None:\n client_nickname = \"\"\n while True:\n # decode the incoming message using utf-8\n message = client.recv(constants.BUFFER_SIZE).decode(constants.ENCODING)\n\n if message.startswith(\"[SENDTO:ALL]\") and client_nickname:\n self.send_to_clients(bytes(message.replace(\"[SENDTO:ALL]\", \"[SENDTO:ALL:\" + client_nickname + \"]\"), constants.ENCODING))\n continue\n elif message.startswith(\"[SENDTO\") and client_nickname:\n message_split = message.split(\"]\")\n name = message_split[0][1:].split(\":\")[1]\n for client_sock, client_name in self.connections.items():\n if name == client_name:\n client_sock.send(bytes(\"[SENDTO:\" + client_nickname + \"]=\" + message_split[1].split(\"=\")[1], constants.ENCODING))\n continue\n elif message.startswith(\"[JOINED]\"):\n client_nickname = message.split(\"=\")[1]\n current_clients = []\n [current_clients.append(client_name) for _, client_name in self.connections.items()]\n if client_nickname in current_clients:\n client.send(bytes(\"[DUPLICATE]\" + client_nickname, constants.ENCODING))\n continue\n time.sleep(0.1)\n self.connections[client] = client_nickname\n print(client_nickname + \" joined that chat!\")\n self.send_to_clients(bytes(\"[JOINED]=\" + client_nickname, constants.ENCODING))\n time.sleep(0.1)\n clients = []\n for _, client_name in self.connections.items():\n clients.append(client_name)\n self.send_to_clients(bytes(\"[CLIENTS]=\" + \"-\".join(clients), constants.ENCODING))\n continue\n elif message.startswith(\"[LEFT]\"):\n client.close()\n try:\n del self.connections[client]\n except KeyError:\n pass\n if len(client_nickname) != 0:\n print(client_nickname + \" left that chat!\")\n self.send_to_clients(bytes(\"[LEFT]=\" + client_nickname, constants.ENCODING))\n time.sleep(0.1)\n clients = []\n for _, client_name in self.connections.items():\n clients.append(client_name)\n self.send_to_clients(bytes(\"[CLIENTS]=\" + \"-\".join(clients), constants.ENCODING))\n break","title":""},{"docid":"32477dbc02a4fd8b914aeba391da4241","score":"0.5373981","text":"def sync_workdir_from_guest(self):\n pass","title":""},{"docid":"24f837691b68ab5bef996b06890572b1","score":"0.53714913","text":"def __sync():\n log.debug('getting in sync with LUA')\n self.__clear_buffers()\n try:\n self.__writeln('UUUUUUUUUUUU') # Send enough characters for auto-baud\n self.__clear_buffers()\n time.sleep(self.autobaud_time) # Wait for autobaud timer to expire\n self.__exchange(';') # Get a defined state\n self.__writeln('print(\"%sync%\");')\n self.__expect('%sync%\\r\\n> ')\n except CommunicationTimeout:\n raise DeviceNotFoundException('Device not found or wrong port')","title":""},{"docid":"b79da3de9e01cbaa7f18501d85175fb4","score":"0.537101","text":"def refresh(self):\r\n self.update(self['__client__'].get(self['id']))","title":""},{"docid":"8f6649f50890cb790c023c2fb653b6f9","score":"0.5370351","text":"async def vaillant_update(self):\n new_room: Room = self.hub.find_component(self._room)\n\n if new_room:\n _LOGGER.debug(\n \"New / old state: %s / %s\", new_room.child_lock, self._room.child_lock\n )\n else:\n _LOGGER.debug(\"Room %s doesn't exist anymore\", self._room.id)\n self._room = new_room","title":""},{"docid":"eee982f3ec8f07d9c45cee31b2f9d0e2","score":"0.5331417","text":"def roomState(self, user):\n \n\n version = 1\n typeMessage = 4\n \n #to get the room of the client\n sessionToken_1 = self.usersession[user.userAddress][0]\n sessionToken_2 = self.usersession[user.userAddress][1]\n \n sequenceNumber = self.usersequenceNumber[user.userAddress][0]\n self.usersequenceNumber[user.userAddress][1] = 4\n room = user.userChatRoom\n \n \n if room == ROOM_IDS.MAIN_ROOM :\n \n #if the client in the main room , call the function messageMainroom()\n mainbuf = self.messageMainroom()\n roomListLength = len(self.serverProxy.getMovieList())\n #to get the data of the movie room\n roomListbuf = struct.pack('>H', roomListLength)\n for movie in self.serverProxy.getMovieList():\n roomListbuf = roomListbuf + self.messageMovieroom(movie.movieTitle)\n payloadSize = len(mainbuf) + len(roomListbuf)\n #pack the header\n headbuf = struct.pack('>BHBHH', version*16+typeMessage, sessionToken_1, sessionToken_2,\n sequenceNumber, payloadSize)\n #the whole packet\n buf = headbuf +mainbuf +roomListbuf\n \n #if the client in the movie room\n else :\n #to call the function messageMovieroom(room)\n moviebuf = self.messageMovieroom(room)\n payloadSize = len(moviebuf) \n #the header\n headbuf = struct.pack('>BHBHH', version*16+typeMessage, sessionToken_1, sessionToken_2,\n sequenceNumber, payloadSize)\n #the whole packet\n buf = headbuf +moviebuf\n \n # Stop sending more than three times\n if self.usercount[user.userAddress][0] ==3:\n print(\"Connection Lost\")\n if self.leave[user.userAddress] == 0:\n self.serverProxy.removeUser(user.userName)\n self.leave[user.userAddress] = 1\n for user in self.serverProxy.getUserList() :\n if (user.userChatRoom == ROOM_IDS.MAIN_ROOM or user.userChatRoom == room):\n self.roomState(user)\n \n else:\n #send the packet\n print('********Send RoomState***************')\n print(\"roomState :\", buf)\n# print('***********************')\n user.userChatInstance.transport.write(buf)\n # Here we consider retransmitting this message after timing 1s without receiving ack response. \n # Stop sending more than three times\n self.usercount[user.userAddress][0] +=1\n if self.usercount[user.userAddress][0] <= 3:\n self.send[user.userAddress] = [reactor.callLater(1, self.roomState, user)]","title":""},{"docid":"dbd667ec2dbd712e7d6059a91e51623b","score":"0.53257537","text":"def sync_state(self):\n pass","title":""},{"docid":"c03fd5f072f56f6f63ca47b63fa39890","score":"0.5298573","text":"def sync(self):\n self.sot.sync_from(self.network, diff_class=NetworkImporterDiff)","title":""},{"docid":"c1804939a76345fdb971d8e45cef6982","score":"0.5287648","text":"def sync_workdir_to_guest(self):\n pass","title":""},{"docid":"aa7d56dc5a31a9a32b792c1b9a7950b6","score":"0.5284305","text":"def test_receiving_local_device_list_changes(self) -> None:\n # Register two users\n test_device_id = \"TESTDEVICE\"\n alice_user_id = self.register_user(\"alice\", \"correcthorse\")\n alice_access_token = self.login(\n alice_user_id, \"correcthorse\", device_id=test_device_id\n )\n\n bob_user_id = self.register_user(\"bob\", \"ponyponypony\")\n bob_access_token = self.login(bob_user_id, \"ponyponypony\")\n\n # Create a room for them to coexist peacefully in\n new_room_id = self.helper.create_room_as(\n alice_user_id, is_public=True, tok=alice_access_token\n )\n self.assertIsNotNone(new_room_id)\n\n # Have Bob join the room\n self.helper.invite(\n new_room_id, alice_user_id, bob_user_id, tok=alice_access_token\n )\n self.helper.join(new_room_id, bob_user_id, tok=bob_access_token)\n\n # Now have Bob initiate an initial sync (in order to get a since token)\n channel = self.make_request(\n \"GET\",\n \"/sync\",\n access_token=bob_access_token,\n )\n self.assertEqual(channel.code, 200, channel.json_body)\n next_batch_token = channel.json_body[\"next_batch\"]\n\n # ...and then an incremental sync. This should block until the sync stream is woken up,\n # which we hope will happen as a result of Alice updating their device list.\n bob_sync_channel = self.make_request(\n \"GET\",\n f\"/sync?since={next_batch_token}&timeout=30000\",\n access_token=bob_access_token,\n # Start the request, then continue on.\n await_result=False,\n )\n\n # Have alice update their device list\n channel = self.make_request(\n \"PUT\",\n f\"/devices/{test_device_id}\",\n {\n \"display_name\": \"New Device Name\",\n },\n access_token=alice_access_token,\n )\n self.assertEqual(channel.code, HTTPStatus.OK, channel.json_body)\n\n # Check that bob's incremental sync contains the updated device list.\n # If not, the client would only receive the device list update on the\n # *next* sync.\n bob_sync_channel.await_result()\n self.assertEqual(bob_sync_channel.code, 200, bob_sync_channel.json_body)\n\n changed_device_lists = bob_sync_channel.json_body.get(\"device_lists\", {}).get(\n \"changed\", []\n )\n self.assertIn(alice_user_id, changed_device_lists, bob_sync_channel.json_body)","title":""},{"docid":"0d546284fbd9db4f7f2d2f8987984821","score":"0.5283178","text":"def test_bin_enter_room(session):\n\n client_bin._on_data(smpacket.SMPacketClientNSSMONL(\n packet=smpacket.SMOPacketClientEnterRoom(enter=1, room=\"Room client-json\", password=\"password\")\n ).binary)\n\n room = session.query(models.Room).filter_by(name=\"Room client-json\").first()\n user1 = session.query(models.User).filter_by(name=\"clientbin-user1\").first()\n user2 = session.query(models.User).filter_by(name=\"clientbin-user2\").first()\n\n assert client_bin.room == room.id\n\n assert user1.room == room\n assert user2.room == room\n\n assert user1.level(room.id) == 1\n assert user2.level(room.id) == 1","title":""},{"docid":"0fae9f3b314c8164142e4753ec1d2be6","score":"0.5249324","text":"def SendMazeDataToClient(self, client_channel):\n print(f\"SendMazeDataToClient({client_channel.id})\")\n client_hash = client_channel.client_hash\n client_channel.Send({\n \"action\": \"sendmazedatatoclient\",\n \"maze\": self.maze_rows\n })","title":""},{"docid":"991fdb8042df46d95c68ad5a4d137c2c","score":"0.52418274","text":"def sync(self):\n\n sync_count = 4\n\n rtt = 999\n for i in range(sync_count):\n st1 = time.time()\n self.senderThread.send({'type': 'sync'})\n reply = self.syncQueue.get()\n st2 = time.time()\n new_rtt = st2 - st1\n if new_rtt < rtt:\n rtt = new_rtt\n self.clockOffset = reply['ct'] + rtt / 2 - st2\n\n print 'synchronized with client', self.id, ': clockOffset =',\\\n self.clockOffset","title":""},{"docid":"dd748414da362133cd5d41060895911c","score":"0.52354807","text":"def unsync(self):\n self.request(\"sync -\")","title":""},{"docid":"ab5253e23202244440d824154c61b19e","score":"0.5229868","text":"def setStartRoom(self, value): \n self.__startRoom = value","title":""},{"docid":"1431cc525cbc29e4f49340033dc21545","score":"0.5228958","text":"def test_json_enter_room(session):\n\n client_json._on_data(smpacket.SMPacketClientNSSMONL(\n packet=smpacket.SMOPacketClientEnterRoom(enter=1, room=\"Room client-bin\", password=\"password\")\n ).json)\n\n room = session.query(models.Room).filter_by(name=\"Room client-bin\").first()\n user = session.query(models.User).filter_by(name=\"clientjson-user1\").first()\n\n assert client_json.room == room.id\n assert user.room == room\n assert user.level(room.id) == 1","title":""},{"docid":"6dce936dea892167030b991746164bc6","score":"0.52219886","text":"def _synchronize(networked_peer):\n # TODO\n if networked_peer:\n pass","title":""},{"docid":"f3b9709b753c66a72e7cb49f0dbfd84b","score":"0.5206759","text":"def start_in(self, room_name):\n self.initial_room_id = self.room_names_to_ids[room_name]\n return self","title":""},{"docid":"2a9f378aa67893ce7bdd14c84c59b185","score":"0.5203585","text":"def sync(self):\n # cannot be implemented as we have no means of retrieving the current player list\n connected_clients = self.getPlayerList()\n for c in self.clients.getList():\n if c.cid not in connected_clients:\n c.disconnect()\n return connected_clients","title":""},{"docid":"89785bb3822f546ca24fb785e88140f2","score":"0.5180891","text":"async def _sync_friends():\n global _SYNCED, _WATCHES\n expected = set([str(k, \"utf8\").lower() for k in _WATCHES.keys()])\n while True:\n actual = await _friends()\n if actual is None:\n raise eqcmd.CommandError(\"Failed to retrieve friends\")\n if actual == expected:\n _SYNCED = True\n return\n for person in expected.symmetric_difference(actual):\n if person in _ONLINE_STATES:\n del _ONLINE_STATES[person]\n await _toggle_friend(person)","title":""},{"docid":"ff11c57d1ae4403d6d6a43eeab448114","score":"0.51796305","text":"def start(self, event):\n self.send_presence()\n self.get_roster()\n\n self.send_message(mto=self.recipient,\n mbody=self.msg,\n mtype='chat')\n\n # Using wait=True ensures that the send queue will be\n # emptied before ending the session.\n self.disconnect(wait=True)","title":""},{"docid":"ff11c57d1ae4403d6d6a43eeab448114","score":"0.51796305","text":"def start(self, event):\n self.send_presence()\n self.get_roster()\n\n self.send_message(mto=self.recipient,\n mbody=self.msg,\n mtype='chat')\n\n # Using wait=True ensures that the send queue will be\n # emptied before ending the session.\n self.disconnect(wait=True)","title":""},{"docid":"c94e606c10a4f53404b110f6e8ce15ee","score":"0.51696575","text":"def sync(self, sync_id: Optional[int] = None) -> \"Server\":\n if self._boot_status not in (BootStatus.BOOTING, BootStatus.ONLINE):\n raise ServerOffline\n Sync(\n sync_id=sync_id if sync_id is not None else self._get_next_sync_id()\n ).communicate(server=self)\n return self","title":""},{"docid":"f1dfd874ee437576482d0a3084fdadc2","score":"0.51671684","text":"def client_thread(connection, address):\n connection.send(\"Chatroom instantiated.\")\n \n # Continue indefinitely.\n while True:\n try:\n message = connection.recv(2048)\n if message:\n \n broadcast_message = address[0] + \" : \" + message\n print(broadcast_message)\n broadcast(broadcast_message, connection)\n else:\n remove(connection)\n except:\n continue","title":""},{"docid":"3963d121ff34c950c1ce18df6a000aef","score":"0.51654917","text":"def SyncWithPresenter(self):\n pass","title":""},{"docid":"cf90cf581dd7a81bfd4b672bfc71b1f5","score":"0.51643354","text":"def test_not_receiving_local_device_list_changes(self) -> None:\n # Register two users\n test_device_id = \"TESTDEVICE\"\n alice_user_id = self.register_user(\"alice\", \"correcthorse\")\n alice_access_token = self.login(\n alice_user_id, \"correcthorse\", device_id=test_device_id\n )\n\n bob_user_id = self.register_user(\"bob\", \"ponyponypony\")\n bob_access_token = self.login(bob_user_id, \"ponyponypony\")\n\n # These users do not share a room. They are lonely.\n\n # Have Bob initiate an initial sync (in order to get a since token)\n channel = self.make_request(\n \"GET\",\n \"/sync\",\n access_token=bob_access_token,\n )\n self.assertEqual(channel.code, HTTPStatus.OK, channel.json_body)\n next_batch_token = channel.json_body[\"next_batch\"]\n\n # ...and then an incremental sync. This should block until the sync stream is woken up,\n # which we hope will happen as a result of Alice updating their device list.\n bob_sync_channel = self.make_request(\n \"GET\",\n f\"/sync?since={next_batch_token}&timeout=1000\",\n access_token=bob_access_token,\n # Start the request, then continue on.\n await_result=False,\n )\n\n # Have alice update their device list\n channel = self.make_request(\n \"PUT\",\n f\"/devices/{test_device_id}\",\n {\n \"display_name\": \"New Device Name\",\n },\n access_token=alice_access_token,\n )\n self.assertEqual(channel.code, HTTPStatus.OK, channel.json_body)\n\n # Check that bob's incremental sync does not contain the updated device list.\n bob_sync_channel.await_result()\n self.assertEqual(\n bob_sync_channel.code, HTTPStatus.OK, bob_sync_channel.json_body\n )\n\n changed_device_lists = bob_sync_channel.json_body.get(\"device_lists\", {}).get(\n \"changed\", []\n )\n self.assertNotIn(\n alice_user_id, changed_device_lists, bob_sync_channel.json_body\n )","title":""},{"docid":"7598d7a7e07e1c6d02124efc3705fdd0","score":"0.51580983","text":"def _checkin_client(self, ts, client):\n # If the connection is now stale, don't return it to the pool.\n # Push an empty slot instead so that it will be refreshed when needed.\n if client.is_connected():\n now = int(time.time())\n if ts + self.timeout > now:\n self.clients.put((ts, client))\n else:\n if self.maxsize is not None:\n self.clients.put(EMPTY_SLOT)","title":""},{"docid":"99b038b03062231ddeb4d68ab73a806e","score":"0.51447207","text":"async def sync(self):\n mime = 'setup.sync'\n await self.send_request(mime, block=False)","title":""},{"docid":"d0bbf30e8fcf3d5f9d497207fda2a451","score":"0.5141951","text":"def resetclient():\n global _client\n _client = NullClient()","title":""},{"docid":"c400ec9c3cc50f94f8749a82c09810ec","score":"0.5131805","text":"def update_clients(id, data):\n data['id'] = id\n socketio.emit('update_track', data, json=True)","title":""},{"docid":"2e8c31191dc8de846924deb3f591a781","score":"0.5116015","text":"def start_clients():\n for client in CLIENTS:\n if client.running:\n continue\n \n Task(client.connect(), KOKORO)\n \n if (current_thread() is not KOKORO):\n KOKORO.wake_up()","title":""},{"docid":"b2b4c6278547deb911006a4f9ca5c414","score":"0.511151","text":"async def sync_from_mudlet(self, *rooms, clear=False):\n db=self.db\n done = set()\n broken = set()\n todo = deque()\n explore = set()\n more = set()\n area_names = {int(k):v for k,v in (await self.mud.getAreaTableSwap())[0].items()}\n area_known = set()\n area_rev = {}\n for k,v in area_names.items():\n area_rev[v] = k\n self.__logger.debug(\"AREAS:%r\",area_names)\n await self.print(_(\"Start syncing. Please be patient.\"))\n\n if clear:\n r_old = {}\n for r in rooms:\n r_old[r.id_old] = id_mudlet\n db.q(db.Room).update().values(id_mudlet = None).execute()\n for r in rooms:\n r.id_mudlet = r_old[r.id_old]\n\n for r in rooms:\n todo.append(r)\n\n def know_area(self, ra):\n if isinstance(ra,str):\n ra = area_rev[area]\n if ra not in area_known:\n if db.q(db.Area).filter(db.Area.id == ra).one_or_none() is None:\n a = db.Area(id=ra,name=area_names[ra])\n db.add(a)\n db.commit()\n area_known.add(ra)\n return ra\n\n while todo:\n r = todo.pop()\n if r.id_old in done:\n continue\n done.add(r.id_old)\n if not (len(done)%100):\n await self.print(_(\"{done} rooms ...\"), done=len(done))\n\n try:\n y = await r.mud_exits\n except NoData:\n # didn't go there yet?\n self.__logger.debug(\"EXPLORE %s %s\",r.id_str,r.name)\n explore.add(r.id_old)\n continue\n\n # Iterate exits but do the \"standard\" directions first,\n # then the reversible nonstandard ways, then the others.\n def exits(y):\n for d,mid in y.items():\n if self.dr.is_std_dir(d):\n yield d,mid\n for d,mid in y.items():\n if not self.dr.is_std_dir(d) and self.dr.loc2rev(d) is not None:\n yield d,mid\n for d,mid in y.items():\n if not self.dr.is_std_dir(d) and self.dr.loc2rev(d) is None:\n yield d,mid\n\n for d,mid in exits(y):\n if not mid:\n continue # unknown\n try:\n x = r.exit_at(d)\n nr = x.dst\n except KeyError:\n x = None\n nr = None\n if nr is not None:\n continue\n\n try:\n if nr is None:\n nr = db.r_mudlet(mid)\n elif nr.id_mudlet is not None:\n continue\n except NoData:\n name = await self.mud.getRoomName(mid)\n name = self.dr.clean_shortname(name[0]) if name and name[0] else None\n gmcp = await self.mud.getRoomHashByID(mid)\n gmcp = gmcp[0] if gmcp and gmcp[0] else None\n\n nr = await self.new_room(name, id_gmcp=gmcp, id_mudlet=mid, offset_from=r, offset_dir=d, explode=self.conf['mudlet_explode'])\n\n if x is None:\n x,_xf = await r.set_exit(d,nr,skip_mud=True)\n elif x.dst is None:\n x.dst = nr\n todo.append(nr)\n db.commit()\n\n await self.print(_(\"Finished, {done} rooms processed\"), done=len(done))","title":""},{"docid":"f7508219d8098a05aa12c6691cf3f63f","score":"0.5110341","text":"def on_pushButton_synchro_client_launch_clicked(self):\r\n # TODO: not implemented yet\r\n goslaunchera3.action.rsyncGos(self, \"@CLIENT\",self.label_synchro_client_state, self.progressBar_synchro_client_fichier,self.progressBar_synchro_client_global,self.label_synchro_client_debit,self.pushButton_synchro_client_launch)","title":""},{"docid":"4c5fa05fea940fffa35d268fff8fd520","score":"0.50803006","text":"def sync(ctx, spec, dry_run):\n engine = ctx.obj[\"engine\"]\n do_sync(engine, spec, dry_run)","title":""},{"docid":"1e01384d90bc68883ade07eb62c0810b","score":"0.5068519","text":"def start(self, event):\n self.get_roster()\n self.send_presence()\n self.plugin['xep_0045'].joinMUC(self.room,\n self.nick,\n # If a room password is needed, use:\n # password=the_room_password,\n wait=True)","title":""},{"docid":"964c4c8aaaca293c8d12c62d6fed3f8e","score":"0.506675","text":"def updateClients(self, client):\n self.clients.append(client)","title":""},{"docid":"a279078b5d9dab7c683e205980a60605","score":"0.5065684","text":"def sync(self):\n resp = self.action('sync')\n resp.raise_if_err()\n return resp","title":""},{"docid":"c81533a8a811bc8a86056a63e755475b","score":"0.5056151","text":"def _sync(self, state):\n\n if self.software_only: return\n self.dispenser_select.sync(state)","title":""},{"docid":"b5942542f0d3c015c759ab1bf98de66f","score":"0.50473946","text":"def room(self, room):\n\n self._room = room","title":""},{"docid":"9348303c6c6258f7e62aee9db9a9f332","score":"0.50335497","text":"def reset_client(self):\n self.GAME_ON = False\n self.tcp_server_port = None\n self.server_ip = None","title":""},{"docid":"fa9e191fc6fdeabc8a9ede53c89b4b56","score":"0.5033359","text":"def join(self, room):\n self.socket.rooms.add(self._get_room_name(room))","title":""},{"docid":"fa9e191fc6fdeabc8a9ede53c89b4b56","score":"0.5033359","text":"def join(self, room):\n self.socket.rooms.add(self._get_room_name(room))","title":""},{"docid":"649cb44f9c372790f1c2d1c0758992a8","score":"0.50316966","text":"def _sync(self, state):\r\n\r\n if self.software_only: return\r\n self.dispenser_select.sync(state)","title":""},{"docid":"8581d8b16cf4d27adf49fb4e2e118238","score":"0.50132996","text":"def connected(client):\n with warnings.catch_warnings():\n # Ignore warning that \"ismaster\" is always routed to primary even\n # if client's read preference isn't PRIMARY.\n warnings.simplefilter(\"ignore\", UserWarning)\n client.admin.command('ismaster') # Force connection.\n\n return client","title":""},{"docid":"dd31ce447fce85e124d0d22f1f4e9a05","score":"0.5012992","text":"def test_multiplayer_sync_response():\n test = DummyMultiplayerGame()\n messages = []\n\n for client in test.clients:\n for actor in client.actors:\n message = send_dummy_message(actor, response='sync')\n messages.append(message)\n\n # Make sure the message is handled like usual by the sender.\n\n for observer in client.observers:\n assert observer.dummy_messages_received == messages\n assert client.world.dummy_messages_executed == messages\n\n # Make sure the server relays the message to all the other \n # observers participating in the game.\n\n test.update()\n\n for observer in test.observers:\n assert observer.dummy_messages_received == messages\n for world in test.worlds:\n assert world.dummy_messages_executed == messages\n\n # Make sure the clients are instructed to sync up.\n\n for observer in test.client_observers:\n assert observer.dummy_sync_responses_received == messages\n for world in test.client_worlds:\n assert world.dummy_sync_responses_executed == messages","title":""},{"docid":"039623ae0448a85b90eb0d21c5723984","score":"0.49944133","text":"async def sync(self, sync_id: Optional[int] = None) -> \"AsyncServer\":\n if self._boot_status not in (BootStatus.BOOTING, BootStatus.ONLINE):\n raise ServerOffline\n await Sync(\n sync_id=sync_id if sync_id is not None else self._get_next_sync_id()\n ).communicate_async(server=self)\n return self","title":""},{"docid":"2bd923a10f00b7a03f173339c43cd2e3","score":"0.4993173","text":"def start(self, event):\n\t\tself.get_roster()\n\t\tself.send_presence()\n\t\tself.plugin['xep_0045'].joinMUC(self.room,\n\t\t\tself.nick,\n\t\t\t# If a room password is needed, use:\n\t\t\t# password=the_room_password,\n\t\t\twait=True)","title":""},{"docid":"cb5bd70f6dfeee97a127ba093c0a42e3","score":"0.49927256","text":"def EnterRoom(clientid, roomname):\n try:\n room = objmgr.GetRoomByName(roomname)\n except KeyError:\n return Response(clientid, 'Invalid room name: %s' % (roomname,))\n\n player = objmgr.GetPlayerByClientId(clientid)\n room.EnterRoom(player)\n\n return Response(clientid, 'You are now in %s' % (room.name,))","title":""},{"docid":"a485d26451f23b3ab0f50694db8d39e4","score":"0.49908218","text":"def incremental_sync_with_gap(self, sync_config, since_token):\n now_token = yield self.event_sources.get_current_token()\n\n rooms = yield self.store.get_rooms_for_user(sync_config.user.to_string())\n room_ids = [room.room_id for room in rooms]\n\n presence_source = self.event_sources.sources[\"presence\"]\n presence, presence_key = yield presence_source.get_new_events(\n user=sync_config.user,\n from_key=since_token.presence_key,\n limit=sync_config.filter.presence_limit(),\n room_ids=room_ids,\n # /sync doesn't support guest access, they can't get to this point in code\n is_guest=False,\n )\n now_token = now_token.copy_and_replace(\"presence_key\", presence_key)\n\n now_token, ephemeral_by_room = yield self.ephemeral_by_room(\n sync_config, now_token, since_token\n )\n\n rm_handler = self.hs.get_handlers().room_member_handler\n app_service = yield self.store.get_app_service_by_user_id(\n sync_config.user.to_string()\n )\n if app_service:\n rooms = yield self.store.get_app_service_rooms(app_service)\n joined_room_ids = set(r.room_id for r in rooms)\n else:\n joined_room_ids = yield rm_handler.get_joined_rooms_for_user(\n sync_config.user\n )\n\n timeline_limit = sync_config.filter.timeline_limit()\n\n room_events, _ = yield self.store.get_room_events_stream(\n sync_config.user.to_string(),\n from_key=since_token.room_key,\n to_key=now_token.room_key,\n limit=timeline_limit + 1,\n )\n\n tags_by_room = yield self.store.get_updated_tags(\n sync_config.user.to_string(),\n since_token.account_data_key,\n )\n\n joined = []\n archived = []\n if len(room_events) <= timeline_limit:\n # There is no gap in any of the rooms. Therefore we can just\n # partition the new events by room and return them.\n logger.debug(\"Got %i events for incremental sync - not limited\",\n len(room_events))\n\n invite_events = []\n leave_events = []\n events_by_room_id = {}\n for event in room_events:\n events_by_room_id.setdefault(event.room_id, []).append(event)\n if event.room_id not in joined_room_ids:\n if (event.type == EventTypes.Member\n and event.state_key == sync_config.user.to_string()):\n if event.membership == Membership.INVITE:\n invite_events.append(event)\n elif event.membership in (Membership.LEAVE, Membership.BAN):\n leave_events.append(event)\n\n for room_id in joined_room_ids:\n recents = events_by_room_id.get(room_id, [])\n logger.debug(\"Events for room %s: %r\", room_id, recents)\n state = {\n (event.type, event.state_key): event\n for event in recents if event.is_state()}\n limited = False\n\n if recents:\n prev_batch = now_token.copy_and_replace(\n \"room_key\", recents[0].internal_metadata.before\n )\n else:\n prev_batch = now_token\n\n just_joined = yield self.check_joined_room(sync_config, state)\n if just_joined:\n logger.debug(\"User has just joined %s: needs full state\",\n room_id)\n state = yield self.get_state_at(room_id, now_token)\n # the timeline is inherently limited if we've just joined\n limited = True\n\n room_sync = JoinedSyncResult(\n room_id=room_id,\n timeline=TimelineBatch(\n events=recents,\n prev_batch=prev_batch,\n limited=limited,\n ),\n state=state,\n ephemeral=ephemeral_by_room.get(room_id, []),\n account_data=self.account_data_for_room(\n room_id, tags_by_room\n ),\n )\n logger.debug(\"Result for room %s: %r\", room_id, room_sync)\n\n if room_sync:\n joined.append(room_sync)\n\n else:\n logger.debug(\"Got %i events for incremental sync - hit limit\",\n len(room_events))\n\n invite_events = yield self.store.get_invites_for_user(\n sync_config.user.to_string()\n )\n\n leave_events = yield self.store.get_leave_and_ban_events_for_user(\n sync_config.user.to_string()\n )\n\n for room_id in joined_room_ids:\n room_sync = yield self.incremental_sync_with_gap_for_room(\n room_id, sync_config, since_token, now_token,\n ephemeral_by_room, tags_by_room\n )\n if room_sync:\n joined.append(room_sync)\n\n for leave_event in leave_events:\n room_sync = yield self.incremental_sync_for_archived_room(\n sync_config, leave_event, since_token, tags_by_room\n )\n archived.append(room_sync)\n\n invited = [\n InvitedSyncResult(room_id=event.room_id, invite=event)\n for event in invite_events\n ]\n\n defer.returnValue(SyncResult(\n presence=presence,\n joined=joined,\n invited=invited,\n archived=archived,\n next_batch=now_token,\n ))","title":""},{"docid":"28714f8887493fbb2141ce720979c262","score":"0.49907303","text":"def handle_client_connect_event():\n # when the client connects, send all existing data\n print('dashboard client connected, sending any existing data...')\n socketio.emit('existingData', existingData)\n global thread","title":""},{"docid":"5461ec7ce6982a7c3855f282cfdc89c6","score":"0.4987587","text":"def sync(self):\r\n\r\n # Ensure to rerun only once to avoid infinite loops\r\n # caused by a constantly changing state value at each run.\r\n #\r\n # Example: state.value += 1\r\n if self._state[\"is_rerun\"]:\r\n self._state[\"is_rerun\"] = False\r\n \r\n elif self._state[\"hash\"] is not None:\r\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(self._state[\"data\"], None):\r\n self._state[\"is_rerun\"] = True\r\n self._state[\"session\"].request_rerun()\r\n\r\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)","title":""},{"docid":"0b164f74c505c12292147abc7c6f4357","score":"0.49763384","text":"def sync(self) -> None:\n\n # Ensure to rerun only once to avoid infinite loops\n # caused by a constantly changing session value at each run.\n #\n # Example: session.value += 1\n if self._state[\"is_rerun\"]:\n self._state[\"is_rerun\"] = False\n\n elif self._state[\"hash\"] is not None:\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(\n self._state[\"data\"], None\n ):\n self._state[\"is_rerun\"] = True\n self._state[\"session\"].request_rerun()\n\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)","title":""},{"docid":"030d20a4d8ceadf744091ffdb75b472c","score":"0.49734926","text":"def enterRoom(self):\n\n\n\t\t# Sets the room = player x,y location\n\t\tif (self.world.tile_exists(self.player.location_x, self.player.location_y)):\n\t\t\tself.room = self.world.tile_exists(self.player.location_x, self.player.location_y)\n\n\t\telse:\n\t\t\tself.world.generate_world(self.player.location_x, self.player.location_y)\n\t\t\tself.room = self.world.tile_exists(self.player.location_x, self.player.location_y)\n\n\t\t\n\n\t\tavailable_actions = self.room.available_actions()","title":""},{"docid":"a1b3ca2cffe0143a985f3050c242d978","score":"0.49679375","text":"def runclient():\n global mcast\n mcast = McastClient(MCAST_PORT,MCAST_ADDR)\n\n while 1:\n mcast.send(\"5+5\")\n time.sleep(5)","title":""},{"docid":"29e3cac73dfc82940258c2f767eb4811","score":"0.49630406","text":"def sync(self):\n\n # Ensure to rerun only once to avoid infinite loops\n # caused by a constantly changing state value at each run.\n #\n # Example: state.value += 1\n if self._state[\"is_rerun\"]:\n self._state[\"is_rerun\"] = False\n \n elif self._state[\"hash\"] is not None:\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(self._state[\"data\"], None):\n self._state[\"is_rerun\"] = True\n self._state[\"session\"].request_rerun()\n\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)","title":""},{"docid":"18ef190a286d00ff1dc973955d8f9fdb","score":"0.49612492","text":"async def vaillant_update(self):\n new_room: Room = self.hub.find_component(self.room)\n new_device: Device = self._find_device(new_room, self.device.sgtin)\n\n if new_room:\n if new_device:\n _LOGGER.debug(\n \"New / old state: %s / %s\",\n new_device.battery_low,\n self.device.battery_low,\n )\n else:\n _LOGGER.debug(\"Device %s doesn't exist anymore\", self.device.sgtin)\n else:\n _LOGGER.debug(\"Room %s doesn't exist anymore\", self.room.id)\n self.room = new_room\n self.device = new_device","title":""},{"docid":"b37729195b22d51fd9f1aa3fa1f8bab8","score":"0.49547064","text":"def sync(self):\n\n # Ensure to rerun only once to avoid infinite loops\n # caused by a constantly changing state value at each run.\n #\n # Example: state.value += 1\n if self._state[\"is_rerun\"]:\n self._state[\"is_rerun\"] = False\n\n elif self._state[\"hash\"] is not None:\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(\n self._state[\"data\"], None):\n self._state[\"is_rerun\"] = True\n self._state[\"session\"].request_rerun()\n\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(\n self._state[\"data\"], None)","title":""},{"docid":"1962cc15abac4892272ca9a654276139","score":"0.49545133","text":"def join_room_socketio(data):\n challenge = db.session.query(Challenge).get(data[\"challenge_id\"])\n if challenge:\n chat_room = challenge.chat_room\n join_room(chat_room.room_id)\n history = chat_room.get_history()\n emit(\"join_room\", {\"history\": history})","title":""},{"docid":"44742eb52d96d6d533c77d679e84613d","score":"0.49509966","text":"def joinRoom(self,data):\n roomId, = struct.unpack('>H', data[8 : 10])\n usernow = self.serverProxy.getUserByName(self.userName)\n \n # room id is wrong\n if roomId not in self.roomID:\n self.roomState(usernow)\n else: \n #the client joins to main room\n if roomId == 1 :\n self.serverProxy.stopStreamingMovie(usernow.userChatRoom)\n userPastroom = usernow.userChatRoom\n self.serverProxy.updateUserChatroom(usernow.userName, ROOM_IDS.MAIN_ROOM)\n for user in self.serverProxy.getUserList():\n if (user.userChatRoom == ROOM_IDS.MAIN_ROOM or user.userChatRoom == userPastroom):\n self.roomState(user)\n #the client joins to movie room\n else :\n for movie in self.serverProxy.getMovieList() :\n if movie.movieId == roomId :\n self.serverProxy.startStreamingMovie(movie.movieTitle)\n self.serverProxy.updateUserChatroom(usernow.userName, movie.movieTitle)\n for user in self.serverProxy.getUserList():\n if (user.userChatRoom == movie.movieTitle or user.userChatRoom == ROOM_IDS.MAIN_ROOM): \n self.roomState(user)","title":""},{"docid":"4f28335f3f9c9246c282b31529135bbb","score":"0.49460658","text":"def sync(path):\r\n path = path.rstrip('\\\\').rstrip('/')\r\n click.confirm(f'This will DELETE remote data that is not present.\\nAre you sure you want to sync {path}?', abort=True)\r\n \r\n try:\r\n coach = get_coach()\r\n coach.sync_local(path)\r\n except Exception as e:\r\n click.echo(e)","title":""},{"docid":"8600b3b6b671d5f8c6fe9a332524d7bf","score":"0.4940865","text":"def sequence_master_sync(self, c, sync=None):\n if sync is None:\n sync = c['master_sync']\n else:\n c['master_sync'] = sync\n return sync","title":""},{"docid":"00a6ae505c43959042b7029cf7070f45","score":"0.49383396","text":"def save(self, *args, **kwargs):\n # Check if changes have been done to the room itself\n if self._room != self.room:\n self.notify_room()\n super(Player, self).save(*args, **kwargs)","title":""},{"docid":"2c1c6ba7b5c525949a38d88d31bac190","score":"0.49371472","text":"def sync(self):\n odir=self.get_temp_dir()\n if not os.path.exists(odir):\n os.makedirs(odir)\n\n remote_url=self.get_remote_coadd_file('g')\n remote_url = remote_url.replace('_g','{_g,_r,_i}')\n cmd = r\"\"\"\n rsync \\\n -aP \\\n --password-file $DES_RSYNC_PASSFILE \\\n %(remote_url)s \\\n %(local_dir)s/\n \"\"\" % dict(\n remote_url=remote_url,\n local_dir=odir,\n )\n\n print(cmd)\n subprocess.check_call(cmd,shell=True)","title":""},{"docid":"81bd268d8f30368faf706b54739698e5","score":"0.49305964","text":"def start(self, event):\n debugger_print(\"session_start was received; self.start was called\")\n self.send_presence()\n self.get_roster()\n \"\"\" self.get_roster(callback=self.print_roster)\n debugger_print(\"CLIENT ROSTER:\")\n debugger_print(self.client_roster) \"\"\"","title":""},{"docid":"dcf800e50654da7df8c999ad2a2bd880","score":"0.49289438","text":"def sync(self):\n\n # Ensure to rerun only once to avoid infinite loops\n # caused by a constantly changing state value at each run.\n #\n # Example: state.value += 1\n if self._state[\"is_rerun\"]:\n self._state[\"is_rerun\"] = False\n\n elif self._state[\"hash\"] is not None:\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(self._state[\"data\"], None):\n self._state[\"is_rerun\"] = True\n self._state[\"session\"].request_rerun()\n\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)","title":""},{"docid":"dfd1220be4b617973ca1e0634bd58af8","score":"0.49222025","text":"def enqueue_state(self, lobby: bool = True) -> None:\n if not self.chat:\n point_of_interest()\n\n # TODO: hmm this is pretty bad, writes twice\n\n # send password only to users currently in the match.\n self.chat.enqueue(packets.updateMatch(self, send_pw=True))\n\n if lobby and (lchan := glob.channels['#lobby']) and lchan.players:\n lchan.enqueue(packets.updateMatch(self, send_pw=False))","title":""},{"docid":"d7d1ad57f6b93521cfbc13977fabbdc5","score":"0.492037","text":"def do_green_room(self):\n if self._is_client_owner:\n if self.privacy_settings.set_greenroom():\n self.send_bot_msg('*Green room is enabled.*')\n self.rtmp_parameter['greenroom'] = True\n else:\n self.send_bot_msg('*Green room is disabled.*')\n self.rtmp_parameter['greenroom'] = False","title":""},{"docid":"7ca106992aec42bd315c2bc298b0acbb","score":"0.4911232","text":"def send_update_game(self):\n game = Game.objects.get_or_none(id=self.id)\n if not game:\n return\n current_player = game.get_current_player()\n if not current_player:\n current_player = \"\"\n current_player_order = 0\n else:\n current_player_order = current_player.order\n current_player = current_player.user.username\n async_to_sync(self.channel_layer.group_send)(\n self.room_group_name,\n {\n \"type\": \"update_game\",\n \"game\": game.as_json(),\n \"current_player\": current_player,\n \"current_player_order\": current_player_order,\n }\n )","title":""},{"docid":"50c51163b454055008f8ba32f5ababd1","score":"0.49042723","text":"def cmd_sync(args):\n\n if args.get(0):\n # Optional branch specifier.\n branch = fuzzy_match_branch(args.get(0))\n if branch:\n is_external = True\n original_branch = get_current_branch_name()\n else:\n print(\"{0} doesn't exist. Use a branch that does.\".format(\n colored.yellow(args.get(0))))\n sys.exit(1)\n else:\n # Sync current branch.\n branch = get_current_branch_name()\n is_external = False\n\n if branch in get_branch_names(local=False):\n\n if is_external:\n switch_to(branch)\n\n if repo.is_dirty():\n status_log(stash_it, 'Saving local changes.', sync=True)\n\n status_log(smart_pull, 'Pulling commits from the server.')\n status_log(push, 'Pushing commits to the server.', branch)\n\n if unstash_index(sync=True):\n status_log(unstash_it, 'Restoring local changes.', sync=True)\n\n if is_external:\n switch_to(original_branch)\n\n else:\n print('{0} has not been published yet.'.format(\n colored.yellow(branch)))\n sys.exit(1)","title":""},{"docid":"3a565dabc3ef2326732058946c1a71cf","score":"0.48988125","text":"def updateclientmodels(sock, updatedweights):\n client.load_state_dict(updatedweights)\n for clientss in connectedclients:\n try:\n if clientss != sock:\n send_msg(clientss, updatedweights)\n except:\n pass","title":""},{"docid":"c64aa9141bbb42188fba31680647f721","score":"0.48850664","text":"async def _sync_db():\n global _WATCHES, _ONLINE_STATES\n while True:\n _WATCHES.close()\n _WATCHES = dbm.open(os.path.join(os.environ[\"HOME\"], \"r99infowatches.db\"), \"c\")\n _ONLINE_STATES.close()\n _ONLINE_STATES = shelve.open(os.path.join(os.environ[\"HOME\"], \"r99infoonline.db\"), \"c\", 2)\n await asyncio.sleep(5)","title":""},{"docid":"dc0702450078db1cdaecef1e3e76a382","score":"0.48746255","text":"def sync(self):\n self.syncing_function = self.stim_syncer.sync(self.bcontrol_loader,\n self.raw_data_loader)","title":""},{"docid":"f4b79856311e39cd4fcacfa7502c89ef","score":"0.48694023","text":"async def __aenter__(self):\n client_id = self.client_id\n logger.info(\"OPEN %s %s\", self.ip_address, client_id)\n\n server = self.server\n queue = asyncio.Queue(loop=server.loop)\n\n for client in server.clients.values():\n await queue.put(Event(client.data, event_type=\"created\"))\n\n self.queue = queue\n server.clients[client_id] = self\n await server.add_event(Event(self.data, event_type=\"created\"))\n\n return self","title":""},{"docid":"a4ecc93cdacc00ac478770a2c6789685","score":"0.48664892","text":"def incremental_sync_with_gap_for_room(self, room_id, sync_config,\n since_token, now_token,\n ephemeral_by_room, tags_by_room):\n logger.debug(\"Doing incremental sync for room %s between %s and %s\",\n room_id, since_token, now_token)\n\n # TODO(mjark): Check for redactions we might have missed.\n\n batch = yield self.load_filtered_recents(\n room_id, sync_config, now_token, since_token,\n )\n\n logging.debug(\"Recents %r\", batch)\n\n current_state = yield self.get_state_at(room_id, now_token)\n\n state_at_previous_sync = yield self.get_state_at(\n room_id, stream_position=since_token\n )\n\n state = yield self.compute_state_delta(\n since_token=since_token,\n previous_state=state_at_previous_sync,\n current_state=current_state,\n )\n\n just_joined = yield self.check_joined_room(sync_config, state)\n if just_joined:\n state = yield self.get_state_at(room_id, now_token)\n\n room_sync = JoinedSyncResult(\n room_id=room_id,\n timeline=batch,\n state=state,\n ephemeral=ephemeral_by_room.get(room_id, []),\n account_data=self.account_data_for_room(\n room_id, tags_by_room\n ),\n )\n\n logging.debug(\"Room sync: %r\", room_sync)\n\n defer.returnValue(room_sync)","title":""},{"docid":"40777ae1de874dee374bb0a56ab3ffa2","score":"0.48618433","text":"def test_sync_backwards_typing(self) -> None:\n typing_url = \"/rooms/%s/typing/%s?access_token=%s\"\n sync_url = \"/sync?timeout=3000000&access_token=%s&since=%s\"\n\n # Register the user who gets notified\n user_id = self.register_user(\"user\", \"pass\")\n access_token = self.login(\"user\", \"pass\")\n\n # Register the user who sends the message\n other_user_id = self.register_user(\"otheruser\", \"pass\")\n other_access_token = self.login(\"otheruser\", \"pass\")\n\n # Create a room\n room = self.helper.create_room_as(user_id, tok=access_token)\n\n # Invite the other person\n self.helper.invite(room=room, src=user_id, tok=access_token, targ=other_user_id)\n\n # The other user joins\n self.helper.join(room=room, user=other_user_id, tok=other_access_token)\n\n # The other user sends some messages\n self.helper.send(room, body=\"Hi!\", tok=other_access_token)\n self.helper.send(room, body=\"There!\", tok=other_access_token)\n\n # Start typing.\n channel = self.make_request(\n \"PUT\",\n typing_url % (room, other_user_id, other_access_token),\n b'{\"typing\": true, \"timeout\": 30000}',\n )\n self.assertEqual(200, channel.code)\n\n channel = self.make_request(\"GET\", \"/sync?access_token=%s\" % (access_token,))\n self.assertEqual(200, channel.code)\n next_batch = channel.json_body[\"next_batch\"]\n\n # Stop typing.\n channel = self.make_request(\n \"PUT\",\n typing_url % (room, other_user_id, other_access_token),\n b'{\"typing\": false}',\n )\n self.assertEqual(200, channel.code)\n\n # Start typing.\n channel = self.make_request(\n \"PUT\",\n typing_url % (room, other_user_id, other_access_token),\n b'{\"typing\": true, \"timeout\": 30000}',\n )\n self.assertEqual(200, channel.code)\n\n # Should return immediately\n channel = self.make_request(\"GET\", sync_url % (access_token, next_batch))\n self.assertEqual(200, channel.code)\n next_batch = channel.json_body[\"next_batch\"]\n\n # Reset typing serial back to 0, as if the master had.\n typing = self.hs.get_typing_handler()\n typing._latest_room_serial = 0\n\n # Since it checks the state token, we need some state to update to\n # invalidate the stream token.\n self.helper.send(room, body=\"There!\", tok=other_access_token)\n\n channel = self.make_request(\"GET\", sync_url % (access_token, next_batch))\n self.assertEqual(200, channel.code)\n next_batch = channel.json_body[\"next_batch\"]\n\n # This should time out! But it does not, because our stream token is\n # ahead, and therefore it's saying the typing (that we've actually\n # already seen) is new, since it's got a token above our new, now-reset\n # stream token.\n channel = self.make_request(\"GET\", sync_url % (access_token, next_batch))\n self.assertEqual(200, channel.code)\n next_batch = channel.json_body[\"next_batch\"]\n\n # Clear the typing information, so that it doesn't think everything is\n # in the future.\n typing._reset()\n\n # Now it SHOULD fail as it never completes!\n with self.assertRaises(TimedOutException):\n self.make_request(\"GET\", sync_url % (access_token, next_batch))","title":""},{"docid":"929b045475245ddd3763a313623a8404","score":"0.4861605","text":"def __init__(self,room):\r\n self.move_to(room)","title":""},{"docid":"30d2b587275ae879c9d55be99d478047","score":"0.48561555","text":"async def getCompactRoom(self, room_id: str) -> Room:\n\t\treturn await self.call(\"getCompactRoom\", room_id)","title":""},{"docid":"e6ec225892dbc19c52d9a97142ca091a","score":"0.48492607","text":"def reqsync(votername, config):\n\t\n\tprint(\"sync\")\n\tprint(votername)","title":""}],"string":"[\n {\n \"docid\": \"9b07bce640e05bbb4999c959ad52528c\",\n \"score\": \"0.61042523\",\n \"text\": \"def test_incremental_sync(self) -> None:\\n channel = self.make_request(\\\"GET\\\", \\\"/sync\\\", access_token=self.tok)\\n self.assertEqual(channel.code, 200, channel.result)\\n next_batch = channel.json_body[\\\"next_batch\\\"]\\n\\n self.helper.send(self.excluded_room_id, tok=self.tok)\\n self.helper.send(self.included_room_id, tok=self.tok)\\n\\n channel = self.make_request(\\n \\\"GET\\\",\\n f\\\"/sync?since={next_batch}\\\",\\n access_token=self.tok,\\n )\\n self.assertEqual(channel.code, 200, channel.result)\\n\\n self.assertNotIn(self.excluded_room_id, channel.json_body[\\\"rooms\\\"][\\\"join\\\"])\\n self.assertIn(self.included_room_id, channel.json_body[\\\"rooms\\\"][\\\"join\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18bbaf3630da66a54dd83f06f3f92d1c\",\n \"score\": \"0.60239524\",\n \"text\": \"def _update_client(self, _: Optional[Dict[str, Any]] = None) -> None:\\n self.async_write_ha_state()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77e9910d01f22c3f4ca877baf0acfce6\",\n \"score\": \"0.5994365\",\n \"text\": \"def sync():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0388f648e0d6a66ccb6f632ce43a6c8\",\n \"score\": \"0.59534687\",\n \"text\": \"def sync(self):\\n self._device.CheckCall(self._BuildCommand('sync'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07868d6159df5bb5e6a4f8a4b4cf4ffe\",\n \"score\": \"0.5818409\",\n \"text\": \"def _sync(self, timeout_ms=30000):\\n response = self.api.sync(self.sync_token, timeout_ms, filter=self.sync_filter)\\n self.sync_token = response[\\\"next_batch\\\"]\\n\\n for presence_update in response['presence']['events']:\\n for callback in self.presence_listeners.values():\\n self.call(callback, presence_update)\\n\\n for room_id, invite_room in response['rooms']['invite'].items():\\n for listener in self.invite_listeners:\\n self.call(listener, room_id, invite_room['invite_state'])\\n\\n for room_id, left_room in response['rooms']['leave'].items():\\n for listener in self.left_listeners:\\n self.call(listener, room_id, left_room)\\n if room_id in self.rooms:\\n del self.rooms[room_id]\\n\\n for room_id, sync_room in response['rooms']['join'].items():\\n if room_id not in self.rooms:\\n self._mkroom(room_id)\\n room = self.rooms[room_id]\\n # TODO: the rest of this for loop should be in room object method\\n room.prev_batch = sync_room[\\\"timeline\\\"][\\\"prev_batch\\\"]\\n\\n for event in sync_room[\\\"state\\\"][\\\"events\\\"]:\\n event['room_id'] = room_id\\n self.call(room._process_state_event, event)\\n\\n for event in sync_room[\\\"timeline\\\"][\\\"events\\\"]:\\n event['room_id'] = room_id\\n self.call(room._put_event, event)\\n\\n # TODO: global listeners can still exist but work by each\\n # room.listeners[uuid] having reference to global listener\\n\\n # Dispatch for client (global) listeners\\n for listener in self.listeners:\\n if (\\n listener['event_type'] is None or\\n listener['event_type'] == event['type']\\n ):\\n self.call(listener['callback'], event)\\n\\n for event in sync_room['ephemeral']['events']:\\n event['room_id'] = room_id\\n self.call(room._put_ephemeral_event, event)\\n\\n for listener in self.ephemeral_listeners:\\n if (\\n listener['event_type'] is None or\\n listener['event_type'] == event['type']\\n ):\\n self.call(listener['callback'], event)\\n\\n for event in sync_room['account_data']['events']:\\n room.account_data[event['type']] = event['content']\\n\\n for event in response['account_data']['events']:\\n self.account_data[event['type']] = event['content']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92e0e1c36a51820b934e7b7c56bc4ad4\",\n \"score\": \"0.57885987\",\n \"text\": \"def sync(self):\\n self.synced = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e73918b5bc3aa43985f5177095ae22e6\",\n \"score\": \"0.56940854\",\n \"text\": \"def sync(self):\\n self.debug('Synchronizing clients')\\n plist = self.getPlayerList(maxRetries=4)\\n self.verbose2('plist: %s' % plist)\\n mlist = {}\\n\\n for cid, c in plist.iteritems():\\n client = self.clients.getByCID(cid)\\n if client:\\n self.verbose2('client found: %s' % client.name)\\n if client.guid and 'guid' in c and not self.IpsOnly:\\n if b3.functions.fuzzyGuidMatch(client.guid, c['guid']):\\n # player matches\\n self.debug('in-sync %s == %s', client.guid, c['guid'])\\n mlist[str(cid)] = client\\n else:\\n self.debug('no-sync %s <> %s', client.guid, c['guid'])\\n client.disconnect()\\n elif client.ip and 'ip' in c:\\n if client.ip == c['ip']:\\n # player matches\\n self.debug('in-sync %s == %s', client.ip, c['ip'])\\n mlist[str(cid)] = client\\n else:\\n self.debug('no-sync %s <> %s', client.ip, c['ip'])\\n client.disconnect()\\n else:\\n self.debug('no-sync: no guid or ip found')\\n else:\\n self.verbose2('no client found for cid: %s' % cid)\\n \\n return mlist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16e8b92329a80d8ee32e71adf6f2f858\",\n \"score\": \"0.5647099\",\n \"text\": \"def sync():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67ed3503835c5d7801972f80c079495f\",\n \"score\": \"0.5581597\",\n \"text\": \"def sync(self):\\n plist = self.getPlayerList()\\n mlist = dict()\\n for cid, c in plist.iteritems():\\n client = self.getByCidOrJoinPlayer(cid)\\n if client:\\n if client.guid and 'guid' in c.keys():\\n if client.guid == c['guid']:\\n # player matches\\n self.debug('in-sync %s == %s', client.guid, c['guid'])\\n mlist[str(cid)] = client\\n else:\\n self.debug('no-sync %s <> %s', client.guid, c['guid'])\\n client.disconnect()\\n elif client.ip and 'ip' in c.keys():\\n if client.ip == c['ip']:\\n # player matches\\n self.debug('in-sync %s == %s', client.ip, c['ip'])\\n mlist[str(cid)] = client\\n else:\\n self.debug('no-sync %s <> %s', client.ip, c['ip'])\\n client.disconnect()\\n else:\\n self.debug('no-sync: no guid or ip found')\\n \\n return mlist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"673bffcb453dc91261f3b4dcf2e64eb0\",\n \"score\": \"0.5563498\",\n \"text\": \"def sync(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d624f76faa44f4debc4e8f46d548c289\",\n \"score\": \"0.5550224\",\n \"text\": \"def client():\\n print(\\\"CLIENT STARTED\\\", argv)\\n client_path = argv[-2]\\n server_path = argv[-1]\\n sync_paths(client_path, server_path)\\n try:\\n while True:\\n if path_content_to_string(client_path) != path_content_to_string(server_path):\\n print(\\\"Client syncing...\\\")\\n sync_paths(client_path, server_path)\\n print(\\\"Client sleeping 1 second\\\")\\n sleep(1.0)\\n finally:\\n print(\\\"CLIENT DONE\\\", argv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13825b2fbb897390fc0a687cc74d7d7b\",\n \"score\": \"0.5508697\",\n \"text\": \"def sync():\\n settings = _load_settings()\\n status = _load_settings(\\\".TDB\\\", check=[])\\n settings.update(status)\\n settings[\\\"database\\\"]\\n client, msg = _connect(settings, new_db=False)\\n click.echo(msg)\\n click.echo(_sync(client))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddc23127af72e14f8101e49832da15ad\",\n \"score\": \"0.54933107\",\n \"text\": \"def _from_client_commands(self):\\n while self._running:\\n all_data = receive(self._from_client_connections.keys(), 0.1)\\n\\n for data in all_data:\\n if data[\\\"type\\\"] == \\\"change\\\":\\n self._paddles[data[\\\"sender\\\"]].update_location(\\n data[\\\"change\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe77edd5a0fe8ef672d278c6f472464d\",\n \"score\": \"0.5472058\",\n \"text\": \"def send_sync(self) :\\n self.send(Protocol.syc(\\\"socket\\\", \\\"1.1.1\\\").pack())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128fc13c86af4b99bf4a54e3f94c0cda\",\n \"score\": \"0.5413396\",\n \"text\": \"def background_thread():\\n count = 0\\n while True:\\n # count += 1\\n # socketio.emit('my_response',\\n # {'data': 'Server generated event', 'count': count},\\n # namespace=namespace)\\n for room in all_rooms.values():\\n room.sync()\\n socketio.sleep(3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b65ff4e7a16279a6e3c71a7321984bc5\",\n \"score\": \"0.541258\",\n \"text\": \"def test_user_with_no_rooms_receives_self_device_list_updates(self) -> None:\\n device_id = \\\"TESTDEVICE\\\"\\n\\n # Register a user and login, creating a device\\n self.user_id = self.register_user(\\\"kermit\\\", \\\"monkey\\\")\\n self.tok = self.login(\\\"kermit\\\", \\\"monkey\\\", device_id=device_id)\\n\\n # Request an initial sync\\n channel = self.make_request(\\\"GET\\\", \\\"/sync\\\", access_token=self.tok)\\n self.assertEqual(channel.code, 200, channel.json_body)\\n next_batch = channel.json_body[\\\"next_batch\\\"]\\n\\n # Now, make an incremental sync request.\\n # It won't return until something has happened\\n incremental_sync_channel = self.make_request(\\n \\\"GET\\\",\\n f\\\"/sync?since={next_batch}&timeout=30000\\\",\\n access_token=self.tok,\\n await_result=False,\\n )\\n\\n # Change our device's display name\\n channel = self.make_request(\\n \\\"PUT\\\",\\n f\\\"devices/{device_id}\\\",\\n {\\n \\\"display_name\\\": \\\"freeze ray\\\",\\n },\\n access_token=self.tok,\\n )\\n self.assertEqual(channel.code, 200, channel.json_body)\\n\\n # The sync should now have returned\\n incremental_sync_channel.await_result(timeout_ms=20000)\\n self.assertEqual(incremental_sync_channel.code, 200, channel.json_body)\\n\\n # We should have received notification that the (user's) device has changed\\n device_list_changes = incremental_sync_channel.json_body.get(\\n \\\"device_lists\\\", {}\\n ).get(\\\"changed\\\", [])\\n\\n self.assertIn(\\n self.user_id, device_list_changes, incremental_sync_channel.json_body\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8f49d8b21dfa53e69dc72d8ebf2869c\",\n \"score\": \"0.540039\",\n \"text\": \"def client():\\n return LunoSyncClient()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4708cee57f8aaae70e5d09db89eb6301\",\n \"score\": \"0.53896546\",\n \"text\": \"def open(self):\\n self.client_id = uuid.uuid4().hex\\n CLIENTS[self.client_id] = self\\n [self.write_message(msg) for msg in CACHE]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b862e2566a3bc61ad1af675c30aab5d\",\n \"score\": \"0.53893983\",\n \"text\": \"def SeedServerStateOnClient(self, client_channel):\\n print(f\\\"SeedServerStateOnClient({client_channel.id})\\\")\\n for target_channel in self.clients.values():\\n if target_channel.client_hash != client_channel.client_hash:\\n for object_hash in target_channel.objects:\\n client_channel.Send({\\n \\\"action\\\": \\\"cloneobjectfromserver\\\",\\n \\\"data\\\": target_channel.objects[object_hash]\\n })\\n print(f\\\" >> {target_channel.id} : {object_hash}\\\")\\n # print(f\\\"seedserver sent cloneobjectfromserver \\\"\\n # + f\\\"{target_channel.objects[object_hash]} to {client_channel.id}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebcef1ef603f30b086087a34c113a087\",\n \"score\": \"0.53885525\",\n \"text\": \"def sync_clients_save(sender, instance, **kwargs):\\n channel_layer = get_channel_layer()\\n async_to_sync(channel_layer.group_send)(\\n \\\"sync_clients\\\", {\\\"type\\\": \\\"sync_clients.save\\\", \\\"model\\\": sender.__name__}\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54ab3e394fa4034a973afd9a7a0319cd\",\n \"score\": \"0.53870213\",\n \"text\": \"def sync(self):\\n cu_sync_device()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26796fc310a405c43a9b0f121818330b\",\n \"score\": \"0.53840584\",\n \"text\": \"def pri_chat(s_send, username):\\n \\n uname = s_send.recv(1024).decode()\\n\\n if uname == 'DEL\\\\n': # client request to delete room\\n s_send.send(b'OK')\\n\\n roomName = s_send.recv(1024).decode()\\n s_send.send(b'OK')\\n \\n # send quit command to all clients in roomName\\n r = rooms[roomName]\\n for c in r:\\n r[c].send(b'PRIVCHAT')\\n r[c].recv(1024) # 'OK'\\n r[c].send(b'QUIT')\\n r[c].recv(1024) # 'OK'\\n r[c].send(roomName.encode())\\n r[c].recv(1024) # 'OK'\\n rooms.pop(roomName)\\n \\n # remove all files belong to roomName\\n for r, d, f in os.walk('uploads'):\\n for i in f:\\n room = i.split('+', 1)[0]\\n if room == 'all':\\n continue\\n room = room.split(' ')\\n room = room[0] + '\\\\n' + room[1]\\n if room == roomName:\\n os.remove('uploads/' + i)\\n break\\n return\\n\\n # create new room\\n if not uname in online_users:\\n s_send.send(b'NO')\\n else:\\n s_send.send(b'OK')\\n\\n # create room\\n roomName = username + '\\\\n' + uname\\n rooms[roomName] = {}\\n rooms[roomName][username] = rooms['all'][username]\\n rooms[roomName][uname] = rooms['all'][uname]\\n\\n # send command create room to all client in room\\n r = rooms[roomName]\\n for c in r:\\n r[c].send(b'PRIVCHAT')\\n r[c].recv(1024) # 'OK'\\n r[c].send(roomName.encode() + b' @' + username.encode() + b' + @' + uname.encode())\\n r[c].recv(1024) # 'OK'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19595cde2466916c71e7f26e1236f9bb\",\n \"score\": \"0.5382695\",\n \"text\": \"def __init__(self):\\n self.lock = asyncio.Lock()\\n self.client_connections = dict()\\n self.game = g.Game()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efdf521aebdb491ac6db4689dab5e10e\",\n \"score\": \"0.5378586\",\n \"text\": \"def update(self, client: socket) -> None:\\n client_nickname = \\\"\\\"\\n while True:\\n # decode the incoming message using utf-8\\n message = client.recv(constants.BUFFER_SIZE).decode(constants.ENCODING)\\n\\n if message.startswith(\\\"[SENDTO:ALL]\\\") and client_nickname:\\n self.send_to_clients(bytes(message.replace(\\\"[SENDTO:ALL]\\\", \\\"[SENDTO:ALL:\\\" + client_nickname + \\\"]\\\"), constants.ENCODING))\\n continue\\n elif message.startswith(\\\"[SENDTO\\\") and client_nickname:\\n message_split = message.split(\\\"]\\\")\\n name = message_split[0][1:].split(\\\":\\\")[1]\\n for client_sock, client_name in self.connections.items():\\n if name == client_name:\\n client_sock.send(bytes(\\\"[SENDTO:\\\" + client_nickname + \\\"]=\\\" + message_split[1].split(\\\"=\\\")[1], constants.ENCODING))\\n continue\\n elif message.startswith(\\\"[JOINED]\\\"):\\n client_nickname = message.split(\\\"=\\\")[1]\\n current_clients = []\\n [current_clients.append(client_name) for _, client_name in self.connections.items()]\\n if client_nickname in current_clients:\\n client.send(bytes(\\\"[DUPLICATE]\\\" + client_nickname, constants.ENCODING))\\n continue\\n time.sleep(0.1)\\n self.connections[client] = client_nickname\\n print(client_nickname + \\\" joined that chat!\\\")\\n self.send_to_clients(bytes(\\\"[JOINED]=\\\" + client_nickname, constants.ENCODING))\\n time.sleep(0.1)\\n clients = []\\n for _, client_name in self.connections.items():\\n clients.append(client_name)\\n self.send_to_clients(bytes(\\\"[CLIENTS]=\\\" + \\\"-\\\".join(clients), constants.ENCODING))\\n continue\\n elif message.startswith(\\\"[LEFT]\\\"):\\n client.close()\\n try:\\n del self.connections[client]\\n except KeyError:\\n pass\\n if len(client_nickname) != 0:\\n print(client_nickname + \\\" left that chat!\\\")\\n self.send_to_clients(bytes(\\\"[LEFT]=\\\" + client_nickname, constants.ENCODING))\\n time.sleep(0.1)\\n clients = []\\n for _, client_name in self.connections.items():\\n clients.append(client_name)\\n self.send_to_clients(bytes(\\\"[CLIENTS]=\\\" + \\\"-\\\".join(clients), constants.ENCODING))\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32477dbc02a4fd8b914aeba391da4241\",\n \"score\": \"0.5373981\",\n \"text\": \"def sync_workdir_from_guest(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24f837691b68ab5bef996b06890572b1\",\n \"score\": \"0.53714913\",\n \"text\": \"def __sync():\\n log.debug('getting in sync with LUA')\\n self.__clear_buffers()\\n try:\\n self.__writeln('UUUUUUUUUUUU') # Send enough characters for auto-baud\\n self.__clear_buffers()\\n time.sleep(self.autobaud_time) # Wait for autobaud timer to expire\\n self.__exchange(';') # Get a defined state\\n self.__writeln('print(\\\"%sync%\\\");')\\n self.__expect('%sync%\\\\r\\\\n> ')\\n except CommunicationTimeout:\\n raise DeviceNotFoundException('Device not found or wrong port')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b79da3de9e01cbaa7f18501d85175fb4\",\n \"score\": \"0.537101\",\n \"text\": \"def refresh(self):\\r\\n self.update(self['__client__'].get(self['id']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f6649f50890cb790c023c2fb653b6f9\",\n \"score\": \"0.5370351\",\n \"text\": \"async def vaillant_update(self):\\n new_room: Room = self.hub.find_component(self._room)\\n\\n if new_room:\\n _LOGGER.debug(\\n \\\"New / old state: %s / %s\\\", new_room.child_lock, self._room.child_lock\\n )\\n else:\\n _LOGGER.debug(\\\"Room %s doesn't exist anymore\\\", self._room.id)\\n self._room = new_room\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eee982f3ec8f07d9c45cee31b2f9d0e2\",\n \"score\": \"0.5331417\",\n \"text\": \"def roomState(self, user):\\n \\n\\n version = 1\\n typeMessage = 4\\n \\n #to get the room of the client\\n sessionToken_1 = self.usersession[user.userAddress][0]\\n sessionToken_2 = self.usersession[user.userAddress][1]\\n \\n sequenceNumber = self.usersequenceNumber[user.userAddress][0]\\n self.usersequenceNumber[user.userAddress][1] = 4\\n room = user.userChatRoom\\n \\n \\n if room == ROOM_IDS.MAIN_ROOM :\\n \\n #if the client in the main room , call the function messageMainroom()\\n mainbuf = self.messageMainroom()\\n roomListLength = len(self.serverProxy.getMovieList())\\n #to get the data of the movie room\\n roomListbuf = struct.pack('>H', roomListLength)\\n for movie in self.serverProxy.getMovieList():\\n roomListbuf = roomListbuf + self.messageMovieroom(movie.movieTitle)\\n payloadSize = len(mainbuf) + len(roomListbuf)\\n #pack the header\\n headbuf = struct.pack('>BHBHH', version*16+typeMessage, sessionToken_1, sessionToken_2,\\n sequenceNumber, payloadSize)\\n #the whole packet\\n buf = headbuf +mainbuf +roomListbuf\\n \\n #if the client in the movie room\\n else :\\n #to call the function messageMovieroom(room)\\n moviebuf = self.messageMovieroom(room)\\n payloadSize = len(moviebuf) \\n #the header\\n headbuf = struct.pack('>BHBHH', version*16+typeMessage, sessionToken_1, sessionToken_2,\\n sequenceNumber, payloadSize)\\n #the whole packet\\n buf = headbuf +moviebuf\\n \\n # Stop sending more than three times\\n if self.usercount[user.userAddress][0] ==3:\\n print(\\\"Connection Lost\\\")\\n if self.leave[user.userAddress] == 0:\\n self.serverProxy.removeUser(user.userName)\\n self.leave[user.userAddress] = 1\\n for user in self.serverProxy.getUserList() :\\n if (user.userChatRoom == ROOM_IDS.MAIN_ROOM or user.userChatRoom == room):\\n self.roomState(user)\\n \\n else:\\n #send the packet\\n print('********Send RoomState***************')\\n print(\\\"roomState :\\\", buf)\\n# print('***********************')\\n user.userChatInstance.transport.write(buf)\\n # Here we consider retransmitting this message after timing 1s without receiving ack response. \\n # Stop sending more than three times\\n self.usercount[user.userAddress][0] +=1\\n if self.usercount[user.userAddress][0] <= 3:\\n self.send[user.userAddress] = [reactor.callLater(1, self.roomState, user)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbd667ec2dbd712e7d6059a91e51623b\",\n \"score\": \"0.53257537\",\n \"text\": \"def sync_state(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c03fd5f072f56f6f63ca47b63fa39890\",\n \"score\": \"0.5298573\",\n \"text\": \"def sync(self):\\n self.sot.sync_from(self.network, diff_class=NetworkImporterDiff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1804939a76345fdb971d8e45cef6982\",\n \"score\": \"0.5287648\",\n \"text\": \"def sync_workdir_to_guest(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa7d56dc5a31a9a32b792c1b9a7950b6\",\n \"score\": \"0.5284305\",\n \"text\": \"def test_receiving_local_device_list_changes(self) -> None:\\n # Register two users\\n test_device_id = \\\"TESTDEVICE\\\"\\n alice_user_id = self.register_user(\\\"alice\\\", \\\"correcthorse\\\")\\n alice_access_token = self.login(\\n alice_user_id, \\\"correcthorse\\\", device_id=test_device_id\\n )\\n\\n bob_user_id = self.register_user(\\\"bob\\\", \\\"ponyponypony\\\")\\n bob_access_token = self.login(bob_user_id, \\\"ponyponypony\\\")\\n\\n # Create a room for them to coexist peacefully in\\n new_room_id = self.helper.create_room_as(\\n alice_user_id, is_public=True, tok=alice_access_token\\n )\\n self.assertIsNotNone(new_room_id)\\n\\n # Have Bob join the room\\n self.helper.invite(\\n new_room_id, alice_user_id, bob_user_id, tok=alice_access_token\\n )\\n self.helper.join(new_room_id, bob_user_id, tok=bob_access_token)\\n\\n # Now have Bob initiate an initial sync (in order to get a since token)\\n channel = self.make_request(\\n \\\"GET\\\",\\n \\\"/sync\\\",\\n access_token=bob_access_token,\\n )\\n self.assertEqual(channel.code, 200, channel.json_body)\\n next_batch_token = channel.json_body[\\\"next_batch\\\"]\\n\\n # ...and then an incremental sync. This should block until the sync stream is woken up,\\n # which we hope will happen as a result of Alice updating their device list.\\n bob_sync_channel = self.make_request(\\n \\\"GET\\\",\\n f\\\"/sync?since={next_batch_token}&timeout=30000\\\",\\n access_token=bob_access_token,\\n # Start the request, then continue on.\\n await_result=False,\\n )\\n\\n # Have alice update their device list\\n channel = self.make_request(\\n \\\"PUT\\\",\\n f\\\"/devices/{test_device_id}\\\",\\n {\\n \\\"display_name\\\": \\\"New Device Name\\\",\\n },\\n access_token=alice_access_token,\\n )\\n self.assertEqual(channel.code, HTTPStatus.OK, channel.json_body)\\n\\n # Check that bob's incremental sync contains the updated device list.\\n # If not, the client would only receive the device list update on the\\n # *next* sync.\\n bob_sync_channel.await_result()\\n self.assertEqual(bob_sync_channel.code, 200, bob_sync_channel.json_body)\\n\\n changed_device_lists = bob_sync_channel.json_body.get(\\\"device_lists\\\", {}).get(\\n \\\"changed\\\", []\\n )\\n self.assertIn(alice_user_id, changed_device_lists, bob_sync_channel.json_body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d546284fbd9db4f7f2d2f8987984821\",\n \"score\": \"0.5283178\",\n \"text\": \"def test_bin_enter_room(session):\\n\\n client_bin._on_data(smpacket.SMPacketClientNSSMONL(\\n packet=smpacket.SMOPacketClientEnterRoom(enter=1, room=\\\"Room client-json\\\", password=\\\"password\\\")\\n ).binary)\\n\\n room = session.query(models.Room).filter_by(name=\\\"Room client-json\\\").first()\\n user1 = session.query(models.User).filter_by(name=\\\"clientbin-user1\\\").first()\\n user2 = session.query(models.User).filter_by(name=\\\"clientbin-user2\\\").first()\\n\\n assert client_bin.room == room.id\\n\\n assert user1.room == room\\n assert user2.room == room\\n\\n assert user1.level(room.id) == 1\\n assert user2.level(room.id) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fae9f3b314c8164142e4753ec1d2be6\",\n \"score\": \"0.5249324\",\n \"text\": \"def SendMazeDataToClient(self, client_channel):\\n print(f\\\"SendMazeDataToClient({client_channel.id})\\\")\\n client_hash = client_channel.client_hash\\n client_channel.Send({\\n \\\"action\\\": \\\"sendmazedatatoclient\\\",\\n \\\"maze\\\": self.maze_rows\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"991fdb8042df46d95c68ad5a4d137c2c\",\n \"score\": \"0.52418274\",\n \"text\": \"def sync(self):\\n\\n sync_count = 4\\n\\n rtt = 999\\n for i in range(sync_count):\\n st1 = time.time()\\n self.senderThread.send({'type': 'sync'})\\n reply = self.syncQueue.get()\\n st2 = time.time()\\n new_rtt = st2 - st1\\n if new_rtt < rtt:\\n rtt = new_rtt\\n self.clockOffset = reply['ct'] + rtt / 2 - st2\\n\\n print 'synchronized with client', self.id, ': clockOffset =',\\\\\\n self.clockOffset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd748414da362133cd5d41060895911c\",\n \"score\": \"0.52354807\",\n \"text\": \"def unsync(self):\\n self.request(\\\"sync -\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab5253e23202244440d824154c61b19e\",\n \"score\": \"0.5229868\",\n \"text\": \"def setStartRoom(self, value): \\n self.__startRoom = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1431cc525cbc29e4f49340033dc21545\",\n \"score\": \"0.5228958\",\n \"text\": \"def test_json_enter_room(session):\\n\\n client_json._on_data(smpacket.SMPacketClientNSSMONL(\\n packet=smpacket.SMOPacketClientEnterRoom(enter=1, room=\\\"Room client-bin\\\", password=\\\"password\\\")\\n ).json)\\n\\n room = session.query(models.Room).filter_by(name=\\\"Room client-bin\\\").first()\\n user = session.query(models.User).filter_by(name=\\\"clientjson-user1\\\").first()\\n\\n assert client_json.room == room.id\\n assert user.room == room\\n assert user.level(room.id) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dce936dea892167030b991746164bc6\",\n \"score\": \"0.52219886\",\n \"text\": \"def _synchronize(networked_peer):\\n # TODO\\n if networked_peer:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b9709b753c66a72e7cb49f0dbfd84b\",\n \"score\": \"0.5206759\",\n \"text\": \"def start_in(self, room_name):\\n self.initial_room_id = self.room_names_to_ids[room_name]\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a9f378aa67893ce7bdd14c84c59b185\",\n \"score\": \"0.5203585\",\n \"text\": \"def sync(self):\\n # cannot be implemented as we have no means of retrieving the current player list\\n connected_clients = self.getPlayerList()\\n for c in self.clients.getList():\\n if c.cid not in connected_clients:\\n c.disconnect()\\n return connected_clients\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89785bb3822f546ca24fb785e88140f2\",\n \"score\": \"0.5180891\",\n \"text\": \"async def _sync_friends():\\n global _SYNCED, _WATCHES\\n expected = set([str(k, \\\"utf8\\\").lower() for k in _WATCHES.keys()])\\n while True:\\n actual = await _friends()\\n if actual is None:\\n raise eqcmd.CommandError(\\\"Failed to retrieve friends\\\")\\n if actual == expected:\\n _SYNCED = True\\n return\\n for person in expected.symmetric_difference(actual):\\n if person in _ONLINE_STATES:\\n del _ONLINE_STATES[person]\\n await _toggle_friend(person)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff11c57d1ae4403d6d6a43eeab448114\",\n \"score\": \"0.51796305\",\n \"text\": \"def start(self, event):\\n self.send_presence()\\n self.get_roster()\\n\\n self.send_message(mto=self.recipient,\\n mbody=self.msg,\\n mtype='chat')\\n\\n # Using wait=True ensures that the send queue will be\\n # emptied before ending the session.\\n self.disconnect(wait=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff11c57d1ae4403d6d6a43eeab448114\",\n \"score\": \"0.51796305\",\n \"text\": \"def start(self, event):\\n self.send_presence()\\n self.get_roster()\\n\\n self.send_message(mto=self.recipient,\\n mbody=self.msg,\\n mtype='chat')\\n\\n # Using wait=True ensures that the send queue will be\\n # emptied before ending the session.\\n self.disconnect(wait=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c94e606c10a4f53404b110f6e8ce15ee\",\n \"score\": \"0.51696575\",\n \"text\": \"def sync(self, sync_id: Optional[int] = None) -> \\\"Server\\\":\\n if self._boot_status not in (BootStatus.BOOTING, BootStatus.ONLINE):\\n raise ServerOffline\\n Sync(\\n sync_id=sync_id if sync_id is not None else self._get_next_sync_id()\\n ).communicate(server=self)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1dfd874ee437576482d0a3084fdadc2\",\n \"score\": \"0.51671684\",\n \"text\": \"def client_thread(connection, address):\\n connection.send(\\\"Chatroom instantiated.\\\")\\n \\n # Continue indefinitely.\\n while True:\\n try:\\n message = connection.recv(2048)\\n if message:\\n \\n broadcast_message = address[0] + \\\" : \\\" + message\\n print(broadcast_message)\\n broadcast(broadcast_message, connection)\\n else:\\n remove(connection)\\n except:\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3963d121ff34c950c1ce18df6a000aef\",\n \"score\": \"0.51654917\",\n \"text\": \"def SyncWithPresenter(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf90cf581dd7a81bfd4b672bfc71b1f5\",\n \"score\": \"0.51643354\",\n \"text\": \"def test_not_receiving_local_device_list_changes(self) -> None:\\n # Register two users\\n test_device_id = \\\"TESTDEVICE\\\"\\n alice_user_id = self.register_user(\\\"alice\\\", \\\"correcthorse\\\")\\n alice_access_token = self.login(\\n alice_user_id, \\\"correcthorse\\\", device_id=test_device_id\\n )\\n\\n bob_user_id = self.register_user(\\\"bob\\\", \\\"ponyponypony\\\")\\n bob_access_token = self.login(bob_user_id, \\\"ponyponypony\\\")\\n\\n # These users do not share a room. They are lonely.\\n\\n # Have Bob initiate an initial sync (in order to get a since token)\\n channel = self.make_request(\\n \\\"GET\\\",\\n \\\"/sync\\\",\\n access_token=bob_access_token,\\n )\\n self.assertEqual(channel.code, HTTPStatus.OK, channel.json_body)\\n next_batch_token = channel.json_body[\\\"next_batch\\\"]\\n\\n # ...and then an incremental sync. This should block until the sync stream is woken up,\\n # which we hope will happen as a result of Alice updating their device list.\\n bob_sync_channel = self.make_request(\\n \\\"GET\\\",\\n f\\\"/sync?since={next_batch_token}&timeout=1000\\\",\\n access_token=bob_access_token,\\n # Start the request, then continue on.\\n await_result=False,\\n )\\n\\n # Have alice update their device list\\n channel = self.make_request(\\n \\\"PUT\\\",\\n f\\\"/devices/{test_device_id}\\\",\\n {\\n \\\"display_name\\\": \\\"New Device Name\\\",\\n },\\n access_token=alice_access_token,\\n )\\n self.assertEqual(channel.code, HTTPStatus.OK, channel.json_body)\\n\\n # Check that bob's incremental sync does not contain the updated device list.\\n bob_sync_channel.await_result()\\n self.assertEqual(\\n bob_sync_channel.code, HTTPStatus.OK, bob_sync_channel.json_body\\n )\\n\\n changed_device_lists = bob_sync_channel.json_body.get(\\\"device_lists\\\", {}).get(\\n \\\"changed\\\", []\\n )\\n self.assertNotIn(\\n alice_user_id, changed_device_lists, bob_sync_channel.json_body\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7598d7a7e07e1c6d02124efc3705fdd0\",\n \"score\": \"0.51580983\",\n \"text\": \"def _checkin_client(self, ts, client):\\n # If the connection is now stale, don't return it to the pool.\\n # Push an empty slot instead so that it will be refreshed when needed.\\n if client.is_connected():\\n now = int(time.time())\\n if ts + self.timeout > now:\\n self.clients.put((ts, client))\\n else:\\n if self.maxsize is not None:\\n self.clients.put(EMPTY_SLOT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99b038b03062231ddeb4d68ab73a806e\",\n \"score\": \"0.51447207\",\n \"text\": \"async def sync(self):\\n mime = 'setup.sync'\\n await self.send_request(mime, block=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0bbf30e8fcf3d5f9d497207fda2a451\",\n \"score\": \"0.5141951\",\n \"text\": \"def resetclient():\\n global _client\\n _client = NullClient()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c400ec9c3cc50f94f8749a82c09810ec\",\n \"score\": \"0.5131805\",\n \"text\": \"def update_clients(id, data):\\n data['id'] = id\\n socketio.emit('update_track', data, json=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e8c31191dc8de846924deb3f591a781\",\n \"score\": \"0.5116015\",\n \"text\": \"def start_clients():\\n for client in CLIENTS:\\n if client.running:\\n continue\\n \\n Task(client.connect(), KOKORO)\\n \\n if (current_thread() is not KOKORO):\\n KOKORO.wake_up()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2b4c6278547deb911006a4f9ca5c414\",\n \"score\": \"0.511151\",\n \"text\": \"async def sync_from_mudlet(self, *rooms, clear=False):\\n db=self.db\\n done = set()\\n broken = set()\\n todo = deque()\\n explore = set()\\n more = set()\\n area_names = {int(k):v for k,v in (await self.mud.getAreaTableSwap())[0].items()}\\n area_known = set()\\n area_rev = {}\\n for k,v in area_names.items():\\n area_rev[v] = k\\n self.__logger.debug(\\\"AREAS:%r\\\",area_names)\\n await self.print(_(\\\"Start syncing. Please be patient.\\\"))\\n\\n if clear:\\n r_old = {}\\n for r in rooms:\\n r_old[r.id_old] = id_mudlet\\n db.q(db.Room).update().values(id_mudlet = None).execute()\\n for r in rooms:\\n r.id_mudlet = r_old[r.id_old]\\n\\n for r in rooms:\\n todo.append(r)\\n\\n def know_area(self, ra):\\n if isinstance(ra,str):\\n ra = area_rev[area]\\n if ra not in area_known:\\n if db.q(db.Area).filter(db.Area.id == ra).one_or_none() is None:\\n a = db.Area(id=ra,name=area_names[ra])\\n db.add(a)\\n db.commit()\\n area_known.add(ra)\\n return ra\\n\\n while todo:\\n r = todo.pop()\\n if r.id_old in done:\\n continue\\n done.add(r.id_old)\\n if not (len(done)%100):\\n await self.print(_(\\\"{done} rooms ...\\\"), done=len(done))\\n\\n try:\\n y = await r.mud_exits\\n except NoData:\\n # didn't go there yet?\\n self.__logger.debug(\\\"EXPLORE %s %s\\\",r.id_str,r.name)\\n explore.add(r.id_old)\\n continue\\n\\n # Iterate exits but do the \\\"standard\\\" directions first,\\n # then the reversible nonstandard ways, then the others.\\n def exits(y):\\n for d,mid in y.items():\\n if self.dr.is_std_dir(d):\\n yield d,mid\\n for d,mid in y.items():\\n if not self.dr.is_std_dir(d) and self.dr.loc2rev(d) is not None:\\n yield d,mid\\n for d,mid in y.items():\\n if not self.dr.is_std_dir(d) and self.dr.loc2rev(d) is None:\\n yield d,mid\\n\\n for d,mid in exits(y):\\n if not mid:\\n continue # unknown\\n try:\\n x = r.exit_at(d)\\n nr = x.dst\\n except KeyError:\\n x = None\\n nr = None\\n if nr is not None:\\n continue\\n\\n try:\\n if nr is None:\\n nr = db.r_mudlet(mid)\\n elif nr.id_mudlet is not None:\\n continue\\n except NoData:\\n name = await self.mud.getRoomName(mid)\\n name = self.dr.clean_shortname(name[0]) if name and name[0] else None\\n gmcp = await self.mud.getRoomHashByID(mid)\\n gmcp = gmcp[0] if gmcp and gmcp[0] else None\\n\\n nr = await self.new_room(name, id_gmcp=gmcp, id_mudlet=mid, offset_from=r, offset_dir=d, explode=self.conf['mudlet_explode'])\\n\\n if x is None:\\n x,_xf = await r.set_exit(d,nr,skip_mud=True)\\n elif x.dst is None:\\n x.dst = nr\\n todo.append(nr)\\n db.commit()\\n\\n await self.print(_(\\\"Finished, {done} rooms processed\\\"), done=len(done))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7508219d8098a05aa12c6691cf3f63f\",\n \"score\": \"0.5110341\",\n \"text\": \"def on_pushButton_synchro_client_launch_clicked(self):\\r\\n # TODO: not implemented yet\\r\\n goslaunchera3.action.rsyncGos(self, \\\"@CLIENT\\\",self.label_synchro_client_state, self.progressBar_synchro_client_fichier,self.progressBar_synchro_client_global,self.label_synchro_client_debit,self.pushButton_synchro_client_launch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c5fa05fea940fffa35d268fff8fd520\",\n \"score\": \"0.50803006\",\n \"text\": \"def sync(ctx, spec, dry_run):\\n engine = ctx.obj[\\\"engine\\\"]\\n do_sync(engine, spec, dry_run)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e01384d90bc68883ade07eb62c0810b\",\n \"score\": \"0.5068519\",\n \"text\": \"def start(self, event):\\n self.get_roster()\\n self.send_presence()\\n self.plugin['xep_0045'].joinMUC(self.room,\\n self.nick,\\n # If a room password is needed, use:\\n # password=the_room_password,\\n wait=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"964c4c8aaaca293c8d12c62d6fed3f8e\",\n \"score\": \"0.506675\",\n \"text\": \"def updateClients(self, client):\\n self.clients.append(client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a279078b5d9dab7c683e205980a60605\",\n \"score\": \"0.5065684\",\n \"text\": \"def sync(self):\\n resp = self.action('sync')\\n resp.raise_if_err()\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c81533a8a811bc8a86056a63e755475b\",\n \"score\": \"0.5056151\",\n \"text\": \"def _sync(self, state):\\n\\n if self.software_only: return\\n self.dispenser_select.sync(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5942542f0d3c015c759ab1bf98de66f\",\n \"score\": \"0.50473946\",\n \"text\": \"def room(self, room):\\n\\n self._room = room\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9348303c6c6258f7e62aee9db9a9f332\",\n \"score\": \"0.50335497\",\n \"text\": \"def reset_client(self):\\n self.GAME_ON = False\\n self.tcp_server_port = None\\n self.server_ip = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa9e191fc6fdeabc8a9ede53c89b4b56\",\n \"score\": \"0.5033359\",\n \"text\": \"def join(self, room):\\n self.socket.rooms.add(self._get_room_name(room))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa9e191fc6fdeabc8a9ede53c89b4b56\",\n \"score\": \"0.5033359\",\n \"text\": \"def join(self, room):\\n self.socket.rooms.add(self._get_room_name(room))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"649cb44f9c372790f1c2d1c0758992a8\",\n \"score\": \"0.50316966\",\n \"text\": \"def _sync(self, state):\\r\\n\\r\\n if self.software_only: return\\r\\n self.dispenser_select.sync(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8581d8b16cf4d27adf49fb4e2e118238\",\n \"score\": \"0.50132996\",\n \"text\": \"def connected(client):\\n with warnings.catch_warnings():\\n # Ignore warning that \\\"ismaster\\\" is always routed to primary even\\n # if client's read preference isn't PRIMARY.\\n warnings.simplefilter(\\\"ignore\\\", UserWarning)\\n client.admin.command('ismaster') # Force connection.\\n\\n return client\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd31ce447fce85e124d0d22f1f4e9a05\",\n \"score\": \"0.5012992\",\n \"text\": \"def test_multiplayer_sync_response():\\n test = DummyMultiplayerGame()\\n messages = []\\n\\n for client in test.clients:\\n for actor in client.actors:\\n message = send_dummy_message(actor, response='sync')\\n messages.append(message)\\n\\n # Make sure the message is handled like usual by the sender.\\n\\n for observer in client.observers:\\n assert observer.dummy_messages_received == messages\\n assert client.world.dummy_messages_executed == messages\\n\\n # Make sure the server relays the message to all the other \\n # observers participating in the game.\\n\\n test.update()\\n\\n for observer in test.observers:\\n assert observer.dummy_messages_received == messages\\n for world in test.worlds:\\n assert world.dummy_messages_executed == messages\\n\\n # Make sure the clients are instructed to sync up.\\n\\n for observer in test.client_observers:\\n assert observer.dummy_sync_responses_received == messages\\n for world in test.client_worlds:\\n assert world.dummy_sync_responses_executed == messages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039623ae0448a85b90eb0d21c5723984\",\n \"score\": \"0.49944133\",\n \"text\": \"async def sync(self, sync_id: Optional[int] = None) -> \\\"AsyncServer\\\":\\n if self._boot_status not in (BootStatus.BOOTING, BootStatus.ONLINE):\\n raise ServerOffline\\n await Sync(\\n sync_id=sync_id if sync_id is not None else self._get_next_sync_id()\\n ).communicate_async(server=self)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bd923a10f00b7a03f173339c43cd2e3\",\n \"score\": \"0.4993173\",\n \"text\": \"def start(self, event):\\n\\t\\tself.get_roster()\\n\\t\\tself.send_presence()\\n\\t\\tself.plugin['xep_0045'].joinMUC(self.room,\\n\\t\\t\\tself.nick,\\n\\t\\t\\t# If a room password is needed, use:\\n\\t\\t\\t# password=the_room_password,\\n\\t\\t\\twait=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb5bd70f6dfeee97a127ba093c0a42e3\",\n \"score\": \"0.49927256\",\n \"text\": \"def EnterRoom(clientid, roomname):\\n try:\\n room = objmgr.GetRoomByName(roomname)\\n except KeyError:\\n return Response(clientid, 'Invalid room name: %s' % (roomname,))\\n\\n player = objmgr.GetPlayerByClientId(clientid)\\n room.EnterRoom(player)\\n\\n return Response(clientid, 'You are now in %s' % (room.name,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a485d26451f23b3ab0f50694db8d39e4\",\n \"score\": \"0.49908218\",\n \"text\": \"def incremental_sync_with_gap(self, sync_config, since_token):\\n now_token = yield self.event_sources.get_current_token()\\n\\n rooms = yield self.store.get_rooms_for_user(sync_config.user.to_string())\\n room_ids = [room.room_id for room in rooms]\\n\\n presence_source = self.event_sources.sources[\\\"presence\\\"]\\n presence, presence_key = yield presence_source.get_new_events(\\n user=sync_config.user,\\n from_key=since_token.presence_key,\\n limit=sync_config.filter.presence_limit(),\\n room_ids=room_ids,\\n # /sync doesn't support guest access, they can't get to this point in code\\n is_guest=False,\\n )\\n now_token = now_token.copy_and_replace(\\\"presence_key\\\", presence_key)\\n\\n now_token, ephemeral_by_room = yield self.ephemeral_by_room(\\n sync_config, now_token, since_token\\n )\\n\\n rm_handler = self.hs.get_handlers().room_member_handler\\n app_service = yield self.store.get_app_service_by_user_id(\\n sync_config.user.to_string()\\n )\\n if app_service:\\n rooms = yield self.store.get_app_service_rooms(app_service)\\n joined_room_ids = set(r.room_id for r in rooms)\\n else:\\n joined_room_ids = yield rm_handler.get_joined_rooms_for_user(\\n sync_config.user\\n )\\n\\n timeline_limit = sync_config.filter.timeline_limit()\\n\\n room_events, _ = yield self.store.get_room_events_stream(\\n sync_config.user.to_string(),\\n from_key=since_token.room_key,\\n to_key=now_token.room_key,\\n limit=timeline_limit + 1,\\n )\\n\\n tags_by_room = yield self.store.get_updated_tags(\\n sync_config.user.to_string(),\\n since_token.account_data_key,\\n )\\n\\n joined = []\\n archived = []\\n if len(room_events) <= timeline_limit:\\n # There is no gap in any of the rooms. Therefore we can just\\n # partition the new events by room and return them.\\n logger.debug(\\\"Got %i events for incremental sync - not limited\\\",\\n len(room_events))\\n\\n invite_events = []\\n leave_events = []\\n events_by_room_id = {}\\n for event in room_events:\\n events_by_room_id.setdefault(event.room_id, []).append(event)\\n if event.room_id not in joined_room_ids:\\n if (event.type == EventTypes.Member\\n and event.state_key == sync_config.user.to_string()):\\n if event.membership == Membership.INVITE:\\n invite_events.append(event)\\n elif event.membership in (Membership.LEAVE, Membership.BAN):\\n leave_events.append(event)\\n\\n for room_id in joined_room_ids:\\n recents = events_by_room_id.get(room_id, [])\\n logger.debug(\\\"Events for room %s: %r\\\", room_id, recents)\\n state = {\\n (event.type, event.state_key): event\\n for event in recents if event.is_state()}\\n limited = False\\n\\n if recents:\\n prev_batch = now_token.copy_and_replace(\\n \\\"room_key\\\", recents[0].internal_metadata.before\\n )\\n else:\\n prev_batch = now_token\\n\\n just_joined = yield self.check_joined_room(sync_config, state)\\n if just_joined:\\n logger.debug(\\\"User has just joined %s: needs full state\\\",\\n room_id)\\n state = yield self.get_state_at(room_id, now_token)\\n # the timeline is inherently limited if we've just joined\\n limited = True\\n\\n room_sync = JoinedSyncResult(\\n room_id=room_id,\\n timeline=TimelineBatch(\\n events=recents,\\n prev_batch=prev_batch,\\n limited=limited,\\n ),\\n state=state,\\n ephemeral=ephemeral_by_room.get(room_id, []),\\n account_data=self.account_data_for_room(\\n room_id, tags_by_room\\n ),\\n )\\n logger.debug(\\\"Result for room %s: %r\\\", room_id, room_sync)\\n\\n if room_sync:\\n joined.append(room_sync)\\n\\n else:\\n logger.debug(\\\"Got %i events for incremental sync - hit limit\\\",\\n len(room_events))\\n\\n invite_events = yield self.store.get_invites_for_user(\\n sync_config.user.to_string()\\n )\\n\\n leave_events = yield self.store.get_leave_and_ban_events_for_user(\\n sync_config.user.to_string()\\n )\\n\\n for room_id in joined_room_ids:\\n room_sync = yield self.incremental_sync_with_gap_for_room(\\n room_id, sync_config, since_token, now_token,\\n ephemeral_by_room, tags_by_room\\n )\\n if room_sync:\\n joined.append(room_sync)\\n\\n for leave_event in leave_events:\\n room_sync = yield self.incremental_sync_for_archived_room(\\n sync_config, leave_event, since_token, tags_by_room\\n )\\n archived.append(room_sync)\\n\\n invited = [\\n InvitedSyncResult(room_id=event.room_id, invite=event)\\n for event in invite_events\\n ]\\n\\n defer.returnValue(SyncResult(\\n presence=presence,\\n joined=joined,\\n invited=invited,\\n archived=archived,\\n next_batch=now_token,\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28714f8887493fbb2141ce720979c262\",\n \"score\": \"0.49907303\",\n \"text\": \"def handle_client_connect_event():\\n # when the client connects, send all existing data\\n print('dashboard client connected, sending any existing data...')\\n socketio.emit('existingData', existingData)\\n global thread\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5461ec7ce6982a7c3855f282cfdc89c6\",\n \"score\": \"0.4987587\",\n \"text\": \"def sync(self):\\r\\n\\r\\n # Ensure to rerun only once to avoid infinite loops\\r\\n # caused by a constantly changing state value at each run.\\r\\n #\\r\\n # Example: state.value += 1\\r\\n if self._state[\\\"is_rerun\\\"]:\\r\\n self._state[\\\"is_rerun\\\"] = False\\r\\n \\r\\n elif self._state[\\\"hash\\\"] is not None:\\r\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None):\\r\\n self._state[\\\"is_rerun\\\"] = True\\r\\n self._state[\\\"session\\\"].request_rerun()\\r\\n\\r\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b164f74c505c12292147abc7c6f4357\",\n \"score\": \"0.49763384\",\n \"text\": \"def sync(self) -> None:\\n\\n # Ensure to rerun only once to avoid infinite loops\\n # caused by a constantly changing session value at each run.\\n #\\n # Example: session.value += 1\\n if self._state[\\\"is_rerun\\\"]:\\n self._state[\\\"is_rerun\\\"] = False\\n\\n elif self._state[\\\"hash\\\"] is not None:\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(\\n self._state[\\\"data\\\"], None\\n ):\\n self._state[\\\"is_rerun\\\"] = True\\n self._state[\\\"session\\\"].request_rerun()\\n\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"030d20a4d8ceadf744091ffdb75b472c\",\n \"score\": \"0.49734926\",\n \"text\": \"def enterRoom(self):\\n\\n\\n\\t\\t# Sets the room = player x,y location\\n\\t\\tif (self.world.tile_exists(self.player.location_x, self.player.location_y)):\\n\\t\\t\\tself.room = self.world.tile_exists(self.player.location_x, self.player.location_y)\\n\\n\\t\\telse:\\n\\t\\t\\tself.world.generate_world(self.player.location_x, self.player.location_y)\\n\\t\\t\\tself.room = self.world.tile_exists(self.player.location_x, self.player.location_y)\\n\\n\\t\\t\\n\\n\\t\\tavailable_actions = self.room.available_actions()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1b3ca2cffe0143a985f3050c242d978\",\n \"score\": \"0.49679375\",\n \"text\": \"def runclient():\\n global mcast\\n mcast = McastClient(MCAST_PORT,MCAST_ADDR)\\n\\n while 1:\\n mcast.send(\\\"5+5\\\")\\n time.sleep(5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29e3cac73dfc82940258c2f767eb4811\",\n \"score\": \"0.49630406\",\n \"text\": \"def sync(self):\\n\\n # Ensure to rerun only once to avoid infinite loops\\n # caused by a constantly changing state value at each run.\\n #\\n # Example: state.value += 1\\n if self._state[\\\"is_rerun\\\"]:\\n self._state[\\\"is_rerun\\\"] = False\\n \\n elif self._state[\\\"hash\\\"] is not None:\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None):\\n self._state[\\\"is_rerun\\\"] = True\\n self._state[\\\"session\\\"].request_rerun()\\n\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18ef190a286d00ff1dc973955d8f9fdb\",\n \"score\": \"0.49612492\",\n \"text\": \"async def vaillant_update(self):\\n new_room: Room = self.hub.find_component(self.room)\\n new_device: Device = self._find_device(new_room, self.device.sgtin)\\n\\n if new_room:\\n if new_device:\\n _LOGGER.debug(\\n \\\"New / old state: %s / %s\\\",\\n new_device.battery_low,\\n self.device.battery_low,\\n )\\n else:\\n _LOGGER.debug(\\\"Device %s doesn't exist anymore\\\", self.device.sgtin)\\n else:\\n _LOGGER.debug(\\\"Room %s doesn't exist anymore\\\", self.room.id)\\n self.room = new_room\\n self.device = new_device\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b37729195b22d51fd9f1aa3fa1f8bab8\",\n \"score\": \"0.49547064\",\n \"text\": \"def sync(self):\\n\\n # Ensure to rerun only once to avoid infinite loops\\n # caused by a constantly changing state value at each run.\\n #\\n # Example: state.value += 1\\n if self._state[\\\"is_rerun\\\"]:\\n self._state[\\\"is_rerun\\\"] = False\\n\\n elif self._state[\\\"hash\\\"] is not None:\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(\\n self._state[\\\"data\\\"], None):\\n self._state[\\\"is_rerun\\\"] = True\\n self._state[\\\"session\\\"].request_rerun()\\n\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(\\n self._state[\\\"data\\\"], None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1962cc15abac4892272ca9a654276139\",\n \"score\": \"0.49545133\",\n \"text\": \"def join_room_socketio(data):\\n challenge = db.session.query(Challenge).get(data[\\\"challenge_id\\\"])\\n if challenge:\\n chat_room = challenge.chat_room\\n join_room(chat_room.room_id)\\n history = chat_room.get_history()\\n emit(\\\"join_room\\\", {\\\"history\\\": history})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44742eb52d96d6d533c77d679e84613d\",\n \"score\": \"0.49509966\",\n \"text\": \"def joinRoom(self,data):\\n roomId, = struct.unpack('>H', data[8 : 10])\\n usernow = self.serverProxy.getUserByName(self.userName)\\n \\n # room id is wrong\\n if roomId not in self.roomID:\\n self.roomState(usernow)\\n else: \\n #the client joins to main room\\n if roomId == 1 :\\n self.serverProxy.stopStreamingMovie(usernow.userChatRoom)\\n userPastroom = usernow.userChatRoom\\n self.serverProxy.updateUserChatroom(usernow.userName, ROOM_IDS.MAIN_ROOM)\\n for user in self.serverProxy.getUserList():\\n if (user.userChatRoom == ROOM_IDS.MAIN_ROOM or user.userChatRoom == userPastroom):\\n self.roomState(user)\\n #the client joins to movie room\\n else :\\n for movie in self.serverProxy.getMovieList() :\\n if movie.movieId == roomId :\\n self.serverProxy.startStreamingMovie(movie.movieTitle)\\n self.serverProxy.updateUserChatroom(usernow.userName, movie.movieTitle)\\n for user in self.serverProxy.getUserList():\\n if (user.userChatRoom == movie.movieTitle or user.userChatRoom == ROOM_IDS.MAIN_ROOM): \\n self.roomState(user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f28335f3f9c9246c282b31529135bbb\",\n \"score\": \"0.49460658\",\n \"text\": \"def sync(path):\\r\\n path = path.rstrip('\\\\\\\\').rstrip('/')\\r\\n click.confirm(f'This will DELETE remote data that is not present.\\\\nAre you sure you want to sync {path}?', abort=True)\\r\\n \\r\\n try:\\r\\n coach = get_coach()\\r\\n coach.sync_local(path)\\r\\n except Exception as e:\\r\\n click.echo(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8600b3b6b671d5f8c6fe9a332524d7bf\",\n \"score\": \"0.4940865\",\n \"text\": \"def sequence_master_sync(self, c, sync=None):\\n if sync is None:\\n sync = c['master_sync']\\n else:\\n c['master_sync'] = sync\\n return sync\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00a6ae505c43959042b7029cf7070f45\",\n \"score\": \"0.49383396\",\n \"text\": \"def save(self, *args, **kwargs):\\n # Check if changes have been done to the room itself\\n if self._room != self.room:\\n self.notify_room()\\n super(Player, self).save(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c1c6ba7b5c525949a38d88d31bac190\",\n \"score\": \"0.49371472\",\n \"text\": \"def sync(self):\\n odir=self.get_temp_dir()\\n if not os.path.exists(odir):\\n os.makedirs(odir)\\n\\n remote_url=self.get_remote_coadd_file('g')\\n remote_url = remote_url.replace('_g','{_g,_r,_i}')\\n cmd = r\\\"\\\"\\\"\\n rsync \\\\\\n -aP \\\\\\n --password-file $DES_RSYNC_PASSFILE \\\\\\n %(remote_url)s \\\\\\n %(local_dir)s/\\n \\\"\\\"\\\" % dict(\\n remote_url=remote_url,\\n local_dir=odir,\\n )\\n\\n print(cmd)\\n subprocess.check_call(cmd,shell=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81bd268d8f30368faf706b54739698e5\",\n \"score\": \"0.49305964\",\n \"text\": \"def start(self, event):\\n debugger_print(\\\"session_start was received; self.start was called\\\")\\n self.send_presence()\\n self.get_roster()\\n \\\"\\\"\\\" self.get_roster(callback=self.print_roster)\\n debugger_print(\\\"CLIENT ROSTER:\\\")\\n debugger_print(self.client_roster) \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcf800e50654da7df8c999ad2a2bd880\",\n \"score\": \"0.49289438\",\n \"text\": \"def sync(self):\\n\\n # Ensure to rerun only once to avoid infinite loops\\n # caused by a constantly changing state value at each run.\\n #\\n # Example: state.value += 1\\n if self._state[\\\"is_rerun\\\"]:\\n self._state[\\\"is_rerun\\\"] = False\\n\\n elif self._state[\\\"hash\\\"] is not None:\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None):\\n self._state[\\\"is_rerun\\\"] = True\\n self._state[\\\"session\\\"].request_rerun()\\n\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfd1220be4b617973ca1e0634bd58af8\",\n \"score\": \"0.49222025\",\n \"text\": \"def enqueue_state(self, lobby: bool = True) -> None:\\n if not self.chat:\\n point_of_interest()\\n\\n # TODO: hmm this is pretty bad, writes twice\\n\\n # send password only to users currently in the match.\\n self.chat.enqueue(packets.updateMatch(self, send_pw=True))\\n\\n if lobby and (lchan := glob.channels['#lobby']) and lchan.players:\\n lchan.enqueue(packets.updateMatch(self, send_pw=False))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d1ad57f6b93521cfbc13977fabbdc5\",\n \"score\": \"0.492037\",\n \"text\": \"def do_green_room(self):\\n if self._is_client_owner:\\n if self.privacy_settings.set_greenroom():\\n self.send_bot_msg('*Green room is enabled.*')\\n self.rtmp_parameter['greenroom'] = True\\n else:\\n self.send_bot_msg('*Green room is disabled.*')\\n self.rtmp_parameter['greenroom'] = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ca106992aec42bd315c2bc298b0acbb\",\n \"score\": \"0.4911232\",\n \"text\": \"def send_update_game(self):\\n game = Game.objects.get_or_none(id=self.id)\\n if not game:\\n return\\n current_player = game.get_current_player()\\n if not current_player:\\n current_player = \\\"\\\"\\n current_player_order = 0\\n else:\\n current_player_order = current_player.order\\n current_player = current_player.user.username\\n async_to_sync(self.channel_layer.group_send)(\\n self.room_group_name,\\n {\\n \\\"type\\\": \\\"update_game\\\",\\n \\\"game\\\": game.as_json(),\\n \\\"current_player\\\": current_player,\\n \\\"current_player_order\\\": current_player_order,\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50c51163b454055008f8ba32f5ababd1\",\n \"score\": \"0.49042723\",\n \"text\": \"def cmd_sync(args):\\n\\n if args.get(0):\\n # Optional branch specifier.\\n branch = fuzzy_match_branch(args.get(0))\\n if branch:\\n is_external = True\\n original_branch = get_current_branch_name()\\n else:\\n print(\\\"{0} doesn't exist. Use a branch that does.\\\".format(\\n colored.yellow(args.get(0))))\\n sys.exit(1)\\n else:\\n # Sync current branch.\\n branch = get_current_branch_name()\\n is_external = False\\n\\n if branch in get_branch_names(local=False):\\n\\n if is_external:\\n switch_to(branch)\\n\\n if repo.is_dirty():\\n status_log(stash_it, 'Saving local changes.', sync=True)\\n\\n status_log(smart_pull, 'Pulling commits from the server.')\\n status_log(push, 'Pushing commits to the server.', branch)\\n\\n if unstash_index(sync=True):\\n status_log(unstash_it, 'Restoring local changes.', sync=True)\\n\\n if is_external:\\n switch_to(original_branch)\\n\\n else:\\n print('{0} has not been published yet.'.format(\\n colored.yellow(branch)))\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a565dabc3ef2326732058946c1a71cf\",\n \"score\": \"0.48988125\",\n \"text\": \"def updateclientmodels(sock, updatedweights):\\n client.load_state_dict(updatedweights)\\n for clientss in connectedclients:\\n try:\\n if clientss != sock:\\n send_msg(clientss, updatedweights)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c64aa9141bbb42188fba31680647f721\",\n \"score\": \"0.48850664\",\n \"text\": \"async def _sync_db():\\n global _WATCHES, _ONLINE_STATES\\n while True:\\n _WATCHES.close()\\n _WATCHES = dbm.open(os.path.join(os.environ[\\\"HOME\\\"], \\\"r99infowatches.db\\\"), \\\"c\\\")\\n _ONLINE_STATES.close()\\n _ONLINE_STATES = shelve.open(os.path.join(os.environ[\\\"HOME\\\"], \\\"r99infoonline.db\\\"), \\\"c\\\", 2)\\n await asyncio.sleep(5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc0702450078db1cdaecef1e3e76a382\",\n \"score\": \"0.48746255\",\n \"text\": \"def sync(self):\\n self.syncing_function = self.stim_syncer.sync(self.bcontrol_loader,\\n self.raw_data_loader)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4b79856311e39cd4fcacfa7502c89ef\",\n \"score\": \"0.48694023\",\n \"text\": \"async def __aenter__(self):\\n client_id = self.client_id\\n logger.info(\\\"OPEN %s %s\\\", self.ip_address, client_id)\\n\\n server = self.server\\n queue = asyncio.Queue(loop=server.loop)\\n\\n for client in server.clients.values():\\n await queue.put(Event(client.data, event_type=\\\"created\\\"))\\n\\n self.queue = queue\\n server.clients[client_id] = self\\n await server.add_event(Event(self.data, event_type=\\\"created\\\"))\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ecc93cdacc00ac478770a2c6789685\",\n \"score\": \"0.48664892\",\n \"text\": \"def incremental_sync_with_gap_for_room(self, room_id, sync_config,\\n since_token, now_token,\\n ephemeral_by_room, tags_by_room):\\n logger.debug(\\\"Doing incremental sync for room %s between %s and %s\\\",\\n room_id, since_token, now_token)\\n\\n # TODO(mjark): Check for redactions we might have missed.\\n\\n batch = yield self.load_filtered_recents(\\n room_id, sync_config, now_token, since_token,\\n )\\n\\n logging.debug(\\\"Recents %r\\\", batch)\\n\\n current_state = yield self.get_state_at(room_id, now_token)\\n\\n state_at_previous_sync = yield self.get_state_at(\\n room_id, stream_position=since_token\\n )\\n\\n state = yield self.compute_state_delta(\\n since_token=since_token,\\n previous_state=state_at_previous_sync,\\n current_state=current_state,\\n )\\n\\n just_joined = yield self.check_joined_room(sync_config, state)\\n if just_joined:\\n state = yield self.get_state_at(room_id, now_token)\\n\\n room_sync = JoinedSyncResult(\\n room_id=room_id,\\n timeline=batch,\\n state=state,\\n ephemeral=ephemeral_by_room.get(room_id, []),\\n account_data=self.account_data_for_room(\\n room_id, tags_by_room\\n ),\\n )\\n\\n logging.debug(\\\"Room sync: %r\\\", room_sync)\\n\\n defer.returnValue(room_sync)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40777ae1de874dee374bb0a56ab3ffa2\",\n \"score\": \"0.48618433\",\n \"text\": \"def test_sync_backwards_typing(self) -> None:\\n typing_url = \\\"/rooms/%s/typing/%s?access_token=%s\\\"\\n sync_url = \\\"/sync?timeout=3000000&access_token=%s&since=%s\\\"\\n\\n # Register the user who gets notified\\n user_id = self.register_user(\\\"user\\\", \\\"pass\\\")\\n access_token = self.login(\\\"user\\\", \\\"pass\\\")\\n\\n # Register the user who sends the message\\n other_user_id = self.register_user(\\\"otheruser\\\", \\\"pass\\\")\\n other_access_token = self.login(\\\"otheruser\\\", \\\"pass\\\")\\n\\n # Create a room\\n room = self.helper.create_room_as(user_id, tok=access_token)\\n\\n # Invite the other person\\n self.helper.invite(room=room, src=user_id, tok=access_token, targ=other_user_id)\\n\\n # The other user joins\\n self.helper.join(room=room, user=other_user_id, tok=other_access_token)\\n\\n # The other user sends some messages\\n self.helper.send(room, body=\\\"Hi!\\\", tok=other_access_token)\\n self.helper.send(room, body=\\\"There!\\\", tok=other_access_token)\\n\\n # Start typing.\\n channel = self.make_request(\\n \\\"PUT\\\",\\n typing_url % (room, other_user_id, other_access_token),\\n b'{\\\"typing\\\": true, \\\"timeout\\\": 30000}',\\n )\\n self.assertEqual(200, channel.code)\\n\\n channel = self.make_request(\\\"GET\\\", \\\"/sync?access_token=%s\\\" % (access_token,))\\n self.assertEqual(200, channel.code)\\n next_batch = channel.json_body[\\\"next_batch\\\"]\\n\\n # Stop typing.\\n channel = self.make_request(\\n \\\"PUT\\\",\\n typing_url % (room, other_user_id, other_access_token),\\n b'{\\\"typing\\\": false}',\\n )\\n self.assertEqual(200, channel.code)\\n\\n # Start typing.\\n channel = self.make_request(\\n \\\"PUT\\\",\\n typing_url % (room, other_user_id, other_access_token),\\n b'{\\\"typing\\\": true, \\\"timeout\\\": 30000}',\\n )\\n self.assertEqual(200, channel.code)\\n\\n # Should return immediately\\n channel = self.make_request(\\\"GET\\\", sync_url % (access_token, next_batch))\\n self.assertEqual(200, channel.code)\\n next_batch = channel.json_body[\\\"next_batch\\\"]\\n\\n # Reset typing serial back to 0, as if the master had.\\n typing = self.hs.get_typing_handler()\\n typing._latest_room_serial = 0\\n\\n # Since it checks the state token, we need some state to update to\\n # invalidate the stream token.\\n self.helper.send(room, body=\\\"There!\\\", tok=other_access_token)\\n\\n channel = self.make_request(\\\"GET\\\", sync_url % (access_token, next_batch))\\n self.assertEqual(200, channel.code)\\n next_batch = channel.json_body[\\\"next_batch\\\"]\\n\\n # This should time out! But it does not, because our stream token is\\n # ahead, and therefore it's saying the typing (that we've actually\\n # already seen) is new, since it's got a token above our new, now-reset\\n # stream token.\\n channel = self.make_request(\\\"GET\\\", sync_url % (access_token, next_batch))\\n self.assertEqual(200, channel.code)\\n next_batch = channel.json_body[\\\"next_batch\\\"]\\n\\n # Clear the typing information, so that it doesn't think everything is\\n # in the future.\\n typing._reset()\\n\\n # Now it SHOULD fail as it never completes!\\n with self.assertRaises(TimedOutException):\\n self.make_request(\\\"GET\\\", sync_url % (access_token, next_batch))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"929b045475245ddd3763a313623a8404\",\n \"score\": \"0.4861605\",\n \"text\": \"def __init__(self,room):\\r\\n self.move_to(room)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30d2b587275ae879c9d55be99d478047\",\n \"score\": \"0.48561555\",\n \"text\": \"async def getCompactRoom(self, room_id: str) -> Room:\\n\\t\\treturn await self.call(\\\"getCompactRoom\\\", room_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6ec225892dbc19c52d9a97142ca091a\",\n \"score\": \"0.48492607\",\n \"text\": \"def reqsync(votername, config):\\n\\t\\n\\tprint(\\\"sync\\\")\\n\\tprint(votername)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":85,"cells":{"query_id":{"kind":"string","value":"0a48dcc0e5d8acc80bdcf94d7eb2230f"},"query":{"kind":"string","value":"Return only compounds with no RO5 violations"},"positive_passages":{"kind":"list like","value":[{"docid":"3e3ac8481a00d1140725d7ed8624fbd8","score":"0.71112686","text":"def filter_ro5(mols):\n return mols.filter(molecule_properties__num_ro5_violations=0)","title":""}],"string":"[\n {\n \"docid\": \"3e3ac8481a00d1140725d7ed8624fbd8\",\n \"score\": \"0.71112686\",\n \"text\": \"def filter_ro5(mols):\\n return mols.filter(molecule_properties__num_ro5_violations=0)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ab98fc705bde3128e358773730bc475c","score":"0.56570566","text":"def find_redundant_compounds(self): \n\n stoich = self.stoichiometry\n\n # Transposing, to use the qr algorithm\n stoich_t = stoich.T\n n_rows = stoich_t.shape[0]\n n_cols = stoich_t.shape[1]\n \n if n_cols > n_rows:\n zeros = np.zeros((n_cols - n_rows, n_cols))\n padded = np.vstack((stoich_t, zeros))\n else:\n zeros = np.zeros((n_rows, n_rows - n_cols))\n padded = np.hstack((stoich_t, zeros))\n \n Q, R, P = qr(padded, pivoting=True)\n dependent = P[np.abs(R.diagonal()) < 1e-10]\n\n # Trimming padded values\n redundant = stoich.index[[i for i in dependent if i < n_cols]]\n\n msg = str(len(redundant)) + ' compounds identified as redundant'\n omfa.logger.info(msg)\n for i in range(len(redundant)):\n omfa.logger.info('\\t' + str(i+1) + ' ' + redundant[i])\n\n return(redundant)","title":""},{"docid":"12450f44c51ad811c8dc97c57c31b3e9","score":"0.5574998","text":"def missed_ground_truth(self):\n return [box for box in self.ground_truth\n if box[4] < self.confidence_threshold]","title":""},{"docid":"a70b8ae2aedd3e86cb1151a90a79af55","score":"0.5422111","text":"def _check_for_residual_space(self):\n covered_volume = sum(rect.volume for rect in self.hyperrectangles)\n if not np.isclose(covered_volume, 1):\n raise ValueError(\"The passed in rectangles do not cover \"\n \"all of [0,1]^n\")","title":""},{"docid":"2f1bf8c3b8493d019bb12f38d0773f29","score":"0.5327456","text":"def free_throw_filter(shot_rows):\r\n filtered_shot_rows = []\r\n for shot_row in shot_rows:\r\n if (not (shot_row[0] == 0 and abs(shot_row[1]) in [28,42]) \r\n and (\"Free\" not in shot_row[3] and \"Lay\" not in shot_row[3])):\r\n filtered_shot_rows.append(shot_row)\r\n return filtered_shot_rows","title":""},{"docid":"26da5fb3389411856906a7947052208b","score":"0.5311303","text":"def in_Ro5(mol):\n \n h_donor = Lipinski.NumHDonors(mol)\n h_accept = Lipinski.NumHAcceptors(mol)\n mw = Descriptors.MolWt(mol)\n logP = Descriptors.MolLogP(mol)\n \n Ro5 = h_donor <= 5 and h_accept <= 10 and mw <= 500 and logP < 5\n return(Ro5)","title":""},{"docid":"885b92e61fe8896700dd4986651fb218","score":"0.5289528","text":"def test_query_cone_badfilt(self):\n tst_args = { 'ra': '53.157662568', 'dec': '-27.8075199236', 'size': '0.0002777' }\n lst = self.imgr.query_cone(tst_args, filt='BADfilt')\n assert lst is not None\n assert len(lst) == 0","title":""},{"docid":"7d9dddf2e36a7689f925f215df5cdc2d","score":"0.52892494","text":"def test_query_cone_badcoll(self):\n tst_args = { 'ra': '53.157662568', 'dec': '-27.8075199236', 'size': '0.0002777' }\n lst = self.imgr.query_cone(tst_args, collection='BADcoll')\n assert lst is not None\n assert len(lst) == 0","title":""},{"docid":"c6890bf33820ca81b87f1a57e73c647f","score":"0.52669555","text":"def accept(potList):\n if potList['NOE'].violations()>0:\n return False\n if potList['RDC'].rms()>1.2: #this might be tightened some\n return False\n if potList['CDIH'].violations()>0:\n return False\n if potList['BOND'].violations()>0:\n return False\n if potList['ANGL'].violations()>0:\n return False\n if potList['IMPR'].violations()>1:\n return False\n \n return True","title":""},{"docid":"4e348f5da5cdf43d96764dd347d6c4f9","score":"0.52626145","text":"def test_query_cone_badboth(self):\n tst_args = { 'ra': '53.157662568', 'dec': '-27.8075199236', 'size': '0.0002777' }\n lst = self.imgr.query_cone(tst_args, filt='BADfilt', collection='BADcoll')\n assert lst is not None\n assert len(lst) == 0","title":""},{"docid":"f027d6a06547fc706bd6745380c6b521","score":"0.52531433","text":"def check_all_checks(cls):\n all_checked = set((b.scope, b.name) for b in cls.all)\n unchecked = all_checked - cls.all_checks\n if unchecked:\n print(\"** These Boogers were never checked:\", file=sys.stderr)\n print(\"\\n\".join(\": \".join(check) for check in unchecked), file=sys.stderr)","title":""},{"docid":"a0197ea31b4ab0fbfcef49167914505c","score":"0.5250094","text":"def testFilterNonCrosProjects(self):\n base_func = itertools.cycle(['chromiumos', 'chromeos']).next\n patches = self.GetPatches(8)\n for patch in patches:\n patch.project = '%s/%i' % (base_func(), _GetNumber())\n patch.tracking_branch = str(_GetNumber())\n\n non_cros_patches = self.GetPatches(2)\n for patch in non_cros_patches:\n patch.project = str(_GetNumber())\n\n filtered_patches = patches[:4]\n allowed_patches = []\n projects = {}\n for idx, patch in enumerate(patches[4:]):\n fails = bool(idx % 2)\n # Vary the revision so we can validate that it checks the branch.\n revision = ('monkeys' if fails\n else 'refs/heads/%s' % patch.tracking_branch)\n if fails:\n filtered_patches.append(patch)\n else:\n allowed_patches.append(patch)\n projects.setdefault(patch.project, {})['revision'] = revision\n\n manifest = MockManifest(self.build_root, projects=projects)\n\n self.mox.ReplayAll()\n results = validation_pool.ValidationPool._FilterNonCrosProjects(\n patches + non_cros_patches, manifest)\n\n def compare(list1, list2):\n mangle = lambda c:(c.id, c.project, c.tracking_branch)\n self.assertEqual(list1, list2,\n msg=\"Comparison failed:\\n list1: %r\\n list2: %r\"\n % (map(mangle, list1), map(mangle, list2)))\n\n compare(results[0], allowed_patches)\n compare(results[1], filtered_patches)","title":""},{"docid":"447fc7a12873a8292aa15de75339b044","score":"0.5234065","text":"def rejects(self):\n #: lines that were rejected b/c they were for a card that isnt supported\n return self.reject_lines","title":""},{"docid":"41d35c2829fd5870fdd86df234a9d487","score":"0.5187228","text":"def find_dead_end_compounds(self): \n\n stoich = self.stoichiometry\n\n dead_end = [i for i in stoich.index if sum(stoich.ix[i, stoich.columns] != 0) <= 1]\n\n msg = str(len(dead_end)) + ' compounds identified as dead-end'\n omfa.logger.info(msg)\n for i in range(len(dead_end)):\n omfa.logger.info('\\t' + str(i+1) + ' ' + dead_end[i])\n\n return(dead_end)","title":""},{"docid":"d8cdf8999dec2a4fea373a740a320b63","score":"0.51506704","text":"def dead_reactions(self):\n\t\treturn list(filter(lambda reaction: abs(reaction.upper_bound) < CONST_EPSILON and abs(reaction.lower_bound) < CONST_EPSILON, self.__cobra_model.reactions))","title":""},{"docid":"b54d36ca30a1f8ecb81873b85a8e782b","score":"0.5139265","text":"def get_clean_data_frame(self,df):\n g = df['r-free final : no ncs'] > 0\n g &= (df['r-free final : cartesian ncs restraints'] > 0)\n g &= (df['r-free final : torsion ncs restraints'] > 0)\n return df[g]","title":""},{"docid":"017570a040a16f38ff744ef3e9792111","score":"0.51170933","text":"def get_unsolved(grid):\n\n unsolved = []\n\n for row in grid.grid:\n for square in row:\n if len(square.possibles) > 1:\n unsolved.append(square)\n return unsolved","title":""},{"docid":"a3aa2257d43b58ec25a37228f45141f4","score":"0.508613","text":"def test_NoIons(self):\n pdb, path = get_pdb_from_remote_or_db(\"1izi\", \"all\", test_data.__path__[0])\n os.remove(path)\n self.assertItemsEqual(['Q50'], set(pdb.select(CurationSelections.LIGAND_SELECTION).getResnames()))","title":""},{"docid":"fd18071ab2ff4016399cb2562abfa5e2","score":"0.50748146","text":"def check_rolled_records(self, today):\n rolled_objects = PrescribedBurn.objects.filter(date=today, rolled=True).exclude(form_name=PrescribedBurn.FORM_268A) #.exclude(completed=True)\n unset_objects = list(set(rolled_objects.filter(area__isnull=True, distance__isnull=True)).union(rolled_objects.filter(status__isnull=True)))\n return unset_objects","title":""},{"docid":"c59921fba65d776ddc88cb917bc1754f","score":"0.5068812","text":"def missing_cards(tuple_of_cards):\n cards = set(tuple_of_cards)\n return [card for card in CARDS if card not in cards]","title":""},{"docid":"99cc4a7f4247add7fe385c4572ac7980","score":"0.50678724","text":"def _remove_noise_in_o2m():\n if line.reconcile_partial_id:\n if currency_id == line.currency_id.id:\n if line.amount_residual_currency <= 0:\n return True\n else:\n if line.amount_residual <= 0:\n return True\n return False","title":""},{"docid":"8cf1ad501f309f67b8858669bb5dd036","score":"0.5063048","text":"def summarizeExcessCap(self):\n f = lambda c: e_cap(c, c.assignedRoom)\n return 100 * sum(map(f, self.courses_filt))/len(self.courses_filt)","title":""},{"docid":"b1152ac514cd417c5933bd379eea7288","score":"0.5059752","text":"def remove_bad_values(self,bad_value=-99.9):\n for cml in self.set:\n cml.remove_bad_values(bad_value)","title":""},{"docid":"7b30cb2e21a7f393de01db4e0963c26c","score":"0.505736","text":"def test_remove_census_not_in_ycom(self):\n census = pd.read_csv('climops/data/acs2015_county_data.csv')\n census_remove_pr = prepare_data.remove_census_not_in_ycom(census)\n #check to se whether remaining dataset includes PR (remaining obs\n # should be 0).\n test_set = census_remove_pr[census_remove_pr['State'] == 'Puerto Rico']\n pr_obs = test_set.shape\n self.assertTrue(pr_obs[0] == 0)","title":""},{"docid":"bcc7fa560d693296706d17cdfb97b26f","score":"0.50406694","text":"def _elf_prune_problematic_conformers(\n cls, molecule: \"Molecule\"\n ) -> List[unit.Quantity]:\n\n valid_conformers = []\n\n for i, conformer in enumerate(molecule.conformers):\n\n is_problematic, reason = cls._elf_is_problematic_conformer(\n molecule, conformer\n )\n\n if is_problematic:\n logger.warning(f\"Discarding conformer {i}: {reason}\")\n else:\n valid_conformers.append(conformer)\n\n return valid_conformers","title":""},{"docid":"157eb57161fa6c8ff3d8b3ec445b8337","score":"0.50365067","text":"def items_that_need_coverage(self):\n q = Edition.missing_coverage_from(self._db, [], self.coverage_source)\n clause = and_(Edition.data_source_id==LicensePool.data_source_id,\n Edition.primary_identifier_id==LicensePool.identifier_id)\n q = q.join(LicensePool, clause)\n q = q.filter(LicensePool.open_access == True).filter(\n Edition.open_access_download_url==None\n )\n return q","title":""},{"docid":"d1c6b355c4dbc1394ad8bc3fbc30b5e1","score":"0.50348026","text":"def exclude_from_overlap_check(self):","title":""},{"docid":"210a4699118872d44edc147c9fcd426a","score":"0.50296766","text":"def _check_bundles(cursor):\n b = cursor.all(\"\"\"\n SELECT bundles_total, balance\n FROM (\n SELECT owner, sum(amount) AS bundles_total\n FROM cash_bundles b\n GROUP BY owner\n ) foo\n JOIN participants p ON p.id = owner\n WHERE bundles_total <> balance\n \"\"\")\n assert len(b) == 0, \"bundles are out of whack: {}\".format(b)","title":""},{"docid":"210a4699118872d44edc147c9fcd426a","score":"0.50296766","text":"def _check_bundles(cursor):\n b = cursor.all(\"\"\"\n SELECT bundles_total, balance\n FROM (\n SELECT owner, sum(amount) AS bundles_total\n FROM cash_bundles b\n GROUP BY owner\n ) foo\n JOIN participants p ON p.id = owner\n WHERE bundles_total <> balance\n \"\"\")\n assert len(b) == 0, \"bundles are out of whack: {}\".format(b)","title":""},{"docid":"7ec860c4062330cdb17aae869356ea1f","score":"0.50128496","text":"def keep_science_exposures(self,df):\n bands= df.loc[:,'band']\n isGRZ= (bands == 'g') | (bands == 'r') | (bands == 'z') \n isObject= df.loc[:,'obstype'] == 'object'\n isScience= df['object'].str.lower().str.contains('decals')\n longExp= df['exptime'] > 30\n print('isGRZ %d/%d' % (len(df[isGRZ]),len(df)))\n print('isObject %d/%d' % (len(df[isObject]),len(df)))\n print('isScience %d/%d' % (len(df[isScience]),len(df)))\n print('longExp %d/%d' % (len(df[longExp]),len(df)))\n allCuts= ((isGRZ) & \n (isObject) &\n (isScience) &\n (longExp))\n print('allCuts %d/%d' % (len(df[allCuts]),len(df)))\n return df[allCuts]","title":""},{"docid":"73357c82d674e4222b1117b4260aa2e5","score":"0.49999732","text":"def test_no_clips_in_project(self):\n pid = create_project(name=\"test_project2\")\n code, res = get_clips(data={PROJECT_ID: pid})\n self.assertEqual(code, 200)\n self.assertEqual(len(res[CLIPS]), 0)","title":""},{"docid":"a7c59f387e1ac5847c79e892b6884f9c","score":"0.49900657","text":"def ALL_REGIONS_WITHOUT_CONTENT_RATINGS():\n return set(ALL_REGIONS) - set(ALL_REGIONS_WITH_CONTENT_RATINGS())","title":""},{"docid":"d3223e00cc4b2c770e93434536de2846","score":"0.49888155","text":"def crit_5(t):\r\n #Escolheu-se nao obter as laterais programaticamente porque \r\n #diminui bastante a complexidade deste criterio \r\n posicoes = [\"b1\", \"a2\", \"c2\", \"b3\"] \r\n posicoes = [cria_posicao(*pos) for pos in posicoes] \r\n for pos in posicoes: \r\n if pecas_iguais(obter_peca(pos), cria_peca()): \r\n return pos","title":""},{"docid":"03495209168bd332fe21a059f8119712","score":"0.49805313","text":"def get_empty_squares(self):","title":""},{"docid":"7420a2febd494b7ceb2a72811ff67aab","score":"0.4971833","text":"def test_is_unidentified(self):\n self.assertEqual(Compound().is_unidentified, True)\n self.assertEqual(Compound(names=['Coumarin 343']).is_unidentified, False)\n self.assertEqual(Compound(labels=['3a']).is_unidentified, False)\n self.assertEqual(Compound(names=['Coumarin 343'], labels=['3a']).is_unidentified, False)\n self.assertEqual(Compound(melting_points=[MeltingPoint(value='250')]).is_unidentified, True)","title":""},{"docid":"6e7d73c959b9ffb2fabb192b74cf9e7b","score":"0.49689627","text":"def filter_acceptable(self, query):\n # type: (Any) -> Any\n return query.filter(opentuner.resultsdb.models.Result.size < 1.0)","title":""},{"docid":"c72635e364b0c90caefdbdd3fdea1379","score":"0.49637672","text":"def not_approved_ltcs(self) -> int:\n return self.__test_design_metric(row_nr=9)","title":""},{"docid":"42cd6c958f4ce87ef29e7458b054f330","score":"0.49596122","text":"def test_incompatible_cable_stitches():\n in_data = [1, 1, 1, 4, 15, 1, 1, 1]\n with pytest.raises(KnitPaintCheckException) as err:\n resolve_cable_stitches(in_data, len(in_data))\n problems = err.value.problems\n assert len(problems) == 2\n assert problems[0].course == 0\n assert problems[0].wale == 3\n assert problems[1].course == 0\n assert problems[1].wale == 4","title":""},{"docid":"e54d5d0d66550fbc9bea42e0ffab4b00","score":"0.4951831","text":"def excessCap(self):\n f = lambda c: e_cap(c, c.assignedRoom)\n return map(f, self.courses_filt)","title":""},{"docid":"3124fb24fd596395e1dbb1e6028153b1","score":"0.49452466","text":"def get_nonstrict_lines(lines):\n return filter(lambda l: l.upper().find(\"NON\") != -1 or empty_section(l), lines)","title":""},{"docid":"78f036fac00d9b5c861922eb512ea1f9","score":"0.49444968","text":"def remove_compounds(self, compounds):\n\n # Checking if string to avoid iterating by character\n if isinstance(compounds, basestring):\n compounds = [compounds]\n # Carrying on to check if integer to maintain consistency\n elif isinstance(compounds, (int, long)):\n compounds = [compounds]\n\n # Identify invalid compounds\n invalid = set(compounds) - set(self.stoichiometry.index)\n\n if len(invalid) > 0:\n msg = 'The following compounds are invalid: ' + ', '.join(invalid)\n omfa.logger.error(msg)\n raise omfa.ModelError(msg)\n else:\n msg = 'Dropping ' + ', '.join(compounds)\n omfa.logger.info(msg)\n self.stoichiometry = self.stoichiometry.drop(compounds)","title":""},{"docid":"6867bec609242f22886d5c47e56d8f32","score":"0.49380073","text":"def rule_1(self):\n return [cell for cell in self.cells if cell.neighbors(self.cells) < 2]","title":""},{"docid":"107fa14844cbfda199098f861da25940","score":"0.4931519","text":"def everything_incorrect(self):\n return self.results.everything_incorrect()","title":""},{"docid":"107fa14844cbfda199098f861da25940","score":"0.4931519","text":"def everything_incorrect(self):\n return self.results.everything_incorrect()","title":""},{"docid":"304f8c04da55f527d1ce97fed607bd6e","score":"0.4929644","text":"def misclosure_error(self):\n for survey in self.project:\n survey.misclosure_error()","title":""},{"docid":"0cad7409c85610e5c28bf78f51df1292","score":"0.4926443","text":"def to_ignore(self, enclosure):\n return all((\n enclosure['product'] == 'SGPIOEnclosure',\n '-MINI-' not in enclosure['model'],\n not enclosure['model'].startswith('R20'),\n ))","title":""},{"docid":"1c92c7c424273853240d6e9d049b227c","score":"0.49256653","text":"def filter_biotherapeutic(mols):\n return mols.filter(biotherapeutic__isnull=False)","title":""},{"docid":"9fcb99deba127260759e6ccb0aa128e6","score":"0.49252394","text":"def test_get_appliances_with_no_appliances(self):\n # Populate appliance table\n a1 = Appliance.objects.create(name=\"A 1\")\n a2 = Appliance.objects.create(name=\"A 2\")\n a1.save()\n a2.save()\n # Create empty recipe to test against\n recipe = Recipe.objects.create(title=\"Empty\", instructions=\"Empty\")\n\n self.assertEquals(list(recipe.get_appliances()), [], \"Empty recipe returns appliances.\")","title":""},{"docid":"e70f4cb3943c603fbba73a7b8edac89e","score":"0.4921788","text":"def outstanding(self):\n return (\n self.filter(suppressed_at__isnull=True)\n .filter(expired_at__isnull=True)\n .filter(vol_prospect_contact_events=None)\n )","title":""},{"docid":"87b238b389c2b23f57d2576bf1a10af2","score":"0.4920438","text":"def malformed_cards(tuple_of_objects):\n return [obj for obj in tuple_of_objects if not is_card(obj)]","title":""},{"docid":"49227fe7a92dd013292a181c617fd8f2","score":"0.49042115","text":"def test_find_clashes_forall_other_series(self):\n par = self.real_parameters\n par.aions = True\n par.xions = True\n par.zions = True\n par.bions = False\n par.yions = False\n #par.bMinusNH3 = True\n #par.bMinusH2O = True\n #par.bPlusH2O = True\n\n par.q3_window = 4.5\n\n q3_low, q3_high = self.real_parameters.get_q3range_transitions()\n precursor = self.precursor\n transitions = precursor.calculate_transitions(q3_low, q3_high)\n \n nonunique = c_getnonuis._find_clashes_forall_other_series( \n tuple(transitions), self.interfering_precursors, par, q3_low, q3_high,\n par.q3_window, par.ppm, precursor.q1 - par.q1_window, False)\n\n self.assertEqual( len( nonunique ), 4)\n self.assertEqual( nonunique.keys(), [3,4,5,6] )\n\n self.assertEqual( len( nonunique[3] ), 1)\n self.assertEqual( len( nonunique[4] ), 2)\n self.assertEqual( len( nonunique[5] ), 3)\n self.assertEqual( len( nonunique[6] ), 1)\n\n # we have one interference with 3\n self.assertTrue( abs(nonunique[3][0][0] - 456.756009652) < self.EPSILON )\n self.assertTrue( abs(nonunique[3][0][1] - 500.787837374) < self.EPSILON )\n self.assertEqual( nonunique[3][0][2], 0) # empty\n self.assertEqual( nonunique[3][0][3], 665) # peptide key\n self.assertEqual( nonunique[3][0][4], 'x')\n self.assertEqual( nonunique[3][0][5], 8)\n self.assertEqual( nonunique[3][0][6], 'GGLIVELGDK') # sequence\n self.assertEqual( nonunique[3][0][-1], 2) # charge\n\n # we have two interferences with 4\n self.assertTrue( abs(nonunique[4][0][0] - 443.22541272) < self.EPSILON )\n self.assertTrue( abs(nonunique[4][0][1] - 506.58461326) < self.EPSILON )\n self.assertEqual( nonunique[4][0][2], 0) # empty\n self.assertEqual( nonunique[4][0][3], 618) # peptide key\n self.assertEqual( nonunique[4][0][4], 'a')\n self.assertEqual( nonunique[4][0][5], 10)\n self.assertEqual( nonunique[4][0][6], 'NGTDGGLQVAIDAMR') # sequence\n self.assertEqual( nonunique[4][0][-1], 2) # charge\n\n self.assertTrue( abs(nonunique[4][1][0] - 443.7267378235) < self.EPSILON )\n self.assertTrue( abs(nonunique[4][1][1] - 506.58461326 ) < self.EPSILON )\n self.assertEqual( nonunique[4][1][2], 0) # empty\n self.assertEqual( nonunique[4][1][3], 618) # peptide key\n self.assertEqual( nonunique[4][1][4], 'z')\n self.assertEqual( nonunique[4][1][5], 8)\n self.assertEqual( nonunique[4][1][6], 'NGTDGGLQVAIDAMR') # sequence\n self.assertEqual( nonunique[4][1][-1], 2) # charge\n\n # we have three interferences with 5\n self.assertTrue( abs(nonunique[5][0][0] - 557.280912722) < self.EPSILON )\n self.assertEqual( nonunique[5][0][4], 'a')\n self.assertEqual( nonunique[5][0][5], 12)\n self.assertEqual( nonunique[5][0][6], 'NGTDGGLQVAIDAMR') # sequence\n self.assertEqual( nonunique[5][0][-1], 2) # charge\n\n self.assertTrue( abs(nonunique[5][1][0] - 550.28240465) < self.EPSILON )\n self.assertEqual( nonunique[5][1][4], 'x')\n self.assertEqual( nonunique[5][1][5], 10)\n self.assertEqual( nonunique[5][1][6], 'NGTDGGLQVAIDAMR') # sequence\n self.assertEqual( nonunique[5][1][-1], 2) # charge\n\n self.assertTrue( abs(nonunique[5][2][0] - 557.2902278235) < self.EPSILON )\n self.assertEqual( nonunique[5][2][4], 'z')\n self.assertEqual( nonunique[5][2][5], 11)\n self.assertEqual( nonunique[5][2][6], 'NGTDGGLQVAIDAMR') # sequence\n self.assertEqual( nonunique[5][2][-1], 2) # charge\n\n # we have one interference with 6\n self.assertTrue( abs(nonunique[6][0][0] - 665.327537823 ) < self.EPSILON )\n self.assertEqual( nonunique[6][0][2], 0) # empty\n self.assertEqual( nonunique[6][0][3], 618) # peptide key\n self.assertEqual( nonunique[6][0][4], 'z')\n self.assertEqual( nonunique[6][0][5], 13)\n self.assertEqual( nonunique[6][0][6], 'NGTDGGLQVAIDAMR') # sequence\n self.assertEqual( nonunique[6][0][-1], 2) # charge","title":""},{"docid":"714e4eaab013bb1cc8404881a64c63e6","score":"0.4903402","text":"def get_strict_lines(lines):\n return filter(lambda l: l.upper().find('NON') == -1 or empty_section(l), lines)","title":""},{"docid":"d681cf368d3845ab503eaa4ba27899eb","score":"0.4903144","text":"def filter_by_rect_and_find():\n\n reduced_possibilities = {}\n for area_coord, area_possibilities in remaining_possibilities.items():\n accurate_area_possibilities = [possibility for possibility in area_possibilities\n if is_zone_free(possibility, grid)]\n if len(accurate_area_possibilities) == 0: # not shape fit in the area, the grid cannot be solved\n Log.info(f'<<< unsolvable - impossible for {area_info(area_coord, grid)}')\n return None\n elif len(accurate_area_possibilities) == 1: # found an area solution\n Log.info(f'rectangle added for {area_info(area_coord, grid)} - from rectangles')\n add_rectangle(accurate_area_possibilities[0], grid)\n else:\n reduced_possibilities[area_coord] = accurate_area_possibilities\n # logging purpose only\n eliminated_possibilities = set(area_possibilities) - set(accurate_area_possibilities)\n if len(eliminated_possibilities) > 0:\n Log.debug(f'eliminate {len(eliminated_possibilities)} inaccurate rectangles'\n f' for {area_info(area_coord, grid)}')\n return reduced_possibilities","title":""},{"docid":"3f45c4a57e8ec273835b754952c068ff","score":"0.4891872","text":"def identifyOAssetholders(calc1):\n has_int1 = (calc1.array('e00300') != 0)\n has_int2 = (calc1.array('e00400') != 0)\n hasoassets = has_int1 | has_int2\n return hasoassets","title":""},{"docid":"cff9679195f9f2bd95ad7a1ec74ef0a1","score":"0.48909375","text":"def get_sellability_report(melons):\n\n # Fill in the rest ","title":""},{"docid":"1f826aed53f154939605fe2635c3b810","score":"0.48850587","text":"def test_ready_computations_excludes_r2c(self):\n name = Name(\"/test/NFN\")\n request_name = Name(\"/test/R2C\")\n self.computationList.add_computation(name, 0, Interest(name))\n self.computationList.container[0].add_name_to_await_list(request_name)\n self.assertEqual(len(self.computationList.container[0].awaiting_data), 1)\n ready_comps = self.computationList.get_ready_computations()\n self.assertEqual(ready_comps, [NFNComputationTableEntry(name)])","title":""},{"docid":"aebf17e049ef781dab2a00f7a05c5cef","score":"0.48828843","text":"def testall(composite, examples, badExamples=[]):\n wrong = []\n for example in examples:\n if composite.GetActivityQuantBounds():\n answer = composite.QuantizeActivity(example)[-1]\n else:\n answer = example[-1]\n res, conf = composite.ClassifyExample(example)\n if res != answer:\n wrong.append((res, conf))\n badExamples.append(example)\n\n return wrong","title":""},{"docid":"7a523913bc344e3b9c4712e342b8252b","score":"0.48817363","text":"def filter_diagnosed(self):\n queryset = self.filter(created__date=datetime.today())\n return queryset.filter(status__in=[Encounter.STATUS.WAIT_RESULT, Encounter.STATUS.RECEIVE_RESULT,\n Encounter.STATUS.CHECKED_OUT, Encounter.STATUS.WAIT_PAY,\n Encounter.STATUS.WAIT_DISPENSE, Encounter.STATUS.DISCHARGED])","title":""},{"docid":"3f9508e44021d77921e50b74a099138d","score":"0.48805833","text":"def clean_redundancies(self) -> None:\n candidates_to_remove = [[candidate for candidate in candidate_set][0]\n for candidate_set in self._mapping.values() if len(candidate_set) == 1]\n sets_to_check = [candidate_set for candidate_set in self._mapping.values() if len(candidate_set) > 1]\n for candidate_to_remove in candidates_to_remove:\n for set_to_check in sets_to_check:\n if candidate_to_remove in set_to_check:\n set_to_check.remove(candidate_to_remove)","title":""},{"docid":"dc3b517d8384bc9c7e0368296c0df2d7","score":"0.4876179","text":"def get_exclusions():\n nb_vdw_list = []\n model = cmd.get_model('all')\n for at in model.atom:\n nb = cmd.index('(index %s extend 2)' % at.index)\n nb_vdw_list.extend([tuple(sorted([at.index-1, i[1]-1])) for i in nb])\n nb_vdw = set(nb_vdw_list)\n return nb_vdw","title":""},{"docid":"c29c87ffcb2ab80446bdd62fa76bd6a4","score":"0.4874136","text":"def refine_compounds(compounds, mols, gaps, failed_mols):\n for idx in sorted(failed_mols, reverse=True):\n del compounds[idx]\n del mols[idx]\n del gaps[idx]","title":""},{"docid":"8776b01c5322597487bbaf5a49bb93ba","score":"0.48680595","text":"def test_image_metadata_by_filter_badcoll(self):\n res = self.imgr.image_metadata_by_filter('F444W', collection='BADcoll')\n print(res)\n assert res is not None\n assert len(res) == 0","title":""},{"docid":"8f2bab583554447bb46643d540df70db","score":"0.4858687","text":"def _yield_compounds(self): # noqa: ANN101\n undefined_compounds = []\n for name in self.model.compounds:\n if not self.model.compounds[name]:\n undefined_compounds.append(name)\n if undefined_compounds:\n raise ValueError( # noqa: TRY003\n f\"undefined compounds: {', '.join(undefined_compounds)}\", # noqa: EM102\n ) # noqa: RUF100\n\n logfiles_table = Table(\n Column(\"no\", justify=\"right\"),\n Column(\"compound\", justify=\"left\"),\n Column(\"path\", justify=\"left\"),\n title=\"logfiles\",\n box=self.box_style,\n )\n compounds_table = Table(\n Column(\"no\", justify=\"right\"),\n Column(\"compound\", justify=\"left\"),\n Column(\"elec. energy\\n〈Eₕ〉\", justify=\"center\"),\n Column(\"spin mult.\", justify=\"center\"),\n Column(\"smallest vibfreqs\\n〈cm⁻¹〉\", justify=\"center\"),\n Column(\"point group\", justify=\"center\"),\n title=\"compounds\",\n box=self.box_style,\n )\n for i, (name, data) in enumerate(self.model.compounds.items()):\n path_text = None\n if data.logfile is not None:\n path_text = Text(data.logfile)\n path_text.highlight_regex(r\"[^\\/]+$\", \"bright_blue\")\n logfiles_table.add_row(f\"{i:d}\", name, path_text)\n\n vibfreqs_text = None\n if data.vibfreqs is not None:\n vibfreqs_text = Text(\n \", \".join([f\"{vibfreq:+7.1f}\" for vibfreq in data.vibfreqs[:3]]),\n )\n vibfreqs_text.highlight_regex(r\"-\\d+\\.\\d\", \"bright_yellow\")\n\n point_group = coords.find_point_group(\n atommasses=data.atommasses,\n atomcoords=data.atomcoords,\n )\n compounds_table.add_row(\n f\"{i:d}\",\n name,\n f\"{data.energy / (constants.hartree * constants.N_A):17.12f}\",\n f\"{data.mult}\",\n vibfreqs_text,\n point_group,\n )\n yield logfiles_table\n yield compounds_table","title":""},{"docid":"a56df535398c5a5953995a1035a300db","score":"0.48563296","text":"def test_find_clashes_forall_other_series_by(self):\n par = self.real_parameters\n q3_low, q3_high = self.real_parameters.get_q3range_transitions()\n precursor = self.precursor\n transitions = precursor.calculate_transitions(q3_low, q3_high)\n \n nonunique = c_getnonuis._find_clashes_forall_other_series( \n tuple(transitions), self.interfering_precursors, par, q3_low, q3_high,\n par.q3_window, par.ppm, precursor.q1 - par.q1_window, False)\n\n self.assertEqual( len( nonunique ), 3)\n self.assertEqual( nonunique.keys(), [0,2,4] )\n\n self.assertTrue( abs(nonunique[0][0][0] - 842.4008) < self.EPSILON )\n self.assertTrue( abs(nonunique[0][0][1] - 506.584613) < self.EPSILON )\n self.assertEqual( nonunique[0][0][2], 0) # empty\n self.assertEqual( nonunique[0][0][3], 618) # peptide key\n self.assertEqual( nonunique[0][0][4], 'b')\n self.assertEqual( nonunique[0][0][5], 9)\n self.assertEqual( nonunique[0][0][6], 'NGTDGGLQVAIDAMR') # sequence\n self.assertEqual( nonunique[0][0][-1], 1) # charge\n\n self.assertTrue( abs(nonunique[2][0][0] - 565.8035) < self.EPSILON )\n self.assertEqual( nonunique[2][0][4], 'y')\n self.assertEqual( nonunique[2][0][5], 11)\n self.assertEqual( nonunique[2][0][6], 'NGTDGGLQVAIDAMR') # sequence\n self.assertEqual( nonunique[2][0][-1], 2) # charge\n\n self.assertTrue( abs(nonunique[4][0][0] - 440.287275) < self.EPSILON )\n self.assertTrue( abs(nonunique[4][0][1] - 500.787837374 ) < self.EPSILON )\n self.assertEqual( nonunique[4][0][2], 0) # empty\n self.assertEqual( nonunique[4][0][3], 665) # peptide key\n self.assertEqual( nonunique[4][0][4], 'b')\n self.assertEqual( nonunique[4][0][5], 5)\n self.assertEqual( nonunique[4][0][6], 'GGLIVELGDK') # sequence\n self.assertEqual( nonunique[4][0][-1], 1) # charge","title":""},{"docid":"347082105d2d5e24af21546f4c5501fd","score":"0.48525828","text":"def find_not_removed_watchee(self, email):\n res = self.watcher_cc.aggregate([\n {\"$match\": {\"email_addr\": email}},\n {\"$project\": {\n \"not_removed\": {\"$filter\": {\n \"input\": \"$crn\",\n \"as\": \"courses\",\n \"cond\": {\"$eq\": [\"$$courses.removed\", False]}\n }}\n }}\n ])\n not_removed = []\n for i in res:\n not_removed.extend(i[\"not_removed\"])\n return not_removed","title":""},{"docid":"13a1992ca1cc119a7814cb5f0860d0a7","score":"0.48480925","text":"def check_constraints_w_enforcement(self):\n\n return exclusions.open()","title":""},{"docid":"6add7d15eb78d51eb98c5e1f5166390c","score":"0.48479852","text":"def test_capture_branch_no_rxn():\n u4br = {\"U234\": {\"U235\": 0.5, \"U235_m1\": 0.5}}\n\n chain_file = Path(__file__).parents[1] / \"chain_simple.xml\"\n chain = Chain.from_xml(chain_file)\n\n u5m = nuclide.Nuclide(\"U235_m1\")\n\n chain.nuclides.append(u5m)\n chain.nuclide_dict[u5m.name] = len(chain.nuclides) - 1\n\n with pytest.raises(AttributeError, match=\"U234\"):\n chain.set_branch_ratios(u4br)","title":""},{"docid":"01fcfafaf5fd59a43c311cb305222420","score":"0.48459184","text":"def drop_less_significant(cleaned):\r\n cleaned=cleaned.groupby(\"SOC_CODE\").filter(lambda x: len(x)>15)\r\n cleaned=cleaned.groupby(\"SOC_TITLE\").filter(lambda x: len(x)>15)\r\n cleaned=cleaned.groupby(\"EMPLOYER_NAME\").filter(lambda x: len(x)>15)\r\n cleaned=cleaned.groupby(\"WORKSITE_STATE\").filter(lambda x: len(x)>15)\r\n return cleaned","title":""},{"docid":"bdf4d15fe7c8d91749369cfa9df82dac","score":"0.48420027","text":"def category_conflicts(course):\n cat_found = dict.fromkeys(GE_AREAS.keys(), False)\n conflicts = list()\n\n for area in course.ge_areas:\n for cat, areas in GE_AREAS.items():\n if cat not in conflicts and area in areas:\n if cat_found[cat] == 1:\n conflicts.append(cat)\n else:\n cat_found[cat] = 1\n\n return conflicts","title":""},{"docid":"4c3abc4d12592b9219c5ca52dd8a59ad","score":"0.48231664","text":"def test_exclude_precise(self):\n exclude = [\"H2O\", \"H2O_PLUS\", \"H2O_MINUS\", \"CO2\", \"LOI\"]\n df = pd.DataFrame(data=self.one_row)\n df.columns = self.cols\n devdf = devolatilise(df, exclude=exclude)\n # There should be all those which weren't excluded\n self.assertTrue(\n np.array(\n [\n i in devdf.columns\n for i in [i for i in df.columns if i not in exclude]\n ]\n ).all()\n )\n # There should be no new things which where unexpected included\n self.assertTrue(np.array([i in df.columns for i in devdf.columns]).all())","title":""},{"docid":"c9f69a1d0632f2a2c6ff614398d89a9e","score":"0.48216188","text":"def false_detections(self):\n return [det for det in self.detections\n if det[4] >= self.confidence_threshold and not det[5]]","title":""},{"docid":"7941a33eb6230cff9bad2b85fec83f45","score":"0.48207965","text":"def test_0(self):\n grid = Grid([[Color.black, Color.white, Color.black],\n [Color.white, Color.black, Color.green]])\n self.assertFalse(grid.all_rows_distinct_colors(BWG))","title":""},{"docid":"9c03417bea93707ac4d075431282b193","score":"0.48172104","text":"def get_cpx_unsatisfied_cts(self, cts, sol, tolerance=1e-6):\n unsatisfied = []\n for ct in cts:\n if not ct.is_satisfied(sol, tolerance):\n # use mixin API to convert to cplex lingo\n cpx_lhs, cpx_sense, cpx_rhs = self.linear_ct_to_cplex(ct)\n unsatisfied.append( (ct, cpx_lhs, cpx_sense, cpx_rhs) )\n return unsatisfied","title":""},{"docid":"443172915c8b0fde212760e5fde44d4d","score":"0.48160225","text":"def filter_must(self):\r\n return filter(\r\n lambda x: isinstance(x.constraint, CommitmentConstraint),\r\n self\r\n )","title":""},{"docid":"d7b66572d57d5669e463c27cb6c194f2","score":"0.48029044","text":"def test_get_compound_dataframe():\n directory = r'../assets\\data\\instrument_data\\agilent_gc_residual_solvents\\sequence-1'\n filename = 'REPORT01.xls'\n data_file = os.path.join(directory, filename)\n workbook_data = pd.read_excel(data_file, sheet_name=None)\n compounds = instruments.get_compound_dataframe(workbook_data)\n assert 'measurement' in compounds.columns\n return compounds","title":""},{"docid":"2a2f146da8a5596b47c1e67d8602fb62","score":"0.48009792","text":"def get_uncalibrated_jsons(self):\n ps = dep_util.get_files_ext(self.path_rigs, \"json\")\n files = [x for x in ps if \"calibrated\" not in x]\n return sorted(files, key=lambda s: s.casefold())","title":""},{"docid":"e35c88be061a0ab8081044226a75f920","score":"0.47851583","text":"def is_guilty_to_lesser(self):\n return self.verdict == pc.VERDICT_GUILTY and self.offense_records.count() == 2 and not self.has_equivalent_offense_records()","title":""},{"docid":"fb05c96f4426214551232da2608c3f49","score":"0.47788897","text":"def test_none(self):\n df = self.df.head(0).copy()\n for cation in [\"Mg\", \"Fe\"]:\n with self.subTest(cation=cation):\n aggdf = aggregate_element(df, to=cation)\n # Check that only one form is returned","title":""},{"docid":"c57d5e912254eaa591da580b0ba3801d","score":"0.47734663","text":"def test_list_no_match(self):\n func = create_constrained_inputcubelist_converter(\n \"airspeed_velocity_of_unladen_swallow\",\n )\n with self.assertRaisesRegex(ConstraintMismatchError, \"^Got 0 cubes\"):\n func(self.wind_cubes)","title":""},{"docid":"53acbfd6ddcc5196be3b266dc1b99262","score":"0.47680584","text":"def missing(self):\r\n #-----------------\r\n\r\n return set(range(1,10)) - set([cell.value for cell in self.cells])","title":""},{"docid":"eb6b7ae3a5d1875fa0c6ddf0e3132995","score":"0.4767532","text":"def test_3d_Greyscale_cine_clip_all(self):\n from deid.dicom import DicomCleaner\n\n dicom_file = get_file(self.dataset, \"GREYSCALE_CINE.zip\", self.tmpdir)\n deid = os.path.join(self.deidpath, \"remove_coordinates_us_all.dicom\")\n\n client = DicomCleaner(output_folder=self.tmpdir, deid=deid)\n out = client.detect(dicom_file)\n self.assertTrue(out[\"flagged\"])\n\n client.clean()\n cleanedfile = client.save_dicom()\n\n outputfile = read_file(cleanedfile)\n outputpixels = outputfile.pixel_array\n\n inputfile = read_file(dicom_file)\n inputpixels = inputfile.pixel_array\n compare = inputpixels == outputpixels\n self.assertFalse(compare.all())\n\n inputpixels[:, :, :] = 0\n compare = inputpixels == outputpixels\n self.assertTrue(compare.all())","title":""},{"docid":"524b7abd1329753777149e1bd5400ab9","score":"0.47633016","text":"def roc_conds(data, mort, ret_aucs = False):\n conds = []\n aucs = []\n for d in range(pl.shape(data)[1]):\n col = pl.copy(data[:,d])\n mort_nonan = pl.copy(mort)\n if sum(pl.isnan(col)) > 0:\n mort_nonan = pl.copy(mort[~pl.isnan(col)])\n col = col[~pl.isnan(col)]\n auc = roc_auc_score(mort_nonan, col)\n if auc < 0.5:\n conds.append(operator.lt)\n else:\n conds.append(operator.gt)\n aucs.append(auc)#pl.amax([auc, 1-auc]))\n if ret_aucs:\n return conds, aucs\n else:\n return conds","title":""},{"docid":"179b058186838562280f96c33b67ac45","score":"0.47622558","text":"def check(self):\n errors = [err for rule in self.rules for err in rule.check(self.dictionary)]\n errors = list(set(errors))\n errors = self.sorting.sort(errors)\n errors = self.filtering.filter(errors)\n if errors:\n raise CertumException(f\"\\n\\n{self.printing.print(errors)}\")","title":""},{"docid":"8c003d8b531ab8478d1604907bbdf996","score":"0.47564667","text":"def test_incomplete_cable_stitches_1():\n in_data = [1, 1, 4, 4, 1, 1]\n with pytest.raises(KnitPaintCheckException) as err:\n resolve_cable_stitches(in_data, len(in_data))\n problems = err.value.problems\n assert len(problems) > 0","title":""},{"docid":"5faacdea569b09a42668df3a8acb9f26","score":"0.47448885","text":"def valid_mice(self):\n return [m.label for m in self.mice if not m.is_ignored]","title":""},{"docid":"2f27c6f0b751941ed2e48d39bc39256c","score":"0.47410977","text":"def quick_check_of_OCS_emissions(target='OCS'):\n #\n root = '/users/ts551/scratch/GC/rundirs/'\n file_str = 'geosfp_4x5_tropchem.v12.2.1.AQSA.{}'\n suffix = 'CH3I.ALL.test_other_sources.repeat.II.OCS/'\n run_dict = {\n # intial test runs\n 'OCS_TEST' : root + file_str.format(suffix),\n }\n # use the run_dict from - obs.get_ground_surface_OCS_obs_DIRECT\n wds = run_dict # for debugging...\n target = 'OCS' # for testing\n #\n filename = 'HEMCO_diagnostics.201401010000.nc'\n # Get a dictionary of all the data\n dsDH = GetEmissionsFromHEMCONetCDFsAsDatasets(wds=run_dict)\n # - Analysis the totals\n # Extract the annual totals to a dataFrame\n df = pd.DataFrame()\n for run in dsDH.keys():\n print(run)\n # Get the values and variable names\n vars2use = [i for i in dsDH[run].data_vars if i != 'AREA']\n vars2use = list(sorted(vars2use))\n vals = [ dsDH[run][i].sum().values for i in vars2use ]\n # Save the summed values to the dataframe\n df[run] = pd.Series( dict(zip(vars2use, vals)) )\n# print( dsDH[run].sum() )\n # Print the DataFrame to screen\n print(df)","title":""},{"docid":"6e1819d93ded8d3039247376e215a669","score":"0.47367904","text":"def test_bulk_exclusion_does_not_leave_too_few_candidates(self):\n\n votes = 17 * (('A Suitable Boy', ), ) + \\\n 12 * (('Farewell My Lovely', ), ) + \\\n 2 * (('What I Loved', ), ) + \\\n 1 * (('Gone Girl', ), )\n\n candidates = [\n 'A Suitable Boy',\n 'Farewell My Lovely',\n 'What I Loved',\n 'The Da Vinci Code',\n 'Angels and Demons',\n 'Gone Girl'\n ]\n vacancies = 4\n\n stv_round = Round(vacancies, candidates, votes)\n bulk_exclusions = stv_round._bulk_exclusions()\n\n self.assertTrue(len(bulk_exclusions) == 0)","title":""},{"docid":"79c534b9ff58df76ab396b9d671c404d","score":"0.4735367","text":"def remove_never_executed(self):\n for cond in self.branch_conds:\n partialbr = []\n for branch in cond.get_branches():\n text = branch.get_name()\n if \"never executed\" not in text:\n partialbr.append(branch)\n if len(partialbr) == 0:\n self.only_never_executed.append(cond)\n else:\n cond.branches = partialbr\n self.interesting_conds.append(cond)","title":""},{"docid":"1611ad5988ab48b2e43bc21659909bb5","score":"0.47326818","text":"def drop_score_empty(): # 删去评分为空的公式，如果第一列名为空，第一列也会被删\n empty_check_list = [u'企业总评分'.encode('utf-8')]\n for file_n in category_finance_files:\n print file_n\n\n dcu.merge_rows(file_n + '_index', file_url=corporation_index_file_url,\n dst_file_url=corporation_index_file_url)\n dcu.drop_rows_too_many_empty(file_n + '_index', file_url=corporation_index_file_url,\n dst_file_url=corporation_index_file_url, columns=empty_check_list, thresh=1)","title":""},{"docid":"f64f7b8888e3d289d4e75abef923ed5e","score":"0.47240153","text":"def test_incomplete_cable_stitches_2():\n in_data = [1, 4, 4, 1, 4, 4, 1]\n with pytest.raises(KnitPaintCheckException) as err:\n resolve_cable_stitches(in_data, len(in_data))\n problems = err.value.problems\n assert len(problems) > 0","title":""},{"docid":"08d0bdc5e3b17bd2ab695d0d70f05b01","score":"0.47177666","text":"def analyze_unconditional_status(self):\n #Initialize status to unknown for all blocks\n for block in self.iterate_blocks(BLACK+WHITE+EMPTY):\n block.status = \"unknown\"\n #import pdb; pdb.set_trace()\n self.analyze_color_unconditional_status(BLACK)\n self.analyze_color_unconditional_status(WHITE)\n #cleanup\n for block in self.iterate_blocks(BLACK+WHITE+EMPTY):\n del block.eye","title":""},{"docid":"5f332ab5832d3d9b8e552a7d070709c5","score":"0.47138566","text":"def __unused(self):\n return [c for c in self.candidates if not self.redundant_feature(c)\n and related(c, single=True)]","title":""},{"docid":"fbc4ae1e41bd59e5c97b6c597cabcf7d","score":"0.4712267","text":"def test_non_offenses_get_removed(self):\n result = remove_non_offenses(self.sheet.fillna(\"None\"))\n for item in result[\"Triggering Offense Category\"]:\n self.assertTrue(\"None\" not in item)","title":""},{"docid":"e5c7dd13bd22c07c2a4307033c92eab8","score":"0.4704868","text":"def get_ignores(self, dt_boxes, gt_boxes):\n if gt_boxes.size:\n ioas = bbox_overlaps(dt_boxes, gt_boxes, mode='iof')\n ioas = np.max(ioas, axis=1)\n rows = np.where(ioas > self.iou_thres)[0]\n return len(rows)\n else:\n return 0","title":""},{"docid":"f65aeb3ff0572d53675cba9e510a392f","score":"0.46947604","text":"def find_non_matching_alleles(sumstat_rec, vcf_rec):\n alts_to_remove = []\n for ref, alt in vcf_rec.yeild_alleles():\n if not compatible_alleles_either_strand(sumstat_rec.other_al,\n sumstat_rec.effect_al,\n ref,\n alt):\n alts_to_remove.append(alt)\n return alts_to_remove","title":""},{"docid":"e8a57d37abbe932d2ea52fbc4fcc8fd4","score":"0.46943173","text":"def filter_boxes(self, boxes, box_confidences, box_class_probs):\n return None","title":""},{"docid":"f5aed6297ee749d4756f874651f124a1","score":"0.4692294","text":"def remove_unwanted_analytes_from_data_file(self):\n # removes unwanted analytes\n self.trimmed_data_frame = self.raw_xml_data_frame[self.raw_xml_data_frame['id17'].isin([1.0, 2.0, 3.0])]","title":""},{"docid":"b70246c6b06b4413139a5669eba2b87b","score":"0.46901798","text":"def whether_uncovered(self, m, n, c, Filled):\n for i in self.covered(m, n, c):\n if i in Filled:\n return False\n return True","title":""},{"docid":"23c6dca894fa0e96fa1030d4b1e70a53","score":"0.46892768","text":"def for_opd_encounter(self):\n return self.filter(type__in=[Division.TYPE.CLINIC,\n Division.TYPE.PREMIUM_CLINIC,\n Division.TYPE.SHORTSTAY,\n Division.TYPE.SPECIMEN_COLLECTOR])","title":""},{"docid":"587c8fe6f783634f6d71b71078d4fde5","score":"0.4686451","text":"def testAllLosing(self):\r\n\r\n allLoseStrings = [\"c\", \"co\", \"coo\", \"bag\", \"bar\"]\r\n errors = [prefix for prefix in allLoseStrings if self.winning(prefix)]\r\n assert not errors,\\\r\n (\"the following are incorrectly marked as winning: \" +\r\n str(errors))","title":""},{"docid":"667c6c5d4557eb48fff0ee1570036396","score":"0.46835056","text":"def remove_sparse(self, n=2):\n bad=(self.total<n).any(axis=1)\n self.filter_snps(~bad)\n \n print(\"Removed \" +str(sum(bad)) + \n \" SNPs with <\" + str(n) + \" alleles in one population\", \n file=sys.stderr)","title":""}],"string":"[\n {\n \"docid\": \"ab98fc705bde3128e358773730bc475c\",\n \"score\": \"0.56570566\",\n \"text\": \"def find_redundant_compounds(self): \\n\\n stoich = self.stoichiometry\\n\\n # Transposing, to use the qr algorithm\\n stoich_t = stoich.T\\n n_rows = stoich_t.shape[0]\\n n_cols = stoich_t.shape[1]\\n \\n if n_cols > n_rows:\\n zeros = np.zeros((n_cols - n_rows, n_cols))\\n padded = np.vstack((stoich_t, zeros))\\n else:\\n zeros = np.zeros((n_rows, n_rows - n_cols))\\n padded = np.hstack((stoich_t, zeros))\\n \\n Q, R, P = qr(padded, pivoting=True)\\n dependent = P[np.abs(R.diagonal()) < 1e-10]\\n\\n # Trimming padded values\\n redundant = stoich.index[[i for i in dependent if i < n_cols]]\\n\\n msg = str(len(redundant)) + ' compounds identified as redundant'\\n omfa.logger.info(msg)\\n for i in range(len(redundant)):\\n omfa.logger.info('\\\\t' + str(i+1) + ' ' + redundant[i])\\n\\n return(redundant)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12450f44c51ad811c8dc97c57c31b3e9\",\n \"score\": \"0.5574998\",\n \"text\": \"def missed_ground_truth(self):\\n return [box for box in self.ground_truth\\n if box[4] < self.confidence_threshold]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a70b8ae2aedd3e86cb1151a90a79af55\",\n \"score\": \"0.5422111\",\n \"text\": \"def _check_for_residual_space(self):\\n covered_volume = sum(rect.volume for rect in self.hyperrectangles)\\n if not np.isclose(covered_volume, 1):\\n raise ValueError(\\\"The passed in rectangles do not cover \\\"\\n \\\"all of [0,1]^n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f1bf8c3b8493d019bb12f38d0773f29\",\n \"score\": \"0.5327456\",\n \"text\": \"def free_throw_filter(shot_rows):\\r\\n filtered_shot_rows = []\\r\\n for shot_row in shot_rows:\\r\\n if (not (shot_row[0] == 0 and abs(shot_row[1]) in [28,42]) \\r\\n and (\\\"Free\\\" not in shot_row[3] and \\\"Lay\\\" not in shot_row[3])):\\r\\n filtered_shot_rows.append(shot_row)\\r\\n return filtered_shot_rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26da5fb3389411856906a7947052208b\",\n \"score\": \"0.5311303\",\n \"text\": \"def in_Ro5(mol):\\n \\n h_donor = Lipinski.NumHDonors(mol)\\n h_accept = Lipinski.NumHAcceptors(mol)\\n mw = Descriptors.MolWt(mol)\\n logP = Descriptors.MolLogP(mol)\\n \\n Ro5 = h_donor <= 5 and h_accept <= 10 and mw <= 500 and logP < 5\\n return(Ro5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"885b92e61fe8896700dd4986651fb218\",\n \"score\": \"0.5289528\",\n \"text\": \"def test_query_cone_badfilt(self):\\n tst_args = { 'ra': '53.157662568', 'dec': '-27.8075199236', 'size': '0.0002777' }\\n lst = self.imgr.query_cone(tst_args, filt='BADfilt')\\n assert lst is not None\\n assert len(lst) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d9dddf2e36a7689f925f215df5cdc2d\",\n \"score\": \"0.52892494\",\n \"text\": \"def test_query_cone_badcoll(self):\\n tst_args = { 'ra': '53.157662568', 'dec': '-27.8075199236', 'size': '0.0002777' }\\n lst = self.imgr.query_cone(tst_args, collection='BADcoll')\\n assert lst is not None\\n assert len(lst) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6890bf33820ca81b87f1a57e73c647f\",\n \"score\": \"0.52669555\",\n \"text\": \"def accept(potList):\\n if potList['NOE'].violations()>0:\\n return False\\n if potList['RDC'].rms()>1.2: #this might be tightened some\\n return False\\n if potList['CDIH'].violations()>0:\\n return False\\n if potList['BOND'].violations()>0:\\n return False\\n if potList['ANGL'].violations()>0:\\n return False\\n if potList['IMPR'].violations()>1:\\n return False\\n \\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e348f5da5cdf43d96764dd347d6c4f9\",\n \"score\": \"0.52626145\",\n \"text\": \"def test_query_cone_badboth(self):\\n tst_args = { 'ra': '53.157662568', 'dec': '-27.8075199236', 'size': '0.0002777' }\\n lst = self.imgr.query_cone(tst_args, filt='BADfilt', collection='BADcoll')\\n assert lst is not None\\n assert len(lst) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f027d6a06547fc706bd6745380c6b521\",\n \"score\": \"0.52531433\",\n \"text\": \"def check_all_checks(cls):\\n all_checked = set((b.scope, b.name) for b in cls.all)\\n unchecked = all_checked - cls.all_checks\\n if unchecked:\\n print(\\\"** These Boogers were never checked:\\\", file=sys.stderr)\\n print(\\\"\\\\n\\\".join(\\\": \\\".join(check) for check in unchecked), file=sys.stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0197ea31b4ab0fbfcef49167914505c\",\n \"score\": \"0.5250094\",\n \"text\": \"def testFilterNonCrosProjects(self):\\n base_func = itertools.cycle(['chromiumos', 'chromeos']).next\\n patches = self.GetPatches(8)\\n for patch in patches:\\n patch.project = '%s/%i' % (base_func(), _GetNumber())\\n patch.tracking_branch = str(_GetNumber())\\n\\n non_cros_patches = self.GetPatches(2)\\n for patch in non_cros_patches:\\n patch.project = str(_GetNumber())\\n\\n filtered_patches = patches[:4]\\n allowed_patches = []\\n projects = {}\\n for idx, patch in enumerate(patches[4:]):\\n fails = bool(idx % 2)\\n # Vary the revision so we can validate that it checks the branch.