\n 'origination_date': None,\n 'reversion_date': None,\n 'rate_term': None,\n 'loan_amount': None,\n 'initial_rate': None,\n 'reversion_rate': None,\n 'term': None,\n 'interest_only_amount': None,\n 'upfront_fees': None,\n 'upfront_costs': None,\n 'entity_eir': None\n }","def __init__(self):\n self._url = \"\"\n self._verb = VERBS.GET\n self._params = {}\n self._data = {}","def __init__(self, request_data, objects, resource_uri=None, limit=None,\r\n offset=0, max_limit=100, collection_name='objects'):\r\n self.request_data = request_data\r\n self.objects = objects\r\n self.limit = limit\r\n self.max_limit = max_limit\r\n self.offset = offset\r\n self.resource_uri = resource_uri\r\n self.collection_name = collection_name","def _init_(self):\n self.res = {}","def __init__(self, preston, base_url):\n self.data = {}\n self._preston = preston\n self.base_url = base_url","def initialize(self):\n self.data = None\n self.errors = []","def __init__(self, application, data):\n self.application = application\n self.data = data","def __init__(self):\n\n # TODO: Add login and data grab logic","def __init__(self, request):\n self._data = request.get_json(silent=True)","def __init__(self):\n\n self.storage: list = Storage()\n\n # Start for get data in API and set in storage\n self._set_proxies_in_storage()","def __init__(self, api, site_id):\n self.api = api\n self.site_id = site_id\n\n self.data = {}\n self.attributes = {}","def __init__(self, *args, **kwargs):\r\n super(StubOraService, self).__init__(*args, **kwargs)\r\n\r\n # Create a dict to map student ID's to their state\r\n self._students = dict()\r\n\r\n # By default, no problems are available for peer grading\r\n # You can add to this list using the `register_location` HTTP end-point\r\n # This is a dict mapping problem locations to problem names\r\n self.problems = dict()","def __init__(self, name, config):\n # self.rest = rest\n self._name = name\n self._config = config\n self._attributes = {}","def __init__(self, api):\n self.api = api\n self.data = None","def __init__(self):\n self.relation = ''\n self.attributes = []\n self.attribute_types = dict()\n self.attribute_data = dict()\n self.comment = []\n self.data = []\n pass","def __init__(self, data_dir, base_url):\n self.data_dir = data_dir\n self.base_url = base_url","def __init__(self, data={}):\n self._update_(data)","def __init__(self):\n self.ju = ju.JSONUtil()\n self.apiC = api.API()","def __init__(self, auth):\n super(Socrata, self).__init__(auth)\n self.views = Views(auth)\n self.sources = Sources(auth)\n self.configs = Configs(auth)","def __init__(self, data: dict):\n self._data = {\n '': 'Location', # this is required\n 'street': '',\n 'suburb': '',\n 'location': '',\n 'stop': ''\n }\n\n self._data.update(data)","def __init__(self, data: dict = {}):\n pass","def __init__(self, json_data=None):\r\n super(NSPatset, self).__init__()\r\n self.options = {'name': '',\r\n 'comment': '',\r\n 'indextype': '',\r\n\t 'description': '',\r\n '__count': ''}\r\n \r\n self.resourcetype = NSPatset.get_resourcetype()\r\n \r\n if not (json_data is None):\r\n for key in json_data.keys():\r\n if key in self.options.keys():\r\n self.options[key] = json_data[key]","def __init__(self):\n\n data_extract=DataExtracter()\n self.data = tuple()","def __init__(self, load_data=True):\n if not load_data:\n return\n\n # Load data files\n data_paths = [\"app_data\", \"..app_data\"]\n adp = None\n\n for data_path in data_paths:\n if os.path.exists(os.path.join(os.curdir, data_path)):\n adp = os.path.join(os.curdir, data_path)\n\n if not adp:\n _logger.error(\"app data path not found.\")\n return\n\n # load all files in the rest_api/app_data path.\n files = glob(os.path.join(adp, \"*\"))\n for filename in files:\n\n if filename.endswith(\".txt\"):\n key = os.path.basename(filename).replace(\".txt\", \"\")\n # decode and encode are required for the cryillic names in the files.\n self._app_data[key] = [\n line.strip().decode(\"utf-8\") for line in open(filename, \"rb\").readlines()\n ]\n\n if filename.endswith(\".csv\"):\n key = os.path.basename(filename).replace(\".csv\", \"\")\n self._app_data[key] = list(csv.DictReader(open(filename)))\n\n if filename.endswith(\".json\"):\n key = os.path.basename(filename).replace(\".json\", \"\")\n self._app_data[key] = json.loads(open(filename).read())","def __init__(self, request, data):\n self.request = request\n self.data = data","def __init__(self, collection, json_data = None):\n\n super(Entity, self).__init__(json_data)\n self.collection = collection\n self.return_error_code = True","def initialize(self, **kwargs):","def __init__(self):\n self._data = []","def __init__(self):\n self._data = []","def __init__(self):\n self._data = []","def __init__(self):\n self._data = []","def __init__(self):\n self._data = []","def __init__(self):\n self._data = []","def __init__(self, **kwargs):\n self.data_dict = dict()\n self.data_list = dict()\n self.user_id = kwargs[\"user_id\"]","def __init__(self):\n self._values = {\n 'ver': 2,\n 'name': None,\n 'duration': None,\n 'success': True,\n }\n self._initialize()","def __init__(self, dat):\n self.data = dat","def __init__(self):\n self.data = []\n self.record = {}","def __init__(self, json_data, id_mappings):\n self._data = json_data\n self._id_mappings = id_mappings\n self._driven_routes = []\n self._invoices = []","def __init__(self, page_index, page_size, total_items, data):\n self.page_index = page_index\n self.page_size = page_size\n self.total_items = total_items\n self.data = data","def __init__(self, data):\n self.json = data\n self.id = data.get(\"ID\", None)\n self.name = data.get(\"Name\", None)\n self.domain = data.get(\"Domain\", None)\n self.brand_id = data.get(\"BrandID\", None)\n self.account_id = data.get(\"AccountID\", None)\n self.brand_name = data.get(\"BrandName\", None)\n self.country_id = data.get(\"CountryID\", None)\n self.country_name = data.get(\"CountryName\", None)\n self.account_name = data.get(\"AccountName\", None)\n self.pre_order = data.get(\"PreOrder\", None)\n self.type_id = data.get(\"TypeID\", None)\n self.type_name = data.get(\"TypeName\", None)\n self.nominal_code_id = data.get(\"NominalCodeID\", None)\n self.external_shop_id = data.get(\"ExtShopID\", None)\n self.pseudo_stock_level_type = data.get(\"PseudoStockLevelType\", None)\n self.currency_symbol = data.get(\"CurrencySymbol\", None)\n self.loyalty_point_per_value = data.get(\"LoyaltyPointPerValue\", None)\n self.loyalty_value_per_point = data.get(\"LoyaltyValuePerPoint\", None)\n self.disabled = data.get(\"disabled\", None)\n self.deleted = data.get(\"deleted\", None)\n self.note = data.get(\"Note\", None)","def init(self, *args, **kwargs):\n raise NotImplementedError(\"ErddapArgoDataFetcher.init not implemented\")","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def __init__(self, data, url, *args, **kwargs):\n super(RabjContainer, self).__init__()\n self.data = data\n self.url = url\n self.container_factory = RabjContainerFactory(self.url)","def __init__(self):\n self.data = None\n self.conn = None\n self.database = None\n self.table = None\n self.manage = None\n self.limiting = 0","def __init__( self, mid = DATA, sid = None, data = None ):\n\n # load arguments into object state\n self.super_init( vars() )","def _init(self):\n pass","def __init__(self, data, node):\n self.data = data\n self.node = node # This is the data structure which holds the data for this node, e.g. lat, lon, etc.","def init(self) -> None:\n ...","def __init__(self, data=None, filename=None, schema=None):\n self.data = None\n self.schema = None\n self.filename = None\n if schema:\n self.load_schema(schema)\n if filename:\n self.load_file(filename)\n if data:\n self.load_data(data)","def __init__(self, param, data):\n self.param = param\n self.data = data","def __init__(self, json_dict, endpoint):\n self._values = json_dict\n self._endpoint = endpoint","def initialize(self):\n\t\tpass","def init(self) -> None:","def __init__(self):\n self._sections = {}\n self._filters = []\n self._id = 0","def __init__(self, auth, home=None):\n self.auth = auth\n self.homedata = None\n self.home_ids = []\n self.home_names = []\n self.room_names = []\n self.schedules = []\n self.home = home\n self.home_id = None","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def __init__(self, data):\n self.__data = data\n self.__next = None","def __init__(self):\n super(Resource, self).__init__()\n\n # ____building form class for resources validation____\n self.request = Requests(self.__class__.__name__)\n self.response = Response()","def __init__(self) -> None:\n self._entry_data_for_reauth: Mapping[str, Any] = {}\n self._geo_id: str | None = None","def initialize(self):\r\n pass","def initialize(self):\r\n pass","def __init__(self, make_response=None, code=200, data=''):\n if isinstance(data, tuple):\n self.data = data[0]\n headers = data[1]\n else:\n self.data = data\n headers = {}\n\n self.headers = headers\n self.code = code\n self.make_response = make_response","def __init__(self):\n self.data = {}\n self.refresh()","def __init__(self):\n self._data = None\n self._forecast_data = None\n self._today_data = None\n self.last_updated = None","def __init__(self, config: Dict[str, Any], data: Data, verbose: bool) -> None:\n self.__verbose = verbose\n self.__data = data\n self.__config = config","def __init__(self):\n self._data = [] # non-public underlying Python list as storage","def __init__(self, host, base_url, auth):\r\n self.host = host\r\n self.base_url = base_url\r\n self.auth = auth\r\n \r\n log.info(\"API Host: %s/%s\" % (self.host, self.base_url))\r\n log.debug(\"Accepting json, auth: Basic %s\" % self.auth)\r\n self.__headers = {\"Authorization\": \"Basic %s\" % self.auth,\r\n \"Accept\": \"application/json\"}\r\n \r\n self.__resource_meta = {}\r\n self.__connection = HTTPSConnection(self.host)","def __init__(self, data):\n self.data = self.get_api_reference_html(data)\n self.parsed_data = []","def __init__(self, data=None, next=None):\r\n pass","def __init__(self, data):\n self._data_dict = data\n self._path = ()","def __init__(self, name):\n self.name = name\n self.data = {}\n self.use_success = False","def __init__(self, obj_type, results, heading, metadata):\n self.obj_type = obj_type\n self.results = results\n self.heading = heading\n self.metadata = metadata","def __init__(self, train, test, head):\n\n # Save the URL's, in case we want it later\n self.train_url = train\n self.test_url = test\n self.head_url = head\n\n # Grab the data, and store the internal variables\n self._get_data()","def __init__(self, **kwargs):\n DataLoader.__init__(self, **kwargs)","def init(self, userdata, conn):\r\n pass","def __init__(self, data):\n self.jssp_instance_data = data","def __init__(self):\n self.metadata = {}\n self.geometry = {'array': None, \n 'geom': None, \n 'wkt': None}","def __init__(self):\n super().__init__()\n self.data = None"],"string":"[\n \"def __init__(self):\\n\\t\\tsuper().__init__()\\n\\t\\t\\n\\t\\t# Typically a list of data here\\n\\t\\t# Typically a dict of header keys and values here\",\n \"def __init__(self, data):\\n self.data = data\\n return\",\n \"def __init__(self, data=None):\\n self.data = data\",\n \"def __init__(self):\\n self.data = json.loads(resource_string(__name__, 'data/oz_postcodes.json'))\",\n \"def __init__(self, data):\\n\\t\\tassert isinstance(data, str), \\\"Data location must be provided in type 'str'!\\\"\\n\\t\\t\\n\\t\\t# load the location provided\\n\\t\\tdata = json.loads(open(data).read())\\n\\n\\t\\t# check for correct format\\n\\t\\tassert isinstance(data, list), \\\"Data must be of type 'list'!\\\"\\n\\n\\t\\tfor element in data:\\n\\t\\t\\tassert isinstance(element, dict), \\\"Each element of data must be of type 'dict'!\\\"\\n\\n\\t\\tself.data = data\",\n \"def __init__(self, data):\\n self.data = data\",\n \"def __init__(self, data):\\n self.data = data\",\n \"def __init__(self, data):\\n self.data = data\",\n \"def __init__(self, data):\\n self.data = data\",\n \"def __init__( self\\n , _o_data\\n ):\\n self.o_data = _o_data\",\n \"def __init__(self, rest=AuthenticatingRestProxy(RestRequests())):\\n self._rest = rest\",\n \"def __init__(self,data):\\n\\n self.data = data\",\n \"def __init__(self):\\n\\n # initialise the empty mappings dictionary\\n self.data = {\\n 'loan_id': None,\\n 'product': None,\\n 'origination_date': None,\\n 'reversion_date': None,\\n 'rate_term': None,\\n 'loan_amount': None,\\n 'initial_rate': None,\\n 'reversion_rate': None,\\n 'term': None,\\n 'interest_only_amount': None,\\n 'upfront_fees': None,\\n 'upfront_costs': None,\\n 'entity_eir': None\\n }\",\n \"def __init__(self):\\n self._url = \\\"\\\"\\n self._verb = VERBS.GET\\n self._params = {}\\n self._data = {}\",\n \"def __init__(self, request_data, objects, resource_uri=None, limit=None,\\r\\n offset=0, max_limit=100, collection_name='objects'):\\r\\n self.request_data = request_data\\r\\n self.objects = objects\\r\\n self.limit = limit\\r\\n self.max_limit = max_limit\\r\\n self.offset = offset\\r\\n self.resource_uri = resource_uri\\r\\n self.collection_name = collection_name\",\n \"def _init_(self):\\n self.res = {}\",\n \"def __init__(self, preston, base_url):\\n self.data = {}\\n self._preston = preston\\n self.base_url = base_url\",\n \"def initialize(self):\\n self.data = None\\n self.errors = []\",\n \"def __init__(self, application, data):\\n self.application = application\\n self.data = data\",\n \"def __init__(self):\\n\\n # TODO: Add login and data grab logic\",\n \"def __init__(self, request):\\n self._data = request.get_json(silent=True)\",\n \"def __init__(self):\\n\\n self.storage: list = Storage()\\n\\n # Start for get data in API and set in storage\\n self._set_proxies_in_storage()\",\n \"def __init__(self, api, site_id):\\n self.api = api\\n self.site_id = site_id\\n\\n self.data = {}\\n self.attributes = {}\",\n \"def __init__(self, *args, **kwargs):\\r\\n super(StubOraService, self).__init__(*args, **kwargs)\\r\\n\\r\\n # Create a dict to map student ID's to their state\\r\\n self._students = dict()\\r\\n\\r\\n # By default, no problems are available for peer grading\\r\\n # You can add to this list using the `register_location` HTTP end-point\\r\\n # This is a dict mapping problem locations to problem names\\r\\n self.problems = dict()\",\n \"def __init__(self, name, config):\\n # self.rest = rest\\n self._name = name\\n self._config = config\\n self._attributes = {}\",\n \"def __init__(self, api):\\n self.api = api\\n self.data = None\",\n \"def __init__(self):\\n self.relation = ''\\n self.attributes = []\\n self.attribute_types = dict()\\n self.attribute_data = dict()\\n self.comment = []\\n self.data = []\\n pass\",\n \"def __init__(self, data_dir, base_url):\\n self.data_dir = data_dir\\n self.base_url = base_url\",\n \"def __init__(self, data={}):\\n self._update_(data)\",\n \"def __init__(self):\\n self.ju = ju.JSONUtil()\\n self.apiC = api.API()\",\n \"def __init__(self, auth):\\n super(Socrata, self).__init__(auth)\\n self.views = Views(auth)\\n self.sources = Sources(auth)\\n self.configs = Configs(auth)\",\n \"def __init__(self, data: dict):\\n self._data = {\\n '': 'Location', # this is required\\n 'street': '',\\n 'suburb': '',\\n 'location': '',\\n 'stop': ''\\n }\\n\\n self._data.update(data)\",\n \"def __init__(self, data: dict = {}):\\n pass\",\n \"def __init__(self, json_data=None):\\r\\n super(NSPatset, self).__init__()\\r\\n self.options = {'name': '',\\r\\n 'comment': '',\\r\\n 'indextype': '',\\r\\n\\t 'description': '',\\r\\n '__count': ''}\\r\\n \\r\\n self.resourcetype = NSPatset.get_resourcetype()\\r\\n \\r\\n if not (json_data is None):\\r\\n for key in json_data.keys():\\r\\n if key in self.options.keys():\\r\\n self.options[key] = json_data[key]\",\n \"def __init__(self):\\n\\n data_extract=DataExtracter()\\n self.data = tuple()\",\n \"def __init__(self, load_data=True):\\n if not load_data:\\n return\\n\\n # Load data files\\n data_paths = [\\\"app_data\\\", \\\"..app_data\\\"]\\n adp = None\\n\\n for data_path in data_paths:\\n if os.path.exists(os.path.join(os.curdir, data_path)):\\n adp = os.path.join(os.curdir, data_path)\\n\\n if not adp:\\n _logger.error(\\\"app data path not found.\\\")\\n return\\n\\n # load all files in the rest_api/app_data path.\\n files = glob(os.path.join(adp, \\\"*\\\"))\\n for filename in files:\\n\\n if filename.endswith(\\\".txt\\\"):\\n key = os.path.basename(filename).replace(\\\".txt\\\", \\\"\\\")\\n # decode and encode are required for the cryillic names in the files.\\n self._app_data[key] = [\\n line.strip().decode(\\\"utf-8\\\") for line in open(filename, \\\"rb\\\").readlines()\\n ]\\n\\n if filename.endswith(\\\".csv\\\"):\\n key = os.path.basename(filename).replace(\\\".csv\\\", \\\"\\\")\\n self._app_data[key] = list(csv.DictReader(open(filename)))\\n\\n if filename.endswith(\\\".json\\\"):\\n key = os.path.basename(filename).replace(\\\".json\\\", \\\"\\\")\\n self._app_data[key] = json.loads(open(filename).read())\",\n \"def __init__(self, request, data):\\n self.request = request\\n self.data = data\",\n \"def __init__(self, collection, json_data = None):\\n\\n super(Entity, self).__init__(json_data)\\n self.collection = collection\\n self.return_error_code = True\",\n \"def initialize(self, **kwargs):\",\n \"def __init__(self):\\n self._data = []\",\n \"def __init__(self):\\n self._data = []\",\n \"def __init__(self):\\n self._data = []\",\n \"def __init__(self):\\n self._data = []\",\n \"def __init__(self):\\n self._data = []\",\n \"def __init__(self):\\n self._data = []\",\n \"def __init__(self, **kwargs):\\n self.data_dict = dict()\\n self.data_list = dict()\\n self.user_id = kwargs[\\\"user_id\\\"]\",\n \"def __init__(self):\\n self._values = {\\n 'ver': 2,\\n 'name': None,\\n 'duration': None,\\n 'success': True,\\n }\\n self._initialize()\",\n \"def __init__(self, dat):\\n self.data = dat\",\n \"def __init__(self):\\n self.data = []\\n self.record = {}\",\n \"def __init__(self, json_data, id_mappings):\\n self._data = json_data\\n self._id_mappings = id_mappings\\n self._driven_routes = []\\n self._invoices = []\",\n \"def __init__(self, page_index, page_size, total_items, data):\\n self.page_index = page_index\\n self.page_size = page_size\\n self.total_items = total_items\\n self.data = data\",\n \"def __init__(self, data):\\n self.json = data\\n self.id = data.get(\\\"ID\\\", None)\\n self.name = data.get(\\\"Name\\\", None)\\n self.domain = data.get(\\\"Domain\\\", None)\\n self.brand_id = data.get(\\\"BrandID\\\", None)\\n self.account_id = data.get(\\\"AccountID\\\", None)\\n self.brand_name = data.get(\\\"BrandName\\\", None)\\n self.country_id = data.get(\\\"CountryID\\\", None)\\n self.country_name = data.get(\\\"CountryName\\\", None)\\n self.account_name = data.get(\\\"AccountName\\\", None)\\n self.pre_order = data.get(\\\"PreOrder\\\", None)\\n self.type_id = data.get(\\\"TypeID\\\", None)\\n self.type_name = data.get(\\\"TypeName\\\", None)\\n self.nominal_code_id = data.get(\\\"NominalCodeID\\\", None)\\n self.external_shop_id = data.get(\\\"ExtShopID\\\", None)\\n self.pseudo_stock_level_type = data.get(\\\"PseudoStockLevelType\\\", None)\\n self.currency_symbol = data.get(\\\"CurrencySymbol\\\", None)\\n self.loyalty_point_per_value = data.get(\\\"LoyaltyPointPerValue\\\", None)\\n self.loyalty_value_per_point = data.get(\\\"LoyaltyValuePerPoint\\\", None)\\n self.disabled = data.get(\\\"disabled\\\", None)\\n self.deleted = data.get(\\\"deleted\\\", None)\\n self.note = data.get(\\\"Note\\\", None)\",\n \"def init(self, *args, **kwargs):\\n raise NotImplementedError(\\\"ErddapArgoDataFetcher.init not implemented\\\")\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def __init__(self, data, url, *args, **kwargs):\\n super(RabjContainer, self).__init__()\\n self.data = data\\n self.url = url\\n self.container_factory = RabjContainerFactory(self.url)\",\n \"def __init__(self):\\n self.data = None\\n self.conn = None\\n self.database = None\\n self.table = None\\n self.manage = None\\n self.limiting = 0\",\n \"def __init__( self, mid = DATA, sid = None, data = None ):\\n\\n # load arguments into object state\\n self.super_init( vars() )\",\n \"def _init(self):\\n pass\",\n \"def __init__(self, data, node):\\n self.data = data\\n self.node = node # This is the data structure which holds the data for this node, e.g. lat, lon, etc.\",\n \"def init(self) -> None:\\n ...\",\n \"def __init__(self, data=None, filename=None, schema=None):\\n self.data = None\\n self.schema = None\\n self.filename = None\\n if schema:\\n self.load_schema(schema)\\n if filename:\\n self.load_file(filename)\\n if data:\\n self.load_data(data)\",\n \"def __init__(self, param, data):\\n self.param = param\\n self.data = data\",\n \"def __init__(self, json_dict, endpoint):\\n self._values = json_dict\\n self._endpoint = endpoint\",\n \"def initialize(self):\\n\\t\\tpass\",\n \"def init(self) -> None:\",\n \"def __init__(self):\\n self._sections = {}\\n self._filters = []\\n self._id = 0\",\n \"def __init__(self, auth, home=None):\\n self.auth = auth\\n self.homedata = None\\n self.home_ids = []\\n self.home_names = []\\n self.room_names = []\\n self.schedules = []\\n self.home = home\\n self.home_id = None\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def __init__(self, data):\\n self.__data = data\\n self.__next = None\",\n \"def __init__(self):\\n super(Resource, self).__init__()\\n\\n # ____building form class for resources validation____\\n self.request = Requests(self.__class__.__name__)\\n self.response = Response()\",\n \"def __init__(self) -> None:\\n self._entry_data_for_reauth: Mapping[str, Any] = {}\\n self._geo_id: str | None = None\",\n \"def initialize(self):\\r\\n pass\",\n \"def initialize(self):\\r\\n pass\",\n \"def __init__(self, make_response=None, code=200, data=''):\\n if isinstance(data, tuple):\\n self.data = data[0]\\n headers = data[1]\\n else:\\n self.data = data\\n headers = {}\\n\\n self.headers = headers\\n self.code = code\\n self.make_response = make_response\",\n \"def __init__(self):\\n self.data = {}\\n self.refresh()\",\n \"def __init__(self):\\n self._data = None\\n self._forecast_data = None\\n self._today_data = None\\n self.last_updated = None\",\n \"def __init__(self, config: Dict[str, Any], data: Data, verbose: bool) -> None:\\n self.__verbose = verbose\\n self.__data = data\\n self.__config = config\",\n \"def __init__(self):\\n self._data = [] # non-public underlying Python list as storage\",\n \"def __init__(self, host, base_url, auth):\\r\\n self.host = host\\r\\n self.base_url = base_url\\r\\n self.auth = auth\\r\\n \\r\\n log.info(\\\"API Host: %s/%s\\\" % (self.host, self.base_url))\\r\\n log.debug(\\\"Accepting json, auth: Basic %s\\\" % self.auth)\\r\\n self.__headers = {\\\"Authorization\\\": \\\"Basic %s\\\" % self.auth,\\r\\n \\\"Accept\\\": \\\"application/json\\\"}\\r\\n \\r\\n self.__resource_meta = {}\\r\\n self.__connection = HTTPSConnection(self.host)\",\n \"def __init__(self, data):\\n self.data = self.get_api_reference_html(data)\\n self.parsed_data = []\",\n \"def __init__(self, data=None, next=None):\\r\\n pass\",\n \"def __init__(self, data):\\n self._data_dict = data\\n self._path = ()\",\n \"def __init__(self, name):\\n self.name = name\\n self.data = {}\\n self.use_success = False\",\n \"def __init__(self, obj_type, results, heading, metadata):\\n self.obj_type = obj_type\\n self.results = results\\n self.heading = heading\\n self.metadata = metadata\",\n \"def __init__(self, train, test, head):\\n\\n # Save the URL's, in case we want it later\\n self.train_url = train\\n self.test_url = test\\n self.head_url = head\\n\\n # Grab the data, and store the internal variables\\n self._get_data()\",\n \"def __init__(self, **kwargs):\\n DataLoader.__init__(self, **kwargs)\",\n \"def init(self, userdata, conn):\\r\\n pass\",\n \"def __init__(self, data):\\n self.jssp_instance_data = data\",\n \"def __init__(self):\\n self.metadata = {}\\n self.geometry = {'array': None, \\n 'geom': None, \\n 'wkt': None}\",\n \"def __init__(self):\\n super().__init__()\\n self.data = None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67280114","0.6717379","0.6682897","0.6569234","0.6562224","0.6559889","0.6559889","0.6559889","0.6559889","0.65542865","0.6554195","0.65215546","0.64844275","0.6484227","0.64589953","0.6457893","0.6446397","0.6441969","0.6429045","0.639269","0.6382177","0.63117296","0.6291355","0.62845725","0.6280303","0.62712806","0.62509006","0.62476456","0.62449867","0.6243475","0.624268","0.6206099","0.62042826","0.61739314","0.61731005","0.6155436","0.61500543","0.6144375","0.61397177","0.61386114","0.61386114","0.61386114","0.61386114","0.61386114","0.61386114","0.6118273","0.6117178","0.61061317","0.6104838","0.609766","0.6093743","0.6088006","0.60875094","0.6080044","0.6080044","0.6080044","0.6080044","0.6080044","0.6080044","0.6080044","0.6080044","0.60796356","0.6062388","0.60555977","0.60503805","0.60483325","0.6023364","0.6013485","0.6012509","0.600199","0.59915775","0.59852237","0.5982926","0.5981618","0.5981509","0.5981509","0.5981509","0.5981509","0.5981509","0.59800524","0.597805","0.5973813","0.59710586","0.59710586","0.5969996","0.59626514","0.59593433","0.59572405","0.59567654","0.59559375","0.5953282","0.5953109","0.5948291","0.5939889","0.59323657","0.5929308","0.59283864","0.59232813","0.59210604","0.5919722","0.5913913"],"string":"[\n \"0.67280114\",\n \"0.6717379\",\n \"0.6682897\",\n \"0.6569234\",\n \"0.6562224\",\n \"0.6559889\",\n \"0.6559889\",\n \"0.6559889\",\n \"0.6559889\",\n \"0.65542865\",\n \"0.6554195\",\n \"0.65215546\",\n \"0.64844275\",\n \"0.6484227\",\n \"0.64589953\",\n \"0.6457893\",\n \"0.6446397\",\n \"0.6441969\",\n \"0.6429045\",\n \"0.639269\",\n \"0.6382177\",\n \"0.63117296\",\n \"0.6291355\",\n \"0.62845725\",\n \"0.6280303\",\n \"0.62712806\",\n \"0.62509006\",\n \"0.62476456\",\n \"0.62449867\",\n \"0.6243475\",\n \"0.624268\",\n \"0.6206099\",\n \"0.62042826\",\n \"0.61739314\",\n \"0.61731005\",\n \"0.6155436\",\n \"0.61500543\",\n \"0.6144375\",\n \"0.61397177\",\n \"0.61386114\",\n \"0.61386114\",\n \"0.61386114\",\n \"0.61386114\",\n \"0.61386114\",\n \"0.61386114\",\n \"0.6118273\",\n \"0.6117178\",\n \"0.61061317\",\n \"0.6104838\",\n \"0.609766\",\n \"0.6093743\",\n \"0.6088006\",\n \"0.60875094\",\n \"0.6080044\",\n \"0.6080044\",\n \"0.6080044\",\n \"0.6080044\",\n \"0.6080044\",\n \"0.6080044\",\n \"0.6080044\",\n \"0.6080044\",\n \"0.60796356\",\n \"0.6062388\",\n \"0.60555977\",\n \"0.60503805\",\n \"0.60483325\",\n \"0.6023364\",\n \"0.6013485\",\n \"0.6012509\",\n \"0.600199\",\n \"0.59915775\",\n \"0.59852237\",\n \"0.5982926\",\n \"0.5981618\",\n \"0.5981509\",\n \"0.5981509\",\n \"0.5981509\",\n \"0.5981509\",\n \"0.5981509\",\n \"0.59800524\",\n \"0.597805\",\n \"0.5973813\",\n \"0.59710586\",\n \"0.59710586\",\n \"0.5969996\",\n \"0.59626514\",\n \"0.59593433\",\n \"0.59572405\",\n \"0.59567654\",\n \"0.59559375\",\n \"0.5953282\",\n \"0.5953109\",\n \"0.5948291\",\n \"0.5939889\",\n \"0.59323657\",\n \"0.5929308\",\n \"0.59283864\",\n \"0.59232813\",\n \"0.59210604\",\n \"0.5919722\",\n \"0.5913913\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":93894,"cells":{"query":{"kind":"string","value":"Get the latest data from aREST device."},"document":{"kind":"string","value":"def update(self):\n try:\n response = requests.get('{}/digital/{}'.format(\n self._resource, self._pin), timeout=10)\n self.data = {'state': response.json()['return_value']}\n except requests.exceptions.ConnectionError:\n _LOGGER.error(\"No route to device '%s'. Is device offline?\",\n self._resource)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def read_rest(self):\n # PROTECTED REGION ID(AsyncTabata.rest_read) ENABLED START #\n return self._rest\n # PROTECTED REGION END # // AsyncTabata.rest_read","def get_latest_data():\n try:\n print '\\nRequesting new data.....\\n'\n response = get(\"https://api.myjson.com/bins/2csub\")\n if response.status_code is 200:\n print '\\nSuccess (200) in downloading data\\n'\n current_json = response.json()\n set_backup_data(current_json)\n else: \n current_json = get_backup_data()\n except ConnectionError:\n current_json = get_backup_data()\n return current_json","def get(self, data):\n ret = self._rest_call({}, 'GET')\n return json.loads(ret[2])","def get(self):\n try:\n log.debug(\"Device info : \")\n #get the payload to influx DB\n url = \"http://localhost:8086/query\"\n querystring = {\"pretty\": \"true\", \"db\": \"IOT\",\n \"q\":\"SELECT DISTINCT(deviceId) FROM(SELECT deviceId,q1 FROM \\\"ttd_devices\\\" ) \" }\n response = requests.request(\"GET\", url, params=querystring)\n r_d=json.loads(response.text)\n result_d=[]\n for rec in r_d['results'][0]['series']:\n for element in rec['values']:\n result_d.append(element[1])\n result={}\n result['status'] = 1\n result['message']=result_d\n return_status = 200\n except ValueError as e:\n result = {}\n log.exception('Value Exception while fetching device list')\n result['status'] = 0\n return_status = 400\n result['message'] = e.args[0]\n except :\n result = {}\n log.exception('Exception while fetching the device data')\n return_status = 500\n result['status'] = 0\n result['message'] = 'Internal Error has occurred while fetching devie data'\n finally:\n resp = Response(json.dumps(result), status=return_status, mimetype=\"application/json\")\n return resp","def getTheData(self, dev):\n if self.debugLevel >= 2 and self.debug:\n self.debugLog(u\"getTheData FrontViewAPI method called.\")\n\n # dev.updateStateOnServer('deviceIsOnline', value=True, uiValue=\"Download\")\n try:\n url = 'http://' + dev.pluginProps['sourceXML'] + '/FrontView'\n r = requests.get(url,timeout=5)\n result = r.json()\n if self.debugLevel >= 2 and self.debug:\n self.debugLog(u\"Result:\" + unicode(result))\n self.WaitInterval = 1\n dev.updateStateOnServer('deviceIsOnline', value=True, uiValue=\"Online\")\n dev.setErrorStateOnServer(None)\n # dev.updateStateOnServer('deviceTimestamp', value=t.time())\n return result\n\n except Exception as error:\n\n indigo.server.log(u\"Error connecting to Device:\" + dev.name)\n self.WaitInterval = 60\n if self.debugLevel >= 2 and self.debug:\n self.debugLog(u\"Device is offline. No data to return. \")\n dev.updateStateOnServer('deviceIsOnline', value=False, uiValue=\"Offline\")\n # dev.updateStateOnServer('deviceTimestamp', value=t.time())\n dev.setErrorStateOnServer(u'Offline')\n result = \"\"\n return result","def get_data():\r\n try:\r\n BME280_TEMPERATURE.labels(device_name=device_name).set(bme280.temperature)\r\n except Exception as e:\r\n logger.error(\"Failed to update temperature. Exception: {}\".format(e))\r\n try:\r\n BME280_RELATIVE_HUMIDITY.labels(device_name=device_name).set(bme280.relative_humidity)\r\n except Exception as e:\r\n logger.error(\"Failed to update relative humidity. Exception: {}\".format(e))\r\n try:\r\n BME280_PRESSURE.labels(device_name=device_name).set(bme280.pressure)\r\n except Exception as e:\r\n logger.error(\"Failed to update pressure. Exception: {}\".format(e))\r\n try:\r\n BME280_ALTITUDE.labels(device_name=device_name).set(bme280.altitude)\r\n except Exception as e:\r\n logger.error(\"Failed to update altitude. Exception: {}\".format(e))\r\n\r\n return Response(generate_latest(), mimetype=CONTENT_TYPE_LATEST)","def get_all(self):\n try:\n return self.current_data\n except:\n print('No data received from sensor')","def do_last_reading(self, device):\n if hasattr(self, device):\n print_info('*' * 80)\n print(\"{}:\".format(device.upper()))\n\n rec = None\n if device == 'weather':\n rec = self.db.current.find_one({'type': 'weather'})\n elif device == 'environment':\n rec = self.db.current.find_one({'type': 'environment'})\n\n pprint(rec)\n print_info('*' * 80)","def last_update(self):\r\n request = http.Request('GET', '/metadata/last_update.json')\r\n return request, parsers.parse_json","def getLatestData(self):\n jsonText = self.session.get(self.jsonURL).text\n\n # Somehow, the output I am getting has some garbage at the beginning.\n # So, skipping all text before first instance of \"{\".\n jsonText = jsonText[jsonText.find(\"{\"):]\n latestData = json.loads(jsonText)\n return latestData","def get(self, latest=None, start_date=None, end_date=None):\r\n params = base.get_params(None, locals())\r\n request = http.Request('GET', self.get_url(), params)\r\n\r\n return request, parsers.parse_json","def fetchRest(self, *args, **kwargs):\n return KorAPClient.fetchRest(self, *args, **kwargs)","def get(self, request, pk):\n sensor_obj = get_object_or_404(SensorData, id=pk)\n serializer = SensorDataSerializer(sensor_obj)\n return Response(serializer.data, 200)","def get(self):\n\t\treturn self.connection.get(self.base_uri)","def get(self, data):\n pass","def refresh(self):\n self.dto = self.res.get()\n log.debug(f\"Refreshed {self.url}\")","def get_data(last):\n Table = \"ServerRoom\"\n filter = \"\"\n if last == \"lastone\":\n data = request_meteodata(\"SELECT * from `ServerRoom` ORDER BY id DESC LIMIT 1 \")\n if len(data) == 0:\n return [SensorData(datetime.datetime.now(), 0, 0)]\n res = []\n for d in data:\n res.append(SensorData(d[1], d[2], d[3]))\n return res\n if last != \"All\":\n limit = datetime.datetime.now().astimezone(utz)\n if last == \"24hours\":\n limit -= datetime.timedelta(hours=24)\n else:\n limit = limit.replace(hour=0, minute=0, second=0, microsecond=0)\n if last == \"3days\":\n limit -= datetime.timedelta(days=3)\n elif last == \"7days\":\n limit -= datetime.timedelta(days=7)\n elif last == \"month\":\n limit = limit.replace(day=1)\n elif last == \"30days\":\n limit -= datetime.timedelta(days=30)\n elif last == \"year\":\n limit = limit.replace(day=1, month=1)\n filter = \" WHERE `date` > '\" + str(limit) + \"'\"\n order = \" ORDER BY `date` ASC\"\n req = \"SELECT * FROM `\" + Table + \"`\" + filter + order\n data = request_meteodata(req)\n if len(data) == 0:\n print(\"no data: get all\")\n req = \"SELECT * FROM `\" + Table + \"`\" + order\n data = request_meteodata(req)\n res = []\n for d in data:\n res.append(SensorData(d[1], d[2], d[3]))\n return res","def get():","def get():","def get_latest_ts_data(self):\n\n from .timeseriesdata import TimeSeriesData\n\n try:\n data = TimeSeriesData.objects.filter(\n sensor=self,\n ).latest(\"ts\")\n except TimeSeriesData.DoesNotExist:\n # If the device hasn't made any timeseries data yet.\n return {}\n\n return data","def get(self):\r\n return http.Request('GET', self.get_url()), parsers.parse_json","def get(self):\r\n return http.Request('GET', self.get_url()), parsers.parse_json","def last(self):\n data = self._http_get(\"last\")\n return data.json()","def _rest_call(self, data, action):\n path = '/wm/device/?ipv4=' + data\n conn = httplib.HTTPConnection(self.host, self.port)\n conn.request('GET', path)\n response = conn.getresponse()\n ret = (response.status, response.reason, response.read())\n conn.close()\n return ret","def main():\n print get_latest_data()","async def last_read(self):\n try:\n asyncio.set_event_loop(self.loop)\n asyncio.get_event_loop().create_task(self.browse())\n await self.browse()\n\n # parse the return reads and extract the most recent one\n # (i.e. last not None)\n jsonResponse = json.loads(self.raw_data)\n lastRead = None\n for read in jsonResponse['reads']:\n if read['value'] is None:\n break\n lastRead = read\n _LOGGER.debug(\"lastRead = %s\", lastRead)\n\n self.startTime = lastRead['startTime']\n self.endTime = lastRead['endTime']\n self.last_read_val = lastRead['value']\n self.unit_of_measurement = jsonResponse['unit']\n\n _LOGGER.debug(\"last read = %s %s %s %s\", self.startTime, self.endTime, self.last_read_val, self.unit_of_measurement)\n\n return self.startTime, self.endTime, self.last_read_val, self.unit_of_measurement\n except:\n raise MeterError(\"Error requesting meter data\")","def get(self, request, *args, **kwargs):\n self.request = request\n device = self.get_object()\n\n response_data = (\"\", \"-----\")\n\n try:\n zb_model = apps.get_model(\"zigbee\", \"ZigbeeDevice\")\n metadata = zb_model.objects.get_metadata_fields(device)\n\n if metadata.first():\n response_data = metadata\n except (AssertionError, TypeError) as ex:\n logger.info(\"Device has no metadata - %s - %s\", device, ex)\n\n json_data = list(response_data)\n return JsonResponse({\"data\": json_data}, safe=False)","async def get(self):\n\n pass","async def get_data(self, endpoint):\n try:\n with async_timeout.timeout(5, loop=self._loop):\n response = await self._session.get(f\"{endpoint}\")\n\n _LOGGER.debug(\"Response from Dingz device: %s\", response.status)\n self.data = await response.json()\n _LOGGER.debug(self.data)\n except (asyncio.TimeoutError, aiohttp.ClientError):\n _LOGGER.error(\"Can not load data from Dingz device\")\n self.data = None\n raise exceptions.DingzConnectionError()","def getData(tme=currentTime):\n # attempts request 10 times\n for attempt in range(10):\n try:\n # make a request to the url and return it in json format\n url = \"https://api.darksky.net/forecast/%s/%s,%s,%s?exclude=minutely,hourly,daily,alerts,flags\" % (API_KEY, LAT, LNG, tme)\n return get(url).json()\n except:\n # Wait .05 seconds and try again\n sleep(.05)\n pass","def get(self):\n return self.get_data()","def request_device_readings_max(device_uuid):\n\n # Set the db that we want and open the connection\n if app.config['TESTING']:\n conn = sqlite3.connect('test_database.db')\n else:\n conn = sqlite3.connect('database.db')\n conn.row_factory = sqlite3.Row\n cur = conn.cursor()\n # Execute the query\n cur.execute('select MAX(value) from readings where device_uuid=\"{}\"'.format(device_uuid))\n rows = cur.fetchall()\n\n # Return the JSON\n return jsonify([dict(zip(['value'], row)) for row in rows]), 200","def get_all(self):\n result_get = GetRest(function = self.function).performRequest()\n return result_get","def bring(self, controller='', device='', limit=-1, lastTime=0):\n self.dp.URL = self.URL\n return self.dp.getData(controller=controller, device=device, limit=limit, lastTime=lastTime)","def test_device_readings_get_temperature(self):\n request = self.client().get('/devices/{}/readings/?type=temperature'.format(self.device_uuid))\n\n self.assertEqual(len(request.json), 3)","async def fetch_data(self) -> T:","def latest_data(self):\n if self._data:\n return self._data[0]\n return None","def get_json(host, mqtt_port, rest_port, url):\n\n if url is None:\n url = \"http://\" + host + \":\" + str(rest_port) + \\\n \"/rest/v1/things\"\n click.echo(\"Getting json code from url \" + url + \".\")\n service = Service(host, mqtt_port, rest_port)\n json_code = service.get_data_with_http(url)\n if json_code is not None:\n click.secho(\"Successfully retrieved the json code!\", fg=\"green\",\n bold=True)\n click.echo(json_code)\n else:\n click.secho(\"There was an error retrieving the json code!\",\n fg=\"red\", bold=True)","def get_one(self, timestamp):\n return self._connection.get(self._dbname + '/' + timestamp).json()","def get(self):\n # Check if last_refreshed is saved in the storage\n if \"last_refreshed\" in storage:\n self.refresh_infos(storage[\"last_refreshed\"])\n else:\n # last_refreshed not found in the storage\n soccerInfo = SoccerInfo.objects.first()\n if soccerInfo and soccerInfo[\"last_refreshed\"]:\n self.refresh_infos(soccerInfo[\"last_refreshed\"])\n else:\n self.refresh_infos()\n return storage[\"cached_soccerInfos\"], 200","def latest_data(self):\n if self._data:\n return self._data\n return None","def query_rest_server(ts1, ts2, data_url, idstr):\n s = requests.Session()\n options = {'t1': int(ts1), 't2': int(ts2), 'flavor': 'raw', 'n': 1}\n uri = \"{}/data/?id={}\".format(data_url, idstr)\n t_start = time.time()\n try:\n resp = s.get(uri, params=options)\n except requests.ConnectionError as e:\n logging.error('ConnectionError: %s', e)\n logging.error('check status of ssh tunnel to trending server')\n if resp.status_code != 200:\n logging.error('invalid response %s from Trending Server',\n resp.status_code)\n return None\n logging.debug('URL=%s', resp.url)\n logging.debug('#---->%s: dt=%.3f', \"channels\", (time.time() - t_start))\n s.close()\n return resp.content","def refresh(request):\n \n t, created = LastUpdate.objects.get_or_create(id=1)\n if created:\n t.save()\n t = t.unix_time()\n else:\n t = t.unix_time()\n latest = int(t) - 2\n return JsonResponse({'latest':int(latest)})","def request_device_readings(device_uuid):\n\n # Set the db that we want and open the connection\n if app.config['TESTING']:\n conn = sqlite3.connect('test_database.db')\n else:\n conn = sqlite3.connect('database.db')\n conn.row_factory = sqlite3.Row\n cur = conn.cursor()\n \n if request.method == 'POST':\n # Grab the post parameters\n post_data = json.loads(request.data)\n sensor_type = post_data.get('type')\n value = post_data.get('value')\n date_created = post_data.get('date_created', int(time.time()))\n\n # Insert data into db\n cur.execute('insert into readings (device_uuid,type,value,date_created) VALUES (?,?,?,?)',\n (device_uuid, sensor_type, value, date_created))\n \n conn.commit()\n\n # Return success\n return 'success', 201\n else:\n # Execute the query\n cur.execute('select * from readings where device_uuid=\"{}\"'.format(device_uuid))\n rows = cur.fetchall()\n\n # Return the JSON\n return jsonify([dict(zip(['device_uuid', 'type', 'value', 'date_created'], row)) for row in rows]), 200","def get_last_data(request):\n\n return_list = []\n if request.POST:\n try:\n conn = psycopg2.connect(dbname=DB_NAME,\n user=DB_USER,\n password=DB_PASSWORD,\n host=DB_HOST)\n cur = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n\n cur.execute(\"SELECT sn, time, icsmain, icsz1, icsz2, icsz3, icsboiler, t1, t2, t3, t4, t5, t6, t7,\"\n \"rt1, rt2, rt3, endswitch, weather FROM devicedata \"\n \"WHERE sn='BRANDON_SN_US' \"\n \"ORDER BY id DESC LIMIT 5\")\n\n get_device_data = cur.fetchall()\n\n cur.close()\n conn.close()\n\n for info in get_device_data:\n item_dict = {\"sn\": info[0], \"time\": str(info[1]), \"icsmain\": info[2], \"icsz1\": info[3], \"icsz2\": info[4],\n \"icsz3\": info[5], \"icsboiler\": info[6], \"t1\": info[7], \"t2\": info[8], \"t3\": info[9],\n \"t4\": info[10], \"t5\": info[11], \"t6\": info[12], \"t7\": info[13], \"rt1\": info[14], \"rt2\": info[15],\n \"rt3\": info[16], \"endswitch\": info[17]}\n return_list.append(item_dict)\n except:\n pass\n\n return JsonResponse(return_list, safe=False)","async def get_device_list(self):\n self.logger.debug(\"Retrieving device list information.\")\n #url = 'https://{}/api/user/device'.format(self.apiHost) #suddenly stopped worrking, so use\n '''\n #full version\n url = 'https://{}/api/user/device?lang=en&apiKey={}&getTags=1&version={}&ts={}&nonce={}&appid={}&imei={}&os={}&model={}&romVersion={}&appVersion={}'.format(self.apiHost,\n self.apikey,\n self.timestamp,\n self._version,\n self._nonce,\n self._appid,\n self._imei,\n self._os,\n self._model,\n self._romVersion,\n self._appVersion)\n '''\n url = 'https://{}/api/user/device?version={}&appid={}'.format(self.apiHost, self._version, self._appid)\n headers = {\n 'Authorization': 'Bearer %s' % self.authenticationToken,\n }\n self.logger.debug('url: %s, headers: %s' % (url, headers))\n async with ClientSession() as session:\n async with session.get(url, headers=headers) as response:\n json_response = await response.json()\n \n self.logger.debug('received response status: %s' % response.status) \n self.logger.debug('received response: %s' % self.pprint(json_response))\n if response.status != 200:\n self.logger.error('error: %s received' % response.status)\n return\n \n if json_response.get(\"devicelist\"):\n self.logger.info('New response format found')\n json_response = json_response[\"devicelist\"]\n \n self.logger.debug('number of device(s) is: %d' % len(json_response))\n \n self._devices = json_response #list of devices and current configurations\n \n self._create_client_devices()\n \n '''\n Example Response:\n [\n {\n \"__v\": 0,\n \"_id\": \"5becffa6d2b4a3c34cb79b38\",\n \"apikey\": \"530303a6-cf2c-4246-894c-xxxxxxxxxxx\",\n \"brandName\": \"AUTOSLIDE\",\n \"createdAt\": \"2018-11-15T05:09:58.341Z\",\n \"deviceStatus\": \"\",\n \"deviceUrl\": \"\",\n \"deviceid\": \"100050xxxxx\",\n \"devicekey\": \"4123ec79-d2c3-4d32-930a-xxxxxxxxxxxxx\",\n \"extra\": {\n \"_id\": \"xxxxxxxxxxxxxxxx\",\n \"extra\": {\n \"apmac\": \"xx:xx:xx:xx:xx:xx\",\n \"brandId\": \"5a6fcf00f620073c67efc280\",\n \"description\": \"20180813001\",\n \"mac\": \"xx:xx:xx0:xx:xx:xx\",\n \"manufacturer\": \"\\u9752\\u5c9b\\u6fb3\\u601d\\u5fb7\\u667a\\u80fd\\u95e8\\u63a7\\u7cfb\\u7edf\\u6709\\u9650\\u516c\\u53f8\",\n \"model\": \"PSA-BTA-GL\",\n \"modelInfo\": \"5af3f5332c8642b001540dac\",\n \"ui\": \"\\u63a8\\u62c9\\u5ba0\\u7269\\u95e8\",\n \"uiid\": 54\n }\n },\n \"group\": \"\",\n \"groups\": [],\n \"ip\": \"xxx.xx.xx.xxx\",\n \"location\": \"\",\n \"name\": \"Patio Door\",\n \"offlineTime\": \"2018-12-31T07:23:31.018Z\",\n \"online\": true,\n \"onlineTime\": \"2018-12-31T12:19:33.216Z\",\n \"params\": {\n \"a\": \"3\",\n \"b\": \"3\",\n \"c\": \"1\",\n \"d\": \"1\",\n \"e\": \"1\",\n \"f\": \"1\",\n \"fwVersion\": \"2.0.2\",\n \"g\": \"0\",\n \"h\": \"1\",\n \"i\": \"0\",\n \"j\": \"00\",\n \"k\": \"0\",\n \"l\": \"1\",\n \"m\": \"2\",\n \"n\": \"0\",\n \"rssi\": -53,\n \"staMac\": \"xx:xx:xx:xx:xx:xx\"\n },\n \"productModel\": \"WFA-1\",\n \"settings\": {\n \"alarmNotify\": 1,\n \"opsHistory\": 1,\n \"opsNotify\": 0\n },\n \"sharedTo\": [\n {\n \"note\": \"\",\n \"permit\": 15,\n \"phoneNumber\": \"e-mail@gmail.com\",\n \"shareTime\": 1542259546087\n }\n ],\n \"showBrand\": true,\n \"type\": \"10\",\n \"uiid\": 54\n }\n ]\n \n or New format:\n {\n \"devicelist\": [\n {\n \"__v\": 0,\n \"_id\": \"5c3665d012d28ae6ba4943c8\",\n \"apikey\": \"530303a6-cf2c-4246-894c-50855b00e6d8\",\n \"brandLogoUrl\": \"https://us-ota.coolkit.cc/logo/KRZ54OifuGmjoEMxT1YYM3Ybu2fj5K2C.png\",\n \"brandName\": \"Sonoff\",\n \"createdAt\": \"2019-01-09T21:21:20.402Z\",\n \"devConfig\": {},\n \"devGroups\": [],\n \"deviceStatus\": \"\",\n ... as before\n '''","def get(self, start=None, limit=None):\r\n params = base.get_params(None, locals())\r\n return http.Request('GET', self.get_url(), params), parsers.parse_json","def getAll(self):\n result_get = GetRest(function = self.function).performRequest()\n return result_get","async def get(self):\n raise NotImplementedError()","def latest_release_get():\n try:\n return json_response.success({'version': version.latest_version()})\n except version.Error as e:\n return json_response.error(str(e)), 200","def get(self):\n dev = self.request.get('device')\n reg = self.request.get('registry')\n states = self.request.get('states')\n\n self.response.headers['Content-Type'] = 'text/plain'\n if (not dev) or len(dev)==0:\n self.response.write('parameter dev not found')\n elif (not reg) or len(reg)==0:\n self.response.write('parameter reg not found')\n elif (not states) or states <= 0:\n self.response.write('invalid or no states found')\n else:\n # Get user account\n ds = Datastore()\n user = ds.get_registry(reg)\n if len(user) == 0:\n self.response.write(\"Registry does not exist\")\n else:\n region = get_region_from_user(user)\n\n # Add Device on IOT Core\n iot = IOT()\n success, message = iot.get_data(dev, reg, states, region)\n if success:\n try:\n for msg in message['deviceStates']:\n msg['binaryData'] = decode64(msg['binaryData'])\n self.response.write(message)\n except:\n self.response.write([])\n else:\n self.response.write(message)","def request_device_readings_max(device_uuid):\n\n if request.data:\n post_data = json.loads(request.data)\n type = post_data.get('type', None)\n if not type or type not in ('temperature', 'humidity'):\n return 'error on the required type data', 400\n start = post_data.get('start', None)\n end = post_data.get('end', None)\n else:\n return 'missing data in the request parameters', 400\n\n # Set the db that we want and open the connection\n if app.config['TESTING']:\n conn = sqlite3.connect('test_database.db')\n else:\n conn = sqlite3.connect('database.db')\n conn.row_factory = sqlite3.Row\n cur = conn.cursor()\n\n sql = 'SELECT *, MAX(r.value) from readings r WHERE r.type = ? AND r.device_uuid = ?'\n params = [type, device_uuid]\n if start:\n sql += 'AND r.date_created >= ?'\n params += [start]\n if end:\n sql += 'AND r.date_created <= ?'\n params += [end]\n\n # Execute the query\n cur.execute(sql, params)\n row = cur.fetchone()\n\n if not row[0]:\n return 'No results found', 200\n\n # Return the JSON\n return jsonify(dict(zip(['device_uuid', 'type', 'value', 'date_created'], row))), 200","def get_data(link):\n data = re.get(link)\n jsondata = data.json()\n for weatherstation in jsondata['weatherStations']:\n FetchandStore.sensordict.update({weatherstation[\"id\"]:weatherstation[\"sensorValues\"]})\n for sensorvalue in weatherstation[\"sensorValues\"]:\n FetchandStore.sensors.append({\"id\": sensorvalue[\"roadStationId\"], \"name\": sensorvalue[\"oldName\"],\n \"value\": sensorvalue[\"sensorValue\"], \"unit\": sensorvalue[\"sensorUnit\"],\n \"datetime\": sensorvalue[\"measuredTime\"]})\n return FetchandStore.sensors","def next(self):\n self.read()\n response = {\n 'humidity': self.humidity,\n 'temperature': self.temperature\n }\n return response","def GET_sensors(self):\n self.sensors.GetAll()\n self.sensors.sensors['charging-state'] = \\\n pyrobot.CHARGING_STATES[self.sensors.sensors['charging-state']]\n print simplejson.dumps(self.sensors.sensors)","def data(self):\r\n if self.metadata is None:\r\n self.refresh()\r\n return self.metadata","def data(self):\n self._get_latest_content()\n return self._data.get('data', {})","async def get_device_data(self):\n pass","def fetch_data(self):","def get_data(self):\n self._send_command(self._adapter.get_data())","def get_data_from_endpoint(self, from_, to_, endpoint):\n endpoint = self.make_endpoint(endpoint)\n from_, to_ = str(from_), str(to_)\n payload = {\n 'auth': self.auth_token,\n 'id': self.monitor_id,\n 'start': from_,\n 'end': to_,\n 'extendLimit': 'true',\n 'fullContents': 'true'\n }\n\n r = self.session.get(endpoint, params=payload)\n ratelimit_remaining = r.headers['X-RateLimit-Remaining']\n #print ('Remaining Ratelimit = ' + str(ratelimit_remaining))\n\n # If the header is empty or 0 then wait for a ratelimit refresh.\n if (not ratelimit_remaining) or (float(ratelimit_remaining) < 1):\n #print('Waiting for ratelimit refresh...')\n sleep(self.ratelimit_refresh)\n\n return r","def get(self):\n devs = Device.query.all()\n lista = []\n for d in devs:\n lista.append(d.json())\n return lista","def get(self):\n self.finish(json.dumps(self.build_response_dict()))","def get(self, path):\n base = 'http://%s:%d' % (self.host, self.port)\n url = '%s/%s' % (base, path)\n\n conn = urllib2.urlopen(url)\n payload = conn.read()\n try: \n payload = json.loads(payload)\n except:\n pass\n\n conn.close()\n\n return payload","def _get(self, endpoint):\n res = self._request(\"get\", endpoint)\n if not res.content:\n return {}\n try:\n res = res.json()\n except ValueError:\n raise ValueError(\"Cannot parse {} as JSON\".format(res))\n if \"error\" in res:\n raise AirthingsError(res[\"error\"])\n return res","def download_data(self, format = 'srt'):\n resp, content = httplib2.Http(\".cache\").request(self.url, \"GET\")\n suburl = json.loads(content)['url']\n resp, content = httplib2.Http(\".cache\").request(suburl, \"GET\")\n\n return content","def refresh_details(self) -> 'outputs.RefreshDetailsResponse':\n return pulumi.get(self, \"refresh_details\")","def getLastData(self) -> ghidra.program.model.listing.Data:\n ...","def fetch_timestamp(self):\r\n return self.__public_request('GET', '/api/v1/timestamp')","def apicall():\r\n# try:\r\n print request.get_json()\r\n test_json = request.get_json()\r\n logger.info(\"input json object loaded\")\r\n logger.info(test_json)\r\n k=MetaData(test_json)\r\n int_res=k.getData()\r\n print '------------------------------'\r\n print int_res\r\n return jsonify(int_res)","def test_get_measurement_history(self):\n device = DeviceFactory(node=Node.objects.first(), external_id='123', type__code=SecureDeviceType.SRT321,\n device_param__type__code=SecureDeviceParameterType.MEASURED_TEMPERATURE)\n d_id_1 = device.external_id\n\n now_loc = datetime.datetime.now(bst)\n ts_loc = now_loc - datetime.timedelta(seconds=30)\n ts_str = ts_loc.strftime('%Y-%m-%dT%H:%M:%S')\n\n data = self.create_secure_server_push_data(d_id_1, ts_str)\n\n SecureClient.process_push_data(data)\n time.sleep(.5)\n\n # get newer timestamp\n ts_str = now_loc.strftime('%Y-%m-%dT%H:%M:%S')\n data = self.create_secure_server_push_data(d_id_1, ts_str, value=\"23.5\")\n\n SecureClient.process_push_data(data)\n\n token = Token.objects.get(user__username=email)\n device_param = device.parameters.first()\n client = APIClient()\n client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)\n url = reverse('api:device_measurements', kwargs={'device_parameter_id': device_param.id})\n\n time.sleep(.5)\n\n response = client.get(url, format='json')\n\n self.assertTrue(response.status_code == 200)\n self.assertTrue(len(response.data) >= 2)","def lastest_datapoint(instance_name: str):\n if request.method == \"GET\":\n with lock:\n if not instances.get(instance_name):\n return jsonify(message=\"Instance Does Not Exist\"), 404\n return jsonify(asdict(instances.get(instance_name).latest()))\n return Response(status=200)","def send_get(self, uri, data=None, ojson=None):\n return self.__send_request('GET', uri=uri, data=data, ojson=ojson)","def flask_get_devices():\n try:\n # retrieve the authorization token\n token = retrieve_auth_token(request)\n\n # retrieve pagination\n page_number, per_page = get_pagination(request)\n\n params = {\n 'page_number': page_number,\n 'per_page': per_page,\n 'sortBy': request.args.get('sortBy', None),\n 'attr': request.args.getlist('attr'),\n 'attr_type': request.args.getlist('attr_type'),\n 'label': request.args.get('label', None),\n 'template': request.args.get('template', None),\n 'idsOnly': request.args.get('idsOnly', 'false'),\n }\n\n result = DeviceHandler.get_devices(token, params)\n LOGGER.info(f' Getting latest added device(s).')\n\n return make_response(jsonify(result), 200)\n except HTTPRequestError as e:\n LOGGER.error(f' {e.message} - {e.error_code}.')\n if isinstance(e.message, dict):\n return make_response(jsonify(e.message), e.error_code)\n\n return format_response(e.error_code, e.message)","def get_data():\n pass","def user_sends_get_call_to_the_devices():\n web_app.list_devices()","def get(self):\r\n return self.data","def request_device_readings(device_uuid):\n\n # Set the db that we want and open the connection\n if app.config['TESTING']:\n conn = sqlite3.connect('test_database.db')\n else:\n conn = sqlite3.connect('database.db')\n conn.row_factory = sqlite3.Row\n cur = conn.cursor()\n\n if request.method == 'POST':\n # Grab the post parameters\n if request.data:\n post_data = json.loads(request.data)\n else:\n return 'missing data in the request parameters', 400\n\n sensor_type = post_data.get('type', None)\n value = post_data.get('value', None)\n date_created = post_data.get('date_created', int(time.time()))\n\n if not sensor_type or sensor_type not in ('temperature', 'humidity'):\n return 'the sensor type is not valid', 400\n\n if not value or 100 < value or 0 > value:\n return 'the sensor value is not in the mandatory range of 0-100', 400\n\n # Insert data into db\n cur.execute('INSERT INTO readings (device_uuid,type,value,date_created) VALUES (?,?,?,?)',\n (device_uuid, sensor_type, value, date_created))\n\n conn.commit()\n\n # Return success\n return 'success', 201\n else:\n # Grab the query parameters (if any)\n start = end = type = None\n if request.data:\n post_data = json.loads(request.data)\n start = post_data.get('start', None)\n end = post_data.get('end', None)\n type = post_data.get('type', None)\n\n sql = 'SELECT * from readings WHERE device_uuid = ?'\n params = [device_uuid]\n if start:\n sql += 'AND date_created >= ?'\n params += [start]\n if end:\n sql += 'AND date_created <= ?'\n params += [end]\n if type:\n sql += 'AND type = ?'\n params += [type]\n\n # Execute the query\n cur.execute(sql, params)\n rows = cur.fetchall()\n\n # Return the JSON\n return jsonify([dict(zip(['device_uuid', 'type', 'value', 'date_created'], row)) for row in rows]), 200","def fetch_data(self, sensor, address, now_utc, now):\n poller = MiFloraPoller(address, GatttoolBackend)\n ts_utc = int(now_utc.strftime(\"%s\"))\n temp_sensor = self.sensor_template.copy()\n temp_sensor['mac'] = address\n temp_sensor['name'] = sensor\n # liefert den Temperaturwert (in Grad Celsius)\n temp_sensor['temperature'] = poller.parameter_value(\"temperature\")\n # gibt die lichtstaerke an\n temp_sensor['light'] = poller.parameter_value(\"light\")\n # gibt die Feuchtigkeit an\n temp_sensor['moisture'] = poller.parameter_value(\"moisture\")\n # gibt die Leitfaehigkeit des Bodens an.\n temp_sensor['conductivity'] = poller.parameter_value(\"conductivity\")\n temp_sensor['battery'] = poller.parameter_value(\"battery\")\n temp_sensor['date_iso'] = now.replace(microsecond=0).isoformat()\n temp_sensor['ts_utc'] = ts_utc\n # poller.firmware_version() # – liefert die aktuelle Firmware Version als Text.\n temp_sensor['firmware'] = poller.firmware_version()\n return temp_sensor","async def async_update(self):\n try:\n self._data = requests.get(self._build_url(), timeout=10, headers={'accept-encoding': None}).json()\n _LOGGER.debug(\"TOON fetched data = %s\", self._data)\n except (requests.exceptions.RequestException) as error:\n _LOGGER.error(\"Unable to connect to TOON: %s\", error)\n self._data = None","def get(self, part, id):\r\n params = base.get_params(None, locals())\r\n\r\n request = http.Request('GET', self.get_url(), params)\r\n return request, parsers.parse_json","def get(self, limit=None, offset=None):\r\n\r\n params = base.get_params(None, locals())\r\n\r\n return http.Request('GET', self.get_url(), params), parsers.parse_json","def get(self, limit=None, offset=None):\r\n\r\n params = base.get_params(None, locals())\r\n\r\n return http.Request('GET', self.get_url(), params), parsers.parse_json","def get_devices(self):\n return self.api_request('GET', self.url + '/device', {})","def _get_raw_data(self, url, series):\n url = self._get_url(url, series)\n try:\n response = self.http.request(url, headers=self._reqheaders)\n except httplib2.ServerNotFoundError as e:\n raise TVDBConnectError(e.message), None, sys.exc_info()[2]\n rep = response[0]\n log.debug(\n 'http-status:%s,content:%s', \n rep['status'], \n rep['content-type']\n )\n if int(rep['status']) >= 400:\n raise TVDBConnectError(\n 'Failed to get \"%s\" from thetvdb. errno:%s' % (\n series, rep['status']),\n rep['status']\n ) \n return response[1]","def do_GET(self):\n print(\"do_GET: got: \" + str(self.path))\n path_split = self.path.split(\"/\", 2) # ['', 'hello', 'nikolay/uuuuer']\n username = path_split[-1] # nikolay/uuuuer\n print(\"do_GET: Let's open db_conn\")\n connection = db_open()\n birth_date = db_select(username, connection)\n db_close(connection)\n\n days_until_bday = get_days_to_bday(birth_date)\n json_obj = http_construct_json(username, days_until_bday)\n\n self.http_send_reply(json_obj)\n print(\"do_GET: DONE! \\n\")","def get(self):\n return self.request({\n \"path\": \"/\" + UUID\n })","def get():\n #Return the corresponding value\n return json_back()","def get_json(self, url, *, timeout, headers):","def test_device_readings_get_humidity(self):\n request = self.client().get('/devices/{}/readings/?type=humidity'.format(self.device_uuid))\n\n self.assertEqual(len(request.json), 1)","def get(self, path):\n req = self.session.get(self.url + path, verify=False)\n\n jdata = req.json()\n if jdata['status'] != 'ok':\n raise Exception(\"Failed to query get: \\n\" + req.text)\n\n return jdata['data']","def fetch_latest_sample(self, sensor_id):\n conn = psycopg2.connect(self.conn)\n query = \"SELECT * FROM steve_sense_sensor_logs WHERE sensor_id = '{}' ORDER BY time DESC LIMIT 1\".format(\n sensor_id)\n cur = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)\n cur.execute(query)\n data = json.loads(json.dumps(\n cur.fetchall(), use_decimal=True, default=str))\n cur.close()\n conn.close()\n return data","def get_data(self):\n\n self.set_query_string()\n self.realtime_data = super().get_data()\n self.set_coordinate()\n return self.realtime_data","def test_device_readings_get_past_dates(self):\n request = self.client().get(f'/devices/{self.device_uuid}/readings/'\n f'?start={int(self.setup_time - 75)}'\n f'&end={int(self.setup_time + 25)}')\n\n self.assertEqual(len(request.json), 2)","def pull(self):\n data = api.get(endpoint=self.endpoint, resource_id=self.id)\n self.__init__(**data)","def get_all_data():\n return jsonify(service.get_all_data())","def get(self, count=None, max_timestamp=None, min_timestamp=None,\r\n min_id=None, max_id=None):\r\n params = base.get_params(\r\n ('count', 'max_timestamp', 'min_timestamp', 'min_id', 'max_id'),\r\n locals())\r\n request = http.Request('GET', self.get_url(), params)\r\n\r\n return request, parsers.parse_json","def _get(self):\n path = self.router.central_core\n response = self.request(method=\"get\", path=path)\n return response","def get(self, limit):\r\n params = base.get_params(None, locals())\r\n\r\n request = http.Request('GET', self.get_url(), params)\r\n return request, parsers.parse_json","async def async_get(\n self, endpoint: str | None = None, entry: str | None = None\n ) -> Any:\n url = f\"{self.url}/api/\"\n if endpoint:\n url = self.endpoints[endpoint]\n if entry:\n url = f\"{url}{entry}\"\n with async_timeout.timeout(10):\n resp = await self.session.get(\n url=url,\n headers=self.headers,\n raise_for_status=True,\n )\n\n return await resp.json()","def get_data_by_uuid(request, uuid, hours):\n\tif request.method == 'GET':\n\t\treadings = get_data_by_uuid_helper(uuid, hours)\n\t\treturn Response(readings)"],"string":"[\n \"def read_rest(self):\\n # PROTECTED REGION ID(AsyncTabata.rest_read) ENABLED START #\\n return self._rest\\n # PROTECTED REGION END # // AsyncTabata.rest_read\",\n \"def get_latest_data():\\n try:\\n print '\\\\nRequesting new data.....\\\\n'\\n response = get(\\\"https://api.myjson.com/bins/2csub\\\")\\n if response.status_code is 200:\\n print '\\\\nSuccess (200) in downloading data\\\\n'\\n current_json = response.json()\\n set_backup_data(current_json)\\n else: \\n current_json = get_backup_data()\\n except ConnectionError:\\n current_json = get_backup_data()\\n return current_json\",\n \"def get(self, data):\\n ret = self._rest_call({}, 'GET')\\n return json.loads(ret[2])\",\n \"def get(self):\\n try:\\n log.debug(\\\"Device info : \\\")\\n #get the payload to influx DB\\n url = \\\"http://localhost:8086/query\\\"\\n querystring = {\\\"pretty\\\": \\\"true\\\", \\\"db\\\": \\\"IOT\\\",\\n \\\"q\\\":\\\"SELECT DISTINCT(deviceId) FROM(SELECT deviceId,q1 FROM \\\\\\\"ttd_devices\\\\\\\" ) \\\" }\\n response = requests.request(\\\"GET\\\", url, params=querystring)\\n r_d=json.loads(response.text)\\n result_d=[]\\n for rec in r_d['results'][0]['series']:\\n for element in rec['values']:\\n result_d.append(element[1])\\n result={}\\n result['status'] = 1\\n result['message']=result_d\\n return_status = 200\\n except ValueError as e:\\n result = {}\\n log.exception('Value Exception while fetching device list')\\n result['status'] = 0\\n return_status = 400\\n result['message'] = e.args[0]\\n except :\\n result = {}\\n log.exception('Exception while fetching the device data')\\n return_status = 500\\n result['status'] = 0\\n result['message'] = 'Internal Error has occurred while fetching devie data'\\n finally:\\n resp = Response(json.dumps(result), status=return_status, mimetype=\\\"application/json\\\")\\n return resp\",\n \"def getTheData(self, dev):\\n if self.debugLevel >= 2 and self.debug:\\n self.debugLog(u\\\"getTheData FrontViewAPI method called.\\\")\\n\\n # dev.updateStateOnServer('deviceIsOnline', value=True, uiValue=\\\"Download\\\")\\n try:\\n url = 'http://' + dev.pluginProps['sourceXML'] + '/FrontView'\\n r = requests.get(url,timeout=5)\\n result = r.json()\\n if self.debugLevel >= 2 and self.debug:\\n self.debugLog(u\\\"Result:\\\" + unicode(result))\\n self.WaitInterval = 1\\n dev.updateStateOnServer('deviceIsOnline', value=True, uiValue=\\\"Online\\\")\\n dev.setErrorStateOnServer(None)\\n # dev.updateStateOnServer('deviceTimestamp', value=t.time())\\n return result\\n\\n except Exception as error:\\n\\n indigo.server.log(u\\\"Error connecting to Device:\\\" + dev.name)\\n self.WaitInterval = 60\\n if self.debugLevel >= 2 and self.debug:\\n self.debugLog(u\\\"Device is offline. No data to return. \\\")\\n dev.updateStateOnServer('deviceIsOnline', value=False, uiValue=\\\"Offline\\\")\\n # dev.updateStateOnServer('deviceTimestamp', value=t.time())\\n dev.setErrorStateOnServer(u'Offline')\\n result = \\\"\\\"\\n return result\",\n \"def get_data():\\r\\n try:\\r\\n BME280_TEMPERATURE.labels(device_name=device_name).set(bme280.temperature)\\r\\n except Exception as e:\\r\\n logger.error(\\\"Failed to update temperature. Exception: {}\\\".format(e))\\r\\n try:\\r\\n BME280_RELATIVE_HUMIDITY.labels(device_name=device_name).set(bme280.relative_humidity)\\r\\n except Exception as e:\\r\\n logger.error(\\\"Failed to update relative humidity. Exception: {}\\\".format(e))\\r\\n try:\\r\\n BME280_PRESSURE.labels(device_name=device_name).set(bme280.pressure)\\r\\n except Exception as e:\\r\\n logger.error(\\\"Failed to update pressure. Exception: {}\\\".format(e))\\r\\n try:\\r\\n BME280_ALTITUDE.labels(device_name=device_name).set(bme280.altitude)\\r\\n except Exception as e:\\r\\n logger.error(\\\"Failed to update altitude. Exception: {}\\\".format(e))\\r\\n\\r\\n return Response(generate_latest(), mimetype=CONTENT_TYPE_LATEST)\",\n \"def get_all(self):\\n try:\\n return self.current_data\\n except:\\n print('No data received from sensor')\",\n \"def do_last_reading(self, device):\\n if hasattr(self, device):\\n print_info('*' * 80)\\n print(\\\"{}:\\\".format(device.upper()))\\n\\n rec = None\\n if device == 'weather':\\n rec = self.db.current.find_one({'type': 'weather'})\\n elif device == 'environment':\\n rec = self.db.current.find_one({'type': 'environment'})\\n\\n pprint(rec)\\n print_info('*' * 80)\",\n \"def last_update(self):\\r\\n request = http.Request('GET', '/metadata/last_update.json')\\r\\n return request, parsers.parse_json\",\n \"def getLatestData(self):\\n jsonText = self.session.get(self.jsonURL).text\\n\\n # Somehow, the output I am getting has some garbage at the beginning.\\n # So, skipping all text before first instance of \\\"{\\\".\\n jsonText = jsonText[jsonText.find(\\\"{\\\"):]\\n latestData = json.loads(jsonText)\\n return latestData\",\n \"def get(self, latest=None, start_date=None, end_date=None):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def fetchRest(self, *args, **kwargs):\\n return KorAPClient.fetchRest(self, *args, **kwargs)\",\n \"def get(self, request, pk):\\n sensor_obj = get_object_or_404(SensorData, id=pk)\\n serializer = SensorDataSerializer(sensor_obj)\\n return Response(serializer.data, 200)\",\n \"def get(self):\\n\\t\\treturn self.connection.get(self.base_uri)\",\n \"def get(self, data):\\n pass\",\n \"def refresh(self):\\n self.dto = self.res.get()\\n log.debug(f\\\"Refreshed {self.url}\\\")\",\n \"def get_data(last):\\n Table = \\\"ServerRoom\\\"\\n filter = \\\"\\\"\\n if last == \\\"lastone\\\":\\n data = request_meteodata(\\\"SELECT * from `ServerRoom` ORDER BY id DESC LIMIT 1 \\\")\\n if len(data) == 0:\\n return [SensorData(datetime.datetime.now(), 0, 0)]\\n res = []\\n for d in data:\\n res.append(SensorData(d[1], d[2], d[3]))\\n return res\\n if last != \\\"All\\\":\\n limit = datetime.datetime.now().astimezone(utz)\\n if last == \\\"24hours\\\":\\n limit -= datetime.timedelta(hours=24)\\n else:\\n limit = limit.replace(hour=0, minute=0, second=0, microsecond=0)\\n if last == \\\"3days\\\":\\n limit -= datetime.timedelta(days=3)\\n elif last == \\\"7days\\\":\\n limit -= datetime.timedelta(days=7)\\n elif last == \\\"month\\\":\\n limit = limit.replace(day=1)\\n elif last == \\\"30days\\\":\\n limit -= datetime.timedelta(days=30)\\n elif last == \\\"year\\\":\\n limit = limit.replace(day=1, month=1)\\n filter = \\\" WHERE `date` > '\\\" + str(limit) + \\\"'\\\"\\n order = \\\" ORDER BY `date` ASC\\\"\\n req = \\\"SELECT * FROM `\\\" + Table + \\\"`\\\" + filter + order\\n data = request_meteodata(req)\\n if len(data) == 0:\\n print(\\\"no data: get all\\\")\\n req = \\\"SELECT * FROM `\\\" + Table + \\\"`\\\" + order\\n data = request_meteodata(req)\\n res = []\\n for d in data:\\n res.append(SensorData(d[1], d[2], d[3]))\\n return res\",\n \"def get():\",\n \"def get():\",\n \"def get_latest_ts_data(self):\\n\\n from .timeseriesdata import TimeSeriesData\\n\\n try:\\n data = TimeSeriesData.objects.filter(\\n sensor=self,\\n ).latest(\\\"ts\\\")\\n except TimeSeriesData.DoesNotExist:\\n # If the device hasn't made any timeseries data yet.\\n return {}\\n\\n return data\",\n \"def get(self):\\r\\n return http.Request('GET', self.get_url()), parsers.parse_json\",\n \"def get(self):\\r\\n return http.Request('GET', self.get_url()), parsers.parse_json\",\n \"def last(self):\\n data = self._http_get(\\\"last\\\")\\n return data.json()\",\n \"def _rest_call(self, data, action):\\n path = '/wm/device/?ipv4=' + data\\n conn = httplib.HTTPConnection(self.host, self.port)\\n conn.request('GET', path)\\n response = conn.getresponse()\\n ret = (response.status, response.reason, response.read())\\n conn.close()\\n return ret\",\n \"def main():\\n print get_latest_data()\",\n \"async def last_read(self):\\n try:\\n asyncio.set_event_loop(self.loop)\\n asyncio.get_event_loop().create_task(self.browse())\\n await self.browse()\\n\\n # parse the return reads and extract the most recent one\\n # (i.e. last not None)\\n jsonResponse = json.loads(self.raw_data)\\n lastRead = None\\n for read in jsonResponse['reads']:\\n if read['value'] is None:\\n break\\n lastRead = read\\n _LOGGER.debug(\\\"lastRead = %s\\\", lastRead)\\n\\n self.startTime = lastRead['startTime']\\n self.endTime = lastRead['endTime']\\n self.last_read_val = lastRead['value']\\n self.unit_of_measurement = jsonResponse['unit']\\n\\n _LOGGER.debug(\\\"last read = %s %s %s %s\\\", self.startTime, self.endTime, self.last_read_val, self.unit_of_measurement)\\n\\n return self.startTime, self.endTime, self.last_read_val, self.unit_of_measurement\\n except:\\n raise MeterError(\\\"Error requesting meter data\\\")\",\n \"def get(self, request, *args, **kwargs):\\n self.request = request\\n device = self.get_object()\\n\\n response_data = (\\\"\\\", \\\"-----\\\")\\n\\n try:\\n zb_model = apps.get_model(\\\"zigbee\\\", \\\"ZigbeeDevice\\\")\\n metadata = zb_model.objects.get_metadata_fields(device)\\n\\n if metadata.first():\\n response_data = metadata\\n except (AssertionError, TypeError) as ex:\\n logger.info(\\\"Device has no metadata - %s - %s\\\", device, ex)\\n\\n json_data = list(response_data)\\n return JsonResponse({\\\"data\\\": json_data}, safe=False)\",\n \"async def get(self):\\n\\n pass\",\n \"async def get_data(self, endpoint):\\n try:\\n with async_timeout.timeout(5, loop=self._loop):\\n response = await self._session.get(f\\\"{endpoint}\\\")\\n\\n _LOGGER.debug(\\\"Response from Dingz device: %s\\\", response.status)\\n self.data = await response.json()\\n _LOGGER.debug(self.data)\\n except (asyncio.TimeoutError, aiohttp.ClientError):\\n _LOGGER.error(\\\"Can not load data from Dingz device\\\")\\n self.data = None\\n raise exceptions.DingzConnectionError()\",\n \"def getData(tme=currentTime):\\n # attempts request 10 times\\n for attempt in range(10):\\n try:\\n # make a request to the url and return it in json format\\n url = \\\"https://api.darksky.net/forecast/%s/%s,%s,%s?exclude=minutely,hourly,daily,alerts,flags\\\" % (API_KEY, LAT, LNG, tme)\\n return get(url).json()\\n except:\\n # Wait .05 seconds and try again\\n sleep(.05)\\n pass\",\n \"def get(self):\\n return self.get_data()\",\n \"def request_device_readings_max(device_uuid):\\n\\n # Set the db that we want and open the connection\\n if app.config['TESTING']:\\n conn = sqlite3.connect('test_database.db')\\n else:\\n conn = sqlite3.connect('database.db')\\n conn.row_factory = sqlite3.Row\\n cur = conn.cursor()\\n # Execute the query\\n cur.execute('select MAX(value) from readings where device_uuid=\\\"{}\\\"'.format(device_uuid))\\n rows = cur.fetchall()\\n\\n # Return the JSON\\n return jsonify([dict(zip(['value'], row)) for row in rows]), 200\",\n \"def get_all(self):\\n result_get = GetRest(function = self.function).performRequest()\\n return result_get\",\n \"def bring(self, controller='', device='', limit=-1, lastTime=0):\\n self.dp.URL = self.URL\\n return self.dp.getData(controller=controller, device=device, limit=limit, lastTime=lastTime)\",\n \"def test_device_readings_get_temperature(self):\\n request = self.client().get('/devices/{}/readings/?type=temperature'.format(self.device_uuid))\\n\\n self.assertEqual(len(request.json), 3)\",\n \"async def fetch_data(self) -> T:\",\n \"def latest_data(self):\\n if self._data:\\n return self._data[0]\\n return None\",\n \"def get_json(host, mqtt_port, rest_port, url):\\n\\n if url is None:\\n url = \\\"http://\\\" + host + \\\":\\\" + str(rest_port) + \\\\\\n \\\"/rest/v1/things\\\"\\n click.echo(\\\"Getting json code from url \\\" + url + \\\".