\\n revision = ('monkeys' if fails\\n else 'refs/heads/%s' % patch.tracking_branch)\\n if fails:\\n filtered_patches.append(patch)\\n else:\\n allowed_patches.append(patch)\\n projects.setdefault(patch.project, {})['revision'] = revision\\n\\n manifest = MockManifest(self.build_root, projects=projects)\\n\\n self.mox.ReplayAll()\\n results = validation_pool.ValidationPool._FilterNonCrosProjects(\\n patches + non_cros_patches, manifest)\\n\\n def compare(list1, list2):\\n mangle = lambda c:(c.id, c.project, c.tracking_branch)\\n self.assertEqual(list1, list2,\\n msg=\\\"Comparison failed:\\\\n list1: %r\\\\n list2: %r\\\"\\n % (map(mangle, list1), map(mangle, list2)))\\n\\n compare(results[0], allowed_patches)\\n compare(results[1], filtered_patches)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"447fc7a12873a8292aa15de75339b044\",\n \"score\": \"0.5234065\",\n \"text\": \"def rejects(self):\\n #: lines that were rejected b/c they were for a card that isnt supported\\n return self.reject_lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41d35c2829fd5870fdd86df234a9d487\",\n \"score\": \"0.5187228\",\n \"text\": \"def find_dead_end_compounds(self): \\n\\n stoich = self.stoichiometry\\n\\n dead_end = [i for i in stoich.index if sum(stoich.ix[i, stoich.columns] != 0) <= 1]\\n\\n msg = str(len(dead_end)) + ' compounds identified as dead-end'\\n omfa.logger.info(msg)\\n for i in range(len(dead_end)):\\n omfa.logger.info('\\\\t' + str(i+1) + ' ' + dead_end[i])\\n\\n return(dead_end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8cdf8999dec2a4fea373a740a320b63\",\n \"score\": \"0.51506704\",\n \"text\": \"def dead_reactions(self):\\n\\t\\treturn list(filter(lambda reaction: abs(reaction.upper_bound) < CONST_EPSILON and abs(reaction.lower_bound) < CONST_EPSILON, self.__cobra_model.reactions))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b54d36ca30a1f8ecb81873b85a8e782b\",\n \"score\": \"0.5139265\",\n \"text\": \"def get_clean_data_frame(self,df):\\n g = df['r-free final : no ncs'] > 0\\n g &= (df['r-free final : cartesian ncs restraints'] > 0)\\n g &= (df['r-free final : torsion ncs restraints'] > 0)\\n return df[g]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"017570a040a16f38ff744ef3e9792111\",\n \"score\": \"0.51170933\",\n \"text\": \"def get_unsolved(grid):\\n\\n unsolved = []\\n\\n for row in grid.grid:\\n for square in row:\\n if len(square.possibles) > 1:\\n unsolved.append(square)\\n return unsolved\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3aa2257d43b58ec25a37228f45141f4\",\n \"score\": \"0.508613\",\n \"text\": \"def test_NoIons(self):\\n pdb, path = get_pdb_from_remote_or_db(\\\"1izi\\\", \\\"all\\\", test_data.__path__[0])\\n os.remove(path)\\n self.assertItemsEqual(['Q50'], set(pdb.select(CurationSelections.LIGAND_SELECTION).getResnames()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd18071ab2ff4016399cb2562abfa5e2\",\n \"score\": \"0.50748146\",\n \"text\": \"def check_rolled_records(self, today):\\n rolled_objects = PrescribedBurn.objects.filter(date=today, rolled=True).exclude(form_name=PrescribedBurn.FORM_268A) #.exclude(completed=True)\\n unset_objects = list(set(rolled_objects.filter(area__isnull=True, distance__isnull=True)).union(rolled_objects.filter(status__isnull=True)))\\n return unset_objects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c59921fba65d776ddc88cb917bc1754f\",\n \"score\": \"0.5068812\",\n \"text\": \"def missing_cards(tuple_of_cards):\\n cards = set(tuple_of_cards)\\n return [card for card in CARDS if card not in cards]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99cc4a7f4247add7fe385c4572ac7980\",\n \"score\": \"0.50678724\",\n \"text\": \"def _remove_noise_in_o2m():\\n if line.reconcile_partial_id:\\n if currency_id == line.currency_id.id:\\n if line.amount_residual_currency <= 0:\\n return True\\n else:\\n if line.amount_residual <= 0:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf1ad501f309f67b8858669bb5dd036\",\n \"score\": \"0.5063048\",\n \"text\": \"def summarizeExcessCap(self):\\n f = lambda c: e_cap(c, c.assignedRoom)\\n return 100 * sum(map(f, self.courses_filt))/len(self.courses_filt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1152ac514cd417c5933bd379eea7288\",\n \"score\": \"0.5059752\",\n \"text\": \"def remove_bad_values(self,bad_value=-99.9):\\n for cml in self.set:\\n cml.remove_bad_values(bad_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b30cb2e21a7f393de01db4e0963c26c\",\n \"score\": \"0.505736\",\n \"text\": \"def test_remove_census_not_in_ycom(self):\\n census = pd.read_csv('climops/data/acs2015_county_data.csv')\\n census_remove_pr = prepare_data.remove_census_not_in_ycom(census)\\n #check to se whether remaining dataset includes PR (remaining obs\\n # should be 0).\\n test_set = census_remove_pr[census_remove_pr['State'] == 'Puerto Rico']\\n pr_obs = test_set.shape\\n self.assertTrue(pr_obs[0] == 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcc7fa560d693296706d17cdfb97b26f\",\n \"score\": \"0.50406694\",\n \"text\": \"def _elf_prune_problematic_conformers(\\n cls, molecule: \\\"Molecule\\\"\\n ) -> List[unit.Quantity]:\\n\\n valid_conformers = []\\n\\n for i, conformer in enumerate(molecule.conformers):\\n\\n is_problematic, reason = cls._elf_is_problematic_conformer(\\n molecule, conformer\\n )\\n\\n if is_problematic:\\n logger.warning(f\\\"Discarding conformer {i}: {reason}\\\")\\n else:\\n valid_conformers.append(conformer)\\n\\n return valid_conformers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"157eb57161fa6c8ff3d8b3ec445b8337\",\n \"score\": \"0.50365067\",\n \"text\": \"def items_that_need_coverage(self):\\n q = Edition.missing_coverage_from(self._db, [], self.coverage_source)\\n clause = and_(Edition.data_source_id==LicensePool.data_source_id,\\n Edition.primary_identifier_id==LicensePool.identifier_id)\\n q = q.join(LicensePool, clause)\\n q = q.filter(LicensePool.open_access == True).filter(\\n Edition.open_access_download_url==None\\n )\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1c6b355c4dbc1394ad8bc3fbc30b5e1\",\n \"score\": \"0.50348026\",\n \"text\": \"def exclude_from_overlap_check(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"210a4699118872d44edc147c9fcd426a\",\n \"score\": \"0.50296766\",\n \"text\": \"def _check_bundles(cursor):\\n b = cursor.all(\\\"\\\"\\\"\\n SELECT bundles_total, balance\\n FROM (\\n SELECT owner, sum(amount) AS bundles_total\\n FROM cash_bundles b\\n GROUP BY owner\\n ) foo\\n JOIN participants p ON p.id = owner\\n WHERE bundles_total <> balance\\n \\\"\\\"\\\")\\n assert len(b) == 0, \\\"bundles are out of whack: {}\\\".format(b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"210a4699118872d44edc147c9fcd426a\",\n \"score\": \"0.50296766\",\n \"text\": \"def _check_bundles(cursor):\\n b = cursor.all(\\\"\\\"\\\"\\n SELECT bundles_total, balance\\n FROM (\\n SELECT owner, sum(amount) AS bundles_total\\n FROM cash_bundles b\\n GROUP BY owner\\n ) foo\\n JOIN participants p ON p.id = owner\\n WHERE bundles_total <> balance\\n \\\"\\\"\\\")\\n assert len(b) == 0, \\\"bundles are out of whack: {}\\\".format(b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ec860c4062330cdb17aae869356ea1f\",\n \"score\": \"0.50128496\",\n \"text\": \"def keep_science_exposures(self,df):\\n bands= df.loc[:,'band']\\n isGRZ= (bands == 'g') | (bands == 'r') | (bands == 'z') \\n isObject= df.loc[:,'obstype'] == 'object'\\n isScience= df['object'].str.lower().str.contains('decals')\\n longExp= df['exptime'] > 30\\n print('isGRZ %d/%d' % (len(df[isGRZ]),len(df)))\\n print('isObject %d/%d' % (len(df[isObject]),len(df)))\\n print('isScience %d/%d' % (len(df[isScience]),len(df)))\\n print('longExp %d/%d' % (len(df[longExp]),len(df)))\\n allCuts= ((isGRZ) & \\n (isObject) &\\n (isScience) &\\n (longExp))\\n print('allCuts %d/%d' % (len(df[allCuts]),len(df)))\\n return df[allCuts]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73357c82d674e4222b1117b4260aa2e5\",\n \"score\": \"0.49999732\",\n \"text\": \"def test_no_clips_in_project(self):\\n pid = create_project(name=\\\"test_project2\\\")\\n code, res = get_clips(data={PROJECT_ID: pid})\\n self.assertEqual(code, 200)\\n self.assertEqual(len(res[CLIPS]), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7c59f387e1ac5847c79e892b6884f9c\",\n \"score\": \"0.49900657\",\n \"text\": \"def ALL_REGIONS_WITHOUT_CONTENT_RATINGS():\\n return set(ALL_REGIONS) - set(ALL_REGIONS_WITH_CONTENT_RATINGS())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3223e00cc4b2c770e93434536de2846\",\n \"score\": \"0.49888155\",\n \"text\": \"def crit_5(t):\\r\\n #Escolheu-se nao obter as laterais programaticamente porque \\r\\n #diminui bastante a complexidade deste criterio \\r\\n posicoes = [\\\"b1\\\", \\\"a2\\\", \\\"c2\\\", \\\"b3\\\"] \\r\\n posicoes = [cria_posicao(*pos) for pos in posicoes] \\r\\n for pos in posicoes: \\r\\n if pecas_iguais(obter_peca(pos), cria_peca()): \\r\\n return pos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03495209168bd332fe21a059f8119712\",\n \"score\": \"0.49805313\",\n \"text\": \"def get_empty_squares(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7420a2febd494b7ceb2a72811ff67aab\",\n \"score\": \"0.4971833\",\n \"text\": \"def test_is_unidentified(self):\\n self.assertEqual(Compound().is_unidentified, True)\\n self.assertEqual(Compound(names=['Coumarin 343']).is_unidentified, False)\\n self.assertEqual(Compound(labels=['3a']).is_unidentified, False)\\n self.assertEqual(Compound(names=['Coumarin 343'], labels=['3a']).is_unidentified, False)\\n self.assertEqual(Compound(melting_points=[MeltingPoint(value='250')]).is_unidentified, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e7d73c959b9ffb2fabb192b74cf9e7b\",\n \"score\": \"0.49689627\",\n \"text\": \"def filter_acceptable(self, query):\\n # type: (Any) -> Any\\n return query.filter(opentuner.resultsdb.models.Result.size < 1.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c72635e364b0c90caefdbdd3fdea1379\",\n \"score\": \"0.49637672\",\n \"text\": \"def not_approved_ltcs(self) -> int:\\n return self.__test_design_metric(row_nr=9)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42cd6c958f4ce87ef29e7458b054f330\",\n \"score\": \"0.49596122\",\n \"text\": \"def test_incompatible_cable_stitches():\\n in_data = [1, 1, 1, 4, 15, 1, 1, 1]\\n with pytest.raises(KnitPaintCheckException) as err:\\n resolve_cable_stitches(in_data, len(in_data))\\n problems = err.value.problems\\n assert len(problems) == 2\\n assert problems[0].course == 0\\n assert problems[0].wale == 3\\n assert problems[1].course == 0\\n assert problems[1].wale == 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54d5d0d66550fbc9bea42e0ffab4b00\",\n \"score\": \"0.4951831\",\n \"text\": \"def excessCap(self):\\n f = lambda c: e_cap(c, c.assignedRoom)\\n return map(f, self.courses_filt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3124fb24fd596395e1dbb1e6028153b1\",\n \"score\": \"0.49452466\",\n \"text\": \"def get_nonstrict_lines(lines):\\n return filter(lambda l: l.upper().find(\\\"NON\\\") != -1 or empty_section(l), lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78f036fac00d9b5c861922eb512ea1f9\",\n \"score\": \"0.49444968\",\n \"text\": \"def remove_compounds(self, compounds):\\n\\n # Checking if string to avoid iterating by character\\n if isinstance(compounds, basestring):\\n compounds = [compounds]\\n # Carrying on to check if integer to maintain consistency\\n elif isinstance(compounds, (int, long)):\\n compounds = [compounds]\\n\\n # Identify invalid compounds\\n invalid = set(compounds) - set(self.stoichiometry.index)\\n\\n if len(invalid) > 0:\\n msg = 'The following compounds are invalid: ' + ', '.join(invalid)\\n omfa.logger.error(msg)\\n raise omfa.ModelError(msg)\\n else:\\n msg = 'Dropping ' + ', '.join(compounds)\\n omfa.logger.info(msg)\\n self.stoichiometry = self.stoichiometry.drop(compounds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6867bec609242f22886d5c47e56d8f32\",\n \"score\": \"0.49380073\",\n \"text\": \"def rule_1(self):\\n return [cell for cell in self.cells if cell.neighbors(self.cells) < 2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"107fa14844cbfda199098f861da25940\",\n \"score\": \"0.4931519\",\n \"text\": \"def everything_incorrect(self):\\n return self.results.everything_incorrect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"107fa14844cbfda199098f861da25940\",\n \"score\": \"0.4931519\",\n \"text\": \"def everything_incorrect(self):\\n return self.results.everything_incorrect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"304f8c04da55f527d1ce97fed607bd6e\",\n \"score\": \"0.4929644\",\n \"text\": \"def misclosure_error(self):\\n for survey in self.project:\\n survey.misclosure_error()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cad7409c85610e5c28bf78f51df1292\",\n \"score\": \"0.4926443\",\n \"text\": \"def to_ignore(self, enclosure):\\n return all((\\n enclosure['product'] == 'SGPIOEnclosure',\\n '-MINI-' not in enclosure['model'],\\n not enclosure['model'].startswith('R20'),\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c92c7c424273853240d6e9d049b227c\",\n \"score\": \"0.49256653\",\n \"text\": \"def filter_biotherapeutic(mols):\\n return mols.filter(biotherapeutic__isnull=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fcb99deba127260759e6ccb0aa128e6\",\n \"score\": \"0.49252394\",\n \"text\": \"def test_get_appliances_with_no_appliances(self):\\n # Populate appliance table\\n a1 = Appliance.objects.create(name=\\\"A 1\\\")\\n a2 = Appliance.objects.create(name=\\\"A 2\\\")\\n a1.save()\\n a2.save()\\n # Create empty recipe to test against\\n recipe = Recipe.objects.create(title=\\\"Empty\\\", instructions=\\\"Empty\\\")\\n\\n self.assertEquals(list(recipe.get_appliances()), [], \\\"Empty recipe returns appliances.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e70f4cb3943c603fbba73a7b8edac89e\",\n \"score\": \"0.4921788\",\n \"text\": \"def outstanding(self):\\n return (\\n self.filter(suppressed_at__isnull=True)\\n .filter(expired_at__isnull=True)\\n .filter(vol_prospect_contact_events=None)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87b238b389c2b23f57d2576bf1a10af2\",\n \"score\": \"0.4920438\",\n \"text\": \"def malformed_cards(tuple_of_objects):\\n return [obj for obj in tuple_of_objects if not is_card(obj)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49227fe7a92dd013292a181c617fd8f2\",\n \"score\": \"0.49042115\",\n \"text\": \"def test_find_clashes_forall_other_series(self):\\n par = self.real_parameters\\n par.aions = True\\n par.xions = True\\n par.zions = True\\n par.bions = False\\n par.yions = False\\n #par.bMinusNH3 = True\\n #par.bMinusH2O = True\\n #par.bPlusH2O = True\\n\\n par.q3_window = 4.5\\n\\n q3_low, q3_high = self.real_parameters.get_q3range_transitions()\\n precursor = self.precursor\\n transitions = precursor.calculate_transitions(q3_low, q3_high)\\n \\n nonunique = c_getnonuis._find_clashes_forall_other_series( \\n tuple(transitions), self.interfering_precursors, par, q3_low, q3_high,\\n par.q3_window, par.ppm, precursor.q1 - par.q1_window, False)\\n\\n self.assertEqual( len( nonunique ), 4)\\n self.assertEqual( nonunique.keys(), [3,4,5,6] )\\n\\n self.assertEqual( len( nonunique[3] ), 1)\\n self.assertEqual( len( nonunique[4] ), 2)\\n self.assertEqual( len( nonunique[5] ), 3)\\n self.assertEqual( len( nonunique[6] ), 1)\\n\\n # we have one interference with 3\\n self.assertTrue( abs(nonunique[3][0][0] - 456.756009652) < self.EPSILON )\\n self.assertTrue( abs(nonunique[3][0][1] - 500.787837374) < self.EPSILON )\\n self.assertEqual( nonunique[3][0][2], 0) # empty\\n self.assertEqual( nonunique[3][0][3], 665) # peptide key\\n self.assertEqual( nonunique[3][0][4], 'x')\\n self.assertEqual( nonunique[3][0][5], 8)\\n self.assertEqual( nonunique[3][0][6], 'GGLIVELGDK') # sequence\\n self.assertEqual( nonunique[3][0][-1], 2) # charge\\n\\n # we have two interferences with 4\\n self.assertTrue( abs(nonunique[4][0][0] - 443.22541272) < self.EPSILON )\\n self.assertTrue( abs(nonunique[4][0][1] - 506.58461326) < self.EPSILON )\\n self.assertEqual( nonunique[4][0][2], 0) # empty\\n self.assertEqual( nonunique[4][0][3], 618) # peptide key\\n self.assertEqual( nonunique[4][0][4], 'a')\\n self.assertEqual( nonunique[4][0][5], 10)\\n self.assertEqual( nonunique[4][0][6], 'NGTDGGLQVAIDAMR') # sequence\\n self.assertEqual( nonunique[4][0][-1], 2) # charge\\n\\n self.assertTrue( abs(nonunique[4][1][0] - 443.7267378235) < self.EPSILON )\\n self.assertTrue( abs(nonunique[4][1][1] - 506.58461326 ) < self.EPSILON )\\n self.assertEqual( nonunique[4][1][2], 0) # empty\\n self.assertEqual( nonunique[4][1][3], 618) # peptide key\\n self.assertEqual( nonunique[4][1][4], 'z')\\n self.assertEqual( nonunique[4][1][5], 8)\\n self.assertEqual( nonunique[4][1][6], 'NGTDGGLQVAIDAMR') # sequence\\n self.assertEqual( nonunique[4][1][-1], 2) # charge\\n\\n # we have three interferences with 5\\n self.assertTrue( abs(nonunique[5][0][0] - 557.280912722) < self.EPSILON )\\n self.assertEqual( nonunique[5][0][4], 'a')\\n self.assertEqual( nonunique[5][0][5], 12)\\n self.assertEqual( nonunique[5][0][6], 'NGTDGGLQVAIDAMR') # sequence\\n self.assertEqual( nonunique[5][0][-1], 2) # charge\\n\\n self.assertTrue( abs(nonunique[5][1][0] - 550.28240465) < self.EPSILON )\\n self.assertEqual( nonunique[5][1][4], 'x')\\n self.assertEqual( nonunique[5][1][5], 10)\\n self.assertEqual( nonunique[5][1][6], 'NGTDGGLQVAIDAMR') # sequence\\n self.assertEqual( nonunique[5][1][-1], 2) # charge\\n\\n self.assertTrue( abs(nonunique[5][2][0] - 557.2902278235) < self.EPSILON )\\n self.assertEqual( nonunique[5][2][4], 'z')\\n self.assertEqual( nonunique[5][2][5], 11)\\n self.assertEqual( nonunique[5][2][6], 'NGTDGGLQVAIDAMR') # sequence\\n self.assertEqual( nonunique[5][2][-1], 2) # charge\\n\\n # we have one interference with 6\\n self.assertTrue( abs(nonunique[6][0][0] - 665.327537823 ) < self.EPSILON )\\n self.assertEqual( nonunique[6][0][2], 0) # empty\\n self.assertEqual( nonunique[6][0][3], 618) # peptide key\\n self.assertEqual( nonunique[6][0][4], 'z')\\n self.assertEqual( nonunique[6][0][5], 13)\\n self.assertEqual( nonunique[6][0][6], 'NGTDGGLQVAIDAMR') # sequence\\n self.assertEqual( nonunique[6][0][-1], 2) # charge\",\n \"title\": \"\"\n },\n {\n \"docid\": \"714e4eaab013bb1cc8404881a64c63e6\",\n \"score\": \"0.4903402\",\n \"text\": \"def get_strict_lines(lines):\\n return filter(lambda l: l.upper().find('NON') == -1 or empty_section(l), lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d681cf368d3845ab503eaa4ba27899eb\",\n \"score\": \"0.4903144\",\n \"text\": \"def filter_by_rect_and_find():\\n\\n reduced_possibilities = {}\\n for area_coord, area_possibilities in remaining_possibilities.items():\\n accurate_area_possibilities = [possibility for possibility in area_possibilities\\n if is_zone_free(possibility, grid)]\\n if len(accurate_area_possibilities) == 0: # not shape fit in the area, the grid cannot be solved\\n Log.info(f'<<< unsolvable - impossible for {area_info(area_coord, grid)}')\\n return None\\n elif len(accurate_area_possibilities) == 1: # found an area solution\\n Log.info(f'rectangle added for {area_info(area_coord, grid)} - from rectangles')\\n add_rectangle(accurate_area_possibilities[0], grid)\\n else:\\n reduced_possibilities[area_coord] = accurate_area_possibilities\\n # logging purpose only\\n eliminated_possibilities = set(area_possibilities) - set(accurate_area_possibilities)\\n if len(eliminated_possibilities) > 0:\\n Log.debug(f'eliminate {len(eliminated_possibilities)} inaccurate rectangles'\\n f' for {area_info(area_coord, grid)}')\\n return reduced_possibilities\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f45c4a57e8ec273835b754952c068ff\",\n \"score\": \"0.4891872\",\n \"text\": \"def identifyOAssetholders(calc1):\\n has_int1 = (calc1.array('e00300') != 0)\\n has_int2 = (calc1.array('e00400') != 0)\\n hasoassets = has_int1 | has_int2\\n return hasoassets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cff9679195f9f2bd95ad7a1ec74ef0a1\",\n \"score\": \"0.48909375\",\n \"text\": \"def get_sellability_report(melons):\\n\\n # Fill in the rest \",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f826aed53f154939605fe2635c3b810\",\n \"score\": \"0.48850587\",\n \"text\": \"def test_ready_computations_excludes_r2c(self):\\n name = Name(\\\"/test/NFN\\\")\\n request_name = Name(\\\"/test/R2C\\\")\\n self.computationList.add_computation(name, 0, Interest(name))\\n self.computationList.container[0].add_name_to_await_list(request_name)\\n self.assertEqual(len(self.computationList.container[0].awaiting_data), 1)\\n ready_comps = self.computationList.get_ready_computations()\\n self.assertEqual(ready_comps, [NFNComputationTableEntry(name)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aebf17e049ef781dab2a00f7a05c5cef\",\n \"score\": \"0.48828843\",\n \"text\": \"def testall(composite, examples, badExamples=[]):\\n wrong = []\\n for example in examples:\\n if composite.GetActivityQuantBounds():\\n answer = composite.QuantizeActivity(example)[-1]\\n else:\\n answer = example[-1]\\n res, conf = composite.ClassifyExample(example)\\n if res != answer:\\n wrong.append((res, conf))\\n badExamples.append(example)\\n\\n return wrong\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a523913bc344e3b9c4712e342b8252b\",\n \"score\": \"0.48817363\",\n \"text\": \"def filter_diagnosed(self):\\n queryset = self.filter(created__date=datetime.today())\\n return queryset.filter(status__in=[Encounter.STATUS.WAIT_RESULT, Encounter.STATUS.RECEIVE_RESULT,\\n Encounter.STATUS.CHECKED_OUT, Encounter.STATUS.WAIT_PAY,\\n Encounter.STATUS.WAIT_DISPENSE, Encounter.STATUS.DISCHARGED])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f9508e44021d77921e50b74a099138d\",\n \"score\": \"0.48805833\",\n \"text\": \"def clean_redundancies(self) -> None:\\n candidates_to_remove = [[candidate for candidate in candidate_set][0]\\n for candidate_set in self._mapping.values() if len(candidate_set) == 1]\\n sets_to_check = [candidate_set for candidate_set in self._mapping.values() if len(candidate_set) > 1]\\n for candidate_to_remove in candidates_to_remove:\\n for set_to_check in sets_to_check:\\n if candidate_to_remove in set_to_check:\\n set_to_check.remove(candidate_to_remove)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc3b517d8384bc9c7e0368296c0df2d7\",\n \"score\": \"0.4876179\",\n \"text\": \"def get_exclusions():\\n nb_vdw_list = []\\n model = cmd.get_model('all')\\n for at in model.atom:\\n nb = cmd.index('(index %s extend 2)' % at.index)\\n nb_vdw_list.extend([tuple(sorted([at.index-1, i[1]-1])) for i in nb])\\n nb_vdw = set(nb_vdw_list)\\n return nb_vdw\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29c87ffcb2ab80446bdd62fa76bd6a4\",\n \"score\": \"0.4874136\",\n \"text\": \"def refine_compounds(compounds, mols, gaps, failed_mols):\\n for idx in sorted(failed_mols, reverse=True):\\n del compounds[idx]\\n del mols[idx]\\n del gaps[idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8776b01c5322597487bbaf5a49bb93ba\",\n \"score\": \"0.48680595\",\n \"text\": \"def test_image_metadata_by_filter_badcoll(self):\\n res = self.imgr.image_metadata_by_filter('F444W', collection='BADcoll')\\n print(res)\\n assert res is not None\\n assert len(res) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f2bab583554447bb46643d540df70db\",\n \"score\": \"0.4858687\",\n \"text\": \"def _yield_compounds(self): # noqa: ANN101\\n undefined_compounds = []\\n for name in self.model.compounds:\\n if not self.model.compounds[name]:\\n undefined_compounds.append(name)\\n if undefined_compounds:\\n raise ValueError( # noqa: TRY003\\n f\\\"undefined compounds: {', '.join(undefined_compounds)}\\\", # noqa: EM102\\n ) # noqa: RUF100\\n\\n logfiles_table = Table(\\n Column(\\\"no\\\", justify=\\\"right\\\"),\\n Column(\\\"compound\\\", justify=\\\"left\\\"),\\n Column(\\\"path\\\", justify=\\\"left\\\"),\\n title=\\\"logfiles\\\",\\n box=self.box_style,\\n )\\n compounds_table = Table(\\n Column(\\\"no\\\", justify=\\\"right\\\"),\\n Column(\\\"compound\\\", justify=\\\"left\\\"),\\n Column(\\\"elec. energy\\\\n〈Eₕ〉\\\", justify=\\\"center\\\"),\\n Column(\\\"spin mult.\\\", justify=\\\"center\\\"),\\n Column(\\\"smallest vibfreqs\\\\n〈cm⁻¹〉\\\", justify=\\\"center\\\"),\\n Column(\\\"point group\\\", justify=\\\"center\\\"),\\n title=\\\"compounds\\\",\\n box=self.box_style,\\n )\\n for i, (name, data) in enumerate(self.model.compounds.items()):\\n path_text = None\\n if data.logfile is not None:\\n path_text = Text(data.logfile)\\n path_text.highlight_regex(r\\\"[^\\\\/]+$\\\", \\\"bright_blue\\\")\\n logfiles_table.add_row(f\\\"{i:d}\\\", name, path_text)\\n\\n vibfreqs_text = None\\n if data.vibfreqs is not None:\\n vibfreqs_text = Text(\\n \\\", \\\".join([f\\\"{vibfreq:+7.1f}\\\" for vibfreq in data.vibfreqs[:3]]),\\n )\\n vibfreqs_text.highlight_regex(r\\\"-\\\\d+\\\\.\\\\d\\\", \\\"bright_yellow\\\")\\n\\n point_group = coords.find_point_group(\\n atommasses=data.atommasses,\\n atomcoords=data.atomcoords,\\n )\\n compounds_table.add_row(\\n f\\\"{i:d}\\\",\\n name,\\n f\\\"{data.energy / (constants.hartree * constants.N_A):17.12f}\\\",\\n f\\\"{data.mult}\\\",\\n vibfreqs_text,\\n point_group,\\n )\\n yield logfiles_table\\n yield compounds_table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a56df535398c5a5953995a1035a300db\",\n \"score\": \"0.48563296\",\n \"text\": \"def test_find_clashes_forall_other_series_by(self):\\n par = self.real_parameters\\n q3_low, q3_high = self.real_parameters.get_q3range_transitions()\\n precursor = self.precursor\\n transitions = precursor.calculate_transitions(q3_low, q3_high)\\n \\n nonunique = c_getnonuis._find_clashes_forall_other_series( \\n tuple(transitions), self.interfering_precursors, par, q3_low, q3_high,\\n par.q3_window, par.ppm, precursor.q1 - par.q1_window, False)\\n\\n self.assertEqual( len( nonunique ), 3)\\n self.assertEqual( nonunique.keys(), [0,2,4] )\\n\\n self.assertTrue( abs(nonunique[0][0][0] - 842.4008) < self.EPSILON )\\n self.assertTrue( abs(nonunique[0][0][1] - 506.584613) < self.EPSILON )\\n self.assertEqual( nonunique[0][0][2], 0) # empty\\n self.assertEqual( nonunique[0][0][3], 618) # peptide key\\n self.assertEqual( nonunique[0][0][4], 'b')\\n self.assertEqual( nonunique[0][0][5], 9)\\n self.assertEqual( nonunique[0][0][6], 'NGTDGGLQVAIDAMR') # sequence\\n self.assertEqual( nonunique[0][0][-1], 1) # charge\\n\\n self.assertTrue( abs(nonunique[2][0][0] - 565.8035) < self.EPSILON )\\n self.assertEqual( nonunique[2][0][4], 'y')\\n self.assertEqual( nonunique[2][0][5], 11)\\n self.assertEqual( nonunique[2][0][6], 'NGTDGGLQVAIDAMR') # sequence\\n self.assertEqual( nonunique[2][0][-1], 2) # charge\\n\\n self.assertTrue( abs(nonunique[4][0][0] - 440.287275) < self.EPSILON )\\n self.assertTrue( abs(nonunique[4][0][1] - 500.787837374 ) < self.EPSILON )\\n self.assertEqual( nonunique[4][0][2], 0) # empty\\n self.assertEqual( nonunique[4][0][3], 665) # peptide key\\n self.assertEqual( nonunique[4][0][4], 'b')\\n self.assertEqual( nonunique[4][0][5], 5)\\n self.assertEqual( nonunique[4][0][6], 'GGLIVELGDK') # sequence\\n self.assertEqual( nonunique[4][0][-1], 1) # charge\",\n \"title\": \"\"\n },\n {\n \"docid\": \"347082105d2d5e24af21546f4c5501fd\",\n \"score\": \"0.48525828\",\n \"text\": \"def find_not_removed_watchee(self, email):\\n res = self.watcher_cc.aggregate([\\n {\\\"$match\\\": {\\\"email_addr\\\": email}},\\n {\\\"$project\\\": {\\n \\\"not_removed\\\": {\\\"$filter\\\": {\\n \\\"input\\\": \\\"$crn\\\",\\n \\\"as\\\": \\\"courses\\\",\\n \\\"cond\\\": {\\\"$eq\\\": [\\\"$$courses.removed\\\", False]}\\n }}\\n }}\\n ])\\n not_removed = []\\n for i in res:\\n not_removed.extend(i[\\\"not_removed\\\"])\\n return not_removed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13a1992ca1cc119a7814cb5f0860d0a7\",\n \"score\": \"0.48480925\",\n \"text\": \"def check_constraints_w_enforcement(self):\\n\\n return exclusions.open()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6add7d15eb78d51eb98c5e1f5166390c\",\n \"score\": \"0.48479852\",\n \"text\": \"def test_capture_branch_no_rxn():\\n u4br = {\\\"U234\\\": {\\\"U235\\\": 0.5, \\\"U235_m1\\\": 0.5}}\\n\\n chain_file = Path(__file__).parents[1] / \\\"chain_simple.xml\\\"\\n chain = Chain.from_xml(chain_file)\\n\\n u5m = nuclide.Nuclide(\\\"U235_m1\\\")\\n\\n chain.nuclides.append(u5m)\\n chain.nuclide_dict[u5m.name] = len(chain.nuclides) - 1\\n\\n with pytest.raises(AttributeError, match=\\\"U234\\\"):\\n chain.set_branch_ratios(u4br)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01fcfafaf5fd59a43c311cb305222420\",\n \"score\": \"0.48459184\",\n \"text\": \"def drop_less_significant(cleaned):\\r\\n cleaned=cleaned.groupby(\\\"SOC_CODE\\\").filter(lambda x: len(x)>15)\\r\\n cleaned=cleaned.groupby(\\\"SOC_TITLE\\\").filter(lambda x: len(x)>15)\\r\\n cleaned=cleaned.groupby(\\\"EMPLOYER_NAME\\\").filter(lambda x: len(x)>15)\\r\\n cleaned=cleaned.groupby(\\\"WORKSITE_STATE\\\").filter(lambda x: len(x)>15)\\r\\n return cleaned\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdf4d15fe7c8d91749369cfa9df82dac\",\n \"score\": \"0.48420027\",\n \"text\": \"def category_conflicts(course):\\n cat_found = dict.fromkeys(GE_AREAS.keys(), False)\\n conflicts = list()\\n\\n for area in course.ge_areas:\\n for cat, areas in GE_AREAS.items():\\n if cat not in conflicts and area in areas:\\n if cat_found[cat] == 1:\\n conflicts.append(cat)\\n else:\\n cat_found[cat] = 1\\n\\n return conflicts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c3abc4d12592b9219c5ca52dd8a59ad\",\n \"score\": \"0.48231664\",\n \"text\": \"def test_exclude_precise(self):\\n exclude = [\\\"H2O\\\", \\\"H2O_PLUS\\\", \\\"H2O_MINUS\\\", \\\"CO2\\\", \\\"LOI\\\"]\\n df = pd.DataFrame(data=self.one_row)\\n df.columns = self.cols\\n devdf = devolatilise(df, exclude=exclude)\\n # There should be all those which weren't excluded\\n self.assertTrue(\\n np.array(\\n [\\n i in devdf.columns\\n for i in [i for i in df.columns if i not in exclude]\\n ]\\n ).all()\\n )\\n # There should be no new things which where unexpected included\\n self.assertTrue(np.array([i in df.columns for i in devdf.columns]).all())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9f69a1d0632f2a2c6ff614398d89a9e\",\n \"score\": \"0.48216188\",\n \"text\": \"def false_detections(self):\\n return [det for det in self.detections\\n if det[4] >= self.confidence_threshold and not det[5]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7941a33eb6230cff9bad2b85fec83f45\",\n \"score\": \"0.48207965\",\n \"text\": \"def test_0(self):\\n grid = Grid([[Color.black, Color.white, Color.black],\\n [Color.white, Color.black, Color.green]])\\n self.assertFalse(grid.all_rows_distinct_colors(BWG))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c03417bea93707ac4d075431282b193\",\n \"score\": \"0.48172104\",\n \"text\": \"def get_cpx_unsatisfied_cts(self, cts, sol, tolerance=1e-6):\\n unsatisfied = []\\n for ct in cts:\\n if not ct.is_satisfied(sol, tolerance):\\n # use mixin API to convert to cplex lingo\\n cpx_lhs, cpx_sense, cpx_rhs = self.linear_ct_to_cplex(ct)\\n unsatisfied.append( (ct, cpx_lhs, cpx_sense, cpx_rhs) )\\n return unsatisfied\",\n \"title\": \"\"\n },\n {\n \"docid\": \"443172915c8b0fde212760e5fde44d4d\",\n \"score\": \"0.48160225\",\n \"text\": \"def filter_must(self):\\r\\n return filter(\\r\\n lambda x: isinstance(x.constraint, CommitmentConstraint),\\r\\n self\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7b66572d57d5669e463c27cb6c194f2\",\n \"score\": \"0.48029044\",\n \"text\": \"def test_get_compound_dataframe():\\n directory = r'../assets\\\\data\\\\instrument_data\\\\agilent_gc_residual_solvents\\\\sequence-1'\\n filename = 'REPORT01.xls'\\n data_file = os.path.join(directory, filename)\\n workbook_data = pd.read_excel(data_file, sheet_name=None)\\n compounds = instruments.get_compound_dataframe(workbook_data)\\n assert 'measurement' in compounds.columns\\n return compounds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a2f146da8a5596b47c1e67d8602fb62\",\n \"score\": \"0.48009792\",\n \"text\": \"def get_uncalibrated_jsons(self):\\n ps = dep_util.get_files_ext(self.path_rigs, \\\"json\\\")\\n files = [x for x in ps if \\\"calibrated\\\" not in x]\\n return sorted(files, key=lambda s: s.casefold())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e35c88be061a0ab8081044226a75f920\",\n \"score\": \"0.47851583\",\n \"text\": \"def is_guilty_to_lesser(self):\\n return self.verdict == pc.VERDICT_GUILTY and self.offense_records.count() == 2 and not self.has_equivalent_offense_records()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb05c96f4426214551232da2608c3f49\",\n \"score\": \"0.47788897\",\n \"text\": \"def test_none(self):\\n df = self.df.head(0).copy()\\n for cation in [\\\"Mg\\\", \\\"Fe\\\"]:\\n with self.subTest(cation=cation):\\n aggdf = aggregate_element(df, to=cation)\\n # Check that only one form is returned\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c57d5e912254eaa591da580b0ba3801d\",\n \"score\": \"0.47734663\",\n \"text\": \"def test_list_no_match(self):\\n func = create_constrained_inputcubelist_converter(\\n \\\"airspeed_velocity_of_unladen_swallow\\\",\\n )\\n with self.assertRaisesRegex(ConstraintMismatchError, \\\"^Got 0 cubes\\\"):\\n func(self.wind_cubes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53acbfd6ddcc5196be3b266dc1b99262\",\n \"score\": \"0.47680584\",\n \"text\": \"def missing(self):\\r\\n #-----------------\\r\\n\\r\\n return set(range(1,10)) - set([cell.value for cell in self.cells])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb6b7ae3a5d1875fa0c6ddf0e3132995\",\n \"score\": \"0.4767532\",\n \"text\": \"def test_3d_Greyscale_cine_clip_all(self):\\n from deid.dicom import DicomCleaner\\n\\n dicom_file = get_file(self.dataset, \\\"GREYSCALE_CINE.zip\\\", self.tmpdir)\\n deid = os.path.join(self.deidpath, \\\"remove_coordinates_us_all.dicom\\\")\\n\\n client = DicomCleaner(output_folder=self.tmpdir, deid=deid)\\n out = client.detect(dicom_file)\\n self.assertTrue(out[\\\"flagged\\\"])\\n\\n client.clean()\\n cleanedfile = client.save_dicom()\\n\\n outputfile = read_file(cleanedfile)\\n outputpixels = outputfile.pixel_array\\n\\n inputfile = read_file(dicom_file)\\n inputpixels = inputfile.pixel_array\\n compare = inputpixels == outputpixels\\n self.assertFalse(compare.all())\\n\\n inputpixels[:, :, :] = 0\\n compare = inputpixels == outputpixels\\n self.assertTrue(compare.all())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"524b7abd1329753777149e1bd5400ab9\",\n \"score\": \"0.47633016\",\n \"text\": \"def roc_conds(data, mort, ret_aucs = False):\\n conds = []\\n aucs = []\\n for d in range(pl.shape(data)[1]):\\n col = pl.copy(data[:,d])\\n mort_nonan = pl.copy(mort)\\n if sum(pl.isnan(col)) > 0:\\n mort_nonan = pl.copy(mort[~pl.isnan(col)])\\n col = col[~pl.isnan(col)]\\n auc = roc_auc_score(mort_nonan, col)\\n if auc < 0.5:\\n conds.append(operator.lt)\\n else:\\n conds.append(operator.gt)\\n aucs.append(auc)#pl.amax([auc, 1-auc]))\\n if ret_aucs:\\n return conds, aucs\\n else:\\n return conds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"179b058186838562280f96c33b67ac45\",\n \"score\": \"0.47622558\",\n \"text\": \"def check(self):\\n errors = [err for rule in self.rules for err in rule.check(self.dictionary)]\\n errors = list(set(errors))\\n errors = self.sorting.sort(errors)\\n errors = self.filtering.filter(errors)\\n if errors:\\n raise CertumException(f\\\"\\\\n\\\\n{self.printing.print(errors)}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c003d8b531ab8478d1604907bbdf996\",\n \"score\": \"0.47564667\",\n \"text\": \"def test_incomplete_cable_stitches_1():\\n in_data = [1, 1, 4, 4, 1, 1]\\n with pytest.raises(KnitPaintCheckException) as err:\\n resolve_cable_stitches(in_data, len(in_data))\\n problems = err.value.problems\\n assert len(problems) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5faacdea569b09a42668df3a8acb9f26\",\n \"score\": \"0.47448885\",\n \"text\": \"def valid_mice(self):\\n return [m.label for m in self.mice if not m.is_ignored]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f27c6f0b751941ed2e48d39bc39256c\",\n \"score\": \"0.47410977\",\n \"text\": \"def quick_check_of_OCS_emissions(target='OCS'):\\n #\\n root = '/users/ts551/scratch/GC/rundirs/'\\n file_str = 'geosfp_4x5_tropchem.v12.2.1.AQSA.{}'\\n suffix = 'CH3I.ALL.test_other_sources.repeat.II.OCS/'\\n run_dict = {\\n # intial test runs\\n 'OCS_TEST' : root + file_str.format(suffix),\\n }\\n # use the run_dict from - obs.get_ground_surface_OCS_obs_DIRECT\\n wds = run_dict # for debugging...\\n target = 'OCS' # for testing\\n #\\n filename = 'HEMCO_diagnostics.201401010000.nc'\\n # Get a dictionary of all the data\\n dsDH = GetEmissionsFromHEMCONetCDFsAsDatasets(wds=run_dict)\\n # - Analysis the totals\\n # Extract the annual totals to a dataFrame\\n df = pd.DataFrame()\\n for run in dsDH.keys():\\n print(run)\\n # Get the values and variable names\\n vars2use = [i for i in dsDH[run].data_vars if i != 'AREA']\\n vars2use = list(sorted(vars2use))\\n vals = [ dsDH[run][i].sum().values for i in vars2use ]\\n # Save the summed values to the dataframe\\n df[run] = pd.Series( dict(zip(vars2use, vals)) )\\n# print( dsDH[run].sum() )\\n # Print the DataFrame to screen\\n print(df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e1819d93ded8d3039247376e215a669\",\n \"score\": \"0.47367904\",\n \"text\": \"def test_bulk_exclusion_does_not_leave_too_few_candidates(self):\\n\\n votes = 17 * (('A Suitable Boy', ), ) + \\\\\\n 12 * (('Farewell My Lovely', ), ) + \\\\\\n 2 * (('What I Loved', ), ) + \\\\\\n 1 * (('Gone Girl', ), )\\n\\n candidates = [\\n 'A Suitable Boy',\\n 'Farewell My Lovely',\\n 'What I Loved',\\n 'The Da Vinci Code',\\n 'Angels and Demons',\\n 'Gone Girl'\\n ]\\n vacancies = 4\\n\\n stv_round = Round(vacancies, candidates, votes)\\n bulk_exclusions = stv_round._bulk_exclusions()\\n\\n self.assertTrue(len(bulk_exclusions) == 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c534b9ff58df76ab396b9d671c404d\",\n \"score\": \"0.4735367\",\n \"text\": \"def remove_never_executed(self):\\n for cond in self.branch_conds:\\n partialbr = []\\n for branch in cond.get_branches():\\n text = branch.get_name()\\n if \\\"never executed\\\" not in text:\\n partialbr.append(branch)\\n if len(partialbr) == 0:\\n self.only_never_executed.append(cond)\\n else:\\n cond.branches = partialbr\\n self.interesting_conds.append(cond)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1611ad5988ab48b2e43bc21659909bb5\",\n \"score\": \"0.47326818\",\n \"text\": \"def drop_score_empty(): # 删去评分为空的公式，如果第一列名为空，第一列也会被删\\n empty_check_list = [u'企业总评分'.encode('utf-8')]\\n for file_n in category_finance_files:\\n print file_n\\n\\n dcu.merge_rows(file_n + '_index', file_url=corporation_index_file_url,\\n dst_file_url=corporation_index_file_url)\\n dcu.drop_rows_too_many_empty(file_n + '_index', file_url=corporation_index_file_url,\\n dst_file_url=corporation_index_file_url, columns=empty_check_list, thresh=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f64f7b8888e3d289d4e75abef923ed5e\",\n \"score\": \"0.47240153\",\n \"text\": \"def test_incomplete_cable_stitches_2():\\n in_data = [1, 4, 4, 1, 4, 4, 1]\\n with pytest.raises(KnitPaintCheckException) as err:\\n resolve_cable_stitches(in_data, len(in_data))\\n problems = err.value.problems\\n assert len(problems) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08d0bdc5e3b17bd2ab695d0d70f05b01\",\n \"score\": \"0.47177666\",\n \"text\": \"def analyze_unconditional_status(self):\\n #Initialize status to unknown for all blocks\\n for block in self.iterate_blocks(BLACK+WHITE+EMPTY):\\n block.status = \\\"unknown\\\"\\n #import pdb; pdb.set_trace()\\n self.analyze_color_unconditional_status(BLACK)\\n self.analyze_color_unconditional_status(WHITE)\\n #cleanup\\n for block in self.iterate_blocks(BLACK+WHITE+EMPTY):\\n del block.eye\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f332ab5832d3d9b8e552a7d070709c5\",\n \"score\": \"0.47138566\",\n \"text\": \"def __unused(self):\\n return [c for c in self.candidates if not self.redundant_feature(c)\\n and related(c, single=True)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbc4ae1e41bd59e5c97b6c597cabcf7d\",\n \"score\": \"0.4712267\",\n \"text\": \"def test_non_offenses_get_removed(self):\\n result = remove_non_offenses(self.sheet.fillna(\\\"None\\\"))\\n for item in result[\\\"Triggering Offense Category\\\"]:\\n self.assertTrue(\\\"None\\\" not in item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5c7dd13bd22c07c2a4307033c92eab8\",\n \"score\": \"0.4704868\",\n \"text\": \"def get_ignores(self, dt_boxes, gt_boxes):\\n if gt_boxes.size:\\n ioas = bbox_overlaps(dt_boxes, gt_boxes, mode='iof')\\n ioas = np.max(ioas, axis=1)\\n rows = np.where(ioas > self.iou_thres)[0]\\n return len(rows)\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f65aeb3ff0572d53675cba9e510a392f\",\n \"score\": \"0.46947604\",\n \"text\": \"def find_non_matching_alleles(sumstat_rec, vcf_rec):\\n alts_to_remove = []\\n for ref, alt in vcf_rec.yeild_alleles():\\n if not compatible_alleles_either_strand(sumstat_rec.other_al,\\n sumstat_rec.effect_al,\\n ref,\\n alt):\\n alts_to_remove.append(alt)\\n return alts_to_remove\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8a57d37abbe932d2ea52fbc4fcc8fd4\",\n \"score\": \"0.46943173\",\n \"text\": \"def filter_boxes(self, boxes, box_confidences, box_class_probs):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5aed6297ee749d4756f874651f124a1\",\n \"score\": \"0.4692294\",\n \"text\": \"def remove_unwanted_analytes_from_data_file(self):\\n # removes unwanted analytes\\n self.trimmed_data_frame = self.raw_xml_data_frame[self.raw_xml_data_frame['id17'].isin([1.0, 2.0, 3.0])]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b70246c6b06b4413139a5669eba2b87b\",\n \"score\": \"0.46901798\",\n \"text\": \"def whether_uncovered(self, m, n, c, Filled):\\n for i in self.covered(m, n, c):\\n if i in Filled:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23c6dca894fa0e96fa1030d4b1e70a53\",\n \"score\": \"0.46892768\",\n \"text\": \"def for_opd_encounter(self):\\n return self.filter(type__in=[Division.TYPE.CLINIC,\\n Division.TYPE.PREMIUM_CLINIC,\\n Division.TYPE.SHORTSTAY,\\n Division.TYPE.SPECIMEN_COLLECTOR])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"587c8fe6f783634f6d71b71078d4fde5\",\n \"score\": \"0.4686451\",\n \"text\": \"def testAllLosing(self):\\r\\n\\r\\n allLoseStrings = [\\\"c\\\", \\\"co\\\", \\\"coo\\\", \\\"bag\\\", \\\"bar\\\"]\\r\\n errors = [prefix for prefix in allLoseStrings if self.winning(prefix)]\\r\\n assert not errors,\\\\\\r\\n (\\\"the following are incorrectly marked as winning: \\\" +\\r\\n str(errors))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"667c6c5d4557eb48fff0ee1570036396\",\n \"score\": \"0.46835056\",\n \"text\": \"def remove_sparse(self, n=2):\\n bad=(self.total<n).any(axis=1)\\n self.filter_snps(~bad)\\n \\n print(\\\"Removed \\\" +str(sum(bad)) + \\n \\\" SNPs with <\\\" + str(n) + \\\" alleles in one population\\\", \\n file=sys.stderr)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":86,"cells":{"query_id":{"kind":"string","value":"e9edfb819f9cb3682910bd9b2ef8e39d"},"query":{"kind":"string","value":"Make directory and subdirectories."},"positive_passages":{"kind":"list like","value":[{"docid":"efb6daf34dbf00c329c8f286cb5fcfdc","score":"0.64890075","text":"def MakeDirs( self, path ) :\n \n # modules:\n import os\n \n # split in domain and remaining path:\n if ':' in path :\n domain,dpath = path.split(':',1)\n else :\n domain,dpath = 'home',path\n #endif\n # remove trailing '/' if present:\n dpath = dpath.rstrip('/')\n \n # first try to list full path, if this fails try parent, etc.\n # keep list of subdirs to be created:\n subdirs = []\n # init parents directory as full path:\n ppath = dpath\n # loop:\n while len(ppath) > 0 :\n # loop until directory can be listed or if it is not present;\n # list command:\n command = ['ecaccess-file-dir','%s:%s' % (domain,ppath)]\n # try to list, return code -1 for 'file not found':\n stdout,stderr,retcode = self._Call( command )\n # no failure if exists ...\n if retcode == 0 :\n # directory was found, no new mkdirs needed; leave:\n break\n elif retcode == -1 :\n # any subdirs left?\n if '/' in ppath :\n # split into new parent directory\n ppath,subdir = ppath.rsplit('/',1)\n # prepend in list:\n subdirs = [subdir]+subdirs\n else :\n # no subdirs left, the current ppath is a subdir to be created too:\n subdirs = [ppath]+subdirs\n # empty:\n ppath = ''\n #endif\n else :\n # some error ...\n print( stdout )\n print( stderr )\n print( 'ERROR from command: %s' % command )\n raise Exception\n #endif\n #endwhile\n # create subdirs?\n if len(subdirs) > 0 :\n # get creation mode, None if not defined:\n mode = self.GetMode( directory=True )\n # loop:\n for subdir in subdirs :\n # new path:\n if len(ppath) > 0 : ppath = ppath+'/'\n ppath = ppath+subdir\n # create:\n command = ['ecaccess-file-mkdir','%s:%s' % (domain,ppath)]\n stdout,stderr,retcode = self._Call( command )\n # error?\n if retcode != 0 :\n print( stdout )\n print( stderr )\n print( 'ERROR from command: %s' % command )\n raise Exception\n #endif\n # change mode?\n if mode is not None :\n # set mode:\n command = ['ecaccess-file-chmod',mode,'%s:%s' % (domain,ppath)]\n stdout,stderr,retcode = self._Call( command )\n # error?\n if retcode != 0 :\n print( stdout )\n print( stderr )\n print( 'ERROR from command: %s' % command )\n raise Exception\n #endif\n #endif\n #endfor # subdirs\n #endif # create subdirs","title":""}],"string":"[\n {\n \"docid\": \"efb6daf34dbf00c329c8f286cb5fcfdc\",\n \"score\": \"0.64890075\",\n \"text\": \"def MakeDirs( self, path ) :\\n \\n # modules:\\n import os\\n \\n # split in domain and remaining path:\\n if ':' in path :\\n domain,dpath = path.split(':',1)\\n else :\\n domain,dpath = 'home',path\\n #endif\\n # remove trailing '/' if present:\\n dpath = dpath.rstrip('/')\\n \\n # first try to list full path, if this fails try parent, etc.\\n # keep list of subdirs to be created:\\n subdirs = []\\n # init parents directory as full path:\\n ppath = dpath\\n # loop:\\n while len(ppath) > 0 :\\n # loop until directory can be listed or if it is not present;\\n # list command:\\n command = ['ecaccess-file-dir','%s:%s' % (domain,ppath)]\\n # try to list, return code -1 for 'file not found':\\n stdout,stderr,retcode = self._Call( command )\\n # no failure if exists ...\\n if retcode == 0 :\\n # directory was found, no new mkdirs needed; leave:\\n break\\n elif retcode == -1 :\\n # any subdirs left?\\n if '/' in ppath :\\n # split into new parent directory\\n ppath,subdir = ppath.rsplit('/',1)\\n # prepend in list:\\n subdirs = [subdir]+subdirs\\n else :\\n # no subdirs left, the current ppath is a subdir to be created too:\\n subdirs = [ppath]+subdirs\\n # empty:\\n ppath = ''\\n #endif\\n else :\\n # some error ...\\n print( stdout )\\n print( stderr )\\n print( 'ERROR from command: %s' % command )\\n raise Exception\\n #endif\\n #endwhile\\n # create subdirs?\\n if len(subdirs) > 0 :\\n # get creation mode, None if not defined:\\n mode = self.GetMode( directory=True )\\n # loop:\\n for subdir in subdirs :\\n # new path:\\n if len(ppath) > 0 : ppath = ppath+'/'\\n ppath = ppath+subdir\\n # create:\\n command = ['ecaccess-file-mkdir','%s:%s' % (domain,ppath)]\\n stdout,stderr,retcode = self._Call( command )\\n # error?\\n if retcode != 0 :\\n print( stdout )\\n print( stderr )\\n print( 'ERROR from command: %s' % command )\\n raise Exception\\n #endif\\n # change mode?\\n if mode is not None :\\n # set mode:\\n command = ['ecaccess-file-chmod',mode,'%s:%s' % (domain,ppath)]\\n stdout,stderr,retcode = self._Call( command )\\n # error?\\n if retcode != 0 :\\n print( stdout )\\n print( stderr )\\n print( 'ERROR from command: %s' % command )\\n raise Exception\\n #endif\\n #endif\\n #endfor # subdirs\\n #endif # create subdirs\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ce93fd6c8b470bab7977b1f05bf747f8","score":"0.7336835","text":"def mkdirs():\n dirs = [\n PYTHON_DIR,\n BUILDS_DIR,\n SHARED_DIR,\n RUN_DIR,\n STATIC_DIR,\n LOG_DIR,\n MEDIA_DIR,\n ]\n for dir in dirs:\n _run_web(\"mkdir -p %s\" % dir)\n _run_web(\"touch %s\" % CURRENT_DIR)","title":""},{"docid":"542fce1e614cde6b60dd7f8745efacea","score":"0.71491116","text":"def makeDir(self, path,*args):\n if not os.path.exists(path):\n pm.sysFile(path, makeDir=True)\n #os.makedirs(path)","title":""},{"docid":"c0ca4b8d9f0d8f9a12e6fb356fa876ee","score":"0.70842254","text":"def mk_all_dirs(dirname):\n original_dir=os.getcwd()\n subdirs=dirname.split(\"/\")\n for curdir in subdirs:\n if os.path.isfile(curdir):\n os.rename(curdir,os.tempnam(\"./\",curdir+\"_\"))\n if not os.path.isdir(curdir):\n try:\n os.mkdir(curdir)\n except Exception as e:\n print(e)\n os.chdir(curdir)\n\n os.chdir(original_dir)","title":""},{"docid":"79ea8c357728d501d6da80503ffd22d6","score":"0.70482004","text":"def makeDirs(*args):\n for arg in args:\n path = os.path.dirname(arg['target'] if isinstance(arg, flow.Node) else arg)\n if path:\n try:\n os.makedirs(path)\n except EnvironmentError, e:\n if e.errno != errno.EEXIST:\n raise","title":""},{"docid":"d588a389e460c3c9c8f778ea4b51b65b","score":"0.70305526","text":"def _MakeDirs(*args, **kwargs):\n try:\n os.makedirs(*args, **kwargs)\n except OSError:\n pass","title":""},{"docid":"58d1446ddc0395ebcbc48dca23cd8812","score":"0.7022971","text":"def create_dir_tree():\n dirs = paths.get_dirs()\n for d in dirs.values():\n if not os.path.isdir(d):\n print('creating {}'.format(d))\n os.makedirs(d)","title":""},{"docid":"2d390ea27b861aa1da9f0a8a3ab6f19c","score":"0.6991802","text":"def MakeDirs( self, path ) :\n \n # modules:\n import os\n\n # create if necessary:\n if not os.path.isdir(path) :\n # info ..\n self.info( 'create directory: %s' % path )\n # get creation mode:\n mode = self.GetMode( directory=True )\n # with mode?\n if mode is not None :\n # create recursively, convert mode from octal to integer:\n os.makedirs( path, mode=eval('0o'+mode) )\n else :\n # create:\n os.makedirs( path )\n #endif\n #endif","title":""},{"docid":"45046fe26186f8a44326f16e0bb63b68","score":"0.69616294","text":"def make_directory(dir_list):\n for _dir in dir_list:\n if not os.path.exists(_dir):\n os.makedirs(_dir)\n return","title":""},{"docid":"786faaa4d6a71117545ebdd6286d6565","score":"0.69375515","text":"def mkdirs(*args):\n for p in args:\n if not os.path.isdir(p):\n os.mkdir(p)","title":""},{"docid":"830c82f04eafe0dc39878546a07811fe","score":"0.6930362","text":"def make_directories(self, path: str):\n current_dir = \"\"\n\n for directory in os.path.split(path)[0].split(os.sep):\n next_dir = os.path.join(current_dir, directory)\n if not self.directory_exists(current_dir, directory):\n self.ftp.mkd(next_dir)\n current_dir = next_dir","title":""},{"docid":"d27c1ed355184281d7912893dfb499f9","score":"0.6889773","text":"def createAllDirs():\n createDirIfNeeded(DIR_OUTPUT_BLOCKSTATES)\n createDirIfNeeded(DIR_OUTPUT_BLOCKS)\n createDirIfNeeded(DIR_OUTPUT_ITEMS)","title":""},{"docid":"458197556cb42509ccf36f2224dd6be0","score":"0.6878645","text":"def make_directories(self) -> types.TaskDict:\n\n def mkdirs(targets: Sequence[str]) -> None:\n for directory in targets:\n Path(directory).mkdir(exist_ok=True)\n\n task = self.basic_task\n task.update(\n {\n \"name\": \"{}/{}\".format(self._root, MAKE_TASK_NAME),\n \"doc\": \"Create directory hierarchy for {}.\".format(self._root),\n \"title\": utils.title_with_target1(\"MKTREE\"),\n \"actions\": [mkdirs],\n \"targets\": self.directories,\n \"uptodate\": [True],\n }\n )\n return task","title":""},{"docid":"117716f9f6008b4c9fd9afffe44d11a1","score":"0.6869847","text":"def create_dirs(f):\n make_dir(OUTPUT_DIR)\n make_dir_rec(OUTPUT_DIR + \"/ckpt/\")\n make_dir_rec(OUTPUT_DIR + \"/performance/\")","title":""},{"docid":"f443860cd378406f87a7ba14bf96293a","score":"0.68128544","text":"def MakeDir(dst):\n print 'mkdir -p ' + dst\n oshelpers.Mkdir(['-p', dst])","title":""},{"docid":"cc440fbfa828238f949e02fa27d046a1","score":"0.6782039","text":"def make_dirs(dirs):\n\tfor d in dirs:\n\t\tos.makedirs(d, exist_ok=True)","title":""},{"docid":"3dcd7e481eb0f9d6aa91a40cbe09b43a","score":"0.6735967","text":"def make_dir(d):\n if not os.path.exists(d):\n os.makedirs(d)","title":""},{"docid":"0e3903bca5e441d1aa69c6b41121d423","score":"0.6730184","text":"def make_sub_dirs(model_type, ver, main_dir):\n sub_dir_1 = main_dir + model_type + '/'\n sub_dir_2 = sub_dir_1 + ver + '/'\n\n make_directory(main_dir)\n make_directory(sub_dir_1)\n make_directory(sub_dir_2)\n\n return sub_dir_2","title":""},{"docid":"8a25ee436bcd0d47e4348003b8607cb4","score":"0.6705954","text":"def make_dir(path):\n pathlib.Path(path).mkdir(parents=True, exist_ok=True)","title":""},{"docid":"bc5faacd8dba93e8a736e0934eaee9db","score":"0.6704804","text":"def make_dirs(*dirs):\n for dir in dirs:\n if not path.isdir(dir):\n makedirs(dir)","title":""},{"docid":"d40cddb37ff95ee4ec77574702a2fb70","score":"0.67003304","text":"def recursive_directories_create(project_directory, structure, dry_run=False):\n logger = logging.getLogger('optimus')\n \n for item in structure:\n if len(item)>0:\n new_dir = item[0]\n path_dir = os.path.join(project_directory, new_dir)\n if not os.path.exists(path_dir):\n logger.info('* Creating new directory : %s', path_dir)\n if not dry_run:\n os.makedirs(path_dir)\n else:\n logger.warning('* Following path allready exist : %s', path_dir)\n # Follow children directories to create them\n if len(item)>1:\n recursive_directories_create(path_dir, item[1], dry_run=dry_run)\n \n return","title":""},{"docid":"e640d778ad39d38a50f4a469e5e5d9d9","score":"0.6659188","text":"def MakeDirs( path, **kwargs ) :\n \n # obtain GSS ojbect and remaining path:\n gss,rpath = GSS( path, **kwargs )\n\n # create:\n gss.MakeDirs( rpath )","title":""},{"docid":"e7b9268af5d87d5d1d5e780dbc01a411","score":"0.6656963","text":"def make_dirs(path):\n if not os.path.isdir(path):\n os.makedirs(path, mode=0o777)","title":""},{"docid":"612e60776c5bbda74e8c225fc3181c25","score":"0.6554929","text":"def create_directory(path, new_directory):\n for i in range(43):\n\n directory = path + '\\\\' + digit_number(i) + str(i)\n\n for root, dirs, files in os.walk(directory):\n for filename in files:\n copyfile(directory + '\\\\' + filename, new_directory + '\\\\' + str(i) + \"_\" + filename)","title":""},{"docid":"6b10190914ae9538a4c3c6433ca106a3","score":"0.65497","text":"def mkdirs(path):\n if not os.path.isdir(path):\n os.makedirs(path)","title":""},{"docid":"5fa6bb333c56d3ad5adc2c38709b490d","score":"0.65257365","text":"def make_dir_structure(base_dir):\n\n def maybe_makedir(*args):\n\n p = join(base_dir, *args)\n\n if exists(p) and not isdir(p):\n raise IOError(\"File '{}' exists but is not a directory \".format(p))\n\n if not exists(p):\n makedirs(p)\n\n maybe_makedir(DOWNLOAD_DIR)\n maybe_makedir(PACKAGE_DIR)\n maybe_makedir(OLD_DIR)","title":""},{"docid":"1fe5915c27dc576ba391459bb1b3f26a","score":"0.65208316","text":"def folder_maker(path):\n if not os.path.exists(path):\n os.mkdir(path)","title":""},{"docid":"87968f892ef4054ebfabdfbca3b63502","score":"0.6519899","text":"def make_directory(dir_path, mode=0o777):\n if not os.path.exists(dir_path):\n os.makedirs(dir_path, mode=mode)","title":""},{"docid":"2104c9b7aab28f39d76b00a5d45256be","score":"0.65141076","text":"def make_dir_rec(path):\n if not os.path.isdir(path):\n os.makedirs(path, exist_ok=True)","title":""},{"docid":"77af259a6ed67947338e86d591f08a45","score":"0.65131664","text":"def make_dir (dir):\n if not os.path.exists (dir):\n os.mkdir (dir)","title":""},{"docid":"8ddf6c00f9bf1e9ff385e0a712c151dc","score":"0.6507649","text":"def mkdirs(newdir):\n printDBG('mkdirs: \"%s\"' % newdir)\n if os.path.isdir(newdir):\n pass\n elif os.path.isfile(newdir):\n raise OSError(\"cannot create directory, file already exists: '%s'\" % newdir)\n else:\n head, tail = os.path.split(newdir)\n if head and not os.path.isdir(head) and not os.path.ismount(head) and not os.path.islink(head):\n mkdirs(head)\n if tail:\n os.mkdir(newdir)","title":""},{"docid":"95b801b238c3613a58f6e25c97e5b9d1","score":"0.64925635","text":"def mkdir(self,rmdir=False):\n import os\n if rmdir:\n os.rmdir(str(self))\n cur=Path('./')\n for intdir in self.split():\n cur+=Path(intdir)\n if not os.path.isdir(cur):\n os.mkdir(cur)","title":""},{"docid":"d9ec0c2b73c2281a8cd40a92c41b0735","score":"0.648629","text":"def make_dir(fpath) -> None:\n pathlib.Path(fpath).mkdir(parents=True, exist_ok=True)","title":""},{"docid":"d87b17eb611b9a509ec401d082e055af","score":"0.647003","text":"def make_dir(path):\n if not os.path.exists(path):\n os.makedirs(path)","title":""},{"docid":"d87b17eb611b9a509ec401d082e055af","score":"0.647003","text":"def make_dir(path):\n if not os.path.exists(path):\n os.makedirs(path)","title":""},{"docid":"0af557cc3ff3661a85fc4e988ee7d705","score":"0.6455032","text":"def make_dir(dir_path):\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)","title":""},{"docid":"01d601164078d66e2ffa3c51aad36be3","score":"0.64532316","text":"def _make_directory(self, path, do_raise=False):\n try:\n os.makedirs(path)\n except OSError:\n if do_raise: raise\n else:\n os.chown(path, self.owner.pw_uid, self.owner.pw_gid)\n os.chmod(path, 0775)","title":""},{"docid":"4ae22980416872bd8fce06ff3dcc8dee","score":"0.6449504","text":"def make_dir(path):\n try:\n os.mkdir(path)\n except OSError:\n pass","title":""},{"docid":"4ae22980416872bd8fce06ff3dcc8dee","score":"0.6449504","text":"def make_dir(path):\n try:\n os.mkdir(path)\n except OSError:\n pass","title":""},{"docid":"4ae22980416872bd8fce06ff3dcc8dee","score":"0.6449504","text":"def make_dir(path):\n try:\n os.mkdir(path)\n except OSError:\n pass","title":""},{"docid":"7865ebc96ef41e1835fdb2eb847585d0","score":"0.6440436","text":"def makeDirectory(DIR):\n \n if not os.path.exists(DIR):\n os.makedirs(DIR)\n print \">>> made directory \" + DIR\n \n\n\n\n\n ##############\n # Categories #\n ##############","title":""},{"docid":"272dcba0213acdbd54529d588eb8d745","score":"0.6428357","text":"def make_directories(path):\n try:\n os.makedirs(os.path.dirname(path))\n except OSError, e:\n if e.errno != errno.EEXIST: # don't complain if already exists\n raise","title":""},{"docid":"a5ba4fbe3aa5b2ada08e66228d6a4a02","score":"0.6424633","text":"def make_dirs_if_needed(*dirs) :\n\tfor dir in dirs :\n\t\tif not exists(dir) :\n\t\t\tmakedirs(dir)","title":""},{"docid":"61162d699e776d88bb8da21674860458","score":"0.6400825","text":"def replicate_directory_tree(input_dir, output_dir):\n def transplant_dir(target, dirname):\n x = dirname.replace(input_dir, target)\n if not os.path.exists(x):\n LOGGER.info('Creating: {}'.format(x))\n os.makedirs(x)\n\n dir_visitor(\n input_dir,\n functools.partial(transplant_dir, output_dir)\n )","title":""},{"docid":"ad5ddf63aa8169d732db5815ad657277","score":"0.63999045","text":"def make_dir(dir_name):\n try:\n os.makedirs(dir_name)\n except OSError:\n pass","title":""},{"docid":"ecfe1510e749ff34acd0faad2494225e","score":"0.637565","text":"def make_dir(path):\n try:\n os.makedirs(path)\n except OSError:\n if not os.path.isdir(path):\n raise","title":""},{"docid":"22a08bd2fb487d5d46b8cc9cfff9247b","score":"0.63677007","text":"def make_directory(path):\n os.makedirs(path, exist_ok=True)","title":""},{"docid":"420e190b9c95c8c605ea8d09c34f349a","score":"0.6364349","text":"def makeDirectory(DIR):\n if not os.path.exists(DIR):\n os.makedirs(DIR)\n #print \">>> made directory \" + DIR","title":""},{"docid":"822edd48b61ff8317464ac3be0c8ecde","score":"0.63573384","text":"def directoryCreate (\n \n self,\n path = None\n ) :\n\n return self.makeDirectory( path )","title":""},{"docid":"2cc8708b739cdbfb958203e8a43a03d9","score":"0.63418084","text":"def create_dir(self):\n dir1 = os.path.dirname(self.dst + \"/\")\n if not os.path.exists(dir1):\n print \"Making directory: \" + dir1\n os.makedirs(dir1)","title":""},{"docid":"f3c3552b52cb80543c91e58a31d7c360","score":"0.63380724","text":"def mkdir(self, parents: bool = False, exist_ok: bool = False):\n pass","title":""},{"docid":"99cabd05fa6b79e637804fdd45810747","score":"0.63192296","text":"def set_up_directory_simple(rootdir, classname):\n dir_path = os.path.join(rootdir, classname)\n make_directory(dir_path)\n return dir_path","title":""},{"docid":"810e3e2fff199bd58a803549731af5e3","score":"0.63168776","text":"def mkdir(d):\n if not test(\"-d %s\" % d):\n run(\"mkdir -p %s\" % d)","title":""},{"docid":"751699902181c6e16a45feb86b887fd0","score":"0.63147205","text":"def __create_dir(dir):\n if not dir.exists():\n dir.mkdir(parents=True)","title":""},{"docid":"e338187034b2526155ffdeaae411e8d9","score":"0.6303725","text":"def dir_create(dir_path):\r\n if not os.path.isdir(dir_path):\r\n os.makedirs(dir_path)","title":""},{"docid":"6aac36f54f602af9748bcadb8931eda6","score":"0.626529","text":"def make_dir(dirname, verbose=True):\n absdir = fix_path(dirname)\n if not os.path.isdir(absdir):\n os.makedirs(absdir)\n if verbose:\n print >>sys.stderr, \"Created directory: %s\" % dirname","title":""},{"docid":"d11fd45a96d5ba5601d3ba52c15353d2","score":"0.6244863","text":"def make_assembly_subdir(self):\n if self.assembly_subdir_needed:\n if self.assembly_subdir is not None:\n if not os.path.isdir(self.assembly_subdir):\n os.makedirs(self.assembly_subdir)\n else:\n pass","title":""},{"docid":"54f3207fdb84f7df7a91dd7f535b1b57","score":"0.62383574","text":"def createDir(path):\n if not os.path.isdir(path):\n os.makedirs(path)","title":""},{"docid":"fbbe786214f5f9fcfe729508a2907c35","score":"0.62380534","text":"def directory_setup(path):\n for i in count(1):\n new_path = path + str(i)\n try:\n makedirs(new_path)\n break\n except:\n pass\n return new_path","title":""},{"docid":"28233374daa1caf4ed54300ba5f827d0","score":"0.6236168","text":"def makeDirs(dir, absMode = None, reuseExisting = False):\n\n # Don't remake dir if already exists\n if (reuseExisting):\n if os.path.isdir(dir):\n return\n\n # calc path depth\n depth = 0\n (head, tail) = os.path.split(dir)\n while head:\n if tail:\n depth += 1 \n if head == '/':\n break\n (head, tail) = os.path.split(head)\n \n retries = 0\n # Set umask to zero, to enable the creation of 777 dirs\n oldUmask = os.umask(0) \n while retries <= depth:\n retries += 1\n try:\n # Create the dir\n if (absMode is not None):\n os.makedirs(dir, absMode)\n else:\n os.makedirs(dir)\n except:\n # maybe the directory was created by another thread\n if os.path.isdir(dir):\n break\n else:\n if retries == depth:\n os.umask(oldUmask)\n raise\n\n os.umask(oldUmask)","title":""},{"docid":"291f67063b72608be2acefa0a69c04f4","score":"0.6226757","text":"def mkdirs(newdir, mode=None):\n if mode is None:\n mode = 0o750\n if os.path.isdir(newdir):\n pass\n elif os.path.isfile(newdir):\n raise OSError(\"a file with the same name as the desired \"\n \"dir, '%s', already exists.\" % newdir)\n else:\n os.makedirs(newdir, mode)","title":""},{"docid":"cf39f8535de9c2f1be6e7d7f586861ee","score":"0.6210859","text":"def mkdirs(path):\n os.makedirs(path, exist_ok=True)","title":""},{"docid":"36aee08538fdeb51f51c3bb91d010438","score":"0.62106806","text":"def make_dir(path, name):\n\n dir = os.path.join(path, name)\n check_dir = os.path.isdir(dir)\n\n if not check_dir:\n os.makedirs(dir)\n print(f'New directory, {name}, made in {path}.')\n else:\n print(f'{name} directory already exists in {path}.')\n return dir","title":""},{"docid":"400b403da03372443bf5f8d9570efc4a","score":"0.6205585","text":"def setup_work_directory(self):\n for subdirectory in self.WORK_DIRECTORIES:\n Path(self.work_directory, subdirectory).mkdir(parents=True, exist_ok=True)","title":""},{"docid":"383d0d06f29c51cfbd59eff1b179fc88","score":"0.62014335","text":"def create_dirs(path):\n if path and not os.path.isdir(path):\n os.makedirs(path)","title":""},{"docid":"dc1b60059e5e167505cf5fa7c7ae0f09","score":"0.6201415","text":"def mkdirs(filename: typing.Union[os.PathLike, str], /, *, mode: int = 0o777) -> None:\n dirname = os.path.dirname(filename)\n if not dirname:\n return\n log.debug('os.makedirs(%r)', dirname)\n os.makedirs(dirname, mode=mode, exist_ok=True)","title":""},{"docid":"5d82ac0e019dd658274ee7bdbc7da138","score":"0.6195935","text":"def mkdirs(self, *directories: str) -> None:\n for directory in list(directories):\n try:\n os.makedirs(directory)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise","title":""},{"docid":"a73e4dd67c0449a80f960ace25cbe4a3","score":"0.61948305","text":"def mkdir(self, folder: str):","title":""},{"docid":"6786034af029e3c41b8c46efca3d4abc","score":"0.6191701","text":"def recursive_makedir(path: str, is_file=False):\n try:\n target = path if not is_file else os_file_separator.join(path.split(os_file_separator)[:-1])\n makedirs(target)\n except FileExistsError as ex:\n assert ex.errno == errno.EEXIST","title":""},{"docid":"a3c0462ec6e07bd2b077c04f850d7d98","score":"0.6188425","text":"def mkdirs(path):\r\n if not os.path.isdir(path):\r\n try:\r\n os.makedirs(path)\r\n except OSError as err:\r\n if err.errno != errno.EEXIST or not os.path.isdir(path):\r\n raise","title":""},{"docid":"1f0ba42992865d9738bafafcbffb7b4e","score":"0.6187536","text":"def folder_creator(path):\n if not os.path.exists(path):\n os.makedirs(path)","title":""},{"docid":"31d20e0de407373a8fb829f507fdc4ce","score":"0.6184182","text":"def setUpDirs():\n if not os.path.isdir(\"videos\"):\n os.mkdir(\"videos\")\n\n if not os.path.isdir(\"thumbnails\"):\n os.mkdir(\"thumbnails\")\n\n if not os.path.isdir(\"music\"):\n os.mkdir(\"music\")\n\n if not os.path.isdir(\"music/wav\"):\n os.mkdir(Path(\"music/wav\"))\n\n if not os.path.isdir(\"music/mp3\"):\n os.mkdir(Path(\"music/mp3\"))\n\n if not os.path.isdir(\"faces\"):\n os.mkdir(Path(\"faces\"))\n\n if not os.path.isdir(\"temp\"):\n os.mkdir(Path(\"temp\"))","title":""},{"docid":"cc3bb9e5a51fb57c169610ad532eabb6","score":"0.6183504","text":"def create_directories(get_torrents, get_magnets, dest_dir):\n if get_torrents:\n folder = os.path.join(dest_dir, \"torrents\")\n file_io.create_directory(folder)\n\n if get_magnets:\n folder = os.path.join(dest_dir, \"magnets\")\n file_io.create_directory(folder)","title":""},{"docid":"8f2dec531d09c23b9833bb6ca19f500a","score":"0.6180252","text":"def create_dir(path):\n os.makedirs(path)","title":""},{"docid":"a8018f37c0776cdb2b2d356fd183cd12","score":"0.61692864","text":"def _makedir(self, system):\n dirname = self._filenames(system)[\"dirname\"]\n if not os.path.exists(dirname):\n os.makedirs(dirname)","title":""},{"docid":"e4f474922a5bd9770605224c45a9ec88","score":"0.6165306","text":"def directory_creator(self, path):\n if not os.path.exists(path):\n os.makedirs(path)","title":""},{"docid":"c011d60610ab3fc192ade74fbf84ebb3","score":"0.61579937","text":"def MakeDirectoryIfAbsent(path):\n if not os.path.isdir(path):\n os.makedirs(path)","title":""},{"docid":"1da648de619005d516f09730453223f2","score":"0.61553186","text":"def create_dirs(target_dirs):\n for dir_path in target_dirs:\n if not os.path.isdir(dir_path):\n os.makedirs(dir_path)","title":""},{"docid":"6e5fd68f978c2abff32699c84cf807cc","score":"0.61537683","text":"def setup_dirs(conf, root):\n conf.vardir = root / \"var/lib/fastpath\"\n conf.cachedir = conf.vardir / \"cache\"\n conf.s3cachedir = conf.cachedir / \"s3\"\n conf.dfdir = conf.vardir / \"dataframes\"\n conf.outdir = conf.vardir / \"output\"\n conf.msmtdir = conf.outdir / \"measurements\"\n for p in (\n conf.vardir,\n conf.cachedir,\n conf.s3cachedir,\n conf.dfdir,\n conf.outdir,\n conf.msmtdir,\n ):\n p.mkdir(parents=True, exist_ok=True)","title":""},{"docid":"bb24a4158950a54dc14f547d8a2fc8c5","score":"0.61509436","text":"def mkdir(path):\n\tif not os.path.isdir(path):\n\t\tos.makedirs(path)","title":""},{"docid":"82b1460d019655d95bc8844555d4608e","score":"0.61493057","text":"def make_dir(base_dir, name):\n if path.exists(base_dir) and path.isdir(base_dir):\n directory = path.join(base_dir, name)\n if path.exists(directory) and path.isdir(directory):\n #raise RuntimeError(\"Directory already exists: {}\".format(directory))\n return directory\n else:\n makedirs(directory)\n return directory\n else:\n raise RuntimeError(\"Directory does not exist: {}\".format(base_dir))","title":""},{"docid":"7742470e80ebe4a70fe0a8468b9b64b4","score":"0.61452585","text":"def create_dirs(idiomas):\n directorios=os.listdir(\"./\")\n if (\"graphics\" not in directorios):\n os.makedirs(\"./graphics\")\n else:\n dir_lang=os.listdir(\"./graphics/\")\n for idioma in idiomas:\n if idioma not in dir_lang:\n os.makedirs(\"./graphics/\"+idioma+\"/data\")\n if(\"data\" not in directorios):\n os.makedirs(\"./data\")","title":""},{"docid":"1af956ea1f8afc72313e94093e37ae0e","score":"0.613982","text":"def mkdir(path):\n if not exists(path):\n makedirs(path)","title":""},{"docid":"2bca5e99be6d6710f3b84824884a19a7","score":"0.61383635","text":"def _make_folders(self, folders: List[str]):\n self._make_initial_folders()\n for folder in folders:\n self._mkdir(folder)","title":""},{"docid":"abc571b0a16121be0f37301c68836ee6","score":"0.61260813","text":"def new_dirs():\n for target in FIELDS:\n if not os.path.exists(target):\n print(':: Create folder')\n os.makedirs(target)","title":""},{"docid":"9b1e067a1793470df1cf279baae8de82","score":"0.6119317","text":"def mkdirs(paths):\n if isinstance(paths, list) and not isinstance(paths, str):\n for path in paths:\n mkdir(path)\n else:\n mkdir(paths)","title":""},{"docid":"9b1e067a1793470df1cf279baae8de82","score":"0.6119317","text":"def mkdirs(paths):\n if isinstance(paths, list) and not isinstance(paths, str):\n for path in paths:\n mkdir(path)\n else:\n mkdir(paths)","title":""},{"docid":"b4df1e8db9535b588dce22e1a37727c6","score":"0.6117014","text":"def CreacionDirectoriosProyecto(Root):\n os.makedirs(os.path.join(Root, 'input'), exist_ok=True)\n os.makedirs(os.path.join(Root, 'input\\processed'), exist_ok=True)\n os.makedirs(os.path.join(Root, 'library'), exist_ok=True)\n os.makedirs(os.path.join(Root, 'output'), exist_ok=True)","title":""},{"docid":"82dc71342a6ff2779c3dde128684aa10","score":"0.6111609","text":"def create_dir(self, relpath):\n return self.get_dir(relpath).create()","title":""},{"docid":"80569fcdbdd5bc308de052972d1b5e7f","score":"0.61066616","text":"def make_dir(datapath, mypath):\n if not os.path.exists(datapath + os.sep + mypath):\n os.makedirs(datapath + os.sep + mypath)\n pass","title":""},{"docid":"e525cb19975ee6e8c790e0219c8e6dc0","score":"0.6106294","text":"def make_directory(path):\n if not os.path.exists(path):\n os.mkdir(path)\n return None","title":""},{"docid":"6a69620517d73a8483ad6af42edfda97","score":"0.6105435","text":"def mktree(path):\n if not os.path.isdir(path):\n debug(\"mktree {}\", path)\n os.makedirs(path)","title":""},{"docid":"98fbe08fe2a147d3df527b523999b3a2","score":"0.61048704","text":"def mkdircond(directory):\n if not os.path.isdir(directory):\n os.mkdir(directory)","title":""},{"docid":"24e5c6e65e347a03323b508787f8672d","score":"0.6104711","text":"def do_mkdir(self, path):\n self.dropbox.file_create_folder(self.current_path + \"/\" + path)","title":""},{"docid":"e93025724e397343b4a1195d282cb56e","score":"0.61009216","text":"def create_dir(path):\n if (os.path.exists(path)) and (os.listdir(path) != []):\n shutil.rmtree(path)\n os.makedirs(path)\n if not os.path.exists(path):\n os.makedirs(path)","title":""},{"docid":"30966f589e31ee707b856994a87b8f7f","score":"0.60996324","text":"def mkdirs(self, dir):\n\n dirPath = self.jvm.org.apache.hadoop.fs.Path(dir)\n self.logger.info(\"Making directory %s\" % dir)\n return self.hadoop_fs.mkdirs(dirPath)","title":""},{"docid":"a639f519d3879d1e31500ad5bfa5ccab","score":"0.60952425","text":"def setup_dirs():\n pathlib.Path(RESULTS_DIR).mkdir(parents=True, exist_ok=True)\n pathlib.Path(DOWNLOADS_DIR).mkdir(parents=True, exist_ok=True)","title":""},{"docid":"526dd079101b01c55668cc00edb78412","score":"0.6093531","text":"def mkdir(new_dir, auth, parents=False):\n dirname = os.path.basename(new_dir)\n headers = dict(auth)\n headers['Content-Type'] = 'application/json'\n data = json.dumps({\n \"name\": dirname,\n \"folder\": {}\n })\n\n # make dir. This somehow only works with the ID. So get the ID before.\n parent = os.path.dirname(new_dir)\n parent_meta = get_metadata(file=parent, auth=auth)\n\n if parent_meta.status_code == 404 and parents: # parent does not exist but should be created!\n mkdir(parent, auth, True) # recurse into parents\n parent_meta = get_metadata(file=parent, auth=auth)\n\n parent_id = dict(parent_meta.json_body()).get('id', '00000000')\n res = requests.post(base_url + \"/drive/items/\" + parent_id + \"/children\", headers=headers, data=data)\n return Result(res)","title":""},{"docid":"1a3bf831319a44f8647d448b15afb58b","score":"0.6092909","text":"def make_parent_dir(path):\n parent_dir = get_dir(path)\n mkdir(parent_dir)","title":""},{"docid":"bcb22c79da211755c5ece8c6ed925e34","score":"0.60842854","text":"def main():\n for root, _dirs, files in os.walk(DIR):\n for file in files:\n try:\n in_file = f\"{root}/{file}\"\n out_dir = os.path.join(os.pardir, root)\n pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)\n generate(in_file, f\"{out_dir}/{file}\")\n except ValueError:\n pass","title":""},{"docid":"0684a8519b7897800a61615a3261223d","score":"0.60746723","text":"def createFolders(self):\n if not os.path.exists(self.imgFolder):\n os.makedirs(self.imgFolder)\n if not os.path.exists(self.xformFolder):\n os.makedirs(self.xformFolder)\n if not os.path.exists(self.txtFolder):\n os.makedirs(self.txtFolder)\n if not os.path.exists(self.gifFolder):\n os.makedirs(self.gifFolder)","title":""}],"string":"[\n {\n \"docid\": \"ce93fd6c8b470bab7977b1f05bf747f8\",\n \"score\": \"0.7336835\",\n \"text\": \"def mkdirs():\\n dirs = [\\n PYTHON_DIR,\\n BUILDS_DIR,\\n SHARED_DIR,\\n RUN_DIR,\\n STATIC_DIR,\\n LOG_DIR,\\n MEDIA_DIR,\\n ]\\n for dir in dirs:\\n _run_web(\\\"mkdir -p %s\\\" % dir)\\n _run_web(\\\"touch %s\\\" % CURRENT_DIR)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"542fce1e614cde6b60dd7f8745efacea\",\n \"score\": \"0.71491116\",\n \"text\": \"def makeDir(self, path,*args):\\n if not os.path.exists(path):\\n pm.sysFile(path, makeDir=True)\\n #os.makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0ca4b8d9f0d8f9a12e6fb356fa876ee\",\n \"score\": \"0.70842254\",\n \"text\": \"def mk_all_dirs(dirname):\\n original_dir=os.getcwd()\\n subdirs=dirname.split(\\\"/\\\")\\n for curdir in subdirs:\\n if os.path.isfile(curdir):\\n os.rename(curdir,os.tempnam(\\\"./\\\",curdir+\\\"_\\\"))\\n if not os.path.isdir(curdir):\\n try:\\n os.mkdir(curdir)\\n except Exception as e:\\n print(e)\\n os.chdir(curdir)\\n\\n os.chdir(original_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79ea8c357728d501d6da80503ffd22d6\",\n \"score\": \"0.70482004\",\n \"text\": \"def makeDirs(*args):\\n for arg in args:\\n path = os.path.dirname(arg['target'] if isinstance(arg, flow.Node) else arg)\\n if path:\\n try:\\n os.makedirs(path)\\n except EnvironmentError, e:\\n if e.errno != errno.EEXIST:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d588a389e460c3c9c8f778ea4b51b65b\",\n \"score\": \"0.70305526\",\n \"text\": \"def _MakeDirs(*args, **kwargs):\\n try:\\n os.makedirs(*args, **kwargs)\\n except OSError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58d1446ddc0395ebcbc48dca23cd8812\",\n \"score\": \"0.7022971\",\n \"text\": \"def create_dir_tree():\\n dirs = paths.get_dirs()\\n for d in dirs.values():\\n if not os.path.isdir(d):\\n print('creating {}'.format(d))\\n os.makedirs(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d390ea27b861aa1da9f0a8a3ab6f19c\",\n \"score\": \"0.6991802\",\n \"text\": \"def MakeDirs( self, path ) :\\n \\n # modules:\\n import os\\n\\n # create if necessary:\\n if not os.path.isdir(path) :\\n # info ..\\n self.info( 'create directory: %s' % path )\\n # get creation mode:\\n mode = self.GetMode( directory=True )\\n # with mode?\\n if mode is not None :\\n # create recursively, convert mode from octal to integer:\\n os.makedirs( path, mode=eval('0o'+mode) )\\n else :\\n # create:\\n os.makedirs( path )\\n #endif\\n #endif\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45046fe26186f8a44326f16e0bb63b68\",\n \"score\": \"0.69616294\",\n \"text\": \"def make_directory(dir_list):\\n for _dir in dir_list:\\n if not os.path.exists(_dir):\\n os.makedirs(_dir)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"786faaa4d6a71117545ebdd6286d6565\",\n \"score\": \"0.69375515\",\n \"text\": \"def mkdirs(*args):\\n for p in args:\\n if not os.path.isdir(p):\\n os.mkdir(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"830c82f04eafe0dc39878546a07811fe\",\n \"score\": \"0.6930362\",\n \"text\": \"def make_directories(self, path: str):\\n current_dir = \\\"\\\"\\n\\n for directory in os.path.split(path)[0].split(os.sep):\\n next_dir = os.path.join(current_dir, directory)\\n if not self.directory_exists(current_dir, directory):\\n self.ftp.mkd(next_dir)\\n current_dir = next_dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d27c1ed355184281d7912893dfb499f9\",\n \"score\": \"0.6889773\",\n \"text\": \"def createAllDirs():\\n createDirIfNeeded(DIR_OUTPUT_BLOCKSTATES)\\n createDirIfNeeded(DIR_OUTPUT_BLOCKS)\\n createDirIfNeeded(DIR_OUTPUT_ITEMS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"458197556cb42509ccf36f2224dd6be0\",\n \"score\": \"0.6878645\",\n \"text\": \"def make_directories(self) -> types.TaskDict:\\n\\n def mkdirs(targets: Sequence[str]) -> None:\\n for directory in targets:\\n Path(directory).mkdir(exist_ok=True)\\n\\n task = self.basic_task\\n task.update(\\n {\\n \\\"name\\\": \\\"{}/{}\\\".format(self._root, MAKE_TASK_NAME),\\n \\\"doc\\\": \\\"Create directory hierarchy for {}.\\\".format(self._root),\\n \\\"title\\\": utils.title_with_target1(\\\"MKTREE\\\"),\\n \\\"actions\\\": [mkdirs],\\n \\\"targets\\\": self.directories,\\n \\\"uptodate\\\": [True],\\n }\\n )\\n return task\",\n \"title\": \"\"\n },\n {\n \"docid\": \"117716f9f6008b4c9fd9afffe44d11a1\",\n \"score\": \"0.6869847\",\n \"text\": \"def create_dirs(f):\\n make_dir(OUTPUT_DIR)\\n make_dir_rec(OUTPUT_DIR + \\\"/ckpt/\\\")\\n make_dir_rec(OUTPUT_DIR + \\\"/performance/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f443860cd378406f87a7ba14bf96293a\",\n \"score\": \"0.68128544\",\n \"text\": \"def MakeDir(dst):\\n print 'mkdir -p ' + dst\\n oshelpers.Mkdir(['-p', dst])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc440fbfa828238f949e02fa27d046a1\",\n \"score\": \"0.6782039\",\n \"text\": \"def make_dirs(dirs):\\n\\tfor d in dirs:\\n\\t\\tos.makedirs(d, exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dcd7e481eb0f9d6aa91a40cbe09b43a\",\n \"score\": \"0.6735967\",\n \"text\": \"def make_dir(d):\\n if not os.path.exists(d):\\n os.makedirs(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e3903bca5e441d1aa69c6b41121d423\",\n \"score\": \"0.6730184\",\n \"text\": \"def make_sub_dirs(model_type, ver, main_dir):\\n sub_dir_1 = main_dir + model_type + '/'\\n sub_dir_2 = sub_dir_1 + ver + '/'\\n\\n make_directory(main_dir)\\n make_directory(sub_dir_1)\\n make_directory(sub_dir_2)\\n\\n return sub_dir_2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a25ee436bcd0d47e4348003b8607cb4\",\n \"score\": \"0.6705954\",\n \"text\": \"def make_dir(path):\\n pathlib.Path(path).mkdir(parents=True, exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc5faacd8dba93e8a736e0934eaee9db\",\n \"score\": \"0.6704804\",\n \"text\": \"def make_dirs(*dirs):\\n for dir in dirs:\\n if not path.isdir(dir):\\n makedirs(dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d40cddb37ff95ee4ec77574702a2fb70\",\n \"score\": \"0.67003304\",\n \"text\": \"def recursive_directories_create(project_directory, structure, dry_run=False):\\n logger = logging.getLogger('optimus')\\n \\n for item in structure:\\n if len(item)>0:\\n new_dir = item[0]\\n path_dir = os.path.join(project_directory, new_dir)\\n if not os.path.exists(path_dir):\\n logger.info('* Creating new directory : %s', path_dir)\\n if not dry_run:\\n os.makedirs(path_dir)\\n else:\\n logger.warning('* Following path allready exist : %s', path_dir)\\n # Follow children directories to create them\\n if len(item)>1:\\n recursive_directories_create(path_dir, item[1], dry_run=dry_run)\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e640d778ad39d38a50f4a469e5e5d9d9\",\n \"score\": \"0.6659188\",\n \"text\": \"def MakeDirs( path, **kwargs ) :\\n \\n # obtain GSS ojbect and remaining path:\\n gss,rpath = GSS( path, **kwargs )\\n\\n # create:\\n gss.MakeDirs( rpath )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7b9268af5d87d5d1d5e780dbc01a411\",\n \"score\": \"0.6656963\",\n \"text\": \"def make_dirs(path):\\n if not os.path.isdir(path):\\n os.makedirs(path, mode=0o777)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"612e60776c5bbda74e8c225fc3181c25\",\n \"score\": \"0.6554929\",\n \"text\": \"def create_directory(path, new_directory):\\n for i in range(43):\\n\\n directory = path + '\\\\\\\\' + digit_number(i) + str(i)\\n\\n for root, dirs, files in os.walk(directory):\\n for filename in files:\\n copyfile(directory + '\\\\\\\\' + filename, new_directory + '\\\\\\\\' + str(i) + \\\"_\\\" + filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b10190914ae9538a4c3c6433ca106a3\",\n \"score\": \"0.65497\",\n \"text\": \"def mkdirs(path):\\n if not os.path.isdir(path):\\n os.makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fa6bb333c56d3ad5adc2c38709b490d\",\n \"score\": \"0.65257365\",\n \"text\": \"def make_dir_structure(base_dir):\\n\\n def maybe_makedir(*args):\\n\\n p = join(base_dir, *args)\\n\\n if exists(p) and not isdir(p):\\n raise IOError(\\\"File '{}' exists but is not a directory \\\".format(p))\\n\\n if not exists(p):\\n makedirs(p)\\n\\n maybe_makedir(DOWNLOAD_DIR)\\n maybe_makedir(PACKAGE_DIR)\\n maybe_makedir(OLD_DIR)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fe5915c27dc576ba391459bb1b3f26a\",\n \"score\": \"0.65208316\",\n \"text\": \"def folder_maker(path):\\n if not os.path.exists(path):\\n os.mkdir(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87968f892ef4054ebfabdfbca3b63502\",\n \"score\": \"0.6519899\",\n \"text\": \"def make_directory(dir_path, mode=0o777):\\n if not os.path.exists(dir_path):\\n os.makedirs(dir_path, mode=mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2104c9b7aab28f39d76b00a5d45256be\",\n \"score\": \"0.65141076\",\n \"text\": \"def make_dir_rec(path):\\n if not os.path.isdir(path):\\n os.makedirs(path, exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77af259a6ed67947338e86d591f08a45\",\n \"score\": \"0.65131664\",\n \"text\": \"def make_dir (dir):\\n if not os.path.exists (dir):\\n os.mkdir (dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ddf6c00f9bf1e9ff385e0a712c151dc\",\n \"score\": \"0.6507649\",\n \"text\": \"def mkdirs(newdir):\\n printDBG('mkdirs: \\\"%s\\\"' % newdir)\\n if os.path.isdir(newdir):\\n pass\\n elif os.path.isfile(newdir):\\n raise OSError(\\\"cannot create directory, file already exists: '%s'\\\" % newdir)\\n else:\\n head, tail = os.path.split(newdir)\\n if head and not os.path.isdir(head) and not os.path.ismount(head) and not os.path.islink(head):\\n mkdirs(head)\\n if tail:\\n os.mkdir(newdir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95b801b238c3613a58f6e25c97e5b9d1\",\n \"score\": \"0.64925635\",\n \"text\": \"def mkdir(self,rmdir=False):\\n import os\\n if rmdir:\\n os.rmdir(str(self))\\n cur=Path('./')\\n for intdir in self.split():\\n cur+=Path(intdir)\\n if not os.path.isdir(cur):\\n os.mkdir(cur)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9ec0c2b73c2281a8cd40a92c41b0735\",\n \"score\": \"0.648629\",\n \"text\": \"def make_dir(fpath) -> None:\\n pathlib.Path(fpath).mkdir(parents=True, exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d87b17eb611b9a509ec401d082e055af\",\n \"score\": \"0.647003\",\n \"text\": \"def make_dir(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d87b17eb611b9a509ec401d082e055af\",\n \"score\": \"0.647003\",\n \"text\": \"def make_dir(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af557cc3ff3661a85fc4e988ee7d705\",\n \"score\": \"0.6455032\",\n \"text\": \"def make_dir(dir_path):\\n if not os.path.exists(dir_path):\\n os.makedirs(dir_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01d601164078d66e2ffa3c51aad36be3\",\n \"score\": \"0.64532316\",\n \"text\": \"def _make_directory(self, path, do_raise=False):\\n try:\\n os.makedirs(path)\\n except OSError:\\n if do_raise: raise\\n else:\\n os.chown(path, self.owner.pw_uid, self.owner.pw_gid)\\n os.chmod(path, 0775)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ae22980416872bd8fce06ff3dcc8dee\",\n \"score\": \"0.6449504\",\n \"text\": \"def make_dir(path):\\n try:\\n os.mkdir(path)\\n except OSError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ae22980416872bd8fce06ff3dcc8dee\",\n \"score\": \"0.6449504\",\n \"text\": \"def make_dir(path):\\n try:\\n os.mkdir(path)\\n except OSError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ae22980416872bd8fce06ff3dcc8dee\",\n \"score\": \"0.6449504\",\n \"text\": \"def make_dir(path):\\n try:\\n os.mkdir(path)\\n except OSError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7865ebc96ef41e1835fdb2eb847585d0\",\n \"score\": \"0.6440436\",\n \"text\": \"def makeDirectory(DIR):\\n \\n if not os.path.exists(DIR):\\n os.makedirs(DIR)\\n print \\\">>> made directory \\\" + DIR\\n \\n\\n\\n\\n\\n ##############\\n # Categories #\\n ##############\",\n \"title\": \"\"\n },\n {\n \"docid\": \"272dcba0213acdbd54529d588eb8d745\",\n \"score\": \"0.6428357\",\n \"text\": \"def make_directories(path):\\n try:\\n os.makedirs(os.path.dirname(path))\\n except OSError, e:\\n if e.errno != errno.EEXIST: # don't complain if already exists\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5ba4fbe3aa5b2ada08e66228d6a4a02\",\n \"score\": \"0.6424633\",\n \"text\": \"def make_dirs_if_needed(*dirs) :\\n\\tfor dir in dirs :\\n\\t\\tif not exists(dir) :\\n\\t\\t\\tmakedirs(dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61162d699e776d88bb8da21674860458\",\n \"score\": \"0.6400825\",\n \"text\": \"def replicate_directory_tree(input_dir, output_dir):\\n def transplant_dir(target, dirname):\\n x = dirname.replace(input_dir, target)\\n if not os.path.exists(x):\\n LOGGER.info('Creating: {}'.format(x))\\n os.makedirs(x)\\n\\n dir_visitor(\\n input_dir,\\n functools.partial(transplant_dir, output_dir)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad5ddf63aa8169d732db5815ad657277\",\n \"score\": \"0.63999045\",\n \"text\": \"def make_dir(dir_name):\\n try:\\n os.makedirs(dir_name)\\n except OSError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecfe1510e749ff34acd0faad2494225e\",\n \"score\": \"0.637565\",\n \"text\": \"def make_dir(path):\\n try:\\n os.makedirs(path)\\n except OSError:\\n if not os.path.isdir(path):\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22a08bd2fb487d5d46b8cc9cfff9247b\",\n \"score\": \"0.63677007\",\n \"text\": \"def make_directory(path):\\n os.makedirs(path, exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"420e190b9c95c8c605ea8d09c34f349a\",\n \"score\": \"0.6364349\",\n \"text\": \"def makeDirectory(DIR):\\n if not os.path.exists(DIR):\\n os.makedirs(DIR)\\n #print \\\">>> made directory \\\" + DIR\",\n \"title\": \"\"\n },\n {\n \"docid\": \"822edd48b61ff8317464ac3be0c8ecde\",\n \"score\": \"0.63573384\",\n \"text\": \"def directoryCreate (\\n \\n self,\\n path = None\\n ) :\\n\\n return self.makeDirectory( path )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cc8708b739cdbfb958203e8a43a03d9\",\n \"score\": \"0.63418084\",\n \"text\": \"def create_dir(self):\\n dir1 = os.path.dirname(self.dst + \\\"/\\\")\\n if not os.path.exists(dir1):\\n print \\\"Making directory: \\\" + dir1\\n os.makedirs(dir1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c3552b52cb80543c91e58a31d7c360\",\n \"score\": \"0.63380724\",\n \"text\": \"def mkdir(self, parents: bool = False, exist_ok: bool = False):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99cabd05fa6b79e637804fdd45810747\",\n \"score\": \"0.63192296\",\n \"text\": \"def set_up_directory_simple(rootdir, classname):\\n dir_path = os.path.join(rootdir, classname)\\n make_directory(dir_path)\\n return dir_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"810e3e2fff199bd58a803549731af5e3\",\n \"score\": \"0.63168776\",\n \"text\": \"def mkdir(d):\\n if not test(\\\"-d %s\\\" % d):\\n run(\\\"mkdir -p %s\\\" % d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"751699902181c6e16a45feb86b887fd0\",\n \"score\": \"0.63147205\",\n \"text\": \"def __create_dir(dir):\\n if not dir.exists():\\n dir.mkdir(parents=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e338187034b2526155ffdeaae411e8d9\",\n \"score\": \"0.6303725\",\n \"text\": \"def dir_create(dir_path):\\r\\n if not os.path.isdir(dir_path):\\r\\n os.makedirs(dir_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aac36f54f602af9748bcadb8931eda6\",\n \"score\": \"0.626529\",\n \"text\": \"def make_dir(dirname, verbose=True):\\n absdir = fix_path(dirname)\\n if not os.path.isdir(absdir):\\n os.makedirs(absdir)\\n if verbose:\\n print >>sys.stderr, \\\"Created directory: %s\\\" % dirname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d11fd45a96d5ba5601d3ba52c15353d2\",\n \"score\": \"0.6244863\",\n \"text\": \"def make_assembly_subdir(self):\\n if self.assembly_subdir_needed:\\n if self.assembly_subdir is not None:\\n if not os.path.isdir(self.assembly_subdir):\\n os.makedirs(self.assembly_subdir)\\n else:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54f3207fdb84f7df7a91dd7f535b1b57\",\n \"score\": \"0.62383574\",\n \"text\": \"def createDir(path):\\n if not os.path.isdir(path):\\n os.makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbbe786214f5f9fcfe729508a2907c35\",\n \"score\": \"0.62380534\",\n \"text\": \"def directory_setup(path):\\n for i in count(1):\\n new_path = path + str(i)\\n try:\\n makedirs(new_path)\\n break\\n except:\\n pass\\n return new_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28233374daa1caf4ed54300ba5f827d0\",\n \"score\": \"0.6236168\",\n \"text\": \"def makeDirs(dir, absMode = None, reuseExisting = False):\\n\\n # Don't remake dir if already exists\\n if (reuseExisting):\\n if os.path.isdir(dir):\\n return\\n\\n # calc path depth\\n depth = 0\\n (head, tail) = os.path.split(dir)\\n while head:\\n if tail:\\n depth += 1 \\n if head == '/':\\n break\\n (head, tail) = os.path.split(head)\\n \\n retries = 0\\n # Set umask to zero, to enable the creation of 777 dirs\\n oldUmask = os.umask(0) \\n while retries <= depth:\\n retries += 1\\n try:\\n # Create the dir\\n if (absMode is not None):\\n os.makedirs(dir, absMode)\\n else:\\n os.makedirs(dir)\\n except:\\n # maybe the directory was created by another thread\\n if os.path.isdir(dir):\\n break\\n else:\\n if retries == depth:\\n os.umask(oldUmask)\\n raise\\n\\n os.umask(oldUmask)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"291f67063b72608be2acefa0a69c04f4\",\n \"score\": \"0.6226757\",\n \"text\": \"def mkdirs(newdir, mode=None):\\n if mode is None:\\n mode = 0o750\\n if os.path.isdir(newdir):\\n pass\\n elif os.path.isfile(newdir):\\n raise OSError(\\\"a file with the same name as the desired \\\"\\n \\\"dir, '%s', already exists.\\\" % newdir)\\n else:\\n os.makedirs(newdir, mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf39f8535de9c2f1be6e7d7f586861ee\",\n \"score\": \"0.6210859\",\n \"text\": \"def mkdirs(path):\\n os.makedirs(path, exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36aee08538fdeb51f51c3bb91d010438\",\n \"score\": \"0.62106806\",\n \"text\": \"def make_dir(path, name):\\n\\n dir = os.path.join(path, name)\\n check_dir = os.path.isdir(dir)\\n\\n if not check_dir:\\n os.makedirs(dir)\\n print(f'New directory, {name}, made in {path}.')\\n else:\\n print(f'{name} directory already exists in {path}.')\\n return dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"400b403da03372443bf5f8d9570efc4a\",\n \"score\": \"0.6205585\",\n \"text\": \"def setup_work_directory(self):\\n for subdirectory in self.WORK_DIRECTORIES:\\n Path(self.work_directory, subdirectory).mkdir(parents=True, exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"383d0d06f29c51cfbd59eff1b179fc88\",\n \"score\": \"0.62014335\",\n \"text\": \"def create_dirs(path):\\n if path and not os.path.isdir(path):\\n os.makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc1b60059e5e167505cf5fa7c7ae0f09\",\n \"score\": \"0.6201415\",\n \"text\": \"def mkdirs(filename: typing.Union[os.PathLike, str], /, *, mode: int = 0o777) -> None:\\n dirname = os.path.dirname(filename)\\n if not dirname:\\n return\\n log.debug('os.makedirs(%r)', dirname)\\n os.makedirs(dirname, mode=mode, exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d82ac0e019dd658274ee7bdbc7da138\",\n \"score\": \"0.6195935\",\n \"text\": \"def mkdirs(self, *directories: str) -> None:\\n for directory in list(directories):\\n try:\\n os.makedirs(directory)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a73e4dd67c0449a80f960ace25cbe4a3\",\n \"score\": \"0.61948305\",\n \"text\": \"def mkdir(self, folder: str):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6786034af029e3c41b8c46efca3d4abc\",\n \"score\": \"0.6191701\",\n \"text\": \"def recursive_makedir(path: str, is_file=False):\\n try:\\n target = path if not is_file else os_file_separator.join(path.split(os_file_separator)[:-1])\\n makedirs(target)\\n except FileExistsError as ex:\\n assert ex.errno == errno.EEXIST\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c0462ec6e07bd2b077c04f850d7d98\",\n \"score\": \"0.6188425\",\n \"text\": \"def mkdirs(path):\\r\\n if not os.path.isdir(path):\\r\\n try:\\r\\n os.makedirs(path)\\r\\n except OSError as err:\\r\\n if err.errno != errno.EEXIST or not os.path.isdir(path):\\r\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f0ba42992865d9738bafafcbffb7b4e\",\n \"score\": \"0.6187536\",\n \"text\": \"def folder_creator(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d20e0de407373a8fb829f507fdc4ce\",\n \"score\": \"0.6184182\",\n \"text\": \"def setUpDirs():\\n if not os.path.isdir(\\\"videos\\\"):\\n os.mkdir(\\\"videos\\\")\\n\\n if not os.path.isdir(\\\"thumbnails\\\"):\\n os.mkdir(\\\"thumbnails\\\")\\n\\n if not os.path.isdir(\\\"music\\\"):\\n os.mkdir(\\\"music\\\")\\n\\n if not os.path.isdir(\\\"music/wav\\\"):\\n os.mkdir(Path(\\\"music/wav\\\"))\\n\\n if not os.path.isdir(\\\"music/mp3\\\"):\\n os.mkdir(Path(\\\"music/mp3\\\"))\\n\\n if not os.path.isdir(\\\"faces\\\"):\\n os.mkdir(Path(\\\"faces\\\"))\\n\\n if not os.path.isdir(\\\"temp\\\"):\\n os.mkdir(Path(\\\"temp\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc3bb9e5a51fb57c169610ad532eabb6\",\n \"score\": \"0.6183504\",\n \"text\": \"def create_directories(get_torrents, get_magnets, dest_dir):\\n if get_torrents:\\n folder = os.path.join(dest_dir, \\\"torrents\\\")\\n file_io.create_directory(folder)\\n\\n if get_magnets:\\n folder = os.path.join(dest_dir, \\\"magnets\\\")\\n file_io.create_directory(folder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f2dec531d09c23b9833bb6ca19f500a\",\n \"score\": \"0.6180252\",\n \"text\": \"def create_dir(path):\\n os.makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8018f37c0776cdb2b2d356fd183cd12\",\n \"score\": \"0.61692864\",\n \"text\": \"def _makedir(self, system):\\n dirname = self._filenames(system)[\\\"dirname\\\"]\\n if not os.path.exists(dirname):\\n os.makedirs(dirname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4f474922a5bd9770605224c45a9ec88\",\n \"score\": \"0.6165306\",\n \"text\": \"def directory_creator(self, path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c011d60610ab3fc192ade74fbf84ebb3\",\n \"score\": \"0.61579937\",\n \"text\": \"def MakeDirectoryIfAbsent(path):\\n if not os.path.isdir(path):\\n os.makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1da648de619005d516f09730453223f2\",\n \"score\": \"0.61553186\",\n \"text\": \"def create_dirs(target_dirs):\\n for dir_path in target_dirs:\\n if not os.path.isdir(dir_path):\\n os.makedirs(dir_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e5fd68f978c2abff32699c84cf807cc\",\n \"score\": \"0.61537683\",\n \"text\": \"def setup_dirs(conf, root):\\n conf.vardir = root / \\\"var/lib/fastpath\\\"\\n conf.cachedir = conf.vardir / \\\"cache\\\"\\n conf.s3cachedir = conf.cachedir / \\\"s3\\\"\\n conf.dfdir = conf.vardir / \\\"dataframes\\\"\\n conf.outdir = conf.vardir / \\\"output\\\"\\n conf.msmtdir = conf.outdir / \\\"measurements\\\"\\n for p in (\\n conf.vardir,\\n conf.cachedir,\\n conf.s3cachedir,\\n conf.dfdir,\\n conf.outdir,\\n conf.msmtdir,\\n ):\\n p.mkdir(parents=True, exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb24a4158950a54dc14f547d8a2fc8c5\",\n \"score\": \"0.61509436\",\n \"text\": \"def mkdir(path):\\n\\tif not os.path.isdir(path):\\n\\t\\tos.makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82b1460d019655d95bc8844555d4608e\",\n \"score\": \"0.61493057\",\n \"text\": \"def make_dir(base_dir, name):\\n if path.exists(base_dir) and path.isdir(base_dir):\\n directory = path.join(base_dir, name)\\n if path.exists(directory) and path.isdir(directory):\\n #raise RuntimeError(\\\"Directory already exists: {}\\\".format(directory))\\n return directory\\n else:\\n makedirs(directory)\\n return directory\\n else:\\n raise RuntimeError(\\\"Directory does not exist: {}\\\".format(base_dir))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7742470e80ebe4a70fe0a8468b9b64b4\",\n \"score\": \"0.61452585\",\n \"text\": \"def create_dirs(idiomas):\\n directorios=os.listdir(\\\"./\\\")\\n if (\\\"graphics\\\" not in directorios):\\n os.makedirs(\\\"./graphics\\\")\\n else:\\n dir_lang=os.listdir(\\\"./graphics/\\\")\\n for idioma in idiomas:\\n if idioma not in dir_lang:\\n os.makedirs(\\\"./graphics/\\\"+idioma+\\\"/data\\\")\\n if(\\\"data\\\" not in directorios):\\n os.makedirs(\\\"./data\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1af956ea1f8afc72313e94093e37ae0e\",\n \"score\": \"0.613982\",\n \"text\": \"def mkdir(path):\\n if not exists(path):\\n makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bca5e99be6d6710f3b84824884a19a7\",\n \"score\": \"0.61383635\",\n \"text\": \"def _make_folders(self, folders: List[str]):\\n self._make_initial_folders()\\n for folder in folders:\\n self._mkdir(folder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abc571b0a16121be0f37301c68836ee6\",\n \"score\": \"0.61260813\",\n \"text\": \"def new_dirs():\\n for target in FIELDS:\\n if not os.path.exists(target):\\n print(':: Create folder')\\n os.makedirs(target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b1e067a1793470df1cf279baae8de82\",\n \"score\": \"0.6119317\",\n \"text\": \"def mkdirs(paths):\\n if isinstance(paths, list) and not isinstance(paths, str):\\n for path in paths:\\n mkdir(path)\\n else:\\n mkdir(paths)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b1e067a1793470df1cf279baae8de82\",\n \"score\": \"0.6119317\",\n \"text\": \"def mkdirs(paths):\\n if isinstance(paths, list) and not isinstance(paths, str):\\n for path in paths:\\n mkdir(path)\\n else:\\n mkdir(paths)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4df1e8db9535b588dce22e1a37727c6\",\n \"score\": \"0.6117014\",\n \"text\": \"def CreacionDirectoriosProyecto(Root):\\n os.makedirs(os.path.join(Root, 'input'), exist_ok=True)\\n os.makedirs(os.path.join(Root, 'input\\\\processed'), exist_ok=True)\\n os.makedirs(os.path.join(Root, 'library'), exist_ok=True)\\n os.makedirs(os.path.join(Root, 'output'), exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82dc71342a6ff2779c3dde128684aa10\",\n \"score\": \"0.6111609\",\n \"text\": \"def create_dir(self, relpath):\\n return self.get_dir(relpath).create()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80569fcdbdd5bc308de052972d1b5e7f\",\n \"score\": \"0.61066616\",\n \"text\": \"def make_dir(datapath, mypath):\\n if not os.path.exists(datapath + os.sep + mypath):\\n os.makedirs(datapath + os.sep + mypath)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e525cb19975ee6e8c790e0219c8e6dc0\",\n \"score\": \"0.6106294\",\n \"text\": \"def make_directory(path):\\n if not os.path.exists(path):\\n os.mkdir(path)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a69620517d73a8483ad6af42edfda97\",\n \"score\": \"0.6105435\",\n \"text\": \"def mktree(path):\\n if not os.path.isdir(path):\\n debug(\\\"mktree {}\\\", path)\\n os.makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98fbe08fe2a147d3df527b523999b3a2\",\n \"score\": \"0.61048704\",\n \"text\": \"def mkdircond(directory):\\n if not os.path.isdir(directory):\\n os.mkdir(directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24e5c6e65e347a03323b508787f8672d\",\n \"score\": \"0.6104711\",\n \"text\": \"def do_mkdir(self, path):\\n self.dropbox.file_create_folder(self.current_path + \\\"/\\\" + path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e93025724e397343b4a1195d282cb56e\",\n \"score\": \"0.61009216\",\n \"text\": \"def create_dir(path):\\n if (os.path.exists(path)) and (os.listdir(path) != []):\\n shutil.rmtree(path)\\n os.makedirs(path)\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30966f589e31ee707b856994a87b8f7f\",\n \"score\": \"0.60996324\",\n \"text\": \"def mkdirs(self, dir):\\n\\n dirPath = self.jvm.org.apache.hadoop.fs.Path(dir)\\n self.logger.info(\\\"Making directory %s\\\" % dir)\\n return self.hadoop_fs.mkdirs(dirPath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a639f519d3879d1e31500ad5bfa5ccab\",\n \"score\": \"0.60952425\",\n \"text\": \"def setup_dirs():\\n pathlib.Path(RESULTS_DIR).mkdir(parents=True, exist_ok=True)\\n pathlib.Path(DOWNLOADS_DIR).mkdir(parents=True, exist_ok=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"526dd079101b01c55668cc00edb78412\",\n \"score\": \"0.6093531\",\n \"text\": \"def mkdir(new_dir, auth, parents=False):\\n dirname = os.path.basename(new_dir)\\n headers = dict(auth)\\n headers['Content-Type'] = 'application/json'\\n data = json.dumps({\\n \\\"name\\\": dirname,\\n \\\"folder\\\": {}\\n })\\n\\n # make dir. This somehow only works with the ID. So get the ID before.\\n parent = os.path.dirname(new_dir)\\n parent_meta = get_metadata(file=parent, auth=auth)\\n\\n if parent_meta.status_code == 404 and parents: # parent does not exist but should be created!\\n mkdir(parent, auth, True) # recurse into parents\\n parent_meta = get_metadata(file=parent, auth=auth)\\n\\n parent_id = dict(parent_meta.json_body()).get('id', '00000000')\\n res = requests.post(base_url + \\\"/drive/items/\\\" + parent_id + \\\"/children\\\", headers=headers, data=data)\\n return Result(res)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a3bf831319a44f8647d448b15afb58b\",\n \"score\": \"0.6092909\",\n \"text\": \"def make_parent_dir(path):\\n parent_dir = get_dir(path)\\n mkdir(parent_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcb22c79da211755c5ece8c6ed925e34\",\n \"score\": \"0.60842854\",\n \"text\": \"def main():\\n for root, _dirs, files in os.walk(DIR):\\n for file in files:\\n try:\\n in_file = f\\\"{root}/{file}\\\"\\n out_dir = os.path.join(os.pardir, root)\\n pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)\\n generate(in_file, f\\\"{out_dir}/{file}\\\")\\n except ValueError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0684a8519b7897800a61615a3261223d\",\n \"score\": \"0.60746723\",\n \"text\": \"def createFolders(self):\\n if not os.path.exists(self.imgFolder):\\n os.makedirs(self.imgFolder)\\n if not os.path.exists(self.xformFolder):\\n os.makedirs(self.xformFolder)\\n if not os.path.exists(self.txtFolder):\\n os.makedirs(self.txtFolder)\\n if not os.path.exists(self.gifFolder):\\n os.makedirs(self.gifFolder)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":87,"cells":{"query_id":{"kind":"string","value":"689ef4dc44033992faded2f451aa3274"},"query":{"kind":"string","value":"Sets the diameter of the"},"positive_passages":{"kind":"list like","value":[{"docid":"f8714e9170fbddd16ab67ffce4d4ee82","score":"0.6536274","text":"def set_diameter(self, value):\n if self._device.successfully_set_diameter(value):\n return \"{}\".format(self.Stopped)\n else:\n run_status = self.get_run_status()\n return \"{}NA{}\".format(self._return, run_status)","title":""}],"string":"[\n {\n \"docid\": \"f8714e9170fbddd16ab67ffce4d4ee82\",\n \"score\": \"0.6536274\",\n \"text\": \"def set_diameter(self, value):\\n if self._device.successfully_set_diameter(value):\\n return \\\"{}\\\".format(self.Stopped)\\n else:\\n run_status = self.get_run_status()\\n return \\\"{}NA{}\\\".format(self._return, run_status)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2e8922b60569a2d01d86bc413100f22d","score":"0.85909635","text":"def diameter(self, diameter):\n\n self._diameter = diameter","title":""},{"docid":"7353f3cffe18dd73e85d4804f159418a","score":"0.701887","text":"def diameter(self):\r\n return self.radius * 2","title":""},{"docid":"74d5b5f3e71278a0157da9f8a56cb49c","score":"0.678239","text":"def diameter(self):\n return self.r*2","title":""},{"docid":"4b2693c86e5831b0531d69370087029c","score":"0.6764692","text":"def diameter(self):\r\n return self.r*2","title":""},{"docid":"a33acde53617599535ece21fec6e7441","score":"0.6754572","text":"def test_set_diameter():\n d1 = Circle(3)\n d2 = Circle(10)\n\n d1.diameter = 12\n assert d1.diameter == 12\n assert d1.radius == 6\n\n d2.diameter = 3\n assert d2.diameter == 3\n assert d2.radius == 1.5","title":""},{"docid":"cf11a07caa1ef66d9fe2cc80dda00191","score":"0.6695417","text":"def diameter(self):\r\n return self._r*2","title":""},{"docid":"392b72a4926740f235673612bf698911","score":"0.6691262","text":"def diameter(self):\n return self._diameter","title":""},{"docid":"516d48c330f15e0e01ab3488cff766b7","score":"0.66901207","text":"def diameter(self):\n return self._r*2","title":""},{"docid":"6ff3bf2aed186d2fb5f7d28687d6554a","score":"0.6498333","text":"def set_soma_size_from_Diam(self, diam):\n if self.use_morphology:\n return # do not do this if we are using morphology\n # print(\"Setting soma size from Diameter\",)\n # assert self.use_morphology is False # do not reset values if we are using hoc file\n self.somaarea = 1e-8 * 4.0 * np.pi * (diam / 2.0) ** 2 # in microns^2\n self.totcap = self.c_m * self.somaarea * 1e6\n # lstd = diam # 1E4 * ((self.somaarea / np.pi) ** 0.5) # convert from cm to um\n self.soma.diam = diam\n self.soma.L = diam","title":""},{"docid":"04d98e8ff68c4754081fdcfd48c5f62b","score":"0.61506265","text":"def size(self, size):\n self.width = size\n self.height = size","title":""},{"docid":"1980dda368e870a37bf7051c0e49803a","score":"0.6136078","text":"def from_diameter(cls, diameter):\n return cls(diameter / 2)","title":""},{"docid":"50c6af3b733aed903f92d4679eeb5507","score":"0.6123093","text":"def diameter(self) -> BaPSFConstant:\n return self._diameter","title":""},{"docid":"c6827446bc5713d2faf3dcc15a19f6c4","score":"0.60529727","text":"def size(self, value):\n self.width = value\n self.height = value","title":""},{"docid":"c6827446bc5713d2faf3dcc15a19f6c4","score":"0.60529727","text":"def size(self, value):\n self.width = value\n self.height = value","title":""},{"docid":"c6827446bc5713d2faf3dcc15a19f6c4","score":"0.60529727","text":"def size(self, value):\n self.width = value\n self.height = value","title":""},{"docid":"c6827446bc5713d2faf3dcc15a19f6c4","score":"0.60529727","text":"def size(self, value):\n self.width = value\n self.height = value","title":""},{"docid":"41cfae0eb1c66bdffbdbcca65263749a","score":"0.6037229","text":"def test_sphere_set_diameter():\n s1 = Sphere(3)\n s2 = Sphere(10)\n\n s1.diameter = 6\n assert s1.diameter == 6\n assert s1.radius == 3\n assert s2.radius == 10\n assert s2.diameter == 20\n\n s2.diameter = 3\n assert s2.diameter == 3\n assert s2.radius == 1.5","title":""},{"docid":"679e4b613aef9caa0710f13c8cbf8b2d","score":"0.60282296","text":"def density(self, density):\n\n self._density = density","title":""},{"docid":"679e4b613aef9caa0710f13c8cbf8b2d","score":"0.60282296","text":"def density(self, density):\n\n self._density = density","title":""},{"docid":"679e4b613aef9caa0710f13c8cbf8b2d","score":"0.60282296","text":"def density(self, density):\n\n self._density = density","title":""},{"docid":"3e42d44b052cf3c6f00fb7931b2d1143","score":"0.6002557","text":"def set_dspacing(self, d):\n self.d = d","title":""},{"docid":"811a40ccee4e58310b9ae7711b5db77d","score":"0.59813046","text":"def size(self, value):\n\n self.width = value\n self.height = value","title":""},{"docid":"300896b4392f07cbdd0382b87f3c5666","score":"0.5973357","text":"def set_size(self, width: int, height: int):\n self.__width = width\n self.__height = height","title":""},{"docid":"46c3b1aec624bd92f3ee37e1e3b3a375","score":"0.5933524","text":"def setRadius(self, radius=1.0):\n self.radius = radius\n self.radiusSq = self.radius**2","title":""},{"docid":"d4a4940b1b6258ebff4120eada3dfdbc","score":"0.59329057","text":"def size(self, size):\n self.dimensions = (self.x, self.y, size[0], size[1])\n self.invalidate()","title":""},{"docid":"77490edad6c71efec0f24826723590e3","score":"0.5931716","text":"def update_size(self):\n self.size = self.orange + self.blue + self.yellow","title":""},{"docid":"c3ed10a728fff63c5d72b07bbadbe191","score":"0.5900453","text":"def setDimensions(self, dim):\n self.dim = dim\n L, H = dim\n x, y = self.coords\n self.vertices[:] = [[x-0.5*L, y-0.5*H],\n [x+0.5*L, y-0.5*H],\n [x+0.5*L, y+0.5*H],\n [x-0.5*L, y+0.5*H]]\n self.volume = L*H","title":""},{"docid":"5537eaf9628ccd716186ffbbc2260935","score":"0.5887625","text":"def size(self, value):\n super().__setattr__('width', value)\n super().__setattr__('heigth', value)","title":""},{"docid":"00e8a54a06e38a617bb45132c8e67631","score":"0.58542734","text":"def diamond(self, x, y, size, scale):\n\n top = self.get(x, y - size)\n right = self.get(x + size, y)\n bottom = self.get(x, y + size)\n left = self.get(x - size, y)\n\n average = ((top + right + bottom + left) / 4)\n self.set(x, y, average + scale)","title":""},{"docid":"258422a8e3c1c642414e173aead0f120","score":"0.5847665","text":"def set_density(self, units, density=NO_DENSITY):\n\n check_type('the density for Material ID=\"{0}\"'.format(self._id),\n density, Real)\n check_value('density units', units, DENSITY_UNITS)\n\n if density == NO_DENSITY and units is not 'sum':\n msg = 'Unable to set the density Material ID=\"{0}\" ' \\\n 'because a density must be set when not using ' \\\n 'sum unit'.format(self._id)\n raise ValueError(msg)\n\n self._density = density\n self._density_units = units","title":""},{"docid":"374b34da3f58a88f1b07ed0b06a25cc4","score":"0.58173096","text":"def set_size(self, xs=1.0, ys=1.0):\n self.xSize = xs\n self.ySize = ys","title":""},{"docid":"76fb2de55c9e138786d5f0b93edff6df","score":"0.5807616","text":"def setRadius(self, rad):\r\n self.radius = rad","title":""},{"docid":"4d0312509c1d42b9733a8bf6ca4f57da","score":"0.58073926","text":"def set_size(self, size):\n\t\tself.size = size","title":""},{"docid":"fad65005837c9a8660b1391298208202","score":"0.57615745","text":"def diameter(self):\n return max(starmap(self.distance, combinations(self._graph, 2)))","title":""},{"docid":"36be5eafe929a4e0e67fcf30e7e43cf8","score":"0.57609415","text":"def set_size(self, size):\r\n\r\n pass","title":""},{"docid":"2f03de8a762801128e00ce22aac71ebc","score":"0.5749086","text":"def set_radius( self, radius ):\n self.radius = radius\n self.invalidate()","title":""},{"docid":"2f03de8a762801128e00ce22aac71ebc","score":"0.5749086","text":"def set_radius( self, radius ):\n self.radius = radius\n self.invalidate()","title":""},{"docid":"2b00b72d4670fc6713a10f0c1b8f0605","score":"0.5734548","text":"def set_value(self, width, height):\n self.__width = width\n self.__height = height","title":""},{"docid":"0e2c72bb0e1764ef56fef43638534b9a","score":"0.57194686","text":"def set_fraction(self, value):\n if value < 0:\n value *= -1\n value = min(value, 1)\n if self.horizontal:\n width = int(self.width * value)\n height = self.height\n else:\n width = self.width\n height = int(self.height * value)\n self.canvas.coords(self.meter, self.xpos, self.ypos,\n self.xpos + width, self.ypos + height)","title":""},{"docid":"f8b48f6f4293c11219299e53e0aaacce","score":"0.5716819","text":"def thickness(self, th: float) -> None:\n self._lvis.thickness = th","title":""},{"docid":"ee00dca4f32972b00c4a2e790994ce6a","score":"0.57103926","text":"def set_size(self, size):\n self.set_property(\"size\", size)\n self.emit(\"appearance_changed\")","title":""},{"docid":"10cc5b03ead2e040c2475848956152d6","score":"0.57092535","text":"def setSize(self, sizeX, sizeY):\n self.size = LVecBase2i(sizeX, sizeY)","title":""},{"docid":"a4e9d41ffa82d1e83950508322e8c850","score":"0.5688525","text":"def setDimensions(self, dim):\n self.dim = dim\n L, W, H = dim\n x, y, z = self.coords\n self.vertices[:] = [[x-0.5*L, y-0.5*W, z-0.5*H],\n [x-0.5*L, y+0.5*W, z-0.5*H],\n [x+0.5*L, y+0.5*W, z-0.5*H],\n [x+0.5*L, y-0.5*W, z-0.5*H],\n [x-0.5*L, y-0.5*W, z+0.5*H],\n [x-0.5*L, y+0.5*W, z+0.5*H],\n [x+0.5*L, y+0.5*W, z+0.5*H],\n [x+0.5*L, y-0.5*W, z+0.5*H]]\n self.volume = L*W*H","title":""},{"docid":"4b18f5d928654a994cfc030e158be8e4","score":"0.5669085","text":"def measure_diode(self):\n self.mode = 'diode'","title":""},{"docid":"8e3f61423aa8e39de9982809b89314cd","score":"0.56675017","text":"def set_size(self, size):\r\n pass","title":""},{"docid":"9239fd74a378c7247109906676d52e5c","score":"0.5664343","text":"def sized(self, size: float):\n self._p.size = size\n return self","title":""},{"docid":"0209b3616ad8d3caada9093541843702","score":"0.5663205","text":"def setSize(self, size):\n pass","title":""},{"docid":"79cc7fc15f9595982a4337f696b4555c","score":"0.56621206","text":"def _setHeight(self, value):\r\n self.size.height = value","title":""},{"docid":"6da91e7a714dd88e3d86a53c275565bc","score":"0.5637468","text":"def set_size(self, size):\n self.size = size\n self.call()","title":""},{"docid":"5e2bc91a482f63e79141f56a9a7fa5f6","score":"0.5637214","text":"def set_size(self, xs, ys, zs):\n self.cone.set_size(xs*1.0, ys*1.0, zs*1.0)\n self.tip.set_size(xs*0.15, ys*0.15, zs*0.15)\n self.tip.set_location(self.cone.getX(), self.cone.getY() + self.cone.ySize/0.7, self.cone.getZ())","title":""},{"docid":"55da39fddd18e01265e90b7a3b94b334","score":"0.56249297","text":"def set_dx(self, dx):\n assert dx >= 0., \"dx must be nonnegative\"\n self.dx = float(dx)","title":""},{"docid":"938e1d60942e9c117b41fb0b011245b2","score":"0.5608492","text":"def set_radius(self, radius: float) -> None:\n pass","title":""},{"docid":"454238b3cd47cc4f3ad57be430b735bd","score":"0.55843496","text":"def set_size(self, width, height):\n self.left = -width / 2\n self.right = width / 2\n if self.bottom_up:\n self.top = -height / 2\n self.bottom = height / 2\n else:\n self.top = height / 2\n self.bottom = -height / 2\n self.width = width\n self.height = height","title":""},{"docid":"1f46a0897ff81df55d02747c985965b9","score":"0.55642694","text":"def _radius_changed(self):\n\n diameter = self.radius * 2\n self.bounds = [diameter, diameter]\n\n return","title":""},{"docid":"0df08e9a8116477c2242459b447a61db","score":"0.556325","text":"def setDensity(self,zRange, rho):\n self.setProperty(self.rho, zRange, rho)","title":""},{"docid":"bf17474110c96e3c77a30412be41cf26","score":"0.55541277","text":"def set_radius(self):\n self._radius = (\n (self._location[0]-1,self._location[1]-1),\n (self._location[0]-1,self._location[1]),\n (self._location[0]-1,self._location[1]+1),\n (self._location[0],self._location[1]+1),\n (self._location[0]+1,self._location[1]+1),\n (self._location[0]+1,self._location[1]),\n (self._location[0]+1,self._location[1]-1),\n (self._location[0],self._location[1]-1))","title":""},{"docid":"762785a66468fefe30c27c2a58e8699d","score":"0.55431616","text":"def SetDiscRadius(self, _arg: 'double const') -> \"void\":\n return _itkHoughTransform2DLinesImageFilterPython.itkHoughTransform2DLinesImageFilterFF_SetDiscRadius(self, _arg)","title":""},{"docid":"7b12832229cb2560003a8df5b7c12b19","score":"0.5542856","text":"def density(self, value):\n self._density = value\n if self._ipoly is not None:\n self.densities[self._ipoly] = value\n self.polygons[self._ipoly].set_color(self._density2color(value))\n # self._update_data()\n self._update_data_plot()\n self.canvas.draw()","title":""},{"docid":"a3bca098f2e33d3d96a4ff5c81709832","score":"0.55379707","text":"def set_box_size(self, size):\n self.height_offset = size\n self.height = self.y + self.height_offset\n self.width_offset = size\n self.width = self.x + self.width_offset","title":""},{"docid":"451feab23deedf6d4012ac4029843b90","score":"0.5528222","text":"def set_size(self, options):\r\n\r\n # Set dimensions or scale for the chart.\r\n self.width = options.get('width', self.width)\r\n self.height = options.get('height', self.height)\r\n self.x_scale = options.get('x_scale', 1)\r\n self.y_scale = options.get('y_scale', 1)\r\n self.x_offset = options.get('x_offset', 0)\r\n self.x_offset = options.get('y_offset', 0)","title":""},{"docid":"d4e57727699ee763bcc898394042d13d","score":"0.54924905","text":"def SetThickness(self, num):\n self.thickness = num\n self.pen = wx.Pen(self.colour, self.thickness, wx.SOLID)\n self.Notify()","title":""},{"docid":"f7dda58e5276fb465efd615c98eaab08","score":"0.54830927","text":"def setDim(self,dim):\n self.dim = dim","title":""},{"docid":"1dce63ed407cf315881c0b72a8e6631e","score":"0.5478455","text":"def set_size(self, xs=1.0, ys=1.0, zs=1.0):\n self.xSize = xs\n self.ySize = ys\n self.zSize = zs","title":""},{"docid":"08efe673d7479c501228a51a6a8d0411","score":"0.5474349","text":"def setDp(self, dp): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"89be1fbfca8beab5cd6f04defd190868","score":"0.54719377","text":"def set_size(self, size):\n if size < 2:\n raise ValueError(\"Size must be greater than 1.\")\n self.size = size","title":""},{"docid":"44bb93382ce0d0fae40990ed08af856f","score":"0.5466153","text":"def _set_pixel_size(self) -> None:\n if self.constellation in [\n Constellation.L8,\n Constellation.L9,\n Constellation.L7,\n ] or (\n self.constellation in [Constellation.L4, Constellation.L5]\n and self.instrument == LandsatInstrument.TM\n ):\n pixel_size = 30.0\n else:\n pixel_size = 60.0\n\n self.pixel_size = pixel_size","title":""},{"docid":"18095b36ae3497c75e37d1975fce6f65","score":"0.5446678","text":"def test_diameter():\n c1 = Circle(10)\n c2 = Circle(4)\n\n assert c1.diameter == 20\n assert c2.diameter == 8\n\n c2.radius = 12\n c1.radius = 180\n assert c2.diameter == 24\n assert c1.diameter == 360","title":""},{"docid":"d83c9faa49c8787b20a531664fc1ead4","score":"0.5443408","text":"def setPatchSize(self, patchSizeX, patchSizeY):\n self.patchSize = LVecBase2i(patchSizeX, patchSizeY)","title":""},{"docid":"44d68aeb8546298576b01c17b39c2715","score":"0.54306895","text":"def __setattr__(self, name, value):\r\n if name in ('cx', 'cy'):\r\n value_str = str(int(value))\r\n self.set(name, value_str)\r\n else:\r\n super(CT_SlideSize, self).__setattr__(name, value)","title":""},{"docid":"600b331502dbf03c007ef2f407d6eb79","score":"0.54270935","text":"def setSize(self, x=None, y=None, z=None, size=None):\n if size is not None:\n x = size.x()\n y = size.y()\n z = size.z()\n self.__size = [x, y, z]\n self.update()","title":""},{"docid":"d11e27c1b0c940b5b5620e565a8369e7","score":"0.54261667","text":"def set_dy(self, dy):\n assert dy >= 0., \"dy must be nonnegative\"\n self.dy = float(dy)","title":""},{"docid":"4baebfb8054d85aa42a1c5e5986725e2","score":"0.54051864","text":"def setPixelSize(self, pixel_size):\n self.hdf5.attrs['pixel_size'] = pixel_size","title":""},{"docid":"b00c6f038307e2f7d5b09c612daccf1e","score":"0.5402376","text":"def set_mdot_charge(self): \n\n self.mdot_charge =( \n (self.RPM / 2. * self.displacement * self.eta_V *\n self.air.rho) / 60.\n ) \n # charge flow (kg/s) in engine","title":""},{"docid":"afe88b6fab3928584dffe948cdc9fd0b","score":"0.53907526","text":"def size(self, size):\n\n\t\tif self.fixed:\n\t\t\traise ValueError('cannot change the size of fixed dimension {}'.format(self.name))\n\n\t\t# size must be a postive integer\n\t\tif size >= 0 and type(size) == int:\n\t\t\tself._size = size\n\t\telse:\n\t\t\traise TypeError('size must be positive int')","title":""},{"docid":"91ba1656824a8632f2459354105b448f","score":"0.53904915","text":"def set_size(self, size):\n self._check_scene_editor()\n self.scene_editor.set_size(size)","title":""},{"docid":"ab4a82cee33ccb00484a0248f122b88d","score":"0.5373509","text":"def setDof(self, dof):\n self.dof = dof\n self.norm = None","title":""},{"docid":"4f1f31cc85dd8747286d3867eca52793","score":"0.53572285","text":"def setDesiredVelocity(self, dxdes):\n\t\tself.dxdes = dxdes","title":""},{"docid":"308baea7b07df81c23d77107e5280195","score":"0.5340301","text":"def set_aperture(self, aperture_size, sky_annulus=None):\n\n self.aperture_size = aperture_size\n if isinstance(sky_annulus, (list, tuple)) and len(sky_annulus) == 2:\n self.sky_annulus = sky_annulus\n else:\n self.sky_annulus = (2.0 * self.aper, 4.0 * self.aper)","title":""},{"docid":"60c0370c307b54599c7e956c39c1de2f","score":"0.5333745","text":"def _set_height(self, height: int) -> None:","title":""},{"docid":"e2184447ffeb416252f5c4b032649ace","score":"0.5308759","text":"def set_tick(self, size: float = 1) -> None:\n self.set_dxf_attrib(\"dimtsz\", size)","title":""},{"docid":"b5e413d26ce490de4279852532162627","score":"0.53087366","text":"def set_size(self, new_size):\n self.size = new_size","title":""},{"docid":"d193d1a1c4295dd9bab67272222355aa","score":"0.5305684","text":"def setPlotSize(self, _plotSizeWidth, _plotSizeHeight): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"659eda28dde6b456c359fdffcbd9887f","score":"0.5302359","text":"def __init__(self, size=6):\n self.sides = size","title":""},{"docid":"7fcada28f08f7e6ad9d69d368cf88cb9","score":"0.53008664","text":"def SetSize(self, value):\n\n self._size = value","title":""},{"docid":"357b36843ef2a421d8db6d3a6b344ee4","score":"0.53003496","text":"def diameter(self):\n if self.__Order == 0: return 0\n return self.distance_matrix().max()","title":""},{"docid":"ec660baa7a7c50eeaa5640e354d2cabe","score":"0.5296701","text":"def circum_ference(diameter):\n return 22*diameter/7","title":""},{"docid":"e7f4e2a6cd8eadcf6833d75943730fb7","score":"0.5290093","text":"def setSize(self, width, height):\n self.WIDTH = width\n self.HEIGHT = height\n self.PIXELCOUNT = self.WIDTH * self.HEIGHT\n self.progressStep = self.PIXELCOUNT / 100\n self.camera = Camera(HomVector(0, 0, 6, 1), HomVector(0, 0, 0, 1), HomVector(0, 1, 0, 0), 45, self.WIDTH, self.HEIGHT)","title":""},{"docid":"95c55420b28ba1ac428a6f8a31c656bd","score":"0.5280665","text":"def radius(self, radius):\n\n self._radius = radius","title":""},{"docid":"95c55420b28ba1ac428a6f8a31c656bd","score":"0.5280665","text":"def radius(self, radius):\n\n self._radius = radius","title":""},{"docid":"cbcefa294f9801516cc919d9bb8b2084","score":"0.5267082","text":"def density(self, value):\n self._data[\"density\"] = float(value)\n self._cache.delete(\"mass_properties\")","title":""},{"docid":"212fb16280274096f5b36e5653685767","score":"0.5265638","text":"def set_size(size):","title":""},{"docid":"1bd5c9671dcee33c9929f4f32e76d89e","score":"0.5263938","text":"def set_size(self, width, height):\n\n self.width = width\n self.height = height\n self.render_list.size(width, height)\n return self","title":""},{"docid":"2eadee9f06119520e665d432a1ba68db","score":"0.5262886","text":"def radius(self, radius: float):\n\n self._radius = radius","title":""},{"docid":"e1aedf8d2b58ece5d986463a75957809","score":"0.5258798","text":"def _changeNeuronSize(self,neuron,new_radius):\r\n\r\n x = neuron[\"x\"]\r\n y = neuron[\"y\"]\r\n\r\n # point 1\r\n UL_x = self._convertToPixels(x - new_radius) + 1\r\n UL_y = self._convertToPixels(y - new_radius) + 1\r\n # point 2\r\n LR_x = self._convertToPixels(x + new_radius)\r\n LR_y = self._convertToPixels(y + new_radius)\r\n\r\n self.canvas.coords(neuron[\"circle\"][\"object\"], UL_x, UL_y, LR_x, LR_y)","title":""},{"docid":"0a0161464fac0dd184f54d8cf74308af","score":"0.5254805","text":"def topo_diameter(self):\n import math\n \n Temp = 0\n for i in range(self.nodenum):\n for j in range(self.nodenum):\n pathlist = []\n self.pathij(i, j, pathlist)\n distance = []\n \n for k in range(len(pathlist)):\n distance.append(len(pathlist[k]) - 1)\n \n if(len(distance) == 0):\n continue\n else:\n if(min(distance) >= Temp):\n Temp = min(distance)\n \n self.topodiameter = Temp","title":""},{"docid":"4fb1ef5db6543e8745a1ac1ef89cfd9d","score":"0.52368236","text":"def set_dc(self, dc):\n self.dc = float(dc)","title":""},{"docid":"001918febd29662654208c30aa10985f","score":"0.5233458","text":"def SetOtherSize( self, size ):\n\t\tif isinstance( size, (int,long)):\n\t\t\tself.otherSize = size\n\t\telse:\n\t\t\tif self.dockSide in ('l','r'):\n\t\t\t\tself.otherSize = size[0]\n\t\t\telse:\n\t\t\t\tself.otherSize = size[1]\n\t\treturn self.otherSize","title":""},{"docid":"fcc7a652747410f3efb57533467544e1","score":"0.5224733","text":"def size(self, size):\n self._size = size\n self._resize_img()","title":""},{"docid":"1bd211125b5958b5cc2c2e1adefcc990","score":"0.5221301","text":"def set_voxel_size(self, size):\n self.voxel_size = size","title":""},{"docid":"aeed35ca2196b1dcc999ceb16b014499","score":"0.51937336","text":"def __init__(self, (x,y), size):\n self.x = x\n self.y = y\n self.size = size\n self.colour = (0,128,255)\n self.thickness = 1\n self.speed = 0.01\n self.angle = math.pi/2","title":""}],"string":"[\n {\n \"docid\": \"2e8922b60569a2d01d86bc413100f22d\",\n \"score\": \"0.85909635\",\n \"text\": \"def diameter(self, diameter):\\n\\n self._diameter = diameter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7353f3cffe18dd73e85d4804f159418a\",\n \"score\": \"0.701887\",\n \"text\": \"def diameter(self):\\r\\n return self.radius * 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74d5b5f3e71278a0157da9f8a56cb49c\",\n \"score\": \"0.678239\",\n \"text\": \"def diameter(self):\\n return self.r*2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b2693c86e5831b0531d69370087029c\",\n \"score\": \"0.6764692\",\n \"text\": \"def diameter(self):\\r\\n return self.r*2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a33acde53617599535ece21fec6e7441\",\n \"score\": \"0.6754572\",\n \"text\": \"def test_set_diameter():\\n d1 = Circle(3)\\n d2 = Circle(10)\\n\\n d1.diameter = 12\\n assert d1.diameter == 12\\n assert d1.radius == 6\\n\\n d2.diameter = 3\\n assert d2.diameter == 3\\n assert d2.radius == 1.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf11a07caa1ef66d9fe2cc80dda00191\",\n \"score\": \"0.6695417\",\n \"text\": \"def diameter(self):\\r\\n return self._r*2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"392b72a4926740f235673612bf698911\",\n \"score\": \"0.6691262\",\n \"text\": \"def diameter(self):\\n return self._diameter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"516d48c330f15e0e01ab3488cff766b7\",\n \"score\": \"0.66901207\",\n \"text\": \"def diameter(self):\\n return self._r*2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ff3bf2aed186d2fb5f7d28687d6554a\",\n \"score\": \"0.6498333\",\n \"text\": \"def set_soma_size_from_Diam(self, diam):\\n if self.use_morphology:\\n return # do not do this if we are using morphology\\n # print(\\\"Setting soma size from Diameter\\\",)\\n # assert self.use_morphology is False # do not reset values if we are using hoc file\\n self.somaarea = 1e-8 * 4.0 * np.pi * (diam / 2.0) ** 2 # in microns^2\\n self.totcap = self.c_m * self.somaarea * 1e6\\n # lstd = diam # 1E4 * ((self.somaarea / np.pi) ** 0.5) # convert from cm to um\\n self.soma.diam = diam\\n self.soma.L = diam\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04d98e8ff68c4754081fdcfd48c5f62b\",\n \"score\": \"0.61506265\",\n \"text\": \"def size(self, size):\\n self.width = size\\n self.height = size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1980dda368e870a37bf7051c0e49803a\",\n \"score\": \"0.6136078\",\n \"text\": \"def from_diameter(cls, diameter):\\n return cls(diameter / 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50c6af3b733aed903f92d4679eeb5507\",\n \"score\": \"0.6123093\",\n \"text\": \"def diameter(self) -> BaPSFConstant:\\n return self._diameter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6827446bc5713d2faf3dcc15a19f6c4\",\n \"score\": \"0.60529727\",\n \"text\": \"def size(self, value):\\n self.width = value\\n self.height = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6827446bc5713d2faf3dcc15a19f6c4\",\n \"score\": \"0.60529727\",\n \"text\": \"def size(self, value):\\n self.width = value\\n self.height = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6827446bc5713d2faf3dcc15a19f6c4\",\n \"score\": \"0.60529727\",\n \"text\": \"def size(self, value):\\n self.width = value\\n self.height = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6827446bc5713d2faf3dcc15a19f6c4\",\n \"score\": \"0.60529727\",\n \"text\": \"def size(self, value):\\n self.width = value\\n self.height = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41cfae0eb1c66bdffbdbcca65263749a\",\n \"score\": \"0.6037229\",\n \"text\": \"def test_sphere_set_diameter():\\n s1 = Sphere(3)\\n s2 = Sphere(10)\\n\\n s1.diameter = 6\\n assert s1.diameter == 6\\n assert s1.radius == 3\\n assert s2.radius == 10\\n assert s2.diameter == 20\\n\\n s2.diameter = 3\\n assert s2.diameter == 3\\n assert s2.radius == 1.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679e4b613aef9caa0710f13c8cbf8b2d\",\n \"score\": \"0.60282296\",\n \"text\": \"def density(self, density):\\n\\n self._density = density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679e4b613aef9caa0710f13c8cbf8b2d\",\n \"score\": \"0.60282296\",\n \"text\": \"def density(self, density):\\n\\n self._density = density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679e4b613aef9caa0710f13c8cbf8b2d\",\n \"score\": \"0.60282296\",\n \"text\": \"def density(self, density):\\n\\n self._density = density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e42d44b052cf3c6f00fb7931b2d1143\",\n \"score\": \"0.6002557\",\n \"text\": \"def set_dspacing(self, d):\\n self.d = d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"811a40ccee4e58310b9ae7711b5db77d\",\n \"score\": \"0.59813046\",\n \"text\": \"def size(self, value):\\n\\n self.width = value\\n self.height = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"300896b4392f07cbdd0382b87f3c5666\",\n \"score\": \"0.5973357\",\n \"text\": \"def set_size(self, width: int, height: int):\\n self.__width = width\\n self.__height = height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46c3b1aec624bd92f3ee37e1e3b3a375\",\n \"score\": \"0.5933524\",\n \"text\": \"def setRadius(self, radius=1.0):\\n self.radius = radius\\n self.radiusSq = self.radius**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4a4940b1b6258ebff4120eada3dfdbc\",\n \"score\": \"0.59329057\",\n \"text\": \"def size(self, size):\\n self.dimensions = (self.x, self.y, size[0], size[1])\\n self.invalidate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77490edad6c71efec0f24826723590e3\",\n \"score\": \"0.5931716\",\n \"text\": \"def update_size(self):\\n self.size = self.orange + self.blue + self.yellow\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3ed10a728fff63c5d72b07bbadbe191\",\n \"score\": \"0.5900453\",\n \"text\": \"def setDimensions(self, dim):\\n self.dim = dim\\n L, H = dim\\n x, y = self.coords\\n self.vertices[:] = [[x-0.5*L, y-0.5*H],\\n [x+0.5*L, y-0.5*H],\\n [x+0.5*L, y+0.5*H],\\n [x-0.5*L, y+0.5*H]]\\n self.volume = L*H\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5537eaf9628ccd716186ffbbc2260935\",\n \"score\": \"0.5887625\",\n \"text\": \"def size(self, value):\\n super().__setattr__('width', value)\\n super().__setattr__('heigth', value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e8a54a06e38a617bb45132c8e67631\",\n \"score\": \"0.58542734\",\n \"text\": \"def diamond(self, x, y, size, scale):\\n\\n top = self.get(x, y - size)\\n right = self.get(x + size, y)\\n bottom = self.get(x, y + size)\\n left = self.get(x - size, y)\\n\\n average = ((top + right + bottom + left) / 4)\\n self.set(x, y, average + scale)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"258422a8e3c1c642414e173aead0f120\",\n \"score\": \"0.5847665\",\n \"text\": \"def set_density(self, units, density=NO_DENSITY):\\n\\n check_type('the density for Material ID=\\\"{0}\\\"'.format(self._id),\\n density, Real)\\n check_value('density units', units, DENSITY_UNITS)\\n\\n if density == NO_DENSITY and units is not 'sum':\\n msg = 'Unable to set the density Material ID=\\\"{0}\\\" ' \\\\\\n 'because a density must be set when not using ' \\\\\\n 'sum unit'.format(self._id)\\n raise ValueError(msg)\\n\\n self._density = density\\n self._density_units = units\",\n \"title\": \"\"\n },\n {\n \"docid\": \"374b34da3f58a88f1b07ed0b06a25cc4\",\n \"score\": \"0.58173096\",\n \"text\": \"def set_size(self, xs=1.0, ys=1.0):\\n self.xSize = xs\\n self.ySize = ys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76fb2de55c9e138786d5f0b93edff6df\",\n \"score\": \"0.5807616\",\n \"text\": \"def setRadius(self, rad):\\r\\n self.radius = rad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d0312509c1d42b9733a8bf6ca4f57da\",\n \"score\": \"0.58073926\",\n \"text\": \"def set_size(self, size):\\n\\t\\tself.size = size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fad65005837c9a8660b1391298208202\",\n \"score\": \"0.57615745\",\n \"text\": \"def diameter(self):\\n return max(starmap(self.distance, combinations(self._graph, 2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36be5eafe929a4e0e67fcf30e7e43cf8\",\n \"score\": \"0.57609415\",\n \"text\": \"def set_size(self, size):\\r\\n\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f03de8a762801128e00ce22aac71ebc\",\n \"score\": \"0.5749086\",\n \"text\": \"def set_radius( self, radius ):\\n self.radius = radius\\n self.invalidate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f03de8a762801128e00ce22aac71ebc\",\n \"score\": \"0.5749086\",\n \"text\": \"def set_radius( self, radius ):\\n self.radius = radius\\n self.invalidate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b00b72d4670fc6713a10f0c1b8f0605\",\n \"score\": \"0.5734548\",\n \"text\": \"def set_value(self, width, height):\\n self.__width = width\\n self.__height = height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e2c72bb0e1764ef56fef43638534b9a\",\n \"score\": \"0.57194686\",\n \"text\": \"def set_fraction(self, value):\\n if value < 0:\\n value *= -1\\n value = min(value, 1)\\n if self.horizontal:\\n width = int(self.width * value)\\n height = self.height\\n else:\\n width = self.width\\n height = int(self.height * value)\\n self.canvas.coords(self.meter, self.xpos, self.ypos,\\n self.xpos + width, self.ypos + height)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8b48f6f4293c11219299e53e0aaacce\",\n \"score\": \"0.5716819\",\n \"text\": \"def thickness(self, th: float) -> None:\\n self._lvis.thickness = th\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee00dca4f32972b00c4a2e790994ce6a\",\n \"score\": \"0.57103926\",\n \"text\": \"def set_size(self, size):\\n self.set_property(\\\"size\\\", size)\\n self.emit(\\\"appearance_changed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10cc5b03ead2e040c2475848956152d6\",\n \"score\": \"0.57092535\",\n \"text\": \"def setSize(self, sizeX, sizeY):\\n self.size = LVecBase2i(sizeX, sizeY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4e9d41ffa82d1e83950508322e8c850\",\n \"score\": \"0.5688525\",\n \"text\": \"def setDimensions(self, dim):\\n self.dim = dim\\n L, W, H = dim\\n x, y, z = self.coords\\n self.vertices[:] = [[x-0.5*L, y-0.5*W, z-0.5*H],\\n [x-0.5*L, y+0.5*W, z-0.5*H],\\n [x+0.5*L, y+0.5*W, z-0.5*H],\\n [x+0.5*L, y-0.5*W, z-0.5*H],\\n [x-0.5*L, y-0.5*W, z+0.5*H],\\n [x-0.5*L, y+0.5*W, z+0.5*H],\\n [x+0.5*L, y+0.5*W, z+0.5*H],\\n [x+0.5*L, y-0.5*W, z+0.5*H]]\\n self.volume = L*W*H\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b18f5d928654a994cfc030e158be8e4\",\n \"score\": \"0.5669085\",\n \"text\": \"def measure_diode(self):\\n self.mode = 'diode'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e3f61423aa8e39de9982809b89314cd\",\n \"score\": \"0.56675017\",\n \"text\": \"def set_size(self, size):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9239fd74a378c7247109906676d52e5c\",\n \"score\": \"0.5664343\",\n \"text\": \"def sized(self, size: float):\\n self._p.size = size\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0209b3616ad8d3caada9093541843702\",\n \"score\": \"0.5663205\",\n \"text\": \"def setSize(self, size):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79cc7fc15f9595982a4337f696b4555c\",\n \"score\": \"0.56621206\",\n \"text\": \"def _setHeight(self, value):\\r\\n self.size.height = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6da91e7a714dd88e3d86a53c275565bc\",\n \"score\": \"0.5637468\",\n \"text\": \"def set_size(self, size):\\n self.size = size\\n self.call()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e2bc91a482f63e79141f56a9a7fa5f6\",\n \"score\": \"0.5637214\",\n \"text\": \"def set_size(self, xs, ys, zs):\\n self.cone.set_size(xs*1.0, ys*1.0, zs*1.0)\\n self.tip.set_size(xs*0.15, ys*0.15, zs*0.15)\\n self.tip.set_location(self.cone.getX(), self.cone.getY() + self.cone.ySize/0.7, self.cone.getZ())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55da39fddd18e01265e90b7a3b94b334\",\n \"score\": \"0.56249297\",\n \"text\": \"def set_dx(self, dx):\\n assert dx >= 0., \\\"dx must be nonnegative\\\"\\n self.dx = float(dx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"938e1d60942e9c117b41fb0b011245b2\",\n \"score\": \"0.5608492\",\n \"text\": \"def set_radius(self, radius: float) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"454238b3cd47cc4f3ad57be430b735bd\",\n \"score\": \"0.55843496\",\n \"text\": \"def set_size(self, width, height):\\n self.left = -width / 2\\n self.right = width / 2\\n if self.bottom_up:\\n self.top = -height / 2\\n self.bottom = height / 2\\n else:\\n self.top = height / 2\\n self.bottom = -height / 2\\n self.width = width\\n self.height = height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f46a0897ff81df55d02747c985965b9\",\n \"score\": \"0.55642694\",\n \"text\": \"def _radius_changed(self):\\n\\n diameter = self.radius * 2\\n self.bounds = [diameter, diameter]\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0df08e9a8116477c2242459b447a61db\",\n \"score\": \"0.556325\",\n \"text\": \"def setDensity(self,zRange, rho):\\n self.setProperty(self.rho, zRange, rho)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf17474110c96e3c77a30412be41cf26\",\n \"score\": \"0.55541277\",\n \"text\": \"def set_radius(self):\\n self._radius = (\\n (self._location[0]-1,self._location[1]-1),\\n (self._location[0]-1,self._location[1]),\\n (self._location[0]-1,self._location[1]+1),\\n (self._location[0],self._location[1]+1),\\n (self._location[0]+1,self._location[1]+1),\\n (self._location[0]+1,self._location[1]),\\n (self._location[0]+1,self._location[1]-1),\\n (self._location[0],self._location[1]-1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"762785a66468fefe30c27c2a58e8699d\",\n \"score\": \"0.55431616\",\n \"text\": \"def SetDiscRadius(self, _arg: 'double const') -> \\\"void\\\":\\n return _itkHoughTransform2DLinesImageFilterPython.itkHoughTransform2DLinesImageFilterFF_SetDiscRadius(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b12832229cb2560003a8df5b7c12b19\",\n \"score\": \"0.5542856\",\n \"text\": \"def density(self, value):\\n self._density = value\\n if self._ipoly is not None:\\n self.densities[self._ipoly] = value\\n self.polygons[self._ipoly].set_color(self._density2color(value))\\n # self._update_data()\\n self._update_data_plot()\\n self.canvas.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3bca098f2e33d3d96a4ff5c81709832\",\n \"score\": \"0.55379707\",\n \"text\": \"def set_box_size(self, size):\\n self.height_offset = size\\n self.height = self.y + self.height_offset\\n self.width_offset = size\\n self.width = self.x + self.width_offset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"451feab23deedf6d4012ac4029843b90\",\n \"score\": \"0.5528222\",\n \"text\": \"def set_size(self, options):\\r\\n\\r\\n # Set dimensions or scale for the chart.\\r\\n self.width = options.get('width', self.width)\\r\\n self.height = options.get('height', self.height)\\r\\n self.x_scale = options.get('x_scale', 1)\\r\\n self.y_scale = options.get('y_scale', 1)\\r\\n self.x_offset = options.get('x_offset', 0)\\r\\n self.x_offset = options.get('y_offset', 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4e57727699ee763bcc898394042d13d\",\n \"score\": \"0.54924905\",\n \"text\": \"def SetThickness(self, num):\\n self.thickness = num\\n self.pen = wx.Pen(self.colour, self.thickness, wx.SOLID)\\n self.Notify()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7dda58e5276fb465efd615c98eaab08\",\n \"score\": \"0.54830927\",\n \"text\": \"def setDim(self,dim):\\n self.dim = dim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dce63ed407cf315881c0b72a8e6631e\",\n \"score\": \"0.5478455\",\n \"text\": \"def set_size(self, xs=1.0, ys=1.0, zs=1.0):\\n self.xSize = xs\\n self.ySize = ys\\n self.zSize = zs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08efe673d7479c501228a51a6a8d0411\",\n \"score\": \"0.5474349\",\n \"text\": \"def setDp(self, dp): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89be1fbfca8beab5cd6f04defd190868\",\n \"score\": \"0.54719377\",\n \"text\": \"def set_size(self, size):\\n if size < 2:\\n raise ValueError(\\\"Size must be greater than 1.\\\")\\n self.size = size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44bb93382ce0d0fae40990ed08af856f\",\n \"score\": \"0.5466153\",\n \"text\": \"def _set_pixel_size(self) -> None:\\n if self.constellation in [\\n Constellation.L8,\\n Constellation.L9,\\n Constellation.L7,\\n ] or (\\n self.constellation in [Constellation.L4, Constellation.L5]\\n and self.instrument == LandsatInstrument.TM\\n ):\\n pixel_size = 30.0\\n else:\\n pixel_size = 60.0\\n\\n self.pixel_size = pixel_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18095b36ae3497c75e37d1975fce6f65\",\n \"score\": \"0.5446678\",\n \"text\": \"def test_diameter():\\n c1 = Circle(10)\\n c2 = Circle(4)\\n\\n assert c1.diameter == 20\\n assert c2.diameter == 8\\n\\n c2.radius = 12\\n c1.radius = 180\\n assert c2.diameter == 24\\n assert c1.diameter == 360\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d83c9faa49c8787b20a531664fc1ead4\",\n \"score\": \"0.5443408\",\n \"text\": \"def setPatchSize(self, patchSizeX, patchSizeY):\\n self.patchSize = LVecBase2i(patchSizeX, patchSizeY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44d68aeb8546298576b01c17b39c2715\",\n \"score\": \"0.54306895\",\n \"text\": \"def __setattr__(self, name, value):\\r\\n if name in ('cx', 'cy'):\\r\\n value_str = str(int(value))\\r\\n self.set(name, value_str)\\r\\n else:\\r\\n super(CT_SlideSize, self).__setattr__(name, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"600b331502dbf03c007ef2f407d6eb79\",\n \"score\": \"0.54270935\",\n \"text\": \"def setSize(self, x=None, y=None, z=None, size=None):\\n if size is not None:\\n x = size.x()\\n y = size.y()\\n z = size.z()\\n self.__size = [x, y, z]\\n self.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d11e27c1b0c940b5b5620e565a8369e7\",\n \"score\": \"0.54261667\",\n \"text\": \"def set_dy(self, dy):\\n assert dy >= 0., \\\"dy must be nonnegative\\\"\\n self.dy = float(dy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4baebfb8054d85aa42a1c5e5986725e2\",\n \"score\": \"0.54051864\",\n \"text\": \"def setPixelSize(self, pixel_size):\\n self.hdf5.attrs['pixel_size'] = pixel_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b00c6f038307e2f7d5b09c612daccf1e\",\n \"score\": \"0.5402376\",\n \"text\": \"def set_mdot_charge(self): \\n\\n self.mdot_charge =( \\n (self.RPM / 2. * self.displacement * self.eta_V *\\n self.air.rho) / 60.\\n ) \\n # charge flow (kg/s) in engine\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afe88b6fab3928584dffe948cdc9fd0b\",\n \"score\": \"0.53907526\",\n \"text\": \"def size(self, size):\\n\\n\\t\\tif self.fixed:\\n\\t\\t\\traise ValueError('cannot change the size of fixed dimension {}'.format(self.name))\\n\\n\\t\\t# size must be a postive integer\\n\\t\\tif size >= 0 and type(size) == int:\\n\\t\\t\\tself._size = size\\n\\t\\telse:\\n\\t\\t\\traise TypeError('size must be positive int')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91ba1656824a8632f2459354105b448f\",\n \"score\": \"0.53904915\",\n \"text\": \"def set_size(self, size):\\n self._check_scene_editor()\\n self.scene_editor.set_size(size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab4a82cee33ccb00484a0248f122b88d\",\n \"score\": \"0.5373509\",\n \"text\": \"def setDof(self, dof):\\n self.dof = dof\\n self.norm = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f1f31cc85dd8747286d3867eca52793\",\n \"score\": \"0.53572285\",\n \"text\": \"def setDesiredVelocity(self, dxdes):\\n\\t\\tself.dxdes = dxdes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"308baea7b07df81c23d77107e5280195\",\n \"score\": \"0.5340301\",\n \"text\": \"def set_aperture(self, aperture_size, sky_annulus=None):\\n\\n self.aperture_size = aperture_size\\n if isinstance(sky_annulus, (list, tuple)) and len(sky_annulus) == 2:\\n self.sky_annulus = sky_annulus\\n else:\\n self.sky_annulus = (2.0 * self.aper, 4.0 * self.aper)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60c0370c307b54599c7e956c39c1de2f\",\n \"score\": \"0.5333745\",\n \"text\": \"def _set_height(self, height: int) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2184447ffeb416252f5c4b032649ace\",\n \"score\": \"0.5308759\",\n \"text\": \"def set_tick(self, size: float = 1) -> None:\\n self.set_dxf_attrib(\\\"dimtsz\\\", size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5e413d26ce490de4279852532162627\",\n \"score\": \"0.53087366\",\n \"text\": \"def set_size(self, new_size):\\n self.size = new_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d193d1a1c4295dd9bab67272222355aa\",\n \"score\": \"0.5305684\",\n \"text\": \"def setPlotSize(self, _plotSizeWidth, _plotSizeHeight): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"659eda28dde6b456c359fdffcbd9887f\",\n \"score\": \"0.5302359\",\n \"text\": \"def __init__(self, size=6):\\n self.sides = size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fcada28f08f7e6ad9d69d368cf88cb9\",\n \"score\": \"0.53008664\",\n \"text\": \"def SetSize(self, value):\\n\\n self._size = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"357b36843ef2a421d8db6d3a6b344ee4\",\n \"score\": \"0.53003496\",\n \"text\": \"def diameter(self):\\n if self.__Order == 0: return 0\\n return self.distance_matrix().max()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec660baa7a7c50eeaa5640e354d2cabe\",\n \"score\": \"0.5296701\",\n \"text\": \"def circum_ference(diameter):\\n return 22*diameter/7\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7f4e2a6cd8eadcf6833d75943730fb7\",\n \"score\": \"0.5290093\",\n \"text\": \"def setSize(self, width, height):\\n self.WIDTH = width\\n self.HEIGHT = height\\n self.PIXELCOUNT = self.WIDTH * self.HEIGHT\\n self.progressStep = self.PIXELCOUNT / 100\\n self.camera = Camera(HomVector(0, 0, 6, 1), HomVector(0, 0, 0, 1), HomVector(0, 1, 0, 0), 45, self.WIDTH, self.HEIGHT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95c55420b28ba1ac428a6f8a31c656bd\",\n \"score\": \"0.5280665\",\n \"text\": \"def radius(self, radius):\\n\\n self._radius = radius\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95c55420b28ba1ac428a6f8a31c656bd\",\n \"score\": \"0.5280665\",\n \"text\": \"def radius(self, radius):\\n\\n self._radius = radius\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbcefa294f9801516cc919d9bb8b2084\",\n \"score\": \"0.5267082\",\n \"text\": \"def density(self, value):\\n self._data[\\\"density\\\"] = float(value)\\n self._cache.delete(\\\"mass_properties\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"212fb16280274096f5b36e5653685767\",\n \"score\": \"0.5265638\",\n \"text\": \"def set_size(size):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bd5c9671dcee33c9929f4f32e76d89e\",\n \"score\": \"0.5263938\",\n \"text\": \"def set_size(self, width, height):\\n\\n self.width = width\\n self.height = height\\n self.render_list.size(width, height)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eadee9f06119520e665d432a1ba68db\",\n \"score\": \"0.5262886\",\n \"text\": \"def radius(self, radius: float):\\n\\n self._radius = radius\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1aedf8d2b58ece5d986463a75957809\",\n \"score\": \"0.5258798\",\n \"text\": \"def _changeNeuronSize(self,neuron,new_radius):\\r\\n\\r\\n x = neuron[\\\"x\\\"]\\r\\n y = neuron[\\\"y\\\"]\\r\\n\\r\\n # point 1\\r\\n UL_x = self._convertToPixels(x - new_radius) + 1\\r\\n UL_y = self._convertToPixels(y - new_radius) + 1\\r\\n # point 2\\r\\n LR_x = self._convertToPixels(x + new_radius)\\r\\n LR_y = self._convertToPixels(y + new_radius)\\r\\n\\r\\n self.canvas.coords(neuron[\\\"circle\\\"][\\\"object\\\"], UL_x, UL_y, LR_x, LR_y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a0161464fac0dd184f54d8cf74308af\",\n \"score\": \"0.5254805\",\n \"text\": \"def topo_diameter(self):\\n import math\\n \\n Temp = 0\\n for i in range(self.nodenum):\\n for j in range(self.nodenum):\\n pathlist = []\\n self.pathij(i, j, pathlist)\\n distance = []\\n \\n for k in range(len(pathlist)):\\n distance.append(len(pathlist[k]) - 1)\\n \\n if(len(distance) == 0):\\n continue\\n else:\\n if(min(distance) >= Temp):\\n Temp = min(distance)\\n \\n self.topodiameter = Temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb1ef5db6543e8745a1ac1ef89cfd9d\",\n \"score\": \"0.52368236\",\n \"text\": \"def set_dc(self, dc):\\n self.dc = float(dc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"001918febd29662654208c30aa10985f\",\n \"score\": \"0.5233458\",\n \"text\": \"def SetOtherSize( self, size ):\\n\\t\\tif isinstance( size, (int,long)):\\n\\t\\t\\tself.otherSize = size\\n\\t\\telse:\\n\\t\\t\\tif self.dockSide in ('l','r'):\\n\\t\\t\\t\\tself.otherSize = size[0]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.otherSize = size[1]\\n\\t\\treturn self.otherSize\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcc7a652747410f3efb57533467544e1\",\n \"score\": \"0.5224733\",\n \"text\": \"def size(self, size):\\n self._size = size\\n self._resize_img()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bd211125b5958b5cc2c2e1adefcc990\",\n \"score\": \"0.5221301\",\n \"text\": \"def set_voxel_size(self, size):\\n self.voxel_size = size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeed35ca2196b1dcc999ceb16b014499\",\n \"score\": \"0.51937336\",\n \"text\": \"def __init__(self, (x,y), size):\\n self.x = x\\n self.y = y\\n self.size = size\\n self.colour = (0,128,255)\\n self.thickness = 1\\n self.speed = 0.01\\n self.angle = math.pi/2\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":88,"cells":{"query_id":{"kind":"string","value":"8c9d60bda2e7faa6539e0ead3b569e28"},"query":{"kind":"string","value":"Initialize the emitter with an event loop."},"positive_passages":{"kind":"list like","value":[{"docid":"3d57c407a3975b589594f2da47098ef7","score":"0.673187","text":"def __init__(self, loop=None):\n self._loop = loop or asyncio.get_event_loop()\n self._listeners = collections.defaultdict(list)\n self._once = collections.defaultdict(list)\n self._max_listeners = self.DEFAULT_MAX_LISTENERS","title":""}],"string":"[\n {\n \"docid\": \"3d57c407a3975b589594f2da47098ef7\",\n \"score\": \"0.673187\",\n \"text\": \"def __init__(self, loop=None):\\n self._loop = loop or asyncio.get_event_loop()\\n self._listeners = collections.defaultdict(list)\\n self._once = collections.defaultdict(list)\\n self._max_listeners = self.DEFAULT_MAX_LISTENERS\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e0c3369da3f4c1abd76581fe10e69f2f","score":"0.69916075","text":"def __init__(self, loop=None) -> None:\n\n self.loop = loop if loop else asyncio.get_event_loop()","title":""},{"docid":"1fb97ccd138ea175404892d6753ba676","score":"0.6815965","text":"def setUp(self):\n self.loop = asyncio.new_event_loop()\n asyncio.set_event_loop(self.loop)","title":""},{"docid":"1fb97ccd138ea175404892d6753ba676","score":"0.6815965","text":"def setUp(self):\n self.loop = asyncio.new_event_loop()\n asyncio.set_event_loop(self.loop)","title":""},{"docid":"587b2fbc314808e7183dcfc4a9280b14","score":"0.6783072","text":"def _init_process(self) -> None:\n # create new event loop after fork\n asyncio.get_event_loop().close()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)","title":""},{"docid":"eaf4fdb1ba981668590d4593254d7e8d","score":"0.67776525","text":"def __init__(self, loop: Optional[AbstractEventLoop] = None) -> None:\n self.loop: AbstractEventLoop = Helper.ensure_loop(loop)\n self._shutdown_event: Event = Event(loop=self.loop)\n self._shutdown_from_signal: bool = False\n\n # SIGINT for local debugging\n self.loop.add_signal_handler(SIGINT, self._initiate_shutdown_signal)\n self.loop.add_signal_handler(SIGTERM, self._initiate_shutdown_signal)","title":""},{"docid":"9bc256bc25d8441dc118b3d3a70ac00d","score":"0.6658748","text":"def new_event_loop():\n return Loop()","title":""},{"docid":"9556f5dfb622bc049a47a7a5df6ffd87","score":"0.65241927","text":"def create_io_loop(self):\n assert self.ioloop is None # This should only ever be run once\n\n def _run(loop):\n asyncio.set_event_loop(loop)\n loop.run_forever()\n\n self.ioloop = asyncio.new_event_loop()\n self.ioloop.set_exception_handler(self._ioloop_exc_handler)\n threading.Thread(target=partial(_run, self.ioloop), name=\"Thread-asyncio\", daemon=True).start()","title":""},{"docid":"86e89cba19b488613da5d7df70149df1","score":"0.638678","text":"def __init__(self, event_manager):\r\n self.__eventManager = event_manager","title":""},{"docid":"1d3c4f78fa64fa0b3c56b3f2d0239296","score":"0.6357002","text":"def __init__(self, options: Options, loop: Optional[asyncio.AbstractEventLoop] = None):\n self.loop = loop or asyncio.get_event_loop()\n super().__init__(self.loop)\n self.options = options\n\n self._future = self.loop.create_future()\n self._engine = EngineService(self)\n self._socket = SocketService(self)\n\n self.on('message_received', self._on_message_received)\n self.on('socket_open', self._on_socket_open)","title":""},{"docid":"ca43c8b669973b423461a191a4ed877c","score":"0.6318867","text":"def set_event_loop(self, loop: typing.Optional[asyncio.AbstractEventLoop]) -> None:\n self._local._set_called = True\n assert loop is None or isinstance(loop, asyncio.AbstractEventLoop)\n self._local._loop = loop\n\n if (\n self._watcher is not None\n and threading.current_thread() is threading.main_thread()\n ):\n self._watcher.attach_loop(loop)","title":""},{"docid":"228ed8ce030c442248b06bbf93163688","score":"0.63083553","text":"def event_loop():\n return asyncio.get_event_loop()","title":""},{"docid":"e494d5dc916560caa19c75f4512e8ff3","score":"0.62807494","text":"def get_event_loop(self):\n loop = super().get_event_loop()\n # Do something with loop ...\n return loop","title":""},{"docid":"122243c37574fad0bc0a1edb232ec86d","score":"0.6272463","text":"async def _init_main_loop(self):","title":""},{"docid":"c501c2b433049be04684f942b0eaed70","score":"0.6232068","text":"def __init__(self,\n prodables: List[Prodable]=None,\n loop=None,\n debug=False,\n autoStart=True):\n self.prodables = list(prodables) if prodables is not None \\\n else [] # type: List[Prodable]\n\n # if sys.platform == 'linux':\n # asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())\n\n if loop:\n self.loop = loop\n else:\n try:\n # if sys.platform == 'win32':\n # loop = asyncio.ProactorEventLoop()\n # asyncio.set_event_loop(loop)\n evl = asyncio.get_event_loop()\n if evl.is_closed():\n raise RuntimeError(\"event loop was closed\")\n except Exception as ex:\n logger.debug(\"Looper could not get default event loop; \"\n \"creating a new one: {}\".format(ex))\n # Trying out uvloop for linux\n evl = asyncio.new_event_loop()\n asyncio.set_event_loop(evl)\n self.loop = evl\n\n logger.info(\"Starting up indy-node\")\n self.runFut = self.loop.create_task(self.runForever()) # type: Task\n self.running = True # type: bool\n\n self.loop.set_debug(bool(int(os.getenv('PYTHONASYNCIODEBUG', debug))))\n\n # TODO: uncomment this when python bug fixed (not just closed, but solved!)\n # https://bugs.python.org/issue23548\n #\n # signals = [item for item in dir(signal)\n # if item.startswith(\"SIG\") and item[3] != \"_\"]\n\n self.signals = [\"SIGINT\", \"SIGTERM\"]\n\n setSignal = \\\n signal.signal if sys.platform == 'win32' \\\n else self.loop.add_signal_handler\n\n for sigName in self.signals:\n try:\n logger.debug(\"Setting handler for {}\".format(sigName))\n sigNum = getattr(signal, sigName)\n setSignal(sigNum, self.handleSignal)\n except RuntimeError as e:\n logger.debug(\n \"Cannot set handler for {} because {}\".format(sigName, e))\n\n self.autoStart = autoStart # type: bool\n if self.autoStart:\n self.startall()","title":""},{"docid":"0c50fe5b3413c092ae58fff6b9ef481e","score":"0.61680555","text":"def __init__(\n self, loop: asyncio.AbstractEventLoop, logger: logging.Logger, probe_routine: Callable[[], Awaitable[bool]]\n ):\n super().__init__()\n self.loop = loop\n self.startup_complete = loop.create_future()\n self.disconnected = loop.create_future()\n self.exited = loop.create_future()\n self._logger = logger\n self._probe = probe_routine","title":""},{"docid":"426600a2b9f22e238812ea45c9e8e4aa","score":"0.6110142","text":"def __init__(\n self, loop: asyncio.AbstractEventLoop, identifier: Optional[str] = None\n ):\n super().__init__()\n self.loop = loop\n self.identifier = identifier","title":""},{"docid":"097d78fd0462e27b001b785319986bce","score":"0.6069578","text":"def __init__(self, **kwargs):\n self.communicator = self.open_listener_connection(DATA[\"id\"])\n self.loop = asyncio.get_event_loop()\n asyncio.ensure_future(self.communicator.start_listening())\n super().__init__(**kwargs)","title":""},{"docid":"10117bdba94a726d593aef6a9989c90f","score":"0.6060124","text":"def init(self):\n listener_class = get_listener_class()\n logger.info('Using %s as a listener' % (listener_class, ))\n self.listener = listener_class()","title":""},{"docid":"cdbfd4efe2b6ecfc2a3915549f7f6f6e","score":"0.601411","text":"async def __aenter__(self):\n self.loop = asyncio.get_event_loop()\n await self.setup()\n return self","title":""},{"docid":"a1eb6bf9ace8dc84eeb62b9e8c6b65be","score":"0.5978569","text":"def setup(self, owner: Entity, loop: ActionLoop) -> None:\n self.owner = owner\n self.loop = loop\n self.start_tick = loop.get_tick()","title":""},{"docid":"a80e82fa995ec7b8e5dea7bae7a9da51","score":"0.5948992","text":"def event_loop(self):\n return self.__loop_from_run_thread","title":""},{"docid":"7546a93ea888d8631d4b63076bf72997","score":"0.59326774","text":"def autonomousInit(self):\r\n self.auto_loop_counter = 0","title":""},{"docid":"09ec980a5d9135d831e02fa5b1f49941","score":"0.59053296","text":"def event_loop():\n loop = uvloop.Loop()\n yield loop\n loop.close()","title":""},{"docid":"512fa841417a0688afe9fbbba9c995b9","score":"0.587469","text":"def _init_events(self):\n self._init_pipe()\n LOG.info('Starting native event thread')\n event_thread = native_threading.Thread(target = self.read_from_tap)\n event_thread.setDaemon(True)\n event_thread.start()\n LOG.info('Starting green dispatch thread')\n dispatch_thread = hub.spawn(self.dispatch_thread)","title":""},{"docid":"d847e53bc910548e9b3eba5e8210624c","score":"0.5860455","text":"def __init__(self, args=None):\n self.stop_request = Event()\n self.force_stop = False\n self.started = False\n self.completed = False\n self.config = None\n self.args = args\n self.workerPool = []\n self.manager = None\n self._setup_loggers(args=args)\n # attach to the logging queue\n self.logger.info(\"Logging Setup Complete.\")\n\n self._generator_queue_size = getattr(self.args, \"generator_queue_size\", 500)\n if self._generator_queue_size < 0:\n self._generator_queue_size = 0\n self.logger.info(\n \"Set generator queue size:{}\".format(self._generator_queue_size)\n )\n\n if self.args and \"configfile\" in self.args and self.args.configfile:\n self._load_config(self.args.configfile, args=args)","title":""},{"docid":"3ac5fd2daba7cc0a11c8782f7babb4d6","score":"0.58336735","text":"def __init__(self, loop, run, *args, **kwargs):\n self.loop = loop\n self.run = run\n self.args = args\n self.kwargs = kwargs\n self.watcher = None","title":""},{"docid":"4ce2e6580fda6ff071869c45cc7ddecf","score":"0.5827722","text":"def event_loop():\n loop = asyncio.get_event_loop_policy().new_event_loop()\n yield loop\n loop.close()","title":""},{"docid":"b2dc75ab97179382e49fbfdeed970b17","score":"0.5813702","text":"async def on_start(self):\n await self.real_init()","title":""},{"docid":"a30fbb44ffd972dfb5dbdf5ad583dc6e","score":"0.58116555","text":"def get_or_create_event_loop() -> asyncio.AbstractEventLoop:\n try:\n loop = asyncio.get_event_loop()\n except (DeprecationWarning, RuntimeError):\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n\n return loop","title":""},{"docid":"7ca0b75fa14a64a7bc1427da032f763c","score":"0.5756037","text":"async def on_init(self):\n pass","title":""},{"docid":"990ab8967ff19dec856ebfdfd6ec89b4","score":"0.57422704","text":"def test_eventloop_factory(get_io_loop):\n assert isinstance(get_io_loop, eventloop.NonBlockingPoll)","title":""},{"docid":"3cef879a89efbb8de4a58922132477c7","score":"0.5739024","text":"def main_loop(self):\n\n raise NotImplementedError()","title":""},{"docid":"d56d500419201411cc89d5215b7a8419","score":"0.5714047","text":"def main_loop(self):\n LOGGER.info('Entering main event loop...')\n while self._handle_faucet_events():\n pass","title":""},{"docid":"6a0dfce6f921340de1ba9eebbc105f6d","score":"0.5713494","text":"def event_loop(request: FixtureRequest) -> Iterator[asyncio.AbstractEventLoop]:\n loop = asyncio.get_event_loop_policy().new_event_loop()\n yield loop\n loop.close()","title":""},{"docid":"4b896883eb6a9b880b4c64f1bea0e1ff","score":"0.57091033","text":"def on_loop_start(self):\n pass","title":""},{"docid":"d4d9bcbcdd80a51ba907c39943cf77ab","score":"0.56964403","text":"def __init__(self, event_queue):\n\n Thread.__init__(self)\n self.error_handler = NullEventHandler()\n self._event_queue = event_queue\n self._stop_flag = 0","title":""},{"docid":"8bc37cf9e8894adb53061cdcf4d6f9fe","score":"0.5687923","text":"def new_event_loop(self) -> PyObjCEventLoop:\n return PyObjCEventLoop()","title":""},{"docid":"c27b05432f7e3b90299b283012b2e3ab","score":"0.5656225","text":"def init_kernel(self): # type: () -> None\n # Reset the core\n self.core.reset()\n\n # Set direction of pins\n self.ttl_out.output()\n self.ttl_in.input()\n\n # Wait until event is submitted\n self.core.wait_until_mu(now_mu())\n\n # Record DMA burst\n self._record_dma_burst()","title":""},{"docid":"59cf8d38e2e9094386f033fceb0df908","score":"0.56523794","text":"def initialize(self):\n self.log(self.args)\n\n self.remote = self.args[\"remote\"]\n self.media_player = self.args[\"media_player\"]\n\n self.listen_event(self.handle_event, \"deconz_event\")","title":""},{"docid":"3952c92236e7f2db9eb7dd55b6e730d9","score":"0.5652089","text":"def setup(self):\n\n # NOTE: this thread is never closed\n thread = threading.Thread(target=Server(self.nao).serve)\n thread.daemon = True\n thread.start()\n\n self.nao.speak(\"Ready to connect!\")","title":""},{"docid":"b6baec542f13bda1f55a48bfe794d757","score":"0.5641426","text":"def event_loop(self):\n self._stop = False\n while not self._stop:\n self.handle()","title":""},{"docid":"8ad77c761671c4a4d5b68ab5ea83c1a5","score":"0.56314456","text":"def initialize(self):\n #self.listen_event(self.receive_telegram_text, 'telegram_text')\n self.settings = self.get_app(\"settings\")\n self.listen_event(self.receive_telegram_command, 'telegram_command')\n self.listen_event(self.receive_telegram_callback, 'telegram_callback')","title":""},{"docid":"59eed19f8de179da931e19028cffc616","score":"0.5625552","text":"def __init__(self, target):\n self._target = target\n self._active = Event()\n self._thread = None","title":""},{"docid":"e96de400566530d0753ec5f1066bc016","score":"0.56232387","text":"async def init(self):\n pass","title":""},{"docid":"3b8ae8f50c71825ac83f06cd5f4aeef6","score":"0.56219727","text":"def __init__(self, context, detach=False):\n self.context = context\n\n # the thread pool to convert block execution of task into async process\n self.thread_pool = ThreadPoolExecutor(4)\n self.wait_queue = queues.Queue()\n self.exec_queue = queues.Queue()\n\n def engine_loop():\n _ioloop = ioloop.IOLoop.current()\n _ioloop.add_callback(self.daemon_loop)\n _ioloop.start()\n\n # if the detached mode is enabled\n # use a seperated thread to boot the io-loop\n self.loop_thread = None\n if detach:\n logger.debug(\"engine running in detach mode\")\n self.loop_thread = threading.Thread(target=engine_loop)\n self.loop_thread.start()","title":""},{"docid":"1271b33e391bd72271f95d266c6d4a4a","score":"0.5619265","text":"def event_manager(**kwargs):\n manager = EventManager(**kwargs)\n return manager.run()","title":""},{"docid":"aa895b64990f0c04782569a15c978d40","score":"0.5610994","text":"def create_listener(self):\n self._listener = ListenerThread(self._app, self.options)","title":""},{"docid":"1ecfaf521575c5b3322ba8ca7f424a34","score":"0.56055164","text":"def start(self):\n self.run = True\n asyncio.ensure_future(self.listen(), loop=self.loop)","title":""},{"docid":"c9d72f324e9ea93f7760a45b7bf96811","score":"0.5600224","text":"def start(self, loop):\n raise NotImplementedError(\"subclass {} should implement this method\"\n .format(self))","title":""},{"docid":"2f534f24c501f10f24ed4736234bfda6","score":"0.55800086","text":"async def initialize(self):\n pass","title":""},{"docid":"02f5810db0693c43783ba81e1854bd30","score":"0.55662316","text":"def init(self):\n self.runner.init()","title":""},{"docid":"2fe39f58e2bff124fd8b391ff5ca11ea","score":"0.5553751","text":"def setup(self):\n # set the Event lock\n self.lock.set()\n\n # setup configuration\n self.config = self.get_config()\n\n if self.config.get('cli_command'):\n self.handle_cli_command(self.config['cli_command'])\n sys.exit()\n\n if self.config['debug']:\n syslog(\n LOG_INFO,\n 'py3status started with config {}'.format(self.config)\n )\n\n # setup i3status thread\n self.i3status_thread = I3status(\n self.lock,\n self.config['i3status_config_path'],\n self.config['standalone']\n )\n if self.config['standalone']:\n self.i3status_thread.mock()\n else:\n self.i3status_thread.start()\n while not self.i3status_thread.ready:\n if not self.i3status_thread.is_alive():\n err = self.i3status_thread.error\n raise IOError(err)\n sleep(0.1)\n if self.config['debug']:\n syslog(\n LOG_INFO,\n 'i3status thread {} with config {}'.format(\n 'started' if not self.config['standalone'] else 'mocked',\n self.i3status_thread.config\n )\n )\n\n # setup input events thread\n self.events_thread = Events(\n self.lock,\n self.config,\n self.modules,\n self.i3status_thread.config\n )\n self.events_thread.start()\n if self.config['debug']:\n syslog(LOG_INFO, 'events thread started')\n\n # suppress modules' ouput wrt issue #20\n if not self.config['debug']:\n sys.stdout = open('/dev/null', 'w')\n sys.stderr = open('/dev/null', 'w')\n\n # get the list of py3status configured modules\n self.py3_modules = self.i3status_thread.config['py3_modules']\n\n # get a dict of all user provided modules\n user_modules = self.get_user_modules()\n if self.config['debug']:\n syslog(LOG_INFO, 'user_modules={}'.format(user_modules))\n\n if self.py3_modules:\n # load and spawn i3status.conf configured modules threads\n self.load_modules(self.py3_modules, user_modules)\n else:\n # legacy behaviour code\n # load and spawn user modules threads based on inclusion folders\n self.load_modules(user_modules, user_modules)","title":""},{"docid":"7724df2272e36e68d0c1ce3729fa3a83","score":"0.5553123","text":"def create_listener(self, logger):\n self._event_mask = (CGEventMaskBit(kCGEventLeftMouseDown) |\n CGEventMaskBit(kCGEventFlagsChanged) |\n CGEventMaskBit(kCGEventKeyDown))\n self._event_tap = CGEventTapCreate(kCGHIDEventTap,\n kCGHeadInsertEventTap,\n kCGEventTapOptionDefault,\n self._event_mask,\n _callback_func,\n logger)\n\n self._runLoopSource = CFMachPortCreateRunLoopSource(\n kCFAllocatorDefault, self._event_tap, 0)\n CFRunLoopAddSource(CFRunLoopGetCurrent(), self._runLoopSource,\n kCFRunLoopCommonModes)\n CGEventTapEnable(self._event_tap, True)\n CFRunLoopRun()","title":""},{"docid":"38597fd1ba66c3c35ddae685e088130e","score":"0.55393517","text":"def main(self) -> None:\n self.loop.run()","title":""},{"docid":"3951c07b9b577bae2e7b522c31f73931","score":"0.5534345","text":"def start_event_listener(self):\n self._sdl.start_event_listener()","title":""},{"docid":"f5547529649ee9dc0b74674f0230ee2f","score":"0.55334944","text":"def install():\n __asyncio.set_event_loop_policy(EventLoopPolicy())","title":""},{"docid":"a601e1f5433201403735e306e35f22db","score":"0.5532086","text":"def start_glib_loop(cls):\n GObject.threads_init()\n Gst.init(None)\n\n cls.glib_loop = GObject.MainLoop()\n cls.glib_thread = threading.Thread(target=cls.glib_loop.run)\n cls.glib_thread.start()","title":""},{"docid":"e2bec545a3c07c9b40ae66a0f0c5893f","score":"0.5532003","text":"def run(self):\n self.running = True\n self.evManager.Post(InitializeEvent())\n while self.running:\n newTick = TickEvent()\n self.evManager.Post(newTick)","title":""},{"docid":"a4446993c8b673c88b433e564435202a","score":"0.55284584","text":"def run(self) -> None:\n if self._was_started.is_set() and not self._was_stopped.is_set():\n asyncio.set_event_loop(self._loop)\n self._loop.run_forever()\n else:\n raise Exception(\"Cannot call directly! Begin EventLoopThreadSafe by start method.\")","title":""},{"docid":"5de1abdf8072071e6c46b8dbe29a5585","score":"0.55279434","text":"def set_ioloop(self, ioloop=None):\n self._ioloop_set_to = ioloop","title":""},{"docid":"1dd075090003f8d5616f8e61016c1b39","score":"0.5518384","text":"def start(self):\n self._connection = self.connect()\n self._connection.ioloop.start()","title":""},{"docid":"487587861c4c2d1678e357d54e105fdf","score":"0.5509251","text":"async def session_start(self):\n self.ast_ctx.add_logger_handler(self.console)\n _LOGGER.info(\"Starting session %s\", self.global_ctx_name)\n\n self.tasks[\"housekeep\"] = {asyncio.create_task(self.housekeep_run())}\n self.tasks[\"startup_timeout\"] = {asyncio.create_task(self.startup_timeout())}\n\n self.iopub_server, self.iopub_port = await self.start_one_server(self.iopub_listen)\n self.heartbeat_server, self.heartbeat_port = await self.start_one_server(self.heartbeat_listen)\n self.control_server, self.control_port = await self.start_one_server(self.control_listen)\n self.stdin_server, self.stdin_port = await self.start_one_server(self.stdin_listen)\n self.shell_server, self.shell_port = await self.start_one_server(self.shell_listen)\n\n #\n # For debugging, can use the real ZMQ library instead on certain sockets; comment out\n # the corresponding asyncio.start_server() call above if you enable the ZMQ-based\n # functions here. You can then turn of verbosity level 4 (-vvvv) in hass_pyscript_kernel.py\n # to see all the byte data in case you need to debug the simple ZMQ implementation here.\n # The two most important zmq functions are shown below.\n #\n # import zmq\n # import zmq.asyncio\n #\n # def zmq_bind(socket, connection, port):\n # \"\"\"Bind a socket.\"\"\"\n # if port <= 0:\n # return socket.bind_to_random_port(connection)\n # # _LOGGER.debug(f\"binding to %s:%s\" % (connection, port))\n # socket.bind(\"%s:%s\" % (connection, port))\n # return port\n #\n # zmq_ctx = zmq.asyncio.Context()\n #\n # ##########################################\n # # Shell using real ZMQ for debugging:\n # async def shell_listen_zmq():\n # \"\"\"Task that listens to shell messages using ZMQ.\"\"\"\n # try:\n # _LOGGER.debug(\"shell_listen_zmq connected\")\n # connection = self.config[\"transport\"] + \"://\" + self.config[\"ip\"]\n # shell_socket = zmq_ctx.socket(zmq.ROUTER)\n # self.shell_port = zmq_bind(shell_socket, connection, -1)\n # _LOGGER.debug(\"shell_listen_zmq connected\")\n # while 1:\n # msg = await shell_socket.recv_multipart()\n # await self.shell_handler(shell_socket, msg)\n # except asyncio.CancelledError:\n # raise\n # except Exception:\n # _LOGGER.error(\"shell_listen exception %s\", traceback.format_exc(-1))\n # await self.housekeep_q.put([\"shutdown\"])\n #\n # ##########################################\n # # IOPub using real ZMQ for debugging:\n # # IOPub/Sub:\n # async def iopub_listen_zmq():\n # \"\"\"Task that listens to iopub messages using ZMQ.\"\"\"\n # try:\n # _LOGGER.debug(\"iopub_listen_zmq connected\")\n # connection = self.config[\"transport\"] + \"://\" + self.config[\"ip\"]\n # iopub_socket = zmq_ctx.socket(zmq.PUB)\n # self.iopub_port = zmq_bind(self.iopub_socket, connection, -1)\n # self.iopub_socket.add(iopub_socket)\n # while 1:\n # wire_msg = await iopub_socket.recv_multipart()\n # _LOGGER.debug(\"iopub received %s\", wire_msg)\n # except asyncio.CancelledError:\n # raise\n # except EOFError:\n # await self.housekeep_q.put([\"shutdown\"])\n # _LOGGER.debug(\"iopub_listen got eof\")\n # except Exception as err:\n # _LOGGER.error(\"iopub_listen exception %s\", err)\n # await self.housekeep_q.put([\"shutdown\"])\n #\n # self.tasks[\"shell\"] = {asyncio.create_task(shell_listen_zmq())}\n # self.tasks[\"iopub\"] = {asyncio.create_task(iopub_listen_zmq())}\n #","title":""},{"docid":"c1858a35b41b675f2f849587cc96c0d8","score":"0.5507766","text":"def __init__(self, original_emitter):\n self.original_emitter = original_emitter","title":""},{"docid":"ddfe7ea246c6dfe7934abbedd7b0643a","score":"0.5497893","text":"def start(self) -> None:\n self.start_async()\n assert self.startup_complete is not None # `start_async` will create it, but mypy doesn't know that\n self._loop.run_until_complete(self.startup_complete)","title":""},{"docid":"4dc9af9c4911d39aa5caa0739606a752","score":"0.5496606","text":"def __init__(self, **kwargs):\r\n kwargs['daemon'] = kwargs.get('daemon', True)\r\n super().__init__(**kwargs)\r\n\r\n self.running = threading.Event()\r\n self.running.set()","title":""},{"docid":"edb794732a685a4caeb0099c43856d80","score":"0.5488664","text":"def start(self):\n self.__reset_command_loop()\n self.__start_listener_thread()\n self.__start_command_thread()","title":""},{"docid":"13d7026ee0d04330d510be43bf1c8135","score":"0.54775965","text":"def init_kernel(self): # type: () -> None\n # Reset the core\n self.core.reset()\n\n # Set direction of pin\n self.ttl_out.output()\n\n # Wait until event is submitted\n self.core.wait_until_mu(now_mu())\n\n # Record DMA burst\n self._record_dma_burst()","title":""},{"docid":"c6edd84510fc3901d18e6e4bd9091634","score":"0.547686","text":"def _start_loop(self):\n self.th_loop = Thread(target=asyncore.loop, args=(), )\n self.th_loop.start()","title":""},{"docid":"ecce3b93380b5d278eb1f0f67433c8d6","score":"0.5474614","text":"def get_loop() -> asyncio.AbstractEventLoop:\n if os.name == \"nt\":\n # for subprocess' pipes on Windows\n loop = asyncio.ProactorEventLoop() # type: ignore\n asyncio.set_event_loop(loop)\n else:\n try:\n loop = asyncio.get_event_loop()\n except RuntimeError:\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n asyncio.set_child_watcher(ThreadedChildWatcher())\n return loop","title":""},{"docid":"895bdfe5ea69d4056eda76e8376f1e85","score":"0.5461658","text":"def start_event_loop(self, timeout=0):\n if hasattr(self, '_event_loop'):\n raise RuntimeError(\"Event loop already running\")\n id = wx.NewId()\n timer = wx.Timer(self, id=id)\n if timeout > 0:\n timer.Start(timeout * 1000, oneShot=True)\n self.Bind(wx.EVT_TIMER, self.stop_event_loop, id=id)\n\n # Event loop handler for start/stop event loop\n self._event_loop = wxc.EventLoop()\n self._event_loop.Run()\n timer.Stop()","title":""},{"docid":"f7bbc688dd349d33ac3c8def717f1cd6","score":"0.5458578","text":"def get_new_event_loop():\n if uvloop:\n return uvloop.new_event_loop()\n else:\n return asyncio.new_event_loop()","title":""},{"docid":"42fbc7a66f76816df4f995257d119afe","score":"0.54584116","text":"def __init__(self, *args, **kw):\n self.mutex = threading.Lock()\n if \"mutex\" not in kw:\n kw[\"mutex\"] = self.mutex\n EventDispatcherBase.__init__(self, *args, **kw)","title":""},{"docid":"44abfe11e59fa940dd54fea873e733d9","score":"0.5453497","text":"def initialize(self):\n print(\"Engine 1 is initializing. Registering device...\")\n self._registerDevice(\"device1\")\n self._setDevice(\"device1\", { \"time\" : self._time, \"timestep\": 0 })","title":""},{"docid":"0167afce13f1f510b52dbb26c1383e2b","score":"0.54530144","text":"def initialize_websockets_manager(self):\n self.manager = WebSocketManager()\n self.manager.start()","title":""},{"docid":"d29c3d07a5996614ef90e6117d47b709","score":"0.54521775","text":"def __init__(self):\n self._services = []\n self.backdoor_port = eventlet_backdoor.initialize_if_enabled(CONF)","title":""},{"docid":"e94a0ddb5945adc67bef4da14d0d7c9f","score":"0.5444111","text":"def setup(bot: Bot) -> None:\n bot.add_cog(Events(bot))","title":""},{"docid":"9a632e83ffabef8dbd4fd9d017fb93f0","score":"0.54405904","text":"def get_loop():\n global _BG_LOOP\n try:\n return _BG_LOOP\n except NameError:\n _BG_LOOP = asyncio.new_event_loop()\n _set_async_debug_and_log_level(_BG_LOOP)\n thread = Thread(target=_loop_main, args=(_BG_LOOP,))\n thread.daemon = True # <-- thread will exit when main thread exists\n thread.start()\n _setup_cleanup(_BG_LOOP, thread)\n return _BG_LOOP","title":""},{"docid":"e11221a005a964b171c516512bee266d","score":"0.5437474","text":"def initialize_listening(self):\n self.listened_time = 0","title":""},{"docid":"14d125ec782de84de7b90f1185683830","score":"0.5435087","text":"def connect(self, loop):\n raise NotImplementedError","title":""},{"docid":"0bdb1a3ab98835c3c41fc77abcb829a3","score":"0.5433524","text":"def __init__(self, middleman, config):\n self.analytics = Analytics(config)\n self.middleman = middleman\n self.thread = None\n\n server = Server(\n {'/': self.init},\n io_loop=IOLoop.current(),\n port=config.PORT,\n num_procs=1)\n\n server.start()\n self.server = server\n server.io_loop.add_callback(server.show, \"/\")\n server.io_loop.start()","title":""},{"docid":"4b6a75744dccca240e366093ff32ae91","score":"0.543057","text":"def initEvents(self):\r\n SceneManager().registerEventReader(self.do_action)","title":""},{"docid":"3c16d382f5ed9e2e495df4037bfea794","score":"0.5425537","text":"def start(self):\n if self._eager_init:\n self.bus.log(\n \"initializing Aglyph singleton and borg component objects\")\n self.init_singletons()\n self.init_borgs()\n self.bus.log(\"starting Aglyph dependency injection support\")\n self.bus.subscribe(\"aglyph-assemble\", self.assemble)\n self.bus.subscribe(\"aglyph-init-singletons\", self.init_singletons)\n self.bus.subscribe(\"aglyph-clear-singletons\", self.clear_singletons)\n self.bus.subscribe(\"aglyph-init-borgs\", self.init_borgs)\n self.bus.subscribe(\"aglyph-clear-borgs\", self.clear_borgs)\n self.bus.subscribe(\"aglyph-clear-weakrefs\", self.clear_weakrefs)","title":""},{"docid":"fe8dab6986fe4154855098026917c1f2","score":"0.5417963","text":"def initialize(self):\n self._load_timers()\n self._reset_timer_index()\n if self.active_timers:\n self.log.info(\"found {} active timers\".format(str(len(self.active_timers))))\n self._show_gui()\n self._start_display_update()\n self._start_expiration_check()\n\n # To prevent beeping while listening\n self.add_event(\"recognizer_loop:wakeword\", self.handle_wake_word_detected)\n self.add_event(\n \"mycroft.speech.recognition.unknown\", self.handle_speech_recognition_unknown\n )\n self.add_event(\"speak\", self.handle_speak)\n self.add_event(\"skill.timer.stop\", self.handle_timer_stop)","title":""},{"docid":"2d9b87adc86234be4355d912692e0ab4","score":"0.54103523","text":"def start_loop(self):\n pass","title":""},{"docid":"15d943ccc1a47b79f2ce5ab04a5f4aca","score":"0.54057145","text":"def io_loop(self):\n if self._io_loop is None:\n self._io_loop = DaemonIOLoop()\n self._io_loop._cod_daemon = self\n return self._io_loop","title":""},{"docid":"ef3ba4bb25dbc8b536a937736dcd924c","score":"0.5401579","text":"def get_loop():\n\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n try:\n loop = asyncio.get_event_loop()\n except RuntimeError:\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n return loop","title":""},{"docid":"745de160cfe20f6bd2a10d59f17d732e","score":"0.5398136","text":"def run(self):\n self._connection = self.connect()\n self._connection.ioloop.start()","title":""},{"docid":"a8bdddc8c0bba6c02e434c1d71958791","score":"0.53943837","text":"def get_event_loop(self) -> asyncio.AbstractEventLoop:\n if (\n self._local._loop is None\n and not self._local._set_called\n and threading.current_thread() is threading.main_thread()\n ):\n self.set_event_loop(self.new_event_loop())\n\n if self._local._loop is None:\n raise RuntimeError(\n \"There is no current event loop in thread %r.\"\n % threading.current_thread().name\n )\n\n return self._local._loop","title":""},{"docid":"f205ade07c19418dea2cab0f25740e7d","score":"0.5391418","text":"def run_soon(coros):\n if _CURRENT_EVENT_LOOP is None:\n raise RuntimeError('no running event loop')\n\n _CURRENT_EVENT_LOOP.run_soon(coros)","title":""},{"docid":"9aece9ae91ddc17961ff2ddbf0f027b9","score":"0.5387953","text":"def run(self):\n self.io_loop = IOLoop.current()\n self.event.set()\n\n opts = salt.config.client_config('/etc/salt/master')\n stream = salt.utils.event.get_event('master', io_loop=self.io_loop,\n transport=opts['transport'], opts=opts)\n stream.set_event_handler(self._handle_event_recv)\n\n self.io_loop.start()","title":""},{"docid":"6469f57255af5d95879c26d25cf3c477","score":"0.53871477","text":"def init(self) -> None:\n self._configure_started = True\n self.do_init()\n self._configure_finished = True","title":""},{"docid":"66e5449e7afbad7c5da50b690de47c42","score":"0.5386321","text":"def __init__(self):\n\t\tself.servo = InitioServo();\n\t\tself._initialisedPorts = {};\n\t\tRPIO.wait_for_interrupts(threaded=True)","title":""},{"docid":"2f0fbd86f19d055f7f43cd316a07f348","score":"0.5383896","text":"def __init__(self, email: str, password: str) -> None:\n self.loop = asyncio.new_event_loop()\n self.loop.set_exception_handler(sse_exception_handler)\n self.websession: ClientSession = ClientSession(loop=self.loop, read_timeout=None)\n self.event_bus: EventBus = EventBus()\n self.state = NOT_RUNNING\n self.available = False\n self.stop_event: Optional[asyncio.Event] = None\n # Instance information\n self.access_token: Optional[str] = None\n self.email = email\n self.password = password\n # Initialize thread\n super().__init__(target=self.run)","title":""},{"docid":"49bf192ccf31961c16e90fe73e3c1ad7","score":"0.5382844","text":"def start(self):\n from threading import Thread\n self.loop.add_callback(self._go)\n self._loop_thread = Thread(target=self.loop.start)\n self._loop_thread.start()","title":""},{"docid":"256f6d40c49a28f2b81a0a3bd3bd7616","score":"0.5381491","text":"def initialize_server(self):\n \n # Pub/sub pattern for async connections\n self.plot_context = zmq.Context()\n self.plot_socket = self.plot_context.socket(zmq.PUB) \n self.plot_socket.bind(\"tcp://*:6020\")\n self.plot_topic = 20000","title":""},{"docid":"9f720ddccd1d518ed494a1d7f6870b02","score":"0.5372912","text":"def run_loop(self):\n pass","title":""},{"docid":"9f658d9b68dfeffb5ed194222db9a8c4","score":"0.53652525","text":"def __init__(self, hostIP=\"0.0.0.0\", log=None):\n if log:\n self.log = log\n else:\n self.log = logging.getLogger(\"Pixelblaze.{}\".format(__class__.__name__))\n self.loop = asyncio.get_event_loop()\n self.hostIP = hostIP\n self.transport = None\n self._exit = False\n self.devices = {}\n self.autoSync = False\n self.new_data = asyncio.Event() #event trigger for new data\n # must run async self.start()","title":""},{"docid":"c80470d97edf5fdcc01c450790d84b5d","score":"0.53593564","text":"def event_loop (timeout=30):\n global event_loop_is_running, with_timeout, sleep_relative\n # replace time.time with our tsc-based version\n time.time, time.original_time = tsc_time.now_raw_posix_fsec, time.time\n with_timeout = _original_with_timeout\n sleep_relative = _original_sleep_relative\n if install_signal_handlers:\n signal_handler.register(signal.SIGTERM, sigterm_handler)\n signal_handler.register(signal.SIGINT, sigterm_handler)\n spawn (tick_updater).set_name ('tick_updater')\n try:\n event_loop_is_running = True\n _original_event_loop (timeout)\n finally:\n event_loop_is_running = False\n # put it back\n time.time = time.original_time","title":""},{"docid":"179ca14ec4bef8ab9a27d57f4be0d8b4","score":"0.53558624","text":"def run(self):\n self.network_event_loop.run_forever()","title":""},{"docid":"1235760f29fd2168cd88c2492196e5cb","score":"0.53547347","text":"def __init__(self, poll_interval=1):\n\n Thread.__init__(self)\n self.poll_interval = poll_interval\n self._stop_flag = 0\n self._event_generators = []\n self._event_queue = Queue()\n self._event_processor = EventProcessor(self._event_queue)\n self._event_processor.start()","title":""}],"string":"[\n {\n \"docid\": \"e0c3369da3f4c1abd76581fe10e69f2f\",\n \"score\": \"0.69916075\",\n \"text\": \"def __init__(self, loop=None) -> None:\\n\\n self.loop = loop if loop else asyncio.get_event_loop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb97ccd138ea175404892d6753ba676\",\n \"score\": \"0.6815965\",\n \"text\": \"def setUp(self):\\n self.loop = asyncio.new_event_loop()\\n asyncio.set_event_loop(self.loop)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb97ccd138ea175404892d6753ba676\",\n \"score\": \"0.6815965\",\n \"text\": \"def setUp(self):\\n self.loop = asyncio.new_event_loop()\\n asyncio.set_event_loop(self.loop)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"587b2fbc314808e7183dcfc4a9280b14\",\n \"score\": \"0.6783072\",\n \"text\": \"def _init_process(self) -> None:\\n # create new event loop after fork\\n asyncio.get_event_loop().close()\\n loop = asyncio.new_event_loop()\\n asyncio.set_event_loop(loop)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaf4fdb1ba981668590d4593254d7e8d\",\n \"score\": \"0.67776525\",\n \"text\": \"def __init__(self, loop: Optional[AbstractEventLoop] = None) -> None:\\n self.loop: AbstractEventLoop = Helper.ensure_loop(loop)\\n self._shutdown_event: Event = Event(loop=self.loop)\\n self._shutdown_from_signal: bool = False\\n\\n # SIGINT for local debugging\\n self.loop.add_signal_handler(SIGINT, self._initiate_shutdown_signal)\\n self.loop.add_signal_handler(SIGTERM, self._initiate_shutdown_signal)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc256bc25d8441dc118b3d3a70ac00d\",\n \"score\": \"0.6658748\",\n \"text\": \"def new_event_loop():\\n return Loop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9556f5dfb622bc049a47a7a5df6ffd87\",\n \"score\": \"0.65241927\",\n \"text\": \"def create_io_loop(self):\\n assert self.ioloop is None # This should only ever be run once\\n\\n def _run(loop):\\n asyncio.set_event_loop(loop)\\n loop.run_forever()\\n\\n self.ioloop = asyncio.new_event_loop()\\n self.ioloop.set_exception_handler(self._ioloop_exc_handler)\\n threading.Thread(target=partial(_run, self.ioloop), name=\\\"Thread-asyncio\\\", daemon=True).start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86e89cba19b488613da5d7df70149df1\",\n \"score\": \"0.638678\",\n \"text\": \"def __init__(self, event_manager):\\r\\n self.__eventManager = event_manager\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d3c4f78fa64fa0b3c56b3f2d0239296\",\n \"score\": \"0.6357002\",\n \"text\": \"def __init__(self, options: Options, loop: Optional[asyncio.AbstractEventLoop] = None):\\n self.loop = loop or asyncio.get_event_loop()\\n super().__init__(self.loop)\\n self.options = options\\n\\n self._future = self.loop.create_future()\\n self._engine = EngineService(self)\\n self._socket = SocketService(self)\\n\\n self.on('message_received', self._on_message_received)\\n self.on('socket_open', self._on_socket_open)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca43c8b669973b423461a191a4ed877c\",\n \"score\": \"0.6318867\",\n \"text\": \"def set_event_loop(self, loop: typing.Optional[asyncio.AbstractEventLoop]) -> None:\\n self._local._set_called = True\\n assert loop is None or isinstance(loop, asyncio.AbstractEventLoop)\\n self._local._loop = loop\\n\\n if (\\n self._watcher is not None\\n and threading.current_thread() is threading.main_thread()\\n ):\\n self._watcher.attach_loop(loop)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"228ed8ce030c442248b06bbf93163688\",\n \"score\": \"0.63083553\",\n \"text\": \"def event_loop():\\n return asyncio.get_event_loop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e494d5dc916560caa19c75f4512e8ff3\",\n \"score\": \"0.62807494\",\n \"text\": \"def get_event_loop(self):\\n loop = super().get_event_loop()\\n # Do something with loop ...\\n return loop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"122243c37574fad0bc0a1edb232ec86d\",\n \"score\": \"0.6272463\",\n \"text\": \"async def _init_main_loop(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c501c2b433049be04684f942b0eaed70\",\n \"score\": \"0.6232068\",\n \"text\": \"def __init__(self,\\n prodables: List[Prodable]=None,\\n loop=None,\\n debug=False,\\n autoStart=True):\\n self.prodables = list(prodables) if prodables is not None \\\\\\n else [] # type: List[Prodable]\\n\\n # if sys.platform == 'linux':\\n # asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())\\n\\n if loop:\\n self.loop = loop\\n else:\\n try:\\n # if sys.platform == 'win32':\\n # loop = asyncio.ProactorEventLoop()\\n # asyncio.set_event_loop(loop)\\n evl = asyncio.get_event_loop()\\n if evl.is_closed():\\n raise RuntimeError(\\\"event loop was closed\\\")\\n except Exception as ex:\\n logger.debug(\\\"Looper could not get default event loop; \\\"\\n \\\"creating a new one: {}\\\".format(ex))\\n # Trying out uvloop for linux\\n evl = asyncio.new_event_loop()\\n asyncio.set_event_loop(evl)\\n self.loop = evl\\n\\n logger.info(\\\"Starting up indy-node\\\")\\n self.runFut = self.loop.create_task(self.runForever()) # type: Task\\n self.running = True # type: bool\\n\\n self.loop.set_debug(bool(int(os.getenv('PYTHONASYNCIODEBUG', debug))))\\n\\n # TODO: uncomment this when python bug fixed (not just closed, but solved!)\\n # https://bugs.python.org/issue23548\\n #\\n # signals = [item for item in dir(signal)\\n # if item.startswith(\\\"SIG\\\") and item[3] != \\\"_\\\"]\\n\\n self.signals = [\\\"SIGINT\\\", \\\"SIGTERM\\\"]\\n\\n setSignal = \\\\\\n signal.signal if sys.platform == 'win32' \\\\\\n else self.loop.add_signal_handler\\n\\n for sigName in self.signals:\\n try:\\n logger.debug(\\\"Setting handler for {}\\\".format(sigName))\\n sigNum = getattr(signal, sigName)\\n setSignal(sigNum, self.handleSignal)\\n except RuntimeError as e:\\n logger.debug(\\n \\\"Cannot set handler for {} because {}\\\".format(sigName, e))\\n\\n self.autoStart = autoStart # type: bool\\n if self.autoStart:\\n self.startall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c50fe5b3413c092ae58fff6b9ef481e\",\n \"score\": \"0.61680555\",\n \"text\": \"def __init__(\\n self, loop: asyncio.AbstractEventLoop, logger: logging.Logger, probe_routine: Callable[[], Awaitable[bool]]\\n ):\\n super().__init__()\\n self.loop = loop\\n self.startup_complete = loop.create_future()\\n self.disconnected = loop.create_future()\\n self.exited = loop.create_future()\\n self._logger = logger\\n self._probe = probe_routine\",\n \"title\": \"\"\n },\n {\n \"docid\": \"426600a2b9f22e238812ea45c9e8e4aa\",\n \"score\": \"0.6110142\",\n \"text\": \"def __init__(\\n self, loop: asyncio.AbstractEventLoop, identifier: Optional[str] = None\\n ):\\n super().__init__()\\n self.loop = loop\\n self.identifier = identifier\",\n \"title\": \"\"\n },\n {\n \"docid\": \"097d78fd0462e27b001b785319986bce\",\n \"score\": \"0.6069578\",\n \"text\": \"def __init__(self, **kwargs):\\n self.communicator = self.open_listener_connection(DATA[\\\"id\\\"])\\n self.loop = asyncio.get_event_loop()\\n asyncio.ensure_future(self.communicator.start_listening())\\n super().__init__(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10117bdba94a726d593aef6a9989c90f\",\n \"score\": \"0.6060124\",\n \"text\": \"def init(self):\\n listener_class = get_listener_class()\\n logger.info('Using %s as a listener' % (listener_class, ))\\n self.listener = listener_class()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdbfd4efe2b6ecfc2a3915549f7f6f6e\",\n \"score\": \"0.601411\",\n \"text\": \"async def __aenter__(self):\\n self.loop = asyncio.get_event_loop()\\n await self.setup()\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1eb6bf9ace8dc84eeb62b9e8c6b65be\",\n \"score\": \"0.5978569\",\n \"text\": \"def setup(self, owner: Entity, loop: ActionLoop) -> None:\\n self.owner = owner\\n self.loop = loop\\n self.start_tick = loop.get_tick()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a80e82fa995ec7b8e5dea7bae7a9da51\",\n \"score\": \"0.5948992\",\n \"text\": \"def event_loop(self):\\n return self.__loop_from_run_thread\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7546a93ea888d8631d4b63076bf72997\",\n \"score\": \"0.59326774\",\n \"text\": \"def autonomousInit(self):\\r\\n self.auto_loop_counter = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09ec980a5d9135d831e02fa5b1f49941\",\n \"score\": \"0.59053296\",\n \"text\": \"def event_loop():\\n loop = uvloop.Loop()\\n yield loop\\n loop.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"512fa841417a0688afe9fbbba9c995b9\",\n \"score\": \"0.587469\",\n \"text\": \"def _init_events(self):\\n self._init_pipe()\\n LOG.info('Starting native event thread')\\n event_thread = native_threading.Thread(target = self.read_from_tap)\\n event_thread.setDaemon(True)\\n event_thread.start()\\n LOG.info('Starting green dispatch thread')\\n dispatch_thread = hub.spawn(self.dispatch_thread)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d847e53bc910548e9b3eba5e8210624c\",\n \"score\": \"0.5860455\",\n \"text\": \"def __init__(self, args=None):\\n self.stop_request = Event()\\n self.force_stop = False\\n self.started = False\\n self.completed = False\\n self.config = None\\n self.args = args\\n self.workerPool = []\\n self.manager = None\\n self._setup_loggers(args=args)\\n # attach to the logging queue\\n self.logger.info(\\\"Logging Setup Complete.\\\")\\n\\n self._generator_queue_size = getattr(self.args, \\\"generator_queue_size\\\", 500)\\n if self._generator_queue_size < 0:\\n self._generator_queue_size = 0\\n self.logger.info(\\n \\\"Set generator queue size:{}\\\".format(self._generator_queue_size)\\n )\\n\\n if self.args and \\\"configfile\\\" in self.args and self.args.configfile:\\n self._load_config(self.args.configfile, args=args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ac5fd2daba7cc0a11c8782f7babb4d6\",\n \"score\": \"0.58336735\",\n \"text\": \"def __init__(self, loop, run, *args, **kwargs):\\n self.loop = loop\\n self.run = run\\n self.args = args\\n self.kwargs = kwargs\\n self.watcher = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ce2e6580fda6ff071869c45cc7ddecf\",\n \"score\": \"0.5827722\",\n \"text\": \"def event_loop():\\n loop = asyncio.get_event_loop_policy().new_event_loop()\\n yield loop\\n loop.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2dc75ab97179382e49fbfdeed970b17\",\n \"score\": \"0.5813702\",\n \"text\": \"async def on_start(self):\\n await self.real_init()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a30fbb44ffd972dfb5dbdf5ad583dc6e\",\n \"score\": \"0.58116555\",\n \"text\": \"def get_or_create_event_loop() -> asyncio.AbstractEventLoop:\\n try:\\n loop = asyncio.get_event_loop()\\n except (DeprecationWarning, RuntimeError):\\n loop = asyncio.new_event_loop()\\n asyncio.set_event_loop(loop)\\n\\n return loop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ca0b75fa14a64a7bc1427da032f763c\",\n \"score\": \"0.5756037\",\n \"text\": \"async def on_init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"990ab8967ff19dec856ebfdfd6ec89b4\",\n \"score\": \"0.57422704\",\n \"text\": \"def test_eventloop_factory(get_io_loop):\\n assert isinstance(get_io_loop, eventloop.NonBlockingPoll)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cef879a89efbb8de4a58922132477c7\",\n \"score\": \"0.5739024\",\n \"text\": \"def main_loop(self):\\n\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d56d500419201411cc89d5215b7a8419\",\n \"score\": \"0.5714047\",\n \"text\": \"def main_loop(self):\\n LOGGER.info('Entering main event loop...')\\n while self._handle_faucet_events():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a0dfce6f921340de1ba9eebbc105f6d\",\n \"score\": \"0.5713494\",\n \"text\": \"def event_loop(request: FixtureRequest) -> Iterator[asyncio.AbstractEventLoop]:\\n loop = asyncio.get_event_loop_policy().new_event_loop()\\n yield loop\\n loop.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b896883eb6a9b880b4c64f1bea0e1ff\",\n \"score\": \"0.57091033\",\n \"text\": \"def on_loop_start(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4d9bcbcdd80a51ba907c39943cf77ab\",\n \"score\": \"0.56964403\",\n \"text\": \"def __init__(self, event_queue):\\n\\n Thread.__init__(self)\\n self.error_handler = NullEventHandler()\\n self._event_queue = event_queue\\n self._stop_flag = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc37cf9e8894adb53061cdcf4d6f9fe\",\n \"score\": \"0.5687923\",\n \"text\": \"def new_event_loop(self) -> PyObjCEventLoop:\\n return PyObjCEventLoop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c27b05432f7e3b90299b283012b2e3ab\",\n \"score\": \"0.5656225\",\n \"text\": \"def init_kernel(self): # type: () -> None\\n # Reset the core\\n self.core.reset()\\n\\n # Set direction of pins\\n self.ttl_out.output()\\n self.ttl_in.input()\\n\\n # Wait until event is submitted\\n self.core.wait_until_mu(now_mu())\\n\\n # Record DMA burst\\n self._record_dma_burst()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59cf8d38e2e9094386f033fceb0df908\",\n \"score\": \"0.56523794\",\n \"text\": \"def initialize(self):\\n self.log(self.args)\\n\\n self.remote = self.args[\\\"remote\\\"]\\n self.media_player = self.args[\\\"media_player\\\"]\\n\\n self.listen_event(self.handle_event, \\\"deconz_event\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3952c92236e7f2db9eb7dd55b6e730d9\",\n \"score\": \"0.5652089\",\n \"text\": \"def setup(self):\\n\\n # NOTE: this thread is never closed\\n thread = threading.Thread(target=Server(self.nao).serve)\\n thread.daemon = True\\n thread.start()\\n\\n self.nao.speak(\\\"Ready to connect!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6baec542f13bda1f55a48bfe794d757\",\n \"score\": \"0.5641426\",\n \"text\": \"def event_loop(self):\\n self._stop = False\\n while not self._stop:\\n self.handle()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ad77c761671c4a4d5b68ab5ea83c1a5\",\n \"score\": \"0.56314456\",\n \"text\": \"def initialize(self):\\n #self.listen_event(self.receive_telegram_text, 'telegram_text')\\n self.settings = self.get_app(\\\"settings\\\")\\n self.listen_event(self.receive_telegram_command, 'telegram_command')\\n self.listen_event(self.receive_telegram_callback, 'telegram_callback')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59eed19f8de179da931e19028cffc616\",\n \"score\": \"0.5625552\",\n \"text\": \"def __init__(self, target):\\n self._target = target\\n self._active = Event()\\n self._thread = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e96de400566530d0753ec5f1066bc016\",\n \"score\": \"0.56232387\",\n \"text\": \"async def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b8ae8f50c71825ac83f06cd5f4aeef6\",\n \"score\": \"0.56219727\",\n \"text\": \"def __init__(self, context, detach=False):\\n self.context = context\\n\\n # the thread pool to convert block execution of task into async process\\n self.thread_pool = ThreadPoolExecutor(4)\\n self.wait_queue = queues.Queue()\\n self.exec_queue = queues.Queue()\\n\\n def engine_loop():\\n _ioloop = ioloop.IOLoop.current()\\n _ioloop.add_callback(self.daemon_loop)\\n _ioloop.start()\\n\\n # if the detached mode is enabled\\n # use a seperated thread to boot the io-loop\\n self.loop_thread = None\\n if detach:\\n logger.debug(\\\"engine running in detach mode\\\")\\n self.loop_thread = threading.Thread(target=engine_loop)\\n self.loop_thread.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1271b33e391bd72271f95d266c6d4a4a\",\n \"score\": \"0.5619265\",\n \"text\": \"def event_manager(**kwargs):\\n manager = EventManager(**kwargs)\\n return manager.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa895b64990f0c04782569a15c978d40\",\n \"score\": \"0.5610994\",\n \"text\": \"def create_listener(self):\\n self._listener = ListenerThread(self._app, self.options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ecfaf521575c5b3322ba8ca7f424a34\",\n \"score\": \"0.56055164\",\n \"text\": \"def start(self):\\n self.run = True\\n asyncio.ensure_future(self.listen(), loop=self.loop)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9d72f324e9ea93f7760a45b7bf96811\",\n \"score\": \"0.5600224\",\n \"text\": \"def start(self, loop):\\n raise NotImplementedError(\\\"subclass {} should implement this method\\\"\\n .format(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f534f24c501f10f24ed4736234bfda6\",\n \"score\": \"0.55800086\",\n \"text\": \"async def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02f5810db0693c43783ba81e1854bd30\",\n \"score\": \"0.55662316\",\n \"text\": \"def init(self):\\n self.runner.init()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fe39f58e2bff124fd8b391ff5ca11ea\",\n \"score\": \"0.5553751\",\n \"text\": \"def setup(self):\\n # set the Event lock\\n self.lock.set()\\n\\n # setup configuration\\n self.config = self.get_config()\\n\\n if self.config.get('cli_command'):\\n self.handle_cli_command(self.config['cli_command'])\\n sys.exit()\\n\\n if self.config['debug']:\\n syslog(\\n LOG_INFO,\\n 'py3status started with config {}'.format(self.config)\\n )\\n\\n # setup i3status thread\\n self.i3status_thread = I3status(\\n self.lock,\\n self.config['i3status_config_path'],\\n self.config['standalone']\\n )\\n if self.config['standalone']:\\n self.i3status_thread.mock()\\n else:\\n self.i3status_thread.start()\\n while not self.i3status_thread.ready:\\n if not self.i3status_thread.is_alive():\\n err = self.i3status_thread.error\\n raise IOError(err)\\n sleep(0.1)\\n if self.config['debug']:\\n syslog(\\n LOG_INFO,\\n 'i3status thread {} with config {}'.format(\\n 'started' if not self.config['standalone'] else 'mocked',\\n self.i3status_thread.config\\n )\\n )\\n\\n # setup input events thread\\n self.events_thread = Events(\\n self.lock,\\n self.config,\\n self.modules,\\n self.i3status_thread.config\\n )\\n self.events_thread.start()\\n if self.config['debug']:\\n syslog(LOG_INFO, 'events thread started')\\n\\n # suppress modules' ouput wrt issue #20\\n if not self.config['debug']:\\n sys.stdout = open('/dev/null', 'w')\\n sys.stderr = open('/dev/null', 'w')\\n\\n # get the list of py3status configured modules\\n self.py3_modules = self.i3status_thread.config['py3_modules']\\n\\n # get a dict of all user provided modules\\n user_modules = self.get_user_modules()\\n if self.config['debug']:\\n syslog(LOG_INFO, 'user_modules={}'.format(user_modules))\\n\\n if self.py3_modules:\\n # load and spawn i3status.conf configured modules threads\\n self.load_modules(self.py3_modules, user_modules)\\n else:\\n # legacy behaviour code\\n # load and spawn user modules threads based on inclusion folders\\n self.load_modules(user_modules, user_modules)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7724df2272e36e68d0c1ce3729fa3a83\",\n \"score\": \"0.5553123\",\n \"text\": \"def create_listener(self, logger):\\n self._event_mask = (CGEventMaskBit(kCGEventLeftMouseDown) |\\n CGEventMaskBit(kCGEventFlagsChanged) |\\n CGEventMaskBit(kCGEventKeyDown))\\n self._event_tap = CGEventTapCreate(kCGHIDEventTap,\\n kCGHeadInsertEventTap,\\n kCGEventTapOptionDefault,\\n self._event_mask,\\n _callback_func,\\n logger)\\n\\n self._runLoopSource = CFMachPortCreateRunLoopSource(\\n kCFAllocatorDefault, self._event_tap, 0)\\n CFRunLoopAddSource(CFRunLoopGetCurrent(), self._runLoopSource,\\n kCFRunLoopCommonModes)\\n CGEventTapEnable(self._event_tap, True)\\n CFRunLoopRun()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38597fd1ba66c3c35ddae685e088130e\",\n \"score\": \"0.55393517\",\n \"text\": \"def main(self) -> None:\\n self.loop.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3951c07b9b577bae2e7b522c31f73931\",\n \"score\": \"0.5534345\",\n \"text\": \"def start_event_listener(self):\\n self._sdl.start_event_listener()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5547529649ee9dc0b74674f0230ee2f\",\n \"score\": \"0.55334944\",\n \"text\": \"def install():\\n __asyncio.set_event_loop_policy(EventLoopPolicy())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a601e1f5433201403735e306e35f22db\",\n \"score\": \"0.5532086\",\n \"text\": \"def start_glib_loop(cls):\\n GObject.threads_init()\\n Gst.init(None)\\n\\n cls.glib_loop = GObject.MainLoop()\\n cls.glib_thread = threading.Thread(target=cls.glib_loop.run)\\n cls.glib_thread.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2bec545a3c07c9b40ae66a0f0c5893f\",\n \"score\": \"0.5532003\",\n \"text\": \"def run(self):\\n self.running = True\\n self.evManager.Post(InitializeEvent())\\n while self.running:\\n newTick = TickEvent()\\n self.evManager.Post(newTick)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4446993c8b673c88b433e564435202a\",\n \"score\": \"0.55284584\",\n \"text\": \"def run(self) -> None:\\n if self._was_started.is_set() and not self._was_stopped.is_set():\\n asyncio.set_event_loop(self._loop)\\n self._loop.run_forever()\\n else:\\n raise Exception(\\\"Cannot call directly! Begin EventLoopThreadSafe by start method.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5de1abdf8072071e6c46b8dbe29a5585\",\n \"score\": \"0.55279434\",\n \"text\": \"def set_ioloop(self, ioloop=None):\\n self._ioloop_set_to = ioloop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dd075090003f8d5616f8e61016c1b39\",\n \"score\": \"0.5518384\",\n \"text\": \"def start(self):\\n self._connection = self.connect()\\n self._connection.ioloop.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"487587861c4c2d1678e357d54e105fdf\",\n \"score\": \"0.5509251\",\n \"text\": \"async def session_start(self):\\n self.ast_ctx.add_logger_handler(self.console)\\n _LOGGER.info(\\\"Starting session %s\\\", self.global_ctx_name)\\n\\n self.tasks[\\\"housekeep\\\"] = {asyncio.create_task(self.housekeep_run())}\\n self.tasks[\\\"startup_timeout\\\"] = {asyncio.create_task(self.startup_timeout())}\\n\\n self.iopub_server, self.iopub_port = await self.start_one_server(self.iopub_listen)\\n self.heartbeat_server, self.heartbeat_port = await self.start_one_server(self.heartbeat_listen)\\n self.control_server, self.control_port = await self.start_one_server(self.control_listen)\\n self.stdin_server, self.stdin_port = await self.start_one_server(self.stdin_listen)\\n self.shell_server, self.shell_port = await self.start_one_server(self.shell_listen)\\n\\n #\\n # For debugging, can use the real ZMQ library instead on certain sockets; comment out\\n # the corresponding asyncio.start_server() call above if you enable the ZMQ-based\\n # functions here. You can then turn of verbosity level 4 (-vvvv) in hass_pyscript_kernel.py\\n # to see all the byte data in case you need to debug the simple ZMQ implementation here.\\n # The two most important zmq functions are shown below.\\n #\\n # import zmq\\n # import zmq.asyncio\\n #\\n # def zmq_bind(socket, connection, port):\\n # \\\"\\\"\\\"Bind a socket.\\\"\\\"\\\"\\n # if port <= 0:\\n # return socket.bind_to_random_port(connection)\\n # # _LOGGER.debug(f\\\"binding to %s:%s\\\" % (connection, port))\\n # socket.bind(\\\"%s:%s\\\" % (connection, port))\\n # return port\\n #\\n # zmq_ctx = zmq.asyncio.Context()\\n #\\n # ##########################################\\n # # Shell using real ZMQ for debugging:\\n # async def shell_listen_zmq():\\n # \\\"\\\"\\\"Task that listens to shell messages using ZMQ.\\\"\\\"\\\"\\n # try:\\n # _LOGGER.debug(\\\"shell_listen_zmq connected\\\")\\n # connection = self.config[\\\"transport\\\"] + \\\"://\\\" + self.config[\\\"ip\\\"]\\n # shell_socket = zmq_ctx.socket(zmq.ROUTER)\\n # self.shell_port = zmq_bind(shell_socket, connection, -1)\\n # _LOGGER.debug(\\\"shell_listen_zmq connected\\\")\\n # while 1:\\n # msg = await shell_socket.recv_multipart()\\n # await self.shell_handler(shell_socket, msg)\\n # except asyncio.CancelledError:\\n # raise\\n # except Exception:\\n # _LOGGER.error(\\\"shell_listen exception %s\\\", traceback.format_exc(-1))\\n # await self.housekeep_q.put([\\\"shutdown\\\"])\\n #\\n # ##########################################\\n # # IOPub using real ZMQ for debugging:\\n # # IOPub/Sub:\\n # async def iopub_listen_zmq():\\n # \\\"\\\"\\\"Task that listens to iopub messages using ZMQ.\\\"\\\"\\\"\\n # try:\\n # _LOGGER.debug(\\\"iopub_listen_zmq connected\\\")\\n # connection = self.config[\\\"transport\\\"] + \\\"://\\\" + self.config[\\\"ip\\\"]\\n # iopub_socket = zmq_ctx.socket(zmq.PUB)\\n # self.iopub_port = zmq_bind(self.iopub_socket, connection, -1)\\n # self.iopub_socket.add(iopub_socket)\\n # while 1:\\n # wire_msg = await iopub_socket.recv_multipart()\\n # _LOGGER.debug(\\\"iopub received %s\\\", wire_msg)\\n # except asyncio.CancelledError:\\n # raise\\n # except EOFError:\\n # await self.housekeep_q.put([\\\"shutdown\\\"])\\n # _LOGGER.debug(\\\"iopub_listen got eof\\\")\\n # except Exception as err:\\n # _LOGGER.error(\\\"iopub_listen exception %s\\\", err)\\n # await self.housekeep_q.put([\\\"shutdown\\\"])\\n #\\n # self.tasks[\\\"shell\\\"] = {asyncio.create_task(shell_listen_zmq())}\\n # self.tasks[\\\"iopub\\\"] = {asyncio.create_task(iopub_listen_zmq())}\\n #\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1858a35b41b675f2f849587cc96c0d8\",\n \"score\": \"0.5507766\",\n \"text\": \"def __init__(self, original_emitter):\\n self.original_emitter = original_emitter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddfe7ea246c6dfe7934abbedd7b0643a\",\n \"score\": \"0.5497893\",\n \"text\": \"def start(self) -> None:\\n self.start_async()\\n assert self.startup_complete is not None # `start_async` will create it, but mypy doesn't know that\\n self._loop.run_until_complete(self.startup_complete)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dc9af9c4911d39aa5caa0739606a752\",\n \"score\": \"0.5496606\",\n \"text\": \"def __init__(self, **kwargs):\\r\\n kwargs['daemon'] = kwargs.get('daemon', True)\\r\\n super().__init__(**kwargs)\\r\\n\\r\\n self.running = threading.Event()\\r\\n self.running.set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edb794732a685a4caeb0099c43856d80\",\n \"score\": \"0.5488664\",\n \"text\": \"def start(self):\\n self.__reset_command_loop()\\n self.__start_listener_thread()\\n self.__start_command_thread()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13d7026ee0d04330d510be43bf1c8135\",\n \"score\": \"0.54775965\",\n \"text\": \"def init_kernel(self): # type: () -> None\\n # Reset the core\\n self.core.reset()\\n\\n # Set direction of pin\\n self.ttl_out.output()\\n\\n # Wait until event is submitted\\n self.core.wait_until_mu(now_mu())\\n\\n # Record DMA burst\\n self._record_dma_burst()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6edd84510fc3901d18e6e4bd9091634\",\n \"score\": \"0.547686\",\n \"text\": \"def _start_loop(self):\\n self.th_loop = Thread(target=asyncore.loop, args=(), )\\n self.th_loop.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecce3b93380b5d278eb1f0f67433c8d6\",\n \"score\": \"0.5474614\",\n \"text\": \"def get_loop() -> asyncio.AbstractEventLoop:\\n if os.name == \\\"nt\\\":\\n # for subprocess' pipes on Windows\\n loop = asyncio.ProactorEventLoop() # type: ignore\\n asyncio.set_event_loop(loop)\\n else:\\n try:\\n loop = asyncio.get_event_loop()\\n except RuntimeError:\\n loop = asyncio.new_event_loop()\\n asyncio.set_event_loop(loop)\\n asyncio.set_child_watcher(ThreadedChildWatcher())\\n return loop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"895bdfe5ea69d4056eda76e8376f1e85\",\n \"score\": \"0.5461658\",\n \"text\": \"def start_event_loop(self, timeout=0):\\n if hasattr(self, '_event_loop'):\\n raise RuntimeError(\\\"Event loop already running\\\")\\n id = wx.NewId()\\n timer = wx.Timer(self, id=id)\\n if timeout > 0:\\n timer.Start(timeout * 1000, oneShot=True)\\n self.Bind(wx.EVT_TIMER, self.stop_event_loop, id=id)\\n\\n # Event loop handler for start/stop event loop\\n self._event_loop = wxc.EventLoop()\\n self._event_loop.Run()\\n timer.Stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7bbc688dd349d33ac3c8def717f1cd6\",\n \"score\": \"0.5458578\",\n \"text\": \"def get_new_event_loop():\\n if uvloop:\\n return uvloop.new_event_loop()\\n else:\\n return asyncio.new_event_loop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42fbc7a66f76816df4f995257d119afe\",\n \"score\": \"0.54584116\",\n \"text\": \"def __init__(self, *args, **kw):\\n self.mutex = threading.Lock()\\n if \\\"mutex\\\" not in kw:\\n kw[\\\"mutex\\\"] = self.mutex\\n EventDispatcherBase.__init__(self, *args, **kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44abfe11e59fa940dd54fea873e733d9\",\n \"score\": \"0.5453497\",\n \"text\": \"def initialize(self):\\n print(\\\"Engine 1 is initializing. Registering device...\\\")\\n self._registerDevice(\\\"device1\\\")\\n self._setDevice(\\\"device1\\\", { \\\"time\\\" : self._time, \\\"timestep\\\": 0 })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0167afce13f1f510b52dbb26c1383e2b\",\n \"score\": \"0.54530144\",\n \"text\": \"def initialize_websockets_manager(self):\\n self.manager = WebSocketManager()\\n self.manager.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d29c3d07a5996614ef90e6117d47b709\",\n \"score\": \"0.54521775\",\n \"text\": \"def __init__(self):\\n self._services = []\\n self.backdoor_port = eventlet_backdoor.initialize_if_enabled(CONF)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e94a0ddb5945adc67bef4da14d0d7c9f\",\n \"score\": \"0.5444111\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(Events(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a632e83ffabef8dbd4fd9d017fb93f0\",\n \"score\": \"0.54405904\",\n \"text\": \"def get_loop():\\n global _BG_LOOP\\n try:\\n return _BG_LOOP\\n except NameError:\\n _BG_LOOP = asyncio.new_event_loop()\\n _set_async_debug_and_log_level(_BG_LOOP)\\n thread = Thread(target=_loop_main, args=(_BG_LOOP,))\\n thread.daemon = True # <-- thread will exit when main thread exists\\n thread.start()\\n _setup_cleanup(_BG_LOOP, thread)\\n return _BG_LOOP\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e11221a005a964b171c516512bee266d\",\n \"score\": \"0.5437474\",\n \"text\": \"def initialize_listening(self):\\n self.listened_time = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14d125ec782de84de7b90f1185683830\",\n \"score\": \"0.5435087\",\n \"text\": \"def connect(self, loop):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bdb1a3ab98835c3c41fc77abcb829a3\",\n \"score\": \"0.5433524\",\n \"text\": \"def __init__(self, middleman, config):\\n self.analytics = Analytics(config)\\n self.middleman = middleman\\n self.thread = None\\n\\n server = Server(\\n {'/': self.init},\\n io_loop=IOLoop.current(),\\n port=config.PORT,\\n num_procs=1)\\n\\n server.start()\\n self.server = server\\n server.io_loop.add_callback(server.show, \\\"/\\\")\\n server.io_loop.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b6a75744dccca240e366093ff32ae91\",\n \"score\": \"0.543057\",\n \"text\": \"def initEvents(self):\\r\\n SceneManager().registerEventReader(self.do_action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c16d382f5ed9e2e495df4037bfea794\",\n \"score\": \"0.5425537\",\n \"text\": \"def start(self):\\n if self._eager_init:\\n self.bus.log(\\n \\\"initializing Aglyph singleton and borg component objects\\\")\\n self.init_singletons()\\n self.init_borgs()\\n self.bus.log(\\\"starting Aglyph dependency injection support\\\")\\n self.bus.subscribe(\\\"aglyph-assemble\\\", self.assemble)\\n self.bus.subscribe(\\\"aglyph-init-singletons\\\", self.init_singletons)\\n self.bus.subscribe(\\\"aglyph-clear-singletons\\\", self.clear_singletons)\\n self.bus.subscribe(\\\"aglyph-init-borgs\\\", self.init_borgs)\\n self.bus.subscribe(\\\"aglyph-clear-borgs\\\", self.clear_borgs)\\n self.bus.subscribe(\\\"aglyph-clear-weakrefs\\\", self.clear_weakrefs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe8dab6986fe4154855098026917c1f2\",\n \"score\": \"0.5417963\",\n \"text\": \"def initialize(self):\\n self._load_timers()\\n self._reset_timer_index()\\n if self.active_timers:\\n self.log.info(\\\"found {} active timers\\\".format(str(len(self.active_timers))))\\n self._show_gui()\\n self._start_display_update()\\n self._start_expiration_check()\\n\\n # To prevent beeping while listening\\n self.add_event(\\\"recognizer_loop:wakeword\\\", self.handle_wake_word_detected)\\n self.add_event(\\n \\\"mycroft.speech.recognition.unknown\\\", self.handle_speech_recognition_unknown\\n )\\n self.add_event(\\\"speak\\\", self.handle_speak)\\n self.add_event(\\\"skill.timer.stop\\\", self.handle_timer_stop)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9b87adc86234be4355d912692e0ab4\",\n \"score\": \"0.54103523\",\n \"text\": \"def start_loop(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15d943ccc1a47b79f2ce5ab04a5f4aca\",\n \"score\": \"0.54057145\",\n \"text\": \"def io_loop(self):\\n if self._io_loop is None:\\n self._io_loop = DaemonIOLoop()\\n self._io_loop._cod_daemon = self\\n return self._io_loop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef3ba4bb25dbc8b536a937736dcd924c\",\n \"score\": \"0.5401579\",\n \"text\": \"def get_loop():\\n\\n warnings.filterwarnings(\\\"ignore\\\", category=DeprecationWarning)\\n try:\\n loop = asyncio.get_event_loop()\\n except RuntimeError:\\n loop = asyncio.new_event_loop()\\n asyncio.set_event_loop(loop)\\n return loop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"745de160cfe20f6bd2a10d59f17d732e\",\n \"score\": \"0.5398136\",\n \"text\": \"def run(self):\\n self._connection = self.connect()\\n self._connection.ioloop.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8bdddc8c0bba6c02e434c1d71958791\",\n \"score\": \"0.53943837\",\n \"text\": \"def get_event_loop(self) -> asyncio.AbstractEventLoop:\\n if (\\n self._local._loop is None\\n and not self._local._set_called\\n and threading.current_thread() is threading.main_thread()\\n ):\\n self.set_event_loop(self.new_event_loop())\\n\\n if self._local._loop is None:\\n raise RuntimeError(\\n \\\"There is no current event loop in thread %r.\\\"\\n % threading.current_thread().name\\n )\\n\\n return self._local._loop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f205ade07c19418dea2cab0f25740e7d\",\n \"score\": \"0.5391418\",\n \"text\": \"def run_soon(coros):\\n if _CURRENT_EVENT_LOOP is None:\\n raise RuntimeError('no running event loop')\\n\\n _CURRENT_EVENT_LOOP.run_soon(coros)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9aece9ae91ddc17961ff2ddbf0f027b9\",\n \"score\": \"0.5387953\",\n \"text\": \"def run(self):\\n self.io_loop = IOLoop.current()\\n self.event.set()\\n\\n opts = salt.config.client_config('/etc/salt/master')\\n stream = salt.utils.event.get_event('master', io_loop=self.io_loop,\\n transport=opts['transport'], opts=opts)\\n stream.set_event_handler(self._handle_event_recv)\\n\\n self.io_loop.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6469f57255af5d95879c26d25cf3c477\",\n \"score\": \"0.53871477\",\n \"text\": \"def init(self) -> None:\\n self._configure_started = True\\n self.do_init()\\n self._configure_finished = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66e5449e7afbad7c5da50b690de47c42\",\n \"score\": \"0.5386321\",\n \"text\": \"def __init__(self):\\n\\t\\tself.servo = InitioServo();\\n\\t\\tself._initialisedPorts = {};\\n\\t\\tRPIO.wait_for_interrupts(threaded=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f0fbd86f19d055f7f43cd316a07f348\",\n \"score\": \"0.5383896\",\n \"text\": \"def __init__(self, email: str, password: str) -> None:\\n self.loop = asyncio.new_event_loop()\\n self.loop.set_exception_handler(sse_exception_handler)\\n self.websession: ClientSession = ClientSession(loop=self.loop, read_timeout=None)\\n self.event_bus: EventBus = EventBus()\\n self.state = NOT_RUNNING\\n self.available = False\\n self.stop_event: Optional[asyncio.Event] = None\\n # Instance information\\n self.access_token: Optional[str] = None\\n self.email = email\\n self.password = password\\n # Initialize thread\\n super().__init__(target=self.run)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49bf192ccf31961c16e90fe73e3c1ad7\",\n \"score\": \"0.5382844\",\n \"text\": \"def start(self):\\n from threading import Thread\\n self.loop.add_callback(self._go)\\n self._loop_thread = Thread(target=self.loop.start)\\n self._loop_thread.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"256f6d40c49a28f2b81a0a3bd3bd7616\",\n \"score\": \"0.5381491\",\n \"text\": \"def initialize_server(self):\\n \\n # Pub/sub pattern for async connections\\n self.plot_context = zmq.Context()\\n self.plot_socket = self.plot_context.socket(zmq.PUB) \\n self.plot_socket.bind(\\\"tcp://*:6020\\\")\\n self.plot_topic = 20000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f720ddccd1d518ed494a1d7f6870b02\",\n \"score\": \"0.5372912\",\n \"text\": \"def run_loop(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f658d9b68dfeffb5ed194222db9a8c4\",\n \"score\": \"0.53652525\",\n \"text\": \"def __init__(self, hostIP=\\\"0.0.0.0\\\", log=None):\\n if log:\\n self.log = log\\n else:\\n self.log = logging.getLogger(\\\"Pixelblaze.{}\\\".format(__class__.__name__))\\n self.loop = asyncio.get_event_loop()\\n self.hostIP = hostIP\\n self.transport = None\\n self._exit = False\\n self.devices = {}\\n self.autoSync = False\\n self.new_data = asyncio.Event() #event trigger for new data\\n # must run async self.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c80470d97edf5fdcc01c450790d84b5d\",\n \"score\": \"0.53593564\",\n \"text\": \"def event_loop (timeout=30):\\n global event_loop_is_running, with_timeout, sleep_relative\\n # replace time.time with our tsc-based version\\n time.time, time.original_time = tsc_time.now_raw_posix_fsec, time.time\\n with_timeout = _original_with_timeout\\n sleep_relative = _original_sleep_relative\\n if install_signal_handlers:\\n signal_handler.register(signal.SIGTERM, sigterm_handler)\\n signal_handler.register(signal.SIGINT, sigterm_handler)\\n spawn (tick_updater).set_name ('tick_updater')\\n try:\\n event_loop_is_running = True\\n _original_event_loop (timeout)\\n finally:\\n event_loop_is_running = False\\n # put it back\\n time.time = time.original_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"179ca14ec4bef8ab9a27d57f4be0d8b4\",\n \"score\": \"0.53558624\",\n \"text\": \"def run(self):\\n self.network_event_loop.run_forever()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1235760f29fd2168cd88c2492196e5cb\",\n \"score\": \"0.53547347\",\n \"text\": \"def __init__(self, poll_interval=1):\\n\\n Thread.__init__(self)\\n self.poll_interval = poll_interval\\n self._stop_flag = 0\\n self._event_generators = []\\n self._event_queue = Queue()\\n self._event_processor = EventProcessor(self._event_queue)\\n self._event_processor.start()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":89,"cells":{"query_id":{"kind":"string","value":"7937c999a5afa4e76d49a4c8dc45d0e3"},"query":{"kind":"string","value":"Returns the point at time t (0>1) along the curve."},"positive_passages":{"kind":"list like","value":[{"docid":"4d96d37707793141520f29d32bd33f79","score":"0.72235733","text":"def pointAtTime(self,t):\n x = (1 - t) * (1 - t) * self[0].x + 2 * (1 - t) * t * self[1].x + t * t * self[2].x;\n y = (1 - t) * (1 - t) * self[0].y + 2 * (1 - t) * t * self[1].y + t * t * self[2].y;\n return Point(x,y)","title":""}],"string":"[\n {\n \"docid\": \"4d96d37707793141520f29d32bd33f79\",\n \"score\": \"0.72235733\",\n \"text\": \"def pointAtTime(self,t):\\n x = (1 - t) * (1 - t) * self[0].x + 2 * (1 - t) * t * self[1].x + t * t * self[2].x;\\n y = (1 - t) * (1 - t) * self[0].y + 2 * (1 - t) * t * self[1].y + t * t * self[2].y;\\n return Point(x,y)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"df6681abd3f94fe59f3e39c4d68cc2c1","score":"0.6694643","text":"def get(self, t):\n # return self.value[np.where((self.time - t) <= 0)[0][-1]] # last one that is <= 0\n return self._value[\n (self._time.searchsorted(t + 1) - 1).clip(0, len(self._value) - 1)\n ]","title":""},{"docid":"26b3d9c97fc621f35b7769a3cf410da5","score":"0.66819525","text":"def at(self, t):\n return \\\n self._avg + (self._lastValue - self._avg)*(1 - (1 - self.x.alpha)**( t - self._lastTime)) \\\n if self._avg is not None else None","title":""},{"docid":"f78a9033d29c8a7a04871a994512a5ba","score":"0.6530438","text":"def exact(self,x,t):\n alpha = 4\n period = 2*pi/self.mlength\n beta = alpha*period\n u = sin(beta*(x-(1-beta**2)*t)) + 1\n return u","title":""},{"docid":"6aba8059fb89079e6ab0e1d18a206c3e","score":"0.6412015","text":"def getSegment(self,t):\n if len(self.times)==0:\n raise ValueError(\"Empty trajectory\")\n if len(self.times)==1:\n return (-1,0)\n if t > self.times[-1]:\n return (len(self.times),0)\n if t < self.times[0]:\n return (0,0)\n i = bisect.bisect_right(self.times,t)\n p=i-1\n u=(t-self.times[p])/(self.times[i]-self.times[p])\n if i==0:\n return (-1,0)\n assert u >= 0 and u <= 1\n return (p,u)","title":""},{"docid":"f2d2f13af33820e7f19bba5902bb3438","score":"0.6325142","text":"def sample_trajectory(trajectory, t):\n # First point\n if t <= 0.0:\n return copy.deepcopy(trajectory.points[0])\n # Last point\n if t >= trajectory.points[-1].time_from_start.to_sec():\n return copy.deepcopy(trajectory.points[-1])\n # Finds the (middle) segment containing t\n i = 0\n while trajectory.points[i + 1].time_from_start.to_sec() < t:\n i += 1\n return interp_cubic(trajectory.points[i], trajectory.points[i + 1], t)","title":""},{"docid":"f3f7100b7d57926b7b71c8919c575ee0","score":"0.63108504","text":"def _getSegment(self,t):\n if len(self.trajectoryTimes)==0:\n raise ValueError(\"Empty trajectory\")\n if len(self.trajectoryTimes)==1:\n return (-1,0)\n if t >= self.trajectoryTimes[-1]:\n return (len(self.trajectoryTimes)-1,0)\n if t <= self.trajectoryTimes[0]:\n return (0,0)\n i = bisect.bisect_right(self.trajectoryTimes,t)\n p=i-1\n assert i > 0 and i < len(self.trajectoryTimes),\"Invalid time index \"+str(t)+\" in \"+str(self.trajectoryTimes)\n u=(t-self.trajectoryTimes[p])/(self.trajectoryTimes[i]-self.trajectoryTimes[p])\n if i==0:\n return (-1,0)\n assert u >= 0 and u <= 1\n return (p,u)","title":""},{"docid":"c7c0f839e694ce9962e8031e40223cf9","score":"0.627243","text":"def bezier_point(cps, t):\n\n p = ((1 - t) ** 3) * cps[0, :]\n p += 3 * t * ((1 - t) ** 2) * cps[1, :]\n p += 3 * (t ** 2) * (1 - t) * cps[2, :]\n p += (t ** 3) * cps[3, :]\n\n return p","title":""},{"docid":"6c29e21036eda671ad8ea0549b5ae16f","score":"0.6193054","text":"def fn(t):\n # must use exp() from numpy to handle input from linspace\n return 0.1*(-3.0*np.exp(-t) + np.exp(2.0*t)*np.sin(t) + 3.0*np.exp(2.0*t)*np.cos(t) + 2.0)","title":""},{"docid":"3253902be54de0fca2a7256fd3a39547","score":"0.61587834","text":"def fp(self, t, y):\n pass","title":""},{"docid":"0c5dcf3e68541659ad36d4386d8658ed","score":"0.6094813","text":"def p(x,t):\n\n if x <= (-1 * v_max * t):\n return p_max\n elif x >= (v_max * t):\n return 0.0\n else:\n return 0.5 * (1. - x/(v_max*t))*p_max","title":""},{"docid":"2c6db1613fc639cf4de9f33ab57625b8","score":"0.6055308","text":"def position_at_time(self, t):\n t += TIME_OFFSET # Compensate for start time\n\n n = 2 * np.pi / self.period # mean motion\n M = n * t # mean anomaly\n\n def kepler_root_fn(E_):\n return (E_ - self.ecc * np.sin(E_) - M) ** 2\n result = minimize(kepler_root_fn, 0)\n if not result.success:\n raise Exception(\"ERROR\")\n E = result.x[0]\n\n theta = 2 * np.arctan(np.sqrt(((1 + self.ecc) / (1 - self.ecc)) * np.tan(E / 2) ** 2))\n\n if E % (2 * np.pi) > np.pi:\n theta = 2 * np.pi - theta\n\n #theta = np.arccos((np.cos(E) - self.ecc) / (1 - self.ecc * np.cos(E)))\n r = self.semimajor * (1 - self.ecc * np.cos(E))\n\n theta *= self.sign # Compensate for direction\n\n x = r * np.cos(theta)\n y = r * np.sin(theta)\n return (x, y)","title":""},{"docid":"628098083ea3ed0b9506e12d00d192db","score":"0.60532224","text":"def P_t(self, past, present):\n\t\treturn self.p_t_coef*self.P_a(past)#*self.p_q","title":""},{"docid":"41358e99b0527b64732154c0543a0501","score":"0.60171324","text":"def control_signal(t):\n return 0 if t < 0.5 else -1","title":""},{"docid":"71a484e6d46c9ab0fdcf1798c8429253","score":"0.60109663","text":"def bezierCurveCLinear(self, p0, p1, t):\n\n if t <= 0:\n return p1[0], p1[1]\n\n elif t >= 1:\n return p0[0], p0[1]\n\n position = (1-t)*p0 + t*p1\n\n return position[0], position[1]","title":""},{"docid":"64a79d315b02aac31626cff8a18d38dc","score":"0.5996135","text":"def at(self, t):\n\n return self.bez(t)","title":""},{"docid":"c8846cad9676337303d23c7dbab07e4b","score":"0.59881216","text":"def pt0_from_t(SA, t, p):\n\n SA = np.maximum(SA, 0)\n\n s1 = SA * (35. / SSO)\n\n pt0 = t + p * (8.65483913395442e-6 -\n s1 * 1.41636299744881e-6 -\n p * 7.38286467135737e-9 +\n t * (-8.38241357039698e-6 +\n s1 * 2.83933368585534e-8 +\n t * 1.77803965218656e-8 +\n p * 1.71155619208233e-10))\n\n dentropy_dt = cp0 / ((Kelvin + pt0) * (1 - 0.05 * (1 - SA / SSO)))\n\n true_entropy_part = entropy_part(SA, t, p)\n\n for Number_of_iterations in range(0, 2, 1):\n pt0_old = pt0\n dentropy = entropy_part_zerop(SA, pt0_old) - true_entropy_part\n # Half way the mod. method (McDougall and Wotherspoon, 2012).\n pt0 = pt0_old - dentropy / dentropy_dt\n pt0m = 0.5 * (pt0 + pt0_old)\n dentropy_dt = -gibbs_pt0_pt0(SA, pt0m)\n pt0 = pt0_old - dentropy / dentropy_dt\n\n # maximum error of 6.3x10^-9 degrees C for one iteration. maximum error is\n # 1.8x10^-14 degrees C for two iterations (two iterations is the default,\n # \"for Number_of_iterations = 1:2\"). These errors are over the full\n # \"oceanographic funnel\" of McDougall et al. (2010), which reaches down to\n # p = 8000 dbar.\n\n return pt0","title":""},{"docid":"7b4650f9f86749dc275529ccac586c5b","score":"0.5983686","text":"def point_from_triangle(t):\n A, B, C = t[0], t[1], t[2]\n x = np.random.uniform(0, 1)\n y = np.random.uniform(0, 1)\n p = ( A[0] + x * (B[0] - A[0]) + y * (C[0] - A[0]), A[1] + x * (B[1] - A[1]) + y * (C[1] - A[1]) )\n\n if point_in_traingle(p, A, B, C):\n return p\n else: \n return ( A[0] + A[0] + (B[0] - A[0]) + (C[0] - A[0]) - p[0], A[1] + A[1] + (B[1] - A[1]) + (C[1] - A[1]) - p[1] )","title":""},{"docid":"dd54d47773be8e0c826929fde713ab9c","score":"0.5979847","text":"def tsat(p, bounds = False):\n if bounds:\n ok = (sat(0.01) <= p <= Pc1)\n else: ok = True\n if ok:\n from scipy.optimize import fsolve\n def f(t): return sat(t)-p\n from math import log\n t0=max(4606.0/(24.02-log(p))-273.15,5.0) # starting estimate\n t=fsolve(f,t0)\n import collections # need to check this as some versions of SciPy return an array from fsolve\n if isinstance(t,collections.Iterable): return t[0]\n else: return t\n else: return None","title":""},{"docid":"5a4962aad30a9cd3ca2db4bb49cf1aa0","score":"0.59513766","text":"def time_to_position(self, x):\n if x == 0: return self.t_0\n\n state0 = self.state_before_position(x)\n\n # p0 is the last position before the desired position\n \n d = x - p0.x\n\n if p0.a == 0:\n # d = v * t \n # t = d / v\n t = d / p0.v\n else:\n # d = v * t + 0.5 * a * t**2\n t = quadratic(0.5 * p0.a, p0.v, -d)\n \n print(\"state \" + str(p0))\n print(\"t_0\", self.t_0)\n print(\"time to position\", x, p0.t + t)\n \n return p0.t + t","title":""},{"docid":"50449250875a308362fd19c1d8d1589a","score":"0.59355855","text":"def derivativeAt(self, t):\n if self._avg is None:\n return None\n dt = t - self._lastTime\n return -(self._lastValue - self._avg)*math.log(1 - self.x.alpha)*(1 - self.x.alpha)**dt","title":""},{"docid":"2b6445b613193e5066ff6a31741f5743","score":"0.5922067","text":"def get_position_by_time(self, t):\n if t<=0 or t<=self.startTime:\n return None\n i = int((t-self.startTime)/NetflixSession.TS_GRANULARITY)\n if i >= len(self.position):\n return None\n return self.position[i]","title":""},{"docid":"95611c2e87f0d20fcd252e46d23c0dac","score":"0.5907894","text":"def stdp(t):\n\n if t > 0:\n return np.exp(-t/tau) \n elif t < 0:\n return -np.exp(t/tau)\n else:\n return 0","title":""},{"docid":"ff45196d44dd2130c764c17dbfe8f877","score":"0.5897936","text":"def pt(self, t):\n return foot(vec.zero, self) + t*self.dir","title":""},{"docid":"17cae00aefb3c38a9519f7be4f2f81a0","score":"0.58472365","text":"def linearbezier(p0, p1, t):\n ## p0 and p1 are of type numpy.array\n return p0+t*(p1-p0)","title":""},{"docid":"6d4a0bf1811fcc6bc0449dfbcf9af3c9","score":"0.5799749","text":"def pt_from_t(SA, t, p, p_ref=0):\n\n p_ref = np.asanyarray(p_ref)\n\n SA = np.maximum(SA, 0)\n\n s1 = SA * 35. / SSO\n\n pt = (t + (p - p_ref) * (8.65483913395442e-6 -\n s1 * 1.41636299744881e-6 -\n (p + p_ref) * 7.38286467135737e-9 +\n t * (-8.38241357039698e-6 +\n s1 * 2.83933368585534e-8 +\n t * 1.77803965218656e-8 +\n (p + p_ref) * 1.71155619208233e-10)))\n\n dentropy_dt = cp0 / ((Kelvin + pt) *\n (1 - 0.05 * (1 - SA / SSO)))\n\n true_entropy_part = entropy_part(SA, t, p)\n\n for Number_of_iterations in range(0, 2, 1):\n pt_old = pt\n dentropy = entropy_part(SA, pt_old, p_ref) - true_entropy_part\n pt = pt_old - dentropy / dentropy_dt # half way through the method\n ptm = 0.5 * (pt + pt_old)\n dentropy_dt = -gibbs(n0, n2, n0, SA, ptm, p_ref)\n pt = pt_old - dentropy / dentropy_dt\n\n # maximum error of 6.3x10^-9 degrees C for one iteration. maximum error\n # is 1.8x10^-14 degrees C for two iterations (two iterations is the default,\n # \"for Number_of_iterations = 1:2). These errors are over the full\n # \"oceanographic funnel\" of McDougall et al. (2010), which reaches down to\n # p = 8000 dbar.\n\n return pt","title":""},{"docid":"60b197e5cdc1687a0f746a86a9f5a81a","score":"0.5793214","text":"def _evaluate_at_time_lw(self, t: float) -> float:\n\n if 0 < t < self._onset + self._decay_duration:\n if t <= self._onset:\n return np.square(t/self._onset)\n else:\n return (np.exp(-(t-self._onset)/self._decay)-self._epsilon)/(1.-self._epsilon)\n else:\n return 0.","title":""},{"docid":"849ec256637303755d7debca77a21b4f","score":"0.5764049","text":"def rhs(x, y, t):\n\n return 5 * x * (x_end - x) * y * (y_end - y) + 10 * a * t * (y * (y_end - y) + x * (x_end - x))","title":""},{"docid":"83777d12b52de145243a1f9e2ea35954","score":"0.57630384","text":"def get(self, t):\n\n beta = self.schedule(t)\n dim = self.dim\n return (1 - beta) * jnp.eye(dim) + beta * jnp.ones((dim, dim)) / dim","title":""},{"docid":"cfd8cd75dcf11e7b3aaa720d98845d2a","score":"0.5747864","text":"def Klookup(self, t):\n idx_closest = (np.abs(self.ttraj - t)).argmin()\n\n # limit to valid indices\n if idx_closest >= (self.K.shape[0] - 1):\n return self.K[-1]\n elif idx_closest <= 0:\n return self.K[0]\n else:\n # linearly interpolate between two points\n t_closest = self.ttraj[idx_closest]\n if t_closest > t:\n dt = self.ttraj[idx_closest] - self.ttraj[idx_closest - 1]\n return (t_closest - t) * self.K[idx_closest - 1] / dt + (t + dt - t_closest) * self.K[idx_closest] / dt\n elif t > t_closest:\n dt = self.ttraj[idx_closest + 1] - self.ttraj[idx_closest]\n return (t - t_closest) * self.K[idx_closest + 1] / dt + (t_closest + dt - t) * self.K[idx_closest] / dt\n else:\n return self.K[idx_closest]","title":""},{"docid":"501146cd970eac51b86862c4d7aefe23","score":"0.5746347","text":"def time2depthpt(self, t, point):\n # Find the nearest profile\n coords = np.array(self.coords)\n closest = np.abs(coords - point).argmin()\n\n time = self.data[closest][:, 0]\n depth = self.data[closest][:, 1]\n\n return np.interp(t, time, depth)","title":""},{"docid":"57d86501a89087fe30912d7181b4e762","score":"0.5741742","text":"def EvaluateCurve(curve_id, t, segment_index=-1):\r\n curve = rhutil.coercecurve(curve_id, segment_index, True)\r\n return curve.PointAt(t)","title":""},{"docid":"54a9cbdb4f54b65505c366e7d08d96cc","score":"0.5734176","text":"def get_value(datablocks, channel_name, t, lpfilter=None):\n x, y = get_waveform(datablocks, channel_name, lpfilter)\n # First index where time is just greater than t\n result = np.where(x >= t)\n index = result[0][0]\n return y[index]","title":""},{"docid":"978754abc990fe8f1987c71f019283b6","score":"0.57319343","text":"def objective(point):\n\n t = point['t']\n x = point['x']\n a = 2 * np.pi\n b = a * t \n c = a * x\n d = np.exp(- (x + 1) ** (t + 1)) * np.cos(c)\n e = np.sin((t + 2) * c) + np.sin((t + 2)**2 * c) + np.sin((t + 2)**3 * c)\n f = d * e\n\t\n\t\n\t\n \"\"\"\n f(t,x) = x^2+t\n \"\"\"\t\n # t = point['t']\n # x = point['x']\t\n # f = 20*x**2+t\n\n return f","title":""},{"docid":"33bfcc14f4bab592b1442d138c53f9de","score":"0.5709217","text":"def c2pt(t, *p):\n val = 0\n for j in range(0, len(p), 2):\n val += 2*p[j]*np.exp(-p[j+1]*T)*np.cosh(p[j+1]*(T - t))\n return val","title":""},{"docid":"00c8cbf96fe0a0581000526457d1493b","score":"0.56889415","text":"def func(cls, t, tau, amp, offset):\n return offset + amp * (1 - np.exp(- t / tau))","title":""},{"docid":"011bc5b84117c4b7b14c383991702196","score":"0.5664827","text":"def lt_de(t, T):\n rhs = 20 * m.sin(3 * t) - 3 * T\n return rhs","title":""},{"docid":"5b61971802152ffbf6c7c953f0977e15","score":"0.56464267","text":"def myCurve(x):\n return np.abs(x) + x * np.sin(x)","title":""},{"docid":"09b3e0aa2293bcd0f67f55e09421a1c0","score":"0.5580157","text":"def get(self, t):\n beta = self.schedule(t)\n dim = self.dim\n width = self.width\n\n band = jnp.tri(\n dim, dim, width // 2, dtype=jnp.int32) - jnp.tri(\n dim, dim, -width // 2, dtype=jnp.int32)\n\n arr = band / band.sum(0, keepdims=True)\n\n return beta * arr + (1 - beta) * jnp.eye(dim)","title":""},{"docid":"2a84555b38822b4ede3df84cd82c70fe","score":"0.55614114","text":"def time2depthpt(self, t, point):\n return t*self.velocity/2.0","title":""},{"docid":"9eeda22685517dc295d3b900b90e094d","score":"0.55608034","text":"def state_at_t(self, t) -> Tuple[float, float]:\n if self.a * self.total_delta_loc < 0:\n if t < .5 * (self.delta_t - (self.v_initial / self.a)):\n # Deceleration - Phase 1\n return self.accelerating_state(t)\n else:\n # Acceleration - Phase 2\n return self.decelerating_state(t)\n\n accel_end = self.accelerate_end_t()\n if t < accel_end:\n return self.accelerating_state(t)\n accel_end_p = self.accelerate_end_p()\n decel_begin = self.decelerate_begin_t(accel_end)\n if t < decel_begin:\n return self.steady_state(t, accel_end, accel_end_p)\n return self.decelerating_state(t)","title":""},{"docid":"55c67b317cb9713b3c093810a6145b34","score":"0.55558425","text":"def weather_at_time(self, t: torch.Tensor) -> torch.Tensor:\n\n inbetween = (\n ((t % self._normalized_timestep_delta) / self._normalized_timestep_delta)\n .unsqueeze(1)\n .to(self._device)\n )\n left_index = (t // self._normalized_timestep_delta).long().to(self._device)\n\n if any(torch.isnan(t)):\n raise ValueError()\n if any(left_index < 0):\n left_index = torch.zeros_like(left_index)\n if any(left_index >= len(self._normalized_weather_tensor) - 2):\n left_index = torch.zeros_like(left_index) - 2\n\n right_index = left_index + 1\n\n left_weather = self._normalized_weather_tensor[left_index]\n right_weather = self._normalized_weather_tensor[right_index]\n\n return left_weather * (1 - inbetween) + right_weather * inbetween","title":""},{"docid":"4342a5e924f2f97902f46a87a4ba23aa","score":"0.55510455","text":"def getValueT(self,t=None):\n\n\t\tp = []\n\t\tfor index, val in enumerate(self.coef):\n\t\t\tp.append(val * t + self.point[index]) \n\t\t\t# p[1] = val * t + self.point[index]\n\t\t\t# p[2] = val * t + self.point[index]\n\t\treturn p","title":""},{"docid":"dd67fce3431965b73879ff9032368247","score":"0.5541475","text":"def yValue(self, time):\n #----------------------\n i = self._index(time)\n if i is not None:\n if i >= (self._poly.size() - 1):\n i = self._poly.size() - 2\n p0 = self._poly.at(i)\n p1 = self._poly.at(i+1)\n if p0.x() == p1.x(): return (p0.y() + p1.y())/2.0\n return p0.y() + (time - p0.x())*(p1.y() - p0.y())/(p1.x() - p0.x())","title":""},{"docid":"57eb1349f0beebdb135cef26e7a8bd31","score":"0.5521312","text":"def value(self, time):\n\n # 1. ensure time is within bounds else return boundary keyframe\n # if time <= self.times[0] or time >= self.times[-1]:\n # return self.values[0 if time <= self.times[0] else -1]\n\n # 2. search for closest index entry in self.times, using bisect_left function\n i = bisect_left(self.times, time%self.times[-1]) # _i is the time index just before t\n\n # 3. using the retrieved index, interpolate between the two neighboring values\n # in self.values, using the initially stored self.interpolate function\n fraction = (time%self.times[-1]-self.times[i-1]) / (self.times[i]-self.times[i-1])\n return self.interpolate(self.values[i-1], self.values[i], fraction)","title":""},{"docid":"9320ee0c79c40beb966dfe6b2b12db22","score":"0.5517267","text":"def Rx(t):\n return np.array(((1,0,0),(0,np.cos(t),-np.sin(t)),(0,np.sin(t),np.cos(t))))","title":""},{"docid":"5c8f9784a3dad0884113cbe7c617bda0","score":"0.5515756","text":"def at_dt(self, t):\n\n return self.bez(t, self.derive[:])","title":""},{"docid":"5d97bf37db187eebda63ba7c5963fd4c","score":"0.5515326","text":"def calc(self, t):\n\n if t < self.x[0]:\n return None\n elif t > self.x[-1]:\n return None\n\n i = self.__search_index(t)\n dx = t - self.x[i]\n result = self.a[i] + self.b[i] * dx + \\\n self.c[i] * dx ** 2.0 + self.d[i] * dx ** 3.0\n\n return result","title":""},{"docid":"f974947e48308375cd41f79504a50118","score":"0.5510095","text":"def est_am(t,y):\n ch=t>t_eq\n y1=y[ch]\n return y1[-1]-y1[0]","title":""},{"docid":"89b8c08cc433340a1ca3586e15a9b1bc","score":"0.550863","text":"def forward_curve(self, x):\n y = scipy.interpolate.splev(x, self.tck, der=0)\n return y","title":""},{"docid":"eda7341692115079bbe420f60d75925b","score":"0.5503552","text":"def getSignal(t):\n s1 = np.sin(2*np.pi*100*t)\n s2 = 2*np.sin(2*np.pi*500*t)\n # create a transient \"chirp\" (zero s2 signal before/after chirp)\n mask = np.where(np.logical_and(t>10, t<12), 1.0, 0.0)\n s2 = s2 * mask\n # add some noise into the mix\n nse = 0.01*np.random.randn(len(t))/1.0\n s = s1 + s2 + nse\n return s # the signal","title":""},{"docid":"36882814fd88d49ceac91a73111b8822","score":"0.54710823","text":"def findNearest(self,t,timevec):\n tnow = self.SecondsSince(t)\n tvec = self.SecondsSince(timevec)\n \n #tdist = np.abs(tnow - tvec)\n \n #idx = np.argwhere(tdist == tdist.min())\n \n #return int(idx[0])\n return np.searchsorted(tvec,tnow)[0]","title":""},{"docid":"02f8de0469a570f2ff10b62d6fcc58e8","score":"0.5462917","text":"def _evaluate_at_time_lw(self, t: float) -> float:\n return 0.","title":""},{"docid":"6032c4321e5fcc21980dcaa09a133dac","score":"0.5456722","text":"def get_t(t21,t2p1, timestep): \n if t21 < 0 or t2p1 < 0:\n #w2 or w2p will come at t<0\n t_min = min(t21, t2p1) - early_buffer\n else:\n t_min = -early_buffer\n #last pulse dictates when emitted coherences should start to be watched for integration\n if t21 > 0 or t2p1 > 0:\n #w2 or w2p will come at t>0\n t_max = max(t21, t2p1) + late_buffer\n else:\n t_max = late_buffer\n #note: dtype float16 can screw up t working properly!\n t = np.arange(t_min, t_max, timestep)\n #tprime_i = t_max - late_buffer - early_buffer\n return t","title":""},{"docid":"f1c8fcf5b50529812bbeff28e91816fa","score":"0.5451557","text":"def value(self, time):\n\n # 1. ensure time is within bounds else return boundary keyframe\n if time <= self.times[0]:\n return self.values[0]\n if time >= self.times[-1]:\n return self.values[-1]\n\n # 2. search for closest index entry in self.times, using bisect_left function\n t_i = bisect_left(self.times, time) - 1 # note the -1\n\n # 3. using the retrieved index, interpolate between the two neighboring values\n # in self.values, using the initially stored self.interpolate function\n f = (time - self.times[t_i]) / (self.times[t_i + 1] - self.times[t_i])\n return self.interpolate(self.values[t_i], self.values[t_i + 1], f)","title":""},{"docid":"efc7cdd95796b7367a1608ab254237ac","score":"0.54389536","text":"def _sample_position(self, t, N=1):\n v = np.zeros(N)\n iv = np.ones(N, dtype=bool)\n x = self.startPosition * np.ones(N)\n continue_ = True\n i = 1\n t = t - self.startTime # should be positive.\n size = v.shape[0]\n while continue_:\n n_samples = iv.sum()\n v[iv] += self.T.rvs(n_samples)\n\n # if the next time is beyond reach, we do not add a new jump\n iv[v >= t] = False\n n_samples = iv.sum()\n\n x[iv] += self.J.rvs(n_samples)\n\n size = iv.sum() # any left?\n empty = size == 0\n\n i += 1\n if empty:\n continue_ = False\n\n if i > 10000:\n warnings.warn(\"The algorithm did not converge. Be sure 'T' is a non negative random \\\n variable, or set 't' to a smaller value.\")\n break\n\n return x","title":""},{"docid":"3f2a367331b2ae237fe894c6805c1373","score":"0.5409791","text":"def path(t):\n t = gs.array(t)\n t = gs.cast(t, initial_cotangent_vec.dtype)\n t = gs.to_ndarray(t, to_ndim=1)\n\n cotangent_vecs = gs.einsum(\"i,...k->...ik\", t, initial_cotangent_vec)\n\n points_at_time_t = [\n self.exp(tv, pt, n_steps=n_steps)\n for tv, pt in zip(cotangent_vecs, initial_point)\n ]\n points_at_time_t = gs.stack(points_at_time_t, axis=0)\n\n return (\n points_at_time_t[0] if n_initial_conditions == 1 else points_at_time_t\n )","title":""},{"docid":"7e916fd07c3010a6d073f8bf25380f1f","score":"0.5408187","text":"def interp(self,t,data):\n # helps to keep mod T here for integrators\n t = np.mod(t,self.T)\n fn = interp1d(self.tLC,data)\n \n #print('t,fn',t,fn(t))\n \n return fn(t)","title":""},{"docid":"75929f6d25cbb52eff53177f44c63aa5","score":"0.5399461","text":"def ToPoint(self) -> _n_1_t_1:","title":""},{"docid":"b6ac56e27ba8a6cb2df8ec9e2efb1ef1","score":"0.5399192","text":"def time2depthpt(self, t, point):\n # Find the nearest profile\n coords = np.array(self.coords)\n closest = np.abs(coords - point).argmin()\n profile = self.data[closest]\n\n # HACK TO MAKE FAKE VELOCITIES\n velocity = np.ones(profile.data.size) * 2000.0\n\n samp = profile.stats[\"sampling_rate\"]\n t_profile = np.arange(velocity.size) * 1.0 / samp\n vrms = np.cumsum(velocity) / (np.arange(velocity.size)+1)\n vrms = np.interp(t, t_profile, vrms)\n return t*vrms/2.","title":""},{"docid":"f642acbb32b4c47686fc9687fc16e155","score":"0.5388491","text":"def isPointOnCurve(*args, **kwargs):\n \n pass","title":""},{"docid":"f051eb29df6c6b48de6ce04874d84744","score":"0.5374317","text":"def O(self,t,x,p):\n return x","title":""},{"docid":"2323ac8c24359c184549681ff9d1045b","score":"0.5364349","text":"def time(self, t, s=1.0, complete=True):\n w = self.w0\n\n x = t / s\n\n output = np.exp(1j * w * x)\n\n if complete:\n output -= np.exp(-0.5 * (w ** 2))\n\n output *= np.exp(-0.5 * (x ** 2)) * np.pi ** (-0.25)\n\n return output","title":""},{"docid":"5ae02d0df6086ff67e6da8169bbd44ac","score":"0.5362421","text":"def state(k, x, t):\n return exp(1j*(k*x - omega*t))","title":""},{"docid":"331a394258f5359453cf93fb876218fa","score":"0.5360645","text":"def quadraticbezier(p0, p1, p2, t):\n ## p0, p1 and p2 are of type numpy.array\n return (1-t)**2*p0+2*(1-t)*t*p1+t**2*p2","title":""},{"docid":"cc2985354355e1fe87456aacdca3c8f3","score":"0.5360297","text":"def sinc_p(t, t0, Tb):\n\n p = np.sinc(np.pi * (t - t0) / Tb)\n return (p)","title":""},{"docid":"8fc93ab057115be1d01909abb130e0ca","score":"0.5358552","text":"def get_timestamp_by_time(self, t):\n if t<=0 or t<=self.startTime:\n return None\n i = int((t-self.startTime)/NetflixSession.TS_GRANULARITY)\n if i >= len(self.timeStamps):\n return None\n return self.timeStamps[i]","title":""},{"docid":"2e1ed925682763bd477ee53df1f4794f","score":"0.5357576","text":"def next_change(self, t=None):\n if t is None:\n t = self.engine.get_now()\n\n delta = random.expovariate(1.0 / self.interval) # expovariate delivers long-tailed distribution between 0 and infinity, centred on given value\n return t + delta","title":""},{"docid":"1a7fbd7defadbe4f74956360ca6cabfb","score":"0.53535336","text":"def interval(self, t):\n return np.random.poisson(self.time_variance(t))","title":""},{"docid":"2b3a23bc342344cdbec79aaa5f41f100","score":"0.53485364","text":"def true_posterior(self, t):\n return (sigmoid(t)**self.a)*(sigmoid(-t)**self.b)/beta(self.a, self.b)","title":""},{"docid":"037f7936f651b07572c42a40308dcf23","score":"0.533736","text":"def generate_pnt(self, s, t, *args):\n pnt = TrajectoryPoint()\n # Trajectory time stamp\n pnt.t = t\n # Set position vector\n pnt.pos = self.generate_pos(s).tolist()\n # Set rotation quaternion\n pnt.rotq = self.generate_quat(s)\n return pnt","title":""},{"docid":"fcbb72c7f6790b4d3e6c239a3c1f41a6","score":"0.5330515","text":"def take_step(self,p):\n dpos = np.random.randn(2)\n dpos = dpos / np.linalg.norm(dpos)\n temp = self.pos + dpos * p\n if temp[0]>= 0 and temp[0]<=1 and temp[1]>= 0 and temp[1]<=1:\n self.pos = temp","title":""},{"docid":"5ed2732aa09b659bf58b65c4cdd0e96c","score":"0.5329308","text":"def Price(t):\n A = 100.\n return .5*A*np.sine(t) + A","title":""},{"docid":"66a1c2aaf585333da657e650474ba79d","score":"0.5327765","text":"def is_in_t(pt):\n flag = True\n if pt[0] < 0 or pt[1] < 0:\n flag = False\n if pt[0] + pt[1] > 1:\n flag = False\n return flag","title":""},{"docid":"6e1de4504ffd58e59e1053934e64d216","score":"0.5325302","text":"def sigmoid(t):\n return (np.exp(t))/(1+(np.exp(t)))","title":""},{"docid":"3688f4880480de3c62a053429809f68e","score":"0.5319139","text":"def plot_me(self, t):\n return np.array([np.cos(2*np.pi*t), np.sin(2*np.pi*t)])","title":""},{"docid":"2b0d92acbbcb3881c7b04c8d85dbdecb","score":"0.5317961","text":"def f1(y, t):\n\n u1 = y[0]\n u2 = y[1]\n u3 = t\n return u2, -2 * params[\"gamma\"] * u2 - np.sin(u1) + params[\"mu\"] * np.sin(params[\"omega\"] * u3)","title":""},{"docid":"a9c139f6f789c7c9897e44700cd5aa56","score":"0.5314853","text":"def sigmoid(t):\n return 1.0 / (1.0 + np.exp(-t))","title":""},{"docid":"2e8bd6921587eb2edd707f3968305d73","score":"0.5311136","text":"def kt(self, t):\n K = np.searchsorted(self.data.t, t)\n N = len(self)\n return np.clip(K, 0, N-1)","title":""},{"docid":"5edd6ac6ced0629b5c26e79a5ac365e0","score":"0.5307474","text":"def trajectory_ex(t_start=0, t_stop=1, dt=2**(-4), x0=np.array([1])):\n parameters = [1.5, 1]\n number_of_timesteps = int((t_stop - t_start) / dt)\n x = np.zeros((number_of_timesteps + 1, len(x0)))\n x_exact = np.zeros_like(x)\n x[0, :] = x0\n x_exact[0, :] = x0\n time = np.linspace(t_start, t_stop, number_of_timesteps + 1)\n\n random_force = np.random.normal(scale=np.sqrt(dt), size=(number_of_timesteps, 1))\n\n for step in range(number_of_timesteps):\n x[step + 1, :] = x[step, :] + parameters[0]*x[step, :]*dt + parameters[1] * x[step, :] * random_force[step]\n x_exact[step+1, :] = x0*np.exp((parameters[0]-parameters[1]**2/2)*time[step]+parameters[1]*np.sum(random_force[:step+1]))\n\n plt.plot(time, x, label='Numerical solution')\n plt.plot(time, x_exact, label='Exact solution')\n plt.legend()\n plt.show()","title":""},{"docid":"ee114c73fbdab147225971456e36a8f6","score":"0.5297463","text":"def sigmoid(t):\n \n return 1.0 / (1.0 + np.exp(-t))","title":""},{"docid":"e93395abe21453359df5e2f86ddbec22","score":"0.52953637","text":"def getFirstPoint(self):\r\n return Trackpoint(self.trackdata[self.trackdataStart:self.trackdataStart + Trackpoint.TRACKPOINTLEN])","title":""},{"docid":"e8a907ebcfb41fb01d335c4a66f58979","score":"0.5291992","text":"def interp_tT(t):\n return interp(t, conditions[:, 0], conditions[:, 1])","title":""},{"docid":"c25c14c125f44a908dc21329584908a4","score":"0.5285083","text":"def simple_harmonic_oscillator(p, t):\n return p[0] + p[1] * np.cos(p[2] * t - p[3])","title":""},{"docid":"4e544b08b240864f702c80dd40b390cb","score":"0.5267563","text":"def f(self, t, y):\n pass","title":""},{"docid":"b15632e6bc69a368ffd79374489089bf","score":"0.52611744","text":"def Xrt(t, amp, centre, period = 365): \n\n x = t * (2 * pi / period) \n\n return (amp*centre * sin(x)) + centre","title":""},{"docid":"cdd2c8cfb52a9f9a708db0fd0b5d02f6","score":"0.5255001","text":"def current_point(self):\n try:\n data = self.read_reg(TD_STATUS, 1)\n # if touch points==1\n if data is not None and data[0] == 0x1:\n # Read Xhigh, Xlow, Yhigh and Ylow\n data_buf = self.read_reg(PN_XH, 4)\n x = ((data_buf[0] & 0x0F) << 8) | (data_buf[1])\n y = ((data_buf[2] & 0x0F) << 8) | (data_buf[3])\n return (x, y)\n except Exception as e:\n return e # debug\n return None","title":""},{"docid":"b0dbd036caae087aa7bcdd74a09ff6bf","score":"0.52548707","text":"def interpolate_linear(t, t0, x0, t1, x1):\n if t0 > t1:\n t0, x0, t1, x1 = t1, x1, t0, x0\n if t0 == t1 and not x0 == x1:\n raise ValueError(\"Interpolated quantity can not have two \"\n \"different values at the same time\")\n if t < t0 or t > t1:\n raise ValueError(\"Time argument outside of the given interval\")\n if t == t0:\n return x0\n if t == t1:\n return x1\n timeDelta = 1.0*(t1 - t0)\n t = (t - t0)/timeDelta\n onemt = 1.0 - t\n return onemt*x0 + t*x1","title":""},{"docid":"2234a4f341fbef9a66a08f9ebecd2607","score":"0.52531505","text":"def edge_point(self, edge, t=0.5):\n if t == 0.0:\n return self.edge_start(edge)\n if t == 1.0:\n return self.edge_end(edge)\n if t == 0.5:\n return self.edge_midpoint(edge)\n\n a, b = self.edge_coordinates(edge)\n ab = subtract_vectors(b, a)\n return Point(*add_vectors(a, scale_vector(ab, t)))","title":""},{"docid":"98cb9bdf5d9ce9fc779c4a405a4ac317","score":"0.5249009","text":"def point_in_time(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"point_in_time\")","title":""},{"docid":"dd850db1ad2991f87dc6b321fd962dd2","score":"0.5246322","text":"def pdf(self, t, l):\n r, c, tau = self.r, self.c, self.tau\n\n t = np.atleast_1d(t)\n\n tcut = (l+r)/c\n tmin = (r-l)/c\n\n x = t.copy()\n x[t > tcut] = tcut\n\n y = c*x*tau*np.exp(tmin/tau)*(r+l-c*tau)\n y += np.exp(x/tau)*(l**2-r**2+c**2*x*tau)*tau\n y += x*(l**2-r**2)*(expi(tmin/tau) - expi(x/tau))\n y /= 4*c*l*x*tau\n y *= np.exp(-t/tau)/tau\n y[t < tmin] = 0\n\n return y if y.size > 1 else y.item()","title":""},{"docid":"06c714a6db88ab6d623bd0a7a6b7d0ba","score":"0.5241759","text":"def log_t(u, t):\n if t == 1.0:\n return u.log()\n else:\n return (u.pow(1.0 - t) - 1.0) / (1.0 - t)","title":""},{"docid":"553cbb4db0feaadc0f78c5bfbcaa9f0c","score":"0.5236777","text":"def get_phase(self, t, TA=None):\n \n if TA is None:\n TA = self.true_anomaly(t)\n \n phase = (TA-self.true_anomaly(self.t0))/(2.*np.pi)\n phase = phase + 1.*(phase<0.).astype(int)\n return phase","title":""},{"docid":"2a9ba343afc0de8d070313bde35efe51","score":"0.5235387","text":"def __interpolate(self, t):\r\n try:\r\n c = [0 for _ in range(self.n)]\r\n w = [0 for _ in range(self.n)]\r\n for i in range(0, self.n):\r\n w[i] = self.Y[i]\r\n for j in reversed(range(i)):\r\n w[j] = (w[j + 1] - w[j]) / (self.X[i] - self.X[j])\r\n c[i] = w[0]\r\n s = c[self.n - 1]\r\n for i in reversed(range(self.n - 1)):\r\n s = s * (t - self.X[i]) + c[i]\r\n return s\r\n except Exception as e:\r\n raise","title":""},{"docid":"1925a5c4de8821953bd94db3a32c50a0","score":"0.5232862","text":"def run(self, t):\r\n temp_x = self.x\r\n temp_t = self.t\r\n traj = np.zeros((len(self.x), int(t/self.dt)))\r\n for i in range(len(traj.T)):\r\n traj.T[i] = self.x\r\n self.step()\r\n self.x = temp_x\r\n self.t = temp_t\r\n self.y = self.output()\r\n return traj","title":""},{"docid":"091ffb581b0f4ba35758e514b61bab70","score":"0.52275234","text":"def interp_cubic(p0, p1, t_abs):\n T = (p1.time_from_start - p0.time_from_start).to_sec()\n t = t_abs - p0.time_from_start.to_sec()\n q = [0] * 7\n qdot = [0] * 7\n qddot = [0] * 7\n for i in range(len(p0.positions)):\n a = p0.positions[i]\n b = p0.velocities[i]\n c = (-3 * p0.positions[i] + 3 * p1.positions[i] - 2 * T * p0.velocities[i] - T * p1.velocities[i]) / T**2\n d = (2 * p0.positions[i] - 2 * p1.positions[i] + T * p0.velocities[i] + T * p1.velocities[i]) / T**3\n\n q[i] = a + b * t + c * t**2 + d * t**3\n qdot[i] = b + 2 * c * t + 3 * d * t**2\n qddot[i] = 2 * c + 6 * d * t\n return JointTrajectoryPoint(positions=q, velocities=qdot, accelerations=qddot, time_from_start=rospy.Duration(t_abs))","title":""},{"docid":"d2e4bb68773d57eca852839ac36577a5","score":"0.5226863","text":"def _A(self, t, T):\r\n P0T = self.curve.P_M(T)\r\n P0t = self.curve.P_M(T)\r\n f0t = self.curve.instantFwd(t)\r\n par = self._B(t, T)*f0t\\\r\n \t-((self.sigma**2)/(4*self.a)*(1-np.exp(-2*self.a*t))*(self._B(t, T)**2))\r\n return P0T/P0t * np.exp(par)","title":""},{"docid":"bf4162a969c1b4c81c4444e540f2eef3","score":"0.522631","text":"def tuple_to_point(t):\n return Point(t[0], t[1]) if t else None","title":""},{"docid":"931fd18e756832d3c7de73e4773dd208","score":"0.5223334","text":"def seg_velocity_time(v0,v1,t):\n \n v0 = float(v0)\n v1 = float(v1)\n x = float(t)\n \n a = (v1-v0)/t\n\n x = a * (t**2) / 2.\n\n return abs(x), v0, v1, t, a","title":""},{"docid":"fc2ea06aae009702465bff8b3b510f5a","score":"0.5210453","text":"def update(self, t):\n\n x = np.zeros((3,))\n x_dot = np.zeros((3,))\n x_ddot = np.zeros((3,))\n x_dddot = np.zeros((3,))\n x_ddddot = np.zeros((3,))\n yaw = 0\n yaw_dot = 0\n\n # STUDENT CODE HERE\n\n # from waypoint_traj\n if np.shape(self.points) == (3,) or np.shape(self.points) == (1, 3):\n x = np.reshape(self.points, (3,))\n elif np.shape(self.points) != (3,) or np.shape(self.points) != (1, 3):\n if t > self.point_t[-1]:\n x = self.points[-1]\n else:\n x = self.f(t)\n\n # if np.shape(self.points) == (3,) or np.shape(self.points) == (1,3):\n # x = np.reshape(self.points, (3,))\n # elif np.shape(self.points) != (3,) or np.shape(self.points) != (1,3):\n # if t >= self.t[-1]:\n # x = self.points[-1]\n # elif t == self.t[0]:\n # x = self.points[0]\n # else:\n # for i in range(len(self.t)-1):\n # if self.t[i] < t <= self.t[i+1]:\n # x_dot = self.v\n # x = x_dot*(t-self.t[i]) + self.points[i]\n #\n\n\n flat_output = {'x': x, 'x_dot': x_dot, 'x_ddot': x_ddot, 'x_dddot': x_dddot, 'x_ddddot': x_ddddot,\n 'yaw': yaw, 'yaw_dot': yaw_dot}\n return flat_output","title":""},{"docid":"f175c55466e23a469d255393e97ec7b4","score":"0.5204807","text":"def PreciseTime():","title":""}],"string":"[\n {\n \"docid\": \"df6681abd3f94fe59f3e39c4d68cc2c1\",\n \"score\": \"0.6694643\",\n \"text\": \"def get(self, t):\\n # return self.value[np.where((self.time - t) <= 0)[0][-1]] # last one that is <= 0\\n return self._value[\\n (self._time.searchsorted(t + 1) - 1).clip(0, len(self._value) - 1)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26b3d9c97fc621f35b7769a3cf410da5\",\n \"score\": \"0.66819525\",\n \"text\": \"def at(self, t):\\n return \\\\\\n self._avg + (self._lastValue - self._avg)*(1 - (1 - self.x.alpha)**( t - self._lastTime)) \\\\\\n if self._avg is not None else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f78a9033d29c8a7a04871a994512a5ba\",\n \"score\": \"0.6530438\",\n \"text\": \"def exact(self,x,t):\\n alpha = 4\\n period = 2*pi/self.mlength\\n beta = alpha*period\\n u = sin(beta*(x-(1-beta**2)*t)) + 1\\n return u\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aba8059fb89079e6ab0e1d18a206c3e\",\n \"score\": \"0.6412015\",\n \"text\": \"def getSegment(self,t):\\n if len(self.times)==0:\\n raise ValueError(\\\"Empty trajectory\\\")\\n if len(self.times)==1:\\n return (-1,0)\\n if t > self.times[-1]:\\n return (len(self.times),0)\\n if t < self.times[0]:\\n return (0,0)\\n i = bisect.bisect_right(self.times,t)\\n p=i-1\\n u=(t-self.times[p])/(self.times[i]-self.times[p])\\n if i==0:\\n return (-1,0)\\n assert u >= 0 and u <= 1\\n return (p,u)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d2f13af33820e7f19bba5902bb3438\",\n \"score\": \"0.6325142\",\n \"text\": \"def sample_trajectory(trajectory, t):\\n # First point\\n if t <= 0.0:\\n return copy.deepcopy(trajectory.points[0])\\n # Last point\\n if t >= trajectory.points[-1].time_from_start.to_sec():\\n return copy.deepcopy(trajectory.points[-1])\\n # Finds the (middle) segment containing t\\n i = 0\\n while trajectory.points[i + 1].time_from_start.to_sec() < t:\\n i += 1\\n return interp_cubic(trajectory.points[i], trajectory.points[i + 1], t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3f7100b7d57926b7b71c8919c575ee0\",\n \"score\": \"0.63108504\",\n \"text\": \"def _getSegment(self,t):\\n if len(self.trajectoryTimes)==0:\\n raise ValueError(\\\"Empty trajectory\\\")\\n if len(self.trajectoryTimes)==1:\\n return (-1,0)\\n if t >= self.trajectoryTimes[-1]:\\n return (len(self.trajectoryTimes)-1,0)\\n if t <= self.trajectoryTimes[0]:\\n return (0,0)\\n i = bisect.bisect_right(self.trajectoryTimes,t)\\n p=i-1\\n assert i > 0 and i < len(self.trajectoryTimes),\\\"Invalid time index \\\"+str(t)+\\\" in \\\"+str(self.trajectoryTimes)\\n u=(t-self.trajectoryTimes[p])/(self.trajectoryTimes[i]-self.trajectoryTimes[p])\\n if i==0:\\n return (-1,0)\\n assert u >= 0 and u <= 1\\n return (p,u)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7c0f839e694ce9962e8031e40223cf9\",\n \"score\": \"0.627243\",\n \"text\": \"def bezier_point(cps, t):\\n\\n p = ((1 - t) ** 3) * cps[0, :]\\n p += 3 * t * ((1 - t) ** 2) * cps[1, :]\\n p += 3 * (t ** 2) * (1 - t) * cps[2, :]\\n p += (t ** 3) * cps[3, :]\\n\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c29e21036eda671ad8ea0549b5ae16f\",\n \"score\": \"0.6193054\",\n \"text\": \"def fn(t):\\n # must use exp() from numpy to handle input from linspace\\n return 0.1*(-3.0*np.exp(-t) + np.exp(2.0*t)*np.sin(t) + 3.0*np.exp(2.0*t)*np.cos(t) + 2.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3253902be54de0fca2a7256fd3a39547\",\n \"score\": \"0.61587834\",\n \"text\": \"def fp(self, t, y):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c5dcf3e68541659ad36d4386d8658ed\",\n \"score\": \"0.6094813\",\n \"text\": \"def p(x,t):\\n\\n if x <= (-1 * v_max * t):\\n return p_max\\n elif x >= (v_max * t):\\n return 0.0\\n else:\\n return 0.5 * (1. - x/(v_max*t))*p_max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c6db1613fc639cf4de9f33ab57625b8\",\n \"score\": \"0.6055308\",\n \"text\": \"def position_at_time(self, t):\\n t += TIME_OFFSET # Compensate for start time\\n\\n n = 2 * np.pi / self.period # mean motion\\n M = n * t # mean anomaly\\n\\n def kepler_root_fn(E_):\\n return (E_ - self.ecc * np.sin(E_) - M) ** 2\\n result = minimize(kepler_root_fn, 0)\\n if not result.success:\\n raise Exception(\\\"ERROR\\\")\\n E = result.x[0]\\n\\n theta = 2 * np.arctan(np.sqrt(((1 + self.ecc) / (1 - self.ecc)) * np.tan(E / 2) ** 2))\\n\\n if E % (2 * np.pi) > np.pi:\\n theta = 2 * np.pi - theta\\n\\n #theta = np.arccos((np.cos(E) - self.ecc) / (1 - self.ecc * np.cos(E)))\\n r = self.semimajor * (1 - self.ecc * np.cos(E))\\n\\n theta *= self.sign # Compensate for direction\\n\\n x = r * np.cos(theta)\\n y = r * np.sin(theta)\\n return (x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"628098083ea3ed0b9506e12d00d192db\",\n \"score\": \"0.60532224\",\n \"text\": \"def P_t(self, past, present):\\n\\t\\treturn self.p_t_coef*self.P_a(past)#*self.p_q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41358e99b0527b64732154c0543a0501\",\n \"score\": \"0.60171324\",\n \"text\": \"def control_signal(t):\\n return 0 if t < 0.5 else -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71a484e6d46c9ab0fdcf1798c8429253\",\n \"score\": \"0.60109663\",\n \"text\": \"def bezierCurveCLinear(self, p0, p1, t):\\n\\n if t <= 0:\\n return p1[0], p1[1]\\n\\n elif t >= 1:\\n return p0[0], p0[1]\\n\\n position = (1-t)*p0 + t*p1\\n\\n return position[0], position[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64a79d315b02aac31626cff8a18d38dc\",\n \"score\": \"0.5996135\",\n \"text\": \"def at(self, t):\\n\\n return self.bez(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8846cad9676337303d23c7dbab07e4b\",\n \"score\": \"0.59881216\",\n \"text\": \"def pt0_from_t(SA, t, p):\\n\\n SA = np.maximum(SA, 0)\\n\\n s1 = SA * (35. / SSO)\\n\\n pt0 = t + p * (8.65483913395442e-6 -\\n s1 * 1.41636299744881e-6 -\\n p * 7.38286467135737e-9 +\\n t * (-8.38241357039698e-6 +\\n s1 * 2.83933368585534e-8 +\\n t * 1.77803965218656e-8 +\\n p * 1.71155619208233e-10))\\n\\n dentropy_dt = cp0 / ((Kelvin + pt0) * (1 - 0.05 * (1 - SA / SSO)))\\n\\n true_entropy_part = entropy_part(SA, t, p)\\n\\n for Number_of_iterations in range(0, 2, 1):\\n pt0_old = pt0\\n dentropy = entropy_part_zerop(SA, pt0_old) - true_entropy_part\\n # Half way the mod. method (McDougall and Wotherspoon, 2012).\\n pt0 = pt0_old - dentropy / dentropy_dt\\n pt0m = 0.5 * (pt0 + pt0_old)\\n dentropy_dt = -gibbs_pt0_pt0(SA, pt0m)\\n pt0 = pt0_old - dentropy / dentropy_dt\\n\\n # maximum error of 6.3x10^-9 degrees C for one iteration. maximum error is\\n # 1.8x10^-14 degrees C for two iterations (two iterations is the default,\\n # \\\"for Number_of_iterations = 1:2\\\"). These errors are over the full\\n # \\\"oceanographic funnel\\\" of McDougall et al. (2010), which reaches down to\\n # p = 8000 dbar.\\n\\n return pt0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b4650f9f86749dc275529ccac586c5b\",\n \"score\": \"0.5983686\",\n \"text\": \"def point_from_triangle(t):\\n A, B, C = t[0], t[1], t[2]\\n x = np.random.uniform(0, 1)\\n y = np.random.uniform(0, 1)\\n p = ( A[0] + x * (B[0] - A[0]) + y * (C[0] - A[0]), A[1] + x * (B[1] - A[1]) + y * (C[1] - A[1]) )\\n\\n if point_in_traingle(p, A, B, C):\\n return p\\n else: \\n return ( A[0] + A[0] + (B[0] - A[0]) + (C[0] - A[0]) - p[0], A[1] + A[1] + (B[1] - A[1]) + (C[1] - A[1]) - p[1] )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd54d47773be8e0c826929fde713ab9c\",\n \"score\": \"0.5979847\",\n \"text\": \"def tsat(p, bounds = False):\\n if bounds:\\n ok = (sat(0.01) <= p <= Pc1)\\n else: ok = True\\n if ok:\\n from scipy.optimize import fsolve\\n def f(t): return sat(t)-p\\n from math import log\\n t0=max(4606.0/(24.02-log(p))-273.15,5.0) # starting estimate\\n t=fsolve(f,t0)\\n import collections # need to check this as some versions of SciPy return an array from fsolve\\n if isinstance(t,collections.Iterable): return t[0]\\n else: return t\\n else: return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a4962aad30a9cd3ca2db4bb49cf1aa0\",\n \"score\": \"0.59513766\",\n \"text\": \"def time_to_position(self, x):\\n if x == 0: return self.t_0\\n\\n state0 = self.state_before_position(x)\\n\\n # p0 is the last position before the desired position\\n \\n d = x - p0.x\\n\\n if p0.a == 0:\\n # d = v * t \\n # t = d / v\\n t = d / p0.v\\n else:\\n # d = v * t + 0.5 * a * t**2\\n t = quadratic(0.5 * p0.a, p0.v, -d)\\n \\n print(\\\"state \\\" + str(p0))\\n print(\\\"t_0\\\", self.t_0)\\n print(\\\"time to position\\\", x, p0.t + t)\\n \\n return p0.t + t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50449250875a308362fd19c1d8d1589a\",\n \"score\": \"0.59355855\",\n \"text\": \"def derivativeAt(self, t):\\n if self._avg is None:\\n return None\\n dt = t - self._lastTime\\n return -(self._lastValue - self._avg)*math.log(1 - self.x.alpha)*(1 - self.x.alpha)**dt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b6445b613193e5066ff6a31741f5743\",\n \"score\": \"0.5922067\",\n \"text\": \"def get_position_by_time(self, t):\\n if t<=0 or t<=self.startTime:\\n return None\\n i = int((t-self.startTime)/NetflixSession.TS_GRANULARITY)\\n if i >= len(self.position):\\n return None\\n return self.position[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95611c2e87f0d20fcd252e46d23c0dac\",\n \"score\": \"0.5907894\",\n \"text\": \"def stdp(t):\\n\\n if t > 0:\\n return np.exp(-t/tau) \\n elif t < 0:\\n return -np.exp(t/tau)\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff45196d44dd2130c764c17dbfe8f877\",\n \"score\": \"0.5897936\",\n \"text\": \"def pt(self, t):\\n return foot(vec.zero, self) + t*self.dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17cae00aefb3c38a9519f7be4f2f81a0\",\n \"score\": \"0.58472365\",\n \"text\": \"def linearbezier(p0, p1, t):\\n ## p0 and p1 are of type numpy.array\\n return p0+t*(p1-p0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d4a0bf1811fcc6bc0449dfbcf9af3c9\",\n \"score\": \"0.5799749\",\n \"text\": \"def pt_from_t(SA, t, p, p_ref=0):\\n\\n p_ref = np.asanyarray(p_ref)\\n\\n SA = np.maximum(SA, 0)\\n\\n s1 = SA * 35. / SSO\\n\\n pt = (t + (p - p_ref) * (8.65483913395442e-6 -\\n s1 * 1.41636299744881e-6 -\\n (p + p_ref) * 7.38286467135737e-9 +\\n t * (-8.38241357039698e-6 +\\n s1 * 2.83933368585534e-8 +\\n t * 1.77803965218656e-8 +\\n (p + p_ref) * 1.71155619208233e-10)))\\n\\n dentropy_dt = cp0 / ((Kelvin + pt) *\\n (1 - 0.05 * (1 - SA / SSO)))\\n\\n true_entropy_part = entropy_part(SA, t, p)\\n\\n for Number_of_iterations in range(0, 2, 1):\\n pt_old = pt\\n dentropy = entropy_part(SA, pt_old, p_ref) - true_entropy_part\\n pt = pt_old - dentropy / dentropy_dt # half way through the method\\n ptm = 0.5 * (pt + pt_old)\\n dentropy_dt = -gibbs(n0, n2, n0, SA, ptm, p_ref)\\n pt = pt_old - dentropy / dentropy_dt\\n\\n # maximum error of 6.3x10^-9 degrees C for one iteration. maximum error\\n # is 1.8x10^-14 degrees C for two iterations (two iterations is the default,\\n # \\\"for Number_of_iterations = 1:2). These errors are over the full\\n # \\\"oceanographic funnel\\\" of McDougall et al. (2010), which reaches down to\\n # p = 8000 dbar.\\n\\n return pt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60b197e5cdc1687a0f746a86a9f5a81a\",\n \"score\": \"0.5793214\",\n \"text\": \"def _evaluate_at_time_lw(self, t: float) -> float:\\n\\n if 0 < t < self._onset + self._decay_duration:\\n if t <= self._onset:\\n return np.square(t/self._onset)\\n else:\\n return (np.exp(-(t-self._onset)/self._decay)-self._epsilon)/(1.-self._epsilon)\\n else:\\n return 0.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"849ec256637303755d7debca77a21b4f\",\n \"score\": \"0.5764049\",\n \"text\": \"def rhs(x, y, t):\\n\\n return 5 * x * (x_end - x) * y * (y_end - y) + 10 * a * t * (y * (y_end - y) + x * (x_end - x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83777d12b52de145243a1f9e2ea35954\",\n \"score\": \"0.57630384\",\n \"text\": \"def get(self, t):\\n\\n beta = self.schedule(t)\\n dim = self.dim\\n return (1 - beta) * jnp.eye(dim) + beta * jnp.ones((dim, dim)) / dim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfd8cd75dcf11e7b3aaa720d98845d2a\",\n \"score\": \"0.5747864\",\n \"text\": \"def Klookup(self, t):\\n idx_closest = (np.abs(self.ttraj - t)).argmin()\\n\\n # limit to valid indices\\n if idx_closest >= (self.K.shape[0] - 1):\\n return self.K[-1]\\n elif idx_closest <= 0:\\n return self.K[0]\\n else:\\n # linearly interpolate between two points\\n t_closest = self.ttraj[idx_closest]\\n if t_closest > t:\\n dt = self.ttraj[idx_closest] - self.ttraj[idx_closest - 1]\\n return (t_closest - t) * self.K[idx_closest - 1] / dt + (t + dt - t_closest) * self.K[idx_closest] / dt\\n elif t > t_closest:\\n dt = self.ttraj[idx_closest + 1] - self.ttraj[idx_closest]\\n return (t - t_closest) * self.K[idx_closest + 1] / dt + (t_closest + dt - t) * self.K[idx_closest] / dt\\n else:\\n return self.K[idx_closest]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"501146cd970eac51b86862c4d7aefe23\",\n \"score\": \"0.5746347\",\n \"text\": \"def time2depthpt(self, t, point):\\n # Find the nearest profile\\n coords = np.array(self.coords)\\n closest = np.abs(coords - point).argmin()\\n\\n time = self.data[closest][:, 0]\\n depth = self.data[closest][:, 1]\\n\\n return np.interp(t, time, depth)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57d86501a89087fe30912d7181b4e762\",\n \"score\": \"0.5741742\",\n \"text\": \"def EvaluateCurve(curve_id, t, segment_index=-1):\\r\\n curve = rhutil.coercecurve(curve_id, segment_index, True)\\r\\n return curve.PointAt(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54a9cbdb4f54b65505c366e7d08d96cc\",\n \"score\": \"0.5734176\",\n \"text\": \"def get_value(datablocks, channel_name, t, lpfilter=None):\\n x, y = get_waveform(datablocks, channel_name, lpfilter)\\n # First index where time is just greater than t\\n result = np.where(x >= t)\\n index = result[0][0]\\n return y[index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"978754abc990fe8f1987c71f019283b6\",\n \"score\": \"0.57319343\",\n \"text\": \"def objective(point):\\n\\n t = point['t']\\n x = point['x']\\n a = 2 * np.pi\\n b = a * t \\n c = a * x\\n d = np.exp(- (x + 1) ** (t + 1)) * np.cos(c)\\n e = np.sin((t + 2) * c) + np.sin((t + 2)**2 * c) + np.sin((t + 2)**3 * c)\\n f = d * e\\n\\t\\n\\t\\n\\t\\n \\\"\\\"\\\"\\n f(t,x) = x^2+t\\n \\\"\\\"\\\"\\t\\n # t = point['t']\\n # x = point['x']\\t\\n # f = 20*x**2+t\\n\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33bfcc14f4bab592b1442d138c53f9de\",\n \"score\": \"0.5709217\",\n \"text\": \"def c2pt(t, *p):\\n val = 0\\n for j in range(0, len(p), 2):\\n val += 2*p[j]*np.exp(-p[j+1]*T)*np.cosh(p[j+1]*(T - t))\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00c8cbf96fe0a0581000526457d1493b\",\n \"score\": \"0.56889415\",\n \"text\": \"def func(cls, t, tau, amp, offset):\\n return offset + amp * (1 - np.exp(- t / tau))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"011bc5b84117c4b7b14c383991702196\",\n \"score\": \"0.5664827\",\n \"text\": \"def lt_de(t, T):\\n rhs = 20 * m.sin(3 * t) - 3 * T\\n return rhs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b61971802152ffbf6c7c953f0977e15\",\n \"score\": \"0.56464267\",\n \"text\": \"def myCurve(x):\\n return np.abs(x) + x * np.sin(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09b3e0aa2293bcd0f67f55e09421a1c0\",\n \"score\": \"0.5580157\",\n \"text\": \"def get(self, t):\\n beta = self.schedule(t)\\n dim = self.dim\\n width = self.width\\n\\n band = jnp.tri(\\n dim, dim, width // 2, dtype=jnp.int32) - jnp.tri(\\n dim, dim, -width // 2, dtype=jnp.int32)\\n\\n arr = band / band.sum(0, keepdims=True)\\n\\n return beta * arr + (1 - beta) * jnp.eye(dim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a84555b38822b4ede3df84cd82c70fe\",\n \"score\": \"0.55614114\",\n \"text\": \"def time2depthpt(self, t, point):\\n return t*self.velocity/2.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eeda22685517dc295d3b900b90e094d\",\n \"score\": \"0.55608034\",\n \"text\": \"def state_at_t(self, t) -> Tuple[float, float]:\\n if self.a * self.total_delta_loc < 0:\\n if t < .5 * (self.delta_t - (self.v_initial / self.a)):\\n # Deceleration - Phase 1\\n return self.accelerating_state(t)\\n else:\\n # Acceleration - Phase 2\\n return self.decelerating_state(t)\\n\\n accel_end = self.accelerate_end_t()\\n if t < accel_end:\\n return self.accelerating_state(t)\\n accel_end_p = self.accelerate_end_p()\\n decel_begin = self.decelerate_begin_t(accel_end)\\n if t < decel_begin:\\n return self.steady_state(t, accel_end, accel_end_p)\\n return self.decelerating_state(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55c67b317cb9713b3c093810a6145b34\",\n \"score\": \"0.55558425\",\n \"text\": \"def weather_at_time(self, t: torch.Tensor) -> torch.Tensor:\\n\\n inbetween = (\\n ((t % self._normalized_timestep_delta) / self._normalized_timestep_delta)\\n .unsqueeze(1)\\n .to(self._device)\\n )\\n left_index = (t // self._normalized_timestep_delta).long().to(self._device)\\n\\n if any(torch.isnan(t)):\\n raise ValueError()\\n if any(left_index < 0):\\n left_index = torch.zeros_like(left_index)\\n if any(left_index >= len(self._normalized_weather_tensor) - 2):\\n left_index = torch.zeros_like(left_index) - 2\\n\\n right_index = left_index + 1\\n\\n left_weather = self._normalized_weather_tensor[left_index]\\n right_weather = self._normalized_weather_tensor[right_index]\\n\\n return left_weather * (1 - inbetween) + right_weather * inbetween\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4342a5e924f2f97902f46a87a4ba23aa\",\n \"score\": \"0.55510455\",\n \"text\": \"def getValueT(self,t=None):\\n\\n\\t\\tp = []\\n\\t\\tfor index, val in enumerate(self.coef):\\n\\t\\t\\tp.append(val * t + self.point[index]) \\n\\t\\t\\t# p[1] = val * t + self.point[index]\\n\\t\\t\\t# p[2] = val * t + self.point[index]\\n\\t\\treturn p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd67fce3431965b73879ff9032368247\",\n \"score\": \"0.5541475\",\n \"text\": \"def yValue(self, time):\\n #----------------------\\n i = self._index(time)\\n if i is not None:\\n if i >= (self._poly.size() - 1):\\n i = self._poly.size() - 2\\n p0 = self._poly.at(i)\\n p1 = self._poly.at(i+1)\\n if p0.x() == p1.x(): return (p0.y() + p1.y())/2.0\\n return p0.y() + (time - p0.x())*(p1.y() - p0.y())/(p1.x() - p0.x())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57eb1349f0beebdb135cef26e7a8bd31\",\n \"score\": \"0.5521312\",\n \"text\": \"def value(self, time):\\n\\n # 1. ensure time is within bounds else return boundary keyframe\\n # if time <= self.times[0] or time >= self.times[-1]:\\n # return self.values[0 if time <= self.times[0] else -1]\\n\\n # 2. search for closest index entry in self.times, using bisect_left function\\n i = bisect_left(self.times, time%self.times[-1]) # _i is the time index just before t\\n\\n # 3. using the retrieved index, interpolate between the two neighboring values\\n # in self.values, using the initially stored self.interpolate function\\n fraction = (time%self.times[-1]-self.times[i-1]) / (self.times[i]-self.times[i-1])\\n return self.interpolate(self.values[i-1], self.values[i], fraction)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9320ee0c79c40beb966dfe6b2b12db22\",\n \"score\": \"0.5517267\",\n \"text\": \"def Rx(t):\\n return np.array(((1,0,0),(0,np.cos(t),-np.sin(t)),(0,np.sin(t),np.cos(t))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c8f9784a3dad0884113cbe7c617bda0\",\n \"score\": \"0.5515756\",\n \"text\": \"def at_dt(self, t):\\n\\n return self.bez(t, self.derive[:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d97bf37db187eebda63ba7c5963fd4c\",\n \"score\": \"0.5515326\",\n \"text\": \"def calc(self, t):\\n\\n if t < self.x[0]:\\n return None\\n elif t > self.x[-1]:\\n return None\\n\\n i = self.__search_index(t)\\n dx = t - self.x[i]\\n result = self.a[i] + self.b[i] * dx + \\\\\\n self.c[i] * dx ** 2.0 + self.d[i] * dx ** 3.0\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f974947e48308375cd41f79504a50118\",\n \"score\": \"0.5510095\",\n \"text\": \"def est_am(t,y):\\n ch=t>t_eq\\n y1=y[ch]\\n return y1[-1]-y1[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89b8c08cc433340a1ca3586e15a9b1bc\",\n \"score\": \"0.550863\",\n \"text\": \"def forward_curve(self, x):\\n y = scipy.interpolate.splev(x, self.tck, der=0)\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda7341692115079bbe420f60d75925b\",\n \"score\": \"0.5503552\",\n \"text\": \"def getSignal(t):\\n s1 = np.sin(2*np.pi*100*t)\\n s2 = 2*np.sin(2*np.pi*500*t)\\n # create a transient \\\"chirp\\\" (zero s2 signal before/after chirp)\\n mask = np.where(np.logical_and(t>10, t<12), 1.0, 0.0)\\n s2 = s2 * mask\\n # add some noise into the mix\\n nse = 0.01*np.random.randn(len(t))/1.0\\n s = s1 + s2 + nse\\n return s # the signal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36882814fd88d49ceac91a73111b8822\",\n \"score\": \"0.54710823\",\n \"text\": \"def findNearest(self,t,timevec):\\n tnow = self.SecondsSince(t)\\n tvec = self.SecondsSince(timevec)\\n \\n #tdist = np.abs(tnow - tvec)\\n \\n #idx = np.argwhere(tdist == tdist.min())\\n \\n #return int(idx[0])\\n return np.searchsorted(tvec,tnow)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02f8de0469a570f2ff10b62d6fcc58e8\",\n \"score\": \"0.5462917\",\n \"text\": \"def _evaluate_at_time_lw(self, t: float) -> float:\\n return 0.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6032c4321e5fcc21980dcaa09a133dac\",\n \"score\": \"0.5456722\",\n \"text\": \"def get_t(t21,t2p1, timestep): \\n if t21 < 0 or t2p1 < 0:\\n #w2 or w2p will come at t<0\\n t_min = min(t21, t2p1) - early_buffer\\n else:\\n t_min = -early_buffer\\n #last pulse dictates when emitted coherences should start to be watched for integration\\n if t21 > 0 or t2p1 > 0:\\n #w2 or w2p will come at t>0\\n t_max = max(t21, t2p1) + late_buffer\\n else:\\n t_max = late_buffer\\n #note: dtype float16 can screw up t working properly!\\n t = np.arange(t_min, t_max, timestep)\\n #tprime_i = t_max - late_buffer - early_buffer\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c8fcf5b50529812bbeff28e91816fa\",\n \"score\": \"0.5451557\",\n \"text\": \"def value(self, time):\\n\\n # 1. ensure time is within bounds else return boundary keyframe\\n if time <= self.times[0]:\\n return self.values[0]\\n if time >= self.times[-1]:\\n return self.values[-1]\\n\\n # 2. search for closest index entry in self.times, using bisect_left function\\n t_i = bisect_left(self.times, time) - 1 # note the -1\\n\\n # 3. using the retrieved index, interpolate between the two neighboring values\\n # in self.values, using the initially stored self.interpolate function\\n f = (time - self.times[t_i]) / (self.times[t_i + 1] - self.times[t_i])\\n return self.interpolate(self.values[t_i], self.values[t_i + 1], f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efc7cdd95796b7367a1608ab254237ac\",\n \"score\": \"0.54389536\",\n \"text\": \"def _sample_position(self, t, N=1):\\n v = np.zeros(N)\\n iv = np.ones(N, dtype=bool)\\n x = self.startPosition * np.ones(N)\\n continue_ = True\\n i = 1\\n t = t - self.startTime # should be positive.\\n size = v.shape[0]\\n while continue_:\\n n_samples = iv.sum()\\n v[iv] += self.T.rvs(n_samples)\\n\\n # if the next time is beyond reach, we do not add a new jump\\n iv[v >= t] = False\\n n_samples = iv.sum()\\n\\n x[iv] += self.J.rvs(n_samples)\\n\\n size = iv.sum() # any left?\\n empty = size == 0\\n\\n i += 1\\n if empty:\\n continue_ = False\\n\\n if i > 10000:\\n warnings.warn(\\\"The algorithm did not converge. Be sure 'T' is a non negative random \\\\\\n variable, or set 't' to a smaller value.\\\")\\n break\\n\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f2a367331b2ae237fe894c6805c1373\",\n \"score\": \"0.5409791\",\n \"text\": \"def path(t):\\n t = gs.array(t)\\n t = gs.cast(t, initial_cotangent_vec.dtype)\\n t = gs.to_ndarray(t, to_ndim=1)\\n\\n cotangent_vecs = gs.einsum(\\\"i,...k->...ik\\\", t, initial_cotangent_vec)\\n\\n points_at_time_t = [\\n self.exp(tv, pt, n_steps=n_steps)\\n for tv, pt in zip(cotangent_vecs, initial_point)\\n ]\\n points_at_time_t = gs.stack(points_at_time_t, axis=0)\\n\\n return (\\n points_at_time_t[0] if n_initial_conditions == 1 else points_at_time_t\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e916fd07c3010a6d073f8bf25380f1f\",\n \"score\": \"0.5408187\",\n \"text\": \"def interp(self,t,data):\\n # helps to keep mod T here for integrators\\n t = np.mod(t,self.T)\\n fn = interp1d(self.tLC,data)\\n \\n #print('t,fn',t,fn(t))\\n \\n return fn(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75929f6d25cbb52eff53177f44c63aa5\",\n \"score\": \"0.5399461\",\n \"text\": \"def ToPoint(self) -> _n_1_t_1:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6ac56e27ba8a6cb2df8ec9e2efb1ef1\",\n \"score\": \"0.5399192\",\n \"text\": \"def time2depthpt(self, t, point):\\n # Find the nearest profile\\n coords = np.array(self.coords)\\n closest = np.abs(coords - point).argmin()\\n profile = self.data[closest]\\n\\n # HACK TO MAKE FAKE VELOCITIES\\n velocity = np.ones(profile.data.size) * 2000.0\\n\\n samp = profile.stats[\\\"sampling_rate\\\"]\\n t_profile = np.arange(velocity.size) * 1.0 / samp\\n vrms = np.cumsum(velocity) / (np.arange(velocity.size)+1)\\n vrms = np.interp(t, t_profile, vrms)\\n return t*vrms/2.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f642acbb32b4c47686fc9687fc16e155\",\n \"score\": \"0.5388491\",\n \"text\": \"def isPointOnCurve(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f051eb29df6c6b48de6ce04874d84744\",\n \"score\": \"0.5374317\",\n \"text\": \"def O(self,t,x,p):\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2323ac8c24359c184549681ff9d1045b\",\n \"score\": \"0.5364349\",\n \"text\": \"def time(self, t, s=1.0, complete=True):\\n w = self.w0\\n\\n x = t / s\\n\\n output = np.exp(1j * w * x)\\n\\n if complete:\\n output -= np.exp(-0.5 * (w ** 2))\\n\\n output *= np.exp(-0.5 * (x ** 2)) * np.pi ** (-0.25)\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ae02d0df6086ff67e6da8169bbd44ac\",\n \"score\": \"0.5362421\",\n \"text\": \"def state(k, x, t):\\n return exp(1j*(k*x - omega*t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"331a394258f5359453cf93fb876218fa\",\n \"score\": \"0.5360645\",\n \"text\": \"def quadraticbezier(p0, p1, p2, t):\\n ## p0, p1 and p2 are of type numpy.array\\n return (1-t)**2*p0+2*(1-t)*t*p1+t**2*p2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc2985354355e1fe87456aacdca3c8f3\",\n \"score\": \"0.5360297\",\n \"text\": \"def sinc_p(t, t0, Tb):\\n\\n p = np.sinc(np.pi * (t - t0) / Tb)\\n return (p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fc93ab057115be1d01909abb130e0ca\",\n \"score\": \"0.5358552\",\n \"text\": \"def get_timestamp_by_time(self, t):\\n if t<=0 or t<=self.startTime:\\n return None\\n i = int((t-self.startTime)/NetflixSession.TS_GRANULARITY)\\n if i >= len(self.timeStamps):\\n return None\\n return self.timeStamps[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e1ed925682763bd477ee53df1f4794f\",\n \"score\": \"0.5357576\",\n \"text\": \"def next_change(self, t=None):\\n if t is None:\\n t = self.engine.get_now()\\n\\n delta = random.expovariate(1.0 / self.interval) # expovariate delivers long-tailed distribution between 0 and infinity, centred on given value\\n return t + delta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a7fbd7defadbe4f74956360ca6cabfb\",\n \"score\": \"0.53535336\",\n \"text\": \"def interval(self, t):\\n return np.random.poisson(self.time_variance(t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b3a23bc342344cdbec79aaa5f41f100\",\n \"score\": \"0.53485364\",\n \"text\": \"def true_posterior(self, t):\\n return (sigmoid(t)**self.a)*(sigmoid(-t)**self.b)/beta(self.a, self.b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"037f7936f651b07572c42a40308dcf23\",\n \"score\": \"0.533736\",\n \"text\": \"def generate_pnt(self, s, t, *args):\\n pnt = TrajectoryPoint()\\n # Trajectory time stamp\\n pnt.t = t\\n # Set position vector\\n pnt.pos = self.generate_pos(s).tolist()\\n # Set rotation quaternion\\n pnt.rotq = self.generate_quat(s)\\n return pnt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcbb72c7f6790b4d3e6c239a3c1f41a6\",\n \"score\": \"0.5330515\",\n \"text\": \"def take_step(self,p):\\n dpos = np.random.randn(2)\\n dpos = dpos / np.linalg.norm(dpos)\\n temp = self.pos + dpos * p\\n if temp[0]>= 0 and temp[0]<=1 and temp[1]>= 0 and temp[1]<=1:\\n self.pos = temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed2732aa09b659bf58b65c4cdd0e96c\",\n \"score\": \"0.5329308\",\n \"text\": \"def Price(t):\\n A = 100.\\n return .5*A*np.sine(t) + A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66a1c2aaf585333da657e650474ba79d\",\n \"score\": \"0.5327765\",\n \"text\": \"def is_in_t(pt):\\n flag = True\\n if pt[0] < 0 or pt[1] < 0:\\n flag = False\\n if pt[0] + pt[1] > 1:\\n flag = False\\n return flag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e1de4504ffd58e59e1053934e64d216\",\n \"score\": \"0.5325302\",\n \"text\": \"def sigmoid(t):\\n return (np.exp(t))/(1+(np.exp(t)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3688f4880480de3c62a053429809f68e\",\n \"score\": \"0.5319139\",\n \"text\": \"def plot_me(self, t):\\n return np.array([np.cos(2*np.pi*t), np.sin(2*np.pi*t)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b0d92acbbcb3881c7b04c8d85dbdecb\",\n \"score\": \"0.5317961\",\n \"text\": \"def f1(y, t):\\n\\n u1 = y[0]\\n u2 = y[1]\\n u3 = t\\n return u2, -2 * params[\\\"gamma\\\"] * u2 - np.sin(u1) + params[\\\"mu\\\"] * np.sin(params[\\\"omega\\\"] * u3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9c139f6f789c7c9897e44700cd5aa56\",\n \"score\": \"0.5314853\",\n \"text\": \"def sigmoid(t):\\n return 1.0 / (1.0 + np.exp(-t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e8bd6921587eb2edd707f3968305d73\",\n \"score\": \"0.5311136\",\n \"text\": \"def kt(self, t):\\n K = np.searchsorted(self.data.t, t)\\n N = len(self)\\n return np.clip(K, 0, N-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5edd6ac6ced0629b5c26e79a5ac365e0\",\n \"score\": \"0.5307474\",\n \"text\": \"def trajectory_ex(t_start=0, t_stop=1, dt=2**(-4), x0=np.array([1])):\\n parameters = [1.5, 1]\\n number_of_timesteps = int((t_stop - t_start) / dt)\\n x = np.zeros((number_of_timesteps + 1, len(x0)))\\n x_exact = np.zeros_like(x)\\n x[0, :] = x0\\n x_exact[0, :] = x0\\n time = np.linspace(t_start, t_stop, number_of_timesteps + 1)\\n\\n random_force = np.random.normal(scale=np.sqrt(dt), size=(number_of_timesteps, 1))\\n\\n for step in range(number_of_timesteps):\\n x[step + 1, :] = x[step, :] + parameters[0]*x[step, :]*dt + parameters[1] * x[step, :] * random_force[step]\\n x_exact[step+1, :] = x0*np.exp((parameters[0]-parameters[1]**2/2)*time[step]+parameters[1]*np.sum(random_force[:step+1]))\\n\\n plt.plot(time, x, label='Numerical solution')\\n plt.plot(time, x_exact, label='Exact solution')\\n plt.legend()\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee114c73fbdab147225971456e36a8f6\",\n \"score\": \"0.5297463\",\n \"text\": \"def sigmoid(t):\\n \\n return 1.0 / (1.0 + np.exp(-t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e93395abe21453359df5e2f86ddbec22\",\n \"score\": \"0.52953637\",\n \"text\": \"def getFirstPoint(self):\\r\\n return Trackpoint(self.trackdata[self.trackdataStart:self.trackdataStart + Trackpoint.TRACKPOINTLEN])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8a907ebcfb41fb01d335c4a66f58979\",\n \"score\": \"0.5291992\",\n \"text\": \"def interp_tT(t):\\n return interp(t, conditions[:, 0], conditions[:, 1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c25c14c125f44a908dc21329584908a4\",\n \"score\": \"0.5285083\",\n \"text\": \"def simple_harmonic_oscillator(p, t):\\n return p[0] + p[1] * np.cos(p[2] * t - p[3])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e544b08b240864f702c80dd40b390cb\",\n \"score\": \"0.5267563\",\n \"text\": \"def f(self, t, y):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b15632e6bc69a368ffd79374489089bf\",\n \"score\": \"0.52611744\",\n \"text\": \"def Xrt(t, amp, centre, period = 365): \\n\\n x = t * (2 * pi / period) \\n\\n return (amp*centre * sin(x)) + centre\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdd2c8cfb52a9f9a708db0fd0b5d02f6\",\n \"score\": \"0.5255001\",\n \"text\": \"def current_point(self):\\n try:\\n data = self.read_reg(TD_STATUS, 1)\\n # if touch points==1\\n if data is not None and data[0] == 0x1:\\n # Read Xhigh, Xlow, Yhigh and Ylow\\n data_buf = self.read_reg(PN_XH, 4)\\n x = ((data_buf[0] & 0x0F) << 8) | (data_buf[1])\\n y = ((data_buf[2] & 0x0F) << 8) | (data_buf[3])\\n return (x, y)\\n except Exception as e:\\n return e # debug\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dbd036caae087aa7bcdd74a09ff6bf\",\n \"score\": \"0.52548707\",\n \"text\": \"def interpolate_linear(t, t0, x0, t1, x1):\\n if t0 > t1:\\n t0, x0, t1, x1 = t1, x1, t0, x0\\n if t0 == t1 and not x0 == x1:\\n raise ValueError(\\\"Interpolated quantity can not have two \\\"\\n \\\"different values at the same time\\\")\\n if t < t0 or t > t1:\\n raise ValueError(\\\"Time argument outside of the given interval\\\")\\n if t == t0:\\n return x0\\n if t == t1:\\n return x1\\n timeDelta = 1.0*(t1 - t0)\\n t = (t - t0)/timeDelta\\n onemt = 1.0 - t\\n return onemt*x0 + t*x1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2234a4f341fbef9a66a08f9ebecd2607\",\n \"score\": \"0.52531505\",\n \"text\": \"def edge_point(self, edge, t=0.5):\\n if t == 0.0:\\n return self.edge_start(edge)\\n if t == 1.0:\\n return self.edge_end(edge)\\n if t == 0.5:\\n return self.edge_midpoint(edge)\\n\\n a, b = self.edge_coordinates(edge)\\n ab = subtract_vectors(b, a)\\n return Point(*add_vectors(a, scale_vector(ab, t)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98cb9bdf5d9ce9fc779c4a405a4ac317\",\n \"score\": \"0.5249009\",\n \"text\": \"def point_in_time(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"point_in_time\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd850db1ad2991f87dc6b321fd962dd2\",\n \"score\": \"0.5246322\",\n \"text\": \"def pdf(self, t, l):\\n r, c, tau = self.r, self.c, self.tau\\n\\n t = np.atleast_1d(t)\\n\\n tcut = (l+r)/c\\n tmin = (r-l)/c\\n\\n x = t.copy()\\n x[t > tcut] = tcut\\n\\n y = c*x*tau*np.exp(tmin/tau)*(r+l-c*tau)\\n y += np.exp(x/tau)*(l**2-r**2+c**2*x*tau)*tau\\n y += x*(l**2-r**2)*(expi(tmin/tau) - expi(x/tau))\\n y /= 4*c*l*x*tau\\n y *= np.exp(-t/tau)/tau\\n y[t < tmin] = 0\\n\\n return y if y.size > 1 else y.item()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06c714a6db88ab6d623bd0a7a6b7d0ba\",\n \"score\": \"0.5241759\",\n \"text\": \"def log_t(u, t):\\n if t == 1.0:\\n return u.log()\\n else:\\n return (u.pow(1.0 - t) - 1.0) / (1.0 - t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"553cbb4db0feaadc0f78c5bfbcaa9f0c\",\n \"score\": \"0.5236777\",\n \"text\": \"def get_phase(self, t, TA=None):\\n \\n if TA is None:\\n TA = self.true_anomaly(t)\\n \\n phase = (TA-self.true_anomaly(self.t0))/(2.*np.pi)\\n phase = phase + 1.*(phase<0.).astype(int)\\n return phase\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a9ba343afc0de8d070313bde35efe51\",\n \"score\": \"0.5235387\",\n \"text\": \"def __interpolate(self, t):\\r\\n try:\\r\\n c = [0 for _ in range(self.n)]\\r\\n w = [0 for _ in range(self.n)]\\r\\n for i in range(0, self.n):\\r\\n w[i] = self.Y[i]\\r\\n for j in reversed(range(i)):\\r\\n w[j] = (w[j + 1] - w[j]) / (self.X[i] - self.X[j])\\r\\n c[i] = w[0]\\r\\n s = c[self.n - 1]\\r\\n for i in reversed(range(self.n - 1)):\\r\\n s = s * (t - self.X[i]) + c[i]\\r\\n return s\\r\\n except Exception as e:\\r\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1925a5c4de8821953bd94db3a32c50a0\",\n \"score\": \"0.5232862\",\n \"text\": \"def run(self, t):\\r\\n temp_x = self.x\\r\\n temp_t = self.t\\r\\n traj = np.zeros((len(self.x), int(t/self.dt)))\\r\\n for i in range(len(traj.T)):\\r\\n traj.T[i] = self.x\\r\\n self.step()\\r\\n self.x = temp_x\\r\\n self.t = temp_t\\r\\n self.y = self.output()\\r\\n return traj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"091ffb581b0f4ba35758e514b61bab70\",\n \"score\": \"0.52275234\",\n \"text\": \"def interp_cubic(p0, p1, t_abs):\\n T = (p1.time_from_start - p0.time_from_start).to_sec()\\n t = t_abs - p0.time_from_start.to_sec()\\n q = [0] * 7\\n qdot = [0] * 7\\n qddot = [0] * 7\\n for i in range(len(p0.positions)):\\n a = p0.positions[i]\\n b = p0.velocities[i]\\n c = (-3 * p0.positions[i] + 3 * p1.positions[i] - 2 * T * p0.velocities[i] - T * p1.velocities[i]) / T**2\\n d = (2 * p0.positions[i] - 2 * p1.positions[i] + T * p0.velocities[i] + T * p1.velocities[i]) / T**3\\n\\n q[i] = a + b * t + c * t**2 + d * t**3\\n qdot[i] = b + 2 * c * t + 3 * d * t**2\\n qddot[i] = 2 * c + 6 * d * t\\n return JointTrajectoryPoint(positions=q, velocities=qdot, accelerations=qddot, time_from_start=rospy.Duration(t_abs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2e4bb68773d57eca852839ac36577a5\",\n \"score\": \"0.5226863\",\n \"text\": \"def _A(self, t, T):\\r\\n P0T = self.curve.P_M(T)\\r\\n P0t = self.curve.P_M(T)\\r\\n f0t = self.curve.instantFwd(t)\\r\\n par = self._B(t, T)*f0t\\\\\\r\\n \\t-((self.sigma**2)/(4*self.a)*(1-np.exp(-2*self.a*t))*(self._B(t, T)**2))\\r\\n return P0T/P0t * np.exp(par)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf4162a969c1b4c81c4444e540f2eef3\",\n \"score\": \"0.522631\",\n \"text\": \"def tuple_to_point(t):\\n return Point(t[0], t[1]) if t else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"931fd18e756832d3c7de73e4773dd208\",\n \"score\": \"0.5223334\",\n \"text\": \"def seg_velocity_time(v0,v1,t):\\n \\n v0 = float(v0)\\n v1 = float(v1)\\n x = float(t)\\n \\n a = (v1-v0)/t\\n\\n x = a * (t**2) / 2.\\n\\n return abs(x), v0, v1, t, a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc2ea06aae009702465bff8b3b510f5a\",\n \"score\": \"0.5210453\",\n \"text\": \"def update(self, t):\\n\\n x = np.zeros((3,))\\n x_dot = np.zeros((3,))\\n x_ddot = np.zeros((3,))\\n x_dddot = np.zeros((3,))\\n x_ddddot = np.zeros((3,))\\n yaw = 0\\n yaw_dot = 0\\n\\n # STUDENT CODE HERE\\n\\n # from waypoint_traj\\n if np.shape(self.points) == (3,) or np.shape(self.points) == (1, 3):\\n x = np.reshape(self.points, (3,))\\n elif np.shape(self.points) != (3,) or np.shape(self.points) != (1, 3):\\n if t > self.point_t[-1]:\\n x = self.points[-1]\\n else:\\n x = self.f(t)\\n\\n # if np.shape(self.points) == (3,) or np.shape(self.points) == (1,3):\\n # x = np.reshape(self.points, (3,))\\n # elif np.shape(self.points) != (3,) or np.shape(self.points) != (1,3):\\n # if t >= self.t[-1]:\\n # x = self.points[-1]\\n # elif t == self.t[0]:\\n # x = self.points[0]\\n # else:\\n # for i in range(len(self.t)-1):\\n # if self.t[i] < t <= self.t[i+1]:\\n # x_dot = self.v\\n # x = x_dot*(t-self.t[i]) + self.points[i]\\n #\\n\\n\\n flat_output = {'x': x, 'x_dot': x_dot, 'x_ddot': x_ddot, 'x_dddot': x_dddot, 'x_ddddot': x_ddddot,\\n 'yaw': yaw, 'yaw_dot': yaw_dot}\\n return flat_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f175c55466e23a469d255393e97ec7b4\",\n \"score\": \"0.5204807\",\n \"text\": \"def PreciseTime():\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":90,"cells":{"query_id":{"kind":"string","value":"70ee95c12ccaa52305a62aa13c0596da"},"query":{"kind":"string","value":"List the keys under path, if any, of the original store."},"positive_passages":{"kind":"list like","value":[{"docid":"67ed1062aee6a8d18ebc3daeff4a9172","score":"0.6513379","text":"def listdir(self, path: str = None) -> List[str]:\n return zarr.storage.listdir(self._store, path)","title":""}],"string":"[\n {\n \"docid\": \"67ed1062aee6a8d18ebc3daeff4a9172\",\n \"score\": \"0.6513379\",\n \"text\": \"def listdir(self, path: str = None) -> List[str]:\\n return zarr.storage.listdir(self._store, path)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"fedbcb88517228f6e49224e6c7ab64ae","score":"0.7502651","text":"def traversed_keys(self, root_path: str) -> List[str]:\n return self.redis_client_traversed.hkeys(root_path)","title":""},{"docid":"37907698267ce4b2bc7e19d9e795d099","score":"0.7457224","text":"def listdir(self):\n\n if self._path not in self._fs._dirs:\n raise UnlistableError()\n\n i = chain(self._fs._dirs, self._fs._store.iterkeys())\n\n # Linear-time search. Could be better.\n p = self._path\n l = len(p) + 1\n ks = [t[-1] for t in i if t[:-1] == p and len(t) == l]\n\n return ks","title":""},{"docid":"6aa2649548acf2fae83833a7b837eb59","score":"0.7161985","text":"def keys(self):\n try:\n return self.__storage__.keys()\n except:\n return []\n #return self.__state.keys()","title":""},{"docid":"4a635d4524904026319caa0ac5886e96","score":"0.70467705","text":"def list_keys(self):\n self.automate_handle()\n return self.kcl.list_keys(self.handle)","title":""},{"docid":"98048518d74d20c1495ac43433054718","score":"0.699317","text":"def listkeys(self):\n\n\t\tif not self.root is None:\n\t\t\treturn self.root.listkeys()\n\t\telse:\n\t\t\treturn []","title":""},{"docid":"45eeb5bca6857d1d25aa73e938ae45c5","score":"0.69510627","text":"def keys(self):\n return self._store.keys()","title":""},{"docid":"45eeb5bca6857d1d25aa73e938ae45c5","score":"0.69510627","text":"def keys(self):\n return self._store.keys()","title":""},{"docid":"45eeb5bca6857d1d25aa73e938ae45c5","score":"0.69510627","text":"def keys(self):\n return self._store.keys()","title":""},{"docid":"04948e5f43fafcd2400c8aed44838e71","score":"0.6910418","text":"def get_keys(self):\n return self.keys","title":""},{"docid":"fd1769d606ca9bde8a2471be456ab835","score":"0.68885016","text":"def keys():\n return _keys","title":""},{"docid":"f5e5e753065a864b38650caf0302cce3","score":"0.68775463","text":"def get_keys(path: Union[str, Path]) -> List[str]:\n path = _get_valid_hdf_path(path)\n with tables.open_file(str(path)) as file:\n keys = _get_keys(file.root)\n return keys","title":""},{"docid":"5a372f201e68abc39bffcc3aac400665","score":"0.6820589","text":"def keys(self) -> List[str]:\n pass","title":""},{"docid":"248d3b0e5677f9481e5f979648f53060","score":"0.67937136","text":"def listkeys(self):\n\n\t\tl = []\n\n\t\tif not self.left is None:\n\t\t\tl.extend(self.left.listkeys())\n\n\t\tl.append(self.key)\n\n\t\tif not self.right is None:\n\t\t\tl.extend(self.right.listkeys())\n\n\t\treturn l","title":""},{"docid":"30a0c2b213c93766d87dd58404c453af","score":"0.6758386","text":"def iterkeys(self):\n\n return self.storage.iterkeys()\n #return self.__state.iterkeys()","title":""},{"docid":"8b75ad9866dd3db3a6acf04809f3244f","score":"0.6737822","text":"def _keys(self) -> AbstractSet[str]:\n if self._cached_keys is None:\n self._cached_keys = self._store.keys()\n return self._cached_keys","title":""},{"docid":"2cdf586783a5d2dfdf31ac601eebb700","score":"0.67342883","text":"def key_list(self):\n return self._key_list","title":""},{"docid":"2554256781cb92b2dd821df2bcc5ea5b","score":"0.67309415","text":"def keys(self):\n return self._storage.keys()","title":""},{"docid":"4153849c74df4f61db7313f41d3aafcf","score":"0.67070645","text":"def get_keys(self):\n return list(self.keys())","title":""},{"docid":"3ee6ae19c1d139f4a0de2ec41ffc1b01","score":"0.6659498","text":"def get_keys(self):\n raise NotImplemented","title":""},{"docid":"8bc31ba0e19e019a92abfe0768933eb1","score":"0.66281956","text":"def keys(self):\n return [row[0] for row in self._database.execute(\"SELECT Key FROM Shelf\")]","title":""},{"docid":"b030dd0d7b687bdb1a22992ff50df2c8","score":"0.66176766","text":"def keys(self):\n\t\treturn self._keys","title":""},{"docid":"8b8f721f3c601846849adc0c6dc12638","score":"0.6596256","text":"def keys(self):\n return list(self)","title":""},{"docid":"8b8f721f3c601846849adc0c6dc12638","score":"0.6596256","text":"def keys(self):\n return list(self)","title":""},{"docid":"ad7e7602fd6334c336c9c38bec029463","score":"0.65922254","text":"def keys(self):\n return list(self._keys)","title":""},{"docid":"254e5846c5c1098d482491c50b970772","score":"0.6525932","text":"def all_paths_list(self):\n return self._db.fetch_col(\"SELECT path FROM cms_paths_hashes\")","title":""},{"docid":"7f417b44442ae3427692726d77ad1d71","score":"0.6514444","text":"def list_all():\n return KEY_DIR.glob(\"*.key\")","title":""},{"docid":"beb9bdda6c89a1b3ea8ec6322a052365","score":"0.6507522","text":"def get_keys(self):\n return self.f_redis.keys()","title":""},{"docid":"8f5fdf09a60d34c3cdb1fbc75ab81939","score":"0.64822406","text":"def keys(self):\n keys = []\n for item in self.stored_configuration:\n if item['key'] != 'common':\n keys.append(item['key'])\n return keys","title":""},{"docid":"6ac40797b587a34f4854483ecdcad7e8","score":"0.6475047","text":"def list_keys(self, bucket_name, prefix=None):\n key_list = []\n\n if prefix:\n root = os.path.join(STORAGE_FOLDER, bucket_name, prefix)\n else:\n root = os.path.join(STORAGE_FOLDER, bucket_name)\n\n for path, subdirs, files in os.walk(root):\n for name in files:\n if prefix:\n key_list.append(os.path.join(prefix, path.replace(root+'/', ''), name))\n else:\n key_list.append(os.path.join(path.replace(root+'/', ''), name))\n\n return key_list","title":""},{"docid":"eda326825acb2c8b532dd76eb0c0d138","score":"0.6473079","text":"def keys(self):\n return self.root.keys","title":""},{"docid":"e33cbc56d83d6e36896055f547aaf68c","score":"0.6464677","text":"def list_keys(self, prefix=''):\n try:\n bucket = self.get_buck()\n lister = bucket.list(prefix)\n\n for key in lister:\n yield key.name\n except ssl.SSLError:\n yield","title":""},{"docid":"a957b057e47e489e888c7f86f18d75a4","score":"0.64603865","text":"def keys(self):\n return list(self.iterkeys())","title":""},{"docid":"a957b057e47e489e888c7f86f18d75a4","score":"0.64603865","text":"def keys(self):\n return list(self.iterkeys())","title":""},{"docid":"373ff0d62d40854573ab78c8abfde618","score":"0.6441174","text":"def keys(self):\n return [u for u in self.iterkeys()]","title":""},{"docid":"20d5a421312e5052c939c42f60d4b434","score":"0.6433927","text":"def keys(self, bucket, user=None):\n return (d[2] for d in os.walk(os.path.join(self.directory, bucket)))","title":""},{"docid":"db8f59e316f81d5ee25d35061252d038","score":"0.6374483","text":"def keys(self):\n\t\tif not self._cache_populated:\n\t\t\tself._populate_cache()\n\t\treturn self._cache.keys()","title":""},{"docid":"b2ff1fac0752a1e5f939401b0831727f","score":"0.63630563","text":"def list_keys(self, bucket_name, prefix=None):\n prefix = prefix or ''\n redis_prefix = self._format_key(bucket_name, prefix)\n\n pdir = '/'.join(redis_prefix.split('/')[:-1]) + '/'\n dir_keys = [key.decode() for key in self._client.smembers(pdir)]\n key_list = []\n\n for key in dir_keys:\n full_key = pdir + key\n if full_key.startswith(redis_prefix):\n if full_key.endswith('/'):\n key_list.extend(self._walk(bucket_name, full_key))\n else:\n key_list.append(full_key)\n\n offset = len(bucket_name) + 1\n return [key[offset:] for key in key_list]","title":""},{"docid":"133076af977a0e9f16863d647f05d497","score":"0.636247","text":"def keys(self) -> Iterator[str]:\n raise NotImplementedError","title":""},{"docid":"9a31c7ec9fe6a54e4949060bba7c2b9c","score":"0.63469154","text":"def keys(self):\n return (k for k in self._keys)","title":""},{"docid":"00584b6ece7bac0308ed7cc8d54f1df9","score":"0.6345846","text":"def keys():","title":""},{"docid":"790aa09d21b90d1d7be2db819d2416e8","score":"0.6340655","text":"def list_by_prefix(self, prefix):\n filtered = self._bucket.objects.filter(Prefix=prefix)\n return list(obj.key for obj in filtered)","title":""},{"docid":"219f0f05773fc5e803fcfd43f5945a00","score":"0.63325286","text":"def keys(self):\n return self.__keys","title":""},{"docid":"80ae1eaa5973a681b843812a47fead9d","score":"0.6330527","text":"def keys(tokens):\n\n return [\n token.path\n for token in tokens\n if isinstance(token, Key)\n ]","title":""},{"docid":"e2faac7fc5d7115ca72fafeecadedc0c","score":"0.6322146","text":"def keys(self):\n return list(self._registry.keys())","title":""},{"docid":"98fd02ff1df531f81d1b094b7df307d4","score":"0.6317723","text":"def keys(self):","title":""},{"docid":"37f77f0978327d530c472fdc8c3bf2e8","score":"0.63081914","text":"def keys(self) -> dict_keys:\n return self._stored.keys()","title":""},{"docid":"3294f2c18118a9800b0f4450677bba3d","score":"0.6307059","text":"def keys(self):\n \n keylist = []\n \n for key in self.iterkeys():\n keylist.append(key)\n\n return keylist","title":""},{"docid":"bd688856634ffd47d815f213de5aa8f4","score":"0.629564","text":"def keys(self):\n\n return list(self.keys())","title":""},{"docid":"72964863465444a1d961d2de8ef33bfa","score":"0.62811106","text":"def get_keys(self):\n try:\n self.cur.execute(\"SELECT DISTINCT ON (key) key \"\n \"FROM public.entities\")\n logger.debug(\"Retrieving keys from shopify database\")\n list_of_keys = [x[0] for x in self.cur.fetchall()]\n return list_of_keys\n except psycopg2.Error as e:\n self.conn.rollback()\n logger.exception(e.pgerror)\n raise DatabaseError(e.pgerror)","title":""},{"docid":"793ac9bd72f142e6c73ff450b789ea69","score":"0.6276947","text":"def list(self):\n session_keys = self.redis.keys(self.key_template[:-2] + '*')\n return [s[len(self.key_template)-2:] for s in session_keys]","title":""},{"docid":"a8ab1a13381ab73aaf8e9d6a5132faab","score":"0.62743986","text":"def keys(self):\n raise NotImplementedError","title":""},{"docid":"a8ab1a13381ab73aaf8e9d6a5132faab","score":"0.62743986","text":"def keys(self):\n raise NotImplementedError","title":""},{"docid":"62fc10b349675a667522b7c536ed41fc","score":"0.62735236","text":"def getKeysList(self, dict):\n list = []\n for key in dict.keys():\n list.append(key)\n return list","title":""},{"docid":"07684118902c7556820c8c1dc58c8093","score":"0.6259769","text":"def list(self):\n return list(self.keys())","title":""},{"docid":"0faf41de3d66b04c280bf651148b1a16","score":"0.62584966","text":"def keys(self):\n super_keys = dict.keys(self)\n my_keys = []\n for keys in super_keys:\n my_keys += list(keys)\n\n return my_keys","title":""},{"docid":"d72024e9cf85c881b55f1d85b86e430c","score":"0.6239055","text":"def keys(self):\n raise NotImplementedError()","title":""},{"docid":"f37a9781d4606912a55fb338ac59acf3","score":"0.6238693","text":"def untranslated_keys(self):\n return [self._storage[k][1] for k in self._storage]","title":""},{"docid":"5cef338a24bbbc498021bd3bb96537f4","score":"0.62290853","text":"def list(self) -> List[str]:\n return list(self.keys())","title":""},{"docid":"da2e54603781cb53effeebf6754d304e","score":"0.62283486","text":"def keys(self):\n self.synch()\n for k in self.storage.keys():\n yield self.key_conv.deserialize(k)","title":""},{"docid":"232005db55523656bd6766be3b7f94bf","score":"0.62247896","text":"def iterkeys(self):\n if not self._data:\n self._data = self.poll()\n\n for file in self._data:\n yield file[\"path\"]","title":""},{"docid":"f51df4dba857fab3c10ad83034469eb6","score":"0.6216685","text":"def keys(self):\n return list(self._DL.keys())","title":""},{"docid":"652a68454bbf2f1762686e6cdbf71f03","score":"0.6215189","text":"def keys(self) -> Sequence[str]:\n return self.__keys","title":""},{"docid":"026e17ed2ccf1063a2e095b099c0ae64","score":"0.6213427","text":"def namelist(self):\n namelist = self.parent.namelist()\n return [item for item in namelist if item.startswith(self.key)]","title":""},{"docid":"9eab05d3f9e0be11d5e7e216436a93dd","score":"0.6209803","text":"def keys(self):\n return self.Keys","title":""},{"docid":"0e4d461dbb1e2977f376d560cd407798","score":"0.6203239","text":"def _internal_kv_list(prefix):\n if isinstance(prefix, bytes):\n pattern = prefix + b\"*\"\n else:\n pattern = prefix + \"*\"\n return ray.worker.global_worker.redis_client.keys(pattern=pattern)","title":""},{"docid":"fb80083cc8bc110a4b2e20fee2aa2e09","score":"0.61997396","text":"def keys(self):\n return self.rpcl('','')","title":""},{"docid":"05f7a192c55ef0aec52679874a22e7bb","score":"0.617626","text":"def keys(self):\n return [v[0] for v in self._dict.values()]","title":""},{"docid":"b665ef778d4ab6227aed9d01b17d17ed","score":"0.61623245","text":"def ls(self, path):\n return os.path.listdir(path)","title":""},{"docid":"deb897fa2984ed2457df63c766acffd7","score":"0.61621964","text":"def get_keys(self):\n return list(map((lambda x: x[DEST]), self.routes.copy()))","title":""},{"docid":"22ff98b5cd16ecc03e788e6e6972da09","score":"0.6158181","text":"def keys(self):\n return self._db_keys(self._db, self._reconstruct_obj)","title":""},{"docid":"4bafb6eafc072380c780c9953009d867","score":"0.61563","text":"def keys(self):\n return list(self.