\\\")\\n service = Service(host, mqtt_port, rest_port)\\n json_code = service.get_data_with_http(url)\\n if json_code is not None:\\n click.secho(\\\"Successfully retrieved the json code!\\\", fg=\\\"green\\\",\\n bold=True)\\n click.echo(json_code)\\n else:\\n click.secho(\\\"There was an error retrieving the json code!\\\",\\n fg=\\\"red\\\", bold=True)\",\n \"def get_one(self, timestamp):\\n return self._connection.get(self._dbname + '/' + timestamp).json()\",\n \"def get(self):\\n # Check if last_refreshed is saved in the storage\\n if \\\"last_refreshed\\\" in storage:\\n self.refresh_infos(storage[\\\"last_refreshed\\\"])\\n else:\\n # last_refreshed not found in the storage\\n soccerInfo = SoccerInfo.objects.first()\\n if soccerInfo and soccerInfo[\\\"last_refreshed\\\"]:\\n self.refresh_infos(soccerInfo[\\\"last_refreshed\\\"])\\n else:\\n self.refresh_infos()\\n return storage[\\\"cached_soccerInfos\\\"], 200\",\n \"def latest_data(self):\\n if self._data:\\n return self._data\\n return None\",\n \"def query_rest_server(ts1, ts2, data_url, idstr):\\n s = requests.Session()\\n options = {'t1': int(ts1), 't2': int(ts2), 'flavor': 'raw', 'n': 1}\\n uri = \\\"{}/data/?id={}\\\".format(data_url, idstr)\\n t_start = time.time()\\n try:\\n resp = s.get(uri, params=options)\\n except requests.ConnectionError as e:\\n logging.error('ConnectionError: %s', e)\\n logging.error('check status of ssh tunnel to trending server')\\n if resp.status_code != 200:\\n logging.error('invalid response %s from Trending Server',\\n resp.status_code)\\n return None\\n logging.debug('URL=%s', resp.url)\\n logging.debug('#---->%s: dt=%.3f', \\\"channels\\\", (time.time() - t_start))\\n s.close()\\n return resp.content\",\n \"def refresh(request):\\n \\n t, created = LastUpdate.objects.get_or_create(id=1)\\n if created:\\n t.save()\\n t = t.unix_time()\\n else:\\n t = t.unix_time()\\n latest = int(t) - 2\\n return JsonResponse({'latest':int(latest)})\",\n \"def request_device_readings(device_uuid):\\n\\n # Set the db that we want and open the connection\\n if app.config['TESTING']:\\n conn = sqlite3.connect('test_database.db')\\n else:\\n conn = sqlite3.connect('database.db')\\n conn.row_factory = sqlite3.Row\\n cur = conn.cursor()\\n \\n if request.method == 'POST':\\n # Grab the post parameters\\n post_data = json.loads(request.data)\\n sensor_type = post_data.get('type')\\n value = post_data.get('value')\\n date_created = post_data.get('date_created', int(time.time()))\\n\\n # Insert data into db\\n cur.execute('insert into readings (device_uuid,type,value,date_created) VALUES (?,?,?,?)',\\n (device_uuid, sensor_type, value, date_created))\\n \\n conn.commit()\\n\\n # Return success\\n return 'success', 201\\n else:\\n # Execute the query\\n cur.execute('select * from readings where device_uuid=\\\"{}\\\"'.format(device_uuid))\\n rows = cur.fetchall()\\n\\n # Return the JSON\\n return jsonify([dict(zip(['device_uuid', 'type', 'value', 'date_created'], row)) for row in rows]), 200\",\n \"def get_last_data(request):\\n\\n return_list = []\\n if request.POST:\\n try:\\n conn = psycopg2.connect(dbname=DB_NAME,\\n user=DB_USER,\\n password=DB_PASSWORD,\\n host=DB_HOST)\\n cur = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\\n\\n cur.execute(\\\"SELECT sn, time, icsmain, icsz1, icsz2, icsz3, icsboiler, t1, t2, t3, t4, t5, t6, t7,\\\"\\n \\\"rt1, rt2, rt3, endswitch, weather FROM devicedata \\\"\\n \\\"WHERE sn='BRANDON_SN_US' \\\"\\n \\\"ORDER BY id DESC LIMIT 5\\\")\\n\\n get_device_data = cur.fetchall()\\n\\n cur.close()\\n conn.close()\\n\\n for info in get_device_data:\\n item_dict = {\\\"sn\\\": info[0], \\\"time\\\": str(info[1]), \\\"icsmain\\\": info[2], \\\"icsz1\\\": info[3], \\\"icsz2\\\": info[4],\\n \\\"icsz3\\\": info[5], \\\"icsboiler\\\": info[6], \\\"t1\\\": info[7], \\\"t2\\\": info[8], \\\"t3\\\": info[9],\\n \\\"t4\\\": info[10], \\\"t5\\\": info[11], \\\"t6\\\": info[12], \\\"t7\\\": info[13], \\\"rt1\\\": info[14], \\\"rt2\\\": info[15],\\n \\\"rt3\\\": info[16], \\\"endswitch\\\": info[17]}\\n return_list.append(item_dict)\\n except:\\n pass\\n\\n return JsonResponse(return_list, safe=False)\",\n \"async def get_device_list(self):\\n self.logger.debug(\\\"Retrieving device list information.\\\")\\n #url = 'https://{}/api/user/device'.format(self.apiHost) #suddenly stopped worrking, so use\\n '''\\n #full version\\n url = 'https://{}/api/user/device?lang=en&apiKey={}&getTags=1&version={}&ts={}&nonce={}&appid={}&imei={}&os={}&model={}&romVersion={}&appVersion={}'.format(self.apiHost,\\n self.apikey,\\n self.timestamp,\\n self._version,\\n self._nonce,\\n self._appid,\\n self._imei,\\n self._os,\\n self._model,\\n self._romVersion,\\n self._appVersion)\\n '''\\n url = 'https://{}/api/user/device?version={}&appid={}'.format(self.apiHost, self._version, self._appid)\\n headers = {\\n 'Authorization': 'Bearer %s' % self.authenticationToken,\\n }\\n self.logger.debug('url: %s, headers: %s' % (url, headers))\\n async with ClientSession() as session:\\n async with session.get(url, headers=headers) as response:\\n json_response = await response.json()\\n \\n self.logger.debug('received response status: %s' % response.status) \\n self.logger.debug('received response: %s' % self.pprint(json_response))\\n if response.status != 200:\\n self.logger.error('error: %s received' % response.status)\\n return\\n \\n if json_response.get(\\\"devicelist\\\"):\\n self.logger.info('New response format found')\\n json_response = json_response[\\\"devicelist\\\"]\\n \\n self.logger.debug('number of device(s) is: %d' % len(json_response))\\n \\n self._devices = json_response #list of devices and current configurations\\n \\n self._create_client_devices()\\n \\n '''\\n Example Response:\\n [\\n {\\n \\\"__v\\\": 0,\\n \\\"_id\\\": \\\"5becffa6d2b4a3c34cb79b38\\\",\\n \\\"apikey\\\": \\\"530303a6-cf2c-4246-894c-xxxxxxxxxxx\\\",\\n \\\"brandName\\\": \\\"AUTOSLIDE\\\",\\n \\\"createdAt\\\": \\\"2018-11-15T05:09:58.341Z\\\",\\n \\\"deviceStatus\\\": \\\"\\\",\\n \\\"deviceUrl\\\": \\\"\\\",\\n \\\"deviceid\\\": \\\"100050xxxxx\\\",\\n \\\"devicekey\\\": \\\"4123ec79-d2c3-4d32-930a-xxxxxxxxxxxxx\\\",\\n \\\"extra\\\": {\\n \\\"_id\\\": \\\"xxxxxxxxxxxxxxxx\\\",\\n \\\"extra\\\": {\\n \\\"apmac\\\": \\\"xx:xx:xx:xx:xx:xx\\\",\\n \\\"brandId\\\": \\\"5a6fcf00f620073c67efc280\\\",\\n \\\"description\\\": \\\"20180813001\\\",\\n \\\"mac\\\": \\\"xx:xx:xx0:xx:xx:xx\\\",\\n \\\"manufacturer\\\": \\\"\\\\u9752\\\\u5c9b\\\\u6fb3\\\\u601d\\\\u5fb7\\\\u667a\\\\u80fd\\\\u95e8\\\\u63a7\\\\u7cfb\\\\u7edf\\\\u6709\\\\u9650\\\\u516c\\\\u53f8\\\",\\n \\\"model\\\": \\\"PSA-BTA-GL\\\",\\n \\\"modelInfo\\\": \\\"5af3f5332c8642b001540dac\\\",\\n \\\"ui\\\": \\\"\\\\u63a8\\\\u62c9\\\\u5ba0\\\\u7269\\\\u95e8\\\",\\n \\\"uiid\\\": 54\\n }\\n },\\n \\\"group\\\": \\\"\\\",\\n \\\"groups\\\": [],\\n \\\"ip\\\": \\\"xxx.xx.xx.xxx\\\",\\n \\\"location\\\": \\\"\\\",\\n \\\"name\\\": \\\"Patio Door\\\",\\n \\\"offlineTime\\\": \\\"2018-12-31T07:23:31.018Z\\\",\\n \\\"online\\\": true,\\n \\\"onlineTime\\\": \\\"2018-12-31T12:19:33.216Z\\\",\\n \\\"params\\\": {\\n \\\"a\\\": \\\"3\\\",\\n \\\"b\\\": \\\"3\\\",\\n \\\"c\\\": \\\"1\\\",\\n \\\"d\\\": \\\"1\\\",\\n \\\"e\\\": \\\"1\\\",\\n \\\"f\\\": \\\"1\\\",\\n \\\"fwVersion\\\": \\\"2.0.2\\\",\\n \\\"g\\\": \\\"0\\\",\\n \\\"h\\\": \\\"1\\\",\\n \\\"i\\\": \\\"0\\\",\\n \\\"j\\\": \\\"00\\\",\\n \\\"k\\\": \\\"0\\\",\\n \\\"l\\\": \\\"1\\\",\\n \\\"m\\\": \\\"2\\\",\\n \\\"n\\\": \\\"0\\\",\\n \\\"rssi\\\": -53,\\n \\\"staMac\\\": \\\"xx:xx:xx:xx:xx:xx\\\"\\n },\\n \\\"productModel\\\": \\\"WFA-1\\\",\\n \\\"settings\\\": {\\n \\\"alarmNotify\\\": 1,\\n \\\"opsHistory\\\": 1,\\n \\\"opsNotify\\\": 0\\n },\\n \\\"sharedTo\\\": [\\n {\\n \\\"note\\\": \\\"\\\",\\n \\\"permit\\\": 15,\\n \\\"phoneNumber\\\": \\\"e-mail@gmail.com\\\",\\n \\\"shareTime\\\": 1542259546087\\n }\\n ],\\n \\\"showBrand\\\": true,\\n \\\"type\\\": \\\"10\\\",\\n \\\"uiid\\\": 54\\n }\\n ]\\n \\n or New format:\\n {\\n \\\"devicelist\\\": [\\n {\\n \\\"__v\\\": 0,\\n \\\"_id\\\": \\\"5c3665d012d28ae6ba4943c8\\\",\\n \\\"apikey\\\": \\\"530303a6-cf2c-4246-894c-50855b00e6d8\\\",\\n \\\"brandLogoUrl\\\": \\\"https://us-ota.coolkit.cc/logo/KRZ54OifuGmjoEMxT1YYM3Ybu2fj5K2C.png\\\",\\n \\\"brandName\\\": \\\"Sonoff\\\",\\n \\\"createdAt\\\": \\\"2019-01-09T21:21:20.402Z\\\",\\n \\\"devConfig\\\": {},\\n \\\"devGroups\\\": [],\\n \\\"deviceStatus\\\": \\\"\\\",\\n ... as before\\n '''\",\n \"def get(self, start=None, limit=None):\\r\\n params = base.get_params(None, locals())\\r\\n return http.Request('GET', self.get_url(), params), parsers.parse_json\",\n \"def getAll(self):\\n result_get = GetRest(function = self.function).performRequest()\\n return result_get\",\n \"async def get(self):\\n raise NotImplementedError()\",\n \"def latest_release_get():\\n try:\\n return json_response.success({'version': version.latest_version()})\\n except version.Error as e:\\n return json_response.error(str(e)), 200\",\n \"def get(self):\\n dev = self.request.get('device')\\n reg = self.request.get('registry')\\n states = self.request.get('states')\\n\\n self.response.headers['Content-Type'] = 'text/plain'\\n if (not dev) or len(dev)==0:\\n self.response.write('parameter dev not found')\\n elif (not reg) or len(reg)==0:\\n self.response.write('parameter reg not found')\\n elif (not states) or states <= 0:\\n self.response.write('invalid or no states found')\\n else:\\n # Get user account\\n ds = Datastore()\\n user = ds.get_registry(reg)\\n if len(user) == 0:\\n self.response.write(\\\"Registry does not exist\\\")\\n else:\\n region = get_region_from_user(user)\\n\\n # Add Device on IOT Core\\n iot = IOT()\\n success, message = iot.get_data(dev, reg, states, region)\\n if success:\\n try:\\n for msg in message['deviceStates']:\\n msg['binaryData'] = decode64(msg['binaryData'])\\n self.response.write(message)\\n except:\\n self.response.write([])\\n else:\\n self.response.write(message)\",\n \"def request_device_readings_max(device_uuid):\\n\\n if request.data:\\n post_data = json.loads(request.data)\\n type = post_data.get('type', None)\\n if not type or type not in ('temperature', 'humidity'):\\n return 'error on the required type data', 400\\n start = post_data.get('start', None)\\n end = post_data.get('end', None)\\n else:\\n return 'missing data in the request parameters', 400\\n\\n # Set the db that we want and open the connection\\n if app.config['TESTING']:\\n conn = sqlite3.connect('test_database.db')\\n else:\\n conn = sqlite3.connect('database.db')\\n conn.row_factory = sqlite3.Row\\n cur = conn.cursor()\\n\\n sql = 'SELECT *, MAX(r.value) from readings r WHERE r.type = ? AND r.device_uuid = ?'\\n params = [type, device_uuid]\\n if start:\\n sql += 'AND r.date_created >= ?'\\n params += [start]\\n if end:\\n sql += 'AND r.date_created <= ?'\\n params += [end]\\n\\n # Execute the query\\n cur.execute(sql, params)\\n row = cur.fetchone()\\n\\n if not row[0]:\\n return 'No results found', 200\\n\\n # Return the JSON\\n return jsonify(dict(zip(['device_uuid', 'type', 'value', 'date_created'], row))), 200\",\n \"def get_data(link):\\n data = re.get(link)\\n jsondata = data.json()\\n for weatherstation in jsondata['weatherStations']:\\n FetchandStore.sensordict.update({weatherstation[\\\"id\\\"]:weatherstation[\\\"sensorValues\\\"]})\\n for sensorvalue in weatherstation[\\\"sensorValues\\\"]:\\n FetchandStore.sensors.append({\\\"id\\\": sensorvalue[\\\"roadStationId\\\"], \\\"name\\\": sensorvalue[\\\"oldName\\\"],\\n \\\"value\\\": sensorvalue[\\\"sensorValue\\\"], \\\"unit\\\": sensorvalue[\\\"sensorUnit\\\"],\\n \\\"datetime\\\": sensorvalue[\\\"measuredTime\\\"]})\\n return FetchandStore.sensors\",\n \"def next(self):\\n self.read()\\n response = {\\n 'humidity': self.humidity,\\n 'temperature': self.temperature\\n }\\n return response\",\n \"def GET_sensors(self):\\n self.sensors.GetAll()\\n self.sensors.sensors['charging-state'] = \\\\\\n pyrobot.CHARGING_STATES[self.sensors.sensors['charging-state']]\\n print simplejson.dumps(self.sensors.sensors)\",\n \"def data(self):\\r\\n if self.metadata is None:\\r\\n self.refresh()\\r\\n return self.metadata\",\n \"def data(self):\\n self._get_latest_content()\\n return self._data.get('data', {})\",\n \"async def get_device_data(self):\\n pass\",\n \"def fetch_data(self):\",\n \"def get_data(self):\\n self._send_command(self._adapter.get_data())\",\n \"def get_data_from_endpoint(self, from_, to_, endpoint):\\n endpoint = self.make_endpoint(endpoint)\\n from_, to_ = str(from_), str(to_)\\n payload = {\\n 'auth': self.auth_token,\\n 'id': self.monitor_id,\\n 'start': from_,\\n 'end': to_,\\n 'extendLimit': 'true',\\n 'fullContents': 'true'\\n }\\n\\n r = self.session.get(endpoint, params=payload)\\n ratelimit_remaining = r.headers['X-RateLimit-Remaining']\\n #print ('Remaining Ratelimit = ' + str(ratelimit_remaining))\\n\\n # If the header is empty or 0 then wait for a ratelimit refresh.\\n if (not ratelimit_remaining) or (float(ratelimit_remaining) < 1):\\n #print('Waiting for ratelimit refresh...')\\n sleep(self.ratelimit_refresh)\\n\\n return r\",\n \"def get(self):\\n devs = Device.query.all()\\n lista = []\\n for d in devs:\\n lista.append(d.json())\\n return lista\",\n \"def get(self):\\n self.finish(json.dumps(self.build_response_dict()))\",\n \"def get(self, path):\\n base = 'http://%s:%d' % (self.host, self.port)\\n url = '%s/%s' % (base, path)\\n\\n conn = urllib2.urlopen(url)\\n payload = conn.read()\\n try: \\n payload = json.loads(payload)\\n except:\\n pass\\n\\n conn.close()\\n\\n return payload\",\n \"def _get(self, endpoint):\\n res = self._request(\\\"get\\\", endpoint)\\n if not res.content:\\n return {}\\n try:\\n res = res.json()\\n except ValueError:\\n raise ValueError(\\\"Cannot parse {} as JSON\\\".format(res))\\n if \\\"error\\\" in res:\\n raise AirthingsError(res[\\\"error\\\"])\\n return res\",\n \"def download_data(self, format = 'srt'):\\n resp, content = httplib2.Http(\\\".cache\\\").request(self.url, \\\"GET\\\")\\n suburl = json.loads(content)['url']\\n resp, content = httplib2.Http(\\\".cache\\\").request(suburl, \\\"GET\\\")\\n\\n return content\",\n \"def refresh_details(self) -> 'outputs.RefreshDetailsResponse':\\n return pulumi.get(self, \\\"refresh_details\\\")\",\n \"def getLastData(self) -> ghidra.program.model.listing.Data:\\n ...\",\n \"def fetch_timestamp(self):\\r\\n return self.__public_request('GET', '/api/v1/timestamp')\",\n \"def apicall():\\r\\n# try:\\r\\n print request.get_json()\\r\\n test_json = request.get_json()\\r\\n logger.info(\\\"input json object loaded\\\")\\r\\n logger.info(test_json)\\r\\n k=MetaData(test_json)\\r\\n int_res=k.getData()\\r\\n print '------------------------------'\\r\\n print int_res\\r\\n return jsonify(int_res)\",\n \"def test_get_measurement_history(self):\\n device = DeviceFactory(node=Node.objects.first(), external_id='123', type__code=SecureDeviceType.SRT321,\\n device_param__type__code=SecureDeviceParameterType.MEASURED_TEMPERATURE)\\n d_id_1 = device.external_id\\n\\n now_loc = datetime.datetime.now(bst)\\n ts_loc = now_loc - datetime.timedelta(seconds=30)\\n ts_str = ts_loc.strftime('%Y-%m-%dT%H:%M:%S')\\n\\n data = self.create_secure_server_push_data(d_id_1, ts_str)\\n\\n SecureClient.process_push_data(data)\\n time.sleep(.5)\\n\\n # get newer timestamp\\n ts_str = now_loc.strftime('%Y-%m-%dT%H:%M:%S')\\n data = self.create_secure_server_push_data(d_id_1, ts_str, value=\\\"23.5\\\")\\n\\n SecureClient.process_push_data(data)\\n\\n token = Token.objects.get(user__username=email)\\n device_param = device.parameters.first()\\n client = APIClient()\\n client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)\\n url = reverse('api:device_measurements', kwargs={'device_parameter_id': device_param.id})\\n\\n time.sleep(.5)\\n\\n response = client.get(url, format='json')\\n\\n self.assertTrue(response.status_code == 200)\\n self.assertTrue(len(response.data) >= 2)\",\n \"def lastest_datapoint(instance_name: str):\\n if request.method == \\\"GET\\\":\\n with lock:\\n if not instances.get(instance_name):\\n return jsonify(message=\\\"Instance Does Not Exist\\\"), 404\\n return jsonify(asdict(instances.get(instance_name).latest()))\\n return Response(status=200)\",\n \"def send_get(self, uri, data=None, ojson=None):\\n return self.__send_request('GET', uri=uri, data=data, ojson=ojson)\",\n \"def flask_get_devices():\\n try:\\n # retrieve the authorization token\\n token = retrieve_auth_token(request)\\n\\n # retrieve pagination\\n page_number, per_page = get_pagination(request)\\n\\n params = {\\n 'page_number': page_number,\\n 'per_page': per_page,\\n 'sortBy': request.args.get('sortBy', None),\\n 'attr': request.args.getlist('attr'),\\n 'attr_type': request.args.getlist('attr_type'),\\n 'label': request.args.get('label', None),\\n 'template': request.args.get('template', None),\\n 'idsOnly': request.args.get('idsOnly', 'false'),\\n }\\n\\n result = DeviceHandler.get_devices(token, params)\\n LOGGER.info(f' Getting latest added device(s).')\\n\\n return make_response(jsonify(result), 200)\\n except HTTPRequestError as e:\\n LOGGER.error(f' {e.message} - {e.error_code}.')\\n if isinstance(e.message, dict):\\n return make_response(jsonify(e.message), e.error_code)\\n\\n return format_response(e.error_code, e.message)\",\n \"def get_data():\\n pass\",\n \"def user_sends_get_call_to_the_devices():\\n web_app.list_devices()\",\n \"def get(self):\\r\\n return self.data\",\n \"def request_device_readings(device_uuid):\\n\\n # Set the db that we want and open the connection\\n if app.config['TESTING']:\\n conn = sqlite3.connect('test_database.db')\\n else:\\n conn = sqlite3.connect('database.db')\\n conn.row_factory = sqlite3.Row\\n cur = conn.cursor()\\n\\n if request.method == 'POST':\\n # Grab the post parameters\\n if request.data:\\n post_data = json.loads(request.data)\\n else:\\n return 'missing data in the request parameters', 400\\n\\n sensor_type = post_data.get('type', None)\\n value = post_data.get('value', None)\\n date_created = post_data.get('date_created', int(time.time()))\\n\\n if not sensor_type or sensor_type not in ('temperature', 'humidity'):\\n return 'the sensor type is not valid', 400\\n\\n if not value or 100 < value or 0 > value:\\n return 'the sensor value is not in the mandatory range of 0-100', 400\\n\\n # Insert data into db\\n cur.execute('INSERT INTO readings (device_uuid,type,value,date_created) VALUES (?,?,?,?)',\\n (device_uuid, sensor_type, value, date_created))\\n\\n conn.commit()\\n\\n # Return success\\n return 'success', 201\\n else:\\n # Grab the query parameters (if any)\\n start = end = type = None\\n if request.data:\\n post_data = json.loads(request.data)\\n start = post_data.get('start', None)\\n end = post_data.get('end', None)\\n type = post_data.get('type', None)\\n\\n sql = 'SELECT * from readings WHERE device_uuid = ?'\\n params = [device_uuid]\\n if start:\\n sql += 'AND date_created >= ?'\\n params += [start]\\n if end:\\n sql += 'AND date_created <= ?'\\n params += [end]\\n if type:\\n sql += 'AND type = ?'\\n params += [type]\\n\\n # Execute the query\\n cur.execute(sql, params)\\n rows = cur.fetchall()\\n\\n # Return the JSON\\n return jsonify([dict(zip(['device_uuid', 'type', 'value', 'date_created'], row)) for row in rows]), 200\",\n \"def fetch_data(self, sensor, address, now_utc, now):\\n poller = MiFloraPoller(address, GatttoolBackend)\\n ts_utc = int(now_utc.strftime(\\\"%s\\\"))\\n temp_sensor = self.sensor_template.copy()\\n temp_sensor['mac'] = address\\n temp_sensor['name'] = sensor\\n # liefert den Temperaturwert (in Grad Celsius)\\n temp_sensor['temperature'] = poller.parameter_value(\\\"temperature\\\")\\n # gibt die lichtstaerke an\\n temp_sensor['light'] = poller.parameter_value(\\\"light\\\")\\n # gibt die Feuchtigkeit an\\n temp_sensor['moisture'] = poller.parameter_value(\\\"moisture\\\")\\n # gibt die Leitfaehigkeit des Bodens an.\\n temp_sensor['conductivity'] = poller.parameter_value(\\\"conductivity\\\")\\n temp_sensor['battery'] = poller.parameter_value(\\\"battery\\\")\\n temp_sensor['date_iso'] = now.replace(microsecond=0).isoformat()\\n temp_sensor['ts_utc'] = ts_utc\\n # poller.firmware_version() # – liefert die aktuelle Firmware Version als Text.\\n temp_sensor['firmware'] = poller.firmware_version()\\n return temp_sensor\",\n \"async def async_update(self):\\n try:\\n self._data = requests.get(self._build_url(), timeout=10, headers={'accept-encoding': None}).json()\\n _LOGGER.debug(\\\"TOON fetched data = %s\\\", self._data)\\n except (requests.exceptions.RequestException) as error:\\n _LOGGER.error(\\\"Unable to connect to TOON: %s\\\", error)\\n self._data = None\",\n \"def get(self, part, id):\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def get(self, limit=None, offset=None):\\r\\n\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n return http.Request('GET', self.get_url(), params), parsers.parse_json\",\n \"def get(self, limit=None, offset=None):\\r\\n\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n return http.Request('GET', self.get_url(), params), parsers.parse_json\",\n \"def get_devices(self):\\n return self.api_request('GET', self.url + '/device', {})\",\n \"def _get_raw_data(self, url, series):\\n url = self._get_url(url, series)\\n try:\\n response = self.http.request(url, headers=self._reqheaders)\\n except httplib2.ServerNotFoundError as e:\\n raise TVDBConnectError(e.message), None, sys.exc_info()[2]\\n rep = response[0]\\n log.debug(\\n 'http-status:%s,content:%s', \\n rep['status'], \\n rep['content-type']\\n )\\n if int(rep['status']) >= 400:\\n raise TVDBConnectError(\\n 'Failed to get \\\"%s\\\" from thetvdb. errno:%s' % (\\n series, rep['status']),\\n rep['status']\\n ) \\n return response[1]\",\n \"def do_GET(self):\\n print(\\\"do_GET: got: \\\" + str(self.path))\\n path_split = self.path.split(\\\"/\\\", 2) # ['', 'hello', 'nikolay/uuuuer']\\n username = path_split[-1] # nikolay/uuuuer\\n print(\\\"do_GET: Let's open db_conn\\\")\\n connection = db_open()\\n birth_date = db_select(username, connection)\\n db_close(connection)\\n\\n days_until_bday = get_days_to_bday(birth_date)\\n json_obj = http_construct_json(username, days_until_bday)\\n\\n self.http_send_reply(json_obj)\\n print(\\\"do_GET: DONE! \\\\n\\\")\",\n \"def get(self):\\n return self.request({\\n \\\"path\\\": \\\"/\\\" + UUID\\n })\",\n \"def get():\\n #Return the corresponding value\\n return json_back()\",\n \"def get_json(self, url, *, timeout, headers):\",\n \"def test_device_readings_get_humidity(self):\\n request = self.client().get('/devices/{}/readings/?type=humidity'.format(self.device_uuid))\\n\\n self.assertEqual(len(request.json), 1)\",\n \"def get(self, path):\\n req = self.session.get(self.url + path, verify=False)\\n\\n jdata = req.json()\\n if jdata['status'] != 'ok':\\n raise Exception(\\\"Failed to query get: \\\\n\\\" + req.text)\\n\\n return jdata['data']\",\n \"def fetch_latest_sample(self, sensor_id):\\n conn = psycopg2.connect(self.conn)\\n query = \\\"SELECT * FROM steve_sense_sensor_logs WHERE sensor_id = '{}' ORDER BY time DESC LIMIT 1\\\".format(\\n sensor_id)\\n cur = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)\\n cur.execute(query)\\n data = json.loads(json.dumps(\\n cur.fetchall(), use_decimal=True, default=str))\\n cur.close()\\n conn.close()\\n return data\",\n \"def get_data(self):\\n\\n self.set_query_string()\\n self.realtime_data = super().get_data()\\n self.set_coordinate()\\n return self.realtime_data\",\n \"def test_device_readings_get_past_dates(self):\\n request = self.client().get(f'/devices/{self.device_uuid}/readings/'\\n f'?start={int(self.setup_time - 75)}'\\n f'&end={int(self.setup_time + 25)}')\\n\\n self.assertEqual(len(request.json), 2)\",\n \"def pull(self):\\n data = api.get(endpoint=self.endpoint, resource_id=self.id)\\n self.__init__(**data)\",\n \"def get_all_data():\\n return jsonify(service.get_all_data())\",\n \"def get(self, count=None, max_timestamp=None, min_timestamp=None,\\r\\n min_id=None, max_id=None):\\r\\n params = base.get_params(\\r\\n ('count', 'max_timestamp', 'min_timestamp', 'min_id', 'max_id'),\\r\\n locals())\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def _get(self):\\n path = self.router.central_core\\n response = self.request(method=\\\"get\\\", path=path)\\n return response\",\n \"def get(self, limit):\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"async def async_get(\\n self, endpoint: str | None = None, entry: str | None = None\\n ) -> Any:\\n url = f\\\"{self.url}/api/\\\"\\n if endpoint:\\n url = self.endpoints[endpoint]\\n if entry:\\n url = f\\\"{url}{entry}\\\"\\n with async_timeout.timeout(10):\\n resp = await self.session.get(\\n url=url,\\n headers=self.headers,\\n raise_for_status=True,\\n )\\n\\n return await resp.json()\",\n \"def get_data_by_uuid(request, uuid, hours):\\n\\tif request.method == 'GET':\\n\\t\\treadings = get_data_by_uuid_helper(uuid, hours)\\n\\t\\treturn Response(readings)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.675981","0.6415501","0.6267848","0.62456656","0.6203895","0.61584896","0.6130172","0.6068994","0.6045216","0.6005045","0.5990922","0.5987145","0.596897","0.5952027","0.5932656","0.5929275","0.59236604","0.58907956","0.58907956","0.5875404","0.58722246","0.58722246","0.5865079","0.58548415","0.58446544","0.5832561","0.5802658","0.5800276","0.57464135","0.5742417","0.57316554","0.5731276","0.5729541","0.5727287","0.572589","0.57255435","0.5716081","0.5713022","0.5711993","0.57003915","0.569636","0.5676609","0.5669425","0.5663296","0.5647207","0.5644878","0.56389","0.5636129","0.563346","0.5606823","0.5604955","0.55985534","0.5597364","0.5597338","0.55946773","0.55911565","0.55803007","0.5578798","0.5578307","0.55774736","0.5572823","0.557275","0.55721396","0.55714375","0.55686724","0.55651873","0.55480117","0.5543548","0.5542715","0.55371165","0.5534315","0.5533284","0.55331624","0.55301636","0.55296654","0.5526007","0.550946","0.55068","0.5506579","0.55011487","0.54937667","0.54891604","0.54891604","0.5481548","0.54755884","0.54702866","0.5466888","0.5456822","0.5453962","0.5453158","0.544885","0.5441642","0.5439217","0.5438087","0.54352087","0.54322195","0.5428722","0.54273224","0.54256254","0.5414149","0.5413595"],"string":"[\n \"0.675981\",\n \"0.6415501\",\n \"0.6267848\",\n \"0.62456656\",\n \"0.6203895\",\n \"0.61584896\",\n \"0.6130172\",\n \"0.6068994\",\n \"0.6045216\",\n \"0.6005045\",\n \"0.5990922\",\n \"0.5987145\",\n \"0.596897\",\n \"0.5952027\",\n \"0.5932656\",\n \"0.5929275\",\n \"0.59236604\",\n \"0.58907956\",\n \"0.58907956\",\n \"0.5875404\",\n \"0.58722246\",\n \"0.58722246\",\n \"0.5865079\",\n \"0.58548415\",\n \"0.58446544\",\n \"0.5832561\",\n \"0.5802658\",\n \"0.5800276\",\n \"0.57464135\",\n \"0.5742417\",\n \"0.57316554\",\n \"0.5731276\",\n \"0.5729541\",\n \"0.5727287\",\n \"0.572589\",\n \"0.57255435\",\n \"0.5716081\",\n \"0.5713022\",\n \"0.5711993\",\n \"0.57003915\",\n \"0.569636\",\n \"0.5676609\",\n \"0.5669425\",\n \"0.5663296\",\n \"0.5647207\",\n \"0.5644878\",\n \"0.56389\",\n \"0.5636129\",\n \"0.563346\",\n \"0.5606823\",\n \"0.5604955\",\n \"0.55985534\",\n \"0.5597364\",\n \"0.5597338\",\n \"0.55946773\",\n \"0.55911565\",\n \"0.55803007\",\n \"0.5578798\",\n \"0.5578307\",\n \"0.55774736\",\n \"0.5572823\",\n \"0.557275\",\n \"0.55721396\",\n \"0.55714375\",\n \"0.55686724\",\n \"0.55651873\",\n \"0.55480117\",\n \"0.5543548\",\n \"0.5542715\",\n \"0.55371165\",\n \"0.5534315\",\n \"0.5533284\",\n \"0.55331624\",\n \"0.55301636\",\n \"0.55296654\",\n \"0.5526007\",\n \"0.550946\",\n \"0.55068\",\n \"0.5506579\",\n \"0.55011487\",\n \"0.54937667\",\n \"0.54891604\",\n \"0.54891604\",\n \"0.5481548\",\n \"0.54755884\",\n \"0.54702866\",\n \"0.5466888\",\n \"0.5456822\",\n \"0.5453962\",\n \"0.5453158\",\n \"0.544885\",\n \"0.5441642\",\n \"0.5439217\",\n \"0.5438087\",\n \"0.54352087\",\n \"0.54322195\",\n \"0.5428722\",\n \"0.54273224\",\n \"0.54256254\",\n \"0.5414149\",\n \"0.5413595\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":93895,"cells":{"query":{"kind":"string","value":"Tests that the output html is generated correclty by the clustering function"},"document":{"kind":"string","value":"def test_output_html(tmp_path):\n\n # This will be the file to\n temporary_file = os.path.join(tmp_path, 'test-cluster.html')\n\n # Run clustering on small dummy data (see test_clustering.py)\n cluster = TextClustering(embedding_random_state=42,\n reducer_random_state=43,\n clustering_random_state=44)\n\n X = ['Wellcome Trust',\n 'The Wellcome Trust',\n 'Sir Henry Wellcome',\n 'Francis Crick',\n 'Crick Institute',\n 'Francis Harry Crick']\n\n cluster.fit(X)\n\n # Run the visualisation function with output_file=temporary_file\n visualize_clusters(clustering=cluster, output_file_path=temporary_file, radius=0.01,\n alpha=0.5, output_in_notebook=False)\n\n # Assert that the html was generated correctly\n assert os.path.exists(temporary_file)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_html_output(self):\n pass","def cluster_classification_tex(f,browsing_matrix,diversifying_matrix, weblog,session_data_threshold,cluster_type,classification_column_diversity,classification_wanted_transaction):\n divpat_classification_wanted_transaction = classification_wanted_transaction\n divpat_N_classification_wanted_transaction=len(divpat_classification_wanted_transaction)\n f.write(\"\\n% 6. Cluster Classification\")\n columns_latex = '|'+'c|'*len(session_data_threshold[cluster_type].unique())\n f.write(\"\\n\\\\newcommand{\\\\%s}{%s}\"%('DivColumnsLatex',columns_latex)) \n columns_blank = ' ' + '& '*(len(session_data_threshold[cluster_type].unique()) -1)\n f.write(\"\\n\\\\newcommand{\\\\%s}{%s}\"%('DivColumnsBlank',columns_blank)) \n cluster_list = []\n ieuc_clusters = []\n star_chain_like_clusters = []\n length_clusters = []\n browsing_pattern_1 = []\n browsing_pattern_2 = []\n browsing_pattern_3 = []\n diversifying_pattern_1 = []\n diversifying_pattern_2 = []\n diversifying_pattern_3 = []\n cluster_ids = session_data_threshold[cluster_type].unique()\n cluster_ids.sort()\n for cluster_id in cluster_ids:\n cluster_list.append(str(cluster_id))\n \n cluster_session_list=session_data_threshold[session_data_threshold[cluster_type]==cluster_id].session_id.values\n temp_cluster_weblog=weblog[weblog.session_id.isin(cluster_session_list)]\n pa,pa_names = proportional_abundance(temp_cluster_weblog,'requested_'+classification_column_diversity)\n cluster_entropy=ShannonEntropy(pa,normalize=True)\n \n ieuc_clusters.append(str(round(np.power(2.0,cluster_entropy),2)))\n star_chain_like_clusters.append(star_chain_str(session_data_threshold[session_data_threshold[cluster_type]==cluster_id].star_chain_like.mean()))\n length_clusters.append(length(session_data_threshold[session_data_threshold[cluster_type]==cluster_id].requests.mean()))\n # Browsing patterns\n r,c=np.unravel_index(browsing_matrix[cluster_id][:-1,:-1].argsort(axis=None)[::-1][:3],dims=(divpat_N_classification_wanted_transaction,divpat_N_classification_wanted_transaction))\n browsing_pattern_1.append('%.1f\\%%: %s$\\\\rightarrow$%s'%(100.0*browsing_matrix[cluster_id][r[0],c[0]],divpat_classification_wanted_transaction[r[0]],divpat_classification_wanted_transaction[c[0]]))\n browsing_pattern_2.append('%.1f\\%%: %s$\\\\rightarrow$%s'%(100.0*browsing_matrix[cluster_id][r[1],c[1]],divpat_classification_wanted_transaction[r[1]],divpat_classification_wanted_transaction[c[1]]))\n browsing_pattern_3.append('%.1f\\%%: %s$\\\\rightarrow$%s'%(100.0*browsing_matrix[cluster_id][r[2],c[2]],divpat_classification_wanted_transaction[r[2]],divpat_classification_wanted_transaction[c[2]]))\n \n # Diversifying patterns\n r,c=np.unravel_index(np.nan_to_num(diversifying_matrix[cluster_id])[:-1,:-1].argsort(axis=None)[::-1][:3],dims=(divpat_N_classification_wanted_transaction,divpat_N_classification_wanted_transaction))\n diversifying_pattern_1.append('%.1f\\%%: %s$\\\\rightarrow$%s'%(100.0*diversifying_matrix[cluster_id][r[0],c[0]],divpat_classification_wanted_transaction[r[0]],divpat_classification_wanted_transaction[c[0]]))\n diversifying_pattern_2.append('%.1f\\%%: %s$\\\\rightarrow$%s'%(100.0*diversifying_matrix[cluster_id][r[1],c[1]],divpat_classification_wanted_transaction[r[1]],divpat_classification_wanted_transaction[c[1]]))\n diversifying_pattern_3.append('%.1f\\%%: %s$\\\\rightarrow$%s'%(100.0*diversifying_matrix[cluster_id][r[2],c[2]],divpat_classification_wanted_transaction[r[2]],divpat_classification_wanted_transaction[c[2]]))\n\n del temp_cluster_weblog\n \n f.write(\"\\n\\\\newcommand{\\\\%s}{%s}\"%('DivClusterList',' & '.join(cluster_list)))\n f.write(\"\\n\\\\newcommand{\\\\%s}{%s}\"%('DivIEUCClusters',' & '.join(ieuc_clusters)))\n f.write(\"\\n\\\\newcommand{\\\\%s}{%s}\"%('StarChainClusters',' & '.join(star_chain_like_clusters)))\n f.write(\"\\n\\\\newcommand{\\\\%s}{%s}\"%('LengthClusters',' & '.join(length_clusters)))\n f.write(\"\\n\\\\newcommand{\\\\%s}{%s}\"%('BrowsingPatternClustersOne',' & '.join(browsing_pattern_1)))\n f.write(\"\\n\\\\newcommand{\\\\%s}{%s}\"%('BrowsingPatternClustersTwo',' & '.join(browsing_pattern_2)))\n f.write(\"\\n\\\\newcommand{\\\\%s}{%s}\"%('BrowsingPatternClustersThree',' & '.join(browsing_pattern_3)))\n f.write(\"\\n\\\\newcommand{\\\\%s}{%s}\"%('DiversifyingPatternClustersOne',' & '.join(diversifying_pattern_1)))\n f.write(\"\\n\\\\newcommand{\\\\%s}{%s}\"%('DiversifyingPatternClustersTwo',' & '.join(diversifying_pattern_2)))\n f.write(\"\\n\\\\newcommand{\\\\%s}{%s}\"%('DiversifyingPatternClustersThree',' & '.join(diversifying_pattern_3)))\n\n return f;","def test_introduction_6():\n import pandas as pd\n from tcrdist.repertoire import TCRrep\n\n df = pd.read_csv(\"dash.csv\")\n tr = TCRrep(cell_df = df, \n organism = 'mouse', \n chains = ['beta','alpha'], \n db_file = 'alphabeta_gammadelta_db.tsv')\n \n from tcrdist.rep_diff import hcluster_diff, member_summ\n from hierdiff import plot_hclust_props\n\n # diff testing is pasted on binary comparison, so all epitope not 'PA' are set to 'X'\n tr.clone_df['PA'] = ['PA' if x == 'PA' else 'X' for x in tr.clone_df.epitope]\n \n res, Z= hcluster_diff(tr.clone_df, tr.pw_beta, x_cols = ['PA'], count_col = 'count')\n \n res_summary = member_summ(res_df = res, clone_df = tr.clone_df, addl_cols=['epitope'])\n \n res_detailed = pd.concat([res, res_summary], axis = 1)\n \n html = plot_hclust_props(Z,\n title='PA Epitope Example',\n res=res_detailed,\n tooltip_cols=['cdr3_b_aa','v_b_gene', 'j_b_gene','epitope'],\n alpha=0.00001, colors = ['blue','gray'],\n alpha_col='pvalue')\n\n with open('hierdiff_example.html', 'w') as fh:\n fh.write(html)","def cluster_and_render(conf, dbname, outname=\"./text.html\", nclusters=8):\n\n\n db = sqlite3.connect(dbname)\n r = db.execute(\"select min(year), max(year) from counts where conf=?\", (conf,))\n minyear, maxyear = r.fetchone()\n\n # total words per year for normalization purposes\n r = db.execute(\"select year, count(*) from counts where conf=? order by year\", (conf,))\n year2c = dict([(year, c) for year, c in r])\n yearcounts = dict2arr(year2c, range(minyear, maxyear+1), 1)\n\n\n def add_content(subcluster, content, suffix):\n \"\"\"\n Render the cluster as an image\n \"\"\"\n\n fname = './plots/%s_%s.png' % (conf, suffix)\n\n # pick the top 10 terms\n subcluster = sorted(subcluster, key=lambda t: max(t[1:].astype(float)), reverse=True)\n subcluster = subcluster[:10]\n\n words = np.array(subcluster)[:,0]\n ys = np.array(subcluster)[:,1:].astype(float)\n mean = [np.mean(ys[:,i]) for i in xrange(ys.shape[1])]\n maxmean = max(mean)\n idx = mean.index(maxmean)\n\n # this is used to make the top-k list in the HTML later\n content.append(('', words, fname, idx))\n\n\n data = []\n for arr in subcluster:\n word = arr[0]\n for x, y in enumerate(map(float, arr[1:])):\n data.append(dict(\n group=\"normal\",\n word=word,\n x=xs[x],\n y=y, \n alpha=0.3\n ))\n\n # add a line for the mean\n for x, y in enumerate(mean):\n data.append(dict(group=\"aggregate\", word='___mean___', x=xs[x], y=y, alpha=1))\n\n if 1:\n maxy = max(10, max(pluckone(data, 'y')))\n if maxy <= 10:\n breaks = [0, 5, 10]\n\n\n # pygg lets you write ggplot2 syntax in python\n p = ggplot(data, aes(x='x', y='y', group='word', color='group', alpha='alpha'))\n p += geom_line(size=1)\n p += scale_color_manual(values=\"c('normal' = '#7777dd','aggregate' = 'black')\", guide=\"FALSE\")\n p += scale_alpha_continuous(guide=\"FALSE\")\n if 1:\n if maxy <= 10:\n p += scale_y_continuous(lim=[0, maxy], breaks=breaks, labels = \"function (x) as.integer(x)\")\n else:\n p += scale_y_continuous(lim=[0, maxy], labels = \"function (x) as.integer(x)\")\n p += legend_bottom\n p += theme(**{\n \"axis.title\":element_blank()\n })\n ggsave(fname, p, width=10, height=4, libs=['grid'])\n \n\n\n def vectors():\n \"\"\"\n Extract a matrix of term count vectors\n\n Return: [\n [word, count1, count2, ...],\n ...\n ]\n \"\"\"\n r = db.execute(\"select word, year, c from counts where conf=? order by word, year\", (conf,))\n vects = defaultdict(dict)\n for w,y,c in r:\n l = vects[w]\n l[y] = float(c) \n\n\n ret = []\n for w in vects:\n d = vects[w]\n\n # if word is super uncommon, skip it\n if (max(d.values()) <= 3):\n continue\n if (max([v / (1.+year2c.get(y,0)) for y, v in d.items()]) < .1): \n continue\n\n # some years may not have the word\n counts = dict2arr(d, xrange(minyear, maxyear+1), 1.0)\n\n \n # naive window averaging smoothing over the trend curve\n smooth = []\n for i in xrange(len(counts)):\n smooth.append(np.mean(counts[max(0,i-2):i+2]))\n if max(smooth) > 2:\n ret.append([w] + smooth)\n return np.array(ret)\n\n\n vects = vectors()\n # dimensions: words (row) x year (col)\n data = vects[:,1:].astype(float)\n\n # there's a bajillion ways to normalize the counts before clustering.\n # we do the following:\n\n # 1. divide by the total number of words in that year\n # (normalize by column)\n for idx, base in enumerate(yearcounts):\n data[:,idx] /= float(base)\n\n # 2. ensure zero mean and 1 std\n # (normalize by row)\n data = np.array([(l - np.mean(l)) / (max(l)) for l in data ])\n\n\n clusterer = KMeans(nclusters, n_init=50, init='k-means++')\n clusterer.fit(data) \n labels = clusterer.labels_\n xs = np.array(range(minyear, maxyear+1))\n\n content = []\n\n # each label is a cluster\n for label in set(labels):\n idxs = labels == label\n cluster = vects[idxs]\n\n # sort the words/clusters by their max count\n cluster = sorted(cluster, key=lambda t: max(t[1:].astype(float)), reverse=True)\n if not len(cluster): continue\n cluster = np.array(cluster)\n words = cluster[:,0]\n words = list(words)\n\n add_content(cluster, content, label)\n\n content.sort(key=lambda c: c[-1])\n\n\n\n # make HTML\n from jinja2 import Template\n template = Template(file('./clustertemplate.html').read())\n\n with file(outname, 'w') as f:\n f.write( template.render(content=content))","def test_main(self):\r\n\r\n expected = \">FS8APND01D3TW3 | cluster size: 94 \\nCTCCCGTAGGAGTCTGGGCCGTATCTCAGTCCCAATGTGGCCGGTCACCCTCTCAGGCCGGCTACCCGTCAAAGCCTTGGTAAGCCACTACCCCACCAACAAGCTGATAAGCCGCGAGTCCATCCCCAACCGCCGAAACTTTCCAACCCCCACCATGCAGCAGGAGCTCCTATCCGGTATTAGCCCCAGTTTCCTGAAGTTATCCCAAAGTCAAGGGCAGGTTACTCACGTGTTACTCACCCGTTCGCC\\n\"\r\n\r\n expected_map = \"\"\"FS8APND01EWRS4:\r\nFS8APND01DXG45:\r\nFS8APND01D3TW3:\\tFS8APND01CSXFN\\tFS8APND01DQ8MX\\tFS8APND01DY7QW\\tFS8APND01B5QNI\\tFS8APND01CQ6OG\\tFS8APND01C7IGN\\tFS8APND01DHSGH\\tFS8APND01DJ17E\\tFS8APND01CUXOA\\tFS8APND01EUTYG\\tFS8APND01EKK7T\\tFS8APND01D582W\\tFS8APND01B5GWU\\tFS8APND01D7N2A\\tFS8APND01BJGHZ\\tFS8APND01D6DYZ\\tFS8APND01C6ZIM\\tFS8APND01D2X6Y\\tFS8APND01BUYCE\\tFS8APND01BNUEY\\tFS8APND01DKLOE\\tFS8APND01C24PP\\tFS8APND01EBWQX\\tFS8APND01ELDYW\\tFS8APND01B0GCS\\tFS8APND01D4QXI\\tFS8APND01EMYD9\\tFS8APND01EA2SK\\tFS8APND01DZOSO\\tFS8APND01DHYAZ\\tFS8APND01C7UD9\\tFS8APND01BTZFV\\tFS8APND01CR78R\\tFS8APND01B39IE\\tFS8APND01ECVC0\\tFS8APND01DM3PL\\tFS8APND01DELWS\\tFS8APND01CIEK8\\tFS8APND01D7ZOZ\\tFS8APND01CZSAI\\tFS8APND01DYOVR\\tFS8APND01BX9XY\\tFS8APND01DEWJA\\tFS8APND01BEKIW\\tFS8APND01DCKB9\\tFS8APND01EEYIS\\tFS8APND01DDKEA\\tFS8APND01DSZLO\\tFS8APND01C6EBC\\tFS8APND01EE15M\\tFS8APND01ELO9B\\tFS8APND01C58QY\\tFS8APND01DONCG\\tFS8APND01DVXX2\\tFS8APND01BL5YT\\tFS8APND01BIL2V\\tFS8APND01EBSYQ\\tFS8APND01CCX8R\\tFS8APND01B2YCJ\\tFS8APND01B1JG4\\tFS8APND01DJ024\\tFS8APND01BIJY0\\tFS8APND01CIA4G\\tFS8APND01DV74M\\tFS8APND01ECAX5\\tFS8APND01DC3TZ\\tFS8APND01EJVO6\\tFS8APND01D4VFG\\tFS8APND01DYYYO\\tFS8APND01D1EDD\\tFS8APND01DQUOT\\tFS8APND01A2NSJ\\tFS8APND01DDC8I\\tFS8APND01BP1T2\\tFS8APND01DPY6U\\tFS8APND01CIQGV\\tFS8APND01BPUT8\\tFS8APND01BDNH4\\tFS8APND01DOZDN\\tFS8APND01DS866\\tFS8APND01DGS2J\\tFS8APND01EDK32\\tFS8APND01EPA0T\\tFS8APND01CK3JM\\tFS8APND01BKLWW\\tFS8APND01DV0BO\\tFS8APND01DPNXE\\tFS8APND01B7LUA\\tFS8APND01BTTE2\\tFS8APND01CKO4X\\tFS8APND01DGGBY\\tFS8APND01C4NHX\\tFS8APND01DYPQN\r\nFS8APND01BSTVP:\r\nFS8APND01EFK0W:\r\nFS8APND01DCIOO:\r\nFS8APND01CKOMZ:\r\n\"\"\"\r\n\r\n command = \" \".join([\"denoiser.py\",\r\n \"--force\", \"-o\", self.test_dir, \"-i\",\r\n \"%s/qiime/support_files/denoiser/TestData/denoiser_test_set.sff.txt\" % PROJECT_HOME])\r\n\r\n result = Popen(command, shell=True, universal_newlines=True,\r\n stdout=PIPE, stderr=STDOUT).stdout.read()\r\n self.result_dir = self.test_dir\r\n\r\n observed = \"\".join(list(open(self.result_dir + \"centroids.fasta\")))\r\n self.assertEqual(observed, expected)\r\n\r\n self.assertEqual(\r\n len(list(parse_fasta(open(self.result_dir + \"singletons.fasta\")))),\r\n 6)\r\n\r\n observed = \"\".join(\r\n list(open(self.result_dir + \"denoiser_mapping.txt\")))\r\n self.assertEqual(observed, expected_map)","def test_main(self):\n\n expected = \"\"\">FS8APND01D3TW3 | cluster size: 94 \nCTCCCGTAGGAGTCTGGGCCGTATCTCAGTCCCAATGTGGCCGGTCACCCTCTCAGGCCGGCTACCCGTCAAAGCCTTGGTAAGCCACTACCCCACCAACAAGCTGATAAGCCGCGAGTCCATCCCCAACCGCCGAAACTTTCCAACCCCCACCATGCAGCAGGAGCTCCTATCCGGTATTAGCCCCAGTTTCCTGAAGTTATCCCAAAGTCAAGGGCAGGTTACTCACGTGTTACTCACCCGTTCGCC\n\"\"\"\n\n expected_map = \"\"\"FS8APND01EWRS4:\nFS8APND01DXG45:\nFS8APND01D3TW3:\\tFS8APND01CSXFN\\tFS8APND01DQ8MX\\tFS8APND01DY7QW\\tFS8APND01B5QNI\\tFS8APND01CQ6OG\\tFS8APND01C7IGN\\tFS8APND01DHSGH\\tFS8APND01DJ17E\\tFS8APND01CUXOA\\tFS8APND01EUTYG\\tFS8APND01EKK7T\\tFS8APND01D582W\\tFS8APND01B5GWU\\tFS8APND01D7N2A\\tFS8APND01BJGHZ\\tFS8APND01D6DYZ\\tFS8APND01C6ZIM\\tFS8APND01D2X6Y\\tFS8APND01BUYCE\\tFS8APND01BNUEY\\tFS8APND01DKLOE\\tFS8APND01C24PP\\tFS8APND01EBWQX\\tFS8APND01ELDYW\\tFS8APND01B0GCS\\tFS8APND01D4QXI\\tFS8APND01EMYD9\\tFS8APND01EA2SK\\tFS8APND01DZOSO\\tFS8APND01DHYAZ\\tFS8APND01C7UD9\\tFS8APND01BTZFV\\tFS8APND01CR78R\\tFS8APND01B39IE\\tFS8APND01ECVC0\\tFS8APND01DM3PL\\tFS8APND01DELWS\\tFS8APND01CIEK8\\tFS8APND01D7ZOZ\\tFS8APND01CZSAI\\tFS8APND01DYOVR\\tFS8APND01BX9XY\\tFS8APND01DEWJA\\tFS8APND01BEKIW\\tFS8APND01DCKB9\\tFS8APND01EEYIS\\tFS8APND01DDKEA\\tFS8APND01DSZLO\\tFS8APND01C6EBC\\tFS8APND01EE15M\\tFS8APND01ELO9B\\tFS8APND01C58QY\\tFS8APND01DONCG\\tFS8APND01DVXX2\\tFS8APND01BL5YT\\tFS8APND01BIL2V\\tFS8APND01EBSYQ\\tFS8APND01CCX8R\\tFS8APND01B2YCJ\\tFS8APND01B1JG4\\tFS8APND01DJ024\\tFS8APND01BIJY0\\tFS8APND01CIA4G\\tFS8APND01DV74M\\tFS8APND01ECAX5\\tFS8APND01DC3TZ\\tFS8APND01EJVO6\\tFS8APND01D4VFG\\tFS8APND01DYYYO\\tFS8APND01D1EDD\\tFS8APND01DQUOT\\tFS8APND01A2NSJ\\tFS8APND01DDC8I\\tFS8APND01BP1T2\\tFS8APND01DPY6U\\tFS8APND01CIQGV\\tFS8APND01BPUT8\\tFS8APND01BDNH4\\tFS8APND01DOZDN\\tFS8APND01DS866\\tFS8APND01DGS2J\\tFS8APND01EDK32\\tFS8APND01EPA0T\\tFS8APND01CK3JM\\tFS8APND01BKLWW\\tFS8APND01DV0BO\\tFS8APND01DPNXE\\tFS8APND01B7LUA\\tFS8APND01BTTE2\\tFS8APND01CKO4X\\tFS8APND01DGGBY\\tFS8APND01C4NHX\\tFS8APND01DYPQN\nFS8APND01BSTVP:\nFS8APND01EFK0W:\nFS8APND01DCIOO:\nFS8APND01CKOMZ:\n\"\"\"\n\n command = \" \".join( [\"%s/denoiser.py\" % get_qiime_scripts_dir(),\n \"--force\", \"-o\", self.test_dir, \"-i\",\n \"%s/qiime/support_files/denoiser/TestData/denoiser_test_set.sff.txt\" % PROJECT_HOME] );\n\n result = Popen(command,shell=True,universal_newlines=True,\\\n stdout=PIPE,stderr=STDOUT).stdout.read()\n self.result_dir = self.test_dir\n\n observed = \"\".join(list(open(self.result_dir+ \"centroids.fasta\")))\n self.assertEqual(observed, expected)\n \n self.assertEqual(len(list(MinimalFastaParser(open(self.result_dir + \"singletons.fasta\")))), 6)\n\n observed = \"\".join(list(open(self.result_dir+ \"denoiser_mapping.txt\")))\n self.assertEqual(observed, expected_map)","def test_main_on_cluster(self):\r\n\r\n command = \" \".join([\"denoiser.py\",\r\n \"--force\", \"-o\", self.test_dir, \"-c\", \"-n\", \"2\",\r\n \"-i\", \"%s/qiime/support_files/denoiser/TestData/denoiser_test_set.sff.txt\" % PROJECT_HOME,\r\n \"-f\", \"%s/qiime/support_files/denoiser/TestData/test_set_seqs.fna\" % PROJECT_HOME])\r\n\r\n result = Popen(command, shell=True, universal_newlines=True,\r\n stdout=PIPE, stderr=STDOUT).stdout.read()\r\n self.result_dir = self.test_dir\r\n\r\n observed = \"\".join(list(open(self.result_dir + \"centroids.fasta\")))\r\n self.assertEqual(observed, self.expected)","def test_main_on_cluster(self):\n\n command = \" \".join( [\"%s/denoiser.py\" % get_qiime_scripts_dir(),\n \"--force\",\"-o\", self.test_dir, \"-c\", \"-n\", \"2\",\n \"-i\", \"%s/qiime/support_files/denoiser/TestData/denoiser_test_set.sff.txt\" % PROJECT_HOME, \n \"-f\", \"%s/qiime/support_files/denoiser/TestData/test_set_seqs.fna\" % PROJECT_HOME] )\n\n result = Popen(command,shell=True,universal_newlines=True,\\\n stdout=PIPE,stderr=STDOUT).stdout.read()\n self.result_dir = self.test_dir\n\n observed = \"\".join(list(open(self.result_dir+ \"centroids.fasta\")))\n self.assertEqual(observed, self.expected)","def test_main_split_cluster(self):\r\n\r\n command = \" \".join([\"denoiser.py\",\r\n \"-S\", \"--force\", '-c', '-n 2',\r\n \"-i\", \"%s/qiime/support_files/denoiser/TestData/denoiser_test_set.sff.txt\" % PROJECT_HOME,\r\n \"-f\", \"%s/qiime/support_files/denoiser/TestData/test_set_seqs.fna\" % PROJECT_HOME,\r\n \"-o\", self.test_dir])\r\n\r\n result = Popen(command, shell=True, universal_newlines=True,\r\n stdout=PIPE, stderr=STDOUT).stdout.read()\r\n self.result_dir = self.test_dir\r\n\r\n for subdir in [\"0/\", \"1/\"]:\r\n observed = \"\".join(\r\n list(open(self.result_dir + subdir + \"centroids.fasta\")))\r\n self.assertEqual(observed, expected_centroids[subdir])\r\n\r\n observed = \"\".join(\r\n list(open(self.result_dir + subdir + \"denoiser_mapping.txt\")))\r\n self.assertEqual(observed, expected_map_string_on_cluster[subdir])","def Clustering(typeVector, behaviour, sampleType):\r\n ProduceVector.produceVector(typeVector, behaviour, sampleType)\r\n vector = ProduceVector.getVector()\r\n\r\n if sampleType == \"\":\r\n sampleType2 = \"ransomware\"\r\n if sampleType == \"2\":\r\n sampleType2 = \"backdoor\"\r\n if sampleType == \"3\":\r\n sampleType2 = \"trojan\"\r\n\r\n if (behaviour == \"f\") or (behaviour == \"fc\"): \r\n\r\n calculateStandardisation(vector)\r\n vector = getStandardisation()\r\n\r\n typeOfVector = typeVector + \" \" + behaviour\r\n\r\n nGram, syscallRep = typeOfVector.split()\r\n\r\n if syscallRep == \"b\":\r\n nGram += \"Full Representation Bit Vector\"\r\n if syscallRep == \"f\":\r\n nGram += \"Full Representation Frequency Vector\"\r\n if syscallRep == \"bc\":\r\n nGram += \"Category Bit Vector\"\r\n if syscallRep == \"fc\":\r\n nGram += \"Category Frequency Vector\"\r\n\r\n\r\n fileName = sampleType2 + \" \" + syscallRep + \" standardisation \" + nGram + \".txt\"\r\n\r\n file = open(fileName, \"w\")\r\n file.write(str(vector.tolist()))\r\n file.close()\r\n\r\n calculateEuclideanDistance(vector) \r\n\r\n typeOfVector = typeVector + \" \" + behaviour\r\n\r\n nGram, syscallRep = typeOfVector.split()\r\n\r\n if syscallRep == \"b\":\r\n nGram += \"Full Representation Bit Vector\"\r\n if syscallRep == \"f\":\r\n nGram += \"Full Representation Frequency Vector\"\r\n if syscallRep == \"bc\":\r\n nGram += \"Category Bit Vector\"\r\n if syscallRep == \"fc\":\r\n nGram += \"Category Frequency Vector\" \r\n\r\n fileName = sampleType2 + \" \" + syscallRep + \" matrix \" + nGram + \".txt\"\r\n\r\n file = open(fileName, \"w\")\r\n file.write(str(getEuclideanDistance().tolist()))\r\n file.close()\r\n\r\n print(\"Producing a dendrogram\")\r\n\r\n\r\n typeOfVector = typeVector + \" \" + behaviour\r\n\r\n setBestCluster(Validation.evaluate(getEuclideanDistance(), vector, typeOfVector, sampleType))","def distortion_of_kmeans_clustering(data_table):\n num_iritations = 5\n singleton_list = []\n for line in data_table:\n singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))\n distortion_list = []\n for num in range(20, 5, -1):\n cluster_list = kmeans_clustering(singleton_list,num, num_iritations)\n distortion = compute_distortion(data_table, cluster_list)\n distortion_list.append(distortion)\n return distortion_list\n\n#####################################################################\n# Code to load cancer data, compute a clustering and\n# visualize the results\n\n\n# def run_example():\n# \"\"\"\n# Load a data table, compute a list of clusters and\n# plot a list of clusters\n#\n# Set DESKTOP = True/False to use either matplotlib or simplegui\n# \"\"\"\n# data_table = load_data_table(DATA_3108_URL)\n# singleton_list = []\n# for line in data_table:\n# singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))\n num_clusters = 16\n # cluster_list = sequential_clustering(singleton_list, num_clusters)\n # print(\"Displaying\", len(cluster_list), \"sequential clusters\")\n #\n # cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, num_clusters)\n # print(\"Displaying\", len(cluster_list), \"hierarchical clusters\")\n #\n # cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, num_clusters, 5)\n # print(\"Displaying\", len(cluster_list), \"k-means clusters\")\n\n # draw the clusters using matplotlib or simplegui\n #\n # if DESKTOP:\n # # alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)\n # alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True) #add cluster centers\n\n # else:\n # alg_clusters_simplegui.PlotClusters(data_table, cluster_list) # use toggle in GUI to add cluster centers","def report(\n df: pd.DataFrame,\n id_col: str = \"Compound_Id\",\n columns: List[str] = [\"Compound_Id\", \"Smiles\"],\n title: str = \"Cluster Report\",\n intro: str = \"Large clusters first, similar clusters together.\",\n):\n\n def add_cluster(cl_no, sim_to=None):\n if sim_to is None:\n sim_to = \"\"\n html.append(\"<hr>\")\n else:\n sim_to = f\"(similar to {sim_to})\"\n mf_cl = mf.MolFrame(df.query(\"Cluster_No == @cl_no\")[columns])\n mf_cl = mf_cl.add_mols()\n html.append(\n f\"<br><h2>Cluster {cl_no} ({len(mf_cl.data)} Members) {sim_to}</h2><br>\"\n )\n grid = mfv.html_grid(mf_cl.data, id_col=\"Compound_Id\")\n html.append(grid)\n\n if id_col not in columns:\n columns = [id_col] + columns\n if \"Smiles\" not in columns:\n columns.append(\"Smiles\")\n df_repr = df.query(\"IsRepr == 'Yes'\").reset_index().drop(\"index\", axis=1)\n chem_sim = {}\n for idx, rec0 in df_repr.iterrows():\n for _, rec1 in df_repr.iloc[idx + 1 :].iterrows():\n cl0 = rec0[\"Cluster_No\"]\n cl1 = rec1[\"Cluster_No\"]\n sim = mf.chem_sim(rec0[\"Smiles\"], rec1[\"Smiles\"])\n chem_sim[(cl0, cl1)] = sim\n chem_sim[(cl1, cl0)] = sim\n\n cl_sizes = (\n df[[\"Cluster_No\", \"Compound_Id\"]]\n .groupby(by=\"Cluster_No\")\n .count()\n .reset_index()\n .rename(columns={\"Compound_Id\": \"Size\"})\n )\n cl_sizes = cl_sizes.sort_values(\"Size\", ascending=False)\n cl_order = {x: True for x in cl_sizes[\"Cluster_No\"].values}\n\n html = [f\"<h1>{title}</h1><br>{intro}<br><br>\"]\n while len(cl_order) > 0:\n cl_no = list(cl_order.keys())[0]\n add_cluster(cl_no)\n cl_order.pop(cl_no)\n to_remove = []\n for sim_cl in cl_order:\n if chem_sim[(cl_no, sim_cl)] > 0.45:\n add_cluster(sim_cl, cl_no)\n to_remove.append(sim_cl)\n for x in to_remove:\n cl_order.pop(x)\n\n mfht.write(mfht.page(\"\\n\".join(html)), \"Clusters.html\")","def test_gethtml_multiple(self):\r\n mock_module = VerticalWithModulesFactory.create()\r\n out_html = mock_module.render('student_view').content\r\n self.assertTrue('Test numerical problem.' in out_html)\r\n self.assertTrue('Another test numerical problem.' in out_html)","def test_main_split_cluster(self):\n \n command = \" \".join( [\"%s/denoiser.py\" % get_qiime_scripts_dir(),\n \"-S\", \"--force\", '-c', '-n 2',\n \"-i\", \"%s/qiime/support_files/denoiser/TestData/denoiser_test_set.sff.txt\" % PROJECT_HOME,\n \"-f\", \"%s/qiime/support_files/denoiser/TestData/test_set_seqs.fna\" % PROJECT_HOME,\n \"-o\", self.test_dir] )\n\n result = Popen(command,shell=True,universal_newlines=True,\\\n stdout=PIPE,stderr=STDOUT).stdout.read()\n self.result_dir = self.test_dir\n\n for subdir in [\"0/\",\"1/\"]:\n observed = \"\".join(list(open(self.result_dir+ subdir+\"centroids.fasta\")))\n self.assertEqual(observed, expected_centroids[subdir])\n\n observed = \"\".join(list(open(self.result_dir+ subdir+\"denoiser_mapping.txt\")))\n self.assertEqual(observed, expected_map_string_on_cluster[subdir])","def test(dist_param, picker_param, iters):\n orig = '/home/zby/MAGISTERKA/MGR/results/oryginal.clustered.t'\n cl_orig = read_clustered(orig)\n name_tag = ''\n ndist = dist_param[1:]\n npick = picker_param[1:]\n for index in drange(4, 20, 0.5):\n name_tag = \"{}_{}_{}\".format(index, npick, ndist)\n tf_conf = configs.TfidfConfig(\n root_name('all_merged.txt', None),\n tfidf_name('merged.stem{}.stop', name_tag),\n tfidf_name('merged.stem{}.stop.txt', name_tag),\n None,\n tfidf_name('merged.stem{}.tfidf', name_tag),\n 10,\n 0,\n None)\n execute(tf_conf)\n tf_conf = configs.TfidfConfig(\n root_name('all_merged.txt', None),\n None,\n tfidf_name('merged.stem{}.stop.txt', name_tag),\n tfidf_name('merged.stem{}.stop', name_tag),\n tfidf_name('merged.stem{}.stop.tfidf', name_tag),\n None,\n None,\n None)\n execute(tf_conf)\n #input, out, picker, distance, iterations,\n clust_cfg = configs.ClusteringConfig(\n tfidf_name('merged.stem{}.stop.tfidf', name_tag),\n tfidf_name('merged.stem{}.stop.clustered.t', name_tag),\n picker_param,\n dist_param,\n iters,\n None\n )\n execute(clust_cfg)\n clust2 = read_clustered(tfidf_name('merged.stem{}.stop.clustered.t', name_tag))\n var, norm = variation_of_information(cl_orig, clust2)\n print(\"**** FOR var {} VOI is {}\".format(name_tag, norm))","def test_get_root_html(self):\n pass","def test_extract_cluster_size(self):\r\n\r\n header1 = \">GCC6FHY01EQVIC | cluster size: 5\"\r\n header2 = \">blabla\"\r\n\r\n self.assertEqual(extract_cluster_size(header1), 5)\r\n self.assertEqual(extract_cluster_size(header2), 0)","def test_rr_summary_cluster(model, clusters):\n test_result = model.fit(groupvar=clusters, cov_type=\"cluster\").summary()\n assert isinstance(test_result.tables, list)\n assert len(test_result.tables) == 3\n assert len(test_result.extra_txt) > 0","def test_cluster_graph(self):\n g = Graph.build(edges=[\n ('a', 'e', 8), ('a', 'c', 7), ('a', 'd', 9), ('a', 'f', 10), ('a', 'b', 8),\n ('e', 'f', 6), ('e', 'd', 5), ('e', 'b', 10), ('e', 'c', 1),\n ('c', 'f', 5), ('c', 'd', 4), ('c', 'b', 9),\n ('d', 'f', 1), ('d', 'b', 7),\n ('f', 'b', 8)\n ], directed=False)\n\n (clusters, distances) = cluster_graph(g, 4)\n expectedClusters = {\n 'a': ['a'],\n 'c': ['c', 'e'],\n 'b': ['b'],\n 'd': ['d', 'f']\n }\n expectedDistances = {\n ('b', 'c'): 9,\n ('c', 'd'): 4,\n ('a', 'd'): 9,\n ('a', 'b'): 8,\n ('a', 'c'): 7,\n ('b', 'd'): 7\n }\n self.assertEqual(clusters, expectedClusters,\n 'should return correct clusters')\n self.assertEqual(distances, expectedDistances,\n 'should compute distances')\n\n (clusters, distances) = cluster_graph(g, 3)\n expectedClusters = {\n 'a': ['a'],\n 'b': ['b'],\n 'c': ['c', 'e', 'd', 'f']\n }\n expectedDistances = {\n ('b', 'c'): 7,\n ('a', 'b'): 8,\n ('a', 'c'): 7\n }\n self.assertEqual(clusters, expectedClusters,\n 'should return correct clusters')\n self.assertEqual(distances, expectedDistances,\n 'should compute distances')","def test_export_html(self):\r\n resp = self.client.get_html(self.url)\r\n self.assertEquals(resp.status_code, 200)\r\n self.assertContains(resp, \"Export My Course Content\")","def test_get_root_html1(self):\n pass","def test_get_root_html2(self):\n pass","def _repr_html_(self):\n return (\n f'<b>GalaxyCluster:</b> {self.unique_id} '\n f'(ra={self.ra}, dec={self.dec}) at z={self.z}'\n f'<br>> <b>with columns:</b> {self._str_colnames()}'\n f'<br>> {len(self.galcat)} source galaxies'\n f'<br>{self.galcat._html_table()}'\n )","def test_pcca_1():\n n = 1000 #number of data points\n kk = 3 #number of points where data accumulates\n k = 10 #number of cluster_centers\n factor = 0.1 #how much is the data perturbed\n data = np.zeros((n,1))\n for i in range(0,n):\n data[i] = i % kk + factor * np.random.rand() * math.pow(-1,int(2*np.random.rand()))\n #plt.scatter(data[:,0],np.zeros((n,1)))\n \n clustering = cl.KMeans(data,k)\n cluster_centers = clustering.cluster_centers\n cluster_labels = clustering.cluster_labels\n \n #plt.scatter(cluster_centers[:],np.zeros((k,1)),c='r')\n \n estimator = est.Estimator(cluster_labels, 1, 1)\n matrix = estimator.reversible_transition_matrix\n msm = ana.MarkovStateModel(matrix)\n \n n_pcca_states = 4;\n #fig, ax = plt.subplots(figsize=(6.5, 5))\n pcca_labels = msm.metastable_set_assignments(n_pcca_states)\n #im = ax.scatter(cluster_centers[:, 0], np.zeros((k,1)), c=pcca_labels, s=200)\n #cbar = fig.colorbar(im, ax=ax)\n error = 0;\n for j in range(0,kk):\n for i in range(0,k):\n if (round(cluster_centers[i,0]) == j):\n test = i\n for i in range(0,k):\n if (np.abs(cluster_centers[i,0] - cluster_centers[test,0]) < 2*factor):\n if (not pcca_labels[i] == pcca_labels[test]):\n error = 1\n print(error)\n assert_true(error == 0)","def test_gettesttools_html(self):\n pass","def testHTML(self):\n\n html = self.E.html()","def print_outputs(self, alpha=None):\n if alpha:\n cluster_dir = self.alpha_cluster_dir\n optimal_k = self.tl_model.optimal_k\n area = (self.tl_model.LON_E-self.tl_model.LON_W)*(self.tl_model.LAT_N-self.tl_model.LAT_S)\n else:\n cluster_dir = self.cluster_dir\n optimal_k = self.optimal_k\n area = (self.LON_E-self.LON_W)*(self.LAT_N-self.LAT_S)\n ind_cluster_plots_dir = str(Path(self.cluster_dir) / \"indiv_cluster_plots\")\n self.ind_cluster_plots_dir = ind_cluster_plots_dir\n os.makedirs(self.ind_cluster_plots_dir, exist_ok=True)\n print(f'self.ind_cluster_plots_dir is @:\\n{self.ind_cluster_plots_dir}')\n\n print(f'cluster_dir: \"{cluster_dir}\", optimal_k: \"{optimal_k}\"')\n min_area = 2000\n if area > min_area: too_large = True\n else: too_large = False\n\n\n if not utils.find('*_prelim_SOMscplot_*.png', cluster_dir): visualization.print_som_scatterplot_with_dmap(self, cluster_dir)\n if not utils.find('*_kmeans*.png', cluster_dir): visualization.print_kmeans_scatterplot(self, cluster_dir, optimal_k)\n self.grid_width = visualization.grid_width(optimal_k)\n self.cbar_pos = np.max([(((clus)//self.grid_width)*self.grid_width)\n for i,clus in enumerate(np.arange(optimal_k))\n if i==(((clus)//self.grid_width)*self.grid_width)])\n if not utils.find('*_ARmonthfrac_*.png', cluster_dir): visualization.print_ar_plot(self, cluster_dir, optimal_k)\n if not utils.find('*_ARmonthfrac_granular_*.png', cluster_dir): visualization.print_ar_plot_granular(self, cluster_dir, optimal_k)\n if not utils.find('*_RFplot_mean_*.png', cluster_dir): visualization.print_rf_mean_plots(self, cluster_dir, optimal_k)\n if not utils.find('*_RFplot_max_v2*.png', cluster_dir): visualization.print_rf_max_plots(self, cluster_dir, optimal_k)\n if not utils.find('*_RFplot_rainday_gt1mm_v3_*.png', cluster_dir): visualization.print_rf_rainday_gt1mm_plots(self, cluster_dir, optimal_k)\n if not utils.find('*_RFplot_rainday_gt1mm_SGonly_v3_*.png', cluster_dir): visualization.print_rf_rainday_gt1mm_SGonly_plots(self, cluster_dir, optimal_k)\n if not utils.find('*_RFplot_heavyrainday_gt50mm_v2_*.png', cluster_dir): visualization.print_rf_heavyrf_gt50mm_plots(self, cluster_dir, optimal_k)\n if not utils.find('*_RFplot_90th_percentile_v2*.png', cluster_dir): visualization.print_rf_90th_percentile_plots(self, cluster_dir, optimal_k)\n if not utils.find('*_qp_v3*.png', cluster_dir): visualization.print_quiver_plots(self, cluster_dir, optimal_k)\n if not utils.find('*_qp_v5*.png', cluster_dir): visualization.print_quiver_plots(self, cluster_dir, optimal_k)\n if not utils.find('*_qp_v1_ANOM*.png', cluster_dir): visualization.print_quiver_ANOM_whole(self, cluster_dir, optimal_k)\n if not utils.find('*_qp_sgonly*.png', cluster_dir): visualization.print_quiver_plots_sgonly(self, cluster_dir, optimal_k)\n \n if not utils.find('*qp_baseline*.png', cluster_dir): visualization.print_quiver_baseline(self, cluster_dir, optimal_k)\n if not utils.find('*qp_baseline_Regionalonly*.png', cluster_dir): visualization.print_quiver_baseline_regional(self, cluster_dir, optimal_k)\n if not utils.find('*_rhum_v3-at*.png', cluster_dir): visualization.print_rhum_plots(self, cluster_dir, optimal_k)\n if not utils.find('*_rhum_sgonly-at*.png', cluster_dir): visualization.print_rhum_plots_sgonly(self, cluster_dir, optimal_k)\n if not utils.find('*_rhum_v5_ANOM-at*.png', cluster_dir): visualization.print_RHUM_ANOM_whole(self, cluster_dir, optimal_k)\n if not utils.find('*rhum_baseline*.png', cluster_dir): visualization.print_rhum_baseline(self, cluster_dir, optimal_k)\n\n if not utils.find('*RFplot_90th_percentile_baseline*.png', cluster_dir): visualization.print_RF_baselines(self, cluster_dir, optimal_k, \n too_large)\n\n if not utils.find('*_qp_Regionalonly*.png', cluster_dir): visualization.print_quiver_Regionalonly(self, cluster_dir, optimal_k)\n if not utils.find('*_rhum_Regionalonly*.png', cluster_dir): visualization.print_RHUM_Regionalonly(self, cluster_dir, optimal_k)\n if not utils.find('*_rhum_Regionalonly_ANOM_v2*.png', cluster_dir): visualization.print_RHUM_ANOM_Regionalonly(self, cluster_dir, optimal_k)\n if not utils.find('*_qp_Regionalonly_ANOM*.png', cluster_dir): visualization.print_quiver_ANOM_Regionalonly(self, cluster_dir, optimal_k)\n if not utils.find('*_RFplot_90th_percentile_SGonly_ANOM_v1_*.png', cluster_dir): visualization.print_rf_90th_percentile_SGonly_ANOM_plots(self, cluster_dir, optimal_k)\n if not utils.find('*_RFplot_rainday_gt1mm_SGonly_ANOM_v3_*.png', cluster_dir): visualization.print_rf_rainday_gt1mm_SGonly_ANOM_plots(self, cluster_dir, optimal_k)\n if not utils.find('*_RFplot_heavy_gt50mm_SGonly_ANOM_v2_*.png', cluster_dir): visualization.print_rf_heavy_gt50mm_SGonly_ANOM_plots(self, cluster_dir, optimal_k)\n if not utils.find('*_RFplot_rainday_gt1mm_ANOM_v1_*.png', cluster_dir): visualization.print_rf_rainday_gt1mm_ANOM_plots(self, cluster_dir, optimal_k)\n\n if not utils.find('*_RFplot_heavy_gt50mm_ANOM_v2_*.png', cluster_dir): visualization.print_rf_heavy_gt50mm_ANOM_plots(self, cluster_dir, optimal_k)\n if not utils.find('*_RFplot_rainday_gt1mm_zscores_v2_*.png', cluster_dir): visualization.print_rf_gt1mm_zscore(self, cluster_dir, optimal_k, \n too_large)\n if not utils.find('*_RFplot_heavy_gt50mm_zscores_v2_*.png', cluster_dir): visualization.print_rf_heavy_gt50mm_zscore(self, cluster_dir, optimal_k, \n too_large)\n if not utils.find('*_RFplot_rainday_gt1mm_SGonly_zscores_v2_*.png', cluster_dir): visualization.print_rf_heavy_gt1mm_SGonly_zscore(self, cluster_dir, optimal_k, \n too_large)\n if not utils.find('*_RFplot_heavy_gt50mm_SGonly_zscores_v3_*.png', cluster_dir): visualization.print_rf_heavy_gt50mm_SGonly_zscore(self, cluster_dir, optimal_k, \n too_large)\n if not utils.find('*_RFplot_90th_percentile_ANOM_v1_*.png', cluster_dir): visualization.print_rf_90th_percentile_ANOM_plots(self, cluster_dir, optimal_k, \n too_large)\n \n if not utils.find('*_RFplot_rainday_gt1mm_Regionalonly_ANOM_v2_*.png', cluster_dir): visualization.print_rf_gt1mm_ANOM_Regionalonly(self, cluster_dir, optimal_k)\n if not utils.find('*_RFplot_heavy_gt50mm_Regionalonly_ANOM_v2_*.png', cluster_dir): visualization.print_rf_gt50mm_ANOM_Regionalonly(self, cluster_dir, optimal_k)\n \n\n if not alpha:\n if not utils.find('*RFprec_to_ClusterLabels_dataset_vals_5xcoarsened_maxed.pkl', ind_cluster_plots_dir):\n print(f'5X coarsened RF full dataset not found. Generating now @:\\n{ind_cluster_plots_dir}')\n self.full_rf_5Xcoarsened_vals_path = prepare.prepare_RF_vals_5x_coarsened(self, ind_cluster_plots_dir)\n else:\n self.full_rf_5Xcoarsened_vals_path = utils.find('*RFprec_to_ClusterLabels_dataset_vals_5xcoarsened_maxed.pkl', ind_cluster_plots_dir)[0]\n # for clus in range(optimal_k):\n # \"\"\"\n # Individual test plots\n # \"\"\"\n # if not utils.find(f\"*RF_proportion_above_90thpercentile_cluster_{int(clus+1)}.png\", ind_cluster_plots_dir): visualization.print_ind_clus_proportion_above_90thpercentile(self, ind_cluster_plots_dir, clus)\n # if not utils.find(f\"*RF_proportion_under_10thpercentile_cluster_v2_{int(clus+1)}.png\", ind_cluster_plots_dir): visualization.print_ind_clus_proportion_under_10thpercentile(self, ind_cluster_plots_dir, clus)\n # if not utils.find(f\"*RF_proportion_above_fullmodel_mean_cluster_{int(clus+1)}.png\", ind_cluster_plots_dir): visualization.print_ind_clus_proportion_above_fullmodel_mean(self, ind_cluster_plots_dir, clus)\n # if not utils.find(f\"*RF_proportion_above_250mm_cluster_{int(clus+1)}.png\", ind_cluster_plots_dir): visualization.print_ind_clus_proportion_above_250mm(self, ind_cluster_plots_dir, clus)\n \n \n print(\n f'Outputs are available @: \\n{cluster_dir}\\n\\nThese are clustering results for domain {self.domain} ({self.period}), '\\\n f'SOM-trained with hpparams {self.hyperparameters} \\nand 2nd-level k-means clustered with k at {optimal_k}.')","def test_summary_with_results(self):\n add_constituency_result_line('X, 10, C')\n r = self.client.get('/summary')\n self.assertEqual(r.status_code, 200)\n soup = BeautifulSoup(r.data, 'html.parser')\n self.assertIs(soup.find(id='no-results'), None)\n self.assertIsNot(soup.find(id='results-table'), None)","def test_si_sample_html_partial(self):\n sample = load_sample('si-game.sample.html')\n doc = Document('http://sportsillustrated.cnn.com/baseball/mlb/gameflash/2012/04/16/40630_preview.html',\n sample)\n res = doc.get_clean_article()\n self.assertEqual('<div><div class=\"', res[0:17])","def test_get_root_html3(self):\n pass","def compute_cluster_similarities(emb_clusters1, emb_clusters2, compare, order, clmethod, plot):\n def compute_sim(e, e1, cls, cls1):\n sims = np.empty((20, 20))\n xticks, yticks = [], []\n for i, c in enumerate(cls):\n yticks.append(', '.join(c[1]) + (f' {round(c[3], 5)}' if order == 'avgfreq' else ''))\n for j, c1 in enumerate(cls1):\n if len(xticks) < 20:\n xticks.append(', '.join(c1[1]) + (f' {round(c1[3], 5)}' if order == 'avgfreq' else ''))\n sims[i, j] = jaccard_similarity_score(c[2], c1[2])\n jaccard_similarities[f'{e}-{e1}'] = sims\n\n if plot:\n if order == 'clustermap':\n similarity_clustermap(sims, xticks, yticks, f'{e}-{e1}_{clmethod}')\n elif order == 'default' or order == 'avgfreq':\n similarity_heatmap(sims, xticks, yticks, f'{e}-{e1}_{clmethod}', order)\n else:\n pass\n\n jaccard_similarities = {}\n if compare == 'cross':\n for ie, (e, cls) in enumerate(emb_clusters1.items()):\n for ie1, (e1, cls1) in enumerate(emb_clusters2.items()):\n if ie < ie1:\n compute_sim(e, e1, cls, cls1)\n elif compare == 'dot':\n for (e, cls), (e1, cls1) in zip(emb_clusters1.items(), emb_clusters2.items()):\n compute_sim(e, e1, cls, cls1)\n\n return jaccard_similarities","def gen_html_report(landmarks_list, usage_flag, output_folder):\n labelled_landmarks = landmarks_list[0]\n\n if usage_flag == 2:\n detected_landmarks = landmarks_list[1]\n assert len(labelled_landmarks.keys()) == len(detected_landmarks.keys())\n\n # sort the labelled landmarks according to detection error\n error_summary = error_analysis(labelled_landmarks, detected_landmarks)\n\n landmark_name_list = labelled_landmarks[list(labelled_landmarks.keys())[0]].keys()\n for landmark_idx, landmark_name in enumerate(landmark_name_list):\n print(\"Generating html report for landmark {}: {}.\".format(landmark_idx, landmark_name))\n image_link_template = r\"<div class='content'><img border=0 src= '{0}' hspace=1 width={1} class='pic'></div>\"\n error_info_template = r'<b>Labelled</b>: [{0:.2f}, {1:.2f}, {2:.2f}];'\n document_text = r'\"<h1>check predicted coordinates:</h1>\"'\n document_text += \"\\n\"\n\n if usage_flag == 1:\n image_list = list(labelled_landmarks.keys())\n for image_idx, image_name in enumerate(image_list):\n label_landmark_world = labelled_landmarks[image_name][landmark_name]\n document_text = \\\n gen_row_for_html(usage_flag, image_link_template, error_info_template,\n document_text, image_list, image_idx, landmark_idx,\n [label_landmark_world], None)\n \n elif usage_flag == 2:\n image_list = error_summary.all_cases[landmark_name]\n error_sorted_index = error_summary.error_sorted_index\n for image_idx in error_sorted_index[landmark_name]:\n image_name = image_list[image_idx]\n label_landmark_world = labelled_landmarks[image_name][landmark_name]\n detected_landmark_world = detected_landmarks[image_name][landmark_name]\n error_info_template = r'<b>Labelled</b>: [{0:.2f}, {1:.2f}, {2:.2f}];'\n error_info_template += r'<b>Detected</b>: [{3:.2f}, {4:.2f}, {5:.2f}]; '\n error_info_template += r'<b>Type</b>: {6};'\n error_info_template += r'<b>Error</b>: x:{7:.2f}; y:{8:.2f}; z:{9:.2f}; L2:{10:.2f};'\n document_text = \\\n gen_row_for_html(usage_flag, image_link_template, error_info_template,\n document_text, image_list, image_idx, landmark_name,\n [label_landmark_world, detected_landmark_world],\n error_summary)\n\n else:\n raise ValueError('Undefined usage flag!')\n\n if usage_flag == 1:\n analysis_text = gen_analysis_text(len(image_list), usage_flag,\n labelled_landmarks, landmark_name, None)\n\n elif usage_flag == 2:\n analysis_text = gen_analysis_text(len(image_list), usage_flag,\n labelled_landmarks, landmark_name, error_summary)\n\n else:\n raise ValueError('Undefined usage float!')\n\n html_report_name = 'result_analysis.html'\n html_report_folder = os.path.join(output_folder, 'lm{}'.format(landmark_idx))\n if not os.path.isdir(html_report_folder):\n os.makedirs(html_report_folder)\n \n html_report_path = os.path.join(html_report_folder, html_report_name)\n write_html_report_for_single_landmark(document_text, analysis_text, html_report_path, width=200)\n\n if usage_flag == 2:\n summary_csv_report_name = 'summary.csv'\n summary_csv_path = os.path.join(output_folder, summary_csv_report_name)\n write_summary_csv_report_for_all_landmarks(error_summary, summary_csv_path)","def cluster(approach, datapath):\n report_path = test(datapath, approach, params[approach])\n c.echo('Report compiled at {0}.'