__entries.keys())","title":""},{"docid":"5238be11fe4067485df1496700309826","score":"0.6155828","text":"def keys(self):\n\t\treturn dir(self).keys()","title":""},{"docid":"7f44d426ec669cada50cdb8b053dd1d1","score":"0.6155747","text":"def GetDirList(self, path):\n props = self.__norm_path_keys(self.client.propfind(path, depth=1))\n # remove this path\n del props[os.path.normpath(path)]\n return [os.path.basename(p) for p in props.keys()]","title":""},{"docid":"b08b394b2ec094d9e63b6135b374a229","score":"0.6152318","text":"def getKeys(self):\n return self.keysArray","title":""},{"docid":"71c40711a586ec5b74c38621429fbb81","score":"0.61424494","text":"def keys(self):\n return [n.key for n in self.nodes()]","title":""},{"docid":"f536b62d04a5a65664a12d7de225fa4d","score":"0.61362773","text":"def list(self):\n # find all files in the encrypted directory\n keys = self.bucket.list('%s/' % self.gpg_email)\n document_names = map(lambda key: key.name, keys)\n\n # don't show directory in listing\n document_names = map(lambda name: name.replace('%s/' % self.gpg_email, ''),\n document_names)\n return document_names","title":""},{"docid":"deebd9689ab2697995a39c07f78188b3","score":"0.6134762","text":"def listdir(self, path):\n return ([], self.container.list_objects(path=path))","title":""},{"docid":"e63eb5ecce410a99fb597ceef9ed5225","score":"0.61342365","text":"def collect_keys(self, only_parents=False):\n result = self.parent and self.parent.collect_keys() or []\n if not only_parents:\n result += getattr(self, 'keys', ())\n return result","title":""},{"docid":"dd50b1d0af6b75ff189b7804111a2fb8","score":"0.6124687","text":"def ls(self, path):\n return self.list(path)","title":""},{"docid":"d69f855cd704991c22a0e910fcaf101e","score":"0.6115973","text":"def keys(self) -> Iterable[KV]:\n return self._result_cache.keys()","title":""},{"docid":"c9e7bb7e87a309261f733e1f38f09b9a","score":"0.61143994","text":"def keys(self) -> AbstractSet[str]:\n return self._keys()","title":""},{"docid":"97941c89e839aae2ea9d80b6fc8b6e5a","score":"0.6113517","text":"def keys(self) -> KeysView:\n return self._dict.keys() # type: ignore","title":""},{"docid":"490a2c29acf38d9b6da253b53f79e106","score":"0.6104836","text":"def keys(self, private=False):\n return self.gnupg.list_keys(private)","title":""},{"docid":"ee18f5e305db99a4c3825ff9a6441801","score":"0.61041945","text":"def iterkeys(self):\n for name, folder in self.iteritems(): yield name","title":""},{"docid":"eeb450d8acea98109104563cdb0cc5d7","score":"0.61017627","text":"def list_stores(self):\n return [store.prefix for store in self._stores.values()]","title":""},{"docid":"390f94d2ef19b0e8a9c2b488c4e0ac65","score":"0.6101698","text":"def keys(self):\n for k in self.__index.keys():\n yield k","title":""},{"docid":"629e819b1c6c3c046be9829a053ffd6e","score":"0.6099832","text":"def keys(self) -> AsyncIterable[str]:","title":""},{"docid":"2b3a8aadd87e7109b072b083dd1cda7d","score":"0.6098269","text":"def keys(self):\n yield from self.index.keys()","title":""},{"docid":"c767932774d8273a47e4f698acee4b9e","score":"0.60764456","text":"def keys(self, pattern):\n return self.client.keys(pattern)","title":""},{"docid":"82a4340ed21793cf0973a84111307855","score":"0.6074092","text":"def get_stored_list(self, key):\n return self._redis.lrange(name=key, start=0, end=-1)","title":""},{"docid":"fe9269b9884ed18df9b6e4212e2385eb","score":"0.60739213","text":"def keys(self):\n return self.__cfg.keys()","title":""},{"docid":"fa35043b6c4e2db2085d541e98a591ef","score":"0.60543495","text":"def loadkeys(keys):\n return []","title":""},{"docid":"0b48b5a3598bf1f8006f65c5ccd7b85a","score":"0.6053376","text":"def get_unchaged_keys(aug_store, current):\n\n prefixes = [\":\".join(key.split(\":\")[:-1]) for key in aug_store.keys()]\n stable =[k for k in prefixes if k not in current]\n unchanged = [k for k in aug_store.keys() if \":\".join(k.split(\":\")[:-1]) in stable ]\n\n return unchanged","title":""},{"docid":"d95b5690b34c954ba1cf803d2085b175","score":"0.60490924","text":"def keys(self, key: Optional[Text] = None) -> List[Text]:\n if key is None:\n return list(self.data.keys())\n\n if key in self.data:\n return list(self.data[key].keys())\n\n return []","title":""},{"docid":"8f8be2bd6274aed26ebb00a42565a0fe","score":"0.60289943","text":"def _keys(self, **kwargs):\n layer = self.defaultLayer\n return layer.keys()","title":""},{"docid":"1e8148ee8673e96851b03c26cb5dd6ec","score":"0.6023591","text":"def keys():\n raise NotImplemented","title":""},{"docid":"5d961f06ef2a3bd949a9c1d099e0d6de","score":"0.6014965","text":"def traversed_path(self) -> List[str]:\n if not self.redis_client_traversed.hget(self.status.root_path, self.start_path):\n return list()\n return json.loads(\n self.redis_client_traversed.hget(\n self.status.root_path, self.start_path\n ).decode()\n )","title":""},{"docid":"74da75dbc15ec2db31fb417d74caa72c","score":"0.6014804","text":"def items(self, path_root=None):\n keys = list(self._dict.keys())\n keys.sort()\n ret = []\n\n for key in keys:\n # filter out expired items\n if self._dict[key].is_expired():\n continue\n\n # filter out keys that do not match the root path\n if path_root and not key.startswith(path_root):\n continue\n\n ret.append((key, self._dict[key].value))\n\n return ret","title":""},{"docid":"dcee6b77dd54326aa5fd4caba48d350e","score":"0.60106474","text":"def keys(self):\n\t\treturn self.data.keys()","title":""},{"docid":"0e6b77a08672b3cf8a7e3f6083ae5c93","score":"0.6007989","text":"def listkeys(self) -> requests.Response:\n url = \"{}/api/v1/keys\".format(self.url)\n return self._httpsession.get(url, headers=self._header())","title":""}],"string":"[\n {\n \"docid\": \"fedbcb88517228f6e49224e6c7ab64ae\",\n \"score\": \"0.7502651\",\n \"text\": \"def traversed_keys(self, root_path: str) -> List[str]:\\n return self.redis_client_traversed.hkeys(root_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37907698267ce4b2bc7e19d9e795d099\",\n \"score\": \"0.7457224\",\n \"text\": \"def listdir(self):\\n\\n if self._path not in self._fs._dirs:\\n raise UnlistableError()\\n\\n i = chain(self._fs._dirs, self._fs._store.iterkeys())\\n\\n # Linear-time search. Could be better.\\n p = self._path\\n l = len(p) + 1\\n ks = [t[-1] for t in i if t[:-1] == p and len(t) == l]\\n\\n return ks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aa2649548acf2fae83833a7b837eb59\",\n \"score\": \"0.7161985\",\n \"text\": \"def keys(self):\\n try:\\n return self.__storage__.keys()\\n except:\\n return []\\n #return self.__state.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a635d4524904026319caa0ac5886e96\",\n \"score\": \"0.70467705\",\n \"text\": \"def list_keys(self):\\n self.automate_handle()\\n return self.kcl.list_keys(self.handle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98048518d74d20c1495ac43433054718\",\n \"score\": \"0.699317\",\n \"text\": \"def listkeys(self):\\n\\n\\t\\tif not self.root is None:\\n\\t\\t\\treturn self.root.listkeys()\\n\\t\\telse:\\n\\t\\t\\treturn []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45eeb5bca6857d1d25aa73e938ae45c5\",\n \"score\": \"0.69510627\",\n \"text\": \"def keys(self):\\n return self._store.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45eeb5bca6857d1d25aa73e938ae45c5\",\n \"score\": \"0.69510627\",\n \"text\": \"def keys(self):\\n return self._store.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45eeb5bca6857d1d25aa73e938ae45c5\",\n \"score\": \"0.69510627\",\n \"text\": \"def keys(self):\\n return self._store.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04948e5f43fafcd2400c8aed44838e71\",\n \"score\": \"0.6910418\",\n \"text\": \"def get_keys(self):\\n return self.keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd1769d606ca9bde8a2471be456ab835\",\n \"score\": \"0.68885016\",\n \"text\": \"def keys():\\n return _keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5e5e753065a864b38650caf0302cce3\",\n \"score\": \"0.68775463\",\n \"text\": \"def get_keys(path: Union[str, Path]) -> List[str]:\\n path = _get_valid_hdf_path(path)\\n with tables.open_file(str(path)) as file:\\n keys = _get_keys(file.root)\\n return keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a372f201e68abc39bffcc3aac400665\",\n \"score\": \"0.6820589\",\n \"text\": \"def keys(self) -> List[str]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"248d3b0e5677f9481e5f979648f53060\",\n \"score\": \"0.67937136\",\n \"text\": \"def listkeys(self):\\n\\n\\t\\tl = []\\n\\n\\t\\tif not self.left is None:\\n\\t\\t\\tl.extend(self.left.listkeys())\\n\\n\\t\\tl.append(self.key)\\n\\n\\t\\tif not self.right is None:\\n\\t\\t\\tl.extend(self.right.listkeys())\\n\\n\\t\\treturn l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30a0c2b213c93766d87dd58404c453af\",\n \"score\": \"0.6758386\",\n \"text\": \"def iterkeys(self):\\n\\n return self.storage.iterkeys()\\n #return self.__state.iterkeys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b75ad9866dd3db3a6acf04809f3244f\",\n \"score\": \"0.6737822\",\n \"text\": \"def _keys(self) -> AbstractSet[str]:\\n if self._cached_keys is None:\\n self._cached_keys = self._store.keys()\\n return self._cached_keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cdf586783a5d2dfdf31ac601eebb700\",\n \"score\": \"0.67342883\",\n \"text\": \"def key_list(self):\\n return self._key_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2554256781cb92b2dd821df2bcc5ea5b\",\n \"score\": \"0.67309415\",\n \"text\": \"def keys(self):\\n return self._storage.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4153849c74df4f61db7313f41d3aafcf\",\n \"score\": \"0.67070645\",\n \"text\": \"def get_keys(self):\\n return list(self.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ee6ae19c1d139f4a0de2ec41ffc1b01\",\n \"score\": \"0.6659498\",\n \"text\": \"def get_keys(self):\\n raise NotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc31ba0e19e019a92abfe0768933eb1\",\n \"score\": \"0.66281956\",\n \"text\": \"def keys(self):\\n return [row[0] for row in self._database.execute(\\\"SELECT Key FROM Shelf\\\")]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b030dd0d7b687bdb1a22992ff50df2c8\",\n \"score\": \"0.66176766\",\n \"text\": \"def keys(self):\\n\\t\\treturn self._keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b8f721f3c601846849adc0c6dc12638\",\n \"score\": \"0.6596256\",\n \"text\": \"def keys(self):\\n return list(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b8f721f3c601846849adc0c6dc12638\",\n \"score\": \"0.6596256\",\n \"text\": \"def keys(self):\\n return list(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad7e7602fd6334c336c9c38bec029463\",\n \"score\": \"0.65922254\",\n \"text\": \"def keys(self):\\n return list(self._keys)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"254e5846c5c1098d482491c50b970772\",\n \"score\": \"0.6525932\",\n \"text\": \"def all_paths_list(self):\\n return self._db.fetch_col(\\\"SELECT path FROM cms_paths_hashes\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f417b44442ae3427692726d77ad1d71\",\n \"score\": \"0.6514444\",\n \"text\": \"def list_all():\\n return KEY_DIR.glob(\\\"*.key\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beb9bdda6c89a1b3ea8ec6322a052365\",\n \"score\": \"0.6507522\",\n \"text\": \"def get_keys(self):\\n return self.f_redis.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f5fdf09a60d34c3cdb1fbc75ab81939\",\n \"score\": \"0.64822406\",\n \"text\": \"def keys(self):\\n keys = []\\n for item in self.stored_configuration:\\n if item['key'] != 'common':\\n keys.append(item['key'])\\n return keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ac40797b587a34f4854483ecdcad7e8\",\n \"score\": \"0.6475047\",\n \"text\": \"def list_keys(self, bucket_name, prefix=None):\\n key_list = []\\n\\n if prefix:\\n root = os.path.join(STORAGE_FOLDER, bucket_name, prefix)\\n else:\\n root = os.path.join(STORAGE_FOLDER, bucket_name)\\n\\n for path, subdirs, files in os.walk(root):\\n for name in files:\\n if prefix:\\n key_list.append(os.path.join(prefix, path.replace(root+'/', ''), name))\\n else:\\n key_list.append(os.path.join(path.replace(root+'/', ''), name))\\n\\n return key_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda326825acb2c8b532dd76eb0c0d138\",\n \"score\": \"0.6473079\",\n \"text\": \"def keys(self):\\n return self.root.keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e33cbc56d83d6e36896055f547aaf68c\",\n \"score\": \"0.6464677\",\n \"text\": \"def list_keys(self, prefix=''):\\n try:\\n bucket = self.get_buck()\\n lister = bucket.list(prefix)\\n\\n for key in lister:\\n yield key.name\\n except ssl.SSLError:\\n yield\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a957b057e47e489e888c7f86f18d75a4\",\n \"score\": \"0.64603865\",\n \"text\": \"def keys(self):\\n return list(self.iterkeys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a957b057e47e489e888c7f86f18d75a4\",\n \"score\": \"0.64603865\",\n \"text\": \"def keys(self):\\n return list(self.iterkeys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"373ff0d62d40854573ab78c8abfde618\",\n \"score\": \"0.6441174\",\n \"text\": \"def keys(self):\\n return [u for u in self.iterkeys()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20d5a421312e5052c939c42f60d4b434\",\n \"score\": \"0.6433927\",\n \"text\": \"def keys(self, bucket, user=None):\\n return (d[2] for d in os.walk(os.path.join(self.directory, bucket)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db8f59e316f81d5ee25d35061252d038\",\n \"score\": \"0.6374483\",\n \"text\": \"def keys(self):\\n\\t\\tif not self._cache_populated:\\n\\t\\t\\tself._populate_cache()\\n\\t\\treturn self._cache.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ff1fac0752a1e5f939401b0831727f\",\n \"score\": \"0.63630563\",\n \"text\": \"def list_keys(self, bucket_name, prefix=None):\\n prefix = prefix or ''\\n redis_prefix = self._format_key(bucket_name, prefix)\\n\\n pdir = '/'.join(redis_prefix.split('/')[:-1]) + '/'\\n dir_keys = [key.decode() for key in self._client.smembers(pdir)]\\n key_list = []\\n\\n for key in dir_keys:\\n full_key = pdir + key\\n if full_key.startswith(redis_prefix):\\n if full_key.endswith('/'):\\n key_list.extend(self._walk(bucket_name, full_key))\\n else:\\n key_list.append(full_key)\\n\\n offset = len(bucket_name) + 1\\n return [key[offset:] for key in key_list]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"133076af977a0e9f16863d647f05d497\",\n \"score\": \"0.636247\",\n \"text\": \"def keys(self) -> Iterator[str]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a31c7ec9fe6a54e4949060bba7c2b9c\",\n \"score\": \"0.63469154\",\n \"text\": \"def keys(self):\\n return (k for k in self._keys)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00584b6ece7bac0308ed7cc8d54f1df9\",\n \"score\": \"0.6345846\",\n \"text\": \"def keys():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"790aa09d21b90d1d7be2db819d2416e8\",\n \"score\": \"0.6340655\",\n \"text\": \"def list_by_prefix(self, prefix):\\n filtered = self._bucket.objects.filter(Prefix=prefix)\\n return list(obj.key for obj in filtered)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"219f0f05773fc5e803fcfd43f5945a00\",\n \"score\": \"0.63325286\",\n \"text\": \"def keys(self):\\n return self.__keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ae1eaa5973a681b843812a47fead9d\",\n \"score\": \"0.6330527\",\n \"text\": \"def keys(tokens):\\n\\n return [\\n token.path\\n for token in tokens\\n if isinstance(token, Key)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2faac7fc5d7115ca72fafeecadedc0c\",\n \"score\": \"0.6322146\",\n \"text\": \"def keys(self):\\n return list(self._registry.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98fd02ff1df531f81d1b094b7df307d4\",\n \"score\": \"0.6317723\",\n \"text\": \"def keys(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37f77f0978327d530c472fdc8c3bf2e8\",\n \"score\": \"0.63081914\",\n \"text\": \"def keys(self) -> dict_keys:\\n return self._stored.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3294f2c18118a9800b0f4450677bba3d\",\n \"score\": \"0.6307059\",\n \"text\": \"def keys(self):\\n \\n keylist = []\\n \\n for key in self.iterkeys():\\n keylist.append(key)\\n\\n return keylist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd688856634ffd47d815f213de5aa8f4\",\n \"score\": \"0.629564\",\n \"text\": \"def keys(self):\\n\\n return list(self.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72964863465444a1d961d2de8ef33bfa\",\n \"score\": \"0.62811106\",\n \"text\": \"def get_keys(self):\\n try:\\n self.cur.execute(\\\"SELECT DISTINCT ON (key) key \\\"\\n \\\"FROM public.entities\\\")\\n logger.debug(\\\"Retrieving keys from shopify database\\\")\\n list_of_keys = [x[0] for x in self.cur.fetchall()]\\n return list_of_keys\\n except psycopg2.Error as e:\\n self.conn.rollback()\\n logger.exception(e.pgerror)\\n raise DatabaseError(e.pgerror)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"793ac9bd72f142e6c73ff450b789ea69\",\n \"score\": \"0.6276947\",\n \"text\": \"def list(self):\\n session_keys = self.redis.keys(self.key_template[:-2] + '*')\\n return [s[len(self.key_template)-2:] for s in session_keys]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8ab1a13381ab73aaf8e9d6a5132faab\",\n \"score\": \"0.62743986\",\n \"text\": \"def keys(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8ab1a13381ab73aaf8e9d6a5132faab\",\n \"score\": \"0.62743986\",\n \"text\": \"def keys(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62fc10b349675a667522b7c536ed41fc\",\n \"score\": \"0.62735236\",\n \"text\": \"def getKeysList(self, dict):\\n list = []\\n for key in dict.keys():\\n list.append(key)\\n return list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07684118902c7556820c8c1dc58c8093\",\n \"score\": \"0.6259769\",\n \"text\": \"def list(self):\\n return list(self.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0faf41de3d66b04c280bf651148b1a16\",\n \"score\": \"0.62584966\",\n \"text\": \"def keys(self):\\n super_keys = dict.keys(self)\\n my_keys = []\\n for keys in super_keys:\\n my_keys += list(keys)\\n\\n return my_keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d72024e9cf85c881b55f1d85b86e430c\",\n \"score\": \"0.6239055\",\n \"text\": \"def keys(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f37a9781d4606912a55fb338ac59acf3\",\n \"score\": \"0.6238693\",\n \"text\": \"def untranslated_keys(self):\\n return [self._storage[k][1] for k in self._storage]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cef338a24bbbc498021bd3bb96537f4\",\n \"score\": \"0.62290853\",\n \"text\": \"def list(self) -> List[str]:\\n return list(self.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da2e54603781cb53effeebf6754d304e\",\n \"score\": \"0.62283486\",\n \"text\": \"def keys(self):\\n self.synch()\\n for k in self.storage.keys():\\n yield self.key_conv.deserialize(k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"232005db55523656bd6766be3b7f94bf\",\n \"score\": \"0.62247896\",\n \"text\": \"def iterkeys(self):\\n if not self._data:\\n self._data = self.poll()\\n\\n for file in self._data:\\n yield file[\\\"path\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f51df4dba857fab3c10ad83034469eb6\",\n \"score\": \"0.6216685\",\n \"text\": \"def keys(self):\\n return list(self._DL.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"652a68454bbf2f1762686e6cdbf71f03\",\n \"score\": \"0.6215189\",\n \"text\": \"def keys(self) -> Sequence[str]:\\n return self.__keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"026e17ed2ccf1063a2e095b099c0ae64\",\n \"score\": \"0.6213427\",\n \"text\": \"def namelist(self):\\n namelist = self.parent.namelist()\\n return [item for item in namelist if item.startswith(self.key)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eab05d3f9e0be11d5e7e216436a93dd\",\n \"score\": \"0.6209803\",\n \"text\": \"def keys(self):\\n return self.Keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e4d461dbb1e2977f376d560cd407798\",\n \"score\": \"0.6203239\",\n \"text\": \"def _internal_kv_list(prefix):\\n if isinstance(prefix, bytes):\\n pattern = prefix + b\\\"*\\\"\\n else:\\n pattern = prefix + \\\"*\\\"\\n return ray.worker.global_worker.redis_client.keys(pattern=pattern)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb80083cc8bc110a4b2e20fee2aa2e09\",\n \"score\": \"0.61997396\",\n \"text\": \"def keys(self):\\n return self.rpcl('','')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05f7a192c55ef0aec52679874a22e7bb\",\n \"score\": \"0.617626\",\n \"text\": \"def keys(self):\\n return [v[0] for v in self._dict.values()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b665ef778d4ab6227aed9d01b17d17ed\",\n \"score\": \"0.61623245\",\n \"text\": \"def ls(self, path):\\n return os.path.listdir(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb897fa2984ed2457df63c766acffd7\",\n \"score\": \"0.61621964\",\n \"text\": \"def get_keys(self):\\n return list(map((lambda x: x[DEST]), self.routes.copy()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22ff98b5cd16ecc03e788e6e6972da09\",\n \"score\": \"0.6158181\",\n \"text\": \"def keys(self):\\n return self._db_keys(self._db, self._reconstruct_obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bafb6eafc072380c780c9953009d867\",\n \"score\": \"0.61563\",\n \"text\": \"def keys(self):\\n return list(self.__entries.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5238be11fe4067485df1496700309826\",\n \"score\": \"0.6155828\",\n \"text\": \"def keys(self):\\n\\t\\treturn dir(self).keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f44d426ec669cada50cdb8b053dd1d1\",\n \"score\": \"0.6155747\",\n \"text\": \"def GetDirList(self, path):\\n props = self.__norm_path_keys(self.client.propfind(path, depth=1))\\n # remove this path\\n del props[os.path.normpath(path)]\\n return [os.path.basename(p) for p in props.keys()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b08b394b2ec094d9e63b6135b374a229\",\n \"score\": \"0.6152318\",\n \"text\": \"def getKeys(self):\\n return self.keysArray\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71c40711a586ec5b74c38621429fbb81\",\n \"score\": \"0.61424494\",\n \"text\": \"def keys(self):\\n return [n.key for n in self.nodes()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f536b62d04a5a65664a12d7de225fa4d\",\n \"score\": \"0.61362773\",\n \"text\": \"def list(self):\\n # find all files in the encrypted directory\\n keys = self.bucket.list('%s/' % self.gpg_email)\\n document_names = map(lambda key: key.name, keys)\\n\\n # don't show directory in listing\\n document_names = map(lambda name: name.replace('%s/' % self.gpg_email, ''),\\n document_names)\\n return document_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deebd9689ab2697995a39c07f78188b3\",\n \"score\": \"0.6134762\",\n \"text\": \"def listdir(self, path):\\n return ([], self.container.list_objects(path=path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63eb5ecce410a99fb597ceef9ed5225\",\n \"score\": \"0.61342365\",\n \"text\": \"def collect_keys(self, only_parents=False):\\n result = self.parent and self.parent.collect_keys() or []\\n if not only_parents:\\n result += getattr(self, 'keys', ())\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd50b1d0af6b75ff189b7804111a2fb8\",\n \"score\": \"0.6124687\",\n \"text\": \"def ls(self, path):\\n return self.list(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d69f855cd704991c22a0e910fcaf101e\",\n \"score\": \"0.6115973\",\n \"text\": \"def keys(self) -> Iterable[KV]:\\n return self._result_cache.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9e7bb7e87a309261f733e1f38f09b9a\",\n \"score\": \"0.61143994\",\n \"text\": \"def keys(self) -> AbstractSet[str]:\\n return self._keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97941c89e839aae2ea9d80b6fc8b6e5a\",\n \"score\": \"0.6113517\",\n \"text\": \"def keys(self) -> KeysView:\\n return self._dict.keys() # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"490a2c29acf38d9b6da253b53f79e106\",\n \"score\": \"0.6104836\",\n \"text\": \"def keys(self, private=False):\\n return self.gnupg.list_keys(private)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee18f5e305db99a4c3825ff9a6441801\",\n \"score\": \"0.61041945\",\n \"text\": \"def iterkeys(self):\\n for name, folder in self.iteritems(): yield name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeb450d8acea98109104563cdb0cc5d7\",\n \"score\": \"0.61017627\",\n \"text\": \"def list_stores(self):\\n return [store.prefix for store in self._stores.values()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"390f94d2ef19b0e8a9c2b488c4e0ac65\",\n \"score\": \"0.6101698\",\n \"text\": \"def keys(self):\\n for k in self.__index.keys():\\n yield k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"629e819b1c6c3c046be9829a053ffd6e\",\n \"score\": \"0.6099832\",\n \"text\": \"def keys(self) -> AsyncIterable[str]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b3a8aadd87e7109b072b083dd1cda7d\",\n \"score\": \"0.6098269\",\n \"text\": \"def keys(self):\\n yield from self.index.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c767932774d8273a47e4f698acee4b9e\",\n \"score\": \"0.60764456\",\n \"text\": \"def keys(self, pattern):\\n return self.client.keys(pattern)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82a4340ed21793cf0973a84111307855\",\n \"score\": \"0.6074092\",\n \"text\": \"def get_stored_list(self, key):\\n return self._redis.lrange(name=key, start=0, end=-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe9269b9884ed18df9b6e4212e2385eb\",\n \"score\": \"0.60739213\",\n \"text\": \"def keys(self):\\n return self.__cfg.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa35043b6c4e2db2085d541e98a591ef\",\n \"score\": \"0.60543495\",\n \"text\": \"def loadkeys(keys):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b48b5a3598bf1f8006f65c5ccd7b85a\",\n \"score\": \"0.6053376\",\n \"text\": \"def get_unchaged_keys(aug_store, current):\\n\\n prefixes = [\\\":\\\".join(key.split(\\\":\\\")[:-1]) for key in aug_store.keys()]\\n stable =[k for k in prefixes if k not in current]\\n unchanged = [k for k in aug_store.keys() if \\\":\\\".join(k.split(\\\":\\\")[:-1]) in stable ]\\n\\n return unchanged\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d95b5690b34c954ba1cf803d2085b175\",\n \"score\": \"0.60490924\",\n \"text\": \"def keys(self, key: Optional[Text] = None) -> List[Text]:\\n if key is None:\\n return list(self.data.keys())\\n\\n if key in self.data:\\n return list(self.data[key].keys())\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f8be2bd6274aed26ebb00a42565a0fe\",\n \"score\": \"0.60289943\",\n \"text\": \"def _keys(self, **kwargs):\\n layer = self.defaultLayer\\n return layer.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e8148ee8673e96851b03c26cb5dd6ec\",\n \"score\": \"0.6023591\",\n \"text\": \"def keys():\\n raise NotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d961f06ef2a3bd949a9c1d099e0d6de\",\n \"score\": \"0.6014965\",\n \"text\": \"def traversed_path(self) -> List[str]:\\n if not self.redis_client_traversed.hget(self.status.root_path, self.start_path):\\n return list()\\n return json.loads(\\n self.redis_client_traversed.hget(\\n self.status.root_path, self.start_path\\n ).decode()\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74da75dbc15ec2db31fb417d74caa72c\",\n \"score\": \"0.6014804\",\n \"text\": \"def items(self, path_root=None):\\n keys = list(self._dict.keys())\\n keys.sort()\\n ret = []\\n\\n for key in keys:\\n # filter out expired items\\n if self._dict[key].is_expired():\\n continue\\n\\n # filter out keys that do not match the root path\\n if path_root and not key.startswith(path_root):\\n continue\\n\\n ret.append((key, self._dict[key].value))\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcee6b77dd54326aa5fd4caba48d350e\",\n \"score\": \"0.60106474\",\n \"text\": \"def keys(self):\\n\\t\\treturn self.data.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e6b77a08672b3cf8a7e3f6083ae5c93\",\n \"score\": \"0.6007989\",\n \"text\": \"def listkeys(self) -> requests.Response:\\n url = \\\"{}/api/v1/keys\\\".format(self.url)\\n return self._httpsession.get(url, headers=self._header())\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":91,"cells":{"query_id":{"kind":"string","value":"444eac86840ee38810c270d702dc34e7"},"query":{"kind":"string","value":"Return True if the node is significant at the 5% level according to the chisquared distrubtion with 1 df."},"positive_passages":{"kind":"list like","value":[{"docid":"8a857b67f48c7662b3e4bb4e57d94841","score":"0.76875913","text":"def _significant(self, node):\n\t\tx_square_cutoff = 3.841 #1df @ 5% level\n\t\tx_square_node = float(\"inf\") \n\n\t\treturn x_square_node > x_square_cutoff","title":""}],"string":"[\n {\n \"docid\": \"8a857b67f48c7662b3e4bb4e57d94841\",\n \"score\": \"0.76875913\",\n \"text\": \"def _significant(self, node):\\n\\t\\tx_square_cutoff = 3.841 #1df @ 5% level\\n\\t\\tx_square_node = float(\\\"inf\\\") \\n\\n\\t\\treturn x_square_node > x_square_cutoff\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"556cfbe6e05fad6380053e80fac12608","score":"0.5665601","text":"def techChange_sunny(p):\n return p[:, 0] > 0.325","title":""},{"docid":"fc21c6804aef56340de033ee2840d9c3","score":"0.5657123","text":"def __feat_num_significant(self, col, df_uni_var_norm, df_uni_var_un_norm, target_col, group_col, add_null):\n df_target_col = df_uni_var_norm[[target_col, group_col, col]]\n df_bin_col = self.__bin_generator.create_percentile_bins(df_target_col.copy(), [col], \n num_bins=self.__config[Constants.num_bins_numerical],\n add_null=add_null)\n binned_feats = [feat for feat in df_bin_col.columns if col in feat]\n\n sig = False\n\n for feat in binned_feats:\n num_c, len_c, num_t, len_t = self.__feat_info(df_bin_col[[target_col, group_col, feat]],\n feat,\n target_col,\n group_col)\n sig = self.__sig_check(num_c, len_c, num_t, len_t)\n if sig:\n break\n\n # if none of the binned features are significant return False, 0 impact\n if not sig:\n return False, 0, 0, 0, 0, 0\n\n # contribution on the non-normalized data set\n df_target_col = df_uni_var_un_norm[[target_col, group_col, col]]\n df_bin_col = self.__bin_generator.create_percentile_bins(df_target_col.copy(), [col], \n num_bins=self.__config[Constants.num_bins_numerical],\n add_null=add_null)\n binned_feats = [feat for feat in df_bin_col.columns if col in feat]\n\n expected = []\n actual = []\n contribution = []\n is_sig = []\n\n for feat in binned_feats:\n num_c, len_c, num_t, len_t = self.__feat_info(df_bin_col[[target_col, group_col, feat]], feat, target_col,\n group_col)\n contribution.append(num_t - num_c * len_t / len_c)\n actual.append(num_t)\n expected.append(num_c * len_t / len_c)\n is_sig.append(self.__sig_check(num_c, len_c, num_t, len_t))\n\n return True, binned_feats, is_sig, expected, actual, contribution","title":""},{"docid":"288f86a1ae143dbb536b20452075b74a","score":"0.5455548","text":"def has_percent_row(minerals: List[str], df: pd.DataFrame) -> bool:\n return any(abs(x - 100) < EPSILON for x in df[minerals].sum(axis=1))","title":""},{"docid":"ff1e36da300e087e37992359ce0f5fef","score":"0.5452519","text":"def __feat_cat_significant(self, col, df_uni_var_norm, df_uni_var_un_norm, target_col, group_col):\n num_c, len_c, num_t, len_t = self.__feat_info_cat(col, df_uni_var_norm, target_col, group_col)\n\n sig = self.__sig_check(num_c, len_c, num_t, len_t)\n if not sig:\n return False, 0, 0, 0\n\n # If number of drops is significant return the number of impacted calls on the original data set.\n num_c, len_c, num_t, len_t = self.__feat_info_cat(col, df_uni_var_un_norm, target_col, group_col)\n\n return True, num_c * len_t / len_c, num_t, num_t - num_c * len_t / len_c","title":""},{"docid":"b6fe87d44a0d2a0ee2b6fd0d2541e5b9","score":"0.5441717","text":"def SSA_sensitivity(database_size):\r\n return 9 + (5/database_size)","title":""},{"docid":"5301dfea61004bf4bdbac600cf7216bc","score":"0.5410713","text":"def pravdepodobnost():\n cislo = np.random.rand()\n\n if cislo >= 0.99:\n return False\n else:\n return True","title":""},{"docid":"5098cb0757edb133d8c30fea185d9d22","score":"0.5357177","text":"def __bool__(self):\n\n return self.fitness == 28","title":""},{"docid":"683f5fdb57b992fb4a186835a57ea876","score":"0.5337217","text":"def safe(self):\n return math.fabs(numpy.linalg.det(self.scatter)) > 1e-6","title":""},{"docid":"a5f1e777edb7f9d8a24ff1ec74a515fa","score":"0.53181183","text":"def weighted(self):\n\t\tmatrix = self.get_matrix()\n\t\tdim = self.get_nodes()\n\t\tfor i in range(dim):\n\t\t\tfor j in range(dim):\n\t\t\t\tif matrix[i][j] != 0 and matrix[i][j] != 1:\n\t\t\t\t\treturn True\n\t\treturn False","title":""},{"docid":"0992ec16f99913effc93c5560db59ca2","score":"0.5310074","text":"def is_over_weight(self,df):\r\n return int(df['bmi'] > 25 and df['bmi'] < 29.9)","title":""},{"docid":"0b7c3c8f1c1433adb239fa23ee9359f8","score":"0.529007","text":"def getUtileDistinction_KS(self, node, p_significanceLevel=float(0.001)):\n\n assert node.nodeType == NodeLeaf\n\n root_utils = self.getEFDRs(node) # Get all expected future discounted returns for all instances in a node\n\n cds = self.getCandidateDistinctions(node) # Get all the candidate distinctions\n\n p_min = float(1)\n cd_min = None\n\n for cd in cds: # test all possible distinctions until find the one satisfy KS test\n child_utils = []\n self.splitToFringe(node, cd) # split to fringe node with split candidate\n # self.split(node,cd)\n for c in node.children:\n if len(c.instances) < self.minSplitInstances: # if not enough instance in a node, stop split\n continue\n child_utils.append(\n self.getEFDRs(c)) # Get all expected future discounted returns for all instances in a children\n # self.unsplit(node) # delete split fringe node\n\n for i, cu in enumerate(child_utils):\n k, p = ks_2samp(root_utils, cu)\n # print p\n if p < p_significanceLevel and p < p_min: # significance_level=0.00005, if p below it, this distinction is significant\n p_min = p\n cd_min = cd\n\n if len(root_utils) == len(cu):\n print 'find you'\n\n print(\"KS passed, p={}, d = {}, back={}\".format(p, cd.dimension, cd.back_idx))\n # print(root_utils)\n # print(cu)\n # elif p< 0.1:\n # print(\"KS failed, p={}. d= {}, back={}\".format(p,cd.dimension,cd.back_idx))\n self.unsplit(node) # delete split fringe node\n\n if cd_min:\n print(\"Will be split, p={}, d={}, back={}\".format(p_min, cd_min.dimension_name, cd_min.back_idx))\n # else:\n # print \"Not found\"\n return cd_min\n else:\n return None","title":""},{"docid":"698d753fdfdfbd3df9a67c0e551473ee","score":"0.5286751","text":"def __convergence_test(self, cost):\n return cost <= self.threshold","title":""},{"docid":"d3c6761a824f02195d5148bb3216555d","score":"0.52796507","text":"def cf_true(x):\n return is_cf(x) and x > CF.cutoff","title":""},{"docid":"1c938fb0c1bd723663ef85e4090583af","score":"0.5265434","text":"def _compute_gfes(self):\n return self._det_dcv is not None","title":""},{"docid":"cf4ee9468f107cf84d46f7d39832f313","score":"0.5253931","text":"def test_Cacciato09Cens5():\n model = Cacciato09Cens(threshold=9.5)\n clf = model.clf(model.median_prim_galprop(prim_haloprop=1e12), prim_haloprop=1e12)\n assert np.isclose(\n np.sqrt(2 * np.pi * model.param_dict[\"sigma\"] ** 2) ** (-1),\n clf,\n rtol=1e-6,\n atol=1.0e-6,\n )\n model.param_dict[\"sigma\"] = 0.24\n clf = model.clf(model.median_prim_galprop(prim_haloprop=1e13), prim_haloprop=1e13)\n assert np.isclose(\n np.sqrt(2 * np.pi * model.param_dict[\"sigma\"] ** 2) ** (-1),\n clf,\n rtol=1e-6,\n atol=1.0e-6,\n )","title":""},{"docid":"5dba6ffbdb1d51f49cb8e69eddf025bb","score":"0.5246929","text":"def validate_t(self, solution, t):\r\n no_sense_prob = 0\r\n active_nodes = [i for i, a in enumerate(solution) if a != cf.SLEEP and self.network.get_node(i).energy >= (cf.SENSING_ENERGY +cf.COMMUNICATION_ENERGY)]\r\n\r\n for n in active_nodes:\r\n no_sense_prob += self.sensing_log_matrix[t][n]\r\n if no_sense_prob >= self.sensing_log_threshold:\r\n return True\r\n if no_sense_prob < self.sensing_log_threshold:\r\n return False\r\n else:\r\n return True","title":""},{"docid":"6f91ded376aa0411f143afd5996a6780","score":"0.5231144","text":"def is_statistically_significant(self):\n return self.fall_in_critical_region()","title":""},{"docid":"a2f9b8f3d95405f594c4a6feed052ef0","score":"0.52248955","text":"def SSE_sensitivity():\r\n return 7","title":""},{"docid":"69a5e030bab96f26aafc90941f1d4701","score":"0.52139014","text":"def oversampled(semibmaj, semibmin, x=30):\n return semibmaj > x or semibmin > x","title":""},{"docid":"66d1cbd6c11e381fe8e633e2980fa584","score":"0.5210077","text":"def set_threshold(df, threshold):\n return df[\"rating\"] >= threshold","title":""},{"docid":"be9ea8fc2921361876d9ca665f8d5279","score":"0.52058774","text":"def check_eigval(self):\n iok = True\n thresh = self.param.thresh","title":""},{"docid":"5312fc342c14a75d5b5bcf167fbf75d2","score":"0.5201662","text":"def snowcover(self):\n return self.swe > 0.0","title":""},{"docid":"b414b0df04d5a58e342c96c42f8e4cd1","score":"0.51848173","text":"def is_on(self) -> bool:\n return (\n self._func_channel.dimLevel is not None\n and self._func_channel.dimLevel > 0.0\n )","title":""},{"docid":"d9404e8cc9aeaf2e2e7dff934e2b5ecd","score":"0.51786757","text":"def has_unit_root(self, data_array, sig=.05):\n adf = adfuller(data_array)\n pvalue = adf[1]\n # print(pvalue)\n if pvalue < sig:\n return False\n else:\n return","title":""},{"docid":"d8633bc4986c4e6f82f4a311ce24ac23","score":"0.51761365","text":"def is_perfect(self):\n h = self._get_max_depth(self._root)\n num = self._get_num_of_node(self._root)\n return (num == (2 ** h - 1))","title":""},{"docid":"49dff13affac125d2382cb69718c501a","score":"0.5174382","text":"def is_normal(r , level = 0.1):\n statistic, p_value=scipy.stats.jarque_bera(r)\n return p_value > level","title":""},{"docid":"162d90b19b80cbf02fa982cc5c9759b6","score":"0.51675516","text":"def check_f(row):\n if row['TRTCD1'] == 50.0:\n return 1\n if row['TRTCD2'] == 50.0:\n return 1\n if row['TRTCD3'] == 50.0:\n return 1\n return 0","title":""},{"docid":"b028c3185d861084186314f0ba48356d","score":"0.5159486","text":"def check_dataset(self, dataset):\n in_support = np.sum(\n (dataset.x >= self.min_x) * (dataset.x <= self.max_x),\n axis=1) == self.num_p\n print(\"percent in support\", np.sum(in_support) / dataset.num_obs)\n return np.sum(in_support) == dataset.num_obs","title":""},{"docid":"f08a77fec455c5dbad94dc0effede95d","score":"0.51545405","text":"def test(self, df, target):\n\n model = DecisionTreeClassifier(random_state=0)\n\n kf = KFold(n_splits=10, shuffle=True, random_state=12345678)\n res = cross_val_score(model, df, target, cv=kf, scoring='f1_weighted')\n\n return res.mean()","title":""},{"docid":"3bfe43c45443a2150c231e4242705995","score":"0.5126107","text":"def need_add_outlier3(self, node, shortened_path):\n if node.id not in self.outliers3:\n return True\n if shortened_path < self.outliers3[node.id].shortened:\n return True","title":""},{"docid":"f20af5806bf23cb9e74cadb961322499","score":"0.51235455","text":"def get_decision(mean_diff):\n\n if mean_diff > -5:\n return True\n else:\n return False","title":""},{"docid":"0c83d57c90ee944456989aff7ddda5de","score":"0.51080835","text":"def s_in_small_g(self):\n\n for node in self.core_s:\n if not self.core_s[node]:\n return False\n return True","title":""},{"docid":"6c025a1d41543ea942da0893991f103f","score":"0.51033574","text":"def _node_cut(self, configs, wf):\n ne = configs.configs.shape[1]\n d2 = 0.0\n for e in range(ne):\n d2 += np.sum(wf.gradient(e, configs.electron(e)) ** 2, axis=0)\n r = 1.0 / (d2 * ne * ne)\n return r < self.nodal_cutoff ** 2","title":""},{"docid":"ab2ca107a549b01047162c7415a92ab1","score":"0.5095412","text":"def FNratio_binned(E): \n if E <=0 :\n fnr = 0.0\n elif E > 0.0 and E <= 0.5 :\n fnr = 1.56980937443\n elif E > 0.5 and E <= 1.0 :\n fnr = 1.68239230683\n elif E > 1.0 and E <= 1.5 :\n fnr = 1.59944451298\n elif E> 1.5 and E<=2.0 :\n fnr = 1.4945939881\n elif E>2.0 and E<=2.5 :\n fnr = 1.35689923574\n elif E>2.5 and E<=3.0 :\n fnr = 1.23797378825\n elif E>3.0 and E<=3.5 :\n fnr = 1.17535790024\n elif E>3.5 and E<=4.0 :\n fnr = 1.23006538251\n elif E>4.0 and E<=4.5 :\n fnr = 1.41616622315\n elif E>4.5 and E<=5.0 :\n fnr = 1.55534133432\n elif E>5.0 and E<=5.5 :\n fnr = 1.57093938166\n elif E>5.5 and E<=6.0 :\n fnr = 1.46452790065\n elif E>6.0 and E<=6.5 :\n fnr = 1.3737572329\n elif E>6.5 and E<=7.0 :\n fnr = 1.29550053164\n elif E>7.0 and E<=7.5 :\n fnr = 1.27981366277\n elif E>7.5 and E<=8.0 :\n fnr = 1.24848762549\n elif E>8.0 and E<=9.0 :\n fnr = 1.26719442633\n elif E>9.0 and E<=10.0 :\n fnr = 1.24833629993\n elif E>10.0 and E<=11.0 :\n fnr = 1.26389158134\n elif E>11.0 and E<=12.0 :\n fnr = 1.34670943259\n elif E>12.0 and E<=14.0 :\n fnr = 1.41853479724\n elif E>14.0 and E<=16.0 :\n fnr = 1.56070845743\n elif E>16.0 and E<=18.0 :\n fnr = 1.55427712082\n elif E>18.0:\n fnr = 1.55427712082\n return (fnr*1.0e-6)","title":""},{"docid":"3f9f1fcb4afbf62e2e877b2bcd1d36e5","score":"0.5087331","text":"def test_Cacciato09Sats5():\n model = Cacciato09Sats(threshold=9.0)\n lum_mc = model.mc_prim_galprop(prim_haloprop=np.ones(int(1e5)) * 5e13, seed=1)\n assert np.all(lum_mc >= 10 ** model.threshold)\n\n # Check that luminosities follow the expected distribution.\n def cdf(lum):\n return np.array(\n [\n (\n model.mean_occupation(prim_haloprop=5e13)\n - model.mean_occupation(prim_haloprop=5e13, prim_galprop_min=l)\n )\n / model.mean_occupation(prim_haloprop=5e13)\n for l in lum\n ]\n )\n\n p_value = kstest(lum_mc, cdf)[1]\n assert p_value > 0.001","title":""},{"docid":"04afc103353679d41c0db2a774391d17","score":"0.50792843","text":"def sat(n: int, x=329437923.5):\n return abs(n - x) <= 0.5","title":""},{"docid":"b3cd06496ec28e40ef1b7260e38aca1a","score":"0.5044325","text":"def _stat_chi2(self):\n model = self._model(self.data.e_ref)\n data = self.data.table[\"dnde\"].quantity\n sigma = self.data.table[\"dnde_err\"].quantity\n return ((data - model) / sigma).to(\"\").value ** 2","title":""},{"docid":"bd060625ac1a8a4f67faa8d28ad9dee0","score":"0.5042267","text":"def has_solution(self, edges, dom_val):\n s = dom_val\n for e in edges:\n s = s | e\n return s == self.graph.dominant_value","title":""},{"docid":"0ec2f5126db1b1de8baa297e7c548b0f","score":"0.50391954","text":"def chi_squared(df, target_col_str):\n\n print('Ho: No significant relationship, feature is independent of target\\n')\n\n categorical_cols = df.select_dtypes(['object']).columns.tolist()\n print('Features tested: {}'.format(categorical_cols))\n\n def chi_test(c):\n ct = pd.crosstab(df[target_col_str], df[c])\n chi2, p, dof, expected = scs.chi2_contingency(ct)\n exp_under_five_percent = np.sum(expected < 5) / len(expected.flat)\n\n if exp_under_five_percent > 0.2:\n return None\n else:\n return p\n\n chi_dict = {c: chi_test(c) for c in categorical_cols}\n\n chi_df = pd.DataFrame.from_dict(chi_dict, orient='index')\n chi_df.columns = ['p_value']\n\n chi_df = chi_df.sort_values(by='p_value', ascending=True)\n\n plt.style.use('fivethirtyeight')\n\n chi_df['p_value'].sort_values(ascending=True).plot(kind='barh', use_index=True, color='deepskyblue',\n title='Chi-squared Test for Independence with Target')\n plt.xlabel('p-value')\n plt.xscale('log')\n p_line = plt.axvline(x=0.05, color='darkorange', label='p-value cut-off', lw=2)\n plt.legend([p_line], ['Significant p-value\\ncut-off (95% CI)'], bbox_to_anchor=(1.05, 1), loc=2)\n plt.grid(False)\n plt.show()\n\n return chi_df","title":""},{"docid":"606d65620831db9fc7dbc53be0e0d683","score":"0.5024171","text":"def classify(y):\r\n c = np.where(y < 0.5, 0, 1)\r\n return c","title":""},{"docid":"3f1d9496fa1a47bebc3971643245d4a6","score":"0.5011161","text":"def check_dataset(self, dataset):\n new_x = self.pca.transform(dataset.x.reshape((dataset.x.shape[0], -1)))\n in_support = np.sum(\n (new_x >= self.min_x) * (new_x <= self.max_x),\n axis=1) == self.num_p\n print(\"percent in support\", np.sum(in_support) / dataset.num_obs)\n return np.sum(in_support) == dataset.num_obs","title":""},{"docid":"a19bcab237bce40c63a9889fef3286ef","score":"0.50060344","text":"def true_decision_function(input_features):\n if input_features[0] <= n_bins // 2:\n return -1\n else:\n if input_features[1] <= n_bins // 3:\n return -1\n else:\n return 1","title":""},{"docid":"11de9fca78d1d04b63910f6710ce9449","score":"0.5001753","text":"def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool:\n for prev_node in nncf_graph.get_previous_nodes(node):\n if prev_node.node_type in OP_NAMES_QUANTIZE_NODE:\n return True\n return False","title":""},{"docid":"94f154bce994db0008fcf9d2069cd4f7","score":"0.50000864","text":"def operator_test5():\n\n l = 5.0\n dx = 0.02\n x = np.arange(0, l+dx, dx)\n\n vx = pk.linear_ramp(x)\n H = pk.build_hamiltonian(x, vx, dx=dx)\n\n evals, evecs = np.linalg.eigh(H)\n\n for i in range(5):\n psi = evecs[:, i]\n psi = pk.normalize_wfn(x, psi)\n pdf = pk.prob_density(psi)\n\n plt.plot(x, psi)\n plt.plot(x, pdf)\n plt.show()","title":""},{"docid":"079cd691420fc207745ae4d3c28ce298","score":"0.49968618","text":"def cond_exp_tree(self, x, tree, S, algo=\"shap\", N=50000, data=None):\n\n if type(data) != type(self.data):\n data = self.data\n\n a = tree.children_left\n b = tree.children_right\n f = tree.feature\n t = tree.threshold\n r_w = tree.weighted_n_node_samples\n v = np.array([tree.value[i][0][0] for i in range(tree.node_count)])\n index = range(x.shape[0])\n\n def G(i, w):\n\n if f[i] == -2:\n return float(w * v[i])\n else:\n if (f[i] in S):\n if x[f[i]] <= t[i]:\n return G(a[i], w)\n else:\n return G(b[i], w)\n else:\n return G(a[i], w * r_w[a[i]] / r_w[i]) + G(b[i], w * r_w[b[i]] / r_w[i])\n\n def explore(i, tab):\n\n if f[i] == -2:\n tab[i] = 1\n else:\n if f[i] in S:\n if x[f[i]] <= t[i]:\n explore(a[i], tab)\n else:\n explore(b[i], tab)\n else:\n explore(a[i], tab)\n explore(b[i], tab)\n\n def get_partition(leaf_id, part):\n\n left = np.where(tree.children_left == leaf_id)[0]\n right = np.where(tree.children_right == leaf_id)[0]\n\n if (len(left) == 0) * (len(right) == 0):\n return (part)\n\n else:\n if len(right) != 0:\n right = int(right[0])\n\n part[f[right]] = np.concatenate((part[f[right]], np.array([[t[right], np.inf]])))\n return get_partition(right, part)\n else:\n left = int(left[0])\n part[f[left]] = np.concatenate((part[f[left]], np.array([[-np.inf, t[left]]])))\n return get_partition(left, part)\n\n def get_final_partition(part):\n final_partition = {}\n for i, var_part in enumerate(part):\n final_partition[i] = [np.max(var_part[:, 0]), np.min(var_part[:, 1])]\n return final_partition\n\n def explore_part(i, tab, partition_leaves, partition_global, prob_global, S, S_bar, data, intv=False):\n if f[i] == -2:\n # tab[i] = 1\n compatible_leaves.append(i)\n partition_global[i] = partition_leaves\n partition_leaves = np.squeeze(np.array(partition_leaves))\n # partition_leaves = np.reshape(np.array(partition_leaves), (self.d + 2, 2)).T\n # partition_leaves = pd.DataFrame(partition_leaves)[S]\n low = FloatVector(partition_leaves[:, 0][S_bar])\n up = FloatVector(partition_leaves[:, 1][S_bar])\n\n prob_global[i] = cond.pcmvnorm(low, up, self.r_mean, self.r_cov, c_index, given, FloatVector(x[S]))[0]\n # print(prob_global[i])\n\n else:\n if f[i] in S:\n if x[f[i]] <= t[i]:\n part = partition_leaves.copy()\n part[f[i]] = np.concatenate((part[f[i]], np.array([[-np.inf, t[i]]])))\n part[f[i]] = np.array([[np.max(part[f[i]][:, 0]), np.min(part[f[i]][:, 1])]])\n explore_part(a[i], tab, part, partition_global, prob_global, S, S_bar, data, intv)\n else:\n part = partition_leaves.copy()\n part[f[i]] = np.concatenate((part[f[i]], np.array([[t[i], np.inf]])))\n part[f[i]] = np.array([[np.max(part[f[i]][:, 0]), np.min(part[f[i]][:, 1])]])\n explore_part(b[i], tab, part, partition_global, prob_global, S, S_bar, data, intv)\n else:\n part = partition_leaves.copy()\n part[f[i]] = np.concatenate((part[f[i]], np.array([[-np.inf, t[i]]])))\n part[f[i]] = np.array([[np.max(part[f[i]][:, 0]), np.min(part[f[i]][:, 1])]])\n\n part_2 = partition_leaves.copy()\n part_2[f[i]] = np.concatenate((part_2[f[i]], np.array([[t[i], np.inf]])))\n part_2[f[i]] = np.array([[np.max(part_2[f[i]][:, 0]), np.min(part_2[f[i]][:, 1])]])\n\n explore_part(a[i], tab, part, partition_global, prob_global, S, S_bar, data, intv)\n explore_part(b[i], tab, part_2, partition_global, prob_global, S, S_bar, data, intv)\n\n def explore_partition(i, tab, partition_leaves, partition_global, prob_global, S, S_bar, data, intv=False):\n\n if f[i] == -2:\n # tab[i] = 1\n compatible_leaves.append(i)\n partition_global[i] = partition_leaves\n partition_leaves = np.squeeze(np.array(partition_leaves))\n\n if not intv:\n section_x = np.prod(\n [(data[:, s] <= partition_leaves[s, 1]) * (data[:, s] >= partition_leaves[s, 0]) for s in S],\n axis=0)\n else:\n section_x = np.prod(\n [(data[:, s] <= partition_leaves[s, 1]) * (data[:, s] >= partition_leaves[s, 0]) for s in\n S_bar], axis=0)\n\n prob_global[i] = np.sum(section_x)\n\n else:\n if f[i] in S:\n if x[f[i]] <= t[i]:\n part = partition_leaves.copy()\n part[f[i]] = np.concatenate((part[f[i]], np.array([[-np.inf, t[i]]])))\n part[f[i]] = np.array([[np.max(part[f[i]][:, 0]), np.min(part[f[i]][:, 1])]])\n explore_partition(a[i], tab, part, partition_global, prob_global, S, S_bar, data, intv)\n else:\n part = partition_leaves.copy()\n part[f[i]] = np.concatenate((part[f[i]], np.array([[t[i], np.inf]])))\n part[f[i]] = np.array([[np.max(part[f[i]][:, 0]), np.min(part[f[i]][:, 1])]])\n explore_partition(b[i], tab, part, partition_global, prob_global, S, S_bar, data, intv)\n else:\n part = partition_leaves.copy()\n part[f[i]] = np.concatenate((part[f[i]], np.array([[-np.inf, t[i]]])))\n part[f[i]] = np.array([[np.max(part[f[i]][:, 0]), np.min(part[f[i]][:, 1])]])\n\n part_2 = partition_leaves.copy()\n part_2[f[i]] = np.concatenate((part_2[f[i]], np.array([[t[i], np.inf]])))\n part_2[f[i]] = np.array([[np.max(part_2[f[i]][:, 0]), np.min(part_2[f[i]][:, 1])]])\n\n explore_partition(a[i], tab, part, partition_global, prob_global, S, S_bar, data, intv)\n explore_partition(b[i], tab, part_2, partition_global, prob_global, S, S_bar, data, intv)\n\n if algo == 'shap':\n return G(0, 1)\n\n elif algo == 'plugin':\n if S == []:\n compatible_leaves = np.zeros(tree.node_count)\n explore(0, compatible_leaves)\n compatible_leaves = [i for i in range(tree.node_count) if compatible_leaves[i] == 1]\n p = r_w[compatible_leaves] / np.sum(r_w[compatible_leaves])\n\n elif len(S) == len(x):\n y_pred = tree.predict(np.expand_dims(np.array(x, dtype=np.float32), axis=0))\n return np.mean(y_pred)\n\n else:\n S_bar = [i for i in self.index if i not in S]\n partition_leaves = [np.array([[-np.inf, np.inf]]) for i in range(data.shape[1])]\n partition_global = {i: [np.array([[-np.inf, np.inf]]) for i in range(data.shape[1])]\n for i in range(tree.node_count)}\n prob_global = {}\n compatible_leaves = []\n explore_partition(0, compatible_leaves, partition_leaves, partition_global, prob_global, S, S_bar, data,\n False)\n\n nbs_leaf = np.array([prob_global[key] for key in compatible_leaves])\n p = r_w[compatible_leaves] / nbs_leaf\n\n return np.sum(p * v[compatible_leaves])\n\n\n elif algo == 'plugin_interventionalv2':\n return self.cond_exp_tree_intv(tree, x, S, v, explore, get_partition, get_final_partition, data)\n\n elif algo == 'plugin_interventional':\n S_bar = [i for i in self.index if i not in S]\n if S == []:\n compatible_leaves = np.zeros(tree.node_count)\n explore(0, compatible_leaves)\n compatible_leaves = [i for i in range(tree.node_count) if compatible_leaves[i] == 1]\n p = r_w[compatible_leaves] / np.sum(r_w[compatible_leaves])\n\n elif len(S) == len(x):\n y_pred = tree.predict(np.expand_dims(np.array(x, dtype=np.float32), axis=0))\n return np.mean(y_pred)\n\n else:\n partition_leaves = [np.array([[-np.inf, np.inf]]) for i in range(data.shape[1])]\n partition_global = {i: [np.array([[-np.inf, np.inf]]) for i in range(data.shape[1])]\n for i in range(tree.node_count)}\n prob_global = {}\n compatible_leaves = []\n explore_partition(0, compatible_leaves, partition_leaves, partition_global, prob_global, S, S_bar, data,\n True)\n p = np.array([prob_global[key] / len(data) for key in compatible_leaves])\n\n return np.sum(p * v[compatible_leaves])\n\n elif algo == 'monte_carlo':\n if len(S) != len(index):\n S_bar = [i for i in index if i not in S]\n rg_data = np.zeros(shape=(N, len(index)))\n rg_data[:, S] = x[S]\n rg = sampleMVN(N, self.mean, self.cov, S_bar, S, x[S])\n rg_data[:, S_bar] = rg\n\n y_pred = tree.predict(np.array(rg_data, dtype=np.float32))\n\n else:\n y_pred = tree.predict(np.expand_dims(np.array(x, dtype=np.float32), axis=0))\n\n return np.mean(y_pred)\n\n elif algo == 'monte_carlo_interventional':\n if len(S) != len(index):\n rg = sampleMarMVN(N, self.mean, self.cov, self.index, [])\n rg_data = pd.DataFrame(rg, columns=[str(i) for i in self.index])\n\n def get_given_data(idx):\n val = np.array([x[idx]])\n val = np.tile(val, N)\n return val\n\n for val_id in S:\n rg_data[str(val_id)] = get_given_data(val_id)\n\n rg_data = rg_data[sorted(rg_data.columns)]\n y_pred = tree.predict(np.array(rg_data.values, dtype=np.float32))\n\n else:\n y_pred = tree.predict(np.expand_dims(np.array(x, dtype=np.float32), axis=0))\n\n return np.mean(y_pred)\n\n elif algo == 'exact':\n S_bar = [i for i in self.index if i not in S]\n given = IntVector(np.array([i + 1 for i in S]))\n c_index = np.array([i + 1 for i in index if i not in S])\n # S_bar = [i for i in index if i not in S]\n # if S == []:\n # compatible_leaves = np.zeros(tree.node_count)\n # explore(0, compatible_leaves)\n # compatible_leaves = [i for i in range(tree.node_count) if compatible_leaves[i] == 1]\n # p = r_w[compatible_leaves] / np.sum(r_w[compatible_leaves])\n\n if len(S) == len(x):\n y_pred = tree.predict(np.expand_dims(np.array(x, dtype=np.float32), axis=0))\n return np.mean(y_pred)\n\n else:\n\n # S_bar = [i for i in self.index if i not in S]\n partition_leaves = [np.array([[-np.inf, np.inf]]) for i in range(data.shape[1])]\n partition_global = {i: [np.array([[-np.inf, np.inf]]) for i in range(data.shape[1])]\n for i in range(tree.node_count)}\n prob_global = {}\n compatible_leaves = []\n explore_part(0, compatible_leaves, partition_leaves, partition_global, prob_global, S, S_bar, data,\n False)\n\n p = np.array([prob_global[key] for key in compatible_leaves])\n # print(p)\n # p = r_w[compatible_leaves] / nbs_leaf\n\n return np.sum(p * v[compatible_leaves])\n\n elif algo == 'exact_v1':\n\n if len(S) != len(index):\n given = IntVector(np.array([i + 1 for i in S]))\n c_index = np.array([i + 1 for i in index if i not in S])\n S_bar = [i for i in index if i not in S]\n\n compatible_leaves = np.zeros(tree.node_count)\n explore(0, compatible_leaves)\n\n leaf_idx = [i for i in range(tree.node_count) if compatible_leaves[i] == 1]\n leaf_proba = []\n for leaf_id in leaf_idx:\n partition_leaves = [np.array([[-np.inf, np.inf]]) for i in range(x.shape[0])]\n partition_leaves = get_partition(leaf_id, partition_leaves)\n partition_leaves = pd.DataFrame(get_final_partition(partition_leaves))[S_bar]\n\n low = FloatVector(partition_leaves.iloc[0])\n up = FloatVector(partition_leaves.iloc[1])\n\n leaf_proba.append(\n cond.pcmvnorm(low, up, self.r_mean, self.r_cov, c_index, given, FloatVector(x[S]))[0])\n\n leaf_proba = np.array(leaf_proba)\n return np.sum(leaf_proba * v[compatible_leaves == 1])\n\n else:\n y_pred = tree.predict(np.expand_dims(np.array(x, dtype=np.float32), axis=0))\n return np.mean(y_pred)\n else:\n raise ValueError(\"This algo is not implemented. Available estimators are: plugin, exact, monte_carlo,\\\n monte_carlo_interventional, plugin_interventional\")","title":""},{"docid":"1d085c7705ab79aad4831a67c51c9dcd","score":"0.49960086","text":"def _goodSN(self, sourceCat):\n if self.config.minSnr <= 0:\n return True\n else:\n with np.errstate(invalid=\"ignore\"): # suppress NAN warnings\n return sourceCat.get(self.fluxKey)/sourceCat.get(self.fluxSigmaKey) > self.config.minSnr","title":""},{"docid":"12e8d9d7c98d0b896b71d5272f0ffd0b","score":"0.49935928","text":"def get_differentially_expressed_proteins_from_log2FC_df(method, sample1, sample2, specie, ratio, two_sided = False):\n ARATH_FC_matrix, CAEEL_FC_matrix, HUMAN_FC_matrix = get_log2FC_ratio_matrices()\n \n if specie == \"HUMAN\":\n FC_matrix = HUMAN_FC_matrix\n FC_treshold = ratio * FC_matrix[sample2][sample1] #The smaples are reversed because the log2FC in this function is reversed.\n file_name = method+\"_\"+\"log2FC\"+\"_\"+sample1+\"_\"+sample2+\"_\"+specie+\".csv\"\n df_log2FC = pd.read_csv(file_name, sep = \"\\t\")\n diffExp = np.sum(df_log2FC[df_log2FC > FC_treshold] > 0) #This comparison should be more then\n elif specie == \"ARATH\":\n FC_matrix = ARATH_FC_matrix\n FC_treshold = ratio * FC_matrix[sample2][sample1] #The smaples are reversed because the log2FC in this function is reversed.\n file_name = method+\"_\"+\"log2FC\"+\"_\"+sample1+\"_\"+sample2+\"_\"+specie+\".csv\"\n df_log2FC = pd.read_csv(file_name, sep = \"\\t\")\n diffExp = np.sum(df_log2FC[df_log2FC < -ratio] > 0) + np.sum(df_log2FC[df_log2FC > ratio] > 0) #This comparison should be if less or more than ratio\n elif specie == \"CAEEL\":\n FC_matrix = CAEEL_FC_matrix\n FC_treshold = ratio * FC_matrix[sample2][sample1] #The smaples are reversed because the log2FC in this function is reversed.\n file_name = method+\"_\"+\"log2FC\"+\"_\"+sample1+\"_\"+sample2+\"_\"+specie+\".csv\"\n df_log2FC = pd.read_csv(file_name, sep = \"\\t\")\n diffExp = np.sum(df_log2FC[df_log2FC < FC_treshold] > 0) #This comparison should be less than\n else:\n print(\"no species specificed\")\n # return \n return diffExp","title":""},{"docid":"6cdf7774a775c38dc3bb1c50407afc16","score":"0.49933684","text":"def probable(self, threshold=0.3):\r\n return self.score > threshold","title":""},{"docid":"e594385a28bad52ea0422bbc0ccaba45","score":"0.49910995","text":"def leaf_node_check(df):\n class_count = set(df['class'].values.tolist())\n if len(class_count) == 2:\n return False\n else:\n return True","title":""},{"docid":"8d2dc40beacc1f3b48c20feae0a48073","score":"0.49809405","text":"def is_silent(self, data) -> bool:\n return np.sqrt(np.mean(data ** 2)) < self.threshold","title":""},{"docid":"7176da4a18060754fd6873ff7f344235","score":"0.4973127","text":"def _is_pos_def(self, mat: np.ndarray) -> bool:\n return np.all(np.linalg.eigvals(mat) > 0)","title":""},{"docid":"a5b22ae6142b2c791fc5a096ba9dd32f","score":"0.49697605","text":"def summarize_sensitivity(sens_df):\n cols = ['star', 'date', '[Fe/H]', 'logg', 'addmode', 'temperature', 'vsini']\n detrate = sens_df.groupby(cols).apply(lambda d: (d.significance > 5).sum() / float(len(d)))\n detrate = detrate.reset_index().rename(columns={0: 'detrate'})\n significance = sens_df.groupby(cols).apply(lambda d: np.nanmean(d.significance))\n significance = significance.reset_index().rename(columns={0: 'significance'})\n detrate['significance'] = significance['significance']\n return detrate","title":""},{"docid":"ebbe54b6adec2df35a2cd4b3a46163d8","score":"0.49613017","text":"def test_distributions1(dist):\n assert entropy(dist) <= np.log2(len(dist.pmf))","title":""},{"docid":"7fda52437ae6a85c4134d1741fa0a5f0","score":"0.49526456","text":"def test_unsupervised_density():\n # !TODO: Implement a suitable scenario.\n pass","title":""},{"docid":"bd915870af46803e5ec1eb4d8dd16c49","score":"0.4946569","text":"def prob55():","title":""},{"docid":"77b13ec72bb195ee995150e1b95e7433","score":"0.49439466","text":"def test_5vIQ(self):\n for i in range(1000):\n self.assertTrue(61 <= Consignment._get_percent(None, 5) <= 80)","title":""},{"docid":"6fc001d90fc77d9d7be6395036917507","score":"0.4943591","text":"def _is_ok_feature(self, feature) -> bool:\n if feature.isnull().mean() >= self.max_nan_rate:\n return False\n if (feature.value_counts().values[0] / feature.shape[0]) >= self.max_constant_rate:\n return False\n return True","title":""},{"docid":"d4f12f27ec44d7c85e9b5e30c30a9158","score":"0.494273","text":"def test_correct_score(self):\n dist = Bernoulli(VISIBLE_UNITS, HIDDEN_UNITS, weights=self.weights, bias_hidden=self.bias_hidden, bias_visible=self.bias_visible)\n target = np.sum(self.weights) + np.sum(self.bias_hidden) + np.sum(self.bias_visible)\n actual = np.abs(dist.score_energy(self.vis_inp, self.hid_inp))\n self.assertEqual(target, actual, \"Didn't match. Target: \" + str(target) + \", Actual: \" + str(actual))","title":""},{"docid":"c0d027fa8cb6d3e97644e9593d97007b","score":"0.493798","text":"def get_consistence(self, node):\n return self.active[self.node_index[node]].sum(0) != 0","title":""},{"docid":"7c5ada0c0b35792369c00069f44d3a45","score":"0.4937976","text":"def is_weight(w):\n \"*** YOUR CODE HERE ***\"\n return root(w)!=None","title":""},{"docid":"1cc787c5105bf00a5c54960b299f26a7","score":"0.4936937","text":"def _test_dist(self,group1,elem1,criterion):\n\n test=True\n for elem0 in group1:\n distsq=np.sum(np.power(elem0-elem1,2))\n if distsq < criterion**2:\n test=False\n break\n\n return test","title":""},{"docid":"920bf55811ff330da85305e32d152711","score":"0.49358174","text":"def detectability_calculator(Theta_CDF,min_CDF,max_CDF,SNR_in):\n \n #input type checking\n assert type(min_CDF) == float, 'min_CDF should be a float.'\n assert type(max_CDF) == float, 'max_CDF should be a float.'\n assert type(SNR_in) == float, 'SNR_in should be a float.'\n \n if SNR_in <= (8 / max_CDF):\n det = 0 #always undetectable\n #set separately so Theta_CDF doesn't get input above 1\n #the max_CDF (just below 1) has to be included because the function\n #Theta_dist is not defined for the small range between max_CDF and 1\n #and this can occasionally cause problems if just 8 is used\n elif SNR_in >= (8 / min_CDF):\n det = 1 #always detectable\n #similarly, Theta_CDF is not defined between 0 and min_CDF\n else:\n threshold = 8 / SNR_in\n #how much the SNR would need to be lowered to hit the threshold SNR=8\n undet = Theta_CDF(threshold)\n #the proportion of Theta values that would lower SNR below 8\n det = 1 - undet #proportion that would keep SNR above 8\n \n #output type conversion\n det = float(det)\n \n return det","title":""},{"docid":"20f342a41fcc353b8e6e6b2622f6f4db","score":"0.4930901","text":"def crowded2(t, data):\n cell_score = 0\n cell_score += n_check(t, data)\n cell_score += s_check(t, data)\n cell_score += e_check(t, data)\n cell_score += w_check(t, data)\n cell_score += ne_check(t, data)\n cell_score += nw_check(t, data)\n cell_score += sw_check(t, data)\n cell_score += se_check(t, data)\n if cell_score >= 5:\n return True\n return False","title":""},{"docid":"76d4ad7012679e0d0f613dbea0d73401","score":"0.4929446","text":"def too_many_significant(ts,len_data,criticalVal,acfVals,upper_significance,lower_significance,numSignificantAllowed=5): \n num_significant=0\n #determine how many points are significant based on upper and lower bound levels\n for i in range(0, len(acfVals)):\n if acfVals[i]>upper_significance[i] or acfVals[i]<lower_significance[i]:\n num_significant+=1\n \n return num_significant > numSignificantAllowed","title":""},{"docid":"2eb74c9f4d717d40c19afb0e7d15d8ea","score":"0.49262944","text":"def gtd_logic(df, G, label_column, column_prefix, progress=True):\n\n # Create a collection of shortest Paths from the root node (VRN) to each of the leave nodes\n\n P = find_shortest_paths(G, progress=progress)\n\n # Calculate the Gain Ratio for each feature in the data (therby for each node in the Graph)\n\n gr_values = calc_gr(df, label_column, progress=progress)\n\n SF = set()\n\n # Create a dictionary to keep track of the availability of nodes\n\n node_availability = {}\n\n for node in list(G.nodes()):\n\n node_availability[node] = True\n\n if progress:\n iterator = tqdm(\n P, desc=\"Greedy Top Down - (3/3) Finding most relevant nodes.\")\n else:\n iterator = P\n\n for path in iterator:\n\n # Get candidate_nodes for that path. Candidate Nodes are nodes that are part of the path AND are still available\n\n candidate_nodes = [\n node for node in path if node_availability[node] == True]\n\n # Check if there are any available nodes in the path.\n\n if len(candidate_nodes) > 0:\n\n # Get the Candidate Node with the highest Gain Ratio\n\n max_node = get_max_node(candidate_nodes, gr_values, column_prefix)\n\n # Add that Node to the Selected Features Set (SF)\n\n SF.add(max_node)\n\n # Change the availability of that node, it's ancestors and it's descendants to False\n\n node_availability[max_node] = False\n\n for ancestor in nx.ancestors(G, max_node):\n\n node_availability[ancestor] = False\n\n for decendant in nx.descendants(G, max_node):\n\n node_availability[decendant] = False\n\n # Check if there are any available nodes left. If not, end the algorithm.\n\n if all(availability == False for availability in node_availability.values()):\n\n break\n\n return SF","title":""},{"docid":"2baa67545bb3503fadc4575b425e5b58","score":"0.4923733","text":"def q7(self, infile):\n\t\treturn 0.1","title":""},{"docid":"922def58e2ccb9565bb1d0dbc91069d1","score":"0.4921992","text":"def percent_heterozygous(self):\n return (self.genotypes==1).sum() / self.size","title":""},{"docid":"076ab58fe92feca3846916edf0dc751e","score":"0.4913728","text":"def column_based_infogain_right(df):\n\n if entype == 'bag':\n individual_entropy_list_right = []\n for each_column in df.columns:\n if each_column != 'class' and each_column != 'bruises?-no':\n df_right = df[[each_column, \"class\"]][df[each_column] == 1].groupby(\"class\").count()\n df_right_sum = df_right.sum()\n entropy = 0.0\n outer_dist = df_right_sum[each_column] / len(df)\n for each_index in df_right.index:\n inner_element = (df_right.loc[each_index, each_column] / df_right_sum[each_column])\n entropy += inner_element * np.log2(inner_element)\n final_entropy = -entropy * outer_dist\n individual_entropy_list_right.append(final_entropy)\n return individual_entropy_list_right\n\n else:\n right_df = df\n header_list = get_column_list(right_df)\n individual_entropy_list_right = []\n for each_column in header_list:\n if each_column != 'class' and each_column != 'weight':\n right_child_entropy = []\n right_temp_df = pd.DataFrame(right_df[[each_column, \"class\", 'weight']][right_df[each_column] == 1],\n columns=[each_column, 'class', 'weight'])\n right_child_entropy.append(round(right_temp_df.loc[right_temp_df['class'] == 1, 'weight'].sum(), 2))\n right_child_entropy.append(round(right_temp_df.loc[right_temp_df['class'] == -1, 'weight'].sum(), 2))\n individual_entropy_list_right.append(entropy_calculation(right_child_entropy, right_df))\n return individual_entropy_list_right","title":""},{"docid":"9bb4b4560a0684e56b16123ee244524f","score":"0.49126238","text":"def observed_class_distribution_is_pure(self):\n count = 0\n for _, weight in self._stats.items():\n if weight != 0:\n count += 1\n if count == 2: # No need to count beyond this point\n break\n return count < 2","title":""},{"docid":"26926a84fc6132413beef3d47ac5e74a","score":"0.4904977","text":"def is_on(self) -> bool:\n func_channel = self._device.functionalChannels[self._channel]\n return func_channel.dimLevel is not None and func_channel.dimLevel > 0.0","title":""},{"docid":"f1262293adbbb02348947abb2dc8207d","score":"0.4897986","text":"def omni(self):\r\n\t\treturn Sstats.normaltest(self.e)","title":""},{"docid":"0f8e42511507492255de2e4ecd9dfa9a","score":"0.48942423","text":"def migrate(self):\r\n\r\n if sl.random() < self.params['mu'] * self.fitness():\r\n return True\r\n else:\r\n return False","title":""},{"docid":"07a58eefa3687e1c9710d2a8425a2e8b","score":"0.4888489","text":"def alifestd_has_multiple_roots(phylogeny_df: pd.DataFrame) -> bool:\n return (\n (phylogeny_df[\"ancestor_list\"] == \"[]\").sum()\n + (phylogeny_df[\"ancestor_list\"] == \"[NONE]\").sum()\n + (phylogeny_df[\"ancestor_list\"] == \"[none]\").sum()\n + (phylogeny_df[\"ancestor_list\"] == \"[None]\").sum()\n ) >= 2","title":""},{"docid":"def834de491b51ef8e25d28b20cfb44d","score":"0.48781985","text":"def is_train_sample(df):\n entry_idx = df.index.names.index(\"entry\")\n is_odd = (df.index.get_level_values(entry_idx) % 2).to_numpy(dtype=bool)\n is_train = is_odd\n return is_train","title":""},{"docid":"9970eba9ee6e5787493ea3e526abd2bf","score":"0.48741898","text":"def keep_going(sample,d=1,k=31):\n log(sample)\n if (statistics.stdev(sample)/math.sqrt(k))>= d:\n return True\n return False","title":""},{"docid":"a615b6db2e3aec6716df3a141b88fdf0","score":"0.48723033","text":"def check_pos_def(self):\n return np.all(np.linalg.eigvals(self.A) > 0)","title":""},{"docid":"43949de78d4c811f75ebc917618015f8","score":"0.4871751","text":"def test_logistic_sigmoid_derivative(self):\n f = self.logistic_sigmoid\n f_prime = self.logistic_sigmoid_derivative\n for x in self.test_points:\n self.assertAlmostEqual(f_prime(x),\n (f(x) * (1 - f(x))) / self.std_dev,\n msg=\"Logistic sigmoid satisfies f' = f(1-f)/sigma\")","title":""},{"docid":"c5d105f66cc93024db4eb9213e2ca103","score":"0.48715493","text":"def is_normal(r, level=0.01):\n statistic, p_value = scipy.stats.jarque_bera(r)\n return p_value > level","title":""},{"docid":"9bcc4e69a233a6c320c4fdc4a16164fe","score":"0.48659343","text":"def p_value(self):\n return 0.5 * chi2.sf(self.ts, 1)","title":""},{"docid":"fb49264abd56def98310b7fee34412ef","score":"0.4865289","text":"def call_significant_sites(fg_counts, bg_counts, max_pvalue):\n print fg_counts.max(), fg_counts.min(), bg_counts\n return poisson.sf(fg_counts, bg_counts) < max_pvalue","title":""},{"docid":"1c939c86348d25341fb2efedb64f7630","score":"0.4860694","text":"def column_based_infogain_left(df):\n\n if entype == \"bag\":\n\n individual_entropy_list_left = []\n for each_column in df.columns:\n if each_column != 'class' and each_column != 'bruises?-no':\n df_left = df[[each_column, \"class\"]][df[each_column] == 0].groupby(\"class\").count()\n df_left_sum = df_left.sum()\n entropy = 0.0\n outer_dist = df_left_sum[each_column] / len(df)\n for each_index in df_left.index:\n inner_element = (df_left.loc[each_index, each_column] / df_left_sum[each_column])\n entropy += inner_element * np.log2(inner_element)\n final_entropy = -entropy * outer_dist\n individual_entropy_list_left.append(final_entropy)\n return individual_entropy_list_left\n\n else:\n\n left_df = df\n header_list = get_column_list(left_df)\n individual_entropy_list_left = []\n for each_column in header_list:\n if each_column != 'class' and each_column != 'weight':\n left_child_entropy = []\n left_temp_df = pd.DataFrame(left_df[[each_column, \"class\", 'weight']][left_df[each_column] == 0],\n columns=[each_column, 'class', 'weight'])\n left_child_entropy.append(round(left_temp_df.loc[left_temp_df['class'] == 1, 'weight'].sum(), 2))\n left_child_entropy.append(round(left_temp_df.loc[left_temp_df['class'] == -1, 'weight'].sum(), 2))\n individual_entropy_list_left.append(entropy_calculation(left_child_entropy, left_df))\n return individual_entropy_list_left","title":""},{"docid":"4bf5748a30421b620cf49808b20477b3","score":"0.4857367","text":"def is_converged(self):\n prob = np.array(self._prob.values())\n return np.all(np.logical_or(prob<THRESHOLD_LOWER_BOUND, prob>THRESHOLD_UPPER_BOUND))","title":""},{"docid":"ff0aa15b762290ea7d30469e2fa573c5","score":"0.4856261","text":"def little_variation(M,m,p):\r\n return ( M*(1-float(p)/100) < m < M*(1+float(p)/100) )","title":""},{"docid":"bab722b9d5d3d54dfb4e3df6a8ed5327","score":"0.48550674","text":"def decision(probability):\n\treturn random.random() > probability","title":""},{"docid":"bc67082adc2ad74b8b71f37f6e98621c","score":"0.48462313","text":"def check_convergence(metrics):\n print(\"###### Evaluating sample size convergence ######\")\n trained = False\n direction = convergence_operator()\n threshold = settings[\"data\"][\"convergence_limit\"]\n \n if settings[\"data\"][\"convergence_relative\"]:\n window_size = 1 ###########\n if len(metrics) < 2:\n return False\n else:\n diff = np.diff(metrics)\n\n if direction(np.mean(diff[-window_size:]),0):\n if abs(np.mean(diff[-window_size:])) < threshold:\n trained = True\n else:\n if direction(metrics[-1],threshold):\n trained = True\n\n print(f\"Sample size convergence metric: {settings['data']['convergence']} - {metrics[-1]}\")\n\n return trained","title":""},{"docid":"8ca3e27b67167e7f2666dab16fd5a798","score":"0.48436168","text":"def isSimilarTo(self, other):\n cython.declare(T=cython.double)\n cython.declare(Tdata=list)\n \n Tdata = [300,400,500,600,800,1000,1500,2000]\n for T in Tdata:\n if not (0.8 < self.getHeatCapacity(T) / other.getHeatCapacity(T) < 1.25):\n return False\n elif not (0.8 < self.getEnthalpy(T) / other.getEnthalpy(T) < 1.25):\n return False\n elif not (0.8 < self.getEntropy(T) / other.getEntropy(T) < 1.25):\n return False\n elif not (0.8 < self.getFreeEnergy(T) / other.getFreeEnergy(T) < 1.25):\n return False\n\n return True","title":""},{"docid":"21af97891471a69dd66f8031370dca12","score":"0.48369926","text":"def deepCheckWeight( file ):\n from math import isnan\n from RootTools.core.Sample import Sample\n\n # convert dpm file pathes\n sample = Sample.fromFiles(name=\"sample\", treeName=\"Events\", files=file)\n # check for branch:\n l = sample.chain.GetListOfBranches()\n if not 'weight' in [ l.At(i).GetName() for i in range(l.GetSize()) ]:\n return 0\n val = sample.getYieldFromDraw(weightString=\"weight\" )['val']\n del sample\n #logger.debug(\"Val in deepCheckWeight: %r\", val) \n return not isnan(val)","title":""},{"docid":"262e2256c8c79645ca80e4f534171a72","score":"0.48350605","text":"def revised_szeged_index(self):\n if not self.is_connected():\n return False\n s = 0.0\n D = self.distance_matrix()\n for u, v in self.edges():\n diff = D[u, :] - D[v, :]\n o = (diff == 0).sum()\n s += ((diff > 0).sum() + .5 * o) * ((diff < 0).sum() + .5 * o)\n return s","title":""},{"docid":"141a0ee3c8bacdd041380cc8cf78e683","score":"0.48326996","text":"def eccenbin(df, thresh):\n col = 'pl_highecc'\n df[col] = None\n for i, row in df.iterrows():\n if row.pl_orbeccen + row.pl_orbeccenerr1 < thresh:\n df.ix[i,col] = False\n if row.pl_orbeccen + row.pl_orbeccenerr2 > thresh:\n df.ix[i,col] = True\n return df","title":""},{"docid":"631de088809027f2138a2345a5cd0de0","score":"0.48291457","text":"def validate(self, s):\r\n nodes = [i for i, a in enumerate(s) if\r\n a != cf.SLEEP and self.network.get_node(i).energy >= (cf.SENSING_ENERGY +cf.COMMUNICATION_ENERGY)] # get list of the active nodes\r\n\r\n for t in range(cf.NUM_TARGETS):\r\n no_sense_prob = 0\r\n for n in nodes:\r\n no_sense_prob += self.sensing_log_matrix[t][n]\r\n if no_sense_prob >= self.sensing_log_threshold:\r\n break\r\n\r\n if no_sense_prob < self.sensing_log_threshold:\r\n return False\r\n\r\n return True","title":""},{"docid":"5da2b496988fd6be3bc9e8a543ce23bf","score":"0.48289847","text":"def isNormal(self):\n for A in self.subsets:\n if self.function(A) < 0:\n return False\n return True","title":""},{"docid":"39932d3df9d7d30eb30db4223c906b83","score":"0.48279193","text":"def test_Cacciato09Cens7():\n model = Cacciato09Cens(threshold=9.5)\n lum_mc = model.mc_prim_galprop(prim_haloprop=np.ones(int(1e5)) * 5e11, seed=1)\n assert np.all(lum_mc >= 10 ** model.threshold)\n\n # Check that luminosities follow the expected distribution.\n def cdf(lum):\n return np.array(\n [\n (\n model.mean_occupation(prim_haloprop=5e11)\n - model.mean_occupation(prim_haloprop=5e11, prim_galprop_min=l)\n )\n / model.mean_occupation(prim_haloprop=5e11)\n for l in lum\n ]\n )\n\n p_value = kstest(lum_mc, cdf)[1]\n\n assert p_value > 0.001","title":""},{"docid":"43cc7959fef16c09edae3765d2865a57","score":"0.48257014","text":"def sconfitto(self):\n return self.vita <= 0","title":""},{"docid":"3ca71d30d2546f4d73416375191400e6","score":"0.48147565","text":"def prob52():","title":""},{"docid":"cea9e25bd2ef57be3a2c3708ab493ad9","score":"0.4813539","text":"def _likelihood():\n return True","title":""},{"docid":"48faa7192845e3219bee90823c958449","score":"0.4811759","text":"def instance_assigned_simulations(self):\n if not isinstance(self.parentnode, M4_16):\n if any(self.parentnode.fpga_slots):\n return True\n return False","title":""},{"docid":"79c69108d2758db2cec36bf7b66e975c","score":"0.4807606","text":"def do_a_decision(the_random_number):\n if the_random_number <= 5.0:\n return 'small decision'\n else:\n return 'big decision'","title":""},{"docid":"f1a377cf80837ddfc35f2f9828f7f0ef","score":"0.48062202","text":"def check_is_gvh5074(self) -> bool:\n return self._mfg_data_check(9, 6)","title":""},{"docid":"26c3b26971b975055f96264c569a0beb","score":"0.48012188","text":"def activate(self, values):\n\n # First calculate the strength with which the perceptron fires\n strength = np.dot(values, self.weights)\n\n # Then return 0 or 1 depending on strength compared to threshold\n return int(strength > self.threshold)","title":""},{"docid":"27897bd2cadb1632642978bb57693c87","score":"0.48001188","text":"def is_normal(r, level=0.01):\n if isinstance(r, pd.DataFrame):\n return r.aggregate(is_normal)\n else:\n statistic, p_value = scipy.stats.jarque_bera(r)\n return p_value > level","title":""}],"string":"[\n {\n \"docid\": \"556cfbe6e05fad6380053e80fac12608\",\n \"score\": \"0.5665601\",\n \"text\": \"def techChange_sunny(p):\\n return p[:, 0] > 0.325\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc21c6804aef56340de033ee2840d9c3\",\n \"score\": \"0.5657123\",\n \"text\": \"def __feat_num_significant(self, col, df_uni_var_norm, df_uni_var_un_norm, target_col, group_col, add_null):\\n df_target_col = df_uni_var_norm[[target_col, group_col, col]]\\n df_bin_col = self.__bin_generator.create_percentile_bins(df_target_col.copy(), [col], \\n num_bins=self.__config[Constants.num_bins_numerical],\\n add_null=add_null)\\n binned_feats = [feat for feat in df_bin_col.columns if col in feat]\\n\\n sig = False\\n\\n for feat in binned_feats:\\n num_c, len_c, num_t, len_t = self.__feat_info(df_bin_col[[target_col, group_col, feat]],\\n feat,\\n target_col,\\n group_col)\\n sig = self.__sig_check(num_c, len_c, num_t, len_t)\\n if sig:\\n break\\n\\n # if none of the binned features are significant return False, 0 impact\\n if not sig:\\n return False, 0, 0, 0, 0, 0\\n\\n # contribution on the non-normalized data set\\n df_target_col = df_uni_var_un_norm[[target_col, group_col, col]]\\n df_bin_col = self.__bin_generator.create_percentile_bins(df_target_col.copy(), [col], \\n num_bins=self.__config[Constants.num_bins_numerical],\\n add_null=add_null)\\n binned_feats = [feat for feat in df_bin_col.columns if col in feat]\\n\\n expected = []\\n actual = []\\n contribution = []\\n is_sig = []\\n\\n for feat in binned_feats:\\n num_c, len_c, num_t, len_t = self.__feat_info(df_bin_col[[target_col, group_col, feat]], feat, target_col,\\n group_col)\\n contribution.append(num_t - num_c * len_t / len_c)\\n actual.append(num_t)\\n expected.append(num_c * len_t / len_c)\\n is_sig.append(self.__sig_check(num_c, len_c, num_t, len_t))\\n\\n return True, binned_feats, is_sig, expected, actual, contribution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"288f86a1ae143dbb536b20452075b74a\",\n \"score\": \"0.5455548\",\n \"text\": \"def has_percent_row(minerals: List[str], df: pd.DataFrame) -> bool:\\n return any(abs(x - 100) < EPSILON for x in df[minerals].sum(axis=1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff1e36da300e087e37992359ce0f5fef\",\n \"score\": \"0.5452519\",\n \"text\": \"def __feat_cat_significant(self, col, df_uni_var_norm, df_uni_var_un_norm, target_col, group_col):\\n num_c, len_c, num_t, len_t = self.__feat_info_cat(col, df_uni_var_norm, target_col, group_col)\\n\\n sig = self.__sig_check(num_c, len_c, num_t, len_t)\\n if not sig:\\n return False, 0, 0, 0\\n\\n # If number of drops is significant return the number of impacted calls on the original data set.\\n num_c, len_c, num_t, len_t = self.__feat_info_cat(col, df_uni_var_un_norm, target_col, group_col)\\n\\n return True, num_c * len_t / len_c, num_t, num_t - num_c * len_t / len_c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6fe87d44a0d2a0ee2b6fd0d2541e5b9\",\n \"score\": \"0.5441717\",\n \"text\": \"def SSA_sensitivity(database_size):\\r\\n return 9 + (5/database_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5301dfea61004bf4bdbac600cf7216bc\",\n \"score\": \"0.5410713\",\n \"text\": \"def pravdepodobnost():\\n cislo = np.random.rand()\\n\\n if cislo >= 0.99:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5098cb0757edb133d8c30fea185d9d22\",\n \"score\": \"0.5357177\",\n \"text\": \"def __bool__(self):\\n\\n return self.fitness == 28\",\n \"title\": \"\"\n },\n {\n \"docid\": \"683f5fdb57b992fb4a186835a57ea876\",\n \"score\": \"0.5337217\",\n \"text\": \"def safe(self):\\n return math.fabs(numpy.linalg.det(self.scatter)) > 1e-6\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f1e777edb7f9d8a24ff1ec74a515fa\",\n \"score\": \"0.53181183\",\n \"text\": \"def weighted(self):\\n\\t\\tmatrix = self.get_matrix()\\n\\t\\tdim = self.get_nodes()\\n\\t\\tfor i in range(dim):\\n\\t\\t\\tfor j in range(dim):\\n\\t\\t\\t\\tif matrix[i][j] != 0 and matrix[i][j] != 1:\\n\\t\\t\\t\\t\\treturn True\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0992ec16f99913effc93c5560db59ca2\",\n \"score\": \"0.5310074\",\n \"text\": \"def is_over_weight(self,df):\\r\\n return int(df['bmi'] > 25 and df['bmi'] < 29.9)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7c3c8f1c1433adb239fa23ee9359f8\",\n \"score\": \"0.529007\",\n \"text\": \"def getUtileDistinction_KS(self, node, p_significanceLevel=float(0.001)):\\n\\n assert node.nodeType == NodeLeaf\\n\\n root_utils = self.getEFDRs(node) # Get all expected future discounted returns for all instances in a node\\n\\n cds = self.getCandidateDistinctions(node) # Get all the candidate distinctions\\n\\n p_min = float(1)\\n cd_min = None\\n\\n for cd in cds: # test all possible distinctions until find the one satisfy KS test\\n child_utils = []\\n self.splitToFringe(node, cd) # split to fringe node with split candidate\\n # self.split(node,cd)\\n for c in node.children:\\n if len(c.instances) < self.minSplitInstances: # if not enough instance in a node, stop split\\n continue\\n child_utils.append(\\n self.getEFDRs(c)) # Get all expected future discounted returns for all instances in a children\\n # self.unsplit(node) # delete split fringe node\\n\\n for i, cu in enumerate(child_utils):\\n k, p = ks_2samp(root_utils, cu)\\n # print p\\n if p < p_significanceLevel and p < p_min: # significance_level=0.00005, if p below it, this distinction is significant\\n p_min = p\\n cd_min = cd\\n\\n if len(root_utils) == len(cu):\\n print 'find you'\\n\\n print(\\\"KS passed, p={}, d = {}, back={}\\\".format(p, cd.dimension, cd.back_idx))\\n # print(root_utils)\\n # print(cu)\\n # elif p< 0.1:\\n # print(\\\"KS failed, p={}. d= {}, back={}\\\".format(p,cd.dimension,cd.back_idx))\\n self.unsplit(node) # delete split fringe node\\n\\n if cd_min:\\n print(\\\"Will be split, p={}, d={}, back={}\\\".format(p_min, cd_min.dimension_name, cd_min.back_idx))\\n # else:\\n # print \\\"Not found\\\"\\n return cd_min\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"698d753fdfdfbd3df9a67c0e551473ee\",\n \"score\": \"0.5286751\",\n \"text\": \"def __convergence_test(self, cost):\\n return cost <= self.threshold\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3c6761a824f02195d5148bb3216555d\",\n \"score\": \"0.52796507\",\n \"text\": \"def cf_true(x):\\n return is_cf(x) and x > CF.cutoff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c938fb0c1bd723663ef85e4090583af\",\n \"score\": \"0.5265434\",\n \"text\": \"def _compute_gfes(self):\\n return self._det_dcv is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf4ee9468f107cf84d46f7d39832f313\",\n \"score\": \"0.5253931\",\n \"text\": \"def test_Cacciato09Cens5():\\n model = Cacciato09Cens(threshold=9.5)\\n clf = model.clf(model.median_prim_galprop(prim_haloprop=1e12), prim_haloprop=1e12)\\n assert np.isclose(\\n np.sqrt(2 * np.pi * model.param_dict[\\\"sigma\\\"] ** 2) ** (-1),\\n clf,\\n rtol=1e-6,\\n atol=1.0e-6,\\n )\\n model.param_dict[\\\"sigma\\\"] = 0.24\\n clf = model.clf(model.median_prim_galprop(prim_haloprop=1e13), prim_haloprop=1e13)\\n assert np.isclose(\\n np.sqrt(2 * np.pi * model.param_dict[\\\"sigma\\\"] ** 2) ** (-1),\\n clf,\\n rtol=1e-6,\\n atol=1.0e-6,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dba6ffbdb1d51f49cb8e69eddf025bb\",\n \"score\": \"0.5246929\",\n \"text\": \"def validate_t(self, solution, t):\\r\\n no_sense_prob = 0\\r\\n active_nodes = [i for i, a in enumerate(solution) if a != cf.SLEEP and self.network.get_node(i).energy >= (cf.SENSING_ENERGY +cf.COMMUNICATION_ENERGY)]\\r\\n\\r\\n for n in active_nodes:\\r\\n no_sense_prob += self.sensing_log_matrix[t][n]\\r\\n if no_sense_prob >= self.sensing_log_threshold:\\r\\n return True\\r\\n if no_sense_prob < self.sensing_log_threshold:\\r\\n return False\\r\\n else:\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f91ded376aa0411f143afd5996a6780\",\n \"score\": \"0.5231144\",\n \"text\": \"def is_statistically_significant(self):\\n return self.fall_in_critical_region()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2f9b8f3d95405f594c4a6feed052ef0\",\n \"score\": \"0.52248955\",\n \"text\": \"def SSE_sensitivity():\\r\\n return 7\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a5e030bab96f26aafc90941f1d4701\",\n \"score\": \"0.52139014\",\n \"text\": \"def oversampled(semibmaj, semibmin, x=30):\\n return semibmaj > x or semibmin > x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66d1cbd6c11e381fe8e633e2980fa584\",\n \"score\": \"0.5210077\",\n \"text\": \"def set_threshold(df, threshold):\\n return df[\\\"rating\\\"] >= threshold\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9ea8fc2921361876d9ca665f8d5279\",\n \"score\": \"0.52058774\",\n \"text\": \"def check_eigval(self):\\n iok = True\\n thresh = self.param.thresh\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5312fc342c14a75d5b5bcf167fbf75d2\",\n \"score\": \"0.5201662\",\n \"text\": \"def snowcover(self):\\n return self.swe > 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b414b0df04d5a58e342c96c42f8e4cd1\",\n \"score\": \"0.51848173\",\n \"text\": \"def is_on(self) -> bool:\\n return (\\n self._func_channel.dimLevel is not None\\n and self._func_channel.dimLevel > 0.0\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9404e8cc9aeaf2e2e7dff934e2b5ecd\",\n \"score\": \"0.51786757\",\n \"text\": \"def has_unit_root(self, data_array, sig=.05):\\n adf = adfuller(data_array)\\n pvalue = adf[1]\\n # print(pvalue)\\n if pvalue < sig:\\n return False\\n else:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8633bc4986c4e6f82f4a311ce24ac23\",\n \"score\": \"0.51761365\",\n \"text\": \"def is_perfect(self):\\n h = self._get_max_depth(self._root)\\n num = self._get_num_of_node(self._root)\\n return (num == (2 ** h - 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49dff13affac125d2382cb69718c501a\",\n \"score\": \"0.5174382\",\n \"text\": \"def is_normal(r , level = 0.1):\\n statistic, p_value=scipy.stats.jarque_bera(r)\\n return p_value > level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"162d90b19b80cbf02fa982cc5c9759b6\",\n \"score\": \"0.51675516\",\n \"text\": \"def check_f(row):\\n if row['TRTCD1'] == 50.0:\\n return 1\\n if row['TRTCD2'] == 50.0:\\n return 1\\n if row['TRTCD3'] == 50.0:\\n return 1\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b028c3185d861084186314f0ba48356d\",\n \"score\": \"0.5159486\",\n \"text\": \"def check_dataset(self, dataset):\\n in_support = np.sum(\\n (dataset.x >= self.min_x) * (dataset.x <= self.max_x),\\n axis=1) == self.num_p\\n print(\\\"percent in support\\\", np.sum(in_support) / dataset.num_obs)\\n return np.sum(in_support) == dataset.num_obs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f08a77fec455c5dbad94dc0effede95d\",\n \"score\": \"0.51545405\",\n \"text\": \"def test(self, df, target):\\n\\n model = DecisionTreeClassifier(random_state=0)\\n\\n kf = KFold(n_splits=10, shuffle=True, random_state=12345678)\\n res = cross_val_score(model, df, target, cv=kf, scoring='f1_weighted')\\n\\n return res.mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bfe43c45443a2150c231e4242705995\",\n \"score\": \"0.5126107\",\n \"text\": \"def need_add_outlier3(self, node, shortened_path):\\n if node.id not in self.outliers3:\\n return True\\n if shortened_path < self.outliers3[node.id].shortened:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f20af5806bf23cb9e74cadb961322499\",\n \"score\": \"0.51235455\",\n \"text\": \"def get_decision(mean_diff):\\n\\n if mean_diff > -5:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c83d57c90ee944456989aff7ddda5de\",\n \"score\": \"0.51080835\",\n \"text\": \"def s_in_small_g(self):\\n\\n for node in self.core_s:\\n if not self.core_s[node]:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c025a1d41543ea942da0893991f103f\",\n \"score\": \"0.51033574\",\n \"text\": \"def _node_cut(self, configs, wf):\\n ne = configs.configs.shape[1]\\n d2 = 0.0\\n for e in range(ne):\\n d2 += np.sum(wf.gradient(e, configs.electron(e)) ** 2, axis=0)\\n r = 1.0 / (d2 * ne * ne)\\n return r < self.nodal_cutoff ** 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab2ca107a549b01047162c7415a92ab1\",\n \"score\": \"0.5095412\",\n \"text\": \"def FNratio_binned(E): \\n if E <=0 :\\n fnr = 0.0\\n elif E > 0.0 and E <= 0.5 :\\n fnr = 1.56980937443\\n elif E > 0.5 and E <= 1.0 :\\n fnr = 1.68239230683\\n elif E > 1.0 and E <= 1.5 :\\n fnr = 1.59944451298\\n elif E> 1.5 and E<=2.0 :\\n fnr = 1.4945939881\\n elif E>2.0 and E<=2.5 :\\n fnr = 1.35689923574\\n elif E>2.5 and E<=3.0 :\\n fnr = 1.23797378825\\n elif E>3.0 and E<=3.5 :\\n fnr = 1.17535790024\\n elif E>3.5 and E<=4.0 :\\n fnr = 1.23006538251\\n elif E>4.0 and E<=4.5 :\\n fnr = 1.41616622315\\n elif E>4.5 and E<=5.0 :\\n fnr = 1.55534133432\\n elif E>5.0 and E<=5.5 :\\n fnr = 1.57093938166\\n elif E>5.5 and E<=6.0 :\\n fnr = 1.46452790065\\n elif E>6.0 and E<=6.5 :\\n fnr = 1.3737572329\\n elif E>6.5 and E<=7.0 :\\n fnr = 1.29550053164\\n elif E>7.0 and E<=7.5 :\\n fnr = 1.27981366277\\n elif E>7.5 and E<=8.0 :\\n fnr = 1.24848762549\\n elif E>8.0 and E<=9.0 :\\n fnr = 1.26719442633\\n elif E>9.0 and E<=10.0 :\\n fnr = 1.24833629993\\n elif E>10.0 and E<=11.0 :\\n fnr = 1.26389158134\\n elif E>11.0 and E<=12.0 :\\n fnr = 1.34670943259\\n elif E>12.0 and E<=14.0 :\\n fnr = 1.41853479724\\n elif E>14.0 and E<=16.0 :\\n fnr = 1.56070845743\\n elif E>16.0 and E<=18.0 :\\n fnr = 1.55427712082\\n elif E>18.0:\\n fnr = 1.55427712082\\n return (fnr*1.0e-6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f9f1fcb4afbf62e2e877b2bcd1d36e5\",\n \"score\": \"0.5087331\",\n \"text\": \"def test_Cacciato09Sats5():\\n model = Cacciato09Sats(threshold=9.0)\\n lum_mc = model.mc_prim_galprop(prim_haloprop=np.ones(int(1e5)) * 5e13, seed=1)\\n assert np.all(lum_mc >= 10 ** model.threshold)\\n\\n # Check that luminosities follow the expected distribution.\\n def cdf(lum):\\n return np.array(\\n [\\n (\\n model.mean_occupation(prim_haloprop=5e13)\\n - model.mean_occupation(prim_haloprop=5e13, prim_galprop_min=l)\\n )\\n / model.mean_occupation(prim_haloprop=5e13)\\n for l in lum\\n ]\\n )\\n\\n p_value = kstest(lum_mc, cdf)[1]\\n assert p_value > 0.001\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04afc103353679d41c0db2a774391d17\",\n \"score\": \"0.50792843\",\n \"text\": \"def sat(n: int, x=329437923.5):\\n return abs(n - x) <= 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3cd06496ec28e40ef1b7260e38aca1a\",\n \"score\": \"0.5044325\",\n \"text\": \"def _stat_chi2(self):\\n model = self._model(self.data.e_ref)\\n data = self.data.table[\\\"dnde\\\"].quantity\\n sigma = self.data.table[\\\"dnde_err\\\"].quantity\\n return ((data - model) / sigma).to(\\\"\\\").value ** 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd060625ac1a8a4f67faa8d28ad9dee0\",\n \"score\": \"0.5042267\",\n \"text\": \"def has_solution(self, edges, dom_val):\\n s = dom_val\\n for e in edges:\\n s = s | e\\n return s == self.graph.dominant_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ec2f5126db1b1de8baa297e7c548b0f\",\n \"score\": \"0.50391954\",\n \"text\": \"def chi_squared(df, target_col_str):\\n\\n print('Ho: No significant relationship, feature is independent of target\\\\n')\\n\\n categorical_cols = df.select_dtypes(['object']).columns.tolist()\\n print('Features tested: {}'.format(categorical_cols))\\n\\n def chi_test(c):\\n ct = pd.crosstab(df[target_col_str], df[c])\\n chi2, p, dof, expected = scs.chi2_contingency(ct)\\n exp_under_five_percent = np.sum(expected < 5) / len(expected.flat)\\n\\n if exp_under_five_percent > 0.2:\\n return None\\n else:\\n return p\\n\\n chi_dict = {c: chi_test(c) for c in categorical_cols}\\n\\n chi_df = pd.DataFrame.from_dict(chi_dict, orient='index')\\n chi_df.columns = ['p_value']\\n\\n chi_df = chi_df.sort_values(by='p_value', ascending=True)\\n\\n plt.style.use('fivethirtyeight')\\n\\n chi_df['p_value'].sort_values(ascending=True).plot(kind='barh', use_index=True, color='deepskyblue',\\n title='Chi-squared Test for Independence with Target')\\n plt.xlabel('p-value')\\n plt.xscale('log')\\n p_line = plt.axvline(x=0.05, color='darkorange', label='p-value cut-off', lw=2)\\n plt.legend([p_line], ['Significant p-value\\\\ncut-off (95% CI)'], bbox_to_anchor=(1.05, 1), loc=2)\\n plt.grid(False)\\n plt.show()\\n\\n return chi_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"606d65620831db9fc7dbc53be0e0d683\",\n \"score\": \"0.5024171\",\n \"text\": \"def classify(y):\\r\\n c = np.where(y < 0.5, 0, 1)\\r\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f1d9496fa1a47bebc3971643245d4a6\",\n \"score\": \"0.5011161\",\n \"text\": \"def check_dataset(self, dataset):\\n new_x = self.pca.transform(dataset.x.reshape((dataset.x.shape[0], -1)))\\n in_support = np.sum(\\n (new_x >= self.min_x) * (new_x <= self.max_x),\\n axis=1) == self.num_p\\n print(\\\"percent in support\\\", np.sum(in_support) / dataset.num_obs)\\n return np.sum(in_support) == dataset.num_obs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a19bcab237bce40c63a9889fef3286ef\",\n \"score\": \"0.50060344\",\n \"text\": \"def true_decision_function(input_features):\\n if input_features[0] <= n_bins // 2:\\n return -1\\n else:\\n if input_features[1] <= n_bins // 3:\\n return -1\\n else:\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11de9fca78d1d04b63910f6710ce9449\",\n \"score\": \"0.5001753\",\n \"text\": \"def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool:\\n for prev_node in nncf_graph.get_previous_nodes(node):\\n if prev_node.node_type in OP_NAMES_QUANTIZE_NODE:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94f154bce994db0008fcf9d2069cd4f7\",\n \"score\": \"0.50000864\",\n \"text\": \"def operator_test5():\\n\\n l = 5.0\\n dx = 0.02\\n x = np.arange(0, l+dx, dx)\\n\\n vx = pk.linear_ramp(x)\\n H = pk.build_hamiltonian(x, vx, dx=dx)\\n\\n evals, evecs = np.linalg.eigh(H)\\n\\n for i in range(5):\\n psi = evecs[:, i]\\n psi = pk.normalize_wfn(x, psi)\\n pdf = pk.prob_density(psi)\\n\\n plt.plot(x, psi)\\n plt.plot(x, pdf)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"079cd691420fc207745ae4d3c28ce298\",\n \"score\": \"0.49968618\",\n \"text\": \"def cond_exp_tree(self, x, tree, S, algo=\\\"shap\\\", N=50000, data=None):\\n\\n if type(data) != type(self.data):\\n data = self.data\\n\\n a = tree.children_left\\n b = tree.children_right\\n f = tree.feature\\n t = tree.threshold\\n r_w = tree.weighted_n_node_samples\\n v = np.array([tree.value[i][0][0] for i in range(tree.node_count)])\\n index = range(x.shape[0])\\n\\n def G(i, w):\\n\\n if f[i] == -2:\\n return float(w * v[i])\\n else:\\n if (f[i] in S):\\n if x[f[i]] <= t[i]:\\n return G(a[i], w)\\n else:\\n return G(b[i], w)\\n else:\\n return G(a[i], w * r_w[a[i]] / r_w[i]) + G(b[i], w * r_w[b[i]] / r_w[i])\\n\\n def explore(i, tab):\\n\\n if f[i] == -2:\\n tab[i] = 1\\n else:\\n if f[i] in S:\\n if x[f[i]] <= t[i]:\\n explore(a[i], tab)\\n else:\\n explore(b[i], tab)\\n else:\\n explore(a[i], tab)\\n explore(b[i], tab)\\n\\n def get_partition(leaf_id, part):\\n\\n left = np.where(tree.children_left == leaf_id)[0]\\n right = np.where(tree.children_right == leaf_id)[0]\\n\\n if (len(left) == 0) * (len(right) == 0):\\n return (part)\\n\\n else:\\n if len(right) != 0:\\n right = int(right[0])\\n\\n part[f[right]] = np.concatenate((part[f[right]], np.array([[t[right], np.inf]])))\\n return get_partition(right, part)\\n else:\\n left = int(left[0])\\n part[f[left]] = np.concatenate((part[f[left]], np.array([[-np.inf, t[left]]])))\\n return get_partition(left, part)\\n\\n def get_final_partition(part):\\n final_partition = {}\\n for i, var_part in enumerate(part):\\n final_partition[i] = [np.max(var_part[:, 0]), np.min(var_part[:, 1])]\\n return final_partition\\n\\n def explore_part(i, tab, partition_leaves, partition_global, prob_global, S, S_bar, data, intv=False):\\n if f[i] == -2:\\n # tab[i] = 1\\n compatible_leaves.append(i)\\n partition_global[i] = partition_leaves\\n partition_leaves = np.squeeze(np.array(partition_leaves))\\n # partition_leaves = np.reshape(np.array(partition_leaves), (self.d + 2, 2)).T\\n # partition_leaves = pd.DataFrame(partition_leaves)[S]\\n low = FloatVector(partition_leaves[:, 0][S_bar])\\n up = FloatVector(partition_leaves[:, 1][S_bar])\\n\\n prob_global[i] = cond.pcmvnorm(low, up, self.r_mean, self.r_cov, c_index, given, FloatVector(x[S]))[0]\\n # print(prob_global[i])\\n\\n else:\\n if f[i] in S:\\n if x[f[i]] <= t[i]:\\n part = partition_leaves.copy()\\n part[f[i]] = np.concatenate((part[f[i]], np.array([[-np.inf, t[i]]])))\\n part[f[i]] = np.array([[np.max(part[f[i]][:, 0]), np.min(part[f[i]][:, 1])]])\\n explore_part(a[i], tab, part, partition_global, prob_global, S, S_bar, data, intv)\\n else:\\n part = partition_leaves.copy()\\n part[f[i]] = np.concatenate((part[f[i]], np.array([[t[i], np.inf]])))\\n part[f[i]] = np.array([[np.max(part[f[i]][:, 0]), np.min(part[f[i]][:, 1])]])\\n explore_part(b[i], tab, part, partition_global, prob_global, S, S_bar, data, intv)\\n else:\\n part = partition_leaves.copy()\\n part[f[i]] = np.concatenate((part[f[i]], np.array([[-np.inf, t[i]]])))\\n part[f[i]] = np.array([[np.max(part[f[i]][:, 0]), np.min(part[f[i]][:, 1])]])\\n\\n part_2 = partition_leaves.copy()\\n part_2[f[i]] = np.concatenate((part_2[f[i]], np.array([[t[i], np.inf]])))\\n part_2[f[i]] = np.array([[np.max(part_2[f[i]][:, 0]), np.min(part_2[f[i]][:, 1])]])\\n\\n explore_part(a[i], tab, part, partition_global, prob_global, S, S_bar, data, intv)\\n explore_part(b[i], tab, part_2, partition_global, prob_global, S, S_bar, data, intv)\\n\\n def explore_partition(i, tab, partition_leaves, partition_global, prob_global, S, S_bar, data, intv=False):\\n\\n if f[i] == -2:\\n # tab[i] = 1\\n compatible_leaves.append(i)\\n partition_global[i] = partition_leaves\\n partition_leaves = np.squeeze(np.array(partition_leaves))\\n\\n if not intv:\\n section_x = np.prod(\\n [(data[:, s] <= partition_leaves[s, 1]) * (data[:, s] >= partition_leaves[s, 0]) for s in S],\\n axis=0)\\n else:\\n section_x = np.prod(\\n [(data[:, s] <= partition_leaves[s, 1]) * (data[:, s] >= partition_leaves[s, 0]) for s in\\n S_bar], axis=0)\\n\\n prob_global[i] = np.sum(section_x)\\n\\n else:\\n if f[i] in S:\\n if x[f[i]] <= t[i]:\\n part = partition_leaves.copy()\\n part[f[i]] = np.concatenate((part[f[i]], np.array([[-np.inf, t[i]]])))\\n part[f[i]] = np.array([[np.max(part[f[i]][:, 0]), np.min(part[f[i]][:, 1])]])\\n explore_partition(a[i], tab, part, partition_global, prob_global, S, S_bar, data, intv)\\n else:\\n part = partition_leaves.copy()\\n part[f[i]] = np.concatenate((part[f[i]], np.array([[t[i], np.inf]])))\\n part[f[i]] = np.array([[np.max(part[f[i]][:, 0]), np.min(part[f[i]][:, 1])]])\\n explore_partition(b[i], tab, part, partition_global, prob_global, S, S_bar, data, intv)\\n else:\\n part = partition_leaves.copy()\\n part[f[i]] = np.concatenate((part[f[i]], np.array([[-np.inf, t[i]]])))\\n part[f[i]] = np.array([[np.max(part[f[i]][:, 0]), np.min(part[f[i]][:, 1])]])\\n\\n part_2 = partition_leaves.copy()\\n part_2[f[i]] = np.concatenate((part_2[f[i]], np.array([[t[i], np.inf]])))\\n part_2[f[i]] = np.array([[np.max(part_2[f[i]][:, 0]), np.min(part_2[f[i]][:, 1])]])\\n\\n explore_partition(a[i], tab, part, partition_global, prob_global, S, S_bar, data, intv)\\n explore_partition(b[i], tab, part_2, partition_global, prob_global, S, S_bar, data, intv)\\n\\n if algo == 'shap':\\n return G(0, 1)\\n\\n elif algo == 'plugin':\\n if S == []:\\n compatible_leaves = np.zeros(tree.node_count)\\n explore(0, compatible_leaves)\\n compatible_leaves = [i for i in range(tree.node_count) if compatible_leaves[i] == 1]\\n p = r_w[compatible_leaves] / np.sum(r_w[compatible_leaves])\\n\\n elif len(S) == len(x):\\n y_pred = tree.predict(np.expand_dims(np.array(x, dtype=np.float32), axis=0))\\n return np.mean(y_pred)\\n\\n else:\\n S_bar = [i for i in self.index if i not in S]\\n partition_leaves = [np.array([[-np.inf, np.inf]]) for i in range(data.shape[1])]\\n partition_global = {i: [np.array([[-np.inf, np.inf]]) for i in range(data.shape[1])]\\n for i in range(tree.node_count)}\\n prob_global = {}\\n compatible_leaves = []\\n explore_partition(0, compatible_leaves, partition_leaves, partition_global, prob_global, S, S_bar, data,\\n False)\\n\\n nbs_leaf = np.array([prob_global[key] for key in compatible_leaves])\\n p = r_w[compatible_leaves] / nbs_leaf\\n\\n return np.sum(p * v[compatible_leaves])\\n\\n\\n elif algo == 'plugin_interventionalv2':\\n return self.cond_exp_tree_intv(tree, x, S, v, explore, get_partition, get_final_partition, data)\\n\\n elif algo == 'plugin_interventional':\\n S_bar = [i for i in self.index if i not in S]\\n if S == []:\\n compatible_leaves = np.zeros(tree.node_count)\\n explore(0, compatible_leaves)\\n compatible_leaves = [i for i in range(tree.node_count) if compatible_leaves[i] == 1]\\n p = r_w[compatible_leaves] / np.sum(r_w[compatible_leaves])\\n\\n elif len(S) == len(x):\\n y_pred = tree.predict(np.expand_dims(np.array(x, dtype=np.float32), axis=0))\\n return np.mean(y_pred)\\n\\n else:\\n partition_leaves = [np.array([[-np.inf, np.inf]]) for i in range(data.shape[1])]\\n partition_global = {i: [np.array([[-np.inf, np.inf]]) for i in range(data.shape[1])]\\n for i in range(tree.node_count)}\\n prob_global = {}\\n compatible_leaves = []\\n explore_partition(0, compatible_leaves, partition_leaves, partition_global, prob_global, S, S_bar, data,\\n True)\\n p = np.array([prob_global[key] / len(data) for key in compatible_leaves])\\n\\n return np.sum(p * v[compatible_leaves])\\n\\n elif algo == 'monte_carlo':\\n if len(S) != len(index):\\n S_bar = [i for i in index if i not in S]\\n rg_data = np.zeros(shape=(N, len(index)))\\n rg_data[:, S] = x[S]\\n rg = sampleMVN(N, self.mean, self.cov, S_bar, S, x[S])\\n rg_data[:, S_bar] = rg\\n\\n y_pred = tree.predict(np.array(rg_data, dtype=np.float32))\\n\\n else:\\n y_pred = tree.predict(np.expand_dims(np.array(x, dtype=np.float32), axis=0))\\n\\n return np.mean(y_pred)\\n\\n elif algo == 'monte_carlo_interventional':\\n if len(S) != len(index):\\n rg = sampleMarMVN(N, self.mean, self.cov, self.index, [])\\n rg_data = pd.DataFrame(rg, columns=[str(i) for i in self.index])\\n\\n def get_given_data(idx):\\n val = np.array([x[idx]])\\n val = np.tile(val, N)\\n return val\\n\\n for val_id in S:\\n rg_data[str(val_id)] = get_given_data(val_id)\\n\\n rg_data = rg_data[sorted(rg_data.columns)]\\n y_pred = tree.predict(np.array(rg_data.values, dtype=np.float32))\\n\\n else:\\n y_pred = tree.predict(np.expand_dims(np.array(x, dtype=np.float32), axis=0))\\n\\n return np.mean(y_pred)\\n\\n elif algo == 'exact':\\n S_bar = [i for i in self.index if i not in S]\\n given = IntVector(np.array([i + 1 for i in S]))\\n c_index = np.array([i + 1 for i in index if i not in S])\\n # S_bar = [i for i in index if i not in S]\\n # if S == []:\\n # compatible_leaves = np.zeros(tree.node_count)\\n # explore(0, compatible_leaves)\\n # compatible_leaves = [i for i in range(tree.node_count) if compatible_leaves[i] == 1]\\n # p = r_w[compatible_leaves] / np.sum(r_w[compatible_leaves])\\n\\n if len(S) == len(x):\\n y_pred = tree.predict(np.expand_dims(np.array(x, dtype=np.float32), axis=0))\\n return np.mean(y_pred)\\n\\n else:\\n\\n # S_bar = [i for i in self.index if i not in S]\\n partition_leaves = [np.array([[-np.inf, np.inf]]) for i in range(data.shape[1])]\\n partition_global = {i: [np.array([[-np.inf, np.inf]]) for i in range(data.shape[1])]\\n for i in range(tree.node_count)}\\n prob_global = {}\\n compatible_leaves = []\\n explore_part(0, compatible_leaves, partition_leaves, partition_global, prob_global, S, S_bar, data,\\n False)\\n\\n p = np.array([prob_global[key] for key in compatible_leaves])\\n # print(p)\\n # p = r_w[compatible_leaves] / nbs_leaf\\n\\n return np.sum(p * v[compatible_leaves])\\n\\n elif algo == 'exact_v1':\\n\\n if len(S) != len(index):\\n given = IntVector(np.array([i + 1 for i in S]))\\n c_index = np.array([i + 1 for i in index if i not in S])\\n S_bar = [i for i in index if i not in S]\\n\\n compatible_leaves = np.zeros(tree.node_count)\\n explore(0, compatible_leaves)\\n\\n leaf_idx = [i for i in range(tree.node_count) if compatible_leaves[i] == 1]\\n leaf_proba = []\\n for leaf_id in leaf_idx:\\n partition_leaves = [np.array([[-np.inf, np.inf]]) for i in range(x.shape[0])]\\n partition_leaves = get_partition(leaf_id, partition_leaves)\\n partition_leaves = pd.DataFrame(get_final_partition(partition_leaves))[S_bar]\\n\\n low = FloatVector(partition_leaves.iloc[0])\\n up = FloatVector(partition_leaves.iloc[1])\\n\\n leaf_proba.append(\\n cond.pcmvnorm(low, up, self.r_mean, self.r_cov, c_index, given, FloatVector(x[S]))[0])\\n\\n leaf_proba = np.array(leaf_proba)\\n return np.sum(leaf_proba * v[compatible_leaves == 1])\\n\\n else:\\n y_pred = tree.predict(np.expand_dims(np.array(x, dtype=np.float32), axis=0))\\n return np.mean(y_pred)\\n else:\\n raise ValueError(\\\"This algo is not implemented. Available estimators are: plugin, exact, monte_carlo,\\\\\\n monte_carlo_interventional, plugin_interventional\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d085c7705ab79aad4831a67c51c9dcd\",\n \"score\": \"0.49960086\",\n \"text\": \"def _goodSN(self, sourceCat):\\n if self.config.minSnr <= 0:\\n return True\\n else:\\n with np.errstate(invalid=\\\"ignore\\\"): # suppress NAN warnings\\n return sourceCat.get(self.fluxKey)/sourceCat.get(self.fluxSigmaKey) > self.config.minSnr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e8d9d7c98d0b896b71d5272f0ffd0b\",\n \"score\": \"0.49935928\",\n \"text\": \"def get_differentially_expressed_proteins_from_log2FC_df(method, sample1, sample2, specie, ratio, two_sided = False):\\n ARATH_FC_matrix, CAEEL_FC_matrix, HUMAN_FC_matrix = get_log2FC_ratio_matrices()\\n \\n if specie == \\\"HUMAN\\\":\\n FC_matrix = HUMAN_FC_matrix\\n FC_treshold = ratio * FC_matrix[sample2][sample1] #The smaples are reversed because the log2FC in this function is reversed.\\n file_name = method+\\\"_\\\"+\\\"log2FC\\\"+\\\"_\\\"+sample1+\\\"_\\\"+sample2+\\\"_\\\"+specie+\\\".csv\\\"\\n df_log2FC = pd.read_csv(file_name, sep = \\\"\\\\t\\\")\\n diffExp = np.sum(df_log2FC[df_log2FC > FC_treshold] > 0) #This comparison should be more then\\n elif specie == \\\"ARATH\\\":\\n FC_matrix = ARATH_FC_matrix\\n FC_treshold = ratio * FC_matrix[sample2][sample1] #The smaples are reversed because the log2FC in this function is reversed.\\n file_name = method+\\\"_\\\"+\\\"log2FC\\\"+\\\"_\\\"+sample1+\\\"_\\\"+sample2+\\\"_\\\"+specie+\\\".csv\\\"\\n df_log2FC = pd.read_csv(file_name, sep = \\\"\\\\t\\\")\\n diffExp = np.sum(df_log2FC[df_log2FC < -ratio] > 0) + np.sum(df_log2FC[df_log2FC > ratio] > 0) #This comparison should be if less or more than ratio\\n elif specie == \\\"CAEEL\\\":\\n FC_matrix = CAEEL_FC_matrix\\n FC_treshold = ratio * FC_matrix[sample2][sample1] #The smaples are reversed because the log2FC in this function is reversed.\\n file_name = method+\\\"_\\\"+\\\"log2FC\\\"+\\\"_\\\"+sample1+\\\"_\\\"+sample2+\\\"_\\\"+specie+\\\".csv\\\"\\n df_log2FC = pd.read_csv(file_name, sep = \\\"\\\\t\\\")\\n diffExp = np.sum(df_log2FC[df_log2FC < FC_treshold] > 0) #This comparison should be less than\\n else:\\n print(\\\"no species specificed\\\")\\n # return \\n return diffExp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cdf7774a775c38dc3bb1c50407afc16\",\n \"score\": \"0.49933684\",\n \"text\": \"def probable(self, threshold=0.3):\\r\\n return self.score > threshold\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e594385a28bad52ea0422bbc0ccaba45\",\n \"score\": \"0.49910995\",\n \"text\": \"def leaf_node_check(df):\\n class_count = set(df['class'].values.tolist())\\n if len(class_count) == 2:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d2dc40beacc1f3b48c20feae0a48073\",\n \"score\": \"0.49809405\",\n \"text\": \"def is_silent(self, data) -> bool:\\n return np.sqrt(np.mean(data ** 2)) < self.threshold\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7176da4a18060754fd6873ff7f344235\",\n \"score\": \"0.4973127\",\n \"text\": \"def _is_pos_def(self, mat: np.ndarray) -> bool:\\n return np.all(np.linalg.eigvals(mat) > 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b22ae6142b2c791fc5a096ba9dd32f\",\n \"score\": \"0.49697605\",\n \"text\": \"def summarize_sensitivity(sens_df):\\n cols = ['star', 'date', '[Fe/H]', 'logg', 'addmode', 'temperature', 'vsini']\\n detrate = sens_df.groupby(cols).apply(lambda d: (d.significance > 5).sum() / float(len(d)))\\n detrate = detrate.reset_index().rename(columns={0: 'detrate'})\\n significance = sens_df.groupby(cols).apply(lambda d: np.nanmean(d.significance))\\n significance = significance.reset_index().rename(columns={0: 'significance'})\\n detrate['significance'] = significance['significance']\\n return detrate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebbe54b6adec2df35a2cd4b3a46163d8\",\n \"score\": \"0.49613017\",\n \"text\": \"def test_distributions1(dist):\\n assert entropy(dist) <= np.log2(len(dist.pmf))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fda52437ae6a85c4134d1741fa0a5f0\",\n \"score\": \"0.49526456\",\n \"text\": \"def test_unsupervised_density():\\n # !TODO: Implement a suitable scenario.\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd915870af46803e5ec1eb4d8dd16c49\",\n \"score\": \"0.4946569\",\n \"text\": \"def prob55():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77b13ec72bb195ee995150e1b95e7433\",\n \"score\": \"0.49439466\",\n \"text\": \"def test_5vIQ(self):\\n for i in range(1000):\\n self.assertTrue(61 <= Consignment._get_percent(None, 5) <= 80)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc001d90fc77d9d7be6395036917507\",\n \"score\": \"0.4943591\",\n \"text\": \"def _is_ok_feature(self, feature) -> bool:\\n if feature.isnull().mean() >= self.max_nan_rate:\\n return False\\n if (feature.value_counts().values[0] / feature.shape[0]) >= self.max_constant_rate:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4f12f27ec44d7c85e9b5e30c30a9158\",\n \"score\": \"0.494273\",\n \"text\": \"def test_correct_score(self):\\n dist = Bernoulli(VISIBLE_UNITS, HIDDEN_UNITS, weights=self.weights, bias_hidden=self.bias_hidden, bias_visible=self.bias_visible)\\n target = np.sum(self.weights) + np.sum(self.bias_hidden) + np.sum(self.bias_visible)\\n actual = np.abs(dist.score_energy(self.vis_inp, self.hid_inp))\\n self.assertEqual(target, actual, \\\"Didn't match. Target: \\\" + str(target) + \\\", Actual: \\\" + str(actual))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d027fa8cb6d3e97644e9593d97007b\",\n \"score\": \"0.493798\",\n \"text\": \"def get_consistence(self, node):\\n return self.active[self.node_index[node]].sum(0) != 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5ada0c0b35792369c00069f44d3a45\",\n \"score\": \"0.4937976\",\n \"text\": \"def is_weight(w):\\n \\\"*** YOUR CODE HERE ***\\\"\\n return root(w)!=None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cc787c5105bf00a5c54960b299f26a7\",\n \"score\": \"0.4936937\",\n \"text\": \"def _test_dist(self,group1,elem1,criterion):\\n\\n test=True\\n for elem0 in group1:\\n distsq=np.sum(np.power(elem0-elem1,2))\\n if distsq < criterion**2:\\n test=False\\n break\\n\\n return test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"920bf55811ff330da85305e32d152711\",\n \"score\": \"0.49358174\",\n \"text\": \"def detectability_calculator(Theta_CDF,min_CDF,max_CDF,SNR_in):\\n \\n #input type checking\\n assert type(min_CDF) == float, 'min_CDF should be a float.'\\n assert type(max_CDF) == float, 'max_CDF should be a float.'\\n assert type(SNR_in) == float, 'SNR_in should be a float.'\\n \\n if SNR_in <= (8 / max_CDF):\\n det = 0 #always undetectable\\n #set separately so Theta_CDF doesn't get input above 1\\n #the max_CDF (just below 1) has to be included because the function\\n #Theta_dist is not defined for the small range between max_CDF and 1\\n #and this can occasionally cause problems if just 8 is used\\n elif SNR_in >= (8 / min_CDF):\\n det = 1 #always detectable\\n #similarly, Theta_CDF is not defined between 0 and min_CDF\\n else:\\n threshold = 8 / SNR_in\\n #how much the SNR would need to be lowered to hit the threshold SNR=8\\n undet = Theta_CDF(threshold)\\n #the proportion of Theta values that would lower SNR below 8\\n det = 1 - undet #proportion that would keep SNR above 8\\n \\n #output type conversion\\n det = float(det)\\n \\n return det\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f342a41fcc353b8e6e6b2622f6f4db\",\n \"score\": \"0.4930901\",\n \"text\": \"def crowded2(t, data):\\n cell_score = 0\\n cell_score += n_check(t, data)\\n cell_score += s_check(t, data)\\n cell_score += e_check(t, data)\\n cell_score += w_check(t, data)\\n cell_score += ne_check(t, data)\\n cell_score += nw_check(t, data)\\n cell_score += sw_check(t, data)\\n cell_score += se_check(t, data)\\n if cell_score >= 5:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76d4ad7012679e0d0f613dbea0d73401\",\n \"score\": \"0.4929446\",\n \"text\": \"def too_many_significant(ts,len_data,criticalVal,acfVals,upper_significance,lower_significance,numSignificantAllowed=5): \\n num_significant=0\\n #determine how many points are significant based on upper and lower bound levels\\n for i in range(0, len(acfVals)):\\n if acfVals[i]>upper_significance[i] or acfVals[i]<lower_significance[i]:\\n num_significant+=1\\n \\n return num_significant > numSignificantAllowed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eb74c9f4d717d40c19afb0e7d15d8ea\",\n \"score\": \"0.49262944\",\n \"text\": \"def gtd_logic(df, G, label_column, column_prefix, progress=True):\\n\\n # Create a collection of shortest Paths from the root node (VRN) to each of the leave nodes\\n\\n P = find_shortest_paths(G, progress=progress)\\n\\n # Calculate the Gain Ratio for each feature in the data (therby for each node in the Graph)\\n\\n gr_values = calc_gr(df, label_column, progress=progress)\\n\\n SF = set()\\n\\n # Create a dictionary to keep track of the availability of nodes\\n\\n node_availability = {}\\n\\n for node in list(G.nodes()):\\n\\n node_availability[node] = True\\n\\n if progress:\\n iterator = tqdm(\\n P, desc=\\\"Greedy Top Down - (3/3) Finding most relevant nodes.\\\")\\n else:\\n iterator = P\\n\\n for path in iterator:\\n\\n # Get candidate_nodes for that path. Candidate Nodes are nodes that are part of the path AND are still available\\n\\n candidate_nodes = [\\n node for node in path if node_availability[node] == True]\\n\\n # Check if there are any available nodes in the path.\\n\\n if len(candidate_nodes) > 0:\\n\\n # Get the Candidate Node with the highest Gain Ratio\\n\\n max_node = get_max_node(candidate_nodes, gr_values, column_prefix)\\n\\n # Add that Node to the Selected Features Set (SF)\\n\\n SF.add(max_node)\\n\\n # Change the availability of that node, it's ancestors and it's descendants to False\\n\\n node_availability[max_node] = False\\n\\n for ancestor in nx.ancestors(G, max_node):\\n\\n node_availability[ancestor] = False\\n\\n for decendant in nx.descendants(G, max_node):\\n\\n node_availability[decendant] = False\\n\\n # Check if there are any available nodes left. If not, end the algorithm.\\n\\n if all(availability == False for availability in node_availability.values()):\\n\\n break\\n\\n return SF\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2baa67545bb3503fadc4575b425e5b58\",\n \"score\": \"0.4923733\",\n \"text\": \"def q7(self, infile):\\n\\t\\treturn 0.1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"922def58e2ccb9565bb1d0dbc91069d1\",\n \"score\": \"0.4921992\",\n \"text\": \"def percent_heterozygous(self):\\n return (self.genotypes==1).sum() / self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"076ab58fe92feca3846916edf0dc751e\",\n \"score\": \"0.4913728\",\n \"text\": \"def column_based_infogain_right(df):\\n\\n if entype == 'bag':\\n individual_entropy_list_right = []\\n for each_column in df.columns:\\n if each_column != 'class' and each_column != 'bruises?-no':\\n df_right = df[[each_column, \\\"class\\\"]][df[each_column] == 1].groupby(\\\"class\\\").count()\\n df_right_sum = df_right.sum()\\n entropy = 0.0\\n outer_dist = df_right_sum[each_column] / len(df)\\n for each_index in df_right.index:\\n inner_element = (df_right.loc[each_index, each_column] / df_right_sum[each_column])\\n entropy += inner_element * np.log2(inner_element)\\n final_entropy = -entropy * outer_dist\\n individual_entropy_list_right.append(final_entropy)\\n return individual_entropy_list_right\\n\\n else:\\n right_df = df\\n header_list = get_column_list(right_df)\\n individual_entropy_list_right = []\\n for each_column in header_list:\\n if each_column != 'class' and each_column != 'weight':\\n right_child_entropy = []\\n right_temp_df = pd.DataFrame(right_df[[each_column, \\\"class\\\", 'weight']][right_df[each_column] == 1],\\n columns=[each_column, 'class', 'weight'])\\n right_child_entropy.append(round(right_temp_df.loc[right_temp_df['class'] == 1, 'weight'].sum(), 2))\\n right_child_entropy.append(round(right_temp_df.loc[right_temp_df['class'] == -1, 'weight'].sum(), 2))\\n individual_entropy_list_right.append(entropy_calculation(right_child_entropy, right_df))\\n return individual_entropy_list_right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb4b4560a0684e56b16123ee244524f\",\n \"score\": \"0.49126238\",\n \"text\": \"def observed_class_distribution_is_pure(self):\\n count = 0\\n for _, weight in self._stats.items():\\n if weight != 0:\\n count += 1\\n if count == 2: # No need to count beyond this point\\n break\\n return count < 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26926a84fc6132413beef3d47ac5e74a\",\n \"score\": \"0.4904977\",\n \"text\": \"def is_on(self) -> bool:\\n func_channel = self._device.functionalChannels[self._channel]\\n return func_channel.dimLevel is not None and func_channel.dimLevel > 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1262293adbbb02348947abb2dc8207d\",\n \"score\": \"0.4897986\",\n \"text\": \"def omni(self):\\r\\n\\t\\treturn Sstats.normaltest(self.e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f8e42511507492255de2e4ecd9dfa9a\",\n \"score\": \"0.48942423\",\n \"text\": \"def migrate(self):\\r\\n\\r\\n if sl.random() < self.params['mu'] * self.fitness():\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07a58eefa3687e1c9710d2a8425a2e8b\",\n \"score\": \"0.4888489\",\n \"text\": \"def alifestd_has_multiple_roots(phylogeny_df: pd.DataFrame) -> bool:\\n return (\\n (phylogeny_df[\\\"ancestor_list\\\"] == \\\"[]\\\").sum()\\n + (phylogeny_df[\\\"ancestor_list\\\"] == \\\"[NONE]\\\").sum()\\n + (phylogeny_df[\\\"ancestor_list\\\"] == \\\"[none]\\\").sum()\\n + (phylogeny_df[\\\"ancestor_list\\\"] == \\\"[None]\\\").sum()\\n ) >= 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"def834de491b51ef8e25d28b20cfb44d\",\n \"score\": \"0.48781985\",\n \"text\": \"def is_train_sample(df):\\n entry_idx = df.index.names.index(\\\"entry\\\")\\n is_odd = (df.index.get_level_values(entry_idx) % 2).to_numpy(dtype=bool)\\n is_train = is_odd\\n return is_train\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9970eba9ee6e5787493ea3e526abd2bf\",\n \"score\": \"0.48741898\",\n \"text\": \"def keep_going(sample,d=1,k=31):\\n log(sample)\\n if (statistics.stdev(sample)/math.sqrt(k))>= d:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a615b6db2e3aec6716df3a141b88fdf0\",\n \"score\": \"0.48723033\",\n \"text\": \"def check_pos_def(self):\\n return np.all(np.linalg.eigvals(self.A) > 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43949de78d4c811f75ebc917618015f8\",\n \"score\": \"0.4871751\",\n \"text\": \"def test_logistic_sigmoid_derivative(self):\\n f = self.logistic_sigmoid\\n f_prime = self.logistic_sigmoid_derivative\\n for x in self.test_points:\\n self.assertAlmostEqual(f_prime(x),\\n (f(x) * (1 - f(x))) / self.std_dev,\\n msg=\\\"Logistic sigmoid satisfies f' = f(1-f)/sigma\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5d105f66cc93024db4eb9213e2ca103\",\n \"score\": \"0.48715493\",\n \"text\": \"def is_normal(r, level=0.01):\\n statistic, p_value = scipy.stats.jarque_bera(r)\\n return p_value > level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bcc4e69a233a6c320c4fdc4a16164fe\",\n \"score\": \"0.48659343\",\n \"text\": \"def p_value(self):\\n return 0.5 * chi2.sf(self.ts, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb49264abd56def98310b7fee34412ef\",\n \"score\": \"0.4865289\",\n \"text\": \"def call_significant_sites(fg_counts, bg_counts, max_pvalue):\\n print fg_counts.max(), fg_counts.min(), bg_counts\\n return poisson.sf(fg_counts, bg_counts) < max_pvalue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c939c86348d25341fb2efedb64f7630\",\n \"score\": \"0.4860694\",\n \"text\": \"def column_based_infogain_left(df):\\n\\n if entype == \\\"bag\\\":\\n\\n individual_entropy_list_left = []\\n for each_column in df.columns:\\n if each_column != 'class' and each_column != 'bruises?-no':\\n df_left = df[[each_column, \\\"class\\\"]][df[each_column] == 0].groupby(\\\"class\\\").count()\\n df_left_sum = df_left.sum()\\n entropy = 0.0\\n outer_dist = df_left_sum[each_column] / len(df)\\n for each_index in df_left.index:\\n inner_element = (df_left.loc[each_index, each_column] / df_left_sum[each_column])\\n entropy += inner_element * np.log2(inner_element)\\n final_entropy = -entropy * outer_dist\\n individual_entropy_list_left.append(final_entropy)\\n return individual_entropy_list_left\\n\\n else:\\n\\n left_df = df\\n header_list = get_column_list(left_df)\\n individual_entropy_list_left = []\\n for each_column in header_list:\\n if each_column != 'class' and each_column != 'weight':\\n left_child_entropy = []\\n left_temp_df = pd.DataFrame(left_df[[each_column, \\\"class\\\", 'weight']][left_df[each_column] == 0],\\n columns=[each_column, 'class', 'weight'])\\n left_child_entropy.append(round(left_temp_df.loc[left_temp_df['class'] == 1, 'weight'].sum(), 2))\\n left_child_entropy.append(round(left_temp_df.loc[left_temp_df['class'] == -1, 'weight'].sum(), 2))\\n individual_entropy_list_left.append(entropy_calculation(left_child_entropy, left_df))\\n return individual_entropy_list_left\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bf5748a30421b620cf49808b20477b3\",\n \"score\": \"0.4857367\",\n \"text\": \"def is_converged(self):\\n prob = np.array(self._prob.values())\\n return np.all(np.logical_or(prob<THRESHOLD_LOWER_BOUND, prob>THRESHOLD_UPPER_BOUND))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff0aa15b762290ea7d30469e2fa573c5\",\n \"score\": \"0.4856261\",\n \"text\": \"def little_variation(M,m,p):\\r\\n return ( M*(1-float(p)/100) < m < M*(1+float(p)/100) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bab722b9d5d3d54dfb4e3df6a8ed5327\",\n \"score\": \"0.48550674\",\n \"text\": \"def decision(probability):\\n\\treturn random.random() > probability\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc67082adc2ad74b8b71f37f6e98621c\",\n \"score\": \"0.48462313\",\n \"text\": \"def check_convergence(metrics):\\n print(\\\"###### Evaluating sample size convergence ######\\\")\\n trained = False\\n direction = convergence_operator()\\n threshold = settings[\\\"data\\\"][\\\"convergence_limit\\\"]\\n \\n if settings[\\\"data\\\"][\\\"convergence_relative\\\"]:\\n window_size = 1 ###########\\n if len(metrics) < 2:\\n return False\\n else:\\n diff = np.diff(metrics)\\n\\n if direction(np.mean(diff[-window_size:]),0):\\n if abs(np.mean(diff[-window_size:])) < threshold:\\n trained = True\\n else:\\n if direction(metrics[-1],threshold):\\n trained = True\\n\\n print(f\\\"Sample size convergence metric: {settings['data']['convergence']} - {metrics[-1]}\\\")\\n\\n return trained\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ca3e27b67167e7f2666dab16fd5a798\",\n \"score\": \"0.48436168\",\n \"text\": \"def isSimilarTo(self, other):\\n cython.declare(T=cython.double)\\n cython.declare(Tdata=list)\\n \\n Tdata = [300,400,500,600,800,1000,1500,2000]\\n for T in Tdata:\\n if not (0.8 < self.getHeatCapacity(T) / other.getHeatCapacity(T) < 1.25):\\n return False\\n elif not (0.8 < self.getEnthalpy(T) / other.getEnthalpy(T) < 1.25):\\n return False\\n elif not (0.8 < self.getEntropy(T) / other.getEntropy(T) < 1.25):\\n return False\\n elif not (0.8 < self.getFreeEnergy(T) / other.getFreeEnergy(T) < 1.25):\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21af97891471a69dd66f8031370dca12\",\n \"score\": \"0.48369926\",\n \"text\": \"def deepCheckWeight( file ):\\n from math import isnan\\n from RootTools.core.Sample import Sample\\n\\n # convert dpm file pathes\\n sample = Sample.fromFiles(name=\\\"sample\\\", treeName=\\\"Events\\\", files=file)\\n # check for branch:\\n l = sample.chain.GetListOfBranches()\\n if not 'weight' in [ l.At(i).GetName() for i in range(l.GetSize()) ]:\\n return 0\\n val = sample.getYieldFromDraw(weightString=\\\"weight\\\" )['val']\\n del sample\\n #logger.debug(\\\"Val in deepCheckWeight: %r\\\", val) \\n return not isnan(val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"262e2256c8c79645ca80e4f534171a72\",\n \"score\": \"0.48350605\",\n \"text\": \"def revised_szeged_index(self):\\n if not self.is_connected():\\n return False\\n s = 0.0\\n D = self.distance_matrix()\\n for u, v in self.edges():\\n diff = D[u, :] - D[v, :]\\n o = (diff == 0).sum()\\n s += ((diff > 0).sum() + .5 * o) * ((diff < 0).sum() + .5 * o)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"141a0ee3c8bacdd041380cc8cf78e683\",\n \"score\": \"0.48326996\",\n \"text\": \"def eccenbin(df, thresh):\\n col = 'pl_highecc'\\n df[col] = None\\n for i, row in df.iterrows():\\n if row.pl_orbeccen + row.pl_orbeccenerr1 < thresh:\\n df.ix[i,col] = False\\n if row.pl_orbeccen + row.pl_orbeccenerr2 > thresh:\\n df.ix[i,col] = True\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"631de088809027f2138a2345a5cd0de0\",\n \"score\": \"0.48291457\",\n \"text\": \"def validate(self, s):\\r\\n nodes = [i for i, a in enumerate(s) if\\r\\n a != cf.SLEEP and self.network.get_node(i).energy >= (cf.SENSING_ENERGY +cf.COMMUNICATION_ENERGY)] # get list of the active nodes\\r\\n\\r\\n for t in range(cf.NUM_TARGETS):\\r\\n no_sense_prob = 0\\r\\n for n in nodes:\\r\\n no_sense_prob += self.sensing_log_matrix[t][n]\\r\\n if no_sense_prob >= self.sensing_log_threshold:\\r\\n break\\r\\n\\r\\n if no_sense_prob < self.sensing_log_threshold:\\r\\n return False\\r\\n\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5da2b496988fd6be3bc9e8a543ce23bf\",\n \"score\": \"0.48289847\",\n \"text\": \"def isNormal(self):\\n for A in self.subsets:\\n if self.function(A) < 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39932d3df9d7d30eb30db4223c906b83\",\n \"score\": \"0.48279193\",\n \"text\": \"def test_Cacciato09Cens7():\\n model = Cacciato09Cens(threshold=9.5)\\n lum_mc = model.mc_prim_galprop(prim_haloprop=np.ones(int(1e5)) * 5e11, seed=1)\\n assert np.all(lum_mc >= 10 ** model.threshold)\\n\\n # Check that luminosities follow the expected distribution.\\n def cdf(lum):\\n return np.array(\\n [\\n (\\n model.mean_occupation(prim_haloprop=5e11)\\n - model.mean_occupation(prim_haloprop=5e11, prim_galprop_min=l)\\n )\\n / model.mean_occupation(prim_haloprop=5e11)\\n for l in lum\\n ]\\n )\\n\\n p_value = kstest(lum_mc, cdf)[1]\\n\\n assert p_value > 0.001\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43cc7959fef16c09edae3765d2865a57\",\n \"score\": \"0.48257014\",\n \"text\": \"def sconfitto(self):\\n return self.vita <= 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ca71d30d2546f4d73416375191400e6\",\n \"score\": \"0.48147565\",\n \"text\": \"def prob52():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cea9e25bd2ef57be3a2c3708ab493ad9\",\n \"score\": \"0.4813539\",\n \"text\": \"def _likelihood():\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48faa7192845e3219bee90823c958449\",\n \"score\": \"0.4811759\",\n \"text\": \"def instance_assigned_simulations(self):\\n if not isinstance(self.parentnode, M4_16):\\n if any(self.parentnode.fpga_slots):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c69108d2758db2cec36bf7b66e975c\",\n \"score\": \"0.4807606\",\n \"text\": \"def do_a_decision(the_random_number):\\n if the_random_number <= 5.0:\\n return 'small decision'\\n else:\\n return 'big decision'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1a377cf80837ddfc35f2f9828f7f0ef\",\n \"score\": \"0.48062202\",\n \"text\": \"def check_is_gvh5074(self) -> bool:\\n return self._mfg_data_check(9, 6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26c3b26971b975055f96264c569a0beb\",\n \"score\": \"0.48012188\",\n \"text\": \"def activate(self, values):\\n\\n # First calculate the strength with which the perceptron fires\\n strength = np.dot(values, self.weights)\\n\\n # Then return 0 or 1 depending on strength compared to threshold\\n return int(strength > self.threshold)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27897bd2cadb1632642978bb57693c87\",\n \"score\": \"0.48001188\",\n \"text\": \"def is_normal(r, level=0.01):\\n if isinstance(r, pd.DataFrame):\\n return r.aggregate(is_normal)\\n else:\\n statistic, p_value = scipy.stats.jarque_bera(r)\\n return p_value > level\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":92,"cells":{"query_id":{"kind":"string","value":"0e10ce96f1b52e7bb623b034794ee907"},"query":{"kind":"string","value":"Get the primary database driver."},"positive_passages":{"kind":"list like","value":[{"docid":"287097578a00ab75ea8ce1b57de8262b","score":"0.8098757","text":"def db_driver(self) -> DbDriver:\n\n primary_db_driver = None\n\n # noinspection PyUnusedLocal\n def filter_db_drivers(service_id, service, config):\n nonlocal primary_db_driver\n if not isinstance(service, DbDriver):\n return False\n if config.get('primary', False):\n if primary_db_driver is not None:\n raise RuntimeError('There can only be a single primary database driver')\n primary_db_driver = service\n return True\n\n drivers = self._db_drivers.find_services(service_filter=filter_db_drivers)\n if primary_db_driver is None:\n if len(drivers) == 1:\n primary_db_driver = drivers[0]\n elif len(drivers) == 0:\n raise RuntimeError('No database driver found')\n else:\n raise RuntimeError('With multiple database drivers, one must be configured to be the primary one')\n\n # noinspection PyTypeChecker\n return primary_db_driver","title":""}],"string":"[\n {\n \"docid\": \"287097578a00ab75ea8ce1b57de8262b\",\n \"score\": \"0.8098757\",\n \"text\": \"def db_driver(self) -> DbDriver:\\n\\n primary_db_driver = None\\n\\n # noinspection PyUnusedLocal\\n def filter_db_drivers(service_id, service, config):\\n nonlocal primary_db_driver\\n if not isinstance(service, DbDriver):\\n return False\\n if config.get('primary', False):\\n if primary_db_driver is not None:\\n raise RuntimeError('There can only be a single primary database driver')\\n primary_db_driver = service\\n return True\\n\\n drivers = self._db_drivers.find_services(service_filter=filter_db_drivers)\\n if primary_db_driver is None:\\n if len(drivers) == 1:\\n primary_db_driver = drivers[0]\\n elif len(drivers) == 0:\\n raise RuntimeError('No database driver found')\\n else:\\n raise RuntimeError('With multiple database drivers, one must be configured to be the primary one')\\n\\n # noinspection PyTypeChecker\\n return primary_db_driver\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ea9c2f86bbbbb9465beea3207fedac07","score":"0.7569865","text":"def driver(self):\n return self._c.driver","title":""},{"docid":"00e26464acdf461fbf5fae34c90c48af","score":"0.7451214","text":"def _get_driver(self):\n if self._config and \"driver\" in self._config and \\\n \"name\" in self._config[\"driver\"]:\n return self._config[\"driver\"][\"name\"]\n return None","title":""},{"docid":"b826cb94f0ed30aba10f9feb5ec471bc","score":"0.7443861","text":"def driver(self):\n return self._dialect.driver","title":""},{"docid":"d3b559d3029683909163d6bf0b7df8b1","score":"0.7383822","text":"def _get_dbdriver_instance():\n if CONF.db_type == 'sql':\n return IMPL\n elif CONF.db_type == 'etcd':\n import zun.db.etcd.api as etcd_api\n return etcd_api.get_connection()\n else:\n raise exception.ConfigInvalid(\n _(\"db_type value of %s is invalid, \"\n \"must be sql or etcd\") % CONF.db_type)","title":""},{"docid":"4d2a775aff21e4422057c3c5da7ae8b1","score":"0.7305779","text":"def driver(self) -> str:\n return pulumi.get(self, \"driver\")","title":""},{"docid":"4d2a775aff21e4422057c3c5da7ae8b1","score":"0.7305779","text":"def driver(self) -> str:\n return pulumi.get(self, \"driver\")","title":""},{"docid":"4d2a775aff21e4422057c3c5da7ae8b1","score":"0.7305779","text":"def driver(self) -> str:\n return pulumi.get(self, \"driver\")","title":""},{"docid":"4d2a775aff21e4422057c3c5da7ae8b1","score":"0.7305779","text":"def driver(self) -> str:\n return pulumi.get(self, \"driver\")","title":""},{"docid":"c9a3c6f85770664aa32b0eec808123ae","score":"0.7287726","text":"def driver(self) -> str:\n return self.__driver","title":""},{"docid":"6ce23da40f54c7aabd97ffcba11073c7","score":"0.71992856","text":"def driver(self):\n\t\tif self.dev:\n\t\t\treturn Driver(pyf.fp_dev_get_driver(self.dev))\n\t\tif self.dscv:\n\t\t\treturn Driver(pyf.fp_dscv_dev_get_driver(self.dscv))","title":""},{"docid":"61eb3dbadc66fefcee473a3acba55c01","score":"0.71473116","text":"def driver_id(self):\n\t\treturn pyf.fp_driver_get_driver_id(self.drv)","title":""},{"docid":"099e179c479331282caf566dcce39a2a","score":"0.6994633","text":"def get_driver(self):\n if self.plaid is not None:\n driver = self.plaid.drivers\n elif IS_MACOS:\n driver = pynvx.cudaSystemGetDriverVersion(ignore=True)\n else:\n try:\n driver = pynvml.nvmlSystemGetDriverVersion().decode(\"utf-8\")\n except pynvml.NVMLError:\n driver = \"No Nvidia driver found\"\n if self.logger:\n self.logger.debug(\"GPU Driver: %s\", driver)\n return driver","title":""},{"docid":"460279b03197b4aded83a2dda7a82ab9","score":"0.6889848","text":"def driver_id(self):\n return self._driver_id","title":""},{"docid":"460279b03197b4aded83a2dda7a82ab9","score":"0.6889848","text":"def driver_id(self):\n return self._driver_id","title":""},{"docid":"fc264c5da0fb911c4f1af47378da7587","score":"0.6871765","text":"def driver_protocol(self):\n return self._driver_protocol","title":""},{"docid":"9ea33acf37bea67703d3d70b1b16b034","score":"0.67590284","text":"def get_driver(conf):\n return _get_driver(conf.storage.driver,\n conf.storage)","title":""},{"docid":"9fd7baca2772dca05a4ad15202fac1c7","score":"0.6708061","text":"def driver(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"driver\")","title":""},{"docid":"9fd7baca2772dca05a4ad15202fac1c7","score":"0.6708061","text":"def driver(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"driver\")","title":""},{"docid":"9fd7baca2772dca05a4ad15202fac1c7","score":"0.6708061","text":"def driver(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"driver\")","title":""},{"docid":"9fd7baca2772dca05a4ad15202fac1c7","score":"0.6708061","text":"def driver(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"driver\")","title":""},{"docid":"313baa47fc4a233ccaef980cf1fc9979","score":"0.66667956","text":"def get_db():\n return DatabaseServer.get_instance()","title":""},{"docid":"b077c475de5d840bc19797a77b29aac7","score":"0.6631761","text":"def get_backend_driver(self):\n if self.backend_driver is None:\n context = get_application_context()\n\n if context == CONTEXT_NORMAL:\n driver = TestBackendDriver\n elif context == CONTEXT_TEST:\n driver = TestBackendDriver\n else:\n raise RuntimeError(\"unknonw application context: %s\" % context)\n\n self.backend_driver = driver\n\n return self.backend_driver","title":""},{"docid":"fe96f4d8ac3006493fdce764d97789a2","score":"0.6545788","text":"def get_default_engine():\n\n return get_component(DatabasePackage.COMPONENT_NAME).get_default_engine()","title":""},{"docid":"1f8977be62ab87aae144f765fa556a0d","score":"0.65122044","text":"def get_driver():\n pass","title":""},{"docid":"fcd1d36106cc80f0829044b41f4aca49","score":"0.6484046","text":"def driver(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"driver\")","title":""},{"docid":"fcd1d36106cc80f0829044b41f4aca49","score":"0.6484046","text":"def driver(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"driver\")","title":""},{"docid":"fcd1d36106cc80f0829044b41f4aca49","score":"0.6484046","text":"def driver(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"driver\")","title":""},{"docid":"fcd1d36106cc80f0829044b41f4aca49","score":"0.6484046","text":"def driver(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"driver\")","title":""},{"docid":"26d9ca1035448806024b894d36c6749e","score":"0.64766526","text":"def _get_driver(name, conf):\n d = driver.DriverManager('gnocchi.storage',\n name).driver\n return d(conf)","title":""},{"docid":"4bcaddbc686ed78ab0567a9280989a6a","score":"0.64453536","text":"def driver_version(self):\n return self._driver_version","title":""},{"docid":"fde50d6509d43221f0acd754b7ecdaac","score":"0.6436149","text":"def default_database(self) -> Optional[str]:\n return pulumi.get(self, \"default_database\")","title":""},{"docid":"fde50d6509d43221f0acd754b7ecdaac","score":"0.6436149","text":"def default_database(self) -> Optional[str]:\n return pulumi.get(self, \"default_database\")","title":""},{"docid":"f327b0818c9a5c9f7a7fb0b85da396e8","score":"0.64345896","text":"def driver_vendor(self):\n return self._driver_vendor","title":""},{"docid":"76f7f45ccd9c16b7d23b9289bcc69fdc","score":"0.64014137","text":"def db_engine(self):\n if self.__connection is not None:\n return self.__db_name, self.__connection.version\n else:\n print(\"Not connected\")","title":""},{"docid":"e7deb58f9bdab2a972269f126cfc3c94","score":"0.63967466","text":"def get_db():\n if not hasattr(g, 'oracle_db'):\n g.oracle_db = connect_db()\n return g.oracle_db","title":""},{"docid":"375a5cef41b15eef2192ac2df8c67a3a","score":"0.63757753","text":"def get_db_name():\n\tconfig = SafeConfigParser()\n\tconfig.read('config.ini')\n\tdb = config.get('Mongo_Config', 'database')\n\treturn db","title":""},{"docid":"48ec4937c08e08712b544de929038494","score":"0.6345324","text":"def driver_id(self):\n\t\tif self.data_ptr:\n\t\t\treturn pyf.fp_print_data_get_driver_id(self.data_ptr)\n\t\telif self.dscv_ptr:\n\t\t\treturn pyf.fp_dscv_print_get_driver_id(self.dscv_ptr)\n\t\traise \"no print\"","title":""},{"docid":"185da7a87920d3a9bbfc8d501d683351","score":"0.6331698","text":"def get_connection(self):\n return self._db","title":""},{"docid":"fbfd73bf5ce96ee551fd76117b708692","score":"0.63186634","text":"def get_driver(backend):\n host, port = get_neo4j_host_and_port()\n scheme = \"bolt://%s:%d\" % (host, port)\n return Driver(backend, scheme, get_authorization())","title":""},{"docid":"a06f60d16dfafbb82597f4ce753ac8bf","score":"0.6311597","text":"def get_driver(cls, name):\r\n\r\n LOG.debug('Looking for driver %s in %s', name, cls.__plugin_ns__)\r\n\r\n mgr = driver.DriverManager(cls.__plugin_ns__, name)\r\n\r\n return mgr.driver","title":""},{"docid":"88825e67a67039d31a776941f613cf69","score":"0.63104516","text":"def get_db():\n\n return db_wrapper.database","title":""},{"docid":"b324045365417bd6c28e4f3d33da5245","score":"0.6293812","text":"def get_db():\n\tif not hasattr(g, 'sqlite_db'):\n\t\tg.sqlite_db = connect_db()\n\n\treturn g.sqlite_db","title":""},{"docid":"819edf44e055902535c91115abcd34c9","score":"0.62842745","text":"def get_connection(self):\n return self._db_connection","title":""},{"docid":"6b7a4d5a7903c237f33675483627c924","score":"0.62807256","text":"def get_db():\n if not hasattr(g, \"sqlite_db\"):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"42a923c024478558ce79f1773ad39b2f","score":"0.6277844","text":"def database(self) -> str:\n return pulumi.get(self, \"database\")","title":""},{"docid":"42a923c024478558ce79f1773ad39b2f","score":"0.6277844","text":"def database(self) -> str:\n return pulumi.get(self, \"database\")","title":""},{"docid":"5d1fc753492278446ac1e3ed211f0f7d","score":"0.62718475","text":"def getdb(self):\n return self._database","title":""},{"docid":"9b08fecb3d74bdf9fbf7f400fdff4c75","score":"0.62621504","text":"def get_db():\r\n if not hasattr(g, 'sqlite_db'):\r\n g.sqlite_db = connect_db()\r\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"9707f4a0631a0b305b746ea7c9346b40","score":"0.6261481","text":"def get_db():\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"930d7ae3738ea14ac11e56ffde65f641","score":"0.6240257","text":"def getdriver(self):\n idn = self.idn()\n if not idn:\n logger.warning(\"IDN not found %s.\")\n return\n vendor = self.__namecleanup(idn[0])\n model = self.__namecleanup(idn[1])\n self.vendormodel = vendor + model\n return self.vendormodel","title":""},{"docid":"7ddabf3e66c156791d9ee8b574f88ed7","score":"0.623206","text":"def get_db():\n\tif not hasattr(g, 'sqlite_db'):\n\t\tg.sqlite_db = connect_db()\n\treturn g.sqlite_db","title":""},{"docid":"a72d16d940dd7c8d83d383602b67d820","score":"0.6231732","text":"def backend(self):\n return self.provider.get_backend(self.backend_name)","title":""},{"docid":"fe16e72d56ad3ed1ccf3dbcdc5105d5d","score":"0.6230065","text":"def get_database(self):\n if self.get('dbtype') == 'sqlite':\n return pw.SqliteDatabase(self.get('dbname'),\n pragmas={'journal_mode': 'wal',\n 'cache_size': -1 * 64000})\n else:\n if self.get('username') is None or self.get('password') is None:\n raise AttributeError('[ERROR] dbtype %s requires username and'\n ' password.\\n' % str(self.get('dbtype')))\n if self.get('hostname') is None:\n self.set('hostname', 'localhost')\n if self.get('dbtype') == 'mysql':\n if self.get('port') is None or self.get('port') == '':\n self.set('port', str(3306))\n return pw.MySQLDatabase(\n self.get('dbname'),\n user=self.get('username'),\n password=self.get('password'),\n host=self.get('hostname'),\n port=int(self.get('port')))\n elif self.get('dbtype') == 'postgres':\n if self.get('port') is None or self.get('port') == '':\n self.set('port', str(5432))\n return pw.PostgresqlDatabase(\n self.get('dbname'),\n user=self.get('username'),\n password=self.get('password'),\n host=self.get('hostname'),\n port=int(self.get('port')))","title":""},{"docid":"47570e6202f938364da72d5505982373","score":"0.6204253","text":"def get_default_database_name():\n\n return get_component(DatabasePackage.COMPONENT_NAME).get_default_database_name()","title":""},{"docid":"96e855f6a1253725e79bf5540b9a4070","score":"0.61888283","text":"def get_database(self, database=None):\n if not self.connection:\n raise ConnectionFailure('No connection')\n if not database:\n if not self._database:\n raise Exception('No database submitted')\n database = self._database\n return self.connection[database]","title":""},{"docid":"8caa5ad96118eb4b45416c937cf6044c","score":"0.6181815","text":"def database_name(self):\n return self.__db","title":""},{"docid":"f60e77c828d268907e32bb29dd75b701","score":"0.616323","text":"def get_database(self):\n\n return self.__database","title":""},{"docid":"981bc7087205722b9b2c024e67037393","score":"0.6155437","text":"def dbname(self):\n\n if self.isolate_db:\n return get_random_identifier(\n max_len=MAX_IDENTIFIER_LEN,\n prefix_str=DbSession.DB_PREFIX_NAME\n )\n else:\n return self._base_conn_params['dbname']","title":""},{"docid":"cbc8d0bf5c2e22c2681c2cabf39ae4e6","score":"0.6142031","text":"def get_database(self):\n return self.__c","title":""},{"docid":"6cb31a1bd2d06a29de506194ee3c99e7","score":"0.61184365","text":"def db_name(self):\n if self.__connection is not None:\n return self.__db_name\n else:\n print(\"Not connected\")","title":""},{"docid":"0a4680bb02cca30e7ffa42b1f229e458","score":"0.6111468","text":"def get_db():\n if not hasattr(g, 'pg_db'):\n g.pg_db = connect_db()\n return g.pg_db","title":""},{"docid":"fe73dc58c6df927526f37f85c88dcf3a","score":"0.6088333","text":"def get_database(self, database=None):\n database = database if database !=None else self.database\n \n if self._database is None:\n conn = self.get_connection()\n db = conn[database]\n self._database = db\n \n return self._database","title":""},{"docid":"e8a8e3595f5f19e15305c4161573d1cb","score":"0.60825795","text":"def getDatabaseName(self):\n return self._base.getDatabaseName()","title":""},{"docid":"34a13446b9632ae323261b298f130474","score":"0.6080003","text":"def get_db():\n if not hasattr(g, 'sqlite_db'): #store db in a global variable 'g'\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"292564626349474c7eec3dfa443766de","score":"0.60793155","text":"def get_db():\n if 'app_db' not in g:\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"e1439839b958f651e44403a553f35cd2","score":"0.60618937","text":"def get_db_client():\n db, user, password, host = get_db_parameters()\n return psycopg2.connect(database=db, user=user, host=host, password=password)","title":""},{"docid":"8adcb3f0dc5f64fa10bf010c80e2dea4","score":"0.60596836","text":"def db(self):\n if not self._database:\n if cfg.uri and 'replicaSet' in cfg.uri:\n conn = pymongo.MongoReplicaSetClient(cfg.uri)\n else:\n conn = pymongo.MongoClient(cfg.uri)\n\n self._database = conn[cfg.database]\n\n return self._database","title":""},{"docid":"1c0602f968e269866160a0b6f8d5b658","score":"0.60579604","text":"def get_db():\r\n # top = _app_ctx_stack.top\r\n # if not hasattr(top, 'sqlite_db'):\r\n # top.sqlite_db = connect_db()\r\n # return top.sqlite_db\r\n if not hasattr(g, 'sqlite_db'):\r\n g.sqlite_db = connect_db()\r\n return g.sqlite_db","title":""},{"docid":"67e1b128e7c4ecc63f1cf9de6ca95a3c","score":"0.60555863","text":"def dbname(self):\n return self._dbname","title":""},{"docid":"67e1b128e7c4ecc63f1cf9de6ca95a3c","score":"0.60555863","text":"def dbname(self):\n return self._dbname","title":""},{"docid":"daedd58f99d639df264379f95bc6e118","score":"0.60529375","text":"def sql_engine(self) -> str:\n return pulumi.get(self, \"sql_engine\")","title":""},{"docid":"7cfdea3e647f215200e143568a04bff3","score":"0.6052886","text":"def get_db():\n ## todo: use connection pooling\n import psycopg2\n params = get_db_connection_params()\n return psycopg2.connect(**params)","title":""},{"docid":"01ec1dedd6d8d723f7b08b6ece50e4e1","score":"0.60479516","text":"def get_db():\n\n #return globalDB\n if not hasattr(g, 'sqlite_db'):\n g.sqlite_db = connect_db()\n return g.sqlite_db","title":""},{"docid":"77ff73efddcfbcb35d499add2395163f","score":"0.6044578","text":"def get_db_host():\n if TEST_POSTGRESQL and not TEST_WITH_TLS:\n return PG_UNIX_HOST\n else:\n return DB_HOST","title":""}],"string":"[\n {\n \"docid\": \"ea9c2f86bbbbb9465beea3207fedac07\",\n \"score\": \"0.7569865\",\n \"text\": \"def driver(self):\\n return self._c.driver\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e26464acdf461fbf5fae34c90c48af\",\n \"score\": \"0.7451214\",\n \"text\": \"def _get_driver(self):\\n if self._config and \\\"driver\\\" in self._config and \\\\\\n \\\"name\\\" in self._config[\\\"driver\\\"]:\\n return self._config[\\\"driver\\\"][\\\"name\\\"]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b826cb94f0ed30aba10f9feb5ec471bc\",\n \"score\": \"0.7443861\",\n \"text\": \"def driver(self):\\n return self._dialect.driver\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b559d3029683909163d6bf0b7df8b1\",\n \"score\": \"0.7383822\",\n \"text\": \"def _get_dbdriver_instance():\\n if CONF.db_type == 'sql':\\n return IMPL\\n elif CONF.db_type == 'etcd':\\n import zun.db.etcd.api as etcd_api\\n return etcd_api.get_connection()\\n else:\\n raise exception.ConfigInvalid(\\n _(\\\"db_type value of %s is invalid, \\\"\\n \\\"must be sql or etcd\\\") % CONF.db_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d2a775aff21e4422057c3c5da7ae8b1\",\n \"score\": \"0.7305779\",\n \"text\": \"def driver(self) -> str:\\n return pulumi.get(self, \\\"driver\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d2a775aff21e4422057c3c5da7ae8b1\",\n \"score\": \"0.7305779\",\n \"text\": \"def driver(self) -> str:\\n return pulumi.get(self, \\\"driver\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d2a775aff21e4422057c3c5da7ae8b1\",\n \"score\": \"0.7305779\",\n \"text\": \"def driver(self) -> str:\\n return pulumi.get(self, \\\"driver\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d2a775aff21e4422057c3c5da7ae8b1\",\n \"score\": \"0.7305779\",\n \"text\": \"def driver(self) -> str:\\n return pulumi.get(self, \\\"driver\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9a3c6f85770664aa32b0eec808123ae\",\n \"score\": \"0.7287726\",\n \"text\": \"def driver(self) -> str:\\n return self.__driver\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ce23da40f54c7aabd97ffcba11073c7\",\n \"score\": \"0.71992856\",\n \"text\": \"def driver(self):\\n\\t\\tif self.dev:\\n\\t\\t\\treturn Driver(pyf.fp_dev_get_driver(self.dev))\\n\\t\\tif self.dscv:\\n\\t\\t\\treturn Driver(pyf.fp_dscv_dev_get_driver(self.dscv))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61eb3dbadc66fefcee473a3acba55c01\",\n \"score\": \"0.71473116\",\n \"text\": \"def driver_id(self):\\n\\t\\treturn pyf.fp_driver_get_driver_id(self.drv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"099e179c479331282caf566dcce39a2a\",\n \"score\": \"0.6994633\",\n \"text\": \"def get_driver(self):\\n if self.plaid is not None:\\n driver = self.plaid.drivers\\n elif IS_MACOS:\\n driver = pynvx.cudaSystemGetDriverVersion(ignore=True)\\n else:\\n try:\\n driver = pynvml.nvmlSystemGetDriverVersion().decode(\\\"utf-8\\\")\\n except pynvml.NVMLError:\\n driver = \\\"No Nvidia driver found\\\"\\n if self.logger:\\n self.logger.debug(\\\"GPU Driver: %s\\\", driver)\\n return driver\",\n \"title\": \"\"\n },\n {\n \"docid\": \"460279b03197b4aded83a2dda7a82ab9\",\n \"score\": \"0.6889848\",\n \"text\": \"def driver_id(self):\\n return self._driver_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"460279b03197b4aded83a2dda7a82ab9\",\n \"score\": \"0.6889848\",\n \"text\": \"def driver_id(self):\\n return self._driver_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc264c5da0fb911c4f1af47378da7587\",\n \"score\": \"0.6871765\",\n \"text\": \"def driver_protocol(self):\\n return self._driver_protocol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ea33acf37bea67703d3d70b1b16b034\",\n \"score\": \"0.67590284\",\n \"text\": \"def get_driver(conf):\\n return _get_driver(conf.storage.driver,\\n conf.storage)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fd7baca2772dca05a4ad15202fac1c7\",\n \"score\": \"0.6708061\",\n \"text\": \"def driver(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"driver\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fd7baca2772dca05a4ad15202fac1c7\",\n \"score\": \"0.6708061\",\n \"text\": \"def driver(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"driver\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fd7baca2772dca05a4ad15202fac1c7\",\n \"score\": \"0.6708061\",\n \"text\": \"def driver(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"driver\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fd7baca2772dca05a4ad15202fac1c7\",\n \"score\": \"0.6708061\",\n \"text\": \"def driver(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"driver\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"313baa47fc4a233ccaef980cf1fc9979\",\n \"score\": \"0.66667956\",\n \"text\": \"def get_db():\\n return DatabaseServer.get_instance()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b077c475de5d840bc19797a77b29aac7\",\n \"score\": \"0.6631761\",\n \"text\": \"def get_backend_driver(self):\\n if self.backend_driver is None:\\n context = get_application_context()\\n\\n if context == CONTEXT_NORMAL:\\n driver = TestBackendDriver\\n elif context == CONTEXT_TEST:\\n driver = TestBackendDriver\\n else:\\n raise RuntimeError(\\\"unknonw application context: %s\\\" % context)\\n\\n self.backend_driver = driver\\n\\n return self.backend_driver\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe96f4d8ac3006493fdce764d97789a2\",\n \"score\": \"0.6545788\",\n \"text\": \"def get_default_engine():\\n\\n return get_component(DatabasePackage.COMPONENT_NAME).get_default_engine()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f8977be62ab87aae144f765fa556a0d\",\n \"score\": \"0.65122044\",\n \"text\": \"def get_driver():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcd1d36106cc80f0829044b41f4aca49\",\n \"score\": \"0.6484046\",\n \"text\": \"def driver(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"driver\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcd1d36106cc80f0829044b41f4aca49\",\n \"score\": \"0.6484046\",\n \"text\": \"def driver(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"driver\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcd1d36106cc80f0829044b41f4aca49\",\n \"score\": \"0.6484046\",\n \"text\": \"def driver(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"driver\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcd1d36106cc80f0829044b41f4aca49\",\n \"score\": \"0.6484046\",\n \"text\": \"def driver(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"driver\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26d9ca1035448806024b894d36c6749e\",\n \"score\": \"0.64766526\",\n \"text\": \"def _get_driver(name, conf):\\n d = driver.DriverManager('gnocchi.storage',\\n name).driver\\n return d(conf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcaddbc686ed78ab0567a9280989a6a\",\n \"score\": \"0.64453536\",\n \"text\": \"def driver_version(self):\\n return self._driver_version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fde50d6509d43221f0acd754b7ecdaac\",\n \"score\": \"0.6436149\",\n \"text\": \"def default_database(self) -> Optional[str]:\\n return pulumi.get(self, \\\"default_database\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fde50d6509d43221f0acd754b7ecdaac\",\n \"score\": \"0.6436149\",\n \"text\": \"def default_database(self) -> Optional[str]:\\n return pulumi.get(self, \\\"default_database\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f327b0818c9a5c9f7a7fb0b85da396e8\",\n \"score\": \"0.64345896\",\n \"text\": \"def driver_vendor(self):\\n return self._driver_vendor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76f7f45ccd9c16b7d23b9289bcc69fdc\",\n \"score\": \"0.64014137\",\n \"text\": \"def db_engine(self):\\n if self.__connection is not None:\\n return self.__db_name, self.__connection.version\\n else:\\n print(\\\"Not connected\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7deb58f9bdab2a972269f126cfc3c94\",\n \"score\": \"0.63967466\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'oracle_db'):\\n g.oracle_db = connect_db()\\n return g.oracle_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"375a5cef41b15eef2192ac2df8c67a3a\",\n \"score\": \"0.63757753\",\n \"text\": \"def get_db_name():\\n\\tconfig = SafeConfigParser()\\n\\tconfig.read('config.ini')\\n\\tdb = config.get('Mongo_Config', 'database')\\n\\treturn db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48ec4937c08e08712b544de929038494\",\n \"score\": \"0.6345324\",\n \"text\": \"def driver_id(self):\\n\\t\\tif self.data_ptr:\\n\\t\\t\\treturn pyf.fp_print_data_get_driver_id(self.data_ptr)\\n\\t\\telif self.dscv_ptr:\\n\\t\\t\\treturn pyf.fp_dscv_print_get_driver_id(self.dscv_ptr)\\n\\t\\traise \\\"no print\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"185da7a87920d3a9bbfc8d501d683351\",\n \"score\": \"0.6331698\",\n \"text\": \"def get_connection(self):\\n return self._db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbfd73bf5ce96ee551fd76117b708692\",\n \"score\": \"0.63186634\",\n \"text\": \"def get_driver(backend):\\n host, port = get_neo4j_host_and_port()\\n scheme = \\\"bolt://%s:%d\\\" % (host, port)\\n return Driver(backend, scheme, get_authorization())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a06f60d16dfafbb82597f4ce753ac8bf\",\n \"score\": \"0.6311597\",\n \"text\": \"def get_driver(cls, name):\\r\\n\\r\\n LOG.debug('Looking for driver %s in %s', name, cls.__plugin_ns__)\\r\\n\\r\\n mgr = driver.DriverManager(cls.__plugin_ns__, name)\\r\\n\\r\\n return mgr.driver\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88825e67a67039d31a776941f613cf69\",\n \"score\": \"0.63104516\",\n \"text\": \"def get_db():\\n\\n return db_wrapper.database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b324045365417bd6c28e4f3d33da5245\",\n \"score\": \"0.6293812\",\n \"text\": \"def get_db():\\n\\tif not hasattr(g, 'sqlite_db'):\\n\\t\\tg.sqlite_db = connect_db()\\n\\n\\treturn g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"819edf44e055902535c91115abcd34c9\",\n \"score\": \"0.62842745\",\n \"text\": \"def get_connection(self):\\n return self._db_connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b7a4d5a7903c237f33675483627c924\",\n \"score\": \"0.62807256\",\n \"text\": \"def get_db():\\n if not hasattr(g, \\\"sqlite_db\\\"):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42a923c024478558ce79f1773ad39b2f\",\n \"score\": \"0.6277844\",\n \"text\": \"def database(self) -> str:\\n return pulumi.get(self, \\\"database\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42a923c024478558ce79f1773ad39b2f\",\n \"score\": \"0.6277844\",\n \"text\": \"def database(self) -> str:\\n return pulumi.get(self, \\\"database\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d1fc753492278446ac1e3ed211f0f7d\",\n \"score\": \"0.62718475\",\n \"text\": \"def getdb(self):\\n return self._database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b08fecb3d74bdf9fbf7f400fdff4c75\",\n \"score\": \"0.62621504\",\n \"text\": \"def get_db():\\r\\n if not hasattr(g, 'sqlite_db'):\\r\\n g.sqlite_db = connect_db()\\r\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9707f4a0631a0b305b746ea7c9346b40\",\n \"score\": \"0.6261481\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"930d7ae3738ea14ac11e56ffde65f641\",\n \"score\": \"0.6240257\",\n \"text\": \"def getdriver(self):\\n idn = self.idn()\\n if not idn:\\n logger.warning(\\\"IDN not found %s.\\\")\\n return\\n vendor = self.__namecleanup(idn[0])\\n model = self.__namecleanup(idn[1])\\n self.vendormodel = vendor + model\\n return self.vendormodel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ddabf3e66c156791d9ee8b574f88ed7\",\n \"score\": \"0.623206\",\n \"text\": \"def get_db():\\n\\tif not hasattr(g, 'sqlite_db'):\\n\\t\\tg.sqlite_db = connect_db()\\n\\treturn g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a72d16d940dd7c8d83d383602b67d820\",\n \"score\": \"0.6231732\",\n \"text\": \"def backend(self):\\n return self.provider.get_backend(self.backend_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe16e72d56ad3ed1ccf3dbcdc5105d5d\",\n \"score\": \"0.6230065\",\n \"text\": \"def get_database(self):\\n if self.get('dbtype') == 'sqlite':\\n return pw.SqliteDatabase(self.get('dbname'),\\n pragmas={'journal_mode': 'wal',\\n 'cache_size': -1 * 64000})\\n else:\\n if self.get('username') is None or self.get('password') is None:\\n raise AttributeError('[ERROR] dbtype %s requires username and'\\n ' password.\\\\n' % str(self.get('dbtype')))\\n if self.get('hostname') is None:\\n self.set('hostname', 'localhost')\\n if self.get('dbtype') == 'mysql':\\n if self.get('port') is None or self.get('port') == '':\\n self.set('port', str(3306))\\n return pw.MySQLDatabase(\\n self.get('dbname'),\\n user=self.get('username'),\\n password=self.get('password'),\\n host=self.get('hostname'),\\n port=int(self.get('port')))\\n elif self.get('dbtype') == 'postgres':\\n if self.get('port') is None or self.get('port') == '':\\n self.set('port', str(5432))\\n return pw.PostgresqlDatabase(\\n self.get('dbname'),\\n user=self.get('username'),\\n password=self.get('password'),\\n host=self.get('hostname'),\\n port=int(self.get('port')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47570e6202f938364da72d5505982373\",\n \"score\": \"0.6204253\",\n \"text\": \"def get_default_database_name():\\n\\n return get_component(DatabasePackage.COMPONENT_NAME).get_default_database_name()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96e855f6a1253725e79bf5540b9a4070\",\n \"score\": \"0.61888283\",\n \"text\": \"def get_database(self, database=None):\\n if not self.connection:\\n raise ConnectionFailure('No connection')\\n if not database:\\n if not self._database:\\n raise Exception('No database submitted')\\n database = self._database\\n return self.connection[database]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8caa5ad96118eb4b45416c937cf6044c\",\n \"score\": \"0.6181815\",\n \"text\": \"def database_name(self):\\n return self.__db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f60e77c828d268907e32bb29dd75b701\",\n \"score\": \"0.616323\",\n \"text\": \"def get_database(self):\\n\\n return self.__database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"981bc7087205722b9b2c024e67037393\",\n \"score\": \"0.6155437\",\n \"text\": \"def dbname(self):\\n\\n if self.isolate_db:\\n return get_random_identifier(\\n max_len=MAX_IDENTIFIER_LEN,\\n prefix_str=DbSession.DB_PREFIX_NAME\\n )\\n else:\\n return self._base_conn_params['dbname']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbc8d0bf5c2e22c2681c2cabf39ae4e6\",\n \"score\": \"0.6142031\",\n \"text\": \"def get_database(self):\\n return self.__c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cb31a1bd2d06a29de506194ee3c99e7\",\n \"score\": \"0.61184365\",\n \"text\": \"def db_name(self):\\n if self.__connection is not None:\\n return self.__db_name\\n else:\\n print(\\\"Not connected\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4680bb02cca30e7ffa42b1f229e458\",\n \"score\": \"0.6111468\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'pg_db'):\\n g.pg_db = connect_db()\\n return g.pg_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe73dc58c6df927526f37f85c88dcf3a\",\n \"score\": \"0.6088333\",\n \"text\": \"def get_database(self, database=None):\\n database = database if database !=None else self.database\\n \\n if self._database is None:\\n conn = self.get_connection()\\n db = conn[database]\\n self._database = db\\n \\n return self._database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8a8e3595f5f19e15305c4161573d1cb\",\n \"score\": \"0.60825795\",\n \"text\": \"def getDatabaseName(self):\\n return self._base.getDatabaseName()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34a13446b9632ae323261b298f130474\",\n \"score\": \"0.6080003\",\n \"text\": \"def get_db():\\n if not hasattr(g, 'sqlite_db'): #store db in a global variable 'g'\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"292564626349474c7eec3dfa443766de\",\n \"score\": \"0.60793155\",\n \"text\": \"def get_db():\\n if 'app_db' not in g:\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1439839b958f651e44403a553f35cd2\",\n \"score\": \"0.60618937\",\n \"text\": \"def get_db_client():\\n db, user, password, host = get_db_parameters()\\n return psycopg2.connect(database=db, user=user, host=host, password=password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8adcb3f0dc5f64fa10bf010c80e2dea4\",\n \"score\": \"0.60596836\",\n \"text\": \"def db(self):\\n if not self._database:\\n if cfg.uri and 'replicaSet' in cfg.uri:\\n conn = pymongo.MongoReplicaSetClient(cfg.uri)\\n else:\\n conn = pymongo.MongoClient(cfg.uri)\\n\\n self._database = conn[cfg.database]\\n\\n return self._database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c0602f968e269866160a0b6f8d5b658\",\n \"score\": \"0.60579604\",\n \"text\": \"def get_db():\\r\\n # top = _app_ctx_stack.top\\r\\n # if not hasattr(top, 'sqlite_db'):\\r\\n # top.sqlite_db = connect_db()\\r\\n # return top.sqlite_db\\r\\n if not hasattr(g, 'sqlite_db'):\\r\\n g.sqlite_db = connect_db()\\r\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67e1b128e7c4ecc63f1cf9de6ca95a3c\",\n \"score\": \"0.60555863\",\n \"text\": \"def dbname(self):\\n return self._dbname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67e1b128e7c4ecc63f1cf9de6ca95a3c\",\n \"score\": \"0.60555863\",\n \"text\": \"def dbname(self):\\n return self._dbname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daedd58f99d639df264379f95bc6e118\",\n \"score\": \"0.60529375\",\n \"text\": \"def sql_engine(self) -> str:\\n return pulumi.get(self, \\\"sql_engine\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cfdea3e647f215200e143568a04bff3\",\n \"score\": \"0.6052886\",\n \"text\": \"def get_db():\\n ## todo: use connection pooling\\n import psycopg2\\n params = get_db_connection_params()\\n return psycopg2.connect(**params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01ec1dedd6d8d723f7b08b6ece50e4e1\",\n \"score\": \"0.60479516\",\n \"text\": \"def get_db():\\n\\n #return globalDB\\n if not hasattr(g, 'sqlite_db'):\\n g.sqlite_db = connect_db()\\n return g.sqlite_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77ff73efddcfbcb35d499add2395163f\",\n \"score\": \"0.6044578\",\n \"text\": \"def get_db_host():\\n if TEST_POSTGRESQL and not TEST_WITH_TLS:\\n return PG_UNIX_HOST\\n else:\\n return DB_HOST\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":93,"cells":{"query_id":{"kind":"string","value":"c3b2cf2d9c71baccb00c198de43cf5af"},"query":{"kind":"string","value":"Iterates columnwise over the marker alignment. Excludes samples."},"positive_passages":{"kind":"list like","value":[{"docid":"300674607fabeb0d2f4d672f073bedbc","score":"0.5351515","text":"def iter_marker_sites(self, start=0, stop=None, size=1):\n if self.markers is None or self.markers.nrows == 0:\n return\n if stop is None:\n stop = self.nsites\n if (stop - start) % size != 0:\n raise ValueError('Alignment cannot be completely divided into '\n 'chucks of size {}'.format(size))\n for i in range(start, stop, size):\n yield [s[i:i+size] for s in self.markers.sequences]","title":""}],"string":"[\n {\n \"docid\": \"300674607fabeb0d2f4d672f073bedbc\",\n \"score\": \"0.5351515\",\n \"text\": \"def iter_marker_sites(self, start=0, stop=None, size=1):\\n if self.markers is None or self.markers.nrows == 0:\\n return\\n if stop is None:\\n stop = self.nsites\\n if (stop - start) % size != 0:\\n raise ValueError('Alignment cannot be completely divided into '\\n 'chucks of size {}'.format(size))\\n for i in range(start, stop, size):\\n yield [s[i:i+size] for s in self.markers.sequences]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a0d44a244dde0b542e3a237cd14452b2","score":"0.55822045","text":"def others_in_column(self, row_, col_):\n for r in range(9):\n if r != row_:\n yield self[r, col_]","title":""},{"docid":"5988acb9ea0c626347aba9bba02a519c","score":"0.54252195","text":"def iter_markers(self):\n for i in self.nmarkers:\n yield Record(\n self.markers.ids[i],\n self.markers.descriptions[i],\n self.markers.sequences[i],\n )","title":""},{"docid":"f46c3bfebc4e391badd53d4010381023","score":"0.52099943","text":"def _get_me_the_result_by_looping_through_each_col(self, fkp, row, col, count_for_output, reference, output,\n alignment_result):\n # push same tokens to alignment_result (#3)\n # if it is not zero, which means the reference and the output are the same in this part.\n if fkp.get_n_match(n_row=row, n_col=col) > 0: # 3\n for i in range(fkp.get_n_match(n_row=row, n_col=col)):\n count_for_output -= 1\n self._alignment_add(reference[fkp.get_n(n_row=row, n_col=col)-1-i], output[count_for_output],\n alignment_result)\n # distinguish insert / del / sub according to #2\n # no matter if the third number is zero or not, we will have to look at the second number;\n return self._second_number(row, col, count_for_output, reference, output, fkp, alignment_result)","title":""},{"docid":"eda9b179be47168f8af1ded2289e7306","score":"0.513998","text":"def _iter_offset(self):\n index = 0\n length = 1\n while True:\n for _ in range(2):\n for _l in range(length):\n yield [self._step * k for k in self._rotation[index]]\n index = (index + 1) % len(self._rotation)\n length += 1","title":""},{"docid":"361f20cabe33898c81536afe79a1d3c3","score":"0.508139","text":"def iterate_across(padded_x, padded_y=None, batch_size=1):\n curr = 0\n data_len = len(padded_x[0])\n index_shuffler = list(range(data_len))\n random.shuffle(index_shuffler)\n while curr < data_len and curr < 1000:\n if padded_y is not None:\n yield [np.array([padded_x[i][j] \\\n for j in index_shuffler[curr:curr+batch_size]]) \\\n for i in range(len(padded_x))], \\\n [np.array([padded_y[i][j] \\\n for j in index_shuffler[curr:curr+batch_size]]) \\\n for i in range(len(padded_y))]\n else:\n yield [np.array([padded_x[i][j] \\\n for j in index_shuffler[curr:curr+batch_size]]) \\\n for i in range(len(padded_x))]\n curr += batch_size","title":""},{"docid":"142e5a5f3fbf15888d339e113da700d9","score":"0.5047755","text":"def align():","title":""},{"docid":"c44099a0e4d84015d6721334ebf9941d","score":"0.4997019","text":"def others_in_row(self, row_, col_):\n for c in range(9):\n if c != col_:\n yield self[row_, c]","title":""},{"docid":"9c4b27d3bd6f934aabb3d79cc41acdc2","score":"0.49119234","text":"def get_markers(self) -> iter:\n for cm in self.mappings:\n yield cm.marker","title":""},{"docid":"53ea1396a36f06157d874ab378077d78","score":"0.4888976","text":"def align_columns() -> Iterable[tuple[int, list[str]]]:\n for page, lines in extract_pages():\n # find a position that is blank in every line\n max_len = max(len(line) for line in lines)\n best_blank = -1\n for i in range(max_len):\n if not all(len(line) <= i or line[i] == \" \" for line in lines):\n continue\n num_lines = len([line for line in lines if len(line) > i])\n if num_lines < 10:\n continue\n best_blank = i\n assert best_blank != -1, f\"failed to find split for {page}\"\n first_column = [line[:best_blank].rstrip() for line in lines]\n second_column = [line[best_blank + 1 :].rstrip() for line in lines]\n yield page, first_column + second_column","title":""},{"docid":"42422da18a8780865576df8848ca2049","score":"0.48882017","text":"def __iter__(self):\n for col in self._selection:\n yield self.df.icol(col)","title":""},{"docid":"6366ee59f36eb689ab6ab9d20a09b0ed","score":"0.48636395","text":"def align(self, column, groups):\n raise NotImplementedError() # pragma: no cover","title":""},{"docid":"04bef7898a1b96c617f1b85624da2194","score":"0.48118094","text":"def iter_rows(self):\n for i in self.nsamples:\n yield Record(\n self.samples.ids[i],\n self.samples.descriptions[i],\n self.samples.sequences[i],\n )\n for i in self.nmarkers:\n yield Record(\n self.markers.ids[i],\n self.markers.descriptions[i],\n self.markers.sequences[i],\n )","title":""},{"docid":"46896471637ee32dc4dba4ef12c0c9ec","score":"0.47870177","text":"def alignment(path_muse, path_note, path_save, columns):\n files_muse = sorted(os.listdir(path_muse)) # read muse files\n files_notes = sorted(os.listdir(path_note)) # read annotation files\n\n for index in range(len(files_muse)): # for each file muse\n muse = pd.read_csv(path_muse + '/' + files_muse[index], index_col=False) # keep the file\n print(index)\n\n muse = muse[['TimeStamp'] + columns] # choose the columns\n if len(muse['TimeStamp'][0]) >= 12: # if there is the date\n muse['TimeStamp'] = list(map(lambda x: x[11:], muse['TimeStamp']))\n\n note = pd.read_csv(path_note + '/' + files_notes[index], index_col=False) #\n\n # fix tables\n first_check = note['check time'][1][:-3]\n note = note[2:-1]\n note['check time'] = list(map(lambda x: (x[:-3]), note['check time']))\n muse['TimeStamp'] = list(map(lambda x: str(x), muse['TimeStamp']))\n\n # fix dimension of csv muse\n print('Dataset size before cleaning samples:' + str(muse.shape))\n old_shape = muse.shape[0]\n muse = muse.dropna()\n muse = muse[(muse[columns] != 0).all(1)]\n print('Size of dataset after cleaning samples:' + str(muse.shape))\n print('Diference: ' + str(old_shape - muse.shape[0]))\n\n # muse csv must be start with the same time of note csv\n first_index = 0\n while pd.to_datetime(first_check) > pd.to_datetime(list(muse['TimeStamp'])[first_index]):\n first_index = first_index + 1\n print(first_index)\n muse = muse[first_index:]\n\n muse = create_new_muse_table(note, muse, first_check)\n\n muse.to_csv(path_save + \"/\" + str(index) + \"alignment.csv\", index=False)","title":""},{"docid":"a2a0e6cc803539a0accb2a194bd02b8f","score":"0.4769264","text":"def iter_multialignments(self):\n sam_iter = iter(self)\n multialignment = [next(sam_iter)]\n for record in sam_iter:\n if record.qname == multialignment[0].qname: # is this expensive?\n multialignment.append(record)\n else:\n yield MultiAlignment(multialignment)\n multialignment = [record]\n yield MultiAlignment(multialignment)","title":""},{"docid":"1fa56691dcc6453eacc6ba779f066afd","score":"0.47459912","text":"def remove_irrelevant_data_from_alignment(ali_plus_top_seq_plus_1):\n # Parse the input alignment.\n alignment = AlignIO.read(ali_plus_top_seq_plus_1, 'nexus')\n\n # Remove the original file.\n os.remove(ali_plus_top_seq_plus_1)\n\n # Remove all but the last two sequences from the alignment.\n alignment = alignment[-2:]\n\n\n # Remove positions in the alignment that have only gaps for both\n # sequences.\n\n # get a list of columns as strings in the original alignment.\n seq_len = alignment.get_alignment_length()\n columns = [alignment[:, col] for col in range(seq_len)] \n\n # Get a list of column indexes for columns that need to be removed.\n col_to_remove = []\n col_index = -1\n for col in columns:\n col_index += 1\n if col[0] == '-' and col[1] == '-':\n col_to_remove.append(col_index)\n\n \n #seq_len = alignment.get_alignment_length()\n #print(seq_len)\n #print(alignment[:,col_to_remove[0]])\n\n # Remove columns (positions) from alignment.\n num = 0\n for col_index in col_to_remove:\n num += 1\n adj_col_index = col_index - num\n alignment = alignment[:,:adj_col_index] + alignment[:,adj_col_index + 1:]\n #if col_index != (seq_len -1):\n # alignment = alignment[:,:adj_col_index] + alignment[:,adj_col_index + 1:]\n #else:\n # alignment = alignment[:,:adj_col_index]\n\n #seq_len = alignment.get_alignment_length()\n #print(seq_len)\n #print(alignment[:,col_to_remove[0]])\n\n # Write modified alignment to the same filepath as the original.\n AlignIO.write(alignment, ali_plus_top_seq_plus_1, 'nexus')","title":""},{"docid":"90651807ce66f66bdf7c3a2fe35d9007","score":"0.46938723","text":"def _test_shifted(sequences):\n\n for i in range(1, len(sequences)):\n\n if sequences[i - 1][:-1].sum() == 0:\n # Change of user, all columns but one\n # are padding.\n continue\n\n assert np.all(sequences[i][1:] == sequences[i - 1][:-1])","title":""},{"docid":"d3a03e5696aa7c858b18a732b64d26cd","score":"0.4686958","text":"def column(self, col_):\n for row in self.raw:\n yield row[col_]","title":""},{"docid":"77b91c3138c977e9a0b69c8d144b7bc1","score":"0.46589667","text":"def _iterate_over_counter_columns(self, func):\n col_count = 0\n for row_index in xrange(self._params['num_counter_rows']):\n row_key = self._generate_row_key(row_index)\n for col_index in xrange(self._params['num_cols']):\n col_name = self._generate_col_name(col_index)\n func(row_key, col_name)\n col_count += 1\n debug(\"iterated over %d counter columns\" % col_count)","title":""},{"docid":"ff8e979417a0fcf1739b14f6ea9f1830","score":"0.4653444","text":"def iterate_over_spatial(self):\n data = self[:]\n\n def calc_slice_idx(idx):\n slice_obj = list(idx[:3]) + [slice(None), ] * (data.ndim - 3)\n return tuple(slice_obj)\n\n for idx in np.ndindex(data.shape[:3]):\n yield self[idx], calc_slice_idx(idx)","title":""},{"docid":"eea3e376a73398e4ab00914b35642bc1","score":"0.46411833","text":"def refine_all_alignment(original, limit=4):\n new_alignment = []\n for i, item in enumerate(original):\n new_alignment.append(refine_individual_alignment(original, i, limit = limit))\n return new_alignment","title":""},{"docid":"7a6246840c523f1a633321e65830459c","score":"0.4640228","text":"def _align_columns(tab_stop_array):\r\n i = 0\r\n while i < len(tab_stop_array[0]):\r\n # Start with leftmost column and work right\r\n # Grab initial tab positions for this column\r\n column = sorted([row[i] for row in tab_stop_array])\r\n # To figure out if a given tab position exceeds MAX_OFFSET, we have to get an accurate value for min_tab first.\r\n # The logic is simplest if we just sort the list.\r\n min_tab = None\r\n max_tab = None\r\n\r\n # Figure out the left and rightmost tabs\r\n for tab in column:\r\n if tab is None:\r\n continue\r\n if min_tab is None or tab < min_tab:\r\n min_tab = tab\r\n if (max_tab is None or tab > max_tab) and tab - \\\r\n min_tab <= MAX_OFFSET:\r\n max_tab = tab\r\n\r\n # Recalculate the rest of the tab stops after offsetting one to align\r\n # with the rest\r\n for j, row in enumerate(tab_stop_array):\r\n aligned_row = row[:i]\r\n original_tab = row[i]\r\n offset = None if original_tab is None else max_tab - original_tab\r\n if offset is None:\r\n # This column is not being used on this row. Leave as None, and\r\n # don't change remaining columns.\r\n aligned_row += [None]\r\n aligned_row += row[i + 1:]\r\n elif offset < 0:\r\n # The field to the left was much wider than the other fields in that column. Shift the columns to the\r\n # right and insert an empty column\r\n aligned_row += [None]\r\n aligned_row += [original_tab]\r\n aligned_row += row[i + 1:]\r\n else:\r\n # Align this field with others in the column, and recalculate\r\n # the remaining columns for this row.\r\n aligned_row += [original_tab + offset]\r\n for tab in row[i + 1:]:\r\n aligned_row += [None if tab is None else tab + offset]\r\n tab_stop_array[j] = aligned_row\r\n\r\n _pad_array(tab_stop_array)\r\n i += 1\r\n return","title":""},{"docid":"008bd0b88e5eed0546f4fabba8a5e83e","score":"0.46262637","text":"def iterating(self):\n\n for l_0, line in enumerate(self.layer):\n self.count = 0\n for c_0, column in enumerate(line):\n if l_0 % 2 == 0:\n self.check_line_1(l_0, c_0)\n else:\n self.check_line_2(l_0, c_0)\n if self.flag_end_line:\n self.flag_end_line = False\n if l_0 + 1 == len(self.layer):\n self.combinations.append(self.layer)\n return\n break","title":""},{"docid":"e339d741d5a5a21b12213e0e2598b63c","score":"0.46220663","text":"def _iter_array_or_map(self):\n while self._block_count != 0:\n if self._block_count < 0:\n self._block_count = -self._block_count\n # Read block size, unused\n self.read_long()\n\n for i in range(self._block_count):\n yield\n self._block_count = self.read_long()","title":""},{"docid":"80abe283cb9df061472336b7537ab8d3","score":"0.46200323","text":"def _adjust_alignment(self):\n col_diff = self._num_columns - len(self._align_list)\n if col_diff <= 0:\n self._align_list = self._align_list[:self._num_columns]\n\n else:\n self._align_list.extend(['>'] * col_diff)","title":""},{"docid":"5acee35efaabe4f84585c8803b0b3aff","score":"0.4612931","text":"def mapaligns(self, pairs):\n # re-map ref_aligns using provided pairs\n # first index is to current (ref_align) sequence, second is to new ref\n # (should be numpy array)\n\n # clear ref_align\n refal = self.ref_align\n ra0 = refal > 0\n self.ref_align = 0*self.ref_align\n # now make the pairs unique in x (own seq)\n (_,uinds) = np.unique(pairs[:,0],return_index=True)\n pairs = pairs[uinds,:]\n # and now use interpolation to remap, return -1 out of range\n self.ref_align[ra0] = np.round(np.interp(refal[ra0],pairs[:,0],pairs[:,1],0,0))\n \n # make sure all of our arrays are in order\n self.makecontiguous()","title":""},{"docid":"809b21ef8646189d16c261175fa49371","score":"0.46081325","text":"def _equalize_batch(padding, gen):\n for batch in gen:\n longest = 0\n for ngs in batch[0]:\n longest = max(longest, len(ngs))\n for i in range(len(batch[0])):\n batch[0][i] = pad_to_length(batch[0][i], longest, padding)\n yield batch","title":""},{"docid":"3eb588113bad5582cf47511920473caf","score":"0.45983833","text":"def whiteIterator(self) :\n for i in range(self.n) :\n for j in range(self.m) :\n if not self.image[i][j] : yield [i,j]","title":""},{"docid":"a6dfe6532a806ab9cf2370ff01d290db","score":"0.4586409","text":"def blackIterator(self) :\n for i in range(self.n) :\n for j in range(self.m) :\n if self.image[i][j] : yield [i,j]","title":""},{"docid":"c31bbbea5c58091145ee2cb687feadf7","score":"0.4584737","text":"def offsetElements(self, i):\n\n #iterate over each tile and subtract\n #if the value is -1, indicating a blank tile, leave it as that\n for y in range(0, len(self.array)):\n for x in range(0, len(self.array[0])):\n if self.array[y][x] != -1:\n self.array[y][x] -= i","title":""},{"docid":"09b39387823ec1a23601b7cc847db985","score":"0.45449036","text":"def main_extracting_features_per_atom(base_path, \n xyz_file, \n out_path, \n out_file_name, \n dic_encode_atom_type, \n lst_atom_types, \n max_vals = [1,1,1], \n centered = True,\n ordering = 'sorted',\n df_bond = pd.DataFrame([1.7], columns = ['bonds'])\n ):\n list_alfa = [0.05, 0.144, 0.5, 1]\n if not (os.path.isdir(out_path)):\n os.mkdir(out_path)\n\n atom_type_col = 'atom_type'\n xyz_cols = ['x', 'y', 'z']\n try:\n df_xyz_plain = xyz_to_df(base_path, xyz_file, dic_encode_atom_type, lst_atom_types, centered = True)\n except:\n print(f'{xyz_file}')\n return\n\n n_atom_types = len(lst_atom_types)\n\n df_xyz_plain['r_dist_min'] = np.linalg.norm((df_xyz_plain[xyz_cols] - df_xyz_plain[xyz_cols].min()).values, axis=1)\n df_xyz_plain['r_dist_max'] = np.linalg.norm((df_xyz_plain[xyz_cols] - df_xyz_plain[xyz_cols].max()).values, axis=1)\n df_xyz_plain['r_dist_mean'] = np.linalg.norm((df_xyz_plain[xyz_cols] - df_xyz_plain[xyz_cols].mean()).values, axis=1)\n\n for alfa in list_alfa:\n for i in range(n_atom_types):\n for j in range(i, n_atom_types):\n atom_1 = lst_atom_types[i]\n atom_2 = lst_atom_types[j]\n coord_1 = df_xyz_plain[xyz_cols][df_xyz_plain['atom_type'] == atom_1]\n coord_2 = df_xyz_plain[xyz_cols][df_xyz_plain['atom_type'] == atom_2]\n\n dist_mat = distance_matrix(coord_1, coord_2)\n density_loc = np.exp(-alfa * dist_mat**2).sum(axis=1)\n df_xyz_plain[f'{atom_1}_{atom_2}_{alfa}'] = density_loc\n # =================================================================\n # 1. Extracting XYZ positions, encoding atoms types, ordering atoms\n # =================================================================\n if ordering == 'org':\n df_xyz = xyz_normalize_centered(df_xyz_plain, max_vals = max_vals, centered = centered)\n\n elif ordering == 'sorted': \n df_xyz = xyz_normalize_centered(df_xyz_plain, max_vals = max_vals, centered = centered)\n df_xyz = df_xyz.sort_values(['x', 'y', 'z'])\n\n elif ordering == 'org_aligned':\n df_xyz_plain[xyz_cols] = xyz_align_PCA(df_xyz_plain[xyz_cols].values)\n df_xyz = xyz_normalize_centered(df_xyz_plain, max_vals = max_vals, centered = centered)\n\n elif ordering == 'sorted_aligned':\n df_xyz_plain[xyz_cols] = xyz_align_PCA(df_xyz_plain[xyz_cols].values)\n df_xyz = xyz_normalize_centered(df_xyz_plain, max_vals = max_vals, centered = centered)\n df_xyz = df_xyz.sort_values(['x', 'y', 'z'])\n\n # ================================\n # 2. Adding coordination of atoms\n # ================================ \n _, coords = return_neighbour_list(df_xyz_plain, df_bond, atom_type_col = 'atom_type', xyz_cols = ['x', 'y', 'z'])\n df_xyz['coordination'] = coords\n\n df_xyz.to_csv(os.path.join(out_path, out_file_name), index=False)","title":""},{"docid":"8a19046787e8b6dba090652a2ab8b127","score":"0.4524642","text":"def column_align(self):\n return self._column_align","title":""},{"docid":"9fb749300f2dff02083957687026b93d","score":"0.45214546","text":"def iter_all_cells(self):\n for row in range(self.table.nrows):\n for col in range(self.table.ncols):\n yield row, col","title":""},{"docid":"62db8e4e2320b1399a973aadf88d224f","score":"0.45199275","text":"def _alignment_histogram_pruning(self, align_beam_size, align_model_probs, bucket_key,\n num_align_models, start, end):\n utils.check_condition(num_align_models == 1, \"Skip alignments only implemented for one alignment model\")\n skip_alignments = np.array([True] * bucket_key[0])\n np_align_model_probs = align_model_probs[0].asnumpy()\n for sent in range(self.batch_size * self.beam_size):\n source_sel = slice(start[sent].asscalar(), end[sent].asscalar())\n rows = slice(sent, (sent + 1))\n sliced_scores = np_align_model_probs[:, rows, source_sel] # .reshape(shape=(1, -1))\n if (source_sel.stop - source_sel.start) > align_beam_size:\n # returns: best_hyp_indices_, best_hyp_pos_indices , best_word_indices\n (_, _, best_word_indices), _ = utils.smallest_k( -1 * sliced_scores, align_beam_size, False)\n for k in best_word_indices:\n if 0 <= k < bucket_key[0]:\n skip_alignments[k] = False\n else:\n for k in range(source_sel.stop - source_sel.start):\n skip_alignments[k] = False\n\n return skip_alignments","title":""},{"docid":"f600fdb9ef8c158e28610e161de5ddb0","score":"0.4515573","text":"def slice(self, aligned):\n slices = {}\n for (mseq, rname, pos), count in aligned.iteritems():\n # check where this read mapped\n read_start = int(pos)\n read_end = read_start + len(mseq.strip('-'))\n coords = self.coords[rname]\n if read_end < coords['Core'][0] or read_start > coords['NS5b'][1]:\n # read falls outside of ORF\n continue\n\n # did it map to one of the target genes?\n for target_gene, target_coords in self.targets.iteritems():\n if target_gene not in slices:\n slices.update({target_gene: {}})\n left, right = coords[target_gene]\n\n for tc in target_coords:\n if tc not in slices[target_gene]:\n slices[target_gene].update({tc: []})\n gene_left, gene_right = tc # unpack tuple\n\n # adjust gene coordinates to genome coordinates\n genome_left = gene_left + left\n genome_right = gene_right + left\n if read_start > genome_left or read_end < genome_right:\n # full coverage of target not possible\n continue\n\n slice = mseq[genome_left:genome_right]\n slices[target_gene][tc].append((rname, slice, count))\n\n return slices","title":""},{"docid":"1858c8f74ce09109bd064850b1b046e4","score":"0.45005003","text":"def _iter_values(self):\n _slice = list(self._none_slices_except_domain_axis)\n slices = []\n for index in range(self.shape[self.labels_axis]):\n _slice[self.labels_axis] = index\n slices.append(tuple(_slice))\n return zip(\n slices,\n np.moveaxis(self.magnitude, self.labels_axis, 0)\n )","title":""},{"docid":"7ea5ed663fbc1ff285496f5e78e963be","score":"0.44990557","text":"def _traverse_column_data_records(self,\n column_name: str,\n *,\n keys: bool = True,\n values: bool = True) -> Iterable[Union[bytes, Tuple[bytes, bytes]]]:\n try:\n datatxn = TxnRegister().begin_reader_txn(self._dataenv)\n schemaColumnRangeKey = schema_db_range_key_from_column_unknown_layout(column_name)\n with datatxn.cursor() as cur:\n if not cur.set_range(schemaColumnRangeKey):\n raise KeyError(f'Traversal of commit references failed. '\n f'No column named `{column_name}` exists.')\n schemaColumnKey = cur.key()\n column_record = schema_column_record_from_db_key(schemaColumnKey)\n startRangeKey = dynamic_layout_data_record_db_start_range_key(column_record)\n yield from self.cursor_range_iterator(datatxn, startRangeKey, keys, values)\n finally:\n TxnRegister().abort_reader_txn(self._dataenv)","title":""},{"docid":"06fb292427ce719e3bfac454d30f0af8","score":"0.4497053","text":"def iter_sites(self, start=0, stop=None, size=1):\n if stop is None:\n stop = self.nsites\n if (stop - start) % size != 0:\n raise ValueError('Alignment cannot be completely divided into '\n 'chucks of size {}'.format(size))\n for i in range(start, stop, size):\n samples = [s[i:i+size] for s in self.samples.sequences]\n if not (self.markers is None or self.markers.nrows == 0):\n markers = [s[i:i+size] for s in self.markers.sequences]\n yield samples + markers\n else:\n yield samples","title":""},{"docid":"0f599eda1779ea5e1588ff1283dbca19","score":"0.4495233","text":"def cols(self):\n for col_heading in self.col_hds:\n yield (self._data[row_heading][col_heading] for row_heading in self.row_hds)","title":""},{"docid":"556f50071cc174a2c820bdb799d2a522","score":"0.44883695","text":"def score_alignment(self,chunk1,chunk2):\n self.tradition_alignment(chunk1,chunk2)\n\n def filter_both_chunks(tmp_a,tmp_b,filter_list):\n \"\"\"Filters two alignment_blocks based on residues that made it into both alignments\n \"\"\"\n filt_chunk1 = [i._resraw for i in tmp_a._res_list]\n filt_chunk2 = [i._resraw for i in tmp_b._res_list]\n tmp_lista = []\n tmp_listb = []\n for i,j in filter_list:\n if i in filt_chunk1 and j in filt_chunk2:\n tmp_lista.append(i)\n tmp_listb.append(j)\n return ([i for i in tmp_a._res_list if i._resraw in tmp_lista],[*tmp_a._res_list]),\\\n ([i for i in tmp_b._res_list if i._resraw in tmp_listb],[*tmp_b._res_list])\n\n self.pep1,self.pep2 = chunk1._chunklist,chunk2._chunklist\n self.len1,self.len2 = len(self.pep1)+1,len(self.pep2)+1\n\n self.init_matrix()\n tmp_dict = defaultdict(list)\n for i,j in self.alignment.alignkey:\n tmp_chunk1 = chunk1._chunklist_dict[i]\n tmp_chunk2 = chunk2._chunklist_dict[j]\n tmp_a,tmp_b = filter_both_chunks(tmp_chunk1,tmp_chunk2,self.alignment.alignkey)\n alignment_score = align_chunks(tmp_a,tmp_b,self.walk)\n for k,l in alignment_score.items():\n tmp_dict[k].append(l)\n for i,j in tmp_dict.items():\n self.rawm[i[0]][i[1]] = min(j)\n self.init_adjustmatrix()","title":""},{"docid":"4f67a7c2d55845aba3a5ae0703a99a73","score":"0.44797224","text":"def _match(self):\n #disable optimized matching\n optimized_rows = None\n optimized_columns = None\n for match in self.__match_rows(optimized_rows):\n #match in rows\n yield match\n for match in self.__match_rows(optimized_columns,\n transpose=True):\n #match in columns and transpose coordinates\n yield match","title":""},{"docid":"65b66a0dfd54b20bd4a14659306b1c97","score":"0.44747886","text":"def filter_multimappers(align_file, data):\n config = dd.get_config(data)\n type_flag = \"\" if bam.is_bam(align_file) else \"S\"\n base, ext = os.path.splitext(align_file)\n out_file = base + \".unique\" + ext\n bed_file = dd.get_variant_regions(data)\n bed_cmd = '-L {0}'.format(bed_file) if bed_file else \" \"\n if utils.file_exists(out_file):\n return out_file\n base_filter = '-F \"[XS] == null and not unmapped {paired_filter} and not duplicate\" '\n if bam.is_paired(align_file):\n paired_filter = \"and paired and proper_pair\"\n else:\n paired_filter = \"\"\n filter_string = base_filter.format(paired_filter=paired_filter)\n sambamba = config_utils.get_program(\"sambamba\", config)\n num_cores = dd.get_num_cores(data)\n with file_transaction(out_file) as tx_out_file:\n cmd = ('{sambamba} view -h{type_flag} '\n '--nthreads {num_cores} '\n '-f bam {bed_cmd} '\n '{filter_string} '\n '{align_file} '\n '> {tx_out_file}')\n message = \"Removing multimapped reads from %s.\" % align_file\n do.run(cmd.format(**locals()), message)\n bam.index(out_file, config)\n return out_file","title":""},{"docid":"6d07347a453302f98fef4c1d2c5f73d6","score":"0.44727546","text":"def _alignment_threshold_pruning(self, align_skip_threshold, align_model_probs, bucket_key,\n num_align_models, start, end):\n\n utils.check_condition(num_align_models == 1, \"Skip alignments only implemented for one alignment model\")\n skip_jumps = mx.nd.zeros((self.batch_size * self.beam_size, bucket_key[0]))\n\n for idx in range(self.batch_size * self.beam_size):\n source_sel = slice(start[idx].asscalar(), end[idx].asscalar())\n target_sel = slice(0, source_sel.stop - source_sel.start)\n skip_jumps[idx, target_sel] = align_model_probs[0][0, idx, source_sel]\n\n skip_alignments = np.all((skip_jumps < align_skip_threshold).asnumpy(), axis=0)\n\n return skip_alignments","title":""},{"docid":"e02a1b99a2af5d5096049a0ded75f0f8","score":"0.44660947","text":"def slice(self, aligned):\n slices = {}\n for (mseq, rname, pos), count in aligned.iteritems():\n # check where this read mapped\n read_start = int(pos) # 1based coord wrt subtype full genome corresponding to read start\n read_end = read_start + len(mseq.strip('-')) # 1based coord wrt subtype full genome corresponding to read end + 1\n if rname not in self.coords:\n print \"Read %s didn't have a corresponding map\" % rname\n continue\n coords = self.coords[rname]\n if read_end < coords['Core'][0] or read_start > coords['NS5b'][1]:\n # read falls outside of ORF\n continue\n\n # did it map to one of the target genes?\n for target_gene, target_coords in self.targets.iteritems():\n if target_gene not in slices:\n slices.update({target_gene: {}})\n left, right = coords[target_gene] # 0based nuc coordinates wrt subtype full genome corresponding to gene start, gene end+1\n\n for tc in target_coords: # 0based nuc coordinates wrt H77 gene corresponding to target start, target end + 1\n if tc not in slices[target_gene]:\n slices[target_gene].update({tc: []})\n # 0based nuc coordinates wrt H77 gene corresponding to target start, target end + 1\n gene_left, gene_right = tc # unpack tuple\n\n # adjust gene coordinates to genome coordinates. Assume no indels wrt H77\n genome_left = gene_left + left # 0based nuc coordinates wrt subtype full genome corresponding to H77 target start\n genome_right = gene_right + left # 0based nuc coordinates wrt subtype full genome corresponding to H77 target end + 1\n\n # If the read begins after the target region or ends before the target region, read_slice_size will be negative.\n read_slice_size = min(genome_right, read_end-1) - max(genome_left, read_start-1)\n target_slice_size = genome_right - genome_left\n if read_slice_size / float(target_slice_size) < self.min_target_width:\n # merged read does not cover the minimum required width of the target region\n continue\n\n slice = mseq[genome_left:genome_right] # mseq should be left-padded wrt subtype full genome\n\n # After slicing merged sequences, we may end up with duplicate slices for the same target gene & coords\n # against the same reference\n slices[target_gene][tc].append((rname, slice, count))\n\n return slices","title":""},{"docid":"91838a895be3d319df0504b7548f3ff9","score":"0.4461923","text":"def test_iter():\n bg = ipythonblocks.BlockGrid(2, 2)\n\n coords = ((0, 0), (0, 1), (1, 0), (1, 1))\n\n for b, c in zip(bg, coords):\n assert b.row == c[0]\n assert b.col == c[1]","title":""},{"docid":"a708c617e8d17c1f92957907c583cfa5","score":"0.445352","text":"def _listed_ea_column_check():\n\n def diff(li1, li2):\n return list(set(li1) - set(li2))\n\n for ea_row in unused_list:\n ddi_index = views_index[ea_row[15]]\n # This check is performed in\n # _ea_in_disposition_col0_and_empty_ipr_d_col\n if ea_row[1] not in ddi_data[ddi_index]:\n continue\n if ea_row[0] in ea_listed_values['IPR Designation'] and \\\n 'IPR Designation' not in \\\n ddi_data[ddi_index][ea_row[1]]['extattrs']:\n continue\n # Processing listable columns.\n for key, value in ea_index.items():\n # Skip's unused keys.\n if key not in ['Datacenter', 'IPR Designation']:\n continue\n # Check for blank column and blank source column.\n if key not in ddi_data[ddi_index][ea_row[1]]['extattrs'] and \\\n ea_row[value] in ['', 'DDI']:\n continue\n # Check Disposition col, check for comma not in IPR D col\n # value, check value in IPR D col to ea ipr d attribute list,\n # check IPR D col value eq ddi value.\n # On not listed IPR D values.\n if key in ea_listed_values:\n ipr_temp_list = []\n ipam_temp_list = []\n # Building list for diff's against DDI data.\n if ea_row[0] in ea_listed_values[key] \\\n and ea_row[0] not in ea_row[value]:\n ipr_temp_list.append(ea_row[0])\n # Extend list if listed values in IPR D column.\n if ',' in ea_row[ea_index[key]]:\n ipr_temp_list.extend([x.strip()\n for x in ea_row[ea_index[key]].\n split(',')\n if x.strip() in\n ea_listed_values[key]])\n # Append list if non-listed values in IPR D column.\n if ',' not in ea_row[ea_index[key]] and \\\n ea_row[ea_index[key]] in \\\n ea_listed_values[key]:\n ipr_temp_list.append(ea_row[ea_index[key]])\n\n # Remove blank elements from list.\n ipr_temp_list = [x for x in ipr_temp_list if x]\n\n # If in IPR and no key listed in IPAM data.\n if ea_row[1] == '192.168.0.0':\n print('myvar: {}'.format(ea_row[1]))\n\n # If no data in IPAM but new data received.\n if key not in \\\n ddi_data[ddi_index][ea_row[1]]['extattrs'] \\\n and ipr_temp_list:\n if len(ipr_temp_list) > 1:\n import_report_data['update'].append(ea_row)\n import_override.append([ea_row[15].strip(),\n ea_row[1].strip(),\n ea_row[14].strip(),\n {key: ','.join(\n ipr_temp_list)}])\n else:\n import_report_data['update'].append(ea_row)\n import_override.append([ea_row[15].strip(),\n ea_row[1].strip(),\n ea_row[14].strip(),\n {key: ipr_temp_list[\n 0]}])\n continue\n\n # Building DDI list for diff against the Multi Att. Columns\n if isinstance(ddi_data[ddi_index][\n ea_row[1]]['extattrs'][\n key]['value'], list):\n ipam_temp_list.extend(ddi_data[ddi_index][\n ea_row[1]]['extattrs'][\n key]['value'])\n else:\n ipam_temp_list.append(ddi_data[ddi_index][\n ea_row[1]]['extattrs'][\n key]['value'])\n\n # Check for diff between listed sets.\n in_ipr_not_ipam = compare(ipr_temp_list, ipam_temp_list)\n if not in_ipr_not_ipam:\n import_report_data['update'].append(ea_row)\n import_override.append([ea_row[15].strip(),\n ea_row[1].strip(),\n ea_row[14].strip(),\n {key: ','.join(\n ipr_temp_list)}])\n continue","title":""},{"docid":"e3b5d92ab399f764c4a006f9d73bc711","score":"0.44291773","text":"def _traverse_column_schema_records(self, keys: bool = True, values: bool = True\n ) -> Iterable[Union[Tuple[bytes], Tuple[bytes, bytes]]]:\n startSchemaRangeKey = schema_record_count_start_range_key()\n try:\n datatxn = TxnRegister().begin_reader_txn(self._dataenv)\n yield from self.cursor_range_iterator(datatxn, startSchemaRangeKey, keys, values)\n finally:\n TxnRegister().abort_reader_txn(self._dataenv)","title":""},{"docid":"1db63069a26bad34c3556090769af3ce","score":"0.44239363","text":"def _test_final_column_no_padding(sequences):\n\n assert np.all(sequences[:, -1] > 0)","title":""},{"docid":"3031aa4345d39ab60ed5e9be81afab47","score":"0.44170517","text":"def getColumnAlignments(self):\n if self._columnAlignments is None:\n return None\n\n alignments = list()\n\n for col in self._visibleColumns:\n alignments.append( self.getColumnAlignment(col) )\n\n return alignments","title":""},{"docid":"e2199c1859f95becc996735ee9c3d00a","score":"0.44155955","text":"def prep_alleles_table_compare(df_alleles,sample_name_1,sample_name_2,MAX_N_ROWS,MIN_FREQUENCY):\n\tdna_to_numbers={'-':0,'A':1,'T':2,'C':3,'G':4,'N':5}\n\tseq_to_numbers= lambda seq: [dna_to_numbers[x] for x in seq]\n\n\tX=[]\n\tannot=[]\n\ty_labels=[]\n\tinsertion_dict=defaultdict(list)\n\tper_element_annot_kws=[]\n\n\tre_find_indels=re.compile(\"(-*-)\")\n\tidx_row=0\n\tfor idx,row in df_alleles.ix[df_alleles['%Reads_'+sample_name_1] + df_alleles['%Reads_'+sample_name_2]>=MIN_FREQUENCY][:MAX_N_ROWS].iterrows():\n\t\tX.append(seq_to_numbers(str.upper(idx)))\n\t\tannot.append(list(idx))\n\t\ty_labels.append('%.2f%% (%d reads) %.2f%% (%d reads) ' % (row['%Reads_'+sample_name_1],row['#Reads_'+sample_name_1],\n\t\t\t\t\t\t\t\t\t\t\t\t\trow['%Reads_'+sample_name_2],row['#Reads_'+sample_name_2]))\n\n\n\t\tfor p in re_find_indels.finditer(row['Reference_Sequence']):\n\t\t\tinsertion_dict[idx_row].append((p.start(),p.end()))\n\n\t\tidx_row+=1\n\n\n\t\tidxs_sub= [i_sub for i_sub in range(len(idx)) if \\\n\t\t\t\t (row['Reference_Sequence'][i_sub]!=idx[i_sub]) and \\\n\t\t\t\t (row['Reference_Sequence'][i_sub]!='-') and\\\n\t\t\t\t (idx[i_sub]!='-')]\n\t\tto_append=np.array([{}]*len(idx),dtype=np.object)\n\t\tto_append[ idxs_sub]={'weight':'bold', 'color':'black','size':16}\n\t\tper_element_annot_kws.append(to_append)\n\n\treturn X,annot,y_labels,insertion_dict,per_element_annot_kws","title":""},{"docid":"294bf27b8f1067ab809de007cb69131b","score":"0.44148335","text":"def compute_shift_neighbor_point(self, j, i, contributing_alignment_points):\n\n self.y_shifts[j][i] = 0.\n self.x_shifts[j][i] = 0.\n for ap in contributing_alignment_points:\n j_ap = self.alignment_points[ap['alignment_point_index']][0]\n i_ap = self.alignment_points[ap['alignment_point_index']][1]\n self.y_shifts[j][i] += ap['weight'] * self.y_shifts[j_ap][i_ap]\n self.x_shifts[j][i] += ap['weight'] * self.x_shifts[j_ap][i_ap]","title":""},{"docid":"f6a826d0e604e0f8a1b594042c08262c","score":"0.44122374","text":"def indices_allpeople_with_allpeople_in_two_frames(meanPositions_b, meanPositions_c, meanPositions_a, printAuxFlag=False, distScale=8*30, b_d=-0.8, b_lr=-0.2):\r\n nb, nc = len(meanPositions_b), len(meanPositions_c)\r\n EDMatrix, EDLMatrix, LRMatrix, TAMatrix = np.ones((nb, nc)), np.ones((nb, nc)), np.ones((nb, nc)), np.ones((nb, nc))\r\n DBAMatrix, DBBMatrix, DBCMatrix = np.ones((nb, nc)), np.ones((nb, nc)), np.ones((nb, nc))\r\n for i, meanPosition_b in enumerate(meanPositions_b):\r\n meanPosition_a = meanPositions_a[i]\r\n for j, meanPosition_c in enumerate(meanPositions_c):\r\n indices = indices_person_with_person_in_three_frames(meanPosition_a, meanPosition_b, meanPosition_c)\r\n EDMatrix[i, j] = indices['ED']\r\n EDLMatrix[i, j] = indices['EDL']\r\n LRMatrix[i, j] = indices['LR']\r\n TAMatrix[i, j] = indices['TA']\r\n DBAMatrix[i, j] = indices['DBA']\r\n DBBMatrix[i, j] = indices['DBB']\r\n DBCMatrix[i, j] = indices['DBC']\r\n # when a person is new-coming, he has no poseVector_a information, and would lead to a constant-zero LR and angel\r\n for nrow in range(LRMatrix.shape[0]):\r\n if np.all(LRMatrix[nrow, :] == 0) and np.all(TAMatrix[nrow, :] == 0):\r\n for ncol in range(LRMatrix.shape[1]):\r\n if np.any(LRMatrix[:, ncol] > 0):\r\n LRMatrix[nrow, ncol] = np.min([x for x in LRMatrix[:, ncol] if x > 0])\r\n if np.any(TAMatrix[:, ncol] > 0):\r\n TAMatrix[nrow, ncol] = np.min([x for x in TAMatrix[:, ncol] if x > 0])\r\n # if EDMatrix.shape[1] == 1:\r\n # NED1Matrix = np.ones((EDMatrix.shape[0], 1))\r\n # else:\r\n # NED1Matrix = (EDMatrix - np.min(EDMatrix, axis=1).reshape(-1, 1)) / (0.00000000000000000000001 + (\r\n # np.max(EDMatrix, axis=1).reshape(-1, 1) - np.min(EDMatrix, axis=1).reshape(-1, 1)))\r\n # if EDMatrix.shape[0] == 1:\r\n # NED2Matrix = np.ones((1, EDMatrix.shape[1]))\r\n # else:\r\n # NED2Matrix = (EDMatrix - np.min(EDMatrix, axis=0).reshape(1, -1)) / (0.00000000000000000000001 + (\r\n # np.max(EDMatrix, axis=0).reshape(1, -1) - np.min(EDMatrix, axis=0).reshape(1, -1)))\r\n\r\n NED1Matrix = EDMatrix / distScale\r\n NED2Matrix = NED1Matrix\r\n \r\n R_ED1Matrix = np.argsort(np.argsort(EDMatrix))\r\n R_ED2Matrix = np.argsort(np.argsort(EDMatrix, axis=0), axis=0)\r\n indicesMatrix = {'ED': EDMatrix, 'EDL': EDLMatrix, 'LR': LRMatrix, 'TA': TAMatrix,\r\n 'DBA': DBAMatrix, 'DBB': DBBMatrix, 'DBC': DBCMatrix, 'R_ED1': R_ED1Matrix, 'R_ED2': R_ED2Matrix}\r\n\r\n VMatrix_toChooseNext = NED1Matrix * b_d + LRMatrix * b_lr\r\n VMatrix_toChoosePrevious = NED2Matrix * b_d + LRMatrix * b_lr\r\n\r\n # these V are the min the best, so we need to use -V to calculate P\r\n P_toChooseNext = V2P(VMatrix_toChooseNext, axis=1)\r\n P_toChoosePrevious = V2P(VMatrix_toChoosePrevious, axis=0)\r\n\r\n # P_toChooseNext = P_toChooseNext[:, :P_toChooseNext.shape[1] - 1]\r\n # P_toChoosePrevious = P_toChoosePrevious[:P_toChoosePrevious.shape[0] - 1, :]\r\n\r\n P = P_toChooseNext * P_toChoosePrevious\r\n if printAuxFlag:\r\n print('\\nDist: \\n{}'.format(EDMatrix))\r\n print('\\nNormarlized Dist: \\n{}'.format(NED1Matrix))\r\n print('\\nLength Ratio: \\n{}'.format(LRMatrix))\r\n print('\\nAngle: \\n{}'.format(TAMatrix))\r\n return P, indicesMatrix","title":""},{"docid":"c02e2eb5cdeda941eebd9a938f60bde9","score":"0.4409557","text":"def internal_iter(self):\n for idx, x in np.ndenumerate(self.internal_t):\n yield x, self.yout[idx, ...]","title":""},{"docid":"6299bff0ad05e6c9ca04350907e91e17","score":"0.4403628","text":"def alignment_stop():\n\n smi = SMI_Beamline()\n yield from smi.modeMeasurement()\n proposal_id('2023_2', '311645_Zhang_1')","title":""},{"docid":"d90fdd225b304891acff7bfe911d7686","score":"0.44032338","text":"def __iter__(self):\n batch = []\n for row in self.reader:\n # Default collate does not work nicely on namedtuples and treat them as lists\n # Using dict will result in the yielded structures being dicts as well\n row_as_dict = row._asdict()\n batch.append(self.transform(row_as_dict) if self.transform else row_as_dict)\n if len(batch) == self.batch_size:\n yield self.collate_fn(batch)\n batch = []\n if batch:\n yield self.collate_fn(batch)","title":""},{"docid":"74cfef2ba0f48aedd7645db60e6e1612","score":"0.44022062","text":"def ialign(images, reference = None, mask = None, fill_value = 0.0, fast = True):\n images = iter(images)\n \n if reference is None:\n reference = next(images)\n yield reference\n\n yield from map(partial(align, reference = reference, mask = mask, fill_value = fill_value, fast = fast), images)","title":""},{"docid":"89155a6f4a71a0e56e4ed835ceb1a325","score":"0.4399901","text":"def _next_column_iterator(previous_column, height, i = None):\n if i is None:\n i = height\n if i == 0:\n yield [-1]*height\n else:\n for column in _next_column_iterator(previous_column, height, i-1):\n min_value = previous_column[i-1]\n if i > 1:\n min_value = max(min_value, column[i-2]+1)\n for value in range(min_value, previous_column[i]+1):\n c = copy.copy(column)\n c[i-1] = value\n yield c","title":""},{"docid":"59559f6d99a5f27fe18e26c27c8131af","score":"0.43922815","text":"def fragment_annot(df, barcodes):\n fw = []\n rv = []\n for i, b in enumerate(barcodes):\n df_b = df.loc[df['Barcode'] == b]\n cloud_idx = 0\n for j in df_b.Reference.unique().tolist():\n ref_idx = cloud_idx\n df_r = df_b.loc[df_b['Reference'] == j]\n start_pos = df_r.Start.tolist()[0] # Leftmost start on reference j. Should be pre-sorted from SAM\n for k in range(len(df_r)):\n if df_r.iloc[k,3] > start_pos + 200000: # Read start outside 2 x max. est. fragment size\n ref_idx += 1\n start_pos = df_r.iloc[k,3]\n read_info = [df_r.iloc[k,0], ref_idx, j + '-' + str(ref_idx)] # ref-enh_num\n rv.append(read_info) if df_r.iloc[k,1] else fw.append(read_info)\n cloud_idx = ref_idx + 1\n return fw, rv","title":""},{"docid":"68d6f06e824b73b96be95020e674c0a0","score":"0.4389992","text":"def remove_element_column(self):\n for i in range(1, len(self.pdb_map)+1):\n ele = self.pdb_map[i][\"element\"]\n e = ele[11]\n self.pdb_map[i][\"element\"]=\" \"+e\n print \"Extra stuff in Element Columns Removed\"\n return self.pdb_map","title":""},{"docid":"9ba00a38e0c18497f2468e44c98702b3","score":"0.4381122","text":"def filter_annoying_cell(self):\n\n idx_center = self.labels.index(self.label_center)\n not_neighbors = []\n for i, label in enumerate(self.labels):\n if label not in self.neighbors[idx_center] and i != idx_center:\n not_neighbors.append([i, self.angles[i]])\n not_neighbors = np.array(not_neighbors)\n if len(not_neighbors) > 2:\n to_delete = not_neighbors[not_neighbors[:, 1].argsort()][2:, 0]\n self.delete(to_delete.astype(int))","title":""},{"docid":"4fcc8b9790e994e95d2df22dd53a85a6","score":"0.4373849","text":"def findMarkers(markerDictList, text):\n global stopWords\n\n rows = []\n for markerType, markerRe, markerDict in markerDictList:\n textLower = text.lower()\n #if markerType in [\"genbankList\", \"genbank\"]:\n #keywords = \n #if not textContainsAny(textLower, keywords):\n #continue\n \n if markerType==\"genbankList\":\n for row in iterGenbankRows(markerRe, markerType, text):\n #yield row\n rows.append(row)\n continue\n\n if markerType in neededWordDict:\n keywords = neededWordDict[markerType]\n if not textContainsAny(textLower, keywords):\n continue\n\n for match in markerRe.finditer(text):\n word = match.group(\"id\")\n if word in stopWords:\n continue\n\n\n if markerType==\"pdb\":\n word = word.lower()\n\n if markerType in [\"hg17\", \"hg18\", \"hg19\"]:\n word = word.replace(\",\", \"\").replace(\" \", \"\")\n\n if markerDict==None:\n idList = [word]\n else:\n idList = markerDict.get(word, None)\n\n if idList != None:\n start = match.start(\"id\")\n end = match.end(\"id\")\n for recogId in idList:\n if word==recogId:\n word=\"\"\n row = [ start, end, markerType, word, recogId]\n #yield result\n rows.append(row)\n\n if len(rows)<MAXROWS:\n return rows\n else:\n return []","title":""},{"docid":"ca7df8187d285acc5433547dca0cbb43","score":"0.4372176","text":"def apply_offset(self):\n \n if self.x_offset or self.y_offset:\n for y_idx, row in enumerate(self.coordinates):\n for x_idx, coord in enumerate(row):\n self.coordinates[y_idx][x_idx] = [coord[0] + self.x_offset, coord[1] + self.y_offset]","title":""},{"docid":"a5f5886c5074e18871ae560302d7925d","score":"0.43685156","text":"def align(self):\n\n # populate the score matrices based on the input parameters\n self.populate_score_matrices()\n\n # print(\"-------M MATRIX-------\")\n # self.m_matrix.print_scores()\n # # print(\"-------IX MATRIX-------\")\n # self.ix_matrix.print_scores()\n # # print(\"-------IY MATRIX-------\")\n # self.iy_matrix.print_scores()\n\n # print(self.find_traceback_start())\n\n # Perform traceback, then write the output to file\n self.traceback()\n self.write_output()\n\n # perform a traceback and write the output to an output file\n ### FILL IN ###","title":""},{"docid":"99088bc9eae65a1377b577b6932371a1","score":"0.43635973","text":"def align(data):\n arr = []\n for d in data:\n # DO\n if d.shape[0] == 3:\n d = np.insert(d, [1, 2], 0, axis=0)\n arr.append(d)\n data = np.array(arr)\n dapi = data[:,0]\n return lasagna.bayer.register_and_offset(data, registration_images=data[:, 0])","title":""},{"docid":"c31c69fa60d9f67aa1436c1069691c88","score":"0.43540114","text":"def _align(x, y, mean_xy, variance_xy, bead_costs):\n m = {}\n for i in range(len(x) + 1):\n for j in range(len(y) + 1):\n if i == j == 0:\n m[0, 0] = (0, 0, 0)\n else:\n m[i, j] = min((m[i-di, j-dj][0] +\n length_cost(x[i-di:i], y[j-dj:j], mean_xy, variance_xy) \\\n + bead_cost, di, dj)\n for (di, dj), bead_cost in BEAD_COSTS.items()\n if i-di>=0 and j-dj>=0)\n\n i, j = len(x), len(y)\n while True:\n (c, di, dj) = m[i, j]\n if di == dj == 0:\n break\n yield (i-di, i), (j-dj, j)\n i -= di\n j -= dj","title":""},{"docid":"b1123b28913e51596181521fead73a7a","score":"0.43500638","text":"def unmap(data, count, inds, fill=0):","title":""},{"docid":"9b2177638e43250dc7a567d67b659953","score":"0.43485883","text":"def alignment_extract(seed, setting = \"AWO\"):\n with open('./work/results_seed' + str(seed) + '/alignments/extracted_matrix.pkl', 'rb') as f:\n extract_matrix = pickle.load(f)\n\n with open(test_de_bpe, encoding=\"utf-8\") as fbpe:\n src_bpe_sents = fbpe.readlines()\n with open(test_de_word, encoding=\"utf-8\") as fword:\n src_word_sents = fword.readlines()\n\n with open(test_en_bpe, encoding=\"utf-8\") as fbpe:\n tgt_bpe_sents = fbpe.readlines()\n with open(test_en_word, encoding=\"utf-8\") as fword:\n tgt_word_sents = fword.readlines()\n\n for mode in [\"attn\", \"summed_afx\"]:\n for l in range(6):\n with open(\"./work/results_seed\" + str(seed) + \"/alignments/hypothesis-{}-{}-{}\".format(mode,l,setting), \"w\") as f:\n for i in range(len(src_bpe_sents)):\n src_bpe_sent = src_bpe_sents[i]\n src_word_sent = src_word_sents[i]\n src_word_to_bpe = convert_bpe_word(src_bpe_sent, src_word_sent)\n src_len = len(src_word_sent.split())\n\n tgt_bpe_sent = tgt_bpe_sents[i]\n tgt_word_sent = tgt_word_sents[i]\n tgt_word_to_bpe = convert_bpe_word(tgt_bpe_sent, tgt_word_sent)\n \n if mode == \"summed_afx\":\n attention_matrix = torch.squeeze(extract_matrix[i][mode][l]).detach().numpy()\n else:\n attention_matrix = torch.squeeze(extract_matrix[i][mode][l]).mean(dim=0).detach().numpy()\n\n if setting == \"AWI\":\n attention_matrix = attention_matrix[list(range(1,len(attention_matrix)))+[0]]\n attention_matrix = get_word_word_attention(attention_matrix, src_word_to_bpe, tgt_word_to_bpe)\n attention_matrix = np.argmax(attention_matrix, -1)\n\n for t, s_a in enumerate(attention_matrix):\n if s_a != src_len:\n f.write(\"{}-{} \".format(t+1, s_a+1))\n f.write(\"\\n\") \n\n for mode in [\"attn\", \"afx\"]:\n for l in range(6):\n for h in range(4):\n with open(\"./work/results_seed\" + str(seed) + \"/alignments/hypothesis-{}-{}-{}-{}\".format(mode,l,h,setting), \"w\") as f:\n for i in range(len(src_bpe_sents)):\n src_bpe_sent = src_bpe_sents[i]\n src_word_sent = src_word_sents[i]\n src_word_to_bpe = convert_bpe_word(src_bpe_sent, src_word_sent)\n src_len = len(src_word_sent.split())\n\n tgt_bpe_sent = tgt_bpe_sents[i]\n tgt_word_sent = tgt_word_sents[i]\n tgt_word_to_bpe = convert_bpe_word(tgt_bpe_sent, tgt_word_sent)\n\n attention_matrix = torch.squeeze(extract_matrix[i][mode][l])[h].detach().numpy()\n if setting == \"AWI\":\n attention_matrix = attention_matrix[list(range(1,len(attention_matrix)))+[0]]\n attention_matrix = get_word_word_attention(attention_matrix, src_word_to_bpe, tgt_word_to_bpe)\n attention_matrix = np.argmax(attention_matrix, -1)\n\n for t, s_a in enumerate(attention_matrix):\n if s_a != src_len:\n f.write(\"{}-{} \".format(t+1, s_a+1))\n f.write(\"\\n\")\n return None","title":""},{"docid":"86c2007a4d655eaf33350a76b400111b","score":"0.43483782","text":"def zihan_alignment():\n proposal_id('2023_1', '000000_tests')\n\n try:\n yield from alignement_gisaxs_hex(angle=0.5, rough_y=0.5)\n except:\n yield from alignement_gisaxs_hex(angle=0.1, rough_y=0.5)\n\n proposal_id('2023_1', '311645_Zhang')","title":""},{"docid":"7f0922a1b6e889b4803e6edc0da63ca6","score":"0.43459523","text":"def iterlabels(self):\n return iter(\n np.moveaxis(self.magnitude, self.labels_axis, 0)\n * self.units\n )","title":""},{"docid":"0de6dbfcfc915d6816eb5c45f1b6c516","score":"0.43381003","text":"def align_cluster( cluster ):\n x_0 = cluster[0][:,0]\n x_f = cluster[0][:,-1]\n distance = lambda x : np.dot(x,x)\n reverse_or_not = lambda x: x[:,::-1] if distance(x[:,0]-x_0) > distance(x[:,0]-x_f) else x\n return [ reverse_or_not(c) for c in cluster ]","title":""},{"docid":"c8adf025194219c181105f446c52690c","score":"0.43354973","text":"def iteridat():\n while True:\n try:\n type, data = self.chunk(lenient=lenient)\n except ValueError as e:\n raise ChunkError(e.args[0])\n if type == b'IEND':\n # http://www.w3.org/TR/PNG/#11IEND\n break\n if type != b'IDAT':\n continue\n # type == b'IDAT'\n # http://www.w3.org/TR/PNG/#11IDAT\n if self.colormap and not self.plte:\n warnings.warn(\"PLTE chunk is required before IDAT chunk\")\n yield data","title":""},{"docid":"0b9a7ba2a402e26bc5c94a0e7f26bab2","score":"0.43353194","text":"def dlmat2imat(dlmat):\n\n for i, row in enumerate(dlmat):\n if i > 0:\n for j, v in enumerate(row):\n if j > 0:\n yield i-1, j-1, v","title":""},{"docid":"0d8ee4ea567471feeceac879e534658d","score":"0.43341258","text":"def _extract_images(self, image):\n _shape_height = image.shape[0]#the height of the image\n _shape_width = image.shape[1]#the width of the image\n\n #search for the first column that is not black\n _x = np.min(np.nonzero(np.any(image, axis = (1,2))))\n\n # search along this column, we can obtain the start pixel row of each image row\n _row_mask = np.any(image[:,_x,:], axis = 1)\n _row_start_y = np.insert(np.nonzero(np.diff(_row_mask.astype(int)) == 1)[0]+1, 0, 0)\n # to be used in later loop\n _row_start_y = np.append(_row_start_y, _shape_height-1)\n _extracted_list = []\n #Search by all the starting rows\n for _y_index in range(_row_start_y.size-1):\n _starting_y = _row_start_y[_y_index]\n\n # we can find all the start and end column of each image\n # (i.e. they are horizonally bounded)\n _column_mask = np.any(image[_starting_y], axis = 1) # all the non-black point\n _column_start_x = np.insert(np.nonzero(np.diff(_column_mask.astype(int)) == 1)[0]+1, 0, 0)\n _column_end_x = np.nonzero(np.diff(_column_mask.astype(int)) == -1)[0]+1\n # check each horizontally bounded region\n for _x_index in range(_column_start_x.size):\n # check the leftmost column\n _image_starting_x = _column_start_x[_x_index]\n\n #search in this column between two starting y\n _image_y_mask = np.any(image[_starting_y:_row_start_y[_y_index + 1],_image_starting_x], axis=1)\n\n #get the end point of y\n _image_end_y = np.where(np.diff(_image_y_mask.astype(int)) == -1)[0][0]+1\n\n #append the image to the final list\n _extracted_list.append(\n image[_starting_y:_starting_y+_image_end_y,\n _image_starting_x:_column_end_x[_x_index]]\n )\n return _extracted_list","title":""},{"docid":"59d83b140eb22b2dd755a449cbf00c28","score":"0.4333457","text":"def motif_pair_dfi(dfi_filtered, motif_pair):\n dfa = dfi_filtered[dfi_filtered.pattern_name == motif_pair[0]]\n dfb = dfi_filtered[dfi_filtered.pattern_name == motif_pair[1]]\n\n dfab = pd.merge(dfa, dfb, on='example_idx', how='outer')\n dfab = dfab[~dfab[['pattern_center_x', 'pattern_center_y']].isnull().any(1)]\n\n dfab['center_diff'] = dfab.pattern_center_y - dfab.pattern_center_x\n if \"pattern_center_aln_x\" in dfab:\n dfab['center_diff_aln'] = dfab.pattern_center_aln_y - dfab.pattern_center_aln_x\n dfab['strand_combination'] = dfab.strand_x + dfab.strand_y\n # assure the right strand combination\n dfab.loc[dfab.center_diff < 0, 'strand_combination'] = dfab[dfab.center_diff < 0]['strand_combination'].map(comp_strand_compbination).values\n\n if motif_pair[0] == motif_pair[1]:\n dfab.loc[dfab['strand_combination'] == \"--\", 'strand_combination'] = \"++\"\n dfab = dfab[dfab.center_diff > 0]\n else:\n dfab.center_diff = np.abs(dfab.center_diff)\n if \"center_diff_aln\" in dfab:\n dfab.center_diff_aln = np.abs(dfab.center_diff_aln)\n if \"center_diff_aln\" in dfab:\n dfab = dfab[dfab.center_diff_aln != 0] # exclude perfect matches\n return dfab","title":""},{"docid":"462892548462bee1f446d8f04e6d1e15","score":"0.43329483","text":"def macs_peaks_knowngene_overall_analysis(macs_peaks_strand_filename,knowngene_genesymbol_filename,annotated_peaks_filename,unannotated_peaks_filename):\n macs_file=open(macs_peaks_strand_filename,'r')\n peaks=macs_file.readlines()\n \n gene_file=open(knowngene_genesymbol_filename,'r')\n genes=gene_file.readlines()\n \n list1=[]\n for i in range(20):\n list1.append(\"chr%s+\" % (i))\n list1.append(\"chr%s-\" % (i))\n list1.append(\"chrM+\")\n list1.append(\"chrM-\")\n list1.append(\"chrX+\")\n list1.append(\"chrX-\")\n list1.append(\"chrY+\")\n list1.append(\"chrY-\")\n \n dic1={}\n for j in list1:\n dic1[j]=[]\n \n for gene in genes[1:]: #the knowngene_genesymbol_file has a header\n chr=gene.split('\\t')[1]+gene.split('\\t')[2] # e.g.,\"chr1\" + \"-\"\n if chr in dic1.keys():\n dic1[chr].append(gene.strip('\\n').split('\\t'))\n dic1[chr][-1][3]=int(dic1[chr][-1][3]) #the txStart position is like \"12345\" and should be stored as 12345\n dic1[chr][-1][6]=int(dic1[chr][-1][6]) #the cdsEnd position is like \"12345\" and should be stored as 12345\n \n annotated_peaks=open(annotated_peaks_filename,'w')\n unannotated_peaks=open(unannotated_peaks_filename,'w')\n a=0 # used to store number of unannoted peaks\n b=0 #used to store number of annotated peaks\n for line in peaks:\n peak=line.strip('\\n').split('\\t')\n peak_chr=peak[0]+peak[10] #e.g., \"chr1\"+ \"-\"\n peak_start=int(peak[1])\n peak_end=int(peak[2])\n peak_summit=int(peak[1])+int(peak[4])\n low=0\n high=len(dic1[peak_chr])-1\n gene_pos=return_loci(dic1[peak_chr],3,6,peak_summit,low,high)#1 and 2 denote the positions in the list for txStart and cdsEnd\n if gene_pos==\"not found!\":\n unannotated_peaks.write(line)\n a=a+1\n else:\n peak.append(dic1[peak_chr][gene_pos][-1])\n new_peak='\\t'.join(peak)+'\\n'\n annotated_peaks.write(new_peak)\n b=b+1\n \n print \"unannotated peaks: %s\\t and annotated peaks: %s\\n\" %(a,b)\n macs_file.close()\n gene_file.close()\n annotated_peaks.close()\n unannotated_peaks.close()","title":""},{"docid":"06e2a0b007927a14887adcd8904811be","score":"0.4317422","text":"def _get_alignment_result(self, fkp, row, col, reference, output):\n\n alignment_result = AlignmentResult()\n count_for_output = 0\n reach_first_cell = False\n while not reach_first_cell:\n # we will only stop when it is first cell\n reach_first_cell, row, col, count_for_output = self._get_me_the_result_by_looping_through_each_col(\n fkp, row, col, count_for_output, reference, output, alignment_result\n )\n\n alignment_result.merge_none_tokens()\n return alignment_result","title":""},{"docid":"2e12e366846d985f622c6904a2250995","score":"0.43162665","text":"async def align(self, alignment = None):\n\t\tif alignment is None:\n\t\t\tif self.reader.sysinfo.ProcessorArchitecture == PROCESSOR_ARCHITECTURE.AMD64:\n\t\t\t\talignment = 8\n\t\t\telse:\n\t\t\t\talignment = 4\n\t\toffset = self.current_position % alignment\n\t\tif offset == 0:\n\t\t\treturn\n\t\toffset_to_aligned = (alignment - offset) % alignment\n\t\tawait self.seek(offset_to_aligned, 1)\n\t\treturn","title":""},{"docid":"51fca61cdff1c4352be24f8e867db2fd","score":"0.43139875","text":"def __mark_new_columns_with_zeros_in_marked_rows(self):\n num_marked_columns = 0\n for index, column in enumerate(self._zero_locations.T):\n if index not in self._marked_columns:\n if column.any():\n row_indices, = np.where(column)\n zeros_in_marked_rows = (set(self._marked_rows) & set(row_indices)) != set([])\n if zeros_in_marked_rows:\n self._marked_columns.append(index)\n num_marked_columns += 1\n return num_marked_columns","title":""},{"docid":"c7f3b5dec13f760f0c240b8acdad2ab4","score":"0.43103832","text":"def centroid_align(stars):\n center = int(stars[0].shape[0] / 2), int(stars[0].shape[1] / 2)\n aligned = []\n\n for star in stars:\n centroid = calculate_centroid(star)\n shift = np.subtract(center, centroid)\n star = fourier_shift(np.fft.fftn(star), shift)\n star = np.fft.ifftn(star).real\n aligned.append(star)\n\n return aligned","title":""},{"docid":"90862dac95aa092d1af86bcb2c7d8a14","score":"0.43101203","text":"def __generateArtifactualLinkageMatrix(self, corrOnly=False):\n\n\t\tdef find_similar_peakwidth(featureMetadata, deltaMZ, deltaOverlap):\n\t\t\t\"\"\"Find 'identical' features based on m/z and peakwidth overlap\n\t\t\t\tinput:\n\t\t\t\t\tfeatureMetada msDataset.featureMetadata\n\t\t\t\t\tdeltaMZ\t\t m/z distance to consider two features identical [ <= ] (same unit as m/z)\n\t\t\t\t\tdelta overlap minimum peak overlap between two grouped features (0-100%)\n\t\t\t\toutput:\n\t\t\t\t\tpandas.DataFrame listing matched features based on deltaMZ and deltaOverlap\n\t\t\t\"\"\"\n\n\t\t\tdef get_match(i, ds, deltaMZ):\n\t\t\t\t\"\"\"Find identical features for a given variable\n\t\t\t\t\toutput:\n\t\t\t\t\t\tpandas.DataFrames listing the matching features based overlap of peakwidth\n\t\t\t\t\"\"\"\n\t\t\t\tmatch = (abs(ds.loc[i, 'Retention Time'] - ds.loc[:, 'Retention Time']) <= (\n\t\t\t\t\t\t\tds.loc[i, 'Peak Width'] + ds.loc[:, 'Peak Width']) / 2) & (\n\t\t\t\t\t\t\t\t\tabs(ds.loc[i, 'm/z'] - ds.loc[:, 'm/z']) <= deltaMZ) # find match\n\t\t\t\treturn (pandas.DataFrame(data={'node1': ds.index[i], 'node2': ds.index[match], 'Peak Overlap': ((((\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ds.loc[\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t i, 'Peak Width'] +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ds.loc[\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t match, 'Peak Width']) / 2) - abs(\n\t\t\t\t\tds.loc[i, 'Retention Time'] - ds.loc[match, 'Retention Time'])) / ((ds.loc[i, 'Peak Width'] +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tds.loc[\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmatch, 'Peak Width']) / 2)) * 100})) # return the matching rows\n\n\t\t\t# get feature overlap\n\t\t\tds = featureMetadata[['Feature Name', 'Retention Time', 'm/z', 'Peak Width']]\n\t\t\t# By concatenating all the matches once, save ~22% compared to do it at each loop round\n\t\t\tmatches = [get_match(i, ds, deltaMZ) for i in range(ds.shape[0])] # get a list of matches\n\t\t\tres = pandas.concat(matches)\n\t\t\tres = res.loc[res.node1 < res.node2] # keeps feat1-feat2, removes feat1-feat1 and feat2-feat1\n\n\t\t\t# filter interactions by overlap\n\t\t\tres = res.loc[res.loc[:, 'Peak Overlap'] >= deltaOverlap, ['node1', 'node2']]\n\t\t\tres.reset_index(drop=True, inplace=True)\n\n\t\t\treturn (res)\n\n\t\t# end find_similar_peakwidth\n\n\t\tdef remove_min_corr_overlap(overlappingFeatures, intensityData, corrCutoff):\n\t\t\t\"\"\" Return the overlap match DataFrame with overlap of metabolites correlated < cut-off removed (and correlation added)\n\t\t\t\tinput:\n\t\t\t\t\toverlappingFeatures pandas.DataFrame as generated by find_similar_peakwidth\n\t\t\t\t\tintensityData\t pandas.DataFrame of data value for each sample (row) / feature (column)\n\t\t\t\t\tcorrCutoff\t\t minimum percentage of overlap (0-1)\n\t\t\t\toutput:\n\t\t\t\t\toverlapping features filtered\n\t\t\t\"\"\"\n\t\t\tlink_corr = numpy.zeros([overlappingFeatures.shape[0]])\n\t\t\tfor jrow in range(0, len(link_corr)):\n\t\t\t\tlink_corr[jrow] = numpy.corrcoef(intensityData[:, overlappingFeatures.loc[jrow, 'node1']],\n\t\t\t\t\t\t\t\t\t\t\t\t intensityData[:, overlappingFeatures.loc[jrow, 'node2']])[0, 1]\n\n\t\t\treturn (overlappingFeatures.loc[link_corr >= corrCutoff,])\n\n\t\t# end remove_min_corr_overlap\n\n\t\t# check required info in featureMetadata for artifactual filtering. If missing, sets self.Attributes['artifactualFilter'] to False\n\t\tif self.Attributes['featureFilters']['artifactualFilter'] == False:\n\t\t\traise ValueError(\n\t\t\t\t'Attributes[\\'artifactualFilter\\'] set to \\'False\\', artifactual filtering cannot be run, use \\'updateMasks(withArtifactualFiltering=False)\\' and \\'generateReport(data, reportType=\\'feature selection\\', withArtifactualFiltering=False)\\'')\n\t\tif 'Feature Name' not in self.featureMetadata.columns:\n\t\t\tself.Attributes['featureFilters']['artifactualFilter'] = False\n\t\t\traise LookupError(\n\t\t\t\t'Missing feature metadata \\\"Feature Name\\\". Artifactual filtering cannot be run, set MSDataset.Attributes[\\'artifactualFilter\\'] = \\'False\\', or use \\'updateMasks(withArtifactualFiltering=False)\\' and \\'generateReport(data, reportType=\\'feature selection\\', withArtifactualFiltering=False)\\'')\n\t\tif 'Retention Time' not in self.featureMetadata.columns:\n\t\t\tself.Attributes['featureFilters']['artifactualFilter'] = False\n\t\t\traise LookupError(\n\t\t\t\t'Missing feature metadata \\\"Retention Time\\\". Artifactual filtering cannot be run, set MSDataset.Attributes[\\'artifactualFilter\\'] = \\'False\\', or use \\'updateMasks(withArtifactualFiltering=False)\\' and \\'generateReport(data, reportType=\\'feature selection\\', withArtifactualFiltering=False)\\'')\n\t\tif 'm/z' not in self.featureMetadata.columns:\n\t\t\tself.Attributes['featureFilters']['artifactualFilter'] = False\n\t\t\traise LookupError(\n\t\t\t\t'Missing feature metadata \\\"m/z\\\". Artifactual filtering cannot be run, set MSDataset.Attributes[\\'artifactualFilter\\'] = \\'False\\', or use \\'updateMasks(withArtifactualFiltering=False)\\' and \\'generateReport(data, reportType=\\'feature selection\\', withArtifactualFiltering=False)\\'')\n\t\tif 'Peak Width' not in self.featureMetadata.columns:\n\t\t\tself.Attributes['featureFilters']['artifactualFilter'] = False\n\t\t\traise LookupError(\n\t\t\t\t'Missing feature metadata \\\"Peak Width\\\". Artifactual filtering cannot be run, set MSDataset.Attributes[\\'artifactualFilter\\'] = \\'False\\', or use \\'updateMasks(withArtifactualFiltering=False)\\' and \\'generateReport(data, reportType=\\'feature selection\\', withArtifactualFiltering=False)\\'')\n\n\t\tif ((not corrOnly) | (corrOnly & self._tempArtifactualLinkageMatrix.empty)):\n\t\t\tself._tempArtifactualLinkageMatrix = find_similar_peakwidth(featureMetadata=self.featureMetadata,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdeltaMZ=self.Attributes['filterParameters'][\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'deltaMzArtifactual'], deltaOverlap=\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.Attributes['filterParameters'][\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'overlapThresholdArtifactual'])\n\t\tartifactualLinkageMatrix = remove_min_corr_overlap(self._tempArtifactualLinkageMatrix, self._intensityData,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.Attributes['filterParameters'][\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'corrThresholdArtifactual'])\n\n\t\treturn (artifactualLinkageMatrix)","title":""},{"docid":"8426a4c876d885b4757e926f40783a44","score":"0.43041614","text":"def get_alignments(self, species, start, stop, strand):\n files = dict()\n visited = set()\n query_size = stop - start\n idx = 0\n for iv in self.tree[species][start: stop]:\n iv_start, iv_end = iv.begin, iv.end\n if (iv_start, iv_end) not in visited:\n visited.add((iv_start, iv_end))\n block_start, block_stop, pathfile = iv.data\n # store all blocks related to a file\n #order.append((iv_start, iv_end, idx))\n files.setdefault(pathfile, list()).append((block_start, block_stop, idx))\n idx += 1\n # because segments can be in multiple files and are not necessary in the right order \n # we remember the segment positions to reorder the alignment at the end\n alignments = dict()\n ordered_alignments = list()\n order = list()\n if DEBUG:\n print(files)\n for pathfile in files:\n # final positions\n positions = sorted(files[pathfile])\n prev_pos = 0\n prev_stop = -1\n with open(pathfile) as handle:\n for file_pos_start, file_pos_stop, file_pos_idx in positions:\n # reading blocks\n assert(file_pos_start > prev_stop) # block cannot overlap, can they?\n file_pos_size = file_pos_stop - file_pos_start\n # we remove prev because we only move of the number of lines between the two blocks\n line = next(islice(handle, file_pos_start - 1 - prev_pos , file_pos_start - prev_pos)) \n align = next(MafIO.MafIterator(handle))\n # look for the sequence of the species that we are interested in\n found = False\n for record in align:\n if record.id == species:\n found = True\n break\n # only keep part of the alignment that we are interest of\n if found:\n start_seq = record.annotations[\"start\"]\n size_seq = record.annotations[\"size\"] \n stop_seq = start_seq + size_seq\n strand_seq = int(record.annotations[\"strand\"])\n srcSize_seq = record.annotations[\"srcSize\"]\n seq = record.seq\n msa_len = len(seq)\n \n qstart = start\n qstop = stop\n if strand_seq < 0:\n # convert query positions\n qstart = srcSize_seq - stop\n #qstop = srcSize_seq - tmp\n qstop = srcSize_seq - start\n # from this point everything is on the plus strand\n max_start = max(start_seq, qstart)\n max_stop = min(stop_seq, qstop)\n strand_p_start = max_start\n strand_p_stop = max_stop\n if strand_seq < 0:\n strand_p_start, strand_p_stop = srcSize_seq - strand_p_stop, srcSize_seq - strand_p_start\n\n abs_start = max_start - start_seq\n abs_stop = max_stop - start_seq\n\n # get new start of alignment \n msa_start, msa_stop, msa_size = seq2msa_startstop(str(seq), abs_start, abs_stop)\n \n if DEBUG:\n print(strand_seq, strand, file_pos_start, file_pos_stop, pathfile)\n print(start_seq, size_seq, srcSize_seq, qstart, qstop, max_start, max_stop, abs_start, abs_stop)\n print(msa_start, msa_stop)\n #print(\">\", record.seq[msa_start: msa_stop])\n #print(\" \", seq[msa_start: msa_stop])\n\n alignments[file_pos_idx] = (align[:, msa_start: msa_stop], strand_seq)\n order.append((strand_p_start, strand_p_stop, file_pos_idx))\n else:\n print(\"Unable to find SeqRecord for species {} in alignment:\".format(species))\n print(align)\n #file_pos_size = number of lines between two blocks\n prev_pos = file_pos_start + file_pos_size \n prev_stop = file_pos_stop\n # order segments and convert to the correct strand if necessary\n if DEBUG:\n print(order)\n order.sort()\n if DEBUG:\n print(order)\n if strand < 0:\n order = order[::-1]\n if DEBUG:\n print(order)\n for iv_start, iv_stop, i in order:\n align, strand_seq = alignments[i]\n if strand < 0:\n if strand_seq < 0:\n ordered_alignments.append(align)\n else:\n ordered_alignments.append(self._reverse_msa(align)) \n else:\n if strand_seq < 0:\n ordered_alignments.append(self._reverse_msa(align))\n else:\n ordered_alignments.append(align)\n return ordered_alignments","title":""},{"docid":"4e0d3137c993e6e3f670d890450d0953","score":"0.42939565","text":"def feed_cells_island(self):\n for landscape in self.map.values():\n landscape.feed_all()","title":""},{"docid":"0cfb6dc63dab0d8384f256528d781f48","score":"0.4291958","text":"def iterstraight(self, raw):\n\n # length of row, in bytes\n rb = self.row_bytes\n a = array('B')\n # The previous (reconstructed) scanline. None indicates first\n # line of image.\n recon = None\n for some in raw:\n a.extend(some)\n while len(a) >= rb + 1:\n filter_type = a[0]\n scanline = a[1:rb+1]\n del a[:rb+1]\n recon = self.undo_filter(filter_type, scanline, recon)\n yield recon\n if len(a) != 0:\n # :file:format We get here with a file format error:\n # when the available bytes (after decompressing) do not\n # pack into exact rows.\n raise FormatError(\n 'Wrong size for decompressed IDAT chunk.')\n assert len(a) == 0","title":""},{"docid":"59a5bac148b6aa49b755f730da69144f","score":"0.4291224","text":"def align_seqs(seq1, seq2, map_qual):\n\n assert len(seq1) == len(seq2) == len(map_qual)\n \n mismatches = []\n for i in range(len(seq1)):\n if seq1[i] == seq2[i]:\n continue\n else:\n if map_qual[i] >= 30:\n mms = [seq1[i], seq2[i], i]\n mismatches.append(mms)\n\n return mismatches","title":""},{"docid":"f861514d7511d76836992e79d9755b20","score":"0.42891642","text":"def flag_cells(self):\n for coordinate in self.get_coordinates():\n\n for cell in self.get_surrounding_cells(coordinate):\n if self.board[cell[0]][cell[1]] == '*':\n continue\n\n self.board[cell[0]][cell[1]] += 1","title":""},{"docid":"d05207a804ff9a2d363afcfb36887169","score":"0.42879254","text":"def _iterate_groups(self):\n for self.groupID, g in enumerate(self.u_groups):\n self.df_g = self.df.loc[self.groups == g]\n if len(self.df_g) < self.min_grp_size:\n continue\n self.df_g.reset_index(inplace=True)\n self._pair_group()","title":""},{"docid":"feecae6fe6b05b2e713df5d18a70f211","score":"0.42837226","text":"def iterate(Primate1, Primate2, side, Primate_inter=None):\n\n individuals = [i for i in os.listdir(Primate2) if '.' not in i]\n\n print('reading affine transformations')\n\n dir = Primate1 + '_to_' + Primate2\n if Primate_inter is not None:\n dir = Primate1 + '_to_' + Primate2 + '_via' + Primate_inter\n\n affine_model = os.path.join(dir, 'affine_trans_' + dir + '_' + side + '.txt')\n int_lon, int_lat, lon_transform, lat_transform = read_affine(affine_model)\n\n for ind in individuals:\n\n nameLon = os.path.join(Primate2, ind, ind + '_' + side + 'white_lon.gii')\n nameLat = os.path.join(Primate2, ind, ind + '_' + side + 'white_lat.gii')\n\n # print('reading coordinates')\n\n r = aims.Reader()\n texLatF = r.read(nameLat)\n texLonF = r.read(nameLon)\n texLat = np.array(texLatF[0])\n texLon = np.array(texLonF[0])\n\n # print('processing longitude')\n\n newLon = rescale(texLon, lon_transform, int_lon)\n\n # print('processing latitude')\n\n newLat = rescale(texLat, lat_transform, int_lat)\n\n # print('writing textures')\n\n nv = texLat.size\n newLatT = aims.TimeTexture_FLOAT(1, nv)\n newLonT = aims.TimeTexture_FLOAT(1, nv)\n\n for i in range(nv):\n newLatT[0][i] = newLat[i]\n newLonT[0][i] = newLon[i]\n\n outLat = os.path.join(dir, Primate1 + '_' + side + 'white_lat_to' + ind + '.gii')\n outLon = os.path.join(dir, Primate1 + '_' + side + 'white_lon_to' + ind + '.gii')\n\n r = aims.Writer()\n r.write(newLatT, outLat)\n r.write(newLonT, outLon)\n\n print('done')","title":""},{"docid":"0ef576af79368e892d5af7bc09fc1b04","score":"0.42796093","text":"def extract(self, x, y, ys):\n#as the input function in dcpg_data.py, we get to know that:\n# x = chunk_pos; x.shape = (32768,)\n# y = cpg_table.pos.values; y.shape = (3971744,)\n#ys = cpg_table.value.values; ys.shape = (3971744,) This is the original input data information\n\n n = len(x) #target length\n m = len(y) #provided length\n k = self.k\n kk = 2 * self.k\n yc = self.__larger_equal(x, y)\n knn_cpg = np.empty((n, kk), dtype=np.float16)\n knn_cpg.fill(np.nan)\n knn_dist = np.empty((n, kk), dtype=np.float32)\n knn_dist.fill(np.nan)\n\n for i in range(n):\n # Left side\n yl = yc[i] - k\n yr = yc[i] - 1\n if yr >= 0:\n xl = 0\n xr = k - 1\n if yl < 0:\n xl += np.abs(yl)\n yl = 0\n xr += 1\n yr += 1\n knn_cpg[i, xl:xr] = ys[yl:yr]\n knn_dist[i, xl:xr] = np.abs(y[yl:yr] - x[i])\n\n # Right side\n yl = yc[i]\n if yl >= m:\n continue\n if x[i] == y[yl]:\n yl += 1\n if yl >= m:\n continue\n yr = yl + k - 1\n xl = 0\n xr = k - 1\n if yr >= m:\n xr -= yr - m + 1\n yr = m - 1\n xl += k\n xr += k + 1\n yr += 1\n knn_cpg[i, xl:xr] = ys[yl:yr]\n knn_dist[i, xl:xr] = np.abs(y[yl:yr] - x[i])\n\n return (knn_cpg, knn_dist)","title":""},{"docid":"bf4a100704a01a5955c235d4efdc6b5c","score":"0.4274558","text":"def _iter_break_from_left_to_right(self):\n yield IdentityTransform(), self","title":""},{"docid":"ff8723c1912591b71c55e55e87d6b9b0","score":"0.42716604","text":"def chromosomesOffsets(self, *args):\n pass","title":""},{"docid":"7e8faccaec9fc773f4ed89fbad97276b","score":"0.4269407","text":"def alignment_csv(out_file):\n writer = csv.DictWriter(out_file, (\n\n 'chadh_slug',\n 'bpo_pub_title',\n 'bpo_title',\n 'bpo_article_type',\n\n 'a_start',\n 'b_start',\n 'size',\n 'a_token_count',\n\n 'a_prefix',\n 'a_snippet',\n 'a_suffix',\n\n 'b_prefix',\n 'b_snippet',\n 'b_suffix',\n ))\n\n writer.writeheader()\n\n alignments = (\n session\n .query(Alignment)\n .join(ChadhNovel, BPOArticle)\n .filter(Alignment.size >= 5)\n .yield_per(1000)\n )\n\n counts = {}\n\n for i, a in enumerate(alignments):\n\n slug = a.chadh_novel.slug\n\n if slug not in counts:\n text = Text(a.chadh_novel.text)\n counts[slug] = len(text.tokens)\n\n writer.writerow(dict(\n\n chadh_slug=a.chadh_novel.slug,\n bpo_pub_title=a.bpo_article.publication_title,\n bpo_title=a.bpo_article.record_title,\n bpo_article_type=a.bpo_article.object_type,\n\n a_start=a.a_start,\n b_start=a.b_start,\n size=a.size,\n a_token_count=counts[slug],\n\n a_prefix=a.a_prefix,\n a_snippet=a.a_snippet,\n a_suffix=a.a_suffix,\n\n b_prefix=a.b_prefix,\n b_snippet=a.b_snippet,\n b_suffix=a.b_suffix,\n\n ))\n\n if i % 1000 == 0:\n print(i)","title":""},{"docid":"30f231716666c378e0ce3209b73f8768","score":"0.42603087","text":"def get_aligned_pairs(self, refset, targetset, unique=True, use_distance=True):\n global_mat, global_matched = self.align(refset, targetset, get_matrix=use_distance)\n for pair in iter_aligned_pairs(refset, targetset, global_mat, global_matched, unique):\n self.pairs_found += 1\n yield pair\n self.log_infos()","title":""},{"docid":"bf685e297261c0138f9adc84a545b657","score":"0.42578217","text":"def _map_segment_pairs_inclined(\n self, i_pair, j_pair, k_pair, borehole_to_borehole):\n i_segment = np.concatenate(\n [i_pair + self._i0Segments[i] for (i, j) in borehole_to_borehole])\n j_segment = np.concatenate(\n [j_pair + self._i0Segments[j] for (i, j) in borehole_to_borehole])\n k_segment = np.tile(k_pair, len(borehole_to_borehole))\n return i_segment, j_segment, k_segment","title":""},{"docid":"efc02674c6ec094fd8de81a0768b562c","score":"0.42540073","text":"def _iter_values(self):\n\n return zip([self._none_slices_except_domain_axis], [self.magnitude])","title":""},{"docid":"9fafddce7d4a9691252df8f62f0057a0","score":"0.4251796","text":"def extract_alignments(self, alignment_output_file = None):\n\n # set the input file if not given\n if not alignment_output_file:\n alignment_output_file = self.alignment_output_file\n alignments = extract_alignments(alignment_output_file)\n self.protein_alignments = alignments","title":""},{"docid":"df79407572c8571b2ec2c997b9dc877a","score":"0.42514947","text":"def tilegen(self):\n for row in self.cells:\n for col in row:\n yield col","title":""},{"docid":"e70efc72557e75fbf8264af798d1a6b7","score":"0.42489278","text":"def _align(data, index_align=0, channel_offsets=None):\n\n # shapes might be different if stitched with different configs\n # keep shape consistent with DO\n\n shape = data[0].shape\n data = lasagna.utils.pile(data)\n data = data[..., :shape[-2], :shape[-1]]\n\n indices = range(len(data))\n indices.pop(index_align)\n indices_fwd = [index_align] + indices\n indices_rev = np.argsort(indices_fwd)\n aligned = lasagna.process.register_and_offset(data[indices_fwd], registration_images=data[indices_fwd,0])\n aligned = aligned[indices_rev]\n if channel_offsets:\n aligned = fix_channel_offsets(aligned, channel_offsets)\n\n return aligned","title":""},{"docid":"e66dc91350c5e4d3f7cb8d857261da95","score":"0.42437807","text":"def inner_mask(self):\n for p in flex.nested_loop(self._grid_size):\n if self._inner_mask_binary[self._grid_idxr(p)]: yield p","title":""},{"docid":"fe5299cf8473c10706cf8839d4cbf644","score":"0.4241394","text":"def outer_mask(self):\n for p in flex.nested_loop(self._grid_size):\n if self._outer_mask_binary[self._grid_idxr(p)]: yield p","title":""},{"docid":"ec14f4d10c412f5629940f9e71a84489","score":"0.4238155","text":"def split_alignment(self):\n aln_list = []\n for name, start, stop in self._linspace.to_list():\n aln = self.get_sites(list(range(start, stop)))\n aln.name = name\n aln._linspace = self._subspaces[name]\n aln.metadata = deepcopy(self.metadata)\n\n aln_list.append(aln)\n return aln_list","title":""},{"docid":"9891a7038593cfc7c1c20f03ad6a5d17","score":"0.42331967","text":"def data_iterator(self, data_info, params, shuffle=False):\n\n collate_fn = lambda d: collate_libri(d, self.mfcc, params, self.word2Idx)\n\n # make a list that decides the order in which we go over the data- this avoids explicit shuffling of data\n data_loader = tdata.DataLoader(self.datasets[data_info['type']], batch_size=params.batch_size, shuffle=shuffle, collate_fn=collate_fn)\n\n # one pass over data\n for data in enumerate(data_loader):\n # fetch waveforms and keywords\n\n yield data[1][0], data[1][1]","title":""}],"string":"[\n {\n \"docid\": \"a0d44a244dde0b542e3a237cd14452b2\",\n \"score\": \"0.55822045\",\n \"text\": \"def others_in_column(self, row_, col_):\\n for r in range(9):\\n if r != row_:\\n yield self[r, col_]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5988acb9ea0c626347aba9bba02a519c\",\n \"score\": \"0.54252195\",\n \"text\": \"def iter_markers(self):\\n for i in self.nmarkers:\\n yield Record(\\n self.markers.ids[i],\\n self.markers.descriptions[i],\\n self.markers.sequences[i],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f46c3bfebc4e391badd53d4010381023\",\n \"score\": \"0.52099943\",\n \"text\": \"def _get_me_the_result_by_looping_through_each_col(self, fkp, row, col, count_for_output, reference, output,\\n alignment_result):\\n # push same tokens to alignment_result (#3)\\n # if it is not zero, which means the reference and the output are the same in this part.\\n if fkp.get_n_match(n_row=row, n_col=col) > 0: # 3\\n for i in range(fkp.get_n_match(n_row=row, n_col=col)):\\n count_for_output -= 1\\n self._alignment_add(reference[fkp.get_n(n_row=row, n_col=col)-1-i], output[count_for_output],\\n alignment_result)\\n # distinguish insert / del / sub according to #2\\n # no matter if the third number is zero or not, we will have to look at the second number;\\n return self._second_number(row, col, count_for_output, reference, output, fkp, alignment_result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda9b179be47168f8af1ded2289e7306\",\n \"score\": \"0.513998\",\n \"text\": \"def _iter_offset(self):\\n index = 0\\n length = 1\\n while True:\\n for _ in range(2):\\n for _l in range(length):\\n yield [self._step * k for k in self._rotation[index]]\\n index = (index + 1) % len(self._rotation)\\n length += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"361f20cabe33898c81536afe79a1d3c3\",\n \"score\": \"0.508139\",\n \"text\": \"def iterate_across(padded_x, padded_y=None, batch_size=1):\\n curr = 0\\n data_len = len(padded_x[0])\\n index_shuffler = list(range(data_len))\\n random.shuffle(index_shuffler)\\n while curr < data_len and curr < 1000:\\n if padded_y is not None:\\n yield [np.array([padded_x[i][j] \\\\\\n for j in index_shuffler[curr:curr+batch_size]]) \\\\\\n for i in range(len(padded_x))], \\\\\\n [np.array([padded_y[i][j] \\\\\\n for j in index_shuffler[curr:curr+batch_size]]) \\\\\\n for i in range(len(padded_y))]\\n else:\\n yield [np.array([padded_x[i][j] \\\\\\n for j in index_shuffler[curr:curr+batch_size]]) \\\\\\n for i in range(len(padded_x))]\\n curr += batch_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"142e5a5f3fbf15888d339e113da700d9\",\n \"score\": \"0.5047755\",\n \"text\": \"def align():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c44099a0e4d84015d6721334ebf9941d\",\n \"score\": \"0.4997019\",\n \"text\": \"def others_in_row(self, row_, col_):\\n for c in range(9):\\n if c != col_:\\n yield self[row_, c]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c4b27d3bd6f934aabb3d79cc41acdc2\",\n \"score\": \"0.49119234\",\n \"text\": \"def get_markers(self) -> iter:\\n for cm in self.mappings:\\n yield cm.marker\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53ea1396a36f06157d874ab378077d78\",\n \"score\": \"0.4888976\",\n \"text\": \"def align_columns() -> Iterable[tuple[int, list[str]]]:\\n for page, lines in extract_pages():\\n # find a position that is blank in every line\\n max_len = max(len(line) for line in lines)\\n best_blank = -1\\n for i in range(max_len):\\n if not all(len(line) <= i or line[i] == \\\" \\\" for line in lines):\\n continue\\n num_lines = len([line for line in lines if len(line) > i])\\n if num_lines < 10:\\n continue\\n best_blank = i\\n assert best_blank != -1, f\\\"failed to find split for {page}\\\"\\n first_column = [line[:best_blank].rstrip() for line in lines]\\n second_column = [line[best_blank + 1 :].rstrip() for line in lines]\\n yield page, first_column + second_column\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42422da18a8780865576df8848ca2049\",\n \"score\": \"0.48882017\",\n \"text\": \"def __iter__(self):\\n for col in self._selection:\\n yield self.df.icol(col)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6366ee59f36eb689ab6ab9d20a09b0ed\",\n \"score\": \"0.48636395\",\n \"text\": \"def align(self, column, groups):\\n raise NotImplementedError() # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04bef7898a1b96c617f1b85624da2194\",\n \"score\": \"0.48118094\",\n \"text\": \"def iter_rows(self):\\n for i in self.nsamples:\\n yield Record(\\n self.samples.ids[i],\\n self.samples.descriptions[i],\\n self.samples.sequences[i],\\n )\\n for i in self.nmarkers:\\n yield Record(\\n self.markers.ids[i],\\n self.markers.descriptions[i],\\n self.markers.sequences[i],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46896471637ee32dc4dba4ef12c0c9ec\",\n \"score\": \"0.47870177\",\n \"text\": \"def alignment(path_muse, path_note, path_save, columns):\\n files_muse = sorted(os.listdir(path_muse)) # read muse files\\n files_notes = sorted(os.listdir(path_note)) # read annotation files\\n\\n for index in range(len(files_muse)): # for each file muse\\n muse = pd.read_csv(path_muse + '/' + files_muse[index], index_col=False) # keep the file\\n print(index)\\n\\n muse = muse[['TimeStamp'] + columns] # choose the columns\\n if len(muse['TimeStamp'][0]) >= 12: # if there is the date\\n muse['TimeStamp'] = list(map(lambda x: x[11:], muse['TimeStamp']))\\n\\n note = pd.read_csv(path_note + '/' + files_notes[index], index_col=False) #\\n\\n # fix tables\\n first_check = note['check time'][1][:-3]\\n note = note[2:-1]\\n note['check time'] = list(map(lambda x: (x[:-3]), note['check time']))\\n muse['TimeStamp'] = list(map(lambda x: str(x), muse['TimeStamp']))\\n\\n # fix dimension of csv muse\\n print('Dataset size before cleaning samples:' + str(muse.shape))\\n old_shape = muse.shape[0]\\n muse = muse.dropna()\\n muse = muse[(muse[columns] != 0).all(1)]\\n print('Size of dataset after cleaning samples:' + str(muse.shape))\\n print('Diference: ' + str(old_shape - muse.shape[0]))\\n\\n # muse csv must be start with the same time of note csv\\n first_index = 0\\n while pd.to_datetime(first_check) > pd.to_datetime(list(muse['TimeStamp'])[first_index]):\\n first_index = first_index + 1\\n print(first_index)\\n muse = muse[first_index:]\\n\\n muse = create_new_muse_table(note, muse, first_check)\\n\\n muse.to_csv(path_save + \\\"/\\\" + str(index) + \\\"alignment.csv\\\", index=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2a0e6cc803539a0accb2a194bd02b8f\",\n \"score\": \"0.4769264\",\n \"text\": \"def iter_multialignments(self):\\n sam_iter = iter(self)\\n multialignment = [next(sam_iter)]\\n for record in sam_iter:\\n if record.qname == multialignment[0].qname: # is this expensive?\\n multialignment.append(record)\\n else:\\n yield MultiAlignment(multialignment)\\n multialignment = [record]\\n yield MultiAlignment(multialignment)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fa56691dcc6453eacc6ba779f066afd\",\n \"score\": \"0.47459912\",\n \"text\": \"def remove_irrelevant_data_from_alignment(ali_plus_top_seq_plus_1):\\n # Parse the input alignment.\\n alignment = AlignIO.read(ali_plus_top_seq_plus_1, 'nexus')\\n\\n # Remove the original file.\\n os.remove(ali_plus_top_seq_plus_1)\\n\\n # Remove all but the last two sequences from the alignment.\\n alignment = alignment[-2:]\\n\\n\\n # Remove positions in the alignment that have only gaps for both\\n # sequences.\\n\\n # get a list of columns as strings in the original alignment.\\n seq_len = alignment.get_alignment_length()\\n columns = [alignment[:, col] for col in range(seq_len)] \\n\\n # Get a list of column indexes for columns that need to be removed.\\n col_to_remove = []\\n col_index = -1\\n for col in columns:\\n col_index += 1\\n if col[0] == '-' and col[1] == '-':\\n col_to_remove.append(col_index)\\n\\n \\n #seq_len = alignment.get_alignment_length()\\n #print(seq_len)\\n #print(alignment[:,col_to_remove[0]])\\n\\n # Remove columns (positions) from alignment.\\n num = 0\\n for col_index in col_to_remove:\\n num += 1\\n adj_col_index = col_index - num\\n alignment = alignment[:,:adj_col_index] + alignment[:,adj_col_index + 1:]\\n #if col_index != (seq_len -1):\\n # alignment = alignment[:,:adj_col_index] + alignment[:,adj_col_index + 1:]\\n #else:\\n # alignment = alignment[:,:adj_col_index]\\n\\n #seq_len = alignment.get_alignment_length()\\n #print(seq_len)\\n #print(alignment[:,col_to_remove[0]])\\n\\n # Write modified alignment to the same filepath as the original.\\n AlignIO.write(alignment, ali_plus_top_seq_plus_1, 'nexus')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90651807ce66f66bdf7c3a2fe35d9007\",\n \"score\": \"0.46938723\",\n \"text\": \"def _test_shifted(sequences):\\n\\n for i in range(1, len(sequences)):\\n\\n if sequences[i - 1][:-1].sum() == 0:\\n # Change of user, all columns but one\\n # are padding.\\n continue\\n\\n assert np.all(sequences[i][1:] == sequences[i - 1][:-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3a03e5696aa7c858b18a732b64d26cd\",\n \"score\": \"0.4686958\",\n \"text\": \"def column(self, col_):\\n for row in self.raw:\\n yield row[col_]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77b91c3138c977e9a0b69c8d144b7bc1\",\n \"score\": \"0.46589667\",\n \"text\": \"def _iterate_over_counter_columns(self, func):\\n col_count = 0\\n for row_index in xrange(self._params['num_counter_rows']):\\n row_key = self._generate_row_key(row_index)\\n for col_index in xrange(self._params['num_cols']):\\n col_name = self._generate_col_name(col_index)\\n func(row_key, col_name)\\n col_count += 1\\n debug(\\\"iterated over %d counter columns\\\" % col_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff8e979417a0fcf1739b14f6ea9f1830\",\n \"score\": \"0.4653444\",\n \"text\": \"def iterate_over_spatial(self):\\n data = self[:]\\n\\n def calc_slice_idx(idx):\\n slice_obj = list(idx[:3]) + [slice(None), ] * (data.ndim - 3)\\n return tuple(slice_obj)\\n\\n for idx in np.ndindex(data.shape[:3]):\\n yield self[idx], calc_slice_idx(idx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eea3e376a73398e4ab00914b35642bc1\",\n \"score\": \"0.46411833\",\n \"text\": \"def refine_all_alignment(original, limit=4):\\n new_alignment = []\\n for i, item in enumerate(original):\\n new_alignment.append(refine_individual_alignment(original, i, limit = limit))\\n return new_alignment\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a6246840c523f1a633321e65830459c\",\n \"score\": \"0.4640228\",\n \"text\": \"def _align_columns(tab_stop_array):\\r\\n i = 0\\r\\n while i < len(tab_stop_array[0]):\\r\\n # Start with leftmost column and work right\\r\\n # Grab initial tab positions for this column\\r\\n column = sorted([row[i] for row in tab_stop_array])\\r\\n # To figure out if a given tab position exceeds MAX_OFFSET, we have to get an accurate value for min_tab first.\\r\\n # The logic is simplest if we just sort the list.\\r\\n min_tab = None\\r\\n max_tab = None\\r\\n\\r\\n # Figure out the left and rightmost tabs\\r\\n for tab in column:\\r\\n if tab is None:\\r\\n continue\\r\\n if min_tab is None or tab < min_tab:\\r\\n min_tab = tab\\r\\n if (max_tab is None or tab > max_tab) and tab - \\\\\\r\\n min_tab <= MAX_OFFSET:\\r\\n max_tab = tab\\r\\n\\r\\n # Recalculate the rest of the tab stops after offsetting one to align\\r\\n # with the rest\\r\\n for j, row in enumerate(tab_stop_array):\\r\\n aligned_row = row[:i]\\r\\n original_tab = row[i]\\r\\n offset = None if original_tab is None else max_tab - original_tab\\r\\n if offset is None:\\r\\n # This column is not being used on this row. Leave as None, and\\r\\n # don't change remaining columns.\\r\\n aligned_row += [None]\\r\\n aligned_row += row[i + 1:]\\r\\n elif offset < 0:\\r\\n # The field to the left was much wider than the other fields in that column. Shift the columns to the\\r\\n # right and insert an empty column\\r\\n aligned_row += [None]\\r\\n aligned_row += [original_tab]\\r\\n aligned_row += row[i + 1:]\\r\\n else:\\r\\n # Align this field with others in the column, and recalculate\\r\\n # the remaining columns for this row.\\r\\n aligned_row += [original_tab + offset]\\r\\n for tab in row[i + 1:]:\\r\\n aligned_row += [None if tab is None else tab + offset]\\r\\n tab_stop_array[j] = aligned_row\\r\\n\\r\\n _pad_array(tab_stop_array)\\r\\n i += 1\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"008bd0b88e5eed0546f4fabba8a5e83e\",\n \"score\": \"0.46262637\",\n \"text\": \"def iterating(self):\\n\\n for l_0, line in enumerate(self.layer):\\n self.count = 0\\n for c_0, column in enumerate(line):\\n if l_0 % 2 == 0:\\n self.check_line_1(l_0, c_0)\\n else:\\n self.check_line_2(l_0, c_0)\\n if self.flag_end_line:\\n self.flag_end_line = False\\n if l_0 + 1 == len(self.layer):\\n self.combinations.append(self.layer)\\n return\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e339d741d5a5a21b12213e0e2598b63c\",\n \"score\": \"0.46220663\",\n \"text\": \"def _iter_array_or_map(self):\\n while self._block_count != 0:\\n if self._block_count < 0:\\n self._block_count = -self._block_count\\n # Read block size, unused\\n self.read_long()\\n\\n for i in range(self._block_count):\\n yield\\n self._block_count = self.read_long()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80abe283cb9df061472336b7537ab8d3\",\n \"score\": \"0.46200323\",\n \"text\": \"def _adjust_alignment(self):\\n col_diff = self._num_columns - len(self._align_list)\\n if col_diff <= 0:\\n self._align_list = self._align_list[:self._num_columns]\\n\\n else:\\n self._align_list.extend(['>'] * col_diff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5acee35efaabe4f84585c8803b0b3aff\",\n \"score\": \"0.4612931\",\n \"text\": \"def mapaligns(self, pairs):\\n # re-map ref_aligns using provided pairs\\n # first index is to current (ref_align) sequence, second is to new ref\\n # (should be numpy array)\\n\\n # clear ref_align\\n refal = self.ref_align\\n ra0 = refal > 0\\n self.ref_align = 0*self.ref_align\\n # now make the pairs unique in x (own seq)\\n (_,uinds) = np.unique(pairs[:,0],return_index=True)\\n pairs = pairs[uinds,:]\\n # and now use interpolation to remap, return -1 out of range\\n self.ref_align[ra0] = np.round(np.interp(refal[ra0],pairs[:,0],pairs[:,1],0,0))\\n \\n # make sure all of our arrays are in order\\n self.makecontiguous()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"809b21ef8646189d16c261175fa49371\",\n \"score\": \"0.46081325\",\n \"text\": \"def _equalize_batch(padding, gen):\\n for batch in gen:\\n longest = 0\\n for ngs in batch[0]:\\n longest = max(longest, len(ngs))\\n for i in range(len(batch[0])):\\n batch[0][i] = pad_to_length(batch[0][i], longest, padding)\\n yield batch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eb588113bad5582cf47511920473caf\",\n \"score\": \"0.45983833\",\n \"text\": \"def whiteIterator(self) :\\n for i in range(self.n) :\\n for j in range(self.m) :\\n if not self.image[i][j] : yield [i,j]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6dfe6532a806ab9cf2370ff01d290db\",\n \"score\": \"0.4586409\",\n \"text\": \"def blackIterator(self) :\\n for i in range(self.n) :\\n for j in range(self.m) :\\n if self.image[i][j] : yield [i,j]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c31bbbea5c58091145ee2cb687feadf7\",\n \"score\": \"0.4584737\",\n \"text\": \"def offsetElements(self, i):\\n\\n #iterate over each tile and subtract\\n #if the value is -1, indicating a blank tile, leave it as that\\n for y in range(0, len(self.array)):\\n for x in range(0, len(self.array[0])):\\n if self.array[y][x] != -1:\\n self.array[y][x] -= i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09b39387823ec1a23601b7cc847db985\",\n \"score\": \"0.45449036\",\n \"text\": \"def main_extracting_features_per_atom(base_path, \\n xyz_file, \\n out_path, \\n out_file_name, \\n dic_encode_atom_type, \\n lst_atom_types, \\n max_vals = [1,1,1], \\n centered = True,\\n ordering = 'sorted',\\n df_bond = pd.DataFrame([1.7], columns = ['bonds'])\\n ):\\n list_alfa = [0.05, 0.144, 0.5, 1]\\n if not (os.path.isdir(out_path)):\\n os.mkdir(out_path)\\n\\n atom_type_col = 'atom_type'\\n xyz_cols = ['x', 'y', 'z']\\n try:\\n df_xyz_plain = xyz_to_df(base_path, xyz_file, dic_encode_atom_type, lst_atom_types, centered = True)\\n except:\\n print(f'{xyz_file}')\\n return\\n\\n n_atom_types = len(lst_atom_types)\\n\\n df_xyz_plain['r_dist_min'] = np.linalg.norm((df_xyz_plain[xyz_cols] - df_xyz_plain[xyz_cols].min()).values, axis=1)\\n df_xyz_plain['r_dist_max'] = np.linalg.norm((df_xyz_plain[xyz_cols] - df_xyz_plain[xyz_cols].max()).values, axis=1)\\n df_xyz_plain['r_dist_mean'] = np.linalg.norm((df_xyz_plain[xyz_cols] - df_xyz_plain[xyz_cols].mean()).values, axis=1)\\n\\n for alfa in list_alfa:\\n for i in range(n_atom_types):\\n for j in range(i, n_atom_types):\\n atom_1 = lst_atom_types[i]\\n atom_2 = lst_atom_types[j]\\n coord_1 = df_xyz_plain[xyz_cols][df_xyz_plain['atom_type'] == atom_1]\\n coord_2 = df_xyz_plain[xyz_cols][df_xyz_plain['atom_type'] == atom_2]\\n\\n dist_mat = distance_matrix(coord_1, coord_2)\\n density_loc = np.exp(-alfa * dist_mat**2).sum(axis=1)\\n df_xyz_plain[f'{atom_1}_{atom_2}_{alfa}'] = density_loc\\n # =================================================================\\n # 1. Extracting XYZ positions, encoding atoms types, ordering atoms\\n # =================================================================\\n if ordering == 'org':\\n df_xyz = xyz_normalize_centered(df_xyz_plain, max_vals = max_vals, centered = centered)\\n\\n elif ordering == 'sorted': \\n df_xyz = xyz_normalize_centered(df_xyz_plain, max_vals = max_vals, centered = centered)\\n df_xyz = df_xyz.sort_values(['x', 'y', 'z'])\\n\\n elif ordering == 'org_aligned':\\n df_xyz_plain[xyz_cols] = xyz_align_PCA(df_xyz_plain[xyz_cols].values)\\n df_xyz = xyz_normalize_centered(df_xyz_plain, max_vals = max_vals, centered = centered)\\n\\n elif ordering == 'sorted_aligned':\\n df_xyz_plain[xyz_cols] = xyz_align_PCA(df_xyz_plain[xyz_cols].values)\\n df_xyz = xyz_normalize_centered(df_xyz_plain, max_vals = max_vals, centered = centered)\\n df_xyz = df_xyz.sort_values(['x', 'y', 'z'])\\n\\n # ================================\\n # 2. Adding coordination of atoms\\n # ================================ \\n _, coords = return_neighbour_list(df_xyz_plain, df_bond, atom_type_col = 'atom_type', xyz_cols = ['x', 'y', 'z'])\\n df_xyz['coordination'] = coords\\n\\n df_xyz.to_csv(os.path.join(out_path, out_file_name), index=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a19046787e8b6dba090652a2ab8b127\",\n \"score\": \"0.4524642\",\n \"text\": \"def column_align(self):\\n return self._column_align\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fb749300f2dff02083957687026b93d\",\n \"score\": \"0.45214546\",\n \"text\": \"def iter_all_cells(self):\\n for row in range(self.table.nrows):\\n for col in range(self.table.ncols):\\n yield row, col\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62db8e4e2320b1399a973aadf88d224f\",\n \"score\": \"0.45199275\",\n \"text\": \"def _alignment_histogram_pruning(self, align_beam_size, align_model_probs, bucket_key,\\n num_align_models, start, end):\\n utils.check_condition(num_align_models == 1, \\\"Skip alignments only implemented for one alignment model\\\")\\n skip_alignments = np.array([True] * bucket_key[0])\\n np_align_model_probs = align_model_probs[0].asnumpy()\\n for sent in range(self.batch_size * self.beam_size):\\n source_sel = slice(start[sent].asscalar(), end[sent].asscalar())\\n rows = slice(sent, (sent + 1))\\n sliced_scores = np_align_model_probs[:, rows, source_sel] # .reshape(shape=(1, -1))\\n if (source_sel.stop - source_sel.start) > align_beam_size:\\n # returns: best_hyp_indices_, best_hyp_pos_indices , best_word_indices\\n (_, _, best_word_indices), _ = utils.smallest_k( -1 * sliced_scores, align_beam_size, False)\\n for k in best_word_indices:\\n if 0 <= k < bucket_key[0]:\\n skip_alignments[k] = False\\n else:\\n for k in range(source_sel.stop - source_sel.start):\\n skip_alignments[k] = False\\n\\n return skip_alignments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f600fdb9ef8c158e28610e161de5ddb0\",\n \"score\": \"0.4515573\",\n \"text\": \"def slice(self, aligned):\\n slices = {}\\n for (mseq, rname, pos), count in aligned.iteritems():\\n # check where this read mapped\\n read_start = int(pos)\\n read_end = read_start + len(mseq.strip('-'))\\n coords = self.coords[rname]\\n if read_end < coords['Core'][0] or read_start > coords['NS5b'][1]:\\n # read falls outside of ORF\\n continue\\n\\n # did it map to one of the target genes?\\n for target_gene, target_coords in self.targets.iteritems():\\n if target_gene not in slices:\\n slices.update({target_gene: {}})\\n left, right = coords[target_gene]\\n\\n for tc in target_coords:\\n if tc not in slices[target_gene]:\\n slices[target_gene].update({tc: []})\\n gene_left, gene_right = tc # unpack tuple\\n\\n # adjust gene coordinates to genome coordinates\\n genome_left = gene_left + left\\n genome_right = gene_right + left\\n if read_start > genome_left or read_end < genome_right:\\n # full coverage of target not possible\\n continue\\n\\n slice = mseq[genome_left:genome_right]\\n slices[target_gene][tc].append((rname, slice, count))\\n\\n return slices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1858c8f74ce09109bd064850b1b046e4\",\n \"score\": \"0.45005003\",\n \"text\": \"def _iter_values(self):\\n _slice = list(self._none_slices_except_domain_axis)\\n slices = []\\n for index in range(self.shape[self.labels_axis]):\\n _slice[self.labels_axis] = index\\n slices.append(tuple(_slice))\\n return zip(\\n slices,\\n np.moveaxis(self.magnitude, self.labels_axis, 0)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ea5ed663fbc1ff285496f5e78e963be\",\n \"score\": \"0.44990557\",\n \"text\": \"def _traverse_column_data_records(self,\\n column_name: str,\\n *,\\n keys: bool = True,\\n values: bool = True) -> Iterable[Union[bytes, Tuple[bytes, bytes]]]:\\n try:\\n datatxn = TxnRegister().begin_reader_txn(self._dataenv)\\n schemaColumnRangeKey = schema_db_range_key_from_column_unknown_layout(column_name)\\n with datatxn.cursor() as cur:\\n if not cur.set_range(schemaColumnRangeKey):\\n raise KeyError(f'Traversal of commit references failed. '\\n f'No column named `{column_name}` exists.')\\n schemaColumnKey = cur.key()\\n column_record = schema_column_record_from_db_key(schemaColumnKey)\\n startRangeKey = dynamic_layout_data_record_db_start_range_key(column_record)\\n yield from self.cursor_range_iterator(datatxn, startRangeKey, keys, values)\\n finally:\\n TxnRegister().abort_reader_txn(self._dataenv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06fb292427ce719e3bfac454d30f0af8\",\n \"score\": \"0.4497053\",\n \"text\": \"def iter_sites(self, start=0, stop=None, size=1):\\n if stop is None:\\n stop = self.nsites\\n if (stop - start) % size != 0:\\n raise ValueError('Alignment cannot be completely divided into '\\n 'chucks of size {}'.format(size))\\n for i in range(start, stop, size):\\n samples = [s[i:i+size] for s in self.samples.sequences]\\n if not (self.markers is None or self.markers.nrows == 0):\\n markers = [s[i:i+size] for s in self.markers.sequences]\\n yield samples + markers\\n else:\\n yield samples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f599eda1779ea5e1588ff1283dbca19\",\n \"score\": \"0.4495233\",\n \"text\": \"def cols(self):\\n for col_heading in self.col_hds:\\n yield (self._data[row_heading][col_heading] for row_heading in self.row_hds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"556f50071cc174a2c820bdb799d2a522\",\n \"score\": \"0.44883695\",\n \"text\": \"def score_alignment(self,chunk1,chunk2):\\n self.tradition_alignment(chunk1,chunk2)\\n\\n def filter_both_chunks(tmp_a,tmp_b,filter_list):\\n \\\"\\\"\\\"Filters two alignment_blocks based on residues that made it into both alignments\\n \\\"\\\"\\\"\\n filt_chunk1 = [i._resraw for i in tmp_a._res_list]\\n filt_chunk2 = [i._resraw for i in tmp_b._res_list]\\n tmp_lista = []\\n tmp_listb = []\\n for i,j in filter_list:\\n if i in filt_chunk1 and j in filt_chunk2:\\n tmp_lista.append(i)\\n tmp_listb.append(j)\\n return ([i for i in tmp_a._res_list if i._resraw in tmp_lista],[*tmp_a._res_list]),\\\\\\n ([i for i in tmp_b._res_list if i._resraw in tmp_listb],[*tmp_b._res_list])\\n\\n self.pep1,self.pep2 = chunk1._chunklist,chunk2._chunklist\\n self.len1,self.len2 = len(self.pep1)+1,len(self.pep2)+1\\n\\n self.init_matrix()\\n tmp_dict = defaultdict(list)\\n for i,j in self.alignment.alignkey:\\n tmp_chunk1 = chunk1._chunklist_dict[i]\\n tmp_chunk2 = chunk2._chunklist_dict[j]\\n tmp_a,tmp_b = filter_both_chunks(tmp_chunk1,tmp_chunk2,self.alignment.alignkey)\\n alignment_score = align_chunks(tmp_a,tmp_b,self.walk)\\n for k,l in alignment_score.items():\\n tmp_dict[k].append(l)\\n for i,j in tmp_dict.items():\\n self.rawm[i[0]][i[1]] = min(j)\\n self.init_adjustmatrix()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f67a7c2d55845aba3a5ae0703a99a73\",\n \"score\": \"0.44797224\",\n \"text\": \"def _match(self):\\n #disable optimized matching\\n optimized_rows = None\\n optimized_columns = None\\n for match in self.__match_rows(optimized_rows):\\n #match in rows\\n yield match\\n for match in self.__match_rows(optimized_columns,\\n transpose=True):\\n #match in columns and transpose coordinates\\n yield match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65b66a0dfd54b20bd4a14659306b1c97\",\n \"score\": \"0.44747886\",\n \"text\": \"def filter_multimappers(align_file, data):\\n config = dd.get_config(data)\\n type_flag = \\\"\\\" if bam.is_bam(align_file) else \\\"S\\\"\\n base, ext = os.path.splitext(align_file)\\n out_file = base + \\\".unique\\\" + ext\\n bed_file = dd.get_variant_regions(data)\\n bed_cmd = '-L {0}'.format(bed_file) if bed_file else \\\" \\\"\\n if utils.file_exists(out_file):\\n return out_file\\n base_filter = '-F \\\"[XS] == null and not unmapped {paired_filter} and not duplicate\\\" '\\n if bam.is_paired(align_file):\\n paired_filter = \\\"and paired and proper_pair\\\"\\n else:\\n paired_filter = \\\"\\\"\\n filter_string = base_filter.format(paired_filter=paired_filter)\\n sambamba = config_utils.get_program(\\\"sambamba\\\", config)\\n num_cores = dd.get_num_cores(data)\\n with file_transaction(out_file) as tx_out_file:\\n cmd = ('{sambamba} view -h{type_flag} '\\n '--nthreads {num_cores} '\\n '-f bam {bed_cmd} '\\n '{filter_string} '\\n '{align_file} '\\n '> {tx_out_file}')\\n message = \\\"Removing multimapped reads from %s.\\\" % align_file\\n do.run(cmd.format(**locals()), message)\\n bam.index(out_file, config)\\n return out_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d07347a453302f98fef4c1d2c5f73d6\",\n \"score\": \"0.44727546\",\n \"text\": \"def _alignment_threshold_pruning(self, align_skip_threshold, align_model_probs, bucket_key,\\n num_align_models, start, end):\\n\\n utils.check_condition(num_align_models == 1, \\\"Skip alignments only implemented for one alignment model\\\")\\n skip_jumps = mx.nd.zeros((self.batch_size * self.beam_size, bucket_key[0]))\\n\\n for idx in range(self.batch_size * self.beam_size):\\n source_sel = slice(start[idx].asscalar(), end[idx].asscalar())\\n target_sel = slice(0, source_sel.stop - source_sel.start)\\n skip_jumps[idx, target_sel] = align_model_probs[0][0, idx, source_sel]\\n\\n skip_alignments = np.all((skip_jumps < align_skip_threshold).asnumpy(), axis=0)\\n\\n return skip_alignments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e02a1b99a2af5d5096049a0ded75f0f8\",\n \"score\": \"0.44660947\",\n \"text\": \"def slice(self, aligned):\\n slices = {}\\n for (mseq, rname, pos), count in aligned.iteritems():\\n # check where this read mapped\\n read_start = int(pos) # 1based coord wrt subtype full genome corresponding to read start\\n read_end = read_start + len(mseq.strip('-')) # 1based coord wrt subtype full genome corresponding to read end + 1\\n if rname not in self.coords:\\n print \\\"Read %s didn't have a corresponding map\\\" % rname\\n continue\\n coords = self.coords[rname]\\n if read_end < coords['Core'][0] or read_start > coords['NS5b'][1]:\\n # read falls outside of ORF\\n continue\\n\\n # did it map to one of the target genes?\\n for target_gene, target_coords in self.targets.iteritems():\\n if target_gene not in slices:\\n slices.update({target_gene: {}})\\n left, right = coords[target_gene] # 0based nuc coordinates wrt subtype full genome corresponding to gene start, gene end+1\\n\\n for tc in target_coords: # 0based nuc coordinates wrt H77 gene corresponding to target start, target end + 1\\n if tc not in slices[target_gene]:\\n slices[target_gene].update({tc: []})\\n # 0based nuc coordinates wrt H77 gene corresponding to target start, target end + 1\\n gene_left, gene_right = tc # unpack tuple\\n\\n # adjust gene coordinates to genome coordinates. Assume no indels wrt H77\\n genome_left = gene_left + left # 0based nuc coordinates wrt subtype full genome corresponding to H77 target start\\n genome_right = gene_right + left # 0based nuc coordinates wrt subtype full genome corresponding to H77 target end + 1\\n\\n # If the read begins after the target region or ends before the target region, read_slice_size will be negative.\\n read_slice_size = min(genome_right, read_end-1) - max(genome_left, read_start-1)\\n target_slice_size = genome_right - genome_left\\n if read_slice_size / float(target_slice_size) < self.min_target_width:\\n # merged read does not cover the minimum required width of the target region\\n continue\\n\\n slice = mseq[genome_left:genome_right] # mseq should be left-padded wrt subtype full genome\\n\\n # After slicing merged sequences, we may end up with duplicate slices for the same target gene & coords\\n # against the same reference\\n slices[target_gene][tc].append((rname, slice, count))\\n\\n return slices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91838a895be3d319df0504b7548f3ff9\",\n \"score\": \"0.4461923\",\n \"text\": \"def test_iter():\\n bg = ipythonblocks.BlockGrid(2, 2)\\n\\n coords = ((0, 0), (0, 1), (1, 0), (1, 1))\\n\\n for b, c in zip(bg, coords):\\n assert b.row == c[0]\\n assert b.col == c[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a708c617e8d17c1f92957907c583cfa5\",\n \"score\": \"0.445352\",\n \"text\": \"def _listed_ea_column_check():\\n\\n def diff(li1, li2):\\n return list(set(li1) - set(li2))\\n\\n for ea_row in unused_list:\\n ddi_index = views_index[ea_row[15]]\\n # This check is performed in\\n # _ea_in_disposition_col0_and_empty_ipr_d_col\\n if ea_row[1] not in ddi_data[ddi_index]:\\n continue\\n if ea_row[0] in ea_listed_values['IPR Designation'] and \\\\\\n 'IPR Designation' not in \\\\\\n ddi_data[ddi_index][ea_row[1]]['extattrs']:\\n continue\\n # Processing listable columns.\\n for key, value in ea_index.items():\\n # Skip's unused keys.\\n if key not in ['Datacenter', 'IPR Designation']:\\n continue\\n # Check for blank column and blank source column.\\n if key not in ddi_data[ddi_index][ea_row[1]]['extattrs'] and \\\\\\n ea_row[value] in ['', 'DDI']:\\n continue\\n # Check Disposition col, check for comma not in IPR D col\\n # value, check value in IPR D col to ea ipr d attribute list,\\n # check IPR D col value eq ddi value.\\n # On not listed IPR D values.\\n if key in ea_listed_values:\\n ipr_temp_list = []\\n ipam_temp_list = []\\n # Building list for diff's against DDI data.\\n if ea_row[0] in ea_listed_values[key] \\\\\\n and ea_row[0] not in ea_row[value]:\\n ipr_temp_list.append(ea_row[0])\\n # Extend list if listed values in IPR D column.\\n if ',' in ea_row[ea_index[key]]:\\n ipr_temp_list.extend([x.strip()\\n for x in ea_row[ea_index[key]].\\n split(',')\\n if x.strip() in\\n ea_listed_values[key]])\\n # Append list if non-listed values in IPR D column.\\n if ',' not in ea_row[ea_index[key]] and \\\\\\n ea_row[ea_index[key]] in \\\\\\n ea_listed_values[key]:\\n ipr_temp_list.append(ea_row[ea_index[key]])\\n\\n # Remove blank elements from list.\\n ipr_temp_list = [x for x in ipr_temp_list if x]\\n\\n # If in IPR and no key listed in IPAM data.\\n if ea_row[1] == '192.168.0.0':\\n print('myvar: {}'.format(ea_row[1]))\\n\\n # If no data in IPAM but new data received.\\n if key not in \\\\\\n ddi_data[ddi_index][ea_row[1]]['extattrs'] \\\\\\n and ipr_temp_list:\\n if len(ipr_temp_list) > 1:\\n import_report_data['update'].append(ea_row)\\n import_override.append([ea_row[15].strip(),\\n ea_row[1].strip(),\\n ea_row[14].strip(),\\n {key: ','.join(\\n ipr_temp_list)}])\\n else:\\n import_report_data['update'].append(ea_row)\\n import_override.append([ea_row[15].strip(),\\n ea_row[1].strip(),\\n ea_row[14].strip(),\\n {key: ipr_temp_list[\\n 0]}])\\n continue\\n\\n # Building DDI list for diff against the Multi Att. Columns\\n if isinstance(ddi_data[ddi_index][\\n ea_row[1]]['extattrs'][\\n key]['value'], list):\\n ipam_temp_list.extend(ddi_data[ddi_index][\\n ea_row[1]]['extattrs'][\\n key]['value'])\\n else:\\n ipam_temp_list.append(ddi_data[ddi_index][\\n ea_row[1]]['extattrs'][\\n key]['value'])\\n\\n # Check for diff between listed sets.\\n in_ipr_not_ipam = compare(ipr_temp_list, ipam_temp_list)\\n if not in_ipr_not_ipam:\\n import_report_data['update'].append(ea_row)\\n import_override.append([ea_row[15].strip(),\\n ea_row[1].strip(),\\n ea_row[14].strip(),\\n {key: ','.join(\\n ipr_temp_list)}])\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b5d92ab399f764c4a006f9d73bc711\",\n \"score\": \"0.44291773\",\n \"text\": \"def _traverse_column_schema_records(self, keys: bool = True, values: bool = True\\n ) -> Iterable[Union[Tuple[bytes], Tuple[bytes, bytes]]]:\\n startSchemaRangeKey = schema_record_count_start_range_key()\\n try:\\n datatxn = TxnRegister().begin_reader_txn(self._dataenv)\\n yield from self.cursor_range_iterator(datatxn, startSchemaRangeKey, keys, values)\\n finally:\\n TxnRegister().abort_reader_txn(self._dataenv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1db63069a26bad34c3556090769af3ce\",\n \"score\": \"0.44239363\",\n \"text\": \"def _test_final_column_no_padding(sequences):\\n\\n assert np.all(sequences[:, -1] > 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3031aa4345d39ab60ed5e9be81afab47\",\n \"score\": \"0.44170517\",\n \"text\": \"def getColumnAlignments(self):\\n if self._columnAlignments is None:\\n return None\\n\\n alignments = list()\\n\\n for col in self._visibleColumns:\\n alignments.append( self.getColumnAlignment(col) )\\n\\n return alignments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2199c1859f95becc996735ee9c3d00a\",\n \"score\": \"0.44155955\",\n \"text\": \"def prep_alleles_table_compare(df_alleles,sample_name_1,sample_name_2,MAX_N_ROWS,MIN_FREQUENCY):\\n\\tdna_to_numbers={'-':0,'A':1,'T':2,'C':3,'G':4,'N':5}\\n\\tseq_to_numbers= lambda seq: [dna_to_numbers[x] for x in seq]\\n\\n\\tX=[]\\n\\tannot=[]\\n\\ty_labels=[]\\n\\tinsertion_dict=defaultdict(list)\\n\\tper_element_annot_kws=[]\\n\\n\\tre_find_indels=re.compile(\\\"(-*-)\\\")\\n\\tidx_row=0\\n\\tfor idx,row in df_alleles.ix[df_alleles['%Reads_'+sample_name_1] + df_alleles['%Reads_'+sample_name_2]>=MIN_FREQUENCY][:MAX_N_ROWS].iterrows():\\n\\t\\tX.append(seq_to_numbers(str.upper(idx)))\\n\\t\\tannot.append(list(idx))\\n\\t\\ty_labels.append('%.2f%% (%d reads) %.2f%% (%d reads) ' % (row['%Reads_'+sample_name_1],row['#Reads_'+sample_name_1],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\trow['%Reads_'+sample_name_2],row['#Reads_'+sample_name_2]))\\n\\n\\n\\t\\tfor p in re_find_indels.finditer(row['Reference_Sequence']):\\n\\t\\t\\tinsertion_dict[idx_row].append((p.start(),p.end()))\\n\\n\\t\\tidx_row+=1\\n\\n\\n\\t\\tidxs_sub= [i_sub for i_sub in range(len(idx)) if \\\\\\n\\t\\t\\t\\t (row['Reference_Sequence'][i_sub]!=idx[i_sub]) and \\\\\\n\\t\\t\\t\\t (row['Reference_Sequence'][i_sub]!='-') and\\\\\\n\\t\\t\\t\\t (idx[i_sub]!='-')]\\n\\t\\tto_append=np.array([{}]*len(idx),dtype=np.object)\\n\\t\\tto_append[ idxs_sub]={'weight':'bold', 'color':'black','size':16}\\n\\t\\tper_element_annot_kws.append(to_append)\\n\\n\\treturn X,annot,y_labels,insertion_dict,per_element_annot_kws\",\n \"title\": \"\"\n },\n {\n \"docid\": \"294bf27b8f1067ab809de007cb69131b\",\n \"score\": \"0.44148335\",\n \"text\": \"def compute_shift_neighbor_point(self, j, i, contributing_alignment_points):\\n\\n self.y_shifts[j][i] = 0.\\n self.x_shifts[j][i] = 0.\\n for ap in contributing_alignment_points:\\n j_ap = self.alignment_points[ap['alignment_point_index']][0]\\n i_ap = self.alignment_points[ap['alignment_point_index']][1]\\n self.y_shifts[j][i] += ap['weight'] * self.y_shifts[j_ap][i_ap]\\n self.x_shifts[j][i] += ap['weight'] * self.x_shifts[j_ap][i_ap]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6a826d0e604e0f8a1b594042c08262c\",\n \"score\": \"0.44122374\",\n \"text\": \"def indices_allpeople_with_allpeople_in_two_frames(meanPositions_b, meanPositions_c, meanPositions_a, printAuxFlag=False, distScale=8*30, b_d=-0.8, b_lr=-0.2):\\r\\n nb, nc = len(meanPositions_b), len(meanPositions_c)\\r\\n EDMatrix, EDLMatrix, LRMatrix, TAMatrix = np.ones((nb, nc)), np.ones((nb, nc)), np.ones((nb, nc)), np.ones((nb, nc))\\r\\n DBAMatrix, DBBMatrix, DBCMatrix = np.ones((nb, nc)), np.ones((nb, nc)), np.ones((nb, nc))\\r\\n for i, meanPosition_b in enumerate(meanPositions_b):\\r\\n meanPosition_a = meanPositions_a[i]\\r\\n for j, meanPosition_c in enumerate(meanPositions_c):\\r\\n indices = indices_person_with_person_in_three_frames(meanPosition_a, meanPosition_b, meanPosition_c)\\r\\n EDMatrix[i, j] = indices['ED']\\r\\n EDLMatrix[i, j] = indices['EDL']\\r\\n LRMatrix[i, j] = indices['LR']\\r\\n TAMatrix[i, j] = indices['TA']\\r\\n DBAMatrix[i, j] = indices['DBA']\\r\\n DBBMatrix[i, j] = indices['DBB']\\r\\n DBCMatrix[i, j] = indices['DBC']\\r\\n # when a person is new-coming, he has no poseVector_a information, and would lead to a constant-zero LR and angel\\r\\n for nrow in range(LRMatrix.shape[0]):\\r\\n if np.all(LRMatrix[nrow, :] == 0) and np.all(TAMatrix[nrow, :] == 0):\\r\\n for ncol in range(LRMatrix.shape[1]):\\r\\n if np.any(LRMatrix[:, ncol] > 0):\\r\\n LRMatrix[nrow, ncol] = np.min([x for x in LRMatrix[:, ncol] if x > 0])\\r\\n if np.any(TAMatrix[:, ncol] > 0):\\r\\n TAMatrix[nrow, ncol] = np.min([x for x in TAMatrix[:, ncol] if x > 0])\\r\\n # if EDMatrix.shape[1] == 1:\\r\\n # NED1Matrix = np.ones((EDMatrix.shape[0], 1))\\r\\n # else:\\r\\n # NED1Matrix = (EDMatrix - np.min(EDMatrix, axis=1).reshape(-1, 1)) / (0.00000000000000000000001 + (\\r\\n # np.max(EDMatrix, axis=1).reshape(-1, 1) - np.min(EDMatrix, axis=1).reshape(-1, 1)))\\r\\n # if EDMatrix.shape[0] == 1:\\r\\n # NED2Matrix = np.ones((1, EDMatrix.shape[1]))\\r\\n # else:\\r\\n # NED2Matrix = (EDMatrix - np.min(EDMatrix, axis=0).reshape(1, -1)) / (0.00000000000000000000001 + (\\r\\n # np.max(EDMatrix, axis=0).reshape(1, -1) - np.min(EDMatrix, axis=0).reshape(1, -1)))\\r\\n\\r\\n NED1Matrix = EDMatrix / distScale\\r\\n NED2Matrix = NED1Matrix\\r\\n \\r\\n R_ED1Matrix = np.argsort(np.argsort(EDMatrix))\\r\\n R_ED2Matrix = np.argsort(np.argsort(EDMatrix, axis=0), axis=0)\\r\\n indicesMatrix = {'ED': EDMatrix, 'EDL': EDLMatrix, 'LR': LRMatrix, 'TA': TAMatrix,\\r\\n 'DBA': DBAMatrix, 'DBB': DBBMatrix, 'DBC': DBCMatrix, 'R_ED1': R_ED1Matrix, 'R_ED2': R_ED2Matrix}\\r\\n\\r\\n VMatrix_toChooseNext = NED1Matrix * b_d + LRMatrix * b_lr\\r\\n VMatrix_toChoosePrevious = NED2Matrix * b_d + LRMatrix * b_lr\\r\\n\\r\\n # these V are the min the best, so we need to use -V to calculate P\\r\\n P_toChooseNext = V2P(VMatrix_toChooseNext, axis=1)\\r\\n P_toChoosePrevious = V2P(VMatrix_toChoosePrevious, axis=0)\\r\\n\\r\\n # P_toChooseNext = P_toChooseNext[:, :P_toChooseNext.shape[1] - 1]\\r\\n # P_toChoosePrevious = P_toChoosePrevious[:P_toChoosePrevious.shape[0] - 1, :]\\r\\n\\r\\n P = P_toChooseNext * P_toChoosePrevious\\r\\n if printAuxFlag:\\r\\n print('\\\\nDist: \\\\n{}'.format(EDMatrix))\\r\\n print('\\\\nNormarlized Dist: \\\\n{}'.format(NED1Matrix))\\r\\n print('\\\\nLength Ratio: \\\\n{}'.format(LRMatrix))\\r\\n print('\\\\nAngle: \\\\n{}'.format(TAMatrix))\\r\\n return P, indicesMatrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c02e2eb5cdeda941eebd9a938f60bde9\",\n \"score\": \"0.4409557\",\n \"text\": \"def internal_iter(self):\\n for idx, x in np.ndenumerate(self.internal_t):\\n yield x, self.yout[idx, ...]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6299bff0ad05e6c9ca04350907e91e17\",\n \"score\": \"0.4403628\",\n \"text\": \"def alignment_stop():\\n\\n smi = SMI_Beamline()\\n yield from smi.modeMeasurement()\\n proposal_id('2023_2', '311645_Zhang_1')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d90fdd225b304891acff7bfe911d7686\",\n \"score\": \"0.44032338\",\n \"text\": \"def __iter__(self):\\n batch = []\\n for row in self.reader:\\n # Default collate does not work nicely on namedtuples and treat them as lists\\n # Using dict will result in the yielded structures being dicts as well\\n row_as_dict = row._asdict()\\n batch.append(self.transform(row_as_dict) if self.transform else row_as_dict)\\n if len(batch) == self.batch_size:\\n yield self.collate_fn(batch)\\n batch = []\\n if batch:\\n yield self.collate_fn(batch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74cfef2ba0f48aedd7645db60e6e1612\",\n \"score\": \"0.44022062\",\n \"text\": \"def ialign(images, reference = None, mask = None, fill_value = 0.0, fast = True):\\n images = iter(images)\\n \\n if reference is None:\\n reference = next(images)\\n yield reference\\n\\n yield from map(partial(align, reference = reference, mask = mask, fill_value = fill_value, fast = fast), images)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89155a6f4a71a0e56e4ed835ceb1a325\",\n \"score\": \"0.4399901\",\n \"text\": \"def _next_column_iterator(previous_column, height, i = None):\\n if i is None:\\n i = height\\n if i == 0:\\n yield [-1]*height\\n else:\\n for column in _next_column_iterator(previous_column, height, i-1):\\n min_value = previous_column[i-1]\\n if i > 1:\\n min_value = max(min_value, column[i-2]+1)\\n for value in range(min_value, previous_column[i]+1):\\n c = copy.copy(column)\\n c[i-1] = value\\n yield c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59559f6d99a5f27fe18e26c27c8131af\",\n \"score\": \"0.43922815\",\n \"text\": \"def fragment_annot(df, barcodes):\\n fw = []\\n rv = []\\n for i, b in enumerate(barcodes):\\n df_b = df.loc[df['Barcode'] == b]\\n cloud_idx = 0\\n for j in df_b.Reference.unique().tolist():\\n ref_idx = cloud_idx\\n df_r = df_b.loc[df_b['Reference'] == j]\\n start_pos = df_r.Start.tolist()[0] # Leftmost start on reference j. Should be pre-sorted from SAM\\n for k in range(len(df_r)):\\n if df_r.iloc[k,3] > start_pos + 200000: # Read start outside 2 x max. est. fragment size\\n ref_idx += 1\\n start_pos = df_r.iloc[k,3]\\n read_info = [df_r.iloc[k,0], ref_idx, j + '-' + str(ref_idx)] # ref-enh_num\\n rv.append(read_info) if df_r.iloc[k,1] else fw.append(read_info)\\n cloud_idx = ref_idx + 1\\n return fw, rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68d6f06e824b73b96be95020e674c0a0\",\n \"score\": \"0.4389992\",\n \"text\": \"def remove_element_column(self):\\n for i in range(1, len(self.pdb_map)+1):\\n ele = self.pdb_map[i][\\\"element\\\"]\\n e = ele[11]\\n self.pdb_map[i][\\\"element\\\"]=\\\" \\\"+e\\n print \\\"Extra stuff in Element Columns Removed\\\"\\n return self.pdb_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ba00a38e0c18497f2468e44c98702b3\",\n \"score\": \"0.4381122\",\n \"text\": \"def filter_annoying_cell(self):\\n\\n idx_center = self.labels.index(self.label_center)\\n not_neighbors = []\\n for i, label in enumerate(self.labels):\\n if label not in self.neighbors[idx_center] and i != idx_center:\\n not_neighbors.append([i, self.angles[i]])\\n not_neighbors = np.array(not_neighbors)\\n if len(not_neighbors) > 2:\\n to_delete = not_neighbors[not_neighbors[:, 1].argsort()][2:, 0]\\n self.delete(to_delete.astype(int))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fcc8b9790e994e95d2df22dd53a85a6\",\n \"score\": \"0.4373849\",\n \"text\": \"def findMarkers(markerDictList, text):\\n global stopWords\\n\\n rows = []\\n for markerType, markerRe, markerDict in markerDictList:\\n textLower = text.lower()\\n #if markerType in [\\\"genbankList\\\", \\\"genbank\\\"]:\\n #keywords = \\n #if not textContainsAny(textLower, keywords):\\n #continue\\n \\n if markerType==\\\"genbankList\\\":\\n for row in iterGenbankRows(markerRe, markerType, text):\\n #yield row\\n rows.append(row)\\n continue\\n\\n if markerType in neededWordDict:\\n keywords = neededWordDict[markerType]\\n if not textContainsAny(textLower, keywords):\\n continue\\n\\n for match in markerRe.finditer(text):\\n word = match.group(\\\"id\\\")\\n if word in stopWords:\\n continue\\n\\n\\n if markerType==\\\"pdb\\\":\\n word = word.lower()\\n\\n if markerType in [\\\"hg17\\\", \\\"hg18\\\", \\\"hg19\\\"]:\\n word = word.replace(\\\",\\\", \\\"\\\").replace(\\\" \\\", \\\"\\\")\\n\\n if markerDict==None:\\n idList = [word]\\n else:\\n idList = markerDict.get(word, None)\\n\\n if idList != None:\\n start = match.start(\\\"id\\\")\\n end = match.end(\\\"id\\\")\\n for recogId in idList:\\n if word==recogId:\\n word=\\\"\\\"\\n row = [ start, end, markerType, word, recogId]\\n #yield result\\n rows.append(row)\\n\\n if len(rows)<MAXROWS:\\n return rows\\n else:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca7df8187d285acc5433547dca0cbb43\",\n \"score\": \"0.4372176\",\n \"text\": \"def apply_offset(self):\\n \\n if self.x_offset or self.y_offset:\\n for y_idx, row in enumerate(self.coordinates):\\n for x_idx, coord in enumerate(row):\\n self.coordinates[y_idx][x_idx] = [coord[0] + self.x_offset, coord[1] + self.y_offset]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f5886c5074e18871ae560302d7925d\",\n \"score\": \"0.43685156\",\n \"text\": \"def align(self):\\n\\n # populate the score matrices based on the input parameters\\n self.populate_score_matrices()\\n\\n # print(\\\"-------M MATRIX-------\\\")\\n # self.m_matrix.print_scores()\\n # # print(\\\"-------IX MATRIX-------\\\")\\n # self.ix_matrix.print_scores()\\n # # print(\\\"-------IY MATRIX-------\\\")\\n # self.iy_matrix.print_scores()\\n\\n # print(self.find_traceback_start())\\n\\n # Perform traceback, then write the output to file\\n self.traceback()\\n self.write_output()\\n\\n # perform a traceback and write the output to an output file\\n ### FILL IN ###\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99088bc9eae65a1377b577b6932371a1\",\n \"score\": \"0.43635973\",\n \"text\": \"def align(data):\\n arr = []\\n for d in data:\\n # DO\\n if d.shape[0] == 3:\\n d = np.insert(d, [1, 2], 0, axis=0)\\n arr.append(d)\\n data = np.array(arr)\\n dapi = data[:,0]\\n return lasagna.bayer.register_and_offset(data, registration_images=data[:, 0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c31c69fa60d9f67aa1436c1069691c88\",\n \"score\": \"0.43540114\",\n \"text\": \"def _align(x, y, mean_xy, variance_xy, bead_costs):\\n m = {}\\n for i in range(len(x) + 1):\\n for j in range(len(y) + 1):\\n if i == j == 0:\\n m[0, 0] = (0, 0, 0)\\n else:\\n m[i, j] = min((m[i-di, j-dj][0] +\\n length_cost(x[i-di:i], y[j-dj:j], mean_xy, variance_xy) \\\\\\n + bead_cost, di, dj)\\n for (di, dj), bead_cost in BEAD_COSTS.items()\\n if i-di>=0 and j-dj>=0)\\n\\n i, j = len(x), len(y)\\n while True:\\n (c, di, dj) = m[i, j]\\n if di == dj == 0:\\n break\\n yield (i-di, i), (j-dj, j)\\n i -= di\\n j -= dj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1123b28913e51596181521fead73a7a\",\n \"score\": \"0.43500638\",\n \"text\": \"def unmap(data, count, inds, fill=0):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b2177638e43250dc7a567d67b659953\",\n \"score\": \"0.43485883\",\n \"text\": \"def alignment_extract(seed, setting = \\\"AWO\\\"):\\n with open('./work/results_seed' + str(seed) + '/alignments/extracted_matrix.pkl', 'rb') as f:\\n extract_matrix = pickle.load(f)\\n\\n with open(test_de_bpe, encoding=\\\"utf-8\\\") as fbpe:\\n src_bpe_sents = fbpe.readlines()\\n with open(test_de_word, encoding=\\\"utf-8\\\") as fword:\\n src_word_sents = fword.readlines()\\n\\n with open(test_en_bpe, encoding=\\\"utf-8\\\") as fbpe:\\n tgt_bpe_sents = fbpe.readlines()\\n with open(test_en_word, encoding=\\\"utf-8\\\") as fword:\\n tgt_word_sents = fword.readlines()\\n\\n for mode in [\\\"attn\\\", \\\"summed_afx\\\"]:\\n for l in range(6):\\n with open(\\\"./work/results_seed\\\" + str(seed) + \\\"/alignments/hypothesis-{}-{}-{}\\\".format(mode,l,setting), \\\"w\\\") as f:\\n for i in range(len(src_bpe_sents)):\\n src_bpe_sent = src_bpe_sents[i]\\n src_word_sent = src_word_sents[i]\\n src_word_to_bpe = convert_bpe_word(src_bpe_sent, src_word_sent)\\n src_len = len(src_word_sent.split())\\n\\n tgt_bpe_sent = tgt_bpe_sents[i]\\n tgt_word_sent = tgt_word_sents[i]\\n tgt_word_to_bpe = convert_bpe_word(tgt_bpe_sent, tgt_word_sent)\\n \\n if mode == \\\"summed_afx\\\":\\n attention_matrix = torch.squeeze(extract_matrix[i][mode][l]).detach().numpy()\\n else:\\n attention_matrix = torch.squeeze(extract_matrix[i][mode][l]).mean(dim=0).detach().numpy()\\n\\n if setting == \\\"AWI\\\":\\n attention_matrix = attention_matrix[list(range(1,len(attention_matrix)))+[0]]\\n attention_matrix = get_word_word_attention(attention_matrix, src_word_to_bpe, tgt_word_to_bpe)\\n attention_matrix = np.argmax(attention_matrix, -1)\\n\\n for t, s_a in enumerate(attention_matrix):\\n if s_a != src_len:\\n f.write(\\\"{}-{} \\\".format(t+1, s_a+1))\\n f.write(\\\"\\\\n\\\") \\n\\n for mode in [\\\"attn\\\", \\\"afx\\\"]:\\n for l in range(6):\\n for h in range(4):\\n with open(\\\"./work/results_seed\\\" + str(seed) + \\\"/alignments/hypothesis-{}-{}-{}-{}\\\".format(mode,l,h,setting), \\\"w\\\") as f:\\n for i in range(len(src_bpe_sents)):\\n src_bpe_sent = src_bpe_sents[i]\\n src_word_sent = src_word_sents[i]\\n src_word_to_bpe = convert_bpe_word(src_bpe_sent, src_word_sent)\\n src_len = len(src_word_sent.split())\\n\\n tgt_bpe_sent = tgt_bpe_sents[i]\\n tgt_word_sent = tgt_word_sents[i]\\n tgt_word_to_bpe = convert_bpe_word(tgt_bpe_sent, tgt_word_sent)\\n\\n attention_matrix = torch.squeeze(extract_matrix[i][mode][l])[h].detach().numpy()\\n if setting == \\\"AWI\\\":\\n attention_matrix = attention_matrix[list(range(1,len(attention_matrix)))+[0]]\\n attention_matrix = get_word_word_attention(attention_matrix, src_word_to_bpe, tgt_word_to_bpe)\\n attention_matrix = np.argmax(attention_matrix, -1)\\n\\n for t, s_a in enumerate(attention_matrix):\\n if s_a != src_len:\\n f.write(\\\"{}-{} \\\".format(t+1, s_a+1))\\n f.write(\\\"\\\\n\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86c2007a4d655eaf33350a76b400111b\",\n \"score\": \"0.43483782\",\n \"text\": \"def zihan_alignment():\\n proposal_id('2023_1', '000000_tests')\\n\\n try:\\n yield from alignement_gisaxs_hex(angle=0.5, rough_y=0.5)\\n except:\\n yield from alignement_gisaxs_hex(angle=0.1, rough_y=0.5)\\n\\n proposal_id('2023_1', '311645_Zhang')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f0922a1b6e889b4803e6edc0da63ca6\",\n \"score\": \"0.43459523\",\n \"text\": \"def iterlabels(self):\\n return iter(\\n np.moveaxis(self.magnitude, self.labels_axis, 0)\\n * self.units\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0de6dbfcfc915d6816eb5c45f1b6c516\",\n \"score\": \"0.43381003\",\n \"text\": \"def align_cluster( cluster ):\\n x_0 = cluster[0][:,0]\\n x_f = cluster[0][:,-1]\\n distance = lambda x : np.dot(x,x)\\n reverse_or_not = lambda x: x[:,::-1] if distance(x[:,0]-x_0) > distance(x[:,0]-x_f) else x\\n return [ reverse_or_not(c) for c in cluster ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8adf025194219c181105f446c52690c\",\n \"score\": \"0.43354973\",\n \"text\": \"def iteridat():\\n while True:\\n try:\\n type, data = self.chunk(lenient=lenient)\\n except ValueError as e:\\n raise ChunkError(e.args[0])\\n if type == b'IEND':\\n # http://www.w3.org/TR/PNG/#11IEND\\n break\\n if type != b'IDAT':\\n continue\\n # type == b'IDAT'\\n # http://www.w3.org/TR/PNG/#11IDAT\\n if self.colormap and not self.plte:\\n warnings.warn(\\\"PLTE chunk is required before IDAT chunk\\\")\\n yield data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b9a7ba2a402e26bc5c94a0e7f26bab2\",\n \"score\": \"0.43353194\",\n \"text\": \"def dlmat2imat(dlmat):\\n\\n for i, row in enumerate(dlmat):\\n if i > 0:\\n for j, v in enumerate(row):\\n if j > 0:\\n yield i-1, j-1, v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d8ee4ea567471feeceac879e534658d\",\n \"score\": \"0.43341258\",\n \"text\": \"def _extract_images(self, image):\\n _shape_height = image.shape[0]#the height of the image\\n _shape_width = image.shape[1]#the width of the image\\n\\n #search for the first column that is not black\\n _x = np.min(np.nonzero(np.any(image, axis = (1,2))))\\n\\n # search along this column, we can obtain the start pixel row of each image row\\n _row_mask = np.any(image[:,_x,:], axis = 1)\\n _row_start_y = np.insert(np.nonzero(np.diff(_row_mask.astype(int)) == 1)[0]+1, 0, 0)\\n # to be used in later loop\\n _row_start_y = np.append(_row_start_y, _shape_height-1)\\n _extracted_list = []\\n #Search by all the starting rows\\n for _y_index in range(_row_start_y.size-1):\\n _starting_y = _row_start_y[_y_index]\\n\\n # we can find all the start and end column of each image\\n # (i.e. they are horizonally bounded)\\n _column_mask = np.any(image[_starting_y], axis = 1) # all the non-black point\\n _column_start_x = np.insert(np.nonzero(np.diff(_column_mask.astype(int)) == 1)[0]+1, 0, 0)\\n _column_end_x = np.nonzero(np.diff(_column_mask.astype(int)) == -1)[0]+1\\n # check each horizontally bounded region\\n for _x_index in range(_column_start_x.size):\\n # check the leftmost column\\n _image_starting_x = _column_start_x[_x_index]\\n\\n #search in this column between two starting y\\n _image_y_mask = np.any(image[_starting_y:_row_start_y[_y_index + 1],_image_starting_x], axis=1)\\n\\n #get the end point of y\\n _image_end_y = np.where(np.diff(_image_y_mask.astype(int)) == -1)[0][0]+1\\n\\n #append the image to the final list\\n _extracted_list.append(\\n image[_starting_y:_starting_y+_image_end_y,\\n _image_starting_x:_column_end_x[_x_index]]\\n )\\n return _extracted_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59d83b140eb22b2dd755a449cbf00c28\",\n \"score\": \"0.4333457\",\n \"text\": \"def motif_pair_dfi(dfi_filtered, motif_pair):\\n dfa = dfi_filtered[dfi_filtered.pattern_name == motif_pair[0]]\\n dfb = dfi_filtered[dfi_filtered.pattern_name == motif_pair[1]]\\n\\n dfab = pd.merge(dfa, dfb, on='example_idx', how='outer')\\n dfab = dfab[~dfab[['pattern_center_x', 'pattern_center_y']].isnull().any(1)]\\n\\n dfab['center_diff'] = dfab.pattern_center_y - dfab.pattern_center_x\\n if \\\"pattern_center_aln_x\\\" in dfab:\\n dfab['center_diff_aln'] = dfab.pattern_center_aln_y - dfab.pattern_center_aln_x\\n dfab['strand_combination'] = dfab.strand_x + dfab.strand_y\\n # assure the right strand combination\\n dfab.loc[dfab.center_diff < 0, 'strand_combination'] = dfab[dfab.center_diff < 0]['strand_combination'].map(comp_strand_compbination).values\\n\\n if motif_pair[0] == motif_pair[1]:\\n dfab.loc[dfab['strand_combination'] == \\\"--\\\", 'strand_combination'] = \\\"++\\\"\\n dfab = dfab[dfab.center_diff > 0]\\n else:\\n dfab.center_diff = np.abs(dfab.center_diff)\\n if \\\"center_diff_aln\\\" in dfab:\\n dfab.center_diff_aln = np.abs(dfab.center_diff_aln)\\n if \\\"center_diff_aln\\\" in dfab:\\n dfab = dfab[dfab.center_diff_aln != 0] # exclude perfect matches\\n return dfab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"462892548462bee1f446d8f04e6d1e15\",\n \"score\": \"0.43329483\",\n \"text\": \"def macs_peaks_knowngene_overall_analysis(macs_peaks_strand_filename,knowngene_genesymbol_filename,annotated_peaks_filename,unannotated_peaks_filename):\\n macs_file=open(macs_peaks_strand_filename,'r')\\n peaks=macs_file.readlines()\\n \\n gene_file=open(knowngene_genesymbol_filename,'r')\\n genes=gene_file.readlines()\\n \\n list1=[]\\n for i in range(20):\\n list1.append(\\\"chr%s+\\\" % (i))\\n list1.append(\\\"chr%s-\\\" % (i))\\n list1.append(\\\"chrM+\\\")\\n list1.append(\\\"chrM-\\\")\\n list1.append(\\\"chrX+\\\")\\n list1.append(\\\"chrX-\\\")\\n list1.append(\\\"chrY+\\\")\\n list1.append(\\\"chrY-\\\")\\n \\n dic1={}\\n for j in list1:\\n dic1[j]=[]\\n \\n for gene in genes[1:]: #the knowngene_genesymbol_file has a header\\n chr=gene.split('\\\\t')[1]+gene.split('\\\\t')[2] # e.g.,\\\"chr1\\\" + \\\"-\\\"\\n if chr in dic1.keys():\\n dic1[chr].append(gene.strip('\\\\n').split('\\\\t'))\\n dic1[chr][-1][3]=int(dic1[chr][-1][3]) #the txStart position is like \\\"12345\\\" and should be stored as 12345\\n dic1[chr][-1][6]=int(dic1[chr][-1][6]) #the cdsEnd position is like \\\"12345\\\" and should be stored as 12345\\n \\n annotated_peaks=open(annotated_peaks_filename,'w')\\n unannotated_peaks=open(unannotated_peaks_filename,'w')\\n a=0 # used to store number of unannoted peaks\\n b=0 #used to store number of annotated peaks\\n for line in peaks:\\n peak=line.strip('\\\\n').split('\\\\t')\\n peak_chr=peak[0]+peak[10] #e.g., \\\"chr1\\\"+ \\\"-\\\"\\n peak_start=int(peak[1])\\n peak_end=int(peak[2])\\n peak_summit=int(peak[1])+int(peak[4])\\n low=0\\n high=len(dic1[peak_chr])-1\\n gene_pos=return_loci(dic1[peak_chr],3,6,peak_summit,low,high)#1 and 2 denote the positions in the list for txStart and cdsEnd\\n if gene_pos==\\\"not found!\\\":\\n unannotated_peaks.write(line)\\n a=a+1\\n else:\\n peak.append(dic1[peak_chr][gene_pos][-1])\\n new_peak='\\\\t'.join(peak)+'\\\\n'\\n annotated_peaks.write(new_peak)\\n b=b+1\\n \\n print \\\"unannotated peaks: %s\\\\t and annotated peaks: %s\\\\n\\\" %(a,b)\\n macs_file.close()\\n gene_file.close()\\n annotated_peaks.close()\\n unannotated_peaks.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06e2a0b007927a14887adcd8904811be\",\n \"score\": \"0.4317422\",\n \"text\": \"def _get_alignment_result(self, fkp, row, col, reference, output):\\n\\n alignment_result = AlignmentResult()\\n count_for_output = 0\\n reach_first_cell = False\\n while not reach_first_cell:\\n # we will only stop when it is first cell\\n reach_first_cell, row, col, count_for_output = self._get_me_the_result_by_looping_through_each_col(\\n fkp, row, col, count_for_output, reference, output, alignment_result\\n )\\n\\n alignment_result.merge_none_tokens()\\n return alignment_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e12e366846d985f622c6904a2250995\",\n \"score\": \"0.43162665\",\n \"text\": \"async def align(self, alignment = None):\\n\\t\\tif alignment is None:\\n\\t\\t\\tif self.reader.sysinfo.ProcessorArchitecture == PROCESSOR_ARCHITECTURE.AMD64:\\n\\t\\t\\t\\talignment = 8\\n\\t\\t\\telse:\\n\\t\\t\\t\\talignment = 4\\n\\t\\toffset = self.current_position % alignment\\n\\t\\tif offset == 0:\\n\\t\\t\\treturn\\n\\t\\toffset_to_aligned = (alignment - offset) % alignment\\n\\t\\tawait self.seek(offset_to_aligned, 1)\\n\\t\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51fca61cdff1c4352be24f8e867db2fd\",\n \"score\": \"0.43139875\",\n \"text\": \"def __mark_new_columns_with_zeros_in_marked_rows(self):\\n num_marked_columns = 0\\n for index, column in enumerate(self._zero_locations.T):\\n if index not in self._marked_columns:\\n if column.any():\\n row_indices, = np.where(column)\\n zeros_in_marked_rows = (set(self._marked_rows) & set(row_indices)) != set([])\\n if zeros_in_marked_rows:\\n self._marked_columns.append(index)\\n num_marked_columns += 1\\n return num_marked_columns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7f3b5dec13f760f0c240b8acdad2ab4\",\n \"score\": \"0.43103832\",\n \"text\": \"def centroid_align(stars):\\n center = int(stars[0].shape[0] / 2), int(stars[0].shape[1] / 2)\\n aligned = []\\n\\n for star in stars:\\n centroid = calculate_centroid(star)\\n shift = np.subtract(center, centroid)\\n star = fourier_shift(np.fft.fftn(star), shift)\\n star = np.fft.ifftn(star).real\\n aligned.append(star)\\n\\n return aligned\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90862dac95aa092d1af86bcb2c7d8a14\",\n \"score\": \"0.43101203\",\n \"text\": \"def __generateArtifactualLinkageMatrix(self, corrOnly=False):\\n\\n\\t\\tdef find_similar_peakwidth(featureMetadata, deltaMZ, deltaOverlap):\\n\\t\\t\\t\\\"\\\"\\\"Find 'identical' features based on m/z and peakwidth overlap\\n\\t\\t\\t\\tinput:\\n\\t\\t\\t\\t\\tfeatureMetada msDataset.featureMetadata\\n\\t\\t\\t\\t\\tdeltaMZ\\t\\t m/z distance to consider two features identical [ <= ] (same unit as m/z)\\n\\t\\t\\t\\t\\tdelta overlap minimum peak overlap between two grouped features (0-100%)\\n\\t\\t\\t\\toutput:\\n\\t\\t\\t\\t\\tpandas.DataFrame listing matched features based on deltaMZ and deltaOverlap\\n\\t\\t\\t\\\"\\\"\\\"\\n\\n\\t\\t\\tdef get_match(i, ds, deltaMZ):\\n\\t\\t\\t\\t\\\"\\\"\\\"Find identical features for a given variable\\n\\t\\t\\t\\t\\toutput:\\n\\t\\t\\t\\t\\t\\tpandas.DataFrames listing the matching features based overlap of peakwidth\\n\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\tmatch = (abs(ds.loc[i, 'Retention Time'] - ds.loc[:, 'Retention Time']) <= (\\n\\t\\t\\t\\t\\t\\t\\tds.loc[i, 'Peak Width'] + ds.loc[:, 'Peak Width']) / 2) & (\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tabs(ds.loc[i, 'm/z'] - ds.loc[:, 'm/z']) <= deltaMZ) # find match\\n\\t\\t\\t\\treturn (pandas.DataFrame(data={'node1': ds.index[i], 'node2': ds.index[match], 'Peak Overlap': ((((\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ds.loc[\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t i, 'Peak Width'] +\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ds.loc[\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t match, 'Peak Width']) / 2) - abs(\\n\\t\\t\\t\\t\\tds.loc[i, 'Retention Time'] - ds.loc[match, 'Retention Time'])) / ((ds.loc[i, 'Peak Width'] +\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tds.loc[\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tmatch, 'Peak Width']) / 2)) * 100})) # return the matching rows\\n\\n\\t\\t\\t# get feature overlap\\n\\t\\t\\tds = featureMetadata[['Feature Name', 'Retention Time', 'm/z', 'Peak Width']]\\n\\t\\t\\t# By concatenating all the matches once, save ~22% compared to do it at each loop round\\n\\t\\t\\tmatches = [get_match(i, ds, deltaMZ) for i in range(ds.shape[0])] # get a list of matches\\n\\t\\t\\tres = pandas.concat(matches)\\n\\t\\t\\tres = res.loc[res.node1 < res.node2] # keeps feat1-feat2, removes feat1-feat1 and feat2-feat1\\n\\n\\t\\t\\t# filter interactions by overlap\\n\\t\\t\\tres = res.loc[res.loc[:, 'Peak Overlap'] >= deltaOverlap, ['node1', 'node2']]\\n\\t\\t\\tres.reset_index(drop=True, inplace=True)\\n\\n\\t\\t\\treturn (res)\\n\\n\\t\\t# end find_similar_peakwidth\\n\\n\\t\\tdef remove_min_corr_overlap(overlappingFeatures, intensityData, corrCutoff):\\n\\t\\t\\t\\\"\\\"\\\" Return the overlap match DataFrame with overlap of metabolites correlated < cut-off removed (and correlation added)\\n\\t\\t\\t\\tinput:\\n\\t\\t\\t\\t\\toverlappingFeatures pandas.DataFrame as generated by find_similar_peakwidth\\n\\t\\t\\t\\t\\tintensityData\\t pandas.DataFrame of data value for each sample (row) / feature (column)\\n\\t\\t\\t\\t\\tcorrCutoff\\t\\t minimum percentage of overlap (0-1)\\n\\t\\t\\t\\toutput:\\n\\t\\t\\t\\t\\toverlapping features filtered\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tlink_corr = numpy.zeros([overlappingFeatures.shape[0]])\\n\\t\\t\\tfor jrow in range(0, len(link_corr)):\\n\\t\\t\\t\\tlink_corr[jrow] = numpy.corrcoef(intensityData[:, overlappingFeatures.loc[jrow, 'node1']],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t intensityData[:, overlappingFeatures.loc[jrow, 'node2']])[0, 1]\\n\\n\\t\\t\\treturn (overlappingFeatures.loc[link_corr >= corrCutoff,])\\n\\n\\t\\t# end remove_min_corr_overlap\\n\\n\\t\\t# check required info in featureMetadata for artifactual filtering. If missing, sets self.Attributes['artifactualFilter'] to False\\n\\t\\tif self.Attributes['featureFilters']['artifactualFilter'] == False:\\n\\t\\t\\traise ValueError(\\n\\t\\t\\t\\t'Attributes[\\\\'artifactualFilter\\\\'] set to \\\\'False\\\\', artifactual filtering cannot be run, use \\\\'updateMasks(withArtifactualFiltering=False)\\\\' and \\\\'generateReport(data, reportType=\\\\'feature selection\\\\', withArtifactualFiltering=False)\\\\'')\\n\\t\\tif 'Feature Name' not in self.featureMetadata.columns:\\n\\t\\t\\tself.Attributes['featureFilters']['artifactualFilter'] = False\\n\\t\\t\\traise LookupError(\\n\\t\\t\\t\\t'Missing feature metadata \\\\\\\"Feature Name\\\\\\\". Artifactual filtering cannot be run, set MSDataset.Attributes[\\\\'artifactualFilter\\\\'] = \\\\'False\\\\', or use \\\\'updateMasks(withArtifactualFiltering=False)\\\\' and \\\\'generateReport(data, reportType=\\\\'feature selection\\\\', withArtifactualFiltering=False)\\\\'')\\n\\t\\tif 'Retention Time' not in self.featureMetadata.columns:\\n\\t\\t\\tself.Attributes['featureFilters']['artifactualFilter'] = False\\n\\t\\t\\traise LookupError(\\n\\t\\t\\t\\t'Missing feature metadata \\\\\\\"Retention Time\\\\\\\". Artifactual filtering cannot be run, set MSDataset.Attributes[\\\\'artifactualFilter\\\\'] = \\\\'False\\\\', or use \\\\'updateMasks(withArtifactualFiltering=False)\\\\' and \\\\'generateReport(data, reportType=\\\\'feature selection\\\\', withArtifactualFiltering=False)\\\\'')\\n\\t\\tif 'm/z' not in self.featureMetadata.columns:\\n\\t\\t\\tself.Attributes['featureFilters']['artifactualFilter'] = False\\n\\t\\t\\traise LookupError(\\n\\t\\t\\t\\t'Missing feature metadata \\\\\\\"m/z\\\\\\\". Artifactual filtering cannot be run, set MSDataset.Attributes[\\\\'artifactualFilter\\\\'] = \\\\'False\\\\', or use \\\\'updateMasks(withArtifactualFiltering=False)\\\\' and \\\\'generateReport(data, reportType=\\\\'feature selection\\\\', withArtifactualFiltering=False)\\\\'')\\n\\t\\tif 'Peak Width' not in self.featureMetadata.columns:\\n\\t\\t\\tself.Attributes['featureFilters']['artifactualFilter'] = False\\n\\t\\t\\traise LookupError(\\n\\t\\t\\t\\t'Missing feature metadata \\\\\\\"Peak Width\\\\\\\". Artifactual filtering cannot be run, set MSDataset.Attributes[\\\\'artifactualFilter\\\\'] = \\\\'False\\\\', or use \\\\'updateMasks(withArtifactualFiltering=False)\\\\' and \\\\'generateReport(data, reportType=\\\\'feature selection\\\\', withArtifactualFiltering=False)\\\\'')\\n\\n\\t\\tif ((not corrOnly) | (corrOnly & self._tempArtifactualLinkageMatrix.empty)):\\n\\t\\t\\tself._tempArtifactualLinkageMatrix = find_similar_peakwidth(featureMetadata=self.featureMetadata,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tdeltaMZ=self.Attributes['filterParameters'][\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'deltaMzArtifactual'], deltaOverlap=\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.Attributes['filterParameters'][\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'overlapThresholdArtifactual'])\\n\\t\\tartifactualLinkageMatrix = remove_min_corr_overlap(self._tempArtifactualLinkageMatrix, self._intensityData,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t self.Attributes['filterParameters'][\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t 'corrThresholdArtifactual'])\\n\\n\\t\\treturn (artifactualLinkageMatrix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8426a4c876d885b4757e926f40783a44\",\n \"score\": \"0.43041614\",\n \"text\": \"def get_alignments(self, species, start, stop, strand):\\n files = dict()\\n visited = set()\\n query_size = stop - start\\n idx = 0\\n for iv in self.tree[species][start: stop]:\\n iv_start, iv_end = iv.begin, iv.end\\n if (iv_start, iv_end) not in visited:\\n visited.add((iv_start, iv_end))\\n block_start, block_stop, pathfile = iv.data\\n # store all blocks related to a file\\n #order.append((iv_start, iv_end, idx))\\n files.setdefault(pathfile, list()).append((block_start, block_stop, idx))\\n idx += 1\\n # because segments can be in multiple files and are not necessary in the right order \\n # we remember the segment positions to reorder the alignment at the end\\n alignments = dict()\\n ordered_alignments = list()\\n order = list()\\n if DEBUG:\\n print(files)\\n for pathfile in files:\\n # final positions\\n positions = sorted(files[pathfile])\\n prev_pos = 0\\n prev_stop = -1\\n with open(pathfile) as handle:\\n for file_pos_start, file_pos_stop, file_pos_idx in positions:\\n # reading blocks\\n assert(file_pos_start > prev_stop) # block cannot overlap, can they?