.format(report_path))","def test_get_distribution_centers(self):\n pass","def generateHtml(self):\n # only the master processor needs to do this\n if not self.master: return\n\n for page in self.layout.pages:\n \n # build the metric dictionary\n metrics = {}\n page.models = []\n for fname in glob.glob(os.path.join(self.output_path,\"*.nc\")):\n with Dataset(fname) as dataset:\n mname = dataset.getncattr(\"name\")\n if mname != \"Benchmark\": page.models.append(mname)\n if not dataset.groups.has_key(page.name): continue\n group = dataset.groups[page.name]\n\n # if the dataset opens, we need to add the model (table row)\n metrics[mname] = {}\n \n # each model will need to have all regions\n for region in self.regions: metrics[mname][region] = {}\n \n # columns in the table will be in the scalars group\n if not group.groups.has_key(\"scalars\"): continue\n \n # we add scalars to the model/region based on the region\n # name being in the variable name. If no region is found,\n # we assume it is the global region.\n grp = group.groups[\"scalars\"]\n for vname in grp.variables.keys():\n found = False\n for region in self.regions:\n if region in vname: \n found = True\n var = grp.variables[vname]\n name = vname.replace(region,\"\")\n metrics[mname][region][name] = Variable(name = name,\n unit = var.units,\n data = var[...])\n if not found:\n var = grp.variables[vname]\n metrics[mname][\"global\"][vname] = Variable(name = vname,\n unit = var.units,\n data = var[...])\n page.setMetrics(metrics)\n \n # write the HTML page\n f = file(os.path.join(self.output_path,\"%s.html\" % (self.name)),\"w\")\n f.write(str(self.layout))\n f.close()","def test_render_student_view(self):\r\n html = self.module_system.render(self.vertical, 'student_view', {}).content\r\n self.assertIn(self.test_html_1, html)\r\n self.assertIn(self.test_html_2, html)","def output_test():\n\toutput_comparison_page(TEST_EVENT_LIST, TEST_COMPARISON_PAGE_FILEPATH)","def output_div(self, output_method):\n instance = self.instance\n G = myGraph(instance.view_num)\n for i in range(instance.view_num):\n view = instance.tables[instance.views[i].table_pos].views[instance.views[i].view_pos]\n G.addNode(view)\n G.getSim()\n result = G.getTopK(instance.view_num)\n order = 1\n export_list = []\n if output_method == 'list':\n for item in result:\n export_list.append(G.nodes[item].output(order))\n order += 1\n return export_list\n elif output_method == 'print':\n for item in result:\n pprint (G.nodes[item].output(order))\n order += 1\n return\n elif output_method == 'single_json' or output_method == 'multiple_jsons':\n path2 = os.getcwd() + '/json/'\n if not os.path.exists(path2):\n os.mkdir(path2)\n if output_method == 'single_json':\n f = open(path2 + self.table_name + '.json','w')\n for item in result:\n f.write(G.nodes[item].output(order) + '\\n')\n order += 1\n f.close() # Notice that f.close() is out of the loop to create only one file\n else: #if output_method == 'multiple_jsons'\n for item in result:\n f = open(path2 + self.table_name + str(order)+'.json','w')\n f.write(G.nodes[item].output(order))\n order += 1\n f.close() # Notice that f.close() is in the loop to create multiple files\n return\n elif output_method == 'single_html' or output_method == 'multiple_htmls':\n path2 = os.getcwd() + '/html/'\n if not os.path.exists(path2):\n os.mkdir(path2)\n page = Page()\n if output_method == 'single_html':\n self.page = Page()\n for item in result:\n view = G.nodes[item]\n self.html_output(order, view, 'single')\n order += 1\n self.page.render('./html/' + self.table_name + '_all' + '.html')\n else: # if output_method == 'multiple_htmls'\n path3 = os.getcwd() + '/html/' + self.table_name\n if not os.path.exists(path3):\n os.mkdir(path3)\n for item in result:\n view = G.nodes[item]\n self.html_output(order, view, 'multiple')\n order += 1\n return","def main(argv=None):\n\n if argv is None:\n argv = sys.argv\n\n # setup command line parser\n parser = E.OptionParser(version=\"%prog version: $Id$\",\n usage=globals()[\"__doc__\"])\n\n parser.add_option(\"-t\", \"--test\", dest=\"test\", type=\"string\",\n help=\"supply help\")\n\n parser.add_option(\"--method\", dest=\"method\", type=\"choice\",\n choices=(\"metrics\", \"summary\", \"module_summary\"),\n help=\"method to summarise clustering\")\n\n parser.add_option(\"--ref-gtf-files\", dest=\"ref_gtf\", type=\"string\",\n help=\"comma separated list of reference gtf files\")\n\n # add common options (-h/--help, ...) and parse command line\n (options, args) = E.Start(parser, argv=argv)\n\n if options.method == \"metrics\":\n infile = argv[-1]\n E.info(\"loading input file: %s\" % infile)\n assert infile\n\n df = pd.read_table(infile,\n sep=\"\\t\",\n header=None,\n index_col=0)\n\n df = df.ix[:, :50]\n cluster_combs = (x for x in itertools.combinations(df.columns,\n 2))\n genes = df.index\n results_dict = {}\n all_clusts = {}\n\n E.info(\"setting up cluster containers\")\n for i in df.columns:\n clusters = set(df[i].values.tolist())\n cluster_dict = {}\n for clust in clusters:\n cluster_dict[clust] = []\n for gene in genes:\n cluster_dict[df[i][gene]].append(gene)\n\n for col in clusters:\n col_set = set()\n clust_col = cluster_dict[col]\n gene_members = itertools.combinations(clust_col,\n 2)\n col_set.update(gene_members)\n cluster_dict[col] = col_set\n all_clusts[i] = cluster_dict\n E.info(\"generating all pair-wise cluster comparisons\")\n E.info(\"calculating adjusted mutual information\")\n for k in cluster_combs:\n clusters1 = all_clusts[k[0]]\n clusters2 = all_clusts[k[1]]\n metric_dict = {}\n metric_dict['AMI'] = TS.adjustedMutualInformation(clusters1,\n clusters2)\n results_dict[k] = metric_dict\n\n res_frame = pd.DataFrame(results_dict).T\n res_frame = res_frame.reset_index()\n res_frame.drop(['level_0'], inplace=True, axis=1)\n res_frame.drop(['level_1'], inplace=True, axis=1)\n\n # flatten rand indices and add to output dataframe\n rand_arrays = TS.randIndexes(df)\n flat_adj_rand = TS.unravel_arrays(rand_arrays[0])\n flat_rand = TS.unravel_arrays(rand_arrays[1])\n res_frame['Rand_Index'] = flat_rand\n res_frame['Adjusted_Rand_Index'] = flat_adj_rand\n E.info(\"aggregating results\")\n\n res_frame.to_csv(options.stdout,\n sep=\"\\t\",\n index_label='idx')\n\n elif options.method == \"summary\":\n infiles = argv[-1]\n list_of_files = infiles.split(\",\")\n\n file_dict = {}\n for fle in list_of_files:\n fname = fle.split(\"/\")[-1]\n condition = fname.split(\"-\")[0]\n ref = fname.split(\"-\")[1]\n df_ = pd.read_table(fle,\n sep=\"\\t\",\n header=0,\n index_col=0)\n df_.columns = ['gene_id', 'cluster']\n clust_dict = {}\n for idx in df_.index:\n cluster = df_.loc[idx]['cluster']\n gene = df_.loc[idx]['gene_id']\n try:\n clust_dict[cluster] += 1\n except KeyError:\n clust_dict[cluster] = 1\n med_size = np.median(clust_dict.values())\n file_dict[fname] = {'condition': condition,\n 'reference': ref,\n 'median_cluster_size': med_size}\n\n outframe = pd.DataFrame(file_dict).T\n outframe.to_csv(options.stdout,\n sep=\"\\t\",\n index_label='idx')\n\n elif options.method == \"module_summary\":\n # get lncRNA/gene lengths from reference gtfs\n ref_gtfs = options.ref_gtf.split(\",\")\n length_dict = {}\n for ref in ref_gtfs:\n oref = IOTools.openFile(ref, \"rb\")\n git = GTF.transcript_iterator(GTF.iterator(oref))\n for gene in git:\n for trans in gene:\n length = trans.end - trans.start\n try:\n length_dict[trans.gene_id] += length\n except KeyError:\n length_dict[trans.gene_id] = length\n oref.close()\n\n infiles = argv[-1]\n list_of_files = infiles.split(\",\")\n\n fdfs = []\n for fle in list_of_files:\n cond = fle.split(\"/\")[-1].split(\"-\")[0]\n refer = fle.split(\"/\")[-1].split(\"-\")[1]\n _df = pd.read_table(fle, sep=\"\\t\",\n header=0, index_col=0)\n _df.columns = ['gene_id', 'cluster']\n clusters = set(_df['cluster'])\n c_dict = {}\n # summarize over each cluster\n for clust in clusters:\n lengths = []\n c_df = _df[_df['cluster'] == clust]\n for lid in c_df['gene_id']:\n lengths.append(length_dict[lid])\n c_dict[clust] = {'cluster_size': len(c_df['gene_id']),\n 'mean_length': np.mean(lengths),\n 'index': (cond, refer),\n 'module': clust}\n cdf = pd.DataFrame(c_dict).T\n # use a multindex for hierarchical indexing\n midx = pd.MultiIndex.from_tuples(cdf['index'])\n cdf.index = midx\n cdf.drop(['index'], inplace=True, axis=1)\n fdfs.append(cdf)\n\n # generate a single output df\n s_df = fdfs[0]\n fdfs.pop(0)\n for df in fdfs:\n s_df = s_df.append(df)\n\n s_df.to_csv(options.stdout,\n index_label=(\"condition\", \"reference\"),\n sep=\"\\t\")\n\n # write footer and output benchmark information.\n E.Stop()","def compare_silhoutte_scores(dfi, samples, range_n_clusters, cluster_dim='features'):\n df = dfi.fillna(0).copy()\n X = df[samples].values\n if cluster_dim == 'samples':\n X = X.T\n \n\n for n_clusters in range_n_clusters:\n # Create a subplot with 1 row and 2 columns\n fig, (ax1, ax2) = plt.subplots(1, 2)\n fig.set_size_inches(18, 7)\n\n # The 1st subplot is the silhouette plot\n # The silhouette coefficient can range from -1, 1 but in this example all\n # lie within [-0.1, 1]\n ax1.set_xlim([-0.1, 1])\n # The (n_clusters+1)*10 is for inserting blank space between silhouette\n # plots of individual clusters, to demarcate them clearly.\n ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n\n # Initialize the clusterer with n_clusters value and a random generator\n # seed of 10 for reproducibility.\n clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n cluster_labels = clusterer.fit_predict(X)\n\n # The silhouette_score gives the average value for all the samples.\n # This gives a perspective into the density and separation of the formed\n # clusters\n silhouette_avg = silhouette_score(X, cluster_labels)\n print(\"For n_clusters =\", n_clusters,\n \"The average silhouette_score is :\", silhouette_avg)\n\n # Compute the silhouette scores for each sample\n sample_silhouette_values = silhouette_samples(X, cluster_labels)\n\n y_lower = 10\n for i in range(n_clusters):\n # Aggregate the silhouette scores for samples belonging to\n # cluster i, and sort them\n ith_cluster_silhouette_values = \\\n sample_silhouette_values[cluster_labels == i]\n\n ith_cluster_silhouette_values.sort()\n\n size_cluster_i = ith_cluster_silhouette_values.shape[0]\n y_upper = y_lower + size_cluster_i\n\n color = cm.spectral(float(i) / n_clusters)\n ax1.fill_betweenx(np.arange(y_lower, y_upper),\n 0, ith_cluster_silhouette_values,\n facecolor=color, edgecolor=color, alpha=0.7)\n\n # Label the silhouette plots with their cluster numbers at the middle\n ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n\n # Compute the new y_lower for next plot\n y_lower = y_upper + 10 # 10 for the 0 samples\n\n ax1.set_title(\"The silhouette plot for the various clusters.\")\n ax1.set_xlabel(\"The silhouette coefficient values\")\n ax1.set_ylabel(\"Cluster label\")\n\n # The vertical line for average silhouette score of all the values\n ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n\n ax1.set_yticks([]) # Clear the yaxis labels / ticks\n ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n\n # 2nd Plot showing the actual clusters formed\n colors = cm.spectral(cluster_labels.astype(float) / n_clusters)\n ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,\n c=colors, edgecolor='k')\n\n # Labeling the clusters\n centers = clusterer.cluster_centers_\n # Draw white circles at cluster centers\n ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\n c=\"white\", alpha=1, s=200, edgecolor='k')\n\n for i, c in enumerate(centers):\n ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\n s=50, edgecolor='k')\n\n ax2.set_title(\"The visualization of the clustered data.\")\n ax2.set_xlabel(\"Feature space for the 1st feature\")\n ax2.set_ylabel(\"Feature space for the 2nd feature\")\n\n plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n \"with n_clusters = %d\" % n_clusters),\n fontsize=14, fontweight='bold')\n\n plt.show()","def test_make_HTML_table(self):\r\n\r\n # test pie charts\r\n fracs_labels_other, fracs_labels, all_counts, other_cat, red, other_frac = \\\r\n get_fracs(self.counts1, 5, 10, 'pie')\r\n\r\n img_data = make_HTML_table(\"Phylum\", other_frac, 10, red, other_cat,\r\n fracs_labels_other, fracs_labels,\r\n self.dir_path, all_counts, 1, self.prefs,\r\n self.color_prefs, 'black', 'white', 'pie',\r\n 'Test1',\r\n self.generate_image_type, self.plot_width,\r\n self.plot_height, self.bar_width, self.dpi, 0,\r\n 'categorical', False, False)\r\n\r\n self.assertEqual(len(img_data), 2)\r\n\r\n # test area charts\r\n fracs_labels_other, fracs_labels, all_counts, other_cat, red, other_frac = \\\r\n get_fracs(self.counts1, 5, 10, 'area')\r\n\r\n img_data = make_HTML_table(\"Phylum\", other_frac, 10, red, other_cat,\r\n fracs_labels_other, fracs_labels,\r\n self.dir_path, all_counts, 1, self.prefs,\r\n self.color_prefs, 'black', 'white', 'pie',\r\n 'Test1',\r\n self.generate_image_type, self.plot_width,\r\n self.plot_height, self.bar_width, self.dpi, 0,\r\n 'categorical', False, False)\r\n\r\n self.assertEqual(len(img_data), 2)\r\n\r\n # test bar charts\r\n fracs_labels_other, fracs_labels, all_counts, other_cat, red, other_frac = \\\r\n get_fracs(self.counts1, 5, 10, 'bar')\r\n\r\n img_data = make_HTML_table(\"Phylum\", other_frac, 10, red, other_cat,\r\n fracs_labels_other, fracs_labels,\r\n self.dir_path, all_counts, 1, self.prefs,\r\n self.color_prefs, 'black', 'white', 'pie',\r\n 'Test1',\r\n self.generate_image_type, self.plot_width,\r\n self.plot_height, self.bar_width, self.dpi, 0,\r\n 'categorical', False, False)\r\n\r\n self.assertEqual(len(img_data), 2)\r\n self._paths_to_clean_up = [\"/tmp/qiimewebfiles/charts/\" + f\r\n for f in listdir(\"/tmp/qiimewebfiles/charts\")]","def test_make_stats(self):\r\n map = self.mapping\r\n stats = \"\"\"Clustersize\\t#\r\n1:\\t\\t2\r\n2:\\t\\t1\r\n5:\\t\\t1\"\"\"\r\n\r\n self.assertEqual(make_stats(map), stats)","def test_store_cluster(self):\r\n\r\n self.tmpdir = mkdtemp(dir=\"./\", suffix=\"_store_clusters/\")\r\n\r\n self.files_to_remove.append(self.tmpdir + \"singletons.fasta\")\r\n self.files_to_remove.append(self.tmpdir + \"centroids.fasta\")\r\n\r\n # empty map results in empty files\r\n store_clusters({}, self.tiny_test, self.tmpdir)\r\n actual_centroids = list(\r\n parse_fasta(open(self.tmpdir + \"centroids.fasta\")))\r\n self.assertEqual(actual_centroids, [])\r\n actual_singletons = list(\r\n parse_fasta(open(self.tmpdir + \"singletons.fasta\")))\r\n self.assertEqual(actual_singletons, [])\r\n\r\n # non-empty map creates non-empty files, centroids sorted by size\r\n mapping = {'FZTHQMS01B8T1H': [],\r\n 'FZTHQMS01DE1KN': ['FZTHQMS01EHAJG'],\r\n 'FZTHQMS01EHAJG': [1, 2, 3]} # content doesn't really matter\r\n\r\n centroids = [(\r\n 'FZTHQMS01EHAJG | cluster size: 4', 'CATGCTGCCTCCCGTAGGAGTTTGGACCGTGTCTCAGTTCCAATGTGGGGGACCTTCCTCTCAGAACCCCTATCCATCGAAGGTTTGGTGAGCCGTTACCTCACCAACTGCCTAATGGAACGCATCCCCATCGATAACCGAAATTCTTTAATAACAAGACCATGCGGTCTGATTATACCATCGGGTATTAATCTTTCTTTCGAAAGGCTATCCCCGAGTTATCGGCAGGTTGGATACGTGTTACTCACCCGTGCGCCGGTCGCCA'),\r\n ('FZTHQMS01DE1KN | cluster size: 2', 'CATGCTGCCTCCCGTAGGAGTTTGGACCGTGTCTCAGTTCCAATGTGGGGGACCTTCCTCTCAGAACCCCTATCCATCGAAGGTTTGGTGAGCCGTTACCTCACCAACTGCCTAATGGAACGCATCCCCATCGATAACCGAAATTCTTTAATAACAAGACCATGCGGTCTGATTATACCATCGGGTATTAATCTTTCTTTCGAAAGGCTATCCCCGAGTTATCGGCAGGTTGGATACGTGTTACTCACCCGTGCGCCGGTCGCCA')]\r\n\r\n singletons = [(\r\n 'FZTHQMS01B8T1H',\r\n 'CATGCTGCCTCCCGTAGGAGTTTGGACCGTGTCTCAGTTCCAATGTGGGGGACCTTCCTCTCAGAACCCCTATCCATCGAAGGTTTGGTGAGCCGTTACCTCACCAACTGCCTAATGGAACGCATCCCCATCGATAACCGAAATTCTTTAATAATTAAACCATGCGGTTTTATTATACCATCGGGTATTAATCTTTCTTTCGAAAGGCTATCCCCGAGTTATCGGCAGGTTGGATACGTGTTACTCACCCGTGCGCCGGTCGCCATCACTTA')]\r\n\r\n store_clusters(mapping, self.tiny_test, self.tmpdir)\r\n actual_centroids = list(\r\n parse_fasta(open(self.tmpdir + \"centroids.fasta\")))\r\n self.assertEqual(actual_centroids, centroids)\r\n actual_singletons = list(\r\n parse_fasta(open(self.tmpdir + \"singletons.fasta\")))\r\n self.assertEqual(actual_singletons, singletons)","def run(self):\n for l in self.uniqueSkel:\n mask = np.arange(len(self.skel))[self.skelLabels == l]\n counts = self.findNearest(mask)\n self.memberships[l] = counts\n\n #self.memberships is an array of as many rows as skeleton labels and as many columns as Voronoi cluster labels,\n #where the i-th row shows for all skeleton points of cluster label i, how many belong to each of the Voronoi\n #cluster labels. More precisely, the j-th column of the i-th row of this array shows how many skeleton points\n #of cluster label i have a closest Voronoi cell center of label j.\n\n print('Out of ' + str(len(self.skel)) + ' skeleton points, ' + str(sum(self.memberships[:, 0])) + ' (' + str(round(sum(self.memberships[:, 0]) * 100/len(self.skel), 3)) + ' %) appear in areas classified as void areas by Voronoi')\n\n for l in self.uniqueSkel:\n members = sum(self.skelLabels == l)\n topVor = np.argsort(self.memberships[l])[::-1][:5] - 1\n counts = np.sort(self.memberships[l])[::-1][:5]\n print('For the ' + str(members) + ' skeleton points with label ' + str(l) + ': ')\n for i in range(5):\n if counts[i] > 0:\n if topVor[i] == -1:\n add = ' ' + str(counts[i]) + ' ( ' + str(round(counts[i] * 100 / members, 3)) + ' %) are not associated with a Voronoi cluster cell'\n else:\n add = ' ' + str(counts[i]) + ' ( ' + str(round(counts[i] * 100/ members, 3)) + ' %) belong to the Voronoi Cluster with label ' + str(topVor[i])\n print(add)\n\n self.plotResults()","def to_single_html(self):\n self.error_throw('output')\n \n if self.rank_method == methods_of_ranking[3]: #'diversified_ranking'\n self.output_div('single_html')\n else:\n self.output('single_html')","def cluster_testing_dist(agg1, agg2, partDiameter):\n agg2_temp = translate_aggregate(agg2, random_point_generator(calculate_LD(agg1), calculate_LD(agg2), calculate_COM(agg1), calculate_COM(agg2), partDiameter))\n agg2_temp = random_rotate_aggregate(agg2_temp)\n\n check = 1\n while check == 1:\n agg2_temp = translate_aggregate(agg2_temp, numpy.array((calculate_COM(agg1)-calculate_COM(agg2_temp))*0.01))\n check, index = test_collision(agg1, agg2_temp, partDiameter)\n \"\"\" Index from this part is not valid! Function returns '99' before collision happens.\n \"\"\"\n if (check == 2):\n # print(index)\n return numpy.linalg.norm(calculate_COM(agg1) - calculate_COM(agg2_temp)), numpy.linalg.norm(calculate_COM(agg1) - agg2_temp[:,index])\n # return numpy.linalg.norm(calculate_COM(agg1) - agg2_temp[0:3,index])\n break","def cluster_classification(weblog,classification_column_transaction,\\\n classification_column_diversity, session_data_threshold, cluster_type, classification_wanted_transaction, verbose = False):\n if verbose== True:\n start_time = timelib.time()\n print(\"\\n * Computing cluster matrices ...\") \n browsing_matrix = {}\n diversifying_matrix = {}\n # Selecting sessions from each cluster\n for cluster_id in session_data_threshold[cluster_type].unique():\n sessions_cluster = session_data_threshold[session_data_threshold[cluster_type]==cluster_id].session_id\n divpat_log = weblog[weblog.session_id.isin(sessions_cluster)]\n # Filtering some requests\n divpat_log=divpat_log[divpat_log['requested_'+classification_column_transaction].isin(classification_wanted_transaction)]\n divpat_log=divpat_log[divpat_log['referrer_'+classification_column_transaction].isin(classification_wanted_transaction)]\n \n # Defining matrices\n diversity_columns=('referrer_'+classification_column_diversity,'requested_'+classification_column_diversity)\n browsing_matrix[cluster_id],_ = compute_browsing_matrix(divpat_log,'referrer_'+classification_column_transaction,'requested_'+classification_column_transaction,labels=classification_wanted_transaction)\n diversifying_matrix[cluster_id],_ = compute_diversifying_matrix(divpat_log,'referrer_'+classification_column_transaction,'requested_'+classification_column_transaction,\\\n diversity_columns,labels = classification_wanted_transaction)\n if verbose == True:\n print(\" Cluster matrices computed in %.1f seconds.\"%(timelib.time() - start_time))\n \n return browsing_matrix, diversifying_matrix;","def create_html_layout(self):\n page = \"\"\"<!DOCTYPE html>\n <!doctype html>\n <html lang=\"en\">\n <head>\n <meta charset=\"utf-8\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1, shrink-to-fit=no\">\n </head>\n </html>\n <head>\n \t<meta charset=\"UTF-8\">\n </head>\n <body>\n <div class=\"container\">\n <div class=\"row\">\n <div class=\"col-sm\">\n <h4>eda report: Exploratory data analysis</h4>\n </div>\n <div class=\"col-sm\">\n <h3>Inspecting dataframe of size: {size}\n </div>\n </div>\n </div>\n \t<table class=\"table table-hover\" style=\".table\">\n <thead>\n <tr style=\"font-size: 15px;\">\n <th width=\"5%\" align=\"left\" scope=\"col\">Variable Name</th>\n <th width=\"12%\" align=\"left\" scope=\"col\">Data Type</th>\n <th width=\"15%\" align=\"left\" scope=\"col\">Histogram</th>\n <th width=\"11%\" align=\"left\" scope=\"col\">Stats</th>\n <th width=\"7%\" align=\"left\" scope=\"col\">Missing NA</th>\n <th width=\"5%\" align=\"left\" scope=\"col\">Outliers</th>\n </tr>\n </thead>\n <tbody>\"\"\".format(size=self.df.size)\n\n end_page = \"\"\" \n </tbody>\n </table>\n </body>\n \"\"\"\n rows_html = []\n for i, column in enumerate(self.df.columns):\n Summary = ColumnSummary(data=self.df[column])\n datatype = Summary.data_type()\n missing = Summary.missing_values()\n stats = Summary.statistic_summary()\n outliers = Summary.outliers()\n Summary.create_histogram(i)\n html = f\"\"\"\n <tr>\n <td style=\"font-size: 15px;\" width=\"10%\" align=\"left\"> {column}</td>\n <td style=\"font-size: 15px;\"width=\"10%\" align=\"left\"> {datatype}</td>\n <td><img class=\"img-fluid\" src=\"hist_images/histogram{i}.png?{random.randint(0,\n 2e9)}\" style=\"width:800px\"> </td>\n <td style=\"font-size: 15px;\">mean: {stats.mean}<br>\n mode: {stats.mode}<br><br>\n min: {stats.min}<br>\n max: {stats.max}<br><br>\n lower-bound: {stats.lower}<br>\n upper-bound: {stats.upper}<b</td>\n <td style=\"font-size: 15px;\">{missing}</td>\n <td style=\"font-size: 15px;\">{outliers}</td>\n </tr>\n \"\"\"\n rows_html.append(html)\n\n merged_html = page + \"\".join(rows_html) + end_page\n return merged_html","def matching_clusterization(self):\n result = []\n self.reclustering(self.groups.copy(deep=True), result)\n self.result = pd.DataFrame(result)\n return self.result.sort_values(by=['cluster_size'], ascending=False)","def visualize_cluster_analysis_result(\n cluster_analysis_result: dict,\n print_hyperparameters: bool = True,\n interactive: bool = False,\n) -> None:\n for clusterer_name, clusterer_result in cluster_analysis_result[\n \"clusterers\"\n ].items():\n num_clusterer_metrics = len(clusterer_result[\"cluster_metrics\"])\n if interactive:\n fig = make_subplots(\n rows=1,\n cols=num_clusterer_metrics,\n )\n fig.update_layout(\n title={\n \"text\": clusterer_name,\n \"y\": 0.9,\n \"x\": 0.5,\n \"xanchor\": \"center\",\n \"yanchor\": \"top\",\n }\n )\n else:\n _, axes = plt.subplots(\n nrows=1,\n ncols=num_clusterer_metrics,\n figsize=(5 * num_clusterer_metrics, 5),\n )\n plt.suptitle(clusterer_name)\n\n clusterer_params = clusterer_result[\"cluster_params\"]\n if print_hyperparameters:\n print(f\"Hyperparameters used for {clusterer_name}:\")\n for i, hyperparams in enumerate(clusterer_params):\n print(f\"{i}: {hyperparams}\")\n xs = list(range(len(clusterer_params)))\n\n if interactive:\n for i, (clusterer_metric_name, clusterer_metric_result) in zip(\n range(1, num_clusterer_metrics + 1),\n clusterer_result[\"cluster_metrics\"].items(),\n ):\n metric_scores = clusterer_metric_result[\"metric_scores\"]\n best_metric_score_idx = clusterer_metric_result[\n \"best_metric_score_indices\"\n ][0]\n\n fig.update_xaxes(title_text=\"Hyperparameter set (index)\", row=1, col=i)\n fig.update_yaxes(\n title_text=f\"{clusterer_metric_name} score\", row=1, col=i\n )\n fig.update_layout(\n {f\"xaxis{i}\": dict(tickmode=\"array\", tickvals=xs, ticktext=xs)},\n )\n fig.add_trace(\n go.Scatter(x=xs, y=metric_scores, name=clusterer_metric_name),\n row=1,\n col=i,\n )\n fig.add_trace(\n go.Scatter(\n x=[best_metric_score_idx],\n y=[metric_scores[best_metric_score_idx]],\n marker={\n \"size\": 10,\n },\n name=\"Best score\",\n showlegend=False,\n ),\n row=1,\n col=i,\n )\n fig.show()\n else:\n for ax, (clusterer_metric_name, clusterer_metric_result) in zip(\n axes, clusterer_result[\"cluster_metrics\"].items()\n ):\n metric_scores = clusterer_metric_result[\"metric_scores\"]\n best_metric_score_idx = clusterer_metric_result[\n \"best_metric_score_indices\"\n ][0]\n\n ax.set_xticks(xs)\n plot_cluster_metric_scores(\n metric_scores=metric_scores,\n hyperparameters=clusterer_params,\n best_score_idx=best_metric_score_idx,\n metric_name=clusterer_metric_name,\n xlabel=\"Hyperparameter set (index)\",\n set_xticks=False,\n set_xtickslabels=False,\n show_plot=False,\n ax=ax,\n )\n plt.tight_layout()\n plt.show()\n\n total_num_clusterer_metrics = len(\n cluster_analysis_result[\"metric_preferred_clusterers\"]\n )\n if interactive:\n fig = make_subplots(\n rows=1,\n cols=total_num_clusterer_metrics,\n )\n fig.update_layout(\n title={\n \"text\": \"Most preferred clusterers (by metrics)\",\n \"y\": 0.9,\n \"x\": 0.5,\n \"xanchor\": \"center\",\n \"yanchor\": \"top\",\n }\n )\n for i, (clusterer_metric_name, clusterer_metric_result) in zip(\n range(1, total_num_clusterer_metrics + 1),\n cluster_analysis_result[\"metric_preferred_clusterers\"].items(),\n ):\n best_metric_scores = clusterer_metric_result[\"best_metric_scores\"]\n clusterer_names = clusterer_metric_result[\"clusterer_names\"]\n xs = list(range(len(clusterer_names)))\n print(best_metric_scores)\n\n fig.update_xaxes(title_text=\"Clusterer\", row=1, col=i)\n fig.update_yaxes(title_text=f\"{clusterer_metric_name} score\", row=1, col=i)\n fig.update_layout(\n {\n f\"xaxis{i}\": dict(\n tickmode=\"array\", tickvals=xs, ticktext=clusterer_names\n )\n },\n )\n fig.add_trace(\n go.Scatter(x=xs, y=best_metric_scores, name=clusterer_metric_name),\n row=1,\n col=i,\n )\n fig.add_trace(\n go.Scatter(\n x=[0],\n y=[best_metric_scores[0]],\n marker={\n \"size\": 10,\n },\n name=\"Best score\",\n showlegend=False,\n ),\n row=1,\n col=i,\n )\n\n fig.show()\n else:\n _, axes = plt.subplots(\n nrows=1,\n ncols=total_num_clusterer_metrics,\n figsize=(5 * total_num_clusterer_metrics, 5),\n )\n plt.suptitle(\"Most preferred clusterers (by metrics)\")\n for ax, (clusterer_metric_name, clusterer_metric_result) in zip(\n axes, cluster_analysis_result[\"metric_preferred_clusterers\"].items()\n ):\n best_metric_scores = clusterer_metric_result[\"best_metric_scores\"]\n clusterer_names = clusterer_metric_result[\"clusterer_names\"]\n print(best_metric_scores)\n plot_cluster_metric_scores(\n metric_scores=best_metric_scores,\n hyperparameters=clusterer_names,\n best_score_idx=0,\n metric_name=clusterer_metric_name,\n xlabel=\"Clusterer\",\n xtickslabels_rotation=45,\n show_plot=False,\n ax=ax,\n )\n plt.tight_layout()\n plt.show()","def plot_clustering(z_run, labels, engine ='plotly', download = False, folder_name ='clustering'):\n \"\"\"\n def plot_clustering_plotly(z_run, labels):\n\n labels = labels[:z_run.shape[0]] # because of weird batch_size\n\n hex_colors = []\n for _ in np.unique(labels):\n hex_colors.append('#%06X' % randint(0, 0xFFFFFF))\n\n colors = [hex_colors[int(i)] for i in labels]\n\n z_run_pca = TruncatedSVD(n_components=3).fit_transform(z_run)\n z_run_tsne = TSNE(perplexity=80, min_grad_norm=1E-12, n_iter=3000).fit_transform(z_run)\n\n trace = Scatter(\n x=z_run_pca[:, 0],\n y=z_run_pca[:, 1],\n mode='markers',\n marker=dict(color=colors)\n )\n data = Data([trace])\n layout = Layout(\n title='PCA on z_run',\n showlegend=False\n )\n fig = Figure(data=data, layout=layout)\n plotly.offline.iplot(fig)\n\n trace = Scatter(\n x=z_run_tsne[:, 0],\n y=z_run_tsne[:, 1],\n mode='markers',\n marker=dict(color=colors)\n )\n data = Data([trace])\n layout = Layout(\n title='tSNE on z_run',\n showlegend=False\n )\n fig = Figure(data=data, layout=layout)\n plotly.offline.iplot(fig)\n \"\"\"\n def plot_clustering_matplotlib(z_run, labels, download, folder_name):\n\n labels = labels[:z_run.shape[0]] # because of weird batch_size\n\n hex_colors = []\n for _ in np.unique(labels):\n hex_colors.append('#%06X' % randint(0, 0xFFFFFF))\n\n colors = [hex_colors[int(i)] for i in labels]\n\n z_run_pca = TruncatedSVD(n_components=3).fit_transform(z_run)\n z_run_tsne = TSNE(perplexity=80, min_grad_norm=1E-12, n_iter=3000).fit_transform(z_run)\n\n plt.scatter(z_run_pca[:, 0], z_run_pca[:, 1], c=colors, marker='*', linewidths=0)\n plt.title('PCA on z_run')\n if download:\n if os.path.exists(folder_name):\n pass\n else:\n os.mkdir(folder_name)\n plt.savefig(folder_name + \"/pca.png\")\n else:\n plt.show()\n\n plt.scatter(z_run_tsne[:, 0], z_run_tsne[:, 1], c=colors, marker='*', linewidths=0)\n plt.title('tSNE on z_run')\n if download:\n if os.path.exists(folder_name):\n pass\n else:\n os.mkdir(folder_name)\n plt.savefig(folder_name + \"/tsne.png\")\n else:\n plt.show()\n\n #if (download == False) & (engine == 'plotly'):\n # plot_clustering_plotly(z_run, labels)\n if (download) & (engine == 'plotly'):\n print(\"Can't download plotly plots\")\n if engine == 'matplotlib':\n plot_clustering_matplotlib(z_run, labels, download, folder_name)","def test_determine_k(self):\n test_dir_name = os.path.dirname(__file__)\n feat_array_fn = os.path.join(\n test_dir_name, \"data\", \"four_clusters.csv\")\n df = pd.read_csv(feat_array_fn)\n feat_array = df[[\"x\", \"y\"]].values\n\n clusterer = Clusterer(feat_array_fn, \"/dev/null\", [])\n best_k = clusterer._determine_k(feat_array, 9)\n\n self.assertEqual(best_k, 4)\n\n feat_array_fn = os.path.join(\n test_dir_name, \"data\", \"iris.csv\")\n df = pd.read_csv(feat_array_fn)\n feat_array = df[[\n \"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\",\n \"Petal.Width\"]].values\n\n clusterer = Clusterer(feat_array_fn, \"/dev/null\", [])\n best_k = clusterer._determine_k(feat_array, 9)\n\n self.assertEqual(best_k, 2)","def test_example(self): \n num_clusters = 2\n iterations = 5\n ts1 = 2.5 * np.random.randn(100,) + 3\n X_1 = pd.Series(ts1)\n ts2 = 2 * np.random.randn(100,) + 5\n X_2 = pd.Series(ts2)\n ts3 = -2.5 * np.random.randn(100,) + 3\n X_3 = pd.Series(ts3)\n list_of_series = [X_1, X_2, X_3]\n from pynuTS.clustering import DTWKmeans\n clts = DTWKmeans(num_clust = num_clusters, num_iter = iterations)\n clts.fit(list_of_series)\n ts4 = 3.5 * np.random.randn(100,) + 2\n ts5 = -3.5 * np.random.randn(100,) + 2\n X_4 = pd.Series(ts4)\n X_5 = pd.Series(ts5)\n list_new = [X_4, X_5]\n clustering_dict = clts.predict(list_new)\n\n assert type(clustering_dict) is dict\n assert len(clustering_dict) == num_clusters","def gen_SNA_html(result_dict):\n html = \"</br>\"\n \n html += \"<p>Is the graph connected? <b>{}</b></p>\".format(result_dict['is_connected'])\n html += \"<p>Top Nodes by Degree:</p>\"\n for ind, i in enumerate(result_dict['top_nodes_dict']['degree']):\n html += \"<p> {}. <b>{}</b></p>\".format(ind + 1, i)\n html += \"<p>Top Nodes by Betweenness:</p>\"\n for ind, i in enumerate(result_dict['top_nodes_dict']['betweenness']):\n html += \"<p> {}. <b>{}</b></p>\".format(ind + 1, i)\n html += \"<p>Top Nodes by Closeness:</p>\"\n for ind, i in enumerate(result_dict['top_nodes_dict']['closeness']):\n html += \"<p> {}. <b>{}</b></p>\".format(ind + 1, i)\n \n html += \"<p>Top Edges by Edge-Betweenness:</p>\"\n for ind, (i, j) in enumerate(result_dict['top_edges_dict']['edge_betweeness']):\n html += \"<p> {}. <b>{} -> {}</b></p>\".format(ind + 1, i, j)\n \n html += \"Detected Communities: \"\n for ind, i in enumerate(result_dict['communities']):\n html += \"<p> {}. {}</p>\".format(ind + 1, (\", \".join(i)))\n \n html += \"</br>\"\n return html","def test_build_graph(self):\n insert_good_data()\n dataframe = get_dataframe()\n results = processing.build_graph(dataframe, figure_path, False)\n # 1\n self.assertEqual(results, \"Updated html File and Opened it\")","def test_constituency_with_results(self):\n add_constituency_result_line('X, 10, C')\n r = self.client.get('/constituencies')\n self.assertEqual(r.status_code, 200)\n soup = BeautifulSoup(r.data, 'html.parser')\n self.assertIs(soup.find(id='no-results'), None)\n self.assertIsNot(soup.find(id='results-table'), None)","def test_case():\n\n input_csv_file_path = 'data/sample_data.csv'\n output_csv_file_path = 'data/result_data.csv'\n generate_html(input_file=input_csv_file_path,\n output_file=output_csv_file_path)\n\n with open(output_csv_file_path, 'r') as file:\n csv_file = csv.DictReader(file)\n\n # assert the col titles in output file\n assert csv_file.fieldnames == ['assessment_id', 'html']\n\n # assert the row count in output file\n row_count = sum(1 for row in csv_file)\n assert row_count == 3","def test_no_cluster(self, script_runner: ScriptRunner, tmp_path: Path) -> None:\n outfile = tmp_path.joinpath(\"pca-cluster.png\")\n logfile = tmp_path.joinpath(\"plot.log\")\n result = script_runner.run(\n \"qaa\",\n \"plot\",\n \"-i\",\n CLUSTER,\n \"-c\",\n CENTROID,\n \"-o\",\n outfile.as_posix(),\n \"-l\",\n logfile.as_posix(),\n \"--pca\",\n \"--verbose\",\n )\n assert result.success\n assert logfile.exists()\n assert outfile.exists()\n assert outfile.stat().st_size > 0","def test_graphs_view_html(self):\n self.assertTemplateUsed(self.response, 'charts/graphs.html')","def main():\n args = get_args()\n proteins_file = args.proteins\n cdhit_file = args.cdhit\n out_file = args.outfile\n\n for arg_name, file in [('--proteins', proteins_file), ('--cdhit',\n cdhit_file)]:\n if not os.path.isfile(file):\n die('{} \"{}\" is not a file'.format(arg_name, file))\n\n clustered = set()\n for line in open(cdhit_file):\n matches = re.search(r'>gi\\|(?P<gi_num>\\d+)\\|', line)\n if matches:\n clustered.add(matches.group('gi_num'))\n\n # if line.startswith('>'):\n # continue\n #\n # flds = line.split()\n # prot_id = flds[2].split('|')[1]\n # if prot_id.isdigit():\n # clustered.add(prot_id)\n\n out_fh = open(out_file, 'wt')\n num_total = 0\n num_unclustered = 0\n\n for rec in SeqIO.parse(proteins_file, 'fasta'):\n num_total += 1\n prot_id = re.sub(r'\\|.*', '', rec.id)\n if not prot_id in clustered:\n num_unclustered += 1\n SeqIO.write(rec, out_fh, 'fasta')\n\n print('Wrote {:,d} of {:,d} unclustered proteins to \"{}\"'.format(\n num_unclustered, num_total, out_file))","def write_index_html(wk_dir,region_dict,metrics_filename,ext=\"png\"):\n # Make lists of the metrics and figure files to display\n metrics_dir = os.path.join(wk_dir,metrics_dir_name)\n metric_list = sorted([\n f for f in os.listdir(metrics_dir) if f.endswith('_summary.csv')])\n plot_list=[]\n fig_list=sorted([f for f in os.listdir(wk_dir+'/'+figure_dir_name)])\n for keyword in ['lag','correlations','twodpdf']:\n plot_list.append([f for f in fig_list if (keyword in f)]) # sort datasets\n subtitle_list=['Autocorrelation','2D Histograms','Correlation maps']\n\n # Start working on html text. Each line is appened to a list that\n # is then written to file.\n html_file=['<html>\\n',\n '<body>','<head><title>ASoP-Coherence</title></head>\\n',\n '<br><h1>ASoP-Coherence results</h1>\\n','<h2>Contents</h2>\\n',\n '<dl>\\n','<dt><a href=\"#Metrics\">Metrics</a></dt>\\n',\n '<dt><a href=\"#Figures\">Figures</a></dt>\\n',\n '<dd><a href=\"#Autocorrelation\">Autocorrelation</a></dd>\\n',\n '<dd><a href=\"#2D-Histograms\">2D Histograms</a></dd>\\n',\n '<dd><a href=\"#Correlation-maps\">Correlation Maps</a></dd>\\n',\n '</dl>\\n''<section id=\"Metrics\">\\n','<br><h2>Metrics</h2>\\n']\n html_file.append('<h3>Intermittency Metrics</h3>\\n')\n\n # Display metrics JSON in dashboard option\n metrics_json = os.path.basename(metrics_filename)\n metrics_relocated = os.path.join(metrics_dir_name,metrics_json)\n tmp='<p><a href=\"'+metrics_relocated+'\" target=\"_blank\">'+metrics_json+'</a></p>\\n'\n html_file.append(tmp)\n\n # Link CSV tables for download\n html_file.append('<h3>Tables</h3>\\n')\n for metric_file in metric_list:\n metric_path = os.path.join(metrics_dir_name,metric_file)\n html_file.append('<p><a href=\"{0}\">{1}</a></p>\\n'.format(metric_path,metric_file))\n html_file.append('<br>\\n')\n html_file.append('</section>\\n')\n\n # Add figures\n html_file.append('<section id=\"Figures\">\\n')\n html_file.append('<h2>Figures</h2>\\n')\n for title,category in zip(subtitle_list,plot_list):\n html_file.append('<section id='+title.replace(' ','-')+'>\\n')\n html_file.append('<h3>{0}</h3>\\n'.format(title))\n # Adjust figure width for autocorrelation\n fwidth = \"647\"\n if title==\"Autocorrelation\":\n fwidth=\"450\"\n for region in region_dict:\n html_file.append('<h4>{0}</h4>\\n'.format(region.replace('_',' ')))\n region_fig = [f for f in category if (region.replace(\" \",\"_\") in f)]\n for fig in region_fig:\n tmp = '<p><a href=\"{0}\" target=\"_blank\" alt={0}>' + \\\n '<img src=\"{0}\" width={1} alt=\"{0}\"></a></p>\\n'\n html_file.append(\n tmp.format(os.path.join(figure_dir_name,fig),fwidth))\n html_file.append('</section>\\n')\n html_file.append('</section>\\n')\n\n html_file.append('</body>\\n</html>\\n')\n filename=wk_dir+'/index.html'\n with open(filename,'w') as html_page:\n html_page.writelines(html_file)","def main():\n\n# The following codes loads the data set into a 2D np array called data\n\twith open('complete_data.csv') as features_file:\n\t\tcsv_reader = csv.DictReader(features_file, delimiter = ',')\n\t\tdata = []\n\t\tcounter = 0\n\t\tfor row in csv_reader:\n\t\t\tprint(\"csv_reader row:\", row)\n\t\t\t# if(counter == 20):\n\t\t\t# \tbreak\n\t\t\tcounter+=1\n\t\t\tcleaned_row = []\n\t\t\tcleaned_row.append(row['track'])\n\t\t\tcleaned_row.append(row['loudness'])\n\t\t\tcleaned_row.append(row['score'])\n\t\t\tdata.append(np.array(cleaned_row))\n\t\tdata = random.sample(list(data), 30)\n\t\tdata = np.array(data)\n\n\n\tX = []\n\tY = []\n\tcounter = 0\n\tfor row in data:\n\t\t# if(counter == 10):\n\t\t# \tbreak\n\t\t# counter+=1\n\t\tY.append(row[0])\n\t\tl = [float(i) for i in row[1:]]\n\t\tX.append(l)\n\tX = np.array(X)\n\tY = np.array(Y)\n\n\tcentroid_indices2,centroids2 = sk_learn_cluster(X,Y,3)\n\n\tplot_word_clusters(data, centroids2, centroid_indices2 )","def test_cluster_npy(self, script_runner: ScriptRunner, tmp_path: Path) -> None:\n outfile = tmp_path.joinpath(\"pca-cluster.png\")\n logfile = tmp_path.joinpath(\"plot.log\")\n result = script_runner.run(\n \"qaa\",\n \"plot\",\n \"-i\",\n CLUSTNPY,\n \"-c\",\n CENTNPY,\n \"--label\",\n LABELS,\n \"-o\",\n outfile.as_posix(),\n \"-l\",\n logfile.as_posix(),\n \"--pca\",\n \"--cluster\",\n \"--verbose\",\n )\n assert result.success\n assert logfile.exists()\n assert outfile.exists()\n assert outfile.stat().st_size > 0","def test_replace_cluster_network(self):\n pass","def _auto_hdiff2(tcrrep,\n html_name = 'DEFAULT.html',\n pwmat_str_b = 'pw_beta',\n pwmat_str_a = 'pw_alpha',\n single = True,\n generate_svgs = True,\n combine_olga = False,\n verbose = True,\n prune = 3,\n default_hcluster_diff_kwargs = _get_default_kwargs(chains = ['beta'])[0],\n default_member_summ_kwargs = _get_default_kwargs(chains = ['beta'])[1],\n default_plot_hclust_props = _get_default_kwargs(chains = ['beta'])[2]):\n import os\n import pandas as pd\n import numpy as np\n import warnings\n from tcrsampler.sampler import TCRsampler\n from palmotif import compute_pal_motif, svg_logo\n from tcrdist.adpt_funcs import get_centroid_seq, get_centroid_seq_alpha\n from tcrdist.summarize import _select\n from tcrdist.repertoire import TCRrep\n from tcrdist.rep_diff import hcluster_diff, member_summ\n from tcrdist.summarize import _select\n from tcrdist.pgen import OlgaModel\n from palmotif import compute_pal_motif, svg_logo\n from hierdiff import plot_hclust_props\n from numpy.random import randint\n from tcrdist.diversity import generalized_simpsons_entropy\n from tcrdist.diversity import fuzzy_diversity\n \n\n # Load clone_df directly from input object\n if default_hcluster_diff_kwargs['clone_df'] is None:\n default_hcluster_diff_kwargs['clone_df'] = getattr(tcrrep, 'clone_df')\n\n # Get appropirate pwmat_str (pw_beta, pw_alpha, or one of the CDRs, e.g., pw_cdr3_b_aa)\n if 'alpha' in tcrrep.chains:\n pwmat_str = pwmat_str_a\n if 'beta' in tcrrep.chains:\n pwmat_str = pwmat_str_b\n\n\n # Get appropirate pairwise matrix\n \n if default_hcluster_diff_kwargs['pwmat'] is None:\n if verbose : print(f\"pwmat set with {pwmat_str}\")\n default_hcluster_diff_kwargs['pwmat'] = getattr(tcrrep, pwmat_str)\n else: \n if verbose : print(\"pwmat was directly provided as a kwarg\")\n \"\"\"Handle the Fact that 2 or more catagoorical levels are need to run hierdiff\"\"\"\n \n x_cols = default_hcluster_diff_kwargs['x_cols']\n if x_cols is None:\n tcrrep.clone_df['dummy'] = \\\n [['X1','X2'][randint(2)] for x in range(tcrrep.clone_df.shape[0])]\n default_hcluster_diff_kwargs['x_cols'] = ['dummy']\n default_hcluster_diff_kwargs['test_method'] = 'fishers' \n warnings.warn(f\"Because x_cols was None, setting random dummy values, and using {default_hcluster_diff_kwargs['test_method']}\\n\", stacklevel=2)\n \n elif tcrrep.clone_df[x_cols].nunique()[0] == 2: \n default_hcluster_diff_kwargs['test_method'] = 'fishers' \n \n elif tcrrep.clone_df[x_cols].nunique()[0] > 2: \n default_hcluster_diff_kwargs['test_method'] = 'chi2'\n \n elif tcrrep.clone_df[x_cols].nunique()[0] < 2 : \n tcrrep.clone_df['dummy'] = \\\n [['X1','X2'][randint(2)] for x in range(tcrrep.clone_df.shape[0])]\n default_hcluster_diff_kwargs['x_cols'] = ['dummy']\n default_hcluster_diff_kwargs['test_method'] = 'fishers' \n warnings.warn(f\"Because x_cols was None, setting random dummy values, and using {default_hcluster_diff_kwargs['test_method']}\\n\", stacklevel=2)\n\n \"\"\" Run hcluster_df \"\"\"\n \n bar = IncrementalBar(f'Run hcluster_diff :', max = 2, suffix='%(percent)d%%')\n bar.next()\n tcrrep.hcluster_df, tcrrep.Z = hcluster_diff(**default_hcluster_diff_kwargs)\n bar.next(); bar.finish()\n\n tcrrep.hcluster_df['prune'] = tcrrep.hcluster_df['K_neighbors'].apply(lambda x: 1 if (x < prune) else 0)#\n \n \"\"\" Do Basic Summary \"\"\"\n mean_distance_ = list()\n percentage_node_25_ = list()\n percentage_node_50_ = list()\n percentage_node_75_ = list()\n n_rows = tcrrep.hcluster_df.shape[0]\n bar = IncrementalBar(f'Evaluate Clusters :', max = n_rows, suffix='%(percent)d%%')\n for i,r in tcrrep.hcluster_df.iterrows():\n bar.next()\n # <dfnode> is dataframe with all the clones at a given tree node\n dfnode = tcrrep.clone_df.iloc[r['neighbors_i'],]\n # <pwnod> is dataframe with all the clones at a given tree node\n pwnode = getattr(tcrrep, pwmat_str)[r['neighbors_i'],:][:,r['neighbors_i']]\n # get the non-diaganol entries.\n node_non_diag_entries = pwnode[~np.eye(pwnode.shape[0],dtype=bool)]\n # Compute the mean distance at the node \n mean_distance_.append(str(round(node_non_diag_entries.mean(),1)))\n\n percentage_node_25 = 100*(node_non_diag_entries < 25).sum() / (node_non_diag_entries.size)\n percentage_node_50 = 100*(node_non_diag_entries < 50).sum() / (node_non_diag_entries.size)#100*((pwnode < 50).sum() - pwnode.shape[0]) / (pwnode.shape[0] * pwnode.shape[1])\n percentage_node_75 = 100*(node_non_diag_entries < 75).sum() / (node_non_diag_entries.size)#100*((pwnode < 100).sum() - pwnode.shape[0]) / (pwnode.shape[0] * pwnode.shape[1])\n percentage_node_25_.append(f\"{round(percentage_node_25,1)}%\")\n percentage_node_50_.append(f\"{round(percentage_node_50,1)}%\")\n percentage_node_75_.append(f\"{round(percentage_node_75,1)}%\")\n bar.next(); bar.finish()\n tcrrep.hcluster_df['mean_dist'] = mean_distance_\n tcrrep.hcluster_df['pct_dist_25'] =\tpercentage_node_25_\n tcrrep.hcluster_df['pct_dist_50'] =\tpercentage_node_50_\n tcrrep.hcluster_df['pct_dist_75'] =\tpercentage_node_75_\n \n \"\"\"\n By default, treat each clone as a single entity if 'count_col' is single \n \"\"\"\n if default_member_summ_kwargs['count_col'] == 'single':\n single = True\n print(\"MAKING 'single' variable\")\n tcrrep.hcluster_df['single'] = 1\n\n if single:\n tcrrep.clone_df['single'] = 1\n default_member_summ_kwargs['count_col'] = 'single'\n\n\n \"\"\" member_summ\"\"\"\n tcrrep.res_summary = \\\n member_summ(res_df = tcrrep.hcluster_df,clone_df = tcrrep.clone_df, **default_member_summ_kwargs)\n\n\n tcrrep.hcluster_df_detailed = pd.concat([tcrrep.hcluster_df.copy(), tcrrep.res_summary.copy()], axis = 1)\n \n \"\"\" Add diversity stats\"\"\"\n tcrrep.clone_df['single'] = 1\n if single:\n fdiv75 = lambda ind : fuzzy_diversity(tcrrep.clone_df.iloc[ind,:]['single'], getattr(tcrrep, pwmat_str)[ind,:][:,ind],order=2, threshold=75)\n fdiv50 = lambda ind : fuzzy_diversity(tcrrep.clone_df.iloc[ind,:]['single'], getattr(tcrrep, pwmat_str)[ind,:][:,ind],order=2, threshold=50)\n fdiv25 = lambda ind : fuzzy_diversity(tcrrep.clone_df.iloc[ind,:]['single'], getattr(tcrrep, pwmat_str)[ind,:][:,ind],order=2, threshold=25)\n else:\n fdiv75 = lambda ind : fuzzy_diversity(tcrrep.clone_df.iloc[ind,:]['count'], getattr(tcrrep, pwmat_str)[ind,:][:,ind],order=2, threshold=75)\n fdiv50 = lambda ind : fuzzy_diversity(tcrrep.clone_df.iloc[ind,:]['count'], getattr(tcrrep, pwmat_str)[ind,:][:,ind],order=2, threshold=50)\n fdiv25 = lambda ind : fuzzy_diversity(tcrrep.clone_df.iloc[ind,:]['count'], getattr(tcrrep, pwmat_str)[ind,:][:,ind],order=2, threshold=25)\n tcrrep.hcluster_df_detailed['fuzzy_simpson_diversity_25'] = [str(round(fdiv25(ind),2)) for ind in tcrrep.hcluster_df_detailed.neighbors_i.to_list()]\n tcrrep.hcluster_df_detailed['fuzzy_simpson_diversity_50'] = [str(round(fdiv50(ind),2)) for ind in tcrrep.hcluster_df_detailed.neighbors_i.to_list()]\n tcrrep.hcluster_df_detailed['fuzzy_simpson_diversity_75'] = [str(round(fdiv75(ind),2)) for ind in tcrrep.hcluster_df_detailed.neighbors_i.to_list()]\n\n \"\"\"Optional Add SVGs to hcluster_detailed\"\"\"\n if 'beta' in tcrrep.chains:\n _tcrsampler_svgs(tcrrep = tcrrep,\n default_background = None, \n default_background_if_missing = None,\n cdr3_name = 'cdr3_b_aa',\n pwmat_str = pwmat_str_b, \n chain = 'beta', \n gene_names = ['v_b_gene','j_b_gene'],\n combine_olga = combine_olga)\n \n if 'alpha' in tcrrep.chains:\n _tcrsampler_svgs(tcrrep = tcrrep,\n default_background = None, \n default_background_if_missing = None,\n cdr3_name = 'cdr3_a_aa',\n pwmat_str = pwmat_str_a, \n chain = 'alpha', \n gene_names = ['v_a_gene','j_a_gene'],\n combine_olga = combine_olga)\n\n\n \"\"\" Plot \"\"\"\n html = plot_hclust_props(tcrrep.Z,\n res=tcrrep.hcluster_df_detailed,\n prune_col = 'prune',\n **default_plot_hclust_props)\n\n \"\"\" Write File \"\"\"\t\t\n with open(html_name, 'w') as fh:\n print(f\"WRITING {html_name}\")\n fh.write(html)","def simple_test_out(self):\n # uses the same dataset as the docs\n actual_res = self.doc_graph.degree_centrality(degree_option=\"out\")\n # also from docs\n expected_res = {1: 0.75, 2: 0.25, 3: 0, 4: 0.25, 5: 0}\n # compare results with expected results\n pandas_res = actual_res.to_pandas()\n for (index, row) in pandas_res.iterrows():\n row_id = int(row[\"id\"])\n expected_centrality = expected_res[row_id]\n self.assertAlmostEqual(expected_centrality, row[\"degree_centrality\"])","def __evaluate_result(self):\n # Evaluate internally using cophenetic correlation coefficient.\n cpcc = self.__clusterer.calc_cophenetic_coeff()\n \n # Show evaluation through a popup window.\n popup = WarningPopup(self.__gui, 'Clustering evaluation',\n 'Cophenetic correlation coefficient : ' + str(cpcc))\n popup._start()","def test_html_repr():\n repr_html = grid._repr_html_()\n assert repr_html is not None","def test_homepage_render(self):\n\n result = self.client.get(\"/\")\n self.assertIn(\"<h1 class=\\\"title\\\">Bark Park!</h1>\", result.data)","def to_multiple_htmls(self):\n self.error_throw('output')\n \n if self.rank_method == methods_of_ranking[3]: #'diversified_ranking'\n self.output_div('multiple_htmls')\n else:\n self.output('multiple_htmls')","def compare(self):\n len0 = len(self.cluster_lists[0])\n len1 = len(self.cluster_lists[1])\n longer_index = 0 if len0 >= len1 else 1\n shorter_index = 1 if len1 <= len0 else 0\n self.stars_length = len(self.cluster_lists[shorter_index]) \n self.starlets_length = len(self.cluster_lists[longer_index]) \n # build the noeds for shorter cluster list, and get the\n # distribution of cluster size.\n for cluster in self.cluster_lists[shorter_index]:\n len_spectra = len(cluster.get_spectra())\n star = ClusterNode(cluster.id, len_spectra) \n self.stars[cluster.id] = star\n\n self.cluster_spectra_num[shorter_index] += len_spectra\n self.cluster_size_dist[shorter_index][len_spectra] = self.cluster_size_dist[shorter_index].get(len_spectra,0) + 1\n # build the noeds for longer cluster list, and get the\n # distribution of cluster size.\n for cluster in self.cluster_lists[longer_index]:\n len_spectra = len(cluster.get_spectra())\n starlet = ClusterNode(cluster.id, len_spectra) \n self.starlets[cluster.id] = starlet\n\n self.cluster_spectra_num[longer_index] += len_spectra\n self.cluster_size_dist[longer_index][len_spectra] = self.cluster_size_dist[longer_index].get(len_spectra,0) + 1\n # do the comparing, and network building\n for i in range (0, len(self.cluster_lists[shorter_index])):\n cluster0 = self.cluster_lists[shorter_index][i] \n for j in range (i, len(self.cluster_lists[longer_index])):\n cluster1 = self.cluster_lists[longer_index][j] \n (shared_spec_num, similarity) = self.calculate_similarity(cluster0, cluster1)\n if similarity == 0:\n continue\n self.similarity_dist[int(similarity*10)] = self.similarity_dist.get(int(similarity*10),0) + 1\n self.shared_spec_num += shared_spec_num\n\n self.stars[cluster0.id].add_nb_node(cluster1.id, similarity, shared_spec_num)\n self.starlets[cluster1.id].add_nb_node(cluster0.id, similarity, shared_spec_num)\n\n self.ave_star_size = self.cluster_spectra_num[shorter_index]/self.stars_length\n self.ave_starlet_size = self.cluster_spectra_num[longer_index]/self.starlets_length","def _layout_computation_results_to_html(self, embed_chart='offline_embed_js_div'):\n pass","def evaluateClusters( features, labels):\r\n\r\n\treturn silhouette_score( features, labels)","def test_fold_clusters(self):\n cfo = open(\"./cstest.fasta\", 'w')\n cfo.close()\n manager = Manager()\n lock = manager.Lock()\n fold_clusters(lock, \"cluster_1\", self.fastaseqs, \"./cstest.fasta\")\n with open(\"./cstest.fasta\") as obsin:\n obs_fold = obsin.read()\n self.del_files.append(\"./cstest.fasta\")\n expected = \">cluster_1\\n%s\\n\" % self.fastastruct\n for header, seq in self.fastaseqs:\n expected = ''.join([expected, \">%s\\n%s\\n\" % (header, seq)])\n self.assertEqual(obs_fold, expected)","def test_clusters(trained_data, centroids):\n\n for c in range(len(centroids)):\n count_1 = 0\n count_0 = 0\n for p in range(len(trained_data)):\n if trained_data[p][-2] == 0 and trained_data[p][-1] == centroids[c]:\n count_0 += 1\n if trained_data[p][-2] == 1 and trained_data[p][-1] == centroids[c]:\n count_1 += 1\n print (\"Centroid \", c+1, \":\", centroids[c])\n print(\"Number of 1's: \", count_1)\n print(\"Number of 0's: \", count_0)\n print(\"Percent 1's: \", round((count_1/(count_1 + count_0))*100,2))\n print(\"Percent 0's: \", round((count_0 / (count_1 + count_0)) * 100,2))\n print(\"****************\")","def demo():\n # declare dummy input vector with two dimensions:\n vectors = numpy.array([[2,4], [0,1], [1,1], [3,2], [4,0], [2,2], [8, 9], [8, 11]])\n\n # compute the distance matrix on the basis of the vectors via sklearn:\n dist_matrix = pairwise_distances(vectors, metric='cityblock')\n\n # plot the distance matrix:\n # dist_matrix.draw() this doesn't work anymore\n\n # initialize a temporal VNC clusterer, here with the Ward linkage method:\n clusterer = VNClusterer(dist_matrix, linkage='ward') # could also be a plain Clusterer()\n\n # start the clustering procedure:\n clusterer.cluster(verbose=1)\n\n labels = ['n'+str(i+1) for i in range(len(vectors))]\n # plot the result as a dendrogram\n clusterer.dendrogram.draw(save=True,\n labels=labels,\n title=\"VNC Analysis (Ward's Linkage)\")","def test_main_random_output(runner: CliRunner) -> None:\n output_random = os.path.join(\n tempfile.gettempdir(),\n \"\".join(secrets.choice(string.hexdigits) for i in range(7)),\n )\n result = runner.invoke(\n __main__.main, f\"-c tests/clippings-es.txt -o {output_random}\"\n )\n assert result.exit_code == 0","def cluster(self):\n assert False","def benchmark(extract_size=800):\n random_file = random_html_file()\n with open(join(DATA_PATH, random_file), 'r') as f:\n html_string = f.read()\n\n # GOOSE\n try:\n g = Goose({'browser_user_agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:61.0) Gecko/20100101 Firefox/61.0',\n 'enable_image_fetching': False})\n goose_article = g.extract(raw_html=html_string)\n goose_result = goose_article.cleaned_text\n except:\n goose_result = ' Goose error.'\n\n # EATIHT\n try:\n eatiht_result = eatiht.extract(html_string)\n except:\n eatiht_result = ' Eatiht error.'\n\n # DRAGNET\n\n try:\n dragnet_result = dragnet.extract_content(html_string)\n except Exception as e:\n dragnet_result = ' Dragnet error: ' + str(e)\n\n # LIBEXTRACT\n\n try:\n textnodes = list(libextract.api.extract(html_string))\n libextract_result = textnodes[0].text_content()\n except:\n libextract_result = ' Libextract error.'\n\n # BOILERPIPE (CanolaExtractor)\n\n try:\n extractor = Extractor(\n extractor='CanolaExtractor', html=html_string)\n boilerpipe_result = extractor.getText()\n except:\n boilerpipe_result = ' Boilerpipe error.'\n\n # NEWSPAPER\n\n try:\n article = Article('url')\n article.download(input_html=html_string)\n article.parse()\n print('Auteurs:', article.authors)\n print('Date de publication:', article.publish_date)\n newspaper_result = article.text\n except:\n newspaper_result = ' Newspaper error.'\n\n # JUSTEXT\n\n try:\n paragraphs = justext.justext(\n html_string, justext.get_stoplist(\"French\"))\n print('PARAGRAPHS')\n for p in paragraphs:\n if not p.is_boilerplate:\n print(p.text)\n justext_result = '\\n'.join(\n paragraph.text for paragraph in paragraphs if not paragraph.is_boilerplate)\n print('JUSTEXT_RESULT', justext_result)\n\n except Exception as e:\n justext_result = ' Justext error: ' + str(e)\n print(justext_result)\n\n # Results\n\n try:\n # finds the url associated with the file in a \"filename-url\" csv\n with open('./data/urls.csv', 'r') as csvfile:\n\n urls = dict((line['id'], line['url'])\n for line in csv.DictReader(csvfile))\n url = urls[random_file[:-5]]\n\n print('\\n\\n >>> URL n.' + random_file[:-5] + ' : ' + url)\n except:\n print('\\n\\n (URL of the html file not found. To print the associated URL, please provide a urls.csv file featuring filename & url in /data)')\n # webbrowser.open(url, autoraise=False)\n path = abspath('temp.html')\n local_url = 'file://' + path\n with open(path, 'w') as f:\n f.write(html_string)\n webbrowser.open(local_url)\n\n # print('\\n\\n /// GOOSE /// \\n')\n # print(goose_result[:extract_size])\n # print('\\n\\n /// EATIHT /// \\n')\n # print(eatiht_result[:extract_size])\n print('\\n ------ [[DRAGNET]] ------',\n len(dragnet_result), 'caractères\\n')\n print(dragnet_result[:extract_size] +\n '\\n...\\n' + dragnet_result[-extract_size:])\n print('\\n ------ [[NEWSPAPER]] ------',\n len(newspaper_result), 'caractères\\n')\n print(newspaper_result[:extract_size] +\n '\\n...\\n' + newspaper_result[-extract_size:])\n print('\\n ------ [[JUSTEXT]] ------',\n len(justext_result), 'caractères\\n')\n print(justext_result[:extract_size] +\n '\\n...\\n' + justext_result[-extract_size:])\n # print('\\n\\n /// LIBEXTRACT /// \\n')\n # print(libextract_result[:extract_size])\n # print('\\n\\n /// BOILERPIPE (CanolaExtractor) /// \\n\\n')\n # print(boilerpipe_result[:extract_size])\n # print('\\n\\n')\n return(url)","def test_gethtml(self):\r\n mock_module = CHModuleFactory.create()\r\n\r\n def fake_get_display_items():\r\n \"\"\"\r\n A mock of get_display_items\r\n \"\"\"\r\n return [FakeChild()]\r\n mock_module.get_display_items = fake_get_display_items\r\n out_html = mock_module.render('student_view').content\r\n self.assertTrue('This is supposed to be test html.' in out_html)\r\n self.assertTrue('i4x://this/is/a/fake/id' in out_html)","def partition_clusters(clustering_matrix, args, nr_clusters=5, method='complete', metric='euclidean', plotting=False):\n # clustering with linkage\n fig = plt.figure(figsize=(8,8))\n ax1 = fig.add_axes([0.09,0.1,0.2,0.6])\n # gives back linkage matrix after hierarchical clustering\n Y = sch.linkage(clustering_matrix, method=method,metric=metric)\n # creates dendogram for plotting and flattening\n Z = sch.dendrogram(Y, orientation='left')\n ax1.set_xticks([])\n ax1.set_yticks([])\n # calculate cluster membership\n # fcluster flattens out dendograms to the specified nr_clusters\n cluster_memberships = sch.fcluster(Y, t=nr_clusters, criterion='maxclust') # ith element in this array is the cluster for i\n idx = np.array(Z['leaves']) # idx ordered in cluster\n \n ax2 = fig.add_axes([0.3,0.71,0.6,0.2])\n Z2 = sch.dendrogram(Y)\n ax2.set_xticks([])\n ax2.set_yticks([])\n\n axmatrix = fig.add_axes([0.3,0.1,0.6,0.6])\n\n clustering_matrix = clustering_matrix[idx,:]\n clustering_matrix = clustering_matrix[:,idx]\n im = axmatrix.matshow(clustering_matrix, aspect='auto', origin='lower', cmap=plt.cm.YlGnBu)\n axmatrix.set_xticks([])\n axmatrix.set_yticks([])\n\n # Plot colorbar.\n axcolor = fig.add_axes([0.91,0.1,0.02,0.6])\n plt.colorbar(im, cax=axcolor)\n if plotting:\n fig.savefig(f'{args.results_root_dir}/clust_{args.clustering_method}_nr_users-{args.num_users}_nr_of_partition_clusters_{nr_clusters}_method_{method}_reconstructed.png')\n\n # Plot filtered\n canvas = np.zeros_like(clustering_matrix)\n for i in range(1,nr_clusters+1):\n mask = np.ones_like(clustering_matrix)\n mask[cluster_memberships[idx]!=i,:] = 0\n mask[:,cluster_memberships[idx]!=i] = 0\n canvas+=clustering_matrix*mask\n fig = plt.figure()\n plt.matshow(canvas,origin='lower')\n if plotting:\n fig.savefig(f'{args.results_root_dir}/clust_{args.clustering_method}_nr_users-{args.num_users}_nr_of_partition_clusters_{nr_clusters}_method_{method}_filtered.png')\n\n d_error = np.sum(clustering_matrix-canvas)\n print(f'Decompostion error: {d_error}, {d_error/np.sum(clustering_matrix)}')\n\n # build cluster id to client id user dict\n cluster_user_dict = { i : idx[cluster_memberships==i] for i in range(1,nr_clusters+1)}\n\n # Test overlaps within clusters\n collected = []\n for i, cluster_members_a in cluster_user_dict.items():\n for j, cluster_members_b in cluster_user_dict.items():\n assert np.all(cluster_members_a != cluster_members_b) or set(cluster_members_a).intersection(set(cluster_members_b)) != {}, f'clusters {i} and {j} are not disjoint'\n collected.extend(cluster_members_a)\n assert np.all(np.arange(0,len(clustering_matrix),1) == np.sort(np.array(collected)))\n\n return cluster_user_dict","def prepare_statistics(self):\n\n # statistics of clustering files\n len0 = len(self.cluster_lists[0])\n len1 = len(self.cluster_lists[1])\n longer_index = 0 if len0 >= len1 else 1\n shorter_index = 1 if len1 <= len0 else 0\n\n percentage_stars = \"%.2f\" % (100.0 * float(self.shared_spec_num)/float(self.cluster_spectra_num[shorter_index]))\n percentage_starlets = \"%.2f\" % (100.0 * float(self.shared_spec_num)/float(self.cluster_spectra_num[longer_index]))\n\n head = \"{0:<25}{1:<20}{2:<20}\\n\".format(\"name\", \"number\", \"description\")\n rows = \"\"\n rows += \"{0:<25}{1:<20}{2:<20}\\n\".format(\"stars No.\", self.stars_length, \"in file with less(or equal) clusters: file\" + str(shorter_index))\n rows += \"{0:<25}{1:<20}{2:<20}\\n\".format(\"starlets No.\", self.starlets_length, \"in file with more(or equal) clusters: file\" + str(longer_index))\n rows += \"{0:<25}{1:<20}{2:<20}\\n\".format(\"identical cluster No.\", self.similarity_dist[10], \"between them\")\n rows += \"{0:<25}{1:<20}{2:<20}\\n\".format(\"spectrum No\", self.cluster_spectra_num[shorter_index], \"in stars\")\n rows += \"{0:<25}{1:<20}{2:<20}\\n\".format(\"spectrum No\", self.cluster_spectra_num[longer_index], \"in starlets \")\n rows += \"{0:<25}{1:<20}{2:<20}\\n\".format(\"shared spectrum No\", self.shared_spec_num, \"between them\")\n rows += \"{0:<25}{1:<20}{2:<20}\\n\".format(\"shared spectrum percent\", percentage_stars, \"in stars\")\n rows += \"{0:<25}{1:<20}{2:<20}\\n\".format(\"shared spectrum percent\", percentage_starlets, \"in starlets\")\n self.tables.append(('statistics of files', head, rows))\n\n # distribution of cluster size in stars\n head = '{0:<20}{1:<20}{2:<20}{3:<20}\\n'.format(\"cluster size\",\"No.\", \"percentage\", \"accumulate pecentage\")\n rows = \"\"\n rows += \"{0:<20}{1:<20}\\n\".format(\"%.2f\" % (self.ave_star_size), \"average\")\n accumulate_num = 0\n for key in sorted(self.cluster_size_dist[shorter_index].keys()):\n value = self.cluster_size_dist[shorter_index][key]\n accumulate_num += value\n percent = \"%.2f\" % (100 * value/self.stars_length)\n accum_percent = \"%.2f\" % (100 * accumulate_num/self.stars_length)\n rows += '{0:<20}{1:<20}{2:<20}{3:<20}\\n'.format(key, value, percent, accum_percent)\n self.tables.append(('distribution of cluster size in stars', head, rows))\n \n head = '{0:<20}{1:<20}{2:<20}{3:<20}\\n'.format(\"cluster size\",\"No.\", \"percentage\", \"accumulate pecentage\")\n rows = \"\"\n rows += \"{0:<20}{1:<20}\\n\".format(\"%.2f\" % (self.ave_starlet_size), \"average\")\n accumulate_num = 0\n for key in sorted(self.cluster_size_dist[longer_index].keys()):\n value = self.cluster_size_dist[longer_index][key]\n accumulate_num += value\n percent = \"%.2f\" % (100 * value/self.starlets_length)\n accum_percent = \"%.2f\" % (100 * accumulate_num/self.starlets_length)\n rows += '{0:<20}{1:<20}{2:<20}{3:<20}\\n'.format(key, value, percent, accum_percent)\n self.tables.append(('distribution of cluster size in starlets', head, rows))\n\n # distribution of similarity\n head = \"{0:<20}{1:<20}{2:<20}{3:<20}\\n\".format(\"similarity score\", \"pairs of clusters\", \"percentage(stars)\", \"percentage(starlets)\")\n rows = \"\"\n for key in reversed(sorted(self.similarity_dist.keys())):\n value = self.similarity_dist[key]\n percent_star = \"%.2f\" % (100.0*value/self.stars_length)\n percent_starlet = \"%.2f\" % (100.0*value/self.starlets_length)\n rows += '{0:<20}{1:<20}{2:<20}{3:<20}\\n'.format(key, value, percent_star, percent_starlet)\n self.tables.append(('distribution of similarity (identical = 10)', head, rows))\n\n # distribution of star divide factors\n head = '{0:<20}{1:<20}{2:<20}\\n'.format(\"divide factor\",\"No.\",\"percentage\")\n rows = \"\"\n rows += \"{0:<20}{1:<20}\\n\".format(\"%.2f\" % (self.ave_divide_factor_star), \"average\")\n for key in sorted(self.star_divide_factor_dist.keys()):\n value = self.star_divide_factor_dist[key]\n percent_star = \"%.2f\" % (100.0*value/self.stars_length)\n rows += '{0:<20}{1:<20}{2:<20}\\n'.format(key, value, percent_star)\n self.tables.append(('distribution of star divide factors', head, rows))\n\n # distribution of starlet divide factors\n head = '{0:<20}{1:<20}{2:<20}\\n'.format(\"divide factor\",\"No.\",\"percentage\")\n rows = \"\"\n rows += \"{0:<20}{1:<20}\\n\".format(\"%.2f\" % (self.ave_divide_factor_starlet), \"average\")\n for key in sorted(self.starlet_divide_factor_dist.keys()):\n value = self.starlet_divide_factor_dist[key]\n percent_starlet = \"%.2f\" % (100.0*value/self.starlets_length)\n rows += '{0:<20}{1:<20}{2:<20}\\n'.format(key, value, percent_starlet)\n self.tables.append(('distribution of starlet divide factors', head, rows))","def test_cluster_csv(self, script_runner: ScriptRunner, tmp_path: Path) -> None:\n outfile = tmp_path.joinpath(\"pca-cluster.png\")\n logfile = tmp_path.joinpath(\"plot.log\")\n result = script_runner.run(\n \"qaa\",\n \"plot\",\n \"-i\",\n CLUSTER,\n \"-c\",\n CENTROID,\n \"-o\",\n outfile.as_posix(),\n \"-l\",\n logfile.as_posix(),\n \"--pca\",\n \"--cluster\",\n \"--verbose\",\n )\n assert result.success\n assert logfile.exists()\n assert outfile.exists()\n assert outfile.stat().st_size > 0","def test_other_clusters(self):\n url = \"?filter[time_scope_units]=month&filter[time_scope_value]=-1&filter[resolution]=monthly&filter[limit]=1&group_by[cluster]=*\" # noqa: E501\n query_params = self.mocked_query_params(url, OCPCpuView)\n handler = OCPReportQueryHandler(query_params)\n\n query_data = handler.execute_query()\n for data in query_data.get(\"data\"):\n for cluster_data in data.get(\"clusters\"):\n cluster_name = cluster_data.get(\"cluster\", \"\")\n if cluster_name == \"Other\":\n for cluster_value in cluster_data.get(\"values\"):\n self.assertTrue(len(cluster_value.get(\"clusters\", [])) == 1)\n self.assertTrue(len(cluster_value.get(\"source_uuid\", [])) == 1)\n elif cluster_name == \"Others\":\n for cluster_value in cluster_data.get(\"values\"):\n self.assertTrue(len(cluster_value.get(\"clusters\", [])) > 1)\n self.assertTrue(len(cluster_value.get(\"source_uuid\", [])) > 1)","def cluster_dstructure_output(self, curs, output_fname, order_1st_id2all_clusters):\n\t\tfrom codense.common import get_gene_no2gene_id\n\t\tgene_no2gene_id = get_gene_no2gene_id(curs)\n\t\tsys.stderr.write(\"Outputting cluster information...\")\n\t\toutf = open(output_fname, 'w')\n\t\tstr_tmp_list0 = []\t#hold the 1st-order clusters\n\t\tfor order_1st_id,all_2nd_order_clusters in order_1st_id2all_clusters.iteritems():\n\t\t\tstr_tmp_list1 = []\t#hold the 2nd-order clusters\n\t\t\tfor order_2nd_id,cluster_list in all_2nd_order_clusters.iteritems():\n\t\t\t\tstr_tmp_list2 = []\t#hold the connected components\n\t\t\t\tfor cluster in cluster_list:\n\t\t\t\t\tstr_tmp = self.return_string_form_of_cluster_dstructure(cluster, gene_no2gene_id)\n\t\t\t\t\tstr_tmp_list2.append(str_tmp)\n\t\t\t\tstr_tmp_list1.append(\"[%s]\"%','.join(str_tmp_list2))\n\t\t\tstr_tmp_list0.append(\"[%s]\"%\",\".join(str_tmp_list1))\n\t\t#'r:=' is for directly read in as an array\n\t\toutf.write(\"r:=[%s]:\"%\",\".join(str_tmp_list0))\n\t\toutf.close()\n\t\tsys.stderr.write(\"Done.\\n\")","def test_read_cluster_network(self):\n pass","def test_create_cluster_network(self):\n pass","def test_result_page(self):\n response = self.client.post(\"/\", data={'1': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\n '2': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\n '3': ['3', '6', '0', '0', '0', '0', '0', '0', '0'],\n '4': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\n '5': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\n '6': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\n '7': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\n '8': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\n '9': ['0', '0', '0', '0', '0', '0', '0', '0', '0']})\n\n self.assertEqual(response.status_code, 302)\n self.assertEqual(response.url, '/result/1')\n\n url = response.url\n new_response = self.client.get(url)\n\n self.assertEqual(new_response.status_code, HTTPStatus.OK)\n self.assertContains(new_response, 'This is matrix that you submitted')\n self.assertContains(new_response, 'This is solved matrix')","def test_output_create_dist(self):\n numbers_dist = sample2dist(\n [\"one\", \"two\", \"three\", \"four\", \"five\", \"six\"])\n VA_numbers = VoseAlias(numbers_dist)\n actual = VA_numbers.dist\n prob = Decimal(1)/Decimal(6)\n expected = {\"one\": prob, \"two\": prob, \"three\": prob,\n \"four\": prob, \"five\": prob, \"six\": prob}\n self.assertEqual(actual, expected)","def SelectClusters(image, background_prediction, result_clustering,\n n_clusters, bands_thresholds=[\"B2\", \"B3\", \"B4\"],\n region_of_interest=None,\n tileScale=PARAMS_CLOUDCLUSTERSCORE_DEFAULT['tileScale']): \n bands_norm_difference = [b + \"_difference\" for b in bands_thresholds]\n\n img_joined = image.subtract(background_prediction)\\\n .select(bands_thresholds, bands_norm_difference)\\\n .addBands(image.select(bands_thresholds))\n\n bands_and_difference_bands = bands_thresholds + bands_norm_difference\n\n multitemporal_score = None\n reflectance_score = None\n\n for i in range(n_clusters):\n img_diff_clus = img_joined.updateMask(\n result_clustering.eq(i)).select(bands_and_difference_bands)\n\n clusteri = img_diff_clus.reduceRegion(ee.Reducer.mean(),\n geometry=region_of_interest,\n bestEffort=True,\n scale=30,\n tileScale=tileScale\n )\n \n clusteri_diff = clusteri.toArray(bands_norm_difference)\n clusteri_refl = clusteri.toArray(bands_thresholds)\n \n clusteri_refl_norm = clusteri_refl.multiply(clusteri_refl).reduce(ee.Reducer.mean(),\n axes=[0]).sqrt().get([0])\n\n clusteridiff_mean = clusteri_diff.reduce(ee.Reducer.mean(), axes=[0]).get([0])\n clusteridiff_norm = clusteri_diff.multiply(clusteri_diff).reduce(ee.Reducer.mean(),\n axes=[0]).sqrt().get([0])\n\n multitemporal_score_clusteri = ee.Algorithms.If(clusteridiff_mean.gt(0),\n clusteridiff_norm,\n clusteridiff_norm.multiply(-1))\n\n multitemporal_score_clusteri = result_clustering.eq(\n i).toFloat().multiply(ee.Number(multitemporal_score_clusteri))\n reflectance_score_clusteri = result_clustering.eq(\n i).toFloat().multiply(ee.Number(clusteri_refl_norm))\n\n if multitemporal_score is None:\n multitemporal_score = multitemporal_score_clusteri\n reflectance_score = reflectance_score_clusteri\n else:\n multitemporal_score = multitemporal_score.add(\n multitemporal_score_clusteri)\n reflectance_score = reflectance_score.add(\n reflectance_score_clusteri)\n\n return multitemporal_score, reflectance_score","def plot_clusters(true_data, preds, cluster_center, cluster_name, savefig=\"\", title=\"\"):\n\n colors = plt.cm.get_cmap('hsv', len(cluster_name)+1) # get colors for each cluster using get_cmap. This will give us len(cluster_name) colors in a object form.\n \n for i, c in enumerate(cluster_name): # iterate through each cluster name\n if c == -1: # -1 is given by DBScan for noise\n clrs = 'grey' # make it grey\n label = 'Noise' # label it 'Noise'\n else:\n clrs = colors(c) # get color for it\n label=f'Cluster {c}' # label it by its name\n df = true_data[preds == c] # get the points from dataset whose prediction was cluster `c`\n x, y = df.iloc[:, 0], df.iloc[:, 1] # x and y axis\n plt.scatter( # plotting the x and y axis\n x, y,\n label=label,\n color=clrs\n )\n if c != -1:\n plt.text(\n cluster_center[i][0] + 0.03, cluster_center[i][1] + 0.1,\n f\"Cluster {i}\",\n weight='bold',\n fontsize=9,\n )\n \n plt.scatter(\n cluster_center[:, 0], cluster_center[:, 1], # plotting the cluster centers\n s=250, marker='*',\n c='red', edgecolor='black',\n label='Centroids'\n )\n \n plt.title(title)\n plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n plt.tight_layout()\n if savefig != \"\" : plt.savefig(f\"{savefig}.png\")\n plt.show()\n plt.close()","def print_cluster(self):\n print('Cluster', self.number)\n for pattern in self.patterns:\n pattern.print_pattern()","def clusterability(features,data):\n #Get together features describing clustering results\n n_nuclei=len(features['label'])\n n_ellongated_nuc=len(data['label'])\n n_clusters = len(set(data['clusters'])-{-1}) # since -1 element denotes noice\n n_uncoupled_nuclei = list(data['clusters']).count(-1) # noise\n n_coupled_nuclei = len(data['clusters'])-n_uncoupled_nuclei\n \n if n_clusters>=2:\n #calculate clustering robustness without noise\n Silohhouette_score= sklm.silhouette_score(data.drop(['clusters','label'],axis=1)[data['clusters']> -1], data['clusters'][data['clusters']> -1])\n else:\n Silohhouette_score = 'NA'\n basic_clustering_features = [Clusterability_Features([n_nuclei,n_ellongated_nuc,n_clusters, n_uncoupled_nuclei,n_coupled_nuclei,Silohhouette_score])]\n basic_clustering_features = pd.DataFrame([o.__dict__ for o in basic_clustering_features])\n\n return basic_clustering_features","def test_visualize_recipe_nutrition(self):\n pass","def main():\n\n dist = \"Euclidean\"\n path = \"\"\n k_v = 2\n error = []\n k_vals = []\n\n for i in range(len(sys.argv)):\n if sys.argv[i] == \"--path\":\n path = sys.argv[i+1]\n if sys.argv[i] == \"--k\":\n k_v = int(sys.argv[i+1])\n if sys.argv[i] == \"[--distance Manhattan]\":\n dist = \"Manhattan\"\n if sys.argv[i] == \"[--distance Minkowski]\":\n dist = \"Minkowski\"\n\n\n training_data = create_data(path)\n\n for k in range(2,10):\n k_vals.append(k)\n if k>2:\n for i in range(len(training_data)):\n training_data[i].remove(training_data[i][-1])\n trained_data, centroids = get_clusters(training_data, k, dist)\n error.append(rms(trained_data, dist))\n plot_error(k_vals, error)\n\n for i in range(len(training_data)):\n training_data[i].remove(training_data[i][-1])\n\n trained_data, centroids = get_clusters(training_data, k_v, dist)\n\n test_clusters(trained_data, centroids)","def main():\n parser = argparse.ArgumentParser(description=\"Wrapper of the scikit-learn AgglomerativeClustering method. \", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))\n parser.add_argument('--config', required=False, help='Configuration file')\n\n # Specific args of each building block\n required_args = parser.add_argument_group('required arguments')\n required_args.add_argument('--input_dataset_path', required=True, help='Path to the input dataset. Accepted formats: csv.')\n required_args.add_argument('--output_results_path', required=True, help='Path to the clustered dataset. Accepted formats: csv.')\n parser.add_argument('--output_plot_path', required=False, help='Path to the clustering plot. Accepted formats: png.')\n\n args = parser.parse_args()\n args.config = args.config or \"{}\"\n properties = settings.ConfReader(config=args.config).get_prop_dic()\n\n # Specific call of each building block\n agglomerative_clustering(input_dataset_path=args.input_dataset_path,\n output_results_path=args.output_results_path,\n output_plot_path=args.output_plot_path,\n properties=properties)","def test(self, test_data_path):\n test_data = read_datafile(test_data_path)\n test_data = self.preprocessor.preprocess(test_data)\n\n data = '__label__' + test_data['claim'].astype(str) + test_data['check_worthiness'].astype(str) + ' ' + \\\n test_data['tweet_text']\n\n output = self.run(data)\n\n df = pd.DataFrame()\n df[\"predicted\"] = output.split()\n df[\"labeled\"] = [d.split()[0] for d in data]\n\n cm = confusion_matrix(df[\"labeled\"], df[\"predicted\"], labels=['__label__11','__label__10','__label__00'])\n\n ax= plt.subplot()\n sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n\n ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n ax.set_title('Confusion Matrix'); \n ax.xaxis.set_ticklabels(['__label__11','__label__10','__label__00']); ax.yaxis.set_ticklabels(['__label__11','__label__10','__label__00']);\n\n plt.show()\n\n return np.sum(cm.diagonal()) / np.sum(cm)","def add_content(subcluster, content, suffix):\n\n fname = './plots/%s_%s.png' % (conf, suffix)\n\n # pick the top 10 terms\n subcluster = sorted(subcluster, key=lambda t: max(t[1:].astype(float)), reverse=True)\n subcluster = subcluster[:10]\n\n words = np.array(subcluster)[:,0]\n ys = np.array(subcluster)[:,1:].astype(float)\n mean = [np.mean(ys[:,i]) for i in xrange(ys.shape[1])]\n maxmean = max(mean)\n idx = mean.index(maxmean)\n\n # this is used to make the top-k list in the HTML later\n content.append(('', words, fname, idx))\n\n\n data = []\n for arr in subcluster:\n word = arr[0]\n for x, y in enumerate(map(float, arr[1:])):\n data.append(dict(\n group=\"normal\",\n word=word,\n x=xs[x],\n y=y, \n alpha=0.3\n ))\n\n # add a line for the mean\n for x, y in enumerate(mean):\n data.append(dict(group=\"aggregate\", word='___mean___', x=xs[x], y=y, alpha=1))\n\n if 1:\n maxy = max(10, max(pluckone(data, 'y')))\n if maxy <= 10:\n breaks = [0, 5, 10]\n\n\n # pygg lets you write ggplot2 syntax in python\n p = ggplot(data, aes(x='x', y='y', group='word', color='group', alpha='alpha'))\n p += geom_line(size=1)\n p += scale_color_manual(values=\"c('normal' = '#7777dd','aggregate' = 'black')\", guide=\"FALSE\")\n p += scale_alpha_continuous(guide=\"FALSE\")\n if 1:\n if maxy <= 10:\n p += scale_y_continuous(lim=[0, maxy], breaks=breaks, labels = \"function (x) as.integer(x)\")\n else:\n p += scale_y_continuous(lim=[0, maxy], labels = \"function (x) as.integer(x)\")\n p += legend_bottom\n p += theme(**{\n \"axis.title\":element_blank()\n })\n ggsave(fname, p, width=10, height=4, libs=['grid'])","def test_call_low_cluster_identity(self):\r\n\r\n # adapted from test_app.test_cd_hit.test_cdhit_clusters_from_seqs\r\n # Should only get 6 clusters\r\n exp_otu_ids = [str(x) for x in range(6)]\r\n\r\n exp_clusters =\\\r\n [['uclust_test_seqs_0', 'uclust_test_seqs_6',\r\n 'uclust_test_seqs_9'], ['uclust_test_seqs_1'], ['uclust_test_seqs_2'],\r\n ['uclust_test_seqs_3',\r\n 'uclust_test_seqs_5',\r\n 'uclust_test_seqs_8'],\r\n ['uclust_test_seqs_4'], ['uclust_test_seqs_7']]\r\n\r\n app = UsearchOtuPicker(params={'save_intermediate_files': False,\r\n 'db_filepath': self.tmp_ref_database,\r\n 'output_dir': self.temp_dir,\r\n 'remove_usearch_logs': True,\r\n 'reference_chimera_detection': False,\r\n 'de_novo_chimera_detection': False,\r\n 'cluster_size_filtering': False,\r\n 'minlen': 12,\r\n 'w': 12,\r\n 'minsize': 1,\r\n 'percent_id': 0.80,\r\n 'percent_id_err': 0.97\r\n })\r\n\r\n obs = app(self.tmp_seq_filepath1)\r\n\r\n obs_otu_ids = sorted(obs.keys())\r\n obs_clusters = sorted(obs.values())\r\n # The relation between otu ids and clusters is abitrary, and\r\n # is not stable due to use of dicts when parsing clusters -- therefore\r\n # just checks that we have the expected group of each\r\n self.assertEqual(obs_otu_ids, exp_otu_ids)\r\n self.assertEqual(obs_clusters, exp_clusters)","def test_get_html(self):\r\n _html = self.peer_grading.get_html()"],"string":"[\n \"def test_html_output(self):\\n pass\",\n \"def cluster_classification_tex(f,browsing_matrix,diversifying_matrix, weblog,session_data_threshold,cluster_type,classification_column_diversity,classification_wanted_transaction):\\n divpat_classification_wanted_transaction = classification_wanted_transaction\\n divpat_N_classification_wanted_transaction=len(divpat_classification_wanted_transaction)\\n f.write(\\\"\\\\n% 6. Cluster Classification\\\")\\n columns_latex = '|'+'c|'*len(session_data_threshold[cluster_type].unique())\\n f.write(\\\"\\\\n\\\\\\\\newcommand{\\\\\\\\%s}{%s}\\\"%('DivColumnsLatex',columns_latex)) \\n columns_blank = ' ' + '& '*(len(session_data_threshold[cluster_type].unique()) -1)\\n f.write(\\\"\\\\n\\\\\\\\newcommand{\\\\\\\\%s}{%s}\\\"%('DivColumnsBlank',columns_blank)) \\n cluster_list = []\\n ieuc_clusters = []\\n star_chain_like_clusters = []\\n length_clusters = []\\n browsing_pattern_1 = []\\n browsing_pattern_2 = []\\n browsing_pattern_3 = []\\n diversifying_pattern_1 = []\\n diversifying_pattern_2 = []\\n diversifying_pattern_3 = []\\n cluster_ids = session_data_threshold[cluster_type].unique()\\n cluster_ids.sort()\\n for cluster_id in cluster_ids:\\n cluster_list.append(str(cluster_id))\\n \\n cluster_session_list=session_data_threshold[session_data_threshold[cluster_type]==cluster_id].session_id.values\\n temp_cluster_weblog=weblog[weblog.session_id.isin(cluster_session_list)]\\n pa,pa_names = proportional_abundance(temp_cluster_weblog,'requested_'+classification_column_diversity)\\n cluster_entropy=ShannonEntropy(pa,normalize=True)\\n \\n ieuc_clusters.append(str(round(np.power(2.0,cluster_entropy),2)))\\n star_chain_like_clusters.append(star_chain_str(session_data_threshold[session_data_threshold[cluster_type]==cluster_id].star_chain_like.mean()))\\n length_clusters.append(length(session_data_threshold[session_data_threshold[cluster_type]==cluster_id].requests.mean()))\\n # Browsing patterns\\n r,c=np.unravel_index(browsing_matrix[cluster_id][:-1,:-1].argsort(axis=None)[::-1][:3],dims=(divpat_N_classification_wanted_transaction,divpat_N_classification_wanted_transaction))\\n browsing_pattern_1.append('%.1f\\\\%%: %s$\\\\\\\\rightarrow$%s'%(100.0*browsing_matrix[cluster_id][r[0],c[0]],divpat_classification_wanted_transaction[r[0]],divpat_classification_wanted_transaction[c[0]]))\\n browsing_pattern_2.append('%.1f\\\\%%: %s$\\\\\\\\rightarrow$%s'%(100.0*browsing_matrix[cluster_id][r[1],c[1]],divpat_classification_wanted_transaction[r[1]],divpat_classification_wanted_transaction[c[1]]))\\n browsing_pattern_3.append('%.1f\\\\%%: %s$\\\\\\\\rightarrow$%s'%(100.0*browsing_matrix[cluster_id][r[2],c[2]],divpat_classification_wanted_transaction[r[2]],divpat_classification_wanted_transaction[c[2]]))\\n \\n # Diversifying patterns\\n r,c=np.unravel_index(np.nan_to_num(diversifying_matrix[cluster_id])[:-1,:-1].argsort(axis=None)[::-1][:3],dims=(divpat_N_classification_wanted_transaction,divpat_N_classification_wanted_transaction))\\n diversifying_pattern_1.append('%.1f\\\\%%: %s$\\\\\\\\rightarrow$%s'%(100.0*diversifying_matrix[cluster_id][r[0],c[0]],divpat_classification_wanted_transaction[r[0]],divpat_classification_wanted_transaction[c[0]]))\\n diversifying_pattern_2.append('%.1f\\\\%%: %s$\\\\\\\\rightarrow$%s'%(100.0*diversifying_matrix[cluster_id][r[1],c[1]],divpat_classification_wanted_transaction[r[1]],divpat_classification_wanted_transaction[c[1]]))\\n diversifying_pattern_3.append('%.1f\\\\%%: %s$\\\\\\\\rightarrow$%s'%(100.0*diversifying_matrix[cluster_id][r[2],c[2]],divpat_classification_wanted_transaction[r[2]],divpat_classification_wanted_transaction[c[2]]))\\n\\n del temp_cluster_weblog\\n \\n f.write(\\\"\\\\n\\\\\\\\newcommand{\\\\\\\\%s}{%s}\\\"%('DivClusterList',' & '.join(cluster_list)))\\n f.write(\\\"\\\\n\\\\\\\\newcommand{\\\\\\\\%s}{%s}\\\"%('DivIEUCClusters',' & '.join(ieuc_clusters)))\\n f.write(\\\"\\\\n\\\\\\\\newcommand{\\\\\\\\%s}{%s}\\\"%('StarChainClusters',' & '.join(star_chain_like_clusters)))\\n f.write(\\\"\\\\n\\\\\\\\newcommand{\\\\\\\\%s}{%s}\\\"%('LengthClusters',' & '.join(length_clusters)))\\n f.write(\\\"\\\\n\\\\\\\\newcommand{\\\\\\\\%s}{%s}\\\"%('BrowsingPatternClustersOne',' & '.join(browsing_pattern_1)))\\n f.write(\\\"\\\\n\\\\\\\\newcommand{\\\\\\\\%s}{%s}\\\"%('BrowsingPatternClustersTwo',' & '.join(browsing_pattern_2)))\\n f.write(\\\"\\\\n\\\\\\\\newcommand{\\\\\\\\%s}{%s}\\\"%('BrowsingPatternClustersThree',' & '.join(browsing_pattern_3)))\\n f.write(\\\"\\\\n\\\\\\\\newcommand{\\\\\\\\%s}{%s}\\\"%('DiversifyingPatternClustersOne',' & '.join(diversifying_pattern_1)))\\n f.write(\\\"\\\\n\\\\\\\\newcommand{\\\\\\\\%s}{%s}\\\"%('DiversifyingPatternClustersTwo',' & '.join(diversifying_pattern_2)))\\n f.write(\\\"\\\\n\\\\\\\\newcommand{\\\\\\\\%s}{%s}\\\"%('DiversifyingPatternClustersThree',' & '.join(diversifying_pattern_3)))\\n\\n return f;\",\n \"def test_introduction_6():\\n import pandas as pd\\n from tcrdist.repertoire import TCRrep\\n\\n df = pd.read_csv(\\\"dash.csv\\\")\\n tr = TCRrep(cell_df = df, \\n organism = 'mouse', \\n chains = ['beta','alpha'], \\n db_file = 'alphabeta_gammadelta_db.tsv')\\n \\n from tcrdist.rep_diff import hcluster_diff, member_summ\\n from hierdiff import plot_hclust_props\\n\\n # diff testing is pasted on binary comparison, so all epitope not 'PA' are set to 'X'\\n tr.clone_df['PA'] = ['PA' if x == 'PA' else 'X' for x in tr.clone_df.epitope]\\n \\n res, Z= hcluster_diff(tr.clone_df, tr.pw_beta, x_cols = ['PA'], count_col = 'count')\\n \\n res_summary = member_summ(res_df = res, clone_df = tr.clone_df, addl_cols=['epitope'])\\n \\n res_detailed = pd.concat([res, res_summary], axis = 1)\\n \\n html = plot_hclust_props(Z,\\n title='PA Epitope Example',\\n res=res_detailed,\\n tooltip_cols=['cdr3_b_aa','v_b_gene', 'j_b_gene','epitope'],\\n alpha=0.00001, colors = ['blue','gray'],\\n alpha_col='pvalue')\\n\\n with open('hierdiff_example.html', 'w') as fh:\\n fh.write(html)\",\n \"def cluster_and_render(conf, dbname, outname=\\\"./text.html\\\", nclusters=8):\\n\\n\\n db = sqlite3.connect(dbname)\\n r = db.execute(\\\"select min(year), max(year) from counts where conf=?\\\", (conf,))\\n minyear, maxyear = r.fetchone()\\n\\n # total words per year for normalization purposes\\n r = db.execute(\\\"select year, count(*) from counts where conf=? order by year\\\", (conf,))\\n year2c = dict([(year, c) for year, c in r])\\n yearcounts = dict2arr(year2c, range(minyear, maxyear+1), 1)\\n\\n\\n def add_content(subcluster, content, suffix):\\n \\\"\\\"\\\"\\n Render the cluster as an image\\n \\\"\\\"\\\"\\n\\n fname = './plots/%s_%s.png' % (conf, suffix)\\n\\n # pick the top 10 terms\\n subcluster = sorted(subcluster, key=lambda t: max(t[1:].astype(float)), reverse=True)\\n subcluster = subcluster[:10]\\n\\n words = np.array(subcluster)[:,0]\\n ys = np.array(subcluster)[:,1:].astype(float)\\n mean = [np.mean(ys[:,i]) for i in xrange(ys.shape[1])]\\n maxmean = max(mean)\\n idx = mean.index(maxmean)\\n\\n # this is used to make the top-k list in the HTML later\\n content.append(('', words, fname, idx))\\n\\n\\n data = []\\n for arr in subcluster:\\n word = arr[0]\\n for x, y in enumerate(map(float, arr[1:])):\\n data.append(dict(\\n group=\\\"normal\\\",\\n word=word,\\n x=xs[x],\\n y=y, \\n alpha=0.3\\n ))\\n\\n # add a line for the mean\\n for x, y in enumerate(mean):\\n data.append(dict(group=\\\"aggregate\\\", word='___mean___', x=xs[x], y=y, alpha=1))\\n\\n if 1:\\n maxy = max(10, max(pluckone(data, 'y')))\\n if maxy <= 10:\\n breaks = [0, 5, 10]\\n\\n\\n # pygg lets you write ggplot2 syntax in python\\n p = ggplot(data, aes(x='x', y='y', group='word', color='group', alpha='alpha'))\\n p += geom_line(size=1)\\n p += scale_color_manual(values=\\\"c('normal' = '#7777dd','aggregate' = 'black')\\\", guide=\\\"FALSE\\\")\\n p += scale_alpha_continuous(guide=\\\"FALSE\\\")\\n if 1:\\n if maxy <= 10:\\n p += scale_y_continuous(lim=[0, maxy], breaks=breaks, labels = \\\"function (x) as.integer(x)\\\")\\n else:\\n p += scale_y_continuous(lim=[0, maxy], labels = \\\"function (x) as.integer(x)\\\")\\n p += legend_bottom\\n p += theme(**{\\n \\\"axis.title\\\":element_blank()\\n })\\n ggsave(fname, p, width=10, height=4, libs=['grid'])\\n \\n\\n\\n def vectors():\\n \\\"\\\"\\\"\\n Extract a matrix of term count vectors\\n\\n Return: [\\n [word, count1, count2, ...],\\n ...\\n ]\\n \\\"\\\"\\\"\\n r = db.execute(\\\"select word, year, c from counts where conf=? order by word, year\\\", (conf,))\\n vects = defaultdict(dict)\\n for w,y,c in r:\\n l = vects[w]\\n l[y] = float(c) \\n\\n\\n ret = []\\n for w in vects:\\n d = vects[w]\\n\\n # if word is super uncommon, skip it\\n if (max(d.values()) <= 3):\\n continue\\n if (max([v / (1.+year2c.get(y,0)) for y, v in d.items()]) < .1): \\n continue\\n\\n # some years may not have the word\\n counts = dict2arr(d, xrange(minyear, maxyear+1), 1.0)\\n\\n \\n # naive window averaging smoothing over the trend curve\\n smooth = []\\n for i in xrange(len(counts)):\\n smooth.append(np.mean(counts[max(0,i-2):i+2]))\\n if max(smooth) > 2:\\n ret.append([w] + smooth)\\n return np.array(ret)\\n\\n\\n vects = vectors()\\n # dimensions: words (row) x year (col)\\n data = vects[:,1:].astype(float)\\n\\n # there's a bajillion ways to normalize the counts before clustering.\\n # we do the following:\\n\\n # 1. divide by the total number of words in that year\\n # (normalize by column)\\n for idx, base in enumerate(yearcounts):\\n data[:,idx] /= float(base)\\n\\n # 2. ensure zero mean and 1 std\\n # (normalize by row)\\n data = np.array([(l - np.mean(l)) / (max(l)) for l in data ])\\n\\n\\n clusterer = KMeans(nclusters, n_init=50, init='k-means++')\\n clusterer.fit(data) \\n labels = clusterer.labels_\\n xs = np.array(range(minyear, maxyear+1))\\n\\n content = []\\n\\n # each label is a cluster\\n for label in set(labels):\\n idxs = labels == label\\n cluster = vects[idxs]\\n\\n # sort the words/clusters by their max count\\n cluster = sorted(cluster, key=lambda t: max(t[1:].astype(float)), reverse=True)\\n if not len(cluster): continue\\n cluster = np.array(cluster)\\n words = cluster[:,0]\\n words = list(words)\\n\\n add_content(cluster, content, label)\\n\\n content.sort(key=lambda c: c[-1])\\n\\n\\n\\n # make HTML\\n from jinja2 import Template\\n template = Template(file('./clustertemplate.html').read())\\n\\n with file(outname, 'w') as f:\\n f.write( template.render(content=content))\",\n \"def test_main(self):\\r\\n\\r\\n expected = \\\">FS8APND01D3TW3 | cluster size: 94 \\\\nCTCCCGTAGGAGTCTGGGCCGTATCTCAGTCCCAATGTGGCCGGTCACCCTCTCAGGCCGGCTACCCGTCAAAGCCTTGGTAAGCCACTACCCCACCAACAAGCTGATAAGCCGCGAGTCCATCCCCAACCGCCGAAACTTTCCAACCCCCACCATGCAGCAGGAGCTCCTATCCGGTATTAGCCCCAGTTTCCTGAAGTTATCCCAAAGTCAAGGGCAGGTTACTCACGTGTTACTCACCCGTTCGCC\\\\n\\\"\\r\\n\\r\\n expected_map = \\\"\\\"\\\"FS8APND01EWRS4:\\r\\nFS8APND01DXG45:\\r\\nFS8APND01D3TW3:\\\\tFS8APND01CSXFN\\\\tFS8APND01DQ8MX\\\\tFS8APND01DY7QW\\\\tFS8APND01B5QNI\\\\tFS8APND01CQ6OG\\\\tFS8APND01C7IGN\\\\tFS8APND01DHSGH\\\\tFS8APND01DJ17E\\\\tFS8APND01CUXOA\\\\tFS8APND01EUTYG\\\\tFS8APND01EKK7T\\\\tFS8APND01D582W\\\\tFS8APND01B5GWU\\\\tFS8APND01D7N2A\\\\tFS8APND01BJGHZ\\\\tFS8APND01D6DYZ\\\\tFS8APND01C6ZIM\\\\tFS8APND01D2X6Y\\\\tFS8APND01BUYCE\\\\tFS8APND01BNUEY\\\\tFS8APND01DKLOE\\\\tFS8APND01C24PP\\\\tFS8APND01EBWQX\\\\tFS8APND01ELDYW\\\\tFS8APND01B0GCS\\\\tFS8APND01D4QXI\\\\tFS8APND01EMYD9\\\\tFS8APND01EA2SK\\\\tFS8APND01DZOSO\\\\tFS8APND01DHYAZ\\\\tFS8APND01C7UD9\\\\tFS8APND01BTZFV\\\\tFS8APND01CR78R\\\\tFS8APND01B39IE\\\\tFS8APND01ECVC0\\\\tFS8APND01DM3PL\\\\tFS8APND01DELWS\\\\tFS8APND01CIEK8\\\\tFS8APND01D7ZOZ\\\\tFS8APND01CZSAI\\\\tFS8APND01DYOVR\\\\tFS8APND01BX9XY\\\\tFS8APND01DEWJA\\\\tFS8APND01BEKIW\\\\tFS8APND01DCKB9\\\\tFS8APND01EEYIS\\\\tFS8APND01DDKEA\\\\tFS8APND01DSZLO\\\\tFS8APND01C6EBC\\\\tFS8APND01EE15M\\\\tFS8APND01ELO9B\\\\tFS8APND01C58QY\\\\tFS8APND01DONCG\\\\tFS8APND01DVXX2\\\\tFS8APND01BL5YT\\\\tFS8APND01BIL2V\\\\tFS8APND01EBSYQ\\\\tFS8APND01CCX8R\\\\tFS8APND01B2YCJ\\\\tFS8APND01B1JG4\\\\tFS8APND01DJ024\\\\tFS8APND01BIJY0\\\\tFS8APND01CIA4G\\\\tFS8APND01DV74M\\\\tFS8APND01ECAX5\\\\tFS8APND01DC3TZ\\\\tFS8APND01EJVO6\\\\tFS8APND01D4VFG\\\\tFS8APND01DYYYO\\\\tFS8APND01D1EDD\\\\tFS8APND01DQUOT\\\\tFS8APND01A2NSJ\\\\tFS8APND01DDC8I\\\\tFS8APND01BP1T2\\\\tFS8APND01DPY6U\\\\tFS8APND01CIQGV\\\\tFS8APND01BPUT8\\\\tFS8APND01BDNH4\\\\tFS8APND01DOZDN\\\\tFS8APND01DS866\\\\tFS8APND01DGS2J\\\\tFS8APND01EDK32\\\\tFS8APND01EPA0T\\\\tFS8APND01CK3JM\\\\tFS8APND01BKLWW\\\\tFS8APND01DV0BO\\\\tFS8APND01DPNXE\\\\tFS8APND01B7LUA\\\\tFS8APND01BTTE2\\\\tFS8APND01CKO4X\\\\tFS8APND01DGGBY\\\\tFS8APND01C4NHX\\\\tFS8APND01DYPQN\\r\\nFS8APND01BSTVP:\\r\\nFS8APND01EFK0W:\\r\\nFS8APND01DCIOO:\\r\\nFS8APND01CKOMZ:\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n command = \\\" \\\".join([\\\"denoiser.py\\\",\\r\\n \\\"--force\\\", \\\"-o\\\", self.test_dir, \\\"-i\\\",\\r\\n \\\"%s/qiime/support_files/denoiser/TestData/denoiser_test_set.sff.txt\\\" % PROJECT_HOME])\\r\\n\\r\\n result = Popen(command, shell=True, universal_newlines=True,\\r\\n stdout=PIPE, stderr=STDOUT).stdout.read()\\r\\n self.result_dir = self.test_dir\\r\\n\\r\\n observed = \\\"\\\".join(list(open(self.result_dir + \\\"centroids.fasta\\\")))\\r\\n self.assertEqual(observed, expected)\\r\\n\\r\\n self.assertEqual(\\r\\n len(list(parse_fasta(open(self.result_dir + \\\"singletons.fasta\\\")))),\\r\\n 6)\\r\\n\\r\\n observed = \\\"\\\".join(\\r\\n list(open(self.result_dir + \\\"denoiser_mapping.txt\\\")))\\r\\n self.assertEqual(observed, expected_map)\",\n \"def test_main(self):\\n\\n expected = \\\"\\\"\\\">FS8APND01D3TW3 | cluster size: 94 \\nCTCCCGTAGGAGTCTGGGCCGTATCTCAGTCCCAATGTGGCCGGTCACCCTCTCAGGCCGGCTACCCGTCAAAGCCTTGGTAAGCCACTACCCCACCAACAAGCTGATAAGCCGCGAGTCCATCCCCAACCGCCGAAACTTTCCAACCCCCACCATGCAGCAGGAGCTCCTATCCGGTATTAGCCCCAGTTTCCTGAAGTTATCCCAAAGTCAAGGGCAGGTTACTCACGTGTTACTCACCCGTTCGCC\\n\\\"\\\"\\\"\\n\\n expected_map = \\\"\\\"\\\"FS8APND01EWRS4:\\nFS8APND01DXG45:\\nFS8APND01D3TW3:\\\\tFS8APND01CSXFN\\\\tFS8APND01DQ8MX\\\\tFS8APND01DY7QW\\\\tFS8APND01B5QNI\\\\tFS8APND01CQ6OG\\\\tFS8APND01C7IGN\\\\tFS8APND01DHSGH\\\\tFS8APND01DJ17E\\\\tFS8APND01CUXOA\\\\tFS8APND01EUTYG\\\\tFS8APND01EKK7T\\\\tFS8APND01D582W\\\\tFS8APND01B5GWU\\\\tFS8APND01D7N2A\\\\tFS8APND01BJGHZ\\\\tFS8APND01D6DYZ\\\\tFS8APND01C6ZIM\\\\tFS8APND01D2X6Y\\\\tFS8APND01BUYCE\\\\tFS8APND01BNUEY\\\\tFS8APND01DKLOE\\\\tFS8APND01C24PP\\\\tFS8APND01EBWQX\\\\tFS8APND01ELDYW\\\\tFS8APND01B0GCS\\\\tFS8APND01D4QXI\\\\tFS8APND01EMYD9\\\\tFS8APND01EA2SK\\\\tFS8APND01DZOSO\\\\tFS8APND01DHYAZ\\\\tFS8APND01C7UD9\\\\tFS8APND01BTZFV\\\\tFS8APND01CR78R\\\\tFS8APND01B39IE\\\\tFS8APND01ECVC0\\\\tFS8APND01DM3PL\\\\tFS8APND01DELWS\\\\tFS8APND01CIEK8\\\\tFS8APND01D7ZOZ\\\\tFS8APND01CZSAI\\\\tFS8APND01DYOVR\\\\tFS8APND01BX9XY\\\\tFS8APND01DEWJA\\\\tFS8APND01BEKIW\\\\tFS8APND01DCKB9\\\\tFS8APND01EEYIS\\\\tFS8APND01DDKEA\\\\tFS8APND01DSZLO\\\\tFS8APND01C6EBC\\\\tFS8APND01EE15M\\\\tFS8APND01ELO9B\\\\tFS8APND01C58QY\\\\tFS8APND01DONCG\\\\tFS8APND01DVXX2\\\\tFS8APND01BL5YT\\\\tFS8APND01BIL2V\\\\tFS8APND01EBSYQ\\\\tFS8APND01CCX8R\\\\tFS8APND01B2YCJ\\\\tFS8APND01B1JG4\\\\tFS8APND01DJ024\\\\tFS8APND01BIJY0\\\\tFS8APND01CIA4G\\\\tFS8APND01DV74M\\\\tFS8APND01ECAX5\\\\tFS8APND01DC3TZ\\\\tFS8APND01EJVO6\\\\tFS8APND01D4VFG\\\\tFS8APND01DYYYO\\\\tFS8APND01D1EDD\\\\tFS8APND01DQUOT\\\\tFS8APND01A2NSJ\\\\tFS8APND01DDC8I\\\\tFS8APND01BP1T2\\\\tFS8APND01DPY6U\\\\tFS8APND01CIQGV\\\\tFS8APND01BPUT8\\\\tFS8APND01BDNH4\\\\tFS8APND01DOZDN\\\\tFS8APND01DS866\\\\tFS8APND01DGS2J\\\\tFS8APND01EDK32\\\\tFS8APND01EPA0T\\\\tFS8APND01CK3JM\\\\tFS8APND01BKLWW\\\\tFS8APND01DV0BO\\\\tFS8APND01DPNXE\\\\tFS8APND01B7LUA\\\\tFS8APND01BTTE2\\\\tFS8APND01CKO4X\\\\tFS8APND01DGGBY\\\\tFS8APND01C4NHX\\\\tFS8APND01DYPQN\\nFS8APND01BSTVP:\\nFS8APND01EFK0W:\\nFS8APND01DCIOO:\\nFS8APND01CKOMZ:\\n\\\"\\\"\\\"\\n\\n command = \\\" \\\".join( [\\\"%s/denoiser.py\\\" % get_qiime_scripts_dir(),\\n \\\"--force\\\", \\\"-o\\\", self.test_dir, \\\"-i\\\",\\n \\\"%s/qiime/support_files/denoiser/TestData/denoiser_test_set.sff.txt\\\" % PROJECT_HOME] );\\n\\n result = Popen(command,shell=True,universal_newlines=True,\\\\\\n stdout=PIPE,stderr=STDOUT).stdout.read()\\n self.result_dir = self.test_dir\\n\\n observed = \\\"\\\".join(list(open(self.result_dir+ \\\"centroids.fasta\\\")))\\n self.assertEqual(observed, expected)\\n \\n self.assertEqual(len(list(MinimalFastaParser(open(self.result_dir + \\\"singletons.fasta\\\")))), 6)\\n\\n observed = \\\"\\\".join(list(open(self.result_dir+ \\\"denoiser_mapping.txt\\\")))\\n self.assertEqual(observed, expected_map)\",\n \"def test_main_on_cluster(self):\\r\\n\\r\\n command = \\\" \\\".join([\\\"denoiser.py\\\",\\r\\n \\\"--force\\\", \\\"-o\\\", self.test_dir, \\\"-c\\\", \\\"-n\\\", \\\"2\\\",\\r\\n \\\"-i\\\", \\\"%s/qiime/support_files/denoiser/TestData/denoiser_test_set.sff.txt\\\" % PROJECT_HOME,\\r\\n \\\"-f\\\", \\\"%s/qiime/support_files/denoiser/TestData/test_set_seqs.fna\\\" % PROJECT_HOME])\\r\\n\\r\\n result = Popen(command, shell=True, universal_newlines=True,\\r\\n stdout=PIPE, stderr=STDOUT).stdout.read()\\r\\n self.result_dir = self.test_dir\\r\\n\\r\\n observed = \\\"\\\".join(list(open(self.result_dir + \\\"centroids.fasta\\\")))\\r\\n self.assertEqual(observed, self.expected)\",\n \"def test_main_on_cluster(self):\\n\\n command = \\\" \\\".join( [\\\"%s/denoiser.py\\\" % get_qiime_scripts_dir(),\\n \\\"--force\\\",\\\"-o\\\", self.test_dir, \\\"-c\\\", \\\"-n\\\", \\\"2\\\",\\n \\\"-i\\\", \\\"%s/qiime/support_files/denoiser/TestData/denoiser_test_set.sff.txt\\\" % PROJECT_HOME, \\n \\\"-f\\\", \\\"%s/qiime/support_files/denoiser/TestData/test_set_seqs.fna\\\" % PROJECT_HOME] )\\n\\n result = Popen(command,shell=True,universal_newlines=True,\\\\\\n stdout=PIPE,stderr=STDOUT).stdout.read()\\n self.result_dir = self.test_dir\\n\\n observed = \\\"\\\".join(list(open(self.result_dir+ \\\"centroids.fasta\\\")))\\n self.assertEqual(observed, self.expected)\",\n \"def test_main_split_cluster(self):\\r\\n\\r\\n command = \\\" \\\".join([\\\"denoiser.py\\\",\\r\\n \\\"-S\\\", \\\"--force\\\", '-c', '-n 2',\\r\\n \\\"-i\\\", \\\"%s/qiime/support_files/denoiser/TestData/denoiser_test_set.sff.txt\\\" % PROJECT_HOME,\\r\\n \\\"-f\\\", \\\"%s/qiime/support_files/denoiser/TestData/test_set_seqs.fna\\\" % PROJECT_HOME,\\r\\n \\\"-o\\\", self.test_dir])\\r\\n\\r\\n result = Popen(command, shell=True, universal_newlines=True,\\r\\n stdout=PIPE, stderr=STDOUT).stdout.read()\\r\\n self.result_dir = self.test_dir\\r\\n\\r\\n for subdir in [\\\"0/\\\", \\\"1/\\\"]:\\r\\n observed = \\\"\\\".join(\\r\\n list(open(self.result_dir + subdir + \\\"centroids.fasta\\\")))\\r\\n self.assertEqual(observed, expected_centroids[subdir])\\r\\n\\r\\n observed = \\\"\\\".join(\\r\\n list(open(self.result_dir + subdir + \\\"denoiser_mapping.txt\\\")))\\r\\n self.assertEqual(observed, expected_map_string_on_cluster[subdir])\",\n \"def Clustering(typeVector, behaviour, sampleType):\\r\\n ProduceVector.produceVector(typeVector, behaviour, sampleType)\\r\\n vector = ProduceVector.getVector()\\r\\n\\r\\n if sampleType == \\\"\\\":\\r\\n sampleType2 = \\\"ransomware\\\"\\r\\n if sampleType == \\\"2\\\":\\r\\n sampleType2 = \\\"backdoor\\\"\\r\\n if sampleType == \\\"3\\\":\\r\\n sampleType2 = \\\"trojan\\\"\\r\\n\\r\\n if (behaviour == \\\"f\\\") or (behaviour == \\\"fc\\\"): \\r\\n\\r\\n calculateStandardisation(vector)\\r\\n vector = getStandardisation()\\r\\n\\r\\n typeOfVector = typeVector + \\\" \\\" + behaviour\\r\\n\\r\\n nGram, syscallRep = typeOfVector.split()\\r\\n\\r\\n if syscallRep == \\\"b\\\":\\r\\n nGram += \\\"Full Representation Bit Vector\\\"\\r\\n if syscallRep == \\\"f\\\":\\r\\n nGram += \\\"Full Representation Frequency Vector\\\"\\r\\n if syscallRep == \\\"bc\\\":\\r\\n nGram += \\\"Category Bit Vector\\\"\\r\\n if syscallRep == \\\"fc\\\":\\r\\n nGram += \\\"Category Frequency Vector\\\"\\r\\n\\r\\n\\r\\n fileName = sampleType2 + \\\" \\\" + syscallRep + \\\" standardisation \\\" + nGram + \\\".txt\\\"\\r\\n\\r\\n file = open(fileName, \\\"w\\\")\\r\\n file.write(str(vector.tolist()))\\r\\n file.close()\\r\\n\\r\\n calculateEuclideanDistance(vector) \\r\\n\\r\\n typeOfVector = typeVector + \\\" \\\" + behaviour\\r\\n\\r\\n nGram, syscallRep = typeOfVector.split()\\r\\n\\r\\n if syscallRep == \\\"b\\\":\\r\\n nGram += \\\"Full Representation Bit Vector\\\"\\r\\n if syscallRep == \\\"f\\\":\\r\\n nGram += \\\"Full Representation Frequency Vector\\\"\\r\\n if syscallRep == \\\"bc\\\":\\r\\n nGram += \\\"Category Bit Vector\\\"\\r\\n if syscallRep == \\\"fc\\\":\\r\\n nGram += \\\"Category Frequency Vector\\\" \\r\\n\\r\\n fileName = sampleType2 + \\\" \\\" + syscallRep + \\\" matrix \\\" + nGram + \\\".txt\\\"\\r\\n\\r\\n file = open(fileName, \\\"w\\\")\\r\\n file.write(str(getEuclideanDistance().tolist()))\\r\\n file.close()\\r\\n\\r\\n print(\\\"Producing a dendrogram\\\")\\r\\n\\r\\n\\r\\n typeOfVector = typeVector + \\\" \\\" + behaviour\\r\\n\\r\\n setBestCluster(Validation.evaluate(getEuclideanDistance(), vector, typeOfVector, sampleType))\",\n \"def distortion_of_kmeans_clustering(data_table):\\n num_iritations = 5\\n singleton_list = []\\n for line in data_table:\\n singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))\\n distortion_list = []\\n for num in range(20, 5, -1):\\n cluster_list = kmeans_clustering(singleton_list,num, num_iritations)\\n distortion = compute_distortion(data_table, cluster_list)\\n distortion_list.append(distortion)\\n return distortion_list\\n\\n#####################################################################\\n# Code to load cancer data, compute a clustering and\\n# visualize the results\\n\\n\\n# def run_example():\\n# \\\"\\\"\\\"\\n# Load a data table, compute a list of clusters and\\n# plot a list of clusters\\n#\\n# Set DESKTOP = True/False to use either matplotlib or simplegui\\n# \\\"\\\"\\\"\\n# data_table = load_data_table(DATA_3108_URL)\\n# singleton_list = []\\n# for line in data_table:\\n# singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))\\n num_clusters = 16\\n # cluster_list = sequential_clustering(singleton_list, num_clusters)\\n # print(\\\"Displaying\\\", len(cluster_list), \\\"sequential clusters\\\")\\n #\\n # cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, num_clusters)\\n # print(\\\"Displaying\\\", len(cluster_list), \\\"hierarchical clusters\\\")\\n #\\n # cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, num_clusters, 5)\\n # print(\\\"Displaying\\\", len(cluster_list), \\\"k-means clusters\\\")\\n\\n # draw the clusters using matplotlib or simplegui\\n #\\n # if DESKTOP:\\n # # alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)\\n # alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True) #add cluster centers\\n\\n # else:\\n # alg_clusters_simplegui.PlotClusters(data_table, cluster_list) # use toggle in GUI to add cluster centers\",\n \"def report(\\n df: pd.DataFrame,\\n id_col: str = \\\"Compound_Id\\\",\\n columns: List[str] = [\\\"Compound_Id\\\", \\\"Smiles\\\"],\\n title: str = \\\"Cluster Report\\\",\\n intro: str = \\\"Large clusters first, similar clusters together.\\\",\\n):\\n\\n def add_cluster(cl_no, sim_to=None):\\n if sim_to is None:\\n sim_to = \\\"\\\"\\n html.append(\\\"<hr>\\\")\\n else:\\n sim_to = f\\\"(similar to {sim_to})\\\"\\n mf_cl = mf.MolFrame(df.query(\\\"Cluster_No == @cl_no\\\")[columns])\\n mf_cl = mf_cl.add_mols()\\n html.append(\\n f\\\"<br><h2>Cluster {cl_no} ({len(mf_cl.data)} Members) {sim_to}</h2><br>\\\"\\n )\\n grid = mfv.html_grid(mf_cl.data, id_col=\\\"Compound_Id\\\")\\n html.append(grid)\\n\\n if id_col not in columns:\\n columns = [id_col] + columns\\n if \\\"Smiles\\\" not in columns:\\n columns.append(\\\"Smiles\\\")\\n df_repr = df.query(\\\"IsRepr == 'Yes'\\\").reset_index().drop(\\\"index\\\", axis=1)\\n chem_sim = {}\\n for idx, rec0 in df_repr.iterrows():\\n for _, rec1 in df_repr.iloc[idx + 1 :].iterrows():\\n cl0 = rec0[\\\"Cluster_No\\\"]\\n cl1 = rec1[\\\"Cluster_No\\\"]\\n sim = mf.chem_sim(rec0[\\\"Smiles\\\"], rec1[\\\"Smiles\\\"])\\n chem_sim[(cl0, cl1)] = sim\\n chem_sim[(cl1, cl0)] = sim\\n\\n cl_sizes = (\\n df[[\\\"Cluster_No\\\", \\\"Compound_Id\\\"]]\\n .groupby(by=\\\"Cluster_No\\\")\\n .count()\\n .reset_index()\\n .rename(columns={\\\"Compound_Id\\\": \\\"Size\\\"})\\n )\\n cl_sizes = cl_sizes.sort_values(\\\"Size\\\", ascending=False)\\n cl_order = {x: True for x in cl_sizes[\\\"Cluster_No\\\"].values}\\n\\n html = [f\\\"<h1>{title}</h1><br>{intro}<br><br>\\\"]\\n while len(cl_order) > 0:\\n cl_no = list(cl_order.keys())[0]\\n add_cluster(cl_no)\\n cl_order.pop(cl_no)\\n to_remove = []\\n for sim_cl in cl_order:\\n if chem_sim[(cl_no, sim_cl)] > 0.45:\\n add_cluster(sim_cl, cl_no)\\n to_remove.append(sim_cl)\\n for x in to_remove:\\n cl_order.pop(x)\\n\\n mfht.write(mfht.page(\\\"\\\\n\\\".join(html)), \\\"Clusters.html\\\")\",\n \"def test_gethtml_multiple(self):\\r\\n mock_module = VerticalWithModulesFactory.create()\\r\\n out_html = mock_module.render('student_view').content\\r\\n self.assertTrue('Test numerical problem.' in out_html)\\r\\n self.assertTrue('Another test numerical problem.' in out_html)\",\n \"def test_main_split_cluster(self):\\n \\n command = \\\" \\\".join( [\\\"%s/denoiser.py\\\" % get_qiime_scripts_dir(),\\n \\\"-S\\\", \\\"--force\\\", '-c', '-n 2',\\n \\\"-i\\\", \\\"%s/qiime/support_files/denoiser/TestData/denoiser_test_set.sff.txt\\\" % PROJECT_HOME,\\n \\\"-f\\\", \\\"%s/qiime/support_files/denoiser/TestData/test_set_seqs.fna\\\" % PROJECT_HOME,\\n \\\"-o\\\", self.test_dir] )\\n\\n result = Popen(command,shell=True,universal_newlines=True,\\\\\\n stdout=PIPE,stderr=STDOUT).stdout.read()\\n self.result_dir = self.test_dir\\n\\n for subdir in [\\\"0/\\\",\\\"1/\\\"]:\\n observed = \\\"\\\".join(list(open(self.result_dir+ subdir+\\\"centroids.fasta\\\")))\\n self.assertEqual(observed, expected_centroids[subdir])\\n\\n observed = \\\"\\\".join(list(open(self.result_dir+ subdir+\\\"denoiser_mapping.txt\\\")))\\n self.assertEqual(observed, expected_map_string_on_cluster[subdir])\",\n \"def test(dist_param, picker_param, iters):\\n orig = '/home/zby/MAGISTERKA/MGR/results/oryginal.clustered.t'\\n cl_orig = read_clustered(orig)\\n name_tag = ''\\n ndist = dist_param[1:]\\n npick = picker_param[1:]\\n for index in drange(4, 20, 0.5):\\n name_tag = \\\"{}_{}_{}\\\".format(index, npick, ndist)\\n tf_conf = configs.TfidfConfig(\\n root_name('all_merged.txt', None),\\n tfidf_name('merged.stem{}.stop', name_tag),\\n tfidf_name('merged.stem{}.stop.txt', name_tag),\\n None,\\n tfidf_name('merged.stem{}.tfidf', name_tag),\\n 10,\\n 0,\\n None)\\n execute(tf_conf)\\n tf_conf = configs.TfidfConfig(\\n root_name('all_merged.txt', None),\\n None,\\n tfidf_name('merged.stem{}.stop.txt', name_tag),\\n tfidf_name('merged.stem{}.stop', name_tag),\\n tfidf_name('merged.stem{}.stop.tfidf', name_tag),\\n None,\\n None,\\n None)\\n execute(tf_conf)\\n #input, out, picker, distance, iterations,\\n clust_cfg = configs.ClusteringConfig(\\n tfidf_name('merged.stem{}.stop.tfidf', name_tag),\\n tfidf_name('merged.stem{}.stop.clustered.t', name_tag),\\n picker_param,\\n dist_param,\\n iters,\\n None\\n )\\n execute(clust_cfg)\\n clust2 = read_clustered(tfidf_name('merged.stem{}.stop.clustered.t', name_tag))\\n var, norm = variation_of_information(cl_orig, clust2)\\n print(\\\"**** FOR var {} VOI is {}\\\".format(name_tag, norm))\",\n \"def test_get_root_html(self):\\n pass\",\n \"def test_extract_cluster_size(self):\\r\\n\\r\\n header1 = \\\">GCC6FHY01EQVIC | cluster size: 5\\\"\\r\\n header2 = \\\">blabla\\\"\\r\\n\\r\\n self.assertEqual(extract_cluster_size(header1), 5)\\r\\n self.assertEqual(extract_cluster_size(header2), 0)\",\n \"def test_rr_summary_cluster(model, clusters):\\n test_result = model.fit(groupvar=clusters, cov_type=\\\"cluster\\\").summary()\\n assert isinstance(test_result.tables, list)\\n assert len(test_result.tables) == 3\\n assert len(test_result.extra_txt) > 0\",\n \"def test_cluster_graph(self):\\n g = Graph.build(edges=[\\n ('a', 'e', 8), ('a', 'c', 7), ('a', 'd', 9), ('a', 'f', 10), ('a', 'b', 8),\\n ('e', 'f', 6), ('e', 'd', 5), ('e', 'b', 10), ('e', 'c', 1),\\n ('c', 'f', 5), ('c', 'd', 4), ('c', 'b', 9),\\n ('d', 'f', 1), ('d', 'b', 7),\\n ('f', 'b', 8)\\n ], directed=False)\\n\\n (clusters, distances) = cluster_graph(g, 4)\\n expectedClusters = {\\n 'a': ['a'],\\n 'c': ['c', 'e'],\\n 'b': ['b'],\\n 'd': ['d', 'f']\\n }\\n expectedDistances = {\\n ('b', 'c'): 9,\\n ('c', 'd'): 4,\\n ('a', 'd'): 9,\\n ('a', 'b'): 8,\\n ('a', 'c'): 7,\\n ('b', 'd'): 7\\n }\\n self.assertEqual(clusters, expectedClusters,\\n 'should return correct clusters')\\n self.assertEqual(distances, expectedDistances,\\n 'should compute distances')\\n\\n (clusters, distances) = cluster_graph(g, 3)\\n expectedClusters = {\\n 'a': ['a'],\\n 'b': ['b'],\\n 'c': ['c', 'e', 'd', 'f']\\n }\\n expectedDistances = {\\n ('b', 'c'): 7,\\n ('a', 'b'): 8,\\n ('a', 'c'): 7\\n }\\n self.assertEqual(clusters, expectedClusters,\\n 'should return correct clusters')\\n self.assertEqual(distances, expectedDistances,\\n 'should compute distances')\",\n \"def test_export_html(self):\\r\\n resp = self.client.get_html(self.url)\\r\\n self.assertEquals(resp.status_code, 200)\\r\\n self.assertContains(resp, \\\"Export My Course Content\\\")\",\n \"def test_get_root_html1(self):\\n pass\",\n \"def test_get_root_html2(self):\\n pass\",\n \"def _repr_html_(self):\\n return (\\n f'<b>GalaxyCluster:</b> {self.unique_id} '\\n f'(ra={self.ra}, dec={self.dec}) at z={self.z}'\\n f'<br>> <b>with columns:</b> {self._str_colnames()}'\\n f'<br>> {len(self.galcat)} source galaxies'\\n f'<br>{self.galcat._html_table()}'\\n )\",\n \"def test_pcca_1():\\n n = 1000 #number of data points\\n kk = 3 #number of points where data accumulates\\n k = 10 #number of cluster_centers\\n factor = 0.1 #how much is the data perturbed\\n data = np.zeros((n,1))\\n for i in range(0,n):\\n data[i] = i % kk + factor * np.random.rand() * math.pow(-1,int(2*np.random.rand()))\\n #plt.scatter(data[:,0],np.zeros((n,1)))\\n \\n clustering = cl.KMeans(data,k)\\n cluster_centers = clustering.cluster_centers\\n cluster_labels = clustering.cluster_labels\\n \\n #plt.scatter(cluster_centers[:],np.zeros((k,1)),c='r')\\n \\n estimator = est.Estimator(cluster_labels, 1, 1)\\n matrix = estimator.reversible_transition_matrix\\n msm = ana.MarkovStateModel(matrix)\\n \\n n_pcca_states = 4;\\n #fig, ax = plt.subplots(figsize=(6.5, 5))\\n pcca_labels = msm.metastable_set_assignments(n_pcca_states)\\n #im = ax.scatter(cluster_centers[:, 0], np.zeros((k,1)), c=pcca_labels, s=200)\\n #cbar = fig.colorbar(im, ax=ax)\\n error = 0;\\n for j in range(0,kk):\\n for i in range(0,k):\\n if (round(cluster_centers[i,0]) == j):\\n test = i\\n for i in range(0,k):\\n if (np.abs(cluster_centers[i,0] - cluster_centers[test,0]) < 2*factor):\\n if (not pcca_labels[i] == pcca_labels[test]):\\n error = 1\\n print(error)\\n assert_true(error == 0)\",\n \"def test_gettesttools_html(self):\\n pass\",\n \"def testHTML(self):\\n\\n html = self.E.html()\",\n \"def print_outputs(self, alpha=None):\\n if alpha:\\n cluster_dir = self.alpha_cluster_dir\\n optimal_k = self.tl_model.optimal_k\\n area = (self.tl_model.LON_E-self.tl_model.LON_W)*(self.tl_model.LAT_N-self.tl_model.LAT_S)\\n else:\\n cluster_dir = self.cluster_dir\\n optimal_k = self.optimal_k\\n area = (self.LON_E-self.LON_W)*(self.LAT_N-self.LAT_S)\\n ind_cluster_plots_dir = str(Path(self.cluster_dir) / \\\"indiv_cluster_plots\\\")\\n self.ind_cluster_plots_dir = ind_cluster_plots_dir\\n os.makedirs(self.ind_cluster_plots_dir, exist_ok=True)\\n print(f'self.ind_cluster_plots_dir is @:\\\\n{self.ind_cluster_plots_dir}')\\n\\n print(f'cluster_dir: \\\"{cluster_dir}\\\", optimal_k: \\\"{optimal_k}\\\"')\\n min_area = 2000\\n if area > min_area: too_large = True\\n else: too_large = False\\n\\n\\n if not utils.find('*_prelim_SOMscplot_*.png', cluster_dir): visualization.print_som_scatterplot_with_dmap(self, cluster_dir)\\n if not utils.find('*_kmeans*.png', cluster_dir): visualization.print_kmeans_scatterplot(self, cluster_dir, optimal_k)\\n self.grid_width = visualization.grid_width(optimal_k)\\n self.cbar_pos = np.max([(((clus)//self.grid_width)*self.grid_width)\\n for i,clus in enumerate(np.arange(optimal_k))\\n if i==(((clus)//self.grid_width)*self.grid_width)])\\n if not utils.find('*_ARmonthfrac_*.png', cluster_dir): visualization.print_ar_plot(self, cluster_dir, optimal_k)\\n if not utils.find('*_ARmonthfrac_granular_*.png', cluster_dir): visualization.print_ar_plot_granular(self, cluster_dir, optimal_k)\\n if not utils.find('*_RFplot_mean_*.png', cluster_dir): visualization.print_rf_mean_plots(self, cluster_dir, optimal_k)\\n if not utils.find('*_RFplot_max_v2*.png', cluster_dir): visualization.print_rf_max_plots(self, cluster_dir, optimal_k)\\n if not utils.find('*_RFplot_rainday_gt1mm_v3_*.png', cluster_dir): visualization.print_rf_rainday_gt1mm_plots(self, cluster_dir, optimal_k)\\n if not utils.find('*_RFplot_rainday_gt1mm_SGonly_v3_*.png', cluster_dir): visualization.print_rf_rainday_gt1mm_SGonly_plots(self, cluster_dir, optimal_k)\\n if not utils.find('*_RFplot_heavyrainday_gt50mm_v2_*.png', cluster_dir): visualization.print_rf_heavyrf_gt50mm_plots(self, cluster_dir, optimal_k)\\n if not utils.find('*_RFplot_90th_percentile_v2*.png', cluster_dir): visualization.print_rf_90th_percentile_plots(self, cluster_dir, optimal_k)\\n if not utils.find('*_qp_v3*.png', cluster_dir): visualization.print_quiver_plots(self, cluster_dir, optimal_k)\\n if not utils.find('*_qp_v5*.png', cluster_dir): visualization.print_quiver_plots(self, cluster_dir, optimal_k)\\n if not utils.find('*_qp_v1_ANOM*.png', cluster_dir): visualization.print_quiver_ANOM_whole(self, cluster_dir, optimal_k)\\n if not utils.find('*_qp_sgonly*.png', cluster_dir): visualization.print_quiver_plots_sgonly(self, cluster_dir, optimal_k)\\n \\n if not utils.find('*qp_baseline*.png', cluster_dir): visualization.print_quiver_baseline(self, cluster_dir, optimal_k)\\n if not utils.find('*qp_baseline_Regionalonly*.png', cluster_dir): visualization.print_quiver_baseline_regional(self, cluster_dir, optimal_k)\\n if not utils.find('*_rhum_v3-at*.png', cluster_dir): visualization.print_rhum_plots(self, cluster_dir, optimal_k)\\n if not utils.find('*_rhum_sgonly-at*.png', cluster_dir): visualization.print_rhum_plots_sgonly(self, cluster_dir, optimal_k)\\n if not utils.find('*_rhum_v5_ANOM-at*.png', cluster_dir): visualization.print_RHUM_ANOM_whole(self, cluster_dir, optimal_k)\\n if not utils.find('*rhum_baseline*.png', cluster_dir): visualization.print_rhum_baseline(self, cluster_dir, optimal_k)\\n\\n if not utils.find('*RFplot_90th_percentile_baseline*.png', cluster_dir): visualization.print_RF_baselines(self, cluster_dir, optimal_k, \\n too_large)\\n\\n if not utils.find('*_qp_Regionalonly*.png', cluster_dir): visualization.print_quiver_Regionalonly(self, cluster_dir, optimal_k)\\n if not utils.find('*_rhum_Regionalonly*.png', cluster_dir): visualization.print_RHUM_Regionalonly(self, cluster_dir, optimal_k)\\n if not utils.find('*_rhum_Regionalonly_ANOM_v2*.png', cluster_dir): visualization.print_RHUM_ANOM_Regionalonly(self, cluster_dir, optimal_k)\\n if not utils.find('*_qp_Regionalonly_ANOM*.png', cluster_dir): visualization.print_quiver_ANOM_Regionalonly(self, cluster_dir, optimal_k)\\n if not utils.find('*_RFplot_90th_percentile_SGonly_ANOM_v1_*.png', cluster_dir): visualization.print_rf_90th_percentile_SGonly_ANOM_plots(self, cluster_dir, optimal_k)\\n if not utils.find('*_RFplot_rainday_gt1mm_SGonly_ANOM_v3_*.png', cluster_dir): visualization.print_rf_rainday_gt1mm_SGonly_ANOM_plots(self, cluster_dir, optimal_k)\\n if not utils.find('*_RFplot_heavy_gt50mm_SGonly_ANOM_v2_*.png', cluster_dir): visualization.print_rf_heavy_gt50mm_SGonly_ANOM_plots(self, cluster_dir, optimal_k)\\n if not utils.find('*_RFplot_rainday_gt1mm_ANOM_v1_*.png', cluster_dir): visualization.print_rf_rainday_gt1mm_ANOM_plots(self, cluster_dir, optimal_k)\\n\\n if not utils.find('*_RFplot_heavy_gt50mm_ANOM_v2_*.png', cluster_dir): visualization.print_rf_heavy_gt50mm_ANOM_plots(self, cluster_dir, optimal_k)\\n if not utils.find('*_RFplot_rainday_gt1mm_zscores_v2_*.png', cluster_dir): visualization.print_rf_gt1mm_zscore(self, cluster_dir, optimal_k, \\n too_large)\\n if not utils.find('*_RFplot_heavy_gt50mm_zscores_v2_*.png', cluster_dir): visualization.print_rf_heavy_gt50mm_zscore(self, cluster_dir, optimal_k, \\n too_large)\\n if not utils.find('*_RFplot_rainday_gt1mm_SGonly_zscores_v2_*.png', cluster_dir): visualization.print_rf_heavy_gt1mm_SGonly_zscore(self, cluster_dir, optimal_k, \\n too_large)\\n if not utils.find('*_RFplot_heavy_gt50mm_SGonly_zscores_v3_*.png', cluster_dir): visualization.print_rf_heavy_gt50mm_SGonly_zscore(self, cluster_dir, optimal_k, \\n too_large)\\n if not utils.find('*_RFplot_90th_percentile_ANOM_v1_*.png', cluster_dir): visualization.print_rf_90th_percentile_ANOM_plots(self, cluster_dir, optimal_k, \\n too_large)\\n \\n if not utils.find('*_RFplot_rainday_gt1mm_Regionalonly_ANOM_v2_*.png', cluster_dir): visualization.print_rf_gt1mm_ANOM_Regionalonly(self, cluster_dir, optimal_k)\\n if not utils.find('*_RFplot_heavy_gt50mm_Regionalonly_ANOM_v2_*.png', cluster_dir): visualization.print_rf_gt50mm_ANOM_Regionalonly(self, cluster_dir, optimal_k)\\n \\n\\n if not alpha:\\n if not utils.find('*RFprec_to_ClusterLabels_dataset_vals_5xcoarsened_maxed.pkl', ind_cluster_plots_dir):\\n print(f'5X coarsened RF full dataset not found. Generating now @:\\\\n{ind_cluster_plots_dir}')\\n self.full_rf_5Xcoarsened_vals_path = prepare.prepare_RF_vals_5x_coarsened(self, ind_cluster_plots_dir)\\n else:\\n self.full_rf_5Xcoarsened_vals_path = utils.find('*RFprec_to_ClusterLabels_dataset_vals_5xcoarsened_maxed.pkl', ind_cluster_plots_dir)[0]\\n # for clus in range(optimal_k):\\n # \\\"\\\"\\\"\\n # Individual test plots\\n # \\\"\\\"\\\"\\n # if not utils.find(f\\\"*RF_proportion_above_90thpercentile_cluster_{int(clus+1)}.png\\\", ind_cluster_plots_dir): visualization.print_ind_clus_proportion_above_90thpercentile(self, ind_cluster_plots_dir, clus)\\n # if not utils.find(f\\\"*RF_proportion_under_10thpercentile_cluster_v2_{int(clus+1)}.png\\\", ind_cluster_plots_dir): visualization.print_ind_clus_proportion_under_10thpercentile(self, ind_cluster_plots_dir, clus)\\n # if not utils.find(f\\\"*RF_proportion_above_fullmodel_mean_cluster_{int(clus+1)}.png\\\", ind_cluster_plots_dir): visualization.print_ind_clus_proportion_above_fullmodel_mean(self, ind_cluster_plots_dir, clus)\\n # if not utils.find(f\\\"*RF_proportion_above_250mm_cluster_{int(clus+1)}.png\\\", ind_cluster_plots_dir): visualization.print_ind_clus_proportion_above_250mm(self, ind_cluster_plots_dir, clus)\\n \\n \\n print(\\n f'Outputs are available @: \\\\n{cluster_dir}\\\\n\\\\nThese are clustering results for domain {self.domain} ({self.period}), '\\\\\\n f'SOM-trained with hpparams {self.hyperparameters} \\\\nand 2nd-level k-means clustered with k at {optimal_k}.')\",\n \"def test_summary_with_results(self):\\n add_constituency_result_line('X, 10, C')\\n r = self.client.get('/summary')\\n self.assertEqual(r.status_code, 200)\\n soup = BeautifulSoup(r.data, 'html.parser')\\n self.assertIs(soup.find(id='no-results'), None)\\n self.assertIsNot(soup.find(id='results-table'), None)\",\n \"def test_si_sample_html_partial(self):\\n sample = load_sample('si-game.sample.html')\\n doc = Document('http://sportsillustrated.cnn.com/baseball/mlb/gameflash/2012/04/16/40630_preview.html',\\n sample)\\n res = doc.get_clean_article()\\n self.assertEqual('<div><div class=\\\"', res[0:17])\",\n \"def test_get_root_html3(self):\\n pass\",\n \"def compute_cluster_similarities(emb_clusters1, emb_clusters2, compare, order, clmethod, plot):\\n def compute_sim(e, e1, cls, cls1):\\n sims = np.empty((20, 20))\\n xticks, yticks = [], []\\n for i, c in enumerate(cls):\\n yticks.append(', '.join(c[1]) + (f' {round(c[3], 5)}' if order == 'avgfreq' else ''))\\n for j, c1 in enumerate(cls1):\\n if len(xticks) < 20:\\n xticks.append(', '.join(c1[1]) + (f' {round(c1[3], 5)}' if order == 'avgfreq' else ''))\\n sims[i, j] = jaccard_similarity_score(c[2], c1[2])\\n jaccard_similarities[f'{e}-{e1}'] = sims\\n\\n if plot:\\n if order == 'clustermap':\\n similarity_clustermap(sims, xticks, yticks, f'{e}-{e1}_{clmethod}')\\n elif order == 'default' or order == 'avgfreq':\\n similarity_heatmap(sims, xticks, yticks, f'{e}-{e1}_{clmethod}', order)\\n else:\\n pass\\n\\n jaccard_similarities = {}\\n if compare == 'cross':\\n for ie, (e, cls) in enumerate(emb_clusters1.items()):\\n for ie1, (e1, cls1) in enumerate(emb_clusters2.items()):\\n if ie < ie1:\\n compute_sim(e, e1, cls, cls1)\\n elif compare == 'dot':\\n for (e, cls), (e1, cls1) in zip(emb_clusters1.items(), emb_clusters2.items()):\\n compute_sim(e, e1, cls, cls1)\\n\\n return jaccard_similarities\",\n \"def gen_html_report(landmarks_list, usage_flag, output_folder):\\n labelled_landmarks = landmarks_list[0]\\n\\n if usage_flag == 2:\\n detected_landmarks = landmarks_list[1]\\n assert len(labelled_landmarks.keys()) == len(detected_landmarks.keys())\\n\\n # sort the labelled landmarks according to detection error\\n error_summary = error_analysis(labelled_landmarks, detected_landmarks)\\n\\n landmark_name_list = labelled_landmarks[list(labelled_landmarks.keys())[0]].keys()\\n for landmark_idx, landmark_name in enumerate(landmark_name_list):\\n print(\\\"Generating html report for landmark {}: {}.\\\".format(landmark_idx, landmark_name))\\n image_link_template = r\\\"<div class='content'><img border=0 src= '{0}' hspace=1 width={1} class='pic'></div>\\\"\\n error_info_template = r'<b>Labelled</b>: [{0:.2f}, {1:.2f}, {2:.2f}];'\\n document_text = r'\\\"<h1>check predicted coordinates:</h1>\\\"'\\n document_text += \\\"\\\\n\\\"\\n\\n if usage_flag == 1:\\n image_list = list(labelled_landmarks.keys())\\n for image_idx, image_name in enumerate(image_list):\\n label_landmark_world = labelled_landmarks[image_name][landmark_name]\\n document_text = \\\\\\n gen_row_for_html(usage_flag, image_link_template, error_info_template,\\n document_text, image_list, image_idx, landmark_idx,\\n [label_landmark_world], None)\\n \\n elif usage_flag == 2:\\n image_list = error_summary.all_cases[landmark_name]\\n error_sorted_index = error_summary.error_sorted_index\\n for image_idx in error_sorted_index[landmark_name]:\\n image_name = image_list[image_idx]\\n label_landmark_world = labelled_landmarks[image_name][landmark_name]\\n detected_landmark_world = detected_landmarks[image_name][landmark_name]\\n error_info_template = r'<b>Labelled</b>: [{0:.2f}, {1:.2f}, {2:.2f}];'\\n error_info_template += r'<b>Detected</b>: [{3:.2f}, {4:.2f}, {5:.2f}]; '\\n error_info_template += r'<b>Type</b>: {6};'\\n error_info_template += r'<b>Error</b>: x:{7:.2f}; y:{8:.2f}; z:{9:.2f}; L2:{10:.2f};'\\n document_text = \\\\\\n gen_row_for_html(usage_flag, image_link_template, error_info_template,\\n document_text, image_list, image_idx, landmark_name,\\n [label_landmark_world, detected_landmark_world],\\n error_summary)\\n\\n else:\\n raise ValueError('Undefined usage flag!')\\n\\n if usage_flag == 1:\\n analysis_text = gen_analysis_text(len(image_list), usage_flag,\\n labelled_landmarks, landmark_name, None)\\n\\n elif usage_flag == 2:\\n analysis_text = gen_analysis_text(len(image_list), usage_flag,\\n labelled_landmarks, landmark_name, error_summary)\\n\\n else:\\n raise ValueError('Undefined usage float!')\\n\\n html_report_name = 'result_analysis.html'\\n html_report_folder = os.path.join(output_folder, 'lm{}'.format(landmark_idx))\\n if not os.path.isdir(html_report_folder):\\n os.makedirs(html_report_folder)\\n \\n html_report_path = os.path.join(html_report_folder, html_report_name)\\n write_html_report_for_single_landmark(document_text, analysis_text, html_report_path, width=200)\\n\\n if usage_flag == 2:\\n summary_csv_report_name = 'summary.csv'\\n summary_csv_path = os.path.join(output_folder, summary_csv_report_name)\\n write_summary_csv_report_for_all_landmarks(error_summary, summary_csv_path)\",\n \"def cluster(approach, datapath):\\n report_path = test(datapath, approach, params[approach])\\n c.echo('Report compiled at {0}.'.format(report_path))\",\n \"def test_get_distribution_centers(self):\\n pass\",\n \"def generateHtml(self):\\n # only the master processor needs to do this\\n if not self.master: return\\n\\n for page in self.layout.pages:\\n \\n # build the metric dictionary\\n metrics = {}\\n page.models = []\\n for fname in glob.glob(os.path.join(self.output_path,\\\"*.nc\\\")):\\n with Dataset(fname) as dataset:\\n mname = dataset.getncattr(\\\"name\\\")\\n if mname != \\\"Benchmark\\\": page.models.append(mname)\\n if not dataset.groups.has_key(page.name): continue\\n group = dataset.groups[page.name]\\n\\n # if the dataset opens, we need to add the model (table row)\\n metrics[mname] = {}\\n \\n # each model will need to have all regions\\n for region in self.regions: metrics[mname][region] = {}\\n \\n # columns in the table will be in the scalars group\\n if not group.groups.has_key(\\\"scalars\\\"): continue\\n \\n # we add scalars to the model/region based on the region\\n # name being in the variable name. If no region is found,\\n # we assume it is the global region.\\n grp = group.groups[\\\"scalars\\\"]\\n for vname in grp.variables.keys():\\n found = False\\n for region in self.regions:\\n if region in vname: \\n found = True\\n var = grp.variables[vname]\\n name = vname.replace(region,\\\"\\\")\\n metrics[mname][region][name] = Variable(name = name,\\n unit = var.units,\\n data = var[...])\\n if not found:\\n var = grp.variables[vname]\\n metrics[mname][\\\"global\\\"][vname] = Variable(name = vname,\\n unit = var.units,\\n data = var[...])\\n page.setMetrics(metrics)\\n \\n # write the HTML page\\n f = file(os.path.join(self.output_path,\\\"%s.html\\\" % (self.name)),\\\"w\\\")\\n f.write(str(self.layout))\\n f.close()\",\n \"def test_render_student_view(self):\\r\\n html = self.module_system.render(self.vertical, 'student_view', {}).content\\r\\n self.assertIn(self.test_html_1, html)\\r\\n self.assertIn(self.test_html_2, html)\",\n \"def output_test():\\n\\toutput_comparison_page(TEST_EVENT_LIST, TEST_COMPARISON_PAGE_FILEPATH)\",\n \"def output_div(self, output_method):\\n instance = self.instance\\n G = myGraph(instance.view_num)\\n for i in range(instance.view_num):\\n view = instance.tables[instance.views[i].table_pos].views[instance.views[i].view_pos]\\n G.addNode(view)\\n G.getSim()\\n result = G.getTopK(instance.view_num)\\n order = 1\\n export_list = []\\n if output_method == 'list':\\n for item in result:\\n export_list.append(G.nodes[item].output(order))\\n order += 1\\n return export_list\\n elif output_method == 'print':\\n for item in result:\\n pprint (G.nodes[item].output(order))\\n order += 1\\n return\\n elif output_method == 'single_json' or output_method == 'multiple_jsons':\\n path2 = os.getcwd() + '/json/'\\n if not os.path.exists(path2):\\n os.mkdir(path2)\\n if output_method == 'single_json':\\n f = open(path2 + self.table_name + '.json','w')\\n for item in result:\\n f.write(G.nodes[item].output(order) + '\\\\n')\\n order += 1\\n f.close() # Notice that f.close() is out of the loop to create only one file\\n else: #if output_method == 'multiple_jsons'\\n for item in result:\\n f = open(path2 + self.table_name + str(order)+'.json','w')\\n f.write(G.nodes[item].output(order))\\n order += 1\\n f.close() # Notice that f.close() is in the loop to create multiple files\\n return\\n elif output_method == 'single_html' or output_method == 'multiple_htmls':\\n path2 = os.getcwd() + '/html/'\\n if not os.path.exists(path2):\\n os.mkdir(path2)\\n page = Page()\\n if output_method == 'single_html':\\n self.page = Page()\\n for item in result:\\n view = G.nodes[item]\\n self.html_output(order, view, 'single')\\n order += 1\\n self.page.render('./html/' + self.table_name + '_all' + '.html')\\n else: # if output_method == 'multiple_htmls'\\n path3 = os.getcwd() + '/html/' + self.table_name\\n if not os.path.exists(path3):\\n os.mkdir(path3)\\n for item in result:\\n view = G.nodes[item]\\n self.html_output(order, view, 'multiple')\\n order += 1\\n return\",\n \"def main(argv=None):\\n\\n if argv is None:\\n argv = sys.argv\\n\\n # setup command line parser\\n parser = E.OptionParser(version=\\\"%prog version: $Id$\\\",\\n usage=globals()[\\\"__doc__\\\"])\\n\\n parser.add_option(\\\"-t\\\", \\\"--test\\\", dest=\\\"test\\\", type=\\\"string\\\",\\n help=\\\"supply help\\\")\\n\\n parser.add_option(\\\"--method\\\", dest=\\\"method\\\", type=\\\"choice\\\",\\n choices=(\\\"metrics\\\", \\\"summary\\\", \\\"module_summary\\\"),\\n help=\\\"method to summarise clustering\\\")\\n\\n parser.add_option(\\\"--ref-gtf-files\\\", dest=\\\"ref_gtf\\\", type=\\\"string\\\",\\n help=\\\"comma separated list of reference gtf files\\\")\\n\\n # add common options (-h/--help, ...) and parse command line\\n (options, args) = E.Start(parser, argv=argv)\\n\\n if options.method == \\\"metrics\\\":\\n infile = argv[-1]\\n E.info(\\\"loading input file: %s\\\" % infile)\\n assert infile\\n\\n df = pd.read_table(infile,\\n sep=\\\"\\\\t\\\",\\n header=None,\\n index_col=0)\\n\\n df = df.ix[:, :50]\\n cluster_combs = (x for x in itertools.combinations(df.columns,\\n 2))\\n genes = df.index\\n results_dict = {}\\n all_clusts = {}\\n\\n E.info(\\\"setting up cluster containers\\\")\\n for i in df.columns:\\n clusters = set(df[i].values.tolist())\\n cluster_dict = {}\\n for clust in clusters:\\n cluster_dict[clust] = []\\n for gene in genes:\\n cluster_dict[df[i][gene]].append(gene)\\n\\n for col in clusters:\\n col_set = set()\\n clust_col = cluster_dict[col]\\n gene_members = itertools.combinations(clust_col,\\n 2)\\n col_set.update(gene_members)\\n cluster_dict[col] = col_set\\n all_clusts[i] = cluster_dict\\n E.info(\\\"generating all pair-wise cluster comparisons\\\")\\n E.info(\\\"calculating adjusted mutual information\\\")\\n for k in cluster_combs:\\n clusters1 = all_clusts[k[0]]\\n clusters2 = all_clusts[k[1]]\\n metric_dict = {}\\n metric_dict['AMI'] = TS.adjustedMutualInformation(clusters1,\\n clusters2)\\n results_dict[k] = metric_dict\\n\\n res_frame = pd.DataFrame(results_dict).T\\n res_frame = res_frame.reset_index()\\n res_frame.drop(['level_0'], inplace=True, axis=1)\\n res_frame.drop(['level_1'], inplace=True, axis=1)\\n\\n # flatten rand indices and add to output dataframe\\n rand_arrays = TS.randIndexes(df)\\n flat_adj_rand = TS.unravel_arrays(rand_arrays[0])\\n flat_rand = TS.unravel_arrays(rand_arrays[1])\\n res_frame['Rand_Index'] = flat_rand\\n res_frame['Adjusted_Rand_Index'] = flat_adj_rand\\n E.info(\\\"aggregating results\\\")\\n\\n res_frame.to_csv(options.stdout,\\n sep=\\\"\\\\t\\\",\\n index_label='idx')\\n\\n elif options.method == \\\"summary\\\":\\n infiles = argv[-1]\\n list_of_files = infiles.split(\\\",\\\")\\n\\n file_dict = {}\\n for fle in list_of_files:\\n fname = fle.split(\\\"/\\\")[-1]\\n condition = fname.split(\\\"-\\\")[0]\\n ref = fname.split(\\\"-\\\")[1]\\n df_ = pd.read_table(fle,\\n sep=\\\"\\\\t\\\",\\n header=0,\\n index_col=0)\\n df_.columns = ['gene_id', 'cluster']\\n clust_dict = {}\\n for idx in df_.index:\\n cluster = df_.loc[idx]['cluster']\\n gene = df_.loc[idx]['gene_id']\\n try:\\n clust_dict[cluster] += 1\\n except KeyError:\\n clust_dict[cluster] = 1\\n med_size = np.median(clust_dict.values())\\n file_dict[fname] = {'condition': condition,\\n 'reference': ref,\\n 'median_cluster_size': med_size}\\n\\n outframe = pd.DataFrame(file_dict).T\\n outframe.to_csv(options.stdout,\\n sep=\\\"\\\\t\\\",\\n index_label='idx')\\n\\n elif options.method == \\\"module_summary\\\":\\n # get lncRNA/gene lengths from reference gtfs\\n ref_gtfs = options.ref_gtf.split(\\\",\\\")\\n length_dict = {}\\n for ref in ref_gtfs:\\n oref = IOTools.openFile(ref, \\\"rb\\\")\\n git = GTF.transcript_iterator(GTF.iterator(oref))\\n for gene in git:\\n for trans in gene:\\n length = trans.end - trans.start\\n try:\\n length_dict[trans.gene_id] += length\\n except KeyError:\\n length_dict[trans.gene_id] = length\\n oref.close()\\n\\n infiles = argv[-1]\\n list_of_files = infiles.split(\\\",\\\")\\n\\n fdfs = []\\n for fle in list_of_files:\\n cond = fle.split(\\\"/\\\")[-1].split(\\\"-\\\")[0]\\n refer = fle.split(\\\"/\\\")[-1].split(\\\"-\\\")[1]\\n _df = pd.read_table(fle, sep=\\\"\\\\t\\\",\\n header=0, index_col=0)\\n _df.columns = ['gene_id', 'cluster']\\n clusters = set(_df['cluster'])\\n c_dict = {}\\n # summarize over each cluster\\n for clust in clusters:\\n lengths = []\\n c_df = _df[_df['cluster'] == clust]\\n for lid in c_df['gene_id']:\\n lengths.append(length_dict[lid])\\n c_dict[clust] = {'cluster_size': len(c_df['gene_id']),\\n 'mean_length': np.mean(lengths),\\n 'index': (cond, refer),\\n 'module': clust}\\n cdf = pd.DataFrame(c_dict).T\\n # use a multindex for hierarchical indexing\\n midx = pd.MultiIndex.from_tuples(cdf['index'])\\n cdf.index = midx\\n cdf.drop(['index'], inplace=True, axis=1)\\n fdfs.append(cdf)\\n\\n # generate a single output df\\n s_df = fdfs[0]\\n fdfs.pop(0)\\n for df in fdfs:\\n s_df = s_df.append(df)\\n\\n s_df.to_csv(options.stdout,\\n index_label=(\\\"condition\\\", \\\"reference\\\"),\\n sep=\\\"\\\\t\\\")\\n\\n # write footer and output benchmark information.\\n E.Stop()\",\n \"def compare_silhoutte_scores(dfi, samples, range_n_clusters, cluster_dim='features'):\\n df = dfi.fillna(0).copy()\\n X = df[samples].values\\n if cluster_dim == 'samples':\\n X = X.T\\n \\n\\n for n_clusters in range_n_clusters:\\n # Create a subplot with 1 row and 2 columns\\n fig, (ax1, ax2) = plt.subplots(1, 2)\\n fig.set_size_inches(18, 7)\\n\\n # The 1st subplot is the silhouette plot\\n # The silhouette coefficient can range from -1, 1 but in this example all\\n # lie within [-0.1, 1]\\n ax1.set_xlim([-0.1, 1])\\n # The (n_clusters+1)*10 is for inserting blank space between silhouette\\n # plots of individual clusters, to demarcate them clearly.\\n ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])\\n\\n # Initialize the clusterer with n_clusters value and a random generator\\n # seed of 10 for reproducibility.\\n clusterer = KMeans(n_clusters=n_clusters, random_state=10)\\n cluster_labels = clusterer.fit_predict(X)\\n\\n # The silhouette_score gives the average value for all the samples.\\n # This gives a perspective into the density and separation of the formed\\n # clusters\\n silhouette_avg = silhouette_score(X, cluster_labels)\\n print(\\\"For n_clusters =\\\", n_clusters,\\n \\\"The average silhouette_score is :\\\", silhouette_avg)\\n\\n # Compute the silhouette scores for each sample\\n sample_silhouette_values = silhouette_samples(X, cluster_labels)\\n\\n y_lower = 10\\n for i in range(n_clusters):\\n # Aggregate the silhouette scores for samples belonging to\\n # cluster i, and sort them\\n ith_cluster_silhouette_values = \\\\\\n sample_silhouette_values[cluster_labels == i]\\n\\n ith_cluster_silhouette_values.sort()\\n\\n size_cluster_i = ith_cluster_silhouette_values.shape[0]\\n y_upper = y_lower + size_cluster_i\\n\\n color = cm.spectral(float(i) / n_clusters)\\n ax1.fill_betweenx(np.arange(y_lower, y_upper),\\n 0, ith_cluster_silhouette_values,\\n facecolor=color, edgecolor=color, alpha=0.7)\\n\\n # Label the silhouette plots with their cluster numbers at the middle\\n ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\\n\\n # Compute the new y_lower for next plot\\n y_lower = y_upper + 10 # 10 for the 0 samples\\n\\n ax1.set_title(\\\"The silhouette plot for the various clusters.\\\")\\n ax1.set_xlabel(\\\"The silhouette coefficient values\\\")\\n ax1.set_ylabel(\\\"Cluster label\\\")\\n\\n # The vertical line for average silhouette score of all the values\\n ax1.axvline(x=silhouette_avg, color=\\\"red\\\", linestyle=\\\"--\\\")\\n\\n ax1.set_yticks([]) # Clear the yaxis labels / ticks\\n ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\\n\\n # 2nd Plot showing the actual clusters formed\\n colors = cm.spectral(cluster_labels.astype(float) / n_clusters)\\n ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,\\n c=colors, edgecolor='k')\\n\\n # Labeling the clusters\\n centers = clusterer.cluster_centers_\\n # Draw white circles at cluster centers\\n ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\\n c=\\\"white\\\", alpha=1, s=200, edgecolor='k')\\n\\n for i, c in enumerate(centers):\\n ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\\n s=50, edgecolor='k')\\n\\n ax2.set_title(\\\"The visualization of the clustered data.\\\")\\n ax2.set_xlabel(\\\"Feature space for the 1st feature\\\")\\n ax2.set_ylabel(\\\"Feature space for the 2nd feature\\\")\\n\\n plt.suptitle((\\\"Silhouette analysis for KMeans clustering on sample data \\\"\\n \\\"with n_clusters = %d\\\" % n_clusters),\\n fontsize=14, fontweight='bold')\\n\\n plt.show()\",\n \"def test_make_HTML_table(self):\\r\\n\\r\\n # test pie charts\\r\\n fracs_labels_other, fracs_labels, all_counts, other_cat, red, other_frac = \\\\\\r\\n get_fracs(self.counts1, 5, 10, 'pie')\\r\\n\\r\\n img_data = make_HTML_table(\\\"Phylum\\\", other_frac, 10, red, other_cat,\\r\\n fracs_labels_other, fracs_labels,\\r\\n self.dir_path, all_counts, 1, self.prefs,\\r\\n self.color_prefs, 'black', 'white', 'pie',\\r\\n 'Test1',\\r\\n self.generate_image_type, self.plot_width,\\r\\n self.plot_height, self.bar_width, self.dpi, 0,\\r\\n 'categorical', False, False)\\r\\n\\r\\n self.assertEqual(len(img_data), 2)\\r\\n\\r\\n # test area charts\\r\\n fracs_labels_other, fracs_labels, all_counts, other_cat, red, other_frac = \\\\\\r\\n get_fracs(self.counts1, 5, 10, 'area')\\r\\n\\r\\n img_data = make_HTML_table(\\\"Phylum\\\", other_frac, 10, red, other_cat,\\r\\n fracs_labels_other, fracs_labels,\\r\\n self.dir_path, all_counts, 1, self.prefs,\\r\\n self.color_prefs, 'black', 'white', 'pie',\\r\\n 'Test1',\\r\\n self.generate_image_type, self.plot_width,\\r\\n self.plot_height, self.bar_width, self.dpi, 0,\\r\\n 'categorical', False, False)\\r\\n\\r\\n self.assertEqual(len(img_data), 2)\\r\\n\\r\\n # test bar charts\\r\\n fracs_labels_other, fracs_labels, all_counts, other_cat, red, other_frac = \\\\\\r\\n get_fracs(self.counts1, 5, 10, 'bar')\\r\\n\\r\\n img_data = make_HTML_table(\\\"Phylum\\\", other_frac, 10, red, other_cat,\\r\\n fracs_labels_other, fracs_labels,\\r\\n self.dir_path, all_counts, 1, self.prefs,\\r\\n self.color_prefs, 'black', 'white', 'pie',\\r\\n 'Test1',\\r\\n self.generate_image_type, self.plot_width,\\r\\n self.plot_height, self.bar_width, self.dpi, 0,\\r\\n 'categorical', False, False)\\r\\n\\r\\n self.assertEqual(len(img_data), 2)\\r\\n self._paths_to_clean_up = [\\\"/tmp/qiimewebfiles/charts/\\\" + f\\r\\n for f in listdir(\\\"/tmp/qiimewebfiles/charts\\\")]\",\n \"def test_make_stats(self):\\r\\n map = self.mapping\\r\\n stats = \\\"\\\"\\\"Clustersize\\\\t#\\r\\n1:\\\\t\\\\t2\\r\\n2:\\\\t\\\\t1\\r\\n5:\\\\t\\\\t1\\\"\\\"\\\"\\r\\n\\r\\n self.assertEqual(make_stats(map), stats)\",\n \"def test_store_cluster(self):\\r\\n\\r\\n self.tmpdir = mkdtemp(dir=\\\"./\\\", suffix=\\\"_store_clusters/\\\")\\r\\n\\r\\n self.files_to_remove.append(self.tmpdir + \\\"singletons.fasta\\\")\\r\\n self.files_to_remove.append(self.tmpdir + \\\"centroids.fasta\\\")\\r\\n\\r\\n # empty map results in empty files\\r\\n store_clusters({}, self.tiny_test, self.tmpdir)\\r\\n actual_centroids = list(\\r\\n parse_fasta(open(self.tmpdir + \\\"centroids.fasta\\\")))\\r\\n self.assertEqual(actual_centroids, [])\\r\\n actual_singletons = list(\\r\\n parse_fasta(open(self.tmpdir + \\\"singletons.fasta\\\")))\\r\\n self.assertEqual(actual_singletons, [])\\r\\n\\r\\n # non-empty map creates non-empty files, centroids sorted by size\\r\\n mapping = {'FZTHQMS01B8T1H': [],\\r\\n 'FZTHQMS01DE1KN': ['FZTHQMS01EHAJG'],\\r\\n 'FZTHQMS01EHAJG': [1, 2, 3]} # content doesn't really matter\\r\\n\\r\\n centroids = [(\\r\\n 'FZTHQMS01EHAJG | cluster size: 4', 'CATGCTGCCTCCCGTAGGAGTTTGGACCGTGTCTCAGTTCCAATGTGGGGGACCTTCCTCTCAGAACCCCTATCCATCGAAGGTTTGGTGAGCCGTTACCTCACCAACTGCCTAATGGAACGCATCCCCATCGATAACCGAAATTCTTTAATAACAAGACCATGCGGTCTGATTATACCATCGGGTATTAATCTTTCTTTCGAAAGGCTATCCCCGAGTTATCGGCAGGTTGGATACGTGTTACTCACCCGTGCGCCGGTCGCCA'),\\r\\n ('FZTHQMS01DE1KN | cluster size: 2', 'CATGCTGCCTCCCGTAGGAGTTTGGACCGTGTCTCAGTTCCAATGTGGGGGACCTTCCTCTCAGAACCCCTATCCATCGAAGGTTTGGTGAGCCGTTACCTCACCAACTGCCTAATGGAACGCATCCCCATCGATAACCGAAATTCTTTAATAACAAGACCATGCGGTCTGATTATACCATCGGGTATTAATCTTTCTTTCGAAAGGCTATCCCCGAGTTATCGGCAGGTTGGATACGTGTTACTCACCCGTGCGCCGGTCGCCA')]\\r\\n\\r\\n singletons = [(\\r\\n 'FZTHQMS01B8T1H',\\r\\n 'CATGCTGCCTCCCGTAGGAGTTTGGACCGTGTCTCAGTTCCAATGTGGGGGACCTTCCTCTCAGAACCCCTATCCATCGAAGGTTTGGTGAGCCGTTACCTCACCAACTGCCTAATGGAACGCATCCCCATCGATAACCGAAATTCTTTAATAATTAAACCATGCGGTTTTATTATACCATCGGGTATTAATCTTTCTTTCGAAAGGCTATCCCCGAGTTATCGGCAGGTTGGATACGTGTTACTCACCCGTGCGCCGGTCGCCATCACTTA')]\\r\\n\\r\\n store_clusters(mapping, self.tiny_test, self.tmpdir)\\r\\n actual_centroids = list(\\r\\n parse_fasta(open(self.tmpdir + \\\"centroids.fasta\\\")))\\r\\n self.assertEqual(actual_centroids, centroids)\\r\\n actual_singletons = list(\\r\\n parse_fasta(open(self.tmpdir + \\\"singletons.fasta\\\")))\\r\\n self.assertEqual(actual_singletons, singletons)\",\n \"def run(self):\\n for l in self.uniqueSkel:\\n mask = np.arange(len(self.skel))[self.skelLabels == l]\\n counts = self.findNearest(mask)\\n self.memberships[l] = counts\\n\\n #self.memberships is an array of as many rows as skeleton labels and as many columns as Voronoi cluster labels,\\n #where the i-th row shows for all skeleton points of cluster label i, how many belong to each of the Voronoi\\n #cluster labels. More precisely, the j-th column of the i-th row of this array shows how many skeleton points\\n #of cluster label i have a closest Voronoi cell center of label j.\\n\\n print('Out of ' + str(len(self.skel)) + ' skeleton points, ' + str(sum(self.memberships[:, 0])) + ' (' + str(round(sum(self.memberships[:, 0]) * 100/len(self.skel), 3)) + ' %) appear in areas classified as void areas by Voronoi')\\n\\n for l in self.uniqueSkel:\\n members = sum(self.skelLabels == l)\\n topVor = np.argsort(self.memberships[l])[::-1][:5] - 1\\n counts = np.sort(self.memberships[l])[::-1][:5]\\n print('For the ' + str(members) + ' skeleton points with label ' + str(l) + ': ')\\n for i in range(5):\\n if counts[i] > 0:\\n if topVor[i] == -1:\\n add = ' ' + str(counts[i]) + ' ( ' + str(round(counts[i] * 100 / members, 3)) + ' %) are not associated with a Voronoi cluster cell'\\n else:\\n add = ' ' + str(counts[i]) + ' ( ' + str(round(counts[i] * 100/ members, 3)) + ' %) belong to the Voronoi Cluster with label ' + str(topVor[i])\\n print(add)\\n\\n self.plotResults()\",\n \"def to_single_html(self):\\n self.error_throw('output')\\n \\n if self.rank_method == methods_of_ranking[3]: #'diversified_ranking'\\n self.output_div('single_html')\\n else:\\n self.output('single_html')\",\n \"def cluster_testing_dist(agg1, agg2, partDiameter):\\n agg2_temp = translate_aggregate(agg2, random_point_generator(calculate_LD(agg1), calculate_LD(agg2), calculate_COM(agg1), calculate_COM(agg2), partDiameter))\\n agg2_temp = random_rotate_aggregate(agg2_temp)\\n\\n check = 1\\n while check == 1:\\n agg2_temp = translate_aggregate(agg2_temp, numpy.array((calculate_COM(agg1)-calculate_COM(agg2_temp))*0.01))\\n check, index = test_collision(agg1, agg2_temp, partDiameter)\\n \\\"\\\"\\\" Index from this part is not valid! Function returns '99' before collision happens.\\n \\\"\\\"\\\"\\n if (check == 2):\\n # print(index)\\n return numpy.linalg.norm(calculate_COM(agg1) - calculate_COM(agg2_temp)), numpy.linalg.norm(calculate_COM(agg1) - agg2_temp[:,index])\\n # return numpy.linalg.norm(calculate_COM(agg1) - agg2_temp[0:3,index])\\n break\",\n \"def cluster_classification(weblog,classification_column_transaction,\\\\\\n classification_column_diversity, session_data_threshold, cluster_type, classification_wanted_transaction, verbose = False):\\n if verbose== True:\\n start_time = timelib.time()\\n print(\\\"\\\\n * Computing cluster matrices ...\\\") \\n browsing_matrix = {}\\n diversifying_matrix = {}\\n # Selecting sessions from each cluster\\n for cluster_id in session_data_threshold[cluster_type].unique():\\n sessions_cluster = session_data_threshold[session_data_threshold[cluster_type]==cluster_id].session_id\\n divpat_log = weblog[weblog.session_id.isin(sessions_cluster)]\\n # Filtering some requests\\n divpat_log=divpat_log[divpat_log['requested_'+classification_column_transaction].isin(classification_wanted_transaction)]\\n divpat_log=divpat_log[divpat_log['referrer_'+classification_column_transaction].isin(classification_wanted_transaction)]\\n \\n # Defining matrices\\n diversity_columns=('referrer_'+classification_column_diversity,'requested_'+classification_column_diversity)\\n browsing_matrix[cluster_id],_ = compute_browsing_matrix(divpat_log,'referrer_'+classification_column_transaction,'requested_'+classification_column_transaction,labels=classification_wanted_transaction)\\n diversifying_matrix[cluster_id],_ = compute_diversifying_matrix(divpat_log,'referrer_'+classification_column_transaction,'requested_'+classification_column_transaction,\\\\\\n diversity_columns,labels = classification_wanted_transaction)\\n if verbose == True:\\n print(\\\" Cluster matrices computed in %.1f seconds.\\\"%(timelib.time() - start_time))\\n \\n return browsing_matrix, diversifying_matrix;\",\n \"def create_html_layout(self):\\n page = \\\"\\\"\\\"<!DOCTYPE html>\\n <!doctype html>\\n <html lang=\\\"en\\\">\\n <head>\\n <meta charset=\\\"utf-8\\\">\\n <meta name=\\\"viewport\\\" content=\\\"width=device-width, initial-scale=1, shrink-to-fit=no\\\">\\n </head>\\n </html>\\n <head>\\n \\t<meta charset=\\\"UTF-8\\\">\\n </head>\\n <body>\\n <div class=\\\"container\\\">\\n <div class=\\\"row\\\">\\n <div class=\\\"col-sm\\\">\\n <h4>eda report: Exploratory data analysis</h4>\\n </div>\\n <div class=\\\"col-sm\\\">\\n <h3>Inspecting dataframe of size: {size}\\n </div>\\n </div>\\n </div>\\n \\t<table class=\\\"table table-hover\\\" style=\\\".table\\\">\\n <thead>\\n <tr style=\\\"font-size: 15px;\\\">\\n <th width=\\\"5%\\\" align=\\\"left\\\" scope=\\\"col\\\">Variable Name</th>\\n <th width=\\\"12%\\\" align=\\\"left\\\" scope=\\\"col\\\">Data Type</th>\\n <th width=\\\"15%\\\" align=\\\"left\\\" scope=\\\"col\\\">Histogram</th>\\n <th width=\\\"11%\\\" align=\\\"left\\\" scope=\\\"col\\\">Stats</th>\\n <th width=\\\"7%\\\" align=\\\"left\\\" scope=\\\"col\\\">Missing NA</th>\\n <th width=\\\"5%\\\" align=\\\"left\\\" scope=\\\"col\\\">Outliers</th>\\n </tr>\\n </thead>\\n <tbody>\\\"\\\"\\\".format(size=self.df.size)\\n\\n end_page = \\\"\\\"\\\" \\n </tbody>\\n </table>\\n </body>\\n \\\"\\\"\\\"\\n rows_html = []\\n for i, column in enumerate(self.df.columns):\\n Summary = ColumnSummary(data=self.df[column])\\n datatype = Summary.data_type()\\n missing = Summary.missing_values()\\n stats = Summary.statistic_summary()\\n outliers = Summary.outliers()\\n Summary.create_histogram(i)\\n html = f\\\"\\\"\\\"\\n <tr>\\n <td style=\\\"font-size: 15px;\\\" width=\\\"10%\\\" align=\\\"left\\\"> {column}</td>\\n <td style=\\\"font-size: 15px;\\\"width=\\\"10%\\\" align=\\\"left\\\"> {datatype}</td>\\n <td><img class=\\\"img-fluid\\\" src=\\\"hist_images/histogram{i}.png?