\\n file_pos_size = file_pos_stop - file_pos_start\\n # we remove prev because we only move of the number of lines between the two blocks\\n line = next(islice(handle, file_pos_start - 1 - prev_pos , file_pos_start - prev_pos)) \\n align = next(MafIO.MafIterator(handle))\\n # look for the sequence of the species that we are interested in\\n found = False\\n for record in align:\\n if record.id == species:\\n found = True\\n break\\n # only keep part of the alignment that we are interest of\\n if found:\\n start_seq = record.annotations[\\\"start\\\"]\\n size_seq = record.annotations[\\\"size\\\"] \\n stop_seq = start_seq + size_seq\\n strand_seq = int(record.annotations[\\\"strand\\\"])\\n srcSize_seq = record.annotations[\\\"srcSize\\\"]\\n seq = record.seq\\n msa_len = len(seq)\\n \\n qstart = start\\n qstop = stop\\n if strand_seq < 0:\\n # convert query positions\\n qstart = srcSize_seq - stop\\n #qstop = srcSize_seq - tmp\\n qstop = srcSize_seq - start\\n # from this point everything is on the plus strand\\n max_start = max(start_seq, qstart)\\n max_stop = min(stop_seq, qstop)\\n strand_p_start = max_start\\n strand_p_stop = max_stop\\n if strand_seq < 0:\\n strand_p_start, strand_p_stop = srcSize_seq - strand_p_stop, srcSize_seq - strand_p_start\\n\\n abs_start = max_start - start_seq\\n abs_stop = max_stop - start_seq\\n\\n # get new start of alignment \\n msa_start, msa_stop, msa_size = seq2msa_startstop(str(seq), abs_start, abs_stop)\\n \\n if DEBUG:\\n print(strand_seq, strand, file_pos_start, file_pos_stop, pathfile)\\n print(start_seq, size_seq, srcSize_seq, qstart, qstop, max_start, max_stop, abs_start, abs_stop)\\n print(msa_start, msa_stop)\\n #print(\\\">\\\", record.seq[msa_start: msa_stop])\\n #print(\\\" \\\", seq[msa_start: msa_stop])\\n\\n alignments[file_pos_idx] = (align[:, msa_start: msa_stop], strand_seq)\\n order.append((strand_p_start, strand_p_stop, file_pos_idx))\\n else:\\n print(\\\"Unable to find SeqRecord for species {} in alignment:\\\".format(species))\\n print(align)\\n #file_pos_size = number of lines between two blocks\\n prev_pos = file_pos_start + file_pos_size \\n prev_stop = file_pos_stop\\n # order segments and convert to the correct strand if necessary\\n if DEBUG:\\n print(order)\\n order.sort()\\n if DEBUG:\\n print(order)\\n if strand < 0:\\n order = order[::-1]\\n if DEBUG:\\n print(order)\\n for iv_start, iv_stop, i in order:\\n align, strand_seq = alignments[i]\\n if strand < 0:\\n if strand_seq < 0:\\n ordered_alignments.append(align)\\n else:\\n ordered_alignments.append(self._reverse_msa(align)) \\n else:\\n if strand_seq < 0:\\n ordered_alignments.append(self._reverse_msa(align))\\n else:\\n ordered_alignments.append(align)\\n return ordered_alignments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e0d3137c993e6e3f670d890450d0953\",\n \"score\": \"0.42939565\",\n \"text\": \"def feed_cells_island(self):\\n for landscape in self.map.values():\\n landscape.feed_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cfb6dc63dab0d8384f256528d781f48\",\n \"score\": \"0.4291958\",\n \"text\": \"def iterstraight(self, raw):\\n\\n # length of row, in bytes\\n rb = self.row_bytes\\n a = array('B')\\n # The previous (reconstructed) scanline. None indicates first\\n # line of image.\\n recon = None\\n for some in raw:\\n a.extend(some)\\n while len(a) >= rb + 1:\\n filter_type = a[0]\\n scanline = a[1:rb+1]\\n del a[:rb+1]\\n recon = self.undo_filter(filter_type, scanline, recon)\\n yield recon\\n if len(a) != 0:\\n # :file:format We get here with a file format error:\\n # when the available bytes (after decompressing) do not\\n # pack into exact rows.\\n raise FormatError(\\n 'Wrong size for decompressed IDAT chunk.')\\n assert len(a) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a5bac148b6aa49b755f730da69144f\",\n \"score\": \"0.4291224\",\n \"text\": \"def align_seqs(seq1, seq2, map_qual):\\n\\n assert len(seq1) == len(seq2) == len(map_qual)\\n \\n mismatches = []\\n for i in range(len(seq1)):\\n if seq1[i] == seq2[i]:\\n continue\\n else:\\n if map_qual[i] >= 30:\\n mms = [seq1[i], seq2[i], i]\\n mismatches.append(mms)\\n\\n return mismatches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f861514d7511d76836992e79d9755b20\",\n \"score\": \"0.42891642\",\n \"text\": \"def flag_cells(self):\\n for coordinate in self.get_coordinates():\\n\\n for cell in self.get_surrounding_cells(coordinate):\\n if self.board[cell[0]][cell[1]] == '*':\\n continue\\n\\n self.board[cell[0]][cell[1]] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d05207a804ff9a2d363afcfb36887169\",\n \"score\": \"0.42879254\",\n \"text\": \"def _iterate_groups(self):\\n for self.groupID, g in enumerate(self.u_groups):\\n self.df_g = self.df.loc[self.groups == g]\\n if len(self.df_g) < self.min_grp_size:\\n continue\\n self.df_g.reset_index(inplace=True)\\n self._pair_group()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feecae6fe6b05b2e713df5d18a70f211\",\n \"score\": \"0.42837226\",\n \"text\": \"def iterate(Primate1, Primate2, side, Primate_inter=None):\\n\\n individuals = [i for i in os.listdir(Primate2) if '.' not in i]\\n\\n print('reading affine transformations')\\n\\n dir = Primate1 + '_to_' + Primate2\\n if Primate_inter is not None:\\n dir = Primate1 + '_to_' + Primate2 + '_via' + Primate_inter\\n\\n affine_model = os.path.join(dir, 'affine_trans_' + dir + '_' + side + '.txt')\\n int_lon, int_lat, lon_transform, lat_transform = read_affine(affine_model)\\n\\n for ind in individuals:\\n\\n nameLon = os.path.join(Primate2, ind, ind + '_' + side + 'white_lon.gii')\\n nameLat = os.path.join(Primate2, ind, ind + '_' + side + 'white_lat.gii')\\n\\n # print('reading coordinates')\\n\\n r = aims.Reader()\\n texLatF = r.read(nameLat)\\n texLonF = r.read(nameLon)\\n texLat = np.array(texLatF[0])\\n texLon = np.array(texLonF[0])\\n\\n # print('processing longitude')\\n\\n newLon = rescale(texLon, lon_transform, int_lon)\\n\\n # print('processing latitude')\\n\\n newLat = rescale(texLat, lat_transform, int_lat)\\n\\n # print('writing textures')\\n\\n nv = texLat.size\\n newLatT = aims.TimeTexture_FLOAT(1, nv)\\n newLonT = aims.TimeTexture_FLOAT(1, nv)\\n\\n for i in range(nv):\\n newLatT[0][i] = newLat[i]\\n newLonT[0][i] = newLon[i]\\n\\n outLat = os.path.join(dir, Primate1 + '_' + side + 'white_lat_to' + ind + '.gii')\\n outLon = os.path.join(dir, Primate1 + '_' + side + 'white_lon_to' + ind + '.gii')\\n\\n r = aims.Writer()\\n r.write(newLatT, outLat)\\n r.write(newLonT, outLon)\\n\\n print('done')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef576af79368e892d5af7bc09fc1b04\",\n \"score\": \"0.42796093\",\n \"text\": \"def extract(self, x, y, ys):\\n#as the input function in dcpg_data.py, we get to know that:\\n# x = chunk_pos; x.shape = (32768,)\\n# y = cpg_table.pos.values; y.shape = (3971744,)\\n#ys = cpg_table.value.values; ys.shape = (3971744,) This is the original input data information\\n\\n n = len(x) #target length\\n m = len(y) #provided length\\n k = self.k\\n kk = 2 * self.k\\n yc = self.__larger_equal(x, y)\\n knn_cpg = np.empty((n, kk), dtype=np.float16)\\n knn_cpg.fill(np.nan)\\n knn_dist = np.empty((n, kk), dtype=np.float32)\\n knn_dist.fill(np.nan)\\n\\n for i in range(n):\\n # Left side\\n yl = yc[i] - k\\n yr = yc[i] - 1\\n if yr >= 0:\\n xl = 0\\n xr = k - 1\\n if yl < 0:\\n xl += np.abs(yl)\\n yl = 0\\n xr += 1\\n yr += 1\\n knn_cpg[i, xl:xr] = ys[yl:yr]\\n knn_dist[i, xl:xr] = np.abs(y[yl:yr] - x[i])\\n\\n # Right side\\n yl = yc[i]\\n if yl >= m:\\n continue\\n if x[i] == y[yl]:\\n yl += 1\\n if yl >= m:\\n continue\\n yr = yl + k - 1\\n xl = 0\\n xr = k - 1\\n if yr >= m:\\n xr -= yr - m + 1\\n yr = m - 1\\n xl += k\\n xr += k + 1\\n yr += 1\\n knn_cpg[i, xl:xr] = ys[yl:yr]\\n knn_dist[i, xl:xr] = np.abs(y[yl:yr] - x[i])\\n\\n return (knn_cpg, knn_dist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf4a100704a01a5955c235d4efdc6b5c\",\n \"score\": \"0.4274558\",\n \"text\": \"def _iter_break_from_left_to_right(self):\\n yield IdentityTransform(), self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff8723c1912591b71c55e55e87d6b9b0\",\n \"score\": \"0.42716604\",\n \"text\": \"def chromosomesOffsets(self, *args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e8faccaec9fc773f4ed89fbad97276b\",\n \"score\": \"0.4269407\",\n \"text\": \"def alignment_csv(out_file):\\n writer = csv.DictWriter(out_file, (\\n\\n 'chadh_slug',\\n 'bpo_pub_title',\\n 'bpo_title',\\n 'bpo_article_type',\\n\\n 'a_start',\\n 'b_start',\\n 'size',\\n 'a_token_count',\\n\\n 'a_prefix',\\n 'a_snippet',\\n 'a_suffix',\\n\\n 'b_prefix',\\n 'b_snippet',\\n 'b_suffix',\\n ))\\n\\n writer.writeheader()\\n\\n alignments = (\\n session\\n .query(Alignment)\\n .join(ChadhNovel, BPOArticle)\\n .filter(Alignment.size >= 5)\\n .yield_per(1000)\\n )\\n\\n counts = {}\\n\\n for i, a in enumerate(alignments):\\n\\n slug = a.chadh_novel.slug\\n\\n if slug not in counts:\\n text = Text(a.chadh_novel.text)\\n counts[slug] = len(text.tokens)\\n\\n writer.writerow(dict(\\n\\n chadh_slug=a.chadh_novel.slug,\\n bpo_pub_title=a.bpo_article.publication_title,\\n bpo_title=a.bpo_article.record_title,\\n bpo_article_type=a.bpo_article.object_type,\\n\\n a_start=a.a_start,\\n b_start=a.b_start,\\n size=a.size,\\n a_token_count=counts[slug],\\n\\n a_prefix=a.a_prefix,\\n a_snippet=a.a_snippet,\\n a_suffix=a.a_suffix,\\n\\n b_prefix=a.b_prefix,\\n b_snippet=a.b_snippet,\\n b_suffix=a.b_suffix,\\n\\n ))\\n\\n if i % 1000 == 0:\\n print(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30f231716666c378e0ce3209b73f8768\",\n \"score\": \"0.42603087\",\n \"text\": \"def get_aligned_pairs(self, refset, targetset, unique=True, use_distance=True):\\n global_mat, global_matched = self.align(refset, targetset, get_matrix=use_distance)\\n for pair in iter_aligned_pairs(refset, targetset, global_mat, global_matched, unique):\\n self.pairs_found += 1\\n yield pair\\n self.log_infos()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf685e297261c0138f9adc84a545b657\",\n \"score\": \"0.42578217\",\n \"text\": \"def _map_segment_pairs_inclined(\\n self, i_pair, j_pair, k_pair, borehole_to_borehole):\\n i_segment = np.concatenate(\\n [i_pair + self._i0Segments[i] for (i, j) in borehole_to_borehole])\\n j_segment = np.concatenate(\\n [j_pair + self._i0Segments[j] for (i, j) in borehole_to_borehole])\\n k_segment = np.tile(k_pair, len(borehole_to_borehole))\\n return i_segment, j_segment, k_segment\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efc02674c6ec094fd8de81a0768b562c\",\n \"score\": \"0.42540073\",\n \"text\": \"def _iter_values(self):\\n\\n return zip([self._none_slices_except_domain_axis], [self.magnitude])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fafddce7d4a9691252df8f62f0057a0\",\n \"score\": \"0.4251796\",\n \"text\": \"def extract_alignments(self, alignment_output_file = None):\\n\\n # set the input file if not given\\n if not alignment_output_file:\\n alignment_output_file = self.alignment_output_file\\n alignments = extract_alignments(alignment_output_file)\\n self.protein_alignments = alignments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df79407572c8571b2ec2c997b9dc877a\",\n \"score\": \"0.42514947\",\n \"text\": \"def tilegen(self):\\n for row in self.cells:\\n for col in row:\\n yield col\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e70efc72557e75fbf8264af798d1a6b7\",\n \"score\": \"0.42489278\",\n \"text\": \"def _align(data, index_align=0, channel_offsets=None):\\n\\n # shapes might be different if stitched with different configs\\n # keep shape consistent with DO\\n\\n shape = data[0].shape\\n data = lasagna.utils.pile(data)\\n data = data[..., :shape[-2], :shape[-1]]\\n\\n indices = range(len(data))\\n indices.pop(index_align)\\n indices_fwd = [index_align] + indices\\n indices_rev = np.argsort(indices_fwd)\\n aligned = lasagna.process.register_and_offset(data[indices_fwd], registration_images=data[indices_fwd,0])\\n aligned = aligned[indices_rev]\\n if channel_offsets:\\n aligned = fix_channel_offsets(aligned, channel_offsets)\\n\\n return aligned\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e66dc91350c5e4d3f7cb8d857261da95\",\n \"score\": \"0.42437807\",\n \"text\": \"def inner_mask(self):\\n for p in flex.nested_loop(self._grid_size):\\n if self._inner_mask_binary[self._grid_idxr(p)]: yield p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe5299cf8473c10706cf8839d4cbf644\",\n \"score\": \"0.4241394\",\n \"text\": \"def outer_mask(self):\\n for p in flex.nested_loop(self._grid_size):\\n if self._outer_mask_binary[self._grid_idxr(p)]: yield p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec14f4d10c412f5629940f9e71a84489\",\n \"score\": \"0.4238155\",\n \"text\": \"def split_alignment(self):\\n aln_list = []\\n for name, start, stop in self._linspace.to_list():\\n aln = self.get_sites(list(range(start, stop)))\\n aln.name = name\\n aln._linspace = self._subspaces[name]\\n aln.metadata = deepcopy(self.metadata)\\n\\n aln_list.append(aln)\\n return aln_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9891a7038593cfc7c1c20f03ad6a5d17\",\n \"score\": \"0.42331967\",\n \"text\": \"def data_iterator(self, data_info, params, shuffle=False):\\n\\n collate_fn = lambda d: collate_libri(d, self.mfcc, params, self.word2Idx)\\n\\n # make a list that decides the order in which we go over the data- this avoids explicit shuffling of data\\n data_loader = tdata.DataLoader(self.datasets[data_info['type']], batch_size=params.batch_size, shuffle=shuffle, collate_fn=collate_fn)\\n\\n # one pass over data\\n for data in enumerate(data_loader):\\n # fetch waveforms and keywords\\n\\n yield data[1][0], data[1][1]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":94,"cells":{"query_id":{"kind":"string","value":"db08e1177f874ec5a2073ffa3d3b937c"},"query":{"kind":"string","value":"Parse swiftrecon output into list of lists grouped by the content of the delimited blocks."},"positive_passages":{"kind":"list like","value":[{"docid":"c075bcdf0c76921e47a2d0a4551ff995","score":"0.71614885","text":"def parse_swift_recon(recon_out):\n\n lines = recon_out.splitlines()\n delimiter_regex = re.compile(r'^={79}')\n collection = []\n\n delimiter_positions = [ind for ind, x in enumerate(lines)\n if delimiter_regex.match(x)]\n\n for ind, delimiter_position in enumerate(delimiter_positions):\n if ind != len(delimiter_positions) - 1: # Are in the last position?\n start = delimiter_position + 1\n end = delimiter_positions[ind + 1]\n collection.append(lines[start:end])\n return collection","title":""}],"string":"[\n {\n \"docid\": \"c075bcdf0c76921e47a2d0a4551ff995\",\n \"score\": \"0.71614885\",\n \"text\": \"def parse_swift_recon(recon_out):\\n\\n lines = recon_out.splitlines()\\n delimiter_regex = re.compile(r'^={79}')\\n collection = []\\n\\n delimiter_positions = [ind for ind, x in enumerate(lines)\\n if delimiter_regex.match(x)]\\n\\n for ind, delimiter_position in enumerate(delimiter_positions):\\n if ind != len(delimiter_positions) - 1: # Are in the last position?\\n start = delimiter_position + 1\\n end = delimiter_positions[ind + 1]\\n collection.append(lines[start:end])\\n return collection\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2d0bf5d3c0c116e3052fefa8030ca8ba","score":"0.65458053","text":"def processBlock(block):\n out = []\n a = \"\"\n for i in block:\n for j in i:\n if a == \"\":\n a = j\n else:\n a = a + \"|\" + j\n\n out = a.split(\"|\")\n return out","title":""},{"docid":"8def8263cd4fa650d980b83ea61f310c","score":"0.6182205","text":"def parse(\n data: str,\n raw: bool = False,\n quiet: bool = False\n) -> List[Dict]:\n jc.utils.compatibility(__name__, info.compatible, quiet)\n jc.utils.input_type_check(data)\n\n raw_output: List = []\n\n if jc.utils.has_data(data):\n\n for line in filter(None, data.splitlines()):\n\n buddy_list = line.split()\n\n raw_output.append(\n {\n 'node': buddy_list[1][:-1],\n 'zone': buddy_list[3],\n 'free_chunks': buddy_list[4:]\n }\n )\n\n return raw_output if raw else _process(raw_output)","title":""},{"docid":"af33e7d182eb59a5bd6714d2604d1e18","score":"0.6090104","text":"def _parse(self):\n res = []\n if not isfile(self.path):\n return res\n sep_pat = re.compile(r'==>\\s*(.+?)\\s*<==')\n with open(self.path, 'r') as f:\n lines = f.read().splitlines()\n for line in lines:\n line = line.strip()\n if not line:\n continue\n m = sep_pat.match(line)\n if m:\n res.append((m.group(1), set(), []))\n elif line.startswith('#'):\n res[-1][2].append(line)\n else:\n res[-1][1].add(line)\n return res","title":""},{"docid":"d4390d0cebd4ce2ddb2035b692d393f1","score":"0.60860026","text":"def parse_conll(stream, filename=None, separator='\\t', is_empty=_line_is_empty):\n\n li, l = 0, None\n try:\n blocks = []\n current_block = []\n for l in stream:\n l = l.rstrip()\n li += 1\n if is_empty(l):\n blocks.append(current_block)\n current_block = []\n else:\n current_block.append(l.split(separator))\n except Exception:\n # whatever goes wrong\n raise ParseError(l, li)\n\n return blocks","title":""},{"docid":"92530a8d4449d64bea3b27da912f8706","score":"0.6025703","text":"def loop_block_to_list(block):\n\n names = [l for l in block[\"lines\"] if l.startswith(\"_\" + block[\"category\"])]\n lines = [l for l in block[\"lines\"][1:] if l not in names]\n names = [l.split(\".\")[1].split()[0] for l in names]\n lines = [split_values(l) for l in lines]\n l = []\n for n in range(len(lines) - 1):\n while n < len(lines) - 1 and\\\n len(lines[n]) + len(lines[n + 1]) <= len(names):\n lines[n] += lines[n + 1]\n lines.pop(n + 1)\n for line in lines:\n l.append({\n name: value for name, value in zip(names, line)\n })\n return l","title":""},{"docid":"f419dd927d7d914b03abc1d95f393f0d","score":"0.5958842","text":"def process_output(output):\n return [line.strip('^$').split('/') for line in output.split('\\n')]","title":""},{"docid":"774b21217cb9181150d8ba1a91159f03","score":"0.59145683","text":"def _extract_multi_block_from_log(log_content, block_name):\n # Parse out the portion we need\n data_started = False\n data_content = []\n data_block_list = []\n model_names = []\n\n for line in log_content.split('\\n'):\n if line.startswith(\"SIMULTANEOUS\"):\n clean_str = line.replace(\"SIMULTANEOUS \", \"\")\n model_names = json.loads(clean_str)\n if line.startswith(\"%s_START\" % block_name):\n data_started = True\n elif line.startswith(\"%s_END\" % block_name):\n data_started = False\n if len(data_content) > 0:\n data_path = ''\n if len(model_names) > len(data_block_list):\n data_path = model_names[len(data_block_list)]\n data_block_list.append([data_path, '\\n'.join(data_content)])\n data_content = []\n elif data_started is True:\n data_content.append(line)\n return data_block_list","title":""},{"docid":"f3ccf5aa0e22f92fa37ec8565dac1e33","score":"0.5887196","text":"def parse(self, text):\r\n return []","title":""},{"docid":"958fecf1816c8c467d193103d247d28f","score":"0.58773464","text":"def parse_list(content):\n csv_list = []\n # Convert into a list of strings\n unicode = content.decode()\n content_list = unicode.split('\\n')\n state = 0\n for line in content_list:\n # print(\"state, line:\", state, line)\n if state == 0:\n # Look for first <PRE>\n if line.startswith('<PRE>'):\n state = 1\n elif state == 1:\n # Look for header\n if line.startswith('No., Day,DD,MMM,YYYY,'):\n csv_list.append(line.split(','))\n state = 2\n elif state == 2:\n # Process rows until blank line\n if line:\n csv_list.append(line.split(','))\n else:\n # Processed all rows so quit\n break\n else:\n continue\n return csv_list","title":""},{"docid":"0d368548f977d370eb3c3c8575391f6a","score":"0.5766159","text":"def separate_records(listofdata):\n\n\troottag = re.compile(r'rootTag')\n\trtlist=[]\n\tfor i,v in enumerate(listofdata):\n \t\tif re.match(roottag, str(v)):\n \trtlist.append(i)\n\n\tlistofrecords = []\t\n\tind = 1\n\tfor r in rtlist:\n \t\tsingle_record = listofdata[r:rtlist[ind+1]]\n \t\tlistofrecords.append(single_record)\n \t\tif ind < len(rtlist)-2:\n \t\tind += 1\n \t\telse:\n \t\tbreak\n \n\treturn listofrecords","title":""},{"docid":"45df3fd00d8018255d01ac47213efaed","score":"0.5765179","text":"def __process_gorn_list(self, s):\n if not s:\n return []\n parts = re.split(r\"\\s*;\\s*\", s)\n seqs = list(map((lambda x : list(map(int, re.split(r\"\\s*,\\s*\", x)))), parts))\n return seqs","title":""},{"docid":"598f7244152fa5e10763c42a012048a4","score":"0.57588154","text":"def rnaforester_parser(lines):\n \n result = []\n \n for block in cluster_parser(lines):\n for struct in line_parser(block):\n result.append(struct)\n\n return result","title":""},{"docid":"c80e989565bfc91cb03e2063b40f99ca","score":"0.57435733","text":"def parse_mol(self,):\n f = open(self.molfile, 'r')\n flag = True\n blocks = []\n block = []\n \n while(flag):\n flag = False\n for i in f:\n flag = True\n if i.strip().startswith('step'):\n if len(block) == 0:\n # append step to list as the first element\n block.append(i.strip().split()[0][4:])\n else:\n blocks.append(block)\n block = []\n # append step to list as the first element\n block.append(i.strip().split()[0][4:])\n break\n # ignore the white lines\n elif len(i.strip()) < 1:\n pass\n else:\n block.append(i.strip())\n blocks.append(block)\n return blocks","title":""},{"docid":"a756a118fe526c1185328529402c0ac8","score":"0.5712363","text":"def get_records(text_block):\n #TODO: Refactor all of these into a classmethod for Record, then\n # set arguments for start, end, find, and non-single-line behavior\n start = text_block.find('(cid:87)(cid:73)(cid:84)(cid:72)(cid:68)'\\\n '(cid:82)(cid:65)(cid:87)(cid:65)(cid:76)(cid:83)(cid:32)'\\\n '(cid:38)(cid:32)(cid:68)(cid:69)(cid:80)(cid:79)(cid:83)'\\\n '(cid:73)(cid:84)(cid:83) (cid:32)')\n end = text_block.find('NET WITHDRAWALS (cid:38) DEPOSITS')\n text_block = text_block[start:end]\n deposits = []\n text_block = text_block.split('\\n')\n \"\"\"\n for line in text_block:\n print line\n \"\"\"\n for i, record in enumerate(text_block):\n if record.find('Deposit ACH') != -1:\n if Deposit.is_single_line(record):\n deposits.append(Deposit.from_record(record))\n elif len(record.split(' ')) < 1:\n continue\n else:\n record = ''.join([text_block[i], text_block[i+1], \n ' ', text_block[i-1]])\n record = Deposit.clean(record)\n record = Deposit.from_record(record)\n deposits.append(record)\n return deposits","title":""},{"docid":"9b2be6e27c1f5ef9bfd32111bd7a6763","score":"0.5686293","text":"def splitBlock(self, stdout):\n results = []\n for line in stdout.splitlines(False):\n # Intentionally skips empty lines.\n if not line:\n continue\n if not line.startswith('__'):\n if results:\n results[-1].append(line)\n else:\n # TODO(maruel): gclient's git stdout is inconsistent.\n # This should fail the test instead!!\n pass\n continue\n\n match = re.match(r'^________ ([a-z]+) \\'(.*)\\' in \\'(.*)\\'$', line)\n if match:\n results.append([[match.group(1), match.group(2), match.group(3)]])\n continue\n\n match = re.match(r'^_____ (.*) is missing, synching instead$', line)\n if match:\n # Blah, it's when a dependency is deleted, we should probably not\n # output this message.\n results.append([line])\n continue\n\n # These two regexps are a bit too broad, they are necessary only for git\n # checkouts.\n if (re.match(r'_____ [^ ]+ at [^ ]+', line) or\n re.match(r'_____ [^ ]+ : Attempting rebase onto [0-9a-f]+...', line)):\n continue\n\n # Fail for any unrecognized lines that start with '__'.\n self.fail(line)\n return results","title":""},{"docid":"05bbe76893762bac7d11ced2dd9f6e92","score":"0.5671271","text":"def parse(data):\n data = LookAheadStringIter(data)\n result = list(parse_recursive(data))\n if data.ahead:\n raise ParseError('Inconsistent nesting of lists', data)\n return result","title":""},{"docid":"95e2f9adfb7a6ed16c1277c2f274ead2","score":"0.5658487","text":"def __process_trees(self, s):\n if not s:\n return []\n tree_strs = s.split(\"|||\")\n return list(map(Tree.fromstring, tree_strs)) ## important fix TS","title":""},{"docid":"d4bd9478a3915907d3ae940892744d02","score":"0.56497824","text":"def parse(lines):\n lines = [i.decode('utf-8').rstrip() for i in lines]\n lines = strip_footer(lines)\n\n out = parse_header_block(lines)\n out['subitems'] = parse_content(lines)\n out['multiple'] = len(out['subitems']) > 1\n\n return out","title":""},{"docid":"668db40667e1fdc1677dfad9c130549c","score":"0.56468004","text":"def sent_split(inlines, abbrevs=None):\n\n outlines = []\n lcont = \"\"\n for l in inlines:\n # if this line is empty, continue to next\n if l.strip() == \"\":\n continue\n\n # protect any abbrevs by using a tag string and the abbrevs index\n if abbrevs is not None:\n for i, a in enumerate(abbrevs):\n\n # if a has a dot then I need to escape it\n if a.find(\".\") != -1:\n a = a.replace(\".\", r\"\\.\")\n\n ab_re = re.compile(r\"\\b\" + a)\n abtag = \"#ABTAG{}#\".format(i)\n ab_re.sub(abtag, l)\n\n # split the current l into groups\n parts = S_RE.split(l.strip())\n lparts = []\n for i, p in enumerate(parts):\n if i > 0 and S_RE.match(p):\n lparts[-1] += \" \" + p\n else:\n lparts.append(p)\n\n # see if there's more than one part\n if len(lparts) > 1: # multi part\n\n # append any lcont to the first\n lparts[0] = lcont + \" \" + lparts[0]\n lcont = \"\"\n\n # see if the last segment will continue to the next\n if not S_RE.match(l.strip()[-1]):\n outlines.extend(\n [p.strip() for p in lparts[0:-1] if p.strip() != \"\"]\n )\n lcont = lparts[-1]\n else:\n outlines.extend([p.strip() for p in lparts if p.strip() != \"\"])\n\n else: # just one part here\n # see if this line will continue to the next\n if not S_RE.match(l.strip()[-1]):\n lcont = lcont + \" \" + l.strip()\n else:\n if lcont + \" \" + l.strip() != \"\":\n outlines.append(lcont + \" \" + l.strip())\n\n # go through outlines and put back strings from abtags\n if abbrevs is not None:\n ABTAG_NUM_RE = re.compile(\"#ABTAG([0-9]+)#\")\n cleanlines = []\n for o in outlines:\n logging.info(\"preclean: \" + o, file=sys.stderr)\n while o.find(\"#ABTAG\") > -1:\n i = int(ABTAG_NUM_RE.search(o).group(1))\n o = (\n o[0:o.find(\"#ABTAG\")]\n + abbrevs[i]\n + \" \"\n + o[o.find(\"#ABTAG\") + 7 + len(str(i)):]\n )\n cleanlines.append(o)\n logging.info(\"cleaned: \" + o, file=sys.stderr)\n return cleanlines\n else:\n return outlines","title":""},{"docid":"12e49697d72620307fdcd596f164f242","score":"0.5633962","text":"def line_parser(block):\n odd = True\n record = False\n first = True\n seq = ''\n con_seq = ''\n struct = ''\n alignment = {}\n for line in block:\n #find alignments\n if line.startswith('seq'):\n if line.__contains__(')') or line.__contains__('('):\n continue\n else:\n sline = line.strip().split()\n name = sline[0]\n tmp_seq = sline[-1]\n if alignment.__contains__(name):\n seq = alignment[name]\n seq = ''.join([seq,tmp_seq])\n alignment[name] = seq\n else:\n alignment[name] = tmp_seq\n\n if line.startswith('Consensus sequence/structure:'): #start\n record = True\n if not first:\n struct = to_pairs(struct)\n yield [alignment,con_seq,struct]\n result = []\n first = True\n elif record:\n if line.startswith(' '):\n line = line.strip()\n if odd:\n con_seq = ''.join([con_seq,line])\n odd = False\n else:\n struct = ''.join([struct,line])\n odd = True\n\n struct = to_pairs(struct)\n yield [alignment,con_seq,struct]","title":""},{"docid":"a35f8fd6e3293a985ccb06e3ad6d7a9d","score":"0.56085324","text":"def parse(stream):\n content = stream.read()\n content = boneyard_re.sub('', content)\n lines = linebreak_re.split(content)\n del content\n return parse_lines(lines)","title":""},{"docid":"ba107700108cb17b31c6891a10f1ef58","score":"0.5605392","text":"def parse(self):\n res = []\n if not isfile(self._log_path):\n return res\n sep_pat = re.compile(r'==>\\s*(.+?)\\s*<==')\n for line in open(self._log_path):\n line = line.strip()\n if not line or line.startswith('#'):\n continue\n m = sep_pat.match(line)\n if m:\n dt = m.group(1)\n res.append((dt, set()))\n else:\n res[-1][1].add(line)\n return res","title":""},{"docid":"a1fb83c031879994c6b2d6bcb5d1fd0e","score":"0.56053007","text":"def __process_span_list(self, s):\n if not s:\n return []\n parts = re.split(r\"\\s*;\\s*\", s)\n seqs = list(map((lambda x : list(map(int, re.split(r\"\\s*\\.\\.\\s*\", x)))), parts))\n return seqs","title":""},{"docid":"66d32cef5a6efa05332fa6fbc15386af","score":"0.5528002","text":"def deduplicate_delimiters(output):\n i = 0\n while i < len(output) - 1:\n if isinstance(output[i], OutputDelimiter) and isinstance(output[i + 1], OutputDelimiter):\n if output[i].string.isspace() and not output[i + 1].string.isspace():\n output.pop(i)\n continue\n elif output[i + 1].string.isspace() and not output[i].string.isspace():\n output.pop(i + 1)\n continue\n elif len(output[i].string) < len(output[i + 1].string):\n output.pop(i)\n continue\n elif len(output[i].string) > len(output[i + 1].string):\n output.pop(i + 1)\n continue\n elif output[i].string.isspace():\n output.pop(i)\n continue\n i += 1\n # Strip leading block delimiters.\n while output and isinstance(output[0], OutputDelimiter) and output[0].string.isspace():\n output.pop(0)\n # Strip trailing block delimiters.\n while output and isinstance(output[-1], OutputDelimiter) and output[-1].string.isspace():\n output.pop(-1)","title":""},{"docid":"03ee338cf906a6665c539bfa0f3b5247","score":"0.54913294","text":"def parse_input(puzzle_data):\n structured_puzzle = list()\n for line in puzzle_data.split(\"\\n\"):\n structured_puzzle.append(line.split(\" \"))\n\n return structured_puzzle","title":""},{"docid":"d81c2c1c335d308e721854cf29f34fd3","score":"0.5482219","text":"def processor(main_dict: dict):\n main_dict[\"group_contents\"] = \\\n main_dict[\"group_contents\"].replace(\"}{\", \"}|{\")\n code_strings_list = main_dict[\"group_contents\"].split(\"|\")\n return code_strings_list","title":""},{"docid":"8bf95ad25f0d0ee9a777781993e56751","score":"0.5477923","text":"def parsed_output(self):\n data = self.output_data\n args = [iter(data)] * 3\n return zip(*args)","title":""},{"docid":"deb5694485fa5bd9c6244f01e931cd13","score":"0.5441826","text":"def parse_and_extract(conllu_path) -> List[Tuple[Inp, Out]]:\n data = []\n with open(conllu_path, \"r\", encoding=\"utf-8\") as data_file:\n for token_list in conllu.parse_incr(data_file): # type: ignore\n data.append(extract(token_list))\n return data","title":""},{"docid":"cfa59e9fd55254f80a663c4e81fd6105","score":"0.54314446","text":"def split_raw_data(raw_data):\n return [\n line if line == \"\\n\"\n else line.split(\"\\t\")\n for line in raw_data\n ]","title":""},{"docid":"fa17a259d594e9a93523c6950b560d5b","score":"0.5430212","text":"def parse_tokens(self, tokens):\n valid_tokens = self.get_valid_tokens(tokens)\n\n if len(valid_tokens) <= 0:\n return []\n\n segmented_tokens = []\n current_tokens = [valid_tokens[0]]\n\n for t in valid_tokens[1:]:\n # Tokens have consecutive indices\n # so same segment\n if current_tokens[-1][0] + 1 == t[0]:\n current_tokens.append(t)\n\n # start new segment\n else:\n segmented_tokens.append(current_tokens)\n current_tokens = [t]\n\n segmented_tokens.append(current_tokens)\n\n segments = []\n\n for token_group in segmented_tokens:\n text = ' '.join(t[3].strip() for t in token_group)\n start = token_group[0][1]\n end = token_group[-1][2]\n segments.append((start, end, text))\n\n return segments","title":""},{"docid":"fb3a1e250dac457027cba191c45d0428","score":"0.54013276","text":"def unpack(self, data):\n return [m.split(self.separator) for m in data.split(\"\\n\")]","title":""},{"docid":"b2e8a57fa800b26fccd0fac20913c753","score":"0.5373687","text":"def group_tokens(self, blocks_and_lines):\n\n def horizontal_distance_between(token1, token2):\n if token1.coordinates[\"x\"] > token2.coordinates[\"x\"]:\n return (\n token1.coordinates[\"x\"]\n - token2.coordinates[\"x\"]\n - token2.coordinates[\"width\"]\n )\n else:\n return (\n token2.coordinates[\"x\"]\n - token1.coordinates[\"x\"]\n - token1.coordinates[\"width\"]\n )\n\n def combine_tokens_into_one_token(token_list):\n x = y = float(\"inf\")\n bottom_right_x = bottom_right_y = 0\n text = \"\"\n\n for token in token_list:\n if not text:\n text = token.text\n else:\n text = text + \" \" + token.text\n\n x = min(token.coordinates[\"x\"], x)\n y = min(token.coordinates[\"y\"], y)\n bottom_right_x = max(\n token.coordinates[\"x\"] + token.coordinates[\"width\"], bottom_right_x\n )\n bottom_right_y = max(\n token.coordinates[\"y\"] + token.coordinates[\"height\"], bottom_right_y\n )\n\n return Token(\n text,\n {\n \"x\": x,\n \"y\": y,\n \"width\": bottom_right_x - x,\n \"height\": bottom_right_y - y,\n },\n \"NA\",\n {**token.token_structure, \"word_num\": \"Grouped\"},\n )\n\n grouped_tokens = []\n\n for block in blocks_and_lines:\n for line in blocks_and_lines[block]:\n current_line = blocks_and_lines[block][line]\n current_group = []\n ADJUSTMENT_FACTOR = 0.25\n\n # take note of all the IS CURRENCY code that is meant to prevent currency and amount from grouping together\n for token in current_line:\n IS_CURRENCY = token.text in currencies\n if current_group: # If there exists tokens in the current group\n height_of_current_group = max(\n list(\n map(\n lambda token: token.coordinates[\"height\"],\n current_group,\n )\n )\n )\n TOO_FAR = (\n horizontal_distance_between(token, current_group[-1])\n > height_of_current_group / 2 + ADJUSTMENT_FACTOR*height_of_current_group\n )\n LAST_TOKEN_ENDS_WITH_COLON = current_group[-1].text[-1] == \":\"\n ALIGNED_HORIZONTALLY = token.is_horizontally_aligned_with(\n current_group[-1]\n )\n\n # When something is a currency, independently add it to the grouped tokens\n if IS_CURRENCY: \n grouped_tokens.append(\n combine_tokens_into_one_token(current_group)\n )\n grouped_tokens.append(token)\n current_group = []\n \n # If something should not belong to the current group, group the current group and start a new group\n elif (\n TOO_FAR\n or LAST_TOKEN_ENDS_WITH_COLON\n or not ALIGNED_HORIZONTALLY\n ): # This token should not be combined into the current group\n grouped_tokens.append(\n combine_tokens_into_one_token(current_group)\n )\n current_group = [token] # Reset the current group\n \n # It meets the criteria for normal append-ment of the token\n else:\n current_group.append(token)\n\n else: # Start a new group, since current_group is empty. Unless it is a currency, in which case, append directly to grouped tokens\n if IS_CURRENCY:\n grouped_tokens.append(token)\n else:\n current_group.append(token)\n\n # After finishing a line, just combine remaining tokens\n if current_group:\n grouped_tokens.append(combine_tokens_into_one_token(current_group))\n\n return grouped_tokens","title":""},{"docid":"b45b7b7fc5810eaa0db8e94970899300","score":"0.5351421","text":"def parse(lines):\n pass","title":""},{"docid":"2c51139f008c7389bc18da4bd16104dc","score":"0.5342632","text":"def parse_input(puzzle_data):\n structured_puzzle = list()\n for line in puzzle_data.split(\"\\n\"):\n structured_puzzle.append(line)\n return structured_puzzle","title":""},{"docid":"17c9edc50893b332ea89ea63e951cff3","score":"0.53323346","text":"def sub_group_lines(lines):\n groups, sub_group = [], []\n\n for line in lines:\n if len(line.rstrip()) > 0:\n sub_group.append(line.rstrip())\n else:\n groups.append(sub_group)\n sub_group = []\n\n if len(sub_group) > 0:\n groups.append(sub_group)\n\n return groups","title":""},{"docid":"45ee9a3e15a7ad670150aad3097bd7a8","score":"0.531893","text":"def parse(self) -> list[list[str]]:\n cells = []\n row = []\n text = self.text\n while text:\n try:\n cell, cell_end, row_done = self.parser.parse(text)\n except ParseError as e:\n if self.debug:\n error = ParseError(f'Error in parse. Cannot match quoted key. '\n f'Text is {text!r}\\ncells length is {len(cells)}\\n'\n f'row length is {len(row)}')\n raise error from e\n else:\n raise\n for formatter in self.formatters:\n cell = formatter.format(cell)\n row.append(cell)\n if row_done:\n cells.append(row)\n row = []\n text = text[cell_end:]\n return cells","title":""},{"docid":"9272d9a18bc118ec1ad1e92a102298e3","score":"0.5317757","text":"def parse_list(parser=lambda x: x, delim=' '):\n return chain(parse_text, lambda value: value.split(delim), map_(parser))","title":""},{"docid":"856fc1de97d3e1f53e335b31e0bff69a","score":"0.5311281","text":"def parse_input():\n\n wires = []\n\n with open('input.txt', 'r') as txt:\n for segments in txt:\n wire = Wire()\n for segment in segments.strip().split(','):\n wire.update(segment)\n wires.append(wire)\n\n return wires","title":""},{"docid":"800f3616a9e08a3b0c53ca49c0977acc","score":"0.5310942","text":"def parseoutput(infilepath, has_headers):\n # open up the file\n f = open(infilepath)\n # this is my way to handle some files having headers and some not\n if has_headers == True:\n # read off the headers \n x = f.readline()\n # for each row in infile strip off newlines and split\n mylist = [r.strip().split('|') for r in f]\n return mylist","title":""},{"docid":"76c1777b899ac82f1cb5e47bb04b1e3b","score":"0.53012073","text":"def parse(self):\n self.data = []\n self.extract_sections()\n for section in self._parsing['sections']:\n self.data.append(self.parse_section(section))\n return self.data","title":""},{"docid":"5863d062b5d4b82b4fe09de2ed7af561","score":"0.52934253","text":"def parse_input_output_string(self, data: str):\n ret = []\n for line in data.split(\"\\n\"):\n if line:\n if line[1] == \" \":\n prev = ret[-1]\n n = (prev[0], prev[1] +\"\\n \" + line.strip())\n ret[-1] = n\n # ret[-1][1] += line.strip()\n else:\n vd = line.split(\" \", 1)\n ret.append((vd[0].strip(),vd[1].strip())) \n \n return ret","title":""},{"docid":"1412ca944e60c113c85eccd508f4bb05","score":"0.527264","text":"def read_list(data):\n assert data.next() == '('\n result = list(parse_recursive(data))\n if not data.ahead or data.next() != ')':\n raise ParseError('Unexpected end of list', data)\n return result","title":""},{"docid":"5169299abeabf63bc9f110e62362caec","score":"0.52621293","text":"def _commands_output_to_list(self, output):\n output = [x.split('=') for x in output.split('\\n')]\n\treturn output","title":""},{"docid":"216ff80ba234f5eb6bd94172bd7d3c79","score":"0.52569085","text":"def parse(lines):\n output = dict()\n\n for line in lines:\n [start, pipes] = line.split(\"<->\")\n output[int(start)] = [int(pipe) for pipe in pipes.split(\",\")]\n\n return output","title":""},{"docid":"d74ca577f5420687b9f96e4463f92cf5","score":"0.5242003","text":"def splitter(data, vendors, EC):\n final_list = []\n split_list = data.split(\"\\n\")\n\n del split_list[len(split_list) - 1]\n\n for item in split_list:\n\n final_list.append(item.split(\";\"))\n\n level_sorter(final_list, vendors, EC)","title":""},{"docid":"c25d139e0b710040e29fe5b512697763","score":"0.52353776","text":"def custom_parser(output: str, command: str, device_type: str, logger_poller) -> list:\n # Install pip install pyATS and genie to use the parser in Netmiko. Now is not necessary for 1 command to parse\n parsed_output = []\n if command == \"show vlan\" and device_type == \"cisco_ios\":\n output = output.split(\"\\n\")\n pattern = r\"^(\\d+)\\s+(\\S+)\\s+\\S+.*$\"\n for line in output:\n match = re.search(pattern, line)\n if match:\n parsed_output.append({\"vlan_id\": match.group(1), \"vlan_name\": match.group(2)})\n else:\n logger_poller.info(f\"Parsed not implemented for command {command} on device_type {device_type}\")\n return parsed_output","title":""},{"docid":"181baa6ca3a82ad9e99cddb505f43718","score":"0.52280134","text":"def parse_colon_record_list(self, response, err):\n results = []\n records = response.split('\\n\\n')\n for record in records:\n obj = self.parse_colon_record(record.strip(), err)\n\n if obj == None:\n continue\n\n results.append(obj)\n\n return results","title":""},{"docid":"7557d97104cf193f6c6a2362cb6115fe","score":"0.5226994","text":"def parse(content: str) -> List[Sentence]:\n return ConlluParserBuilder.build().parse(content)","title":""},{"docid":"2cad2a8522b146e539fb3334ce2a59c5","score":"0.52262414","text":"def parse_input(filename=\"input.txt\"):\n with open(filename, \"r\") as f:\n text = f.read()\n # Parse the groups\n groups = text.split(\"\\n\\n\")\n answers = [group.split(\"\\n\") for group in groups]\n return answers","title":""},{"docid":"dce2742ce3e7af2c87257e6737ec0875","score":"0.5214416","text":"def groups(self):\n groups = []\n with open(self.fname) as reader:\n for line in reader.readlines():\n m = re.match(r\"\\[\\[([^\\[\\]]+)\\|([^\\[\\]]+)\\]\\]\", line)\n if m:\n group = (m.group(1), m.group(2))\n groups.append(group)\n return groups","title":""},{"docid":"be08c7ed23720dab2344331390742144","score":"0.51933235","text":"def items(self):\n self.reset()\n\n for l in diff_split_lines(self.unified_diff, False):\n self._bytes_processed += len(l) + 1\n m = re.match(r'^--- ([^\\s]*)', l)\n if m:\n yield from self.empty_buffer()\n continue\n m = re.match(r'^\\+\\+\\+ ([^\\s]*)', l)\n if m:\n yield from self.empty_buffer()\n continue\n\n m = re.match(r\"@@ -(\\d+),?(\\d*) \\+(\\d+),?(\\d*)\", l)\n if m:\n yield from self.empty_buffer()\n hunk_data = map(lambda x: x == \"\" and 1 or int(x), m.groups())\n self.hunk_off1, self.hunk_size1, self.hunk_off2, self.hunk_size2 = (\n hunk_data\n )\n self.line1, self.line2 = self.hunk_off1, self.hunk_off2\n yield \"H\", (\n self.hunk_off1,\n self.hunk_size1,\n self.hunk_off2,\n self.hunk_size2,\n )\n continue\n\n if re.match(r'^\\[', l):\n yield from self.empty_buffer()\n yield \"C\", l\n\n if re.match(r\"^\\\\ No newline\", l):\n if self.hunk_size2 == 0:\n self.buf[-1] = (\n self.buf[-1][0],\n self.buf[-1][1] + '\\n' + l[2:],\n )\n else:\n self.buf[-1] = (\n self.buf[-1][0] + '\\n' + l[2:],\n self.buf[-1][1],\n )\n continue\n\n if self.hunk_size1 <= 0 and self.hunk_size2 <= 0:\n yield from self.empty_buffer()\n continue\n\n m = re.match(r\"^\\+\\[ (\\d+) lines removed \\]$\", l)\n if m:\n self.add_cpt += int(m.group(1))\n self.hunk_size2 -= int(m.group(1))\n self.buf.append((None, l[1:]))\n continue\n\n if re.match(r\"^\\+\", l):\n self.add_cpt += 1\n self.hunk_size2 -= 1\n self.buf.append((None, l[1:]))\n continue\n\n m = re.match(r\"^-\\[ (\\d+) lines removed \\]$\", l)\n if m:\n self.del_cpt += int(m.group(1))\n self.hunk_size1 -= int(m.group(1))\n self.buf.append((l[1:], None))\n continue\n\n if re.match(r\"^-\", l):\n self.del_cpt += 1\n self.hunk_size1 -= 1\n self.buf.append((l[1:], None))\n continue\n\n if re.match(r\"^ \", l) and self.hunk_size1 and self.hunk_size2:\n yield from self.empty_buffer()\n self.hunk_size1 -= 1\n self.hunk_size2 -= 1\n self.buf.append((l[1:], l[1:]))\n continue\n\n yield from self.empty_buffer()\n\n yield from self.empty_buffer()","title":""},{"docid":"bac22f7e817cba31d52d11a36d797ff0","score":"0.5186413","text":"def parser(self, inp):\n file = open(inp)\n ecotypes = []\n for line in file:\n if \"Ecotype\" in line and \"[\" in line and \"]\" in line:\n start = False\n ecotype = []\n sequence = \"\"\n for char in line:\n if char == '[':\n start = True\n if start == True:\n if char != '[' and char != ',' and char != ' ' and char != ']':\n sequence += char\n elif char == ' ':\n ecotype.append(sequence)\n sequence = \"\"\n elif char == ']':\n ecotype.append(sequence)\n ecotypes.append(ecotype)\n sequence = \"\"\n break\n return ecotypes","title":""},{"docid":"7f7ae34386eb4185f4611582cdad64b4","score":"0.51828325","text":"def get_items(self, block):\r\n items = []\r\n for line in block.split('\\n'):\r\n m = self.CHILD_RE.match(line)\r\n if m:\r\n # This is a new item. Append\r\n items.append(m.group(3))\r\n elif self.INDENT_RE.match(line):\r\n # This is an indented (possibly nested) item.\r\n if items[-1].startswith(' '*markdown.TAB_LENGTH):\r\n # Previous item was indented. Append to that item.\r\n items[-1] = '%s\\n%s' % (items[-1], line)\r\n else:\r\n items.append(line)\r\n else:\r\n # This is another line of previous item. Append to that item.\r\n items[-1] = '%s\\n%s' % (items[-1], line)\r\n return items","title":""},{"docid":"5f6549a2bab8f7c1bff90efd65f43693","score":"0.51791257","text":"def parse(self, format_string):\n\n\t\treturn [\n\t\t\t(\n\t\t\t\tbefore,\n\t\t\t\tidentifiant,\n\t\t\t\tstr(\n\t\t\t\t\tlen(\n\t\t\t\t\t\tre.search('\\t*$', before).group(0)\n\t\t\t\t\t)\n\t\t\t\t) + '\\t' + (param if param is not None else ''),\n\t\t\t\tmodif,\n\t\t\t)\n\t\t\tfor before, identifiant, param, modif\n\t\t\tin super().parse(format_string)\n\t\t]","title":""},{"docid":"580a98f211a45ad7955c8983c132bded","score":"0.5173377","text":"def chunk(lines):\n chunks = []\n chunk = []\n for lineno, line in lines:\n stripped = line.strip()\n if not stripped or stripped[0] == '#':\n if chunk:\n chunks.append(chunk)\n chunk = []\n else:\n chunk.append((lineno, stripped))\n\n if chunk:\n chunks.append(chunk)\n\n return chunks","title":""},{"docid":"60f5a47b3205bb266f8db9785a65e9d2","score":"0.5162477","text":"def get_output_list(self, d):\n output_list = [] \n\n # base output = id | timestamp | body\n output_list.append( Field_id(d).value )\n output_list.append( Field_postedtime(d).value )\n output_list.append( Field_body(d).value )\n\n # urls \n if self.options_urls:\n #\n # TODO: add back this exception handling for -x option\n # https://github.com/DrSkippy/Gnacs/blob/16dd146fb05d02d7c1e3f282254e6718fd13303f/acscsv/twacscsv.py#L97 \n #\n # gnip \n val = Field_gnip_urls(d).value\n if isinstance(val, list): \n try:\n output_list.append( self.buildListString( [ x[\"expanded_url\"] for x in val ] ) ) \n except KeyError:\n output_list.append(Field_gnip_urls(d).default_value)\n else: \n output_list.append( val ) \n # twitter\n val = Field_twitter_entities_urls(d).value \n if isinstance(val, list):\n url_list = self.buildListString( [ x[\"url\"] for x in val ] ) \n exp_url_list = self.buildListString( [ x[\"expanded_url\"] for x in val ] ) \n else:\n url_list = val\n exp_url_list = val \n output_list.append( url_list ) \n output_list.append( exp_url_list ) \n \n # languages \n if self.options_lang:\n # actor\n # - this field has *very* infrequently contained unicode chars. drop them.\n output_list.append( Field_actor_language(d).value.encode('ascii', 'ignore') ) \n # classifications\n output_list.append( Field_gnip_language_value(d).value ) \n output_list.append( Field_twitter_lang(d).value ) \n \n # rules\n if self.options_rules:\n val = Field_gnip_rules(d).value \n if isinstance(val, list):\n # output: '[\" value (tag)\", ... ]'\n output_list.append( \n self.buildListString( \n [ \"{} ({})\".format( x[\"value\"], x[\"tag\"] ) for x in Field_gnip_rules(d).value ]\n )\n ) \n else: \n output_list.append( val ) \n\n # geo-related fields\n if self.options_geo:\n # geo-tag \n val = Field_geo_coordinates(d).value\n # keep self.geoCoordsList for backward compatibility\n self.geoCoordsList = None\n if isinstance(val, list):\n output_list.append( str(val) ) \n self.geoCoordsList = val \n else:\n output_list.append( val ) \n output_list.append( Field_geo_type(d).value )\n val = Field_location_geo_coordinates(d).value \n if isinstance(val, list): \n output_list.append( str(val) ) \n else:\n output_list.append( val ) \n output_list.append( Field_location_geo_type(d).value )\n output_list.append( Field_location_displayname(d).value ) \n output_list.append( Field_location_twitter_country_code(d).value ) \n # user \n output_list.append( Field_actor_utcoffset(d).value ) \n output_list.append( Field_actor_location_displayname(d).value ) \n # profileLocations\n output_list.append( Field_gnip_profilelocations_displayname(d).value ) \n output_list.append( Field_gnip_profilelocations_objecttype(d).value ) \n output_list.append( Field_gnip_profilelocations_address_country(d).value ) \n output_list.append( Field_gnip_profilelocations_address_region(d).value ) \n output_list.append( Field_gnip_profilelocations_address_countrycode(d).value ) \n output_list.append( Field_gnip_profilelocations_address_locality(d).value ) \n output_list.append( Field_gnip_profilelocations_geo_type(d).value ) \n output_list.append( Field_gnip_profilelocations_geo_coordinates(d).value ) \n\n # user\n if self.options_user:\n output_list.append( Field_actor_displayname(d).value ) \n output_list.append( Field_actor_preferredusername(d).value ) \n output_list.append( Field_actor_id(d).value ) \n \n # user connections, klout\n if self.options_influence:\n output_list.append( Field_gnip_klout_score(d).value ) \n output_list.append( Field_actor_followerscount(d).value ) \n output_list.append( Field_actor_friendscount(d).value ) \n output_list.append( Field_actor_listedcount(d).value ) \n output_list.append( Field_actor_statusesCount(d).value ) \n \n # structure\n if self.options_struct:\n output_list.append( Field_activity_type(d).value ) \n\n # done building output list \n return output_list","title":""},{"docid":"54c9aeb9113235841a192435e9b759ca","score":"0.5161918","text":"def _extract_results(self, response):\r\n res = re.compile(self.pattern, re.DOTALL).findall(response)\r\n results = list()\r\n for r in res:\r\n results.append(r)\r\n return results","title":""},{"docid":"87bdfeb9b018da62afb88a5ddc597e41","score":"0.5153912","text":"def test_extract_blocks(self):\n f = StringIO.StringIO(\"\"\"\n// No block\n.foobar {}\n\n// Block1\n//\n// foobar\n//\n// Styleguide\ndiv {}\n\n// Block2\n//\n// Styleguide\n \"\"\")\n\n blocks = list(_extract_blocks(f))\n self.assertEqual(len(blocks), 2)\n self.assertEqual(blocks[0], [\"Block1\", \"\", \"foobar\", \"\", \"Styleguide\"])\n self.assertEqual(blocks[1], [\"Block2\", \"\", \"Styleguide\"])","title":""},{"docid":"95121c78fff829854cb2fd773c48be30","score":"0.51532143","text":"def parseDataFile(self, data):\n\n block = 0\n clientsbool = False\n self.dataDicts = []\n self.dataList = []\n for line in data:\n try:\n if '!CLIENTS:' in line:\n clientsbool = True\n else:\n if line.find(';') == 0:\n if not line.find('; ') == 0:\n block = block + 1\n if not line.find(';\\n') == 0:\n block = block + 1\n if block > 1:\n clientsbool = False\n block = 0\n if clientsbool:\n splitline = line.split(u':', 40)\n self.dataList.append(splitline)\n self.dataDicts.append(profilemapper.profileMapper(splitline))\n except:\n pass","title":""},{"docid":"2d4e5ea8804ccd15d68bd90e6c32b951","score":"0.5145597","text":"def mmcif_lines_to_mmcif_blocks(lines):\n\n category = None\n block, blocks = [], []\n while lines:\n line = lines.pop(0)\n if line.startswith(\"data_\"): continue\n if line.startswith(\"_\"):\n line_category = line.split(\".\")[0]\n if line_category != category:\n if category:\n blocks.append({\"category\": category[1:], \"lines\": block})\n category = line_category\n block = []\n if line.startswith(\"loop_\"):\n if category:\n blocks.append({\"category\": category[1:], \"lines\": block})\n category = lines[0].split(\".\")[0]\n block = []\n block.append(line)\n if block: blocks.append({\"category\": category[1:], \"lines\": block})\n return blocks","title":""},{"docid":"86132f917a800ac0305419ccedc4e10b","score":"0.5145506","text":"def yield_records(handle):\n record = []\n for line in handle:\n if line.startswith('//'):\n if len(record) > 0:\n yield record\n record = []\n else:\n record.append(line.strip())\n if len(record) > 0:\n yield record","title":""},{"docid":"dfd046d167e1e06b4dfb9540eb38f77c","score":"0.5143903","text":"def _to_list(self, text):\n r=[]\n for x in text.split(\"$\"):\n x = x.strip()\n assert x\n r.append(x)\n return tuple(r)","title":""},{"docid":"4153c39511f757926cd1684a4963a3aa","score":"0.5143742","text":"def asList( self ):\r\n out = []\r\n for res in self.__toklist:\r\n if isinstance(res,ParseResults):\r\n out.append( res.asList() )\r\n else:\r\n out.append( res )\r\n return out","title":""},{"docid":"26cede90ab44af2c00ecd9216eb4535e","score":"0.51401764","text":"def parse(self) -> List[Version]:\n versions = []\n while self.next_line():\n assert self.current_line is not None\n if '{' in self.current_line:\n versions.append(self.parse_version())\n else:\n raise ParseError(\n f'Unexpected contents at top level: {self.current_line}')\n\n self.check_no_duplicate_symbols(versions)\n return versions","title":""},{"docid":"08b9e25eaff61e953fbeafdd38239d1c","score":"0.513816","text":"def groupByField (listOfTabbedStrings, fieldGroupedBy):\n\n # Convert listOfTabbedStrings to a list of lists with 'lowest' list\n # representing a list of original tab seperated fields\n listOfListsByTab = []\n while listOfTabbedStrings != []:\n listOfListsByTab.append(listOfTabbedStrings.pop(0).split('\\t'))\n \n exonList = []\n\n listOfExonLists = []\n \n while listOfListsByTab != 'end':\n\n # If there is a fresh and clean exonList:\n # add the first coding region of the first/next gene to exonList\n if exonList == []:\n exonList.append(listOfListsByTab.pop(0))\n\n # If the next BioMart record list matches the one(s) in exonList:\n # add it to exonList\n elif listOfListsByTab[0][fieldGroupedBy] == exonList[0][fieldGroupedBy]:\n exonList.append(listOfListsByTab.pop(0))\n\n # Check to see if you just popped the last record:\n # - export last exonList\n # - cull exonList\n # - set listOfListsByTab to 'end' to stop the loop\n if listOfListsByTab == []:\n listOfExonLists.append(exonList)\n #print len(listOfExonLists), '\\n'\n print exonList[0][0]\n exonList = []\n listOfListsByTab = 'end'\n \n # Otherwise append whole exonList to listOfExonLists and clean exonList for next record group\n else:\n listOfExonLists.append(exonList)\n #print len(listOfExonLists), '\\n'\n print exonList[0][0]\n exonList = []\n \n print 'The groupByField function produced ',len(listOfExonLists),' groups.\\n\\n'\n return listOfExonLists","title":""},{"docid":"83fb01b5c7b0ebf28e38ad7902534ab2","score":"0.5135255","text":"def _pre_process(src_file):\r\n slices = []\r\n handled_slices = []\r\n with open(src_file) as fs:\r\n for line in fs:\r\n slices.extend(re.split(r'({[%{#].+?[%}#]})', line))\r\n\r\n flg_is_comment = False\r\n handled_line = ''\r\n for slice in slices:\r\n if slice.startswith('{#'):\r\n flg_is_comment = True\r\n elif slice.startswith('#}'):\r\n flg_is_comment = False\r\n slice = slice[2:].strip()\r\n\r\n if flg_is_comment:\r\n continue\r\n elif slice.startswith('{%') or slice.startswith('{{'):\r\n if handled_line:\r\n handled_slices.append(handled_line)\r\n handled_line = ''\r\n handled_slices.append(slice)\r\n elif slice:\r\n handled_line += slice\r\n if handled_line:\r\n handled_slices.append(handled_line)\r\n return handled_slices","title":""},{"docid":"0eed58c93ccb327bfdc21cac039903ff","score":"0.5134003","text":"def parse_simple_namelist(filePath, commentchar='!', condense=False ):\n \n lines = []\n with open(filePath, 'r') as f:\n if condense:\n pad = ''\n else:\n pad = ' '\n \n for line in f:\n ulines = unroll_namelist_line(line, commentchar=commentchar, condense=condense)\n lines = lines + ulines\n\n \n return lines","title":""},{"docid":"60a4af7c95339a3859cf5353c33471d5","score":"0.5131401","text":"def parse(self):\n standard_formatters = re.compile(r'\\((.+?)\\)', re.IGNORECASE)\n return standard_formatters.findall(self._fmt)","title":""},{"docid":"04cbccf92ec6ad2e5cc085144aa4a7c8","score":"0.5128008","text":"def process_data(data: str) -> list[list[str]]:\n return [re.findall(r\"\\d+\", line) for line in data.strip().split(\"\\n\")]","title":""},{"docid":"704586cb6d035209facdc55183c05d32","score":"0.51262045","text":"def parse(raspa_output):\n # Reads the string into a newline-separated list, skipping useless lines\n data = [row.strip() for row in raspa_output.splitlines() if row and\n all(d not in row for d in [\"-----\", \"+++++\"])]\n\n # Generally, categories in the output are delimited by equal signs\n delimiters = [i for i, row in enumerate(data) if \"=====\" in row\n and \"Exclusion constraints energy\" not in data[i - 1]]\n\n # Append a row for \"absolute adsorption:\" and \"excess adsorption:\"\n # These values are separated into two rows\n abs_adsorp_rows = [i for i, row in enumerate(data)\n if \"absolute adsorption:\" in row]\n for row in abs_adsorp_rows:\n data[row] += \" \" + data[row + 1]\n data[row + 2] += data[row + 3]\n data[row + 1], data[row + 3] = \" \", \" \"\n\n # Use the delimiters to make a high-level dict. Title is row before\n # delimiter, and content is every row after delimiter, up to the next title\n info = {data[n - 1].strip(\":\"): data[n + 1: delimiters[i + 1] - 1]\n for i, n in enumerate(delimiters[:-1])}\n\n # Let's PARSE!\n for key, values in info.items():\n d, note_index = {}, 1\n for item in values:\n # Takes care of all \"Blocks[ #]\", skipping hard-to-parse parts\n if (\"Block\" in item and \"Box-lengths\" not in key\n and \"Van der Waals:\" not in item):\n blocks = _clean(item.split())\n d[\"\".join(blocks[:2])] = blocks[2:]\n\n # Most of the average data values are parsed in this section\n elif (any(s in item for s in [\"Average \", \"Surface area:\"])\n and \"desorption\" not in key):\n average_data = _clean(item.split())\n # Average values organized by its unit, many patterns here\n if len(average_data) == 8:\n del average_data[2:4]\n d[\" \".join(average_data[4:6])] = average_data[1:4]\n elif len(average_data) == 5:\n d[average_data[-1]] = average_data[1:4]\n elif \"Surface\" in average_data[0]:\n d[average_data[-1]] = average_data[2:5]\n # This is the common case\n else:\n del average_data[2]\n d[average_data[-1]] = average_data[1:4]\n\n # Average box-lengths has its own pattern\n elif \"Box-lengths\" in key:\n box_lengths = _clean(item.split())\n i = 3 if \"angle\" in item else 2\n d[\" \".join(box_lengths[:i])] = box_lengths[i:]\n\n # \"Heat of Desorption\" section\n elif \"desorption\" in key:\n if \"Note\" in item:\n notes = re.split(\"[:\\s]{2,}\", item)\n d[\"%s %d\" % (notes[0], note_index)] = notes[1]\n note_index += 1\n else:\n heat_desorp = _clean(item.split())\n # One line has \"Average\" in front, force it to be normal\n if \"Average\" in item:\n del heat_desorp[0]\n d[heat_desorp[-1]] = heat_desorp[0:3]\n\n # Parts where Van der Waals are included\n elif (\"Host-\" in key or \"-Cation\" in key or\n \"Adsorbate-Adsorbate\" in key) and \"desorption\" not in key:\n van_der = item.split()\n # First Column\n if \"Block\" in van_der[0]:\n sub_data = [_clean(s.split(\":\"))\n for s in re.split(\"\\s{2,}\", item)[1:]]\n sub_dict = {s[0]: s[1] for s in sub_data[:2]}\n d[\"\".join(van_der[:2])] = [float(van_der[2]), sub_dict]\n # Average for each columns\n elif \"Average\" in item:\n avg = _clean(re.split(\"\\s{2,}\", item))\n vdw, coulomb = [_clean(s.split(\": \")) for s in avg[2:4]]\n d[avg[0]] = avg[1]\n d[\"Average %s\" % vdw[0]] = vdw[1]\n d[\"Average %s\" % coulomb[0]] = coulomb[1]\n else:\n d[\"standard deviation\"] = _clean(van_der)\n\n # IMPORTANT STUFF\n elif \"Number of molecules\" in key:\n adsorb_data = _clean(item.rsplit(\" \", 12))\n if \"Component\" in item:\n gas_name = adsorb_data[2].strip(\"[]\")\n d[gas_name] = {}\n else:\n d[gas_name][adsorb_data[0]] = adsorb_data[1:]\n\n # Henry and Widom\n elif \"Average Widom\" in item:\n d[\"Widom\"] = _clean(item.rsplit(\" \", 5))[1:]\n\n elif \"Average Henry\" in item:\n d[\"Henry\"] = _clean(item.rsplit(\" \", 5))[1:]\n\n # Ignore these\n elif any(s in item for s in [\"=====\", \"Starting simulation\",\n \"Finishing simulation\"]):\n continue\n\n # Other strings\n else:\n parsed_data = _clean(re.split(\"[()[\\]:,\\t]\", item))\n d[parsed_data[0]] = parsed_data[1:]\n # Putting subdictionary back into main object\n info[key] = d\n\n return info","title":""},{"docid":"560b8874f8949f62cc58722347c5344d","score":"0.5124573","text":"def get_unformatted_output(raw_data):\n out = []\n\n for entry in raw_data:\n if 'Federalist' in entry['pp']:\n continue\n\n out.append({\n 'sort_by': entry['nm'],\n 'name': format_name(entry['nm']),\n 'party': create_acronym(entry['pp']),\n 'term_start_date': '01-20-%s' % entry['tm'][:4]\n })\n\n return out","title":""},{"docid":"ed614c11901cf38d239c0b504e29b48c","score":"0.5121098","text":"def parse(data, raw=False, quiet=False):\n jc.utils.compatibility(__name__, info.compatible, quiet)\n jc.utils.input_type_check(data)\n\n raw_output = []\n\n # Clear any blank lines\n cleandata = list(filter(None, data.splitlines()))\n\n if not jc.utils.has_data(data):\n return raw_output\n\n # linux output\n if cleandata[0].startswith(' File: '):\n output_line = {}\n\n # stats output contains 8 lines\n for line in cleandata:\n # line #1\n if line.find('File:') == 2:\n if output_line: # Reached a new file stat info.\n raw_output.append(output_line)\n output_line = {}\n\n line_list = line.split(maxsplit=1)\n output_line['file'] = line_list[1]\n\n # populate link_to field if -> found\n if ' -> ' in output_line['file']:\n filename = output_line['file'].split(' -> ')[0].strip('\\u2018').rstrip('\\u2019')\n link = output_line['file'].split(' -> ')[1].strip('\\u2018').rstrip('\\u2019')\n output_line['file'] = filename\n output_line['link_to'] = link\n else:\n filename = output_line['file'].split(' -> ')[0].strip('\\u2018').rstrip('\\u2019')\n output_line['file'] = filename\n\n continue\n\n # line #2\n if line.startswith(' Size:'):\n line_list = line.split(maxsplit=7)\n output_line['size'] = line_list[1]\n output_line['blocks'] = line_list[3]\n output_line['io_blocks'] = line_list[6]\n output_line['type'] = line_list[7]\n continue\n\n # line #3\n if line.startswith('Device:'):\n line_list = line.split()\n output_line['device'] = line_list[1]\n output_line['inode'] = line_list[3]\n output_line['links'] = line_list[5]\n continue\n\n # line #4\n if line.startswith('Access: ('):\n line = line.replace('(', ' ').replace(')', ' ').replace('/', ' ')\n line_list = line.split()\n output_line['access'] = line_list[1]\n output_line['flags'] = line_list[2]\n output_line['uid'] = line_list[4]\n output_line['user'] = line_list[5]\n output_line['gid'] = line_list[7]\n output_line['group'] = line_list[8]\n continue\n\n # line #5\n if line.startswith('Access: 2'):\n line_list = line.split(maxsplit=1)\n output_line['access_time'] = line_list[1]\n continue\n\n # line #6\n if line.startswith('Modify:'):\n line_list = line.split(maxsplit=1)\n output_line['modify_time'] = line_list[1]\n continue\n\n # line #7\n if line.startswith('Change:'):\n line_list = line.split(maxsplit=1)\n output_line['change_time'] = line_list[1]\n continue\n\n # line #8\n if line.startswith(' Birth:'):\n line_list = line.split(maxsplit=1)\n output_line['birth_time'] = line_list[1]\n continue\n\n if output_line:\n raw_output.append(output_line)\n\n # FreeBSD/OSX output\n else:\n for line in cleandata:\n value = shlex.split(line)\n output_line = {\n 'file': ' '.join(value[15:]),\n 'unix_device': value[0],\n 'inode': value[1],\n 'flags': value[2],\n 'links': value[3],\n 'user': value[4],\n 'group': value[5],\n 'rdev': value[6],\n 'size': value[7],\n 'access_time': value[8],\n 'modify_time': value[9],\n 'change_time': value[10],\n 'birth_time': value[11],\n 'block_size': value[12],\n 'blocks': value[13],\n 'unix_flags': value[14]\n }\n\n raw_output.append(output_line)\n\n return raw_output if raw else _process(raw_output)","title":""},{"docid":"307139b65c5c6a3a0cab40a14eac6c9e","score":"0.5116401","text":"def unpack(self, string):\n parsed_list = []\n\n for post in string.split(self.delimiter):\n parsed = self.parse(post)\n if parsed:\n parsed_list.append(parsed)\n return parsed_list","title":""},{"docid":"3a01c0f6e9f0ad94551b4acf956ce922","score":"0.5112231","text":"def _docstring_getblocks(self):\n #If there are no lines to look at, we have nothing to do here.\n if self.ibuffer[0] == self.ibuffer[1]:\n return []\n\n lines = self.context.bufferstr[self.ibuffer[0]:self.ibuffer[1]]\n docblock = []\n result = []\n self._doclines = []\n\n #We need to keep track of the line number for the start of the\n #documentation strings.\n docline = 0\n doclength = 0\n\n first = self.docparser.RE_DOCS.match(lines[0])\n if first is not None:\n docblock.append(first.group(\"docstring\"))\n docline = self.ibuffer[0]\n self._doclines.append(docline)\n doclength += len(lines[0]) + 1 # + 1 for \\n removed by split.\n\n #We need to search backwards in the main buffer string for\n #additional tags to add to the block\n i = self.ibuffer[0] - 1\n while i > 0:\n current = self.context.bufferstr[i]\n docmatch = self.docparser.RE_DOCS.match(current)\n if docmatch is not None:\n docblock.append(docmatch.group(\"docstring\"))\n docline = i\n doclength += len(current) + 1\n else:\n break\n i -= 1\n\n #Reverse the docblock list since we were going backwards and appending.\n if len(docblock) > 0:\n docblock.reverse()\n\n #Now handle the lines following the first line. Also handle the\n #possibility of multiple, separate blocks that are still valid XML.\n #We have to keep going until we have exceed the operational changes\n #or found the decorating element.\n i = self.ibuffer[0] + 1\n while (i < len(self.context.bufferstr) and \n (i < self.ibuffer[1] or len(docblock) > 0)):\n line = self.context.bufferstr[i]\n docmatch = self.docparser.RE_DOCS.match(line)\n if docmatch is not None:\n docblock.append(docmatch.group(\"docstring\"))\n doclength += len(line)\n if docline == 0:\n docline = i\n #Only track actual documentation lines that are within the \n #operations list of lines.\n if i < self.ibuffer[1]:\n self._doclines.append(i)\n\n elif len(docblock) > 0:\n key = self._docstring_key(line)\n result.append((docblock, docline, doclength, key))\n docblock = []\n docline = 0\n doclength = 0\n\n #We need to exit the loop if we have exceeded the length of\n #the operational changes\n if len(docblock) == 0 and i > self.ibuffer[1]:\n break\n i += 1\n\n return result","title":""},{"docid":"77360134491b6ef46a448534416bbd10","score":"0.511177","text":"def parse_body(source):\n\n paragraphs = []\n for blank, input_lines in itertools.groupby(source, _is_blank):\n if not blank:\n as_string = note_re.sub('', '\\n'.join(input_lines))\n if _is_blank(as_string):\n continue\n paragraph = InputParagraph(as_string.split('\\n'))\n paragraph.update_list(paragraphs)\n\n return paragraphs","title":""},{"docid":"c2a9ff947a847a1e19f01fb638e302e7","score":"0.5111172","text":"def _parse_minute_into_tasks(content, group):\n missing_colon_lines = []\n unknown_task_ids = []\n unknown_user = []\n\n task_list = []\n done_list = []\n remove_list = []\n\n for i, line in enumerate(content.splitlines()):\n try:\n if MISSING_COLON_REGEX.search(line):\n missing_colon_lines.append((i, line))\n continue\n\n # Single task for multiple users.\n for names, task in TASK_REGEX.findall(line):\n users = get_list_of_users_from_string(group.id, names)\n task_list.append((i, task, users))\n\n # Single task for individual users.\n for names, task in TASKS_REGEX.findall(line):\n users = get_list_of_users_from_string(group.id, names)\n for user in users:\n task_list.append((i, task, [user]))\n\n # Mark a comma separated list as done.\n for task_id_list in DONE_REGEX.findall(line):\n for b32_task_id in task_id_list.strip().split(\",\"):\n b32_task_id = b32_task_id.strip()\n try:\n task_id = b32.decode(b32_task_id)\n except ValueError:\n unknown_task_ids.append((i, b32_task_id))\n continue\n\n task = pimpy_repository. \\\n find_task_in_group_by_id(task_id, group.id)\n if not task:\n unknown_task_ids.append((i, b32_task_id))\n else:\n done_list.append((i, task))\n\n # Mark a comma separated list as removed.\n for task_id_list in REMOVE_REGEX.findall(line):\n for b32_task_id in task_id_list.strip().split(\",\"):\n b32_task_id = b32_task_id.strip()\n try:\n task_id = b32.decode(b32_task_id)\n except ValueError:\n unknown_task_ids.append((i, b32_task_id))\n continue\n\n task = pimpy_repository. \\\n find_task_in_group_by_id(task_id, group.id)\n if not task:\n unknown_task_ids.append((i, b32_task_id))\n else:\n remove_list.append((i, task))\n\n # Catch invalid user.\n except ValidationException:\n unknown_user.append((i, line))\n\n if len(missing_colon_lines) or len(unknown_task_ids) or len(unknown_user):\n raise InvalidMinuteException(missing_colon_lines, unknown_task_ids,\n unknown_user)\n\n return task_list, done_list, remove_list","title":""},{"docid":"a7c5ea87e48462dd7e7fa67b2f7ae793","score":"0.51104","text":"def parse_data(block):\n lines = block.strip().splitlines()\n indices = []\n symbols = []\n charges = []\n for line in lines[1:]:\n words = line.split()\n indices.append(int(words[0]))\n symbols.append(words[1])\n charges.append(float(words[2]))\n return (np.array(indices), np.array(symbols), np.array(charges))","title":""},{"docid":"40aa9b7d37e59916f5d19911d60a7aa7","score":"0.50997615","text":"def tokenizer(self):\n all_tokens = []\n index = 0 #TODO remove\n # for section, index in zip(self.zettel, range(len(self.zettel))): #TODO\n # if index == 3:\n # self.given_tags = re.split(\";\", section) #TODO make sure to use ';' \n for section in self.zettel:\n if index == len(self.zettel) - 1:\n self.given_tags = re.split(\";\", section)\n else:\n tokens = re.split('\\W+', section)\n tokens = list(filter(None, tokens))\n all_tokens.append(tokens)\n index += 1 #TODO remove\n return all_tokens","title":""},{"docid":"d48816a8237379718698109d4dede931","score":"0.50961757","text":"def parse_lines(self):\n\n\t\tself.lines_raw = Text.DATE_PATTERN.findall(self.data)\n\n\t\tself.lines = []\n\t\tfor i in self.lines_raw:\n\t\t\tdate, rest = i.split(\" - \", 1)\n\t\t\tif ':' in rest:\n\t\t\t\tuser, message = rest.split(\": \", 1)\n\t\t\t\t# message_type = 1 if message == \"<Media omitted>\" else 0\n\t\t\t\tmessage_type = int(message == \"<Media omitted>\")\n\t\t\telse:\n\t\t\t\tuser = \"system\"\n\t\t\t\tmessage = rest\n\t\t\t\tmessage_type = 2\n\n\t\t\ttemp_date = re.findall(\"^(\\d{1,2}/\\d{1,2}/\\d\\d), \", date)[0]\n\t\t\ttemp_hour = date[date.find(',')+2:]\n\t\t\tself.lines.append((\n\t\t\t\t\t\t\tutils.date.parse_date(temp_date), # date\n\t\t\t\t\t\t\tint(temp_hour[:2]) * 60 + int(temp_hour[-2:]), # minutes\n\t\t\t\t\t\t\tuser,\n\t\t\t\t\t\t\tmessage,\n\t\t\t\t\t\t\tmessage_type\n\t\t\t\t\t\t))\n\t\treturn self.lines","title":""},{"docid":"2ef8fdfdf6cfb834d2ff9b32240d3be5","score":"0.5094201","text":"def extract_data(data_list):\n data = []\n for block in data_list:\n state = get_state(block[0])\n date = get_date(block[0])\n b_type = get_type(block[0])\n category = get_category(block[0])\n records = get_records(block[0])\n\n extracted_text = (' ').join([state, date, b_type, category, records])\n entity = get_entity(block, extracted_text)\n\n source = get_source(block[-2])\n url = get_url(block[-1])\n\n data.append([entity, state, date, b_type,\n category, records, source, url])\n return data","title":""},{"docid":"6beb61879e656a61a2ff17aa82c8670d","score":"0.5091759","text":"def extract_tokens(representation, separators=SEPARATOR_CHARACTERS):\n buff = \"\"\n elements = []\n last_token = None\n\n for index, c in enumerate(representation):\n # if separator character is found, push\n # the content of the buffer in the elements list\n if c in separators:\n if buff:\n # If the last found token is invalid,\n # raise and InvalidTokenError\n if not valid_token(buff):\n raise InvalidTokenError(\n \"Duration representation {0} contains \"\n \"an invalid token: {1}\".format(representation, buff)\n )\n\n # If buffer content is a separator word, for example\n # \"and\", just ignore it\n if not buff.strip() in SEPARATOR_TOKENS:\n elements.append(buff)\n\n # Anyway, reset buffer and last token marker\n # to their zero value\n buff = \"\"\n last_token = None\n else:\n token = compute_char_token(c)\n if token is not None \\\n and last_token is not None \\\n and token != last_token:\n elements.append(buff)\n buff = c\n else:\n buff += c\n\n last_token = token\n\n # push the content left in representation\n # in the elements list\n elements.append(buff)\n\n return list(zip(elements[::2], elements[1::2]))","title":""},{"docid":"8ab2eb313b528d432236ade01923b6d3","score":"0.507784","text":"def parse(source):","title":""},{"docid":"b6b49eec132ec12019d9d17be4960870","score":"0.50764745","text":"def _prettytable_to_list(self, str):\n retval = []\n if str is not None and len(str) > 0:\n rows = re.findall(r'\\|(.*?)\\n', str)\n # Remove header\n header_row = rows.pop(0)\n key_names = re.findall(r'\\s*(.*?)\\s*\\|', header_row)\n for row in rows:\n values = re.findall(r'\\s*(.*?)\\s*\\|', row)\n entry_dict = dict(zip(key_names, values))\n retval.append(entry_dict)\n return retval","title":""},{"docid":"27865cb21475e9d05ac6cf9e275e820f","score":"0.50761974","text":"def parse_clamscan(scanned_file: str, raw_output: str) -> list:\n\n summary_regex = re.compile(\"-+ SUMMARY -+\")\n results = []\n\n undefined_path_signatures = set()\n defined_path_signatures = set()\n for line in raw_output.splitlines():\n if summary_regex.match(line):\n # Output complete\n break\n\n result = parse_clamscan_output_line(scanned_file, line)\n if result:\n path = result[0]\n signature = result[1]\n # Ensure that we have a unique set of signatures when the path is undefined (see parse_clamscan_output_line)\n if path == UNKNOWN and signature not in undefined_path_signatures:\n undefined_path_signatures.add(signature)\n elif path != UNKNOWN:\n results.append({\"path\": path, \"signature\": signature})\n defined_path_signatures.add(signature)\n\n # For each signature that doesn't have a defined path, append it.\n for signature in undefined_path_signatures.difference(defined_path_signatures):\n results.append({\"path\": UNKNOWN, \"signature\": signature})\n return results","title":""},{"docid":"01f7b125dcac1634c401862b4caea196","score":"0.5072473","text":"def getBlocks(self, tokzer=None):\n\n def consume_extent(i, tokens, extent=None, detect_change=False):\n \"\"\"Return tokens that belong to the given extent.\n\n It parses all the tokens that follow tokens[i], until getting out\n of the extent. When detect_change is True, it may terminate early\n when detecting preprocessing directives inside the extent.\n \"\"\"\n\n result = []\n if extent is None:\n extent = tokens[i].cursor.extent\n\n while i < len(tokens) and tokens[i].location in extent:\n t = tokens[i]\n if debugBlockParser:\n print(' ' * 2, t.id, t.kind, t.cursor.kind)\n if (detect_change and t.cursor.extent != extent and\n t.cursor.kind == CursorKind.PREPROCESSING_DIRECTIVE):\n break\n result.append(t)\n i += 1\n return (i, result)\n\n def consume_line(i, tokens):\n \"\"\"Return tokens that follow tokens[i] in the same line.\"\"\"\n result = []\n line = tokens[i].location.line\n while i < len(tokens) and tokens[i].location.line == line:\n if tokens[i].cursor.kind == CursorKind.PREPROCESSING_DIRECTIVE:\n break\n result.append(tokens[i])\n i += 1\n return (i, result)\n\n if tokzer is None:\n tokzer = self._tokzer\n tokens = tokzer.tokens\n\n blocks = []\n buf = []\n i = 0\n\n while i < len(tokens):\n t = tokens[i]\n cursor = t.cursor\n\n if debugBlockParser:\n print (\"%d: Processing [%s], kind=[%s], cursor=[%s], \"\n \"extent=[%s]\" % (t.location.line, t.spelling, t.kind,\n cursor.kind,\n self._short_extent(cursor.extent)))\n\n if cursor.kind == CursorKind.PREPROCESSING_DIRECTIVE:\n if buf:\n blocks.append(Block(buf))\n buf = []\n\n j = i\n if j + 1 >= len(tokens):\n raise BadExpectedToken(\"### BAD TOKEN at %s\" % (t.location))\n directive = tokens[j+1].id\n\n if directive == 'define':\n if i+2 >= len(tokens):\n raise BadExpectedToken(\"### BAD TOKEN at %s\" %\n (tokens[i].location))\n\n # Skip '#' and 'define'.\n extent = tokens[i].cursor.extent\n i += 2\n id = ''\n # We need to separate the id from the remaining of\n # the line, especially for the function-like macro.\n if (i + 1 < len(tokens) and tokens[i+1].id == '(' and\n (tokens[i].location.column + len(tokens[i].spelling) ==\n tokens[i+1].location.column)):\n while i < len(tokens):\n id += tokens[i].id\n if tokens[i].spelling == ')':\n i += 1\n break\n i += 1\n else:\n id += tokens[i].id\n # Advance to the next token that follows the macro id\n i += 1\n\n (i, ret) = consume_extent(i, tokens, extent=extent)\n blocks.append(Block(ret, directive=directive,\n lineno=t.location.line, identifier=id))\n\n else:\n (i, ret) = consume_extent(i, tokens)\n blocks.append(Block(ret[2:], directive=directive,\n lineno=t.location.line))\n\n elif cursor.kind == CursorKind.INCLUSION_DIRECTIVE:\n if buf:\n blocks.append(Block(buf))\n buf = []\n directive = tokens[i+1].id\n (i, ret) = consume_extent(i, tokens)\n\n blocks.append(Block(ret[2:], directive=directive,\n lineno=t.location.line))\n\n elif cursor.kind == CursorKind.VAR_DECL:\n if buf:\n blocks.append(Block(buf))\n buf = []\n\n (i, ret) = consume_extent(i, tokens, detect_change=True)\n buf += ret\n\n elif cursor.kind == CursorKind.FUNCTION_DECL:\n if buf:\n blocks.append(Block(buf))\n buf = []\n\n (i, ret) = consume_extent(i, tokens, detect_change=True)\n buf += ret\n\n else:\n (i, ret) = consume_line(i, tokens)\n buf += ret\n\n if buf:\n blocks.append(Block(buf))\n\n # _parsed=True indicates a successful parsing, although may result an\n # empty BlockList.\n self._parsed = True\n\n return BlockList(blocks)","title":""},{"docid":"86b5e15dfc92e4c23fcabeffab0aaaa7","score":"0.50715864","text":"def getContentsAsList(self,showHeader=0,includeLineContents=0):\n\t\titems = []\n\t\tif showHeader == 1: items.append('%s\\n' % (self.sStartOfBlock))\n\t\tif includeLineContents==0:\n\t\t\tallkeys = self.aKeywords.keys()\n\t\t\tallkeys.sort()\n\t\t\tfor k in allkeys:\n\t\t\t\tv = self.aKeywords[k]\n\t\t\t\titems.append(\"%s %s\\n\" % (k,v))\n\t\telse:\n\t\t\tfor ilo in self.aLineContents: # Okay, the guys wants it all\n\t\t\t\tif ilo.isComment == 1: continue\n\t\t\t\titems.append(ilo.getRawLine())\n\t\tfor ilo in self.aLineContents: # fd\n\t\t\tif ilo.isComment == 1: items.append(ilo.getRawLine())\n\t\tif showHeader == 1: items.append(\"%s\\n\" % self.sEndOfBlock)\n\t\treturn items","title":""},{"docid":"ddc19bc83520245a90ca1e9fbfcdbbc9","score":"0.5071156","text":"def parse_packets(bytestream):\r\n\r\n container = packet_stream.parse(bytestream)\r\n\r\n l = [(i.header, i.payload) for i in container.full_packet]\r\n leftovers = \"\".join(chr(i) for i in container.leftovers)\r\n\r\n if DUMP_ALL_PACKETS:\r\n for header, payload in l:\r\n print \"Parsed packet 0x%.2x\" % header\r\n print payload\r\n\r\n return l, leftovers","title":""},{"docid":"3009dad22f514ae7ddb2da351b83318b","score":"0.50671506","text":"def flatten_content_list(content_list: List[bs4.element.NavigableString]) -> str:\n out = ''\n for c in content_list:\n stack = [c]\n while len(stack) > 0:\n tag = stack.pop()\n if tag.string is not None:\n out += tag.string.replace('\\n', ' ').replace(' ', '')\n else:\n stack.extend(tag.contents)\n return out","title":""},{"docid":"49bbf3cbf92bb4636b8ba5721968358d","score":"0.50620216","text":"def getBlocks(self, tokzer=None):\n\n def consume_extent(i, tokens, extent=None, detect_change=False):\n \"\"\"Return tokens that belong to the given extent.\n\n It parses all the tokens that follow tokens[i], until getting out\n of the extent. When detect_change is True, it may terminate early\n when detecting preprocessing directives inside the extent.\n \"\"\"\n\n result = []\n if extent is None:\n extent = tokens[i].cursor.extent\n\n while i < len(tokens) and tokens[i].location in extent:\n t = tokens[i]\n if debugBlockParser:\n print ' ' * 2, t.id, t.kind, t.cursor.kind\n if (detect_change and t.cursor.extent != extent and\n t.cursor.kind == CursorKind.PREPROCESSING_DIRECTIVE):\n break\n result.append(t)\n i += 1\n return (i, result)\n\n def consume_line(i, tokens):\n \"\"\"Return tokens that follow tokens[i] in the same line.\"\"\"\n result = []\n line = tokens[i].location.line\n while i < len(tokens) and tokens[i].location.line == line:\n if tokens[i].cursor.kind == CursorKind.PREPROCESSING_DIRECTIVE:\n break\n result.append(tokens[i])\n i += 1\n return (i, result)\n\n if tokzer is None:\n tokzer = self._tokzer\n tokens = tokzer.tokens\n\n blocks = []\n buf = []\n i = 0\n\n while i < len(tokens):\n t = tokens[i]\n cursor = t.cursor\n\n if debugBlockParser:\n print (\"%d: Processing [%s], kind=[%s], cursor=[%s], \"\n \"extent=[%s]\" % (t.location.line, t.spelling, t.kind,\n cursor.kind,\n self._short_extent(cursor.extent)))\n\n if cursor.kind == CursorKind.PREPROCESSING_DIRECTIVE:\n if buf:\n blocks.append(Block(buf))\n buf = []\n\n j = i\n if j + 1 >= len(tokens):\n raise BadExpectedToken(\"### BAD TOKEN at %s\" % (t.location))\n directive = tokens[j+1].id\n\n if directive == 'define':\n if i+2 >= len(tokens):\n raise BadExpectedToken(\"### BAD TOKEN at %s\" %\n (tokens[i].location))\n\n # Skip '#' and 'define'.\n extent = tokens[i].cursor.extent\n i += 2\n id = ''\n # We need to separate the id from the remaining of\n # the line, especially for the function-like macro.\n if (i + 1 < len(tokens) and tokens[i+1].id == '(' and\n (tokens[i].location.column + len(tokens[i].spelling) ==\n tokens[i+1].location.column)):\n while i < len(tokens):\n id += tokens[i].id\n if tokens[i].spelling == ')':\n i += 1\n break\n i += 1\n else:\n id += tokens[i].id\n # Advance to the next token that follows the macro id\n i += 1\n\n (i, ret) = consume_extent(i, tokens, extent=extent)\n blocks.append(Block(ret, directive=directive,\n lineno=t.location.line, identifier=id))\n\n else:\n (i, ret) = consume_extent(i, tokens)\n blocks.append(Block(ret[2:], directive=directive,\n lineno=t.location.line))\n\n elif cursor.kind == CursorKind.INCLUSION_DIRECTIVE:\n if buf:\n blocks.append(Block(buf))\n buf = []\n directive = tokens[i+1].id\n (i, ret) = consume_extent(i, tokens)\n\n blocks.append(Block(ret[2:], directive=directive,\n lineno=t.location.line))\n\n elif cursor.kind == CursorKind.VAR_DECL:\n if buf:\n blocks.append(Block(buf))\n buf = []\n\n (i, ret) = consume_extent(i, tokens, detect_change=True)\n buf += ret\n\n elif cursor.kind == CursorKind.FUNCTION_DECL:\n if buf:\n blocks.append(Block(buf))\n buf = []\n\n (i, ret) = consume_extent(i, tokens, detect_change=True)\n buf += ret\n\n else:\n (i, ret) = consume_line(i, tokens)\n buf += ret\n\n if buf:\n blocks.append(Block(buf))\n\n # _parsed=True indicates a successful parsing, although may result an\n # empty BlockList.\n self._parsed = True\n\n return BlockList(blocks)","title":""},{"docid":"39c11d0136c2832cb21f968dfc218f4e","score":"0.5059111","text":"def _read_data(cls, input_file):\n with open(input_file, 'r', encoding=\"utf-8\") as f:\n lines = []\n for line in f:\n content = line.split('|')\n sent = content[0].strip()\n label = content[1].replace('\\n', '')\n lines.append([sent, label])\n return lines","title":""},{"docid":"d9f401c12679be2385faa6457db05ee2","score":"0.5056553","text":"def rowParse(self):\n self.rowData = self.RAWdata.split(\"\\n\")\n\n # If final splitted character is EOF delete that void element\n if self.rowData[-1] == \"\":\n del self.rowData[-1]\n\n self.rowData = self.parseComment(self.rowData)","title":""},{"docid":"8419761b95f6e153b2a3d174299d4401","score":"0.50522107","text":"def parse_counters_packets(sketch):\n\tparsed_sketch = []\n\tfor i in range(len(sketch)):\n\t\tparsed_sketch.append([])\n\t\tfor j in range(len(sketch[i])):\n\t\t\tparsed_sketch[i].append(str(sketch[i][j]).split(\"=\")[1].split(\",\")[0])\n\treturn parsed_sketch","title":""},{"docid":"06c7f6939fec7dce7680fc0ef18285d0","score":"0.5051958","text":"def parse_multiple(source):\n return [parse(exp) for exp in split_exps(source)]","title":""},{"docid":"19e8b18d129504fc5643062564df8058","score":"0.5050841","text":"def flatten_parse(parse):\n no_brackets = re.sub(r'[\\[\\], ]+', ' ', str(parse))\n return no_brackets.strip().split(' ')","title":""},{"docid":"41b5addf4a9decdcc17ec16dc0aad5bd","score":"0.5048182","text":"def parse(self, buff, delimiter=DELIMITER, separator=SEPARATOR):\n def pushback_generator(iterator):\n \"\"\"\n Generator which allows to push back a previously picked item\n for example:\n gen = pushback_generator(range(10))\n print next(gen)\n print next(gen)\n v = next(gen)\n print v\n gen.send(v)\n print next(gen)\n :param iterator:\n :return:\n \"\"\"\n for value in iterator:\n back = yield value\n if back is not None:\n yield back\n yield back\n\n custom_r = re.compile(FIX_REGEX_STRING.format(d=re.escape(delimiter),\n s=re.escape(separator)).encode('UTF-8'),\n re.DOTALL)\n tagvals = custom_r.findall(buff)\n\n msg_type = None\n if not self._no_groups and self.spec is not None:\n for i in range(4):\n if tagvals[i][0] == b'35':\n msg_type = self.spec.msg_types.get(tagvals[i][1])\n\n if self.encoding is not None:\n tagvals = ((int_or_str(tval[0], self.encoding), tval[1].decode(self.encoding)) for tval in tagvals)\n elif self.decode_all_as_347:\n tagvals = [(int_or_str(tval[0]), tval[1]) for tval in tagvals]\n encoding = None\n for tag, val in tagvals:\n if tag == 347:\n encoding = val.decode('UTF-8')\n break\n if encoding is not None:\n tagvals = ((t[0], t[1].decode(encoding)) for t in tagvals)\n else:\n tagvals = ((int_or_str(tval[0]), tval[1]) for tval in tagvals)\n # Need to add logic to parse Encoded* tags according to 347\n\n if self._no_groups or self.spec is None or msg_type is None:\n # no groups can be found without a spec, so no point looking up the msg type.\n return self._frg_class(tagvals)\n msg = self._frg_class()\n groups = msg_type.groups\n tagvals = pushback_generator(tagvals)\n for tag, value in tagvals:\n if tag not in groups:\n msg[tag] = value\n else:\n if value == '0':\n msg[tag] = RepeatingGroup.create_repeating_group(tag)\n else:\n contents, last_tagval = self._process_group(tag, tagvals,\n msg_type=msg_type,\n group=groups[tag])\n msg[tag] = contents\n if last_tagval:\n tagvals.send(last_tagval)\n return msg","title":""},{"docid":"cf63c656b8e0cac13faebc2a01d9e92b","score":"0.5044811","text":"def split_bars(self, body):\r\n body = \"\".join(body)\r\n bars = re.split(self.regexPattern, body)\r\n while(\"\" in bars):\r\n bars.remove(\"\")\r\n if bars[0] in self.delimiters:\r\n bars[1] = bars[0]+bars[1]\r\n bars = bars[1:]\r\n bars = [bars[i*2]+bars[i*2+1] for i in range(int(len(bars)/2))]\r\n\r\n return bars","title":""},{"docid":"252bd4859572f726ed18e0c8027780e3","score":"0.50384027","text":"def stream_processing(file_path):\n\n\twith open(file_path, 'r') as f:\n\n\t\t# prepares the date from the file\n\t\tfor line in f:\n\t\t\told_stream = line.strip()\n\t\t\t# a string with the cancelled chars removed \n\t\t\tcleaner_stream = ''\n\t\t\t# \n\t\t\tcleanest_stream = ''\n\t\t\t# cancelled chars flag\n\t\t\tskip_next = False\n\t\t\t# garbage flag\n\t\t\tskip_while = False\n\t\t\t# nesting info for counting the groups\n\t\t\tnesting = 0\n\t\t\t# groups\n\t\t\tgroups = 0\n\n\t\t\t# Part 2\n\t\t\t# garbage chars counter\n\t\t\tgarbage_count = 0\n\n\t\t\t# removes the '!' and the next char from the stream\n\t\t\tfor char in range(0, len(old_stream)):\n\t\t\t\tif skip_next:\n\t\t\t\t\tskip_next = False\n\t\t\t\t\tcontinue\n\n\t\t\t\tif old_stream[char] == '!':\n\t\t\t\t\tskip_next = True\n\t\t\t\telse:\n\t\t\t\t\tcleaner_stream += old_stream[char]\n\n\t\t\t# removes the garbage\n\t\t\tfor char in range(0, len(cleaner_stream)):\n\t\t\t\t# while skip_while is True if the char is not '>' the iteration continues\n\t\t\t\tif skip_while:\n\t\t\t\t\tif cleaner_stream[char] == '>':\n\t\t\t\t\t\tskip_while = False\n\t\t\t\t\telse:\n\t\t\t\t\t\tgarbage_count += 1\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t# if the char is '<', skip while is set to True\n\t\t\t\tif cleaner_stream[char] == '<':\n\t\t\t\t\tskip_while = True\n\t\t\t\telif cleaner_stream[char] == '>':\n\t\t\t\t\tcontinue\n\t\t\t\telse:\n\t\t\t\t\tcleanest_stream += cleaner_stream[char]\n\n\t\t\t# counts the groups\n\t\t\tfor char in cleanest_stream:\n\n\t\t\t\tif char == '{':\n\t\t\t\t\tnesting += 1\n\n\t\t\t\tif char == '}':\n\t\t\t\t\tgroups += nesting\n\t\t\t\t\tnesting -= 1\n\n\n\twith open('output_91', 'w') as f:\n\t\tf.write(str(groups))\n\n\twith open('output_92', 'w') as f:\n\t\tf.write(str(garbage_count))","title":""},{"docid":"134e535f3a50148e34d531d30a00b1cd","score":"0.50335705","text":"def preprocess_lines(self, lines: List[str]) -> List[str]:\n\n def repl(m: re.Match) -> str:\n \"\"\"\n Split lines, adding leading whitespace to the second line.\n *Don't* separate tags if the tags open and close the same element.\n \"\"\"\n m2 = self.tag_name_pat.match(m.group(2))\n m3 = self.tag_name_pat.match(m.group(3))\n tag_name2 = m2 and m2.group(1) or ''\n tag_name3 = m3 and m3.group(1) or ''\n same_element = (\n tag_name2 == tag_name3\n and not m.group(2).startswith('</')\n and m.group(3).startswith('</')\n )\n lws = g.get_leading_ws(m.group(1))\n sep = '' if same_element else '\\n' + lws\n return m.group(1) + m.group(2).rstrip() + sep + m.group(3)\n\n result_lines = []\n for i, line in enumerate(lines):\n s = re.sub(self.adjacent_tags_pat, repl, line)\n result_lines.extend(g.splitLines(s))\n return result_lines","title":""},{"docid":"278c388bea9a38b5a26011502f8b22ef","score":"0.50301296","text":"def parse_slurm(squeue_output: str) -> List[Dict]:\n table = [[str(i) for i in line.split()] for line in squeue_output.splitlines()]\n \"\"\"\n [['JOBID', 'NAME', 'STATE', 'SUBMIT_TIME', 'START_TIME', 'EXEC_HOST', 'REASON'], \n ['418774', 'V_REL_130234420_7_0', 'RUNNING', '2018-05-09T23:34:36', '2018-05-10T09:41:36', 'shangrila01', 'None']]\n \"\"\"\n list_of_dict: List[Dict] = [dict(zip(table[0], row)) for row in table[1:]]\n \"\"\"\n [{'NAME': 'V_REL_130234420_7_0', \n 'START_TIME': '2018-05-10T09:41:36', \n 'REASON': 'None', \n 'JOBID': '418774', \n 'STATE': 'RUNNING', \n 'SUBMIT_TIME': '2018-05-09T23:34:36', \n 'EXEC_HOST': 'shangrila01'}\n ]\n \"\"\"\n return list_of_dict","title":""},{"docid":"43b748036515324d5dc6c9c3f3972916","score":"0.50250113","text":"def _parse_block(self, line_feeder, end_marker, parent, deps,\n visible_if_deps, res=None):\n\n block = [] if res is None else res\n\n while 1:\n # Do we already have a tokenized line that we determined wasn't\n # part of whatever we were parsing earlier? See comment in\n # Config.__init__().\n if self.end_line is not None:\n line = self.end_line\n tokens = self.end_line_tokens\n tokens.unget_all()\n\n self.end_line = None\n self.end_line_tokens = None\n else:\n line = line_feeder.get_next()\n if line is None:\n if end_marker is not None:\n raise Kconfig_Syntax_Error(\"Unexpected end of file {0}\"\n .format(line_feeder.filename))\n return block\n\n tokens = self._tokenize(line, False, line_feeder.filename,\n line_feeder.linenr)\n\n t0 = tokens.get_next()\n if t0 is None:\n continue\n\n # Cases are ordered roughly by frequency, which speeds things up a\n # bit\n\n if t0 == T_CONFIG or t0 == T_MENUCONFIG:\n # The tokenizer will automatically allocate a new Symbol object\n # for any new names it encounters, so we don't need to worry\n # about that here.\n sym = tokens.get_next()\n\n # Symbols defined in multiple places get the parent of their\n # first definition. However, for symbols whose parents are\n # choice statements, the choice statement takes precedence.\n if not sym.is_defined_ or isinstance(parent, Choice):\n sym.parent = parent\n\n sym.is_defined_ = True\n\n self.kconfig_syms.append(sym)\n block.append(sym)\n\n self._parse_properties(line_feeder, sym, deps, visible_if_deps)\n\n elif t0 == T_SOURCE:\n kconfig_file = tokens.get_next()\n exp_kconfig_file = self._expand_sym_refs(kconfig_file)\n f = os.path.join(self.base_dir, exp_kconfig_file)\n if not os.path.exists(f):\n raise IOError('{0}:{1}: sourced file \"{2}\" (expands to '\n '\"{3}\") not found. Perhaps base_dir '\n '(argument to Config.__init__(), currently '\n '\"{4}\") is set to the wrong value.'\n .format(line_feeder.filename,\n line_feeder.linenr,\n kconfig_file, exp_kconfig_file,\n self.base_dir))\n # Add items to the same block\n self._parse_file(f, parent, deps, visible_if_deps, block)\n\n elif t0 == end_marker:\n # We have reached the end of the block\n return block\n\n elif t0 == T_IF:\n # If statements are treated as syntactic sugar for adding\n # dependencies to enclosed items and do not have an explicit\n # object representation.\n\n dep_expr = self._parse_expr(tokens, None, line,\n line_feeder.filename,\n line_feeder.linenr)\n # Add items to the same block\n self._parse_block(line_feeder, T_ENDIF, parent,\n _make_and(dep_expr, deps),\n visible_if_deps, block)\n\n elif t0 == T_COMMENT:\n comment = Comment()\n\n comment.config = self\n comment.parent = parent\n comment.filename = line_feeder.filename\n comment.linenr = line_feeder.linenr\n comment.text = tokens.get_next()\n\n self.comments.append(comment)\n block.append(comment)\n\n self._parse_properties(line_feeder, comment, deps,\n visible_if_deps)\n\n elif t0 == T_MENU:\n menu = Menu()\n\n menu.config = self\n menu.parent = parent\n menu.filename = line_feeder.filename\n menu.linenr = line_feeder.linenr\n menu.title = tokens.get_next()\n\n self.menus.append(menu)\n block.append(menu)\n\n # Parse properties and contents\n self._parse_properties(line_feeder, menu, deps,\n visible_if_deps)\n menu.block = self._parse_block(line_feeder, T_ENDMENU, menu,\n menu.dep_expr,\n _make_and(visible_if_deps,\n menu.visible_if_expr))\n\n elif t0 == T_CHOICE:\n name = tokens.get_next()\n if name is None:\n choice = Choice()\n self.choices.append(choice)\n else:\n # Named choice\n choice = self.named_choices.get(name)\n if choice is None:\n choice = Choice()\n choice.name = name\n self.named_choices[name] = choice\n self.choices.append(choice)\n\n choice.config = self\n choice.parent = parent\n\n choice.def_locations.append((line_feeder.filename,\n line_feeder.linenr))\n\n # Parse properties and contents\n self._parse_properties(line_feeder, choice, deps,\n visible_if_deps)\n choice.block = self._parse_block(line_feeder, T_ENDCHOICE,\n choice, deps, visible_if_deps)\n\n choice._determine_actual_symbols()\n\n # If no type is specified for the choice, its type is that of\n # the first choice item with a specified type\n if choice.type == UNKNOWN:\n for item in choice.actual_symbols:\n if item.type != UNKNOWN:\n choice.type = item.type\n break\n\n # Each choice item of UNKNOWN type gets the type of the choice\n for item in choice.actual_symbols:\n if item.type == UNKNOWN:\n item.type = choice.type\n\n block.append(choice)\n\n elif t0 == T_MAINMENU:\n text = tokens.get_next()\n if self.mainmenu_text is not None:\n self._warn(\"overriding 'mainmenu' text. \"\n 'Old value: \"{0}\", new value: \"{1}\".'\n .format(self.mainmenu_text, text),\n line_feeder.filename, line_feeder.linenr)\n self.mainmenu_text = text\n\n else:\n _parse_error(line, \"unrecognized construct\",\n line_feeder.filename, line_feeder.linenr)","title":""}],"string":"[\n {\n \"docid\": \"2d0bf5d3c0c116e3052fefa8030ca8ba\",\n \"score\": \"0.65458053\",\n \"text\": \"def processBlock(block):\\n out = []\\n a = \\\"\\\"\\n for i in block:\\n for j in i:\\n if a == \\\"\\\":\\n a = j\\n else:\\n a = a + \\\"|\\\" + j\\n\\n out = a.split(\\\"|\\\")\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8def8263cd4fa650d980b83ea61f310c\",\n \"score\": \"0.6182205\",\n \"text\": \"def parse(\\n data: str,\\n raw: bool = False,\\n quiet: bool = False\\n) -> List[Dict]:\\n jc.utils.compatibility(__name__, info.compatible, quiet)\\n jc.utils.input_type_check(data)\\n\\n raw_output: List = []\\n\\n if jc.utils.has_data(data):\\n\\n for line in filter(None, data.splitlines()):\\n\\n buddy_list = line.split()\\n\\n raw_output.append(\\n {\\n 'node': buddy_list[1][:-1],\\n 'zone': buddy_list[3],\\n 'free_chunks': buddy_list[4:]\\n }\\n )\\n\\n return raw_output if raw else _process(raw_output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af33e7d182eb59a5bd6714d2604d1e18\",\n \"score\": \"0.6090104\",\n \"text\": \"def _parse(self):\\n res = []\\n if not isfile(self.path):\\n return res\\n sep_pat = re.compile(r'==>\\\\s*(.+?)\\\\s*<==')\\n with open(self.path, 'r') as f:\\n lines = f.read().splitlines()\\n for line in lines:\\n line = line.strip()\\n if not line:\\n continue\\n m = sep_pat.match(line)\\n if m:\\n res.append((m.group(1), set(), []))\\n elif line.startswith('#'):\\n res[-1][2].append(line)\\n else:\\n res[-1][1].add(line)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4390d0cebd4ce2ddb2035b692d393f1\",\n \"score\": \"0.60860026\",\n \"text\": \"def parse_conll(stream, filename=None, separator='\\\\t', is_empty=_line_is_empty):\\n\\n li, l = 0, None\\n try:\\n blocks = []\\n current_block = []\\n for l in stream:\\n l = l.rstrip()\\n li += 1\\n if is_empty(l):\\n blocks.append(current_block)\\n current_block = []\\n else:\\n current_block.append(l.split(separator))\\n except Exception:\\n # whatever goes wrong\\n raise ParseError(l, li)\\n\\n return blocks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92530a8d4449d64bea3b27da912f8706\",\n \"score\": \"0.6025703\",\n \"text\": \"def loop_block_to_list(block):\\n\\n names = [l for l in block[\\\"lines\\\"] if l.startswith(\\\"_\\\" + block[\\\"category\\\"])]\\n lines = [l for l in block[\\\"lines\\\"][1:] if l not in names]\\n names = [l.split(\\\".\\\")[1].split()[0] for l in names]\\n lines = [split_values(l) for l in lines]\\n l = []\\n for n in range(len(lines) - 1):\\n while n < len(lines) - 1 and\\\\\\n len(lines[n]) + len(lines[n + 1]) <= len(names):\\n lines[n] += lines[n + 1]\\n lines.pop(n + 1)\\n for line in lines:\\n l.append({\\n name: value for name, value in zip(names, line)\\n })\\n return l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f419dd927d7d914b03abc1d95f393f0d\",\n \"score\": \"0.5958842\",\n \"text\": \"def process_output(output):\\n return [line.strip('^$').split('/') for line in output.split('\\\\n')]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"774b21217cb9181150d8ba1a91159f03\",\n \"score\": \"0.59145683\",\n \"text\": \"def _extract_multi_block_from_log(log_content, block_name):\\n # Parse out the portion we need\\n data_started = False\\n data_content = []\\n data_block_list = []\\n model_names = []\\n\\n for line in log_content.split('\\\\n'):\\n if line.startswith(\\\"SIMULTANEOUS\\\"):\\n clean_str = line.replace(\\\"SIMULTANEOUS \\\", \\\"\\\")\\n model_names = json.loads(clean_str)\\n if line.startswith(\\\"%s_START\\\" % block_name):\\n data_started = True\\n elif line.startswith(\\\"%s_END\\\" % block_name):\\n data_started = False\\n if len(data_content) > 0:\\n data_path = ''\\n if len(model_names) > len(data_block_list):\\n data_path = model_names[len(data_block_list)]\\n data_block_list.append([data_path, '\\\\n'.join(data_content)])\\n data_content = []\\n elif data_started is True:\\n data_content.append(line)\\n return data_block_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ccf5aa0e22f92fa37ec8565dac1e33\",\n \"score\": \"0.5887196\",\n \"text\": \"def parse(self, text):\\r\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"958fecf1816c8c467d193103d247d28f\",\n \"score\": \"0.58773464\",\n \"text\": \"def parse_list(content):\\n csv_list = []\\n # Convert into a list of strings\\n unicode = content.decode()\\n content_list = unicode.split('\\\\n')\\n state = 0\\n for line in content_list:\\n # print(\\\"state, line:\\\", state, line)\\n if state == 0:\\n # Look for first <PRE>\\n if line.startswith('<PRE>'):\\n state = 1\\n elif state == 1:\\n # Look for header\\n if line.startswith('No., Day,DD,MMM,YYYY,'):\\n csv_list.append(line.split(','))\\n state = 2\\n elif state == 2:\\n # Process rows until blank line\\n if line:\\n csv_list.append(line.split(','))\\n else:\\n # Processed all rows so quit\\n break\\n else:\\n continue\\n return csv_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d368548f977d370eb3c3c8575391f6a\",\n \"score\": \"0.5766159\",\n \"text\": \"def separate_records(listofdata):\\n\\n\\troottag = re.compile(r'rootTag')\\n\\trtlist=[]\\n\\tfor i,v in enumerate(listofdata):\\n \\t\\tif re.match(roottag, str(v)):\\n \\trtlist.append(i)\\n\\n\\tlistofrecords = []\\t\\n\\tind = 1\\n\\tfor r in rtlist:\\n \\t\\tsingle_record = listofdata[r:rtlist[ind+1]]\\n \\t\\tlistofrecords.append(single_record)\\n \\t\\tif ind < len(rtlist)-2:\\n \\t\\tind += 1\\n \\t\\telse:\\n \\t\\tbreak\\n \\n\\treturn listofrecords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45df3fd00d8018255d01ac47213efaed\",\n \"score\": \"0.5765179\",\n \"text\": \"def __process_gorn_list(self, s):\\n if not s:\\n return []\\n parts = re.split(r\\\"\\\\s*;\\\\s*\\\", s)\\n seqs = list(map((lambda x : list(map(int, re.split(r\\\"\\\\s*,\\\\s*\\\", x)))), parts))\\n return seqs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598f7244152fa5e10763c42a012048a4\",\n \"score\": \"0.57588154\",\n \"text\": \"def rnaforester_parser(lines):\\n \\n result = []\\n \\n for block in cluster_parser(lines):\\n for struct in line_parser(block):\\n result.append(struct)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c80e989565bfc91cb03e2063b40f99ca\",\n \"score\": \"0.57435733\",\n \"text\": \"def parse_mol(self,):\\n f = open(self.molfile, 'r')\\n flag = True\\n blocks = []\\n block = []\\n \\n while(flag):\\n flag = False\\n for i in f:\\n flag = True\\n if i.strip().startswith('step'):\\n if len(block) == 0:\\n # append step to list as the first element\\n block.append(i.strip().split()[0][4:])\\n else:\\n blocks.append(block)\\n block = []\\n # append step to list as the first element\\n block.append(i.strip().split()[0][4:])\\n break\\n # ignore the white lines\\n elif len(i.strip()) < 1:\\n pass\\n else:\\n block.append(i.strip())\\n blocks.append(block)\\n return blocks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a756a118fe526c1185328529402c0ac8\",\n \"score\": \"0.5712363\",\n \"text\": \"def get_records(text_block):\\n #TODO: Refactor all of these into a classmethod for Record, then\\n # set arguments for start, end, find, and non-single-line behavior\\n start = text_block.find('(cid:87)(cid:73)(cid:84)(cid:72)(cid:68)'\\\\\\n '(cid:82)(cid:65)(cid:87)(cid:65)(cid:76)(cid:83)(cid:32)'\\\\\\n '(cid:38)(cid:32)(cid:68)(cid:69)(cid:80)(cid:79)(cid:83)'\\\\\\n '(cid:73)(cid:84)(cid:83) (cid:32)')\\n end = text_block.find('NET WITHDRAWALS (cid:38) DEPOSITS')\\n text_block = text_block[start:end]\\n deposits = []\\n text_block = text_block.split('\\\\n')\\n \\\"\\\"\\\"\\n for line in text_block:\\n print line\\n \\\"\\\"\\\"\\n for i, record in enumerate(text_block):\\n if record.find('Deposit ACH') != -1:\\n if Deposit.is_single_line(record):\\n deposits.append(Deposit.from_record(record))\\n elif len(record.split(' ')) < 1:\\n continue\\n else:\\n record = ''.join([text_block[i], text_block[i+1], \\n ' ', text_block[i-1]])\\n record = Deposit.clean(record)\\n record = Deposit.from_record(record)\\n deposits.append(record)\\n return deposits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b2be6e27c1f5ef9bfd32111bd7a6763\",\n \"score\": \"0.5686293\",\n \"text\": \"def splitBlock(self, stdout):\\n results = []\\n for line in stdout.splitlines(False):\\n # Intentionally skips empty lines.\\n if not line:\\n continue\\n if not line.startswith('__'):\\n if results:\\n results[-1].append(line)\\n else:\\n # TODO(maruel): gclient's git stdout is inconsistent.\\n # This should fail the test instead!!\\n pass\\n continue\\n\\n match = re.match(r'^________ ([a-z]+) \\\\'(.*)\\\\' in \\\\'(.*)\\\\'$', line)\\n if match:\\n results.append([[match.group(1), match.group(2), match.group(3)]])\\n continue\\n\\n match = re.match(r'^_____ (.*) is missing, synching instead$', line)\\n if match:\\n # Blah, it's when a dependency is deleted, we should probably not\\n # output this message.\\n results.append([line])\\n continue\\n\\n # These two regexps are a bit too broad, they are necessary only for git\\n # checkouts.\\n if (re.match(r'_____ [^ ]+ at [^ ]+', line) or\\n re.match(r'_____ [^ ]+ : Attempting rebase onto [0-9a-f]+...', line)):\\n continue\\n\\n # Fail for any unrecognized lines that start with '__'.\\n self.fail(line)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05bbe76893762bac7d11ced2dd9f6e92\",\n \"score\": \"0.5671271\",\n \"text\": \"def parse(data):\\n data = LookAheadStringIter(data)\\n result = list(parse_recursive(data))\\n if data.ahead:\\n raise ParseError('Inconsistent nesting of lists', data)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e2f9adfb7a6ed16c1277c2f274ead2\",\n \"score\": \"0.5658487\",\n \"text\": \"def __process_trees(self, s):\\n if not s:\\n return []\\n tree_strs = s.split(\\\"|||\\\")\\n return list(map(Tree.fromstring, tree_strs)) ## important fix TS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4bd9478a3915907d3ae940892744d02\",\n \"score\": \"0.56497824\",\n \"text\": \"def parse(lines):\\n lines = [i.decode('utf-8').rstrip() for i in lines]\\n lines = strip_footer(lines)\\n\\n out = parse_header_block(lines)\\n out['subitems'] = parse_content(lines)\\n out['multiple'] = len(out['subitems']) > 1\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"668db40667e1fdc1677dfad9c130549c\",\n \"score\": \"0.56468004\",\n \"text\": \"def sent_split(inlines, abbrevs=None):\\n\\n outlines = []\\n lcont = \\\"\\\"\\n for l in inlines:\\n # if this line is empty, continue to next\\n if l.strip() == \\\"\\\":\\n continue\\n\\n # protect any abbrevs by using a tag string and the abbrevs index\\n if abbrevs is not None:\\n for i, a in enumerate(abbrevs):\\n\\n # if a has a dot then I need to escape it\\n if a.find(\\\".\\\") != -1:\\n a = a.replace(\\\".\\\", r\\\"\\\\.\\\")\\n\\n ab_re = re.compile(r\\\"\\\\b\\\" + a)\\n abtag = \\\"#ABTAG{}#\\\".format(i)\\n ab_re.sub(abtag, l)\\n\\n # split the current l into groups\\n parts = S_RE.split(l.strip())\\n lparts = []\\n for i, p in enumerate(parts):\\n if i > 0 and S_RE.match(p):\\n lparts[-1] += \\\" \\\" + p\\n else:\\n lparts.append(p)\\n\\n # see if there's more than one part\\n if len(lparts) > 1: # multi part\\n\\n # append any lcont to the first\\n lparts[0] = lcont + \\\" \\\" + lparts[0]\\n lcont = \\\"\\\"\\n\\n # see if the last segment will continue to the next\\n if not S_RE.match(l.strip()[-1]):\\n outlines.extend(\\n [p.strip() for p in lparts[0:-1] if p.strip() != \\\"\\\"]\\n )\\n lcont = lparts[-1]\\n else:\\n outlines.extend([p.strip() for p in lparts if p.strip() != \\\"\\\"])\\n\\n else: # just one part here\\n # see if this line will continue to the next\\n if not S_RE.match(l.strip()[-1]):\\n lcont = lcont + \\\" \\\" + l.strip()\\n else:\\n if lcont + \\\" \\\" + l.strip() != \\\"\\\":\\n outlines.append(lcont + \\\" \\\" + l.strip())\\n\\n # go through outlines and put back strings from abtags\\n if abbrevs is not None:\\n ABTAG_NUM_RE = re.compile(\\\"#ABTAG([0-9]+)#\\\")\\n cleanlines = []\\n for o in outlines:\\n logging.info(\\\"preclean: \\\" + o, file=sys.stderr)\\n while o.find(\\\"#ABTAG\\\") > -1:\\n i = int(ABTAG_NUM_RE.search(o).group(1))\\n o = (\\n o[0:o.find(\\\"#ABTAG\\\")]\\n + abbrevs[i]\\n + \\\" \\\"\\n + o[o.find(\\\"#ABTAG\\\") + 7 + len(str(i)):]\\n )\\n cleanlines.append(o)\\n logging.info(\\\"cleaned: \\\" + o, file=sys.stderr)\\n return cleanlines\\n else:\\n return outlines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e49697d72620307fdcd596f164f242\",\n \"score\": \"0.5633962\",\n \"text\": \"def line_parser(block):\\n odd = True\\n record = False\\n first = True\\n seq = ''\\n con_seq = ''\\n struct = ''\\n alignment = {}\\n for line in block:\\n #find alignments\\n if line.startswith('seq'):\\n if line.__contains__(')') or line.__contains__('('):\\n continue\\n else:\\n sline = line.strip().split()\\n name = sline[0]\\n tmp_seq = sline[-1]\\n if alignment.__contains__(name):\\n seq = alignment[name]\\n seq = ''.join([seq,tmp_seq])\\n alignment[name] = seq\\n else:\\n alignment[name] = tmp_seq\\n\\n if line.startswith('Consensus sequence/structure:'): #start\\n record = True\\n if not first:\\n struct = to_pairs(struct)\\n yield [alignment,con_seq,struct]\\n result = []\\n first = True\\n elif record:\\n if line.startswith(' '):\\n line = line.strip()\\n if odd:\\n con_seq = ''.join([con_seq,line])\\n odd = False\\n else:\\n struct = ''.join([struct,line])\\n odd = True\\n\\n struct = to_pairs(struct)\\n yield [alignment,con_seq,struct]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a35f8fd6e3293a985ccb06e3ad6d7a9d\",\n \"score\": \"0.56085324\",\n \"text\": \"def parse(stream):\\n content = stream.read()\\n content = boneyard_re.sub('', content)\\n lines = linebreak_re.split(content)\\n del content\\n return parse_lines(lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba107700108cb17b31c6891a10f1ef58\",\n \"score\": \"0.5605392\",\n \"text\": \"def parse(self):\\n res = []\\n if not isfile(self._log_path):\\n return res\\n sep_pat = re.compile(r'==>\\\\s*(.+?)\\\\s*<==')\\n for line in open(self._log_path):\\n line = line.strip()\\n if not line or line.startswith('#'):\\n continue\\n m = sep_pat.match(line)\\n if m:\\n dt = m.group(1)\\n res.append((dt, set()))\\n else:\\n res[-1][1].add(line)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1fb83c031879994c6b2d6bcb5d1fd0e\",\n \"score\": \"0.56053007\",\n \"text\": \"def __process_span_list(self, s):\\n if not s:\\n return []\\n parts = re.split(r\\\"\\\\s*;\\\\s*\\\", s)\\n seqs = list(map((lambda x : list(map(int, re.split(r\\\"\\\\s*\\\\.\\\\.\\\\s*\\\", x)))), parts))\\n return seqs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66d32cef5a6efa05332fa6fbc15386af\",\n \"score\": \"0.5528002\",\n \"text\": \"def deduplicate_delimiters(output):\\n i = 0\\n while i < len(output) - 1:\\n if isinstance(output[i], OutputDelimiter) and isinstance(output[i + 1], OutputDelimiter):\\n if output[i].string.isspace() and not output[i + 1].string.isspace():\\n output.pop(i)\\n continue\\n elif output[i + 1].string.isspace() and not output[i].string.isspace():\\n output.pop(i + 1)\\n continue\\n elif len(output[i].string) < len(output[i + 1].string):\\n output.pop(i)\\n continue\\n elif len(output[i].string) > len(output[i + 1].string):\\n output.pop(i + 1)\\n continue\\n elif output[i].string.isspace():\\n output.pop(i)\\n continue\\n i += 1\\n # Strip leading block delimiters.\\n while output and isinstance(output[0], OutputDelimiter) and output[0].string.isspace():\\n output.pop(0)\\n # Strip trailing block delimiters.\\n while output and isinstance(output[-1], OutputDelimiter) and output[-1].string.isspace():\\n output.pop(-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03ee338cf906a6665c539bfa0f3b5247\",\n \"score\": \"0.54913294\",\n \"text\": \"def parse_input(puzzle_data):\\n structured_puzzle = list()\\n for line in puzzle_data.split(\\\"\\\\n\\\"):\\n structured_puzzle.append(line.split(\\\" \\\"))\\n\\n return structured_puzzle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d81c2c1c335d308e721854cf29f34fd3\",\n \"score\": \"0.5482219\",\n \"text\": \"def processor(main_dict: dict):\\n main_dict[\\\"group_contents\\\"] = \\\\\\n main_dict[\\\"group_contents\\\"].replace(\\\"}{\\\", \\\"}|{\\\")\\n code_strings_list = main_dict[\\\"group_contents\\\"].split(\\\"|\\\")\\n return code_strings_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bf95ad25f0d0ee9a777781993e56751\",\n \"score\": \"0.5477923\",\n \"text\": \"def parsed_output(self):\\n data = self.output_data\\n args = [iter(data)] * 3\\n return zip(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb5694485fa5bd9c6244f01e931cd13\",\n \"score\": \"0.5441826\",\n \"text\": \"def parse_and_extract(conllu_path) -> List[Tuple[Inp, Out]]:\\n data = []\\n with open(conllu_path, \\\"r\\\", encoding=\\\"utf-8\\\") as data_file:\\n for token_list in conllu.parse_incr(data_file): # type: ignore\\n data.append(extract(token_list))\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfa59e9fd55254f80a663c4e81fd6105\",\n \"score\": \"0.54314446\",\n \"text\": \"def split_raw_data(raw_data):\\n return [\\n line if line == \\\"\\\\n\\\"\\n else line.split(\\\"\\\\t\\\")\\n for line in raw_data\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa17a259d594e9a93523c6950b560d5b\",\n \"score\": \"0.5430212\",\n \"text\": \"def parse_tokens(self, tokens):\\n valid_tokens = self.get_valid_tokens(tokens)\\n\\n if len(valid_tokens) <= 0:\\n return []\\n\\n segmented_tokens = []\\n current_tokens = [valid_tokens[0]]\\n\\n for t in valid_tokens[1:]:\\n # Tokens have consecutive indices\\n # so same segment\\n if current_tokens[-1][0] + 1 == t[0]:\\n current_tokens.append(t)\\n\\n # start new segment\\n else:\\n segmented_tokens.append(current_tokens)\\n current_tokens = [t]\\n\\n segmented_tokens.append(current_tokens)\\n\\n segments = []\\n\\n for token_group in segmented_tokens:\\n text = ' '.join(t[3].strip() for t in token_group)\\n start = token_group[0][1]\\n end = token_group[-1][2]\\n segments.append((start, end, text))\\n\\n return segments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb3a1e250dac457027cba191c45d0428\",\n \"score\": \"0.54013276\",\n \"text\": \"def unpack(self, data):\\n return [m.split(self.separator) for m in data.split(\\\"\\\\n\\\")]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2e8a57fa800b26fccd0fac20913c753\",\n \"score\": \"0.5373687\",\n \"text\": \"def group_tokens(self, blocks_and_lines):\\n\\n def horizontal_distance_between(token1, token2):\\n if token1.coordinates[\\\"x\\\"] > token2.coordinates[\\\"x\\\"]:\\n return (\\n token1.coordinates[\\\"x\\\"]\\n - token2.coordinates[\\\"x\\\"]\\n - token2.coordinates[\\\"width\\\"]\\n )\\n else:\\n return (\\n token2.coordinates[\\\"x\\\"]\\n - token1.coordinates[\\\"x\\\"]\\n - token1.coordinates[\\\"width\\\"]\\n )\\n\\n def combine_tokens_into_one_token(token_list):\\n x = y = float(\\\"inf\\\")\\n bottom_right_x = bottom_right_y = 0\\n text = \\\"\\\"\\n\\n for token in token_list:\\n if not text:\\n text = token.text\\n else:\\n text = text + \\\" \\\" + token.text\\n\\n x = min(token.coordinates[\\\"x\\\"], x)\\n y = min(token.coordinates[\\\"y\\\"], y)\\n bottom_right_x = max(\\n token.coordinates[\\\"x\\\"] + token.coordinates[\\\"width\\\"], bottom_right_x\\n )\\n bottom_right_y = max(\\n token.coordinates[\\\"y\\\"] + token.coordinates[\\\"height\\\"], bottom_right_y\\n )\\n\\n return Token(\\n text,\\n {\\n \\\"x\\\": x,\\n \\\"y\\\": y,\\n \\\"width\\\": bottom_right_x - x,\\n \\\"height\\\": bottom_right_y - y,\\n },\\n \\\"NA\\\",\\n {**token.token_structure, \\\"word_num\\\": \\\"Grouped\\\"},\\n )\\n\\n grouped_tokens = []\\n\\n for block in blocks_and_lines:\\n for line in blocks_and_lines[block]:\\n current_line = blocks_and_lines[block][line]\\n current_group = []\\n ADJUSTMENT_FACTOR = 0.25\\n\\n # take note of all the IS CURRENCY code that is meant to prevent currency and amount from grouping together\\n for token in current_line:\\n IS_CURRENCY = token.text in currencies\\n if current_group: # If there exists tokens in the current group\\n height_of_current_group = max(\\n list(\\n map(\\n lambda token: token.coordinates[\\\"height\\\"],\\n current_group,\\n )\\n )\\n )\\n TOO_FAR = (\\n horizontal_distance_between(token, current_group[-1])\\n > height_of_current_group / 2 + ADJUSTMENT_FACTOR*height_of_current_group\\n )\\n LAST_TOKEN_ENDS_WITH_COLON = current_group[-1].text[-1] == \\\":\\\"\\n ALIGNED_HORIZONTALLY = token.is_horizontally_aligned_with(\\n current_group[-1]\\n )\\n\\n # When something is a currency, independently add it to the grouped tokens\\n if IS_CURRENCY: \\n grouped_tokens.append(\\n combine_tokens_into_one_token(current_group)\\n )\\n grouped_tokens.append(token)\\n current_group = []\\n \\n # If something should not belong to the current group, group the current group and start a new group\\n elif (\\n TOO_FAR\\n or LAST_TOKEN_ENDS_WITH_COLON\\n or not ALIGNED_HORIZONTALLY\\n ): # This token should not be combined into the current group\\n grouped_tokens.append(\\n combine_tokens_into_one_token(current_group)\\n )\\n current_group = [token] # Reset the current group\\n \\n # It meets the criteria for normal append-ment of the token\\n else:\\n current_group.append(token)\\n\\n else: # Start a new group, since current_group is empty. Unless it is a currency, in which case, append directly to grouped tokens\\n if IS_CURRENCY:\\n grouped_tokens.append(token)\\n else:\\n current_group.append(token)\\n\\n # After finishing a line, just combine remaining tokens\\n if current_group:\\n grouped_tokens.append(combine_tokens_into_one_token(current_group))\\n\\n return grouped_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b45b7b7fc5810eaa0db8e94970899300\",\n \"score\": \"0.5351421\",\n \"text\": \"def parse(lines):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c51139f008c7389bc18da4bd16104dc\",\n \"score\": \"0.5342632\",\n \"text\": \"def parse_input(puzzle_data):\\n structured_puzzle = list()\\n for line in puzzle_data.split(\\\"\\\\n\\\"):\\n structured_puzzle.append(line)\\n return structured_puzzle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17c9edc50893b332ea89ea63e951cff3\",\n \"score\": \"0.53323346\",\n \"text\": \"def sub_group_lines(lines):\\n groups, sub_group = [], []\\n\\n for line in lines:\\n if len(line.rstrip()) > 0:\\n sub_group.append(line.rstrip())\\n else:\\n groups.append(sub_group)\\n sub_group = []\\n\\n if len(sub_group) > 0:\\n groups.append(sub_group)\\n\\n return groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45ee9a3e15a7ad670150aad3097bd7a8\",\n \"score\": \"0.531893\",\n \"text\": \"def parse(self) -> list[list[str]]:\\n cells = []\\n row = []\\n text = self.text\\n while text:\\n try:\\n cell, cell_end, row_done = self.parser.parse(text)\\n except ParseError as e:\\n if self.debug:\\n error = ParseError(f'Error in parse. Cannot match quoted key. '\\n f'Text is {text!r}\\\\ncells length is {len(cells)}\\\\n'\\n f'row length is {len(row)}')\\n raise error from e\\n else:\\n raise\\n for formatter in self.formatters:\\n cell = formatter.format(cell)\\n row.append(cell)\\n if row_done:\\n cells.append(row)\\n row = []\\n text = text[cell_end:]\\n return cells\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9272d9a18bc118ec1ad1e92a102298e3\",\n \"score\": \"0.5317757\",\n \"text\": \"def parse_list(parser=lambda x: x, delim=' '):\\n return chain(parse_text, lambda value: value.split(delim), map_(parser))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"856fc1de97d3e1f53e335b31e0bff69a\",\n \"score\": \"0.5311281\",\n \"text\": \"def parse_input():\\n\\n wires = []\\n\\n with open('input.txt', 'r') as txt:\\n for segments in txt:\\n wire = Wire()\\n for segment in segments.strip().split(','):\\n wire.update(segment)\\n wires.append(wire)\\n\\n return wires\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800f3616a9e08a3b0c53ca49c0977acc\",\n \"score\": \"0.5310942\",\n \"text\": \"def parseoutput(infilepath, has_headers):\\n # open up the file\\n f = open(infilepath)\\n # this is my way to handle some files having headers and some not\\n if has_headers == True:\\n # read off the headers \\n x = f.readline()\\n # for each row in infile strip off newlines and split\\n mylist = [r.strip().split('|') for r in f]\\n return mylist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76c1777b899ac82f1cb5e47bb04b1e3b\",\n \"score\": \"0.53012073\",\n \"text\": \"def parse(self):\\n self.data = []\\n self.extract_sections()\\n for section in self._parsing['sections']:\\n self.data.append(self.parse_section(section))\\n return self.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5863d062b5d4b82b4fe09de2ed7af561\",\n \"score\": \"0.52934253\",\n \"text\": \"def parse_input_output_string(self, data: str):\\n ret = []\\n for line in data.split(\\\"\\\\n\\\"):\\n if line:\\n if line[1] == \\\" \\\":\\n prev = ret[-1]\\n n = (prev[0], prev[1] +\\\"\\\\n \\\" + line.strip())\\n ret[-1] = n\\n # ret[-1][1] += line.strip()\\n else:\\n vd = line.split(\\\" \\\", 1)\\n ret.append((vd[0].strip(),vd[1].strip())) \\n \\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1412ca944e60c113c85eccd508f4bb05\",\n \"score\": \"0.527264\",\n \"text\": \"def read_list(data):\\n assert data.next() == '('\\n result = list(parse_recursive(data))\\n if not data.ahead or data.next() != ')':\\n raise ParseError('Unexpected end of list', data)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5169299abeabf63bc9f110e62362caec\",\n \"score\": \"0.52621293\",\n \"text\": \"def _commands_output_to_list(self, output):\\n output = [x.split('=') for x in output.split('\\\\n')]\\n\\treturn output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"216ff80ba234f5eb6bd94172bd7d3c79\",\n \"score\": \"0.52569085\",\n \"text\": \"def parse(lines):\\n output = dict()\\n\\n for line in lines:\\n [start, pipes] = line.split(\\\"<->\\\")\\n output[int(start)] = [int(pipe) for pipe in pipes.split(\\\",\\\")]\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d74ca577f5420687b9f96e4463f92cf5\",\n \"score\": \"0.5242003\",\n \"text\": \"def splitter(data, vendors, EC):\\n final_list = []\\n split_list = data.split(\\\"\\\\n\\\")\\n\\n del split_list[len(split_list) - 1]\\n\\n for item in split_list:\\n\\n final_list.append(item.split(\\\";\\\"))\\n\\n level_sorter(final_list, vendors, EC)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c25d139e0b710040e29fe5b512697763\",\n \"score\": \"0.52353776\",\n \"text\": \"def custom_parser(output: str, command: str, device_type: str, logger_poller) -> list:\\n # Install pip install pyATS and genie to use the parser in Netmiko. Now is not necessary for 1 command to parse\\n parsed_output = []\\n if command == \\\"show vlan\\\" and device_type == \\\"cisco_ios\\\":\\n output = output.split(\\\"\\\\n\\\")\\n pattern = r\\\"^(\\\\d+)\\\\s+(\\\\S+)\\\\s+\\\\S+.*$\\\"\\n for line in output:\\n match = re.search(pattern, line)\\n if match:\\n parsed_output.append({\\\"vlan_id\\\": match.group(1), \\\"vlan_name\\\": match.group(2)})\\n else:\\n logger_poller.info(f\\\"Parsed not implemented for command {command} on device_type {device_type}\\\")\\n return parsed_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"181baa6ca3a82ad9e99cddb505f43718\",\n \"score\": \"0.52280134\",\n \"text\": \"def parse_colon_record_list(self, response, err):\\n results = []\\n records = response.split('\\\\n\\\\n')\\n for record in records:\\n obj = self.parse_colon_record(record.strip(), err)\\n\\n if obj == None:\\n continue\\n\\n results.append(obj)\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7557d97104cf193f6c6a2362cb6115fe\",\n \"score\": \"0.5226994\",\n \"text\": \"def parse(content: str) -> List[Sentence]:\\n return ConlluParserBuilder.build().parse(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cad2a8522b146e539fb3334ce2a59c5\",\n \"score\": \"0.52262414\",\n \"text\": \"def parse_input(filename=\\\"input.txt\\\"):\\n with open(filename, \\\"r\\\") as f:\\n text = f.read()\\n # Parse the groups\\n groups = text.split(\\\"\\\\n\\\\n\\\")\\n answers = [group.split(\\\"\\\\n\\\") for group in groups]\\n return answers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dce2742ce3e7af2c87257e6737ec0875\",\n \"score\": \"0.5214416\",\n \"text\": \"def groups(self):\\n groups = []\\n with open(self.fname) as reader:\\n for line in reader.readlines():\\n m = re.match(r\\\"\\\\[\\\\[([^\\\\[\\\\]]+)\\\\|([^\\\\[\\\\]]+)\\\\]\\\\]\\\", line)\\n if m:\\n group = (m.group(1), m.group(2))\\n groups.append(group)\\n return groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be08c7ed23720dab2344331390742144\",\n \"score\": \"0.51933235\",\n \"text\": \"def items(self):\\n self.reset()\\n\\n for l in diff_split_lines(self.unified_diff, False):\\n self._bytes_processed += len(l) + 1\\n m = re.match(r'^--- ([^\\\\s]*)', l)\\n if m:\\n yield from self.empty_buffer()\\n continue\\n m = re.match(r'^\\\\+\\\\+\\\\+ ([^\\\\s]*)', l)\\n if m:\\n yield from self.empty_buffer()\\n continue\\n\\n m = re.match(r\\\"@@ -(\\\\d+),?(\\\\d*) \\\\+(\\\\d+),?(\\\\d*)\\\", l)\\n if m:\\n yield from self.empty_buffer()\\n hunk_data = map(lambda x: x == \\\"\\\" and 1 or int(x), m.groups())\\n self.hunk_off1, self.hunk_size1, self.hunk_off2, self.hunk_size2 = (\\n hunk_data\\n )\\n self.line1, self.line2 = self.hunk_off1, self.hunk_off2\\n yield \\\"H\\\", (\\n self.hunk_off1,\\n self.hunk_size1,\\n self.hunk_off2,\\n self.hunk_size2,\\n )\\n continue\\n\\n if re.match(r'^\\\\[', l):\\n yield from self.empty_buffer()\\n yield \\\"C\\\", l\\n\\n if re.match(r\\\"^\\\\\\\\ No newline\\\", l):\\n if self.hunk_size2 == 0:\\n self.buf[-1] = (\\n self.buf[-1][0],\\n self.buf[-1][1] + '\\\\n' + l[2:],\\n )\\n else:\\n self.buf[-1] = (\\n self.buf[-1][0] + '\\\\n' + l[2:],\\n self.buf[-1][1],\\n )\\n continue\\n\\n if self.hunk_size1 <= 0 and self.hunk_size2 <= 0:\\n yield from self.empty_buffer()\\n continue\\n\\n m = re.match(r\\\"^\\\\+\\\\[ (\\\\d+) lines removed \\\\]$\\\", l)\\n if m:\\n self.add_cpt += int(m.group(1))\\n self.hunk_size2 -= int(m.group(1))\\n self.buf.append((None, l[1:]))\\n continue\\n\\n if re.match(r\\\"^\\\\+\\\", l):\\n self.add_cpt += 1\\n self.hunk_size2 -= 1\\n self.buf.append((None, l[1:]))\\n continue\\n\\n m = re.match(r\\\"^-\\\\[ (\\\\d+) lines removed \\\\]$\\\", l)\\n if m:\\n self.del_cpt += int(m.group(1))\\n self.hunk_size1 -= int(m.group(1))\\n self.buf.append((l[1:], None))\\n continue\\n\\n if re.match(r\\\"^-\\\", l):\\n self.del_cpt += 1\\n self.hunk_size1 -= 1\\n self.buf.append((l[1:], None))\\n continue\\n\\n if re.match(r\\\"^ \\\", l) and self.hunk_size1 and self.hunk_size2:\\n yield from self.empty_buffer()\\n self.hunk_size1 -= 1\\n self.hunk_size2 -= 1\\n self.buf.append((l[1:], l[1:]))\\n continue\\n\\n yield from self.empty_buffer()\\n\\n yield from self.empty_buffer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bac22f7e817cba31d52d11a36d797ff0\",\n \"score\": \"0.5186413\",\n \"text\": \"def parser(self, inp):\\n file = open(inp)\\n ecotypes = []\\n for line in file:\\n if \\\"Ecotype\\\" in line and \\\"[\\\" in line and \\\"]\\\" in line:\\n start = False\\n ecotype = []\\n sequence = \\\"\\\"\\n for char in line:\\n if char == '[':\\n start = True\\n if start == True:\\n if char != '[' and char != ',' and char != ' ' and char != ']':\\n sequence += char\\n elif char == ' ':\\n ecotype.append(sequence)\\n sequence = \\\"\\\"\\n elif char == ']':\\n ecotype.append(sequence)\\n ecotypes.append(ecotype)\\n sequence = \\\"\\\"\\n break\\n return ecotypes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f7ae34386eb4185f4611582cdad64b4\",\n \"score\": \"0.51828325\",\n \"text\": \"def get_items(self, block):\\r\\n items = []\\r\\n for line in block.split('\\\\n'):\\r\\n m = self.CHILD_RE.match(line)\\r\\n if m:\\r\\n # This is a new item. Append\\r\\n items.append(m.group(3))\\r\\n elif self.INDENT_RE.match(line):\\r\\n # This is an indented (possibly nested) item.\\r\\n if items[-1].startswith(' '*markdown.TAB_LENGTH):\\r\\n # Previous item was indented. Append to that item.\\r\\n items[-1] = '%s\\\\n%s' % (items[-1], line)\\r\\n else:\\r\\n items.append(line)\\r\\n else:\\r\\n # This is another line of previous item. Append to that item.\\r\\n items[-1] = '%s\\\\n%s' % (items[-1], line)\\r\\n return items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f6549a2bab8f7c1bff90efd65f43693\",\n \"score\": \"0.51791257\",\n \"text\": \"def parse(self, format_string):\\n\\n\\t\\treturn [\\n\\t\\t\\t(\\n\\t\\t\\t\\tbefore,\\n\\t\\t\\t\\tidentifiant,\\n\\t\\t\\t\\tstr(\\n\\t\\t\\t\\t\\tlen(\\n\\t\\t\\t\\t\\t\\tre.search('\\\\t*$', before).group(0)\\n\\t\\t\\t\\t\\t)\\n\\t\\t\\t\\t) + '\\\\t' + (param if param is not None else ''),\\n\\t\\t\\t\\tmodif,\\n\\t\\t\\t)\\n\\t\\t\\tfor before, identifiant, param, modif\\n\\t\\t\\tin super().parse(format_string)\\n\\t\\t]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"580a98f211a45ad7955c8983c132bded\",\n \"score\": \"0.5173377\",\n \"text\": \"def chunk(lines):\\n chunks = []\\n chunk = []\\n for lineno, line in lines:\\n stripped = line.strip()\\n if not stripped or stripped[0] == '#':\\n if chunk:\\n chunks.append(chunk)\\n chunk = []\\n else:\\n chunk.append((lineno, stripped))\\n\\n if chunk:\\n chunks.append(chunk)\\n\\n return chunks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60f5a47b3205bb266f8db9785a65e9d2\",\n \"score\": \"0.5162477\",\n \"text\": \"def get_output_list(self, d):\\n output_list = [] \\n\\n # base output = id | timestamp | body\\n output_list.append( Field_id(d).value )\\n output_list.append( Field_postedtime(d).value )\\n output_list.append( Field_body(d).value )\\n\\n # urls \\n if self.options_urls:\\n #\\n # TODO: add back this exception handling for -x option\\n # https://github.com/DrSkippy/Gnacs/blob/16dd146fb05d02d7c1e3f282254e6718fd13303f/acscsv/twacscsv.py#L97 \\n #\\n # gnip \\n val = Field_gnip_urls(d).value\\n if isinstance(val, list): \\n try:\\n output_list.append( self.buildListString( [ x[\\\"expanded_url\\\"] for x in val ] ) ) \\n except KeyError:\\n output_list.append(Field_gnip_urls(d).default_value)\\n else: \\n output_list.append( val ) \\n # twitter\\n val = Field_twitter_entities_urls(d).value \\n if isinstance(val, list):\\n url_list = self.buildListString( [ x[\\\"url\\\"] for x in val ] ) \\n exp_url_list = self.buildListString( [ x[\\\"expanded_url\\\"] for x in val ] ) \\n else:\\n url_list = val\\n exp_url_list = val \\n output_list.append( url_list ) \\n output_list.append( exp_url_list ) \\n \\n # languages \\n if self.options_lang:\\n # actor\\n # - this field has *very* infrequently contained unicode chars. drop them.\\n output_list.append( Field_actor_language(d).value.encode('ascii', 'ignore') ) \\n # classifications\\n output_list.append( Field_gnip_language_value(d).value ) \\n output_list.append( Field_twitter_lang(d).value ) \\n \\n # rules\\n if self.options_rules:\\n val = Field_gnip_rules(d).value \\n if isinstance(val, list):\\n # output: '[\\\" value (tag)\\\", ... ]'\\n output_list.append( \\n self.buildListString( \\n [ \\\"{} ({})\\\".format( x[\\\"value\\\"], x[\\\"tag\\\"] ) for x in Field_gnip_rules(d).value ]\\n )\\n ) \\n else: \\n output_list.append( val ) \\n\\n # geo-related fields\\n if self.options_geo:\\n # geo-tag \\n val = Field_geo_coordinates(d).value\\n # keep self.geoCoordsList for backward compatibility\\n self.geoCoordsList = None\\n if isinstance(val, list):\\n output_list.append( str(val) ) \\n self.geoCoordsList = val \\n else:\\n output_list.append( val ) \\n output_list.append( Field_geo_type(d).value )\\n val = Field_location_geo_coordinates(d).value \\n if isinstance(val, list): \\n output_list.append( str(val) ) \\n else:\\n output_list.append( val ) \\n output_list.append( Field_location_geo_type(d).value )\\n output_list.append( Field_location_displayname(d).value ) \\n output_list.append( Field_location_twitter_country_code(d).value ) \\n # user \\n output_list.append( Field_actor_utcoffset(d).value ) \\n output_list.append( Field_actor_location_displayname(d).value ) \\n # profileLocations\\n output_list.append( Field_gnip_profilelocations_displayname(d).value ) \\n output_list.append( Field_gnip_profilelocations_objecttype(d).value ) \\n output_list.append( Field_gnip_profilelocations_address_country(d).value ) \\n output_list.append( Field_gnip_profilelocations_address_region(d).value ) \\n output_list.append( Field_gnip_profilelocations_address_countrycode(d).value ) \\n output_list.append( Field_gnip_profilelocations_address_locality(d).value ) \\n output_list.append( Field_gnip_profilelocations_geo_type(d).value ) \\n output_list.append( Field_gnip_profilelocations_geo_coordinates(d).value ) \\n\\n # user\\n if self.options_user:\\n output_list.append( Field_actor_displayname(d).value ) \\n output_list.append( Field_actor_preferredusername(d).value ) \\n output_list.append( Field_actor_id(d).value ) \\n \\n # user connections, klout\\n if self.options_influence:\\n output_list.append( Field_gnip_klout_score(d).value ) \\n output_list.append( Field_actor_followerscount(d).value ) \\n output_list.append( Field_actor_friendscount(d).value ) \\n output_list.append( Field_actor_listedcount(d).value ) \\n output_list.append( Field_actor_statusesCount(d).value ) \\n \\n # structure\\n if self.options_struct:\\n output_list.append( Field_activity_type(d).value ) \\n\\n # done building output list \\n return output_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54c9aeb9113235841a192435e9b759ca\",\n \"score\": \"0.5161918\",\n \"text\": \"def _extract_results(self, response):\\r\\n res = re.compile(self.pattern, re.DOTALL).findall(response)\\r\\n results = list()\\r\\n for r in res:\\r\\n results.append(r)\\r\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87bdfeb9b018da62afb88a5ddc597e41\",\n \"score\": \"0.5153912\",\n \"text\": \"def test_extract_blocks(self):\\n f = StringIO.StringIO(\\\"\\\"\\\"\\n// No block\\n.foobar {}\\n\\n// Block1\\n//\\n// foobar\\n//\\n// Styleguide\\ndiv {}\\n\\n// Block2\\n//\\n// Styleguide\\n \\\"\\\"\\\")\\n\\n blocks = list(_extract_blocks(f))\\n self.assertEqual(len(blocks), 2)\\n self.assertEqual(blocks[0], [\\\"Block1\\\", \\\"\\\", \\\"foobar\\\", \\\"\\\", \\\"Styleguide\\\"])\\n self.assertEqual(blocks[1], [\\\"Block2\\\", \\\"\\\", \\\"Styleguide\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95121c78fff829854cb2fd773c48be30\",\n \"score\": \"0.51532143\",\n \"text\": \"def parseDataFile(self, data):\\n\\n block = 0\\n clientsbool = False\\n self.dataDicts = []\\n self.dataList = []\\n for line in data:\\n try:\\n if '!CLIENTS:' in line:\\n clientsbool = True\\n else:\\n if line.find(';') == 0:\\n if not line.find('; ') == 0:\\n block = block + 1\\n if not line.find(';\\\\n') == 0:\\n block = block + 1\\n if block > 1:\\n clientsbool = False\\n block = 0\\n if clientsbool:\\n splitline = line.split(u':', 40)\\n self.dataList.append(splitline)\\n self.dataDicts.append(profilemapper.profileMapper(splitline))\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d4e5ea8804ccd15d68bd90e6c32b951\",\n \"score\": \"0.5145597\",\n \"text\": \"def mmcif_lines_to_mmcif_blocks(lines):\\n\\n category = None\\n block, blocks = [], []\\n while lines:\\n line = lines.pop(0)\\n if line.startswith(\\\"data_\\\"): continue\\n if line.startswith(\\\"_\\\"):\\n line_category = line.split(\\\".\\\")[0]\\n if line_category != category:\\n if category:\\n blocks.append({\\\"category\\\": category[1:], \\\"lines\\\": block})\\n category = line_category\\n block = []\\n if line.startswith(\\\"loop_\\\"):\\n if category:\\n blocks.append({\\\"category\\\": category[1:], \\\"lines\\\": block})\\n category = lines[0].split(\\\".\\\")[0]\\n block = []\\n block.append(line)\\n if block: blocks.append({\\\"category\\\": category[1:], \\\"lines\\\": block})\\n return blocks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86132f917a800ac0305419ccedc4e10b\",\n \"score\": \"0.5145506\",\n \"text\": \"def yield_records(handle):\\n record = []\\n for line in handle:\\n if line.startswith('//'):\\n if len(record) > 0:\\n yield record\\n record = []\\n else:\\n record.append(line.strip())\\n if len(record) > 0:\\n yield record\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfd046d167e1e06b4dfb9540eb38f77c\",\n \"score\": \"0.5143903\",\n \"text\": \"def _to_list(self, text):\\n r=[]\\n for x in text.split(\\\"$\\\"):\\n x = x.strip()\\n assert x\\n r.append(x)\\n return tuple(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4153c39511f757926cd1684a4963a3aa\",\n \"score\": \"0.5143742\",\n \"text\": \"def asList( self ):\\r\\n out = []\\r\\n for res in self.__toklist:\\r\\n if isinstance(res,ParseResults):\\r\\n out.append( res.asList() )\\r\\n else:\\r\\n out.append( res )\\r\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26cede90ab44af2c00ecd9216eb4535e\",\n \"score\": \"0.51401764\",\n \"text\": \"def parse(self) -> List[Version]:\\n versions = []\\n while self.next_line():\\n assert self.current_line is not None\\n if '{' in self.current_line:\\n versions.append(self.parse_version())\\n else:\\n raise ParseError(\\n f'Unexpected contents at top level: {self.current_line}')\\n\\n self.check_no_duplicate_symbols(versions)\\n return versions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08b9e25eaff61e953fbeafdd38239d1c\",\n \"score\": \"0.513816\",\n \"text\": \"def groupByField (listOfTabbedStrings, fieldGroupedBy):\\n\\n # Convert listOfTabbedStrings to a list of lists with 'lowest' list\\n # representing a list of original tab seperated fields\\n listOfListsByTab = []\\n while listOfTabbedStrings != []:\\n listOfListsByTab.append(listOfTabbedStrings.pop(0).split('\\\\t'))\\n \\n exonList = []\\n\\n listOfExonLists = []\\n \\n while listOfListsByTab != 'end':\\n\\n # If there is a fresh and clean exonList:\\n # add the first coding region of the first/next gene to exonList\\n if exonList == []:\\n exonList.append(listOfListsByTab.pop(0))\\n\\n # If the next BioMart record list matches the one(s) in exonList:\\n # add it to exonList\\n elif listOfListsByTab[0][fieldGroupedBy] == exonList[0][fieldGroupedBy]:\\n exonList.append(listOfListsByTab.pop(0))\\n\\n # Check to see if you just popped the last record:\\n # - export last exonList\\n # - cull exonList\\n # - set listOfListsByTab to 'end' to stop the loop\\n if listOfListsByTab == []:\\n listOfExonLists.append(exonList)\\n #print len(listOfExonLists), '\\\\n'\\n print exonList[0][0]\\n exonList = []\\n listOfListsByTab = 'end'\\n \\n # Otherwise append whole exonList to listOfExonLists and clean exonList for next record group\\n else:\\n listOfExonLists.append(exonList)\\n #print len(listOfExonLists), '\\\\n'\\n print exonList[0][0]\\n exonList = []\\n \\n print 'The groupByField function produced ',len(listOfExonLists),' groups.\\\\n\\\\n'\\n return listOfExonLists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83fb01b5c7b0ebf28e38ad7902534ab2\",\n \"score\": \"0.5135255\",\n \"text\": \"def _pre_process(src_file):\\r\\n slices = []\\r\\n handled_slices = []\\r\\n with open(src_file) as fs:\\r\\n for line in fs:\\r\\n slices.extend(re.split(r'({[%{#].+?[%}#]})', line))\\r\\n\\r\\n flg_is_comment = False\\r\\n handled_line = ''\\r\\n for slice in slices:\\r\\n if slice.startswith('{#'):\\r\\n flg_is_comment = True\\r\\n elif slice.startswith('#}'):\\r\\n flg_is_comment = False\\r\\n slice = slice[2:].strip()\\r\\n\\r\\n if flg_is_comment:\\r\\n continue\\r\\n elif slice.startswith('{%') or slice.startswith('{{'):\\r\\n if handled_line:\\r\\n handled_slices.append(handled_line)\\r\\n handled_line = ''\\r\\n handled_slices.append(slice)\\r\\n elif slice:\\r\\n handled_line += slice\\r\\n if handled_line:\\r\\n handled_slices.append(handled_line)\\r\\n return handled_slices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eed58c93ccb327bfdc21cac039903ff\",\n \"score\": \"0.5134003\",\n \"text\": \"def parse_simple_namelist(filePath, commentchar='!', condense=False ):\\n \\n lines = []\\n with open(filePath, 'r') as f:\\n if condense:\\n pad = ''\\n else:\\n pad = ' '\\n \\n for line in f:\\n ulines = unroll_namelist_line(line, commentchar=commentchar, condense=condense)\\n lines = lines + ulines\\n\\n \\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a4af7c95339a3859cf5353c33471d5\",\n \"score\": \"0.5131401\",\n \"text\": \"def parse(self):\\n standard_formatters = re.compile(r'\\\\((.+?)\\\\)', re.IGNORECASE)\\n return standard_formatters.findall(self._fmt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04cbccf92ec6ad2e5cc085144aa4a7c8\",\n \"score\": \"0.5128008\",\n \"text\": \"def process_data(data: str) -> list[list[str]]:\\n return [re.findall(r\\\"\\\\d+\\\", line) for line in data.strip().split(\\\"\\\\n\\\")]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"704586cb6d035209facdc55183c05d32\",\n \"score\": \"0.51262045\",\n \"text\": \"def parse(raspa_output):\\n # Reads the string into a newline-separated list, skipping useless lines\\n data = [row.strip() for row in raspa_output.splitlines() if row and\\n all(d not in row for d in [\\\"-----\\\", \\\"+++++\\\"])]\\n\\n # Generally, categories in the output are delimited by equal signs\\n delimiters = [i for i, row in enumerate(data) if \\\"=====\\\" in row\\n and \\\"Exclusion constraints energy\\\" not in data[i - 1]]\\n\\n # Append a row for \\\"absolute adsorption:\\\" and \\\"excess adsorption:\\\"\\n # These values are separated into two rows\\n abs_adsorp_rows = [i for i, row in enumerate(data)\\n if \\\"absolute adsorption:\\\" in row]\\n for row in abs_adsorp_rows:\\n data[row] += \\\" \\\" + data[row + 1]\\n data[row + 2] += data[row + 3]\\n data[row + 1], data[row + 3] = \\\" \\\", \\\" \\\"\\n\\n # Use the delimiters to make a high-level dict. Title is row before\\n # delimiter, and content is every row after delimiter, up to the next title\\n info = {data[n - 1].strip(\\\":\\\"): data[n + 1: delimiters[i + 1] - 1]\\n for i, n in enumerate(delimiters[:-1])}\\n\\n # Let's PARSE!\\n for key, values in info.items():\\n d, note_index = {}, 1\\n for item in values:\\n # Takes care of all \\\"Blocks[ #]\\\", skipping hard-to-parse parts\\n if (\\\"Block\\\" in item and \\\"Box-lengths\\\" not in key\\n and \\\"Van der Waals:\\\" not in item):\\n blocks = _clean(item.split())\\n d[\\\"\\\".join(blocks[:2])] = blocks[2:]\\n\\n # Most of the average data values are parsed in this section\\n elif (any(s in item for s in [\\\"Average \\\", \\\"Surface area:\\\"])\\n and \\\"desorption\\\" not in key):\\n average_data = _clean(item.split())\\n # Average values organized by its unit, many patterns here\\n if len(average_data) == 8:\\n del average_data[2:4]\\n d[\\\" \\\".join(average_data[4:6])] = average_data[1:4]\\n elif len(average_data) == 5:\\n d[average_data[-1]] = average_data[1:4]\\n elif \\\"Surface\\\" in average_data[0]:\\n d[average_data[-1]] = average_data[2:5]\\n # This is the common case\\n else:\\n del average_data[2]\\n d[average_data[-1]] = average_data[1:4]\\n\\n # Average box-lengths has its own pattern\\n elif \\\"Box-lengths\\\" in key:\\n box_lengths = _clean(item.split())\\n i = 3 if \\\"angle\\\" in item else 2\\n d[\\\" \\\".join(box_lengths[:i])] = box_lengths[i:]\\n\\n # \\\"Heat of Desorption\\\" section\\n elif \\\"desorption\\\" in key:\\n if \\\"Note\\\" in item:\\n notes = re.split(\\\"[:\\\\s]{2,}\\\", item)\\n d[\\\"%s %d\\\" % (notes[0], note_index)] = notes[1]\\n note_index += 1\\n else:\\n heat_desorp = _clean(item.split())\\n # One line has \\\"Average\\\" in front, force it to be normal\\n if \\\"Average\\\" in item:\\n del heat_desorp[0]\\n d[heat_desorp[-1]] = heat_desorp[0:3]\\n\\n # Parts where Van der Waals are included\\n elif (\\\"Host-\\\" in key or \\\"-Cation\\\" in key or\\n \\\"Adsorbate-Adsorbate\\\" in key) and \\\"desorption\\\" not in key:\\n van_der = item.split()\\n # First Column\\n if \\\"Block\\\" in van_der[0]:\\n sub_data = [_clean(s.split(\\\":\\\"))\\n for s in re.split(\\\"\\\\s{2,}\\\", item)[1:]]\\n sub_dict = {s[0]: s[1] for s in sub_data[:2]}\\n d[\\\"\\\".join(van_der[:2])] = [float(van_der[2]), sub_dict]\\n # Average for each columns\\n elif \\\"Average\\\" in item:\\n avg = _clean(re.split(\\\"\\\\s{2,}\\\", item))\\n vdw, coulomb = [_clean(s.split(\\\": \\\")) for s in avg[2:4]]\\n d[avg[0]] = avg[1]\\n d[\\\"Average %s\\\" % vdw[0]] = vdw[1]\\n d[\\\"Average %s\\\" % coulomb[0]] = coulomb[1]\\n else:\\n d[\\\"standard deviation\\\"] = _clean(van_der)\\n\\n # IMPORTANT STUFF\\n elif \\\"Number of molecules\\\" in key:\\n adsorb_data = _clean(item.rsplit(\\\" \\\", 12))\\n if \\\"Component\\\" in item:\\n gas_name = adsorb_data[2].strip(\\\"[]\\\")\\n d[gas_name] = {}\\n else:\\n d[gas_name][adsorb_data[0]] = adsorb_data[1:]\\n\\n # Henry and Widom\\n elif \\\"Average Widom\\\" in item:\\n d[\\\"Widom\\\"] = _clean(item.rsplit(\\\" \\\", 5))[1:]\\n\\n elif \\\"Average Henry\\\" in item:\\n d[\\\"Henry\\\"] = _clean(item.rsplit(\\\" \\\", 5))[1:]\\n\\n # Ignore these\\n elif any(s in item for s in [\\\"=====\\\", \\\"Starting simulation\\\",\\n \\\"Finishing simulation\\\"]):\\n continue\\n\\n # Other strings\\n else:\\n parsed_data = _clean(re.split(\\\"[()[\\\\]:,\\\\t]\\\", item))\\n d[parsed_data[0]] = parsed_data[1:]\\n # Putting subdictionary back into main object\\n info[key] = d\\n\\n return info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"560b8874f8949f62cc58722347c5344d\",\n \"score\": \"0.5124573\",\n \"text\": \"def get_unformatted_output(raw_data):\\n out = []\\n\\n for entry in raw_data:\\n if 'Federalist' in entry['pp']:\\n continue\\n\\n out.append({\\n 'sort_by': entry['nm'],\\n 'name': format_name(entry['nm']),\\n 'party': create_acronym(entry['pp']),\\n 'term_start_date': '01-20-%s' % entry['tm'][:4]\\n })\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed614c11901cf38d239c0b504e29b48c\",\n \"score\": \"0.5121098\",\n \"text\": \"def parse(data, raw=False, quiet=False):\\n jc.utils.compatibility(__name__, info.compatible, quiet)\\n jc.utils.input_type_check(data)\\n\\n raw_output = []\\n\\n # Clear any blank lines\\n cleandata = list(filter(None, data.splitlines()))\\n\\n if not jc.utils.has_data(data):\\n return raw_output\\n\\n # linux output\\n if cleandata[0].startswith(' File: '):\\n output_line = {}\\n\\n # stats output contains 8 lines\\n for line in cleandata:\\n # line #1\\n if line.find('File:') == 2:\\n if output_line: # Reached a new file stat info.\\n raw_output.append(output_line)\\n output_line = {}\\n\\n line_list = line.split(maxsplit=1)\\n output_line['file'] = line_list[1]\\n\\n # populate link_to field if -> found\\n if ' -> ' in output_line['file']:\\n filename = output_line['file'].split(' -> ')[0].strip('\\\\u2018').rstrip('\\\\u2019')\\n link = output_line['file'].split(' -> ')[1].strip('\\\\u2018').rstrip('\\\\u2019')\\n output_line['file'] = filename\\n output_line['link_to'] = link\\n else:\\n filename = output_line['file'].split(' -> ')[0].strip('\\\\u2018').rstrip('\\\\u2019')\\n output_line['file'] = filename\\n\\n continue\\n\\n # line #2\\n if line.startswith(' Size:'):\\n line_list = line.split(maxsplit=7)\\n output_line['size'] = line_list[1]\\n output_line['blocks'] = line_list[3]\\n output_line['io_blocks'] = line_list[6]\\n output_line['type'] = line_list[7]\\n continue\\n\\n # line #3\\n if line.startswith('Device:'):\\n line_list = line.split()\\n output_line['device'] = line_list[1]\\n output_line['inode'] = line_list[3]\\n output_line['links'] = line_list[5]\\n continue\\n\\n # line #4\\n if line.startswith('Access: ('):\\n line = line.replace('(', ' ').replace(')', ' ').replace('/', ' ')\\n line_list = line.split()\\n output_line['access'] = line_list[1]\\n output_line['flags'] = line_list[2]\\n output_line['uid'] = line_list[4]\\n output_line['user'] = line_list[5]\\n output_line['gid'] = line_list[7]\\n output_line['group'] = line_list[8]\\n continue\\n\\n # line #5\\n if line.startswith('Access: 2'):\\n line_list = line.split(maxsplit=1)\\n output_line['access_time'] = line_list[1]\\n continue\\n\\n # line #6\\n if line.startswith('Modify:'):\\n line_list = line.split(maxsplit=1)\\n output_line['modify_time'] = line_list[1]\\n continue\\n\\n # line #7\\n if line.startswith('Change:'):\\n line_list = line.split(maxsplit=1)\\n output_line['change_time'] = line_list[1]\\n continue\\n\\n # line #8\\n if line.startswith(' Birth:'):\\n line_list = line.split(maxsplit=1)\\n output_line['birth_time'] = line_list[1]\\n continue\\n\\n if output_line:\\n raw_output.append(output_line)\\n\\n # FreeBSD/OSX output\\n else:\\n for line in cleandata:\\n value = shlex.split(line)\\n output_line = {\\n 'file': ' '.join(value[15:]),\\n 'unix_device': value[0],\\n 'inode': value[1],\\n 'flags': value[2],\\n 'links': value[3],\\n 'user': value[4],\\n 'group': value[5],\\n 'rdev': value[6],\\n 'size': value[7],\\n 'access_time': value[8],\\n 'modify_time': value[9],\\n 'change_time': value[10],\\n 'birth_time': value[11],\\n 'block_size': value[12],\\n 'blocks': value[13],\\n 'unix_flags': value[14]\\n }\\n\\n raw_output.append(output_line)\\n\\n return raw_output if raw else _process(raw_output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307139b65c5c6a3a0cab40a14eac6c9e\",\n \"score\": \"0.5116401\",\n \"text\": \"def unpack(self, string):\\n parsed_list = []\\n\\n for post in string.split(self.delimiter):\\n parsed = self.parse(post)\\n if parsed:\\n parsed_list.append(parsed)\\n return parsed_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a01c0f6e9f0ad94551b4acf956ce922\",\n \"score\": \"0.5112231\",\n \"text\": \"def _docstring_getblocks(self):\\n #If there are no lines to look at, we have nothing to do here.\\n if self.ibuffer[0] == self.ibuffer[1]:\\n return []\\n\\n lines = self.context.bufferstr[self.ibuffer[0]:self.ibuffer[1]]\\n docblock = []\\n result = []\\n self._doclines = []\\n\\n #We need to keep track of the line number for the start of the\\n #documentation strings.\\n docline = 0\\n doclength = 0\\n\\n first = self.docparser.RE_DOCS.match(lines[0])\\n if first is not None:\\n docblock.append(first.group(\\\"docstring\\\"))\\n docline = self.ibuffer[0]\\n self._doclines.append(docline)\\n doclength += len(lines[0]) + 1 # + 1 for \\\\n removed by split.\\n\\n #We need to search backwards in the main buffer string for\\n #additional tags to add to the block\\n i = self.ibuffer[0] - 1\\n while i > 0:\\n current = self.context.bufferstr[i]\\n docmatch = self.docparser.RE_DOCS.match(current)\\n if docmatch is not None:\\n docblock.append(docmatch.group(\\\"docstring\\\"))\\n docline = i\\n doclength += len(current) + 1\\n else:\\n break\\n i -= 1\\n\\n #Reverse the docblock list since we were going backwards and appending.\\n if len(docblock) > 0:\\n docblock.reverse()\\n\\n #Now handle the lines following the first line. Also handle the\\n #possibility of multiple, separate blocks that are still valid XML.\\n #We have to keep going until we have exceed the operational changes\\n #or found the decorating element.\\n i = self.ibuffer[0] + 1\\n while (i < len(self.context.bufferstr) and \\n (i < self.ibuffer[1] or len(docblock) > 0)):\\n line = self.context.bufferstr[i]\\n docmatch = self.docparser.RE_DOCS.match(line)\\n if docmatch is not None:\\n docblock.append(docmatch.group(\\\"docstring\\\"))\\n doclength += len(line)\\n if docline == 0:\\n docline = i\\n #Only track actual documentation lines that are within the \\n #operations list of lines.\\n if i < self.ibuffer[1]:\\n self._doclines.append(i)\\n\\n elif len(docblock) > 0:\\n key = self._docstring_key(line)\\n result.append((docblock, docline, doclength, key))\\n docblock = []\\n docline = 0\\n doclength = 0\\n\\n #We need to exit the loop if we have exceeded the length of\\n #the operational changes\\n if len(docblock) == 0 and i > self.ibuffer[1]:\\n break\\n i += 1\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77360134491b6ef46a448534416bbd10\",\n \"score\": \"0.511177\",\n \"text\": \"def parse_body(source):\\n\\n paragraphs = []\\n for blank, input_lines in itertools.groupby(source, _is_blank):\\n if not blank:\\n as_string = note_re.sub('', '\\\\n'.join(input_lines))\\n if _is_blank(as_string):\\n continue\\n paragraph = InputParagraph(as_string.split('\\\\n'))\\n paragraph.update_list(paragraphs)\\n\\n return paragraphs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2a9ff947a847a1e19f01fb638e302e7\",\n \"score\": \"0.5111172\",\n \"text\": \"def _parse_minute_into_tasks(content, group):\\n missing_colon_lines = []\\n unknown_task_ids = []\\n unknown_user = []\\n\\n task_list = []\\n done_list = []\\n remove_list = []\\n\\n for i, line in enumerate(content.splitlines()):\\n try:\\n if MISSING_COLON_REGEX.search(line):\\n missing_colon_lines.append((i, line))\\n continue\\n\\n # Single task for multiple users.\\n for names, task in TASK_REGEX.findall(line):\\n users = get_list_of_users_from_string(group.id, names)\\n task_list.append((i, task, users))\\n\\n # Single task for individual users.\\n for names, task in TASKS_REGEX.findall(line):\\n users = get_list_of_users_from_string(group.id, names)\\n for user in users:\\n task_list.append((i, task, [user]))\\n\\n # Mark a comma separated list as done.\\n for task_id_list in DONE_REGEX.findall(line):\\n for b32_task_id in task_id_list.strip().split(\\\",\\\"):\\n b32_task_id = b32_task_id.strip()\\n try:\\n task_id = b32.decode(b32_task_id)\\n except ValueError:\\n unknown_task_ids.append((i, b32_task_id))\\n continue\\n\\n task = pimpy_repository. \\\\\\n find_task_in_group_by_id(task_id, group.id)\\n if not task:\\n unknown_task_ids.append((i, b32_task_id))\\n else:\\n done_list.append((i, task))\\n\\n # Mark a comma separated list as removed.\\n for task_id_list in REMOVE_REGEX.findall(line):\\n for b32_task_id in task_id_list.strip().split(\\\",\\\"):\\n b32_task_id = b32_task_id.strip()\\n try:\\n task_id = b32.decode(b32_task_id)\\n except ValueError:\\n unknown_task_ids.append((i, b32_task_id))\\n continue\\n\\n task = pimpy_repository. \\\\\\n find_task_in_group_by_id(task_id, group.id)\\n if not task:\\n unknown_task_ids.append((i, b32_task_id))\\n else:\\n remove_list.append((i, task))\\n\\n # Catch invalid user.\\n except ValidationException:\\n unknown_user.append((i, line))\\n\\n if len(missing_colon_lines) or len(unknown_task_ids) or len(unknown_user):\\n raise InvalidMinuteException(missing_colon_lines, unknown_task_ids,\\n unknown_user)\\n\\n return task_list, done_list, remove_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7c5ea87e48462dd7e7fa67b2f7ae793\",\n \"score\": \"0.51104\",\n \"text\": \"def parse_data(block):\\n lines = block.strip().splitlines()\\n indices = []\\n symbols = []\\n charges = []\\n for line in lines[1:]:\\n words = line.split()\\n indices.append(int(words[0]))\\n symbols.append(words[1])\\n charges.append(float(words[2]))\\n return (np.array(indices), np.array(symbols), np.array(charges))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40aa9b7d37e59916f5d19911d60a7aa7\",\n \"score\": \"0.50997615\",\n \"text\": \"def tokenizer(self):\\n all_tokens = []\\n index = 0 #TODO remove\\n # for section, index in zip(self.zettel, range(len(self.zettel))): #TODO\\n # if index == 3:\\n # self.given_tags = re.split(\\\";\\\", section) #TODO make sure to use ';' \\n for section in self.zettel:\\n if index == len(self.zettel) - 1:\\n self.given_tags = re.split(\\\";\\\", section)\\n else:\\n tokens = re.split('\\\\W+', section)\\n tokens = list(filter(None, tokens))\\n all_tokens.append(tokens)\\n index += 1 #TODO remove\\n return all_tokens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d48816a8237379718698109d4dede931\",\n \"score\": \"0.50961757\",\n \"text\": \"def parse_lines(self):\\n\\n\\t\\tself.lines_raw = Text.DATE_PATTERN.findall(self.data)\\n\\n\\t\\tself.lines = []\\n\\t\\tfor i in self.lines_raw:\\n\\t\\t\\tdate, rest = i.split(\\\" - \\\", 1)\\n\\t\\t\\tif ':' in rest:\\n\\t\\t\\t\\tuser, message = rest.split(\\\": \\\", 1)\\n\\t\\t\\t\\t# message_type = 1 if message == \\\"<Media omitted>\\\" else 0\\n\\t\\t\\t\\tmessage_type = int(message == \\\"<Media omitted>\\\")\\n\\t\\t\\telse:\\n\\t\\t\\t\\tuser = \\\"system\\\"\\n\\t\\t\\t\\tmessage = rest\\n\\t\\t\\t\\tmessage_type = 2\\n\\n\\t\\t\\ttemp_date = re.findall(\\\"^(\\\\d{1,2}/\\\\d{1,2}/\\\\d\\\\d), \\\", date)[0]\\n\\t\\t\\ttemp_hour = date[date.find(',')+2:]\\n\\t\\t\\tself.lines.append((\\n\\t\\t\\t\\t\\t\\t\\tutils.date.parse_date(temp_date), # date\\n\\t\\t\\t\\t\\t\\t\\tint(temp_hour[:2]) * 60 + int(temp_hour[-2:]), # minutes\\n\\t\\t\\t\\t\\t\\t\\tuser,\\n\\t\\t\\t\\t\\t\\t\\tmessage,\\n\\t\\t\\t\\t\\t\\t\\tmessage_type\\n\\t\\t\\t\\t\\t\\t))\\n\\t\\treturn self.lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ef8fdfdf6cfb834d2ff9b32240d3be5\",\n \"score\": \"0.5094201\",\n \"text\": \"def extract_data(data_list):\\n data = []\\n for block in data_list:\\n state = get_state(block[0])\\n date = get_date(block[0])\\n b_type = get_type(block[0])\\n category = get_category(block[0])\\n records = get_records(block[0])\\n\\n extracted_text = (' ').join([state, date, b_type, category, records])\\n entity = get_entity(block, extracted_text)\\n\\n source = get_source(block[-2])\\n url = get_url(block[-1])\\n\\n data.append([entity, state, date, b_type,\\n category, records, source, url])\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6beb61879e656a61a2ff17aa82c8670d\",\n \"score\": \"0.5091759\",\n \"text\": \"def extract_tokens(representation, separators=SEPARATOR_CHARACTERS):\\n buff = \\\"\\\"\\n elements = []\\n last_token = None\\n\\n for index, c in enumerate(representation):\\n # if separator character is found, push\\n # the content of the buffer in the elements list\\n if c in separators:\\n if buff:\\n # If the last found token is invalid,\\n # raise and InvalidTokenError\\n if not valid_token(buff):\\n raise InvalidTokenError(\\n \\\"Duration representation {0} contains \\\"\\n \\\"an invalid token: {1}\\\".format(representation, buff)\\n )\\n\\n # If buffer content is a separator word, for example\\n # \\\"and\\\", just ignore it\\n if not buff.strip() in SEPARATOR_TOKENS:\\n elements.append(buff)\\n\\n # Anyway, reset buffer and last token marker\\n # to their zero value\\n buff = \\\"\\\"\\n last_token = None\\n else:\\n token = compute_char_token(c)\\n if token is not None \\\\\\n and last_token is not None \\\\\\n and token != last_token:\\n elements.append(buff)\\n buff = c\\n else:\\n buff += c\\n\\n last_token = token\\n\\n # push the content left in representation\\n # in the elements list\\n elements.append(buff)\\n\\n return list(zip(elements[::2], elements[1::2]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ab2eb313b528d432236ade01923b6d3\",\n \"score\": \"0.507784\",\n \"text\": \"def parse(source):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6b49eec132ec12019d9d17be4960870\",\n \"score\": \"0.50764745\",\n \"text\": \"def _prettytable_to_list(self, str):\\n retval = []\\n if str is not None and len(str) > 0:\\n rows = re.findall(r'\\\\|(.