{random.randint(0,\\n 2e9)}\\\" style=\\\"width:800px\\\"> </td>\\n <td style=\\\"font-size: 15px;\\\">mean: {stats.mean}<br>\\n mode: {stats.mode}<br><br>\\n min: {stats.min}<br>\\n max: {stats.max}<br><br>\\n lower-bound: {stats.lower}<br>\\n upper-bound: {stats.upper}<b</td>\\n <td style=\\\"font-size: 15px;\\\">{missing}</td>\\n <td style=\\\"font-size: 15px;\\\">{outliers}</td>\\n </tr>\\n \\\"\\\"\\\"\\n rows_html.append(html)\\n\\n merged_html = page + \\\"\\\".join(rows_html) + end_page\\n return merged_html\",\n \"def matching_clusterization(self):\\n result = []\\n self.reclustering(self.groups.copy(deep=True), result)\\n self.result = pd.DataFrame(result)\\n return self.result.sort_values(by=['cluster_size'], ascending=False)\",\n \"def visualize_cluster_analysis_result(\\n cluster_analysis_result: dict,\\n print_hyperparameters: bool = True,\\n interactive: bool = False,\\n) -> None:\\n for clusterer_name, clusterer_result in cluster_analysis_result[\\n \\\"clusterers\\\"\\n ].items():\\n num_clusterer_metrics = len(clusterer_result[\\\"cluster_metrics\\\"])\\n if interactive:\\n fig = make_subplots(\\n rows=1,\\n cols=num_clusterer_metrics,\\n )\\n fig.update_layout(\\n title={\\n \\\"text\\\": clusterer_name,\\n \\\"y\\\": 0.9,\\n \\\"x\\\": 0.5,\\n \\\"xanchor\\\": \\\"center\\\",\\n \\\"yanchor\\\": \\\"top\\\",\\n }\\n )\\n else:\\n _, axes = plt.subplots(\\n nrows=1,\\n ncols=num_clusterer_metrics,\\n figsize=(5 * num_clusterer_metrics, 5),\\n )\\n plt.suptitle(clusterer_name)\\n\\n clusterer_params = clusterer_result[\\\"cluster_params\\\"]\\n if print_hyperparameters:\\n print(f\\\"Hyperparameters used for {clusterer_name}:\\\")\\n for i, hyperparams in enumerate(clusterer_params):\\n print(f\\\"{i}: {hyperparams}\\\")\\n xs = list(range(len(clusterer_params)))\\n\\n if interactive:\\n for i, (clusterer_metric_name, clusterer_metric_result) in zip(\\n range(1, num_clusterer_metrics + 1),\\n clusterer_result[\\\"cluster_metrics\\\"].items(),\\n ):\\n metric_scores = clusterer_metric_result[\\\"metric_scores\\\"]\\n best_metric_score_idx = clusterer_metric_result[\\n \\\"best_metric_score_indices\\\"\\n ][0]\\n\\n fig.update_xaxes(title_text=\\\"Hyperparameter set (index)\\\", row=1, col=i)\\n fig.update_yaxes(\\n title_text=f\\\"{clusterer_metric_name} score\\\", row=1, col=i\\n )\\n fig.update_layout(\\n {f\\\"xaxis{i}\\\": dict(tickmode=\\\"array\\\", tickvals=xs, ticktext=xs)},\\n )\\n fig.add_trace(\\n go.Scatter(x=xs, y=metric_scores, name=clusterer_metric_name),\\n row=1,\\n col=i,\\n )\\n fig.add_trace(\\n go.Scatter(\\n x=[best_metric_score_idx],\\n y=[metric_scores[best_metric_score_idx]],\\n marker={\\n \\\"size\\\": 10,\\n },\\n name=\\\"Best score\\\",\\n showlegend=False,\\n ),\\n row=1,\\n col=i,\\n )\\n fig.show()\\n else:\\n for ax, (clusterer_metric_name, clusterer_metric_result) in zip(\\n axes, clusterer_result[\\\"cluster_metrics\\\"].items()\\n ):\\n metric_scores = clusterer_metric_result[\\\"metric_scores\\\"]\\n best_metric_score_idx = clusterer_metric_result[\\n \\\"best_metric_score_indices\\\"\\n ][0]\\n\\n ax.set_xticks(xs)\\n plot_cluster_metric_scores(\\n metric_scores=metric_scores,\\n hyperparameters=clusterer_params,\\n best_score_idx=best_metric_score_idx,\\n metric_name=clusterer_metric_name,\\n xlabel=\\\"Hyperparameter set (index)\\\",\\n set_xticks=False,\\n set_xtickslabels=False,\\n show_plot=False,\\n ax=ax,\\n )\\n plt.tight_layout()\\n plt.show()\\n\\n total_num_clusterer_metrics = len(\\n cluster_analysis_result[\\\"metric_preferred_clusterers\\\"]\\n )\\n if interactive:\\n fig = make_subplots(\\n rows=1,\\n cols=total_num_clusterer_metrics,\\n )\\n fig.update_layout(\\n title={\\n \\\"text\\\": \\\"Most preferred clusterers (by metrics)\\\",\\n \\\"y\\\": 0.9,\\n \\\"x\\\": 0.5,\\n \\\"xanchor\\\": \\\"center\\\",\\n \\\"yanchor\\\": \\\"top\\\",\\n }\\n )\\n for i, (clusterer_metric_name, clusterer_metric_result) in zip(\\n range(1, total_num_clusterer_metrics + 1),\\n cluster_analysis_result[\\\"metric_preferred_clusterers\\\"].items(),\\n ):\\n best_metric_scores = clusterer_metric_result[\\\"best_metric_scores\\\"]\\n clusterer_names = clusterer_metric_result[\\\"clusterer_names\\\"]\\n xs = list(range(len(clusterer_names)))\\n print(best_metric_scores)\\n\\n fig.update_xaxes(title_text=\\\"Clusterer\\\", row=1, col=i)\\n fig.update_yaxes(title_text=f\\\"{clusterer_metric_name} score\\\", row=1, col=i)\\n fig.update_layout(\\n {\\n f\\\"xaxis{i}\\\": dict(\\n tickmode=\\\"array\\\", tickvals=xs, ticktext=clusterer_names\\n )\\n },\\n )\\n fig.add_trace(\\n go.Scatter(x=xs, y=best_metric_scores, name=clusterer_metric_name),\\n row=1,\\n col=i,\\n )\\n fig.add_trace(\\n go.Scatter(\\n x=[0],\\n y=[best_metric_scores[0]],\\n marker={\\n \\\"size\\\": 10,\\n },\\n name=\\\"Best score\\\",\\n showlegend=False,\\n ),\\n row=1,\\n col=i,\\n )\\n\\n fig.show()\\n else:\\n _, axes = plt.subplots(\\n nrows=1,\\n ncols=total_num_clusterer_metrics,\\n figsize=(5 * total_num_clusterer_metrics, 5),\\n )\\n plt.suptitle(\\\"Most preferred clusterers (by metrics)\\\")\\n for ax, (clusterer_metric_name, clusterer_metric_result) in zip(\\n axes, cluster_analysis_result[\\\"metric_preferred_clusterers\\\"].items()\\n ):\\n best_metric_scores = clusterer_metric_result[\\\"best_metric_scores\\\"]\\n clusterer_names = clusterer_metric_result[\\\"clusterer_names\\\"]\\n print(best_metric_scores)\\n plot_cluster_metric_scores(\\n metric_scores=best_metric_scores,\\n hyperparameters=clusterer_names,\\n best_score_idx=0,\\n metric_name=clusterer_metric_name,\\n xlabel=\\\"Clusterer\\\",\\n xtickslabels_rotation=45,\\n show_plot=False,\\n ax=ax,\\n )\\n plt.tight_layout()\\n plt.show()\",\n \"def plot_clustering(z_run, labels, engine ='plotly', download = False, folder_name ='clustering'):\\n \\\"\\\"\\\"\\n def plot_clustering_plotly(z_run, labels):\\n\\n labels = labels[:z_run.shape[0]] # because of weird batch_size\\n\\n hex_colors = []\\n for _ in np.unique(labels):\\n hex_colors.append('#%06X' % randint(0, 0xFFFFFF))\\n\\n colors = [hex_colors[int(i)] for i in labels]\\n\\n z_run_pca = TruncatedSVD(n_components=3).fit_transform(z_run)\\n z_run_tsne = TSNE(perplexity=80, min_grad_norm=1E-12, n_iter=3000).fit_transform(z_run)\\n\\n trace = Scatter(\\n x=z_run_pca[:, 0],\\n y=z_run_pca[:, 1],\\n mode='markers',\\n marker=dict(color=colors)\\n )\\n data = Data([trace])\\n layout = Layout(\\n title='PCA on z_run',\\n showlegend=False\\n )\\n fig = Figure(data=data, layout=layout)\\n plotly.offline.iplot(fig)\\n\\n trace = Scatter(\\n x=z_run_tsne[:, 0],\\n y=z_run_tsne[:, 1],\\n mode='markers',\\n marker=dict(color=colors)\\n )\\n data = Data([trace])\\n layout = Layout(\\n title='tSNE on z_run',\\n showlegend=False\\n )\\n fig = Figure(data=data, layout=layout)\\n plotly.offline.iplot(fig)\\n \\\"\\\"\\\"\\n def plot_clustering_matplotlib(z_run, labels, download, folder_name):\\n\\n labels = labels[:z_run.shape[0]] # because of weird batch_size\\n\\n hex_colors = []\\n for _ in np.unique(labels):\\n hex_colors.append('#%06X' % randint(0, 0xFFFFFF))\\n\\n colors = [hex_colors[int(i)] for i in labels]\\n\\n z_run_pca = TruncatedSVD(n_components=3).fit_transform(z_run)\\n z_run_tsne = TSNE(perplexity=80, min_grad_norm=1E-12, n_iter=3000).fit_transform(z_run)\\n\\n plt.scatter(z_run_pca[:, 0], z_run_pca[:, 1], c=colors, marker='*', linewidths=0)\\n plt.title('PCA on z_run')\\n if download:\\n if os.path.exists(folder_name):\\n pass\\n else:\\n os.mkdir(folder_name)\\n plt.savefig(folder_name + \\\"/pca.png\\\")\\n else:\\n plt.show()\\n\\n plt.scatter(z_run_tsne[:, 0], z_run_tsne[:, 1], c=colors, marker='*', linewidths=0)\\n plt.title('tSNE on z_run')\\n if download:\\n if os.path.exists(folder_name):\\n pass\\n else:\\n os.mkdir(folder_name)\\n plt.savefig(folder_name + \\\"/tsne.png\\\")\\n else:\\n plt.show()\\n\\n #if (download == False) & (engine == 'plotly'):\\n # plot_clustering_plotly(z_run, labels)\\n if (download) & (engine == 'plotly'):\\n print(\\\"Can't download plotly plots\\\")\\n if engine == 'matplotlib':\\n plot_clustering_matplotlib(z_run, labels, download, folder_name)\",\n \"def test_determine_k(self):\\n test_dir_name = os.path.dirname(__file__)\\n feat_array_fn = os.path.join(\\n test_dir_name, \\\"data\\\", \\\"four_clusters.csv\\\")\\n df = pd.read_csv(feat_array_fn)\\n feat_array = df[[\\\"x\\\", \\\"y\\\"]].values\\n\\n clusterer = Clusterer(feat_array_fn, \\\"/dev/null\\\", [])\\n best_k = clusterer._determine_k(feat_array, 9)\\n\\n self.assertEqual(best_k, 4)\\n\\n feat_array_fn = os.path.join(\\n test_dir_name, \\\"data\\\", \\\"iris.csv\\\")\\n df = pd.read_csv(feat_array_fn)\\n feat_array = df[[\\n \\\"Sepal.Length\\\", \\\"Sepal.Width\\\", \\\"Petal.Length\\\",\\n \\\"Petal.Width\\\"]].values\\n\\n clusterer = Clusterer(feat_array_fn, \\\"/dev/null\\\", [])\\n best_k = clusterer._determine_k(feat_array, 9)\\n\\n self.assertEqual(best_k, 2)\",\n \"def test_example(self): \\n num_clusters = 2\\n iterations = 5\\n ts1 = 2.5 * np.random.randn(100,) + 3\\n X_1 = pd.Series(ts1)\\n ts2 = 2 * np.random.randn(100,) + 5\\n X_2 = pd.Series(ts2)\\n ts3 = -2.5 * np.random.randn(100,) + 3\\n X_3 = pd.Series(ts3)\\n list_of_series = [X_1, X_2, X_3]\\n from pynuTS.clustering import DTWKmeans\\n clts = DTWKmeans(num_clust = num_clusters, num_iter = iterations)\\n clts.fit(list_of_series)\\n ts4 = 3.5 * np.random.randn(100,) + 2\\n ts5 = -3.5 * np.random.randn(100,) + 2\\n X_4 = pd.Series(ts4)\\n X_5 = pd.Series(ts5)\\n list_new = [X_4, X_5]\\n clustering_dict = clts.predict(list_new)\\n\\n assert type(clustering_dict) is dict\\n assert len(clustering_dict) == num_clusters\",\n \"def gen_SNA_html(result_dict):\\n html = \\\"</br>\\\"\\n \\n html += \\\"<p>Is the graph connected? <b>{}</b></p>\\\".format(result_dict['is_connected'])\\n html += \\\"<p>Top Nodes by Degree:</p>\\\"\\n for ind, i in enumerate(result_dict['top_nodes_dict']['degree']):\\n html += \\\"<p> {}. <b>{}</b></p>\\\".format(ind + 1, i)\\n html += \\\"<p>Top Nodes by Betweenness:</p>\\\"\\n for ind, i in enumerate(result_dict['top_nodes_dict']['betweenness']):\\n html += \\\"<p> {}. <b>{}</b></p>\\\".format(ind + 1, i)\\n html += \\\"<p>Top Nodes by Closeness:</p>\\\"\\n for ind, i in enumerate(result_dict['top_nodes_dict']['closeness']):\\n html += \\\"<p> {}. <b>{}</b></p>\\\".format(ind + 1, i)\\n \\n html += \\\"<p>Top Edges by Edge-Betweenness:</p>\\\"\\n for ind, (i, j) in enumerate(result_dict['top_edges_dict']['edge_betweeness']):\\n html += \\\"<p> {}. <b>{} -> {}</b></p>\\\".format(ind + 1, i, j)\\n \\n html += \\\"Detected Communities: \\\"\\n for ind, i in enumerate(result_dict['communities']):\\n html += \\\"<p> {}. {}</p>\\\".format(ind + 1, (\\\", \\\".join(i)))\\n \\n html += \\\"</br>\\\"\\n return html\",\n \"def test_build_graph(self):\\n insert_good_data()\\n dataframe = get_dataframe()\\n results = processing.build_graph(dataframe, figure_path, False)\\n # 1\\n self.assertEqual(results, \\\"Updated html File and Opened it\\\")\",\n \"def test_constituency_with_results(self):\\n add_constituency_result_line('X, 10, C')\\n r = self.client.get('/constituencies')\\n self.assertEqual(r.status_code, 200)\\n soup = BeautifulSoup(r.data, 'html.parser')\\n self.assertIs(soup.find(id='no-results'), None)\\n self.assertIsNot(soup.find(id='results-table'), None)\",\n \"def test_case():\\n\\n input_csv_file_path = 'data/sample_data.csv'\\n output_csv_file_path = 'data/result_data.csv'\\n generate_html(input_file=input_csv_file_path,\\n output_file=output_csv_file_path)\\n\\n with open(output_csv_file_path, 'r') as file:\\n csv_file = csv.DictReader(file)\\n\\n # assert the col titles in output file\\n assert csv_file.fieldnames == ['assessment_id', 'html']\\n\\n # assert the row count in output file\\n row_count = sum(1 for row in csv_file)\\n assert row_count == 3\",\n \"def test_no_cluster(self, script_runner: ScriptRunner, tmp_path: Path) -> None:\\n outfile = tmp_path.joinpath(\\\"pca-cluster.png\\\")\\n logfile = tmp_path.joinpath(\\\"plot.log\\\")\\n result = script_runner.run(\\n \\\"qaa\\\",\\n \\\"plot\\\",\\n \\\"-i\\\",\\n CLUSTER,\\n \\\"-c\\\",\\n CENTROID,\\n \\\"-o\\\",\\n outfile.as_posix(),\\n \\\"-l\\\",\\n logfile.as_posix(),\\n \\\"--pca\\\",\\n \\\"--verbose\\\",\\n )\\n assert result.success\\n assert logfile.exists()\\n assert outfile.exists()\\n assert outfile.stat().st_size > 0\",\n \"def test_graphs_view_html(self):\\n self.assertTemplateUsed(self.response, 'charts/graphs.html')\",\n \"def main():\\n args = get_args()\\n proteins_file = args.proteins\\n cdhit_file = args.cdhit\\n out_file = args.outfile\\n\\n for arg_name, file in [('--proteins', proteins_file), ('--cdhit',\\n cdhit_file)]:\\n if not os.path.isfile(file):\\n die('{} \\\"{}\\\" is not a file'.format(arg_name, file))\\n\\n clustered = set()\\n for line in open(cdhit_file):\\n matches = re.search(r'>gi\\\\|(?P<gi_num>\\\\d+)\\\\|', line)\\n if matches:\\n clustered.add(matches.group('gi_num'))\\n\\n # if line.startswith('>'):\\n # continue\\n #\\n # flds = line.split()\\n # prot_id = flds[2].split('|')[1]\\n # if prot_id.isdigit():\\n # clustered.add(prot_id)\\n\\n out_fh = open(out_file, 'wt')\\n num_total = 0\\n num_unclustered = 0\\n\\n for rec in SeqIO.parse(proteins_file, 'fasta'):\\n num_total += 1\\n prot_id = re.sub(r'\\\\|.*', '', rec.id)\\n if not prot_id in clustered:\\n num_unclustered += 1\\n SeqIO.write(rec, out_fh, 'fasta')\\n\\n print('Wrote {:,d} of {:,d} unclustered proteins to \\\"{}\\\"'.format(\\n num_unclustered, num_total, out_file))\",\n \"def write_index_html(wk_dir,region_dict,metrics_filename,ext=\\\"png\\\"):\\n # Make lists of the metrics and figure files to display\\n metrics_dir = os.path.join(wk_dir,metrics_dir_name)\\n metric_list = sorted([\\n f for f in os.listdir(metrics_dir) if f.endswith('_summary.csv')])\\n plot_list=[]\\n fig_list=sorted([f for f in os.listdir(wk_dir+'/'+figure_dir_name)])\\n for keyword in ['lag','correlations','twodpdf']:\\n plot_list.append([f for f in fig_list if (keyword in f)]) # sort datasets\\n subtitle_list=['Autocorrelation','2D Histograms','Correlation maps']\\n\\n # Start working on html text. Each line is appened to a list that\\n # is then written to file.\\n html_file=['<html>\\\\n',\\n '<body>','<head><title>ASoP-Coherence</title></head>\\\\n',\\n '<br><h1>ASoP-Coherence results</h1>\\\\n','<h2>Contents</h2>\\\\n',\\n '<dl>\\\\n','<dt><a href=\\\"#Metrics\\\">Metrics</a></dt>\\\\n',\\n '<dt><a href=\\\"#Figures\\\">Figures</a></dt>\\\\n',\\n '<dd><a href=\\\"#Autocorrelation\\\">Autocorrelation</a></dd>\\\\n',\\n '<dd><a href=\\\"#2D-Histograms\\\">2D Histograms</a></dd>\\\\n',\\n '<dd><a href=\\\"#Correlation-maps\\\">Correlation Maps</a></dd>\\\\n',\\n '</dl>\\\\n''<section id=\\\"Metrics\\\">\\\\n','<br><h2>Metrics</h2>\\\\n']\\n html_file.append('<h3>Intermittency Metrics</h3>\\\\n')\\n\\n # Display metrics JSON in dashboard option\\n metrics_json = os.path.basename(metrics_filename)\\n metrics_relocated = os.path.join(metrics_dir_name,metrics_json)\\n tmp='<p><a href=\\\"'+metrics_relocated+'\\\" target=\\\"_blank\\\">'+metrics_json+'</a></p>\\\\n'\\n html_file.append(tmp)\\n\\n # Link CSV tables for download\\n html_file.append('<h3>Tables</h3>\\\\n')\\n for metric_file in metric_list:\\n metric_path = os.path.join(metrics_dir_name,metric_file)\\n html_file.append('<p><a href=\\\"{0}\\\">{1}</a></p>\\\\n'.format(metric_path,metric_file))\\n html_file.append('<br>\\\\n')\\n html_file.append('</section>\\\\n')\\n\\n # Add figures\\n html_file.append('<section id=\\\"Figures\\\">\\\\n')\\n html_file.append('<h2>Figures</h2>\\\\n')\\n for title,category in zip(subtitle_list,plot_list):\\n html_file.append('<section id='+title.replace(' ','-')+'>\\\\n')\\n html_file.append('<h3>{0}</h3>\\\\n'.format(title))\\n # Adjust figure width for autocorrelation\\n fwidth = \\\"647\\\"\\n if title==\\\"Autocorrelation\\\":\\n fwidth=\\\"450\\\"\\n for region in region_dict:\\n html_file.append('<h4>{0}</h4>\\\\n'.format(region.replace('_',' ')))\\n region_fig = [f for f in category if (region.replace(\\\" \\\",\\\"_\\\") in f)]\\n for fig in region_fig:\\n tmp = '<p><a href=\\\"{0}\\\" target=\\\"_blank\\\" alt={0}>' + \\\\\\n '<img src=\\\"{0}\\\" width={1} alt=\\\"{0}\\\"></a></p>\\\\n'\\n html_file.append(\\n tmp.format(os.path.join(figure_dir_name,fig),fwidth))\\n html_file.append('</section>\\\\n')\\n html_file.append('</section>\\\\n')\\n\\n html_file.append('</body>\\\\n</html>\\\\n')\\n filename=wk_dir+'/index.html'\\n with open(filename,'w') as html_page:\\n html_page.writelines(html_file)\",\n \"def main():\\n\\n# The following codes loads the data set into a 2D np array called data\\n\\twith open('complete_data.csv') as features_file:\\n\\t\\tcsv_reader = csv.DictReader(features_file, delimiter = ',')\\n\\t\\tdata = []\\n\\t\\tcounter = 0\\n\\t\\tfor row in csv_reader:\\n\\t\\t\\tprint(\\\"csv_reader row:\\\", row)\\n\\t\\t\\t# if(counter == 20):\\n\\t\\t\\t# \\tbreak\\n\\t\\t\\tcounter+=1\\n\\t\\t\\tcleaned_row = []\\n\\t\\t\\tcleaned_row.append(row['track'])\\n\\t\\t\\tcleaned_row.append(row['loudness'])\\n\\t\\t\\tcleaned_row.append(row['score'])\\n\\t\\t\\tdata.append(np.array(cleaned_row))\\n\\t\\tdata = random.sample(list(data), 30)\\n\\t\\tdata = np.array(data)\\n\\n\\n\\tX = []\\n\\tY = []\\n\\tcounter = 0\\n\\tfor row in data:\\n\\t\\t# if(counter == 10):\\n\\t\\t# \\tbreak\\n\\t\\t# counter+=1\\n\\t\\tY.append(row[0])\\n\\t\\tl = [float(i) for i in row[1:]]\\n\\t\\tX.append(l)\\n\\tX = np.array(X)\\n\\tY = np.array(Y)\\n\\n\\tcentroid_indices2,centroids2 = sk_learn_cluster(X,Y,3)\\n\\n\\tplot_word_clusters(data, centroids2, centroid_indices2 )\",\n \"def test_cluster_npy(self, script_runner: ScriptRunner, tmp_path: Path) -> None:\\n outfile = tmp_path.joinpath(\\\"pca-cluster.png\\\")\\n logfile = tmp_path.joinpath(\\\"plot.log\\\")\\n result = script_runner.run(\\n \\\"qaa\\\",\\n \\\"plot\\\",\\n \\\"-i\\\",\\n CLUSTNPY,\\n \\\"-c\\\",\\n CENTNPY,\\n \\\"--label\\\",\\n LABELS,\\n \\\"-o\\\",\\n outfile.as_posix(),\\n \\\"-l\\\",\\n logfile.as_posix(),\\n \\\"--pca\\\",\\n \\\"--cluster\\\",\\n \\\"--verbose\\\",\\n )\\n assert result.success\\n assert logfile.exists()\\n assert outfile.exists()\\n assert outfile.stat().st_size > 0\",\n \"def test_replace_cluster_network(self):\\n pass\",\n \"def _auto_hdiff2(tcrrep,\\n html_name = 'DEFAULT.html',\\n pwmat_str_b = 'pw_beta',\\n pwmat_str_a = 'pw_alpha',\\n single = True,\\n generate_svgs = True,\\n combine_olga = False,\\n verbose = True,\\n prune = 3,\\n default_hcluster_diff_kwargs = _get_default_kwargs(chains = ['beta'])[0],\\n default_member_summ_kwargs = _get_default_kwargs(chains = ['beta'])[1],\\n default_plot_hclust_props = _get_default_kwargs(chains = ['beta'])[2]):\\n import os\\n import pandas as pd\\n import numpy as np\\n import warnings\\n from tcrsampler.sampler import TCRsampler\\n from palmotif import compute_pal_motif, svg_logo\\n from tcrdist.adpt_funcs import get_centroid_seq, get_centroid_seq_alpha\\n from tcrdist.summarize import _select\\n from tcrdist.repertoire import TCRrep\\n from tcrdist.rep_diff import hcluster_diff, member_summ\\n from tcrdist.summarize import _select\\n from tcrdist.pgen import OlgaModel\\n from palmotif import compute_pal_motif, svg_logo\\n from hierdiff import plot_hclust_props\\n from numpy.random import randint\\n from tcrdist.diversity import generalized_simpsons_entropy\\n from tcrdist.diversity import fuzzy_diversity\\n \\n\\n # Load clone_df directly from input object\\n if default_hcluster_diff_kwargs['clone_df'] is None:\\n default_hcluster_diff_kwargs['clone_df'] = getattr(tcrrep, 'clone_df')\\n\\n # Get appropirate pwmat_str (pw_beta, pw_alpha, or one of the CDRs, e.g., pw_cdr3_b_aa)\\n if 'alpha' in tcrrep.chains:\\n pwmat_str = pwmat_str_a\\n if 'beta' in tcrrep.chains:\\n pwmat_str = pwmat_str_b\\n\\n\\n # Get appropirate pairwise matrix\\n \\n if default_hcluster_diff_kwargs['pwmat'] is None:\\n if verbose : print(f\\\"pwmat set with {pwmat_str}\\\")\\n default_hcluster_diff_kwargs['pwmat'] = getattr(tcrrep, pwmat_str)\\n else: \\n if verbose : print(\\\"pwmat was directly provided as a kwarg\\\")\\n \\\"\\\"\\\"Handle the Fact that 2 or more catagoorical levels are need to run hierdiff\\\"\\\"\\\"\\n \\n x_cols = default_hcluster_diff_kwargs['x_cols']\\n if x_cols is None:\\n tcrrep.clone_df['dummy'] = \\\\\\n [['X1','X2'][randint(2)] for x in range(tcrrep.clone_df.shape[0])]\\n default_hcluster_diff_kwargs['x_cols'] = ['dummy']\\n default_hcluster_diff_kwargs['test_method'] = 'fishers' \\n warnings.warn(f\\\"Because x_cols was None, setting random dummy values, and using {default_hcluster_diff_kwargs['test_method']}\\\\n\\\", stacklevel=2)\\n \\n elif tcrrep.clone_df[x_cols].nunique()[0] == 2: \\n default_hcluster_diff_kwargs['test_method'] = 'fishers' \\n \\n elif tcrrep.clone_df[x_cols].nunique()[0] > 2: \\n default_hcluster_diff_kwargs['test_method'] = 'chi2'\\n \\n elif tcrrep.clone_df[x_cols].nunique()[0] < 2 : \\n tcrrep.clone_df['dummy'] = \\\\\\n [['X1','X2'][randint(2)] for x in range(tcrrep.clone_df.shape[0])]\\n default_hcluster_diff_kwargs['x_cols'] = ['dummy']\\n default_hcluster_diff_kwargs['test_method'] = 'fishers' \\n warnings.warn(f\\\"Because x_cols was None, setting random dummy values, and using {default_hcluster_diff_kwargs['test_method']}\\\\n\\\", stacklevel=2)\\n\\n \\\"\\\"\\\" Run hcluster_df \\\"\\\"\\\"\\n \\n bar = IncrementalBar(f'Run hcluster_diff :', max = 2, suffix='%(percent)d%%')\\n bar.next()\\n tcrrep.hcluster_df, tcrrep.Z = hcluster_diff(**default_hcluster_diff_kwargs)\\n bar.next(); bar.finish()\\n\\n tcrrep.hcluster_df['prune'] = tcrrep.hcluster_df['K_neighbors'].apply(lambda x: 1 if (x < prune) else 0)#\\n \\n \\\"\\\"\\\" Do Basic Summary \\\"\\\"\\\"\\n mean_distance_ = list()\\n percentage_node_25_ = list()\\n percentage_node_50_ = list()\\n percentage_node_75_ = list()\\n n_rows = tcrrep.hcluster_df.shape[0]\\n bar = IncrementalBar(f'Evaluate Clusters :', max = n_rows, suffix='%(percent)d%%')\\n for i,r in tcrrep.hcluster_df.iterrows():\\n bar.next()\\n # <dfnode> is dataframe with all the clones at a given tree node\\n dfnode = tcrrep.clone_df.iloc[r['neighbors_i'],]\\n # <pwnod> is dataframe with all the clones at a given tree node\\n pwnode = getattr(tcrrep, pwmat_str)[r['neighbors_i'],:][:,r['neighbors_i']]\\n # get the non-diaganol entries.\\n node_non_diag_entries = pwnode[~np.eye(pwnode.shape[0],dtype=bool)]\\n # Compute the mean distance at the node \\n mean_distance_.append(str(round(node_non_diag_entries.mean(),1)))\\n\\n percentage_node_25 = 100*(node_non_diag_entries < 25).sum() / (node_non_diag_entries.size)\\n percentage_node_50 = 100*(node_non_diag_entries < 50).sum() / (node_non_diag_entries.size)#100*((pwnode < 50).sum() - pwnode.shape[0]) / (pwnode.shape[0] * pwnode.shape[1])\\n percentage_node_75 = 100*(node_non_diag_entries < 75).sum() / (node_non_diag_entries.size)#100*((pwnode < 100).sum() - pwnode.shape[0]) / (pwnode.shape[0] * pwnode.shape[1])\\n percentage_node_25_.append(f\\\"{round(percentage_node_25,1)}%\\\")\\n percentage_node_50_.append(f\\\"{round(percentage_node_50,1)}%\\\")\\n percentage_node_75_.append(f\\\"{round(percentage_node_75,1)}%\\\")\\n bar.next(); bar.finish()\\n tcrrep.hcluster_df['mean_dist'] = mean_distance_\\n tcrrep.hcluster_df['pct_dist_25'] =\\tpercentage_node_25_\\n tcrrep.hcluster_df['pct_dist_50'] =\\tpercentage_node_50_\\n tcrrep.hcluster_df['pct_dist_75'] =\\tpercentage_node_75_\\n \\n \\\"\\\"\\\"\\n By default, treat each clone as a single entity if 'count_col' is single \\n \\\"\\\"\\\"\\n if default_member_summ_kwargs['count_col'] == 'single':\\n single = True\\n print(\\\"MAKING 'single' variable\\\")\\n tcrrep.hcluster_df['single'] = 1\\n\\n if single:\\n tcrrep.clone_df['single'] = 1\\n default_member_summ_kwargs['count_col'] = 'single'\\n\\n\\n \\\"\\\"\\\" member_summ\\\"\\\"\\\"\\n tcrrep.res_summary = \\\\\\n member_summ(res_df = tcrrep.hcluster_df,clone_df = tcrrep.clone_df, **default_member_summ_kwargs)\\n\\n\\n tcrrep.hcluster_df_detailed = pd.concat([tcrrep.hcluster_df.copy(), tcrrep.res_summary.copy()], axis = 1)\\n \\n \\\"\\\"\\\" Add diversity stats\\\"\\\"\\\"\\n tcrrep.clone_df['single'] = 1\\n if single:\\n fdiv75 = lambda ind : fuzzy_diversity(tcrrep.clone_df.iloc[ind,:]['single'], getattr(tcrrep, pwmat_str)[ind,:][:,ind],order=2, threshold=75)\\n fdiv50 = lambda ind : fuzzy_diversity(tcrrep.clone_df.iloc[ind,:]['single'], getattr(tcrrep, pwmat_str)[ind,:][:,ind],order=2, threshold=50)\\n fdiv25 = lambda ind : fuzzy_diversity(tcrrep.clone_df.iloc[ind,:]['single'], getattr(tcrrep, pwmat_str)[ind,:][:,ind],order=2, threshold=25)\\n else:\\n fdiv75 = lambda ind : fuzzy_diversity(tcrrep.clone_df.iloc[ind,:]['count'], getattr(tcrrep, pwmat_str)[ind,:][:,ind],order=2, threshold=75)\\n fdiv50 = lambda ind : fuzzy_diversity(tcrrep.clone_df.iloc[ind,:]['count'], getattr(tcrrep, pwmat_str)[ind,:][:,ind],order=2, threshold=50)\\n fdiv25 = lambda ind : fuzzy_diversity(tcrrep.clone_df.iloc[ind,:]['count'], getattr(tcrrep, pwmat_str)[ind,:][:,ind],order=2, threshold=25)\\n tcrrep.hcluster_df_detailed['fuzzy_simpson_diversity_25'] = [str(round(fdiv25(ind),2)) for ind in tcrrep.hcluster_df_detailed.neighbors_i.to_list()]\\n tcrrep.hcluster_df_detailed['fuzzy_simpson_diversity_50'] = [str(round(fdiv50(ind),2)) for ind in tcrrep.hcluster_df_detailed.neighbors_i.to_list()]\\n tcrrep.hcluster_df_detailed['fuzzy_simpson_diversity_75'] = [str(round(fdiv75(ind),2)) for ind in tcrrep.hcluster_df_detailed.neighbors_i.to_list()]\\n\\n \\\"\\\"\\\"Optional Add SVGs to hcluster_detailed\\\"\\\"\\\"\\n if 'beta' in tcrrep.chains:\\n _tcrsampler_svgs(tcrrep = tcrrep,\\n default_background = None, \\n default_background_if_missing = None,\\n cdr3_name = 'cdr3_b_aa',\\n pwmat_str = pwmat_str_b, \\n chain = 'beta', \\n gene_names = ['v_b_gene','j_b_gene'],\\n combine_olga = combine_olga)\\n \\n if 'alpha' in tcrrep.chains:\\n _tcrsampler_svgs(tcrrep = tcrrep,\\n default_background = None, \\n default_background_if_missing = None,\\n cdr3_name = 'cdr3_a_aa',\\n pwmat_str = pwmat_str_a, \\n chain = 'alpha', \\n gene_names = ['v_a_gene','j_a_gene'],\\n combine_olga = combine_olga)\\n\\n\\n \\\"\\\"\\\" Plot \\\"\\\"\\\"\\n html = plot_hclust_props(tcrrep.Z,\\n res=tcrrep.hcluster_df_detailed,\\n prune_col = 'prune',\\n **default_plot_hclust_props)\\n\\n \\\"\\\"\\\" Write File \\\"\\\"\\\"\\t\\t\\n with open(html_name, 'w') as fh:\\n print(f\\\"WRITING {html_name}\\\")\\n fh.write(html)\",\n \"def simple_test_out(self):\\n # uses the same dataset as the docs\\n actual_res = self.doc_graph.degree_centrality(degree_option=\\\"out\\\")\\n # also from docs\\n expected_res = {1: 0.75, 2: 0.25, 3: 0, 4: 0.25, 5: 0}\\n # compare results with expected results\\n pandas_res = actual_res.to_pandas()\\n for (index, row) in pandas_res.iterrows():\\n row_id = int(row[\\\"id\\\"])\\n expected_centrality = expected_res[row_id]\\n self.assertAlmostEqual(expected_centrality, row[\\\"degree_centrality\\\"])\",\n \"def __evaluate_result(self):\\n # Evaluate internally using cophenetic correlation coefficient.\\n cpcc = self.__clusterer.calc_cophenetic_coeff()\\n \\n # Show evaluation through a popup window.\\n popup = WarningPopup(self.__gui, 'Clustering evaluation',\\n 'Cophenetic correlation coefficient : ' + str(cpcc))\\n popup._start()\",\n \"def test_html_repr():\\n repr_html = grid._repr_html_()\\n assert repr_html is not None\",\n \"def test_homepage_render(self):\\n\\n result = self.client.get(\\\"/\\\")\\n self.assertIn(\\\"<h1 class=\\\\\\\"title\\\\\\\">Bark Park!</h1>\\\", result.data)\",\n \"def to_multiple_htmls(self):\\n self.error_throw('output')\\n \\n if self.rank_method == methods_of_ranking[3]: #'diversified_ranking'\\n self.output_div('multiple_htmls')\\n else:\\n self.output('multiple_htmls')\",\n \"def compare(self):\\n len0 = len(self.cluster_lists[0])\\n len1 = len(self.cluster_lists[1])\\n longer_index = 0 if len0 >= len1 else 1\\n shorter_index = 1 if len1 <= len0 else 0\\n self.stars_length = len(self.cluster_lists[shorter_index]) \\n self.starlets_length = len(self.cluster_lists[longer_index]) \\n # build the noeds for shorter cluster list, and get the\\n # distribution of cluster size.\\n for cluster in self.cluster_lists[shorter_index]:\\n len_spectra = len(cluster.get_spectra())\\n star = ClusterNode(cluster.id, len_spectra) \\n self.stars[cluster.id] = star\\n\\n self.cluster_spectra_num[shorter_index] += len_spectra\\n self.cluster_size_dist[shorter_index][len_spectra] = self.cluster_size_dist[shorter_index].get(len_spectra,0) + 1\\n # build the noeds for longer cluster list, and get the\\n # distribution of cluster size.\\n for cluster in self.cluster_lists[longer_index]:\\n len_spectra = len(cluster.get_spectra())\\n starlet = ClusterNode(cluster.id, len_spectra) \\n self.starlets[cluster.id] = starlet\\n\\n self.cluster_spectra_num[longer_index] += len_spectra\\n self.cluster_size_dist[longer_index][len_spectra] = self.cluster_size_dist[longer_index].get(len_spectra,0) + 1\\n # do the comparing, and network building\\n for i in range (0, len(self.cluster_lists[shorter_index])):\\n cluster0 = self.cluster_lists[shorter_index][i] \\n for j in range (i, len(self.cluster_lists[longer_index])):\\n cluster1 = self.cluster_lists[longer_index][j] \\n (shared_spec_num, similarity) = self.calculate_similarity(cluster0, cluster1)\\n if similarity == 0:\\n continue\\n self.similarity_dist[int(similarity*10)] = self.similarity_dist.get(int(similarity*10),0) + 1\\n self.shared_spec_num += shared_spec_num\\n\\n self.stars[cluster0.id].add_nb_node(cluster1.id, similarity, shared_spec_num)\\n self.starlets[cluster1.id].add_nb_node(cluster0.id, similarity, shared_spec_num)\\n\\n self.ave_star_size = self.cluster_spectra_num[shorter_index]/self.stars_length\\n self.ave_starlet_size = self.cluster_spectra_num[longer_index]/self.starlets_length\",\n \"def _layout_computation_results_to_html(self, embed_chart='offline_embed_js_div'):\\n pass\",\n \"def evaluateClusters( features, labels):\\r\\n\\r\\n\\treturn silhouette_score( features, labels)\",\n \"def test_fold_clusters(self):\\n cfo = open(\\\"./cstest.fasta\\\", 'w')\\n cfo.close()\\n manager = Manager()\\n lock = manager.Lock()\\n fold_clusters(lock, \\\"cluster_1\\\", self.fastaseqs, \\\"./cstest.fasta\\\")\\n with open(\\\"./cstest.fasta\\\") as obsin:\\n obs_fold = obsin.read()\\n self.del_files.append(\\\"./cstest.fasta\\\")\\n expected = \\\">cluster_1\\\\n%s\\\\n\\\" % self.fastastruct\\n for header, seq in self.fastaseqs:\\n expected = ''.join([expected, \\\">%s\\\\n%s\\\\n\\\" % (header, seq)])\\n self.assertEqual(obs_fold, expected)\",\n \"def test_clusters(trained_data, centroids):\\n\\n for c in range(len(centroids)):\\n count_1 = 0\\n count_0 = 0\\n for p in range(len(trained_data)):\\n if trained_data[p][-2] == 0 and trained_data[p][-1] == centroids[c]:\\n count_0 += 1\\n if trained_data[p][-2] == 1 and trained_data[p][-1] == centroids[c]:\\n count_1 += 1\\n print (\\\"Centroid \\\", c+1, \\\":\\\", centroids[c])\\n print(\\\"Number of 1's: \\\", count_1)\\n print(\\\"Number of 0's: \\\", count_0)\\n print(\\\"Percent 1's: \\\", round((count_1/(count_1 + count_0))*100,2))\\n print(\\\"Percent 0's: \\\", round((count_0 / (count_1 + count_0)) * 100,2))\\n print(\\\"****************\\\")\",\n \"def demo():\\n # declare dummy input vector with two dimensions:\\n vectors = numpy.array([[2,4], [0,1], [1,1], [3,2], [4,0], [2,2], [8, 9], [8, 11]])\\n\\n # compute the distance matrix on the basis of the vectors via sklearn:\\n dist_matrix = pairwise_distances(vectors, metric='cityblock')\\n\\n # plot the distance matrix:\\n # dist_matrix.draw() this doesn't work anymore\\n\\n # initialize a temporal VNC clusterer, here with the Ward linkage method:\\n clusterer = VNClusterer(dist_matrix, linkage='ward') # could also be a plain Clusterer()\\n\\n # start the clustering procedure:\\n clusterer.cluster(verbose=1)\\n\\n labels = ['n'+str(i+1) for i in range(len(vectors))]\\n # plot the result as a dendrogram\\n clusterer.dendrogram.draw(save=True,\\n labels=labels,\\n title=\\\"VNC Analysis (Ward's Linkage)\\\")\",\n \"def test_main_random_output(runner: CliRunner) -> None:\\n output_random = os.path.join(\\n tempfile.gettempdir(),\\n \\\"\\\".join(secrets.choice(string.hexdigits) for i in range(7)),\\n )\\n result = runner.invoke(\\n __main__.main, f\\\"-c tests/clippings-es.txt -o {output_random}\\\"\\n )\\n assert result.exit_code == 0\",\n \"def cluster(self):\\n assert False\",\n \"def benchmark(extract_size=800):\\n random_file = random_html_file()\\n with open(join(DATA_PATH, random_file), 'r') as f:\\n html_string = f.read()\\n\\n # GOOSE\\n try:\\n g = Goose({'browser_user_agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:61.0) Gecko/20100101 Firefox/61.0',\\n 'enable_image_fetching': False})\\n goose_article = g.extract(raw_html=html_string)\\n goose_result = goose_article.cleaned_text\\n except:\\n goose_result = ' Goose error.'\\n\\n # EATIHT\\n try:\\n eatiht_result = eatiht.extract(html_string)\\n except:\\n eatiht_result = ' Eatiht error.'\\n\\n # DRAGNET\\n\\n try:\\n dragnet_result = dragnet.extract_content(html_string)\\n except Exception as e:\\n dragnet_result = ' Dragnet error: ' + str(e)\\n\\n # LIBEXTRACT\\n\\n try:\\n textnodes = list(libextract.api.extract(html_string))\\n libextract_result = textnodes[0].text_content()\\n except:\\n libextract_result = ' Libextract error.'\\n\\n # BOILERPIPE (CanolaExtractor)\\n\\n try:\\n extractor = Extractor(\\n extractor='CanolaExtractor', html=html_string)\\n boilerpipe_result = extractor.getText()\\n except:\\n boilerpipe_result = ' Boilerpipe error.'\\n\\n # NEWSPAPER\\n\\n try:\\n article = Article('url')\\n article.download(input_html=html_string)\\n article.parse()\\n print('Auteurs:', article.authors)\\n print('Date de publication:', article.publish_date)\\n newspaper_result = article.text\\n except:\\n newspaper_result = ' Newspaper error.'\\n\\n # JUSTEXT\\n\\n try:\\n paragraphs = justext.justext(\\n html_string, justext.get_stoplist(\\\"French\\\"))\\n print('PARAGRAPHS')\\n for p in paragraphs:\\n if not p.is_boilerplate:\\n print(p.text)\\n justext_result = '\\\\n'.join(\\n paragraph.text for paragraph in paragraphs if not paragraph.is_boilerplate)\\n print('JUSTEXT_RESULT', justext_result)\\n\\n except Exception as e:\\n justext_result = ' Justext error: ' + str(e)\\n print(justext_result)\\n\\n # Results\\n\\n try:\\n # finds the url associated with the file in a \\\"filename-url\\\" csv\\n with open('./data/urls.csv', 'r') as csvfile:\\n\\n urls = dict((line['id'], line['url'])\\n for line in csv.DictReader(csvfile))\\n url = urls[random_file[:-5]]\\n\\n print('\\\\n\\\\n >>> URL n.' + random_file[:-5] + ' : ' + url)\\n except:\\n print('\\\\n\\\\n (URL of the html file not found. To print the associated URL, please provide a urls.csv file featuring filename & url in /data)')\\n # webbrowser.open(url, autoraise=False)\\n path = abspath('temp.html')\\n local_url = 'file://' + path\\n with open(path, 'w') as f:\\n f.write(html_string)\\n webbrowser.open(local_url)\\n\\n # print('\\\\n\\\\n /// GOOSE /// \\\\n')\\n # print(goose_result[:extract_size])\\n # print('\\\\n\\\\n /// EATIHT /// \\\\n')\\n # print(eatiht_result[:extract_size])\\n print('\\\\n ------ [[DRAGNET]] ------',\\n len(dragnet_result), 'caractères\\\\n')\\n print(dragnet_result[:extract_size] +\\n '\\\\n...\\\\n' + dragnet_result[-extract_size:])\\n print('\\\\n ------ [[NEWSPAPER]] ------',\\n len(newspaper_result), 'caractères\\\\n')\\n print(newspaper_result[:extract_size] +\\n '\\\\n...\\\\n' + newspaper_result[-extract_size:])\\n print('\\\\n ------ [[JUSTEXT]] ------',\\n len(justext_result), 'caractères\\\\n')\\n print(justext_result[:extract_size] +\\n '\\\\n...\\\\n' + justext_result[-extract_size:])\\n # print('\\\\n\\\\n /// LIBEXTRACT /// \\\\n')\\n # print(libextract_result[:extract_size])\\n # print('\\\\n\\\\n /// BOILERPIPE (CanolaExtractor) /// \\\\n\\\\n')\\n # print(boilerpipe_result[:extract_size])\\n # print('\\\\n\\\\n')\\n return(url)\",\n \"def test_gethtml(self):\\r\\n mock_module = CHModuleFactory.create()\\r\\n\\r\\n def fake_get_display_items():\\r\\n \\\"\\\"\\\"\\r\\n A mock of get_display_items\\r\\n \\\"\\\"\\\"\\r\\n return [FakeChild()]\\r\\n mock_module.get_display_items = fake_get_display_items\\r\\n out_html = mock_module.render('student_view').content\\r\\n self.assertTrue('This is supposed to be test html.' in out_html)\\r\\n self.assertTrue('i4x://this/is/a/fake/id' in out_html)\",\n \"def partition_clusters(clustering_matrix, args, nr_clusters=5, method='complete', metric='euclidean', plotting=False):\\n # clustering with linkage\\n fig = plt.figure(figsize=(8,8))\\n ax1 = fig.add_axes([0.09,0.1,0.2,0.6])\\n # gives back linkage matrix after hierarchical clustering\\n Y = sch.linkage(clustering_matrix, method=method,metric=metric)\\n # creates dendogram for plotting and flattening\\n Z = sch.dendrogram(Y, orientation='left')\\n ax1.set_xticks([])\\n ax1.set_yticks([])\\n # calculate cluster membership\\n # fcluster flattens out dendograms to the specified nr_clusters\\n cluster_memberships = sch.fcluster(Y, t=nr_clusters, criterion='maxclust') # ith element in this array is the cluster for i\\n idx = np.array(Z['leaves']) # idx ordered in cluster\\n \\n ax2 = fig.add_axes([0.3,0.71,0.6,0.2])\\n Z2 = sch.dendrogram(Y)\\n ax2.set_xticks([])\\n ax2.set_yticks([])\\n\\n axmatrix = fig.add_axes([0.3,0.1,0.6,0.6])\\n\\n clustering_matrix = clustering_matrix[idx,:]\\n clustering_matrix = clustering_matrix[:,idx]\\n im = axmatrix.matshow(clustering_matrix, aspect='auto', origin='lower', cmap=plt.cm.YlGnBu)\\n axmatrix.set_xticks([])\\n axmatrix.set_yticks([])\\n\\n # Plot colorbar.\\n axcolor = fig.add_axes([0.91,0.1,0.02,0.6])\\n plt.colorbar(im, cax=axcolor)\\n if plotting:\\n fig.savefig(f'{args.results_root_dir}/clust_{args.clustering_method}_nr_users-{args.num_users}_nr_of_partition_clusters_{nr_clusters}_method_{method}_reconstructed.png')\\n\\n # Plot filtered\\n canvas = np.zeros_like(clustering_matrix)\\n for i in range(1,nr_clusters+1):\\n mask = np.ones_like(clustering_matrix)\\n mask[cluster_memberships[idx]!=i,:] = 0\\n mask[:,cluster_memberships[idx]!=i] = 0\\n canvas+=clustering_matrix*mask\\n fig = plt.figure()\\n plt.matshow(canvas,origin='lower')\\n if plotting:\\n fig.savefig(f'{args.results_root_dir}/clust_{args.clustering_method}_nr_users-{args.num_users}_nr_of_partition_clusters_{nr_clusters}_method_{method}_filtered.png')\\n\\n d_error = np.sum(clustering_matrix-canvas)\\n print(f'Decompostion error: {d_error}, {d_error/np.sum(clustering_matrix)}')\\n\\n # build cluster id to client id user dict\\n cluster_user_dict = { i : idx[cluster_memberships==i] for i in range(1,nr_clusters+1)}\\n\\n # Test overlaps within clusters\\n collected = []\\n for i, cluster_members_a in cluster_user_dict.items():\\n for j, cluster_members_b in cluster_user_dict.items():\\n assert np.all(cluster_members_a != cluster_members_b) or set(cluster_members_a).intersection(set(cluster_members_b)) != {}, f'clusters {i} and {j} are not disjoint'\\n collected.extend(cluster_members_a)\\n assert np.all(np.arange(0,len(clustering_matrix),1) == np.sort(np.array(collected)))\\n\\n return cluster_user_dict\",\n \"def prepare_statistics(self):\\n\\n # statistics of clustering files\\n len0 = len(self.cluster_lists[0])\\n len1 = len(self.cluster_lists[1])\\n longer_index = 0 if len0 >= len1 else 1\\n shorter_index = 1 if len1 <= len0 else 0\\n\\n percentage_stars = \\\"%.2f\\\" % (100.0 * float(self.shared_spec_num)/float(self.cluster_spectra_num[shorter_index]))\\n percentage_starlets = \\\"%.2f\\\" % (100.0 * float(self.shared_spec_num)/float(self.cluster_spectra_num[longer_index]))\\n\\n head = \\\"{0:<25}{1:<20}{2:<20}\\\\n\\\".format(\\\"name\\\", \\\"number\\\", \\\"description\\\")\\n rows = \\\"\\\"\\n rows += \\\"{0:<25}{1:<20}{2:<20}\\\\n\\\".format(\\\"stars No.\\\", self.stars_length, \\\"in file with less(or equal) clusters: file\\\" + str(shorter_index))\\n rows += \\\"{0:<25}{1:<20}{2:<20}\\\\n\\\".format(\\\"starlets No.\\\", self.starlets_length, \\\"in file with more(or equal) clusters: file\\\" + str(longer_index))\\n rows += \\\"{0:<25}{1:<20}{2:<20}\\\\n\\\".format(\\\"identical cluster No.\\\", self.similarity_dist[10], \\\"between them\\\")\\n rows += \\\"{0:<25}{1:<20}{2:<20}\\\\n\\\".format(\\\"spectrum No\\\", self.cluster_spectra_num[shorter_index], \\\"in stars\\\")\\n rows += \\\"{0:<25}{1:<20}{2:<20}\\\\n\\\".format(\\\"spectrum No\\\", self.cluster_spectra_num[longer_index], \\\"in starlets \\\")\\n rows += \\\"{0:<25}{1:<20}{2:<20}\\\\n\\\".format(\\\"shared spectrum No\\\", self.shared_spec_num, \\\"between them\\\")\\n rows += \\\"{0:<25}{1:<20}{2:<20}\\\\n\\\".format(\\\"shared spectrum percent\\\", percentage_stars, \\\"in stars\\\")\\n rows += \\\"{0:<25}{1:<20}{2:<20}\\\\n\\\".format(\\\"shared spectrum percent\\\", percentage_starlets, \\\"in starlets\\\")\\n self.tables.append(('statistics of files', head, rows))\\n\\n # distribution of cluster size in stars\\n head = '{0:<20}{1:<20}{2:<20}{3:<20}\\\\n'.format(\\\"cluster size\\\",\\\"No.\\\", \\\"percentage\\\", \\\"accumulate pecentage\\\")\\n rows = \\\"\\\"\\n rows += \\\"{0:<20}{1:<20}\\\\n\\\".format(\\\"%.2f\\\" % (self.ave_star_size), \\\"average\\\")\\n accumulate_num = 0\\n for key in sorted(self.cluster_size_dist[shorter_index].keys()):\\n value = self.cluster_size_dist[shorter_index][key]\\n accumulate_num += value\\n percent = \\\"%.2f\\\" % (100 * value/self.stars_length)\\n accum_percent = \\\"%.2f\\\" % (100 * accumulate_num/self.stars_length)\\n rows += '{0:<20}{1:<20}{2:<20}{3:<20}\\\\n'.format(key, value, percent, accum_percent)\\n self.tables.append(('distribution of cluster size in stars', head, rows))\\n \\n head = '{0:<20}{1:<20}{2:<20}{3:<20}\\\\n'.format(\\\"cluster size\\\",\\\"No.\\\", \\\"percentage\\\", \\\"accumulate pecentage\\\")\\n rows = \\\"\\\"\\n rows += \\\"{0:<20}{1:<20}\\\\n\\\".format(\\\"%.2f\\\" % (self.ave_starlet_size), \\\"average\\\")\\n accumulate_num = 0\\n for key in sorted(self.cluster_size_dist[longer_index].keys()):\\n value = self.cluster_size_dist[longer_index][key]\\n accumulate_num += value\\n percent = \\\"%.2f\\\" % (100 * value/self.starlets_length)\\n accum_percent = \\\"%.2f\\\" % (100 * accumulate_num/self.starlets_length)\\n rows += '{0:<20}{1:<20}{2:<20}{3:<20}\\\\n'.format(key, value, percent, accum_percent)\\n self.tables.append(('distribution of cluster size in starlets', head, rows))\\n\\n # distribution of similarity\\n head = \\\"{0:<20}{1:<20}{2:<20}{3:<20}\\\\n\\\".format(\\\"similarity score\\\", \\\"pairs of clusters\\\", \\\"percentage(stars)\\\", \\\"percentage(starlets)\\\")\\n rows = \\\"\\\"\\n for key in reversed(sorted(self.similarity_dist.keys())):\\n value = self.similarity_dist[key]\\n percent_star = \\\"%.2f\\\" % (100.0*value/self.stars_length)\\n percent_starlet = \\\"%.2f\\\" % (100.0*value/self.starlets_length)\\n rows += '{0:<20}{1:<20}{2:<20}{3:<20}\\\\n'.format(key, value, percent_star, percent_starlet)\\n self.tables.append(('distribution of similarity (identical = 10)', head, rows))\\n\\n # distribution of star divide factors\\n head = '{0:<20}{1:<20}{2:<20}\\\\n'.format(\\\"divide factor\\\",\\\"No.\\\",\\\"percentage\\\")\\n rows = \\\"\\\"\\n rows += \\\"{0:<20}{1:<20}\\\\n\\\".format(\\\"%.2f\\\" % (self.ave_divide_factor_star), \\\"average\\\")\\n for key in sorted(self.star_divide_factor_dist.keys()):\\n value = self.star_divide_factor_dist[key]\\n percent_star = \\\"%.2f\\\" % (100.0*value/self.stars_length)\\n rows += '{0:<20}{1:<20}{2:<20}\\\\n'.format(key, value, percent_star)\\n self.tables.append(('distribution of star divide factors', head, rows))\\n\\n # distribution of starlet divide factors\\n head = '{0:<20}{1:<20}{2:<20}\\\\n'.format(\\\"divide factor\\\",\\\"No.\\\",\\\"percentage\\\")\\n rows = \\\"\\\"\\n rows += \\\"{0:<20}{1:<20}\\\\n\\\".format(\\\"%.2f\\\" % (self.ave_divide_factor_starlet), \\\"average\\\")\\n for key in sorted(self.starlet_divide_factor_dist.keys()):\\n value = self.starlet_divide_factor_dist[key]\\n percent_starlet = \\\"%.2f\\\" % (100.0*value/self.starlets_length)\\n rows += '{0:<20}{1:<20}{2:<20}\\\\n'.format(key, value, percent_starlet)\\n self.tables.append(('distribution of starlet divide factors', head, rows))\",\n \"def test_cluster_csv(self, script_runner: ScriptRunner, tmp_path: Path) -> None:\\n outfile = tmp_path.joinpath(\\\"pca-cluster.png\\\")\\n logfile = tmp_path.joinpath(\\\"plot.log\\\")\\n result = script_runner.run(\\n \\\"qaa\\\",\\n \\\"plot\\\",\\n \\\"-i\\\",\\n CLUSTER,\\n \\\"-c\\\",\\n CENTROID,\\n \\\"-o\\\",\\n outfile.as_posix(),\\n \\\"-l\\\",\\n logfile.as_posix(),\\n \\\"--pca\\\",\\n \\\"--cluster\\\",\\n \\\"--verbose\\\",\\n )\\n assert result.success\\n assert logfile.exists()\\n assert outfile.exists()\\n assert outfile.stat().st_size > 0\",\n \"def test_other_clusters(self):\\n url = \\\"?filter[time_scope_units]=month&filter[time_scope_value]=-1&filter[resolution]=monthly&filter[limit]=1&group_by[cluster]=*\\\" # noqa: E501\\n query_params = self.mocked_query_params(url, OCPCpuView)\\n handler = OCPReportQueryHandler(query_params)\\n\\n query_data = handler.execute_query()\\n for data in query_data.get(\\\"data\\\"):\\n for cluster_data in data.get(\\\"clusters\\\"):\\n cluster_name = cluster_data.get(\\\"cluster\\\", \\\"\\\")\\n if cluster_name == \\\"Other\\\":\\n for cluster_value in cluster_data.get(\\\"values\\\"):\\n self.assertTrue(len(cluster_value.get(\\\"clusters\\\", [])) == 1)\\n self.assertTrue(len(cluster_value.get(\\\"source_uuid\\\", [])) == 1)\\n elif cluster_name == \\\"Others\\\":\\n for cluster_value in cluster_data.get(\\\"values\\\"):\\n self.assertTrue(len(cluster_value.get(\\\"clusters\\\", [])) > 1)\\n self.assertTrue(len(cluster_value.get(\\\"source_uuid\\\", [])) > 1)\",\n \"def cluster_dstructure_output(self, curs, output_fname, order_1st_id2all_clusters):\\n\\t\\tfrom codense.common import get_gene_no2gene_id\\n\\t\\tgene_no2gene_id = get_gene_no2gene_id(curs)\\n\\t\\tsys.stderr.write(\\\"Outputting cluster information...\\\")\\n\\t\\toutf = open(output_fname, 'w')\\n\\t\\tstr_tmp_list0 = []\\t#hold the 1st-order clusters\\n\\t\\tfor order_1st_id,all_2nd_order_clusters in order_1st_id2all_clusters.iteritems():\\n\\t\\t\\tstr_tmp_list1 = []\\t#hold the 2nd-order clusters\\n\\t\\t\\tfor order_2nd_id,cluster_list in all_2nd_order_clusters.iteritems():\\n\\t\\t\\t\\tstr_tmp_list2 = []\\t#hold the connected components\\n\\t\\t\\t\\tfor cluster in cluster_list:\\n\\t\\t\\t\\t\\tstr_tmp = self.return_string_form_of_cluster_dstructure(cluster, gene_no2gene_id)\\n\\t\\t\\t\\t\\tstr_tmp_list2.append(str_tmp)\\n\\t\\t\\t\\tstr_tmp_list1.append(\\\"[%s]\\\"%','.join(str_tmp_list2))\\n\\t\\t\\tstr_tmp_list0.append(\\\"[%s]\\\"%\\\",\\\".join(str_tmp_list1))\\n\\t\\t#'r:=' is for directly read in as an array\\n\\t\\toutf.write(\\\"r:=[%s]:\\\"%\\\",\\\".join(str_tmp_list0))\\n\\t\\toutf.close()\\n\\t\\tsys.stderr.write(\\\"Done.\\\\n\\\")\",\n \"def test_read_cluster_network(self):\\n pass\",\n \"def test_create_cluster_network(self):\\n pass\",\n \"def test_result_page(self):\\n response = self.client.post(\\\"/\\\", data={'1': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\\n '2': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\\n '3': ['3', '6', '0', '0', '0', '0', '0', '0', '0'],\\n '4': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\\n '5': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\\n '6': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\\n '7': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\\n '8': ['0', '0', '0', '0', '0', '0', '0', '0', '0'],\\n '9': ['0', '0', '0', '0', '0', '0', '0', '0', '0']})\\n\\n self.assertEqual(response.status_code, 302)\\n self.assertEqual(response.url, '/result/1')\\n\\n url = response.url\\n new_response = self.client.get(url)\\n\\n self.assertEqual(new_response.status_code, HTTPStatus.OK)\\n self.assertContains(new_response, 'This is matrix that you submitted')\\n self.assertContains(new_response, 'This is solved matrix')\",\n \"def test_output_create_dist(self):\\n numbers_dist = sample2dist(\\n [\\\"one\\\", \\\"two\\\", \\\"three\\\", \\\"four\\\", \\\"five\\\", \\\"six\\\"])\\n VA_numbers = VoseAlias(numbers_dist)\\n actual = VA_numbers.dist\\n prob = Decimal(1)/Decimal(6)\\n expected = {\\\"one\\\": prob, \\\"two\\\": prob, \\\"three\\\": prob,\\n \\\"four\\\": prob, \\\"five\\\": prob, \\\"six\\\": prob}\\n self.assertEqual(actual, expected)\",\n \"def SelectClusters(image, background_prediction, result_clustering,\\n n_clusters, bands_thresholds=[\\\"B2\\\", \\\"B3\\\", \\\"B4\\\"],\\n region_of_interest=None,\\n tileScale=PARAMS_CLOUDCLUSTERSCORE_DEFAULT['tileScale']): \\n bands_norm_difference = [b + \\\"_difference\\\" for b in bands_thresholds]\\n\\n img_joined = image.subtract(background_prediction)\\\\\\n .select(bands_thresholds, bands_norm_difference)\\\\\\n .addBands(image.select(bands_thresholds))\\n\\n bands_and_difference_bands = bands_thresholds + bands_norm_difference\\n\\n multitemporal_score = None\\n reflectance_score = None\\n\\n for i in range(n_clusters):\\n img_diff_clus = img_joined.updateMask(\\n result_clustering.eq(i)).select(bands_and_difference_bands)\\n\\n clusteri = img_diff_clus.reduceRegion(ee.Reducer.mean(),\\n geometry=region_of_interest,\\n bestEffort=True,\\n scale=30,\\n tileScale=tileScale\\n )\\n \\n clusteri_diff = clusteri.toArray(bands_norm_difference)\\n clusteri_refl = clusteri.toArray(bands_thresholds)\\n \\n clusteri_refl_norm = clusteri_refl.multiply(clusteri_refl).reduce(ee.Reducer.mean(),\\n axes=[0]).sqrt().get([0])\\n\\n clusteridiff_mean = clusteri_diff.reduce(ee.Reducer.mean(), axes=[0]).get([0])\\n clusteridiff_norm = clusteri_diff.multiply(clusteri_diff).reduce(ee.Reducer.mean(),\\n axes=[0]).sqrt().get([0])\\n\\n multitemporal_score_clusteri = ee.Algorithms.If(clusteridiff_mean.gt(0),\\n clusteridiff_norm,\\n clusteridiff_norm.multiply(-1))\\n\\n multitemporal_score_clusteri = result_clustering.eq(\\n i).toFloat().multiply(ee.Number(multitemporal_score_clusteri))\\n reflectance_score_clusteri = result_clustering.eq(\\n i).toFloat().multiply(ee.Number(clusteri_refl_norm))\\n\\n if multitemporal_score is None:\\n multitemporal_score = multitemporal_score_clusteri\\n reflectance_score = reflectance_score_clusteri\\n else:\\n multitemporal_score = multitemporal_score.add(\\n multitemporal_score_clusteri)\\n reflectance_score = reflectance_score.add(\\n reflectance_score_clusteri)\\n\\n return multitemporal_score, reflectance_score\",\n \"def plot_clusters(true_data, preds, cluster_center, cluster_name, savefig=\\\"\\\", title=\\\"\\\"):\\n\\n colors = plt.cm.get_cmap('hsv', len(cluster_name)+1) # get colors for each cluster using get_cmap. This will give us len(cluster_name) colors in a object form.\\n \\n for i, c in enumerate(cluster_name): # iterate through each cluster name\\n if c == -1: # -1 is given by DBScan for noise\\n clrs = 'grey' # make it grey\\n label = 'Noise' # label it 'Noise'\\n else:\\n clrs = colors(c) # get color for it\\n label=f'Cluster {c}' # label it by its name\\n df = true_data[preds == c] # get the points from dataset whose prediction was cluster `c`\\n x, y = df.iloc[:, 0], df.iloc[:, 1] # x and y axis\\n plt.scatter( # plotting the x and y axis\\n x, y,\\n label=label,\\n color=clrs\\n )\\n if c != -1:\\n plt.text(\\n cluster_center[i][0] + 0.03, cluster_center[i][1] + 0.1,\\n f\\\"Cluster {i}\\\",\\n weight='bold',\\n fontsize=9,\\n )\\n \\n plt.scatter(\\n cluster_center[:, 0], cluster_center[:, 1], # plotting the cluster centers\\n s=250, marker='*',\\n c='red', edgecolor='black',\\n label='Centroids'\\n )\\n \\n plt.title(title)\\n plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\\n plt.tight_layout()\\n if savefig != \\\"\\\" : plt.savefig(f\\\"{savefig}.png\\\")\\n plt.show()\\n plt.close()\",\n \"def print_cluster(self):\\n print('Cluster', self.number)\\n for pattern in self.patterns:\\n pattern.print_pattern()\",\n \"def clusterability(features,data):\\n #Get together features describing clustering results\\n n_nuclei=len(features['label'])\\n n_ellongated_nuc=len(data['label'])\\n n_clusters = len(set(data['clusters'])-{-1}) # since -1 element denotes noice\\n n_uncoupled_nuclei = list(data['clusters']).count(-1) # noise\\n n_coupled_nuclei = len(data['clusters'])-n_uncoupled_nuclei\\n \\n if n_clusters>=2:\\n #calculate clustering robustness without noise\\n Silohhouette_score= sklm.silhouette_score(data.drop(['clusters','label'],axis=1)[data['clusters']> -1], data['clusters'][data['clusters']> -1])\\n else:\\n Silohhouette_score = 'NA'\\n basic_clustering_features = [Clusterability_Features([n_nuclei,n_ellongated_nuc,n_clusters, n_uncoupled_nuclei,n_coupled_nuclei,Silohhouette_score])]\\n basic_clustering_features = pd.DataFrame([o.__dict__ for o in basic_clustering_features])\\n\\n return basic_clustering_features\",\n \"def test_visualize_recipe_nutrition(self):\\n pass\",\n \"def main():\\n\\n dist = \\\"Euclidean\\\"\\n path = \\\"\\\"\\n k_v = 2\\n error = []\\n k_vals = []\\n\\n for i in range(len(sys.argv)):\\n if sys.argv[i] == \\\"--path\\\":\\n path = sys.argv[i+1]\\n if sys.argv[i] == \\\"--k\\\":\\n k_v = int(sys.argv[i+1])\\n if sys.argv[i] == \\\"[--distance Manhattan]\\\":\\n dist = \\\"Manhattan\\\"\\n if sys.argv[i] == \\\"[--distance Minkowski]\\\":\\n dist = \\\"Minkowski\\\"\\n\\n\\n training_data = create_data(path)\\n\\n for k in range(2,10):\\n k_vals.append(k)\\n if k>2:\\n for i in range(len(training_data)):\\n training_data[i].remove(training_data[i][-1])\\n trained_data, centroids = get_clusters(training_data, k, dist)\\n error.append(rms(trained_data, dist))\\n plot_error(k_vals, error)\\n\\n for i in range(len(training_data)):\\n training_data[i].remove(training_data[i][-1])\\n\\n trained_data, centroids = get_clusters(training_data, k_v, dist)\\n\\n test_clusters(trained_data, centroids)\",\n \"def main():\\n parser = argparse.ArgumentParser(description=\\\"Wrapper of the scikit-learn AgglomerativeClustering method. \\\", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))\\n parser.add_argument('--config', required=False, help='Configuration file')\\n\\n # Specific args of each building block\\n required_args = parser.add_argument_group('required arguments')\\n required_args.add_argument('--input_dataset_path', required=True, help='Path to the input dataset. Accepted formats: csv.')\\n required_args.add_argument('--output_results_path', required=True, help='Path to the clustered dataset. Accepted formats: csv.')\\n parser.add_argument('--output_plot_path', required=False, help='Path to the clustering plot. Accepted formats: png.')\\n\\n args = parser.parse_args()\\n args.config = args.config or \\\"{}\\\"\\n properties = settings.ConfReader(config=args.config).get_prop_dic()\\n\\n # Specific call of each building block\\n agglomerative_clustering(input_dataset_path=args.input_dataset_path,\\n output_results_path=args.output_results_path,\\n output_plot_path=args.output_plot_path,\\n properties=properties)\",\n \"def test(self, test_data_path):\\n test_data = read_datafile(test_data_path)\\n test_data = self.preprocessor.preprocess(test_data)\\n\\n data = '__label__' + test_data['claim'].astype(str) + test_data['check_worthiness'].astype(str) + ' ' + \\\\\\n test_data['tweet_text']\\n\\n output = self.run(data)\\n\\n df = pd.DataFrame()\\n df[\\\"predicted\\\"] = output.split()\\n df[\\\"labeled\\\"] = [d.split()[0] for d in data]\\n\\n cm = confusion_matrix(df[\\\"labeled\\\"], df[\\\"predicted\\\"], labels=['__label__11','__label__10','__label__00'])\\n\\n ax= plt.subplot()\\n sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\\n\\n ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \\n ax.set_title('Confusion Matrix'); \\n ax.xaxis.set_ticklabels(['__label__11','__label__10','__label__00']); ax.yaxis.set_ticklabels(['__label__11','__label__10','__label__00']);\\n\\n plt.show()\\n\\n return np.sum(cm.diagonal()) / np.sum(cm)\",\n \"def add_content(subcluster, content, suffix):\\n\\n fname = './plots/%s_%s.png' % (conf, suffix)\\n\\n # pick the top 10 terms\\n subcluster = sorted(subcluster, key=lambda t: max(t[1:].astype(float)), reverse=True)\\n subcluster = subcluster[:10]\\n\\n words = np.array(subcluster)[:,0]\\n ys = np.array(subcluster)[:,1:].astype(float)\\n mean = [np.mean(ys[:,i]) for i in xrange(ys.shape[1])]\\n maxmean = max(mean)\\n idx = mean.index(maxmean)\\n\\n # this is used to make the top-k list in the HTML later\\n content.append(('', words, fname, idx))\\n\\n\\n data = []\\n for arr in subcluster:\\n word = arr[0]\\n for x, y in enumerate(map(float, arr[1:])):\\n data.append(dict(\\n group=\\\"normal\\\",\\n word=word,\\n x=xs[x],\\n y=y, \\n alpha=0.3\\n ))\\n\\n # add a line for the mean\\n for x, y in enumerate(mean):\\n data.append(dict(group=\\\"aggregate\\\", word='___mean___', x=xs[x], y=y, alpha=1))\\n\\n if 1:\\n maxy = max(10, max(pluckone(data, 'y')))\\n if maxy <= 10:\\n breaks = [0, 5, 10]\\n\\n\\n # pygg lets you write ggplot2 syntax in python\\n p = ggplot(data, aes(x='x', y='y', group='word', color='group', alpha='alpha'))\\n p += geom_line(size=1)\\n p += scale_color_manual(values=\\\"c('normal' = '#7777dd','aggregate' = 'black')\\\", guide=\\\"FALSE\\\")\\n p += scale_alpha_continuous(guide=\\\"FALSE\\\")\\n if 1:\\n if maxy <= 10:\\n p += scale_y_continuous(lim=[0, maxy], breaks=breaks, labels = \\\"function (x) as.integer(x)\\\")\\n else:\\n p += scale_y_continuous(lim=[0, maxy], labels = \\\"function (x) as.integer(x)\\\")\\n p += legend_bottom\\n p += theme(**{\\n \\\"axis.title\\\":element_blank()\\n })\\n ggsave(fname, p, width=10, height=4, libs=['grid'])\",\n \"def test_call_low_cluster_identity(self):\\r\\n\\r\\n # adapted from test_app.test_cd_hit.test_cdhit_clusters_from_seqs\\r\\n # Should only get 6 clusters\\r\\n exp_otu_ids = [str(x) for x in range(6)]\\r\\n\\r\\n exp_clusters =\\\\\\r\\n [['uclust_test_seqs_0', 'uclust_test_seqs_6',\\r\\n 'uclust_test_seqs_9'], ['uclust_test_seqs_1'], ['uclust_test_seqs_2'],\\r\\n ['uclust_test_seqs_3',\\r\\n 'uclust_test_seqs_5',\\r\\n 'uclust_test_seqs_8'],\\r\\n ['uclust_test_seqs_4'], ['uclust_test_seqs_7']]\\r\\n\\r\\n app = UsearchOtuPicker(params={'save_intermediate_files': False,\\r\\n 'db_filepath': self.tmp_ref_database,\\r\\n 'output_dir': self.temp_dir,\\r\\n 'remove_usearch_logs': True,\\r\\n 'reference_chimera_detection': False,\\r\\n 'de_novo_chimera_detection': False,\\r\\n 'cluster_size_filtering': False,\\r\\n 'minlen': 12,\\r\\n 'w': 12,\\r\\n 'minsize': 1,\\r\\n 'percent_id': 0.80,\\r\\n 'percent_id_err': 0.97\\r\\n })\\r\\n\\r\\n obs = app(self.tmp_seq_filepath1)\\r\\n\\r\\n obs_otu_ids = sorted(obs.keys())\\r\\n obs_clusters = sorted(obs.values())\\r\\n # The relation between otu ids and clusters is abitrary, and\\r\\n # is not stable due to use of dicts when parsing clusters -- therefore\\r\\n # just checks that we have the expected group of each\\r\\n self.assertEqual(obs_otu_ids, exp_otu_ids)\\r\\n self.assertEqual(obs_clusters, exp_clusters)\",\n \"def test_get_html(self):\\r\\n _html = self.peer_grading.get_html()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68014586","0.64515656","0.6283319","0.6218219","0.61491203","0.6064487","0.5956804","0.5843667","0.5821508","0.5812067","0.5782588","0.5746487","0.572244","0.5716077","0.57043064","0.5677167","0.5629126","0.5620701","0.5605728","0.5572866","0.5569146","0.55277187","0.55172074","0.5495248","0.5485648","0.5473991","0.54627836","0.54423845","0.5434325","0.53935546","0.5393222","0.5381607","0.53619087","0.5357629","0.5356211","0.5354116","0.535177","0.53463775","0.5337603","0.53258765","0.53118914","0.5310749","0.5302253","0.5295331","0.52886957","0.5287403","0.52833563","0.52775025","0.52743906","0.5264615","0.524502","0.52413136","0.5238267","0.52197814","0.5217244","0.52079374","0.5203316","0.5202228","0.5196766","0.5191322","0.5190776","0.5186196","0.51833403","0.5182063","0.51759773","0.5175127","0.51743525","0.5172556","0.5168381","0.51679564","0.51622367","0.516171","0.5149593","0.51430035","0.51417476","0.51400936","0.51281995","0.5123151","0.5116655","0.5113251","0.5106915","0.51012707","0.50991404","0.50939524","0.50891834","0.5087446","0.507477","0.5071149","0.5067674","0.5067106","0.5066648","0.506251","0.50601274","0.5059198","0.5056744","0.5053719","0.50498354","0.50441253","0.50322735","0.5026938"],"string":"[\n \"0.68014586\",\n \"0.64515656\",\n \"0.6283319\",\n \"0.6218219\",\n \"0.61491203\",\n \"0.6064487\",\n \"0.5956804\",\n \"0.5843667\",\n \"0.5821508\",\n \"0.5812067\",\n \"0.5782588\",\n \"0.5746487\",\n \"0.572244\",\n \"0.5716077\",\n \"0.57043064\",\n \"0.5677167\",\n \"0.5629126\",\n \"0.5620701\",\n \"0.5605728\",\n \"0.5572866\",\n \"0.5569146\",\n \"0.55277187\",\n \"0.55172074\",\n \"0.5495248\",\n \"0.5485648\",\n \"0.5473991\",\n \"0.54627836\",\n \"0.54423845\",\n \"0.5434325\",\n \"0.53935546\",\n \"0.5393222\",\n \"0.5381607\",\n \"0.53619087\",\n \"0.5357629\",\n \"0.5356211\",\n \"0.5354116\",\n \"0.535177\",\n \"0.53463775\",\n \"0.5337603\",\n \"0.53258765\",\n \"0.53118914\",\n \"0.5310749\",\n \"0.5302253\",\n \"0.5295331\",\n \"0.52886957\",\n \"0.5287403\",\n \"0.52833563\",\n \"0.52775025\",\n \"0.52743906\",\n \"0.5264615\",\n \"0.524502\",\n \"0.52413136\",\n \"0.5238267\",\n \"0.52197814\",\n \"0.5217244\",\n \"0.52079374\",\n \"0.5203316\",\n \"0.5202228\",\n \"0.5196766\",\n \"0.5191322\",\n \"0.5190776\",\n \"0.5186196\",\n \"0.51833403\",\n \"0.5182063\",\n \"0.51759773\",\n \"0.5175127\",\n \"0.51743525\",\n \"0.5172556\",\n \"0.5168381\",\n \"0.51679564\",\n \"0.51622367\",\n \"0.516171\",\n \"0.5149593\",\n \"0.51430035\",\n \"0.51417476\",\n \"0.51400936\",\n \"0.51281995\",\n \"0.5123151\",\n \"0.5116655\",\n \"0.5113251\",\n \"0.5106915\",\n \"0.51012707\",\n \"0.50991404\",\n \"0.50939524\",\n \"0.50891834\",\n \"0.5087446\",\n \"0.507477\",\n \"0.5071149\",\n \"0.5067674\",\n \"0.5067106\",\n \"0.5066648\",\n \"0.506251\",\n \"0.50601274\",\n \"0.5059198\",\n \"0.5056744\",\n \"0.5053719\",\n \"0.50498354\",\n \"0.50441253\",\n \"0.50322735\",\n \"0.5026938\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7976958"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":93896,"cells":{"query":{"kind":"string","value":"Override the default train() to freeze the BN parameters"},"document":{"kind":"string","value":"def train(self, mode=True, freeze_bn=False):\n super(WideResNet, self).train(mode)\n self.freeze_bn = freeze_bn\n if self.freeze_bn:\n print(\"Freezing Mean/Var of BatchNorm2D.\")\n print(\"Freezing Weight/Bias of BatchNorm2D.\")\n if self.freeze_bn:\n for m in self.modules():\n if isinstance(m, nn.BatchNorm2d):\n m.eval()\n m.weight.requires_grad = False\n m.bias.requires_grad = False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def train(self, *args, **kwargs):\n raise NotImplementedError","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(self, ):\n raise NotImplementedError","def train(self):\n raise NotImplementedError","def train():\n pass","def train(self):\n\t\traise NotImplementedError","def trainNet():","def train(self):\n raise NotImplementedError()","def train(self, mode=True, freeze_bn=False):\n super(NetFeat, self).train(mode)\n self.freeze_bn = freeze_bn\n if self.freeze_bn:\n for m in self.modules():\n if isinstance(m, nn.BatchNorm2d):\n m.eval()\n m.weight.requires_grad = False\n m.bias.requires_grad = False","def train(self, training_steps=10):","def train_naive(): # add arguments as needed\n pass","def train(self)->None:","def train(self):\n return","def __init__(self,\n exp_name,\n ds_train,\n ds_val,\n epochs=210,\n batch_size=16,\n num_workers=4,\n loss='JointsMSELoss',\n lr=0.001,\n lr_decay=True,\n lr_decay_steps=(170, 200),\n lr_decay_gamma=0.1,\n optimizer='Adam',\n weight_decay=0.,\n momentum=0.9,\n nesterov=False,\n pretrained_weight_path=None,\n checkpoint_path=None,\n log_path='./logs',\n use_tensorboard=True,\n model_c=48,\n model_nof_joints=18,\n model_bn_momentum=0.1,\n flip_test_images=True,\n device=None\n ):\n super(GOLFTrain, self).__init__(\n exp_name=exp_name,\n ds_train=ds_train,\n ds_val=ds_val,\n epochs=epochs,\n batch_size=batch_size,\n num_workers=num_workers,\n loss=loss,\n lr=lr,\n lr_decay=lr_decay,\n lr_decay_steps=lr_decay_steps,\n lr_decay_gamma=lr_decay_gamma,\n optimizer=optimizer,\n weight_decay=weight_decay,\n momentum=momentum,\n nesterov=nesterov,\n pretrained_weight_path=pretrained_weight_path,\n checkpoint_path=checkpoint_path,\n log_path=log_path,\n use_tensorboard=use_tensorboard,\n model_c=model_c,\n model_nof_joints=model_nof_joints,\n model_bn_momentum=model_bn_momentum,\n flip_test_images=flip_test_images,\n device=device\n )","def train_model(self, *args, **kwargs):\n raise NotImplementedError","def train(self, mode=True):\n super(RCRNN, self).train(mode)\n if self.freeze_bn:\n print(\"Freezing Mean/Var of BatchNorm2D.\")\n if self.freeze_bn:\n print(\"Freezing Weight/Bias of BatchNorm2D.\")\n if self.freeze_bn:\n for m in self.modules():\n if isinstance(m, nn.BatchNorm2d):\n m.eval()\n if self.freeze_bn:\n m.weight.requires_grad = False\n m.bias.requires_grad = False","def train(self, x={}, **kwargs):\n return 0","def train(self, batch):\n pass","def train(self, mode=True):\n super(CRNN, self).train(mode)\n if self.freeze_bn:\n print(\"Freezing Mean/Var of BatchNorm2D.\")\n if self.freeze_bn:\n print(\"Freezing Weight/Bias of BatchNorm2D.\")\n if self.freeze_bn:\n for m in self.modules():\n if isinstance(m, nn.BatchNorm2d):\n m.eval()\n if self.freeze_bn:\n m.weight.requires_grad = False\n m.bias.requires_grad = False","def forward_train(self, *args, **kwargs):\n pass","def trainModel( self, featureTrain, classTrain):","def train(self, training_data, cfg, **kwargs):\n pass","def train(self) -> Any:\n pass","def __init__(self,\n exp_name,\n ds_train,\n ds_val,\n epochs=210,\n batch_size=16,\n num_workers=4,\n loss='JointsMSELoss',\n lr=0.001,\n lr_decay=True,\n lr_decay_steps=(170, 200),\n lr_decay_gamma=0.1,\n optimizer='Adam',\n weight_decay=0.,\n momentum=0.9,\n nesterov=False,\n pretrained_weight_path=None,\n checkpoint_path=None,\n log_path='./logs',\n use_tensorboard=True,\n model_c=48,\n model_nof_joints=18,\n model_bn_momentum=0.1,\n flip_test_images=True,\n device=None\n ):\n super(Train, self).__init__()\n\n self.exp_name = exp_name\n self.ds_train = ds_train\n self.ds_val = ds_val\n self.epochs = epochs\n self.batch_size = batch_size\n self.num_workers = num_workers\n self.loss = loss\n self.lr = lr\n self.lr_decay = lr_decay\n self.lr_decay_steps = lr_decay_steps\n self.lr_decay_gamma = lr_decay_gamma\n self.optimizer = optimizer\n self.weight_decay = weight_decay\n self.momentum = momentum\n self.nesterov = nesterov\n self.pretrained_weight_path = pretrained_weight_path\n self.checkpoint_path = checkpoint_path\n self.log_path = os.path.join(log_path, self.exp_name)\n self.use_tensorboard = use_tensorboard\n self.model_c = model_c\n self.model_nof_joints = model_nof_joints\n self.model_bn_momentum = model_bn_momentum\n self.flip_test_images = flip_test_images\n self.epoch = 0\n\n\n os.makedirs(self.log_path, 0o755, exist_ok=True) # exist_ok=False to avoid overwriting\n if self.use_tensorboard:\n self.summary_writer = tb.SummaryWriter(self.log_path)\n\n #\n # write all experiment parameters in parameters.txt and in tensorboard text field\n self.parameters = [x + ': ' + str(y) + '\\n' for x, y in locals().items()]\n\n with open(os.path.join(self.log_path, 'parameters.txt'), 'w') as fd:\n fd.writelines(self.parameters)\n if self.use_tensorboard:\n self.summary_writer.add_text('parameters', '\\n'.join(self.parameters))\n\n #\n # load model\n self.model = HRNet(c=self.model_c, nof_joints=self.model_nof_joints,\n bn_momentum=self.model_bn_momentum).cuda()\n\n\n #\n # define loss and optimizers\n if self.loss == 'JointsMSELoss':\n self.loss_fn = JointsMSELoss()\n elif self.loss == 'JointsOHKMMSELoss':\n self.loss_fn = JointsOHKMMSELoss()\n else:\n raise NotImplementedError\n\n if optimizer == 'SGD':\n self.optim = SGD(self.model.parameters(), lr=self.lr, weight_decay=self.weight_decay,\n momentum=self.momentum, nesterov=self.nesterov)\n elif optimizer == 'Adam':\n self.optim = Adam(self.model.parameters(), lr=self.lr, weight_decay=self.weight_decay)\n else:\n raise NotImplementedError\n\n\n # load pre-trained weights (such as those pre-trained on imagenet)\n if self.pretrained_weight_path is not None:\n if self.model_nof_joints == 18:\n pretrained_dict = torch.load(self.pretrained_weight_path)\n pretrained_dict_items = list(pretrained_dict.items())\n pretrained_model = {}\n j = 0\n for k, v in self.model.state_dict().items():\n v = pretrained_dict_items[j][1]\n k = pretrained_dict_items[j][0]\n\n if k == 'final_layer.weight':\n x = torch.rand(1,48,1,1).cuda()\n v = torch.cat([v, x], dim=0)\n if k == 'final_layer.bias':\n x = torch.rand(1).cuda()\n v = torch.cat([v,x],dim=0)\n pretrained_model[k] = v\n j +=1\n model_dict=self.model.state_dict()\n model_dict.update(pretrained_model)\n self.model.load_state_dict(model_dict,strict=True)\n else:\n self.model.load_state_dict(torch.load(self.pretrained_weight_path, strict=True))\n print('Pre-trained weights loaded.')\n\n self.model = nn.DataParallel(self.model.cuda())\n # self.model = nn.DataParallel(self.model.to(self.device))\n #\n # load previous checkpoint\n if self.checkpoint_path is not None:\n print('Loading checkpoint %s...' % self.checkpoint_path)\n if os.path.isdir(self.checkpoint_path):\n path = os.path.join(self.checkpoint_path, 'checkpoint_last.pth')\n else:\n path = self.checkpoint_path\n self.starting_epoch, self.model, self.optim, self.params = load_checkpoint(path, self.model, self.optim,\n self.device)\n else:\n self.starting_epoch = 0\n\n if lr_decay:\n self.lr_scheduler = MultiStepLR(self.optim, list(self.lr_decay_steps), gamma=self.lr_decay_gamma,\n last_epoch=self.starting_epoch if self.starting_epoch else -1)\n\n #\n # load train and val datasets\n self.dl_train = DataLoader(self.ds_train, batch_size=self.batch_size, shuffle=True,\n num_workers=self.num_workers, drop_last=True)\n self.len_dl_train = len(self.dl_train)\n\n # dl_val = DataLoader(self.ds_val, batch_size=1, shuffle=False, num_workers=num_workers)\n self.dl_val = DataLoader(self.ds_val, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers)\n self.len_dl_val = len(self.dl_val)\n\n #\n # initialize variables\n self.mean_loss_train = 0.\n self.mean_acc_train = 0.\n self.mean_loss_val = 0.\n self.mean_acc_val = 0.\n self.mean_mAP_val = 0.\n\n self.best_loss = None\n self.best_acc = None\n self.best_mAP = None","def train(self):\n self.mode = \"train\"\n self.online_net.train()","def train(self):\n self.mode = \"train\"\n self.online_net.train()","def train(self):\n self.training = True","def defreeze_model(self):\n # defreeze all parameters\n for param in self.parameters():\n param.requires_grad = True\n # make the whole network trainable\n self.train()","def _set_learning_phase(self, train: bool = False):\n if train:\n self.net_q.train()\n self.net_ps.train()\n self.net_k.train()\n else:\n self.net_q.eval()\n self.net_ps.eval()\n self.net_k.eval()","def train(self, num_batches: int):","def _train(self, dataset):\n net = buildNetwork(\n dataset.params_length,\n self._default_hidden_layers,\n 1 # a binary classifier only requires one output layer\n )\n ds = SupervisedDataSet(dataset)\n trainer = BackpropTrainer(net, ds)\n trainer.trainUntilConvergence()\n net.activate(params.as_serialized)","def train(self, trainData):\n pass","def __init__(\n self,\n hidden_dims,\n input_dim=3 * 32 * 32,\n num_classes=10,\n dropout=1,\n normalization=None,\n reg=0.0,\n weight_scale=1e-2,\n dtype=np.float32,\n seed=None,\n ):\n self.normalization = normalization\n self.use_dropout = dropout != 1\n self.reg = reg\n self.num_layers = 1 + len(hidden_dims)\n self.dtype = dtype\n self.params = {}\n\n Din, Dout = input_dim, hidden_dims[0]\n for i in range(self.num_layers):\n self.params['W' + str(i+1)] = np.random.normal(scale=weight_scale, size=(Din, Dout))\n self.params['b' + str(i+1)] = np.zeros((Dout,))\n Din = Dout\n if i < len(hidden_dims) - 1:\n Dout = hidden_dims[i+1]\n if i == len(hidden_dims) - 1:\n Dout = num_classes\n \n # BN params initialization\n if self.normalization != None:\n for i in range(self.num_layers - 1):\n self.params['gamma' + str(i+1)] = np.ones(shape=(hidden_dims[i]))\n self.params['beta' + str(i+1)] = np.zeros(shape=(hidden_dims[i]))\n\n # When using dropout we need to pass a dropout_param dictionary to each\n # dropout layer so that the layer knows the dropout probability and the mode\n # (train / test). You can pass the same dropout_param to each dropout layer.\n self.dropout_param = {}\n if self.use_dropout:\n self.dropout_param = {\"mode\": \"train\", \"p\": dropout}\n if seed is not None:\n self.dropout_param[\"seed\"] = seed\n\n # With batch normalization we need to keep track of running means and\n # variances, so we need to pass a special bn_param object to each batch\n # normalization layer. You should pass self.bn_params[0] to the forward pass\n # of the first batch normalization layer, self.bn_params[1] to the forward\n # pass of the second batch normalization layer, etc.\n self.bn_params = []\n if self.normalization == \"batchnorm\":\n self.bn_params = [{\"mode\": \"train\"} for i in range(self.num_layers - 1)]\n if self.normalization == \"layernorm\":\n self.bn_params = [{} for i in range(self.num_layers - 1)]\n\n # Cast all parameters to the correct datatype\n for k, v in self.params.items():\n self.params[k] = v.astype(dtype)","def train(self):\n raise NotImplemented()","def train(self, batch_training=False):\n raise NotImplementedError","def train(self, training_data):\n pass","def _set_train(self):\n\n if not self.model.__dict__['training']:\n self.model.train()","def _train(self):\n self.network.train() # note that here we are calling torch.nn.Module train class method\n epochs_since_improvement = 0\n best_params = None\n self.calculate_validation_loss()\n best_validation_loss = self.validation_average_loss\n\n while epochs_since_improvement < 10:\n self.train_epoch()\n self.calculate_validation_loss()\n if self.validation_average_loss < best_validation_loss:\n epochs_since_improvement = 0\n best_validation_loss = self.validation_average_loss\n best_params = self.network.state_dict()\n else:\n epochs_since_improvement += 1\n LOGGER.info(\"Epochs since improvement in validation_loss: {} \\n\".format(epochs_since_improvement))\n if self.maximum_epochs_allowed is not None and self.epochs_trained >= self.maximum_epochs_allowed:\n break\n LOGGER.info(\"Training complete after {} epochs \\n\".format(self.epochs_trained))\n LOGGER.info(\"Best training loss achieved: {} \\n\".format(self.training_average_loss))\n LOGGER.info(\"Best validation loss achieved: {}\".format(self.validation_average_loss))\n self.learned_params = best_params\n self.network.load_state_dict(best_params)","def train(self, train, dev, test, best_score_got):\n param_dic = {}\n params = []\n best_score = best_score_got\n nepoch_no_imprv = 0 # for early stopping\n self.add_summary() # tensorboard\n self.logger.info(\"no of layers {:}\".format(self.config.layer))\n self.logger.info(\"no of steps {:}\".format(self.config.step))\n self.logger.info(\"train_embeddings {:}\".format(self.config.train_embeddings))\n self.logger.info(\"no of nepochs {:}\".format(self.config.nepochs))\n self.logger.info(\"dropout {:}\".format(self.config.dropout))\n self.logger.info(\"batch_size {:}\".format(self.config.batch_size))\n self.logger.info(\"learning rate {:}\".format(self.config.lr))\n self.logger.info(\"lr_decay {:}\".format(self.config.lr_decay))\n self.logger.info(\"model_type {:}\".format(self.config.model_type))\n param_dic[\"no of layers\"] = self.config.layer\n param_dic[\"no of steps\"] = self.config.step\n param_dic[\"train_embeddings\"] = self.config.train_embeddings\n param_dic[\"no of nepochs\"] = self.config.nepochs\n param_dic[\"dropout\"] = self.config.dropout\n param_dic[\"batch_size\"] = self.config.batch_size\n param_dic[\"learning rate\"] = self.config.lr\n param_dic[\"lr_decay\"] = self.config.lr_decay\n param_dic[\"model_type\"] = self.config.model_type\n\n params.append([\"no of layers {:}\".format(self.config.layer),\n \"no of steps {:}\".format(self.config.step),\n \"train_embeddings {:}\".format(self.config.train_embeddings),\n \"no of nepochs {:}\".format(self.config.nepochs),\n \"dropout {:}\".format(self.config.dropout),\n \"batch_size {:}\".format(self.config.batch_size),\n \"learning rate {:}\".format(self.config.lr),\n \"lr_decay {:}\".format(self.config.lr_decay),\n \"model_type {:}\".format(self.config.model_type)])\n\n for epoch in range(self.config.nepochs):\n self.logger.info(\"Epoch {:} out of {:} of model no. - {:}\".format(epoch + 1,\n self.config.nepochs, len(params)))\n self.logger.info(param_dic)\n\n score = self.run_epoch(train, dev, epoch, test)\n self.config.lr *= self.config.lr_decay # decay learning rate\n\n # early stopping and saving best parameters\n if score >= best_score:\n nepoch_no_imprv = 0\n self.save_session()\n best_score = score\n param_dic[\"best_score\"] = best_score\n self.logger.info(\"- new best score!\")\n self.logger.info(\"Saved best model at epoch {}\".format(epoch + 1))\n\n with open(self.config.dir_model + 'bestModelParams.txt', 'w') as outfile:\n json.dump(param_dic, outfile)\n outfile.close()\n else:\n nepoch_no_imprv += 1\n if nepoch_no_imprv >= self.config.nepoch_no_imprv:\n self.logger.info(\"- early stopping {} epochs without \" \\\n \"improvement\".format(nepoch_no_imprv))\n break\n\n model_detail = \" , \".join(\n [\" : \".join(str(element) for element in item) for item in param_dic.items() if (item[0] != \"best_score\")])\n model_dic = {\"model_detail\": model_detail, \"epoch_number\": epoch_list, \"model_acc\": accuracy_list}\n\n return {\"model_dic\": model_dic, \"params\":params}","def train_all(self):\n for p in self.parameters():\n p.requires_grad = True\n return self","def train():\n # YOUR TRAINING CODE GOES HERE","def set_train(self):\n self.train()\n self.volatile = False","def _initial_setup(self, **train_kwargs):\n super(NetworkValidationBase, self)._initial_setup(**train_kwargs)","def __init__(self, **kwargs):\n super(BertNewsClassifier, self).__init__()\n self.PRE_TRAINED_MODEL_NAME = \"bert-base-uncased\"\n self.bert_model = BertModel.from_pretrained(self.PRE_TRAINED_MODEL_NAME)\n for param in self.bert_model.parameters():\n param.requires_grad = False\n self.drop = nn.Dropout(p=0.2)\n # assigning labels\n self.class_names = [\"World\", \"Sports\", \"Business\", \"Sci/Tech\"]\n n_classes = len(self.class_names)\n\n self.fc1 = nn.Linear(self.bert_model.config.hidden_size, 512)\n self.out = nn.Linear(512, n_classes)\n self.bert_model.embedding = self.bert_model.embeddings\n self.embedding = self.bert_model.embeddings\n\n self.scheduler = None\n self.optimizer = None\n self.args = kwargs\n\n self.train_acc = Accuracy()\n self.val_acc = Accuracy()\n self.test_acc = Accuracy()","def tune_params(self, X_train, Y_train):\n return self.model # No hyper-parameter tuning","def set_train(self):\n self.model.train()","def init(self, train):\n return","def init(self, train):\n return","def init(self, train):\n return","def init(self, train):\n return","def init(self, train):\n return","def train(self,iTrain=1,Nbatch=32,Nlayers=3,Nunits=100,Nepochs=10000,\n ValidationSplit=0.1,Patience=20,Verbose=2):\n if iTrain == 1:\n self.cvstem()\n print(\"========================================================\")\n print(\"=================== NCM CONSTRUCTION ===================\")\n print(\"========================================================\")\n n = self.n\n n_p = self.n_p\n X = np.hstack((self.xs_opt,self.ps_opt))\n y = self.cholMs\n model = Sequential(name=\"NCM\")\n model.add(Dense(Nunits,activation=\"relu\",input_shape=(n+n_p,)))\n for l in range(Nlayers-1):\n model.add(Dense(Nunits,activation=\"relu\"))\n model.add(Dense(int(n*(n+1)/2)))\n model.summary()\n model.compile(loss=\"mean_squared_error\",optimizer=\"adam\")\n es = EarlyStopping(monitor=\"val_loss\",patience=Patience)\n model.fit(X,y,batch_size=Nbatch,epochs=Nepochs,verbose=Verbose,\\\n callbacks=[es],validation_split=ValidationSplit)\n self.model = model\n model.save(\"models/\"+self.fname+\".h5\")\n elif iTrain == 0:\n print(\"========================================================\")\n print(\"=================== NCM CONSTRUCTION ===================\")\n print(\"========================================================\")\n self.model = load_model(\"models/\"+self.fname+\".h5\")\n path = \"models/optvals/\"+self.fname\n self.alp_opt = np.load(path+\"/alp_opt.npy\")\n self.chi_opt = np.load(path+\"/chi_opt.npy\")\n self.nu_opt = np.load(path+\"/nu_opt.npy\")\n self.Jcv_opt = np.load(path+\"/Jcv_opt.npy\")\n print(\"Loading pre-trained NCM ...\")\n print(\"Loading pre-trained NCM END\")\n else:\n raise ValueError(\"Invalid iTrain: iTrain = 1 or 0\")\n print(\"========================================================\")\n print(\"================= NCM CONSTRUCTION END =================\")\n print(\"========================================================\")\n pass","def _train_model(self):\n raise NotImplementedError()","def train(self, trainingData, trainingLabels, validationData, validationLabels ):\n\n self.setWeights(trainingData.shape[1])\n # DO NOT ZERO OUT YOUR WEIGHTS BEFORE STARTING TRAINING, OR\n # THE AUTOGRADER WILL LIKELY DEDUCT POINTS.\n \n # Hyper-parameters. Your can reset them. Default batchSize = 100, weight_decay = 1e-3, learningRate = 1e-2\n \"*** YOU CODE HERE ***\"\n self.batchSize = 100\n self.weight_decay = 1e-3\n self.learningRate = 0.1\n\n def Softmax(x):\n x_max = np.max(x, axis=0)\n x_exp = np.exp(x - x_max)\n x_exp_sum = np.sum(x_exp, axis=0)\n return x_exp / x_exp_sum\n\n for iteration in range(self.max_iterations):\n if iteration % 10 == 0:\n print(\"Starting iteration \", iteration, \"...\")\n self.learningRate *= 0.9\n dataBatches = self.prepareDataBatches(trainingData, trainingLabels)\n for batchData, batchLabel in dataBatches:\n \"*** YOUR CODE HERE ***\"\n Y = np.zeros((len(self.legalLabels), self.batchSize))\n for i in range(self.batchSize):\n Y[batchLabel[i]][i] = 1\n Y_pred = Softmax((batchData @ self.weights + self.bias).T)\n d_weight = ((Y_pred - Y) @ batchData / batchData.shape[0]).T + self.weight_decay * sum(self.weights)\n d_bias = np.mean(Y_pred - Y, axis=1) + self.weight_decay * sum(self.bias)\n self.weights -= d_weight * self.learningRate\n self.bias -= d_bias * self.learningRate","def __init__(self, encoded_network, input_shape, n_classes, batch_size=256,\n log_path=\"./trainer\", mu=0.5, rho=0.5, variable_scope=\"cnn\"):\n super(EarlyStopNASTrainer, self).__init__(\n encoded_network=encoded_network,\n input_shape=input_shape,\n n_classes=n_classes,\n batch_size=batch_size,\n log_path=log_path,\n variable_scope=variable_scope\n )\n # Custom variables for the refined accuracy in BlockQNN implementation\n # pylint: disable=invalid-name\n self.mu = mu\n self.rho = rho\n\n # Updated during training call\n self.density = None\n self.flops = None\n\n # Build the estimator\n self._set_estimator()","def train(self):\n self.target_update_frequency = 10000 # How often to update target network\n\n stash_model = True # Flag for stashing a copy of the model\n model_stash_interval = 10 # Stashes a copy of the model this often\n\n # Number of steps to take before training. Allows buffer to partially fill.\n # Must be at least batch_size to avoid error when sampling from experience replay\n self.pre_training_steps = 10*self.batch_size\n assert(self.pre_training_steps <= self.buffer_size), \"Replay not large enough for pre-training!\"\n assert(self.pre_training_steps >= self.batch_size), \"Buffer not allowed to fill enough before sampling!\"\n # Number of steps to force learner to observe submitted actions, rather than submit its own actions\n self.observations = 2000\n\n self.epsilon = 0.5 # Initial probability of letting the learner submit its own action\n self.eps_decay_rate = 1./(25*20*len(self.training_data)) # Rate at which epsilon decays per submission\n\n lr_decay_freq = 10 # Decay learning rate after a set number of epochs\n min_learning_rate = 1.e-8 # Minimum learning rate allowed to decay to\n\n summaries = {}\n summaries[\"loss\"] = []\n summaries[\"train_acc\"] = []\n summaries[\"val_acc\"] = []\n # Load existing model\n self.ddq_net.sess.run(self.ddq_net.online_ops[\"init\"])\n if(self.load_path):\n self.ddq_net.load(self.load_path)\n print(\"\\nCheckpoint loaded from {}\".format(self.load_path))\n\n # Initialize target network\n self.ddq_net.sess.run(self.ddq_net.target_ops[\"target_init\"])\n\n for self.epoch_count in range(self.n_epoch):\n t0 = time.time()\n learning_rate = self.ddq_net.online_ops[\"learning_rate\"].eval(self.ddq_net.sess)\n if((self.epoch_count>0) and (self.epoch_count % lr_decay_freq == 0) and (learning_rate>= min_learning_rate)):\n # Decay learning rate accoring to schedule\n learning_rate = 0.5*learning_rate\n self.ddq_net.sess.run(self.ddq_net.online_ops[\"learning_rate\"].assign(learning_rate))\n\n # Run single epoch of training\n loss, train_acc, val_acc = self.train_epoch()\n dt = time.time()-t0\n\n print(\" Finished epoch {:2}/{}: lr: {:.4e}, dt {:.2f}, loss {:.6f}, train {:.6f}, val {:.6f}\".format(self.epoch_count+1, self.n_epoch, learning_rate, dt, loss, train_acc, val_acc), flush=True)\n summaries[\"loss\"].append(loss)\n summaries[\"train_acc\"].append(train_acc)\n summaries[\"val_acc\"].append(val_acc)\n\n if(stash_model):\n if(self.epoch_count>0 and (self.epoch_count+1)%model_stash_interval==0):\n # Stash a copy of the current model\n out_path = \"tmp/models/{}_model_E{}.ckpt\".format(self.ddq_net._name, self.epoch_count+1)\n self.ddq_net.save(path=out_path)\n print(\"Stashed a copy of the current model in {}\".format(out_path))\n\n self.ddq_net.save(path=self.ddq_net._path_to_model)\n return summaries","def _set_training_params(self, params):\n self.lyapunov_hybrid_system.lyapunov_relu.load_state_dict(\n params[\"lyap_relu_params\"])\n if not self.R_options.fixed_R:\n self.R_options._variables = params[\"R_params\"].clone()\n if isinstance(self.lyapunov_hybrid_system.system,\n feedback_system.FeedbackSystem):\n self.lyapunov_hybrid_system.system.controller_network.\\\n load_state_dict(params[\"controller_params\"])","def train(self, x_train, y_train, x_val, y_val):\n pass","def __init__(self, parameters: ParametersList, algorithm: ClassVar, algorithm_data: AlgorithmData):\n super(GreedyTrain, self).__init__(parameters, algorithm, algorithm_data)","def train(\n self, max_epochs: int = 30000, batch_size: int = None, train_size: float = 1, lr: float = 0.002, **kwargs\n ):\n\n kwargs[\"max_epochs\"] = max_epochs\n kwargs[\"batch_size\"] = batch_size\n kwargs[\"train_size\"] = train_size\n kwargs[\"lr\"] = lr\n\n super().train(**kwargs)","def train_step(self):\n pass","def __init__(self):\n # Number of examples per epoch of training data.\n self.num_examples_per_epoch = None \n\n # Optimizer for training the model.\n self.optimizer = \"SGD\" #default \"SGD\"\n\n # Learning rate for the initial phase of training.\n self.initial_learning_rate = 2.0 # default 2.0\n self.learning_rate_decay_factor = 0.8\n self.num_epochs_per_decay = 4 #default 8\n\n # If not None, clip gradients to this value.\n self.clip_gradients = 5.0\n\n # How many model checkpoints to keep.\n self.max_checkpoints_to_keep = 2","def train_all(X_train_fuse, Y_train, X_dev_fuse, Y_dev, R_train, R_dev, hyperparams):","def train_one_epoch(self):\n raise NotImplementedError","def train(self):\n\t\tself.model.fit(self.training_data, self.training_labels)","def __init__(self, parameters: ParametersList, algorithm: ClassVar, algorithm_data: AlgorithmData):\n\n super(SupervisedTrain, self).__init__(parameters, algorithm, algorithm_data)\n self.best_score = np.inf","def train():\n args = arguments()\n\n # Create output directories\n create_output_dir(args.outputDir)\n\n # Start Log File\n log_path = os.path.join(args.outputDir, LOG_DIR, time.strftime('%Y-%m-%d_%H-%M-%S.log'))\n log_file = Logger(log_path)\n\n # Log arguments\n arg_str = ''\n for arg in vars(args):\n arg_str += \"\\n\" + \"{:30} {}\".format(str(arg), getattr(args, arg))\n log_file.log_line(\"Arguments\", arg_str)\n log_file.newline()\n\n # Load Params\n configuration = config_cvppp.TrainConfig()\n\n # Log params\n log_file.log_line(\"Config Parameters\\n\", configuration.to_string())\n log_file.newline()\n\n ## Load dataset API (Already logged in the args log step)\n train_dataset, crossVal_dataset = load_datasets(args)\n\n # Init the model\n checkpoint_path = os.path.join(args.outputDir, CHECKPOINT_DIR)\n training_model = model.MaskRCNN('training', configuration, checkpoint_path)\n\n # Load weights\n if args.init == 'last':\n weights_path = training_model.find_last()\n log_file.log_line(\"Initialised with \", weights_path)\n training_model.load_weights(weights_path, by_name=True)\n\n elif args.init == 'rand':\n log_file.log_line(\"Initialised with \", \"random weights\")\n pass\n\n else:\n if not os.path.exists(args.init):\n raise OSError('No weights at: ' + args.init)\n \n log_file.log_line(\"Initialised with \", args.init)\n training_model.load_weights(args.init, by_name=True)\n\n # Train the model\n augmentation = get_augmentation_sequence()\n\n custom_callbacks = None\n\n training_model.train(train_dataset, crossVal_dataset, \n learning_rate=configuration.LEARNING_RATE, \n epochs=args.numEpochs,\n augmentation=augmentation,\n layers='all',\n custom_callbacks=custom_callbacks) # Train all layers\n\n # Close the log file\n log_file.close()","def set_params(self, **params):\n if not hasattr(self, \"_non_sklearn_base\"):\n return super().set_params(**params)\n if not (\n len(params) == 1 and\n (\"nthreads\" in params or \"n_jobs\" in params)\n ):\n self.is_fitted_ = False\n valid_params = self.get_params(deep=False)\n for k,v in params.items():\n if k not in valid_params:\n raise ValueError(\"Invalid parameter: \", k)\n setattr(self, k, v)\n return self","def train_network(self, batch_size, epochs):\n\n if self.eq_train: self.model.fit([self.X_train_high_level, self.X_train_low_level], self.y_train, epochs=epochs, batch_size=batch_size, sample_weight=self.train_weights_eq) \n else: self.model.fit([self.X_train_high_level, self.X_train_low_level], self.y_train, epochs=epochs, batch_size=batch_size, sample_weight=self.train_weights)","def __init__(self, state_size, action_size, fc1_units, fc2_units, buffer_size, batch_size, alpha, gamma, tau,\n local_update_every, target_update_every, seed, a, b, b_increase, b_end, dbl_dqn=False, priority_rpl=False, duel_dqn=False):\n self.state_size = state_size\n self.action_size = action_size\n self.seed = random.seed(seed)\n\n # Hyperparameters\n self.alpha = alpha # Learning rate\n self.gamma = gamma # Discount parameter\n self.tau = tau # Interpolation parameter\n self.local_update_every = local_update_every # Number of actions to take before updating local net weights\n self.target_update_every = target_update_every # Number of actions to take before updating target net weights\n self.batch_size = batch_size # Number of experiences to sample during learning\n self.buffer_size = buffer_size # Size of memory buffer\n self.a = a # Sampling probability (0=random | 1=priority)\n self.b = b # Influence of importance sampling weights over learning\n self.b_increase = b_increase # Amount to increase b by every learning step\n self.b_end = b_end # Maximum value for b\n\n # Agent modifications\n self.dbl_dqn = dbl_dqn # Double Q Learning\n self.priority_rpl = priority_rpl # Prioritised Experience Replay\n self.duel_dqn = duel_dqn # Duelling Q Networks\n\n # Q-Network\n if self.duel_dqn:\n self.qnetwork_local = DuellingQNetwork(state_size, action_size, fc1_units, fc2_units, seed).to(device)\n self.qnetwork_target = DuellingQNetwork(state_size, action_size, fc1_units, fc2_units, seed).to(device)\n else:\n self.qnetwork_local = QNetwork(state_size, action_size, fc1_units, fc2_units, seed).to(device)\n self.qnetwork_target = QNetwork(state_size, action_size, fc1_units, fc2_units, seed).to(device)\n self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=self.alpha)\n\n # Replay memory\n self.memory = ReplayBuffer(action_size, buffer_size, batch_size, seed, priority_rpl)\n # Initialize time step (for updating every local_update_every/target_update_every steps)\n self.t_step = 0","def __init__(self):\n self.num_examples_per_epoch = 99999\n self.optimizer = \"Adam\"\n # Learning rate for the initial phase of training.\n self.initial_learning_rate = 0.0001\n self.learning_rate_decay_factor = 0.5\n self.num_epochs_per_decay = 8.0\n\n # Learning rate when fine tuning the Inception v3 parameters.\n self.train_inception_learning_rate = 0.0001\n\n # If not None, clip gradients to this value.\n self.clip_gradients = 5.0\n\n # How many model checkpoints to keep.\n self.max_checkpoints_to_keep = 5000","def train2(self):\n for epoch in range(self.epochs):\n print \"epoch: \", epoch\n self.train(self.D)\n self.alpha -= 0.002 # decrease the learning rate\n self.min_alpha = model.alpha # fix the learning rate, no decay","def _train(self, log_prob):\n raise NotImplementedError","def train(self, X):\n self.X = X","def train_gradient_boost(self, params, num_boost_round = 50):\n print \"training GB......\"\n dtrain = xgb.DMatrix(self.X, self.y)\n model = xgb.train(params, dtrain, num_boost_round = num_boost_round)\n self.models += [model]","def train(self, train_dataset, val_dataset):\n\n # check fine_tuning option\n model_path = os.path.join(self.check_point, 'model.pt')\n if self.fine_tune and not os.path.exists(model_path):\n raise Exception('Cannot find %s.' % model_path)\n elif self.fine_tune and os.path.exists(model_path):\n if self.verbose:\n print('Loading %s for finetuning.' % model_path)\n self.model = torch.load(model_path)\n '''\n if isinstance(net, torch.nn.DataParallel):\n net = net.module\n model_dict = self.model.state_dict()\n net_dict = net.state_dict()\n # 1. filter out unnecessary keys\n pretrained_dict = {k: v for k, v in net_dict.items() if k in model_dict}\n # 2. overwrite entries in the existing state dict\n model_dict.update(pretrained_dict)\n # 3. load the new state dict\n self.model.load_state_dict(model_dict)\n '''\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n\n # capture best model\n best_val_psnr = -1\n best_psnr = -1\n best_model_state = self.model.state_dict()\n\n with open(os.path.join(self.check_point, 'PSNR' + '.txt'), 'w') as f:\n # Train the model\n for epoch in range(self.num_epochs):\n self._epoch_step(train_dataset, epoch)\n self.scheduler.step()\n\n if epoch % 10 == 0:\n if self.verbose:\n print('Computing PSNR...')\n\n # capture running PSNR on train and val dataset\n train_psnr, train_ssim, _, _, _ = self._check_PSNR(val_dataset)\n self.hist_train_psnr.append(train_psnr)\n\n f.write('epoch%d:\\t%.3f\\n' % (epoch, train_psnr))\n\n if self.verbose:\n print('Average train PSNR:%.3fdB average ssim: %.3f' % (train_psnr, train_ssim))\n print('')\n if best_psnr < train_psnr:\n best_psnr = train_psnr\n # write the model to hard-disk for testing\n if not os.path.exists(self.check_point):\n os.makedirs(self.check_point)\n model_path = os.path.join(self.check_point, 'model.pt')\n torch.save(self.model, model_path)\n print(' Best average psnr: %.3f' % (best_psnr))\n print('')","def training_step(self, **kwargs):\n raise NotImplementedError","def __init__(self,\n n_tasks,\n n_features,\n logdir=None,\n layer_sizes=[1000],\n weight_init_stddevs=[.02],\n bias_init_consts=[1.],\n penalty=0.0,\n penalty_type=\"l2\",\n dropouts=[0.5],\n learning_rate=.001,\n momentum=.9,\n optimizer=\"adam\",\n batch_size=50,\n n_classes=2,\n pad_batches=False,\n verbose=True,\n seed=None,\n **kwargs):\n # Save hyperparameters\n self.n_tasks = n_tasks\n self.n_features = n_features\n self.layer_sizes = layer_sizes\n self.weight_init_stddevs = weight_init_stddevs\n self.bias_init_consts = bias_init_consts\n self.penalty = penalty\n self.penalty_type = penalty_type\n self.dropouts = dropouts\n self.learning_rate = learning_rate\n self.momentum = momentum\n self.optimizer = optimizer\n self.batch_size = batch_size\n self.n_classes = n_classes\n self.pad_batches = pad_batches\n self.verbose = verbose\n self.seed = seed\n\n if logdir is not None:\n if not os.path.exists(logdir):\n os.makedirs(logdir)\n else:\n logdir = tempfile.mkdtemp()\n self.logdir = logdir\n\n # Guard variable to make sure we don't Restore() this model\n # from a disk checkpoint more than once.\n self._restored_model = False\n # Path to save checkpoint files, which matches the\n # replicated supervisor's default path.\n self._save_path = os.path.join(logdir, 'model.ckpt')\n\n self.train_graph = self.construct_graph(training=True, seed=self.seed)\n self.eval_graph = self.construct_graph(training=False, seed=self.seed)","def __init__(\n self,\n net,\n eps,\n sua=False,\n pi=False,\n update_freq=1,\n alpha=1.0,\n constraint_norm=False,\n data_size=50000,\n use_batch_norm=False,\n ):\n self.net = net\n self.state = net.state_dict()\n self.mean_state = copy.deepcopy(self.state)\n self.data_size = data_size\n self.use_batch_norm = use_batch_norm\n\n self.eps = eps\n self.sua = sua\n self.pi = pi\n self.update_freq = update_freq\n self.alpha = alpha\n self.constraint_norm = constraint_norm\n self.params = []\n self._iteration_counter = 0\n for mod in net.modules():\n mod_class = mod.__class__.__name__\n if mod_class in [\"Linear\", \"Conv2d\"]:\n mod.register_forward_pre_hook(self._save_input)\n mod.register_backward_hook(self._save_grad_output)\n params = [mod.weight]\n if mod.bias is not None:\n params.append(mod.bias)\n d = {\"params\": params, \"mod\": mod, \"layer_type\": mod_class}\n self.params.append(d)\n\n elif \"BatchNorm\" in mod_class and use_batch_norm:\n mod.register_forward_pre_hook(self._save_input)\n mod.register_backward_hook(self._save_grad_output)\n\n params = [mod.weight, mod.bias]\n\n d = {\"params\": params, \"mod\": mod, \"layer_type\": mod_class}\n self.params.append(d)\n\n super(KFACLaplace, self).__init__(self.params, {})\n # super(KFACLaplace, self).__init__()","def __init__(self, batch_size=1, epochs=None, learning_rate=None, momentum=None, weights_name=''):\n self.batch_size = batch_size\n self.epochs = epochs\n self.model = None\n self.optimizer = None\n self.cb = None\n self.lr = learning_rate\n self.momentum = momentum\n self.weights_name = weights_name","def trainFreezeBN(self):\n\t\tprint(\"Freezing Mean/Var of BatchNorm2D.\")\n\t\tprint(\"Freezing Weight/Bias of BatchNorm2D.\")\n\t\tfor m in self.modules():\n\t\t\tif isinstance(m, nn.BatchNorm2d):\n\t\t\t\tm.eval()\n\t\t\t\tm.weight.requires_grad = False\n\t\t\t\tm.bias.requires_grad = False","def trainFreezeBN(self):\n\t\tprint(\"Freezing Mean/Var of BatchNorm2D.\")\n\t\tprint(\"Freezing Weight/Bias of BatchNorm2D.\")\n\t\tfor m in self.modules():\n\t\t\tif isinstance(m, nn.BatchNorm2d):\n\t\t\t\tm.eval()\n\t\t\t\tm.weight.requires_grad = False\n\t\t\t\tm.bias.requires_grad = False","def __init__(self, in_features, out_features):\n \n ########################\n # PUT YOUR CODE HERE #\n #######################\n mean = 0\n std_dev = 0.0001\n #print(in_features)\n #print(out_features)\n # create weight matrices\n weight = np.random.normal(mean, std_dev, (out_features, in_features))\n #print(weight.shape)\n grad_weight = np.zeros((in_features, out_features))\n\n # create biases (in batches)\n bias = np.zeros(out_features)\n grad_bias = np.zeros(out_features)\n\n self.params = {'weight': weight, 'bias': bias}\n self.grads = {'weight': bias, 'bias': grad_bias}\n\n ########################\n # END OF YOUR CODE #\n #######################","def train(self, mode=True):\n super(Encoder, self).train(mode)\n self.apply(freeze_batchnorm)","def TrainOneStep(self):\n pass","def __init__(self,seed=None,batch_size=60, use_earlyStopping=False, loaded_model=None):\n self.batch_size = batch_size\n self.pruning_rates = PRUNING_PERCENTAGES\n self.early_stopping = use_earlyStopping\n\n if loaded_model == None:\n self.model = keras.Sequential()\n self.model.add(keras.layers.Flatten(input_shape=(28, 28)))\n for layer in LAYERS:\n (units, activation) = LAYERS[layer]\n self.model.add(keras.layers.Dense(units, activation=activation, kernel_initializer=tf.keras.initializers.glorot_normal(seed=None)))\n \n \n self.model.compile(optimizer=OPTIMIZER_FC,\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n metrics=['accuracy'])\n\n self.weights_init = self.get_weights()\n \n else:\n self.model = loaded_model\n\n if use_earlyStopping:\n self.es = EarlyStopping(monitor='val_loss', patience=10)","def train(self, **kwargs):\n self.solver.train(**kwargs)","def train(train_iter, test_iter, net, loss, trainer, ctx, num_epochs):\n # 加入学习曲线显示\n weight_url = '//home//jim//shanghai_index//data//weights.params'\n logdir_url = '//home//jim//shanghai_index//log'\n sw = SummaryWriter(logdir = logdir_url, flush_secs=10)\n # 提取已有参数\n if os.path.exists(weight_url):\n print(u'已含有旧权重文件，正在载入继续训练并更新')\n net.load_parameters(weight_url, allow_missing = True, ignore_extra = True)\n # 训练\n for epoch in range(num_epochs):\n train_loss, train_acc, train_step, start = 0.0, 0.0, 0, time.time()\n for x1, x2, y in train_iter:\n batch_size = x1.shape[0]\n with autograd.record():\n y_hats = net(x1, x2)\n print('y_hat = {}'.format(y_hats))\n print('y = {}'.format(y))\n ls = loss(y_hats, y)\n print('loss:{}'.format(ls))\n ls.backward()\n trainer.step(batch_size)\n train_loss += np.mean(ls.asnumpy())\n train_acc += acc(y_hats, y)\n train_step += 1\n \n print('epoch {}, loss {}, train acc {}, time {} sec'.format(epoch + 1,\n train_loss/train_step,\n train_acc/train_step,\n time.time() - start))\n # 向tensorboard填数据\n sw.add_scalar(tag = 'Loss_and_acc', \\\n value = {'train_loss': train_loss/train_step, 'train_acc': train_acc/train_step}, \\\n global_step = epoch)\n # 加入某个层权重分布变化等高图\n grads = [i.grad() for i in net.collect_params('.*weight|.*bias').values()]\n param_names = [name for name in net.collect_params('.*weight|.*bias').keys()]\n assert len(grads) == len(param_names)\n # logging the gradients of parameters for checking convergence\n for i, name in enumerate(param_names):\n sw.add_histogram(tag = name, values = grads[i], global_step = epoch, bins = 20)\n\n # 加入保存参数\n net.save_parameters(weight_url)","def train(self, mode=True):\n super(TSN, self).train(mode)\n count = 0\n if self._enable_pbn:\n print(\"Freezing BatchNorm2D except the first one.\")\n for m in self.base_model.modules():\n # print('the type train model : {}'.format(type(m)))\n if isinstance(m, torch.nn.BatchNorm2d) or \\\n isinstance(m, linklink.nn.syncbn_layer.SyncBatchNorm2d):\n count += 1\n if count >= (2 if self._enable_pbn else 1):\n m.eval()\n # print('the freeze module: {} of {}th'.format(type(m), count))\n # shutdown update in frozen mode\n m.weight.requires_grad = False\n m.bias.requires_grad = False","def _setup_training(self, params, **kwargs):\n model_params = params.permute_training_on_top().model\n\n model_kwargs = {**model_params.fixed, **model_params.variable}\n\n model = self.model_cls(**model_kwargs)\n\n training_params = params.permute_training_on_top().training\n losses = training_params.nested_get(\"losses\")\n optimizer_cls = training_params.nested_get(\"optimizer_cls\")\n optimizer_params = training_params.nested_get(\"optimizer_params\")\n train_metrics = training_params.nested_get(\"train_metrics\", {})\n lr_scheduler_cls = training_params.nested_get(\"lr_sched_cls\", None)\n lr_scheduler_params = training_params.nested_get(\"lr_sched_params\",\n {})\n val_metrics = training_params.nested_get(\"val_metrics\", {})\n\n # necessary for resuming training from a given path\n save_path = kwargs.pop(\"save_path\", os.path.join(\n self.save_path,\n \"checkpoints\",\n \"run_%02d\" % self._run))\n\n return self.trainer_cls(\n network=model,\n save_path=save_path,\n losses=losses,\n key_mapping=self.key_mapping,\n optimizer_cls=optimizer_cls,\n optimizer_params=optimizer_params,\n train_metrics=train_metrics,\n val_metrics=val_metrics,\n lr_scheduler_cls=lr_scheduler_cls,\n lr_scheduler_params=lr_scheduler_params,\n optim_fn=self._optim_builder,\n save_freq=self.checkpoint_freq,\n **kwargs\n )","def train(self):\n raise IllegalOperationError(\"Cannot train multiplicative model!\")","def __init__(self, config, data_loader, layer_hyperparams):\n self.config = config\n self.layer_hyperparams = layer_hyperparams\n\n if config.is_train:\n self.train_loader = data_loader[0]\n self.valid_loader = data_loader[1]\n self.num_train = len(self.train_loader.dataset)\n self.num_valid = self.valid_loader.dataset.trials\n else:\n if config.get_embedding:\n self.test_embedding_loader = data_loader\n self.n_embeddings = config.n_embeddings\n else:\n self.test_loader = data_loader\n self.num_test = self.test_loader.dataset.trials\n\n if config.use_batch_norm:\n self.model = SiameseNetWithBN()\n else:\n self.model = SiameseNet()\n \n if config.use_gpu:\n self.model.cuda()\n\n # model params\n self.num_params = sum(\n [p.data.nelement() for p in self.model.parameters()]\n )\n self.num_model = get_num_model(config)\n self.num_layers = len(list(self.model.children()))\n\n print('[*] Number of model parameters: {:,}'.format(self.num_params))\n\n # path params\n self.ckpt_dir = os.path.join(config.ckpt_dir, self.num_model)\n self.logs_dir = os.path.join(config.logs_dir, self.num_model)\n\n # misc params\n self.resume = config.resume\n self.use_gpu = config.use_gpu\n self.dtype = (\n torch.cuda.FloatTensor if self.use_gpu else torch.FloatTensor\n )\n\n # optimization params\n self.best = config.best\n self.best_valid_acc = 0.\n self.epochs = config.epochs\n self.start_epoch = 0\n self.lr_patience = config.lr_patience\n self.train_patience = config.train_patience\n self.counter = 0\n\n # grab layer-wise hyperparams\n self.init_lrs = self.layer_hyperparams['layer_init_lrs']\n self.init_momentums = [config.init_momentum]*self.num_layers\n self.end_momentums = self.layer_hyperparams['layer_end_momentums']\n self.l2_regs = self.layer_hyperparams['layer_l2_regs']\n\n # compute temper rate for momentum\n if self.epochs == 1:\n f = lambda max, min: min\n else:\n f = lambda max, min: (max - min) / (self.epochs-1)\n self.momentum_temper_rates = [\n f(x, y) for x, y in zip(self.end_momentums, self.init_momentums)\n ]\n\n # set global learning rates and momentums\n self.lrs = self.init_lrs\n self.momentums = self.init_momentums\n\n # # initialize optimizer\n # optim_dict = []\n # for i, layer in enumerate(self.model.children()):\n # group = {}\n # group['params'] = layer.parameters()\n # group['lr'] = self.lrs[i]\n # group['momentum'] = self.momentums[i]\n # group['weight_decay'] = self.l2_regs[i]\n # optim_dict.append(group)\n # self.optimizer = optim.SGD(optim_dict)\n # self.optimizer = optim.SGD(\n # self.model.parameters(), lr=1e-3, momentum=0.9, weight_decay=4e-4,\n # )\n self.optimizer = optim.Adam(\n self.model.parameters(), lr=3e-4, weight_decay=6e-5,\n )\n\n # # learning rate scheduler\n # self.scheduler = StepLR(\n # self.optimizer, step_size=self.lr_patience, gamma=0.99,\n # )\n self.debug = dict()","def set_train(self):\n self.train()\n self.volatile = False\n self.scheduled_sampling = self.sample_prob != 0","def train_model(self, *args, **kwargs):\n self.model.train(self.training, *args, **kwargs)","def __init__(\n self,\n model_name: str,\n pretrained: bool = True,\n progress: bool = True,\n spatial_dims: int = 2,\n in_channels: int = 3,\n num_classes: int = 1000,\n norm: str | tuple = (\"batch\", {\"eps\": 1e-3, \"momentum\": 0.01}),\n adv_prop: bool = False,\n ) -> None:\n blocks_args_str = [\n \"r1_k3_s11_e1_i32_o16_se0.25\",\n \"r2_k3_s22_e6_i16_o24_se0.25\",\n \"r2_k5_s22_e6_i24_o40_se0.25\",\n \"r3_k3_s22_e6_i40_o80_se0.25\",\n \"r3_k5_s11_e6_i80_o112_se0.25\",\n \"r4_k5_s22_e6_i112_o192_se0.25\",\n \"r1_k3_s11_e6_i192_o320_se0.25\",\n ]\n\n # check if model_name is valid model\n if model_name not in efficientnet_params:\n model_name_string = \", \".join(efficientnet_params.keys())\n raise ValueError(f\"invalid model_name {model_name} found, must be one of {model_name_string} \")\n\n # get network parameters\n weight_coeff, depth_coeff, image_size, dropout_rate, dropconnect_rate = efficientnet_params[model_name]\n\n # create model and initialize random weights\n super().__init__(\n blocks_args_str=blocks_args_str,\n spatial_dims=spatial_dims,\n in_channels=in_channels,\n num_classes=num_classes,\n width_coefficient=weight_coeff,\n depth_coefficient=depth_coeff,\n dropout_rate=dropout_rate,\n image_size=image_size,\n drop_connect_rate=dropconnect_rate,\n norm=norm,\n )\n\n # only pretrained for when `spatial_dims` is 2\n if pretrained and (spatial_dims == 2):\n _load_state_dict(self, model_name, progress, adv_prop)","def train(self, trainingData, trainingLabels, validationData, validationLabels): \n \n # might be useful in your code later...\n # this is a list of all features in the training set.\n self.features = list(set([ f for datum in trainingData for f in datum.keys() ]));\n \n if (self.automaticTuning):\n kgrid = [0.001, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 20, 50]\n else:\n kgrid = [self.k]\n \n self.trainAndTune(trainingData, trainingLabels, validationData, validationLabels, kgrid)"],"string":"[\n \"def train(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self, ):\\n raise NotImplementedError\",\n \"def train(self):\\n raise NotImplementedError\",\n \"def train():\\n pass\",\n \"def train(self):\\n\\t\\traise NotImplementedError\",\n \"def trainNet():\",\n \"def train(self):\\n raise NotImplementedError()\",\n \"def train(self, mode=True, freeze_bn=False):\\n super(NetFeat, self).train(mode)\\n self.freeze_bn = freeze_bn\\n if self.freeze_bn:\\n for m in self.modules():\\n if isinstance(m, nn.BatchNorm2d):\\n m.eval()\\n m.weight.requires_grad = False\\n m.bias.requires_grad = False\",\n \"def train(self, training_steps=10):\",\n \"def train_naive(): # add arguments as needed\\n pass\",\n \"def train(self)->None:\",\n \"def train(self):\\n return\",\n \"def __init__(self,\\n exp_name,\\n ds_train,\\n ds_val,\\n epochs=210,\\n batch_size=16,\\n num_workers=4,\\n loss='JointsMSELoss',\\n lr=0.001,\\n lr_decay=True,\\n lr_decay_steps=(170, 200),\\n lr_decay_gamma=0.1,\\n optimizer='Adam',\\n weight_decay=0.,\\n momentum=0.9,\\n nesterov=False,\\n pretrained_weight_path=None,\\n checkpoint_path=None,\\n log_path='./logs',\\n use_tensorboard=True,\\n model_c=48,\\n model_nof_joints=18,\\n model_bn_momentum=0.1,\\n flip_test_images=True,\\n device=None\\n ):\\n super(GOLFTrain, self).__init__(\\n exp_name=exp_name,\\n ds_train=ds_train,\\n ds_val=ds_val,\\n epochs=epochs,\\n batch_size=batch_size,\\n num_workers=num_workers,\\n loss=loss,\\n lr=lr,\\n lr_decay=lr_decay,\\n lr_decay_steps=lr_decay_steps,\\n lr_decay_gamma=lr_decay_gamma,\\n optimizer=optimizer,\\n weight_decay=weight_decay,\\n momentum=momentum,\\n nesterov=nesterov,\\n pretrained_weight_path=pretrained_weight_path,\\n checkpoint_path=checkpoint_path,\\n log_path=log_path,\\n use_tensorboard=use_tensorboard,\\n model_c=model_c,\\n model_nof_joints=model_nof_joints,\\n model_bn_momentum=model_bn_momentum,\\n flip_test_images=flip_test_images,\\n device=device\\n )\",\n \"def train_model(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def train(self, mode=True):\\n super(RCRNN, self).train(mode)\\n if self.freeze_bn:\\n print(\\\"Freezing Mean/Var of BatchNorm2D.\\\")\\n if self.freeze_bn:\\n print(\\\"Freezing Weight/Bias of BatchNorm2D.\\\")\\n if self.freeze_bn:\\n for m in self.modules():\\n if isinstance(m, nn.BatchNorm2d):\\n m.eval()\\n if self.freeze_bn:\\n m.weight.requires_grad = False\\n m.bias.requires_grad = False\",\n \"def train(self, x={}, **kwargs):\\n return 0\",\n \"def train(self, batch):\\n pass\",\n \"def train(self, mode=True):\\n super(CRNN, self).train(mode)\\n if self.freeze_bn:\\n print(\\\"Freezing Mean/Var of BatchNorm2D.\\\")\\n if self.freeze_bn:\\n print(\\\"Freezing Weight/Bias of BatchNorm2D.\\\")\\n if self.freeze_bn:\\n for m in self.modules():\\n if isinstance(m, nn.BatchNorm2d):\\n m.eval()\\n if self.freeze_bn:\\n m.weight.requires_grad = False\\n m.bias.requires_grad = False\",\n \"def forward_train(self, *args, **kwargs):\\n pass\",\n \"def trainModel( self, featureTrain, classTrain):\",\n \"def train(self, training_data, cfg, **kwargs):\\n pass\",\n \"def train(self) -> Any:\\n pass\",\n \"def __init__(self,\\n exp_name,\\n ds_train,\\n ds_val,\\n epochs=210,\\n batch_size=16,\\n num_workers=4,\\n loss='JointsMSELoss',\\n lr=0.001,\\n lr_decay=True,\\n lr_decay_steps=(170, 200),\\n lr_decay_gamma=0.1,\\n optimizer='Adam',\\n weight_decay=0.,\\n momentum=0.9,\\n nesterov=False,\\n pretrained_weight_path=None,\\n checkpoint_path=None,\\n log_path='./logs',\\n use_tensorboard=True,\\n model_c=48,\\n model_nof_joints=18,\\n model_bn_momentum=0.1,\\n flip_test_images=True,\\n device=None\\n ):\\n super(Train, self).__init__()\\n\\n self.exp_name = exp_name\\n self.ds_train = ds_train\\n self.ds_val = ds_val\\n self.epochs = epochs\\n self.batch_size = batch_size\\n self.num_workers = num_workers\\n self.loss = loss\\n self.lr = lr\\n self.lr_decay = lr_decay\\n self.lr_decay_steps = lr_decay_steps\\n self.lr_decay_gamma = lr_decay_gamma\\n self.optimizer = optimizer\\n self.weight_decay = weight_decay\\n self.momentum = momentum\\n self.nesterov = nesterov\\n self.pretrained_weight_path = pretrained_weight_path\\n self.checkpoint_path = checkpoint_path\\n self.log_path = os.path.join(log_path, self.exp_name)\\n self.use_tensorboard = use_tensorboard\\n self.model_c = model_c\\n self.model_nof_joints = model_nof_joints\\n self.model_bn_momentum = model_bn_momentum\\n self.flip_test_images = flip_test_images\\n self.epoch = 0\\n\\n\\n os.makedirs(self.log_path, 0o755, exist_ok=True) # exist_ok=False to avoid overwriting\\n if self.use_tensorboard:\\n self.summary_writer = tb.SummaryWriter(self.log_path)\\n\\n #\\n # write all experiment parameters in parameters.txt and in tensorboard text field\\n self.parameters = [x + ': ' + str(y) + '\\\\n' for x, y in locals().items()]\\n\\n with open(os.path.join(self.log_path, 'parameters.txt'), 'w') as fd:\\n fd.writelines(self.parameters)\\n if self.use_tensorboard:\\n self.summary_writer.add_text('parameters', '\\\\n'.join(self.parameters))\\n\\n #\\n # load model\\n self.model = HRNet(c=self.model_c, nof_joints=self.model_nof_joints,\\n bn_momentum=self.model_bn_momentum).cuda()\\n\\n\\n #\\n # define loss and optimizers\\n if self.loss == 'JointsMSELoss':\\n self.loss_fn = JointsMSELoss()\\n elif self.loss == 'JointsOHKMMSELoss':\\n self.loss_fn = JointsOHKMMSELoss()\\n else:\\n raise NotImplementedError\\n\\n if optimizer == 'SGD':\\n self.optim = SGD(self.model.parameters(), lr=self.lr, weight_decay=self.weight_decay,\\n momentum=self.momentum, nesterov=self.nesterov)\\n elif optimizer == 'Adam':\\n self.optim = Adam(self.model.parameters(), lr=self.lr, weight_decay=self.weight_decay)\\n else:\\n raise NotImplementedError\\n\\n\\n # load pre-trained weights (such as those pre-trained on imagenet)\\n if self.pretrained_weight_path is not None:\\n if self.model_nof_joints == 18:\\n pretrained_dict = torch.load(self.pretrained_weight_path)\\n pretrained_dict_items = list(pretrained_dict.items())\\n pretrained_model = {}\\n j = 0\\n for k, v in self.model.state_dict().items():\\n v = pretrained_dict_items[j][1]\\n k = pretrained_dict_items[j][0]\\n\\n if k == 'final_layer.weight':\\n x = torch.rand(1,48,1,1).cuda()\\n v = torch.cat([v, x], dim=0)\\n if k == 'final_layer.bias':\\n x = torch.rand(1).cuda()\\n v = torch.cat([v,x],dim=0)\\n pretrained_model[k] = v\\n j +=1\\n model_dict=self.model.state_dict()\\n model_dict.update(pretrained_model)\\n self.model.load_state_dict(model_dict,strict=True)\\n else:\\n self.model.load_state_dict(torch.load(self.pretrained_weight_path, strict=True))\\n print('Pre-trained weights loaded.')\\n\\n self.model = nn.DataParallel(self.model.cuda())\\n # self.model = nn.DataParallel(self.model.to(self.device))\\n #\\n # load previous checkpoint\\n if self.checkpoint_path is not None:\\n print('Loading checkpoint %s...' % self.checkpoint_path)\\n if os.path.isdir(self.checkpoint_path):\\n path = os.path.join(self.checkpoint_path, 'checkpoint_last.pth')\\n else:\\n path = self.checkpoint_path\\n self.starting_epoch, self.model, self.optim, self.params = load_checkpoint(path, self.model, self.optim,\\n self.device)\\n else:\\n self.starting_epoch = 0\\n\\n if lr_decay:\\n self.lr_scheduler = MultiStepLR(self.optim, list(self.lr_decay_steps), gamma=self.lr_decay_gamma,\\n last_epoch=self.starting_epoch if self.starting_epoch else -1)\\n\\n #\\n # load train and val datasets\\n self.dl_train = DataLoader(self.ds_train, batch_size=self.batch_size, shuffle=True,\\n num_workers=self.num_workers, drop_last=True)\\n self.len_dl_train = len(self.dl_train)\\n\\n # dl_val = DataLoader(self.ds_val, batch_size=1, shuffle=False, num_workers=num_workers)\\n self.dl_val = DataLoader(self.ds_val, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers)\\n self.len_dl_val = len(self.dl_val)\\n\\n #\\n # initialize variables\\n self.mean_loss_train = 0.\\n self.mean_acc_train = 0.\\n self.mean_loss_val = 0.\\n self.mean_acc_val = 0.\\n self.mean_mAP_val = 0.\\n\\n self.best_loss = None\\n self.best_acc = None\\n self.best_mAP = None\",\n \"def train(self):\\n self.mode = \\\"train\\\"\\n self.online_net.train()\",\n \"def train(self):\\n self.mode = \\\"train\\\"\\n self.online_net.train()\",\n \"def train(self):\\n self.training = True\",\n \"def defreeze_model(self):\\n # defreeze all parameters\\n for param in self.parameters():\\n param.requires_grad = True\\n # make the whole network trainable\\n self.train()\",\n \"def _set_learning_phase(self, train: bool = False):\\n if train:\\n self.net_q.train()\\n self.net_ps.train()\\n self.net_k.train()\\n else:\\n self.net_q.eval()\\n self.net_ps.eval()\\n self.net_k.eval()\",\n \"def train(self, num_batches: int):\",\n \"def _train(self, dataset):\\n net = buildNetwork(\\n dataset.params_length,\\n self._default_hidden_layers,\\n 1 # a binary classifier only requires one output layer\\n )\\n ds = SupervisedDataSet(dataset)\\n trainer = BackpropTrainer(net, ds)\\n trainer.trainUntilConvergence()\\n net.activate(params.as_serialized)\",\n \"def train(self, trainData):\\n pass\",\n \"def __init__(\\n self,\\n hidden_dims,\\n input_dim=3 * 32 * 32,\\n num_classes=10,\\n dropout=1,\\n normalization=None,\\n reg=0.0,\\n weight_scale=1e-2,\\n dtype=np.float32,\\n seed=None,\\n ):\\n self.normalization = normalization\\n self.use_dropout = dropout != 1\\n self.reg = reg\\n self.num_layers = 1 + len(hidden_dims)\\n self.dtype = dtype\\n self.params = {}\\n\\n Din, Dout = input_dim, hidden_dims[0]\\n for i in range(self.num_layers):\\n self.params['W' + str(i+1)] = np.random.normal(scale=weight_scale, size=(Din, Dout))\\n self.params['b' + str(i+1)] = np.zeros((Dout,))\\n Din = Dout\\n if i < len(hidden_dims) - 1:\\n Dout = hidden_dims[i+1]\\n if i == len(hidden_dims) - 1:\\n Dout = num_classes\\n \\n # BN params initialization\\n if self.normalization != None:\\n for i in range(self.num_layers - 1):\\n self.params['gamma' + str(i+1)] = np.ones(shape=(hidden_dims[i]))\\n self.params['beta' + str(i+1)] = np.zeros(shape=(hidden_dims[i]))\\n\\n # When using dropout we need to pass a dropout_param dictionary to each\\n # dropout layer so that the layer knows the dropout probability and the mode\\n # (train / test). You can pass the same dropout_param to each dropout layer.\\n self.dropout_param = {}\\n if self.use_dropout:\\n self.dropout_param = {\\\"mode\\\": \\\"train\\\", \\\"p\\\": dropout}\\n if seed is not None:\\n self.dropout_param[\\\"seed\\\"] = seed\\n\\n # With batch normalization we need to keep track of running means and\\n # variances, so we need to pass a special bn_param object to each batch\\n # normalization layer. You should pass self.bn_params[0] to the forward pass\\n # of the first batch normalization layer, self.bn_params[1] to the forward\\n # pass of the second batch normalization layer, etc.\\n self.bn_params = []\\n if self.normalization == \\\"batchnorm\\\":\\n self.bn_params = [{\\\"mode\\\": \\\"train\\\"} for i in range(self.num_layers - 1)]\\n if self.normalization == \\\"layernorm\\\":\\n self.bn_params = [{} for i in range(self.num_layers - 1)]\\n\\n # Cast all parameters to the correct datatype\\n for k, v in self.params.items():\\n self.params[k] = v.astype(dtype)\",\n \"def train(self):\\n raise NotImplemented()\",\n \"def train(self, batch_training=False):\\n raise NotImplementedError\",\n \"def train(self, training_data):\\n pass\",\n \"def _set_train(self):\\n\\n if not self.model.__dict__['training']:\\n self.model.train()\",\n \"def _train(self):\\n self.network.train() # note that here we are calling torch.nn.Module train class method\\n epochs_since_improvement = 0\\n best_params = None\\n self.calculate_validation_loss()\\n best_validation_loss = self.validation_average_loss\\n\\n while epochs_since_improvement < 10:\\n self.train_epoch()\\n self.calculate_validation_loss()\\n if self.validation_average_loss < best_validation_loss:\\n epochs_since_improvement = 0\\n best_validation_loss = self.validation_average_loss\\n best_params = self.network.state_dict()\\n else:\\n epochs_since_improvement += 1\\n LOGGER.info(\\\"Epochs since improvement in validation_loss: {} \\\\n\\\".format(epochs_since_improvement))\\n if self.maximum_epochs_allowed is not None and self.epochs_trained >= self.maximum_epochs_allowed:\\n break\\n LOGGER.info(\\\"Training complete after {} epochs \\\\n\\\".format(self.epochs_trained))\\n LOGGER.info(\\\"Best training loss achieved: {} \\\\n\\\".format(self.training_average_loss))\\n LOGGER.info(\\\"Best validation loss achieved: {}\\\".format(self.validation_average_loss))\\n self.learned_params = best_params\\n self.network.load_state_dict(best_params)\",\n \"def train(self, train, dev, test, best_score_got):\\n param_dic = {}\\n params = []\\n best_score = best_score_got\\n nepoch_no_imprv = 0 # for early stopping\\n self.add_summary() # tensorboard\\n self.logger.info(\\\"no of layers {:}\\\".format(self.config.layer))\\n self.logger.info(\\\"no of steps {:}\\\".format(self.config.step))\\n self.logger.info(\\\"train_embeddings {:}\\\".format(self.config.train_embeddings))\\n self.logger.info(\\\"no of nepochs {:}\\\".format(self.config.nepochs))\\n self.logger.info(\\\"dropout {:}\\\".format(self.config.dropout))\\n self.logger.info(\\\"batch_size {:}\\\".format(self.config.batch_size))\\n self.logger.info(\\\"learning rate {:}\\\".format(self.config.lr))\\n self.logger.info(\\\"lr_decay {:}\\\".format(self.config.lr_decay))\\n self.logger.info(\\\"model_type {:}\\\".format(self.config.model_type))\\n param_dic[\\\"no of layers\\\"] = self.config.layer\\n param_dic[\\\"no of steps\\\"] = self.config.step\\n param_dic[\\\"train_embeddings\\\"] = self.config.train_embeddings\\n param_dic[\\\"no of nepochs\\\"] = self.config.nepochs\\n param_dic[\\\"dropout\\\"] = self.config.dropout\\n param_dic[\\\"batch_size\\\"] = self.config.batch_size\\n param_dic[\\\"learning rate\\\"] = self.config.lr\\n param_dic[\\\"lr_decay\\\"] = self.config.lr_decay\\n param_dic[\\\"model_type\\\"] = self.config.model_type\\n\\n params.append([\\\"no of layers {:}\\\".format(self.config.layer),\\n \\\"no of steps {:}\\\".format(self.config.step),\\n \\\"train_embeddings {:}\\\".format(self.config.train_embeddings),\\n \\\"no of nepochs {:}\\\".format(self.config.nepochs),\\n \\\"dropout {:}\\\".format(self.config.dropout),\\n \\\"batch_size {:}\\\".format(self.config.batch_size),\\n \\\"learning rate {:}\\\".format(self.config.lr),\\n \\\"lr_decay {:}\\\".format(self.config.lr_decay),\\n \\\"model_type {:}\\\".format(self.config.model_type)])\\n\\n for epoch in range(self.config.nepochs):\\n self.logger.info(\\\"Epoch {:} out of {:} of model no. - {:}\\\".format(epoch + 1,\\n self.config.nepochs, len(params)))\\n self.logger.info(param_dic)\\n\\n score = self.run_epoch(train, dev, epoch, test)\\n self.config.lr *= self.config.lr_decay # decay learning rate\\n\\n # early stopping and saving best parameters\\n if score >= best_score:\\n nepoch_no_imprv = 0\\n self.save_session()\\n best_score = score\\n param_dic[\\\"best_score\\\"] = best_score\\n self.logger.info(\\\"- new best score!\\\")\\n self.logger.info(\\\"Saved best model at epoch {}\\\".format(epoch + 1))\\n\\n with open(self.config.dir_model + 'bestModelParams.txt', 'w') as outfile:\\n json.dump(param_dic, outfile)\\n outfile.close()\\n else:\\n nepoch_no_imprv += 1\\n if nepoch_no_imprv >= self.config.nepoch_no_imprv:\\n self.logger.info(\\\"- early stopping {} epochs without \\\" \\\\\\n \\\"improvement\\\".format(nepoch_no_imprv))\\n break\\n\\n model_detail = \\\" , \\\".join(\\n [\\\" : \\\".join(str(element) for element in item) for item in param_dic.items() if (item[0] != \\\"best_score\\\")])\\n model_dic = {\\\"model_detail\\\": model_detail, \\\"epoch_number\\\": epoch_list, \\\"model_acc\\\": accuracy_list}\\n\\n return {\\\"model_dic\\\": model_dic, \\\"params\\\":params}\",\n \"def train_all(self):\\n for p in self.parameters():\\n p.requires_grad = True\\n return self\",\n \"def train():\\n # YOUR TRAINING CODE GOES HERE\",\n \"def set_train(self):\\n self.train()\\n self.volatile = False\",\n \"def _initial_setup(self, **train_kwargs):\\n super(NetworkValidationBase, self)._initial_setup(**train_kwargs)\",\n \"def __init__(self, **kwargs):\\n super(BertNewsClassifier, self).__init__()\\n self.PRE_TRAINED_MODEL_NAME = \\\"bert-base-uncased\\\"\\n self.bert_model = BertModel.from_pretrained(self.PRE_TRAINED_MODEL_NAME)\\n for param in self.bert_model.parameters():\\n param.requires_grad = False\\n self.drop = nn.Dropout(p=0.2)\\n # assigning labels\\n self.class_names = [\\\"World\\\", \\\"Sports\\\", \\\"Business\\\", \\\"Sci/Tech\\\"]\\n n_classes = len(self.class_names)\\n\\n self.fc1 = nn.Linear(self.bert_model.config.hidden_size, 512)\\n self.out = nn.Linear(512, n_classes)\\n self.bert_model.embedding = self.bert_model.embeddings\\n self.embedding = self.bert_model.embeddings\\n\\n self.scheduler = None\\n self.optimizer = None\\n self.args = kwargs\\n\\n self.train_acc = Accuracy()\\n self.val_acc = Accuracy()\\n self.test_acc = Accuracy()\",\n \"def tune_params(self, X_train, Y_train):\\n return self.model # No hyper-parameter tuning\",\n \"def set_train(self):\\n self.model.train()\",\n \"def init(self, train):\\n return\",\n \"def init(self, train):\\n return\",\n \"def init(self, train):\\n return\",\n \"def init(self, train):\\n return\",\n \"def init(self, train):\\n return\",\n \"def train(self,iTrain=1,Nbatch=32,Nlayers=3,Nunits=100,Nepochs=10000,\\n ValidationSplit=0.1,Patience=20,Verbose=2):\\n if iTrain == 1:\\n self.cvstem()\\n print(\\\"========================================================\\\")\\n print(\\\"=================== NCM CONSTRUCTION ===================\\\")\\n print(\\\"========================================================\\\")\\n n = self.n\\n n_p = self.n_p\\n X = np.hstack((self.xs_opt,self.ps_opt))\\n y = self.cholMs\\n model = Sequential(name=\\\"NCM\\\")\\n model.add(Dense(Nunits,activation=\\\"relu\\\",input_shape=(n+n_p,)))\\n for l in range(Nlayers-1):\\n model.add(Dense(Nunits,activation=\\\"relu\\\"))\\n model.add(Dense(int(n*(n+1)/2)))\\n model.summary()\\n model.compile(loss=\\\"mean_squared_error\\\",optimizer=\\\"adam\\\")\\n es = EarlyStopping(monitor=\\\"val_loss\\\",patience=Patience)\\n model.fit(X,y,batch_size=Nbatch,epochs=Nepochs,verbose=Verbose,\\\\\\n callbacks=[es],validation_split=ValidationSplit)\\n self.model = model\\n model.save(\\\"models/\\\"+self.fname+\\\".h5\\\")\\n elif iTrain == 0:\\n print(\\\"========================================================\\\")\\n print(\\\"=================== NCM CONSTRUCTION ===================\\\")\\n print(\\\"========================================================\\\")\\n self.model = load_model(\\\"models/\\\"+self.fname+\\\".h5\\\")\\n path = \\\"models/optvals/\\\"+self.fname\\n self.alp_opt = np.load(path+\\\"/alp_opt.npy\\\")\\n self.chi_opt = np.load(path+\\\"/chi_opt.npy\\\")\\n self.nu_opt = np.load(path+\\\"/nu_opt.npy\\\")\\n self.Jcv_opt = np.load(path+\\\"/Jcv_opt.npy\\\")\\n print(\\\"Loading pre-trained NCM ...\\\")\\n print(\\\"Loading pre-trained NCM END\\\")\\n else:\\n raise ValueError(\\\"Invalid iTrain: iTrain = 1 or 0\\\")\\n print(\\\"========================================================\\\")\\n print(\\\"================= NCM CONSTRUCTION END =================\\\")\\n print(\\\"========================================================\\\")\\n pass\",\n \"def _train_model(self):\\n raise NotImplementedError()\",\n \"def train(self, trainingData, trainingLabels, validationData, validationLabels ):\\n\\n self.setWeights(trainingData.shape[1])\\n # DO NOT ZERO OUT YOUR WEIGHTS BEFORE STARTING TRAINING, OR\\n # THE AUTOGRADER WILL LIKELY DEDUCT POINTS.\\n \\n # Hyper-parameters. Your can reset them. Default batchSize = 100, weight_decay = 1e-3, learningRate = 1e-2\\n \\\"*** YOU CODE HERE ***\\\"\\n self.batchSize = 100\\n self.weight_decay = 1e-3\\n self.learningRate = 0.1\\n\\n def Softmax(x):\\n x_max = np.max(x, axis=0)\\n x_exp = np.exp(x - x_max)\\n x_exp_sum = np.sum(x_exp, axis=0)\\n return x_exp / x_exp_sum\\n\\n for iteration in range(self.max_iterations):\\n if iteration % 10 == 0:\\n print(\\\"Starting iteration \\\", iteration, \\\"...\\\")\\n self.learningRate *= 0.9\\n dataBatches = self.prepareDataBatches(trainingData, trainingLabels)\\n for batchData, batchLabel in dataBatches:\\n \\\"*** YOUR CODE HERE ***\\\"\\n Y = np.zeros((len(self.legalLabels), self.batchSize))\\n for i in range(self.batchSize):\\n Y[batchLabel[i]][i] = 1\\n Y_pred = Softmax((batchData @ self.weights + self.bias).T)\\n d_weight = ((Y_pred - Y) @ batchData / batchData.shape[0]).T + self.weight_decay * sum(self.weights)\\n d_bias = np.mean(Y_pred - Y, axis=1) + self.weight_decay * sum(self.bias)\\n self.weights -= d_weight * self.learningRate\\n self.bias -= d_bias * self.learningRate\",\n \"def __init__(self, encoded_network, input_shape, n_classes, batch_size=256,\\n log_path=\\\"./trainer\\\", mu=0.5, rho=0.5, variable_scope=\\\"cnn\\\"):\\n super(EarlyStopNASTrainer, self).__init__(\\n encoded_network=encoded_network,\\n input_shape=input_shape,\\n n_classes=n_classes,\\n batch_size=batch_size,\\n log_path=log_path,\\n variable_scope=variable_scope\\n )\\n # Custom variables for the refined accuracy in BlockQNN implementation\\n # pylint: disable=invalid-name\\n self.mu = mu\\n self.rho = rho\\n\\n # Updated during training call\\n self.density = None\\n self.flops = None\\n\\n # Build the estimator\\n self._set_estimator()\",\n \"def train(self):\\n self.target_update_frequency = 10000 # How often to update target network\\n\\n stash_model = True # Flag for stashing a copy of the model\\n model_stash_interval = 10 # Stashes a copy of the model this often\\n\\n # Number of steps to take before training. Allows buffer to partially fill.\\n # Must be at least batch_size to avoid error when sampling from experience replay\\n self.pre_training_steps = 10*self.batch_size\\n assert(self.pre_training_steps <= self.buffer_size), \\\"Replay not large enough for pre-training!\\\"\\n assert(self.pre_training_steps >= self.batch_size), \\\"Buffer not allowed to fill enough before sampling!\\\"\\n # Number of steps to force learner to observe submitted actions, rather than submit its own actions\\n self.observations = 2000\\n\\n self.epsilon = 0.5 # Initial probability of letting the learner submit its own action\\n self.eps_decay_rate = 1./(25*20*len(self.training_data)) # Rate at which epsilon decays per submission\\n\\n lr_decay_freq = 10 # Decay learning rate after a set number of epochs\\n min_learning_rate = 1.e-8 # Minimum learning rate allowed to decay to\\n\\n summaries = {}\\n summaries[\\\"loss\\\"] = []\\n summaries[\\\"train_acc\\\"] = []\\n summaries[\\\"val_acc\\\"] = []\\n # Load existing model\\n self.ddq_net.sess.run(self.ddq_net.online_ops[\\\"init\\\"])\\n if(self.load_path):\\n self.ddq_net.load(self.load_path)\\n print(\\\"\\\\nCheckpoint loaded from {}\\\".format(self.load_path))\\n\\n # Initialize target network\\n self.ddq_net.sess.run(self.ddq_net.target_ops[\\\"target_init\\\"])\\n\\n for self.epoch_count in range(self.n_epoch):\\n t0 = time.time()\\n learning_rate = self.ddq_net.online_ops[\\\"learning_rate\\\"].eval(self.ddq_net.sess)\\n if((self.epoch_count>0) and (self.epoch_count % lr_decay_freq == 0) and (learning_rate>= min_learning_rate)):\\n # Decay learning rate accoring to schedule\\n learning_rate = 0.5*learning_rate\\n self.ddq_net.sess.run(self.ddq_net.online_ops[\\\"learning_rate\\\"].assign(learning_rate))\\n\\n # Run single epoch of training\\n loss, train_acc, val_acc = self.train_epoch()\\n dt = time.time()-t0\\n\\n print(\\\" Finished epoch {:2}/{}: lr: {:.4e}, dt {:.2f}, loss {:.6f}, train {:.6f}, val {:.6f}\\\".format(self.epoch_count+1, self.n_epoch, learning_rate, dt, loss, train_acc, val_acc), flush=True)\\n summaries[\\\"loss\\\"].append(loss)\\n summaries[\\\"train_acc\\\"].append(train_acc)\\n summaries[\\\"val_acc\\\"].append(val_acc)\\n\\n if(stash_model):\\n if(self.epoch_count>0 and (self.epoch_count+1)%model_stash_interval==0):\\n # Stash a copy of the current model\\n out_path = \\\"tmp/models/{}_model_E{}.ckpt\\\".format(self.ddq_net._name, self.epoch_count+1)\\n self.ddq_net.save(path=out_path)\\n print(\\\"Stashed a copy of the current model in {}\\\".format(out_path))\\n\\n self.ddq_net.save(path=self.ddq_net._path_to_model)\\n return summaries\",\n \"def _set_training_params(self, params):\\n self.lyapunov_hybrid_system.lyapunov_relu.load_state_dict(\\n params[\\\"lyap_relu_params\\\"])\\n if not self.R_options.fixed_R:\\n self.R_options._variables = params[\\\"R_params\\\"].clone()\\n if isinstance(self.lyapunov_hybrid_system.system,\\n feedback_system.FeedbackSystem):\\n self.lyapunov_hybrid_system.system.controller_network.\\\\\\n load_state_dict(params[\\\"controller_params\\\"])\",\n \"def train(self, x_train, y_train, x_val, y_val):\\n pass\",\n \"def __init__(self, parameters: ParametersList, algorithm: ClassVar, algorithm_data: AlgorithmData):\\n super(GreedyTrain, self).__init__(parameters, algorithm, algorithm_data)\",\n \"def train(\\n self, max_epochs: int = 30000, batch_size: int = None, train_size: float = 1, lr: float = 0.002, **kwargs\\n ):\\n\\n kwargs[\\\"max_epochs\\\"] = max_epochs\\n kwargs[\\\"batch_size\\\"] = batch_size\\n kwargs[\\\"train_size\\\"] = train_size\\n kwargs[\\\"lr\\\"] = lr\\n\\n super().train(**kwargs)\",\n \"def train_step(self):\\n pass\",\n \"def __init__(self):\\n # Number of examples per epoch of training data.\\n self.num_examples_per_epoch = None \\n\\n # Optimizer for training the model.\\n self.optimizer = \\\"SGD\\\" #default \\\"SGD\\\"\\n\\n # Learning rate for the initial phase of training.\\n self.initial_learning_rate = 2.0 # default 2.0\\n self.learning_rate_decay_factor = 0.8\\n self.num_epochs_per_decay = 4 #default 8\\n\\n # If not None, clip gradients to this value.\\n self.clip_gradients = 5.0\\n\\n # How many model checkpoints to keep.\\n self.max_checkpoints_to_keep = 2\",\n \"def train_all(X_train_fuse, Y_train, X_dev_fuse, Y_dev, R_train, R_dev, hyperparams):\",\n \"def train_one_epoch(self):\\n raise NotImplementedError\",\n \"def train(self):\\n\\t\\tself.model.fit(self.training_data, self.training_labels)\",\n \"def __init__(self, parameters: ParametersList, algorithm: ClassVar, algorithm_data: AlgorithmData):\\n\\n super(SupervisedTrain, self).__init__(parameters, algorithm, algorithm_data)\\n self.best_score = np.inf\",\n \"def train():\\n args = arguments()\\n\\n # Create output directories\\n create_output_dir(args.outputDir)\\n\\n # Start Log File\\n log_path = os.path.join(args.outputDir, LOG_DIR, time.strftime('%Y-%m-%d_%H-%M-%S.log'))\\n log_file = Logger(log_path)\\n\\n # Log arguments\\n arg_str = ''\\n for arg in vars(args):\\n arg_str += \\\"\\\\n\\\" + \\\"{:30} {}\\\".format(str(arg), getattr(args, arg))\\n log_file.log_line(\\\"Arguments\\\", arg_str)\\n log_file.newline()\\n\\n # Load Params\\n configuration = config_cvppp.TrainConfig()\\n\\n # Log params\\n log_file.log_line(\\\"Config Parameters\\\\n\\\", configuration.to_string())\\n log_file.newline()\\n\\n ## Load dataset API (Already logged in the args log step)\\n train_dataset, crossVal_dataset = load_datasets(args)\\n\\n # Init the model\\n checkpoint_path = os.path.join(args.outputDir, CHECKPOINT_DIR)\\n training_model = model.MaskRCNN('training', configuration, checkpoint_path)\\n\\n # Load weights\\n if args.init == 'last':\\n weights_path = training_model.find_last()\\n log_file.log_line(\\\"Initialised with \\\", weights_path)\\n training_model.load_weights(weights_path, by_name=True)\\n\\n elif args.init == 'rand':\\n log_file.log_line(\\\"Initialised with \\\", \\\"random weights\\\")\\n pass\\n\\n else:\\n if not os.path.exists(args.init):\\n raise OSError('No weights at: ' + args.init)\\n \\n log_file.log_line(\\\"Initialised with \\\", args.init)\\n training_model.load_weights(args.init, by_name=True)\\n\\n # Train the model\\n augmentation = get_augmentation_sequence()\\n\\n custom_callbacks = None\\n\\n training_model.train(train_dataset, crossVal_dataset, \\n learning_rate=configuration.LEARNING_RATE, \\n epochs=args.numEpochs,\\n augmentation=augmentation,\\n layers='all',\\n custom_callbacks=custom_callbacks) # Train all layers\\n\\n # Close the log file\\n log_file.close()\",\n \"def set_params(self, **params):\\n if not hasattr(self, \\\"_non_sklearn_base\\\"):\\n return super().set_params(**params)\\n if not (\\n len(params) == 1 and\\n (\\\"nthreads\\\" in params or \\\"n_jobs\\\" in params)\\n ):\\n self.is_fitted_ = False\\n valid_params = self.get_params(deep=False)\\n for k,v in params.items():\\n if k not in valid_params:\\n raise ValueError(\\\"Invalid parameter: \\\", k)\\n setattr(self, k, v)\\n return self\",\n \"def train_network(self, batch_size, epochs):\\n\\n if self.eq_train: self.model.fit([self.X_train_high_level, self.X_train_low_level], self.y_train, epochs=epochs, batch_size=batch_size, sample_weight=self.train_weights_eq) \\n else: self.model.fit([self.X_train_high_level, self.X_train_low_level], self.y_train, epochs=epochs, batch_size=batch_size, sample_weight=self.train_weights)\",\n \"def __init__(self, state_size, action_size, fc1_units, fc2_units, buffer_size, batch_size, alpha, gamma, tau,\\n local_update_every, target_update_every, seed, a, b, b_increase, b_end, dbl_dqn=False, priority_rpl=False, duel_dqn=False):\\n self.state_size = state_size\\n self.action_size = action_size\\n self.seed = random.seed(seed)\\n\\n # Hyperparameters\\n self.alpha = alpha # Learning rate\\n self.gamma = gamma # Discount parameter\\n self.tau = tau # Interpolation parameter\\n self.local_update_every = local_update_every # Number of actions to take before updating local net weights\\n self.target_update_every = target_update_every # Number of actions to take before updating target net weights\\n self.batch_size = batch_size # Number of experiences to sample during learning\\n self.buffer_size = buffer_size # Size of memory buffer\\n self.a = a # Sampling probability (0=random | 1=priority)\\n self.b = b # Influence of importance sampling weights over learning\\n self.b_increase = b_increase # Amount to increase b by every learning step\\n self.b_end = b_end # Maximum value for b\\n\\n # Agent modifications\\n self.dbl_dqn = dbl_dqn # Double Q Learning\\n self.priority_rpl = priority_rpl # Prioritised Experience Replay\\n self.duel_dqn = duel_dqn # Duelling Q Networks\\n\\n # Q-Network\\n if self.duel_dqn:\\n self.qnetwork_local = DuellingQNetwork(state_size, action_size, fc1_units, fc2_units, seed).to(device)\\n self.qnetwork_target = DuellingQNetwork(state_size, action_size, fc1_units, fc2_units, seed).to(device)\\n else:\\n self.qnetwork_local = QNetwork(state_size, action_size, fc1_units, fc2_units, seed).to(device)\\n self.qnetwork_target = QNetwork(state_size, action_size, fc1_units, fc2_units, seed).to(device)\\n self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=self.alpha)\\n\\n # Replay memory\\n self.memory = ReplayBuffer(action_size, buffer_size, batch_size, seed, priority_rpl)\\n # Initialize time step (for updating every local_update_every/target_update_every steps)\\n self.t_step = 0\",\n \"def __init__(self):\\n self.num_examples_per_epoch = 99999\\n self.optimizer = \\\"Adam\\\"\\n # Learning rate for the initial phase of training.\\n self.initial_learning_rate = 0.0001\\n self.learning_rate_decay_factor = 0.5\\n self.num_epochs_per_decay = 8.0\\n\\n # Learning rate when fine tuning the Inception v3 parameters.\\n self.train_inception_learning_rate = 0.0001\\n\\n # If not None, clip gradients to this value.\\n self.clip_gradients = 5.0\\n\\n # How many model checkpoints to keep.\\n self.max_checkpoints_to_keep = 5000\",\n \"def train2(self):\\n for epoch in range(self.epochs):\\n print \\\"epoch: \\\", epoch\\n self.train(self.D)\\n self.alpha -= 0.002 # decrease the learning rate\\n self.min_alpha = model.alpha # fix the learning rate, no decay\",\n \"def _train(self, log_prob):\\n raise NotImplementedError\",\n \"def train(self, X):\\n self.X = X\",\n \"def train_gradient_boost(self, params, num_boost_round = 50):\\n print \\\"training GB......\\\"\\n dtrain = xgb.DMatrix(self.X, self.y)\\n model = xgb.train(params, dtrain, num_boost_round = num_boost_round)\\n self.models += [model]\",\n \"def train(self, train_dataset, val_dataset):\\n\\n # check fine_tuning option\\n model_path = os.path.join(self.check_point, 'model.pt')\\n if self.fine_tune and not os.path.exists(model_path):\\n raise Exception('Cannot find %s.' % model_path)\\n elif self.fine_tune and os.path.exists(model_path):\\n if self.verbose:\\n print('Loading %s for finetuning.' % model_path)\\n self.model = torch.load(model_path)\\n '''\\n if isinstance(net, torch.nn.DataParallel):\\n net = net.module\\n model_dict = self.model.state_dict()\\n net_dict = net.state_dict()\\n # 1. filter out unnecessary keys\\n pretrained_dict = {k: v for k, v in net_dict.items() if k in model_dict}\\n # 2. overwrite entries in the existing state dict\\n model_dict.update(pretrained_dict)\\n # 3. load the new state dict\\n self.model.load_state_dict(model_dict)\\n '''\\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\\n\\n # capture best model\\n best_val_psnr = -1\\n best_psnr = -1\\n best_model_state = self.model.state_dict()\\n\\n with open(os.path.join(self.check_point, 'PSNR' + '.txt'), 'w') as f:\\n # Train the model\\n for epoch in range(self.num_epochs):\\n self._epoch_step(train_dataset, epoch)\\n self.scheduler.step()\\n\\n if epoch % 10 == 0:\\n if self.verbose:\\n print('Computing PSNR...')\\n\\n # capture running PSNR on train and val dataset\\n train_psnr, train_ssim, _, _, _ = self._check_PSNR(val_dataset)\\n self.hist_train_psnr.append(train_psnr)\\n\\n f.write('epoch%d:\\\\t%.3f\\\\n' % (epoch, train_psnr))\\n\\n if self.verbose:\\n print('Average train PSNR:%.3fdB average ssim: %.3f' % (train_psnr, train_ssim))\\n print('')\\n if best_psnr < train_psnr:\\n best_psnr = train_psnr\\n # write the model to hard-disk for testing\\n if not os.path.exists(self.check_point):\\n os.makedirs(self.check_point)\\n model_path = os.path.join(self.check_point, 'model.pt')\\n torch.save(self.model, model_path)\\n print(' Best average psnr: %.3f' % (best_psnr))\\n print('')\",\n \"def training_step(self, **kwargs):\\n raise NotImplementedError\",\n \"def __init__(self,\\n n_tasks,\\n n_features,\\n logdir=None,\\n layer_sizes=[1000],\\n weight_init_stddevs=[.02],\\n bias_init_consts=[1.],\\n penalty=0.0,\\n penalty_type=\\\"l2\\\",\\n dropouts=[0.5],\\n learning_rate=.001,\\n momentum=.9,\\n optimizer=\\\"adam\\\",\\n batch_size=50,\\n n_classes=2,\\n pad_batches=False,\\n verbose=True,\\n seed=None,\\n **kwargs):\\n # Save hyperparameters\\n self.n_tasks = n_tasks\\n self.n_features = n_features\\n self.layer_sizes = layer_sizes\\n self.weight_init_stddevs = weight_init_stddevs\\n self.bias_init_consts = bias_init_consts\\n self.penalty = penalty\\n self.penalty_type = penalty_type\\n self.dropouts = dropouts\\n self.learning_rate = learning_rate\\n self.momentum = momentum\\n self.optimizer = optimizer\\n self.batch_size = batch_size\\n self.n_classes = n_classes\\n self.pad_batches = pad_batches\\n self.verbose = verbose\\n self.seed = seed\\n\\n if logdir is not None:\\n if not os.path.exists(logdir):\\n os.makedirs(logdir)\\n else:\\n logdir = tempfile.mkdtemp()\\n self.logdir = logdir\\n\\n # Guard variable to make sure we don't Restore() this model\\n # from a disk checkpoint more than once.