*?)\\\\n', str)\\n # Remove header\\n header_row = rows.pop(0)\\n key_names = re.findall(r'\\\\s*(.*?)\\\\s*\\\\|', header_row)\\n for row in rows:\\n values = re.findall(r'\\\\s*(.*?)\\\\s*\\\\|', row)\\n entry_dict = dict(zip(key_names, values))\\n retval.append(entry_dict)\\n return retval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27865cb21475e9d05ac6cf9e275e820f\",\n \"score\": \"0.50761974\",\n \"text\": \"def parse_clamscan(scanned_file: str, raw_output: str) -> list:\\n\\n summary_regex = re.compile(\\\"-+ SUMMARY -+\\\")\\n results = []\\n\\n undefined_path_signatures = set()\\n defined_path_signatures = set()\\n for line in raw_output.splitlines():\\n if summary_regex.match(line):\\n # Output complete\\n break\\n\\n result = parse_clamscan_output_line(scanned_file, line)\\n if result:\\n path = result[0]\\n signature = result[1]\\n # Ensure that we have a unique set of signatures when the path is undefined (see parse_clamscan_output_line)\\n if path == UNKNOWN and signature not in undefined_path_signatures:\\n undefined_path_signatures.add(signature)\\n elif path != UNKNOWN:\\n results.append({\\\"path\\\": path, \\\"signature\\\": signature})\\n defined_path_signatures.add(signature)\\n\\n # For each signature that doesn't have a defined path, append it.\\n for signature in undefined_path_signatures.difference(defined_path_signatures):\\n results.append({\\\"path\\\": UNKNOWN, \\\"signature\\\": signature})\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01f7b125dcac1634c401862b4caea196\",\n \"score\": \"0.5072473\",\n \"text\": \"def getBlocks(self, tokzer=None):\\n\\n def consume_extent(i, tokens, extent=None, detect_change=False):\\n \\\"\\\"\\\"Return tokens that belong to the given extent.\\n\\n It parses all the tokens that follow tokens[i], until getting out\\n of the extent. When detect_change is True, it may terminate early\\n when detecting preprocessing directives inside the extent.\\n \\\"\\\"\\\"\\n\\n result = []\\n if extent is None:\\n extent = tokens[i].cursor.extent\\n\\n while i < len(tokens) and tokens[i].location in extent:\\n t = tokens[i]\\n if debugBlockParser:\\n print(' ' * 2, t.id, t.kind, t.cursor.kind)\\n if (detect_change and t.cursor.extent != extent and\\n t.cursor.kind == CursorKind.PREPROCESSING_DIRECTIVE):\\n break\\n result.append(t)\\n i += 1\\n return (i, result)\\n\\n def consume_line(i, tokens):\\n \\\"\\\"\\\"Return tokens that follow tokens[i] in the same line.\\\"\\\"\\\"\\n result = []\\n line = tokens[i].location.line\\n while i < len(tokens) and tokens[i].location.line == line:\\n if tokens[i].cursor.kind == CursorKind.PREPROCESSING_DIRECTIVE:\\n break\\n result.append(tokens[i])\\n i += 1\\n return (i, result)\\n\\n if tokzer is None:\\n tokzer = self._tokzer\\n tokens = tokzer.tokens\\n\\n blocks = []\\n buf = []\\n i = 0\\n\\n while i < len(tokens):\\n t = tokens[i]\\n cursor = t.cursor\\n\\n if debugBlockParser:\\n print (\\\"%d: Processing [%s], kind=[%s], cursor=[%s], \\\"\\n \\\"extent=[%s]\\\" % (t.location.line, t.spelling, t.kind,\\n cursor.kind,\\n self._short_extent(cursor.extent)))\\n\\n if cursor.kind == CursorKind.PREPROCESSING_DIRECTIVE:\\n if buf:\\n blocks.append(Block(buf))\\n buf = []\\n\\n j = i\\n if j + 1 >= len(tokens):\\n raise BadExpectedToken(\\\"### BAD TOKEN at %s\\\" % (t.location))\\n directive = tokens[j+1].id\\n\\n if directive == 'define':\\n if i+2 >= len(tokens):\\n raise BadExpectedToken(\\\"### BAD TOKEN at %s\\\" %\\n (tokens[i].location))\\n\\n # Skip '#' and 'define'.\\n extent = tokens[i].cursor.extent\\n i += 2\\n id = ''\\n # We need to separate the id from the remaining of\\n # the line, especially for the function-like macro.\\n if (i + 1 < len(tokens) and tokens[i+1].id == '(' and\\n (tokens[i].location.column + len(tokens[i].spelling) ==\\n tokens[i+1].location.column)):\\n while i < len(tokens):\\n id += tokens[i].id\\n if tokens[i].spelling == ')':\\n i += 1\\n break\\n i += 1\\n else:\\n id += tokens[i].id\\n # Advance to the next token that follows the macro id\\n i += 1\\n\\n (i, ret) = consume_extent(i, tokens, extent=extent)\\n blocks.append(Block(ret, directive=directive,\\n lineno=t.location.line, identifier=id))\\n\\n else:\\n (i, ret) = consume_extent(i, tokens)\\n blocks.append(Block(ret[2:], directive=directive,\\n lineno=t.location.line))\\n\\n elif cursor.kind == CursorKind.INCLUSION_DIRECTIVE:\\n if buf:\\n blocks.append(Block(buf))\\n buf = []\\n directive = tokens[i+1].id\\n (i, ret) = consume_extent(i, tokens)\\n\\n blocks.append(Block(ret[2:], directive=directive,\\n lineno=t.location.line))\\n\\n elif cursor.kind == CursorKind.VAR_DECL:\\n if buf:\\n blocks.append(Block(buf))\\n buf = []\\n\\n (i, ret) = consume_extent(i, tokens, detect_change=True)\\n buf += ret\\n\\n elif cursor.kind == CursorKind.FUNCTION_DECL:\\n if buf:\\n blocks.append(Block(buf))\\n buf = []\\n\\n (i, ret) = consume_extent(i, tokens, detect_change=True)\\n buf += ret\\n\\n else:\\n (i, ret) = consume_line(i, tokens)\\n buf += ret\\n\\n if buf:\\n blocks.append(Block(buf))\\n\\n # _parsed=True indicates a successful parsing, although may result an\\n # empty BlockList.\\n self._parsed = True\\n\\n return BlockList(blocks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86b5e15dfc92e4c23fcabeffab0aaaa7\",\n \"score\": \"0.50715864\",\n \"text\": \"def getContentsAsList(self,showHeader=0,includeLineContents=0):\\n\\t\\titems = []\\n\\t\\tif showHeader == 1: items.append('%s\\\\n' % (self.sStartOfBlock))\\n\\t\\tif includeLineContents==0:\\n\\t\\t\\tallkeys = self.aKeywords.keys()\\n\\t\\t\\tallkeys.sort()\\n\\t\\t\\tfor k in allkeys:\\n\\t\\t\\t\\tv = self.aKeywords[k]\\n\\t\\t\\t\\titems.append(\\\"%s %s\\\\n\\\" % (k,v))\\n\\t\\telse:\\n\\t\\t\\tfor ilo in self.aLineContents: # Okay, the guys wants it all\\n\\t\\t\\t\\tif ilo.isComment == 1: continue\\n\\t\\t\\t\\titems.append(ilo.getRawLine())\\n\\t\\tfor ilo in self.aLineContents: # fd\\n\\t\\t\\tif ilo.isComment == 1: items.append(ilo.getRawLine())\\n\\t\\tif showHeader == 1: items.append(\\\"%s\\\\n\\\" % self.sEndOfBlock)\\n\\t\\treturn items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddc19bc83520245a90ca1e9fbfcdbbc9\",\n \"score\": \"0.5071156\",\n \"text\": \"def parse_packets(bytestream):\\r\\n\\r\\n container = packet_stream.parse(bytestream)\\r\\n\\r\\n l = [(i.header, i.payload) for i in container.full_packet]\\r\\n leftovers = \\\"\\\".join(chr(i) for i in container.leftovers)\\r\\n\\r\\n if DUMP_ALL_PACKETS:\\r\\n for header, payload in l:\\r\\n print \\\"Parsed packet 0x%.2x\\\" % header\\r\\n print payload\\r\\n\\r\\n return l, leftovers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3009dad22f514ae7ddb2da351b83318b\",\n \"score\": \"0.50671506\",\n \"text\": \"def flatten_content_list(content_list: List[bs4.element.NavigableString]) -> str:\\n out = ''\\n for c in content_list:\\n stack = [c]\\n while len(stack) > 0:\\n tag = stack.pop()\\n if tag.string is not None:\\n out += tag.string.replace('\\\\n', ' ').replace(' ', '')\\n else:\\n stack.extend(tag.contents)\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49bbf3cbf92bb4636b8ba5721968358d\",\n \"score\": \"0.50620216\",\n \"text\": \"def getBlocks(self, tokzer=None):\\n\\n def consume_extent(i, tokens, extent=None, detect_change=False):\\n \\\"\\\"\\\"Return tokens that belong to the given extent.\\n\\n It parses all the tokens that follow tokens[i], until getting out\\n of the extent. When detect_change is True, it may terminate early\\n when detecting preprocessing directives inside the extent.\\n \\\"\\\"\\\"\\n\\n result = []\\n if extent is None:\\n extent = tokens[i].cursor.extent\\n\\n while i < len(tokens) and tokens[i].location in extent:\\n t = tokens[i]\\n if debugBlockParser:\\n print ' ' * 2, t.id, t.kind, t.cursor.kind\\n if (detect_change and t.cursor.extent != extent and\\n t.cursor.kind == CursorKind.PREPROCESSING_DIRECTIVE):\\n break\\n result.append(t)\\n i += 1\\n return (i, result)\\n\\n def consume_line(i, tokens):\\n \\\"\\\"\\\"Return tokens that follow tokens[i] in the same line.\\\"\\\"\\\"\\n result = []\\n line = tokens[i].location.line\\n while i < len(tokens) and tokens[i].location.line == line:\\n if tokens[i].cursor.kind == CursorKind.PREPROCESSING_DIRECTIVE:\\n break\\n result.append(tokens[i])\\n i += 1\\n return (i, result)\\n\\n if tokzer is None:\\n tokzer = self._tokzer\\n tokens = tokzer.tokens\\n\\n blocks = []\\n buf = []\\n i = 0\\n\\n while i < len(tokens):\\n t = tokens[i]\\n cursor = t.cursor\\n\\n if debugBlockParser:\\n print (\\\"%d: Processing [%s], kind=[%s], cursor=[%s], \\\"\\n \\\"extent=[%s]\\\" % (t.location.line, t.spelling, t.kind,\\n cursor.kind,\\n self._short_extent(cursor.extent)))\\n\\n if cursor.kind == CursorKind.PREPROCESSING_DIRECTIVE:\\n if buf:\\n blocks.append(Block(buf))\\n buf = []\\n\\n j = i\\n if j + 1 >= len(tokens):\\n raise BadExpectedToken(\\\"### BAD TOKEN at %s\\\" % (t.location))\\n directive = tokens[j+1].id\\n\\n if directive == 'define':\\n if i+2 >= len(tokens):\\n raise BadExpectedToken(\\\"### BAD TOKEN at %s\\\" %\\n (tokens[i].location))\\n\\n # Skip '#' and 'define'.\\n extent = tokens[i].cursor.extent\\n i += 2\\n id = ''\\n # We need to separate the id from the remaining of\\n # the line, especially for the function-like macro.\\n if (i + 1 < len(tokens) and tokens[i+1].id == '(' and\\n (tokens[i].location.column + len(tokens[i].spelling) ==\\n tokens[i+1].location.column)):\\n while i < len(tokens):\\n id += tokens[i].id\\n if tokens[i].spelling == ')':\\n i += 1\\n break\\n i += 1\\n else:\\n id += tokens[i].id\\n # Advance to the next token that follows the macro id\\n i += 1\\n\\n (i, ret) = consume_extent(i, tokens, extent=extent)\\n blocks.append(Block(ret, directive=directive,\\n lineno=t.location.line, identifier=id))\\n\\n else:\\n (i, ret) = consume_extent(i, tokens)\\n blocks.append(Block(ret[2:], directive=directive,\\n lineno=t.location.line))\\n\\n elif cursor.kind == CursorKind.INCLUSION_DIRECTIVE:\\n if buf:\\n blocks.append(Block(buf))\\n buf = []\\n directive = tokens[i+1].id\\n (i, ret) = consume_extent(i, tokens)\\n\\n blocks.append(Block(ret[2:], directive=directive,\\n lineno=t.location.line))\\n\\n elif cursor.kind == CursorKind.VAR_DECL:\\n if buf:\\n blocks.append(Block(buf))\\n buf = []\\n\\n (i, ret) = consume_extent(i, tokens, detect_change=True)\\n buf += ret\\n\\n elif cursor.kind == CursorKind.FUNCTION_DECL:\\n if buf:\\n blocks.append(Block(buf))\\n buf = []\\n\\n (i, ret) = consume_extent(i, tokens, detect_change=True)\\n buf += ret\\n\\n else:\\n (i, ret) = consume_line(i, tokens)\\n buf += ret\\n\\n if buf:\\n blocks.append(Block(buf))\\n\\n # _parsed=True indicates a successful parsing, although may result an\\n # empty BlockList.\\n self._parsed = True\\n\\n return BlockList(blocks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39c11d0136c2832cb21f968dfc218f4e\",\n \"score\": \"0.5059111\",\n \"text\": \"def _read_data(cls, input_file):\\n with open(input_file, 'r', encoding=\\\"utf-8\\\") as f:\\n lines = []\\n for line in f:\\n content = line.split('|')\\n sent = content[0].strip()\\n label = content[1].replace('\\\\n', '')\\n lines.append([sent, label])\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9f401c12679be2385faa6457db05ee2\",\n \"score\": \"0.5056553\",\n \"text\": \"def rowParse(self):\\n self.rowData = self.RAWdata.split(\\\"\\\\n\\\")\\n\\n # If final splitted character is EOF delete that void element\\n if self.rowData[-1] == \\\"\\\":\\n del self.rowData[-1]\\n\\n self.rowData = self.parseComment(self.rowData)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8419761b95f6e153b2a3d174299d4401\",\n \"score\": \"0.50522107\",\n \"text\": \"def parse_counters_packets(sketch):\\n\\tparsed_sketch = []\\n\\tfor i in range(len(sketch)):\\n\\t\\tparsed_sketch.append([])\\n\\t\\tfor j in range(len(sketch[i])):\\n\\t\\t\\tparsed_sketch[i].append(str(sketch[i][j]).split(\\\"=\\\")[1].split(\\\",\\\")[0])\\n\\treturn parsed_sketch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06c7f6939fec7dce7680fc0ef18285d0\",\n \"score\": \"0.5051958\",\n \"text\": \"def parse_multiple(source):\\n return [parse(exp) for exp in split_exps(source)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e8b18d129504fc5643062564df8058\",\n \"score\": \"0.5050841\",\n \"text\": \"def flatten_parse(parse):\\n no_brackets = re.sub(r'[\\\\[\\\\], ]+', ' ', str(parse))\\n return no_brackets.strip().split(' ')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41b5addf4a9decdcc17ec16dc0aad5bd\",\n \"score\": \"0.5048182\",\n \"text\": \"def parse(self, buff, delimiter=DELIMITER, separator=SEPARATOR):\\n def pushback_generator(iterator):\\n \\\"\\\"\\\"\\n Generator which allows to push back a previously picked item\\n for example:\\n gen = pushback_generator(range(10))\\n print next(gen)\\n print next(gen)\\n v = next(gen)\\n print v\\n gen.send(v)\\n print next(gen)\\n :param iterator:\\n :return:\\n \\\"\\\"\\\"\\n for value in iterator:\\n back = yield value\\n if back is not None:\\n yield back\\n yield back\\n\\n custom_r = re.compile(FIX_REGEX_STRING.format(d=re.escape(delimiter),\\n s=re.escape(separator)).encode('UTF-8'),\\n re.DOTALL)\\n tagvals = custom_r.findall(buff)\\n\\n msg_type = None\\n if not self._no_groups and self.spec is not None:\\n for i in range(4):\\n if tagvals[i][0] == b'35':\\n msg_type = self.spec.msg_types.get(tagvals[i][1])\\n\\n if self.encoding is not None:\\n tagvals = ((int_or_str(tval[0], self.encoding), tval[1].decode(self.encoding)) for tval in tagvals)\\n elif self.decode_all_as_347:\\n tagvals = [(int_or_str(tval[0]), tval[1]) for tval in tagvals]\\n encoding = None\\n for tag, val in tagvals:\\n if tag == 347:\\n encoding = val.decode('UTF-8')\\n break\\n if encoding is not None:\\n tagvals = ((t[0], t[1].decode(encoding)) for t in tagvals)\\n else:\\n tagvals = ((int_or_str(tval[0]), tval[1]) for tval in tagvals)\\n # Need to add logic to parse Encoded* tags according to 347\\n\\n if self._no_groups or self.spec is None or msg_type is None:\\n # no groups can be found without a spec, so no point looking up the msg type.\\n return self._frg_class(tagvals)\\n msg = self._frg_class()\\n groups = msg_type.groups\\n tagvals = pushback_generator(tagvals)\\n for tag, value in tagvals:\\n if tag not in groups:\\n msg[tag] = value\\n else:\\n if value == '0':\\n msg[tag] = RepeatingGroup.create_repeating_group(tag)\\n else:\\n contents, last_tagval = self._process_group(tag, tagvals,\\n msg_type=msg_type,\\n group=groups[tag])\\n msg[tag] = contents\\n if last_tagval:\\n tagvals.send(last_tagval)\\n return msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf63c656b8e0cac13faebc2a01d9e92b\",\n \"score\": \"0.5044811\",\n \"text\": \"def split_bars(self, body):\\r\\n body = \\\"\\\".join(body)\\r\\n bars = re.split(self.regexPattern, body)\\r\\n while(\\\"\\\" in bars):\\r\\n bars.remove(\\\"\\\")\\r\\n if bars[0] in self.delimiters:\\r\\n bars[1] = bars[0]+bars[1]\\r\\n bars = bars[1:]\\r\\n bars = [bars[i*2]+bars[i*2+1] for i in range(int(len(bars)/2))]\\r\\n\\r\\n return bars\",\n \"title\": \"\"\n },\n {\n \"docid\": \"252bd4859572f726ed18e0c8027780e3\",\n \"score\": \"0.50384027\",\n \"text\": \"def stream_processing(file_path):\\n\\n\\twith open(file_path, 'r') as f:\\n\\n\\t\\t# prepares the date from the file\\n\\t\\tfor line in f:\\n\\t\\t\\told_stream = line.strip()\\n\\t\\t\\t# a string with the cancelled chars removed \\n\\t\\t\\tcleaner_stream = ''\\n\\t\\t\\t# \\n\\t\\t\\tcleanest_stream = ''\\n\\t\\t\\t# cancelled chars flag\\n\\t\\t\\tskip_next = False\\n\\t\\t\\t# garbage flag\\n\\t\\t\\tskip_while = False\\n\\t\\t\\t# nesting info for counting the groups\\n\\t\\t\\tnesting = 0\\n\\t\\t\\t# groups\\n\\t\\t\\tgroups = 0\\n\\n\\t\\t\\t# Part 2\\n\\t\\t\\t# garbage chars counter\\n\\t\\t\\tgarbage_count = 0\\n\\n\\t\\t\\t# removes the '!' and the next char from the stream\\n\\t\\t\\tfor char in range(0, len(old_stream)):\\n\\t\\t\\t\\tif skip_next:\\n\\t\\t\\t\\t\\tskip_next = False\\n\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\tif old_stream[char] == '!':\\n\\t\\t\\t\\t\\tskip_next = True\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tcleaner_stream += old_stream[char]\\n\\n\\t\\t\\t# removes the garbage\\n\\t\\t\\tfor char in range(0, len(cleaner_stream)):\\n\\t\\t\\t\\t# while skip_while is True if the char is not '>' the iteration continues\\n\\t\\t\\t\\tif skip_while:\\n\\t\\t\\t\\t\\tif cleaner_stream[char] == '>':\\n\\t\\t\\t\\t\\t\\tskip_while = False\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tgarbage_count += 1\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\t# if the char is '<', skip while is set to True\\n\\t\\t\\t\\tif cleaner_stream[char] == '<':\\n\\t\\t\\t\\t\\tskip_while = True\\n\\t\\t\\t\\telif cleaner_stream[char] == '>':\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tcleanest_stream += cleaner_stream[char]\\n\\n\\t\\t\\t# counts the groups\\n\\t\\t\\tfor char in cleanest_stream:\\n\\n\\t\\t\\t\\tif char == '{':\\n\\t\\t\\t\\t\\tnesting += 1\\n\\n\\t\\t\\t\\tif char == '}':\\n\\t\\t\\t\\t\\tgroups += nesting\\n\\t\\t\\t\\t\\tnesting -= 1\\n\\n\\n\\twith open('output_91', 'w') as f:\\n\\t\\tf.write(str(groups))\\n\\n\\twith open('output_92', 'w') as f:\\n\\t\\tf.write(str(garbage_count))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"134e535f3a50148e34d531d30a00b1cd\",\n \"score\": \"0.50335705\",\n \"text\": \"def preprocess_lines(self, lines: List[str]) -> List[str]:\\n\\n def repl(m: re.Match) -> str:\\n \\\"\\\"\\\"\\n Split lines, adding leading whitespace to the second line.\\n *Don't* separate tags if the tags open and close the same element.\\n \\\"\\\"\\\"\\n m2 = self.tag_name_pat.match(m.group(2))\\n m3 = self.tag_name_pat.match(m.group(3))\\n tag_name2 = m2 and m2.group(1) or ''\\n tag_name3 = m3 and m3.group(1) or ''\\n same_element = (\\n tag_name2 == tag_name3\\n and not m.group(2).startswith('</')\\n and m.group(3).startswith('</')\\n )\\n lws = g.get_leading_ws(m.group(1))\\n sep = '' if same_element else '\\\\n' + lws\\n return m.group(1) + m.group(2).rstrip() + sep + m.group(3)\\n\\n result_lines = []\\n for i, line in enumerate(lines):\\n s = re.sub(self.adjacent_tags_pat, repl, line)\\n result_lines.extend(g.splitLines(s))\\n return result_lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"278c388bea9a38b5a26011502f8b22ef\",\n \"score\": \"0.50301296\",\n \"text\": \"def parse_slurm(squeue_output: str) -> List[Dict]:\\n table = [[str(i) for i in line.split()] for line in squeue_output.splitlines()]\\n \\\"\\\"\\\"\\n [['JOBID', 'NAME', 'STATE', 'SUBMIT_TIME', 'START_TIME', 'EXEC_HOST', 'REASON'], \\n ['418774', 'V_REL_130234420_7_0', 'RUNNING', '2018-05-09T23:34:36', '2018-05-10T09:41:36', 'shangrila01', 'None']]\\n \\\"\\\"\\\"\\n list_of_dict: List[Dict] = [dict(zip(table[0], row)) for row in table[1:]]\\n \\\"\\\"\\\"\\n [{'NAME': 'V_REL_130234420_7_0', \\n 'START_TIME': '2018-05-10T09:41:36', \\n 'REASON': 'None', \\n 'JOBID': '418774', \\n 'STATE': 'RUNNING', \\n 'SUBMIT_TIME': '2018-05-09T23:34:36', \\n 'EXEC_HOST': 'shangrila01'}\\n ]\\n \\\"\\\"\\\"\\n return list_of_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b748036515324d5dc6c9c3f3972916\",\n \"score\": \"0.50250113\",\n \"text\": \"def _parse_block(self, line_feeder, end_marker, parent, deps,\\n visible_if_deps, res=None):\\n\\n block = [] if res is None else res\\n\\n while 1:\\n # Do we already have a tokenized line that we determined wasn't\\n # part of whatever we were parsing earlier? See comment in\\n # Config.__init__().\\n if self.end_line is not None:\\n line = self.end_line\\n tokens = self.end_line_tokens\\n tokens.unget_all()\\n\\n self.end_line = None\\n self.end_line_tokens = None\\n else:\\n line = line_feeder.get_next()\\n if line is None:\\n if end_marker is not None:\\n raise Kconfig_Syntax_Error(\\\"Unexpected end of file {0}\\\"\\n .format(line_feeder.filename))\\n return block\\n\\n tokens = self._tokenize(line, False, line_feeder.filename,\\n line_feeder.linenr)\\n\\n t0 = tokens.get_next()\\n if t0 is None:\\n continue\\n\\n # Cases are ordered roughly by frequency, which speeds things up a\\n # bit\\n\\n if t0 == T_CONFIG or t0 == T_MENUCONFIG:\\n # The tokenizer will automatically allocate a new Symbol object\\n # for any new names it encounters, so we don't need to worry\\n # about that here.\\n sym = tokens.get_next()\\n\\n # Symbols defined in multiple places get the parent of their\\n # first definition. However, for symbols whose parents are\\n # choice statements, the choice statement takes precedence.\\n if not sym.is_defined_ or isinstance(parent, Choice):\\n sym.parent = parent\\n\\n sym.is_defined_ = True\\n\\n self.kconfig_syms.append(sym)\\n block.append(sym)\\n\\n self._parse_properties(line_feeder, sym, deps, visible_if_deps)\\n\\n elif t0 == T_SOURCE:\\n kconfig_file = tokens.get_next()\\n exp_kconfig_file = self._expand_sym_refs(kconfig_file)\\n f = os.path.join(self.base_dir, exp_kconfig_file)\\n if not os.path.exists(f):\\n raise IOError('{0}:{1}: sourced file \\\"{2}\\\" (expands to '\\n '\\\"{3}\\\") not found. Perhaps base_dir '\\n '(argument to Config.__init__(), currently '\\n '\\\"{4}\\\") is set to the wrong value.'\\n .format(line_feeder.filename,\\n line_feeder.linenr,\\n kconfig_file, exp_kconfig_file,\\n self.base_dir))\\n # Add items to the same block\\n self._parse_file(f, parent, deps, visible_if_deps, block)\\n\\n elif t0 == end_marker:\\n # We have reached the end of the block\\n return block\\n\\n elif t0 == T_IF:\\n # If statements are treated as syntactic sugar for adding\\n # dependencies to enclosed items and do not have an explicit\\n # object representation.\\n\\n dep_expr = self._parse_expr(tokens, None, line,\\n line_feeder.filename,\\n line_feeder.linenr)\\n # Add items to the same block\\n self._parse_block(line_feeder, T_ENDIF, parent,\\n _make_and(dep_expr, deps),\\n visible_if_deps, block)\\n\\n elif t0 == T_COMMENT:\\n comment = Comment()\\n\\n comment.config = self\\n comment.parent = parent\\n comment.filename = line_feeder.filename\\n comment.linenr = line_feeder.linenr\\n comment.text = tokens.get_next()\\n\\n self.comments.append(comment)\\n block.append(comment)\\n\\n self._parse_properties(line_feeder, comment, deps,\\n visible_if_deps)\\n\\n elif t0 == T_MENU:\\n menu = Menu()\\n\\n menu.config = self\\n menu.parent = parent\\n menu.filename = line_feeder.filename\\n menu.linenr = line_feeder.linenr\\n menu.title = tokens.get_next()\\n\\n self.menus.append(menu)\\n block.append(menu)\\n\\n # Parse properties and contents\\n self._parse_properties(line_feeder, menu, deps,\\n visible_if_deps)\\n menu.block = self._parse_block(line_feeder, T_ENDMENU, menu,\\n menu.dep_expr,\\n _make_and(visible_if_deps,\\n menu.visible_if_expr))\\n\\n elif t0 == T_CHOICE:\\n name = tokens.get_next()\\n if name is None:\\n choice = Choice()\\n self.choices.append(choice)\\n else:\\n # Named choice\\n choice = self.named_choices.get(name)\\n if choice is None:\\n choice = Choice()\\n choice.name = name\\n self.named_choices[name] = choice\\n self.choices.append(choice)\\n\\n choice.config = self\\n choice.parent = parent\\n\\n choice.def_locations.append((line_feeder.filename,\\n line_feeder.linenr))\\n\\n # Parse properties and contents\\n self._parse_properties(line_feeder, choice, deps,\\n visible_if_deps)\\n choice.block = self._parse_block(line_feeder, T_ENDCHOICE,\\n choice, deps, visible_if_deps)\\n\\n choice._determine_actual_symbols()\\n\\n # If no type is specified for the choice, its type is that of\\n # the first choice item with a specified type\\n if choice.type == UNKNOWN:\\n for item in choice.actual_symbols:\\n if item.type != UNKNOWN:\\n choice.type = item.type\\n break\\n\\n # Each choice item of UNKNOWN type gets the type of the choice\\n for item in choice.actual_symbols:\\n if item.type == UNKNOWN:\\n item.type = choice.type\\n\\n block.append(choice)\\n\\n elif t0 == T_MAINMENU:\\n text = tokens.get_next()\\n if self.mainmenu_text is not None:\\n self._warn(\\\"overriding 'mainmenu' text. \\\"\\n 'Old value: \\\"{0}\\\", new value: \\\"{1}\\\".'\\n .format(self.mainmenu_text, text),\\n line_feeder.filename, line_feeder.linenr)\\n self.mainmenu_text = text\\n\\n else:\\n _parse_error(line, \\\"unrecognized construct\\\",\\n line_feeder.filename, line_feeder.linenr)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":95,"cells":{"query_id":{"kind":"string","value":"f21ec1a454b7684d26c31c403a205e32"},"query":{"kind":"string","value":"A callback called when device setup failed to connect after repeated tries"},"positive_passages":{"kind":"list like","value":[{"docid":"3d4150e47b5c449f1fe6ef81e9f5672e","score":"0.6424305","text":"def devices_scan_failed(self):\n pass","title":""}],"string":"[\n {\n \"docid\": \"3d4150e47b5c449f1fe6ef81e9f5672e\",\n \"score\": \"0.6424305\",\n \"text\": \"def devices_scan_failed(self):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6ef6703ff92b4c3939b494a6ae277183","score":"0.6651631","text":"def test_on_connect_error(self):\n # this assumed the ssdb server being tested against doesn't use 1023\n # port. An error should be raised on connect \n bad_connection = Connection(port=1023)\n bad_connection.connect()","title":""},{"docid":"b46d3244f1db949eed1735145acad8d0","score":"0.66482955","text":"def connection_refused_handler(spawn):\n if spawn.device:\n spawn.device.api.execute_clear_line()\n spawn.device.connect()\n return\n raise Exception('Connection refused to device %s' % (str(spawn)))","title":""},{"docid":"7c1f852634a59fe3c39994590ed7631c","score":"0.659285","text":"def connectSensorErrback(self, reason):\n print('Connection attempt to sensor failed: %s' %(reason.getErrorMessage()))","title":""},{"docid":"b8ceb3aad3a1bd81a7265cf050c34873","score":"0.6562177","text":"async def test_setup_failure_on_connection(opp, caplog):\n\n patch_product_identify(None, side_effect=blebox_uniapi.error.ConnectionError)\n\n entry = mock_config()\n entry.add_to_opp(opp)\n\n caplog.set_level(logging.ERROR)\n await opp.config_entries.async_setup(entry.entry_id)\n await opp.async_block_till_done()\n\n assert \"Identify failed at 172.100.123.4:80 ()\" in caplog.text\n assert entry.state is ConfigEntryState.SETUP_RETRY","title":""},{"docid":"6e6ca924d699daf28724dc244acb29b6","score":"0.6475644","text":"def addConnectionFailedCallback(*args, **kwargs):\n \n pass","title":""},{"docid":"64b69eefe5679ddcc5280459033850fb","score":"0.644714","text":"def connectionLost(reason):","title":""},{"docid":"a3329a33416f7bf4146fce5169260f14","score":"0.6442218","text":"def handle_connect_failure(self):\n self._connected = False\n if not self._shutdown:\n _LOGGER.error('Unable to connect to IP Module. Reconnecting...')\n self._alarmPanel._loginTimeoutCallback(False)\n ensure_future(self.reconnect(self._alarmPanel.connection_timeout), loop=self._eventLoop)","title":""},{"docid":"5f47c3cfd01e183bdc7a0f44851180d0","score":"0.6427711","text":"def connectionLost(self, reason):","title":""},{"docid":"3fbd8d9a1e1821c186d11ab059269b92","score":"0.6407843","text":"def on_connection_failure(self, exc):\n self._record_exception(exc)\n time.sleep(self._RECONNECT_DELAY)\n sys.exit()","title":""},{"docid":"998494a7ec477718693842f631779fbb","score":"0.62489235","text":"async def test_setup_failed_connect(opp, caplog):\n\n respx.get(\"http://localhost\").mock(\n side_effect=httpx.RequestError(\"server offline\", request=MagicMock())\n )\n assert await async_setup_component(\n opp,\n binary_sensor.DOMAIN,\n {\n \"binary_sensor\": {\n \"platform\": \"rest\",\n \"resource\": \"http://localhost\",\n \"method\": \"GET\",\n }\n },\n )\n await opp.async_block_till_done()\n assert len(opp.states.async_all()) == 0\n assert \"server offline\" in caplog.text","title":""},{"docid":"0c348be3a2014fb3d90b00f8e01bd8e1","score":"0.6194147","text":"def connectionLost(self, reason):\n pass","title":""},{"docid":"8f50797131aa438a7052dc4f99b4ff5e","score":"0.6179521","text":"def _connection_failed(self, failure, *args, **kwargs):\n log_msg(\"Port test failed to connect: %s\" % (failure), 1)\n self.probeRunning = False\n self.ignoreExits.append(self.circ.get_exit())\n if not self.nextScheduledTest or not self.nextScheduledTest.active():\n #dont count as a failure if the circuit is the thing that failed, not the connection:\n if self.circ.status != \"BUILT\":\n self.circuitFailures += 1\n #otherwise, make sure we get a new circuit to try with\n else:\n self.circ.close()\n self.circ = None\n #dont retry more than self.MAX_TESTS times in a row:\n if self.numProbes - self.circuitFailures < self.MAX_TESTS:\n self._schedule_next_test()\n else:\n log_msg(\"Retried self testing of reachability too many times, stopping.\", 1)\n self._on_unreachable(False)","title":""},{"docid":"307b57c8eddaed0ea53af6180e432de3","score":"0.611997","text":"async def test_setup_failure(opp, caplog):\n\n patch_product_identify(None, side_effect=blebox_uniapi.error.ClientError)\n\n entry = mock_config()\n entry.add_to_opp(opp)\n\n caplog.set_level(logging.ERROR)\n await opp.config_entries.async_setup(entry.entry_id)\n await opp.async_block_till_done()\n\n assert \"Identify failed at 172.100.123.4:80 ()\" in caplog.text\n assert entry.state is ConfigEntryState.SETUP_RETRY","title":""},{"docid":"c25746fe2bfa9dccdf0dea17066df518","score":"0.6088787","text":"def on_connect(client, userdata, flags, rc):\n if rc == 0:\n print(\"connected OK with returned code=\", rc)\n else:\n print(\"Bad connection with returned code=\", rc)","title":""},{"docid":"e67b550e46046a3005bc4c2fdffc0d64","score":"0.6082641","text":"def conn_callback(self):\n print 'Connected to a random stranger!\\n'","title":""},{"docid":"82e07f22e16b2dc6ad90b1bb162a0065","score":"0.6041003","text":"def Connect(self, retry):\n pass","title":""},{"docid":"e2a11272666187a9074e2e008f18d0a0","score":"0.6035678","text":"def test_connect_exception(self):\n poller = MiFloraPoller(self.TEST_MAC, ConnectExceptionBackend)\n with self.assertRaises(BluetoothBackendException):\n poller.firmware_version()\n with self.assertRaises(BluetoothBackendException):\n poller.name()\n with self.assertRaises(BluetoothBackendException):\n poller.parameter_value(MI_MOISTURE)\n with self.assertRaises(BluetoothBackendException):\n poller.parameter_value(MI_MOISTURE)","title":""},{"docid":"95f36bfc7940848a8bcbf8bccad9b6d9","score":"0.6025926","text":"def clientConnectionFailed(self, connector, reason):\n self._onConnection.errback(reason)","title":""},{"docid":"8674e25b2cd3a1a658f3340d15d7c314","score":"0.6003622","text":"def on_connect(self, unused_client, unused_userdata, unused_flags, rc):\n self.print_debug('Connection Result:', error_str(rc))\n self.connected = True","title":""},{"docid":"d0d446c2885413ad1e84f82b13269dd0","score":"0.59826756","text":"async def test_async_setup_entry_not_ready(\n hass: HomeAssistant, aioclient_mock: AiohttpClientMocker\n) -> None:\n entry: ConfigEntry = await async_init_integration(\n hass, aioclient_mock, error=\"cannot_connect\"\n )\n await hass.async_block_till_done()\n assert entry.state == ConfigEntryState.SETUP_RETRY","title":""},{"docid":"0f480747e8747440742fbe871d120159","score":"0.5979643","text":"def on_reconnect(self):","title":""},{"docid":"cb89c6af5489bebbc2e3e2bda220da47","score":"0.595108","text":"def test_connect_exception(self):\n poller = MiTempBtPoller(self.TEST_MAC, ConnectExceptionBackend, retries=0)\n with self.assertRaises(BluetoothBackendException):\n poller.firmware_version()\n with self.assertRaises(BluetoothBackendException):\n poller.name()\n with self.assertRaises(BluetoothBackendException):\n poller.parameter_value(MI_TEMPERATURE)\n with self.assertRaises(BluetoothBackendException):\n poller.parameter_value(MI_HUMIDITY)","title":""},{"docid":"c8135fbe57e761e32d166ebef6ef50df","score":"0.5943264","text":"def clientConnectionLost(self, connector, reason): #{{{\n connector.connect()","title":""},{"docid":"eca39338d01be2705630dd5b9fff588d","score":"0.5935057","text":"def test_failed_config(self):\n self._conn.execute(\"configure terminal\", allow_state_change=True)\n self._conn.execute(\"test failed\")\n self._conn.spawn.timeout = 60\n self._conn.enable()","title":""},{"docid":"bc8f50cc745c6b3e1e03d7c134df275d","score":"0.5917956","text":"def check_for_setup_error(self):\n if not self.ra.is_pool_exists(self.pool):\n LOG.error(\"Setup is incorrect, please check connection settings.\")\n raise exception.VolumeDriverException(\"Bad configuration expected\")","title":""},{"docid":"631654a538a1ec540f042da500487cd5","score":"0.5916882","text":"def connectionBroken(*args, **kwargs):\n \n pass","title":""},{"docid":"24b9a391eb61e807cb74f89725510e55","score":"0.5900489","text":"async def test_error_on_connection_failure(\n hass: HomeAssistant, mock_api: MagicMock\n) -> None:\n result = await hass.config_entries.flow.async_init(\n DOMAIN, context={\"source\": config_entries.SOURCE_USER}\n )\n\n mock_api.side_effect = TransmissionConnectError()\n result2 = await hass.config_entries.flow.async_configure(\n result[\"flow_id\"],\n MOCK_CONFIG_DATA,\n )\n\n assert result2[\"type\"] == FlowResultType.FORM\n assert result2[\"errors\"] == {\"base\": \"cannot_connect\"}","title":""},{"docid":"2378f9c9799121c77bf66454ff88b974","score":"0.58909976","text":"async def test_device_setup_os_error(hass: HomeAssistant) -> None:\n device = get_device(\"Office\")\n mock_api = device.get_mock_api()\n mock_api.auth.side_effect = OSError()\n\n with patch.object(\n hass.config_entries, \"async_forward_entry_setup\"\n ) as mock_forward, patch.object(\n hass.config_entries.flow, \"async_init\"\n ) as mock_init:\n mock_setup = await device.setup_entry(hass, mock_api=mock_api)\n\n assert mock_setup.entry.state is ConfigEntryState.SETUP_RETRY\n assert mock_setup.api.auth.call_count == 1\n assert mock_forward.call_count == 0\n assert mock_init.call_count == 0","title":""},{"docid":"480aae69089cb6801bb313cf4dad7b2d","score":"0.5890543","text":"def clientConnectionLost(self, connector, reason):\n connector.connect()","title":""},{"docid":"480aae69089cb6801bb313cf4dad7b2d","score":"0.5890543","text":"def clientConnectionLost(self, connector, reason):\n connector.connect()","title":""},{"docid":"480aae69089cb6801bb313cf4dad7b2d","score":"0.5890543","text":"def clientConnectionLost(self, connector, reason):\n connector.connect()","title":""},{"docid":"480aae69089cb6801bb313cf4dad7b2d","score":"0.5890543","text":"def clientConnectionLost(self, connector, reason):\n connector.connect()","title":""},{"docid":"480aae69089cb6801bb313cf4dad7b2d","score":"0.5890543","text":"def clientConnectionLost(self, connector, reason):\n connector.connect()","title":""},{"docid":"480aae69089cb6801bb313cf4dad7b2d","score":"0.5890543","text":"def clientConnectionLost(self, connector, reason):\n connector.connect()","title":""},{"docid":"480aae69089cb6801bb313cf4dad7b2d","score":"0.5890543","text":"def clientConnectionLost(self, connector, reason):\n connector.connect()","title":""},{"docid":"480aae69089cb6801bb313cf4dad7b2d","score":"0.5890543","text":"def clientConnectionLost(self, connector, reason):\n connector.connect()","title":""},{"docid":"6423d2face0ba7560cb851368bda102b","score":"0.5879564","text":"def Connect(self):\n error_msg = \"Failed to connect to remote device: \" + self.UNSUPPORTED_REASON\n logging.error(error_msg)\n raise BluefruitLEException(error_msg)","title":""},{"docid":"fa3d5464c3e68a7d3cc7b4e67cc91b44","score":"0.58723104","text":"def _failure_handler(self) -> None:\n self._log.debug('DHCP request failed')\n self._write_ret(ovpn.CC_RET_FAILED)","title":""},{"docid":"1c2036ce04e3102c35dd22553b110d05","score":"0.58659947","text":"def __initialConnectionRefused(self):\n self.__setConnected(False)","title":""},{"docid":"efb70ce6a47a1eba0e7a2cddfec19ce0","score":"0.58597976","text":"async def test_device_setup_network_timeout(hass: HomeAssistant) -> None:\n device = get_device(\"Office\")\n mock_api = device.get_mock_api()\n mock_api.auth.side_effect = blke.NetworkTimeoutError()\n\n with patch.object(\n hass.config_entries, \"async_forward_entry_setup\"\n ) as mock_forward, patch.object(\n hass.config_entries.flow, \"async_init\"\n ) as mock_init:\n mock_setup = await device.setup_entry(hass, mock_api=mock_api)\n\n assert mock_setup.entry.state is ConfigEntryState.SETUP_RETRY\n assert mock_setup.api.auth.call_count == 1\n assert mock_forward.call_count == 0\n assert mock_init.call_count == 0","title":""},{"docid":"32da673b59b16992899728759d40fdf4","score":"0.58591765","text":"def _finishInit(self, whenDone, skt, error, reactor):\n if whenDone:\n self._commonConnection.__init__(self, skt, None, reactor)\n reactor.callLater(0, whenDone)\n else:\n reactor.callLater(0, self.failIfNotConnected, error)","title":""},{"docid":"7e4c265b9abf0129d59e9dcffddcd740","score":"0.58291787","text":"def clientConnectionLost(self, connector, reason):\n print \"connection lost: \", reason\n connector.connect()","title":""},{"docid":"eab6e0cf299e658d3ff774b0bcafc618","score":"0.5828817","text":"def on_connect(client,userdata,flags,rc):\n if rc != 0:\n print(\"connection to MQTT failed: \" + connack_string(rc))\n exit(1)\n else:\n print('connected to MQTT!')","title":""},{"docid":"dc5a8ce7ea6f37693079150ff48139ea","score":"0.58196956","text":"def connection_lost(self, exc):\n if exc:\n print(exc)\n print(\"Lost connection to serial device\")","title":""},{"docid":"fb8a4edd959ae5fd6beb330e1923a797","score":"0.58190507","text":"def after_send_error(self, err):\n pass","title":""},{"docid":"0f8f8a4b940f186af776fb4799df13d5","score":"0.58111495","text":"def on_connect(client, userdata, flags, rc):\r\n if rc == 0:\r\n print(\"Connected to \", LOCALHOST)\r\n else:\r\n print(\"Bad connection\")","title":""},{"docid":"344ee7c9870714785f7400dd02693d0c","score":"0.57919157","text":"def check_for_setup_error(self):\n if self.lun_ostype not in self.ALLOWED_LUN_OS_TYPES:\n msg = _(\"Invalid value for NetApp configuration\"\n \" option netapp_lun_ostype.\")\n LOG.error(msg)\n raise na_utils.NetAppDriverException(msg)\n if self.host_type not in self.ALLOWED_IGROUP_HOST_TYPES:\n msg = _(\"Invalid value for NetApp configuration\"\n \" option netapp_host_type.\")\n LOG.error(msg)\n raise na_utils.NetAppDriverException(msg)\n lun_list = self.zapi_client.get_lun_list()\n self._extract_and_populate_luns(lun_list)\n LOG.debug(\"Success getting list of LUNs from server.\")\n self.loopingcalls.start_tasks()","title":""},{"docid":"58917727f2131d697489f5e31959cfb8","score":"0.5762713","text":"def test_driver_check_for_setup_error_exception(self):\n\n # Since HPE3ParMediator is mocked, we'll hit the except/log.\n self.mock_object(hpe3pardriver, 'LOG')\n self.init_driver()\n self.driver.check_for_setup_error()\n expected_calls = [\n mock.call.debug('HPE3ParShareDriver SHA1: %s', mock.ANY),\n mock.call.debug('Source code SHA1 not logged due to: %s', mock.ANY)\n ]\n hpe3pardriver.LOG.assert_has_calls(expected_calls)","title":""},{"docid":"410a74fdf6aa3a323ec3e35b2b084b1a","score":"0.5761829","text":"def _handler_lost_connection_enter(self, *args, **kwargs):\n super(PlatformAgent, self)._common_state_enter(*args, **kwargs)\n log.error(\"%r: (LC) lost connection to the device. Will attempt to reconnect...\",\n self._platform_id)\n\n self._event_publisher.publish_event(\n event_type='ResourceAgentConnectionLostErrorEvent',\n origin_type=self.ORIGIN_TYPE,\n origin=self.resource_id)\n\n # Setup reconnect timer.\n self._autoreconnect_greenlet = spawn(self._autoreconnect)","title":""},{"docid":"9f353f3e29a2785f629a49f9795aeaaa","score":"0.5735981","text":"async def test_setup_failed(\n hass: HomeAssistant, caplog: pytest.LogCaptureFixture\n) -> None:\n mocked_device = _create_mocked_device(throw_exception=True)\n entry = MockConfigEntry(domain=songpal.DOMAIN, data=CONF_DATA)\n entry.add_to_hass(hass)\n\n with _patch_media_player_device(mocked_device):\n await hass.config_entries.async_setup(entry.entry_id)\n await hass.async_block_till_done()\n all_states = hass.states.async_all()\n assert len(all_states) == 0\n assert \"[name(http://0.0.0.0:10000/sony)] Unable to connect\" in caplog.text\n assert \"Platform songpal not ready yet: Unable to do POST request\" in caplog.text\n assert not any(x.levelno == logging.ERROR for x in caplog.records)\n caplog.clear()\n\n utcnow = dt_util.utcnow()\n type(mocked_device).get_supported_methods = AsyncMock()\n with _patch_media_player_device(mocked_device):\n async_fire_time_changed(hass, utcnow + timedelta(seconds=30))\n await hass.async_block_till_done()\n all_states = hass.states.async_all()\n assert len(all_states) == 1\n assert not any(x.levelno == logging.WARNING for x in caplog.records)\n assert not any(x.levelno == logging.ERROR for x in caplog.records)","title":""},{"docid":"fddf1257d77e9110022c8cb51a7282dc","score":"0.57325023","text":"async def test_config_entry_not_ready(hass: HomeAssistant) -> None:\n config_entry = mock_config_entry(unique_id=\"id_123_not_ready\")\n config_entry.add_to_hass(hass)\n with patch(\n \"homeassistant.components.rituals_perfume_genie.Account.get_devices\",\n side_effect=aiohttp.ClientError,\n ):\n await hass.config_entries.async_setup(config_entry.entry_id)\n assert config_entry.state is ConfigEntryState.SETUP_RETRY","title":""},{"docid":"00a1dee6520e19e8674629cfac2d135f","score":"0.57131076","text":"def on_connect(client, userdata, flags, rc):\n print(\"connected with result code {}\".format(rc))","title":""},{"docid":"330ebb43db70d24c45de90fdb1c2f633","score":"0.57082","text":"async def test_connection_failed(self):\n with patch(\n \"pyinsteon.protocol.protocol.publish_topic\", self.mock_publish_topic\n ):\n try:\n async with async_protocol_manager(connect=False, retry=False):\n await asyncio.sleep(0.1)\n assert False\n except ConnectionError:\n await asyncio.sleep(0.1)\n assert self.topic == \"connection.failed\"","title":""},{"docid":"52317eedbce371c9fd92b82a4a388f0b","score":"0.57081854","text":"def onError(self, conn, exc):","title":""},{"docid":"7462592c810b67da3c920c1361f76dc6","score":"0.5680518","text":"async def test_connection_error(hass):\n\n with patch(\n \"aioairzone.localapi_device.AirzoneLocalApi.validate_airzone\",\n side_effect=ClientConnectorError(MagicMock(), MagicMock()),\n ):\n result = await hass.config_entries.flow.async_init(\n DOMAIN, context={\"source\": SOURCE_USER}, data=CONFIG\n )\n\n assert result[\"errors\"] == {\"base\": \"cannot_connect\"}","title":""},{"docid":"0bcc08166bb77942566f0809e5217359","score":"0.5672495","text":"def DeviceException(self):\n self.dev.DeviceException()","title":""},{"docid":"5de20bf45e13ce926f5c2ceb04bd9eed","score":"0.56696653","text":"async def test_dhcp_fails_to_connect(hass: HomeAssistant) -> None:\n\n with patch(\n \"homeassistant.components.radiotherm.data.radiotherm.get_thermostat\",\n side_effect=RadiothermTstatError,\n ):\n result = await hass.config_entries.flow.async_init(\n DOMAIN,\n context={\"source\": config_entries.SOURCE_DHCP},\n data=dhcp.DhcpServiceInfo(\n hostname=\"radiotherm\",\n ip=\"1.2.3.4\",\n macaddress=\"aa:bb:cc:dd:ee:ff\",\n ),\n )\n await hass.async_block_till_done()\n\n assert result[\"type\"] == data_entry_flow.FlowResultType.ABORT\n assert result[\"reason\"] == \"cannot_connect\"","title":""},{"docid":"0c285dd86319782dc70c0d5b38adaf18","score":"0.5658114","text":"def on_connection_success(self):\n\n pass","title":""},{"docid":"e72eea797f65e797945db8c63cd83e4e","score":"0.56492656","text":"def handle_not_connected(self):\n LOG.error('msm failed, network connection not available')\n self.next_download = time() + FIVE_MINUTES","title":""},{"docid":"62f6f82d502a3061ef478d91f68226e1","score":"0.5642949","text":"def _handler_lost_connection_enter(self, *args, **kwargs):\n super(DataSetAgent, self)._common_state_enter(*args, **kwargs)\n log.error('Dataset agent %s lost connection to the device.',\n self._proc_name)\n\n self._event_publisher.publish_event(\n event_type='ResourceAgentConnectionLostErrorEvent',\n origin_type=self.ORIGIN_TYPE,\n origin=self.resource_id)\n\n # Setup reconnect timer.\n self._autoreconnect_greenlet = gevent.spawn(self._autoreconnect)","title":""},{"docid":"9c5b3df988d0c98224bafe5f91375ccd","score":"0.5627677","text":"def register_ad_error_cb(error):\n print('Failed to register advertisement: ' + str(error))\n mainloop.quit()","title":""},{"docid":"bbf4ccc0873d9e92da439b29f84b73b2","score":"0.5625665","text":"def onConnectionRefuse(self):","title":""},{"docid":"6d236c806ca11a27e6f2eebc5b4cc7ed","score":"0.5622576","text":"def callback_connect(self):\n pass","title":""},{"docid":"7c5897be5640089d7a586d06fa1a2220","score":"0.5610685","text":"def got_error(self, error):\n if isinstance(error, self.dbus.exceptions.DBusException) and \\\n error.get_dbus_name() == DBUS_UNKNOWN_SERVICE:\n logger.debug(\"Network Manager not present\")\n self.call_result_cb(UNKNOWN)\n else:\n logger.error(\"Error contacting NetworkManager: %s\" % \\\n str(error))\n self.call_result_cb(UNKNOWN)","title":""},{"docid":"b000b5d2e3bac6c0670e3fe307acc35c","score":"0.56089234","text":"def _connection_failed(self, link_uri, msg):\n print('Connection to %s failed: %s' % (link_uri, msg))\n self.is_connected = False","title":""},{"docid":"daf763ced6db021fbc1ff497e6b0943b","score":"0.56058013","text":"def failIfNotConnected(self, err):\n if (self.connected or self.disconnected or\n not hasattr(self, \"connector\")):\n return\n\n self._stopReadingAndWriting()\n try:\n self._closeSocket(True)\n except AttributeError:\n pass\n else:\n self._collectSocketDetails()\n self.connector.connectionFailed(failure.Failure(err))\n del self.connector","title":""},{"docid":"81ced1aaf1fe94efa75ce25608893355","score":"0.5605212","text":"def _connection_failed(self, link_uri, msg):\n print(\"Connection to %s failed: %s\" % (link_uri, msg))\n self.is_connected = False","title":""},{"docid":"3cb096a849659ddbc2f48eec8d344d62","score":"0.55960256","text":"def on_connect(self):\r\n return None","title":""},{"docid":"3cb096a849659ddbc2f48eec8d344d62","score":"0.55960256","text":"def on_connect(self):\r\n return None","title":""},{"docid":"82a45ccd2e4e4b3a7a3a1974bfd4093e","score":"0.5592014","text":"def connectionFailed(self, reason=None):\n msg = '[%s] Connection Failed: %s/%s'%(\n self.request.backend.base,\n self.request.backendServer.path, self.request.backend_uri)\n\n if reason:\n msg = '%s (%s)'%(msg, reason.getErrorMessage())\n log.debug(\"Connection Failed: \"+str(reason), 'Fetcher')\n log.err(msg)\n\n # Look for alternative fetchers\n if not self.request.activateNextBackendServer(self):\n # No more backends, send error response back to client\n if reason.check(error.ConnectError):\n self.setResponseCode(http.SERVICE_UNAVAILABLE, \"Connect Error\")\n else:\n self.setResponseCode(http.SERVICE_UNAVAILABLE)\n self.apDataReceived(\"\")\n self.apDataEnd(self.transfered, False)\n #Because of a bug in tcp.Client we may be called twice,\n #Make sure that next time nothing will happen\n #FIXME: This hack is probably not anymore pertinent.\n self.connectionFailed = lambda : log.debug('connectionFailed(2)',\n 'Fetcher','9')","title":""},{"docid":"99ca652745e7cc4c8cd093c1e3ab6660","score":"0.5584071","text":"def test_driver_check_for_setup_error_success(self):\n\n # Generally this is always mocked, but here we reference the class.\n hpe3parmediator.HPE3ParMediator = self.real_hpe_3par_mediator\n\n self.mock_object(hpe3pardriver, 'LOG')\n self.init_driver()\n self.driver.check_for_setup_error()\n expected_calls = [\n mock.call.debug('HPE3ParShareDriver SHA1: %s', mock.ANY),\n mock.call.debug('HPE3ParMediator SHA1: %s', mock.ANY)\n ]\n hpe3pardriver.LOG.assert_has_calls(expected_calls)","title":""},{"docid":"c44e996a2f9b1b96dd1896119fbd29a6","score":"0.55826896","text":"def exception_callback(self, exception):\n log.error('Exception detected in the driver', exc_info=True)\n self._fsm.on_event(ResourceAgentEvent.LOST_CONNECTION)","title":""},{"docid":"df861c7b76afdff058e76553f064380c","score":"0.5571946","text":"async def test_device_setup_update_network_timeout(hass: HomeAssistant) -> None:\n device = get_device(\"Office\")\n mock_api = device.get_mock_api()\n mock_api.check_sensors.side_effect = blke.NetworkTimeoutError()\n\n with patch.object(\n hass.config_entries, \"async_forward_entry_setup\"\n ) as mock_forward, patch.object(\n hass.config_entries.flow, \"async_init\"\n ) as mock_init:\n mock_setup = await device.setup_entry(hass, mock_api=mock_api)\n\n assert mock_setup.entry.state is ConfigEntryState.SETUP_RETRY\n assert mock_setup.api.auth.call_count == 1\n assert mock_setup.api.check_sensors.call_count == 1\n assert mock_forward.call_count == 0\n assert mock_init.call_count == 0","title":""},{"docid":"a497fb8d7c964038a1a5d24eb2f531ce","score":"0.5570269","text":"async def test_device_setup_authentication_error(hass: HomeAssistant) -> None:\n device = get_device(\"Living Room\")\n mock_api = device.get_mock_api()\n mock_api.auth.side_effect = blke.AuthenticationError()\n\n with patch.object(\n hass.config_entries, \"async_forward_entry_setup\"\n ) as mock_forward, patch.object(\n hass.config_entries.flow, \"async_init\"\n ) as mock_init:\n mock_setup = await device.setup_entry(hass, mock_api=mock_api)\n\n assert mock_setup.entry.state is ConfigEntryState.SETUP_ERROR\n assert mock_setup.api.auth.call_count == 1\n assert mock_forward.call_count == 0\n assert mock_init.call_count == 1\n assert mock_init.mock_calls[0][2][\"context\"][\"source\"] == \"reauth\"\n assert mock_init.mock_calls[0][2][\"data\"] == {\n \"name\": device.name,\n **device.get_entry_data(),\n }","title":""},{"docid":"e2e8b9843877ad3d0599119f28c95953","score":"0.55674","text":"def test_connection_failure(self):\n self.assertTrue(Enigma2Error, lambda: enigma2.api.Enigma2Connection(host='1.1.1.1'))","title":""},{"docid":"f270323f113d3126b4ad2f7975732386","score":"0.5562889","text":"async def test_async_setup_entry_auth_failed(hass: HomeAssistant) -> None:\n entry = create_entry(hass)\n with patch_interface() as interface:\n interface.side_effect = steam.api.HTTPError(\"401\")\n await hass.config_entries.async_setup(entry.entry_id)\n await hass.async_block_till_done()\n assert entry.state == ConfigEntryState.SETUP_ERROR\n assert not hass.data.get(DOMAIN)","title":""},{"docid":"f447a97342cd26887d90165464282130","score":"0.55579406","text":"def connectionLost(self, reason):\n if self.delegate is not None:\n self.delegate.didLoseConnection(reason.getErrorMessage())","title":""},{"docid":"2b8b579918a53b5438af4706310a2427","score":"0.5549847","text":"def post_connect_handler(self):\n pass","title":""},{"docid":"2f2ed6b88820afb070fe6ed46ebaae2b","score":"0.5541294","text":"def connectionLost(self, reason):\n if not self.connected:\n self.failIfNotConnected(error.ConnectError(string=reason))\n else:\n self._commonConnection.connectionLost(self, reason)\n self.connector.connectionLost(reason)","title":""},{"docid":"c21214779c2d6a66108cfcdffaddcd11","score":"0.55395067","text":"def on_fail(self, cntx):","title":""},{"docid":"042acef581ab149bb5195d53a68b1e4b","score":"0.553431","text":"async def test_connection_errors_raise_not_ready(\n hass, config_entry, smartthings_mock):\n setattr(hass.config_entries, '_entries', [config_entry])\n api = smartthings_mock.return_value\n api.app.return_value = mock_coro(\n exception=ClientConnectionError())\n\n with pytest.raises(ConfigEntryNotReady):\n await smartthings.async_setup_entry(hass, config_entry)","title":""},{"docid":"972eb9b76aac68a94ce9d9514767c42e","score":"0.55272996","text":"def __on_connect(self, client, userdata, flags, rc):\n logger.debug(\"Connected with rc code: \" + str(rc))\n if rc == 0:\n if self.is_initial_connect:\n self.__publish_bootinfo()\n self.is_initial_connect = False\n self.on_connect_callback()\n else:\n logger.error(\"Failed to connect to mqtt broker: \" + str(rc))","title":""},{"docid":"099df4da2ad5cd5f0133747bf757bd0a","score":"0.5499527","text":"def clientConnectionFailed(self, connector, reason):\r\n if hasattr(self, \"server_restart_mode\"):\r\n self.maxDelay = 1\r\n else:\r\n self.maxDelay = 10\r\n self.portal.sessions.announce_all(\" ...\")\r\n protocol.ReconnectingClientFactory.clientConnectionFailed(self, connector, reason)","title":""},{"docid":"5697ba07f085e4851c4a57648fa7b067","score":"0.5495648","text":"def register_app_error_cb(error):\n print('Failed to register application: ' + str(error))\n mainloop.quit()","title":""},{"docid":"527a980fa1c0d78bcce82092fa59eba7","score":"0.54914784","text":"async def _async_connect(self) -> None:\n LOGGER.debug(\"_async_connect: location: %s\", self._location)\n assert self._location, \"self._location has not been set before connect\"\n\n domain_data = get_domain_data(self.hass)\n try:\n device = await domain_data.upnp_factory.async_create_device(self._location)\n except UpnpError as err:\n raise ConnectError(\"cannot_connect\") from err\n\n if not DmrDevice.is_profile_device(device):\n raise ConnectError(\"not_dmr\")\n\n device = find_device_of_type(device, DmrDevice.DEVICE_TYPES)\n\n if not self._udn:\n self._udn = device.udn\n await self.async_set_unique_id(self._udn)\n\n # Abort if already configured, but update the last-known location\n self._abort_if_unique_id_configured(\n updates={CONF_URL: self._location}, reload_on_update=False\n )\n\n if not self._device_type:\n self._device_type = device.device_type\n\n if not self._name:\n self._name = device.name\n\n if not self._mac and (host := urlparse(self._location).hostname):\n self._mac = await _async_get_mac_address(self.hass, host)","title":""},{"docid":"720b730e78a4c6f0a4d8cb236dcf5938","score":"0.54846907","text":"def on_mqtt_connect(client, userdata, flags, rc):\n print(\"MQTT connection successful\")","title":""},{"docid":"c9a38cf5ba2d50f3e95cef7b40cac857","score":"0.54777217","text":"def connection_lost(self, ex: Optional[Exception]) -> None:\n _LOGGER.debug(\"WizProtocol connection lost: %s\", ex)","title":""},{"docid":"ceea3844dadcbc463ec4081c101eb3e3","score":"0.5477571","text":"def clientConnectionFailed(self, connector, reason):\n reactor.stop()","title":""},{"docid":"093be4f57b8b627e4273624eba16feb7","score":"0.5475054","text":"async def test_config_entry_retry(hass):\n already_migrated_config_entry = MockConfigEntry(\n domain=DOMAIN, data={CONF_HOST: IP_ADDRESS}, unique_id=MAC_ADDRESS\n )\n already_migrated_config_entry.add_to_hass(hass)\n with _patch_discovery(no_device=True), _patch_single_discovery(no_device=True):\n await async_setup_component(hass, tplink.DOMAIN, {tplink.DOMAIN: {}})\n await hass.async_block_till_done()\n assert already_migrated_config_entry.state == ConfigEntryState.SETUP_RETRY","title":""},{"docid":"e0e05b60f952aa20b9c914b583240d3f","score":"0.54732364","text":"def on_retry(self, exc_info):\n ...","title":""},{"docid":"a959dc74a92d947b160856c1cbc46aa6","score":"0.5469504","text":"def on_connect(client, userd, flags, rc):\n syslog.syslog(f\"MQTT connect rc: {str(rc)}\")","title":""},{"docid":"83d85e3369c301b897490170e142e2fe","score":"0.5468484","text":"def test_connectionLost(self):\r\n self.request.connectionLost(Failure(CONNECTION_DONE))\r\n self.handler.lostReason.trap(ConnectionDone)","title":""},{"docid":"34e810d79dc5af0db27ad342e186c235","score":"0.54684293","text":"def connectionInitialized():","title":""},{"docid":"8c02916e987ae978a5d194b436db06d9","score":"0.5462366","text":"def connection_lost(self, exc):\n if not self.suspendAllOperations:\n log.error(\"ERROR Connection Lost : disconnected because the Ethernet/USB connection was externally terminated.\")\n self._performDisconnect(reason=\"termination\", exc=exc)","title":""},{"docid":"97ddde240ce639e24419bce9dc87bbf5","score":"0.5454659","text":"def check_connection(self):\n self.connect()","title":""},{"docid":"ee333a1db1a87d647a564224e6c30635","score":"0.54460835","text":"def test_connection_lost(gateway, connection_transport, reconnect_callback):\n assert gateway.tasks.transport.protocol.transport is None\n gateway.tasks.transport.connect()\n assert gateway.tasks.transport.protocol.transport is connection_transport\n gateway.tasks.transport.protocol.connection_lost(\"error\")\n assert connection_transport.serial.close.call_count == 1\n assert reconnect_callback.call_count == 1\n assert gateway.tasks.transport.protocol.transport is None","title":""},{"docid":"2b240bb606ba7d77f90a5927c27907b0","score":"0.5443004","text":"def test_check_connect(nodaq):\n logger.debug('test_check_connect')\n with pytest.raises(RuntimeError):\n nodaq.wait()","title":""},{"docid":"e63e4d159c35631c28ff284eb8cd375c","score":"0.5441226","text":"def check_device_state(self):\n\n if not (self.switch.cli.conn.check_client() and self.switch.cli.conn.check_shell()):\n try:\n self.switch.ui.connect()\n except (CLISSHException, SSHException, SocketError):\n self.switch.ui.disconnect()\n raise Exception(\"Device is not ready.\")\n\n # Add cli application check","title":""},{"docid":"f7912fe97387787fb5827348571a222e","score":"0.54365784","text":"def connectError(self, reason):\n if self.stream_error_called:\n return\n # Before Twisted 11.x the xmlstream object was passed instead of the\n # disconnect reason. See http://twistedmatrix.com/trac/ticket/2618\n if not isinstance(reason, failure.Failure):\n reason_str = 'Reason unknown'\n else:\n reason_str = str(reason)\n\n # If the connection was established and lost, then we need to report\n # the error back to the client, since he needs to reauthenticate.\n # FIXME: If the connection was lost before anything happened, we could\n # silently retry instead.\n if self.verbose:\n log.msg('connect ERROR: %s' % reason_str)\n\n self.stopTrying()\n\n e = error.Error('remote-connection-failed')\n\n do_expire = True\n\n if self.waiting_requests:\n wr = self.waiting_requests.pop(0)\n wr.doErrback(e)\n else:\n # need to wait for a new request and then expire\n do_expire = False\n\n if self.pint and self.sid in self.pint.sessions:\n if do_expire:\n try:\n self.expire()\n except Exception:\n self.onExpire()\n else:\n s = self.pint.sessions.get(self.sid)\n s.stream_error = e","title":""},{"docid":"16c81482f599a6d7eeef1194ab53f772","score":"0.54336935","text":"async def test_color_palette_select_connection_error(\n hass: HomeAssistant,\n mock_wled: MagicMock,\n) -> None:\n mock_wled.segment.side_effect = WLEDConnectionError\n\n with pytest.raises(HomeAssistantError, match=\"Error communicating with WLED API\"):\n await hass.services.async_call(\n SELECT_DOMAIN,\n SERVICE_SELECT_OPTION,\n {\n ATTR_ENTITY_ID: \"select.wled_rgb_light_segment_1_color_palette\",\n ATTR_OPTION: \"Icefire\",\n },\n blocking=True,\n )\n\n assert (state := hass.states.get(\"select.wled_rgb_light_segment_1_color_palette\"))\n assert state.state == STATE_UNAVAILABLE\n assert mock_wled.segment.call_count == 1\n mock_wled.segment.assert_called_with(segment_id=1, palette=\"Icefire\")","title":""}],"string":"[\n {\n \"docid\": \"6ef6703ff92b4c3939b494a6ae277183\",\n \"score\": \"0.6651631\",\n \"text\": \"def test_on_connect_error(self):\\n # this assumed the ssdb server being tested against doesn't use 1023\\n # port. An error should be raised on connect \\n bad_connection = Connection(port=1023)\\n bad_connection.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b46d3244f1db949eed1735145acad8d0\",\n \"score\": \"0.66482955\",\n \"text\": \"def connection_refused_handler(spawn):\\n if spawn.device:\\n spawn.device.api.execute_clear_line()\\n spawn.device.connect()\\n return\\n raise Exception('Connection refused to device %s' % (str(spawn)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c1f852634a59fe3c39994590ed7631c\",\n \"score\": \"0.659285\",\n \"text\": \"def connectSensorErrback(self, reason):\\n print('Connection attempt to sensor failed: %s' %(reason.getErrorMessage()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8ceb3aad3a1bd81a7265cf050c34873\",\n \"score\": \"0.6562177\",\n \"text\": \"async def test_setup_failure_on_connection(opp, caplog):\\n\\n patch_product_identify(None, side_effect=blebox_uniapi.error.ConnectionError)\\n\\n entry = mock_config()\\n entry.add_to_opp(opp)\\n\\n caplog.set_level(logging.ERROR)\\n await opp.config_entries.async_setup(entry.entry_id)\\n await opp.async_block_till_done()\\n\\n assert \\\"Identify failed at 172.100.123.4:80 ()\\\" in caplog.text\\n assert entry.state is ConfigEntryState.SETUP_RETRY\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e6ca924d699daf28724dc244acb29b6\",\n \"score\": \"0.6475644\",\n \"text\": \"def addConnectionFailedCallback(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64b69eefe5679ddcc5280459033850fb\",\n \"score\": \"0.644714\",\n \"text\": \"def connectionLost(reason):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3329a33416f7bf4146fce5169260f14\",\n \"score\": \"0.6442218\",\n \"text\": \"def handle_connect_failure(self):\\n self._connected = False\\n if not self._shutdown:\\n _LOGGER.error('Unable to connect to IP Module. Reconnecting...')\\n self._alarmPanel._loginTimeoutCallback(False)\\n ensure_future(self.reconnect(self._alarmPanel.connection_timeout), loop=self._eventLoop)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f47c3cfd01e183bdc7a0f44851180d0\",\n \"score\": \"0.6427711\",\n \"text\": \"def connectionLost(self, reason):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fbd8d9a1e1821c186d11ab059269b92\",\n \"score\": \"0.6407843\",\n \"text\": \"def on_connection_failure(self, exc):\\n self._record_exception(exc)\\n time.sleep(self._RECONNECT_DELAY)\\n sys.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"998494a7ec477718693842f631779fbb\",\n \"score\": \"0.62489235\",\n \"text\": \"async def test_setup_failed_connect(opp, caplog):\\n\\n respx.get(\\\"http://localhost\\\").mock(\\n side_effect=httpx.RequestError(\\\"server offline\\\", request=MagicMock())\\n )\\n assert await async_setup_component(\\n opp,\\n binary_sensor.DOMAIN,\\n {\\n \\\"binary_sensor\\\": {\\n \\\"platform\\\": \\\"rest\\\",\\n \\\"resource\\\": \\\"http://localhost\\\",\\n \\\"method\\\": \\\"GET\\\",\\n }\\n },\\n )\\n await opp.async_block_till_done()\\n assert len(opp.states.async_all()) == 0\\n assert \\\"server offline\\\" in caplog.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c348be3a2014fb3d90b00f8e01bd8e1\",\n \"score\": \"0.6194147\",\n \"text\": \"def connectionLost(self, reason):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f50797131aa438a7052dc4f99b4ff5e\",\n \"score\": \"0.6179521\",\n \"text\": \"def _connection_failed(self, failure, *args, **kwargs):\\n log_msg(\\\"Port test failed to connect: %s\\\" % (failure), 1)\\n self.probeRunning = False\\n self.ignoreExits.append(self.circ.get_exit())\\n if not self.nextScheduledTest or not self.nextScheduledTest.active():\\n #dont count as a failure if the circuit is the thing that failed, not the connection:\\n if self.circ.status != \\\"BUILT\\\":\\n self.circuitFailures += 1\\n #otherwise, make sure we get a new circuit to try with\\n else:\\n self.circ.close()\\n self.circ = None\\n #dont retry more than self.MAX_TESTS times in a row:\\n if self.numProbes - self.circuitFailures < self.MAX_TESTS:\\n self._schedule_next_test()\\n else:\\n log_msg(\\\"Retried self testing of reachability too many times, stopping.\\\", 1)\\n self._on_unreachable(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307b57c8eddaed0ea53af6180e432de3\",\n \"score\": \"0.611997\",\n \"text\": \"async def test_setup_failure(opp, caplog):\\n\\n patch_product_identify(None, side_effect=blebox_uniapi.error.ClientError)\\n\\n entry = mock_config()\\n entry.add_to_opp(opp)\\n\\n caplog.set_level(logging.ERROR)\\n await opp.config_entries.async_setup(entry.entry_id)\\n await opp.async_block_till_done()\\n\\n assert \\\"Identify failed at 172.100.123.4:80 ()\\\" in caplog.text\\n assert entry.state is ConfigEntryState.SETUP_RETRY\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c25746fe2bfa9dccdf0dea17066df518\",\n \"score\": \"0.6088787\",\n \"text\": \"def on_connect(client, userdata, flags, rc):\\n if rc == 0:\\n print(\\\"connected OK with returned code=\\\", rc)\\n else:\\n print(\\\"Bad connection with returned code=\\\", rc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e67b550e46046a3005bc4c2fdffc0d64\",\n \"score\": \"0.6082641\",\n \"text\": \"def conn_callback(self):\\n print 'Connected to a random stranger!\\\\n'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82e07f22e16b2dc6ad90b1bb162a0065\",\n \"score\": \"0.6041003\",\n \"text\": \"def Connect(self, retry):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2a11272666187a9074e2e008f18d0a0\",\n \"score\": \"0.6035678\",\n \"text\": \"def test_connect_exception(self):\\n poller = MiFloraPoller(self.TEST_MAC, ConnectExceptionBackend)\\n with self.assertRaises(BluetoothBackendException):\\n poller.firmware_version()\\n with self.assertRaises(BluetoothBackendException):\\n poller.name()\\n with self.assertRaises(BluetoothBackendException):\\n poller.parameter_value(MI_MOISTURE)\\n with self.assertRaises(BluetoothBackendException):\\n poller.parameter_value(MI_MOISTURE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f36bfc7940848a8bcbf8bccad9b6d9\",\n \"score\": \"0.6025926\",\n \"text\": \"def clientConnectionFailed(self, connector, reason):\\n self._onConnection.errback(reason)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8674e25b2cd3a1a658f3340d15d7c314\",\n \"score\": \"0.6003622\",\n \"text\": \"def on_connect(self, unused_client, unused_userdata, unused_flags, rc):\\n self.print_debug('Connection Result:', error_str(rc))\\n self.connected = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0d446c2885413ad1e84f82b13269dd0\",\n \"score\": \"0.59826756\",\n \"text\": \"async def test_async_setup_entry_not_ready(\\n hass: HomeAssistant, aioclient_mock: AiohttpClientMocker\\n) -> None:\\n entry: ConfigEntry = await async_init_integration(\\n hass, aioclient_mock, error=\\\"cannot_connect\\\"\\n )\\n await hass.async_block_till_done()\\n assert entry.state == ConfigEntryState.SETUP_RETRY\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f480747e8747440742fbe871d120159\",\n \"score\": \"0.5979643\",\n \"text\": \"def on_reconnect(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb89c6af5489bebbc2e3e2bda220da47\",\n \"score\": \"0.595108\",\n \"text\": \"def test_connect_exception(self):\\n poller = MiTempBtPoller(self.TEST_MAC, ConnectExceptionBackend, retries=0)\\n with self.assertRaises(BluetoothBackendException):\\n poller.firmware_version()\\n with self.assertRaises(BluetoothBackendException):\\n poller.name()\\n with self.assertRaises(BluetoothBackendException):\\n poller.parameter_value(MI_TEMPERATURE)\\n with self.assertRaises(BluetoothBackendException):\\n poller.parameter_value(MI_HUMIDITY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8135fbe57e761e32d166ebef6ef50df\",\n \"score\": \"0.5943264\",\n \"text\": \"def clientConnectionLost(self, connector, reason): #{{{\\n connector.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eca39338d01be2705630dd5b9fff588d\",\n \"score\": \"0.5935057\",\n \"text\": \"def test_failed_config(self):\\n self._conn.execute(\\\"configure terminal\\\", allow_state_change=True)\\n self._conn.execute(\\\"test failed\\\")\\n self._conn.spawn.timeout = 60\\n self._conn.enable()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc8f50cc745c6b3e1e03d7c134df275d\",\n \"score\": \"0.5917956\",\n \"text\": \"def check_for_setup_error(self):\\n if not self.ra.is_pool_exists(self.pool):\\n LOG.error(\\\"Setup is incorrect, please check connection settings.\\\")\\n raise exception.VolumeDriverException(\\\"Bad configuration expected\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"631654a538a1ec540f042da500487cd5\",\n \"score\": \"0.5916882\",\n \"text\": \"def connectionBroken(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24b9a391eb61e807cb74f89725510e55\",\n \"score\": \"0.5900489\",\n \"text\": \"async def test_error_on_connection_failure(\\n hass: HomeAssistant, mock_api: MagicMock\\n) -> None:\\n result = await hass.config_entries.flow.async_init(\\n DOMAIN, context={\\\"source\\\": config_entries.SOURCE_USER}\\n )\\n\\n mock_api.side_effect = TransmissionConnectError()\\n result2 = await hass.config_entries.flow.async_configure(\\n result[\\\"flow_id\\\"],\\n MOCK_CONFIG_DATA,\\n )\\n\\n assert result2[\\\"type\\\"] == FlowResultType.FORM\\n assert result2[\\\"errors\\\"] == {\\\"base\\\": \\\"cannot_connect\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2378f9c9799121c77bf66454ff88b974\",\n \"score\": \"0.58909976\",\n \"text\": \"async def test_device_setup_os_error(hass: HomeAssistant) -> None:\\n device = get_device(\\\"Office\\\")\\n mock_api = device.get_mock_api()\\n mock_api.auth.side_effect = OSError()\\n\\n with patch.object(\\n hass.config_entries, \\\"async_forward_entry_setup\\\"\\n ) as mock_forward, patch.object(\\n hass.config_entries.flow, \\\"async_init\\\"\\n ) as mock_init:\\n mock_setup = await device.setup_entry(hass, mock_api=mock_api)\\n\\n assert mock_setup.entry.state is ConfigEntryState.SETUP_RETRY\\n assert mock_setup.api.auth.call_count == 1\\n assert mock_forward.call_count == 0\\n assert mock_init.call_count == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480aae69089cb6801bb313cf4dad7b2d\",\n \"score\": \"0.5890543\",\n \"text\": \"def clientConnectionLost(self, connector, reason):\\n connector.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480aae69089cb6801bb313cf4dad7b2d\",\n \"score\": \"0.5890543\",\n \"text\": \"def clientConnectionLost(self, connector, reason):\\n connector.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480aae69089cb6801bb313cf4dad7b2d\",\n \"score\": \"0.5890543\",\n \"text\": \"def clientConnectionLost(self, connector, reason):\\n connector.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480aae69089cb6801bb313cf4dad7b2d\",\n \"score\": \"0.5890543\",\n \"text\": \"def clientConnectionLost(self, connector, reason):\\n connector.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480aae69089cb6801bb313cf4dad7b2d\",\n \"score\": \"0.5890543\",\n \"text\": \"def clientConnectionLost(self, connector, reason):\\n connector.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480aae69089cb6801bb313cf4dad7b2d\",\n \"score\": \"0.5890543\",\n \"text\": \"def clientConnectionLost(self, connector, reason):\\n connector.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480aae69089cb6801bb313cf4dad7b2d\",\n \"score\": \"0.5890543\",\n \"text\": \"def clientConnectionLost(self, connector, reason):\\n connector.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480aae69089cb6801bb313cf4dad7b2d\",\n \"score\": \"0.5890543\",\n \"text\": \"def clientConnectionLost(self, connector, reason):\\n connector.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6423d2face0ba7560cb851368bda102b\",\n \"score\": \"0.5879564\",\n \"text\": \"def Connect(self):\\n error_msg = \\\"Failed to connect to remote device: \\\" + self.UNSUPPORTED_REASON\\n logging.error(error_msg)\\n raise BluefruitLEException(error_msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3d5464c3e68a7d3cc7b4e67cc91b44\",\n \"score\": \"0.58723104\",\n \"text\": \"def _failure_handler(self) -> None:\\n self._log.debug('DHCP request failed')\\n self._write_ret(ovpn.CC_RET_FAILED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c2036ce04e3102c35dd22553b110d05\",\n \"score\": \"0.58659947\",\n \"text\": \"def __initialConnectionRefused(self):\\n self.__setConnected(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efb70ce6a47a1eba0e7a2cddfec19ce0\",\n \"score\": \"0.58597976\",\n \"text\": \"async def test_device_setup_network_timeout(hass: HomeAssistant) -> None:\\n device = get_device(\\\"Office\\\")\\n mock_api = device.get_mock_api()\\n mock_api.auth.side_effect = blke.NetworkTimeoutError()\\n\\n with patch.object(\\n hass.config_entries, \\\"async_forward_entry_setup\\\"\\n ) as mock_forward, patch.object(\\n hass.config_entries.flow, \\\"async_init\\\"\\n ) as mock_init:\\n mock_setup = await device.setup_entry(hass, mock_api=mock_api)\\n\\n assert mock_setup.entry.state is ConfigEntryState.SETUP_RETRY\\n assert mock_setup.api.auth.call_count == 1\\n assert mock_forward.call_count == 0\\n assert mock_init.call_count == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32da673b59b16992899728759d40fdf4\",\n \"score\": \"0.58591765\",\n \"text\": \"def _finishInit(self, whenDone, skt, error, reactor):\\n if whenDone:\\n self._commonConnection.__init__(self, skt, None, reactor)\\n reactor.callLater(0, whenDone)\\n else:\\n reactor.callLater(0, self.failIfNotConnected, error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e4c265b9abf0129d59e9dcffddcd740\",\n \"score\": \"0.58291787\",\n \"text\": \"def clientConnectionLost(self, connector, reason):\\n print \\\"connection lost: \\\", reason\\n connector.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eab6e0cf299e658d3ff774b0bcafc618\",\n \"score\": \"0.5828817\",\n \"text\": \"def on_connect(client,userdata,flags,rc):\\n if rc != 0:\\n print(\\\"connection to MQTT failed: \\\" + connack_string(rc))\\n exit(1)\\n else:\\n print('connected to MQTT!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc5a8ce7ea6f37693079150ff48139ea\",\n \"score\": \"0.58196956\",\n \"text\": \"def connection_lost(self, exc):\\n if exc:\\n print(exc)\\n print(\\\"Lost connection to serial device\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb8a4edd959ae5fd6beb330e1923a797\",\n \"score\": \"0.58190507\",\n \"text\": \"def after_send_error(self, err):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f8f8a4b940f186af776fb4799df13d5\",\n \"score\": \"0.58111495\",\n \"text\": \"def on_connect(client, userdata, flags, rc):\\r\\n if rc == 0:\\r\\n print(\\\"Connected to \\\", LOCALHOST)\\r\\n else:\\r\\n print(\\\"Bad connection\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"344ee7c9870714785f7400dd02693d0c\",\n \"score\": \"0.57919157\",\n \"text\": \"def check_for_setup_error(self):\\n if self.lun_ostype not in self.ALLOWED_LUN_OS_TYPES:\\n msg = _(\\\"Invalid value for NetApp configuration\\\"\\n \\\" option netapp_lun_ostype.\\\")\\n LOG.error(msg)\\n raise na_utils.NetAppDriverException(msg)\\n if self.host_type not in self.ALLOWED_IGROUP_HOST_TYPES:\\n msg = _(\\\"Invalid value for NetApp configuration\\\"\\n \\\" option netapp_host_type.\\\")\\n LOG.error(msg)\\n raise na_utils.NetAppDriverException(msg)\\n lun_list = self.zapi_client.get_lun_list()\\n self._extract_and_populate_luns(lun_list)\\n LOG.debug(\\\"Success getting list of LUNs from server.\\\")\\n self.loopingcalls.start_tasks()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58917727f2131d697489f5e31959cfb8\",\n \"score\": \"0.5762713\",\n \"text\": \"def test_driver_check_for_setup_error_exception(self):\\n\\n # Since HPE3ParMediator is mocked, we'll hit the except/log.\\n self.mock_object(hpe3pardriver, 'LOG')\\n self.init_driver()\\n self.driver.check_for_setup_error()\\n expected_calls = [\\n mock.call.debug('HPE3ParShareDriver SHA1: %s', mock.ANY),\\n mock.call.debug('Source code SHA1 not logged due to: %s', mock.ANY)\\n ]\\n hpe3pardriver.LOG.assert_has_calls(expected_calls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"410a74fdf6aa3a323ec3e35b2b084b1a\",\n \"score\": \"0.5761829\",\n \"text\": \"def _handler_lost_connection_enter(self, *args, **kwargs):\\n super(PlatformAgent, self)._common_state_enter(*args, **kwargs)\\n log.error(\\\"%r: (LC) lost connection to the device. Will attempt to reconnect...\\\",\\n self._platform_id)\\n\\n self._event_publisher.publish_event(\\n event_type='ResourceAgentConnectionLostErrorEvent',\\n origin_type=self.ORIGIN_TYPE,\\n origin=self.resource_id)\\n\\n # Setup reconnect timer.\\n self._autoreconnect_greenlet = spawn(self._autoreconnect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f353f3e29a2785f629a49f9795aeaaa\",\n \"score\": \"0.5735981\",\n \"text\": \"async def test_setup_failed(\\n hass: HomeAssistant, caplog: pytest.LogCaptureFixture\\n) -> None:\\n mocked_device = _create_mocked_device(throw_exception=True)\\n entry = MockConfigEntry(domain=songpal.DOMAIN, data=CONF_DATA)\\n entry.add_to_hass(hass)\\n\\n with _patch_media_player_device(mocked_device):\\n await hass.config_entries.async_setup(entry.entry_id)\\n await hass.async_block_till_done()\\n all_states = hass.states.async_all()\\n assert len(all_states) == 0\\n assert \\\"[name(http://0.0.0.0:10000/sony)] Unable to connect\\\" in caplog.text\\n assert \\\"Platform songpal not ready yet: Unable to do POST request\\\" in caplog.text\\n assert not any(x.levelno == logging.ERROR for x in caplog.records)\\n caplog.clear()\\n\\n utcnow = dt_util.utcnow()\\n type(mocked_device).get_supported_methods = AsyncMock()\\n with _patch_media_player_device(mocked_device):\\n async_fire_time_changed(hass, utcnow + timedelta(seconds=30))\\n await hass.async_block_till_done()\\n all_states = hass.states.async_all()\\n assert len(all_states) == 1\\n assert not any(x.levelno == logging.WARNING for x in caplog.records)\\n assert not any(x.levelno == logging.ERROR for x in caplog.records)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fddf1257d77e9110022c8cb51a7282dc\",\n \"score\": \"0.57325023\",\n \"text\": \"async def test_config_entry_not_ready(hass: HomeAssistant) -> None:\\n config_entry = mock_config_entry(unique_id=\\\"id_123_not_ready\\\")\\n config_entry.add_to_hass(hass)\\n with patch(\\n \\\"homeassistant.components.rituals_perfume_genie.Account.get_devices\\\",\\n side_effect=aiohttp.ClientError,\\n ):\\n await hass.config_entries.async_setup(config_entry.entry_id)\\n assert config_entry.state is ConfigEntryState.SETUP_RETRY\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00a1dee6520e19e8674629cfac2d135f\",\n \"score\": \"0.57131076\",\n \"text\": \"def on_connect(client, userdata, flags, rc):\\n print(\\\"connected with result code {}\\\".format(rc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"330ebb43db70d24c45de90fdb1c2f633\",\n \"score\": \"0.57082\",\n \"text\": \"async def test_connection_failed(self):\\n with patch(\\n \\\"pyinsteon.protocol.protocol.publish_topic\\\", self.mock_publish_topic\\n ):\\n try:\\n async with async_protocol_manager(connect=False, retry=False):\\n await asyncio.sleep(0.1)\\n assert False\\n except ConnectionError:\\n await asyncio.sleep(0.1)\\n assert self.topic == \\\"connection.failed\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52317eedbce371c9fd92b82a4a388f0b\",\n \"score\": \"0.57081854\",\n \"text\": \"def onError(self, conn, exc):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7462592c810b67da3c920c1361f76dc6\",\n \"score\": \"0.5680518\",\n \"text\": \"async def test_connection_error(hass):\\n\\n with patch(\\n \\\"aioairzone.localapi_device.AirzoneLocalApi.validate_airzone\\\",\\n side_effect=ClientConnectorError(MagicMock(), MagicMock()),\\n ):\\n result = await hass.config_entries.flow.async_init(\\n DOMAIN, context={\\\"source\\\": SOURCE_USER}, data=CONFIG\\n )\\n\\n assert result[\\\"errors\\\"] == {\\\"base\\\": \\\"cannot_connect\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bcc08166bb77942566f0809e5217359\",\n \"score\": \"0.5672495\",\n \"text\": \"def DeviceException(self):\\n self.dev.DeviceException()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5de20bf45e13ce926f5c2ceb04bd9eed\",\n \"score\": \"0.56696653\",\n \"text\": \"async def test_dhcp_fails_to_connect(hass: HomeAssistant) -> None:\\n\\n with patch(\\n \\\"homeassistant.components.radiotherm.data.radiotherm.get_thermostat\\\",\\n side_effect=RadiothermTstatError,\\n ):\\n result = await hass.config_entries.flow.async_init(\\n DOMAIN,\\n context={\\\"source\\\": config_entries.SOURCE_DHCP},\\n data=dhcp.DhcpServiceInfo(\\n hostname=\\\"radiotherm\\\",\\n ip=\\\"1.2.3.4\\\",\\n macaddress=\\\"aa:bb:cc:dd:ee:ff\\\",\\n ),\\n )\\n await hass.async_block_till_done()\\n\\n assert result[\\\"type\\\"] == data_entry_flow.FlowResultType.ABORT\\n assert result[\\\"reason\\\"] == \\\"cannot_connect\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c285dd86319782dc70c0d5b38adaf18\",\n \"score\": \"0.5658114\",\n \"text\": \"def on_connection_success(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e72eea797f65e797945db8c63cd83e4e\",\n \"score\": \"0.56492656\",\n \"text\": \"def handle_not_connected(self):\\n LOG.error('msm failed, network connection not available')\\n self.next_download = time() + FIVE_MINUTES\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62f6f82d502a3061ef478d91f68226e1\",\n \"score\": \"0.5642949\",\n \"text\": \"def _handler_lost_connection_enter(self, *args, **kwargs):\\n super(DataSetAgent, self)._common_state_enter(*args, **kwargs)\\n log.error('Dataset agent %s lost connection to the device.',\\n self._proc_name)\\n\\n self._event_publisher.publish_event(\\n event_type='ResourceAgentConnectionLostErrorEvent',\\n origin_type=self.ORIGIN_TYPE,\\n origin=self.resource_id)\\n\\n # Setup reconnect timer.\\n self._autoreconnect_greenlet = gevent.spawn(self._autoreconnect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c5b3df988d0c98224bafe5f91375ccd\",\n \"score\": \"0.5627677\",\n \"text\": \"def register_ad_error_cb(error):\\n print('Failed to register advertisement: ' + str(error))\\n mainloop.quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbf4ccc0873d9e92da439b29f84b73b2\",\n \"score\": \"0.5625665\",\n \"text\": \"def onConnectionRefuse(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d236c806ca11a27e6f2eebc5b4cc7ed\",\n \"score\": \"0.5622576\",\n \"text\": \"def callback_connect(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5897be5640089d7a586d06fa1a2220\",\n \"score\": \"0.5610685\",\n \"text\": \"def got_error(self, error):\\n if isinstance(error, self.dbus.exceptions.DBusException) and \\\\\\n error.get_dbus_name() == DBUS_UNKNOWN_SERVICE:\\n logger.debug(\\\"Network Manager not present\\\")\\n self.call_result_cb(UNKNOWN)\\n else:\\n logger.error(\\\"Error contacting NetworkManager: %s\\\" % \\\\\\n str(error))\\n self.call_result_cb(UNKNOWN)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b000b5d2e3bac6c0670e3fe307acc35c\",\n \"score\": \"0.56089234\",\n \"text\": \"def _connection_failed(self, link_uri, msg):\\n print('Connection to %s failed: %s' % (link_uri, msg))\\n self.is_connected = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daf763ced6db021fbc1ff497e6b0943b\",\n \"score\": \"0.56058013\",\n \"text\": \"def failIfNotConnected(self, err):\\n if (self.connected or self.disconnected or\\n not hasattr(self, \\\"connector\\\")):\\n return\\n\\n self._stopReadingAndWriting()\\n try:\\n self._closeSocket(True)\\n except AttributeError:\\n pass\\n else:\\n self._collectSocketDetails()\\n self.connector.connectionFailed(failure.Failure(err))\\n del self.connector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81ced1aaf1fe94efa75ce25608893355\",\n \"score\": \"0.5605212\",\n \"text\": \"def _connection_failed(self, link_uri, msg):\\n print(\\\"Connection to %s failed: %s\\\" % (link_uri, msg))\\n self.is_connected = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cb096a849659ddbc2f48eec8d344d62\",\n \"score\": \"0.55960256\",\n \"text\": \"def on_connect(self):\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cb096a849659ddbc2f48eec8d344d62\",\n \"score\": \"0.55960256\",\n \"text\": \"def on_connect(self):\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82a45ccd2e4e4b3a7a3a1974bfd4093e\",\n \"score\": \"0.5592014\",\n \"text\": \"def connectionFailed(self, reason=None):\\n msg = '[%s] Connection Failed: %s/%s'%(\\n self.request.backend.base,\\n self.request.backendServer.path, self.request.backend_uri)\\n\\n if reason:\\n msg = '%s (%s)'%(msg, reason.getErrorMessage())\\n log.debug(\\\"Connection Failed: \\\"+str(reason), 'Fetcher')\\n log.err(msg)\\n\\n # Look for alternative fetchers\\n if not self.request.activateNextBackendServer(self):\\n # No more backends, send error response back to client\\n if reason.check(error.ConnectError):\\n self.setResponseCode(http.SERVICE_UNAVAILABLE, \\\"Connect Error\\\")\\n else:\\n self.setResponseCode(http.SERVICE_UNAVAILABLE)\\n self.apDataReceived(\\\"\\\")\\n self.apDataEnd(self.transfered, False)\\n #Because of a bug in tcp.Client we may be called twice,\\n #Make sure that next time nothing will happen\\n #FIXME: This hack is probably not anymore pertinent.\\n self.connectionFailed = lambda : log.debug('connectionFailed(2)',\\n 'Fetcher','9')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99ca652745e7cc4c8cd093c1e3ab6660\",\n \"score\": \"0.5584071\",\n \"text\": \"def test_driver_check_for_setup_error_success(self):\\n\\n # Generally this is always mocked, but here we reference the class.\\n hpe3parmediator.HPE3ParMediator = self.real_hpe_3par_mediator\\n\\n self.mock_object(hpe3pardriver, 'LOG')\\n self.init_driver()\\n self.driver.check_for_setup_error()\\n expected_calls = [\\n mock.call.debug('HPE3ParShareDriver SHA1: %s', mock.ANY),\\n mock.call.debug('HPE3ParMediator SHA1: %s', mock.ANY)\\n ]\\n hpe3pardriver.LOG.assert_has_calls(expected_calls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c44e996a2f9b1b96dd1896119fbd29a6\",\n \"score\": \"0.55826896\",\n \"text\": \"def exception_callback(self, exception):\\n log.error('Exception detected in the driver', exc_info=True)\\n self._fsm.on_event(ResourceAgentEvent.LOST_CONNECTION)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df861c7b76afdff058e76553f064380c\",\n \"score\": \"0.5571946\",\n \"text\": \"async def test_device_setup_update_network_timeout(hass: HomeAssistant) -> None:\\n device = get_device(\\\"Office\\\")\\n mock_api = device.get_mock_api()\\n mock_api.check_sensors.side_effect = blke.NetworkTimeoutError()\\n\\n with patch.object(\\n hass.config_entries, \\\"async_forward_entry_setup\\\"\\n ) as mock_forward, patch.object(\\n hass.config_entries.flow, \\\"async_init\\\"\\n ) as mock_init:\\n mock_setup = await device.setup_entry(hass, mock_api=mock_api)\\n\\n assert mock_setup.entry.state is ConfigEntryState.SETUP_RETRY\\n assert mock_setup.api.auth.call_count == 1\\n assert mock_setup.api.check_sensors.call_count == 1\\n assert mock_forward.call_count == 0\\n assert mock_init.call_count == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a497fb8d7c964038a1a5d24eb2f531ce\",\n \"score\": \"0.5570269\",\n \"text\": \"async def test_device_setup_authentication_error(hass: HomeAssistant) -> None:\\n device = get_device(\\\"Living Room\\\")\\n mock_api = device.get_mock_api()\\n mock_api.auth.side_effect = blke.AuthenticationError()\\n\\n with patch.object(\\n hass.config_entries, \\\"async_forward_entry_setup\\\"\\n ) as mock_forward, patch.object(\\n hass.config_entries.flow, \\\"async_init\\\"\\n ) as mock_init:\\n mock_setup = await device.setup_entry(hass, mock_api=mock_api)\\n\\n assert mock_setup.entry.state is ConfigEntryState.SETUP_ERROR\\n assert mock_setup.api.auth.call_count == 1\\n assert mock_forward.call_count == 0\\n assert mock_init.call_count == 1\\n assert mock_init.mock_calls[0][2][\\\"context\\\"][\\\"source\\\"] == \\\"reauth\\\"\\n assert mock_init.mock_calls[0][2][\\\"data\\\"] == {\\n \\\"name\\\": device.name,\\n **device.get_entry_data(),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2e8b9843877ad3d0599119f28c95953\",\n \"score\": \"0.55674\",\n \"text\": \"def test_connection_failure(self):\\n self.assertTrue(Enigma2Error, lambda: enigma2.api.Enigma2Connection(host='1.1.1.1'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f270323f113d3126b4ad2f7975732386\",\n \"score\": \"0.5562889\",\n \"text\": \"async def test_async_setup_entry_auth_failed(hass: HomeAssistant) -> None:\\n entry = create_entry(hass)\\n with patch_interface() as interface:\\n interface.side_effect = steam.api.HTTPError(\\\"401\\\")\\n await hass.config_entries.async_setup(entry.entry_id)\\n await hass.async_block_till_done()\\n assert entry.state == ConfigEntryState.SETUP_ERROR\\n assert not hass.data.get(DOMAIN)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f447a97342cd26887d90165464282130\",\n \"score\": \"0.55579406\",\n \"text\": \"def connectionLost(self, reason):\\n if self.delegate is not None:\\n self.delegate.didLoseConnection(reason.getErrorMessage())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b8b579918a53b5438af4706310a2427\",\n \"score\": \"0.5549847\",\n \"text\": \"def post_connect_handler(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f2ed6b88820afb070fe6ed46ebaae2b\",\n \"score\": \"0.5541294\",\n \"text\": \"def connectionLost(self, reason):\\n if not self.connected:\\n self.failIfNotConnected(error.ConnectError(string=reason))\\n else:\\n self._commonConnection.connectionLost(self, reason)\\n self.connector.connectionLost(reason)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c21214779c2d6a66108cfcdffaddcd11\",\n \"score\": \"0.55395067\",\n \"text\": \"def on_fail(self, cntx):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"042acef581ab149bb5195d53a68b1e4b\",\n \"score\": \"0.553431\",\n \"text\": \"async def test_connection_errors_raise_not_ready(\\n hass, config_entry, smartthings_mock):\\n setattr(hass.config_entries, '_entries', [config_entry])\\n api = smartthings_mock.return_value\\n api.app.return_value = mock_coro(\\n exception=ClientConnectionError())\\n\\n with pytest.raises(ConfigEntryNotReady):\\n await smartthings.async_setup_entry(hass, config_entry)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972eb9b76aac68a94ce9d9514767c42e\",\n \"score\": \"0.55272996\",\n \"text\": \"def __on_connect(self, client, userdata, flags, rc):\\n logger.debug(\\\"Connected with rc code: \\\" + str(rc))\\n if rc == 0:\\n if self.is_initial_connect:\\n self.__publish_bootinfo()\\n self.is_initial_connect = False\\n self.on_connect_callback()\\n else:\\n logger.error(\\\"Failed to connect to mqtt broker: \\\" + str(rc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"099df4da2ad5cd5f0133747bf757bd0a\",\n \"score\": \"0.5499527\",\n \"text\": \"def clientConnectionFailed(self, connector, reason):\\r\\n if hasattr(self, \\\"server_restart_mode\\\"):\\r\\n self.maxDelay = 1\\r\\n else:\\r\\n self.maxDelay = 10\\r\\n self.portal.sessions.announce_all(\\\" ...\\\")\\r\\n protocol.ReconnectingClientFactory.clientConnectionFailed(self, connector, reason)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5697ba07f085e4851c4a57648fa7b067\",\n \"score\": \"0.5495648\",\n \"text\": \"def register_app_error_cb(error):\\n print('Failed to register application: ' + str(error))\\n mainloop.quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"527a980fa1c0d78bcce82092fa59eba7\",\n \"score\": \"0.54914784\",\n \"text\": \"async def _async_connect(self) -> None:\\n LOGGER.debug(\\\"_async_connect: location: %s\\\", self._location)\\n assert self._location, \\\"self._location has not been set before connect\\\"\\n\\n domain_data = get_domain_data(self.hass)\\n try:\\n device = await domain_data.upnp_factory.async_create_device(self._location)\\n except UpnpError as err:\\n raise ConnectError(\\\"cannot_connect\\\") from err\\n\\n if not DmrDevice.is_profile_device(device):\\n raise ConnectError(\\\"not_dmr\\\")\\n\\n device = find_device_of_type(device, DmrDevice.DEVICE_TYPES)\\n\\n if not self._udn:\\n self._udn = device.udn\\n await self.async_set_unique_id(self._udn)\\n\\n # Abort if already configured, but update the last-known location\\n self._abort_if_unique_id_configured(\\n updates={CONF_URL: self._location}, reload_on_update=False\\n )\\n\\n if not self._device_type:\\n self._device_type = device.device_type\\n\\n if not self._name:\\n self._name = device.name\\n\\n if not self._mac and (host := urlparse(self._location).hostname):\\n self._mac = await _async_get_mac_address(self.hass, host)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"720b730e78a4c6f0a4d8cb236dcf5938\",\n \"score\": \"0.54846907\",\n \"text\": \"def on_mqtt_connect(client, userdata, flags, rc):\\n print(\\\"MQTT connection successful\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9a38cf5ba2d50f3e95cef7b40cac857\",\n \"score\": \"0.54777217\",\n \"text\": \"def connection_lost(self, ex: Optional[Exception]) -> None:\\n _LOGGER.debug(\\\"WizProtocol connection lost: %s\\\", ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceea3844dadcbc463ec4081c101eb3e3\",\n \"score\": \"0.5477571\",\n \"text\": \"def clientConnectionFailed(self, connector, reason):\\n reactor.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"093be4f57b8b627e4273624eba16feb7\",\n \"score\": \"0.5475054\",\n \"text\": \"async def test_config_entry_retry(hass):\\n already_migrated_config_entry = MockConfigEntry(\\n domain=DOMAIN, data={CONF_HOST: IP_ADDRESS}, unique_id=MAC_ADDRESS\\n )\\n already_migrated_config_entry.add_to_hass(hass)\\n with _patch_discovery(no_device=True), _patch_single_discovery(no_device=True):\\n await async_setup_component(hass, tplink.DOMAIN, {tplink.DOMAIN: {}})\\n await hass.async_block_till_done()\\n assert already_migrated_config_entry.state == ConfigEntryState.SETUP_RETRY\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0e05b60f952aa20b9c914b583240d3f\",\n \"score\": \"0.54732364\",\n \"text\": \"def on_retry(self, exc_info):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a959dc74a92d947b160856c1cbc46aa6\",\n \"score\": \"0.5469504\",\n \"text\": \"def on_connect(client, userd, flags, rc):\\n syslog.syslog(f\\\"MQTT connect rc: {str(rc)}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83d85e3369c301b897490170e142e2fe\",\n \"score\": \"0.5468484\",\n \"text\": \"def test_connectionLost(self):\\r\\n self.request.connectionLost(Failure(CONNECTION_DONE))\\r\\n self.handler.lostReason.trap(ConnectionDone)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34e810d79dc5af0db27ad342e186c235\",\n \"score\": \"0.54684293\",\n \"text\": \"def connectionInitialized():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c02916e987ae978a5d194b436db06d9\",\n \"score\": \"0.5462366\",\n \"text\": \"def connection_lost(self, exc):\\n if not self.suspendAllOperations:\\n log.error(\\\"ERROR Connection Lost : disconnected because the Ethernet/USB connection was externally terminated.\\\")\\n self._performDisconnect(reason=\\\"termination\\\", exc=exc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97ddde240ce639e24419bce9dc87bbf5\",\n \"score\": \"0.5454659\",\n \"text\": \"def check_connection(self):\\n self.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee333a1db1a87d647a564224e6c30635\",\n \"score\": \"0.54460835\",\n \"text\": \"def test_connection_lost(gateway, connection_transport, reconnect_callback):\\n assert gateway.tasks.transport.protocol.transport is None\\n gateway.tasks.transport.connect()\\n assert gateway.tasks.transport.protocol.transport is connection_transport\\n gateway.tasks.transport.protocol.connection_lost(\\\"error\\\")\\n assert connection_transport.serial.close.call_count == 1\\n assert reconnect_callback.call_count == 1\\n assert gateway.tasks.transport.protocol.transport is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b240bb606ba7d77f90a5927c27907b0\",\n \"score\": \"0.5443004\",\n \"text\": \"def test_check_connect(nodaq):\\n logger.debug('test_check_connect')\\n with pytest.raises(RuntimeError):\\n nodaq.wait()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63e4d159c35631c28ff284eb8cd375c\",\n \"score\": \"0.5441226\",\n \"text\": \"def check_device_state(self):\\n\\n if not (self.switch.cli.conn.check_client() and self.switch.cli.conn.check_shell()):\\n try:\\n self.switch.ui.connect()\\n except (CLISSHException, SSHException, SocketError):\\n self.switch.ui.disconnect()\\n raise Exception(\\\"Device is not ready.\\\")\\n\\n # Add cli application check\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7912fe97387787fb5827348571a222e\",\n \"score\": \"0.54365784\",\n \"text\": \"def connectError(self, reason):\\n if self.stream_error_called:\\n return\\n # Before Twisted 11.x the xmlstream object was passed instead of the\\n # disconnect reason. See http://twistedmatrix.com/trac/ticket/2618\\n if not isinstance(reason, failure.Failure):\\n reason_str = 'Reason unknown'\\n else:\\n reason_str = str(reason)\\n\\n # If the connection was established and lost, then we need to report\\n # the error back to the client, since he needs to reauthenticate.\\n # FIXME: If the connection was lost before anything happened, we could\\n # silently retry instead.\\n if self.verbose:\\n log.msg('connect ERROR: %s' % reason_str)\\n\\n self.stopTrying()\\n\\n e = error.Error('remote-connection-failed')\\n\\n do_expire = True\\n\\n if self.waiting_requests:\\n wr = self.waiting_requests.pop(0)\\n wr.doErrback(e)\\n else:\\n # need to wait for a new request and then expire\\n do_expire = False\\n\\n if self.pint and self.sid in self.pint.sessions:\\n if do_expire:\\n try:\\n self.expire()\\n except Exception:\\n self.onExpire()\\n else:\\n s = self.pint.sessions.get(self.sid)\\n s.stream_error = e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16c81482f599a6d7eeef1194ab53f772\",\n \"score\": \"0.54336935\",\n \"text\": \"async def test_color_palette_select_connection_error(\\n hass: HomeAssistant,\\n mock_wled: MagicMock,\\n) -> None:\\n mock_wled.segment.side_effect = WLEDConnectionError\\n\\n with pytest.raises(HomeAssistantError, match=\\\"Error communicating with WLED API\\\"):\\n await hass.services.async_call(\\n SELECT_DOMAIN,\\n SERVICE_SELECT_OPTION,\\n {\\n ATTR_ENTITY_ID: \\\"select.wled_rgb_light_segment_1_color_palette\\\",\\n ATTR_OPTION: \\\"Icefire\\\",\\n },\\n blocking=True,\\n )\\n\\n assert (state := hass.states.get(\\\"select.wled_rgb_light_segment_1_color_palette\\\"))\\n assert state.state == STATE_UNAVAILABLE\\n assert mock_wled.segment.call_count == 1\\n mock_wled.segment.assert_called_with(segment_id=1, palette=\\\"Icefire\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":96,"cells":{"query_id":{"kind":"string","value":"63e2a9a9fcbb147fd55d6f76d423eb0b"},"query":{"kind":"string","value":"Cache potential symbols in the symbol table."},"positive_passages":{"kind":"list like","value":[{"docid":"f242891fa3149bdb7fe2262b204e019f","score":"0.5748251","text":"def _enumerateSymbols(self, machoCtx) -> None:\n\n\t\tsymtab: symtab_command = machoCtx.getLoadCommand(\n\t\t\t(LoadCommands.LC_SYMTAB,)\n\t\t)\n\t\tif not symtab:\n\t\t\tself._logger.warning(\"Unable to find LC_SYMTAB.\")\n\t\t\treturn\n\n\t\tlinkeditFile = machoCtx.ctxForAddr(\n\t\t\tmachoCtx.segments[b\"__LINKEDIT\"].seg.vmaddr\n\t\t)\n\n\t\tfor i in range(symtab.nsyms):\n\t\t\tself._statusBar.update()\n\n\t\t\t# Get the symbol and its address\n\t\t\tentryOff = symtab.symoff + (i * nlist_64.SIZE)\n\t\t\tsymbolEntry = nlist_64(linkeditFile.file, entryOff)\n\n\t\t\tsymbolAddr = symbolEntry.n_value\n\t\t\tsymbol = linkeditFile.readString(symtab.stroff + symbolEntry.n_strx)\n\n\t\t\tif symbolAddr == 0:\n\t\t\t\tcontinue\n\t\t\tif not machoCtx.containsAddr(symbolAddr):\n\t\t\t\tself._logger.warning(f\"Invalid address: {symbolAddr}, for symbol entry: {symbol}.\") # noqa\n\t\t\t\tcontinue\n\n\t\t\t# save it to the cache\n\t\t\tif symbolAddr in self._symbolCache:\n\t\t\t\tself._symbolCache[symbolAddr].append(bytes(symbol))\n\t\t\telse:\n\t\t\t\tself._symbolCache[symbolAddr] = [bytes(symbol)]\n\t\t\tpass\n\t\tpass","title":""}],"string":"[\n {\n \"docid\": \"f242891fa3149bdb7fe2262b204e019f\",\n \"score\": \"0.5748251\",\n \"text\": \"def _enumerateSymbols(self, machoCtx) -> None:\\n\\n\\t\\tsymtab: symtab_command = machoCtx.getLoadCommand(\\n\\t\\t\\t(LoadCommands.LC_SYMTAB,)\\n\\t\\t)\\n\\t\\tif not symtab:\\n\\t\\t\\tself._logger.warning(\\\"Unable to find LC_SYMTAB.\\\")\\n\\t\\t\\treturn\\n\\n\\t\\tlinkeditFile = machoCtx.ctxForAddr(\\n\\t\\t\\tmachoCtx.segments[b\\\"__LINKEDIT\\\"].seg.vmaddr\\n\\t\\t)\\n\\n\\t\\tfor i in range(symtab.nsyms):\\n\\t\\t\\tself._statusBar.update()\\n\\n\\t\\t\\t# Get the symbol and its address\\n\\t\\t\\tentryOff = symtab.symoff + (i * nlist_64.SIZE)\\n\\t\\t\\tsymbolEntry = nlist_64(linkeditFile.file, entryOff)\\n\\n\\t\\t\\tsymbolAddr = symbolEntry.n_value\\n\\t\\t\\tsymbol = linkeditFile.readString(symtab.stroff + symbolEntry.n_strx)\\n\\n\\t\\t\\tif symbolAddr == 0:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tif not machoCtx.containsAddr(symbolAddr):\\n\\t\\t\\t\\tself._logger.warning(f\\\"Invalid address: {symbolAddr}, for symbol entry: {symbol}.\\\") # noqa\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t# save it to the cache\\n\\t\\t\\tif symbolAddr in self._symbolCache:\\n\\t\\t\\t\\tself._symbolCache[symbolAddr].append(bytes(symbol))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself._symbolCache[symbolAddr] = [bytes(symbol)]\\n\\t\\t\\tpass\\n\\t\\tpass\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5d0760fc5481f25d465307101e3bd6f3","score":"0.66358835","text":"def _load_symbols_cache(self, cache):\n\n #\n # Symbols\n #\n\n for sym, address in cache['symbols'].items():\n if self._process.file_type == \"PE\" or \\\n (self.elf is not None and self.elf.type_str == 'DYN'):\n address = address + self.base\n\n sym = symbols.Symbol(self._process, name=sym, address=address)\n self._process.modules._symbol_to_address[self.name][sym.name] = sym\n self._process.modules._address_to_symbol[address] = sym\n\n #\n # Sections\n #\n\n if 'plt_offset' in cache:\n self.plt = cache['plt_offset']\n\n #\n # PLT\n #\n\n if 'plt' in cache:\n for i, sym in enumerate(cache['plt']):\n addr = self.plt + ((i+1)*0x10)\n name = 'plt.' + sym\n sym = symbols.Symbol(self._process, name=name, address=addr)\n self._process.modules._symbol_to_address[self.name][name] = sym\n self._process.modules._address_to_symbol[addr] = sym","title":""},{"docid":"fc0072c550ca48cc70aaac6d930d7679","score":"0.65396005","text":"def _symbols_update(self):\n self.symbols_sting = [str(sym) for sym in list(self.expression.free_symbols)]\n self.symbols_sting.sort()\n self.symbols = [symbols(char) for char in self.symbols_sting]","title":""},{"docid":"d2fac262d57dfbd7413fb0e1458ac0e5","score":"0.58945626","text":"def update_local_symbols_dict(self, comp):\n pass","title":""},{"docid":"324c3cf79b34a2def8b3b2a9e35f87fb","score":"0.5756938","text":"def makeMapSymbols(self):\n n = self.keepAnalysisCount\n pen = self.colorList.next()\n filledbrush = pen\n emptybrush = None\n symbol = self.symbolList.next()\n if n == 0:\n clearFlag = True\n else:\n clearFlag = False\n self.currentSymDict = {'pen': pen, 'filledbrush': filledbrush,\n 'emptybrush': emptybrush, 'symbol': symbol,\n 'n': n, 'clearFlag': clearFlag}","title":""},{"docid":"de66b2e3a9f3773a3e811b658b69d887","score":"0.56086206","text":"def symbols_with_same_path(self):\n a, b = Symbol('spam.ham.eggs'), Symbol().spam.ham.eggs\n self.symbs = [a, b]\n return self.symbs","title":""},{"docid":"9940dd1a84f08f221365a89a03aa1e00","score":"0.5607992","text":"def _load_symbols(self):\n\n # Either we're loading everything or what we're looking at right now __should__ be loaded\n if self._process._load_symbols is None or any(True for x in self._process._load_symbols if fnmatch(self.name, x)):\n\n # Clear out old symbols if needed\n self._process.modules._symbol_to_address[self.name] = {}\n\n file_io = common.load_file(self._process, self.path)\n cache = self._read_symbols_cache_dict(file_io)\n\n if cache is not None:\n return self._load_symbols_cache(cache)\n\n if self._process.file_type == 'ELF':\n self._load_symbols_elf(file_io)\n\n elif self._process.file_type == \"PE\":\n self._load_symbols_pe(file_io)\n\n # TODO: Windows\n # TODO: Mac\n\n # If we didn't resolve anything, make sure we noted we tried\n if self.name not in self._process.modules._symbol_to_address:\n self._process.modules._symbol_to_address[self.name] = {}","title":""},{"docid":"8f157c87d5d45117e5803321caad471b","score":"0.558471","text":"def initialize_local_symbols_dict(self, comp, assigned_var):\n pass","title":""},{"docid":"75423c89f4625aada9920528546243c0","score":"0.5582418","text":"def free_symbols(self) -> Set[str]:\n raise NotImplementedError('free_symbols not implemented by \"%s\"' % type(self).__name__)","title":""},{"docid":"965c91085d8c0bf31b4959d99fb5b169","score":"0.5560573","text":"def _resolve_symbols(self):\n # TODO: Should attempt to fill self.imports in accordance with angr's expectations\n # note: no rebasing support\n\n # trigger parsing of exports\n self._parse_exports(self.export_blob)\n\n # Set up search tables for searching\n section_tab = [None] # first section is NO_SECT\n for seg in self.segments:\n section_tab.extend(seg.sections)\n\n # A new memory area is created to hold external (undefined) symbols\n ext_symbol_start = 0xff00000000000000 if self.arch.bits == 64 else 0xff000000\n ext_symbol_end = ext_symbol_start\n\n # Update symbol properties\n for sym in self.symbols:\n\n sym._is_export = sym.name in self.exports_by_name\n\n if sym.is_stab: # stab symbols are debugging information - don't care!\n l.debug(\"Symbol %r is debugging information, skipping\", sym.name)\n continue\n\n if sym.name in self.exports_by_name:\n l.debug(\"Symbol %r is an export\", sym.name)\n sym.is_export = True\n else:\n l.debug(\"Symbol %r is not an export\", sym.name)\n sym.is_export = False\n\n if sym.is_common:\n l.debug(\"Symbol %r is common, updating size\", sym.name)\n sym.size = sym.n_value\n\n if sym.sym_type == SYMBOL_TYPE_SECT:\n l.debug(\"Symbol %r is N_SECT, updating names and address\", sym.name)\n sec = section_tab[sym.n_sect]\n if sec is not None:\n sym.segment_name = sec.segname\n sym.section_name = sec.sectname\n\n sym.relative_addr = sym.n_value\n\n elif sym.is_import():\n l.debug(\"Symbol %r is imported!\", sym.name)\n sym.library_name = self.imported_libraries[sym.library_ordinal]\n\n # if the symbol has no address we assign one from the special memory area\n if sym.relative_addr is None:\n sym.relative_addr = ext_symbol_end\n ext_symbol_end += sym.size\n l.debug(\"Assigning address %#x to symbol %r\", sym.relative_addr, sym.name)\n\n # Add special memory area for symbols:\n self.memory.add_backer(ext_symbol_start, \"\\x00\" * (ext_symbol_end - ext_symbol_start))\n\n # Perform binding\n bh = BindingHelper(self) # TODO: Make this configurable\n bh.do_normal_bind(self.binding_blob)\n bh.do_lazy_bind(self.lazy_binding_blob)\n if self.weak_binding_blob is not None and len(self.weak_binding_blob) > 0:\n l.info(\"Found weak binding blob. According to current state of knowledge, weak binding \"\n \"is only sensible if multiple binaries are involved and is thus skipped.\")\n\n # Add to symbols_by_addr\n # All (resolvable) symbols should be resolved by now\n for sym in self.symbols:\n if not sym.is_stab:\n if sym.relative_addr is not None:\n self._symbols_by_addr[sym.relative_addr] = sym\n else:\n # todo: this might be worth an error\n l.warn(\"Non-stab symbol %r @ %#x has no address.\", sym.name, sym.symtab_offset)","title":""},{"docid":"d3ad03e670aa88ff288689847e04e29b","score":"0.5556712","text":"def set_is_created_containing_these_symbols(self):\n self.set = set(self.symbs)","title":""},{"docid":"caf0f32edf1c8d82e2ccdc12a58f6c73","score":"0.5525948","text":"def IncludingErased(self) -> SymbolTable:","title":""},{"docid":"f4a6d64ce17e563d13be0060e89a0d4b","score":"0.5494512","text":"def warm_cache(self):\n words = self.good.keys() + self.rejected.keys()\n for word in words:\n self.rprobs[word] = self.rejected_given_word(word)\n \n return {'rprobs' : self.rprobs,\n 'good' : self.good,\n 'rejected' : self.rejected}","title":""},{"docid":"605e4a336fe6563cbea85e4f3783316a","score":"0.54501534","text":"def _clear_cache(self):\n self.__symbolic_jacobian = None","title":""},{"docid":"b2575be7d1b8a7fb0a119b9d250533d9","score":"0.5398685","text":"def prepare(self):\n self.find_symbol_table()\n if self._has_symbol is False:\n return None\n self.logger.debug(\"has_symbol: %s\" % self._has_symbol)\n self.get_symbol_table()","title":""},{"docid":"3f7309b7112d6acecd555c8459b37ad8","score":"0.5389967","text":"def _read_symbol_table(self):\n section = self.get_section_by_name(\".symtab\")\n if not section:\n raise Exception(\"Missing symbol table\")\n\n if not isinstance(section, SymbolTableSection):\n raise Exception(\"Invalid symbol table section\")\n\n symbols = {}\n for symbol in section.iter_symbols():\n name_str = symbol.name\n if name_str in symbols:\n logging.debug(\"Duplicate symbol %s\", name_str)\n symbols[name_str] = SymbolSimple(name_str, symbol[\"st_value\"],\n symbol[\"st_size\"])\n return symbols","title":""},{"docid":"070c063cecf57669722560a0981a8c7f","score":"0.5354533","text":"def free_symbols(self) -> Iterable[sympy.Symbol]:\n return get_free_symbols(self.params)","title":""},{"docid":"b932cb1f8beb2aeb9636a4e4ae74432c","score":"0.53413147","text":"def cache_all_data(cls):\n from ir_log import IRProgressBar\n from ir_config import IRConfig\n from ir_mongodb_helper import IRCollection\n bug_id_name = IRConfig.get_instance().get('bug_id_name')\n summary_name = IRConfig.get_instance().get('bug_summary_name')\n description_name = IRConfig.get_instance().get('bug_description_name')\n tfidf_collection = IRCollection(\n 'bug_db_name', 'bug_tfidf_collection_name', 'r')\n cls.set_is_cache(True)\n cls.__cache = {}\n def iter_tfidf(bug):\n cls.__cache[bug[bug_id_name]] = (bug[summary_name],\n bug[description_name])\n IRProgressBar.execute_iteration_for_cursor(tfidf_collection.find(),\n iter_tfidf, \"Caching TFIDF\")","title":""},{"docid":"ffa5e2ae072eba1df0a931b74f25cf0a","score":"0.53243756","text":"def symbolizeAddr(self, addr: int) -> List[bytes]:\n\t\tif addr in self._symbolCache:\n\t\t\treturn self._symbolCache[addr]\n\t\telse:\n\t\t\treturn None","title":""},{"docid":"e731dd71a4be8fb6244ea9fd844f35a1","score":"0.5317222","text":"def non_trivial_symbol(self):\n a, b = Symbol('os.path.isfile'), Symbol('sys.executable')\n self.symbs = [a, b]\n return self.symbs","title":""},{"docid":"9c88c13f1bb7c417c36d2ba46485d50e","score":"0.5298955","text":"def free_symbols(self) -> Set[sp.Basic]:\n return set(\n chain.from_iterable(\n state.get_free_symbols()\n for state in self.states() + self.algebraic_equations()\n )\n )","title":""},{"docid":"d691e4e77a8abc1e93b5e4779cadd38e","score":"0.52566","text":"def set_symbols(self, symbols):\n\t\tself.symbols = symbols","title":""},{"docid":"7b80bd13f5791b52d86bd5ea0f3cca52","score":"0.5239692","text":"def symbols(self):\n return self._symbols","title":""},{"docid":"b28fafedbb403ba7dd3e4099813258b7","score":"0.5223729","text":"def getUncachedGameKeys(self):","title":""},{"docid":"58eede2f0004b3c04cb3294c1207ac05","score":"0.52194476","text":"def getSymbols(self) -> List[ghidra.program.model.symbol.Symbol]:\n ...","title":""},{"docid":"6a47dc84ed9e8628929172e06e72f2dd","score":"0.5188628","text":"def intern(self, string):\n if string in self.oblist:\n return self.oblist[string]\n sym = symbol(string)\n self.oblist[string] = sym\n return sym","title":""},{"docid":"cee80301dcbdd3338e473d782cbbfed5","score":"0.5178622","text":"def scan_cache(force_update=False):\n\n global __CACHED_SCAN__\n if force_update or __CACHED_SCAN__ is None:\n __CACHED_SCAN__ = scan_system()\n return __CACHED_SCAN__","title":""},{"docid":"bf685ba8aa951ff9e726e9efb8c31ad4","score":"0.5176204","text":"def symbols(self) -> set[Symbol]:\n\n # this fails because `free_symbols` is a dict on NumExpr but `set` on Expr\n\n symbols = set()\n for rhs in self.values():\n if isinstance(rhs, (Expr, MatrixBase)):\n symbols |= rhs.free_symbols\n else:\n try:\n symbols |= set(rhs.symbols)\n except AttributeError:\n # RHS doesnt have any symbols; for example might be a numpy array\n pass\n\n # symbols = getattr(rhs, 'free_symbols')\n # try:\n # # rhs is a sympy `Expr` and has `free_symbols` as a set\n # symbols |= rhs.free_symbols\n # except TypeError:\n # # rhs is a slimfit `NumExpr`\n # symbols |= set(rhs.symbols)\n # except AttributeError:\n # # RHS doesnt have any symbols; for example might be a numpy array\n # pass\n return symbols","title":""},{"docid":"2e4416089cae9958605ad2d84b3bc489","score":"0.51651824","text":"def __init__(self):\n self._cache = {0: 0, 1: 1, 2: 1}","title":""},{"docid":"4070f67b1073d8e761fc82f8cf7656e6","score":"0.5159139","text":"def get_priceinfo_cached(self, symbol, thresh):\n cached = self._get_cache_priceinfo(symbol, thresh)\n if not cached:\n cached = self.fetch_priceinfo(symbol)\n if cached:\n self._set_cache_priceinfo(symbol, cached)\n\n numfields = set(['open', 'high', 'low', 'price', 'volume', 'change', 'previous close'])\n return {k: Decimal(v) if k in numfields else v for k, v in cached.items()}","title":""},{"docid":"3a89c9bd2dbf0876f75fb50d2b843d44","score":"0.5138711","text":"def symbol_values(self):\n return dict(zip(self.symbols, self.get_weights()[0]))","title":""},{"docid":"a48824c4d9d1c838a72db8b7c5d0b4fd","score":"0.51371515","text":"def symbols(self):\n symbols = set()\n\n for word, transcriptions in self.entries.items():\n for t in transcriptions:\n symbols.update(t)\n\n return symbols","title":""},{"docid":"d37d540188094bb50310ce54102d922c","score":"0.51330703","text":"def _enumerateSymbolPointers(self) -> Dict[bytes, Tuple[int]]:\n\n\t\t# read all the bind records as they're a source of symbolic info\n\t\tbindRecords: Dict[int, _BindRecord] = {}\n\t\tdyldInfo: dyld_info_command = self._machoCtx.getLoadCommand(\n\t\t\t(LoadCommands.LC_DYLD_INFO, LoadCommands.LC_DYLD_INFO_ONLY)\n\t\t)\n\n\t\tlinkeditFile = self._machoCtx.ctxForAddr(\n\t\t\tself._machoCtx.segments[b\"__LINKEDIT\"].seg.vmaddr\n\t\t)\n\n\t\tif dyldInfo:\n\t\t\trecords: List[_BindRecord] = []\n\t\t\ttry:\n\t\t\t\tif dyldInfo.weak_bind_size:\n\t\t\t\t\t# usually contains records for c++ symbols like \"new\"\n\t\t\t\t\trecords.extend(\n\t\t\t\t\t\t_bindReader(\n\t\t\t\t\t\t\tlinkeditFile,\n\t\t\t\t\t\t\tdyldInfo.weak_bind_off,\n\t\t\t\t\t\t\tdyldInfo.weak_bind_size\n\t\t\t\t\t\t)\n\t\t\t\t\t)\n\t\t\t\t\tpass\n\n\t\t\t\tif dyldInfo.lazy_bind_off:\n\t\t\t\t\trecords.extend(\n\t\t\t\t\t\t_bindReader(\n\t\t\t\t\t\t\tlinkeditFile,\n\t\t\t\t\t\t\tdyldInfo.lazy_bind_off,\n\t\t\t\t\t\t\tdyldInfo.lazy_bind_size\n\t\t\t\t\t\t)\n\t\t\t\t\t)\n\t\t\t\t\tpass\n\n\t\t\t\tfor record in records:\n\t\t\t\t\t# check if we have the info needed\n\t\t\t\t\tif (\n\t\t\t\t\t\trecord.symbol is None\n\t\t\t\t\t\tor record.segment is None\n\t\t\t\t\t\tor record.offset is None\n\t\t\t\t\t):\n\t\t\t\t\t\tself._logger.warning(f\"Incomplete lazy bind record: {record}\")\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\tbindAddr = self._machoCtx.segmentsI[record.segment].seg.vmaddr\n\t\t\t\t\tbindAddr += record.offset\n\t\t\t\t\tbindRecords[bindAddr] = record\n\t\t\t\t\tpass\n\t\t\texcept KeyError as e:\n\t\t\t\tself._logger.error(f\"Unable to read bind records, reasons: {e}\")\n\t\t\tpass\n\n\t\t# enumerate all symbol pointers\n\t\tsymbolPtrs: Dict[bytes, List[int]] = {}\n\n\t\tdef _addToMap(ptrSymbol: bytes, ptrAddr: int, section: section_64):\n\t\t\tif ptrSymbol in symbolPtrs:\n\t\t\t\t# give priority to ptrs in the __auth_got section\n\t\t\t\tif section.sectname == b\"__auth_got\":\n\t\t\t\t\tsymbolPtrs[ptrSymbol].insert(0, ptrAddr)\n\t\t\t\telse:\n\t\t\t\t\tsymbolPtrs[ptrSymbol].append(ptrAddr)\n\t\t\telse:\n\t\t\t\tsymbolPtrs[ptrSymbol] = [ptrAddr]\n\t\t\tpass\n\n\t\tfor segment in self._machoCtx.segmentsI:\n\t\t\tfor sect in segment.sectsI:\n\t\t\t\tsectType = sect.flags & SECTION_TYPE\n\t\t\t\tif (\n\t\t\t\t\tsectType == S_NON_LAZY_SYMBOL_POINTERS\n\t\t\t\t\tor sectType == S_LAZY_SYMBOL_POINTERS\n\t\t\t\t):\n\t\t\t\t\tfor i in range(int(sect.size / 8)):\n\t\t\t\t\t\tself._statusBar.update(status=\"Caching Symbol Pointers\")\n\n\t\t\t\t\t\tptrAddr = sect.addr + (i * 8)\n\n\t\t\t\t\t\t# Try to symbolize through bind records\n\t\t\t\t\t\tif ptrAddr in bindRecords:\n\t\t\t\t\t\t\t_addToMap(bindRecords[ptrAddr].symbol, ptrAddr, sect)\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t# Try to symbolize though indirect symbol entries\n\t\t\t\t\t\tsymbolIndex = linkeditFile.readFormat(\n\t\t\t\t\t\t\t\"<I\",\n\t\t\t\t\t\t\tself._dysymtab.indirectsymoff + ((sect.reserved1 + i) * 4)\n\t\t\t\t\t\t)[0]\n\t\t\t\t\t\tif (\n\t\t\t\t\t\t\tsymbolIndex != 0\n\t\t\t\t\t\t\tand symbolIndex != INDIRECT_SYMBOL_ABS\n\t\t\t\t\t\t\tand symbolIndex != INDIRECT_SYMBOL_LOCAL\n\t\t\t\t\t\t\tand symbolIndex != (INDIRECT_SYMBOL_ABS | INDIRECT_SYMBOL_LOCAL)\n\t\t\t\t\t\t):\n\t\t\t\t\t\t\tsymbolEntry = nlist_64(\n\t\t\t\t\t\t\t\tlinkeditFile.file,\n\t\t\t\t\t\t\t\tself._symtab.symoff + (symbolIndex * nlist_64.SIZE)\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\tsymbol = linkeditFile.readString(\n\t\t\t\t\t\t\t\tself._symtab.stroff + symbolEntry.n_strx\n\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t_addToMap(symbol, ptrAddr, sect)\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t# Try to symbolize though the pointers target\n\t\t\t\t\t\tptrTarget = self._slider.slideAddress(ptrAddr)\n\t\t\t\t\t\tptrFunc = self._arm64Utils.resolveStubChain(ptrTarget)\n\t\t\t\t\t\tif symbols := self._symbolizer.symbolizeAddr(ptrFunc):\n\t\t\t\t\t\t\tfor sym in symbols:\n\t\t\t\t\t\t\t\t_addToMap(sym, ptrAddr, sect)\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t# Skip special cases like __csbitmaps in CoreFoundation\n\t\t\t\t\t\tif self._machoCtx.containsAddr(ptrTarget):\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\tself._logger.warning(f\"Unable to symbolize pointer at {hex(ptrAddr)}, with indirect entry index {hex(sect.reserved1 + i)}, with target function {hex(ptrFunc)}\") # noqa\n\t\t\t\t\t\tpass\n\t\t\t\t\tpass\n\t\t\t\tpass\n\t\t\tpass\n\n\t\treturn symbolPtrs","title":""},{"docid":"bc18719db059b3a7738e714477916e7a","score":"0.5131824","text":"def clear_cache(self):\n self.phrase2vec_cache = {}\n self.membership_cache = {}\n self.interpret_cache = {}","title":""},{"docid":"913b95798a15f24d84e6b7f4fe58be36","score":"0.5119424","text":"def reset_caches(self):\r\n with common.ignored(AttributeError):\r\n del self._used_names","title":""},{"docid":"e3ad59819e704474430d4505e7607173","score":"0.51084596","text":"def get_sym_data(self, objective, constraints, cached_data):\n self.validate_cache(objective, constraints, cached_data)\n prob_data = cached_data[self.name()]\n if prob_data.sym_data is None:\n prob_data.sym_data = SymData(objective, constraints, self)\n return prob_data.sym_data","title":""},{"docid":"c09a463f172d3ba34111a74651468369","score":"0.5107634","text":"def _reset_cache(self):\n\n\t\tsuper()._reset_cache()\n\t\tif self._initialized == False:\n\t\t\treturn\n\n\t\tself.register_buffer(\"_log_sqrt_dofs_pi_cov\", torch.log(torch.sqrt(\n\t\t\tself.dofs * math.pi * self.covs)))","title":""},{"docid":"be98e8d62b3feafb7ef7f3559e6a8896","score":"0.51061755","text":"def _addSymbol(self, symbol):\n setattr(self, symbol, len(vars(self).keys()))","title":""},{"docid":"49a36b30096f5ac81df973e5553a5f40","score":"0.5098728","text":"def update_symbols(self, current_thread_only):\n logging.info(\"Updating symbols\")\n mapped_files = _get_mapped_files()\n # Map all symbols from native libraries packages with the APK.\n for file_mappings in mapped_files:\n filename = file_mappings[0].filename\n if ((filename.startswith('/data/data/') or\n filename.startswith('/data/app')) and\n not filename.endswith('.apk') and\n not filename.endswith('.dex')):\n logging.info('Pre-mapping: %s' % file_mappings[0].filename)\n self._try_to_map(file_mappings)\n\n if current_thread_only:\n self._map_symbols_on_current_thread(mapped_files)\n else:\n logging.info('Updating all threads\\' symbols')\n current_thread = gdb.selected_thread()\n nb_threads = len(_gdb_execute(\"info threads\").split(\"\\n\")) - 2\n for i in xrange(nb_threads):\n try:\n _gdb_execute(\"thread %d\" % (i + 1))\n self._map_symbols_on_current_thread(mapped_files)\n except gdb.error:\n traceback.print_exc()\n current_thread.switch()","title":""},{"docid":"bcf4fdc57e80ec04cb9f4678b9529ffc","score":"0.50945777","text":"def symbol_table(func):\n\n @functools.wraps(func)\n def inner(*args, **kwargs):\n return func(*args, symbol_table=_SYMBOL_TABLE, **kwargs)\n return inner","title":""},{"docid":"eb24d4bf9b2c40aac6d559fbf5143a44","score":"0.5071513","text":"def __dirty(self): \n # You're a dirty, dirty slot table and you should be\n # ashamed of having outdated caches!\n self.availabilitycache = {}\n self.awcache_time = None\n self.awcache = None","title":""},{"docid":"7e77509da934ca4c1ffc4716287111c5","score":"0.50685626","text":"def gensym ():\n global symcount\n sym = 'sym%i' % symcount\n symcount += 1\n return sym","title":""},{"docid":"19a4efcf44e7f053bd4673c0e3e4fd18","score":"0.50659806","text":"def all_symbols(self):","title":""},{"docid":"fc89c3a98c7f988cd8e6e978e44b1492","score":"0.50572646","text":"def new_symbol(s):\n if s not in symbol_table:\n symbol_table[s] = SAtom(s)\n return symbol_table[s]","title":""},{"docid":"44a28ba2735621ae39b0fc0ed743bef5","score":"0.50570285","text":"def encodeSymbol(self, sym):\n return Hmm.__dicLookup(self._symbol_dic, sym)","title":""},{"docid":"51813efb13dc378f21f0acf2645d4d84","score":"0.5043085","text":"def getSymbolsCache(self) -> \"DyldContext\":\n\n\t\tif not self._subCaches or not self.headerContainsField(\"symbolFileUUID\"):\n\t\t\treturn self\n\n\t\tfor cache in self._subCaches:\n\t\t\tif bytes(self.header.symbolFileUUID) == bytes(cache.header.uuid):\n\t\t\t\treturn cache\n\t\t\tpass\n\n\t\treturn None","title":""},{"docid":"92ac29ff2f082812cd51450b38518a1c","score":"0.5038338","text":"def symbols(self):\n return [sympy.Symbol(x) for x in self._symbols_list]","title":""},{"docid":"22b0c43122e94509c8d9bcf28e54a136","score":"0.5021292","text":"def get_symbol_table(self):\n if self.symbol_table_start and self.symbol_table_end:\n self._check_vxworks_endian()\n\n else:\n return False\n\n for i in range(self.symbol_table_start, self.symbol_table_end, self._symbol_interval):\n symbol_name_addr = self._firmware[i + 4:i + 8]\n symbol_dest_addr = self._firmware[i + 8:i + 12]\n if self.big_endian:\n unpack_format = '>I'\n else:\n unpack_format = '<I'\n symbol_name_addr = int(struct.unpack(unpack_format, symbol_name_addr)[0])\n self.logger.debug(\"symbol_name_addr: %s\" % symbol_name_addr)\n symbol_dest_addr = int(struct.unpack(unpack_format, symbol_dest_addr)[0])\n self.logger.debug(\"symbol_dest_addr: %s\" % symbol_dest_addr)\n self._symbol_table.append({\n 'symbol_name_addr': symbol_name_addr,\n 'symbol_name_length': None,\n 'symbol_dest_addr': symbol_dest_addr,\n 'offset': i\n })\n # self.logger.debug(\"self._symbol_table: %s\" % self._symbol_table)\n self.logger.debug(\"len(self._symbol_table): %s\".format(len(self._symbol_table)))\n self._symbol_table = sorted(self._symbol_table, key=lambda x: x['symbol_name_addr'])\n for i in range(len(self._symbol_table) - 1):\n self._symbol_table[i]['symbol_name_length'] = self._symbol_table[i + 1]['symbol_name_addr'] - \\\n self._symbol_table[i]['symbol_name_addr']\n self.logger.debug(\"len(self._symbol_table): %s\".format(len(self._symbol_table)))\n return True","title":""},{"docid":"8e0a1f016789fa9491981cf70c8dc49e","score":"0.50072587","text":"def _pdb_sccs_cache(pdbid):\n global db,pdb,chain_lengths\n \n # Each process has to keep it's own global dictionary because of\n # concurrency issues.\n if pdb == None:\n pdb = {}\n if chain_lengths == None:\n chain_lengths = {}\n \n # Search for the pdb\n if not pdb.has_key(pdbid):\n if db==None:\n db = _connection()\n cursor = db.cursor()\n try:\n query = \"select part_text, sccs from scop_cla where pdb='%s'\" % pdbid\n cursor.execute(query)\n all = cursor.fetchall()\n # Parse parts and chains\n domains = []\n for txt, sccs in all:\n sccs = sccs.strip()\n if len(sccs) == 0:\n sccs = None\n s = SCOPDomain(pdbid, sccs, part_text=txt)\n for part in s.parts:\n # Determine the length of the chain for parts that might not\n # have a start/stop defined.\n if not chain_lengths.has_key((pdbid,part.chain)):\n query = \"select length(sequence) from pdbIndex i join pdbSeqRes r on i.id=r.pdb_key join sequence s on r.sequence_key=s.id where i.pdbId='%s' and r.chain='%s'\" % (pdbid,part.chain)\n #print query\n cursor.execute(query)\n chain_len = cursor.fetchone()[0]\n chain_lengths[(pdbid,part.chain)]=chain_len\n else:\n chain_len = chain_lengths[(pdbid,part.chain)]\n # Tell the par the length of the chains\n part.chain_len = chain_len\n domains.append(s)\n pdb[pdbid]=list(set(domains))\n finally:\n cursor.close()","title":""},{"docid":"650880fe41cb4f84c784371c398c1536","score":"0.5006173","text":"def symbol_fiducials(self):\n return dict(zip(self.symbol_map.sym_list, self.symbol_map.ival_list))","title":""},{"docid":"f373137e1511f0f4611c8ccd33fd5322","score":"0.49984825","text":"def enlistSymbols(self ):\n for e in self.Edges:\n if e[2] not in self.symbols and e[2]!='ε':\n self.symbols.append(e[2])","title":""},{"docid":"80be30f6a5b6d16a3c5b3a896299f9b2","score":"0.4996974","text":"async def cache_instruments(\n self,\n require: Optional[Dict[Mount, PipetteName]] = None,\n ) -> None:\n ...","title":""},{"docid":"5f665bf932835ff2bbeb7ef5b879a8a9","score":"0.4989114","text":"def _reset(self):\n self.cache = SmtLibExecutionCache()\n self.logic = None\n self._current_env = self.pysmt_env\n\n mgr = self.pysmt_env.formula_manager\n self.cache.update({'+':mgr.Plus,\n '-':self._minus_or_uminus,\n '*':mgr.Times,\n '/':self._division,\n '>':mgr.GT,\n '<':mgr.LT,\n '>=':mgr.GE,\n '<=':mgr.LE,\n '=':self._equals_or_iff,\n 'not':mgr.Not,\n 'and':mgr.And,\n 'or':mgr.Or,\n 'xor':mgr.Xor,\n '=>':mgr.Implies,\n '<->':mgr.Iff,\n 'ite':mgr.Ite,\n 'false':mgr.FALSE(),\n 'true':mgr.TRUE(),\n 'to_real':mgr.ToReal,\n 'concat':mgr.BVConcat,\n 'bvnot':mgr.BVNot,\n 'bvand':mgr.BVAnd,\n 'bvor':mgr.BVOr,\n 'bvneg':mgr.BVNeg,\n 'bvadd':mgr.BVAdd,\n 'bvmul':mgr.BVMul,\n 'bvudiv':mgr.BVUDiv,\n 'bvurem':mgr.BVURem,\n 'bvshl':mgr.BVLShl,\n 'bvlshr':mgr.BVLShr,\n 'bvsub':mgr.BVSub,\n 'bvult':mgr.BVULT,\n 'bvxor':mgr.BVXor,\n '_':self._smtlib_underscore,\n # Extended Functions\n 'bvnand':mgr.BVNand,\n 'bvnor':mgr.BVNor,\n 'bvxnor':mgr.BVXnor,\n 'bvcomp':mgr.BVComp,\n 'bvsdiv':mgr.BVSDiv,\n 'bvsrem':mgr.BVSRem,\n 'bvsmod':mgr.BVSMod,\n 'bvashr':mgr.BVAShr,\n 'bvule':mgr.BVULE,\n 'bvugt':mgr.BVUGT,\n 'bvuge':mgr.BVUGE,\n 'bvslt':mgr.BVSLT,\n 'bvsle':mgr.BVSLE,\n 'bvsgt':mgr.BVSGT,\n 'bvsge':mgr.BVSGE,\n })","title":""},{"docid":"a25a01fd803bb24f56de3aedb69ef122","score":"0.4986482","text":"def _save_symbols_cache(self, file_io, cache):\n\n file_io.seek(0, 0)\n h = hashlib.sha256(file_io.read()).hexdigest()\n file_io.seek(0, 0)\n\n this_cache_file = os.path.join(symbol_cache_path, h)\n with open(this_cache_file, \"w\") as f:\n f.write(json.dumps(cache))","title":""},{"docid":"8f64f2a05537ba8241e7cd04ded4dc41","score":"0.4980934","text":"def unset_symbol(self):\n\t\tself.symbols = None","title":""},{"docid":"0cb36f386662591ece2d36147dc26e2f","score":"0.4958083","text":"def _reset_caches(self):\n self._total_nb_possibilities = None\n self._cached_examples = []","title":""},{"docid":"5d722e08f7b423f336e40835591af460","score":"0.49553323","text":"def intern(name):\n if isinstance(name, SchemeSymbol):\n if str(name) in _all_symbols:\n return _all_symbols[str(name)]\n else:\n _all_symbols[str(name)] = name\n return name\n if name in _all_symbols:\n return _all_symbols[name]\n else:\n sym = SchemeSymbol(name)\n _all_symbols[name] = sym\n return sym","title":""},{"docid":"4545e9ddbf6793f87760186b40dc183b","score":"0.49532443","text":"def fill_cache(self, X):\n cache = dict(lX=self._predict(X))\n cache[\"aX\"] = self.activation_(cache[\"lX\"])\n return cache","title":""},{"docid":"897ffa50017b0af2a87db6cdc4972570","score":"0.49398854","text":"def init_cache():\n global memory_cache\n with session_scope() as session:\n last_req_dict = dict(get_last_requested(session))\n hits_dict = dict(get_endpoints_hits(session))\n averages_dict = dict(get_endpoint_averages(session))\n for rule in get_rules():\n memory_cache[rule.endpoint] = EndpointInfo(\n last_requested=last_req_dict.get(rule.endpoint),\n average_duration=averages_dict.get(rule.endpoint),\n hits=hits_dict.get(rule.endpoint),\n )","title":""},{"docid":"d82a1e7eae31a22371aaa9f3ea91fcb2","score":"0.4927952","text":"def _calculate_initial_bought(self):\r\n bought = {} \r\n for s in self.symbol_list:\r\n bought[s] = 'OUT'\r\n return bought","title":""},{"docid":"332c1a1ae5a5c92d9cc46a99be8dc6be","score":"0.49105838","text":"def symbols(self):\n raise NotImplementedError()","title":""},{"docid":"fd9b7c2e38a94bdf7c4e2745766f7ef1","score":"0.49104315","text":"def find_symbol_table(self):\n for offset in range(len(self._firmware)):\n if self.symbol_table_start is None:\n if self._check_symbol_format(offset):\n self.logger.info(\"symbol table start offset: %s\" % (hex(offset)))\n self.symbol_table_start = offset\n self._has_symbol = True\n break\n else:\n break\n\n if self.symbol_table_start:\n for i in range(self.symbol_table_start, len(self._firmware), self._symbol_interval):\n check_data = self._firmware[i:i + self._symbol_interval]\n if len(check_data) < self._symbol_interval:\n self.logger.debug(\"Check_data length is too small\")\n break\n\n if len(check_data) < self._symbol_interval:\n self.logger.debug(\"Check_data length is too small\")\n break\n\n if self._check_symbol_format_simple(check_data):\n self.symbol_table_end = i + self._symbol_interval\n self.logger.debug(\"self.symbol_table_end: {:010x}\".format(self.symbol_table_end))\n\n else:\n self.logger.info(\"symbol table end offset: %s\" % hex(self.symbol_table_end))\n break\n else:\n self.logger.error(\"didn't find symbol table in this image\")\n self._has_symbol = False","title":""},{"docid":"52da5b440a1b2e652cc17bcc84e5f433","score":"0.49103576","text":"def freeze(self) -> None:\n self.__frozen = True\n if self.__key_expr:\n fce = make_lambda(self.__key_expr)\n self.__map = dict(((key, index) for index, key in enumerate((fce(item) for item in self))))","title":""},{"docid":"27119c3b7afe9e18f823dc9b91cbee54","score":"0.48837","text":"def symbol_cost(self, symbol, seen=set()):\n expansions = self.v['grammar'][symbol]\n return min(self.expansion_cost(e, seen | {symbol}) for e in expansions)","title":""},{"docid":"a4c988225c3599bacdbb01f6ff463cfb","score":"0.48803112","text":"def gen_unused_symbols(used, n):\n i = cnt = 0\n while cnt < n:\n s = get_symbol(i)\n i += 1\n if s in used:\n continue\n yield s\n cnt += 1","title":""},{"docid":"2443fcee79c9db7f847b7ec04d5397f3","score":"0.48796088","text":"def manin_symbols(self):\n try:\n return self.__manin_symbols\n except AttributeError:\n self.__manin_symbols = ManinSymbolList_gamma_h(\n group=self.group(), weight=self.weight())\n return self.__manin_symbols","title":""},{"docid":"0626a4682147c14e09a7ab19fb2fbdff","score":"0.48670635","text":"def init_word_cache(self, length=10000):\n for i in range(0, length):\n self.word_cache.append(self.gen_word())","title":""},{"docid":"ff9958340cc4cd6f309cc930fc287a99","score":"0.4865642","text":"def order_collected_historical_data(self):\n for symbol_name in SYMBOLS:\n self.fetched_data[symbol_name] = OrderedDict(sorted(self.fetched_data[symbol_name].items()))","title":""},{"docid":"1e8ff1c58af90a0b648852b5b3e536af","score":"0.48649782","text":"def lookup_symbol(self, symbol):\n try:\n s = self.reverse_grammar[symbol]\n return s\n except KeyError:\n return set()","title":""},{"docid":"4f38bd984bb87eafb2a65e68f1f1337b","score":"0.48638475","text":"def clearcache():\r\n\r\n global cache\r\n cache = {}","title":""},{"docid":"49c43897503206fba647c250c27e88ec","score":"0.48610923","text":"def do_symbols(self, arg):\n symbols = db(db.symbols).select()\n if len(symbols) == 0:\n print('%s %s' % (self.out, 'no symbols stored'))\n return\n for i, sym in enumerate(symbols):\n print('%s %3s: %s - %s' % (self.out, i, sym.ticker, sym.name))","title":""},{"docid":"4b8a32c88ff779907f03763270b410f2","score":"0.4859931","text":"def _load_symbol_data(self):\n return False","title":""},{"docid":"5cc3762142a1e458e72d97eaa8e1277f","score":"0.48566782","text":"def _reset_caches(self):\n self._initialized = False\n self._is_hermitian = None","title":""},{"docid":"049bc1db08196bea951d633202c589db","score":"0.48480937","text":"def gen_symbol_table(min_size=1,max_size=50):\n table = {}\n\n i = randint(min_size,max_size)\n\n \n while i > 0:\n symbol = gen_rand_symbol(randint(3,3))\n if not symbol in table:\n table[symbol] = 0\n i -= 1\n return table","title":""},{"docid":"0a3e8c26f0db20c4764de56c61f25d66","score":"0.48405343","text":"def store_matches(\n self,\n atom_slots: dict[TopologyKey, PotentialKey],\n topology: \"Topology\",\n ) -> None:\n for proper in topology.propers:\n atom_indices = tuple(topology.atom_index(atom) for atom in proper)\n top_key = TopologyKey(atom_indices=atom_indices)\n\n pot_key_ids = tuple(\n _get_potential_key_id(atom_slots, idx) for idx in atom_indices\n )\n\n self.key_map[top_key] = PotentialKey(\n id=POTENTIAL_KEY_SEPARATOR.join(pot_key_ids),\n )","title":""},{"docid":"83176714dca3497dc482b278cbb20df0","score":"0.4837721","text":"def sym_names(self) -> List[str]:\n return list(self._syms.keys())","title":""},{"docid":"deea379dd8c3c83f12c64ca872b77167","score":"0.48372594","text":"def optimize_lookups(self):\n if self._lookup_dict is not None:\n return\n self._list = sorted(self._list, key=lambda x: x.begin)\n if len(self._list) > 1000:\n self._lookup_dict = {}\n cur = 0\n while cur < len(self._list):\n self._lookup_dict[cur] = self._list[cur].begin\n cur += 1000","title":""},{"docid":"d31d56f47fae019ab516ba7a67746e70","score":"0.48329672","text":"def cache(self):\n cached_data = self.state_dict()\n cached_data['epoch'] = self.epoch\n cached_data['best_val_error'] = self._best_val_error\n cached_data['best_loss'] = self._best_loss\n self._cacher.save(cached_data)\n self._cacher_params.save(self.hyperparams.hashable_str)","title":""},{"docid":"cc63e587e1ed72bdef9f2f0965265c05","score":"0.48305044","text":"def _reset_cache(self):\n\n self._noint_mask = None\n self._escat_mask = None\n self._escatonly_mask = None\n self._line_mask = None\n self._lam_escat = None\n self._lam_noint = None\n self._weights_escat = None\n self._weights_noint = None\n self._line_in_infos = None\n self._line_in_nu = None\n self._line_in_L = None\n self._line_out_infos = None\n self._line_out_nu = None\n self._line_out_L = None","title":""},{"docid":"763413c7d471d5ac0c75ff50214fc6b2","score":"0.48247024","text":"def _read_symbols_cache_dict(self, f):\n\n if f is None:\n return None\n\n assert isinstance(f, io.IOBase), 'Unhandled symbol cache load type of {}'.format(type(f))\n\n f.seek(0, 0)\n h = hashlib.sha256(f.read()).hexdigest()\n f.seek(0, 0)\n\n this_cache_file = os.path.join(symbol_cache_path, h)\n\n # Cache miss\n if not os.path.isfile(this_cache_file):\n return None\n\n # Cache hit\n with open(this_cache_file, \"r\") as f:\n return json.loads(f.read())","title":""},{"docid":"cd5a8eb9d63818ecb3fdf6139075ac87","score":"0.4820459","text":"def construct_symbol_table(filename):\n pass","title":""},{"docid":"bdd9db309fa25353d7cf8af32d86bbcc","score":"0.48204294","text":"def replace_with_cached(\n self, expr: expressions.Expression) -> Dict[str, expressions.Expression]:\n\n replaced_inputs: Dict[str, expressions.Expression] = {}\n self._replace_with_cached_recur(expr, replaced_inputs)\n return replaced_inputs","title":""},{"docid":"eecf32044d99d6808e3c23db2667223b","score":"0.48199284","text":"def manin_symbols(self):\n try:\n return self.__manin_symbols\n except AttributeError:\n self.__manin_symbols = ManinSymbolList_gamma0(\n level=self.level(), weight=self.weight())\n return self.__manin_symbols","title":""},{"docid":"bdfca7e1bf42123fc0ddc4ca98b749d4","score":"0.48153427","text":"def reinit(self):\n self.mem = {i: [] for i in range(self.env.nbarms)}","title":""},{"docid":"567c52896377a51900de203c1648781c","score":"0.48125237","text":"def __hash__(self):\n return hash(('Atom', self.symbol))","title":""},{"docid":"32aadcdefb03a2e9c5fb704dd49d91ba","score":"0.48118928","text":"def _calculate_initial_bought(self):\n bought = {}\n for s in self.symbol_list:\n bought[s] = 'OUT'\n return bought","title":""},{"docid":"7945e6f18fde97ced93d36260aa22fba","score":"0.48061186","text":"def create_default_symbol_table():\n\n st = SymbolTable({\n 'SP': 0,\n 'LCL': 1,\n 'ARG': 2,\n 'THIS': 3,\n 'THAT': 4,\n 'SCREEN': 16384,\n 'KBD': 24576,\n })\n\n st.update({'R{}'.format(r): r\n for r in range(16)})\n\n return st","title":""},{"docid":"f7029f3237c4bce00f5971226320d92e","score":"0.48026088","text":"def _populateCache(self):\n cacheSize = self.len/3 + 1\n self.sort(self._compareBy(\"hits\"))\n for i in range(cacheSize):\n self.cache.append(self.words[i])","title":""},{"docid":"f3fdca96dec7badeeb66bfc23282910c","score":"0.4795915","text":"def store_matches(\n self,\n atom_slots: dict[TopologyKey, PotentialKey],\n topology: \"Topology\",\n ) -> None:\n for bond in topology.bonds:\n atom_indices = (\n topology.atom_index(bond.atom1),\n topology.atom_index(bond.atom2),\n )\n top_key = TopologyKey(atom_indices=atom_indices)\n\n pot_key_ids = tuple(\n _get_potential_key_id(atom_slots, idx) for idx in atom_indices\n )\n\n self.key_map[top_key] = PotentialKey(\n id=POTENTIAL_KEY_SEPARATOR.join(pot_key_ids),\n )","title":""},{"docid":"04096965f8cfa97e4588833da73b4295","score":"0.47878587","text":"def isBackedByLocalSymbolMap(self) -> bool:\n ...","title":""},{"docid":"c485b9b330cbfdf325ca87146a0bbbd3","score":"0.47812495","text":"def find_symbols(symbol_dirs):\r\n known_symbols = {}\r\n for symbol_dir in symbol_dirs:\r\n if os.path.exists(symbol_dir):\r\n for dirpath, dirnames, filenames in os.walk(symbol_dir):\r\n dirnames.sort()\r\n for filename in filenames:\r\n if filename.endswith('.sym'):\r\n filepath = os.path.join(dirpath, filename)\r\n filename, _ = os.path.splitext(filename)\r\n if filename.lower() not in known_symbols:\r\n known_symbols[filename.lower()] = filepath\r\n\r\n return known_symbols","title":""},{"docid":"129d62e7da921ea76970fdc1088aa338","score":"0.47739196","text":"def createCache(self):\n \n for i in range(2**self.index):\n self._cache.append({})","title":""},{"docid":"ba273d0dd88c988724380e1a8b93c076","score":"0.47723415","text":"def UpdateAndAttachSymbolTable(self, oldTable):\n\n symbols = list(self.ReturnSymbols())\n if len(symbols) == 0:\n self.symTable = oldTable\n return\n\n newProperties = {sym:[False, -1] for sym in symbols}\n\n outSymbol = self.dest.value\n\n if self.dest.is_SCALAR_VARIABLE():\n newProperties[outSymbol] = [False, -1]\n\n # Rest of the symbols count as a \"use\"\n for sym in symbols:\n if sym == outSymbol:\n continue\n\n newProperties[sym] = [True, self.lineID]\n\n # Check if the outSymbol is used as an input as well\n if self.dest.is_SCALAR_VARIABLE():\n if (self.inp1.value == outSymbol or \n self.inp2.value == outSymbol or \n outSymbol in [i.value for i in self.IOArgs]):\n\n newProperties[outSymbol] = [True, self.lineID] \n\n\n self.symTable = BB.SymSetProperties(oldTable, newProperties)","title":""},{"docid":"5b16a34a45e8737d3f6d13717c611a97","score":"0.47612625","text":"def add_symbol(self, symbol):\n\t\tsymbol = symbol.lower()\n\t\tif symbol not in self.symbols:\n\t\t\tself.symbols.append(symbol)\n\t\treturn True","title":""},{"docid":"70908542bd1317d7797375be2ecddf4b","score":"0.47581547","text":"def bomb_with_cache(self, x, y):\n try:\n return self.cache[(x, y)]\n except AttributeError:\n self.cache = {}\n return self.bomb(x, y)\n except KeyError:\n r = self._bomb(x, y)\n self.cache[(x, y)] = r\n return r","title":""},{"docid":"f322550399b2a0f8de51b149ab475a76","score":"0.47405848","text":"def manin_symbols(self):\n try:\n return self.__manin_symbols\n except AttributeError:\n self.__manin_symbols = ManinSymbolList_gamma1(\n level=self.level(), weight=self.weight())\n return self.__manin_symbols","title":""},{"docid":"ecc786710b87dc9540082526dafa6058","score":"0.47348714","text":"def _symbol(self, symbol):\n\n if self.current_const != \"\":\n if len(self.current_vars) == 1: # Is a coefficient\n self.current_vars[0].val = str(int(self.current_vars[0].val) *\n int(self.current_const))\n else: # Is an exponent\n self.current_vars[-1].power = int(self.current_const)\n self.current_const = \"\"\n\n if len(self.current_vars) > 0:\n current_term = Term({})\n while len(self.current_vars) > 0:\n current_term.set_var(self.current_vars.pop())\n self.poly.add_term(current_term)\n current_var = Variable(val=symbol+\"1\")\n self.current_vars.append(current_var)","title":""},{"docid":"c63c35d1bcf98b0b00bf74ae94690cd6","score":"0.47338057","text":"def set_symbols(self, symbols_str=None):\n symbols_list = []\n if symbols_str:\n for i in symbols_str:\n symbols_list.append(i)\n self.symbols_list = symbols_list","title":""},{"docid":"ab527b3ac5988047f866b348aa4c04bf","score":"0.47334912","text":"def collect(self):\n for func in self._caches:\n cache = {}\n for key in self._caches[func]:\n if (time.time() - self._caches[func][key][1]) < self._timeouts[func]:\n cache[key] = self._caches[func][key]\n self._caches[func] = cache","title":""},{"docid":"74f36c5e66f8968332ba8ae255a959c8","score":"0.47306302","text":"def _clear_caches():\n _ghi_cache.clear()\n _prf_cache.clear()","title":""},{"docid":"a1a196add6434995e90ffc5ded390fc1","score":"0.4728394","text":"def __iter__(self):\n return iter(list(self._symbols.items()))","title":""}],"string":"[\n {\n \"docid\": \"5d0760fc5481f25d465307101e3bd6f3\",\n \"score\": \"0.66358835\",\n \"text\": \"def _load_symbols_cache(self, cache):\\n\\n #\\n # Symbols\\n #\\n\\n for sym, address in cache['symbols'].items():\\n if self._process.file_type == \\\"PE\\\" or \\\\\\n (self.elf is not None and self.elf.type_str == 'DYN'):\\n address = address + self.base\\n\\n sym = symbols.Symbol(self._process, name=sym, address=address)\\n self._process.modules._symbol_to_address[self.name][sym.name] = sym\\n self._process.modules._address_to_symbol[address] = sym\\n\\n #\\n # Sections\\n #\\n\\n if 'plt_offset' in cache:\\n self.plt = cache['plt_offset']\\n\\n #\\n # PLT\\n #\\n\\n if 'plt' in cache:\\n for i, sym in enumerate(cache['plt']):\\n addr = self.plt + ((i+1)*0x10)\\n name = 'plt.' + sym\\n sym = symbols.Symbol(self._process, name=name, address=addr)\\n self._process.modules._symbol_to_address[self.name][name] = sym\\n self._process.modules._address_to_symbol[addr] = sym\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc0072c550ca48cc70aaac6d930d7679\",\n \"score\": \"0.65396005\",\n \"text\": \"def _symbols_update(self):\\n self.symbols_sting = [str(sym) for sym in list(self.expression.free_symbols)]\\n self.symbols_sting.sort()\\n self.symbols = [symbols(char) for char in self.symbols_sting]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2fac262d57dfbd7413fb0e1458ac0e5\",\n \"score\": \"0.58945626\",\n \"text\": \"def update_local_symbols_dict(self, comp):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"324c3cf79b34a2def8b3b2a9e35f87fb\",\n \"score\": \"0.5756938\",\n \"text\": \"def makeMapSymbols(self):\\n n = self.keepAnalysisCount\\n pen = self.colorList.next()\\n filledbrush = pen\\n emptybrush = None\\n symbol = self.symbolList.next()\\n if n == 0:\\n clearFlag = True\\n else:\\n clearFlag = False\\n self.currentSymDict = {'pen': pen, 'filledbrush': filledbrush,\\n 'emptybrush': emptybrush, 'symbol': symbol,\\n 'n': n, 'clearFlag': clearFlag}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de66b2e3a9f3773a3e811b658b69d887\",\n \"score\": \"0.56086206\",\n \"text\": \"def symbols_with_same_path(self):\\n a, b = Symbol('spam.ham.eggs'), Symbol().spam.ham.eggs\\n self.symbs = [a, b]\\n return self.symbs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9940dd1a84f08f221365a89a03aa1e00\",\n \"score\": \"0.5607992\",\n \"text\": \"def _load_symbols(self):\\n\\n # Either we're loading everything or what we're looking at right now __should__ be loaded\\n if self._process._load_symbols is None or any(True for x in self._process._load_symbols if fnmatch(self.name, x)):\\n\\n # Clear out old symbols if needed\\n self._process.modules._symbol_to_address[self.name] = {}\\n\\n file_io = common.load_file(self._process, self.path)\\n cache = self._read_symbols_cache_dict(file_io)\\n\\n if cache is not None:\\n return self._load_symbols_cache(cache)\\n\\n if self._process.file_type == 'ELF':\\n self._load_symbols_elf(file_io)\\n\\n elif self._process.file_type == \\\"PE\\\":\\n self._load_symbols_pe(file_io)\\n\\n # TODO: Windows\\n # TODO: Mac\\n\\n # If we didn't resolve anything, make sure we noted we tried\\n if self.name not in self._process.modules._symbol_to_address:\\n self._process.modules._symbol_to_address[self.name] = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f157c87d5d45117e5803321caad471b\",\n \"score\": \"0.558471\",\n \"text\": \"def initialize_local_symbols_dict(self, comp, assigned_var):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75423c89f4625aada9920528546243c0\",\n \"score\": \"0.5582418\",\n \"text\": \"def free_symbols(self) -> Set[str]:\\n raise NotImplementedError('free_symbols not implemented by \\\"%s\\\"' % type(self).__name__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"965c91085d8c0bf31b4959d99fb5b169\",\n \"score\": \"0.5560573\",\n \"text\": \"def _resolve_symbols(self):\\n # TODO: Should attempt to fill self.imports in accordance with angr's expectations\\n # note: no rebasing support\\n\\n # trigger parsing of exports\\n self._parse_exports(self.export_blob)\\n\\n # Set up search tables for searching\\n section_tab = [None] # first section is NO_SECT\\n for seg in self.segments:\\n section_tab.extend(seg.sections)\\n\\n # A new memory area is created to hold external (undefined) symbols\\n ext_symbol_start = 0xff00000000000000 if self.arch.bits == 64 else 0xff000000\\n ext_symbol_end = ext_symbol_start\\n\\n # Update symbol properties\\n for sym in self.symbols:\\n\\n sym._is_export = sym.name in self.exports_by_name\\n\\n if sym.is_stab: # stab symbols are debugging information - don't care!\\n l.debug(\\\"Symbol %r is debugging information, skipping\\\", sym.name)\\n continue\\n\\n if sym.name in self.exports_by_name:\\n l.debug(\\\"Symbol %r is an export\\\", sym.name)\\n sym.is_export = True\\n else:\\n l.debug(\\\"Symbol %r is not an export\\\", sym.name)\\n sym.is_export = False\\n\\n if sym.is_common:\\n l.debug(\\\"Symbol %r is common, updating size\\\", sym.name)\\n sym.size = sym.n_value\\n\\n if sym.sym_type == SYMBOL_TYPE_SECT:\\n l.debug(\\\"Symbol %r is N_SECT, updating names and address\\\", sym.name)\\n sec = section_tab[sym.n_sect]\\n if sec is not None:\\n sym.segment_name = sec.segname\\n sym.section_name = sec.sectname\\n\\n sym.relative_addr = sym.n_value\\n\\n elif sym.is_import():\\n l.debug(\\\"Symbol %r is imported!\\\", sym.name)\\n sym.library_name = self.imported_libraries[sym.library_ordinal]\\n\\n # if the symbol has no address we assign one from the special memory area\\n if sym.relative_addr is None:\\n sym.relative_addr = ext_symbol_end\\n ext_symbol_end += sym.size\\n l.debug(\\\"Assigning address %#x to symbol %r\\\", sym.relative_addr, sym.name)\\n\\n # Add special memory area for symbols:\\n self.memory.add_backer(ext_symbol_start, \\\"\\\\x00\\\" * (ext_symbol_end - ext_symbol_start))\\n\\n # Perform binding\\n bh = BindingHelper(self) # TODO: Make this configurable\\n bh.do_normal_bind(self.binding_blob)\\n bh.do_lazy_bind(self.lazy_binding_blob)\\n if self.weak_binding_blob is not None and len(self.weak_binding_blob) > 0:\\n l.info(\\\"Found weak binding blob. According to current state of knowledge, weak binding \\\"\\n \\\"is only sensible if multiple binaries are involved and is thus skipped.\\\")\\n\\n # Add to symbols_by_addr\\n # All (resolvable) symbols should be resolved by now\\n for sym in self.symbols:\\n if not sym.is_stab:\\n if sym.relative_addr is not None:\\n self._symbols_by_addr[sym.relative_addr] = sym\\n else:\\n # todo: this might be worth an error\\n l.warn(\\\"Non-stab symbol %r @ %#x has no address.\\\", sym.name, sym.symtab_offset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ad03e670aa88ff288689847e04e29b\",\n \"score\": \"0.5556712\",\n \"text\": \"def set_is_created_containing_these_symbols(self):\\n self.set = set(self.symbs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caf0f32edf1c8d82e2ccdc12a58f6c73\",\n \"score\": \"0.5525948\",\n \"text\": \"def IncludingErased(self) -> SymbolTable:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4a6d64ce17e563d13be0060e89a0d4b\",\n \"score\": \"0.5494512\",\n \"text\": \"def warm_cache(self):\\n words = self.good.keys() + self.rejected.keys()\\n for word in words:\\n self.rprobs[word] = self.rejected_given_word(word)\\n \\n return {'rprobs' : self.rprobs,\\n 'good' : self.good,\\n 'rejected' : self.rejected}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"605e4a336fe6563cbea85e4f3783316a\",\n \"score\": \"0.54501534\",\n \"text\": \"def _clear_cache(self):\\n self.__symbolic_jacobian = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2575be7d1b8a7fb0a119b9d250533d9\",\n \"score\": \"0.5398685\",\n \"text\": \"def prepare(self):\\n self.find_symbol_table()\\n if self._has_symbol is False:\\n return None\\n self.logger.debug(\\\"has_symbol: %s\\\" % self._has_symbol)\\n self.get_symbol_table()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f7309b7112d6acecd555c8459b37ad8\",\n \"score\": \"0.5389967\",\n \"text\": \"def _read_symbol_table(self):\\n section = self.get_section_by_name(\\\".symtab\\\")\\n if not section:\\n raise Exception(\\\"Missing symbol table\\\")\\n\\n if not isinstance(section, SymbolTableSection):\\n raise Exception(\\\"Invalid symbol table section\\\")\\n\\n symbols = {}\\n for symbol in section.iter_symbols():\\n name_str = symbol.name\\n if name_str in symbols:\\n logging.debug(\\\"Duplicate symbol %s\\\", name_str)\\n symbols[name_str] = SymbolSimple(name_str, symbol[\\\"st_value\\\"],\\n symbol[\\\"st_size\\\"])\\n return symbols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"070c063cecf57669722560a0981a8c7f\",\n \"score\": \"0.5354533\",\n \"text\": \"def free_symbols(self) -> Iterable[sympy.Symbol]:\\n return get_free_symbols(self.params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b932cb1f8beb2aeb9636a4e4ae74432c\",\n \"score\": \"0.53413147\",\n \"text\": \"def cache_all_data(cls):\\n from ir_log import IRProgressBar\\n from ir_config import IRConfig\\n from ir_mongodb_helper import IRCollection\\n bug_id_name = IRConfig.get_instance().get('bug_id_name')\\n summary_name = IRConfig.get_instance().get('bug_summary_name')\\n description_name = IRConfig.get_instance().get('bug_description_name')\\n tfidf_collection = IRCollection(\\n 'bug_db_name', 'bug_tfidf_collection_name', 'r')\\n cls.set_is_cache(True)\\n cls.__cache = {}\\n def iter_tfidf(bug):\\n cls.