\\n self._restored_model = False\\n # Path to save checkpoint files, which matches the\\n # replicated supervisor's default path.\\n self._save_path = os.path.join(logdir, 'model.ckpt')\\n\\n self.train_graph = self.construct_graph(training=True, seed=self.seed)\\n self.eval_graph = self.construct_graph(training=False, seed=self.seed)\",\n \"def __init__(\\n self,\\n net,\\n eps,\\n sua=False,\\n pi=False,\\n update_freq=1,\\n alpha=1.0,\\n constraint_norm=False,\\n data_size=50000,\\n use_batch_norm=False,\\n ):\\n self.net = net\\n self.state = net.state_dict()\\n self.mean_state = copy.deepcopy(self.state)\\n self.data_size = data_size\\n self.use_batch_norm = use_batch_norm\\n\\n self.eps = eps\\n self.sua = sua\\n self.pi = pi\\n self.update_freq = update_freq\\n self.alpha = alpha\\n self.constraint_norm = constraint_norm\\n self.params = []\\n self._iteration_counter = 0\\n for mod in net.modules():\\n mod_class = mod.__class__.__name__\\n if mod_class in [\\\"Linear\\\", \\\"Conv2d\\\"]:\\n mod.register_forward_pre_hook(self._save_input)\\n mod.register_backward_hook(self._save_grad_output)\\n params = [mod.weight]\\n if mod.bias is not None:\\n params.append(mod.bias)\\n d = {\\\"params\\\": params, \\\"mod\\\": mod, \\\"layer_type\\\": mod_class}\\n self.params.append(d)\\n\\n elif \\\"BatchNorm\\\" in mod_class and use_batch_norm:\\n mod.register_forward_pre_hook(self._save_input)\\n mod.register_backward_hook(self._save_grad_output)\\n\\n params = [mod.weight, mod.bias]\\n\\n d = {\\\"params\\\": params, \\\"mod\\\": mod, \\\"layer_type\\\": mod_class}\\n self.params.append(d)\\n\\n super(KFACLaplace, self).__init__(self.params, {})\\n # super(KFACLaplace, self).__init__()\",\n \"def __init__(self, batch_size=1, epochs=None, learning_rate=None, momentum=None, weights_name=''):\\n self.batch_size = batch_size\\n self.epochs = epochs\\n self.model = None\\n self.optimizer = None\\n self.cb = None\\n self.lr = learning_rate\\n self.momentum = momentum\\n self.weights_name = weights_name\",\n \"def trainFreezeBN(self):\\n\\t\\tprint(\\\"Freezing Mean/Var of BatchNorm2D.\\\")\\n\\t\\tprint(\\\"Freezing Weight/Bias of BatchNorm2D.\\\")\\n\\t\\tfor m in self.modules():\\n\\t\\t\\tif isinstance(m, nn.BatchNorm2d):\\n\\t\\t\\t\\tm.eval()\\n\\t\\t\\t\\tm.weight.requires_grad = False\\n\\t\\t\\t\\tm.bias.requires_grad = False\",\n \"def trainFreezeBN(self):\\n\\t\\tprint(\\\"Freezing Mean/Var of BatchNorm2D.\\\")\\n\\t\\tprint(\\\"Freezing Weight/Bias of BatchNorm2D.\\\")\\n\\t\\tfor m in self.modules():\\n\\t\\t\\tif isinstance(m, nn.BatchNorm2d):\\n\\t\\t\\t\\tm.eval()\\n\\t\\t\\t\\tm.weight.requires_grad = False\\n\\t\\t\\t\\tm.bias.requires_grad = False\",\n \"def __init__(self, in_features, out_features):\\n \\n ########################\\n # PUT YOUR CODE HERE #\\n #######################\\n mean = 0\\n std_dev = 0.0001\\n #print(in_features)\\n #print(out_features)\\n # create weight matrices\\n weight = np.random.normal(mean, std_dev, (out_features, in_features))\\n #print(weight.shape)\\n grad_weight = np.zeros((in_features, out_features))\\n\\n # create biases (in batches)\\n bias = np.zeros(out_features)\\n grad_bias = np.zeros(out_features)\\n\\n self.params = {'weight': weight, 'bias': bias}\\n self.grads = {'weight': bias, 'bias': grad_bias}\\n\\n ########################\\n # END OF YOUR CODE #\\n #######################\",\n \"def train(self, mode=True):\\n super(Encoder, self).train(mode)\\n self.apply(freeze_batchnorm)\",\n \"def TrainOneStep(self):\\n pass\",\n \"def __init__(self,seed=None,batch_size=60, use_earlyStopping=False, loaded_model=None):\\n self.batch_size = batch_size\\n self.pruning_rates = PRUNING_PERCENTAGES\\n self.early_stopping = use_earlyStopping\\n\\n if loaded_model == None:\\n self.model = keras.Sequential()\\n self.model.add(keras.layers.Flatten(input_shape=(28, 28)))\\n for layer in LAYERS:\\n (units, activation) = LAYERS[layer]\\n self.model.add(keras.layers.Dense(units, activation=activation, kernel_initializer=tf.keras.initializers.glorot_normal(seed=None)))\\n \\n \\n self.model.compile(optimizer=OPTIMIZER_FC,\\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\\n metrics=['accuracy'])\\n\\n self.weights_init = self.get_weights()\\n \\n else:\\n self.model = loaded_model\\n\\n if use_earlyStopping:\\n self.es = EarlyStopping(monitor='val_loss', patience=10)\",\n \"def train(self, **kwargs):\\n self.solver.train(**kwargs)\",\n \"def train(train_iter, test_iter, net, loss, trainer, ctx, num_epochs):\\n # 加入学习曲线显示\\n weight_url = '//home//jim//shanghai_index//data//weights.params'\\n logdir_url = '//home//jim//shanghai_index//log'\\n sw = SummaryWriter(logdir = logdir_url, flush_secs=10)\\n # 提取已有参数\\n if os.path.exists(weight_url):\\n print(u'已含有旧权重文件，正在载入继续训练并更新')\\n net.load_parameters(weight_url, allow_missing = True, ignore_extra = True)\\n # 训练\\n for epoch in range(num_epochs):\\n train_loss, train_acc, train_step, start = 0.0, 0.0, 0, time.time()\\n for x1, x2, y in train_iter:\\n batch_size = x1.shape[0]\\n with autograd.record():\\n y_hats = net(x1, x2)\\n print('y_hat = {}'.format(y_hats))\\n print('y = {}'.format(y))\\n ls = loss(y_hats, y)\\n print('loss:{}'.format(ls))\\n ls.backward()\\n trainer.step(batch_size)\\n train_loss += np.mean(ls.asnumpy())\\n train_acc += acc(y_hats, y)\\n train_step += 1\\n \\n print('epoch {}, loss {}, train acc {}, time {} sec'.format(epoch + 1,\\n train_loss/train_step,\\n train_acc/train_step,\\n time.time() - start))\\n # 向tensorboard填数据\\n sw.add_scalar(tag = 'Loss_and_acc', \\\\\\n value = {'train_loss': train_loss/train_step, 'train_acc': train_acc/train_step}, \\\\\\n global_step = epoch)\\n # 加入某个层权重分布变化等高图\\n grads = [i.grad() for i in net.collect_params('.*weight|.*bias').values()]\\n param_names = [name for name in net.collect_params('.*weight|.*bias').keys()]\\n assert len(grads) == len(param_names)\\n # logging the gradients of parameters for checking convergence\\n for i, name in enumerate(param_names):\\n sw.add_histogram(tag = name, values = grads[i], global_step = epoch, bins = 20)\\n\\n # 加入保存参数\\n net.save_parameters(weight_url)\",\n \"def train(self, mode=True):\\n super(TSN, self).train(mode)\\n count = 0\\n if self._enable_pbn:\\n print(\\\"Freezing BatchNorm2D except the first one.\\\")\\n for m in self.base_model.modules():\\n # print('the type train model : {}'.format(type(m)))\\n if isinstance(m, torch.nn.BatchNorm2d) or \\\\\\n isinstance(m, linklink.nn.syncbn_layer.SyncBatchNorm2d):\\n count += 1\\n if count >= (2 if self._enable_pbn else 1):\\n m.eval()\\n # print('the freeze module: {} of {}th'.format(type(m), count))\\n # shutdown update in frozen mode\\n m.weight.requires_grad = False\\n m.bias.requires_grad = False\",\n \"def _setup_training(self, params, **kwargs):\\n model_params = params.permute_training_on_top().model\\n\\n model_kwargs = {**model_params.fixed, **model_params.variable}\\n\\n model = self.model_cls(**model_kwargs)\\n\\n training_params = params.permute_training_on_top().training\\n losses = training_params.nested_get(\\\"losses\\\")\\n optimizer_cls = training_params.nested_get(\\\"optimizer_cls\\\")\\n optimizer_params = training_params.nested_get(\\\"optimizer_params\\\")\\n train_metrics = training_params.nested_get(\\\"train_metrics\\\", {})\\n lr_scheduler_cls = training_params.nested_get(\\\"lr_sched_cls\\\", None)\\n lr_scheduler_params = training_params.nested_get(\\\"lr_sched_params\\\",\\n {})\\n val_metrics = training_params.nested_get(\\\"val_metrics\\\", {})\\n\\n # necessary for resuming training from a given path\\n save_path = kwargs.pop(\\\"save_path\\\", os.path.join(\\n self.save_path,\\n \\\"checkpoints\\\",\\n \\\"run_%02d\\\" % self._run))\\n\\n return self.trainer_cls(\\n network=model,\\n save_path=save_path,\\n losses=losses,\\n key_mapping=self.key_mapping,\\n optimizer_cls=optimizer_cls,\\n optimizer_params=optimizer_params,\\n train_metrics=train_metrics,\\n val_metrics=val_metrics,\\n lr_scheduler_cls=lr_scheduler_cls,\\n lr_scheduler_params=lr_scheduler_params,\\n optim_fn=self._optim_builder,\\n save_freq=self.checkpoint_freq,\\n **kwargs\\n )\",\n \"def train(self):\\n raise IllegalOperationError(\\\"Cannot train multiplicative model!\\\")\",\n \"def __init__(self, config, data_loader, layer_hyperparams):\\n self.config = config\\n self.layer_hyperparams = layer_hyperparams\\n\\n if config.is_train:\\n self.train_loader = data_loader[0]\\n self.valid_loader = data_loader[1]\\n self.num_train = len(self.train_loader.dataset)\\n self.num_valid = self.valid_loader.dataset.trials\\n else:\\n if config.get_embedding:\\n self.test_embedding_loader = data_loader\\n self.n_embeddings = config.n_embeddings\\n else:\\n self.test_loader = data_loader\\n self.num_test = self.test_loader.dataset.trials\\n\\n if config.use_batch_norm:\\n self.model = SiameseNetWithBN()\\n else:\\n self.model = SiameseNet()\\n \\n if config.use_gpu:\\n self.model.cuda()\\n\\n # model params\\n self.num_params = sum(\\n [p.data.nelement() for p in self.model.parameters()]\\n )\\n self.num_model = get_num_model(config)\\n self.num_layers = len(list(self.model.children()))\\n\\n print('[*] Number of model parameters: {:,}'.format(self.num_params))\\n\\n # path params\\n self.ckpt_dir = os.path.join(config.ckpt_dir, self.num_model)\\n self.logs_dir = os.path.join(config.logs_dir, self.num_model)\\n\\n # misc params\\n self.resume = config.resume\\n self.use_gpu = config.use_gpu\\n self.dtype = (\\n torch.cuda.FloatTensor if self.use_gpu else torch.FloatTensor\\n )\\n\\n # optimization params\\n self.best = config.best\\n self.best_valid_acc = 0.\\n self.epochs = config.epochs\\n self.start_epoch = 0\\n self.lr_patience = config.lr_patience\\n self.train_patience = config.train_patience\\n self.counter = 0\\n\\n # grab layer-wise hyperparams\\n self.init_lrs = self.layer_hyperparams['layer_init_lrs']\\n self.init_momentums = [config.init_momentum]*self.num_layers\\n self.end_momentums = self.layer_hyperparams['layer_end_momentums']\\n self.l2_regs = self.layer_hyperparams['layer_l2_regs']\\n\\n # compute temper rate for momentum\\n if self.epochs == 1:\\n f = lambda max, min: min\\n else:\\n f = lambda max, min: (max - min) / (self.epochs-1)\\n self.momentum_temper_rates = [\\n f(x, y) for x, y in zip(self.end_momentums, self.init_momentums)\\n ]\\n\\n # set global learning rates and momentums\\n self.lrs = self.init_lrs\\n self.momentums = self.init_momentums\\n\\n # # initialize optimizer\\n # optim_dict = []\\n # for i, layer in enumerate(self.model.children()):\\n # group = {}\\n # group['params'] = layer.parameters()\\n # group['lr'] = self.lrs[i]\\n # group['momentum'] = self.momentums[i]\\n # group['weight_decay'] = self.l2_regs[i]\\n # optim_dict.append(group)\\n # self.optimizer = optim.SGD(optim_dict)\\n # self.optimizer = optim.SGD(\\n # self.model.parameters(), lr=1e-3, momentum=0.9, weight_decay=4e-4,\\n # )\\n self.optimizer = optim.Adam(\\n self.model.parameters(), lr=3e-4, weight_decay=6e-5,\\n )\\n\\n # # learning rate scheduler\\n # self.scheduler = StepLR(\\n # self.optimizer, step_size=self.lr_patience, gamma=0.99,\\n # )\\n self.debug = dict()\",\n \"def set_train(self):\\n self.train()\\n self.volatile = False\\n self.scheduled_sampling = self.sample_prob != 0\",\n \"def train_model(self, *args, **kwargs):\\n self.model.train(self.training, *args, **kwargs)\",\n \"def __init__(\\n self,\\n model_name: str,\\n pretrained: bool = True,\\n progress: bool = True,\\n spatial_dims: int = 2,\\n in_channels: int = 3,\\n num_classes: int = 1000,\\n norm: str | tuple = (\\\"batch\\\", {\\\"eps\\\": 1e-3, \\\"momentum\\\": 0.01}),\\n adv_prop: bool = False,\\n ) -> None:\\n blocks_args_str = [\\n \\\"r1_k3_s11_e1_i32_o16_se0.25\\\",\\n \\\"r2_k3_s22_e6_i16_o24_se0.25\\\",\\n \\\"r2_k5_s22_e6_i24_o40_se0.25\\\",\\n \\\"r3_k3_s22_e6_i40_o80_se0.25\\\",\\n \\\"r3_k5_s11_e6_i80_o112_se0.25\\\",\\n \\\"r4_k5_s22_e6_i112_o192_se0.25\\\",\\n \\\"r1_k3_s11_e6_i192_o320_se0.25\\\",\\n ]\\n\\n # check if model_name is valid model\\n if model_name not in efficientnet_params:\\n model_name_string = \\\", \\\".join(efficientnet_params.keys())\\n raise ValueError(f\\\"invalid model_name {model_name} found, must be one of {model_name_string} \\\")\\n\\n # get network parameters\\n weight_coeff, depth_coeff, image_size, dropout_rate, dropconnect_rate = efficientnet_params[model_name]\\n\\n # create model and initialize random weights\\n super().__init__(\\n blocks_args_str=blocks_args_str,\\n spatial_dims=spatial_dims,\\n in_channels=in_channels,\\n num_classes=num_classes,\\n width_coefficient=weight_coeff,\\n depth_coefficient=depth_coeff,\\n dropout_rate=dropout_rate,\\n image_size=image_size,\\n drop_connect_rate=dropconnect_rate,\\n norm=norm,\\n )\\n\\n # only pretrained for when `spatial_dims` is 2\\n if pretrained and (spatial_dims == 2):\\n _load_state_dict(self, model_name, progress, adv_prop)\",\n \"def train(self, trainingData, trainingLabels, validationData, validationLabels): \\n \\n # might be useful in your code later...\\n # this is a list of all features in the training set.\\n self.features = list(set([ f for datum in trainingData for f in datum.keys() ]));\\n \\n if (self.automaticTuning):\\n kgrid = [0.001, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 20, 50]\\n else:\\n kgrid = [self.k]\\n \\n self.trainAndTune(trainingData, trainingLabels, validationData, validationLabels, kgrid)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6963234","0.6939087","0.6939087","0.6939087","0.6939087","0.6939087","0.69190997","0.6901135","0.68561196","0.6850719","0.68132","0.6813139","0.67859966","0.6785637","0.6709828","0.6674066","0.66536355","0.6645038","0.6637477","0.662026","0.66150165","0.6607401","0.6575136","0.6547953","0.6524828","0.6523968","0.65201205","0.65184027","0.6513207","0.6513207","0.6512901","0.64741606","0.64534944","0.6450927","0.6442529","0.644184","0.64375544","0.6435256","0.64191467","0.6416278","0.6407156","0.64040273","0.63971627","0.63966465","0.63945836","0.63865876","0.6371043","0.636316","0.635963","0.63489485","0.63485056","0.63485056","0.63485056","0.63485056","0.63485056","0.6337182","0.6335694","0.63233095","0.6321017","0.6316123","0.6315081","0.63148713","0.63101065","0.63007087","0.6298916","0.62868345","0.6284454","0.62783843","0.6273136","0.625716","0.6250236","0.62435305","0.6242516","0.62417334","0.6231561","0.62301004","0.62277824","0.62266237","0.62265474","0.62167764","0.62131023","0.6205605","0.6201039","0.6197089","0.6196157","0.6196157","0.6195357","0.61939925","0.6191283","0.6187466","0.6187418","0.6187206","0.6183227","0.6179134","0.6173097","0.61558574","0.61538935","0.6153192","0.6145689","0.61395293"],"string":"[\n \"0.6963234\",\n \"0.6939087\",\n \"0.6939087\",\n \"0.6939087\",\n \"0.6939087\",\n \"0.6939087\",\n \"0.69190997\",\n \"0.6901135\",\n \"0.68561196\",\n \"0.6850719\",\n \"0.68132\",\n \"0.6813139\",\n \"0.67859966\",\n \"0.6785637\",\n \"0.6709828\",\n \"0.6674066\",\n \"0.66536355\",\n \"0.6645038\",\n \"0.6637477\",\n \"0.662026\",\n \"0.66150165\",\n \"0.6607401\",\n \"0.6575136\",\n \"0.6547953\",\n \"0.6524828\",\n \"0.6523968\",\n \"0.65201205\",\n \"0.65184027\",\n \"0.6513207\",\n \"0.6513207\",\n \"0.6512901\",\n \"0.64741606\",\n \"0.64534944\",\n \"0.6450927\",\n \"0.6442529\",\n \"0.644184\",\n \"0.64375544\",\n \"0.6435256\",\n \"0.64191467\",\n \"0.6416278\",\n \"0.6407156\",\n \"0.64040273\",\n \"0.63971627\",\n \"0.63966465\",\n \"0.63945836\",\n \"0.63865876\",\n \"0.6371043\",\n \"0.636316\",\n \"0.635963\",\n \"0.63489485\",\n \"0.63485056\",\n \"0.63485056\",\n \"0.63485056\",\n \"0.63485056\",\n \"0.63485056\",\n \"0.6337182\",\n \"0.6335694\",\n \"0.63233095\",\n \"0.6321017\",\n \"0.6316123\",\n \"0.6315081\",\n \"0.63148713\",\n \"0.63101065\",\n \"0.63007087\",\n \"0.6298916\",\n \"0.62868345\",\n \"0.6284454\",\n \"0.62783843\",\n \"0.6273136\",\n \"0.625716\",\n \"0.6250236\",\n \"0.62435305\",\n \"0.6242516\",\n \"0.62417334\",\n \"0.6231561\",\n \"0.62301004\",\n \"0.62277824\",\n \"0.62266237\",\n \"0.62265474\",\n \"0.62167764\",\n \"0.62131023\",\n \"0.6205605\",\n \"0.6201039\",\n \"0.6197089\",\n \"0.6196157\",\n \"0.6196157\",\n \"0.6195357\",\n \"0.61939925\",\n \"0.6191283\",\n \"0.6187466\",\n \"0.6187418\",\n \"0.6187206\",\n \"0.6183227\",\n \"0.6179134\",\n \"0.6173097\",\n \"0.61558574\",\n \"0.61538935\",\n \"0.6153192\",\n \"0.6145689\",\n \"0.61395293\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6582539"},"document_rank":{"kind":"string","value":"22"}}},{"rowIdx":93897,"cells":{"query":{"kind":"string","value":"programmer_type e.g. INTEGER or programmer defined type"},"document":{"kind":"string","value":"def __init__(self, programmer_type):\r\n self.programmer_type = programmer_type"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def type_():\n pass","def opinion_type():\n pass","def _type(self):\n return self._id[1]","def program_type(self, value):\n\n types = {\n 'executableProgram': '1'\n }\n\n self._program_type = types[value]","def Type(self) -> _n_6_t_5:","def _type(self) -> str:\n ...","def getType(self):\n if (self.type == 's'):\n #suit type\n type = \"suit\"\n elif (self.type == 'b'):\n #boss type\n type = \"boss\"\n else:\n notify.error(\"Invalid DNA type: \", self.type)\n\n return type","def mot_type(self):\n if not hasattr(self, \"_mot_type\"):\n try:\n self._mot_type = self.get_par(\"record_type\")\n except:\n self._mot_type = \"old\"\n return self._mot_type","def _ptype(ptype_value, verbose=True):\n types = {\n 0:[\"PT_NULL\",\"Program header table entry unused\"],\n 1:[\"PT_LOAD\",\"Loadable program segment\"],\n 2:[\"PT_DYNAMIC\",\"Dynamic linking information\"],\n 3:[\"PT_INTERP\",\"Program interpreter\"],\n 4:[\"PT_NOTE\",\"Auxiliary information\"],\n 5:[\"PT_SHLIB\",\"Reserved\"],\n 6:[\"PT_PHDR\",\"Entry for header table itself\"],\n 7:[\"PT_TLS\",\"Thread-local storage segment\"],\n 8:[\"PT_NUM\",\"Number of defined types\"],\n 0x60000000:[\"PT_LOOS\",\"Start of OS-specific\"],\n 0x6474e550:[\"PT_GNU_EH_FRAME\",\"GCC .eh_frame_hdr segment\"],\n 0x6474e551:[\"PT_GNU_STACK\",\"Indicates stack executability\"],\n 0x6474e552:[\"PT_GNU_RELRO\",\"Read-only after relocation\"],\n 0x6ffffffa:[\"PT_SUNWBSS\",\"Sun Specific segment\"],\n 0x6ffffffb:[\"PT_SUNWSTACK\",\"Stack segment\"],\n 0x6fffffff:[\"PT_HIOS\",\"End of OS-specific\"],\n 0x70000000:[\"PT_LOPROC\",\"Start of processor-specific\"],\n 0x7fffffff:[\"PT_HIPROC\",\"End of processor-specific\"],\n }\n\n if types.get(ptype_value):\n value = types[ptype_value]\n if verbose:\n return \"%s (%s)\" % (value[0], value[1])\n else:\n return \"%s\" % value[0]\n else:\n return \"UNKNOWN\"","def typecode (self) :\r\n return self.numeric_typecode","def type(name):","def type_others():\n return \"<string>\"","def type(self):\n # easy enough\n return self._dataset._pyre_id.type","def type(self) -> global___Type:","def type(self):\n\t\treturn self.type_","def _transtype(self, systype=None):\n if systype is None:\n systype = self.get_meta(CAMSYS_TYPE, None)\n if systype == \"annotation-type\":\n newtype = AnnotationType\n elif systype == \"relation-type\":\n newtype = RelationType\n else:\n newtype = Tag\n if self.__class__ is not newtype:\n self.__class__ = newtype","def get_type(self):\r\n return self.mm_type + self.meta_model.get_type()","def guess_part_type(self, data):\n if 'administrativEnhet' in data or 'saksbehandler' in data:\n typename = 'intern'\n elif 'kontaktperson' in data \\\n or -1 != data['navn'].find(' AS'):\n typename = 'enhet'\n else:\n typename = 'person'\n return typename","def type(self):\n pass","def type(self):\n pass","def type(self):\n pass","def def_typ(mede):\n\tif mede:\n\t\tmime=mede['mime_type'].split('/')\n\t\ttyp_mime = mime[0]\n\telse:\n\t\t#could be cause by : not a referenced type or hachoir failed.\n\t\ttyp_mime='other'\n\t\t\n\ttyp=0\n\tif typ_mime=='image':\n\t\ttyp=1\n\telif typ_mime=='audio':\n\t\ttyp=2\n\telif typ_mime=='video':\n\t\ttyp=3\n\telif typ_mime=='model':\n\t\ttyp=4\n\telif typ_mime=='text':\n\t\ttyp=5\n\telif typ_mime=='application':\n\t\ttyp=6\n\telse:\n\t\ttyp=7\n\n\treturn typ","def get_engine_type(self):","def unit_type(self) -> str:","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def type(self) -> str:\n return pulumi.get(self, \"type\")","def residue_ramachandran_type(residue) :\n if residue_amino(residue)==\"GLY\" :\n return rama_GLYCINE\n elif residue_amino(residue)==\"PRO\" :\n return rama_PROLINE\n elif residue_amino(next_residue(residue))==\"PRO\" :\n #exlcudes those that are Pro or Gly\n return rama_PRE_PRO\n else :\n return rama_GENERAL","def type_code(self):\n return self._TYPE_CODE","def oglprimtype(self):\n return self._enttype.value","def Type(self):\n if self.currtok[1].name in {\"INT\", \"FLOAT\", \"BOOLEAN\"}:\n type = self.currtok[0]\n self.currtok = next(self.tg)\n return type\n raise SLUCSyntaxError(\"ERROR: Unexpected token {0} on line {1}\".\n format(self.currtok[1], str(self.currtok[2] - 1)))","def get_type(self):\n\n return self.scalertype","def type(self) -> Optional[str]:\n return pulumi.get(self, \"type\")","def type(self) -> Optional[str]:\n return pulumi.get(self, \"type\")","def type(self) -> Optional[str]:\n return pulumi.get(self, \"type\")","def type(self) -> Optional[str]:\n return pulumi.get(self, \"type\")","def type(self) -> Optional[str]:\n return pulumi.get(self, \"type\")","def type(self) -> Optional[str]:\n return pulumi.get(self, \"type\")","def type(self) -> Optional[str]:\n return pulumi.get(self, \"type\")","def user_type(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"user_type\")","def type_(self):\n return self._type","def sample_type_str(t):\n if t == dsl.Type.NUMBER or t == dsl.Type.DIGIT:\n return get_number()\n elif t == dsl.Type.WORD:\n return get_word()\n elif t == dsl.Type.ALPHANUM or t == dsl.Type.CHAR:\n return get_alphanumeric()\n elif t == dsl.Type.ALL_CAPS:\n return get_caps()\n elif t == dsl.Type.PROP_CASE:\n return get_proper_case()\n elif t == dsl.Type.LOWER:\n return get_lower()\n else:\n raise ValueError('Unsupported type: {}'.format(t))","def datatype(parameter):\n return type(parameter)","def input_type():\n pass","def user_type(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_type\")","def user_type(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_type\")","def t(self):\n\t\treturn self.type","def compliance_type(self) -> str:\n return pulumi.get(self, \"compliance_type\")","def stype(self):\n\n return self.__stype","def type(self, value):\n return value","def reviewers_type(self) -> str:\n return pulumi.get(self, \"reviewers_type\")","def __get_type(self):\r\n if self.__tokenizer.token_type() == TYPES_DIC[\"IDENTIFIER\"]:\r\n return self.__tokenizer.identifier()\r\n else:\r\n return self.__tokenizer.keyword()","def user_type(self):\n if \"userType\" in self._prop_dict:\n return self._prop_dict[\"userType\"]\n else:\n return None","def user_type(self):\n if \"userType\" in self._prop_dict:\n return self._prop_dict[\"userType\"]\n else:\n return None","def getType(self): #$NON-NLS-1$\r","def get_type(self) -> str:\n return self.row_dict['type']","def __Type(thing):\n from .native import (Vector, Color, Matrix, VectorField, ColorField,\n MatrixField, ScalarField)\n if isinstance(thing, (int, float, ScalarField)): return 'Scalar'\n if isinstance(thing, (Vector, VectorField)): return 'Vector'\n if isinstance(thing, (Color, ColorField)): return 'Color'\n if isinstance(thing, (Matrix, MatrixField)): return 'Matrix'\n raise ValueError(\"Could not guess type of: \" + thing)","def type(self):\n return self._getValue('type')","def vehicle_type():\n pass","def get_spec_type(self):\r\n return self._spec_type","def clips_to_py_type(ctype):\n\n ptype = None\n if ctype == \"INTEGER\":\n ptype = int\n elif ctype == \"FLOAT\":\n ptype = float\n elif ctype == \"STRING\":\n ptype = str\n elif ctype == \"BOOLEAN\":\n ptype = bool\n return ptype"],"string":"[\n \"def type_():\\n pass\",\n \"def opinion_type():\\n pass\",\n \"def _type(self):\\n return self._id[1]\",\n \"def program_type(self, value):\\n\\n types = {\\n 'executableProgram': '1'\\n }\\n\\n self._program_type = types[value]\",\n \"def Type(self) -> _n_6_t_5:\",\n \"def _type(self) -> str:\\n ...\",\n \"def getType(self):\\n if (self.type == 's'):\\n #suit type\\n type = \\\"suit\\\"\\n elif (self.type == 'b'):\\n #boss type\\n type = \\\"boss\\\"\\n else:\\n notify.error(\\\"Invalid DNA type: \\\", self.type)\\n\\n return type\",\n \"def mot_type(self):\\n if not hasattr(self, \\\"_mot_type\\\"):\\n try:\\n self._mot_type = self.get_par(\\\"record_type\\\")\\n except:\\n self._mot_type = \\\"old\\\"\\n return self._mot_type\",\n \"def _ptype(ptype_value, verbose=True):\\n types = {\\n 0:[\\\"PT_NULL\\\",\\\"Program header table entry unused\\\"],\\n 1:[\\\"PT_LOAD\\\",\\\"Loadable program segment\\\"],\\n 2:[\\\"PT_DYNAMIC\\\",\\\"Dynamic linking information\\\"],\\n 3:[\\\"PT_INTERP\\\",\\\"Program interpreter\\\"],\\n 4:[\\\"PT_NOTE\\\",\\\"Auxiliary information\\\"],\\n 5:[\\\"PT_SHLIB\\\",\\\"Reserved\\\"],\\n 6:[\\\"PT_PHDR\\\",\\\"Entry for header table itself\\\"],\\n 7:[\\\"PT_TLS\\\",\\\"Thread-local storage segment\\\"],\\n 8:[\\\"PT_NUM\\\",\\\"Number of defined types\\\"],\\n 0x60000000:[\\\"PT_LOOS\\\",\\\"Start of OS-specific\\\"],\\n 0x6474e550:[\\\"PT_GNU_EH_FRAME\\\",\\\"GCC .eh_frame_hdr segment\\\"],\\n 0x6474e551:[\\\"PT_GNU_STACK\\\",\\\"Indicates stack executability\\\"],\\n 0x6474e552:[\\\"PT_GNU_RELRO\\\",\\\"Read-only after relocation\\\"],\\n 0x6ffffffa:[\\\"PT_SUNWBSS\\\",\\\"Sun Specific segment\\\"],\\n 0x6ffffffb:[\\\"PT_SUNWSTACK\\\",\\\"Stack segment\\\"],\\n 0x6fffffff:[\\\"PT_HIOS\\\",\\\"End of OS-specific\\\"],\\n 0x70000000:[\\\"PT_LOPROC\\\",\\\"Start of processor-specific\\\"],\\n 0x7fffffff:[\\\"PT_HIPROC\\\",\\\"End of processor-specific\\\"],\\n }\\n\\n if types.get(ptype_value):\\n value = types[ptype_value]\\n if verbose:\\n return \\\"%s (%s)\\\" % (value[0], value[1])\\n else:\\n return \\\"%s\\\" % value[0]\\n else:\\n return \\\"UNKNOWN\\\"\",\n \"def typecode (self) :\\r\\n return self.numeric_typecode\",\n \"def type(name):\",\n \"def type_others():\\n return \\\"<string>\\\"\",\n \"def type(self):\\n # easy enough\\n return self._dataset._pyre_id.type\",\n \"def type(self) -> global___Type:\",\n \"def type(self):\\n\\t\\treturn self.type_\",\n \"def _transtype(self, systype=None):\\n if systype is None:\\n systype = self.get_meta(CAMSYS_TYPE, None)\\n if systype == \\\"annotation-type\\\":\\n newtype = AnnotationType\\n elif systype == \\\"relation-type\\\":\\n newtype = RelationType\\n else:\\n newtype = Tag\\n if self.__class__ is not newtype:\\n self.__class__ = newtype\",\n \"def get_type(self):\\r\\n return self.mm_type + self.meta_model.get_type()\",\n \"def guess_part_type(self, data):\\n if 'administrativEnhet' in data or 'saksbehandler' in data:\\n typename = 'intern'\\n elif 'kontaktperson' in data \\\\\\n or -1 != data['navn'].find(' AS'):\\n typename = 'enhet'\\n else:\\n typename = 'person'\\n return typename\",\n \"def type(self):\\n pass\",\n \"def type(self):\\n pass\",\n \"def type(self):\\n pass\",\n \"def def_typ(mede):\\n\\tif mede:\\n\\t\\tmime=mede['mime_type'].split('/')\\n\\t\\ttyp_mime = mime[0]\\n\\telse:\\n\\t\\t#could be cause by : not a referenced type or hachoir failed.\\n\\t\\ttyp_mime='other'\\n\\t\\t\\n\\ttyp=0\\n\\tif typ_mime=='image':\\n\\t\\ttyp=1\\n\\telif typ_mime=='audio':\\n\\t\\ttyp=2\\n\\telif typ_mime=='video':\\n\\t\\ttyp=3\\n\\telif typ_mime=='model':\\n\\t\\ttyp=4\\n\\telif typ_mime=='text':\\n\\t\\ttyp=5\\n\\telif typ_mime=='application':\\n\\t\\ttyp=6\\n\\telse:\\n\\t\\ttyp=7\\n\\n\\treturn typ\",\n \"def get_engine_type(self):\",\n \"def unit_type(self) -> str:\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def residue_ramachandran_type(residue) :\\n if residue_amino(residue)==\\\"GLY\\\" :\\n return rama_GLYCINE\\n elif residue_amino(residue)==\\\"PRO\\\" :\\n return rama_PROLINE\\n elif residue_amino(next_residue(residue))==\\\"PRO\\\" :\\n #exlcudes those that are Pro or Gly\\n return rama_PRE_PRO\\n else :\\n return rama_GENERAL\",\n \"def type_code(self):\\n return self._TYPE_CODE\",\n \"def oglprimtype(self):\\n return self._enttype.value\",\n \"def Type(self):\\n if self.currtok[1].name in {\\\"INT\\\", \\\"FLOAT\\\", \\\"BOOLEAN\\\"}:\\n type = self.currtok[0]\\n self.currtok = next(self.tg)\\n return type\\n raise SLUCSyntaxError(\\\"ERROR: Unexpected token {0} on line {1}\\\".\\n format(self.currtok[1], str(self.currtok[2] - 1)))\",\n \"def get_type(self):\\n\\n return self.scalertype\",\n \"def type(self) -> Optional[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> Optional[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> Optional[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> Optional[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> Optional[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> Optional[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> Optional[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def user_type(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"user_type\\\")\",\n \"def type_(self):\\n return self._type\",\n \"def sample_type_str(t):\\n if t == dsl.Type.NUMBER or t == dsl.Type.DIGIT:\\n return get_number()\\n elif t == dsl.Type.WORD:\\n return get_word()\\n elif t == dsl.Type.ALPHANUM or t == dsl.Type.CHAR:\\n return get_alphanumeric()\\n elif t == dsl.Type.ALL_CAPS:\\n return get_caps()\\n elif t == dsl.Type.PROP_CASE:\\n return get_proper_case()\\n elif t == dsl.Type.LOWER:\\n return get_lower()\\n else:\\n raise ValueError('Unsupported type: {}'.format(t))\",\n \"def datatype(parameter):\\n return type(parameter)\",\n \"def input_type():\\n pass\",\n \"def user_type(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_type\\\")\",\n \"def user_type(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_type\\\")\",\n \"def t(self):\\n\\t\\treturn self.type\",\n \"def compliance_type(self) -> str:\\n return pulumi.get(self, \\\"compliance_type\\\")\",\n \"def stype(self):\\n\\n return self.__stype\",\n \"def type(self, value):\\n return value\",\n \"def reviewers_type(self) -> str:\\n return pulumi.get(self, \\\"reviewers_type\\\")\",\n \"def __get_type(self):\\r\\n if self.__tokenizer.token_type() == TYPES_DIC[\\\"IDENTIFIER\\\"]:\\r\\n return self.__tokenizer.identifier()\\r\\n else:\\r\\n return self.__tokenizer.keyword()\",\n \"def user_type(self):\\n if \\\"userType\\\" in self._prop_dict:\\n return self._prop_dict[\\\"userType\\\"]\\n else:\\n return None\",\n \"def user_type(self):\\n if \\\"userType\\\" in self._prop_dict:\\n return self._prop_dict[\\\"userType\\\"]\\n else:\\n return None\",\n \"def getType(self): #$NON-NLS-1$\\r\",\n \"def get_type(self) -> str:\\n return self.row_dict['type']\",\n \"def __Type(thing):\\n from .native import (Vector, Color, Matrix, VectorField, ColorField,\\n MatrixField, ScalarField)\\n if isinstance(thing, (int, float, ScalarField)): return 'Scalar'\\n if isinstance(thing, (Vector, VectorField)): return 'Vector'\\n if isinstance(thing, (Color, ColorField)): return 'Color'\\n if isinstance(thing, (Matrix, MatrixField)): return 'Matrix'\\n raise ValueError(\\\"Could not guess type of: \\\" + thing)\",\n \"def type(self):\\n return self._getValue('type')\",\n \"def vehicle_type():\\n pass\",\n \"def get_spec_type(self):\\r\\n return self._spec_type\",\n \"def clips_to_py_type(ctype):\\n\\n ptype = None\\n if ctype == \\\"INTEGER\\\":\\n ptype = int\\n elif ctype == \\\"FLOAT\\\":\\n ptype = float\\n elif ctype == \\\"STRING\\\":\\n ptype = str\\n elif ctype == \\\"BOOLEAN\\\":\\n ptype = bool\\n return ptype\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62550634","0.6194765","0.61409026","0.60830945","0.6071827","0.6035185","0.60270154","0.588981","0.587063","0.58595556","0.5849741","0.58421713","0.5834853","0.5820968","0.58097893","0.5783257","0.5766275","0.57662106","0.57528615","0.57528615","0.57528615","0.57445276","0.57417625","0.57415175","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.5715352","0.571474","0.57050395","0.568064","0.5676557","0.5670967","0.5667034","0.5667034","0.5667034","0.5667034","0.5667034","0.5667034","0.5667034","0.5652451","0.5651366","0.563479","0.5626344","0.56243587","0.5618711","0.5618711","0.5613616","0.5604663","0.55989647","0.5585212","0.5579369","0.55737174","0.5569174","0.5569174","0.556541","0.55635095","0.55579424","0.55346453","0.55326277","0.5528994","0.552748"],"string":"[\n \"0.62550634\",\n \"0.6194765\",\n \"0.61409026\",\n \"0.60830945\",\n \"0.6071827\",\n \"0.6035185\",\n \"0.60270154\",\n \"0.588981\",\n \"0.587063\",\n \"0.58595556\",\n \"0.5849741\",\n \"0.58421713\",\n \"0.5834853\",\n \"0.5820968\",\n \"0.58097893\",\n \"0.5783257\",\n \"0.5766275\",\n \"0.57662106\",\n \"0.57528615\",\n \"0.57528615\",\n \"0.57528615\",\n \"0.57445276\",\n \"0.57417625\",\n \"0.57415175\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.5715352\",\n \"0.571474\",\n \"0.57050395\",\n \"0.568064\",\n \"0.5676557\",\n \"0.5670967\",\n \"0.5667034\",\n \"0.5667034\",\n \"0.5667034\",\n \"0.5667034\",\n \"0.5667034\",\n \"0.5667034\",\n \"0.5667034\",\n \"0.5652451\",\n \"0.5651366\",\n \"0.563479\",\n \"0.5626344\",\n \"0.56243587\",\n \"0.5618711\",\n \"0.5618711\",\n \"0.5613616\",\n \"0.5604663\",\n \"0.55989647\",\n \"0.5585212\",\n \"0.5579369\",\n \"0.55737174\",\n \"0.5569174\",\n \"0.5569174\",\n \"0.556541\",\n \"0.55635095\",\n \"0.55579424\",\n \"0.55346453\",\n \"0.55326277\",\n \"0.5528994\",\n \"0.552748\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65804327"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":93898,"cells":{"query":{"kind":"string","value":"Size in words and programmer_type e.g. integer"},"document":{"kind":"string","value":"def __init__(self, num_params):\r\n self.num_params = num_params"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __get_size(self):\n\t\treturn 4*self.version + 17","def getSize(self):\n return 1","def getSize(self):\n return 1","def getSize(language=None):","def getSize(self) -> int:\n ...","def getSize(self) -> int:\n ...","def word_size(tpname):\n if tpname == \"uint32\":\n return \"2\"\n elif tpname == \"uint16\":\n return \"1\"\n elif Signal.types.has_key(tpname):\n return \"%s\" % (Signal.types[tpname].size)\n else:\n return \"%s_WORD_SIZE\" % (tpname.upper())","def size(self) -> int:","def size_bytes(self):\n size_words = self.size_words()\n if size_words is None:\n return None\n return size_words * intnat.sizeof","def getSize(self) -> long:\n ...","def get_size(self):","def calc_size(self):\r\n pass","def sizeof(tt):\n if not isinstance(tt, type) and not type(tt) == str:\n tt = type(tt)\n try:\n return _sizes[tt]\n except KeyError:\n try:\n sz = ctypes.sizeof(tt)\n except TypeError:\n sz = gbl.gInterpreter.ProcessLine(\"sizeof(%s);\" % (_get_name(tt),))\n _sizes[tt] = sz\n return sz","def sizeof(self, typ):\n return self.context.sizeof(typ)","def const_size():\r\n return 1000.0","def size(self): \r\n pass","def size(self):","def size(name):","def size() -> int:\n ...","def sizeof(obj, human_readable: Optional[bool] = True) -> Union[str, int]:\n # bytes = asizeof(obj)\n bytes = len(pickle.dumps(obj))\n if human_readable:\n return format_bytes_to_human_readable(bytes) # str\n else:\n return bytes # int","def __sizeof__(self, ???):","def size(self):\n pass","def size(self):\n pass","def size(self):\n pass","def __len__(self):\n\t\treturn 8","def get_size(self):\n ...","def __sizeof__(self): # real signature unknown; restored from __doc__\n pass","def test_size_pretty_intonly(self):\n assert smdba.basegate.BaseGate.size_pretty(size=str(0x19000000000), int_only=True) == \"2 TB\"\n assert smdba.basegate.BaseGate.size_pretty(size=str(0x19000000000), int_only=False) == \"1.56 TB\"","def minsize(self):# -> int:\r\n return 0","def __len__(self):\n return int(self.size._value)","def __len__(self):\n return sys.maxsize","def _size(self):\n raise NotImplementedError","def s_size(self) -> Size:\n pass","def get_size(obj):\n size_bytes = sys.getsizeof(obj)\n if size_bytes == 0:\n return \"0B\"\n size_name = (\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\", \"ZB\", \"YB\")\n i = int(math.floor(math.log(size_bytes, 1024)))\n p = math.pow(1024, i)\n s = round(size_bytes / p, 2)\n return \"%s %s\" % (s, size_name[i])","def __source_size(size):\n return struct.pack(b'=L', int(size))","def __len__(self) -> int:\n return self.disp_size ** 2","def width(self) -> int:","def width(self) -> int:","def calc_size(cls) -> int:\n return calcsize('<' + cls.fmt)","def __sizeof__(self):\r\n\r\n S = 0 # Full size of the object\r\n if self.loss_list is not None:\r\n for value in self.loss_list:\r\n S += getsizeof(value)\r\n if self.meshsol_list is not None:\r\n for value in self.meshsol_list:\r\n S += getsizeof(value)\r\n if self.loss_index is not None:\r\n for key, value in self.loss_index.items():\r\n S += getsizeof(value) + getsizeof(key)\r\n S += getsizeof(self.logger_name)\r\n if self.axes_dict is not None:\r\n for key, value in self.axes_dict.items():\r\n S += getsizeof(value) + getsizeof(key)\r\n S += getsizeof(self.Pstator)\r\n S += getsizeof(self.Protor)\r\n S += getsizeof(self.Pmagnet)\r\n S += getsizeof(self.Pprox)\r\n S += getsizeof(self.Pjoule)\r\n if self.coeff_dict is not None:\r\n for key, value in self.coeff_dict.items():\r\n S += getsizeof(value) + getsizeof(key)\r\n return S","def size(self):\n return self.__size","def size():\n return int(os.environ['WORLD_SIZE'])","def update_size(self):\n return 3 + self.memory_unit_size","def size(self):\n return self.unpack_dword(0x4)","def size(self):\n return self.unpack_dword(0x4)","def get_size(self, typ):\n return self.info.get_size(typ)","def size(self):\n return self.size_number","def size(self):\n return struct.calcsize(b\"<ii\") + len(self.body.encode(\"ascii\")) + 2","def _size(self):\n return self._logicalSize","def bitSizeOf() -> int:\n\n return 64","def unitSize( cls, value, typeCode=None ):\n return value.shape[-1]","def bitSizeOf() -> int:\n\n return 32","def cargo_size(self) -> Union[float, int]:\n return self.type_data.cargo_size","def cargo_size(self) -> Union[float, int]:\n return self.type_data.cargo_size","def size(cls):\n return (cls.num_properties()*2 + 2)","def Length(self) -> int:","def Length(self) -> int:","def get_field_size(type_str):\n return ULog._UNPACK_TYPES[type_str][1]","def get_size(self, valueid):","def bitSizeOf() -> int:\n\n return 1","def getSize(self):\n if self.sym != None:\n return self.sym.getSize()\n return self.define.getSize()","def size(self):\r\n raise NotImplementedError","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass"],"string":"[\n \"def __get_size(self):\\n\\t\\treturn 4*self.version + 17\",\n \"def getSize(self):\\n return 1\",\n \"def getSize(self):\\n return 1\",\n \"def getSize(language=None):\",\n \"def getSize(self) -> int:\\n ...\",\n \"def getSize(self) -> int:\\n ...\",\n \"def word_size(tpname):\\n if tpname == \\\"uint32\\\":\\n return \\\"2\\\"\\n elif tpname == \\\"uint16\\\":\\n return \\\"1\\\"\\n elif Signal.types.has_key(tpname):\\n return \\\"%s\\\" % (Signal.types[tpname].size)\\n else:\\n return \\\"%s_WORD_SIZE\\\" % (tpname.upper())\",\n \"def size(self) -> int:\",\n \"def size_bytes(self):\\n size_words = self.size_words()\\n if size_words is None:\\n return None\\n return size_words * intnat.sizeof\",\n \"def getSize(self) -> long:\\n ...\",\n \"def get_size(self):\",\n \"def calc_size(self):\\r\\n pass\",\n \"def sizeof(tt):\\n if not isinstance(tt, type) and not type(tt) == str:\\n tt = type(tt)\\n try:\\n return _sizes[tt]\\n except KeyError:\\n try:\\n sz = ctypes.sizeof(tt)\\n except TypeError:\\n sz = gbl.gInterpreter.ProcessLine(\\\"sizeof(%s);\\\" % (_get_name(tt),))\\n _sizes[tt] = sz\\n return sz\",\n \"def sizeof(self, typ):\\n return self.context.sizeof(typ)\",\n \"def const_size():\\r\\n return 1000.0\",\n \"def size(self): \\r\\n pass\",\n \"def size(self):\",\n \"def size(name):\",\n \"def size() -> int:\\n ...\",\n \"def sizeof(obj, human_readable: Optional[bool] = True) -> Union[str, int]:\\n # bytes = asizeof(obj)\\n bytes = len(pickle.dumps(obj))\\n if human_readable:\\n return format_bytes_to_human_readable(bytes) # str\\n else:\\n return bytes # int\",\n \"def __sizeof__(self, ???):\",\n \"def size(self):\\n pass\",\n \"def size(self):\\n pass\",\n \"def size(self):\\n pass\",\n \"def __len__(self):\\n\\t\\treturn 8\",\n \"def get_size(self):\\n ...\",\n \"def __sizeof__(self): # real signature unknown; restored from __doc__\\n pass\",\n \"def test_size_pretty_intonly(self):\\n assert smdba.basegate.BaseGate.size_pretty(size=str(0x19000000000), int_only=True) == \\\"2 TB\\\"\\n assert smdba.basegate.BaseGate.size_pretty(size=str(0x19000000000), int_only=False) == \\\"1.56 TB\\\"\",\n \"def minsize(self):# -> int:\\r\\n return 0\",\n \"def __len__(self):\\n return int(self.size._value)\",\n \"def __len__(self):\\n return sys.maxsize\",\n \"def _size(self):\\n raise NotImplementedError\",\n \"def s_size(self) -> Size:\\n pass\",\n \"def get_size(obj):\\n size_bytes = sys.getsizeof(obj)\\n if size_bytes == 0:\\n return \\\"0B\\\"\\n size_name = (\\\"B\\\", \\\"KB\\\", \\\"MB\\\", \\\"GB\\\", \\\"TB\\\", \\\"PB\\\", \\\"EB\\\", \\\"ZB\\\", \\\"YB\\\")\\n i = int(math.floor(math.log(size_bytes, 1024)))\\n p = math.pow(1024, i)\\n s = round(size_bytes / p, 2)\\n return \\\"%s %s\\\" % (s, size_name[i])\",\n \"def __source_size(size):\\n return struct.pack(b'=L', int(size))\",\n \"def __len__(self) -> int:\\n return self.disp_size ** 2\",\n \"def width(self) -> int:\",\n \"def width(self) -> int:\",\n \"def calc_size(cls) -> int:\\n return calcsize('<' + cls.fmt)\",\n \"def __sizeof__(self):\\r\\n\\r\\n S = 0 # Full size of the object\\r\\n if self.loss_list is not None:\\r\\n for value in self.loss_list:\\r\\n S += getsizeof(value)\\r\\n if self.meshsol_list is not None:\\r\\n for value in self.meshsol_list:\\r\\n S += getsizeof(value)\\r\\n if self.loss_index is not None:\\r\\n for key, value in self.loss_index.items():\\r\\n S += getsizeof(value) + getsizeof(key)\\r\\n S += getsizeof(self.logger_name)\\r\\n if self.axes_dict is not None:\\r\\n for key, value in self.axes_dict.items():\\r\\n S += getsizeof(value) + getsizeof(key)\\r\\n S += getsizeof(self.Pstator)\\r\\n S += getsizeof(self.Protor)\\r\\n S += getsizeof(self.Pmagnet)\\r\\n S += getsizeof(self.Pprox)\\r\\n S += getsizeof(self.Pjoule)\\r\\n if self.coeff_dict is not None:\\r\\n for key, value in self.coeff_dict.items():\\r\\n S += getsizeof(value) + getsizeof(key)\\r\\n return S\",\n \"def size(self):\\n return self.__size\",\n \"def size():\\n return int(os.environ['WORLD_SIZE'])\",\n \"def update_size(self):\\n return 3 + self.memory_unit_size\",\n \"def size(self):\\n return self.unpack_dword(0x4)\",\n \"def size(self):\\n return self.unpack_dword(0x4)\",\n \"def get_size(self, typ):\\n return self.info.get_size(typ)\",\n \"def size(self):\\n return self.size_number\",\n \"def size(self):\\n return struct.calcsize(b\\\"<ii\\\") + len(self.body.encode(\\\"ascii\\\")) + 2\",\n \"def _size(self):\\n return self._logicalSize\",\n \"def bitSizeOf() -> int:\\n\\n return 64\",\n \"def unitSize( cls, value, typeCode=None ):\\n return value.shape[-1]\",\n \"def bitSizeOf() -> int:\\n\\n return 32\",\n \"def cargo_size(self) -> Union[float, int]:\\n return self.type_data.cargo_size\",\n \"def cargo_size(self) -> Union[float, int]:\\n return self.type_data.cargo_size\",\n \"def size(cls):\\n return (cls.num_properties()*2 + 2)\",\n \"def Length(self) -> int:\",\n \"def Length(self) -> int:\",\n \"def get_field_size(type_str):\\n return ULog._UNPACK_TYPES[type_str][1]\",\n \"def get_size(self, valueid):\",\n \"def bitSizeOf() -> int:\\n\\n return 1\",\n \"def getSize(self):\\n if self.sym != None:\\n return self.sym.getSize()\\n return self.define.getSize()\",\n \"def size(self):\\r\\n raise NotImplementedError\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73081714","0.70577526","0.7009011","0.7003701","0.6970362","0.6970362","0.6962064","0.68956697","0.6845606","0.6825537","0.68143684","0.68104845","0.67622477","0.6742321","0.67140716","0.6710887","0.6704871","0.66993916","0.6625745","0.661541","0.6606087","0.6588984","0.6588984","0.6588984","0.6580205","0.6580026","0.6571008","0.6563584","0.65579945","0.6511632","0.6501965","0.648851","0.6469287","0.64498585","0.6443807","0.64355963","0.643428","0.643428","0.64271164","0.6383088","0.637282","0.6369948","0.63678235","0.635319","0.635319","0.6345653","0.6337143","0.63180685","0.6305096","0.63019186","0.62950784","0.6289167","0.6286926","0.6286926","0.62859625","0.62771195","0.62771195","0.62757254","0.62682","0.62612075","0.62554556","0.6246005","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783","0.6243783"],"string":"[\n \"0.73081714\",\n \"0.70577526\",\n \"0.7009011\",\n \"0.7003701\",\n \"0.6970362\",\n \"0.6970362\",\n \"0.6962064\",\n \"0.68956697\",\n \"0.6845606\",\n \"0.6825537\",\n \"0.68143684\",\n \"0.68104845\",\n \"0.67622477\",\n \"0.6742321\",\n \"0.67140716\",\n \"0.6710887\",\n \"0.6704871\",\n \"0.66993916\",\n \"0.6625745\",\n \"0.661541\",\n \"0.6606087\",\n \"0.6588984\",\n \"0.6588984\",\n \"0.6588984\",\n \"0.6580205\",\n \"0.6580026\",\n \"0.6571008\",\n \"0.6563584\",\n \"0.65579945\",\n \"0.6511632\",\n \"0.6501965\",\n \"0.648851\",\n \"0.6469287\",\n \"0.64498585\",\n \"0.6443807\",\n \"0.64355963\",\n \"0.643428\",\n \"0.643428\",\n \"0.64271164\",\n \"0.6383088\",\n \"0.637282\",\n \"0.6369948\",\n \"0.63678235\",\n \"0.635319\",\n \"0.635319\",\n \"0.6345653\",\n \"0.6337143\",\n \"0.63180685\",\n \"0.6305096\",\n \"0.63019186\",\n \"0.62950784\",\n \"0.6289167\",\n \"0.6286926\",\n \"0.6286926\",\n \"0.62859625\",\n \"0.62771195\",\n \"0.62771195\",\n \"0.62757254\",\n \"0.62682\",\n \"0.62612075\",\n \"0.62554556\",\n \"0.6246005\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\",\n \"0.6243783\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":93899,"cells":{"query":{"kind":"string","value":"Size in words and programmer_type e.g. integer"},"document":{"kind":"string","value":"def __init__(self, num_params):\r\n self.num_params = num_params"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __get_size(self):\n\t\treturn 4*self.version + 17","def getSize(self):\n return 1","def getSize(self):\n return 1","def getSize(language=None):","def getSize(self) -> int:\n ...","def getSize(self) -> int:\n ...","def word_size(tpname):\n if tpname == \"uint32\":\n return \"2\"\n elif tpname == \"uint16\":\n return \"1\"\n elif Signal.types.has_key(tpname):\n return \"%s\" % (Signal.types[tpname].size)\n else:\n return \"%s_WORD_SIZE\" % (tpname.upper())","def size(self) -> int:","def size_bytes(self):\n size_words = self.size_words()\n if size_words is None:\n return None\n return size_words * intnat.sizeof","def getSize(self) -> long:\n ...","def get_size(self):","def calc_size(self):\r\n pass","def sizeof(tt):\n if not isinstance(tt, type) and not type(tt) == str:\n tt = type(tt)\n try:\n return _sizes[tt]\n except KeyError:\n try:\n sz = ctypes.sizeof(tt)\n except TypeError:\n sz = gbl.gInterpreter.ProcessLine(\"sizeof(%s);\" % (_get_name(tt),))\n _sizes[tt] = sz\n return sz","def sizeof(self, typ):\n return self.context.sizeof(typ)","def const_size():\r\n return 1000.0","def size(self): \r\n pass","def size(self):","def size(name):","def size() -> int:\n ...","def sizeof(obj, human_readable: Optional[bool] = True) -> Union[str, int]:\n # bytes = asizeof(obj)\n bytes = len(pickle.dumps(obj))\n if human_readable:\n return format_bytes_to_human_readable(bytes) # str\n else:\n return bytes # int","def __sizeof__(self, ???):","def size(self):\n pass","def size(self):\n pass","def size(self):\n pass","def __len__(self):\n\t\treturn 8","def get_size(self):\n ...","def __sizeof__(self): # real signature unknown; restored from __doc__\n pass","def test_size_pretty_intonly(self):\n assert smdba.basegate.BaseGate.size_pretty(size=str(0x19000000000), int_only=True) == \"2 TB\"\n assert smdba.basegate.BaseGate.size_pretty(size=str(0x19000000000), int_only=False) == \"1.56 TB\"","def minsize(self):# -> int:\r\n return 0","def __len__(self):\n return int(self.size._value)","def __len__(self):\n return sys.maxsize","def _size(self):\n raise NotImplementedError","def s_size(self) -> Size:\n pass","def get_size(obj):\n size_bytes = sys.getsizeof(obj)\n if size_bytes == 0:\n return \"0B\"\n size_name = (\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\", \"ZB\", \"YB\")\n i = int(math.floor(math.log(size_bytes, 1024)))\n p = math.pow(1024, i)\n s = round(size_bytes / p, 2)\n return \"%s %s\" % (s, size_name[i])","def __source_size(size):\n return struct.pack(b'=L', int(size))","def __len__(self) -> int:\n return self.disp_size ** 2","def width(self) -> int:","def width(self) -> int:","def calc_size(cls) -> int:\n return calcsize('<' + cls.fmt)","def __sizeof__(self):\r\n\r\n S = 0 # Full size of the object\r\n if self.loss_list is not None:\r\n for value in self.loss_list:\r\n S += getsizeof(value)\r\n if self.meshsol_list is not None:\r\n for value in self.meshsol_list:\r\n S += getsizeof(value)\r\n if self.loss_index is not None:\r\n for key, value in self.loss_index.items():\r\n S += getsizeof(value) + getsizeof(key)\r\n S += getsizeof(self.logger_name)\r\n if self.axes_dict is not None:\r\n for key, value in self.axes_dict.items():\r\n S += getsizeof(value) + getsizeof(key)\r\n S += getsizeof(self.Pstator)\r\n S += getsizeof(self.Protor)\r\n S += getsizeof(self.Pmagnet)\r\n S += getsizeof(self.Pprox)\r\n S += getsizeof(self.Pjoule)\r\n if self.coeff_dict is not None:\r\n for key, value in self.coeff_dict.items():\r\n S += getsizeof(value) + getsizeof(key)\r\n return S","def size(self):\n return self.__size","def size():\n return int(os.environ['WORLD_SIZE'])","def update_size(self):\n return 3 + self.memory_unit_size","def size(self):\n return self.unpack_dword(0x4)","def size(self):\n return self.unpack_dword(0x4)","def get_size(self, typ):\n return self.info.get_size(typ)","def size(self):\n return self.size_number","def size(self):\n return struct.calcsize(b\"<ii\") + len(self.body.encode(\"ascii\")) + 2","def _size(self):\n return self._logicalSize","def bitSizeOf() -> int:\n\n return 64","def unitSize( cls, value, typeCode=None ):\n return value.shape[-1]","def bitSizeOf() -> int:\n\n return 32","def size(cls):\n return (cls.num_properties()*2 + 2)","def cargo_size(self) -> Union[float, int]:\n return self.type_data.cargo_size","def cargo_size(self) -> Union[float, int]:\n return self.type_data.cargo_size","def Length(self) -> int:","def Length(self) -> int:","def get_field_size(type_str):\n return ULog._UNPACK_TYPES[type_str][1]","def get_size(self, valueid):","def bitSizeOf() -> int:\n\n return 1","def getSize(self):\n if self.sym != None:\n return self.sym.getSize()\n return self.define.getSize()","def size(self):\r\n raise NotImplementedError","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass","def __sizeof__(self, *args, **kwargs): # real signature unknown\n pass"],"string":"[\n \"def __get_size(self):\\n\\t\\treturn 4*self.version + 17\",\n \"def getSize(self):\\n return 1\",\n \"def getSize(self):\\n return 1\",\n \"def getSize(language=None):\",\n \"def getSize(self) -> int:\\n ...\",\n \"def getSize(self) -> int:\\n ...\",\n \"def word_size(tpname):\\n if tpname == \\\"uint32\\\":\\n return \\\"2\\\"\\n elif tpname == \\\"uint16\\\":\\n return \\\"1\\\"\\n elif Signal.types.has_key(tpname):\\n return \\\"%s\\\" % (Signal.types[tpname].size)\\n else:\\n return \\\"%s_WORD_SIZE\\\" % (tpname.upper())\",\n \"def size(self) -> int:\",\n \"def size_bytes(self):\\n size_words = self.size_words()\\n if size_words is None:\\n return None\\n return size_words * intnat.sizeof\",\n \"def getSize(self) -> long:\\n ...\",\n \"def get_size(self):\",\n \"def calc_size(self):\\r\\n pass\",\n \"def sizeof(tt):\\n if not isinstance(tt, type) and not type(tt) == str:\\n tt = type(tt)\\n try:\\n return _sizes[tt]\\n except KeyError:\\n try:\\n sz = ctypes.sizeof(tt)\\n except TypeError:\\n sz = gbl.gInterpreter.ProcessLine(\\\"sizeof(%s);\\\" % (_get_name(tt),))\\n _sizes[tt] = sz\\n return sz\",\n \"def sizeof(self, typ):\\n return self.context.sizeof(typ)\",\n \"def const_size():\\r\\n return 1000.0\",\n \"def size(self): \\r\\n pass\",\n \"def size(self):\",\n \"def size(name):\",\n \"def size() -> int:\\n ...\",\n \"def sizeof(obj, human_readable: Optional[bool] = True) -> Union[str, int]:\\n # bytes = asizeof(obj)\\n bytes = len(pickle.dumps(obj))\\n if human_readable:\\n return format_bytes_to_human_readable(bytes) # str\\n else:\\n return bytes # int\",\n \"def __sizeof__(self, ???):\",\n \"def size(self):\\n pass\",\n \"def size(self):\\n pass\",\n \"def size(self):\\n pass\",\n \"def __len__(self):\\n\\t\\treturn 8\",\n \"def get_size(self):\\n ...\",\n \"def __sizeof__(self): # real signature unknown; restored from __doc__\\n pass\",\n \"def test_size_pretty_intonly(self):\\n assert smdba.basegate.BaseGate.size_pretty(size=str(0x19000000000), int_only=True) == \\\"2 TB\\\"\\n assert smdba.basegate.BaseGate.size_pretty(size=str(0x19000000000), int_only=False) == \\\"1.56 TB\\\"\",\n \"def minsize(self):# -> int:\\r\\n return 0\",\n \"def __len__(self):\\n return int(self.size._value)\",\n \"def __len__(self):\\n return sys.maxsize\",\n \"def _size(self):\\n raise NotImplementedError\",\n \"def s_size(self) -> Size:\\n pass\",\n \"def get_size(obj):\\n size_bytes = sys.getsizeof(obj)\\n if size_bytes == 0:\\n return \\\"0B\\\"\\n size_name = (\\\"B\\\", \\\"KB\\\", \\\"MB\\\", \\\"GB\\\", \\\"TB\\\", \\\"PB\\\", \\\"EB\\\", \\\"ZB\\\", \\\"YB\\\")\\n i = int(math.floor(math.log(size_bytes, 1024)))\\n p = math.pow(1024, i)\\n s = round(size_bytes / p, 2)\\n return \\\"%s %s\\\" % (s, size_name[i])\",\n \"def __source_size(size):\\n return struct.pack(b'=L', int(size))\",\n \"def __len__(self) -> int:\\n return self.disp_size ** 2\",\n \"def width(self) -> int:\",\n \"def width(self) -> int:\",\n \"def calc_size(cls) -> int:\\n return calcsize('<' + cls.fmt)\",\n \"def __sizeof__(self):\\r\\n\\r\\n S = 0 # Full size of the object\\r\\n if self.loss_list is not None:\\r\\n for value in self.loss_list:\\r\\n S += getsizeof(value)\\r\\n if self.meshsol_list is not None:\\r\\n for value in self.meshsol_list:\\r\\n S += getsizeof(value)\\r\\n if self.loss_index is not None:\\r\\n for key, value in self.loss_index.items():\\r\\n S += getsizeof(value) + getsizeof(key)\\r\\n S += getsizeof(self.logger_name)\\r\\n if self.axes_dict is not None:\\r\\n for key, value in self.axes_dict.items():\\r\\n S += getsizeof(value) + getsizeof(key)\\r\\n S += getsizeof(self.Pstator)\\r\\n S += getsizeof(self.Protor)\\r\\n S += getsizeof(self.Pmagnet)\\r\\n S += getsizeof(self.Pprox)\\r\\n S += getsizeof(self.Pjoule)\\r\\n if self.coeff_dict is not None:\\r\\n for key, value in self.coeff_dict.items():\\r\\n S += getsizeof(value) + getsizeof(key)\\r\\n return S\",\n \"def size(self):\\n return self.__size\",\n \"def size():\\n return int(os.environ['WORLD_SIZE'])\",\n \"def update_size(self):\\n return 3 + self.memory_unit_size\",\n \"def size(self):\\n return self.unpack_dword(0x4)\",\n \"def size(self):\\n return self.unpack_dword(0x4)\",\n \"def get_size(self, typ):\\n return self.info.get_size(typ)\",\n \"def size(self):\\n return self.size_number\",\n \"def size(self):\\n return struct.calcsize(b\\\"<ii\\\") + len(self.body.encode(\\\"ascii\\\")) + 2\",\n \"def _size(self):\\n return self._logicalSize\",\n \"def bitSizeOf() -> int:\\n\\n return 64\",\n \"def unitSize( cls, value, typeCode=None ):\\n return value.shape[-1]\",\n \"def bitSizeOf() -> int:\\n\\n return 32\",\n \"def size(cls):\\n return (cls.num_properties()*2 + 2)\",\n \"def cargo_size(self) -> Union[float, int]:\\n return self.type_data.cargo_size\",\n \"def cargo_size(self) -> Union[float, int]:\\n return self.type_data.cargo_size\",\n \"def Length(self) -> int:\",\n \"def Length(self) -> int:\",\n \"def get_field_size(type_str):\\n return ULog._UNPACK_TYPES[type_str][1]\",\n \"def get_size(self, valueid):\",\n \"def bitSizeOf() -> int:\\n\\n return 1\",\n \"def getSize(self):\\n if self.sym != None:\\n return self.sym.getSize()\\n return self.define.getSize()\",\n \"def size(self):\\r\\n raise NotImplementedError\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __sizeof__(self, *args, **kwargs): # real signature unknown\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73083884","0.7057839","0.7009008","0.7002719","0.6969705","0.6969705","0.69600403","0.68952006","0.68450075","0.6824593","0.68134356","0.6809526","0.6760405","0.6741088","0.67141205","0.67103976","0.67043126","0.66976994","0.6625272","0.6613481","0.6605095","0.6588381","0.6588381","0.6588381","0.6580093","0.6579088","0.65704435","0.65627944","0.65579534","0.65103513","0.6501752","0.64875436","0.6467809","0.644768","0.64439917","0.64349145","0.6433541","0.6433541","0.6425805","0.63819337","0.6372254","0.6369516","0.6367561","0.63523114","0.63523114","0.6344148","0.63359934","0.6317431","0.6303389","0.6301053","0.629327","0.6288215","0.62853867","0.6285081","0.6285081","0.62759686","0.62759686","0.62737006","0.62667537","0.62604743","0.6255017","0.6245361","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036","0.6243036"],"string":"[\n \"0.73083884\",\n \"0.7057839\",\n \"0.7009008\",\n \"0.7002719\",\n \"0.6969705\",\n \"0.6969705\",\n \"0.69600403\",\n \"0.68952006\",\n \"0.68450075\",\n \"0.6824593\",\n \"0.68134356\",\n \"0.6809526\",\n \"0.6760405\",\n \"0.6741088\",\n \"0.67141205\",\n \"0.67103976\",\n \"0.67043126\",\n \"0.66976994\",\n \"0.6625272\",\n \"0.6613481\",\n \"0.6605095\",\n \"0.6588381\",\n \"0.6588381\",\n \"0.6588381\",\n \"0.6580093\",\n \"0.6579088\",\n \"0.65704435\",\n \"0.65627944\",\n \"0.65579534\",\n \"0.65103513\",\n \"0.6501752\",\n \"0.64875436\",\n \"0.6467809\",\n \"0.644768\",\n \"0.64439917\",\n \"0.64349145\",\n \"0.6433541\",\n \"0.6433541\",\n \"0.6425805\",\n \"0.63819337\",\n \"0.6372254\",\n \"0.6369516\",\n \"0.6367561\",\n \"0.63523114\",\n \"0.63523114\",\n \"0.6344148\",\n \"0.63359934\",\n \"0.6317431\",\n \"0.6303389\",\n \"0.6301053\",\n \"0.629327\",\n \"0.6288215\",\n \"0.62853867\",\n \"0.6285081\",\n \"0.6285081\",\n \"0.62759686\",\n \"0.62759686\",\n \"0.62737006\",\n \"0.62667537\",\n \"0.62604743\",\n \"0.6255017\",\n \"0.6245361\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\",\n \"0.6243036\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":938,"numItemsPerPage":100,"numTotalItems":95772,"offset":93800,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTUxODY5NCwianRpIjoiNmIwNjU2ZTYtYTE5NS00MjVlLTk3YTMtNTI0MmE3ZTY5ZGNmIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU1MjIyOTQsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.tEIDpay8oIZvFU7xIaLjFGbLe_vj1AT76hvVUSex0_Slv9n7OeIomLltooIoFIEsTW4Qv2dqLUxrFRNjrMmzAw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Test checker for whether matching is stable or not. | def test_check_stability():
residents = [Resident("A"), Resident("B"), Resident("C")]
hospitals = [Hospital("X", 2), Hospital("Y", 2)]
(a, b, c), (x, y) = residents, hospitals
a.set_prefs([x, y])
b.set_prefs([y])
c.set_prefs([y, x])
x.set_prefs([c, a])
y.set_prefs([a, b, c])
gam... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check():",
"def match(self) -> bool:",
"def test_match_correct(self, event: Event, pr_algorithm, minimal_state: State):\n assert pr_algorithm.match(event, minimal_state)",
"def check_stability(self):",
"def test_match_left_none():\r\n runmatch(lcode_left_none)",
"def test_match_right_none()... | [
"0.594465",
"0.594353",
"0.5866137",
"0.58372444",
"0.5782722",
"0.57116014",
"0.569137",
"0.5661478",
"0.5660017",
"0.5641539",
"0.5637666",
"0.56293386",
"0.5627196",
"0.5614456",
"0.56038964",
"0.5542264",
"0.55329216",
"0.5529746",
"0.5523878",
"0.5474978",
"0.54693824",
... | 0.51438534 | 79 |
parses the options for the planning including the targets | def parse_options(site, darkness, date, plan_range):
# parse targets
opts = {}
opts.update(parse_date(date))
opts.update(parse_range(plan_range))
opts.update(parse_site(site))
opts.update(parse_darkness(darkness))
return opts | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse(self):\n opt = self.gather_options()\n opt.isTrain = self.isTrain # train or test\n\n # process opt.suffix\n if opt.suffix:\n suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''\n opt.name = opt.name + suffix\n\n opt.f_map... | [
"0.62074244",
"0.59689",
"0.59588844",
"0.5875803",
"0.5780339",
"0.5754228",
"0.57520694",
"0.5705439",
"0.5700765",
"0.56824964",
"0.5640865",
"0.5615099",
"0.5609186",
"0.5605042",
"0.5589972",
"0.55796164",
"0.5563828",
"0.5488467",
"0.548542",
"0.5478226",
"0.54763347",
... | 0.67171156 | 0 |
parse the range options | def parse_range(option):
return {"range": timedelta(days=option)} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_range(option, opt_str, value, parser):\n # Preserve the original option string for print output.\n\n parser.values.ensure_value(\"raw_%s\" % option.dest, value)\n\n # Place the max and min into a single entry for each found\n # threshold. This lets a user see all possible ranges passed by\n... | [
"0.68641734",
"0.64663",
"0.6461454",
"0.6431249",
"0.64209527",
"0.6322825",
"0.6321341",
"0.6262145",
"0.6225956",
"0.61157084",
"0.60893756",
"0.6082446",
"0.60010207",
"0.5982482",
"0.5957853",
"0.58720285",
"0.58702815",
"0.5854096",
"0.5840488",
"0.5828852",
"0.5792684"... | 0.7645768 | 0 |
parse date and range | def parse_date(option):
date_opt = option
date = Time(datetime.utcnow()).datetime
if date_opt is not None:
date = Time(date_opt, scale="utc").datetime
date = datetime(date.year, date.month, date.day)
return {"date": date} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_date_range(\n date_range: str,\n) -> Tuple[Optional[datetime.date], Optional[datetime.date]]:\n date_range = date_range.lstrip(\"\\\\\")\n split = date_range.split(\"-\")\n if len(split) > 2:\n raise ValueError(\n \"Invalid date range, should only specify one dash in date ra... | [
"0.69496834",
"0.6842823",
"0.67660517",
"0.6615257",
"0.65185654",
"0.64432776",
"0.64398843",
"0.6386422",
"0.6376004",
"0.6292795",
"0.6227388",
"0.62164617",
"0.6211114",
"0.6141747",
"0.6140293",
"0.61234504",
"0.6117977",
"0.6112637",
"0.6109786",
"0.6046468",
"0.604065... | 0.0 | -1 |
main function of the tool that performs the planning | def plan_targets(target, site, darkness, date, plan_range):
targets = iop_targets.resolve_target_list(target)
options = parse_options(site, darkness, date, plan_range)
summarize_options(options, targets)
# Setup the nights to plan and calculate sun rise/set times
planned_nights = iop_nights.setup_n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n\t# GET THE DIALOG CONTENT\n\tpolicyFlag = int(sys.argv[3])\n\tif policyFlag == 0:\n\t\texistedIterNum = 15000\n\t\texistedThetaFileName = 'algorithms/theta/cycle_tree/of0w1.0g5v0.0625l0.05'\n\telif policyFlag == 1:\n\t\texistedIterNum = 10000\n\t\texistedThetaFileName = 'algorithms/theta/cycle_tree/o... | [
"0.7199436",
"0.7175628",
"0.71665376",
"0.7101281",
"0.6975896",
"0.69690853",
"0.6864884",
"0.6853323",
"0.6847819",
"0.6827581",
"0.68167394",
"0.67758334",
"0.67334515",
"0.67334515",
"0.67334515",
"0.67334515",
"0.67334515",
"0.67334515",
"0.67334515",
"0.67334515",
"0.6... | 0.0 | -1 |
Testing to check if the PVC volume was created successfully | def test_pvc_creation(self, mock_logger, mock_run_to_compl, mock_pvc_create):
self.assertIsInstance(init_pvc(self.data, self.filer), PVC) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_pvcvolume_attach(self):\n v = self.cs.volumes.get('pvcvolume')\n self.cs.volumes.attach(v, 1, '/dev/vdc')\n self.cs.assert_called('POST',\n '/volumes/pvcvolume/action')",
"def test_create_volume(self, mock_ghn):\n ctxt = context.get_admin_context(... | [
"0.7457292",
"0.7293168",
"0.7195492",
"0.6962155",
"0.68749386",
"0.6638356",
"0.6575415",
"0.65737724",
"0.65491074",
"0.6500569",
"0.6487423",
"0.64540786",
"0.6361075",
"0.63578856",
"0.63056785",
"0.6305089",
"0.6280213",
"0.62753564",
"0.6267789",
"0.6223779",
"0.619556... | 0.66817355 | 5 |
Testcase for finding if the PVC creation failed with exit 0 | def test_pvc_failure(self, mock_logger, run_to_compl, mock_pvc_create, mock_pvc_delete):
self.assertRaises(SystemExit, init_pvc, self.data, self.filer) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_pvc_creation(self, mock_logger, mock_run_to_compl, mock_pvc_create):\n self.assertIsInstance(init_pvc(self.data, self.filer), PVC)",
"def test_vmfactory_fail(self):\n self.command.output = \"foo.vmx\"\n with self.assertRaises(VMInitError):\n self.command.package = self.in... | [
"0.64863044",
"0.636269",
"0.6359872",
"0.6171155",
"0.61627007",
"0.61235106",
"0.6072229",
"0.60417724",
"0.602527",
"0.5993727",
"0.5867872",
"0.585176",
"0.5840988",
"0.5839947",
"0.5835358",
"0.57915354",
"0.5782399",
"0.5769757",
"0.57690483",
"0.56890714",
"0.56839293"... | 0.7272803 | 0 |
Test that fetched Malware objects are wellformed | def test_index(populate_malware, authenticated_client):
names = [malware.name for malware in populate_malware]
for name in names:
query_json = {'name': name}
rv = authenticated_client.post('/api/entities/filter/',
data=json.dumps(query_json),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_malware_have_techniques(attck_fixture):\n for malware in attck_fixture.enterprise.malwares:\n if malware.techniques:\n assert getattr(malware,'techniques')",
"def test_malware_have_actors(attck_fixture):\n for malware in attck_fixture.enterprise.malwares:\n if malware.acto... | [
"0.6753312",
"0.62137586",
"0.62131786",
"0.6054662",
"0.604343",
"0.59996074",
"0.5981002",
"0.5909198",
"0.5886412",
"0.5814652",
"0.57630396",
"0.57399",
"0.5711823",
"0.57050747",
"0.5703893",
"0.56574583",
"0.563821",
"0.5593698",
"0.55885243",
"0.55871683",
"0.55865103"... | 0.6298893 | 1 |
Tests updating a new object via PUT. | def test_put(populate_malware, authenticated_client):
rv = authenticated_client.get('/api/entities/{0:s}/'.format(populate_malware[0].id))
entity_json = json.loads(rv.data)
rv = authenticated_client.put('/api/entities/{0:s}/'.format(entity_json['id']),
data=json.dumps({'lab... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_put_method(self):\n self.getPage('/blah', method='PUT')\n self.assertStatus('200 OK')\n self.assertHeader('Content-Type', 'application/json')\n self.assertBody('{\"mystring\": \"blah\"}')",
"def taco_test_put_update(self):\n body = '{ \"id\": 400, \"name\": \"item4\", ... | [
"0.8103551",
"0.80811286",
"0.77770305",
"0.77689296",
"0.77537143",
"0.7665791",
"0.7640661",
"0.7604226",
"0.7553142",
"0.7541913",
"0.7483781",
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"0.73470914",
"0.73064786",
"0.7275389",
"0.7244537",
"0.72116286",
"0.71898234",
"0.7184... | 0.0 | -1 |
The logger gets initialized with a name (logger by default), which is also part of the name of the logfiles, if persistence is enabled. If rotation_interval (log rotation interval) is set to 0 (default), all output will be written to a single log file. When rotation is enabled, | def __init__(self, name="logger", loglevel=3, persist=True, flush=True, rotation_interval=1, format="text"):
self.name = name
self.loglevel = loglevel
self.persist = persist
self.flush = flush
self.format = format
self.rotation_interval = rot... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_logbook(name, extension='.txt', level=logging.INFO, soloDir = True):\n formatter = logging.Formatter(fmt='%(asctime)s.%(msecs)03d (%(name)s) - %(message)s', datefmt='%d-%m-%y %H:%M:%S')\n date = datetime.today().strftime('%Y-%m-%d')\n if soloDir:\n log_path = str(settings.DATA_DIR + name ... | [
"0.6630192",
"0.65736866",
"0.65538865",
"0.6480561",
"0.64405483",
"0.64157355",
"0.6408364",
"0.640588",
"0.6376535",
"0.63044286",
"0.62730765",
"0.62329596",
"0.62253886",
"0.6189911",
"0.6173905",
"0.61433166",
"0.61343294",
"0.611963",
"0.6104088",
"0.61026853",
"0.6079... | 0.70223504 | 0 |
convert parsed instruction format into pyparsing grammer | def build_grammer(ast):
# example:
# ['ldw', ['tgt', 'reg'], ',', ['offset', 's7'], '(', ['base', 'reg'], ')']
def lookup(name, type, modifier=None):
g = globals()[type].setResultsName(name).setName(name)
if modifier == plus:
g = pp.Group(pp.delimitedList(g, delim=","))
d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_instruction(instruction: str) -> Tuple[int, int, int]:\n\t# NOOP\n\tif match := NOOP_REGEX.match(instruction):\n\t\tinstruction_type = 0\n\t# ADD\n\telif match := ADD_REGEX.match(instruction):\n\t\tinstruction_type = 1\n\t# MINUS\n\telif match := MINUS_REGEX.match(instruction):\n\t\tinstruction_type = ... | [
"0.605632",
"0.60111564",
"0.5925806",
"0.58522624",
"0.5783257",
"0.57504004",
"0.5728153",
"0.5717114",
"0.5711363",
"0.5704006",
"0.5698306",
"0.5690248",
"0.5654756",
"0.5613274",
"0.56095576",
"0.5582052",
"0.55348366",
"0.5512344",
"0.5498963",
"0.54925406",
"0.5473631"... | 0.51525617 | 59 |
Check if norm layer is in correct train state. | def check_norm_state(modules, train_state):
for mod in modules:
if isinstance(mod, _BatchNorm):
if mod.training != train_state:
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_norm_state(modules, train_state):\n for mod in modules:\n if isinstance(mod, _BatchNorm):\n if mod.training != train_state:\n return False\n return True",
"def has_emb_layer_norm_before(model_state):\n return any(k.startswith(\"emb_layer_norm_before\") for k, p... | [
"0.793346",
"0.67689735",
"0.661185",
"0.6503076",
"0.6476175",
"0.6219339",
"0.61168087",
"0.6102992",
"0.60851747",
"0.5981991",
"0.5981991",
"0.5933828",
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"0.59198314",
"0.590348",
"0.5878359",
"0.5868085",
"0.58631355",
"0.58382577",
"0.5830594",
"0.58257794"... | 0.8030396 | 1 |
Construct a new Transform object An array of operations that manipulate the trace data, for example filtering or sorting the data arrays. | def __init__(self, **kwargs):
super(Transform, self).__init__('transforms')
# Import validators
# -----------------
from plotly.validators.heatmapgl import (transform as v_transform)
# Initialize validators
# ---------------------
# Populate data dict with prop... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, transforms):\n super().__init__(transforms)\n self.S1 = transforms[0]\n self.T1 = transforms[1]\n self.S2 = transforms[2]\n self.T2 = transforms[3]",
"def __init__(self, transforms):\n self.transforms = transforms",
"def __init__(self, transforms):\n... | [
"0.64450544",
"0.6047228",
"0.5957629",
"0.59549063",
"0.5921418",
"0.58241445",
"0.58241445",
"0.58241445",
"0.58241445",
"0.58241445",
"0.58241445",
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"0.57942915",
"0.5784964",
"0.5783769",
"0.5750437",
"0.5715402",
"0.56863594",
"0.56863594",
"0.56863594",
"0.... | 0.51939946 | 55 |
Return the cosine similarity between vector a and b. | def cos_sim(a, b):
numerator, sosA, sosB = 0, 0, 0 #sum of squares for denom
for i in movies: #replace movies
numerator += (ratingai * ratingbi)
sosA += ratingai * ratingai
sosB += ratingbi * ratingbi
denom = sqrt(sosA * sosB) #make sure denom won't be 0!
return numerator / deno... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cosine_similarity(a, b):\n cs = dot_product(a, b)/(norm(a) * norm(b))\n return cs",
"def cosine_similarity(cls, vec_a, vec_b):\n return np.dot(vec_a, vec_b) / \\\n (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))",
"def cosine_similarity(a, b):\n if a.ndim != 1 or b.ndim != 1:\n ... | [
"0.9152629",
"0.9123586",
"0.8628271",
"0.85290915",
"0.8427776",
"0.8416829",
"0.83845985",
"0.83020973",
"0.8284079",
"0.8208634",
"0.820216",
"0.8121326",
"0.8069175",
"0.79891837",
"0.7935514",
"0.78413475",
"0.78242147",
"0.78125125",
"0.77350307",
"0.77096653",
"0.76706... | 0.73320496 | 31 |
Return the adjusted cosine similarity between vector a and b. | def adj_cos_sim(a, b, avg_a, avg_b):
adj_a, adj_b = [], []
for val in a: #replace a, not necessarily a vector if we do OOP
adj_a.append(val - avg_a) #negative res
for val in b:
adj_b.append(val - avg_b)
return cos_sim(adj_a, adj_b) #is this slower than if I wrote it out/expanded it? | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cosine_similarity(a, b):\n cs = dot_product(a, b)/(norm(a) * norm(b))\n return cs",
"def cosine_similarity(cls, vec_a, vec_b):\n return np.dot(vec_a, vec_b) / \\\n (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))",
"def cosine_similarity(a, b):\n if a.ndim != 1 or b.ndim != 1:\n ... | [
"0.87131846",
"0.8546428",
"0.8107705",
"0.79870147",
"0.79809254",
"0.79333097",
"0.7864782",
"0.7748505",
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"0.7564928",
"0.7562569",
"0.75531864",
"0.7344096",
"0.7298929",
"0.72495806",
"0.7220... | 0.7113526 | 24 |
Takes in a movie's ID and returns a list of strings representing the genres pertaining to the movie. Access the movies dataframe to convert the large concatenated string delimited by '|'. | def listGenres(movieId):
genres = movies.at[movieId, 'genres'] #change movies to whatever variable name the movies df has
genres = genres.split('|')
return genres | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_genres(genres):\n\tgenre_list = []\n\tfor genre in genres:\n\t\tgenre_list.append(genre.name)\n\n\treturn \", \".join(genre_list)",
"def _get_genre_vector(self, movie_id, list=False):\n if list:\n return self.movies[self.movies['movie_id'] == movie_id][self.genres].iloc[0].tolist()\n ... | [
"0.6485439",
"0.6418141",
"0.6309844",
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"0.5774838",
"0.577413",
"0.5769358",
"0.57170504",
"0.5686664",... | 0.84673285 | 0 |
Takes in a list of predicted ratings and a list of actual ratings for a set of movies and computes the mean absolute error of our predictions. | def mae(predicted, actual):
#maybe make some assertions, assume have same length & in right order
interm_total = 0
for i in range(len(predicted)):
interm_total += abs(predicted[i] - actual[i])
return interm_total / len(predicted) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mean_absolute_error(predictions, ratings_test):\n return np.abs(predictions - np.array(ratings_test.rating)).mean()",
"def mean_squared_error ( actuals, predictions ):\n return np.mean ( ( actuals - predictions ) ** 2 )\n # End mean_squared_error()",
"def mean_squared_error ( actuals, predictions ... | [
"0.73422986",
"0.7151543",
"0.7151543",
"0.7146762",
"0.70993924",
"0.69311756",
"0.6910379",
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"0.66862285",
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"0.6590158",
"0.658878",
"0.65615636",
"0.6515388",
"0.65142846",
"0.64485",
"0.6440558",
"0.643734... | 0.6110793 | 49 |
Takes in a list of predicted ratings and a list of actual ratings for a set of movies and computes the root mean square error of our predictions. | def rmse(predicted, actual):
#maybe make some assertions, assume have same length & in right order
interm_total = 0
for i in range(len(predicted)):
interm_total += (predicted[i] - actual[i]) ** 2
return sqrt(interm_total / len(predicted)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mean_squared_error ( actuals, predictions ):\n return np.mean ( ( actuals - predictions ) ** 2 )\n # End mean_squared_error()",
"def mean_squared_error ( actuals, predictions ):\n return np.mean ( ( actuals - predictions ) ** 2 )\n # End mean_squared_error()",
"def mean_squared_error(prediction... | [
"0.7167294",
"0.7167294",
"0.7134918",
"0.69895697",
"0.6954634",
"0.69295204",
"0.69183964",
"0.67908305",
"0.67088586",
"0.6700815",
"0.6667378",
"0.6665828",
"0.65780556",
"0.6568806",
"0.6543323",
"0.65049005",
"0.6441019",
"0.6425092",
"0.6396935",
"0.63875955",
"0.63875... | 0.6367347 | 22 |
clean up raw titles taken from BOMojo to facilitate better searches on OMDB/Twitter | def processTitle(title):
cleaned = re.sub(r'[@#\"]+', '', title.lower().strip())
cleaned = re.sub(r'\(\d{4}.*\)', '', cleaned)
cleaned = re.sub(r':.+', '', cleaned).strip()
return cleaned | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_title(self, title):\n\t\t# strip apostrophes\n\t\tif '\\'' in title:\n\t\t\ttitle = re.sub('\\'', '', title)\n\t\tif '.' in title:\n\t\t\ttitle = re.sub('.', '', title)\n\t\treturn title",
"def clean_title(title):\n title = re.sub(\"\\n\", \"\", title) # Remove newlines\n title = ' '.join(title... | [
"0.6604108",
"0.6413314",
"0.6245085",
"0.62396383",
"0.62070096",
"0.6201837",
"0.6178692",
"0.61765945",
"0.60911244",
"0.6089713",
"0.608802",
"0.6075226",
"0.6067024",
"0.60305786",
"0.59994125",
"0.59956473",
"0.594616",
"0.59446096",
"0.5936255",
"0.59050155",
"0.589108... | 0.64549226 | 1 |
output a data table to CSV for visual inspection | def outputData(tname):
table = pd.read_sql("SELECT * FROM {0}".format(tname), ENGINE)
table.to_csv("data/{0}.csv".format(tname), sep=",", header=True, index=False, quoting=csv.QUOTE_NONNUMERIC) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_csv(self):\n self.tableView.df.to_csv('Data export.csv', index=False)\n print('CSV file exported')",
"def generate_csv(table, header):\n with open(\"%s.csv\" % header, \"w\") as csvfile:\n for i in range(len(table)):\n for j in range(len(table[i])):\n i... | [
"0.75195706",
"0.73774415",
"0.719166",
"0.7110487",
"0.7089844",
"0.70896596",
"0.7071763",
"0.70578736",
"0.70538783",
"0.7041552",
"0.70119685",
"0.6945632",
"0.69442695",
"0.6914141",
"0.68514514",
"0.6846723",
"0.68346244",
"0.68259144",
"0.68199605",
"0.680954",
"0.6808... | 0.7156569 | 3 |
pull the next date for which to search for new data | def findNextDate(cnx, firstdate):
cur = cnx.cursor()
cur.execute("SELECT gross_date FROM boxoffice ORDER BY gross_date DESC LIMIT 1")
try:
lastdate = cur.fetchone()[0]
nextdate = datetime.strptime(lastdate, '%Y-%m-%d') + timedelta(days=1)
nextdate = nextdate.strftime('%Y-%m-%d')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def next_release_date(date):\n df = get_release_dates()\n df = df[df['ReleaseDate'] > date]\n return df['ReleaseDate'].iloc[0]",
"def find_next_valid(data, date):\n correct_date = None\n while correct_date is None:\n try:\n _ = data.loc[date]\n correct_date = date\n ... | [
"0.736613",
"0.7089448",
"0.7066551",
"0.69742924",
"0.6841013",
"0.6803448",
"0.6733757",
"0.65900165",
"0.6249408",
"0.62220967",
"0.62220967",
"0.6202062",
"0.61446637",
"0.6117578",
"0.6051559",
"0.6039819",
"0.602323",
"0.5971371",
"0.5857774",
"0.5829712",
"0.5808867",
... | 0.67084557 | 7 |
scrape BOMojo for information on the top N movies in terms of daily gross | def getTopMovies(endpoint, date, count=10):
try:
response = urlreq.urlopen(endpoint.format(date))
soup = BeautifulSoup(response.read(), "html.parser")
table = soup.find('table', border="0", cellpadding="5", cellspacing="1")
tdata = []
for i, row in enumerate(table... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_top_movies(genre: Genre) -> List[Movie]:\n\n html = download_top_movies(genre)\n soup = parse_top_movies(html)\n return [extract_movie(result) for result in soup]",
"def scrape_movie_page(dom):\n # to save the information\n info = []\n\n # find the information block needed\n header =... | [
"0.71330565",
"0.67618537",
"0.6608621",
"0.656355",
"0.6549088",
"0.6464945",
"0.64557415",
"0.6446494",
"0.6415211",
"0.6344131",
"0.6254804",
"0.62027013",
"0.619816",
"0.61576635",
"0.61518717",
"0.61433727",
"0.6072192",
"0.6044976",
"0.60145175",
"0.5932325",
"0.5837514... | 0.72936654 | 0 |
generate a tweepy API object to perform actions | def generateAPI(c_key, c_sec, a_tok, a_sec, **kwargs):
auth = tweepy.OAuthHandler(c_key, c_sec)
auth.set_access_token(a_tok, a_sec)
api = tweepy.API(auth, **kwargs)
return api | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def access_api():\n\n auth = tweepy.OAuthHandler(Consumer_key, Consumer_key_secret)\n auth.set_access_token(Access_token, Access_token_secret)\n api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)\n\n return api",
"def __init__(self):\n auth = tweepy.OAuthHandler(co... | [
"0.72307414",
"0.71253115",
"0.709998",
"0.69709504",
"0.6892117",
"0.6864982",
"0.6747763",
"0.6702982",
"0.6688524",
"0.6688524",
"0.66325337",
"0.66152906",
"0.65276515",
"0.65111357",
"0.6489107",
"0.64700675",
"0.6451166",
"0.644855",
"0.64357334",
"0.6417696",
"0.641477... | 0.69804233 | 3 |
process a given tweet to prepare it for storage | def processTweet(title, tweet, remove_title=False):
# create a title regex and initialize a dictionary to hold results
texp = r"#?" + r" ?".join(processTitle(title).split(" "))
results = {}
# retrieve author metadata
results['author_id'] = tweet.author.id
results['author_name'] ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_tweet(tweet):\n global start_date\n global end_date\n global geo_enabled_tweets\n global retweets\n\n # Check for filters before processing any further\n if args.filter and tweet.source:\n if not args.filter.lower() in tweet.source.lower():\n return\n\n tw_date = ... | [
"0.72721946",
"0.7039031",
"0.6736819",
"0.6640499",
"0.6595347",
"0.6585862",
"0.6540867",
"0.65392375",
"0.64957726",
"0.6471852",
"0.6453129",
"0.6349555",
"0.6304325",
"0.61687505",
"0.61530614",
"0.61504155",
"0.6125002",
"0.61050314",
"0.6098452",
"0.60467815",
"0.60453... | 0.6764535 | 2 |
search twitter for tweets mentioning a given movie title | def searchMovie(api, title, date, count, retweets=False):
since = date
until = datetime.strptime(since, '%Y-%m-%d') + timedelta(days=1)
until = until.strftime('%Y-%m-%d')
query = "\"{0}\" since:{1} until:{2} -filter:links".format(processTitle(title), since, until)
if retweets == False:
que... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search_by_title(title):\n\turl = tmdb_api(\"search/movie\")+\"&query=\"+urllib.quote_plus(title)\n\tresponse = json.load(urllib2.urlopen(url))\n\treturn JSONResponse(response)",
"def search_movies(title: str) -> list[tuple]:\n with connection:\n search = '%' + title + '%'\n return list(conne... | [
"0.6925293",
"0.6724913",
"0.64352965",
"0.6390151",
"0.6380741",
"0.6323461",
"0.6268078",
"0.61828446",
"0.6174598",
"0.610009",
"0.6096859",
"0.60881114",
"0.60233986",
"0.5969585",
"0.59671646",
"0.59104264",
"0.58904886",
"0.5866746",
"0.5842005",
"0.5801552",
"0.5786741... | 0.68453336 | 1 |
retrieve movie metadata from OMDB | def getMovieInfo(endpoint, title, year):
params = {'t': title, 'y': year, 'plot':'short', 'r':'json', 'tomatoes':'true'}
response = requests.get(endpoint, params=params)
try:
response.raise_for_status()
response = response.json()
if 'Error' in response.keys():
raise ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getOmdbInfo(title):\n baseUrl = \"http://www.omdbapi.com/?\"\n # parsing the API credentials to the base url\n credentialsData = urllib.parse.urlencode(credentials)\n finalUrl = baseUrl + credentialsData\n parameters = {\"t\": title} # Parameters to add a query to the url\n try:\n r =... | [
"0.70910835",
"0.6855289",
"0.6734887",
"0.65963125",
"0.6587276",
"0.65848976",
"0.65397507",
"0.651509",
"0.649011",
"0.6487825",
"0.6443921",
"0.63679284",
"0.636016",
"0.63368636",
"0.6247301",
"0.623941",
"0.6220363",
"0.61828387",
"0.6102056",
"0.609643",
"0.6065145",
... | 0.622112 | 16 |
insert a new movie into the movies table | def insertMovie(cnx, title, date, results):
try:
cur = cnx.cursor()
cur.execute('INSERT INTO movies VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)',
(title, date, results['Actors'], results['Director'], results['Genre'], results['Plot'],
results['Rated'], results[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_movie_to_db(self):\n MOVIE.insert_one({\n \"title\": self.title,\n \"year\": self.year\n })",
"def insert(self, movie):\n self._currentMovie = self.movies.add(movie)\n self._updateMovieList = True",
"def create(movie):\n schema = MovieSchema()\n n... | [
"0.81389934",
"0.75338364",
"0.72310257",
"0.7177825",
"0.7136104",
"0.7132212",
"0.69271517",
"0.6926719",
"0.6923478",
"0.68877876",
"0.6880706",
"0.68791896",
"0.67456186",
"0.6744896",
"0.6655135",
"0.6625037",
"0.65993536",
"0.6581402",
"0.6463103",
"0.6446574",
"0.64421... | 0.7341244 | 2 |
main driver program to gather daily data | def gatherData():
# connect to database, set up the tweepy API object, and find the next date to search
cnx = sqlite3.connect(DB_FILE)
api = generateAPI(wait_on_rate_limit=True, wait_on_rate_limit_notify=True, **CREDENTIALS)
nextdate = findNextDate(cnx, FIRSTDATE)
year = nextdate[:4]
# attem... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n print get_latest_data()",
"def main():\n credentials = get_credentials()\n now = datetime.datetime.utcnow().isoformat() + 'Z' # 'Z' indicates UTC time\n max = 7\n events = getEvents(credentials, now, max)\n if not events:\n print('No upcoming events found.')\n for event ... | [
"0.67580193",
"0.6650759",
"0.6525746",
"0.64976233",
"0.6491844",
"0.64623386",
"0.64084315",
"0.6386345",
"0.6351465",
"0.6345402",
"0.63255656",
"0.6315899",
"0.631295",
"0.6292713",
"0.6276271",
"0.6266144",
"0.6226493",
"0.62246346",
"0.6222256",
"0.6209975",
"0.61979985... | 0.0 | -1 |
Return a binding site name from a given agent. | def get_binding_site_name(agent):
# Try to construct a binding site name based on parent
grounding = agent.get_grounding()
# We don't want to accidentally deal with very deep ontological
# cases here such as CHEBI (e.g., GTP) which requires thousands
# of lookups to resolve
if grounding != (None... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def site_name(self, obj):\n site = obj.site\n return (\"%s\" % (site.name))",
"def server_agent_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"server_agent_name\")",
"def server_agent_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"serv... | [
"0.60516745",
"0.5991255",
"0.5991255",
"0.5868282",
"0.5762695",
"0.5751871",
"0.54725695",
"0.54660743",
"0.5430123",
"0.5412076",
"0.5372198",
"0.53381824",
"0.5273435",
"0.51932955",
"0.5154005",
"0.50929475",
"0.50890386",
"0.50785226",
"0.5071362",
"0.4990982",
"0.49901... | 0.83094543 | 0 |
Return site names for a modification. | def get_mod_site_name(mod_condition):
if mod_condition.residue is None:
mod_str = abbrevs[mod_condition.mod_type]
else:
mod_str = mod_condition.residue
mod_pos = mod_condition.position if \
mod_condition.position is not None else ''
name = ('%s%s' % (mod_str, mod_pos))
return... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_site_names(self, include = ['*'], exclude = []):\n \n raise NotImplementedError('get_site_names')",
"def affecteds_names(self):\n return [m.name for m in self.affecteds]",
"def get_urls(self):\r\n if self.mod.filename:\r\n return [x + self.mod.filename for x in se... | [
"0.59734267",
"0.55854976",
"0.54443717",
"0.53902864",
"0.5368823",
"0.5288254",
"0.52330816",
"0.51733387",
"0.5106047",
"0.51046014",
"0.5099322",
"0.5099322",
"0.5074785",
"0.50735295",
"0.5044799",
"0.5037665",
"0.5037665",
"0.49848792",
"0.49837267",
"0.49308565",
"0.48... | 0.48955134 | 21 |
Create and return a new object. See help(type) for accurate signature. | def __new__(*args, **kwargs):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_object():\n return object()",
"def create(cls, _):\n return cls",
"def __newobj__(cls, *args):\n return cls.__new__(cls, *args)",
"def create(cls):\n pass\n return cls()",
"def __new__(cls):\n return object.__new__(cls)",
"def __new__(cls):\n return objec... | [
"0.75212413",
"0.74254465",
"0.7227017",
"0.7224189",
"0.7096888",
"0.7096888",
"0.7059055",
"0.70071834",
"0.69155335",
"0.68690395",
"0.6814675",
"0.6812803",
"0.6802038",
"0.66921675",
"0.6628809",
"0.6586155",
"0.6570953",
"0.6558287",
"0.6547422",
"0.65455115",
"0.654116... | 0.0 | -1 |
Implement setattr(self, name, value). | def __setattr__(self, *args, **kwargs):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __setattr__(self, name, value):\n self.set(**{name: value})",
"def set_attr(self, name, value):\n setattr(self, name, value)",
"def __setattr__(self, name, value):\n if not hasattr(self, name):\n raise AttributeError(\"'%s' object has no attribute '%s'\" % (type(self).__name... | [
"0.868889",
"0.8361373",
"0.8290292",
"0.8283524",
"0.82243794",
"0.8204499",
"0.8099823",
"0.8099823",
"0.8071226",
"0.80402154",
"0.8024459",
"0.79978293",
"0.79883593",
"0.7987577",
"0.79493016",
"0.7936443",
"0.79316676",
"0.7890851",
"0.78896946",
"0.78018266",
"0.775468... | 0.0 | -1 |
The combination of associate_user and load_extra_data functions of djangosocialauth. The reason for combining these two pipeline functions is to decrease the number of database visits. | def social_associate_and_load_data(backend, details, response, uid, user,
social_user=None, *args, **kwargs):
extra_data = backend.extra_data(user, uid, response, details)
created = False
if not social_user and user:
social_user, created = UserSocialAuth.objects.ge... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_extra_data(backend, details, response, uid, user, social_user=None,\n *args, **kwargs):\n social_user = social_user or \\\n UserSocialAuth.get_social_auth(backend.name, uid)\n if social_user:\n extra_data = backend.extra_data(user, uid, response, details)\n... | [
"0.6427262",
"0.5478884",
"0.5240445",
"0.52389956",
"0.5213373",
"0.519633",
"0.51899827",
"0.5171619",
"0.5155315",
"0.51518744",
"0.5148101",
"0.5131034",
"0.5093896",
"0.50883776",
"0.5074174",
"0.5033296",
"0.50316167",
"0.50292736",
"0.5025192",
"0.50241655",
"0.5004713... | 0.6337023 | 1 |
Prettyprint a list of builders from a master. | def pp_builders(builders, master):
columns = ['name', 'slavename', 'category']
title = 'outputting builders for: %s' % master
notfound = 'no builders found.'
pp_internal(builders, columns, title, notfound) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAllBuilders(self):\n names = self.status.getBuilderNames(categories=self.categories)\n builders = [self.status.getBuilder(n) for n in names]\n return builders",
"def list_builders(self) -> List[str]:\n return sorted(_iter_builder_names(self._ns2data_dir))",
"def listBuilderNames(... | [
"0.6192067",
"0.60224825",
"0.55578184",
"0.55578184",
"0.5387139",
"0.53863895",
"0.53175986",
"0.52845865",
"0.5281185",
"0.5277838",
"0.5247651",
"0.52469724",
"0.5176106",
"0.512959",
"0.5071999",
"0.49349397",
"0.49117327",
"0.48857865",
"0.48785007",
"0.48488688",
"0.48... | 0.77710736 | 0 |
Expand builders which have multiple slaves into multiple builders with one slave each. | def dup_slaves(builders):
def arrayify(possible_array):
"""Convert 'string' into ['string']. Leave actual arrays alone."""
if isinstance(possible_array, basestring):
return [possible_array]
return possible_array
duped_slaves = []
for b in builders:
for slave in arrayify(b['slavenames']):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def slavebuilders(self):\n return self._get_slave_builders(lambda s:\n s.slavename in self._online_slaves and\n s.slavename not in self._busy_slaves)",
"def pp_builders(builders, master):\n\n columns = ['name', 'slavename', 'category']\n title = 'outputting builders for: %s' % master\n notfound =... | [
"0.7009561",
"0.5939224",
"0.5697346",
"0.554258",
"0.52212834",
"0.51657134",
"0.51587296",
"0.5150197",
"0.51009035",
"0.5070033",
"0.5056523",
"0.5022392",
"0.5016884",
"0.49988937",
"0.49728",
"0.49724874",
"0.49134463",
"0.48988444",
"0.48966083",
"0.48809367",
"0.484797... | 0.7059593 | 0 |
Convert 'string' into ['string']. Leave actual arrays alone. | def arrayify(possible_array):
if isinstance(possible_array, basestring):
return [possible_array]
return possible_array | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _strings_to_list(one_or_more_strings):\n if isinstance(one_or_more_strings, str):\n return [one_or_more_strings]\n else:\n return list(one_or_more_strings)",
"def str2list(input):\n if isinstance(input, str):\n return [input]\n\n else:\n return input",
"def explode(_... | [
"0.7060984",
"0.7050311",
"0.7019041",
"0.6896074",
"0.68879455",
"0.6805283",
"0.67713535",
"0.67248327",
"0.6600286",
"0.6444824",
"0.6418021",
"0.6392094",
"0.63635457",
"0.63421506",
"0.6302689",
"0.62985396",
"0.6285999",
"0.62579507",
"0.6233276",
"0.6212226",
"0.620852... | 0.68721193 | 5 |
Return a list of builders which match what is specified in 'spec'. | def search(builders, spec):
if 'builder' in spec:
return [b for b in builders if b['name'] ==
spec['builder']]
elif 'hostname' in spec:
return [b for b in builders if b['slavename']
== spec['hostname']]
else:
return [b for b in builders if (b['name'] ==
spec['either... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def choose(builders, spec):\n\n candidates = search(builders, spec)\n buildername = get_builder(candidates, spec.values()[0])\n\n if not buildername:\n return None\n\n blder = [b for b in builders if b['name'] == buildername][0]\n\n return blder",
"def getAllBuilders(self):\n names = self.status.g... | [
"0.7065604",
"0.6366904",
"0.6205621",
"0.59313875",
"0.5801212",
"0.5716414",
"0.5580702",
"0.54017884",
"0.5263097",
"0.52591556",
"0.5248544",
"0.52406526",
"0.52212656",
"0.52202344",
"0.5194147",
"0.5179128",
"0.5170816",
"0.5136215",
"0.51341134",
"0.51207536",
"0.51207... | 0.76802087 | 0 |
Return a pair of (mastername, path) for all masters found. | def get_masters():
# note: ListMasters uses master.cfg hardcoded as part of its search path
def parse_master_name(masterpath):
"""Returns a mastername from a pathname to a master."""
_, tail = os.path.split(masterpath)
sep = '.'
hdr = 'master'
chunks = tail.split(sep)
if not chunks or chunk... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def masters(self) -> pulumi.Output[Optional[Sequence[str]]]:\n return pulumi.get(self, \"masters\")",
"def ListMasters(cue='master.cfg', include_public=True, include_internal=True):\n # Look for \"internal\" masters first.\n path_internal = os.path.join(\n BUILD_DIR, os.pardir, 'build_internal', 'm... | [
"0.7142009",
"0.70371884",
"0.68338263",
"0.64809763",
"0.6472665",
"0.6472665",
"0.6311474",
"0.6304537",
"0.62080437",
"0.60861564",
"0.6067843",
"0.60515904",
"0.60162145",
"0.59996986",
"0.5956191",
"0.5953216",
"0.5837279",
"0.5673788",
"0.56117886",
"0.558528",
"0.55739... | 0.7571945 | 0 |
Returns a mastername from a pathname to a master. | def parse_master_name(masterpath):
_, tail = os.path.split(masterpath)
sep = '.'
hdr = 'master'
chunks = tail.split(sep)
if not chunks or chunks[0] != hdr or len(chunks) < 2:
raise ValueError('unable to parse mastername from path! (%s)' % tail)
return sep.join(chunks[1:]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def choose_master(searchname):\n masters = get_masters()\n masternames = []\n master_lookup = {}\n for mn, path in masters:\n master = {}\n master['mastername'] = mn\n master_lookup[mn] = path\n masternames.append(master)\n\n candidates = [mn for mn in masternames if mn['mastername'] == searchname... | [
"0.6372923",
"0.6317169",
"0.61556184",
"0.610252",
"0.60480016",
"0.600932",
"0.58795077",
"0.58532685",
"0.58285683",
"0.5752388",
"0.57484525",
"0.5719935",
"0.57097894",
"0.56524986",
"0.56494594",
"0.564476",
"0.564476",
"0.564476",
"0.5600287",
"0.5599613",
"0.5592351",... | 0.8282636 | 0 |
Confirm a sequence only contains one unique value and return it. | def only_get_one(seq, key, source):
def uniquify(seq):
return list(frozenset(seq))
res = uniquify([s[key] for s in seq])
if len(res) > 1:
print >>sys.stderr, 'error: %s too many %ss:' % (source, key)
for r in res:
print ' ', r
return None
elif not res:
print 'error: %s zero %ss' % (... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isdistinct(seq):\n return len(seq) == len(set(seq))",
"def get_unique(self):\n return self.serie.nunique()",
"def is_unique(self):\r\n return self._unique",
"def uniq(seq):\r\n #TODO: consider building a set out of seq so that the if condition\r\n #is constant time -JB\r\n retur... | [
"0.6194614",
"0.61632925",
"0.6018149",
"0.60084176",
"0.59649074",
"0.5959102",
"0.5955434",
"0.5943642",
"0.59182596",
"0.5835167",
"0.58102876",
"0.5796282",
"0.5759754",
"0.5730302",
"0.56856775",
"0.56828374",
"0.5673099",
"0.5668738",
"0.55292314",
"0.5515162",
"0.54578... | 0.625032 | 0 |
Return unique builder name from a list of builders. | def get_builder(slaves, keyval):
errstring = 'string \'%s\' matches' % keyval
return only_get_one(slaves, 'name', errstring) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getBuilderNames():",
"def listBuilderNames():",
"def listBuilderNames():",
"def GetBotNamesForBuilder(builders, builder_name):\n bots = []\n pool_names = builders['builders'][builder_name]['bot_pools']\n for pool_name in pool_names:\n for entry in builders['bot_pools'][pool_name]['bots']:\n bo... | [
"0.69416225",
"0.6723085",
"0.6723085",
"0.65255445",
"0.6470424",
"0.63513696",
"0.6288651",
"0.6103744",
"0.5903304",
"0.5774765",
"0.57546216",
"0.56133306",
"0.54580605",
"0.5434363",
"0.5427097",
"0.5427097",
"0.5319753",
"0.5281067",
"0.5212107",
"0.52096176",
"0.519287... | 0.5666771 | 11 |
Search through builders matching 'spec' and return it. | def choose(builders, spec):
candidates = search(builders, spec)
buildername = get_builder(candidates, spec.values()[0])
if not buildername:
return None
blder = [b for b in builders if b['name'] == buildername][0]
return blder | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search(builders, spec):\n if 'builder' in spec:\n return [b for b in builders if b['name'] ==\n spec['builder']]\n elif 'hostname' in spec:\n return [b for b in builders if b['slavename']\n == spec['hostname']]\n else:\n return [b for b in builders if (b['name'] ==\n ... | [
"0.74085414",
"0.5914206",
"0.5404804",
"0.53804606",
"0.53804606",
"0.5173281",
"0.51628286",
"0.5149105",
"0.5062585",
"0.50532424",
"0.5007297",
"0.50054854",
"0.49976838",
"0.49824825",
"0.4926369",
"0.48937625",
"0.48742664",
"0.48677486",
"0.48448572",
"0.47606125",
"0.... | 0.7681437 | 0 |
Connect to chromium LKGR server and get LKGR revision. | def get_lkgr():
try:
conn = urllib.urlopen('http://chromium-status.appspot.com/lkgr')
except IOError:
return (None, 'Error connecting to LKGR server! Is your internet '
'connection working properly?')
try:
rev = int('\n'.join(conn.readlines()))
except IOError:
return (None, 'Error c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetLKGR():\n\n try:\n conn = urllib.urlopen('https://chromium-status.appspot.com/lkgr')\n except IOError:\n return (None, 'Error connecting to LKGR server! Is your internet '\n 'connection working properly?')\n try:\n rev = int('\\n'.join(conn.readlines()))\n except IOError:\n return... | [
"0.7639202",
"0.5643532",
"0.5526256",
"0.5292058",
"0.52112335",
"0.5158212",
"0.5131409",
"0.51192427",
"0.5084575",
"0.5029493",
"0.49947414",
"0.4977814",
"0.49504116",
"0.49502164",
"0.49388358",
"0.49381736",
"0.49336123",
"0.49293384",
"0.49293384",
"0.49240768",
"0.48... | 0.76185536 | 1 |
Verify arguments are correct and prepare args dictionary. | def args_ok(inoptions, pos_args):
if inoptions.factory_properties:
for key in inoptions.factory_properties:
setattr(inoptions, key, inoptions.factory_properties[key])
if inoptions.list_masters:
return True
if inoptions.build_properties and not inoptions.master_dir:
if inoptions.build_properti... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_args(self, args_):\n\n pass",
"def __check_args(self):\n self.__check_args_type()\n self.__check_args_val()",
"def check_arguments(self):\n ## only four test operation is permitted, if given anything apart from this, then it should print error message\n if (self.ar... | [
"0.7524365",
"0.7385789",
"0.7053287",
"0.69999295",
"0.6985941",
"0.68912166",
"0.68684274",
"0.68312424",
"0.67038983",
"0.66816574",
"0.6628343",
"0.6617935",
"0.661433",
"0.6587311",
"0.65323263",
"0.65291506",
"0.6506392",
"0.6493578",
"0.6490966",
"0.6479323",
"0.646865... | 0.6173015 | 45 |
Output error and stop further steps. | def buildException(status, why):
print >>sys.stderr, 'build error encountered:', why
print >>sys.stderr, 'aborting build'
status.code = 1
reactor.callFromThread(reactor.stop) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def finalize_error():\n print('')\n exit(-1)",
"def exit_on_error(self) -> None:\n if self.errors:\n sys.exit(1)",
"def stop_err(msg):\n sys.stderr.write('%s\\n' % msg)\n sys.exit(-1)",
"def call_error():\r\n print(\"Error in input format.\")\r\n sys.exit()",
"def should... | [
"0.76871026",
"0.7620726",
"0.72847825",
"0.722326",
"0.71368927",
"0.70985675",
"0.6998441",
"0.6992924",
"0.69576144",
"0.69307584",
"0.69207406",
"0.6894901",
"0.6878585",
"0.6854247",
"0.6834557",
"0.6831641",
"0.6812907",
"0.6808463",
"0.6802954",
"0.6802954",
"0.6787063... | 0.0 | -1 |
Tear down twisted session. | def finished():
print >>sys.stderr, 'build completed successfully'
reactor.callFromThread(reactor.stop) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shutdown(self):\n logging.info(\"Shutdown\")\n self._sessionmanager.Shutdown()",
"def close(self):\n self.__session.remove()",
"def close(self):\n self.__session.remove()",
"def close(self):\n self.__session.remove()",
"async def shutdown(self, ctx):\n await se... | [
"0.70598125",
"0.6998482",
"0.6998482",
"0.6998482",
"0.6988004",
"0.6942793",
"0.69355035",
"0.6903656",
"0.68920654",
"0.68756866",
"0.68607336",
"0.68400365",
"0.68310785",
"0.6735389",
"0.6730148",
"0.671928",
"0.6656418",
"0.66507643",
"0.6634737",
"0.66242105",
"0.66220... | 0.0 | -1 |
Run the next step, optionally skipping if there is a stepfilter. | def startNextStep(steps, run_status, prog_args):
def getNextStep():
if not steps:
return None
return steps.pop(0)
try:
s = getNextStep()
if hasattr(prog_args, 'step_regex'):
while s and not prog_args.step_regex.search(s.name):
print >>sys.stderr, 'skipping step: ' + s.name
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_step(self) -> None:",
"def next_step(self):\n self.proceed()\n self.execute_current()",
"def step(self, **kwargs):\n pass",
"def step(self, step=None):\n pass",
"def _step(self):\n pass",
"def step(self):\n while self.state != STATE_TERMINAL:\n ... | [
"0.6615308",
"0.65612143",
"0.6438154",
"0.63276404",
"0.6169919",
"0.61310494",
"0.6100939",
"0.60929835",
"0.6047394",
"0.60388684",
"0.6027473",
"0.5933554",
"0.58823085",
"0.5839381",
"0.5839381",
"0.5828136",
"0.5741703",
"0.56705713",
"0.56664586",
"0.56664586",
"0.5660... | 0.5515624 | 35 |
Check if the previous step succeeded before continuing. | def checkStep(rc, steps, run_status, prog_args):
if (rc == FAILURE) or (rc == EXCEPTION):
buildException(run_status, 'previous command failed')
else:
defer.maybeDeferred(lambda x: startNextStep(x,
run_status, prog_args), steps) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ready_to_proceed(self):\n if self.current_step is None or self.step_position == StepPosition.Before:\n return False\n\n for condition, _ in self.current_step.conditions:\n if condition.satisfied():\n return True\n return False",
"def try_advance(self)... | [
"0.6924123",
"0.6897496",
"0.6432004",
"0.64264905",
"0.6425109",
"0.63696253",
"0.6231616",
"0.6207607",
"0.619148",
"0.6149748",
"0.61462504",
"0.6142753",
"0.614151",
"0.6126543",
"0.6112948",
"0.6102814",
"0.6095051",
"0.60858077",
"0.6071269",
"0.60315263",
"0.60169834",... | 0.71053165 | 0 |
Print out a list of steps in the builder. | def generate_steplist(my_factory):
steps = []
stepnames = {}
for factory, cmdargs in my_factory.steps:
cmdargs = cmdargs.copy()
try:
step = factory(**cmdargs)
except:
print >>sys.stderr, ('error while creating step, factory=%s, args=%s'
% (factory, cmdargs))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_steps(steps, printer=logging.info):\n\n # Print steps and respective kwargs in pipeline\n for si, (class_object, kwargs) in enumerate(steps, 1):\n if not kwargs:\n printer('{}) {}'.format(si, class_object.__name__))\n continue\n\n # Width of kwarg keyword, to mak... | [
"0.71910936",
"0.64591366",
"0.6434415",
"0.6221022",
"0.6214201",
"0.620257",
"0.6115325",
"0.61081594",
"0.61029005",
"0.60843486",
"0.6066363",
"0.6058359",
"0.6048852",
"0.60436594",
"0.5989871",
"0.59669226",
"0.59497505",
"0.5945767",
"0.58630234",
"0.5857588",
"0.58519... | 0.0 | -1 |
Given a string, load all masters and pick the master that matches. | def choose_master(searchname):
masters = get_masters()
masternames = []
master_lookup = {}
for mn, path in masters:
master = {}
master['mastername'] = mn
master_lookup[mn] = path
masternames.append(master)
candidates = [mn for mn in masternames if mn['mastername'] == searchname]
errstring ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_masters():\n\n # note: ListMasters uses master.cfg hardcoded as part of its search path\n def parse_master_name(masterpath):\n \"\"\"Returns a mastername from a pathname to a master.\"\"\"\n _, tail = os.path.split(masterpath)\n sep = '.'\n hdr = 'master'\n chunks = tail.split(sep)\n if... | [
"0.59240687",
"0.5650912",
"0.5650912",
"0.5484264",
"0.544708",
"0.5408858",
"0.5354618",
"0.5318847",
"0.51864445",
"0.5169288",
"0.51414424",
"0.5123247",
"0.51117146",
"0.5093722",
"0.5075803",
"0.4926",
"0.48871285",
"0.4880617",
"0.48195487",
"0.47273347",
"0.47038534",... | 0.7108627 | 0 |
Attach build and buildslaves to each step. | def process_steps(steplist, build, buildslave, build_status, basedir):
for step in steplist:
step.setBuild(build)
step.setBuildSlave(buildslave)
step.setStepStatus(build_status.addStepWithName(step.name))
step.setDefaultWorkdir(os.path.join(basedir, 'build'))
step.workdir = os.path.join(basedir, '... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stepStarted(build, step):",
"def build_step(self):\n\n pass",
"def build_step(self):\n pass",
"def build_step(self):\n pass",
"def steps(self, steps):\n\n self._steps = steps",
"def merge_steps(self, steps):\n raise NotImplementedError",
"def stepFinished(build, s... | [
"0.60801065",
"0.6005942",
"0.59210396",
"0.59210396",
"0.5855583",
"0.5763112",
"0.56093055",
"0.5504773",
"0.5465347",
"0.541182",
"0.5409328",
"0.5386782",
"0.538314",
"0.53748685",
"0.53719014",
"0.5365673",
"0.5361277",
"0.53086853",
"0.5272315",
"0.5268723",
"0.5249136"... | 0.6598073 | 0 |
Extract shell commands from step. | def get_commands(steplist):
commands = []
for step in steplist:
if hasattr(step, 'command'):
cmdhash = {}
renderables = []
accumulateClassList(step.__class__, 'renderables', renderables)
for renderable in renderables:
setattr(step, renderable, step.build.render(getattr(step,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_shell(self, shell):",
"def shell_command_strings(self, command):\n return (None, \"$(shell \" + command + \")\", None)",
"def extract_commands(self):\n # import pdb; pdb.set_trace()\n left_i = 0\n right_i = 1\n commands = {}\n cmd = self.cmd\n\n if not c... | [
"0.62787807",
"0.6205541",
"0.59439397",
"0.57891214",
"0.5723175",
"0.56572735",
"0.5631145",
"0.5605977",
"0.5601769",
"0.55819225",
"0.5537334",
"0.551242",
"0.5481799",
"0.5445721",
"0.5445366",
"0.5432864",
"0.5421588",
"0.5414213",
"0.54119444",
"0.5395798",
"0.53597456... | 0.6046613 | 2 |
Initialise the eazy object. | def __init__(self, OUTPATH, OUTPREFIX):
self.path = OUTPATH
self.prefix = OUTPREFIX
self.apply_prior = False
if not self.path.endswith("/"):
self.path = self.path + "/"
self.params, self.apply_prior = self.getParams(self.path, self.prefix)
self.zout = self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def initialise(self):",
"def do_i... | [
"0.7445991",
"0.7445991",
"0.7445991",
"0.7445991",
"0.7445991",
"0.7445991",
"0.7445991",
"0.7445991",
"0.73927194",
"0.7332743",
"0.7290069",
"0.7266646",
"0.7266646",
"0.7266646",
"0.7266646",
"0.7266646",
"0.72549605",
"0.7246767",
"0.7245598",
"0.7245598",
"0.72157526",
... | 0.0 | -1 |
Get the contents of the .param EAZY output file and return as a dictionary object. | def getParams(self, path, prefix, verbose = True):
fpath = path + prefix + ".param"
if not os.path.isfile(fpath):
if verbose: print '.params file not found - please check!'
return False
params = dict(np.loadtxt(fpath, dtype=str, comments="#"))
if bool(params['... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_parameter_file( param_file_name ):\n return json.loads( open( param_file_name).read() )",
"def _read_settings():\n parser = argparse.ArgumentParser(\n description='extract molecular fragments')\n parser.add_argument('-i', type=str, dest='input',\n help='input JSON ... | [
"0.6471408",
"0.60893655",
"0.60254073",
"0.60105526",
"0.60096985",
"0.59862536",
"0.59680444",
"0.5957866",
"0.59509706",
"0.591111",
"0.58881617",
"0.58322906",
"0.5806964",
"0.57989657",
"0.5794131",
"0.57913566",
"0.57668686",
"0.5729273",
"0.5718993",
"0.5717271",
"0.56... | 0.0 | -1 |
Return the contents of the EAZY cache file in a dictionary | def getCache(self, path, prefix, verbose = True):
fpath = path + prefix + "_cache"
if not os.path.isfile(fpath):
if verbose: print "Cache file not found - please check!"
return False
with open(fpath, "rb") as f:
s = np.fromfile(f, count=4, dtype=np.int32)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_cache():\n try:\n cache_file = open(CACHE_FILENAME, 'r', encoding=\"utf-8\")\n cache_contents = cache_file.read()\n cache_dict = json.loads(cache_contents)\n cache_file.close()\n return cache_dict\n except:\n cache_dict = {}\n return cache_dict",
"d... | [
"0.7426107",
"0.7003848",
"0.6940114",
"0.6829228",
"0.66353035",
"0.6561614",
"0.6475884",
"0.64701176",
"0.6441302",
"0.63921094",
"0.639017",
"0.63582116",
"0.6356054",
"0.6356054",
"0.6354886",
"0.63147855",
"0.629293",
"0.624724",
"0.6163521",
"0.6160946",
"0.6151438",
... | 0.5636934 | 64 |
Open the ascii .zout file and return as an astropy.table Table object. | def getOut(self, path, prefix, verbose = True):
fpath = path + prefix + ".zout"
if not os.path.isfile(fpath):
if verbose: print ".zout file not found - please check!"
return False
try:
zout = Table.read(fpath, format="ascii")
if verbose: print ".... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _pdread2astrotable(csvgzdir):\n df = pd.read_csv(csvgzdir)\n tb = Table.from_pandas(df)\n return tb",
"def read_tabix(\n file: PathType, storage_options: Optional[Dict[str, str]] = None\n) -> TabixIndex:\n with open_gzip(file, storage_options=storage_options) as f:\n magic = read_bytes_... | [
"0.6445617",
"0.58485997",
"0.56481296",
"0.5641298",
"0.5635116",
"0.5553415",
"0.5553415",
"0.5550646",
"0.5523641",
"0.54948246",
"0.53368026",
"0.52775514",
"0.52525586",
"0.5251553",
"0.5247824",
"0.52383107",
"0.5213523",
"0.5205577",
"0.5199613",
"0.5189507",
"0.518745... | 0.61672497 | 1 |
Open the .coeff EAZY output file and return contents as a dictionary object | def getCoeff(self, path, prefix, verbose = True):
fpath = path + prefix + ".coeff"
# print 'get coeff path', fpath
if not os.path.isfile(fpath):
if verbose: print ".coeff file not found - please check!"
return False
with open(fpath, "rb") as f:
s = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read_output_file(outfile: os.PathLike):\n with open(outfile, mode=\"r\") as f:\n res = {}\n for line in f:\n model, var, val = line.split(\":\")\n model = model.strip()\n if model not in res:\n res[model] = {}\n ... | [
"0.6290784",
"0.6113737",
"0.6085913",
"0.5871633",
"0.58495826",
"0.58220476",
"0.5799313",
"0.5752368",
"0.56779546",
"0.5649936",
"0.5642193",
"0.5599682",
"0.556524",
"0.5538183",
"0.5530907",
"0.55146635",
"0.5490223",
"0.5482123",
"0.5465694",
"0.5461214",
"0.5453267",
... | 0.5549966 | 13 |
Open the .tempfilt EAZY output file and return contents as a dictionary object | def getTempfilt(self, path, prefix, verbose = True):
fpath = path + prefix + '.tempfilt'
if not os.path.isfile(fpath):
if verbose: print ".tempfilt file not found - please check!"
return False
with open(fpath, "rb") as f:
s = np.fromfile(f, count = 4, dtype... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readtempfilt(MAIN_OUTPUT_FILE='photz', OUTPUT_DIRECTORY='./OUTPUT'):\n\n root = os.path.join(OUTPUT_DIRECTORY, MAIN_OUTPUT_FILE)\n \n ###### .tempfilt\n file_path = root+'.tempfilt'\n \n if os.path.exists(file_path) is False:\n raise ValueError('File, %s, not found.' %(file_path))\n\n ... | [
"0.711481",
"0.6126175",
"0.594898",
"0.56255794",
"0.55975693",
"0.555666",
"0.55090946",
"0.55012673",
"0.548985",
"0.5441883",
"0.5371903",
"0.53018695",
"0.529611",
"0.5256127",
"0.5240169",
"0.5194636",
"0.5193826",
"0.51894754",
"0.51860964",
"0.51855606",
"0.5164739",
... | 0.6364439 | 1 |
Open the .pz EAZY output file and return contents as a dictionary object | def getPz(self, path, prefix, verbose = True):
fpath = path + prefix + ".pz"
if not os.path.isfile(fpath):
if verbose: print ".pz file not found - please check!"
return False
with open(fpath, "rb") as f:
s = np.fromfile(f, count=2, dtype=np.int32)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def file_to_dictionary():\n\n return;",
"def read_file(filename):\n # Read in as nested dictionary\n # hipparcos_data = {'(star catalog number':\n # { 'parallax' : ... , 'apparent_magnitude' : ... , 'blue_minus_visual' : ... },\n # ... }\n\n return hipparcos_data",
"as... | [
"0.6477481",
"0.5687221",
"0.55963707",
"0.555326",
"0.54855376",
"0.54763544",
"0.54614484",
"0.5372779",
"0.5358996",
"0.5338705",
"0.53302336",
"0.5328768",
"0.53002435",
"0.53000194",
"0.529421",
"0.5283907",
"0.525488",
"0.5251823",
"0.5246022",
"0.5234127",
"0.51833165"... | 0.50832134 | 30 |
Open the .zbin EAZY output file and return contents as a dictionary object | def getBin(self, path, prefix, verbose = True):
fpath = path + prefix + ".zbin"
if not os.path.isfile(fpath):
if verbose: print ".zbin file not found - please check!"
return False
with open(fpath, "rb") as f:
s = np.fromfile(f, count=1, dtype=np.int32)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def file_to_dictionary():\n\n return;",
"def read_archive(self):\n print '------------------------------------'\n print ' Archiving the simulation results'\n print ' '\n self.archive = {}\n with open(self.archive_data, 'r') as f:\n for line in f:\n (key, val) = line.split()\n ... | [
"0.6425174",
"0.5940175",
"0.58725595",
"0.5715125",
"0.5622214",
"0.5616681",
"0.55860937",
"0.5580861",
"0.5564032",
"0.5561638",
"0.5522089",
"0.5476602",
"0.547554",
"0.533191",
"0.53222144",
"0.53065765",
"0.52383524",
"0.52364534",
"0.52283925",
"0.5224154",
"0.52168286... | 0.0 | -1 |
Compute the normalised PDF for an object in the output | def getPDF(self, idx, path = False, prefix = False, verbose = True):
if path is False: path = self.path
if prefix is False: prefix = self.prefix
if not isinstance(idx, (list, np.ndarray)):
idx = [idx]
params, apply_prior = self.getParams(path, prefix)
pz_d = sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pdf(self,x):\n returnPdf = self._distribution.pdf(x)\n return returnPdf",
"def pdf(self, x):\n raise NotImplementedError",
"def normal_pdf(x: torch.Tensor) -> torch.Tensor:\n return torch.exp(-(x ** 2 / 2)) / np.sqrt(2 * np.pi)",
"def pdf(self,x):\n if self.transformation:\n pdfVa... | [
"0.6779025",
"0.67060024",
"0.67034996",
"0.65615475",
"0.65343434",
"0.6449618",
"0.642357",
"0.64226717",
"0.6377885",
"0.6377885",
"0.637643",
"0.63481814",
"0.6324596",
"0.63121724",
"0.62875146",
"0.62816155",
"0.6273578",
"0.6232625",
"0.62272394",
"0.62031245",
"0.6193... | 0.0 | -1 |
Write a new .params file, overwritten with new key values | def writeParams(self, outpath = False, newvals = False, clobber = False, verbose = True):
if newvals is not False and type(newvals) is not dict:
if verbose: print "New parameters not in dictionary format - please check!"
return False
outparams = self.params
if newvals ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _write_params(self, force=False):\n\t\tif force or not os.path.exists(self._get_params_filepath()):\n\t\t\tf = open(self._get_params_filepath(), 'w')\n\t\t\tf.write(\"\\n\".join(self.params))\n\t\t\tf.write(\"\\n\")\n\t\t\tf.close()\n\t\t\tlogger.debug(\"Wrote %s\" % (self._get_params_filepath()))\n\t\telse:\n... | [
"0.73747414",
"0.7367924",
"0.6965227",
"0.69542533",
"0.67178345",
"0.6705485",
"0.64723575",
"0.6471904",
"0.6431831",
"0.6351544",
"0.6347792",
"0.63348347",
"0.6263163",
"0.6246471",
"0.61968315",
"0.6188766",
"0.61873174",
"0.61659473",
"0.61176175",
"0.6111604",
"0.6104... | 0.7389937 | 0 |
Grab the current zeropoints from the .zeropoints file | def getZeropoints (self, indir, inpref, verbose = True):
if os.path.isfile(indir+inpref+'.zeropoint'):
zeropoints = np.genfromtxt(indir+inpref+'.zeropoint', usecols=1, dtype=np.float32)
if verbose: print "zeropoints captured successfully!"
return zeropoints
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getPointsFromFile(traceFile):\r\n g = minidom.parse(traceFile)\r\n if g.getElementsByTagName('gpx'):\r\n return getPointsFromGpx(g)\r\n elif g.getElementsByTagName('kml'):\r\n return getPointsFromMercuryFile(traceFile)\r\n else:\r\n return [] #echec -no points\r",
"def get_a... | [
"0.61317664",
"0.59517145",
"0.5922443",
"0.58649415",
"0.5825099",
"0.58034104",
"0.57739806",
"0.5744703",
"0.57242584",
"0.5695771",
"0.56932396",
"0.56530195",
"0.56403214",
"0.5625227",
"0.56025916",
"0.55972755",
"0.5574614",
"0.55499184",
"0.55398184",
"0.5490028",
"0.... | 0.7392869 | 0 |
Calculate the photometric zeropoints for a spectroscopic sample | def calcZeropoints(self, eazypath, indir, inpref, tol = 1e-2, clobber = True,
maxiters = 100, exclude = False, verbose = True, plot = False):
zp_table = Table.read(indir+inpref+".zeropoint", format="ascii.no_header")
newpvals = {"GET_ZP_OFFSETS": 1,
"FIX_ZSPE... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def PA_SB_Zx_func(data, weit_data, pix_size, cx, cy, z0, R_bins):\n\tDa0 = Test_model.angular_diameter_distance(z0).value ## in unit 'Mpc'\n\tNx = data.shape[1]\n\tNy = data.shape[0]\n\tx0 = np.linspace(0, Nx-1, Nx)\n\ty0 = np.linspace(0, Ny-1, Ny)\n\tpix_id = np.array(np.meshgrid(x0,y0))\n\n\t#..center pixel poin... | [
"0.6416052",
"0.63215095",
"0.6161897",
"0.61533886",
"0.60753524",
"0.607235",
"0.6072261",
"0.6034678",
"0.6013616",
"0.596318",
"0.5955432",
"0.5914271",
"0.59079623",
"0.5838066",
"0.5828683",
"0.5815866",
"0.57941943",
"0.57479525",
"0.5717172",
"0.57110345",
"0.5696119"... | 0.0 | -1 |
Run EAZY and collect the output. | def runEAZY(self, eazypath, inpath, inpref):
command = ("cd {directory}; {0} -p {1} -t {2} -z {3}".format(eazypath, inpref+".param", inpref+".translate",
inpref+".zeropoint",
director... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n my_emr = EmrProcessing()\n\n if \"-s\" in sys.argv:\n my_emr.verbose_mode = False\n else:\n my_emr.verbose_mode = True\n print \"\\nStarting Titanic Data Analysis\"\n my_emr.parse_user_selections()\n\n # Setup\n my_emr.clear_local_output_directory()\n my_emr.... | [
"0.57431215",
"0.5409531",
"0.5399925",
"0.5399925",
"0.53816",
"0.52810526",
"0.5280897",
"0.52732193",
"0.5271705",
"0.52603763",
"0.52446514",
"0.5219894",
"0.5172208",
"0.51682454",
"0.5145979",
"0.5121802",
"0.51211095",
"0.50872207",
"0.50812286",
"0.5064914",
"0.506491... | 0.58166707 | 0 |
Plot a comparison between photometric and spectroscopic redshifts | def plotComparison(self, photoz = "z_peak", show = True):
fig, ax = plt.subplots(1, 1, figsize=(5.5, 5))
spec_z = self.zout["z_spec"]
photo_z = self.zout[photoz]
xy_max = np.max([spec_z.max(), photo_z.max()]) + 0.1
prng = np.arange(0, xy_max+0.05, 0.05)
ax.plot(spec_z... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot(self):\n\t\tself.plotOfSpect()",
"def plot(self):\n\t\tself.plotOfSpect().plot()",
"def plot_colour_mag_diagram(params,mags, colours, local_mags, local_colours,\n target, source, blend, RC, blue_filter, red_filter,\n yaxis_filter, tol, log):\n\n ... | [
"0.66576403",
"0.65320396",
"0.64093614",
"0.6394958",
"0.63727075",
"0.6201341",
"0.61825037",
"0.61814046",
"0.6138984",
"0.61138934",
"0.6106851",
"0.60940653",
"0.6091854",
"0.6068858",
"0.6003209",
"0.600191",
"0.5991049",
"0.59887123",
"0.5985631",
"0.5977557",
"0.59421... | 0.0 | -1 |
Calculate some useful statistics for the photoz/specz comparison. | def getCompStats(self, photoz = "z_peak", verbose = True):
specz = self.zout["z_spec"]
photoz = self.zout[photoz]
dz = (photoz - specz)
diff = (dz / (1.+specz))
nmad = 1.4826 * np.median( np.abs( dz - np.median(dz) ) )
mean_offset = np.mean(diff)
median_offset ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testSampleImageStats(self):\n \n imgfiles = []\n imgfiles.append(\"v1_i1_g_m400_s20_f.fits\")\n imgfiles.append(\"v1_i1_g_m400_s20_u16.fits\")\n imgfiles.append(\"v1_i2_g_m400_s20_f.fits\")\n imgfiles.append(\"v1_i2_g_m400_s20_u16.fits\")\n imgfiles.append(\"v2_... | [
"0.62995243",
"0.626098",
"0.61650515",
"0.60723734",
"0.60337234",
"0.6022576",
"0.5963165",
"0.59203196",
"0.5843567",
"0.5823474",
"0.58122724",
"0.5804204",
"0.577855",
"0.56952435",
"0.56760657",
"0.5613303",
"0.56002814",
"0.55723095",
"0.55523425",
"0.5547705",
"0.5523... | 0.78785306 | 0 |
Test to see what happens when only a subset of the potential settings are used | def test_wp_eval_some_settings():
args = {'line_by_line': True, "max_length": 512}
with pytest.raises(ValueError):
result = happy_wp.eval("../data/wp/train-eval.txt", args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_settings(self):\r\n pass",
"def check_settings(self):\n pass",
"def test_checkCustoms(self):\n self.failUnlessEqual(self.nice.opts['myflag'], \"PONY!\")\n self.failUnlessEqual(self.nice.opts['myparam'], \"Tofu WITH A PONY!\")",
"def test_check_required_success():\n se... | [
"0.672732",
"0.665855",
"0.6543287",
"0.6459723",
"0.63275325",
"0.63275325",
"0.62311333",
"0.61921144",
"0.61921144",
"0.61721367",
"0.613498",
"0.6128301",
"0.6111549",
"0.60465807",
"0.6014964",
"0.6011822",
"0.5996574",
"0.59954536",
"0.59722525",
"0.59559226",
"0.593957... | 0.0 | -1 |
Returns the fields needed for an extensible attributes ipam call. | def extensible_attributes():
return 'extensibleattributedef?' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def describe_account_attributes():\n pass",
"def get_api_fields(cls):\n return ['fqdn', 'ttl', 'description', 'views']",
"def getAttributes(self):\n pass",
"def get_ea_attributes(path, logger):\n reattrib = None\n try:\n reattrib = requests.get(PAYLOAD['url'] + \"extensibleattri... | [
"0.64952034",
"0.6412314",
"0.6406711",
"0.62219125",
"0.61058205",
"0.60922253",
"0.6087751",
"0.6087751",
"0.60825884",
"0.607636",
"0.60684806",
"0.60631704",
"0.60596853",
"0.6051178",
"0.6046008",
"0.6033764",
"0.6031279",
"0.6006388",
"0.5993471",
"0.5978762",
"0.596880... | 0.6063322 | 11 |
Returns the fields needed for an extensible attributes ipam call. | def extensible_attributes_list_values():
return "extensibleattributedef?" \
"_return_fields=" \
"list_values," \
"comment," \
"name," \
"type" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def describe_account_attributes():\n pass",
"def get_api_fields(cls):\n return ['fqdn', 'ttl', 'description', 'views']",
"def getAttributes(self):\n pass",
"def get_ea_attributes(path, logger):\n reattrib = None\n try:\n reattrib = requests.get(PAYLOAD['url'] + \"extensibleattri... | [
"0.649405",
"0.64128107",
"0.6405656",
"0.6219948",
"0.61061186",
"0.6090734",
"0.6085757",
"0.6085757",
"0.6083903",
"0.60761714",
"0.6068696",
"0.60633224",
"0.606197",
"0.6059749",
"0.60496956",
"0.6047246",
"0.60332847",
"0.60306746",
"0.6005083",
"0.59933203",
"0.5976819... | 0.59678864 | 21 |
Returns the fields needed for a network views ipam call. | def network_views():
return 'networkview?' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_api_fields(cls):\n return ['fqdn', 'ttl', 'description', 'views']",
"def getViews(read):\n ...",
"def get_vm_data_fields(self):\n\n raise NotImplementedError",
"def info(self):\n info = \">>>> IP\" + '\\n'\n info += \" To: \" + self.destinationIp + '\\n'\n inf... | [
"0.58593136",
"0.57089645",
"0.5631673",
"0.56026655",
"0.5583982",
"0.5524781",
"0.54614687",
"0.5457726",
"0.54543835",
"0.54375136",
"0.5420535",
"0.52939105",
"0.52843255",
"0.5243048",
"0.52313054",
"0.5225801",
"0.5189378",
"0.5185825",
"0.517134",
"0.5154297",
"0.51468... | 0.596402 | 0 |
Returns the fields needed from networks ipam call. | def networks(view):
return "network?" \
"_return_fields=" \
"extattrs," \
"comment," \
"network," \
"network_view," \
"utilization&" \
"network_view=" + view + \
"&_max_results=-25000" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rpc_info():",
"def getFields(iface):\n return getFieldsInOrder(iface)",
"def test_ipam_vlans_read(self):\n pass",
"def get_ip(self):",
"def get_fields() :\n st=hp.synfast(cltt+nltt,nside,new=True,verbose=False,pol=True)\n ff0=nmt.NmtField(mask_lss,[st])\n return ff0",
"def info(sel... | [
"0.5779202",
"0.56970006",
"0.56601065",
"0.56375086",
"0.5631149",
"0.54892325",
"0.54859495",
"0.5472514",
"0.54665065",
"0.5439959",
"0.5439959",
"0.5439959",
"0.54203576",
"0.5398455",
"0.53844625",
"0.5379024",
"0.53763855",
"0.53588057",
"0.5348849",
"0.53384274",
"0.53... | 0.5089324 | 44 |
Returns the fields needed from networkcontainers ipam call. | def networkcontainers(view):
return "networkcontainer?" \
"_return_fields=" \
"extattrs," \
"comment," \
"network," \
"network_view," \
"utilization&" \
"network_view=" + view + \
"&_max_resul... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rpc_info():",
"def getFields(iface):\n return getFieldsInOrder(iface)",
"def get_interface_info(self): # real signature unknown; restored from __doc__\n pass",
"def get_ip(self):",
"def test_ipam_aggregates_read(self):\n pass",
"def test_ipam_ip_addresses_read(self):\n pass",
... | [
"0.5752631",
"0.5632856",
"0.55738837",
"0.5550749",
"0.5510972",
"0.54324913",
"0.5415445",
"0.5317205",
"0.52685046",
"0.5245804",
"0.5200601",
"0.5200096",
"0.5200038",
"0.5169341",
"0.51556444",
"0.51506966",
"0.51506966",
"0.51506966",
"0.51459086",
"0.51456106",
"0.5135... | 0.0 | -1 |
Returns the fields needed from networks ipam call. | def ipv6_networks(view):
return "ipv6network?" \
"_return_fields=" \
"extattrs," \
"comment," \
"network," \
"network_view," \
"utilization&" \
"network_view=" + view + \
"&_max_results=-25000... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rpc_info():",
"def getFields(iface):\n return getFieldsInOrder(iface)",
"def test_ipam_vlans_read(self):\n pass",
"def get_ip(self):",
"def get_fields() :\n st=hp.synfast(cltt+nltt,nside,new=True,verbose=False,pol=True)\n ff0=nmt.NmtField(mask_lss,[st])\n return ff0",
"def info(sel... | [
"0.5775925",
"0.569589",
"0.566016",
"0.5638428",
"0.5630748",
"0.5490142",
"0.54885566",
"0.5472954",
"0.5465002",
"0.5437468",
"0.5437468",
"0.5437468",
"0.5420819",
"0.53993124",
"0.53843606",
"0.53766876",
"0.5373815",
"0.5353859",
"0.5346021",
"0.5335662",
"0.53345245",
... | 0.0 | -1 |
Returns the fields needed from networkcontainers ipam call. | def ipv6_networkcontainers(view):
return "ipv6networkcontainer?" \
"_return_fields=" \
"extattrs," \
"comment," \
"network," \
"network_view," \
"utilization&" \
"network_view=" + view + \
"&_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rpc_info():",
"def getFields(iface):\n return getFieldsInOrder(iface)",
"def get_interface_info(self): # real signature unknown; restored from __doc__\n pass",
"def get_ip(self):",
"def test_ipam_aggregates_read(self):\n pass",
"def test_ipam_ip_addresses_read(self):\n pass",
... | [
"0.5752631",
"0.5632856",
"0.55738837",
"0.5550749",
"0.5510972",
"0.54324913",
"0.5415445",
"0.5317205",
"0.52685046",
"0.5245804",
"0.5200601",
"0.5200096",
"0.5200038",
"0.5169341",
"0.51556444",
"0.51506966",
"0.51506966",
"0.51506966",
"0.51459086",
"0.51456106",
"0.5135... | 0.0 | -1 |
This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. | def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Greens):
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_confusion_matrix(cm, classes=[0,1], normalize=False, title='Confusion matrix', print_matrix=False):\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n... | [
"0.8192804",
"0.8094402",
"0.80277497",
"0.80176693",
"0.7992383",
"0.7989708",
"0.7979215",
"0.79741204",
"0.7959607",
"0.7955097",
"0.79358983",
"0.79329205",
"0.7932711",
"0.79295874",
"0.79247147",
"0.79228914",
"0.792216",
"0.79215133",
"0.7921201",
"0.7920765",
"0.79161... | 0.785076 | 44 |
Wrap records object that are exhaustable (eg generators, file objects, db cursors) so that we can sample them for inspection and inference without losing records. | def wrap_records_object(obj: Any) -> Any:
if isinstance(obj, SampleableIterator):
# Already wrapped
return obj
if isinstance(obj, ResultProxy):
return SampleableCursor(obj)
if isinstance(obj, IOBase):
return SampleableIO(obj)
if isinstance(obj, abc.Generator):
ret... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, *records: ScalarSequence):\n self._records = [r for r in records if r]",
"def records(self):\r\n raise NotImplementedError()",
"def __iter__(self):\n counter = 0\n while True:\n if counter < len(self.all_records):\n yield self.all_records... | [
"0.6080863",
"0.5741171",
"0.55929357",
"0.5570124",
"0.5447731",
"0.54261357",
"0.5404625",
"0.53648776",
"0.5355849",
"0.53219557",
"0.5255752",
"0.5210186",
"0.5188838",
"0.51879054",
"0.5187643",
"0.51652",
"0.51625055",
"0.5120713",
"0.50605875",
"0.50605875",
"0.5032449... | 0.72915584 | 0 |
Construct a new FA session. | def __init__(self, username):
self.username = username
self._requests = requests.Session()
self._requests.headers["User-Agent"] = constants.USER_AGENT
self._requests.headers["Referer"] = constants.FA_ROOT + "/"
self._rate_limit_start = datetime.datetime.now()
self._rate... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_session(aq_instance):\n dirname = os.path.dirname(__file__)\n filename = os.path.join(dirname, 'secrets.json')\n\n with open(filename) as f:\n secrets = json.load(f)\n\n credentials = secrets[aq_instance]\n session = AqSession(\n credentials[\"login\"],\n credentials[... | [
"0.6634107",
"0.6326105",
"0.6310392",
"0.6274315",
"0.6241301",
"0.6186287",
"0.6178623",
"0.61726516",
"0.61697036",
"0.6166564",
"0.6161023",
"0.6105878",
"0.60832137",
"0.60805315",
"0.60735244",
"0.60646576",
"0.6022593",
"0.6021701",
"0.60208005",
"0.6016343",
"0.601144... | 0.0 | -1 |
Rate limit calls to a function. | def _limited_call(self, func, *args, **kwargs):
# Check seconds that have passed
now = datetime.datetime.now()
diff = (now - self._rate_limit_start).total_seconds()
if diff >= 60:
# If greater than a minute, reset the rate limit
self._rate_limit_count = 0
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ratelimited():\n\n lock = threading.Lock()\n\n def decorator(func):\n last_call = time.perf_counter()\n\n @wraps(func)\n def ratelimit(*args, **kwargs):\n limit = kwargs.get(\"ratelimit\", None)\n if limit:\n count, seconds = limit.split(\"/\")\n ... | [
"0.774844",
"0.7687252",
"0.73051584",
"0.7302061",
"0.7221094",
"0.7193447",
"0.7186658",
"0.7117065",
"0.710736",
"0.7102475",
"0.70750374",
"0.7038235",
"0.69855464",
"0.6896162",
"0.68784034",
"0.6843292",
"0.6843292",
"0.6843292",
"0.67920244",
"0.6740426",
"0.66486627",... | 0.8237565 | 0 |
Get a CAPTCHA puzzle. | def get_captcha(self):
res = self._limited_call(self._requests.get,
constants.FA_ROOT + "/captcha.jpg")
data = res.content
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self):\n try:\n imageFilename = random.choice(os.listdir(self.cacheDir))\n imagePath = os.path.join(self.cacheDir, imageFilename)\n with open(imagePath) as imageFile:\n self.image = imageFile.read()\n except IndexError:\n raise GimpCa... | [
"0.61169136",
"0.6112375",
"0.60955966",
"0.5859403",
"0.5839033",
"0.5812558",
"0.5522057",
"0.5405848",
"0.53841287",
"0.5343834",
"0.5302962",
"0.5302095",
"0.52938336",
"0.52660924",
"0.5257254",
"0.52544385",
"0.5173279",
"0.5142943",
"0.51268345",
"0.5119854",
"0.511594... | 0.6154923 | 0 |
Log in to the site. Call .logout to log out, or use this method with the context manager. | def login(self, password, captcha):
url = constants.FA_ROOT + "/login/"
data = {
"action": "login",
"name": self.username,
"pass": password,
"captcha": captcha,
"login": "Login to\u00a0FurAffinity",
}
logger.info("Logging in as... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_in(self):\n\n # Get login page.\n self.get_endpoint(endpoint=self.config['paths']['login'])\n\n # Post log-in data.\n email_form = self.browser.find_element_by_xpath(\"//input[@id='email']\")\n pw_form = self.browser.find_element_by_xpath(\"//input[@id='password']\")\n ... | [
"0.7290045",
"0.7128971",
"0.7029878",
"0.70254457",
"0.6998062",
"0.6989771",
"0.6910118",
"0.69069165",
"0.69020975",
"0.6878334",
"0.68720883",
"0.6850107",
"0.6816924",
"0.67713416",
"0.67486775",
"0.67442846",
"0.6725656",
"0.66482985",
"0.65863806",
"0.65805143",
"0.653... | 0.0 | -1 |
Log out of the site. | def logout(self):
logger.info("Logging out")
self._limited_call(self._requests.get, constants.FA_ROOT + "/logout/") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logOut(self):\n self.client.logout()",
"def logout(self):\n pass",
"def logout():\n rino.login.logout()",
"def logout(self):\n self.__send_command(\"LOGOUT\")",
"def logout():",
"def logout():\n login()",
"def log_out_user(self):\n flask_login.logout_user()",
"de... | [
"0.8635206",
"0.82986826",
"0.8285628",
"0.8208519",
"0.8206995",
"0.8138281",
"0.80890554",
"0.7998981",
"0.7973082",
"0.7925552",
"0.7922266",
"0.7895177",
"0.7888047",
"0.7873234",
"0.78515416",
"0.7807907",
"0.7790976",
"0.77811825",
"0.775594",
"0.7750046",
"0.7744232",
... | 0.78818446 | 13 |
Return submissions found in pages of a base url. | def _scan_submission_page(self, url_format):
submissions = []
try:
page = 1
while True:
url = url_format % page
doc = self._limited_call(self._html_get, url)
logger.debug("Scanning submissions from %s" % url)
coun... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_urls(base_url):\n res = requests.get(base_url, headers=HEADERS)\n res = BeautifulSoup(res.text, 'html.parser')\n res = res.find_all(href=re.compile('pdf'))\n return res",
"def get_submissions(username, problem, cookies, is_clicker, sesssion):\r\n url = '/'.join([courseserver, coursepath, c... | [
"0.5971324",
"0.5961301",
"0.59605527",
"0.5955323",
"0.58842486",
"0.5882445",
"0.58632535",
"0.5807735",
"0.57095593",
"0.5695872",
"0.5692507",
"0.5645808",
"0.5624923",
"0.5583744",
"0.5556068",
"0.55493116",
"0.5507686",
"0.55000055",
"0.54877317",
"0.5482656",
"0.544188... | 0.65737253 | 0 |
Starts the Load Balancer application | def run_lb(name):
global init_param, stop
# Read the initial config parameters
with open('init_config.json') as init_config:
init_param = json.load(init_config)
# Check if all the values are not null
for param, value in init_param.items():
if not value:
logger.warning(f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start(self) -> None:\n if self.bolt_app.logger.level > logging.INFO:\n print(get_boot_message())\n else:\n self.bolt_app.logger.info(get_boot_message())\n\n web.run_app(self.web_app, host=\"0.0.0.0\", port=self.port)",
"def start():\n app.run()",
"def start() -... | [
"0.7244366",
"0.67025673",
"0.6595618",
"0.6525551",
"0.65218407",
"0.65112716",
"0.6436959",
"0.64000326",
"0.6390139",
"0.63894284",
"0.6378471",
"0.6378471",
"0.6368097",
"0.6355661",
"0.6349179",
"0.62332124",
"0.6215053",
"0.6215012",
"0.61955005",
"0.614471",
"0.613681"... | 0.5770426 | 71 |
Gets the IP of the users currently on the WiFi | def get_wifi_users():
response = requests.get(url=f"http://{init_param['ryu_ip']}:8080/\
stats/flow/{init_param['br-int_dpid']}")
rules = response.json()[init_param['br-int_dpid']]
wifi_users = []
for rule in rules:
try:
vlc_ip = rule["match"]["nw_dst"]
except KeyError:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetWirelessIP(self):\n ip = self.wifi.GetIP()\n return ip",
"def get_public_ip():\n public_ip = get('https://api.ipify.org').text\n return public_ip",
"def publicIP(self):\n return self.query('https://plex.tv/:/ip')",
"def checkIP(self):\n\t\tself.get(\"https://ifconfig.me/\")\... | [
"0.7699801",
"0.67521274",
"0.67227936",
"0.6681065",
"0.6656468",
"0.6654231",
"0.6638662",
"0.66331196",
"0.66173965",
"0.6582812",
"0.6568506",
"0.65680355",
"0.6561505",
"0.6549181",
"0.65410495",
"0.6517311",
"0.6516988",
"0.65079516",
"0.65009683",
"0.64814854",
"0.6435... | 0.65245605 | 15 |
Returns the list of the users that are currently on the VLC/mmWave | def get_vlc_users(wifi_list):
vlc_users = [user for user in users if user["vlc_ip"] not in wifi_list]
return vlc_users | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_users(self):\n return self.mycam.devicemgmt.GetUsers()",
"def user_list(self, mapp, url_of_liveserver):\n return mapp.getjson(url_of_liveserver)['result'].keys()",
"def all_users():\n\treturn [unicode(name[:-4]).lower() for name in os.listdir(os.path.join(WORLD_DIR, 'players'))]",
"def ... | [
"0.74427754",
"0.6795005",
"0.672411",
"0.67044204",
"0.6702518",
"0.66885984",
"0.66357285",
"0.6614727",
"0.65836966",
"0.65774393",
"0.6577427",
"0.65748775",
"0.6572361",
"0.65383404",
"0.653604",
"0.6530479",
"0.65299314",
"0.6490531",
"0.6474435",
"0.6440596",
"0.643123... | 0.75741893 | 0 |
Converts time as an option into a datetime.datetime. Returns None if not specified. | def parse_time_option(value):
if not value:
return None
try:
return _EPOCH + datetime.timedelta(seconds=int(value))
except ValueError:
pass
for fmt in ('%Y-%m-%d',):
try:
return datetime.datetime.strptime(value, fmt)
except ValueError:
pass
raise ValueError('Failed to parse %s'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def datetime_from_time(time: datetime.time, date: datetime.date = datetime.date.today()):\n if type(time) == datetime.time:\n return datetime.datetime.combine(date, time)\n else:\n return time",
"def parse_time(time_input, *, force_datetime=False, allow_undefined=False, **kwargs):\n\n if a... | [
"0.68546826",
"0.683096",
"0.67861164",
"0.6604809",
"0.64384985",
"0.6394784",
"0.6331635",
"0.62863314",
"0.62711996",
"0.6268744",
"0.62352127",
"0.620883",
"0.6158795",
"0.6090492",
"0.60857975",
"0.60848105",
"0.6053297",
"0.60125333",
"0.59862024",
"0.5982028",
"0.59811... | 0.67915386 | 2 |
Fetches data from swarming and returns it. | def fetch_data(swarming, start, end, state, tags):
# Split the work in days. That's a lot of requests to do.
queue = Queue.Queue()
threads = []
def run(start, cmd):
data = json.loads(subprocess.check_output(cmd))
queue.put((start, int(data['count'])))
day = start
while day != end:
data = [
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _fetch_data(self):\n pass",
"def run(self):\n results = self.fetch()\n return results",
"def fetch_data(self):",
"async def fetch_data(self) -> T:",
"def data():\n return volumes_fetchers.get_json_data()",
"def get_data():\n return",
"async def _fetch_data(self) -> T:\n ... | [
"0.656309",
"0.6497311",
"0.64277226",
"0.6388809",
"0.6219082",
"0.61422515",
"0.61238706",
"0.6111634",
"0.6094759",
"0.607809",
"0.60757875",
"0.60490596",
"0.5996118",
"0.598899",
"0.59853625",
"0.59853625",
"0.5912132",
"0.5912132",
"0.5912132",
"0.5903248",
"0.5899217",... | 0.5962772 | 16 |
Setup the aREST binary sensor. | def setup_platform(hass, config, add_devices, discovery_info=None):
resource = config.get(CONF_RESOURCE)
pin = config.get(CONF_PIN)
if None in (resource, pin):
_LOGGER.error('Not all required config keys present: %s',
', '.join((CONF_RESOURCE, CONF_PIN)))
return False
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUp(self):\n self.ser = Serial()\n self.device_obj = ZBSensor(self.ser)",
"def setUp(self):\n self.sensor = Sensor('127.1.1.3', 9000)",
"def __init__(self):\n self._read_calibration_data()\n self.configure_sensor(\n TemperatureOversamplings.x08,\n ... | [
"0.6209206",
"0.6132519",
"0.57827735",
"0.57115394",
"0.56619436",
"0.5657306",
"0.56566656",
"0.5559778",
"0.5507198",
"0.55019826",
"0.5441228",
"0.5435611",
"0.5408168",
"0.53973293",
"0.538949",
"0.53633904",
"0.5356179",
"0.5353374",
"0.5349762",
"0.5343878",
"0.5338553... | 0.55974704 | 7 |
Initialize the aREST device. | def __init__(self, arest, resource, name, pin):
self.arest = arest
self._resource = resource
self._name = name
self._pin = pin
self.update()
if self._pin is not None:
request = requests.get('{}/mode/{}/i'.format
(self._resou... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def init(self):\n logger.info(\"Init device: %s\", self._serial)\n self._callback(STATUS_INIT)\n\n self._init_binaries()\n self._init_apks()\n await self._init_forwards()\n\n await adb.shell(self._serial, \"/data/local/tmp/atx-agent server --stop\")\n await ad... | [
"0.6444274",
"0.6330388",
"0.6299139",
"0.62126595",
"0.61097986",
"0.6039827",
"0.5993592",
"0.59869677",
"0.59306645",
"0.5883426",
"0.5880507",
"0.58715373",
"0.5868116",
"0.5868116",
"0.5868116",
"0.5859604",
"0.5851872",
"0.5851872",
"0.5851872",
"0.5851872",
"0.58076674... | 0.56759477 | 41 |
Return the name of the binary sensor. | def name(self):
return self._name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def name(self):\n return f\"{DEFAULT_NAME}_{BINARY_SENSOR}\"",
"def get_sensor_name(self):\n return self.data[1]",
"def name(self):\n return self._sensor.name",
"def name(self):\n return f\"{self.sensor_type['name']} ({self._mac[-5:]})\"",
"def name(self):\n return self.d... | [
"0.82069147",
"0.8016458",
"0.7923548",
"0.76958257",
"0.7668517",
"0.7534579",
"0.72059137",
"0.7146881",
"0.7066772",
"0.6925692",
"0.6923594",
"0.67905974",
"0.67600256",
"0.67595863",
"0.66872895",
"0.6645599",
"0.6642735",
"0.66148776",
"0.6601633",
"0.6601633",
"0.66016... | 0.0 | -1 |
Return true if the binary sensor is on. | def is_on(self):
return bool(self.arest.data.get('state')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_on(self):\n return self._sensor_state()",
"def is_on(self):\n return getattr(self.coordinator.data[0], self._sensor) is True",
"def is_on(self):\n data = self.sensor_data\n if data and data[\"model\"] == \"SML\" and data[\"changed\"]:\n return data[\"state\"] == ST... | [
"0.7853332",
"0.76352257",
"0.7587365",
"0.7494062",
"0.74632245",
"0.74632245",
"0.74632245",
"0.7458867",
"0.7458476",
"0.73671085",
"0.73671085",
"0.73581773",
"0.73362136",
"0.7291605",
"0.7263485",
"0.7212699",
"0.7204223",
"0.718982",
"0.7121019",
"0.7081601",
"0.705846... | 0.7081065 | 20 |
Get the latest data from aREST API. | def update(self):
self.arest.update() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_latest_data():\n try:\n print '\\nRequesting new data.....\\n'\n response = get(\"https://api.myjson.com/bins/2csub\")\n if response.status_code is 200:\n print '\\nSuccess (200) in downloading data\\n'\n current_json = response.json()\n set_backup_d... | [
"0.69037545",
"0.6617101",
"0.648815",
"0.63266337",
"0.63005227",
"0.63005227",
"0.6281676",
"0.6281676",
"0.6278101",
"0.6271919",
"0.6258911",
"0.62241006",
"0.6204899",
"0.61852705",
"0.6169884",
"0.6165374",
"0.61476934",
"0.6141144",
"0.61288834",
"0.60983145",
"0.60928... | 0.0 | -1 |
Initialize the aREST data object. | def __init__(self, resource, pin):
self._resource = resource
self._pin = pin
self.data = {} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n\t\tsuper().__init__()\n\t\t\n\t\t# Typically a list of data here\n\t\t# Typically a dict of header keys and values here",
"def __init__(self, data):\n self.data = data\n return",
"def __init__(self, data=None):\n self.data = data",
"def __init__(self):\n self... | [
"0.67280114",
"0.6717379",
"0.6682897",
"0.6569234",
"0.6562224",
"0.6559889",
"0.6559889",
"0.6559889",
"0.6559889",
"0.65542865",
"0.6554195",
"0.65215546",
"0.64844275",
"0.6484227",
"0.64589953",
"0.6457893",
"0.6446397",
"0.6441969",
"0.6429045",
"0.639269",
"0.6382177",... | 0.0 | -1 |
Get the latest data from aREST device. | def update(self):
try:
response = requests.get('{}/digital/{}'.format(
self._resource, self._pin), timeout=10)
self.data = {'state': response.json()['return_value']}
except requests.exceptions.ConnectionError:
_LOGGER.error("No route to device '%s'. Is... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_rest(self):\n # PROTECTED REGION ID(AsyncTabata.rest_read) ENABLED START #\n return self._rest\n # PROTECTED REGION END # // AsyncTabata.rest_read",
"def get_latest_data():\n try:\n print '\\nRequesting new data.....\\n'\n response = get(\"https://api.myjson.com... | [
"0.675981",
"0.6415501",
"0.6267848",
"0.62456656",
"0.6203895",
"0.61584896",
"0.6130172",
"0.6068994",
"0.6045216",
"0.6005045",
"0.5990922",
"0.5987145",
"0.596897",
"0.5952027",
"0.5932656",
"0.5929275",
"0.59236604",
"0.58907956",
"0.58907956",
"0.5875404",
"0.58722246",... | 0.0 | -1 |
Tests that the output html is generated correclty by the clustering function | def test_output_html(tmp_path):
# This will be the file to
temporary_file = os.path.join(tmp_path, 'test-cluster.html')
# Run clustering on small dummy data (see test_clustering.py)
cluster = TextClustering(embedding_random_state=42,
reducer_random_state=43,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_html_output(self):\n pass",
"def cluster_classification_tex(f,browsing_matrix,diversifying_matrix, weblog,session_data_threshold,cluster_type,classification_column_diversity,classification_wanted_transaction):\n divpat_classification_wanted_transaction = classification_wanted_transaction\n ... | [
"0.68014586",
"0.64515656",
"0.6283319",
"0.6218219",
"0.61491203",
"0.6064487",
"0.5956804",
"0.5843667",
"0.5821508",
"0.5812067",
"0.5782588",
"0.5746487",
"0.572244",
"0.5716077",
"0.57043064",
"0.5677167",
"0.5629126",
"0.5620701",
"0.5605728",
"0.5572866",
"0.5569146",
... | 0.7976958 | 0 |
Override the default train() to freeze the BN parameters | def train(self, mode=True, freeze_bn=False):
super(WideResNet, self).train(mode)
self.freeze_bn = freeze_bn
if self.freeze_bn:
print("Freezing Mean/Var of BatchNorm2D.")
print("Freezing Weight/Bias of BatchNorm2D.")
if self.freeze_bn:
for m in self.mod... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train(self, *args, **kwargs):\n raise NotImplementedError",
"def train(self):\n pass",
"def train(self):\n pass",
"def train(self):\n pass",
"def train(self):\n pass",
"def train(self):\n pass",
"def train(self, ):\n raise NotImplementedError",
"def tr... | [
"0.6963234",
"0.6939087",
"0.6939087",
"0.6939087",
"0.6939087",
"0.6939087",
"0.69190997",
"0.6901135",
"0.68561196",
"0.6850719",
"0.68132",
"0.6813139",
"0.67859966",
"0.6785637",
"0.6709828",
"0.6674066",
"0.66536355",
"0.6645038",
"0.6637477",
"0.662026",
"0.66150165",
... | 0.6582539 | 22 |
programmer_type e.g. INTEGER or programmer defined type | def __init__(self, programmer_type):
self.programmer_type = programmer_type | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def type_():\n pass",
"def opinion_type():\n pass",
"def _type(self):\n return self._id[1]",
"def program_type(self, value):\n\n types = {\n 'executableProgram': '1'\n }\n\n self._program_type = types[value]",
"def Type(self) -> _n_6_t_5:",
"def _type(self) ->... | [
"0.62550634",
"0.6194765",
"0.61409026",
"0.60830945",
"0.6071827",
"0.6035185",
"0.60270154",
"0.588981",
"0.587063",
"0.58595556",
"0.5849741",
"0.58421713",
"0.5834853",
"0.5820968",
"0.58097893",
"0.5783257",
"0.5766275",
"0.57662106",
"0.57528615",
"0.57528615",
"0.57528... | 0.65804327 | 0 |
Size in words and programmer_type e.g. integer | def __init__(self, num_params):
self.num_params = num_params | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __get_size(self):\n\t\treturn 4*self.version + 17",
"def getSize(self):\n return 1",
"def getSize(self):\n return 1",
"def getSize(language=None):",
"def getSize(self) -> int:\n ...",
"def getSize(self) -> int:\n ...",
"def word_size(tpname):\n if tpname == \"uint3... | [
"0.73081714",
"0.70577526",
"0.7009011",
"0.7003701",
"0.6970362",
"0.6970362",
"0.6962064",
"0.68956697",
"0.6845606",
"0.6825537",
"0.68143684",
"0.68104845",
"0.67622477",
"0.6742321",
"0.67140716",
"0.6710887",
"0.6704871",
"0.66993916",
"0.6625745",
"0.661541",
"0.660608... | 0.0 | -1 |
Size in words and programmer_type e.g. integer | def __init__(self, num_params):
self.num_params = num_params | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __get_size(self):\n\t\treturn 4*self.version + 17",
"def getSize(self):\n return 1",
"def getSize(self):\n return 1",
"def getSize(language=None):",
"def getSize(self) -> int:\n ...",
"def getSize(self) -> int:\n ...",
"def word_size(tpname):\n if tpname == \"uint3... | [
"0.73083884",
"0.7057839",
"0.7009008",
"0.7002719",
"0.6969705",
"0.6969705",
"0.69600403",
"0.68952006",
"0.68450075",
"0.6824593",
"0.68134356",
"0.6809526",
"0.6760405",
"0.6741088",
"0.67141205",
"0.67103976",
"0.67043126",
"0.66976994",
"0.6625272",
"0.6613481",
"0.6605... | 0.0 | -1 |
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