__cache[bug[bug_id_name]] = (bug[summary_name],\\n bug[description_name])\\n IRProgressBar.execute_iteration_for_cursor(tfidf_collection.find(),\\n iter_tfidf, \\\"Caching TFIDF\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffa5e2ae072eba1df0a931b74f25cf0a\",\n \"score\": \"0.53243756\",\n \"text\": \"def symbolizeAddr(self, addr: int) -> List[bytes]:\\n\\t\\tif addr in self._symbolCache:\\n\\t\\t\\treturn self._symbolCache[addr]\\n\\t\\telse:\\n\\t\\t\\treturn None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e731dd71a4be8fb6244ea9fd844f35a1\",\n \"score\": \"0.5317222\",\n \"text\": \"def non_trivial_symbol(self):\\n a, b = Symbol('os.path.isfile'), Symbol('sys.executable')\\n self.symbs = [a, b]\\n return self.symbs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c88c13f1bb7c417c36d2ba46485d50e\",\n \"score\": \"0.5298955\",\n \"text\": \"def free_symbols(self) -> Set[sp.Basic]:\\n return set(\\n chain.from_iterable(\\n state.get_free_symbols()\\n for state in self.states() + self.algebraic_equations()\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d691e4e77a8abc1e93b5e4779cadd38e\",\n \"score\": \"0.52566\",\n \"text\": \"def set_symbols(self, symbols):\\n\\t\\tself.symbols = symbols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b80bd13f5791b52d86bd5ea0f3cca52\",\n \"score\": \"0.5239692\",\n \"text\": \"def symbols(self):\\n return self._symbols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b28fafedbb403ba7dd3e4099813258b7\",\n \"score\": \"0.5223729\",\n \"text\": \"def getUncachedGameKeys(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58eede2f0004b3c04cb3294c1207ac05\",\n \"score\": \"0.52194476\",\n \"text\": \"def getSymbols(self) -> List[ghidra.program.model.symbol.Symbol]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a47dc84ed9e8628929172e06e72f2dd\",\n \"score\": \"0.5188628\",\n \"text\": \"def intern(self, string):\\n if string in self.oblist:\\n return self.oblist[string]\\n sym = symbol(string)\\n self.oblist[string] = sym\\n return sym\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cee80301dcbdd3338e473d782cbbfed5\",\n \"score\": \"0.5178622\",\n \"text\": \"def scan_cache(force_update=False):\\n\\n global __CACHED_SCAN__\\n if force_update or __CACHED_SCAN__ is None:\\n __CACHED_SCAN__ = scan_system()\\n return __CACHED_SCAN__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf685ba8aa951ff9e726e9efb8c31ad4\",\n \"score\": \"0.5176204\",\n \"text\": \"def symbols(self) -> set[Symbol]:\\n\\n # this fails because `free_symbols` is a dict on NumExpr but `set` on Expr\\n\\n symbols = set()\\n for rhs in self.values():\\n if isinstance(rhs, (Expr, MatrixBase)):\\n symbols |= rhs.free_symbols\\n else:\\n try:\\n symbols |= set(rhs.symbols)\\n except AttributeError:\\n # RHS doesnt have any symbols; for example might be a numpy array\\n pass\\n\\n # symbols = getattr(rhs, 'free_symbols')\\n # try:\\n # # rhs is a sympy `Expr` and has `free_symbols` as a set\\n # symbols |= rhs.free_symbols\\n # except TypeError:\\n # # rhs is a slimfit `NumExpr`\\n # symbols |= set(rhs.symbols)\\n # except AttributeError:\\n # # RHS doesnt have any symbols; for example might be a numpy array\\n # pass\\n return symbols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e4416089cae9958605ad2d84b3bc489\",\n \"score\": \"0.51651824\",\n \"text\": \"def __init__(self):\\n self._cache = {0: 0, 1: 1, 2: 1}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4070f67b1073d8e761fc82f8cf7656e6\",\n \"score\": \"0.5159139\",\n \"text\": \"def get_priceinfo_cached(self, symbol, thresh):\\n cached = self._get_cache_priceinfo(symbol, thresh)\\n if not cached:\\n cached = self.fetch_priceinfo(symbol)\\n if cached:\\n self._set_cache_priceinfo(symbol, cached)\\n\\n numfields = set(['open', 'high', 'low', 'price', 'volume', 'change', 'previous close'])\\n return {k: Decimal(v) if k in numfields else v for k, v in cached.items()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a89c9bd2dbf0876f75fb50d2b843d44\",\n \"score\": \"0.5138711\",\n \"text\": \"def symbol_values(self):\\n return dict(zip(self.symbols, self.get_weights()[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a48824c4d9d1c838a72db8b7c5d0b4fd\",\n \"score\": \"0.51371515\",\n \"text\": \"def symbols(self):\\n symbols = set()\\n\\n for word, transcriptions in self.entries.items():\\n for t in transcriptions:\\n symbols.update(t)\\n\\n return symbols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d37d540188094bb50310ce54102d922c\",\n \"score\": \"0.51330703\",\n \"text\": \"def _enumerateSymbolPointers(self) -> Dict[bytes, Tuple[int]]:\\n\\n\\t\\t# read all the bind records as they're a source of symbolic info\\n\\t\\tbindRecords: Dict[int, _BindRecord] = {}\\n\\t\\tdyldInfo: dyld_info_command = self._machoCtx.getLoadCommand(\\n\\t\\t\\t(LoadCommands.LC_DYLD_INFO, LoadCommands.LC_DYLD_INFO_ONLY)\\n\\t\\t)\\n\\n\\t\\tlinkeditFile = self._machoCtx.ctxForAddr(\\n\\t\\t\\tself._machoCtx.segments[b\\\"__LINKEDIT\\\"].seg.vmaddr\\n\\t\\t)\\n\\n\\t\\tif dyldInfo:\\n\\t\\t\\trecords: List[_BindRecord] = []\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tif dyldInfo.weak_bind_size:\\n\\t\\t\\t\\t\\t# usually contains records for c++ symbols like \\\"new\\\"\\n\\t\\t\\t\\t\\trecords.extend(\\n\\t\\t\\t\\t\\t\\t_bindReader(\\n\\t\\t\\t\\t\\t\\t\\tlinkeditFile,\\n\\t\\t\\t\\t\\t\\t\\tdyldInfo.weak_bind_off,\\n\\t\\t\\t\\t\\t\\t\\tdyldInfo.weak_bind_size\\n\\t\\t\\t\\t\\t\\t)\\n\\t\\t\\t\\t\\t)\\n\\t\\t\\t\\t\\tpass\\n\\n\\t\\t\\t\\tif dyldInfo.lazy_bind_off:\\n\\t\\t\\t\\t\\trecords.extend(\\n\\t\\t\\t\\t\\t\\t_bindReader(\\n\\t\\t\\t\\t\\t\\t\\tlinkeditFile,\\n\\t\\t\\t\\t\\t\\t\\tdyldInfo.lazy_bind_off,\\n\\t\\t\\t\\t\\t\\t\\tdyldInfo.lazy_bind_size\\n\\t\\t\\t\\t\\t\\t)\\n\\t\\t\\t\\t\\t)\\n\\t\\t\\t\\t\\tpass\\n\\n\\t\\t\\t\\tfor record in records:\\n\\t\\t\\t\\t\\t# check if we have the info needed\\n\\t\\t\\t\\t\\tif (\\n\\t\\t\\t\\t\\t\\trecord.symbol is None\\n\\t\\t\\t\\t\\t\\tor record.segment is None\\n\\t\\t\\t\\t\\t\\tor record.offset is None\\n\\t\\t\\t\\t\\t):\\n\\t\\t\\t\\t\\t\\tself._logger.warning(f\\\"Incomplete lazy bind record: {record}\\\")\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\t\\tbindAddr = self._machoCtx.segmentsI[record.segment].seg.vmaddr\\n\\t\\t\\t\\t\\tbindAddr += record.offset\\n\\t\\t\\t\\t\\tbindRecords[bindAddr] = record\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\texcept KeyError as e:\\n\\t\\t\\t\\tself._logger.error(f\\\"Unable to read bind records, reasons: {e}\\\")\\n\\t\\t\\tpass\\n\\n\\t\\t# enumerate all symbol pointers\\n\\t\\tsymbolPtrs: Dict[bytes, List[int]] = {}\\n\\n\\t\\tdef _addToMap(ptrSymbol: bytes, ptrAddr: int, section: section_64):\\n\\t\\t\\tif ptrSymbol in symbolPtrs:\\n\\t\\t\\t\\t# give priority to ptrs in the __auth_got section\\n\\t\\t\\t\\tif section.sectname == b\\\"__auth_got\\\":\\n\\t\\t\\t\\t\\tsymbolPtrs[ptrSymbol].insert(0, ptrAddr)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tsymbolPtrs[ptrSymbol].append(ptrAddr)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tsymbolPtrs[ptrSymbol] = [ptrAddr]\\n\\t\\t\\tpass\\n\\n\\t\\tfor segment in self._machoCtx.segmentsI:\\n\\t\\t\\tfor sect in segment.sectsI:\\n\\t\\t\\t\\tsectType = sect.flags & SECTION_TYPE\\n\\t\\t\\t\\tif (\\n\\t\\t\\t\\t\\tsectType == S_NON_LAZY_SYMBOL_POINTERS\\n\\t\\t\\t\\t\\tor sectType == S_LAZY_SYMBOL_POINTERS\\n\\t\\t\\t\\t):\\n\\t\\t\\t\\t\\tfor i in range(int(sect.size / 8)):\\n\\t\\t\\t\\t\\t\\tself._statusBar.update(status=\\\"Caching Symbol Pointers\\\")\\n\\n\\t\\t\\t\\t\\t\\tptrAddr = sect.addr + (i * 8)\\n\\n\\t\\t\\t\\t\\t\\t# Try to symbolize through bind records\\n\\t\\t\\t\\t\\t\\tif ptrAddr in bindRecords:\\n\\t\\t\\t\\t\\t\\t\\t_addToMap(bindRecords[ptrAddr].symbol, ptrAddr, sect)\\n\\t\\t\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\t\\t\\t# Try to symbolize though indirect symbol entries\\n\\t\\t\\t\\t\\t\\tsymbolIndex = linkeditFile.readFormat(\\n\\t\\t\\t\\t\\t\\t\\t\\\"<I\\\",\\n\\t\\t\\t\\t\\t\\t\\tself._dysymtab.indirectsymoff + ((sect.reserved1 + i) * 4)\\n\\t\\t\\t\\t\\t\\t)[0]\\n\\t\\t\\t\\t\\t\\tif (\\n\\t\\t\\t\\t\\t\\t\\tsymbolIndex != 0\\n\\t\\t\\t\\t\\t\\t\\tand symbolIndex != INDIRECT_SYMBOL_ABS\\n\\t\\t\\t\\t\\t\\t\\tand symbolIndex != INDIRECT_SYMBOL_LOCAL\\n\\t\\t\\t\\t\\t\\t\\tand symbolIndex != (INDIRECT_SYMBOL_ABS | INDIRECT_SYMBOL_LOCAL)\\n\\t\\t\\t\\t\\t\\t):\\n\\t\\t\\t\\t\\t\\t\\tsymbolEntry = nlist_64(\\n\\t\\t\\t\\t\\t\\t\\t\\tlinkeditFile.file,\\n\\t\\t\\t\\t\\t\\t\\t\\tself._symtab.symoff + (symbolIndex * nlist_64.SIZE)\\n\\t\\t\\t\\t\\t\\t\\t)\\n\\t\\t\\t\\t\\t\\t\\tsymbol = linkeditFile.readString(\\n\\t\\t\\t\\t\\t\\t\\t\\tself._symtab.stroff + symbolEntry.n_strx\\n\\t\\t\\t\\t\\t\\t\\t)\\n\\n\\t\\t\\t\\t\\t\\t\\t_addToMap(symbol, ptrAddr, sect)\\n\\t\\t\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\t\\t\\t# Try to symbolize though the pointers target\\n\\t\\t\\t\\t\\t\\tptrTarget = self._slider.slideAddress(ptrAddr)\\n\\t\\t\\t\\t\\t\\tptrFunc = self._arm64Utils.resolveStubChain(ptrTarget)\\n\\t\\t\\t\\t\\t\\tif symbols := self._symbolizer.symbolizeAddr(ptrFunc):\\n\\t\\t\\t\\t\\t\\t\\tfor sym in symbols:\\n\\t\\t\\t\\t\\t\\t\\t\\t_addToMap(sym, ptrAddr, sect)\\n\\t\\t\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\t\\t\\t# Skip special cases like __csbitmaps in CoreFoundation\\n\\t\\t\\t\\t\\t\\tif self._machoCtx.containsAddr(ptrTarget):\\n\\t\\t\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\t\\t\\tself._logger.warning(f\\\"Unable to symbolize pointer at {hex(ptrAddr)}, with indirect entry index {hex(sect.reserved1 + i)}, with target function {hex(ptrFunc)}\\\") # noqa\\n\\t\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\tpass\\n\\t\\t\\tpass\\n\\n\\t\\treturn symbolPtrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc18719db059b3a7738e714477916e7a\",\n \"score\": \"0.5131824\",\n \"text\": \"def clear_cache(self):\\n self.phrase2vec_cache = {}\\n self.membership_cache = {}\\n self.interpret_cache = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"913b95798a15f24d84e6b7f4fe58be36\",\n \"score\": \"0.5119424\",\n \"text\": \"def reset_caches(self):\\r\\n with common.ignored(AttributeError):\\r\\n del self._used_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3ad59819e704474430d4505e7607173\",\n \"score\": \"0.51084596\",\n \"text\": \"def get_sym_data(self, objective, constraints, cached_data):\\n self.validate_cache(objective, constraints, cached_data)\\n prob_data = cached_data[self.name()]\\n if prob_data.sym_data is None:\\n prob_data.sym_data = SymData(objective, constraints, self)\\n return prob_data.sym_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c09a463f172d3ba34111a74651468369\",\n \"score\": \"0.5107634\",\n \"text\": \"def _reset_cache(self):\\n\\n\\t\\tsuper()._reset_cache()\\n\\t\\tif self._initialized == False:\\n\\t\\t\\treturn\\n\\n\\t\\tself.register_buffer(\\\"_log_sqrt_dofs_pi_cov\\\", torch.log(torch.sqrt(\\n\\t\\t\\tself.dofs * math.pi * self.covs)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be98e8d62b3feafb7ef7f3559e6a8896\",\n \"score\": \"0.51061755\",\n \"text\": \"def _addSymbol(self, symbol):\\n setattr(self, symbol, len(vars(self).keys()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49a36b30096f5ac81df973e5553a5f40\",\n \"score\": \"0.5098728\",\n \"text\": \"def update_symbols(self, current_thread_only):\\n logging.info(\\\"Updating symbols\\\")\\n mapped_files = _get_mapped_files()\\n # Map all symbols from native libraries packages with the APK.\\n for file_mappings in mapped_files:\\n filename = file_mappings[0].filename\\n if ((filename.startswith('/data/data/') or\\n filename.startswith('/data/app')) and\\n not filename.endswith('.apk') and\\n not filename.endswith('.dex')):\\n logging.info('Pre-mapping: %s' % file_mappings[0].filename)\\n self._try_to_map(file_mappings)\\n\\n if current_thread_only:\\n self._map_symbols_on_current_thread(mapped_files)\\n else:\\n logging.info('Updating all threads\\\\' symbols')\\n current_thread = gdb.selected_thread()\\n nb_threads = len(_gdb_execute(\\\"info threads\\\").split(\\\"\\\\n\\\")) - 2\\n for i in xrange(nb_threads):\\n try:\\n _gdb_execute(\\\"thread %d\\\" % (i + 1))\\n self._map_symbols_on_current_thread(mapped_files)\\n except gdb.error:\\n traceback.print_exc()\\n current_thread.switch()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcf4fdc57e80ec04cb9f4678b9529ffc\",\n \"score\": \"0.50945777\",\n \"text\": \"def symbol_table(func):\\n\\n @functools.wraps(func)\\n def inner(*args, **kwargs):\\n return func(*args, symbol_table=_SYMBOL_TABLE, **kwargs)\\n return inner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb24d4bf9b2c40aac6d559fbf5143a44\",\n \"score\": \"0.5071513\",\n \"text\": \"def __dirty(self): \\n # You're a dirty, dirty slot table and you should be\\n # ashamed of having outdated caches!\\n self.availabilitycache = {}\\n self.awcache_time = None\\n self.awcache = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e77509da934ca4c1ffc4716287111c5\",\n \"score\": \"0.50685626\",\n \"text\": \"def gensym ():\\n global symcount\\n sym = 'sym%i' % symcount\\n symcount += 1\\n return sym\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19a4efcf44e7f053bd4673c0e3e4fd18\",\n \"score\": \"0.50659806\",\n \"text\": \"def all_symbols(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc89c3a98c7f988cd8e6e978e44b1492\",\n \"score\": \"0.50572646\",\n \"text\": \"def new_symbol(s):\\n if s not in symbol_table:\\n symbol_table[s] = SAtom(s)\\n return symbol_table[s]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44a28ba2735621ae39b0fc0ed743bef5\",\n \"score\": \"0.50570285\",\n \"text\": \"def encodeSymbol(self, sym):\\n return Hmm.__dicLookup(self._symbol_dic, sym)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51813efb13dc378f21f0acf2645d4d84\",\n \"score\": \"0.5043085\",\n \"text\": \"def getSymbolsCache(self) -> \\\"DyldContext\\\":\\n\\n\\t\\tif not self._subCaches or not self.headerContainsField(\\\"symbolFileUUID\\\"):\\n\\t\\t\\treturn self\\n\\n\\t\\tfor cache in self._subCaches:\\n\\t\\t\\tif bytes(self.header.symbolFileUUID) == bytes(cache.header.uuid):\\n\\t\\t\\t\\treturn cache\\n\\t\\t\\tpass\\n\\n\\t\\treturn None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92ac29ff2f082812cd51450b38518a1c\",\n \"score\": \"0.5038338\",\n \"text\": \"def symbols(self):\\n return [sympy.Symbol(x) for x in self._symbols_list]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22b0c43122e94509c8d9bcf28e54a136\",\n \"score\": \"0.5021292\",\n \"text\": \"def get_symbol_table(self):\\n if self.symbol_table_start and self.symbol_table_end:\\n self._check_vxworks_endian()\\n\\n else:\\n return False\\n\\n for i in range(self.symbol_table_start, self.symbol_table_end, self._symbol_interval):\\n symbol_name_addr = self._firmware[i + 4:i + 8]\\n symbol_dest_addr = self._firmware[i + 8:i + 12]\\n if self.big_endian:\\n unpack_format = '>I'\\n else:\\n unpack_format = '<I'\\n symbol_name_addr = int(struct.unpack(unpack_format, symbol_name_addr)[0])\\n self.logger.debug(\\\"symbol_name_addr: %s\\\" % symbol_name_addr)\\n symbol_dest_addr = int(struct.unpack(unpack_format, symbol_dest_addr)[0])\\n self.logger.debug(\\\"symbol_dest_addr: %s\\\" % symbol_dest_addr)\\n self._symbol_table.append({\\n 'symbol_name_addr': symbol_name_addr,\\n 'symbol_name_length': None,\\n 'symbol_dest_addr': symbol_dest_addr,\\n 'offset': i\\n })\\n # self.logger.debug(\\\"self._symbol_table: %s\\\" % self._symbol_table)\\n self.logger.debug(\\\"len(self._symbol_table): %s\\\".format(len(self._symbol_table)))\\n self._symbol_table = sorted(self._symbol_table, key=lambda x: x['symbol_name_addr'])\\n for i in range(len(self._symbol_table) - 1):\\n self._symbol_table[i]['symbol_name_length'] = self._symbol_table[i + 1]['symbol_name_addr'] - \\\\\\n self._symbol_table[i]['symbol_name_addr']\\n self.logger.debug(\\\"len(self._symbol_table): %s\\\".format(len(self._symbol_table)))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e0a1f016789fa9491981cf70c8dc49e\",\n \"score\": \"0.50072587\",\n \"text\": \"def _pdb_sccs_cache(pdbid):\\n global db,pdb,chain_lengths\\n \\n # Each process has to keep it's own global dictionary because of\\n # concurrency issues.\\n if pdb == None:\\n pdb = {}\\n if chain_lengths == None:\\n chain_lengths = {}\\n \\n # Search for the pdb\\n if not pdb.has_key(pdbid):\\n if db==None:\\n db = _connection()\\n cursor = db.cursor()\\n try:\\n query = \\\"select part_text, sccs from scop_cla where pdb='%s'\\\" % pdbid\\n cursor.execute(query)\\n all = cursor.fetchall()\\n # Parse parts and chains\\n domains = []\\n for txt, sccs in all:\\n sccs = sccs.strip()\\n if len(sccs) == 0:\\n sccs = None\\n s = SCOPDomain(pdbid, sccs, part_text=txt)\\n for part in s.parts:\\n # Determine the length of the chain for parts that might not\\n # have a start/stop defined.\\n if not chain_lengths.has_key((pdbid,part.chain)):\\n query = \\\"select length(sequence) from pdbIndex i join pdbSeqRes r on i.id=r.pdb_key join sequence s on r.sequence_key=s.id where i.pdbId='%s' and r.chain='%s'\\\" % (pdbid,part.chain)\\n #print query\\n cursor.execute(query)\\n chain_len = cursor.fetchone()[0]\\n chain_lengths[(pdbid,part.chain)]=chain_len\\n else:\\n chain_len = chain_lengths[(pdbid,part.chain)]\\n # Tell the par the length of the chains\\n part.chain_len = chain_len\\n domains.append(s)\\n pdb[pdbid]=list(set(domains))\\n finally:\\n cursor.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"650880fe41cb4f84c784371c398c1536\",\n \"score\": \"0.5006173\",\n \"text\": \"def symbol_fiducials(self):\\n return dict(zip(self.symbol_map.sym_list, self.symbol_map.ival_list))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f373137e1511f0f4611c8ccd33fd5322\",\n \"score\": \"0.49984825\",\n \"text\": \"def enlistSymbols(self ):\\n for e in self.Edges:\\n if e[2] not in self.symbols and e[2]!='ε':\\n self.symbols.append(e[2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80be30f6a5b6d16a3c5b3a896299f9b2\",\n \"score\": \"0.4996974\",\n \"text\": \"async def cache_instruments(\\n self,\\n require: Optional[Dict[Mount, PipetteName]] = None,\\n ) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f665bf932835ff2bbeb7ef5b879a8a9\",\n \"score\": \"0.4989114\",\n \"text\": \"def _reset(self):\\n self.cache = SmtLibExecutionCache()\\n self.logic = None\\n self._current_env = self.pysmt_env\\n\\n mgr = self.pysmt_env.formula_manager\\n self.cache.update({'+':mgr.Plus,\\n '-':self._minus_or_uminus,\\n '*':mgr.Times,\\n '/':self._division,\\n '>':mgr.GT,\\n '<':mgr.LT,\\n '>=':mgr.GE,\\n '<=':mgr.LE,\\n '=':self._equals_or_iff,\\n 'not':mgr.Not,\\n 'and':mgr.And,\\n 'or':mgr.Or,\\n 'xor':mgr.Xor,\\n '=>':mgr.Implies,\\n '<->':mgr.Iff,\\n 'ite':mgr.Ite,\\n 'false':mgr.FALSE(),\\n 'true':mgr.TRUE(),\\n 'to_real':mgr.ToReal,\\n 'concat':mgr.BVConcat,\\n 'bvnot':mgr.BVNot,\\n 'bvand':mgr.BVAnd,\\n 'bvor':mgr.BVOr,\\n 'bvneg':mgr.BVNeg,\\n 'bvadd':mgr.BVAdd,\\n 'bvmul':mgr.BVMul,\\n 'bvudiv':mgr.BVUDiv,\\n 'bvurem':mgr.BVURem,\\n 'bvshl':mgr.BVLShl,\\n 'bvlshr':mgr.BVLShr,\\n 'bvsub':mgr.BVSub,\\n 'bvult':mgr.BVULT,\\n 'bvxor':mgr.BVXor,\\n '_':self._smtlib_underscore,\\n # Extended Functions\\n 'bvnand':mgr.BVNand,\\n 'bvnor':mgr.BVNor,\\n 'bvxnor':mgr.BVXnor,\\n 'bvcomp':mgr.BVComp,\\n 'bvsdiv':mgr.BVSDiv,\\n 'bvsrem':mgr.BVSRem,\\n 'bvsmod':mgr.BVSMod,\\n 'bvashr':mgr.BVAShr,\\n 'bvule':mgr.BVULE,\\n 'bvugt':mgr.BVUGT,\\n 'bvuge':mgr.BVUGE,\\n 'bvslt':mgr.BVSLT,\\n 'bvsle':mgr.BVSLE,\\n 'bvsgt':mgr.BVSGT,\\n 'bvsge':mgr.BVSGE,\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a25a01fd803bb24f56de3aedb69ef122\",\n \"score\": \"0.4986482\",\n \"text\": \"def _save_symbols_cache(self, file_io, cache):\\n\\n file_io.seek(0, 0)\\n h = hashlib.sha256(file_io.read()).hexdigest()\\n file_io.seek(0, 0)\\n\\n this_cache_file = os.path.join(symbol_cache_path, h)\\n with open(this_cache_file, \\\"w\\\") as f:\\n f.write(json.dumps(cache))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f64f2a05537ba8241e7cd04ded4dc41\",\n \"score\": \"0.4980934\",\n \"text\": \"def unset_symbol(self):\\n\\t\\tself.symbols = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cb36f386662591ece2d36147dc26e2f\",\n \"score\": \"0.4958083\",\n \"text\": \"def _reset_caches(self):\\n self._total_nb_possibilities = None\\n self._cached_examples = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d722e08f7b423f336e40835591af460\",\n \"score\": \"0.49553323\",\n \"text\": \"def intern(name):\\n if isinstance(name, SchemeSymbol):\\n if str(name) in _all_symbols:\\n return _all_symbols[str(name)]\\n else:\\n _all_symbols[str(name)] = name\\n return name\\n if name in _all_symbols:\\n return _all_symbols[name]\\n else:\\n sym = SchemeSymbol(name)\\n _all_symbols[name] = sym\\n return sym\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4545e9ddbf6793f87760186b40dc183b\",\n \"score\": \"0.49532443\",\n \"text\": \"def fill_cache(self, X):\\n cache = dict(lX=self._predict(X))\\n cache[\\\"aX\\\"] = self.activation_(cache[\\\"lX\\\"])\\n return cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"897ffa50017b0af2a87db6cdc4972570\",\n \"score\": \"0.49398854\",\n \"text\": \"def init_cache():\\n global memory_cache\\n with session_scope() as session:\\n last_req_dict = dict(get_last_requested(session))\\n hits_dict = dict(get_endpoints_hits(session))\\n averages_dict = dict(get_endpoint_averages(session))\\n for rule in get_rules():\\n memory_cache[rule.endpoint] = EndpointInfo(\\n last_requested=last_req_dict.get(rule.endpoint),\\n average_duration=averages_dict.get(rule.endpoint),\\n hits=hits_dict.get(rule.endpoint),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d82a1e7eae31a22371aaa9f3ea91fcb2\",\n \"score\": \"0.4927952\",\n \"text\": \"def _calculate_initial_bought(self):\\r\\n bought = {} \\r\\n for s in self.symbol_list:\\r\\n bought[s] = 'OUT'\\r\\n return bought\",\n \"title\": \"\"\n },\n {\n \"docid\": \"332c1a1ae5a5c92d9cc46a99be8dc6be\",\n \"score\": \"0.49105838\",\n \"text\": \"def symbols(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd9b7c2e38a94bdf7c4e2745766f7ef1\",\n \"score\": \"0.49104315\",\n \"text\": \"def find_symbol_table(self):\\n for offset in range(len(self._firmware)):\\n if self.symbol_table_start is None:\\n if self._check_symbol_format(offset):\\n self.logger.info(\\\"symbol table start offset: %s\\\" % (hex(offset)))\\n self.symbol_table_start = offset\\n self._has_symbol = True\\n break\\n else:\\n break\\n\\n if self.symbol_table_start:\\n for i in range(self.symbol_table_start, len(self._firmware), self._symbol_interval):\\n check_data = self._firmware[i:i + self._symbol_interval]\\n if len(check_data) < self._symbol_interval:\\n self.logger.debug(\\\"Check_data length is too small\\\")\\n break\\n\\n if len(check_data) < self._symbol_interval:\\n self.logger.debug(\\\"Check_data length is too small\\\")\\n break\\n\\n if self._check_symbol_format_simple(check_data):\\n self.symbol_table_end = i + self._symbol_interval\\n self.logger.debug(\\\"self.symbol_table_end: {:010x}\\\".format(self.symbol_table_end))\\n\\n else:\\n self.logger.info(\\\"symbol table end offset: %s\\\" % hex(self.symbol_table_end))\\n break\\n else:\\n self.logger.error(\\\"didn't find symbol table in this image\\\")\\n self._has_symbol = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52da5b440a1b2e652cc17bcc84e5f433\",\n \"score\": \"0.49103576\",\n \"text\": \"def freeze(self) -> None:\\n self.__frozen = True\\n if self.__key_expr:\\n fce = make_lambda(self.__key_expr)\\n self.__map = dict(((key, index) for index, key in enumerate((fce(item) for item in self))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27119c3b7afe9e18f823dc9b91cbee54\",\n \"score\": \"0.48837\",\n \"text\": \"def symbol_cost(self, symbol, seen=set()):\\n expansions = self.v['grammar'][symbol]\\n return min(self.expansion_cost(e, seen | {symbol}) for e in expansions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4c988225c3599bacdbb01f6ff463cfb\",\n \"score\": \"0.48803112\",\n \"text\": \"def gen_unused_symbols(used, n):\\n i = cnt = 0\\n while cnt < n:\\n s = get_symbol(i)\\n i += 1\\n if s in used:\\n continue\\n yield s\\n cnt += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2443fcee79c9db7f847b7ec04d5397f3\",\n \"score\": \"0.48796088\",\n \"text\": \"def manin_symbols(self):\\n try:\\n return self.__manin_symbols\\n except AttributeError:\\n self.__manin_symbols = ManinSymbolList_gamma_h(\\n group=self.group(), weight=self.weight())\\n return self.__manin_symbols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0626a4682147c14e09a7ab19fb2fbdff\",\n \"score\": \"0.48670635\",\n \"text\": \"def init_word_cache(self, length=10000):\\n for i in range(0, length):\\n self.word_cache.append(self.gen_word())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff9958340cc4cd6f309cc930fc287a99\",\n \"score\": \"0.4865642\",\n \"text\": \"def order_collected_historical_data(self):\\n for symbol_name in SYMBOLS:\\n self.fetched_data[symbol_name] = OrderedDict(sorted(self.fetched_data[symbol_name].items()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e8ff1c58af90a0b648852b5b3e536af\",\n \"score\": \"0.48649782\",\n \"text\": \"def lookup_symbol(self, symbol):\\n try:\\n s = self.reverse_grammar[symbol]\\n return s\\n except KeyError:\\n return set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f38bd984bb87eafb2a65e68f1f1337b\",\n \"score\": \"0.48638475\",\n \"text\": \"def clearcache():\\r\\n\\r\\n global cache\\r\\n cache = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49c43897503206fba647c250c27e88ec\",\n \"score\": \"0.48610923\",\n \"text\": \"def do_symbols(self, arg):\\n symbols = db(db.symbols).select()\\n if len(symbols) == 0:\\n print('%s %s' % (self.out, 'no symbols stored'))\\n return\\n for i, sym in enumerate(symbols):\\n print('%s %3s: %s - %s' % (self.out, i, sym.ticker, sym.name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b8a32c88ff779907f03763270b410f2\",\n \"score\": \"0.4859931\",\n \"text\": \"def _load_symbol_data(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cc3762142a1e458e72d97eaa8e1277f\",\n \"score\": \"0.48566782\",\n \"text\": \"def _reset_caches(self):\\n self._initialized = False\\n self._is_hermitian = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"049bc1db08196bea951d633202c589db\",\n \"score\": \"0.48480937\",\n \"text\": \"def gen_symbol_table(min_size=1,max_size=50):\\n table = {}\\n\\n i = randint(min_size,max_size)\\n\\n \\n while i > 0:\\n symbol = gen_rand_symbol(randint(3,3))\\n if not symbol in table:\\n table[symbol] = 0\\n i -= 1\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a3e8c26f0db20c4764de56c61f25d66\",\n \"score\": \"0.48405343\",\n \"text\": \"def store_matches(\\n self,\\n atom_slots: dict[TopologyKey, PotentialKey],\\n topology: \\\"Topology\\\",\\n ) -> None:\\n for proper in topology.propers:\\n atom_indices = tuple(topology.atom_index(atom) for atom in proper)\\n top_key = TopologyKey(atom_indices=atom_indices)\\n\\n pot_key_ids = tuple(\\n _get_potential_key_id(atom_slots, idx) for idx in atom_indices\\n )\\n\\n self.key_map[top_key] = PotentialKey(\\n id=POTENTIAL_KEY_SEPARATOR.join(pot_key_ids),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83176714dca3497dc482b278cbb20df0\",\n \"score\": \"0.4837721\",\n \"text\": \"def sym_names(self) -> List[str]:\\n return list(self._syms.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deea379dd8c3c83f12c64ca872b77167\",\n \"score\": \"0.48372594\",\n \"text\": \"def optimize_lookups(self):\\n if self._lookup_dict is not None:\\n return\\n self._list = sorted(self._list, key=lambda x: x.begin)\\n if len(self._list) > 1000:\\n self._lookup_dict = {}\\n cur = 0\\n while cur < len(self._list):\\n self._lookup_dict[cur] = self._list[cur].begin\\n cur += 1000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d31d56f47fae019ab516ba7a67746e70\",\n \"score\": \"0.48329672\",\n \"text\": \"def cache(self):\\n cached_data = self.state_dict()\\n cached_data['epoch'] = self.epoch\\n cached_data['best_val_error'] = self._best_val_error\\n cached_data['best_loss'] = self._best_loss\\n self._cacher.save(cached_data)\\n self._cacher_params.save(self.hyperparams.hashable_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc63e587e1ed72bdef9f2f0965265c05\",\n \"score\": \"0.48305044\",\n \"text\": \"def _reset_cache(self):\\n\\n self._noint_mask = None\\n self._escat_mask = None\\n self._escatonly_mask = None\\n self._line_mask = None\\n self._lam_escat = None\\n self._lam_noint = None\\n self._weights_escat = None\\n self._weights_noint = None\\n self._line_in_infos = None\\n self._line_in_nu = None\\n self._line_in_L = None\\n self._line_out_infos = None\\n self._line_out_nu = None\\n self._line_out_L = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"763413c7d471d5ac0c75ff50214fc6b2\",\n \"score\": \"0.48247024\",\n \"text\": \"def _read_symbols_cache_dict(self, f):\\n\\n if f is None:\\n return None\\n\\n assert isinstance(f, io.IOBase), 'Unhandled symbol cache load type of {}'.format(type(f))\\n\\n f.seek(0, 0)\\n h = hashlib.sha256(f.read()).hexdigest()\\n f.seek(0, 0)\\n\\n this_cache_file = os.path.join(symbol_cache_path, h)\\n\\n # Cache miss\\n if not os.path.isfile(this_cache_file):\\n return None\\n\\n # Cache hit\\n with open(this_cache_file, \\\"r\\\") as f:\\n return json.loads(f.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd5a8eb9d63818ecb3fdf6139075ac87\",\n \"score\": \"0.4820459\",\n \"text\": \"def construct_symbol_table(filename):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdd9db309fa25353d7cf8af32d86bbcc\",\n \"score\": \"0.48204294\",\n \"text\": \"def replace_with_cached(\\n self, expr: expressions.Expression) -> Dict[str, expressions.Expression]:\\n\\n replaced_inputs: Dict[str, expressions.Expression] = {}\\n self._replace_with_cached_recur(expr, replaced_inputs)\\n return replaced_inputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eecf32044d99d6808e3c23db2667223b\",\n \"score\": \"0.48199284\",\n \"text\": \"def manin_symbols(self):\\n try:\\n return self.__manin_symbols\\n except AttributeError:\\n self.__manin_symbols = ManinSymbolList_gamma0(\\n level=self.level(), weight=self.weight())\\n return self.__manin_symbols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdfca7e1bf42123fc0ddc4ca98b749d4\",\n \"score\": \"0.48153427\",\n \"text\": \"def reinit(self):\\n self.mem = {i: [] for i in range(self.env.nbarms)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"567c52896377a51900de203c1648781c\",\n \"score\": \"0.48125237\",\n \"text\": \"def __hash__(self):\\n return hash(('Atom', self.symbol))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32aadcdefb03a2e9c5fb704dd49d91ba\",\n \"score\": \"0.48118928\",\n \"text\": \"def _calculate_initial_bought(self):\\n bought = {}\\n for s in self.symbol_list:\\n bought[s] = 'OUT'\\n return bought\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7945e6f18fde97ced93d36260aa22fba\",\n \"score\": \"0.48061186\",\n \"text\": \"def create_default_symbol_table():\\n\\n st = SymbolTable({\\n 'SP': 0,\\n 'LCL': 1,\\n 'ARG': 2,\\n 'THIS': 3,\\n 'THAT': 4,\\n 'SCREEN': 16384,\\n 'KBD': 24576,\\n })\\n\\n st.update({'R{}'.format(r): r\\n for r in range(16)})\\n\\n return st\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7029f3237c4bce00f5971226320d92e\",\n \"score\": \"0.48026088\",\n \"text\": \"def _populateCache(self):\\n cacheSize = self.len/3 + 1\\n self.sort(self._compareBy(\\\"hits\\\"))\\n for i in range(cacheSize):\\n self.cache.append(self.words[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3fdca96dec7badeeb66bfc23282910c\",\n \"score\": \"0.4795915\",\n \"text\": \"def store_matches(\\n self,\\n atom_slots: dict[TopologyKey, PotentialKey],\\n topology: \\\"Topology\\\",\\n ) -> None:\\n for bond in topology.bonds:\\n atom_indices = (\\n topology.atom_index(bond.atom1),\\n topology.atom_index(bond.atom2),\\n )\\n top_key = TopologyKey(atom_indices=atom_indices)\\n\\n pot_key_ids = tuple(\\n _get_potential_key_id(atom_slots, idx) for idx in atom_indices\\n )\\n\\n self.key_map[top_key] = PotentialKey(\\n id=POTENTIAL_KEY_SEPARATOR.join(pot_key_ids),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04096965f8cfa97e4588833da73b4295\",\n \"score\": \"0.47878587\",\n \"text\": \"def isBackedByLocalSymbolMap(self) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c485b9b330cbfdf325ca87146a0bbbd3\",\n \"score\": \"0.47812495\",\n \"text\": \"def find_symbols(symbol_dirs):\\r\\n known_symbols = {}\\r\\n for symbol_dir in symbol_dirs:\\r\\n if os.path.exists(symbol_dir):\\r\\n for dirpath, dirnames, filenames in os.walk(symbol_dir):\\r\\n dirnames.sort()\\r\\n for filename in filenames:\\r\\n if filename.endswith('.sym'):\\r\\n filepath = os.path.join(dirpath, filename)\\r\\n filename, _ = os.path.splitext(filename)\\r\\n if filename.lower() not in known_symbols:\\r\\n known_symbols[filename.lower()] = filepath\\r\\n\\r\\n return known_symbols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"129d62e7da921ea76970fdc1088aa338\",\n \"score\": \"0.47739196\",\n \"text\": \"def createCache(self):\\n \\n for i in range(2**self.index):\\n self._cache.append({})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba273d0dd88c988724380e1a8b93c076\",\n \"score\": \"0.47723415\",\n \"text\": \"def UpdateAndAttachSymbolTable(self, oldTable):\\n\\n symbols = list(self.ReturnSymbols())\\n if len(symbols) == 0:\\n self.symTable = oldTable\\n return\\n\\n newProperties = {sym:[False, -1] for sym in symbols}\\n\\n outSymbol = self.dest.value\\n\\n if self.dest.is_SCALAR_VARIABLE():\\n newProperties[outSymbol] = [False, -1]\\n\\n # Rest of the symbols count as a \\\"use\\\"\\n for sym in symbols:\\n if sym == outSymbol:\\n continue\\n\\n newProperties[sym] = [True, self.lineID]\\n\\n # Check if the outSymbol is used as an input as well\\n if self.dest.is_SCALAR_VARIABLE():\\n if (self.inp1.value == outSymbol or \\n self.inp2.value == outSymbol or \\n outSymbol in [i.value for i in self.IOArgs]):\\n\\n newProperties[outSymbol] = [True, self.lineID] \\n\\n\\n self.symTable = BB.SymSetProperties(oldTable, newProperties)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b16a34a45e8737d3f6d13717c611a97\",\n \"score\": \"0.47612625\",\n \"text\": \"def add_symbol(self, symbol):\\n\\t\\tsymbol = symbol.lower()\\n\\t\\tif symbol not in self.symbols:\\n\\t\\t\\tself.symbols.append(symbol)\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70908542bd1317d7797375be2ecddf4b\",\n \"score\": \"0.47581547\",\n \"text\": \"def bomb_with_cache(self, x, y):\\n try:\\n return self.cache[(x, y)]\\n except AttributeError:\\n self.cache = {}\\n return self.bomb(x, y)\\n except KeyError:\\n r = self._bomb(x, y)\\n self.cache[(x, y)] = r\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f322550399b2a0f8de51b149ab475a76\",\n \"score\": \"0.47405848\",\n \"text\": \"def manin_symbols(self):\\n try:\\n return self.__manin_symbols\\n except AttributeError:\\n self.__manin_symbols = ManinSymbolList_gamma1(\\n level=self.level(), weight=self.weight())\\n return self.__manin_symbols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecc786710b87dc9540082526dafa6058\",\n \"score\": \"0.47348714\",\n \"text\": \"def _symbol(self, symbol):\\n\\n if self.current_const != \\\"\\\":\\n if len(self.current_vars) == 1: # Is a coefficient\\n self.current_vars[0].val = str(int(self.current_vars[0].val) *\\n int(self.current_const))\\n else: # Is an exponent\\n self.current_vars[-1].power = int(self.current_const)\\n self.current_const = \\\"\\\"\\n\\n if len(self.current_vars) > 0:\\n current_term = Term({})\\n while len(self.current_vars) > 0:\\n current_term.set_var(self.current_vars.pop())\\n self.poly.add_term(current_term)\\n current_var = Variable(val=symbol+\\\"1\\\")\\n self.current_vars.append(current_var)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c63c35d1bcf98b0b00bf74ae94690cd6\",\n \"score\": \"0.47338057\",\n \"text\": \"def set_symbols(self, symbols_str=None):\\n symbols_list = []\\n if symbols_str:\\n for i in symbols_str:\\n symbols_list.append(i)\\n self.symbols_list = symbols_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab527b3ac5988047f866b348aa4c04bf\",\n \"score\": \"0.47334912\",\n \"text\": \"def collect(self):\\n for func in self._caches:\\n cache = {}\\n for key in self._caches[func]:\\n if (time.time() - self._caches[func][key][1]) < self._timeouts[func]:\\n cache[key] = self._caches[func][key]\\n self._caches[func] = cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74f36c5e66f8968332ba8ae255a959c8\",\n \"score\": \"0.47306302\",\n \"text\": \"def _clear_caches():\\n _ghi_cache.clear()\\n _prf_cache.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1a196add6434995e90ffc5ded390fc1\",\n \"score\": \"0.4728394\",\n \"text\": \"def __iter__(self):\\n return iter(list(self._symbols.items()))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":97,"cells":{"query_id":{"kind":"string","value":"dd99e7bd4990e39891a390e9335bdb98"},"query":{"kind":"string","value":"Plot the learning curve of the different networks trained"},"positive_passages":{"kind":"list like","value":[{"docid":"3ffe038286369685daca2d21ff9f7913","score":"0.6725776","text":"def plot_learning(steps, stats_mean_list=None, stats_std_list=None,\n labels=[], title=\"Learning Curve\", ylabel=\"Loss\",\n y_lim=[0.0, 1.7], share_x=False, share_y=False,\n legend_loc=\"best\", filter_ws=0, step_size=10000,\n xlabel=r\"$\\times 10^4$ Batch Iterations\"):\n fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n # Loop over individual lines, smooth the arrays &\n for i in range(len(labels)):\n if filter_ws > 0:\n temp_mean = savgol_filter(stats_mean_list[i], filter_ws, 3)\n if stats_std_list is not None:\n temp_std = savgol_filter(stats_std_list[i], filter_ws, 3)\n else:\n temp_mean = stats_mean_list[i]\n if stats_std_list is not None:\n temp_std = stats_std_list[i]\n ax.plot(steps, temp_mean, label=labels[i])\n if stats_std_list is not None:\n ax.fill_between(steps,\n temp_mean - 0.5*temp_std,\n temp_mean + 0.5*temp_std, alpha=0.5)\n\n its_ticks = np.arange(step_size, np.max(steps), step_size)\n its_labels_temp = [str(int(it/step_size)) for it in its_ticks]\n its_labels = [it_l for it_l in its_labels_temp]\n its_labels[0] = r\"$1$\"\n ax.set_xticks(its_ticks, minor=False)\n ax.set_xticklabels(its_labels)\n\n ax.legend(loc=legend_loc, fontsize=12)\n ax.set_title(title)\n if not share_y:\n ax.set_ylabel(ylabel)\n else:\n ax.get_yaxis().set_ticks([])\n if not share_x:\n ax.set_xlabel(xlabel)\n else:\n ax.get_xaxis().set_ticks([])\n ax.set_ylim(bottom=y_lim[0], top=y_lim[1])\n ax.spines['top'].set_visible(False)\n ax.spines['right'].set_visible(False)\n ax.legend(fontsize=25)\n return","title":""}],"string":"[\n {\n \"docid\": \"3ffe038286369685daca2d21ff9f7913\",\n \"score\": \"0.6725776\",\n \"text\": \"def plot_learning(steps, stats_mean_list=None, stats_std_list=None,\\n labels=[], title=\\\"Learning Curve\\\", ylabel=\\\"Loss\\\",\\n y_lim=[0.0, 1.7], share_x=False, share_y=False,\\n legend_loc=\\\"best\\\", filter_ws=0, step_size=10000,\\n xlabel=r\\\"$\\\\times 10^4$ Batch Iterations\\\"):\\n fig, ax = plt.subplots(1, 1, figsize=(8, 5))\\n # Loop over individual lines, smooth the arrays &\\n for i in range(len(labels)):\\n if filter_ws > 0:\\n temp_mean = savgol_filter(stats_mean_list[i], filter_ws, 3)\\n if stats_std_list is not None:\\n temp_std = savgol_filter(stats_std_list[i], filter_ws, 3)\\n else:\\n temp_mean = stats_mean_list[i]\\n if stats_std_list is not None:\\n temp_std = stats_std_list[i]\\n ax.plot(steps, temp_mean, label=labels[i])\\n if stats_std_list is not None:\\n ax.fill_between(steps,\\n temp_mean - 0.5*temp_std,\\n temp_mean + 0.5*temp_std, alpha=0.5)\\n\\n its_ticks = np.arange(step_size, np.max(steps), step_size)\\n its_labels_temp = [str(int(it/step_size)) for it in its_ticks]\\n its_labels = [it_l for it_l in its_labels_temp]\\n its_labels[0] = r\\\"$1$\\\"\\n ax.set_xticks(its_ticks, minor=False)\\n ax.set_xticklabels(its_labels)\\n\\n ax.legend(loc=legend_loc, fontsize=12)\\n ax.set_title(title)\\n if not share_y:\\n ax.set_ylabel(ylabel)\\n else:\\n ax.get_yaxis().set_ticks([])\\n if not share_x:\\n ax.set_xlabel(xlabel)\\n else:\\n ax.get_xaxis().set_ticks([])\\n ax.set_ylim(bottom=y_lim[0], top=y_lim[1])\\n ax.spines['top'].set_visible(False)\\n ax.spines['right'].set_visible(False)\\n ax.legend(fontsize=25)\\n return\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c021a9f6d6801419b68365d93bbefc7a","score":"0.7735428","text":"def plot_learning_curve(training_losses, validation_losses): \n plt.ylabel('Loss')\n plt.xlabel('Training Steps')\n plt.plot(training_losses, label=\"training\")\n plt.plot(validation_losses, label=\"validation\")\n plt.legend(loc=1)","title":""},{"docid":"78275ffe2d1cb5c5812d89bcae3e4d5d","score":"0.75846165","text":"def plot_learning_curve(self):\n plt.figure()\n plt.plot(range(self.noi),self.Jv)\n plt.xlabel('Number of iterations')\n plt.ylabel('Cost')\n plt.title('Cost vs Number of iterations curve')","title":""},{"docid":"9cf983abe2d065dbae806a52cd1ff8b9","score":"0.73652726","text":"def plot_lr(learning_rates):\n plt.plot(learning_rates)\n plt.title('Change of learning rate over time')\n plt.xlabel('Epoch')\n plt.ylabel('Learning rate')\n plt.show()","title":""},{"docid":"90a600749107f22359b2581e8270d052","score":"0.73337096","text":"def plot(self):\n fig = plt.figure(figsize=[10, 4])\n\n plt.subplot(121)\n self.plot_lr()\n plt.xlabel('n iter')\n plt.ylabel('learning rate')\n\n plt.subplot(122)\n self.plot_loss(c='r')\n plt.xlabel('n iter')\n plt.ylabel('loss')\n fig.subplots_adjust(wspace=0.3, bottom=0.18)","title":""},{"docid":"c9bb8a2098f33e2fb9265a305f1f6e3c","score":"0.7284015","text":"def train_model(self) -> None:\n self.NN = NeuralNetwork()\n costs = self.network_training(self.NN)\n plt.plot(costs)\n plt.show()","title":""},{"docid":"7f3c8bb6177e1a0774176957da2bbbe5","score":"0.7243582","text":"def plot_learning_curve(MODEL):\n train_accs = np.squeeze(MODEL['train_accs'])\n test_accs = np.squeeze(MODEL['test_accs'])\n fig, ax = plt.subplots()\n ax.plot(100 - train_accs, label='Training set error')\n ax.plot(100 - test_accs, label='Test set error')\n ax.set_ylabel('Prediction error (%)')\n plt.legend()\n ax.set_xlabel('Hundreds of iterations')\n ax.set_title(\"Learning rate =\" + str(MODEL[\"learning_rate\"]))","title":""},{"docid":"acb76bdc2341b630ed30417dc3534fcc","score":"0.72194725","text":"def learning_curves(X_train, y_train, X_test, y_test):\n \n print \"Creating learning curve graphs for max_depths of 1, 3, 6, and 10.....\"\n \n # Create the figure window\n fig = plt.figure(figsize=(10,8))\n \n # We will vary the training set size so that we have 50 different sizes.\n sizes = np.rint(np.linspace(1, len(X_train),50)).astype(int)\n train_err = np.zeros(len(sizes))\n test_err = np.zeros(len(sizes))\n \n # Create four different models based on max_depth\n\n for k, depth in enumerate([1,3,6,10]):\n \n for i,s in enumerate(sizes):\n \n # Setup a decision tree regressor so that it learns a tree with max_depth = depth\n regressor = DecisionTreeRegressor(max_depth =depth)\n \n # Fit the learner to the training data\n regressor.fit(X_train[:s], y_train[:s])\n \n # Find the performance on the training set\n train_err[i] = performance_metric(y_train[:s],regressor.predict(X_train[:s]))\n \n # Find the performance on the testing set\n test_err[i] = performance_metric(y_test,regressor.predict(X_test))\n \n # Subplot the learning curve graph\n ax = fig.add_subplot(2,2, k+1)\n ax.plot(sizes,test_err, lw = 2, label = \"Testing Error\")\n ax.plot(sizes, train_err, lw = 2, label = \"Training Error\")\n ax.legend()\n ax.set_title('max_depth = %s'%(depth))\n ax.set_xlabel(\"Number of Data Points in Training Set\")\n ax.set_ylabel('Total Error')\n ax.set_xlim([0, len(X_train)])\n \n # Visual aesthetics\n fig.suptitle('Decision Tree Regressor Learning Performances', fontsize = 18, y=1.03)\n fig.tight_layout()\n fig.show()","title":""},{"docid":"adf43744f83cac76d420fe7db3bf1b65","score":"0.71618503","text":"def plot_learning(train_loss, eval_loss, path_to_save):\n plt.plot(train_loss)\n plt.plot(eval_loss)\n plt.legend(['Train loss', 'Dev loss'])\n path = os.path.join(path_to_save, 'learning_plot.png')\n plt.savefig(path)\n plt.clf()","title":""},{"docid":"be3c9091e702298fdee0c3a0ddd65f60","score":"0.70671624","text":"def show_learning_curves(rew_matrix, naive_rew):\n plt.plot(range(len(rew_matrix[0])), rew_matrix[0], \"-b\", label = \"Feature 1\")\n plt.plot(range(len(rew_matrix[1])), rew_matrix[1], \"-g\", label = \"Feature 7\")\n plt.plot(range(len(rew_matrix[2])), rew_matrix[2], \"-r\", label = \"Feature 12\")\n plt.plot(range(len(rew_matrix[3])), rew_matrix[3], \"-c\", label = \"Feature 13\")\n plt.plot(range(len(naive_rew)), naive_rew, \"-m\", label = \"Naive policy\")\n plt.xlabel(\"Episode\")\n plt.ylabel(\"Reward (learning phase)\")\n plt.legend()","title":""},{"docid":"8f23da3e535be143870e33a7c2b44233","score":"0.70591086","text":"def draw_learning_curve(numbers, metric):\r\n\r\n plt.xlabel('Simulation Epoch')\r\n plt.ylabel(metric)\r\n plt.title('Learning Curve')\r\n plt.grid(True)\r\n\r\n plt.plot(numbers['x'], numbers[metric], 'r', lw=1)\r\n plt.show()","title":""},{"docid":"4a2e3d953d6903dd83eada7bc67add9c","score":"0.6997576","text":"def plot_learning_curve(classification_method, classification_method_params, data, labels):\n if classification_method == KNeighborsClassifier:\n train_sizes, train_scores, valid_scores = learning_curve(\n classification_method(n_neighbors=classification_method_params[0]), data, labels,\n train_sizes=np.linspace(0.1, 1.0, 25))\n elif classification_method == LogisticRegression:\n train_sizes, train_scores, valid_scores = learning_curve(\n classification_method(C=classification_method_params[0]), data, labels,\n train_sizes=np.linspace(0.1, 1.0, 25))\n else:\n train_sizes, train_scores, valid_scores = learning_curve(\n classification_method(), data, labels, train_sizes=np.linspace(0.1, 1.0, 25))\n plt.figure()\n plt.title(\"Learning Curve ({0})\".format(classification_method.__name__))\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n plt.grid()\n # Compute the mean and std to also plot them on the learning curve.\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(valid_scores, axis=1)\n test_scores_std = np.std(valid_scores, axis=1)\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std,\n alpha=0.1, color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std,\n alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\n plt.legend(loc=\"best\")\n plt.show()","title":""},{"docid":"9af31db2b0dee82bc1bf530fd754f4a6","score":"0.69334346","text":"def plot_cost_and_acc(self):\n x = list(range(1, len(self.cost_hist_tr) + 1))\n plt.plot(x, self.cost_hist_tr, label = \"train loss\")\n plt.plot(x, self.cost_hist_val, label = \"val loss\")\n plt.title(\"Loss over epochs\")\n plt.xlabel(\"Epochs\")\n plt.ylabel(\"Loss\")\n plt.legend()\n plt.show()\n plt.plot(x, self.acc_hist_tr, label = \"Train accuracy\")\n plt.plot(x, self.acc_hist_val, label = \"Val accuracy\")\n plt.title(\"Accuracy over epochs\")\n plt.xlabel(\"Epochs\")\n plt.ylabel(\"Accuracy\")\n plt.legend()\n plt.show()","title":""},{"docid":"d508a117025eb2f127cf2a40bab7e917","score":"0.69325346","text":"def plot(self):\n plt.clf()\n\n plt.plot(np.linspace(0, 1, self.N+1), self.sb_policy, c='tab:orange', label=f\"{self.model_name} Learner\")\n plt.plot(np.linspace(0, 1, self.N+1), self.vi_policy, c='B', label=\"Value Iteration\")\n\n plt.title(\n f\"ACER Train Time: {self.sb_train_time}, VI Train Time: {self.vi_train_time}\\n\"\n f\"tt/ts: {self.params['movement_cost']}/{self.params['sample_cost']}, \"\n f\"N: {int(1/self.params['delta'])}\")\n plt.xlabel(\"Size of Hypothesis Space\")\n plt.ylabel(\"Gym Policy\")\n plt.legend()\n\n plt.xlim((0, 1))\n plt.ylim((0, self.N))\n\n plt.savefig(self.img_path)","title":""},{"docid":"3490ab7802cd264199290c1f38fa36ba","score":"0.6920023","text":"def plot_learning_curves(x_train, y_train, x_test, y_test, clf):\n errors = []\n\n def misclf_err(y_predict, y):\n return (y_predict != y).sum() / float(len(y))\n\n rng = [int(i) for i in np.linspace(0, x_train.shape[0], 11)][1:]\n for r in rng:\n print(f\"Running {r / x_train.shape[0]:.0%}\")\n clf.fit(x_train[:r], y_train[:r])\n\n y_test_predict = clf.predict(x_test)\n\n error = misclf_err(y_test, y_test_predict)\n errors.append(error*100)\n\n _, ax = plt.subplots(1, 1, figsize=(8, 5))\n ax.plot(np.arange(10, 101, 10), errors, label=\"Error\", marker=\"o\")\n\n ax.set_xlim([0, 110])\n ax.set_xticks(np.arange(0, 101, 10))\n ax.yaxis.set_major_formatter(PercentFormatter())\n ax.xaxis.set_major_formatter(PercentFormatter())\n\n plt.ylabel(\"Error\")\n plt.xlabel(\"Training set size in percent\")\n plt.title(\"Learning Curve\")\n plt.grid()\n plt.tight_layout()","title":""},{"docid":"5ee3ef41896c39395171d975f95590ac","score":"0.69093424","text":"def plot_learning_rates():\n # Choose some alpha value - change this\n alpha = 0.\n num_iters = 100\n\n # init theta and run gradient descent\n theta = np.zeros(3)\n theta, J_history = gradientDescentMulti(X, y, theta, alpha, num_iters)\n\n # Plot the convergence graph\n pyplot.plot(np.arange(len(J_history)), J_history, lw=2)\n pyplot.xlabel('Number of iterations')\n pyplot.ylabel('Cost J')\n\n # Display the gradient descent's result\n print('theta computed from gradient descent: {:s}'.format(str(theta)))\n\n # Estimate the price of a 1650 sq-ft, 3 br house\n # ======================= YOUR CODE HERE ===========================\n # Recall that the first column of X is all-ones.\n # Thus, it does not need to be normalized.\n\n price = 0 # You should change this\n\n # ===================================================================\n\n print('Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): ${:.0f}'.format(price))","title":""},{"docid":"5bb6df2fde4efcba7faf73f33ac502db","score":"0.6860948","text":"def plot_cost_per_lr(X, y):\n plt.figure()\n plt.xlabel(\"Number of iterations\")\n plt.ylabel(\"Cost J\")\n\n lr_list = [0.3, 0.1, 0.03, 0.01, 0.003, 0.001]\n losses_per_lr_list = []\n nb_iterations = 50\n for i in range(len(lr_list)):\n lr = lr_list[i]\n model = Model(X.shape[1])\n print('Training model using ', nb_iterations, 'iterations and learning rate=', lr)\n model.train(nb_iterations, X, y, learning_rate=lr)\n J_history = model.losses\n losses_per_lr_list.append(J_history)\n plt.plot(range(nb_iterations), losses_per_lr_list[i], label='alpha= {:.3f}'.format(lr))\n plt.legend()\n plt.savefig('figs/different_learning_rates')","title":""},{"docid":"e1fd06e138e4cb2d23ac6cee1af944b8","score":"0.6855946","text":"def plotGraphs(history):\n\n acc = history.history['acc']\n val_acc = history.history['val_acc']\n\n loss = history.history['loss']\n val_loss = history.history['val_loss'] \n\n epochs = range(1, len(acc) + 1)\n plt.plot(epochs, acc, 'bo', label='Training acc')\n plt.plot(epochs, val_acc, 'b', label='Validation acc')\n\n plt.title('Training and validation accuracy')\n plt.legend() \n plt.figure() \n\n plt.plot(epochs, loss, 'bo', label='Training loss')\n plt.plot(epochs, val_loss, 'b', label='Validation loss')\n plt.title('Training and validation loss')\n plt.legend()\n plt.show()","title":""},{"docid":"527395ebae2613580c45cadf981dc556","score":"0.6851773","text":"def plot_learning_curve(data, prefix):\n mean, lower_bound, upper_bound = utils.mean_confidence_interval(data)\n x_lim = len(data[0])\n plt.plot(mean, color='red')\n plt.fill_between(range(x_lim), lower_bound, upper_bound, color='red', alpha=0.4)\n plt.xlabel(\"Episode\")\n plt.ylabel(\"Avg. number of steps\")\n plt.savefig(prefix + 'learning_curve.png')\n plt.clf()","title":""},{"docid":"3a52cc6f6f07005cb6377c2a6082a5bf","score":"0.6830098","text":"def Graph_Costs_Over_Time(self):\n plt.plot(np.squeeze(self.costs))\n plt.ylabel('cost')\n plt.xlabel('iterations (per tens)')\n plt.title(\"Learning rate =\" + str(self.learning_rate))\n plt.show()","title":""},{"docid":"ea36c3ff0efb092b09449baccd690860","score":"0.6811258","text":"def plot_objectives(self,betas_cyc,betas_rnd,X_train,y_train):\n plt.figure(figsize = (10, 5))\n plt.plot([self.computeobj(betas_cyc[::20][x], X_train, y_train) for x in range(50)], label=\"Cyclic Coordinate Descent\")\n plt.plot([self.computeobj(betas_rnd[::20][x], X_train, y_train) for x in range(50)], label=\"Random Coordinate Descent\")\n plt.xlabel('Iteration')\n plt.ylabel('Objective value')\n plt.title(\"Objective Value vs. Iteration (Lambda = 0.11069)\")\n plt.legend()","title":""},{"docid":"bb5711cfbe8dd3bb4c12d1f08b638d57","score":"0.67987454","text":"def plot_train_metrics(losses, train_acc, precision):\n plt.figure(figsize=(10, 12))\n plt.subplot(311)\n plt.title('Loss')\n plt.plot(losses, color='c')\n plt.subplot(312)\n plt.title('Accuracy')\n plt.plot(train_acc, color='c')\n plt.subplot(313)\n plt.title('Precision')\n plt.plot(precision, color='c')\n plt.show()","title":""},{"docid":"6f89459f9020a92452a96fa11188b218","score":"0.6797175","text":"def plot_learning(self, x, y):\r\n objective_iter = [self.f_beta(x, y, coef) for coef in self.coeff_list]\r\n fig = plt.figure()\r\n plt.plot(range(len(self.coeff_list)), objective_iter)\r\n plt.xlabel('Iteration')\r\n plt.ylabel('Objective Function')\r\n \r\n return fig","title":""},{"docid":"3f9ecb8ff30164dd210a092450c21446","score":"0.67761576","text":"def plot_loss(set_loss, training_loss, parameters, set_name): \n\n plt.clf();\n \n plt.plot(set_loss, color='k', label=set_name);\n plt.plot(training_loss, color='r', linestyle=\":\", label=\"Training Set\");\n\n plt.legend(framealpha=1);\n plt.yscale('log');\n plt.title(f'{parameters[\"dataset\"]}');\n plt.xlabel(\"Epochs\", fontsize=16);\n plt.ylabel(\"Loss\", fontsize=16);\n\n plt.show()","title":""},{"docid":"5128e4f041fe882b585ae76b186b8636","score":"0.6761979","text":"def plot(history):\n plt.plot(history.history['loss'])\n plt.plot(history.history['val_loss'])\n plt.title('model loss')\n plt.ylabel('loss')\n plt.xlabel('epoch')\n plt.legend(['train','validation'],loc='upper right')\n plt.show()\n \n # YOUR CODE HERE","title":""},{"docid":"3485b50690f2e171364716eaa60610c4","score":"0.67561424","text":"def plot_training(history, savedir):\n\n acc = history.history['acc']\n val_acc = history.history['val_acc']\n loss = history.history['loss']\n val_loss = history.history['val_loss']\n epochs = range(len(acc))\n\n plt.plot(epochs, acc, 'r.')\n plt.plot(epochs, val_acc, 'r')\n plt.title('Training and validation accuracy')\n plt.savefig(f'{savedir}/acc_vs_epochs.png')\n\n plt.figure()\n plt.plot(epochs, loss, 'r.')\n plt.plot(epochs, val_loss, 'r-')\n plt.title('Training and validation loss')\n plt.savefig(f'{savedir}/loss_vs_epochs.png')","title":""},{"docid":"9f37da37068724f3771e23f01ce0a585","score":"0.67085624","text":"def plot_learning_curve(X_train=None,\n y_train=None,\n X_test=None,\n y_test=None,\n kernel=None,\n n_run=10,\n title=\"Learning curve\"):\n n_data = len(self.X_train)\n n_samp = np.linspace(n_data//n_run, n_run*(n_data//n_run),\n n_run, dtype=int)\n est = import_module('sklearn.gaussian_process')\n estimator = getattr(est, 'GaussianProcessRegressor')\n\n\n _MAE_train = []\n _MAE_test = []\n for i, n in enumerate(n_samp):\n xtrain = self.X_train[:n]\n if self.scaling:\n scaled_part_train = self.alpha * self.scaling_y_train[:n] \\\n + self.gamma\n scaled_part_test = self.alpha * self.scaling_y_test \\\n + self.gamma\n ytrain = self.y_train[:n] - scaled_part_train\n estimator = estimator(kernel=kernel,\n n_restarts_optimizer=4,\n alpha=0)\n estimator.fit(xtrain, ytrain)\n _MAE_train += [mean_absolute_error(self.y_train[:n],\n estimator.predict(xtrain) + scaled_part_train)]\n _MAE_test += [mean_absolute_error(self.y_test,\n estimator.predict(self.X_test) + scaled_part_test)]\n kernel = estimator.kernel_\n else:\n ytrain = self.y_train[:n]\n estimator = estimator(kernel=kernel,\n n_restarts_optimizer=4,\n alpha=0)\n estimator.fit(xtrain, ytrain)\n _MAE_train += [mean_absolute_error(ytrain,\n estimator.predict(xtrain))]\n _MAE_test += [mean_absolute_error(self.y_test,\n estimator.predict(self.X_test))]\n kernel = estimator.kernel_\n\n plt.figure()\n plt.title(title)\n plt.grid(color='b', linestyle='-', linewidth=0.5)\n plt.xlabel(\"# of training samples\")\n plt.ylabel(\"MAE [eV]\")\n plt.plot(n_samp, _MAE_train, 'o-', color='r',\n label=\"MAE for training set\")\n plt.plot(n_samp, _MAE_test, 'o-', color='g',\n label=\"MAE for testing set\")\n plt.legend(loc='best')\n\n return plt","title":""},{"docid":"970f1b92c648d8455021f1d0b03638b0","score":"0.6684306","text":"def plot_learning_curves(estimators, titles, X, y, ylim=None, cv=None,\n n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 2)):\n nrows = len(estimators)//2\n ncols = (len(estimators)//nrows)+ (0 if len(estimators) % nrows == 0 else 1)\n plt.figure(1)\n fig, axes = plt.subplots(nrows, ncols, figsize=(10, 10))\n \n n = 0\n for col in range(ncols):\n for row in range(nrows):\n estimator = estimators[n]\n title = titles[n]\n axes[row, col].set_title(title) \n if ylim is not None:\n axes[row, col].set_ylim(*ylim) \n axes[row, col].set_xlabel(\"Training examples\")\n axes[row, col].set_ylabel(\"Score\") \n train_sizes, train_scores, test_scores = learning_curve(estimator,\n X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes,\n scoring=\"neg_mean_squared_error\") \n train_scores = np.sqrt(-train_scores)\n test_scores = np.sqrt(-test_scores) \n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n axes[row, col].grid() \n axes[row, col].fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n axes[row, col].fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n axes[row, col].plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\n axes[row, col].plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\n axes[row, col].legend(loc=\"best\")\n \n n += 1\n plt.tight_layout()\n plt.show()\n plt.gcf().clear()","title":""},{"docid":"8b34e5cdd6c4c329bdb2d359077871f4","score":"0.6667418","text":"def create_train_plots(training_losses):\n x = [i for i in range(len(training_losses))]\n plt.clf()\n plt.plot(x, training_losses)\n plt.title('Training Loss per epoch')\n plt.xlabel('Epoch')\n plt.ylabel('Loss')\n plt.xticks(np.arange(1, len(x)+1, 1))\n plt.savefig('../results/train_plot.png')","title":""},{"docid":"d8d6a87f7ef5c34e3df5280d349da6fe","score":"0.6653885","text":"def plot_training(hist, i: int):\n acc = hist.history[\"acc\"]\n val_acc = hist.history[\"val_acc\"]\n epochs = range(len(acc))\n plt.plot(epochs, acc, label=\"Training Accuracy\")\n plt.plot(epochs, val_acc, label=\"Validation Accuracy\")\n plt.title(\"Training and Validation Accuracy\")\n plt.xlabel(\"Epochs\")\n plt.ylabel(\"Accuracy\")\n plt.title(f\"Model {i} Training Accuracy\")\n plt.savefig(f\"figs/{experiment_name}/training_accuracy_m{i}\")\n plt.figure()\n loss = hist.history[\"loss\"]\n val_loss = hist.history[\"val_loss\"]\n plt.plot(epochs, loss, label=\"Training Loss\")\n plt.xlabel(\"Epochs\")\n plt.ylabel(\"Loss\")\n plt.plot(epochs, val_loss, label=\"Validation Loss\")\n plt.title(f\"Model {i} Training Loss\")\n plt.savefig(f\"figs/{experiment_name}/training_loss_m{i}.png\")\n plt.figure()","title":""},{"docid":"e7bcff285ed14d3c4bfbe639a640bd82","score":"0.6644706","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n\t\t\t\t\t\tn_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n\tplt12.figure()\n\tplt12.title(title)\n\tif ylim is not None:\n\t\tplt12.ylim(*ylim)\n\tplt12.xlabel(\"Training examples\")\n\tplt12.ylabel(\"Score\")\n\ttrain_sizes, train_scores, test_scores = learning_curve(\n\t\testimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n\ttrain_scores_mean = np.mean(train_scores, axis=1)\n\ttrain_scores_std = np.std(train_scores, axis=1)\n\ttest_scores_mean = np.mean(test_scores, axis=1)\n\ttest_scores_std = np.std(test_scores, axis=1)\n\tplt12.grid()\n\n\t#plt12.fill_between(train_sizes, train_scores_mean - train_scores_std,train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n\t#plt12.fill_between(train_sizes, test_scores_mean - test_scores_std,test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n\tplt12.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n\t\t\t label=\"Training score\")\n\tplt12.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n\t\t\t label=\"Cross-validation score\")\n\n\tplt12.legend(loc=\"best\")\n\treturn plt12","title":""},{"docid":"2541e6e3dd6ee6d202aee516cd6f428b","score":"0.6633816","text":"def plot_learning(\n data, # dictionary of dictionaries of data\n title = None, # title for plot\n iter_key = \"iter\", # iteration key word\n val_key = \"f1\", # validation key word\n score_type = \"F1\", # score type for label name\n linewidth = 1.0, # linewidth for plot\n offset = 10, # pixel offset for axes\n label_x_offset = 200, # x direction offset for label\n label_y_offset = 1, # y direction offset for label\n x_size = 10, # x size of figure\n y_size = 5, # y size of figure\n x_label = \"training iteration\", # name for x-axis\n x_tick_int = 20000, # interval for ticks on x-axis\n iterations = 100000, # max number of training iterations\n y_label = \"score\", # name for y-axis\n y_tick_int = 10, # interval for ticks on y-axis\n max_score = 100 # max score\n ):\n \n # Create plot figure\n fig = plt.figure(figsize = (x_size, y_size))\n ax = fig.add_subplot(111)\n \n # Plot Style\n# =============================================================================\n# plt.style.use('seaborn-deep')\n# =============================================================================\n \n # Format x and y axes\n plt.xlabel(x_label)\n plt.xlim(0,iterations)\n plt.xticks(np.arange(0,iterations+1,x_tick_int))\n \n plt.ylabel(y_label)\n plt.ylim(0,max_score)\n plt.yticks(np.arange(0,max_score+1,y_tick_int))\n \n # Adjust plot borders\n ax.spines['right'].set_visible(False)\n ax.spines['top'].set_visible(False)\n ax.spines['left'].set_position(('outward', offset))\n ax.spines['bottom'].set_position(('outward', offset))\n \n # Iterate through keys\n for key in data.keys():\n \n assert key in colors.keys(), \"{} is not a support key in: {}\".format(key, colors.keys())\n \n # Get data set\n experiment = data.get(key)\n \n # Get x and y values for plotting\n temp_iter = experiment.get(iter_key)\n temp_val = experiment.get(val_key)\n \n # Plot data on figure\n ax.plot(temp_iter, temp_val, linestyle='-', marker=markers.get(key),\n color=colors.get(key), mec='w', ms=8, clip_on=False)\n \n # Add dataset labels\n plt.text(temp_iter[-1]+label_x_offset, temp_val[-1]-label_y_offset,\n names.get(key)+\" \"+score_type, color=colors.get(key))\n \n # Add title\n if not title is None:\n plt.title(title)\n \n # Adjust layout\n fig.tight_layout()\n \n return fig","title":""},{"docid":"c8257df28ec55cf1b0eb568db6c4f055","score":"0.661555","text":"def print_cost(self):\n costs = self.cost\n plt.plot(np.squeeze(costs))\n plt.ylabel('cost')\n plt.xlabel('iterations (per hundreds)')\n plt.title(\"Learning rate =\" + str(self.learning_rate))\n plt.show()","title":""},{"docid":"f77371b675f0504eed404be81751fdad","score":"0.6612863","text":"def show_learning_curve(file_paths: List[str], model_names: List[str], **kwargs) -> tuple:\n import matplotlib.pyplot as plt\n from matplotlib.ticker import MaxNLocator\n from matplotlib.lines import Line2D\n from matplotlib import colors as mcolors\n\n available_markers = list(Line2D.markers.keys())[2:]\n available_colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS).keys())\n\n total_files = len(file_paths)\n # print(available_markers)\n # markers = available_markers[:total_files]\n # markers_grouped = [markers[n:n + 2] for n in range(0, len(markers), 2)]\n\n markers = kwargs.get(\"markers\", [\"o\", \"v\", \"d\", \"x\", \"*\", \"+\", \"P\", \"s\"])\n markers = markers[:total_files]\n\n if len(markers) != total_files:\n raise ValueError(\n f\"Not enough values for markers. markers contains {len(markers)} elements while it is supposed to\"\n f\"contains at least {total_files} elements.\"\n )\n\n colors = available_colors[:total_files]\n\n fig, axes = plt.subplots(1, 1, figsize=kwargs.get(\"figsize\", (8, 6)))\n\n for idx, (file_path, model_name, marker, color) in enumerate(zip(file_paths, model_names, markers, colors)):\n history: dict = read_log_file(file_path=file_path, model_name=model_name)\n\n epochs: list = history['epoch']\n\n axes.plot(\n epochs,\n history['loss'],\n marker=marker,\n markersize=kwargs.get(\"markersize\", 4),\n linestyle='-',\n lw=1.8,\n label=f'{model_name}',\n color=color\n )\n\n axes.plot(\n epochs,\n history['val_loss'],\n marker=marker,\n markersize=kwargs.get(\"markersize\", 4),\n linestyle='--',\n lw=1.8,\n color=color\n )\n\n fontsize = kwargs.get(\"font_size\", 14)\n axes.set_ylabel('Loss [-]', fontsize=fontsize)\n axes.set_xlabel('Epochs [-]', fontsize=fontsize)\n axes.xaxis.set_major_locator(MaxNLocator(nbins=20, integer=True))\n axes.set_yscale(kwargs.get(\"scale\", \"linear\"))\n axes.tick_params(axis=\"both\", which=\"major\", labelsize=fontsize)\n axes.legend(fontsize=fontsize)\n axes.grid()\n plt.tight_layout()\n\n if kwargs.get(\"save\", True):\n os.makedirs(\"figures/\", exist_ok=True)\n plt.savefig(kwargs.get(\"save_name\", f\"figures/model.png\"), dpi=300)\n if kwargs.get(\"show\", True):\n plt.show()\n\n return fig, axes","title":""},{"docid":"ec1ab34b67710e721f77e0f04cb8e360","score":"0.6609935","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n plt.figure()\n plt.title(title)\n if ylim is not None:\n plt.ylim(*ylim)\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n train_sizes, train_scores, test_scores = learning_curve( estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes = train_sizes)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n \n print(\"train_scores_mean:\")\n print(np.mean(train_scores, axis=1))\n print(\"test_scores_mean:\")\n print (test_scores_mean)\n\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\n\n plt.legend(loc=\"best\")\n return plt","title":""},{"docid":"20205dff9ce7f52fbee690482eb3588c","score":"0.66068953","text":"def plot_learning_curve(self, estimator, title, X, y, save_file_path, ylim=None, cv=None,\n\t\t\t\t\t\t\t\t\t\t\t\t\ttrain_sizes=np.linspace(.1, 1.0, 5)):\n\t\tlogger.info(\"Plotting {}\".format(title))\n\t\tplt.figure()\n\t\tplt.title(title)\n\t\tif ylim is not None:\n\t\t\tplt.ylim(*ylim)\n\t\tplt.xlabel(\"Training examples\")\n\t\tplt.ylabel(\"Score\")\n\t\ttrain_sizes, train_scores, test_scores = learning_curve(\n\t\t\testimator, X, y, cv=cv, train_sizes=train_sizes)\n\t\ttrain_scores_mean = np.mean(train_scores, axis=1)\n\t\ttrain_scores_std = np.std(train_scores, axis=1)\n\t\ttest_scores_mean = np.mean(test_scores, axis=1)\n\t\ttest_scores_std = np.std(test_scores, axis=1)\n\t\tplt.grid()\n\n\t\tplt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1,\n\t\t\t\t\t\t\t\t\t\t color=\"r\")\n\t\tplt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1,\n\t\t\t\t\t\t\t\t\t\t color=\"g\")\n\t\tplt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n\t\tplt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\n\n\t\tplt.legend(loc=\"best\")\n\t\tplt.savefig(\"{}\".format(save_file_path))\n\t\tplt.close()","title":""},{"docid":"14dad150472da33b1b28071d6f9eba2c","score":"0.6601268","text":"def plot(self):\r\n points_to_plot = [[I[self.FEATURES_TO_PLOT[0]], I[self.FEATURES_TO_PLOT[1]], I[-1]] for I in self.TRAINING_DATA]\r\n self.plot_2d_points(points_to_plot)\r\n self.plot_weight_vectors(self.tp.W)\r\n plt.xlabel(self.FEATURES[self.FEATURES_TO_PLOT[0]])\r\n plt.ylabel(self.FEATURES[self.FEATURES_TO_PLOT[1]])\r\n plt.title(\"Iris data with three weight vectors from multi-class perceptron training.\")\r\n plt.show()","title":""},{"docid":"ab2e8b4863826fd64d6f122f3073a6ae","score":"0.6590201","text":"def plot(network):\n\tplt.figure(figsize=(16, 8))\n\tnetwork.plot()\n\tplt.show()","title":""},{"docid":"aa4f6dafc2e0f7f6d36481366059b026","score":"0.658493","text":"def plot_training(history_name):\n acc = history_name.history['acc']\n test_acc = history_name.history['val_acc']\n loss = history_name.history['loss']\n test_loss = history_name.history['val_loss']\n\n epochs = range(1,len(acc) + 1)\n\n plt.plot(epochs, acc, 'bo', label='Training accuracy')\n plt.plot(epochs, test_acc, 'r', label='Validation accuracy')\n plt.title('Training and test accuracy')\n plt.legend()\n plt.axhline(y=0.9, color='black', linestyle='--')\n plt.figure()\n\n plt.plot(epochs, loss, 'bo', label='Training loss')\n plt.plot(epochs, test_loss, 'r', label='Test loss')\n plt.title('Training and test loss')\n plt.legend()\n plt.show()","title":""},{"docid":"c029173e38c4132285925996bf3f9869","score":"0.6583262","text":"def generate_plot(data):\n epochs = list(range(1, len(data) + 1))\n plt.ylabel(\"Loss\")\n plt.xlabel(\"Epochs\")\n plt.plot(epochs, data)\n plt.savefig(\"images/loss.png\")\n plt.show()","title":""},{"docid":"7de3dd97f97b93783806a52dfb16bb65","score":"0.65776277","text":"def plot_learning_curve(metrics_csv_file, x_attribute, y_attribute):\n df = pd.read_csv(metrics_csv_file)[[x_attribute, y_attribute, \"alpha\"]]\n color_list = ['sienna', 'black', 'darkgreen', 'red', 'blue', 'gold',\n 'silver', 'navy', 'orange', 'm', \"lime\", \"grey\"]\n sns.lmplot(x_attribute, y_attribute, data=df, hue='alpha',\n palette=color_list, fit_reg=False, legend_out=False, markers='o',\n scatter_kws={\"s\": 50})\n sns.plt.suptitle(\"Simulated user on validation set\")\n plt.show()","title":""},{"docid":"356d7df7226595b96a1d8fa320289f25","score":"0.6573321","text":"def plot_learning_curve(\n estimator, title, X, y, cv=None, train_sizes=np.linspace(.1, 1.0, 10)\n):\n plt.figure()\n plt.title(title)\n plt.ylim(0.45, 1.01)\n plt.xlabel('Number of samples', labelpad=20)\n plt.ylabel('Score', labelpad=20)\n train_sizes, train_scores, test_scores = learning_curve(\n estimator, X, y, cv=cv, n_jobs=-1, train_sizes=train_sizes)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label='Train score')\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label='Cross-validation score')\n\n plt.legend(loc='best')\n return plt","title":""},{"docid":"8a41de375a0d3ab553e41eb169c682b0","score":"0.65684664","text":"def __plot(self):\n\n if not self.accuracies or not self.losses:\n raise ValueError(\"ERROR: self.accuracies or self.losses not defined. Make sure self.fit() is called with visible=True or that you called self.update_fit()\")\n plt.plot([i+1 for i in range(len(self.accuracies))],self.accuracies)\n plt.xlabel(\"Number of estimators\")\n plt.ylabel(\"Accuracy\")\n plt.show()\n plt.plot([i+1 for i in range(len(self.losses))], self.losses)\n plt.xlabel(\"Number of estimators\")\n plt.ylabel(\"Loss\")\n plt.show()","title":""},{"docid":"e08c3a35211accafbdfeb2b09b7a966c","score":"0.65595156","text":"def plot_cost(train_cost, val_cost, epochs):\n epochs_label = np.arange(0, epochs+1, 1)\n\n fig, ax = plt.subplots()\n\n ax.plot(epochs_label, train_cost, label=\"Training Data\")\n ax.plot(epochs_label, val_cost, label=\"Validation Data\")\n ax.legend()\n ax.set(xlabel='Epochs', ylabel='Cost', ylim=(0, 4), xlim=(0, 22))\n ax.grid()\n\n plt.show()","title":""},{"docid":"272c01e451d339a873f684af02317476","score":"0.6546069","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n plt.figure()\n plt.title(title)\n if ylim is not None:\n plt.ylim(*ylim)\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Accuracy\")\n train_sizes, train_scores, test_scores = learning_curve(\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation\")\n\n plt.legend(loc=\"best\")\n return plt","title":""},{"docid":"e1674e91ec60ccb1d1f11af3ccdcd682","score":"0.6542977","text":"def plot_loss(self):\n history_dict = self.history.history\n history_dict.keys()\n loss = history_dict['loss']\n val_loss = history_dict['val_loss']\n\n epochs = range(1, len(loss) + 1)\n\n # \"bo\" is for \"blue dot\"\n plt.plot(epochs, loss, 'bo', label='Training loss')\n # b is for \"solid blue line\"\n plt.plot(epochs, val_loss, 'b', label='Validation loss')\n plt.title('Training and validation loss')\n plt.xlabel('Epochs')\n plt.ylabel('Loss')\n plt.legend()\n\n plt.show()","title":""},{"docid":"9ba9f10d9151afb9802d84a677b84e16","score":"0.65290445","text":"def plot_learning_accuracy(self):\n x = [i for i in range(0,len(self._epoch_accuracy))]\n \n plt.plot(x, self._epoch_accuracy, label='Accuracy')\n plt.xlabel('Epoch')\n plt.ylabel('Accuracy')\n plt.title('Learning Accuracy\\n')\n plt.legend()\n plt.show()","title":""},{"docid":"023fd3e20414ece902f0b09dc936013d","score":"0.6525452","text":"def plotter(train, val, title):\n plt.style.use('seaborn')\n plt.xlabel('epoch')\n plt.ylabel(str(title))\n plt.plot(train, color = 'green', label = \"training \" + str(title))\n plt.plot(val, color = 'red', label = \"validation \" + str(title))\n plt.legend()\n plt.show()\n #plt.savefig('result/loss')\n #plt.close()","title":""},{"docid":"81d81e38ebd617805455b3e93687c6ef","score":"0.65207815","text":"def plot_training_curve(path):\n import matplotlib.pyplot as plt\n train_err = np.loadtxt(\"{}_train_err.csv\".format(path))\n val_err = np.loadtxt(\"{}_val_err.csv\".format(path))\n train_loss = np.loadtxt(\"{}_train_loss.csv\".format(path))\n val_loss = np.loadtxt(\"{}_val_loss.csv\".format(path))\n plt.title(\"Train vs Validation Error\")\n n = len(train_err) # number of epochs\n plt.plot(range(1,n+1), train_err, label=\"Train\")\n plt.plot(range(1,n+1), val_err, label=\"Validation\")\n plt.xlabel(\"Epoch\")\n plt.ylabel(\"Error\")\n plt.legend(loc='best')\n plt.show()\n plt.title(\"Train vs Validation Loss\")\n plt.plot(range(1,n+1), train_loss, label=\"Train\")\n plt.plot(range(1,n+1), val_loss, label=\"Validation\")\n plt.xlabel(\"Epoch\")\n plt.ylabel(\"Loss\")\n plt.legend(loc='best')\n plt.show()","title":""},{"docid":"1a679329bb7ae6543872bfa4f1835fe0","score":"0.65168494","text":"def plot(self, n_epochs, title=\"Learning Rate Schedule\"):\n # Compute the set of learning rates for each corresponding\n # Epoch\n epochs = np.arange(0, n_epochs)\n lrs = []\n for i in range(n_epochs):\n lrs.append(self())\n # the learning rate schedule\n plt.style.use(\"ggplot\")\n plt.figure()\n plt.plot(epochs, lrs)\n plt.title(title)\n plt.xlabel(\"Epoch #\")\n plt.ylabel(\"Learning Rate\")\n plt.show()","title":""},{"docid":"812823d7b039d34de2c17bb6dde63873","score":"0.6508551","text":"def accuracy_curve_training(history):\n \n fig = plt.figure(figsize=[8,6])\n plt.plot(history.history['acc'],'r',linewidth=3.0)\n plt.legend(['Training Accuracy'],fontsize=18)\n plt.xlabel('Epochs ',fontsize=16)\n plt.ylabel('Accuracy',fontsize=16)\n plt.title('Accuracy Curves',fontsize=16)\n fig.savefig('/services/scratch/perception/cdamhieu/results/curves/accuracy_loss_per_epoch/accuracy_curves_training.png')","title":""},{"docid":"b7ae5f7fe4c4fc9a2975794cb89bd4d2","score":"0.64923316","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n\t\t\t\t\t\tn_jobs=1, train_sizes=numpy.linspace(.1, 1.0, 5)):\n\n\tplt.figure()\n\tplt.title(title)\n\tif ylim is not None:\n\t\tplt.ylim(*ylim)\n\tplt.xlabel(\"Training examples\")\n\tplt.ylabel(\"Score\")\n\ttrain_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n\n\ttrain_scores_mean = numpy.mean(train_scores, axis=1)\n\ttrain_scores_std = numpy.std(train_scores, axis=1)\n\ttest_scores_mean = numpy.mean(test_scores, axis=1)\n\ttest_scores_std = numpy.std(test_scores, axis=1)\n\tplt.grid()\n\n\tplt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n\t\t\t\t\t train_scores_mean + train_scores_std, alpha=0.1,\n\t\t\t\t\t color=\"r\")\n\tplt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n\t\t\t\t\t test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n\tplt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n\t\t\t label=\"Training score\")\n\tplt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n\t\t\t label=\"Cross-validation score\")\n\n\tplt.legend(loc=\"best\")\n\treturn plt","title":""},{"docid":"db919439563f163aabe298f0bbbe7cfa","score":"0.64802206","text":"def loss_curve_training(history):\n \n fig = plt.figure(figsize=[8,6])\n plt.plot(history.history['loss'],'r',linewidth=3.0)\n plt.legend(['Training loss'],fontsize=18)\n plt.xlabel('Epochs ',fontsize=16)\n plt.ylabel('Loss',fontsize=16)\n plt.title('Loss Curves',fontsize=16)\n fig.savefig('/services/scratch/perception/cdamhieu/results/curves/accuracy_loss_per_epoch/loss_curves_training.png')","title":""},{"docid":"7b8f07d04711f360455bd392fd7351b3","score":"0.64736956","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n plt.figure()\n plt.title(title)\n if ylim is not None:\n plt.ylim(*ylim)\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n train_sizes, train_scores, test_scores = learning_curve(\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\n\n plt.legend(loc=\"best\")\n return plt","title":""},{"docid":"7b8f07d04711f360455bd392fd7351b3","score":"0.64736956","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n plt.figure()\n plt.title(title)\n if ylim is not None:\n plt.ylim(*ylim)\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n train_sizes, train_scores, test_scores = learning_curve(\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\n\n plt.legend(loc=\"best\")\n return plt","title":""},{"docid":"6414421bfed99521f4e17f4e4ce2044a","score":"0.6466813","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n plt.figure()\n plt.title(title)\n if ylim is not None:\n plt.ylim(*ylim)\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n train_sizes, train_scores, test_scores = learning_curve(\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\n\n plt.legend(loc=\"best\")\n return plt","title":""},{"docid":"6414421bfed99521f4e17f4e4ce2044a","score":"0.6466813","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n plt.figure()\n plt.title(title)\n if ylim is not None:\n plt.ylim(*ylim)\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n train_sizes, train_scores, test_scores = learning_curve(\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\n\n plt.legend(loc=\"best\")\n return plt","title":""},{"docid":"6414421bfed99521f4e17f4e4ce2044a","score":"0.6466813","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n plt.figure()\n plt.title(title)\n if ylim is not None:\n plt.ylim(*ylim)\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n train_sizes, train_scores, test_scores = learning_curve(\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\n\n plt.legend(loc=\"best\")\n return plt","title":""},{"docid":"6414421bfed99521f4e17f4e4ce2044a","score":"0.6466813","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n plt.figure()\n plt.title(title)\n if ylim is not None:\n plt.ylim(*ylim)\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n train_sizes, train_scores, test_scores = learning_curve(\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\n\n plt.legend(loc=\"best\")\n return plt","title":""},{"docid":"a620d3a1effbe372db273e526432213c","score":"0.64666253","text":"def plot_learning(remaining_pegs):\n episode = [i for i in range(len(remaining_pegs))]\n plt.plot(episode, remaining_pegs)\n plt.xlabel(\"Episode number\")\n plt.ylabel(\"Remaining pegs\")\n plt.show()","title":""},{"docid":"c6d1a41d25320aa94acc92cd23a2ba2a","score":"0.6466579","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):\n plt.figure()\n plt.title(title)\n if ylim is not None:\n plt.ylim(*ylim)\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n train_sizes, train_scores, test_scores = learning_curve(\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\n\n plt.legend(loc=\"best\")\n return plt","title":""},{"docid":"072799999cacae5916320e963a05181d","score":"0.64643675","text":"def update_training_plot(self):\n self.axes1training.cla()\n self.axes2training.cla()\n self.axes3training.cla()\n\n self.axes3training.set_xlabel('Episode')\n\n reward, v, d = self.tr.read_averages()\n\n self.axes1training.plot(reward)\n self.axes2training.plot(v)\n self.axes3training.plot(d)\n\n self.canvas_training.draw()","title":""},{"docid":"b8f41fe53f2b966ef2e9e72c2f18c99b","score":"0.6459311","text":"def show_learning_curve_v2(file_paths: List[str], model_names: List[str], **kwargs) -> tuple:\n import matplotlib.pyplot as plt\n from matplotlib.ticker import MaxNLocator\n from matplotlib.lines import Line2D\n from matplotlib import colors as mcolors\n\n available_markers = list(Line2D.markers.keys())[2:]\n available_colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS).keys())\n\n total_files = len(file_paths)\n # print(available_markers)\n # markers = available_markers[:total_files]\n # markers_grouped = [markers[n:n + 2] for n in range(0, len(markers), 2)]\n\n markers = kwargs.get(\"markers\", [\"o\", \"v\", \"d\", \"x\", \"*\", \"+\", \"P\", \"s\"])\n markers = markers[:total_files]\n\n if len(markers) != total_files:\n raise ValueError(\n f\"Not enough values for markers. markers contains {len(markers)} elements while it is supposed to\"\n f\"contains at least {total_files} elements.\"\n )\n\n colors = available_colors[:total_files]\n\n fig, axes = plt.subplots(1, 1, figsize=kwargs.get(\"figsize\", (8, 6)))\n\n for idx, (file_path, model_name, marker, color) in enumerate(zip(file_paths, model_names, markers, colors)):\n with open(file_path, \"r\") as log_file:\n lines: List[str] = log_file.readlines()\n\n for line_idx, line in enumerate(lines):\n if line.__contains__(\"epoch\"):\n first_index: int = line_idx - 1\n break\n\n epochs: np.ndarray = np.loadtxt(file_path, usecols=1, skiprows=first_index)\n loss: np.ndarray = np.loadtxt(file_path, usecols=3, skiprows=first_index)\n val_loss: np.ndarray = np.loadtxt(file_path, usecols=5, skiprows=first_index)\n\n axes.plot(\n epochs,\n loss,\n # marker=marker,\n # markersize=kwargs.get(\"markersize\", 4),\n linestyle='-',\n lw=1.8,\n label=f'{model_name}',\n color=color\n )\n\n axes.plot(\n epochs,\n val_loss,\n # marker=marker,\n # markersize=kwargs.get(\"markersize\", 4),\n linestyle='--',\n lw=1.8,\n color=color\n )\n\n fontsize = kwargs.get(\"font_size\", 14)\n axes.set_ylabel('Loss RMSE [-]', fontsize=fontsize)\n axes.set_xlabel('Epochs [-]', fontsize=fontsize)\n axes.xaxis.set_major_locator(MaxNLocator(nbins=10, integer=True))\n axes.set_xscale(kwargs.get(\"scale\", \"linear\"))\n axes.set_yscale(kwargs.get(\"scale\", \"linear\"))\n axes.tick_params(axis=\"both\", which=\"major\", labelsize=fontsize)\n axes.legend(fontsize=fontsize)\n axes.grid()\n\n axes.axhline(y=0.00022, xmin=0.0001, xmax=1, lw=2, color=\"k\")\n axes.annotate(s=r\"deepx score ($2.2 \\times 10^{-4}$)\", xy=(1, 0.00023), fontsize=16)\n\n plt.tight_layout()\n\n if kwargs.get(\"save\", True):\n os.makedirs(\"figures/\", exist_ok=True)\n plt.savefig(kwargs.get(\"save_name\", f\"figures/model.png\"), dpi=300)\n if kwargs.get(\"show\", True):\n plt.show()\n\n return fig, axes","title":""},{"docid":"10845d45e8e17f5885f239b3e6352d1c","score":"0.64541495","text":"def plot_graphs(history, component):\n plt.title('Train ' + ' and Validation ' + component)\n plt.plot(history.history[component])\n plt.plot(history.history['val_'+component])\n plt.xlabel('Epochs')\n plt.ylabel(component)\n plt.legend([component, 'val_'+component])\n plt.grid()\n plt.show()","title":""},{"docid":"a739d7b119eb64978aea124e0ed068bb","score":"0.6453572","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\r\n n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\r\n plt.figure(1)\r\n plt.title(title)\r\n if ylim is not None:\r\n plt.ylim(*ylim)\r\n plt.xlabel(\"Training examples\",fontsize = fontsize)\r\n plt.ylabel(\"Score\",fontsize = fontsize)\r\n train_sizes, train_scores, test_scores = learning_curve(\r\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\r\n train_scores_mean = np.mean(train_scores, axis=1)\r\n train_scores_std = np.std(train_scores, axis=1)\r\n test_scores_mean = np.mean(test_scores, axis=1)\r\n test_scores_std = np.std(test_scores, axis=1)\r\n plt.grid()\r\n\r\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\r\n train_scores_mean + train_scores_std, alpha=0.1,\r\n color=\"r\")\r\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\r\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\r\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\r\n label=\"Training score\")\r\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\r\n label=\"Cross-validation score\")\r\n plt.xticks(size = fontsize)\r\n plt.yticks(size = fontsize)\r\n plt.legend(loc=\"best\")\r\n return plt","title":""},{"docid":"4553e7cbf098de1565f12cdd3ffa3bf3","score":"0.64534795","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n plt.figure()\n plt.title(title)\n if ylim is not None:\n plt.ylim(*ylim)\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n train_sizes, train_scores, test_scores = learning_curve(\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\n plt.legend(loc=\"best\")\n return plt","title":""},{"docid":"37852c16230e65af81b1bc776da51a6f","score":"0.6443604","text":"def plot_loss(train_loss, val_loss, epochs):\n epochs_label = np.arange(0, epochs+1, 1)\n\n fig, ax = plt.subplots()\n\n ax.plot(epochs_label, train_loss, label=\"Training Data\")\n ax.plot(epochs_label, val_loss, label=\"Validation Data\")\n ax.legend()\n ax.set(xlabel='Epochs', ylabel='Loss', ylim=(0, 3), xlim=(0, 22))\n ax.grid()\n\n plt.show()","title":""},{"docid":"c7d340bd84d65d1779c15c4254770da7","score":"0.6431484","text":"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n n_jobs=1, train_sizes=np.linspace(0.1, 1.0, 5), heldout_score=None):\n plt.figure()\n plt.title(title)\n if ylim is not None:\n plt.ylim(*ylim)\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n train_sizes, train_scores, test_scores = learning_curve(\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\n\n plt.legend(loc=\"best\")\n\n print \"train scores\\n\", train_scores\n\n print \"test_scores \\n\", test_scores\n \n return plt","title":""},{"docid":"b630a0d5430ecf79a4340cfc0d173906","score":"0.642106","text":"def mat_plot():\n line_x = np.linspace(- 5, 5, 100)\n line_y = square_func(line_x)\n x_start = - 5\n iterations = 50\n learning_rate = 0.7\n x = gradient_descent(x_start, derivative_func, iterations, learning_rate)\n\n # 绘制二阶曲线\n plt.plot(line_x, line_y, c='b')\n\n # 绘制梯度下降过程的点\n plt.scatter(x, square_func(x), c='r', )\n plt.plot(x, square_func(x), c='r',\n label='learning rate={}'.format(learning_rate))\n\n # legend函数显示图例\n plt.legend()\n # show函数显示\n plt.show()","title":""},{"docid":"fe72fc138ed41fcdacd6efda4246bd65","score":"0.64100766","text":"def show_learning_rate(file_paths: List[str], model_names: List[str], **kwargs) -> tuple:\n import matplotlib.pyplot as plt\n from matplotlib.ticker import MaxNLocator\n from matplotlib.lines import Line2D\n from matplotlib import colors as mcolors\n\n available_markers = list(Line2D.markers.keys())[2:]\n available_colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS).keys())\n\n total_files = len(file_paths)\n # print(available_markers)\n # markers = available_markers[:total_files]\n # markers_grouped = [markers[n:n + 2] for n in range(0, len(markers), 2)]\n\n markers = kwargs.get(\"markers\", [\"o\", \"v\", \"d\", \"x\", \"*\", \"+\", \"P\", \"s\"])\n markers = markers[:total_files]\n\n if len(markers) != total_files:\n raise ValueError(\n f\"Not enough values for markers. markers contains {len(markers)} elements while it is supposed to\"\n f\"contains at least {total_files} elements.\"\n )\n\n colors = available_colors[:total_files]\n\n fig, axes = plt.subplots(1, 1, figsize=kwargs.get(\"figsize\", (8, 6)))\n\n for idx, (file_path, model_name, marker, color) in enumerate(zip(file_paths, model_names, markers, colors)):\n with open(file_path, \"r\") as log_file:\n lines: List[str] = log_file.readlines()\n\n for line_idx, line in enumerate(lines):\n if line.__contains__(\"epoch\"):\n first_index: int = line_idx - 1\n break\n\n epochs: np.ndarray = np.loadtxt(file_path, usecols=1, skiprows=first_index)\n lr: np.ndarray = np.loadtxt(file_path, usecols=7, skiprows=first_index)\n\n axes.plot(\n epochs,\n lr,\n # marker=marker,\n # markersize=kwargs.get(\"markersize\", 4),\n linestyle='-',\n lw=1.8,\n label=f'{model_name}',\n color=color\n )\n\n fontsize = kwargs.get(\"font_size\", 14)\n axes.set_ylabel('Learning rate [-]', fontsize=fontsize)\n axes.set_xlabel('Epochs [-]', fontsize=fontsize)\n axes.xaxis.set_major_locator(MaxNLocator(nbins=10, integer=True))\n axes.set_yscale(kwargs.get(\"scale\", \"linear\"))\n axes.tick_params(axis=\"both\", which=\"major\", labelsize=fontsize)\n axes.legend(fontsize=fontsize)\n axes.grid()\n plt.tight_layout()\n\n if kwargs.get(\"save\", True):\n os.makedirs(\"figures/\", exist_ok=True)\n plt.savefig(kwargs.get(\"save_name\", f\"figures/model.png\"), dpi=300)\n if kwargs.get(\"show\", True):\n plt.show()\n\n return fig, axes","title":""},{"docid":"688640d2e8c0fe5fd2cee47fb25a91c2","score":"0.64025366","text":"def dual_plot_accuracy(trainingAccuracy, testingAccuracy, lr):\n plt.xlabel('Number of Epochs')\n plt.plot(trainingAccuracy, color = \"blue\", label = 'training accuracy')\n plt.plot(testingAccuracy, color = \"green\", label = 'testing accuracy')\n plt.title('Training vs Testing Accuracy for Learning Rate : ' + str(lr))\n lgd = plt.legend(loc=9, bbox_to_anchor=(0.5, -0.1),ncol=2)\n plt.savefig('training_vs_testing_'+str(lr)+'.png',bbox_extra_artists=(lgd,), bbox_inches='tight')","title":""},{"docid":"97641d969d7c320f1325b0aae10ed825","score":"0.63984543","text":"def plot_acc(acc_v, acc_t, save_plots_path):\n\n plt.figure()\n plt.plot(acc_v, label='Validation acc')\n plt.plot(acc_t, label='Training acc')\n plt.legend()\n title = 'Accuracy per epoch'\n plt.title(title)\n plt.xlabel(\"Epoch\")\n plt.ylabel(\"Accuracy\")\n plt.savefig(save_plots_path + \"sgd_accuracy_plot.png\")","title":""},{"docid":"e7361f09473514bd94291907f1dbd4e1","score":"0.6388655","text":"def plot_xgboost_learning_curve(iterations, scorestrain, scorescv) -> None:\n plt.figure(figsize=(12, 7))\n plt.plot(iterations,scorestrain, 'r')\n plt.plot(iterations,scorescv, 'b')\n plt.xlabel('# training examples')\n plt.ylabel('MAE')\n plt.legend(['Training Set', 'CV set'], loc='lower right')\n plt.show()","title":""},{"docid":"628e486b012325d2958b9a0a66a600ad","score":"0.63880914","text":"def plotting_graphs(y, tx, k_fold, initial_w, max_iters, gammas, lambdas, optimal_lambda_, optimal_gamma, i, seed=1, model=\"LOG_REG_GD\"):\n training_losses, testing_losses, training_accuracy, testing_accuracy = [], [], [], []\n \n for gamma in range(len(gammas)):\n loss_tr, loss_te, ca_tr, ca_te = total_cross_validation(y, tx, k_fold, initial_w, max_iters, gammas[gamma], optimal_lambda_, seed=1, batch_size=1, model=\"LOG_REG_GD\")\n training_losses.append(loss_tr)\n testing_losses.append(loss_te)\n training_accuracy.append(ca_tr)\n testing_accuracy.append(ca_te)\n cross_validation_visualization(gammas, training_losses, testing_losses, i, parameter = \"gamma_\") # doing only for losses\n\n training_losses, testing_losses, training_accuracy, testing_accuracy = [], [], [], []\n for lambda_ in range(len(lambdas)):\n loss_tr, loss_te, ca_tr, ca_te = total_cross_validation(y, tx, k_fold, initial_w, max_iters, lambdas[lambda_], optimal_gamma, seed=1, batch_size=1, model=\"LOG_REG_GD\")\n training_losses.append(loss_tr)\n testing_losses.append(loss_te)\n training_accuracy.append(ca_tr)\n testing_accuracy.append(ca_te)\n cross_validation_visualization(lambdas, training_losses, testing_losses, i, parameter = \"lambda_\")","title":""},{"docid":"fc049ccea867dacc2fc651de7b8e1382","score":"0.6367363","text":"def plot_lr_func(lr_func: Callable, total_steps: int):\n ys = simulate_lr_func(lr_func, total_steps)\n plot_scatter(ys, ytitle='Learning rate', xtitle='Training step')","title":""},{"docid":"26abfb62c6f3793bd5569d596683fcd8","score":"0.6360308","text":"def plot_cost(c_v, c_t, save_plots_path):\n\n plt.figure()\n plt.plot(c_v, label='Validation loss')\n plt.plot(c_t, label='Training loss')\n plt.legend()\n title = 'Loss per epoch'\n plt.title(title)\n plt.xlabel(\"Epoch\")\n plt.ylabel(\"Loss\")\n plt.savefig(save_plots_path + \"sgd_loss_plot.png\")","title":""},{"docid":"09723e80b7c6529f0b74df7c37864f8b","score":"0.635458","text":"def plot_training_summary(filePath='',savePath='.s/imgs'):\n assert os.path.isfile(filePath) , f'{filePath} does not exist'\n try:\n with open(filePath, 'rb') as f:\n x = pickle.load(f)\n except :\n print(f'could not open {filePath}')\n exit(1)\n\n d_l = np.array(x['discriminator_loss']).ravel()\n g_l = np.array(x['Generator_loss']).ravel()\n acc_history = np.array(x['acc_history'])\n acc = acc_history.sum(axis=1) * 0.5\n acc_real = acc_history[:,1]\n acc_gen = acc_history[:,0]\n kl = np.array(x[\"kl_divergence\"]).ravel()\n\n n = np.arange(len(d_l))\n figname = os.path.split(filePath)[1].replace('.pickle','')\n title = 'Loss and Accuracy plot'+'\\n'+ figname\n\n fig = plt.figure(figsize=(19.20,10.80))\n fig.suptitle(title, fontsize=15,fontweight=\"bold\")\n\n axs1 = plt.subplot(222)\n # axs1.set_title(title,fontsize=5.0,fontweight=\"bold\")\n axs1.plot(n, g_l,label='Generator loss',linewidth=3)\n axs1.plot(n, d_l,label='Discriminator loss',linewidth=3)\n axs1.legend(loc=0, prop={'size': 13})\n axs1.set_ylabel('Loss',fontsize=15.0,fontweight=\"bold\")\n axs1.tick_params(labelsize=10)\n # axs1.tick_params(axis='x',which='both',bottom=False,top=False,labelbottom=False,labelsize=20)\n\n # axs2.plot(n, acc,'r',label='Discriminator accuracy',linewidth=4)\n axs2 = plt.subplot(221)\n axs2.plot(n, acc_gen,label='Accuracy on Generated',linewidth=3)\n axs2.plot(n, acc_real,label='Accuracy on Real',linewidth=3)\n axs2.legend(loc=0,prop={'size': 13})\n axs2.set_ylabel('Accuracy',fontsize=15.0,fontweight=\"bold\")\n # axs2.set_xlabel('Epoch',fontsize=15.0,fontweight=\"bold\")\n axs2.tick_params(labelsize=10)\n\n axs3 = plt.subplot(212)\n n = np.arange(0,(len(kl)*10),10)\n\n axs3.plot(n, kl,label='KL',linewidth=3)\n axs3.legend(loc=0,prop={'size': 13})\n axs3.set_ylabel('KL-Divergence',fontsize=15.0,fontweight=\"bold\")\n axs3.set_xlabel('Epoch',fontsize=15.0,fontweight=\"bold\")\n axs3.tick_params(labelsize=10)\n\n # plt.tight_layout()\n fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n if not os.path.exists(savePath):\n os.makedirs(savePath)\n\n plt.savefig(os.path.join(savePath,figname+'.png'),dpi = 300)\n plt.close('all') #plt.close(fig)","title":""},{"docid":"083d9a6126bd5a94725480fc7cc1e06b","score":"0.6354436","text":"def epoch_vizual(self, train_logs, path):\n fig = plt.figure(figsize=(20, 20))\n plt.rc(\"text\", usetex=True)\n ax1 = fig.add_subplot(2, 1, 1)\n ax2 = fig.add_subplot(2, 1, 2)\n\n ax1.semilogy(train_logs[\"g_grad\"], linewidth=7.0)\n ax1.set_title(\"Norm of Generator gradient\", fontsize=45)\n ax1.set_xlabel(\"Training Iteration\", fontsize=42)\n ax1.set_ylabel(\"Norm of gradient\", fontsize=42)\n ax1.tick_params(axis=\"x\", labelsize=40)\n ax1.tick_params(axis=\"y\", labelsize=40)\n ax1.grid()\n\n ax2.semilogy(train_logs[\"c_grad\"], linewidth=7.0)\n ax2.set_title(\"Norm of Discriminator gradient\", fontsize=45)\n ax2.set_xlabel(\"Training Iteration\", fontsize=42)\n ax2.set_ylabel(\"Norm of gradient\", fontsize=42)\n ax2.tick_params(axis=\"x\", labelsize=40)\n ax2.tick_params(axis=\"y\", labelsize=40)\n ax2.grid()\n\n plt.savefig(path)\n plt.show()","title":""},{"docid":"9b65ffa06b3056b5bc3f64eaf29cd038","score":"0.635388","text":"def visualize_epoch(history):\n # Summarize history for accuracy\n plt.plot(history['acc']) # Training accuracy\n plt.plot(history['val_acc']) # Validation accuracy\n plt.title('Epoch Iteration vs. Accuracy')\n plt.xlabel('Epoch Number')\n plt.ylabel('Accuracy')\n plt.legend(['train', 'val'], loc='upper left')\n plt.show()","title":""},{"docid":"733dc15b79e629a3ffa4cf15031feb6f","score":"0.63411385","text":"def plot_learning_curve(self, resolution=100):\n self.calculate_tour_distance()\n # Get the divisor to limit the number of samples.\n step = len(self.samples) // resolution\n if step == 0:\n step = 1\n # Get samples.\n x = list(range(0, len(self.samples) + 1, step))\n y = [(self.samples + [self.chromosomes[0].tour_distance])[i] for i in x]\n # Setup plot.\n plt.grid(True)\n plt.title('TSP Learning Curve: {0}'.format(str(self.chromosomes[0])))\n plt.xlabel('Samples (every {0} generations)'.format(step))\n plt.ylabel('Lowest Distance')\n plt.plot(x, y)\n # Save plot.\n timestamp = datetime.now().strftime('%m-%d-%Y_%I-%M-%S-%p')\n name = 'learning_curve_{0}.png'.format(timestamp)\n save_dir = os.path.join(os.path.abspath(PLOT_DIR), name)\n plt.savefig(save_dir, dpi=200)","title":""},{"docid":"ce59f4b1b4c650026be0209c9773dc5d","score":"0.63339585","text":"def plot_learning_curve(model, X_train, y_train, scoring='recall'):\n\n cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=42)\n\n train_sizes, train_scores, test_scores = learning_curve(model, \n X_train, \n y_train, \n train_sizes=np.linspace(0.05, 1, 20),\n cv=cv,\n scoring=scoring,\n n_jobs=-1\n )\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n\n plt.plot(train_sizes, train_scores_mean, label = 'Train')\n plt.fill_between(train_sizes, \n train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, \n alpha=0.1)\n\n plt.plot(train_sizes, test_scores_mean, label = 'Val')\n plt.fill_between(train_sizes, \n test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, \n alpha=0.1)\n plt.legend()\n plt.ylabel('score')\n plt.xlabel('train sizes')\n if scoring=='recall':\n plt.ylim(0.6, 1)","title":""},{"docid":"0c249dd68937df33a9202e5c3cded280","score":"0.63328385","text":"def _draw(train_accuracy_list, valid_accuracy_list):\n fig, ax = plt.subplots(1, 1)\n x_axis = np.arange(1, 21)\n ax.plot(x_axis, train_accuracy_list, label='Training')\n ax.plot(x_axis, valid_accuracy_list, label='Validation')\n ax.set_xlabel('K value')\n ax.set_ylabel('Accuracy')\n plt.legend(loc='best')\n plt.show()\n plt.close()","title":""},{"docid":"b853c8a7b0b345faed311cf62880933d","score":"0.633202","text":"def plot_history(history=None):\r\n acc = history.history['acc']\r\n val_acc = history.history['val_acc']\r\n loss = history.history['loss']\r\n val_loss = history.history['val_loss']\r\n epochs = range(1, len(acc) + 1)\r\n plt.plot(epochs, acc, 'bo', label='Training acc')\r\n plt.plot(epochs, val_acc, 'r', label='Validation acc')\r\n plt.title('Training and Validation accuracy')\r\n plt.legend()\r\n\r\n plt.figure()\r\n plt.plot(epochs, loss, 'bo', label='Training loss')\r\n plt.plot(epochs, val_loss, 'r', label='Validation loss')\r\n plt.title('Training and Validation loss')\r\n plt.legend()\r\n plt.show()","title":""},{"docid":"a4919aa76c64c12df39f0a238207283a","score":"0.63313687","text":"def plot_losses(self):\n\n plot_x = range(len(self.losses_log['train']))\n\n for type_data, type_plot, color, marker in [\n ('train', plt.plot, None, None),\n ('valid', plt.plot, None, None),\n ('saved', plt.scatter, 'g', 'x'),\n ]:\n if type_data in self.losses_log.keys():\n plot_y = self.losses_log[type_data]\n if len(plot_y) > 0:\n type_plot(plot_x, plot_y, label=type_data, c=color, marker=marker)\n\n\n plt.legend()\n plt.ylabel('loss')\n plt.xlabel('epoch')\n plt.grid()\n plt.gcf().patch.set_facecolor('white')","title":""},{"docid":"0cc10a02de0562854d72dc0e342be7f5","score":"0.6329032","text":"def plot_learning_curve(classifier, X, y, measurements=[0.1, 0.325, 0.55, 0.775, 1.], metric=None, n_jobs=-1,\n save_to_folder=os.path.join(os.path.dirname(__file__), \"ExperimentResults\")):\n sns.set_style(\"whitegrid\")\n\n plt.figure()\n plt.title(\"Learning curves\")\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n train_sizes, train_scores, test_scores = skms.learning_curve(classifier, X, y, n_jobs=n_jobs,\n train_sizes=measurements, scoring=metric)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\n\n plt.legend(loc=\"best\")\n\n file_name = _get_file_name(\"\", classifier, \"learning_curve\", \"png\")\n if not os.path.exists(save_to_folder):\n os.mkdir(save_to_folder)\n plt.savefig(os.path.join(save_to_folder, file_name))\n plt.close()","title":""},{"docid":"93c89bc0818f3dd895b6dce09e41d5e7","score":"0.63228965","text":"def plot_acc_and_loss():\n # Accuracy results per epoch for training and validation data sets\n acc = history.history['accuracy']\n val_acc = history.history['val_accuracy']\n\n # Loss results per epoch for training and validation data sets\n loss = history.history['loss']\n val_loss = history.history['val_loss']\n\n # Plot training and validation accuracy\n plt.figure(figsize=(8, 8))\n plt.subplot(1, 2, 1)\n plt.plot(range(len(acc)), acc, color='#9ACD32',\n lw=3, label='Training Accuracy')\n plt.plot(range(len(acc)), val_acc, color='#1E90FF',\n lw=3, label='Validation Accuracy')\n plt.xlabel(\"Epoch\")\n plt.legend(loc='lower right')\n plt.title('Training and Validation Accuracy')\n\n # Plot training and validation loss\n plt.subplot(1, 2, 2)\n plt.plot(range(len(loss)), loss, color='#9ACD32',\n lw=3, label='Training Loss')\n plt.plot(range(len(loss)), val_loss, color='#1E90FF',\n lw=3, label='Validation Loss')\n plt.xlabel(\"Epoch\", labelpad=8)\n plt.legend(loc='lower left')\n plt.title('Training and Validation Loss')\n\n plt.savefig('acc_and_loss.png')","title":""},{"docid":"7128363289948844668684bb1c175725","score":"0.63189256","text":"def Plot_Lift_Curve(X, y, Estimators, Target_Class = None, Number_of_Splits = 50, Train_Test_Split = True, Test_Size =0.3, Random_State = None, Stratify = None):\n #Split the Data,if wanted, so that results aren't subject to overfitting\n #Set up figure for axes\n \n if Train_Test_Split:\n X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = Test_Size, random_state = Random_State, stratify = Stratify)\n else:\n X_train, X_test, y_train, y_test = X, X, y, y\n\n \n \n \n if isinstance(Estimators, (list, type(np.array))):\n number_of_axes = len(Estimators)\n \n if number_of_axes%2 ==0:\n length_axis_0 = number_of_axes/2\n else:\n length_axis_0 = (number_of_axes//2)+1\n\n fig, axes = plt.subplots(nrows = int(length_axis_0), ncols= 2, sharex= False, sharey= False, figsize = (12,int(length_axis_0)*4)) \n\n\n else:\n Estimators = np.array([Estimators])\n fig, axes = plt.subplots(1,1, figsize = (12,8))\n axes = np.array([axes])\n \n for ax, Estimator in zip(axes.ravel(), Estimators):\n\n #Fit the model\n model = Estimator.fit(X_train,y_train)\n\n #Get predicted classifications\n predictions = model.predict(X_test)\n #Predict scores so that samples can be ranked\n if hasattr(model, \"predict_proba\"):\n prediction_certainties_NDA = model.predict_proba(X_test)\n\n elif hasattr(model, \"decision_function\"):\n prediction_certainties_NDA = model.decision_function(X_test)\n\n else:\n print(\"Model doesn't have attribute: decision_function or predict_proba\")\n\n #Find which column in predictions to look at for ranking\n class_index = np.where(model.classes_ == Target_Class)[0][0]\n\n #Get the target column to rank by certainty\n if prediction_certainties_NDA.ndim >1:\n prediction_certainty_array = prediction_certainties_NDA[:, class_index]\n else:\n prediction_certainty_array = prediction_certainties_NDA\n\n\n #Get indicies of predictions sorted by certainty of correct classification\n sorted_certainty_indicies = np.argsort(prediction_certainty_array)\n \n #Rank the actual labels by certainty they are of class: \"Target_Class\"\n sorted_actual_labels = y_test[sorted_certainty_indicies]\n \n\n class_prior = sum(y_test == Target_Class)/len(y_test)\n \n\n def Lift(Sorted_Labels_, Portion_Targeted_, Class_Prior_, Target_Class_):\n\n #Work out how many samples we want to target\n number_to_target = np.floor(Portion_Targeted_ * len(Sorted_Labels_))\n number_to_target = int(number_to_target)\n\n #Create boolean for who we correctly targeted\n correctly_targeted_bool = (Sorted_Labels_[:number_to_target] == Target_Class_)\n \n #How many did we get correct\n number_correctly_classified = np.sum(correctly_targeted_bool)\n \n\n #Calculate lift\n lift_score = (number_correctly_classified)/(number_to_target*Class_Prior_) #Denominator is how many we would have got correct if random\n\n return lift_score\n\n #initialise list where lift scores go \n lift_scores_list = [] \n \n X_percent_targeted = np.arange(0,100, 100/Number_of_Splits)\n\n for i in X_percent_targeted:\n portion = (i+1)/(100)\n lift = Lift(Sorted_Labels_ = sorted_actual_labels, Portion_Targeted_ = portion, Class_Prior_ = class_prior, Target_Class_= Target_Class)\n\n lift_scores_list.append(lift)\n \n \n ax.plot(X_percent_targeted, lift_scores_list, \"r-\")\n \n ax.set_xlabel(\"Percentage of people Targeted\")\n ax.set_title(\"Lift Curve for: {}\".format(Estimator))\n ax.set_ylabel(\"Lift\")\n ax.set_yticks(np.arange(max(lift_scores_list)+2)) \n plt.tight_layout()\n\n\n return fig","title":""},{"docid":"119d8040d186ceac358330dcb003c955","score":"0.63074523","text":"def plot_model_training_history(history_object):\n plt.title('model accuracy')\n plt.ylabel('accuracy')\n plt.xlabel('epoch')\n plt.plot(history_object.history['loss'])\n plt.plot(history_object.history['val_loss'])\n plt.legend(['train', 'test'], loc='upper left')\n plt.show()","title":""},{"docid":"ce99cb5b11ba6587b49c039efbe3daaa","score":"0.63073224","text":"def plot_accuracy(self):\n plt.clf() # clear figure\n history_dict = self.history.history\n history_dict.keys()\n acc = history_dict['acc']\n val_acc = history_dict['val_acc']\n epochs = range(1, len(acc) + 1)\n plt.plot(epochs, acc, 'bo', label='Training acc')\n plt.plot(epochs, val_acc, 'b', label='Validation acc')\n plt.title('Training and validation accuracy')\n plt.xlabel('Epochs')\n plt.ylabel('Accuracy')\n plt.legend()\n\n plt.show()","title":""},{"docid":"77cae9211f0e7d8112b5a2eeeec7b228","score":"0.63038343","text":"def plot_learning_curve(model, X_train, y_train, name='test', scoring='recall'):\n\n \n cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=42)\n\n train_sizes, train_scores, test_scores = learning_curve(model.best_estimator_, \n X_train, \n y_train, \n train_sizes=np.linspace(0.05, 1, 20),\n cv=cv,\n scoring=scoring,\n n_jobs=-1\n )\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n\n plt.plot(train_sizes, train_scores_mean, label = 'Train')\n plt.fill_between(train_sizes, \n train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, \n alpha=0.1)\n\n plt.plot(train_sizes, test_scores_mean, label = 'Val')\n plt.fill_between(train_sizes, \n test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, \n alpha=0.1)\n plt.legend()\n plt.ylabel('score')\n plt.xlabel('train sizes')\n if scoring=='recall':\n plt.ylim(0.6, 1)\n plt.savefig('../tree_figs/' + name + '.png', bbox_inches='tight')\n plt.close()","title":""},{"docid":"3e6d91618a6b15768221aadda489d80c","score":"0.6299842","text":"def learning_curve(ww_training, rw_training, ww_test, rw_test):\n np.random.shuffle(ww_training)\n np.random.shuffle(rw_training)\n dif_int = len(ww_training) - len(rw_training)\n if dif_int < 0:\n dif_int = 0\n lower_size_int = len(ww_training) - dif_int\n accuracy_array = np.empty((lower_size_int, 2))\n \n for n_trial in range(1, lower_size_int + 1):\n accuracy_array[n_trial - 1] = [n_trial, 100 * experiment(ww_training[:n_trial], rw_training[:n_trial], ww_test, rw_test)]\n \n plt.title(\"Learning Curve\")\n plt.xlabel(\"Number of Training Items\")\n plt.ylabel(\"Accuracy (%)\")\n plt.plot(accuracy_array[:, 0], accuracy_array[:, 1])","title":""},{"docid":"e7530cb297483c53f83dc100d73434ae","score":"0.6299744","text":"def train(self):\n\n steps = []\n print(\"Training the agent...\")\n for i in tqdm(range(self.n_episodes)):\n # let agent play\n episode_stats = self.play()\n steps.append(episode_stats[\"steps\"])\n\n if i % 10 == 0:\n self.eps = self.eps - i * self.reduction\n\n # learn from experience\n self.learn()\n\n plt.plot(steps)\n plt.show()","title":""},{"docid":"51d14e0aebc445409e2ce9b5ec0dc91a","score":"0.6295639","text":"def training_curve(train_loss, perc_iter, val_loss, psnr, ssim, x, lr, epoch, iters_per_epoch, \n fig: matplotlib.figure.Figure, axis: matplotlib.axes.Axes, linewidth=0.25):\n # Linear scale of loss curve\n ax = axis[0]\n ax.clear()\n line1, = ax.plot(x, val_loss, label=\"Validation Loss\", color='red', linewidth=linewidth)\n line2, = ax.plot(x, train_loss, label=\"Train Loss\", color='blue', linewidth=linewidth)\n ax.plot(x, np.repeat(np.amin(val_loss), len(x)), linestyle=':', linewidth=linewidth)\n ax.set_xlabel(\"Epoch(s) / Iteration: {}\".format(iters_per_epoch))\n ax.set_ylabel(\"Image Loss\")\n ax.set_title(\"Loss\")\n\n if not np.isnan(perc_iter).all():\n ax = axis[4]\n ax.clear()\n line4, = ax.plot(x, perc_iter, label=\"Perceptual Loss\", color='green', linewidth=linewidth)\n ax.set_ylabel(\"Perceptual Loss\")\n\n ax.legend(handles=(line1, line2, line4, )) if not np.isnan(perc_iter).all() else ax.legend(handles=(line1, line2, ))\n\n # Log scale of loss curve\n ax = axis[1]\n ax.clear()\n line1, = ax.plot(x, val_loss, label=\"Validation Loss\", color='red', linewidth=linewidth)\n line2, = ax.plot(x, train_loss, label=\"Train Loss\", color='blue', linewidth=linewidth)\n ax.plot(x, np.repeat(np.amin(val_loss), len(x)), linestyle=':', linewidth=linewidth)\n ax.set_xlabel(\"Epoch(s) / Iteration: {}\".format(iters_per_epoch))\n ax.set_yscale('log')\n ax.set_title(\"Loss(Log scale)\")\n\n if not np.isnan(perc_iter).all():\n ax = axis[5]\n ax.clear()\n line4, = ax.plot(x, perc_iter, label=\"Perceptual Loss\", color='green', linewidth=linewidth)\n ax.set_ylabel(\"Perceptual Loss\")\n\n ax.legend(handles=(line1, line2, line4, )) if not np.isnan(perc_iter).all() else ax.legend(handles=(line1, line2, ))\n\n # Linear scale of PSNR, SSIM\n ax = axis[2]\n ax.clear()\n line1, = ax.plot(x, psnr, label=\"PSNR\", color='blue', linewidth=linewidth)\n ax.plot(x, np.repeat(np.amax(psnr), len(x)), linestyle=':', linewidth=linewidth)\n ax.set_xlabel(\"Epochs(s) / Iteration: {}\".format(iters_per_epoch))\n ax.set_ylabel(\"Average PSNR\")\n ax.set_title(\"Validation Performance\")\n\n ax.legend(handles=(line1, ))\n\n # Learning Rate Curve\n ax = axis[3]\n ax.clear()\n line1, = ax.plot(x, lr, label=\"Learning Rate\", color='cyan', linewidth=linewidth)\n ax.set_xlabel(\"Epochs(s) / Iteration: {}\".format(iters_per_epoch))\n ax.set_title(\"Learning Rate\")\n ax.set_yscale('log')\n\n ax.legend(handles=(line1, ))\n \n return fig, axis","title":""},{"docid":"7f041d83c1a08843dffef745d5adbcd2","score":"0.62874764","text":"def training(override=None):\n name = BEST if override is None else override\n data = read(name)\n\n plt.figure(1)\n\n size = 5\n chunks = list(chunk_list(data[:, 1], size))\n averages = [sum(chunk) / len(chunk) for chunk in chunks]\n\n plt.plot(range(0, len(data), size), averages)\n plt.axhline(y=200, color='g', linestyle='--')\n\n try:\n success = [i[0] for i in data if i[2] >= 200][0] - 200\n plt.axvline(x=success, color='r')\n except IndexError:\n print('[WARN] Learner did not succeed')\n\n plt.title('Learning Performance')\n plt.xlabel('Episode')\n plt.ylabel('Episode Total Reward (Averaged Per {})'.format(size))\n\n plt.savefig(PLOT_DIR + 'training.{}.png'.format(name))","title":""},{"docid":"b9e766f64fb7f208f87f0d8d13661b12","score":"0.6285915","text":"def plot(self, **kwargs):\n plt.plot(self.epochs, self.kl_weights, **kwargs)\n plt.xlabel(\"Epoch\")\n plt.ylabel(\"KL Loss Weight\")","title":""},{"docid":"4e8b3436ae7471fa1b6c26da3f9311fc","score":"0.6276074","text":"def plot_loss(train_log,valid_log):\n \n num_log = len(train_log) # number of element in input array\n \n fig = plt.figure()\n fig.suptitle(\"Loss vs. Epoch\")\n ax = fig.add_subplot(111)\n ax.set_xlabel(\"Epoch\")\n ax.set_ylabel(\"Loss\")\n ax.plot(np.arange(0,num_log),train_log,'r-',label='train_loss')\n ax.plot(np.arange(0,num_log),valid_log,'b-',label='validation_loss')\n ax.legend(loc=4,prop={'size':15})","title":""},{"docid":"e17734e3a4bdc51a40a4b05d75d42eb6","score":"0.6271056","text":"def part_b(t_lst, loss_test_lst, loss_train_lst):\n\n plt.figure()\n plot_graph(loss_test_lst, t_lst, \"q5_part_b_test\", \"\", \"Iteration vs Loss Test\", \"Loss\", \"Iteration\")\n plt.figure()\n plot_graph(loss_train_lst, t_lst, \"q5_part_b_train\", \"\", \"Iteration vs Loss Train\", \"Loss\", \"Iteration\")","title":""},{"docid":"ca9b7d62ac761328a408f091d495eed2","score":"0.62491584","text":"def populate_graph():\n \"\"\"\n plt.figure()\n x = [2,3,4,5,6,7,8,9,10,11,12]\n #train = [0.989662, 0.997180, 0.999060, 0.999060, 0.999060, 1.0, 1.0]\n #test = [0.939850, 0.954887, 0.939850, 0.947368, 0.947368, 0.932331, 0.939850]\n train = [0.986842,0.996241,0.997180,0.998120,0.999060,0.999060,0.999060,0.999060,0.999060,0.999060,0.999060]\n test = [0.932331,0.932331,0.947368,0.947368,0.947368,0.947368,0.947368,0.954887,0.954887,0.954887,0.954887]\n \n\n plt.plot(x,train,'-r', label=\"Train Accuracy\")\n plt.plot(x,test,'-b', label=\"Test Accuracy\")\n plt.title(\"TF-IDF Passes vs Accuracy\")\n plt.xlabel(\"Passes\")\n plt.ylabel(\"Accuracy\")\n plt.axis([2,12,0.93,1.02])\n plt.legend(loc=\"center right\")\n plt.show()\n plt.close()\n \"\"\"\n plt.figure()\n x=[1,2,3,4,5,6,7,8]\n train =[0.965226,0.985902,0.989662,0.990602,0.995301,0.997241,1.0,1.0]\n test = [0.932331,0.954887,0.954887,0.954887,0.962406,0.962406,0.962406,0.962406]\n plt.plot(x,train,'-r', label=\"Train Accuracy\")\n plt.plot(x,test,'-b', label=\"Test Accuracy\")\n plt.title(\"Passes vs Accuracy\")\n plt.xlabel(\"Passes\")\n plt.ylabel(\"Accuracy\")\n plt.axis([1,8,0.93,1.02])\n plt.legend()\n plt.show()\n plt.close()","title":""},{"docid":"d35ac155396f7209e187ac4f38b8de15","score":"0.6243018","text":"def plot_graphs(self):\n\n plt.plot(self.history.history[self.metric])\n plt.plot(self.history.history['val_' + self.metric], '')\n plt.xlabel(\"Epochs\")\n plt.ylabel(self.metric)\n plt.legend([self.metric, 'val_'+self.metric])\n plt.show()","title":""},{"docid":"33e2f6a994e0ade5ccad224d56f48b35","score":"0.6230619","text":"def neural_net_implementation(x, y, tag=\"\"):\n from keras.models import Sequential\n from keras.layers import Dense\n from keras.callbacks import EarlyStopping\n from sklearn.model_selection import train_test_split\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n\n model = Sequential()\n model.add(Dense(50, input_dim=x.shape[1], activation='relu'))\n model.add(Dense(150, activation='relu'))\n model.add(Dense(1))\n model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])\n monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, verbose=1, mode='auto')\n history = model.fit(x, y, validation_data=(x_test, y_test), callbacks=[monitor], verbose=0, epochs=5000)\n\n score, acc = model.evaluate(x_test, y_test)\n print(\"Score (RMSE) : {}\".format(score))\n print(\"accuracy is :{}\".format(acc))\n\n df = pd.DataFrame(history.history)\n df.plot(subplots=True, grid=True, figsize=(10, 15))\n plt.savefig(tag)","title":""},{"docid":"b108737f98f9f52d38eb216dd1cb473d","score":"0.6223654","text":"def plot_learning_curve(estimator,fitted_dt, title, X, y, ylim=None, cv=None,\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 10)):\n\n train_sizes, train_scores, test_scores = learning_curve(\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n\n return train_sizes, train_scores_mean, test_scores_mean","title":""}],"string":"[\n {\n \"docid\": \"c021a9f6d6801419b68365d93bbefc7a\",\n \"score\": \"0.7735428\",\n \"text\": \"def plot_learning_curve(training_losses, validation_losses): \\n plt.ylabel('Loss')\\n plt.xlabel('Training Steps')\\n plt.plot(training_losses, label=\\\"training\\\")\\n plt.plot(validation_losses, label=\\\"validation\\\")\\n plt.legend(loc=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78275ffe2d1cb5c5812d89bcae3e4d5d\",\n \"score\": \"0.75846165\",\n \"text\": \"def plot_learning_curve(self):\\n plt.figure()\\n plt.plot(range(self.noi),self.Jv)\\n plt.xlabel('Number of iterations')\\n plt.ylabel('Cost')\\n plt.title('Cost vs Number of iterations curve')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cf983abe2d065dbae806a52cd1ff8b9\",\n \"score\": \"0.73652726\",\n \"text\": \"def plot_lr(learning_rates):\\n plt.plot(learning_rates)\\n plt.title('Change of learning rate over time')\\n plt.xlabel('Epoch')\\n plt.ylabel('Learning rate')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90a600749107f22359b2581e8270d052\",\n \"score\": \"0.73337096\",\n \"text\": \"def plot(self):\\n fig = plt.figure(figsize=[10, 4])\\n\\n plt.subplot(121)\\n self.plot_lr()\\n plt.xlabel('n iter')\\n plt.ylabel('learning rate')\\n\\n plt.subplot(122)\\n self.plot_loss(c='r')\\n plt.xlabel('n iter')\\n plt.ylabel('loss')\\n fig.subplots_adjust(wspace=0.3, bottom=0.18)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9bb8a2098f33e2fb9265a305f1f6e3c\",\n \"score\": \"0.7284015\",\n \"text\": \"def train_model(self) -> None:\\n self.NN = NeuralNetwork()\\n costs = self.network_training(self.NN)\\n plt.plot(costs)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f3c8bb6177e1a0774176957da2bbbe5\",\n \"score\": \"0.7243582\",\n \"text\": \"def plot_learning_curve(MODEL):\\n train_accs = np.squeeze(MODEL['train_accs'])\\n test_accs = np.squeeze(MODEL['test_accs'])\\n fig, ax = plt.subplots()\\n ax.plot(100 - train_accs, label='Training set error')\\n ax.plot(100 - test_accs, label='Test set error')\\n ax.set_ylabel('Prediction error (%)')\\n plt.legend()\\n ax.set_xlabel('Hundreds of iterations')\\n ax.set_title(\\\"Learning rate =\\\" + str(MODEL[\\\"learning_rate\\\"]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb76bdc2341b630ed30417dc3534fcc\",\n \"score\": \"0.72194725\",\n \"text\": \"def learning_curves(X_train, y_train, X_test, y_test):\\n \\n print \\\"Creating learning curve graphs for max_depths of 1, 3, 6, and 10.....\\\"\\n \\n # Create the figure window\\n fig = plt.figure(figsize=(10,8))\\n \\n # We will vary the training set size so that we have 50 different sizes.\\n sizes = np.rint(np.linspace(1, len(X_train),50)).astype(int)\\n train_err = np.zeros(len(sizes))\\n test_err = np.zeros(len(sizes))\\n \\n # Create four different models based on max_depth\\n\\n for k, depth in enumerate([1,3,6,10]):\\n \\n for i,s in enumerate(sizes):\\n \\n # Setup a decision tree regressor so that it learns a tree with max_depth = depth\\n regressor = DecisionTreeRegressor(max_depth =depth)\\n \\n # Fit the learner to the training data\\n regressor.fit(X_train[:s], y_train[:s])\\n \\n # Find the performance on the training set\\n train_err[i] = performance_metric(y_train[:s],regressor.predict(X_train[:s]))\\n \\n # Find the performance on the testing set\\n test_err[i] = performance_metric(y_test,regressor.predict(X_test))\\n \\n # Subplot the learning curve graph\\n ax = fig.add_subplot(2,2, k+1)\\n ax.plot(sizes,test_err, lw = 2, label = \\\"Testing Error\\\")\\n ax.plot(sizes, train_err, lw = 2, label = \\\"Training Error\\\")\\n ax.legend()\\n ax.set_title('max_depth = %s'%(depth))\\n ax.set_xlabel(\\\"Number of Data Points in Training Set\\\")\\n ax.set_ylabel('Total Error')\\n ax.set_xlim([0, len(X_train)])\\n \\n # Visual aesthetics\\n fig.suptitle('Decision Tree Regressor Learning Performances', fontsize = 18, y=1.03)\\n fig.tight_layout()\\n fig.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adf43744f83cac76d420fe7db3bf1b65\",\n \"score\": \"0.71618503\",\n \"text\": \"def plot_learning(train_loss, eval_loss, path_to_save):\\n plt.plot(train_loss)\\n plt.plot(eval_loss)\\n plt.legend(['Train loss', 'Dev loss'])\\n path = os.path.join(path_to_save, 'learning_plot.png')\\n plt.savefig(path)\\n plt.clf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be3c9091e702298fdee0c3a0ddd65f60\",\n \"score\": \"0.70671624\",\n \"text\": \"def show_learning_curves(rew_matrix, naive_rew):\\n plt.plot(range(len(rew_matrix[0])), rew_matrix[0], \\\"-b\\\", label = \\\"Feature 1\\\")\\n plt.plot(range(len(rew_matrix[1])), rew_matrix[1], \\\"-g\\\", label = \\\"Feature 7\\\")\\n plt.plot(range(len(rew_matrix[2])), rew_matrix[2], \\\"-r\\\", label = \\\"Feature 12\\\")\\n plt.plot(range(len(rew_matrix[3])), rew_matrix[3], \\\"-c\\\", label = \\\"Feature 13\\\")\\n plt.plot(range(len(naive_rew)), naive_rew, \\\"-m\\\", label = \\\"Naive policy\\\")\\n plt.xlabel(\\\"Episode\\\")\\n plt.ylabel(\\\"Reward (learning phase)\\\")\\n plt.legend()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f23da3e535be143870e33a7c2b44233\",\n \"score\": \"0.70591086\",\n \"text\": \"def draw_learning_curve(numbers, metric):\\r\\n\\r\\n plt.xlabel('Simulation Epoch')\\r\\n plt.ylabel(metric)\\r\\n plt.title('Learning Curve')\\r\\n plt.grid(True)\\r\\n\\r\\n plt.plot(numbers['x'], numbers[metric], 'r', lw=1)\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a2e3d953d6903dd83eada7bc67add9c\",\n \"score\": \"0.6997576\",\n \"text\": \"def plot_learning_curve(classification_method, classification_method_params, data, labels):\\n if classification_method == KNeighborsClassifier:\\n train_sizes, train_scores, valid_scores = learning_curve(\\n classification_method(n_neighbors=classification_method_params[0]), data, labels,\\n train_sizes=np.linspace(0.1, 1.0, 25))\\n elif classification_method == LogisticRegression:\\n train_sizes, train_scores, valid_scores = learning_curve(\\n classification_method(C=classification_method_params[0]), data, labels,\\n train_sizes=np.linspace(0.1, 1.0, 25))\\n else:\\n train_sizes, train_scores, valid_scores = learning_curve(\\n classification_method(), data, labels, train_sizes=np.linspace(0.1, 1.0, 25))\\n plt.figure()\\n plt.title(\\\"Learning Curve ({0})\\\".format(classification_method.__name__))\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Score\\\")\\n plt.grid()\\n # Compute the mean and std to also plot them on the learning curve.\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(valid_scores, axis=1)\\n test_scores_std = np.std(valid_scores, axis=1)\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std,\\n alpha=0.1, color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std,\\n alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\", label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\", label=\\\"Cross-validation score\\\")\\n plt.legend(loc=\\\"best\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9af31db2b0dee82bc1bf530fd754f4a6\",\n \"score\": \"0.69334346\",\n \"text\": \"def plot_cost_and_acc(self):\\n x = list(range(1, len(self.cost_hist_tr) + 1))\\n plt.plot(x, self.cost_hist_tr, label = \\\"train loss\\\")\\n plt.plot(x, self.cost_hist_val, label = \\\"val loss\\\")\\n plt.title(\\\"Loss over epochs\\\")\\n plt.xlabel(\\\"Epochs\\\")\\n plt.ylabel(\\\"Loss\\\")\\n plt.legend()\\n plt.show()\\n plt.plot(x, self.acc_hist_tr, label = \\\"Train accuracy\\\")\\n plt.plot(x, self.acc_hist_val, label = \\\"Val accuracy\\\")\\n plt.title(\\\"Accuracy over epochs\\\")\\n plt.xlabel(\\\"Epochs\\\")\\n plt.ylabel(\\\"Accuracy\\\")\\n plt.legend()\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d508a117025eb2f127cf2a40bab7e917\",\n \"score\": \"0.69325346\",\n \"text\": \"def plot(self):\\n plt.clf()\\n\\n plt.plot(np.linspace(0, 1, self.N+1), self.sb_policy, c='tab:orange', label=f\\\"{self.model_name} Learner\\\")\\n plt.plot(np.linspace(0, 1, self.N+1), self.vi_policy, c='B', label=\\\"Value Iteration\\\")\\n\\n plt.title(\\n f\\\"ACER Train Time: {self.sb_train_time}, VI Train Time: {self.vi_train_time}\\\\n\\\"\\n f\\\"tt/ts: {self.params['movement_cost']}/{self.params['sample_cost']}, \\\"\\n f\\\"N: {int(1/self.params['delta'])}\\\")\\n plt.xlabel(\\\"Size of Hypothesis Space\\\")\\n plt.ylabel(\\\"Gym Policy\\\")\\n plt.legend()\\n\\n plt.xlim((0, 1))\\n plt.ylim((0, self.N))\\n\\n plt.savefig(self.img_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3490ab7802cd264199290c1f38fa36ba\",\n \"score\": \"0.6920023\",\n \"text\": \"def plot_learning_curves(x_train, y_train, x_test, y_test, clf):\\n errors = []\\n\\n def misclf_err(y_predict, y):\\n return (y_predict != y).sum() / float(len(y))\\n\\n rng = [int(i) for i in np.linspace(0, x_train.shape[0], 11)][1:]\\n for r in rng:\\n print(f\\\"Running {r / x_train.shape[0]:.0%}\\\")\\n clf.fit(x_train[:r], y_train[:r])\\n\\n y_test_predict = clf.predict(x_test)\\n\\n error = misclf_err(y_test, y_test_predict)\\n errors.append(error*100)\\n\\n _, ax = plt.subplots(1, 1, figsize=(8, 5))\\n ax.plot(np.arange(10, 101, 10), errors, label=\\\"Error\\\", marker=\\\"o\\\")\\n\\n ax.set_xlim([0, 110])\\n ax.set_xticks(np.arange(0, 101, 10))\\n ax.yaxis.set_major_formatter(PercentFormatter())\\n ax.xaxis.set_major_formatter(PercentFormatter())\\n\\n plt.ylabel(\\\"Error\\\")\\n plt.xlabel(\\\"Training set size in percent\\\")\\n plt.title(\\\"Learning Curve\\\")\\n plt.grid()\\n plt.tight_layout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ee3ef41896c39395171d975f95590ac\",\n \"score\": \"0.69093424\",\n \"text\": \"def plot_learning_rates():\\n # Choose some alpha value - change this\\n alpha = 0.\\n num_iters = 100\\n\\n # init theta and run gradient descent\\n theta = np.zeros(3)\\n theta, J_history = gradientDescentMulti(X, y, theta, alpha, num_iters)\\n\\n # Plot the convergence graph\\n pyplot.plot(np.arange(len(J_history)), J_history, lw=2)\\n pyplot.xlabel('Number of iterations')\\n pyplot.ylabel('Cost J')\\n\\n # Display the gradient descent's result\\n print('theta computed from gradient descent: {:s}'.format(str(theta)))\\n\\n # Estimate the price of a 1650 sq-ft, 3 br house\\n # ======================= YOUR CODE HERE ===========================\\n # Recall that the first column of X is all-ones.\\n # Thus, it does not need to be normalized.\\n\\n price = 0 # You should change this\\n\\n # ===================================================================\\n\\n print('Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): ${:.0f}'.format(price))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb6df2fde4efcba7faf73f33ac502db\",\n \"score\": \"0.6860948\",\n \"text\": \"def plot_cost_per_lr(X, y):\\n plt.figure()\\n plt.xlabel(\\\"Number of iterations\\\")\\n plt.ylabel(\\\"Cost J\\\")\\n\\n lr_list = [0.3, 0.1, 0.03, 0.01, 0.003, 0.001]\\n losses_per_lr_list = []\\n nb_iterations = 50\\n for i in range(len(lr_list)):\\n lr = lr_list[i]\\n model = Model(X.shape[1])\\n print('Training model using ', nb_iterations, 'iterations and learning rate=', lr)\\n model.train(nb_iterations, X, y, learning_rate=lr)\\n J_history = model.losses\\n losses_per_lr_list.append(J_history)\\n plt.plot(range(nb_iterations), losses_per_lr_list[i], label='alpha= {:.3f}'.format(lr))\\n plt.legend()\\n plt.savefig('figs/different_learning_rates')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1fd06e138e4cb2d23ac6cee1af944b8\",\n \"score\": \"0.6855946\",\n \"text\": \"def plotGraphs(history):\\n\\n acc = history.history['acc']\\n val_acc = history.history['val_acc']\\n\\n loss = history.history['loss']\\n val_loss = history.history['val_loss'] \\n\\n epochs = range(1, len(acc) + 1)\\n plt.plot(epochs, acc, 'bo', label='Training acc')\\n plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\\n plt.title('Training and validation accuracy')\\n plt.legend() \\n plt.figure() \\n\\n plt.plot(epochs, loss, 'bo', label='Training loss')\\n plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n plt.title('Training and validation loss')\\n plt.legend()\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"527395ebae2613580c45cadf981dc556\",\n \"score\": \"0.6851773\",\n \"text\": \"def plot_learning_curve(data, prefix):\\n mean, lower_bound, upper_bound = utils.mean_confidence_interval(data)\\n x_lim = len(data[0])\\n plt.plot(mean, color='red')\\n plt.fill_between(range(x_lim), lower_bound, upper_bound, color='red', alpha=0.4)\\n plt.xlabel(\\\"Episode\\\")\\n plt.ylabel(\\\"Avg. number of steps\\\")\\n plt.savefig(prefix + 'learning_curve.png')\\n plt.clf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a52cc6f6f07005cb6377c2a6082a5bf\",\n \"score\": \"0.6830098\",\n \"text\": \"def Graph_Costs_Over_Time(self):\\n plt.plot(np.squeeze(self.costs))\\n plt.ylabel('cost')\\n plt.xlabel('iterations (per tens)')\\n plt.title(\\\"Learning rate =\\\" + str(self.learning_rate))\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea36c3ff0efb092b09449baccd690860\",\n \"score\": \"0.6811258\",\n \"text\": \"def plot_objectives(self,betas_cyc,betas_rnd,X_train,y_train):\\n plt.figure(figsize = (10, 5))\\n plt.plot([self.computeobj(betas_cyc[::20][x], X_train, y_train) for x in range(50)], label=\\\"Cyclic Coordinate Descent\\\")\\n plt.plot([self.computeobj(betas_rnd[::20][x], X_train, y_train) for x in range(50)], label=\\\"Random Coordinate Descent\\\")\\n plt.xlabel('Iteration')\\n plt.ylabel('Objective value')\\n plt.title(\\\"Objective Value vs. Iteration (Lambda = 0.11069)\\\")\\n plt.legend()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb5711cfbe8dd3bb4c12d1f08b638d57\",\n \"score\": \"0.67987454\",\n \"text\": \"def plot_train_metrics(losses, train_acc, precision):\\n plt.figure(figsize=(10, 12))\\n plt.subplot(311)\\n plt.title('Loss')\\n plt.plot(losses, color='c')\\n plt.subplot(312)\\n plt.title('Accuracy')\\n plt.plot(train_acc, color='c')\\n plt.subplot(313)\\n plt.title('Precision')\\n plt.plot(precision, color='c')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f89459f9020a92452a96fa11188b218\",\n \"score\": \"0.6797175\",\n \"text\": \"def plot_learning(self, x, y):\\r\\n objective_iter = [self.f_beta(x, y, coef) for coef in self.coeff_list]\\r\\n fig = plt.figure()\\r\\n plt.plot(range(len(self.coeff_list)), objective_iter)\\r\\n plt.xlabel('Iteration')\\r\\n plt.ylabel('Objective Function')\\r\\n \\r\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f9ecb8ff30164dd210a092450c21446\",\n \"score\": \"0.67761576\",\n \"text\": \"def plot_loss(set_loss, training_loss, parameters, set_name): \\n\\n plt.clf();\\n \\n plt.plot(set_loss, color='k', label=set_name);\\n plt.plot(training_loss, color='r', linestyle=\\\":\\\", label=\\\"Training Set\\\");\\n\\n plt.legend(framealpha=1);\\n plt.yscale('log');\\n plt.title(f'{parameters[\\\"dataset\\\"]}');\\n plt.xlabel(\\\"Epochs\\\", fontsize=16);\\n plt.ylabel(\\\"Loss\\\", fontsize=16);\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5128e4f041fe882b585ae76b186b8636\",\n \"score\": \"0.6761979\",\n \"text\": \"def plot(history):\\n plt.plot(history.history['loss'])\\n plt.plot(history.history['val_loss'])\\n plt.title('model loss')\\n plt.ylabel('loss')\\n plt.xlabel('epoch')\\n plt.legend(['train','validation'],loc='upper right')\\n plt.show()\\n \\n # YOUR CODE HERE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3485b50690f2e171364716eaa60610c4\",\n \"score\": \"0.67561424\",\n \"text\": \"def plot_training(history, savedir):\\n\\n acc = history.history['acc']\\n val_acc = history.history['val_acc']\\n loss = history.history['loss']\\n val_loss = history.history['val_loss']\\n epochs = range(len(acc))\\n\\n plt.plot(epochs, acc, 'r.')\\n plt.plot(epochs, val_acc, 'r')\\n plt.title('Training and validation accuracy')\\n plt.savefig(f'{savedir}/acc_vs_epochs.png')\\n\\n plt.figure()\\n plt.plot(epochs, loss, 'r.')\\n plt.plot(epochs, val_loss, 'r-')\\n plt.title('Training and validation loss')\\n plt.savefig(f'{savedir}/loss_vs_epochs.png')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f37da37068724f3771e23f01ce0a585\",\n \"score\": \"0.67085624\",\n \"text\": \"def plot_learning_curve(X_train=None,\\n y_train=None,\\n X_test=None,\\n y_test=None,\\n kernel=None,\\n n_run=10,\\n title=\\\"Learning curve\\\"):\\n n_data = len(self.X_train)\\n n_samp = np.linspace(n_data//n_run, n_run*(n_data//n_run),\\n n_run, dtype=int)\\n est = import_module('sklearn.gaussian_process')\\n estimator = getattr(est, 'GaussianProcessRegressor')\\n\\n\\n _MAE_train = []\\n _MAE_test = []\\n for i, n in enumerate(n_samp):\\n xtrain = self.X_train[:n]\\n if self.scaling:\\n scaled_part_train = self.alpha * self.scaling_y_train[:n] \\\\\\n + self.gamma\\n scaled_part_test = self.alpha * self.scaling_y_test \\\\\\n + self.gamma\\n ytrain = self.y_train[:n] - scaled_part_train\\n estimator = estimator(kernel=kernel,\\n n_restarts_optimizer=4,\\n alpha=0)\\n estimator.fit(xtrain, ytrain)\\n _MAE_train += [mean_absolute_error(self.y_train[:n],\\n estimator.predict(xtrain) + scaled_part_train)]\\n _MAE_test += [mean_absolute_error(self.y_test,\\n estimator.predict(self.X_test) + scaled_part_test)]\\n kernel = estimator.kernel_\\n else:\\n ytrain = self.y_train[:n]\\n estimator = estimator(kernel=kernel,\\n n_restarts_optimizer=4,\\n alpha=0)\\n estimator.fit(xtrain, ytrain)\\n _MAE_train += [mean_absolute_error(ytrain,\\n estimator.predict(xtrain))]\\n _MAE_test += [mean_absolute_error(self.y_test,\\n estimator.predict(self.X_test))]\\n kernel = estimator.kernel_\\n\\n plt.figure()\\n plt.title(title)\\n plt.grid(color='b', linestyle='-', linewidth=0.5)\\n plt.xlabel(\\\"# of training samples\\\")\\n plt.ylabel(\\\"MAE [eV]\\\")\\n plt.plot(n_samp, _MAE_train, 'o-', color='r',\\n label=\\\"MAE for training set\\\")\\n plt.plot(n_samp, _MAE_test, 'o-', color='g',\\n label=\\\"MAE for testing set\\\")\\n plt.legend(loc='best')\\n\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"970f1b92c648d8455021f1d0b03638b0\",\n \"score\": \"0.6684306\",\n \"text\": \"def plot_learning_curves(estimators, titles, X, y, ylim=None, cv=None,\\n n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 2)):\\n nrows = len(estimators)//2\\n ncols = (len(estimators)//nrows)+ (0 if len(estimators) % nrows == 0 else 1)\\n plt.figure(1)\\n fig, axes = plt.subplots(nrows, ncols, figsize=(10, 10))\\n \\n n = 0\\n for col in range(ncols):\\n for row in range(nrows):\\n estimator = estimators[n]\\n title = titles[n]\\n axes[row, col].set_title(title) \\n if ylim is not None:\\n axes[row, col].set_ylim(*ylim) \\n axes[row, col].set_xlabel(\\\"Training examples\\\")\\n axes[row, col].set_ylabel(\\\"Score\\\") \\n train_sizes, train_scores, test_scores = learning_curve(estimator,\\n X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes,\\n scoring=\\\"neg_mean_squared_error\\\") \\n train_scores = np.sqrt(-train_scores)\\n test_scores = np.sqrt(-test_scores) \\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n axes[row, col].grid() \\n axes[row, col].fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n axes[row, col].fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n axes[row, col].plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label=\\\"Training score\\\")\\n axes[row, col].plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label=\\\"Cross-validation score\\\")\\n axes[row, col].legend(loc=\\\"best\\\")\\n \\n n += 1\\n plt.tight_layout()\\n plt.show()\\n plt.gcf().clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b34e5cdd6c4c329bdb2d359077871f4\",\n \"score\": \"0.6667418\",\n \"text\": \"def create_train_plots(training_losses):\\n x = [i for i in range(len(training_losses))]\\n plt.clf()\\n plt.plot(x, training_losses)\\n plt.title('Training Loss per epoch')\\n plt.xlabel('Epoch')\\n plt.ylabel('Loss')\\n plt.xticks(np.arange(1, len(x)+1, 1))\\n plt.savefig('../results/train_plot.png')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8d6a87f7ef5c34e3df5280d349da6fe\",\n \"score\": \"0.6653885\",\n \"text\": \"def plot_training(hist, i: int):\\n acc = hist.history[\\\"acc\\\"]\\n val_acc = hist.history[\\\"val_acc\\\"]\\n epochs = range(len(acc))\\n plt.plot(epochs, acc, label=\\\"Training Accuracy\\\")\\n plt.plot(epochs, val_acc, label=\\\"Validation Accuracy\\\")\\n plt.title(\\\"Training and Validation Accuracy\\\")\\n plt.xlabel(\\\"Epochs\\\")\\n plt.ylabel(\\\"Accuracy\\\")\\n plt.title(f\\\"Model {i} Training Accuracy\\\")\\n plt.savefig(f\\\"figs/{experiment_name}/training_accuracy_m{i}\\\")\\n plt.figure()\\n loss = hist.history[\\\"loss\\\"]\\n val_loss = hist.history[\\\"val_loss\\\"]\\n plt.plot(epochs, loss, label=\\\"Training Loss\\\")\\n plt.xlabel(\\\"Epochs\\\")\\n plt.ylabel(\\\"Loss\\\")\\n plt.plot(epochs, val_loss, label=\\\"Validation Loss\\\")\\n plt.title(f\\\"Model {i} Training Loss\\\")\\n plt.savefig(f\\\"figs/{experiment_name}/training_loss_m{i}.png\\\")\\n plt.figure()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7bcff285ed14d3c4bfbe639a640bd82\",\n \"score\": \"0.6644706\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n\\t\\t\\t\\t\\t\\tn_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\\n\\tplt12.figure()\\n\\tplt12.title(title)\\n\\tif ylim is not None:\\n\\t\\tplt12.ylim(*ylim)\\n\\tplt12.xlabel(\\\"Training examples\\\")\\n\\tplt12.ylabel(\\\"Score\\\")\\n\\ttrain_sizes, train_scores, test_scores = learning_curve(\\n\\t\\testimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\n\\ttrain_scores_mean = np.mean(train_scores, axis=1)\\n\\ttrain_scores_std = np.std(train_scores, axis=1)\\n\\ttest_scores_mean = np.mean(test_scores, axis=1)\\n\\ttest_scores_std = np.std(test_scores, axis=1)\\n\\tplt12.grid()\\n\\n\\t#plt12.fill_between(train_sizes, train_scores_mean - train_scores_std,train_scores_mean + train_scores_std, alpha=0.1, color=\\\"r\\\")\\n\\t#plt12.fill_between(train_sizes, test_scores_mean - test_scores_std,test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n\\tplt12.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n\\t\\t\\t label=\\\"Training score\\\")\\n\\tplt12.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n\\t\\t\\t label=\\\"Cross-validation score\\\")\\n\\n\\tplt12.legend(loc=\\\"best\\\")\\n\\treturn plt12\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2541e6e3dd6ee6d202aee516cd6f428b\",\n \"score\": \"0.6633816\",\n \"text\": \"def plot_learning(\\n data, # dictionary of dictionaries of data\\n title = None, # title for plot\\n iter_key = \\\"iter\\\", # iteration key word\\n val_key = \\\"f1\\\", # validation key word\\n score_type = \\\"F1\\\", # score type for label name\\n linewidth = 1.0, # linewidth for plot\\n offset = 10, # pixel offset for axes\\n label_x_offset = 200, # x direction offset for label\\n label_y_offset = 1, # y direction offset for label\\n x_size = 10, # x size of figure\\n y_size = 5, # y size of figure\\n x_label = \\\"training iteration\\\", # name for x-axis\\n x_tick_int = 20000, # interval for ticks on x-axis\\n iterations = 100000, # max number of training iterations\\n y_label = \\\"score\\\", # name for y-axis\\n y_tick_int = 10, # interval for ticks on y-axis\\n max_score = 100 # max score\\n ):\\n \\n # Create plot figure\\n fig = plt.figure(figsize = (x_size, y_size))\\n ax = fig.add_subplot(111)\\n \\n # Plot Style\\n# =============================================================================\\n# plt.style.use('seaborn-deep')\\n# =============================================================================\\n \\n # Format x and y axes\\n plt.xlabel(x_label)\\n plt.xlim(0,iterations)\\n plt.xticks(np.arange(0,iterations+1,x_tick_int))\\n \\n plt.ylabel(y_label)\\n plt.ylim(0,max_score)\\n plt.yticks(np.arange(0,max_score+1,y_tick_int))\\n \\n # Adjust plot borders\\n ax.spines['right'].set_visible(False)\\n ax.spines['top'].set_visible(False)\\n ax.spines['left'].set_position(('outward', offset))\\n ax.spines['bottom'].set_position(('outward', offset))\\n \\n # Iterate through keys\\n for key in data.keys():\\n \\n assert key in colors.keys(), \\\"{} is not a support key in: {}\\\".format(key, colors.keys())\\n \\n # Get data set\\n experiment = data.get(key)\\n \\n # Get x and y values for plotting\\n temp_iter = experiment.get(iter_key)\\n temp_val = experiment.get(val_key)\\n \\n # Plot data on figure\\n ax.plot(temp_iter, temp_val, linestyle='-', marker=markers.get(key),\\n color=colors.get(key), mec='w', ms=8, clip_on=False)\\n \\n # Add dataset labels\\n plt.text(temp_iter[-1]+label_x_offset, temp_val[-1]-label_y_offset,\\n names.get(key)+\\\" \\\"+score_type, color=colors.get(key))\\n \\n # Add title\\n if not title is None:\\n plt.title(title)\\n \\n # Adjust layout\\n fig.tight_layout()\\n \\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8257df28ec55cf1b0eb568db6c4f055\",\n \"score\": \"0.661555\",\n \"text\": \"def print_cost(self):\\n costs = self.cost\\n plt.plot(np.squeeze(costs))\\n plt.ylabel('cost')\\n plt.xlabel('iterations (per hundreds)')\\n plt.title(\\\"Learning rate =\\\" + str(self.learning_rate))\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f77371b675f0504eed404be81751fdad\",\n \"score\": \"0.6612863\",\n \"text\": \"def show_learning_curve(file_paths: List[str], model_names: List[str], **kwargs) -> tuple:\\n import matplotlib.pyplot as plt\\n from matplotlib.ticker import MaxNLocator\\n from matplotlib.lines import Line2D\\n from matplotlib import colors as mcolors\\n\\n available_markers = list(Line2D.markers.keys())[2:]\\n available_colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS).keys())\\n\\n total_files = len(file_paths)\\n # print(available_markers)\\n # markers = available_markers[:total_files]\\n # markers_grouped = [markers[n:n + 2] for n in range(0, len(markers), 2)]\\n\\n markers = kwargs.get(\\\"markers\\\", [\\\"o\\\", \\\"v\\\", \\\"d\\\", \\\"x\\\", \\\"*\\\", \\\"+\\\", \\\"P\\\", \\\"s\\\"])\\n markers = markers[:total_files]\\n\\n if len(markers) != total_files:\\n raise ValueError(\\n f\\\"Not enough values for markers. markers contains {len(markers)} elements while it is supposed to\\\"\\n f\\\"contains at least {total_files} elements.\\\"\\n )\\n\\n colors = available_colors[:total_files]\\n\\n fig, axes = plt.subplots(1, 1, figsize=kwargs.get(\\\"figsize\\\", (8, 6)))\\n\\n for idx, (file_path, model_name, marker, color) in enumerate(zip(file_paths, model_names, markers, colors)):\\n history: dict = read_log_file(file_path=file_path, model_name=model_name)\\n\\n epochs: list = history['epoch']\\n\\n axes.plot(\\n epochs,\\n history['loss'],\\n marker=marker,\\n markersize=kwargs.get(\\\"markersize\\\", 4),\\n linestyle='-',\\n lw=1.8,\\n label=f'{model_name}',\\n color=color\\n )\\n\\n axes.plot(\\n epochs,\\n history['val_loss'],\\n marker=marker,\\n markersize=kwargs.get(\\\"markersize\\\", 4),\\n linestyle='--',\\n lw=1.8,\\n color=color\\n )\\n\\n fontsize = kwargs.get(\\\"font_size\\\", 14)\\n axes.set_ylabel('Loss [-]', fontsize=fontsize)\\n axes.set_xlabel('Epochs [-]', fontsize=fontsize)\\n axes.xaxis.set_major_locator(MaxNLocator(nbins=20, integer=True))\\n axes.set_yscale(kwargs.get(\\\"scale\\\", \\\"linear\\\"))\\n axes.tick_params(axis=\\\"both\\\", which=\\\"major\\\", labelsize=fontsize)\\n axes.legend(fontsize=fontsize)\\n axes.grid()\\n plt.tight_layout()\\n\\n if kwargs.get(\\\"save\\\", True):\\n os.makedirs(\\\"figures/\\\", exist_ok=True)\\n plt.savefig(kwargs.get(\\\"save_name\\\", f\\\"figures/model.png\\\"), dpi=300)\\n if kwargs.get(\\\"show\\\", True):\\n plt.show()\\n\\n return fig, axes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec1ab34b67710e721f77e0f04cb8e360\",\n \"score\": \"0.6609935\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\\n plt.figure()\\n plt.title(title)\\n if ylim is not None:\\n plt.ylim(*ylim)\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Score\\\")\\n train_sizes, train_scores, test_scores = learning_curve( estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes = train_sizes)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n \\n print(\\\"train_scores_mean:\\\")\\n print(np.mean(train_scores, axis=1))\\n print(\\\"test_scores_mean:\\\")\\n print (test_scores_mean)\\n\\n plt.grid()\\n\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label=\\\"Cross-validation score\\\")\\n\\n plt.legend(loc=\\\"best\\\")\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20205dff9ce7f52fbee690482eb3588c\",\n \"score\": \"0.66068953\",\n \"text\": \"def plot_learning_curve(self, estimator, title, X, y, save_file_path, ylim=None, cv=None,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\ttrain_sizes=np.linspace(.1, 1.0, 5)):\\n\\t\\tlogger.info(\\\"Plotting {}\\\".format(title))\\n\\t\\tplt.figure()\\n\\t\\tplt.title(title)\\n\\t\\tif ylim is not None:\\n\\t\\t\\tplt.ylim(*ylim)\\n\\t\\tplt.xlabel(\\\"Training examples\\\")\\n\\t\\tplt.ylabel(\\\"Score\\\")\\n\\t\\ttrain_sizes, train_scores, test_scores = learning_curve(\\n\\t\\t\\testimator, X, y, cv=cv, train_sizes=train_sizes)\\n\\t\\ttrain_scores_mean = np.mean(train_scores, axis=1)\\n\\t\\ttrain_scores_std = np.std(train_scores, axis=1)\\n\\t\\ttest_scores_mean = np.mean(test_scores, axis=1)\\n\\t\\ttest_scores_std = np.std(test_scores, axis=1)\\n\\t\\tplt.grid()\\n\\n\\t\\tplt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t color=\\\"r\\\")\\n\\t\\tplt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t color=\\\"g\\\")\\n\\t\\tplt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\", label=\\\"Training score\\\")\\n\\t\\tplt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\", label=\\\"Cross-validation score\\\")\\n\\n\\t\\tplt.legend(loc=\\\"best\\\")\\n\\t\\tplt.savefig(\\\"{}\\\".format(save_file_path))\\n\\t\\tplt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14dad150472da33b1b28071d6f9eba2c\",\n \"score\": \"0.6601268\",\n \"text\": \"def plot(self):\\r\\n points_to_plot = [[I[self.FEATURES_TO_PLOT[0]], I[self.FEATURES_TO_PLOT[1]], I[-1]] for I in self.TRAINING_DATA]\\r\\n self.plot_2d_points(points_to_plot)\\r\\n self.plot_weight_vectors(self.tp.W)\\r\\n plt.xlabel(self.FEATURES[self.FEATURES_TO_PLOT[0]])\\r\\n plt.ylabel(self.FEATURES[self.FEATURES_TO_PLOT[1]])\\r\\n plt.title(\\\"Iris data with three weight vectors from multi-class perceptron training.\\\")\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab2e8b4863826fd64d6f122f3073a6ae\",\n \"score\": \"0.6590201\",\n \"text\": \"def plot(network):\\n\\tplt.figure(figsize=(16, 8))\\n\\tnetwork.plot()\\n\\tplt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa4f6dafc2e0f7f6d36481366059b026\",\n \"score\": \"0.658493\",\n \"text\": \"def plot_training(history_name):\\n acc = history_name.history['acc']\\n test_acc = history_name.history['val_acc']\\n loss = history_name.history['loss']\\n test_loss = history_name.history['val_loss']\\n\\n epochs = range(1,len(acc) + 1)\\n\\n plt.plot(epochs, acc, 'bo', label='Training accuracy')\\n plt.plot(epochs, test_acc, 'r', label='Validation accuracy')\\n plt.title('Training and test accuracy')\\n plt.legend()\\n plt.axhline(y=0.9, color='black', linestyle='--')\\n plt.figure()\\n\\n plt.plot(epochs, loss, 'bo', label='Training loss')\\n plt.plot(epochs, test_loss, 'r', label='Test loss')\\n plt.title('Training and test loss')\\n plt.legend()\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c029173e38c4132285925996bf3f9869\",\n \"score\": \"0.6583262\",\n \"text\": \"def generate_plot(data):\\n epochs = list(range(1, len(data) + 1))\\n plt.ylabel(\\\"Loss\\\")\\n plt.xlabel(\\\"Epochs\\\")\\n plt.plot(epochs, data)\\n plt.savefig(\\\"images/loss.png\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7de3dd97f97b93783806a52dfb16bb65\",\n \"score\": \"0.65776277\",\n \"text\": \"def plot_learning_curve(metrics_csv_file, x_attribute, y_attribute):\\n df = pd.read_csv(metrics_csv_file)[[x_attribute, y_attribute, \\\"alpha\\\"]]\\n color_list = ['sienna', 'black', 'darkgreen', 'red', 'blue', 'gold',\\n 'silver', 'navy', 'orange', 'm', \\\"lime\\\", \\\"grey\\\"]\\n sns.lmplot(x_attribute, y_attribute, data=df, hue='alpha',\\n palette=color_list, fit_reg=False, legend_out=False, markers='o',\\n scatter_kws={\\\"s\\\": 50})\\n sns.plt.suptitle(\\\"Simulated user on validation set\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"356d7df7226595b96a1d8fa320289f25\",\n \"score\": \"0.6573321\",\n \"text\": \"def plot_learning_curve(\\n estimator, title, X, y, cv=None, train_sizes=np.linspace(.1, 1.0, 10)\\n):\\n plt.figure()\\n plt.title(title)\\n plt.ylim(0.45, 1.01)\\n plt.xlabel('Number of samples', labelpad=20)\\n plt.ylabel('Score', labelpad=20)\\n train_sizes, train_scores, test_scores = learning_curve(\\n estimator, X, y, cv=cv, n_jobs=-1, train_sizes=train_sizes)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n plt.grid()\\n\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label='Train score')\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label='Cross-validation score')\\n\\n plt.legend(loc='best')\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a41de375a0d3ab553e41eb169c682b0\",\n \"score\": \"0.65684664\",\n \"text\": \"def __plot(self):\\n\\n if not self.accuracies or not self.losses:\\n raise ValueError(\\\"ERROR: self.accuracies or self.losses not defined. Make sure self.fit() is called with visible=True or that you called self.update_fit()\\\")\\n plt.plot([i+1 for i in range(len(self.accuracies))],self.accuracies)\\n plt.xlabel(\\\"Number of estimators\\\")\\n plt.ylabel(\\\"Accuracy\\\")\\n plt.show()\\n plt.plot([i+1 for i in range(len(self.losses))], self.losses)\\n plt.xlabel(\\\"Number of estimators\\\")\\n plt.ylabel(\\\"Loss\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e08c3a35211accafbdfeb2b09b7a966c\",\n \"score\": \"0.65595156\",\n \"text\": \"def plot_cost(train_cost, val_cost, epochs):\\n epochs_label = np.arange(0, epochs+1, 1)\\n\\n fig, ax = plt.subplots()\\n\\n ax.plot(epochs_label, train_cost, label=\\\"Training Data\\\")\\n ax.plot(epochs_label, val_cost, label=\\\"Validation Data\\\")\\n ax.legend()\\n ax.set(xlabel='Epochs', ylabel='Cost', ylim=(0, 4), xlim=(0, 22))\\n ax.grid()\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"272c01e451d339a873f684af02317476\",\n \"score\": \"0.6546069\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\\n plt.figure()\\n plt.title(title)\\n if ylim is not None:\\n plt.ylim(*ylim)\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Accuracy\\\")\\n train_sizes, train_scores, test_scores = learning_curve(\\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n plt.grid()\\n\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label=\\\"Training\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label=\\\"Cross-validation\\\")\\n\\n plt.legend(loc=\\\"best\\\")\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1674e91ec60ccb1d1f11af3ccdcd682\",\n \"score\": \"0.6542977\",\n \"text\": \"def plot_loss(self):\\n history_dict = self.history.history\\n history_dict.keys()\\n loss = history_dict['loss']\\n val_loss = history_dict['val_loss']\\n\\n epochs = range(1, len(loss) + 1)\\n\\n # \\\"bo\\\" is for \\\"blue dot\\\"\\n plt.plot(epochs, loss, 'bo', label='Training loss')\\n # b is for \\\"solid blue line\\\"\\n plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n plt.title('Training and validation loss')\\n plt.xlabel('Epochs')\\n plt.ylabel('Loss')\\n plt.legend()\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ba9f10d9151afb9802d84a677b84e16\",\n \"score\": \"0.65290445\",\n \"text\": \"def plot_learning_accuracy(self):\\n x = [i for i in range(0,len(self._epoch_accuracy))]\\n \\n plt.plot(x, self._epoch_accuracy, label='Accuracy')\\n plt.xlabel('Epoch')\\n plt.ylabel('Accuracy')\\n plt.title('Learning Accuracy\\\\n')\\n plt.legend()\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"023fd3e20414ece902f0b09dc936013d\",\n \"score\": \"0.6525452\",\n \"text\": \"def plotter(train, val, title):\\n plt.style.use('seaborn')\\n plt.xlabel('epoch')\\n plt.ylabel(str(title))\\n plt.plot(train, color = 'green', label = \\\"training \\\" + str(title))\\n plt.plot(val, color = 'red', label = \\\"validation \\\" + str(title))\\n plt.legend()\\n plt.show()\\n #plt.savefig('result/loss')\\n #plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d81e38ebd617805455b3e93687c6ef\",\n \"score\": \"0.65207815\",\n \"text\": \"def plot_training_curve(path):\\n import matplotlib.pyplot as plt\\n train_err = np.loadtxt(\\\"{}_train_err.csv\\\".format(path))\\n val_err = np.loadtxt(\\\"{}_val_err.csv\\\".format(path))\\n train_loss = np.loadtxt(\\\"{}_train_loss.csv\\\".format(path))\\n val_loss = np.loadtxt(\\\"{}_val_loss.csv\\\".format(path))\\n plt.title(\\\"Train vs Validation Error\\\")\\n n = len(train_err) # number of epochs\\n plt.plot(range(1,n+1), train_err, label=\\\"Train\\\")\\n plt.plot(range(1,n+1), val_err, label=\\\"Validation\\\")\\n plt.xlabel(\\\"Epoch\\\")\\n plt.ylabel(\\\"Error\\\")\\n plt.legend(loc='best')\\n plt.show()\\n plt.title(\\\"Train vs Validation Loss\\\")\\n plt.plot(range(1,n+1), train_loss, label=\\\"Train\\\")\\n plt.plot(range(1,n+1), val_loss, label=\\\"Validation\\\")\\n plt.xlabel(\\\"Epoch\\\")\\n plt.ylabel(\\\"Loss\\\")\\n plt.legend(loc='best')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a679329bb7ae6543872bfa4f1835fe0\",\n \"score\": \"0.65168494\",\n \"text\": \"def plot(self, n_epochs, title=\\\"Learning Rate Schedule\\\"):\\n # Compute the set of learning rates for each corresponding\\n # Epoch\\n epochs = np.arange(0, n_epochs)\\n lrs = []\\n for i in range(n_epochs):\\n lrs.append(self())\\n # the learning rate schedule\\n plt.style.use(\\\"ggplot\\\")\\n plt.figure()\\n plt.plot(epochs, lrs)\\n plt.title(title)\\n plt.xlabel(\\\"Epoch #\\\")\\n plt.ylabel(\\\"Learning Rate\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"812823d7b039d34de2c17bb6dde63873\",\n \"score\": \"0.6508551\",\n \"text\": \"def accuracy_curve_training(history):\\n \\n fig = plt.figure(figsize=[8,6])\\n plt.plot(history.history['acc'],'r',linewidth=3.0)\\n plt.legend(['Training Accuracy'],fontsize=18)\\n plt.xlabel('Epochs ',fontsize=16)\\n plt.ylabel('Accuracy',fontsize=16)\\n plt.title('Accuracy Curves',fontsize=16)\\n fig.savefig('/services/scratch/perception/cdamhieu/results/curves/accuracy_loss_per_epoch/accuracy_curves_training.png')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7ae5f7fe4c4fc9a2975794cb89bd4d2\",\n \"score\": \"0.64923316\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n\\t\\t\\t\\t\\t\\tn_jobs=1, train_sizes=numpy.linspace(.1, 1.0, 5)):\\n\\n\\tplt.figure()\\n\\tplt.title(title)\\n\\tif ylim is not None:\\n\\t\\tplt.ylim(*ylim)\\n\\tplt.xlabel(\\\"Training examples\\\")\\n\\tplt.ylabel(\\\"Score\\\")\\n\\ttrain_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\n\\n\\ttrain_scores_mean = numpy.mean(train_scores, axis=1)\\n\\ttrain_scores_std = numpy.std(train_scores, axis=1)\\n\\ttest_scores_mean = numpy.mean(test_scores, axis=1)\\n\\ttest_scores_std = numpy.std(test_scores, axis=1)\\n\\tplt.grid()\\n\\n\\tplt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n\\t\\t\\t\\t\\t train_scores_mean + train_scores_std, alpha=0.1,\\n\\t\\t\\t\\t\\t color=\\\"r\\\")\\n\\tplt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n\\t\\t\\t\\t\\t test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n\\tplt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n\\t\\t\\t label=\\\"Training score\\\")\\n\\tplt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n\\t\\t\\t label=\\\"Cross-validation score\\\")\\n\\n\\tplt.legend(loc=\\\"best\\\")\\n\\treturn plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db919439563f163aabe298f0bbbe7cfa\",\n \"score\": \"0.64802206\",\n \"text\": \"def loss_curve_training(history):\\n \\n fig = plt.figure(figsize=[8,6])\\n plt.plot(history.history['loss'],'r',linewidth=3.0)\\n plt.legend(['Training loss'],fontsize=18)\\n plt.xlabel('Epochs ',fontsize=16)\\n plt.ylabel('Loss',fontsize=16)\\n plt.title('Loss Curves',fontsize=16)\\n fig.savefig('/services/scratch/perception/cdamhieu/results/curves/accuracy_loss_per_epoch/loss_curves_training.png')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b8f07d04711f360455bd392fd7351b3\",\n \"score\": \"0.64736956\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\\n plt.figure()\\n plt.title(title)\\n if ylim is not None:\\n plt.ylim(*ylim)\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Score\\\")\\n train_sizes, train_scores, test_scores = learning_curve(\\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n plt.grid()\\n\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label=\\\"Cross-validation score\\\")\\n\\n plt.legend(loc=\\\"best\\\")\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b8f07d04711f360455bd392fd7351b3\",\n \"score\": \"0.64736956\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\\n plt.figure()\\n plt.title(title)\\n if ylim is not None:\\n plt.ylim(*ylim)\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Score\\\")\\n train_sizes, train_scores, test_scores = learning_curve(\\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n plt.grid()\\n\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label=\\\"Cross-validation score\\\")\\n\\n plt.legend(loc=\\\"best\\\")\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6414421bfed99521f4e17f4e4ce2044a\",\n \"score\": \"0.6466813\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\\n plt.figure()\\n plt.title(title)\\n if ylim is not None:\\n plt.ylim(*ylim)\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Score\\\")\\n train_sizes, train_scores, test_scores = learning_curve(\\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n plt.grid()\\n\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label=\\\"Cross-validation score\\\")\\n\\n plt.legend(loc=\\\"best\\\")\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6414421bfed99521f4e17f4e4ce2044a\",\n \"score\": \"0.6466813\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\\n plt.figure()\\n plt.title(title)\\n if ylim is not None:\\n plt.ylim(*ylim)\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Score\\\")\\n train_sizes, train_scores, test_scores = learning_curve(\\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n plt.grid()\\n\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label=\\\"Cross-validation score\\\")\\n\\n plt.legend(loc=\\\"best\\\")\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6414421bfed99521f4e17f4e4ce2044a\",\n \"score\": \"0.6466813\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\\n plt.figure()\\n plt.title(title)\\n if ylim is not None:\\n plt.ylim(*ylim)\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Score\\\")\\n train_sizes, train_scores, test_scores = learning_curve(\\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n plt.grid()\\n\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label=\\\"Cross-validation score\\\")\\n\\n plt.legend(loc=\\\"best\\\")\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6414421bfed99521f4e17f4e4ce2044a\",\n \"score\": \"0.6466813\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\\n plt.figure()\\n plt.title(title)\\n if ylim is not None:\\n plt.ylim(*ylim)\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Score\\\")\\n train_sizes, train_scores, test_scores = learning_curve(\\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n plt.grid()\\n\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label=\\\"Cross-validation score\\\")\\n\\n plt.legend(loc=\\\"best\\\")\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a620d3a1effbe372db273e526432213c\",\n \"score\": \"0.64666253\",\n \"text\": \"def plot_learning(remaining_pegs):\\n episode = [i for i in range(len(remaining_pegs))]\\n plt.plot(episode, remaining_pegs)\\n plt.xlabel(\\\"Episode number\\\")\\n plt.ylabel(\\\"Remaining pegs\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6d1a41d25320aa94acc92cd23a2ba2a\",\n \"score\": \"0.6466579\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):\\n plt.figure()\\n plt.title(title)\\n if ylim is not None:\\n plt.ylim(*ylim)\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Score\\\")\\n train_sizes, train_scores, test_scores = learning_curve(\\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n plt.grid()\\n\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label=\\\"Cross-validation score\\\")\\n\\n plt.legend(loc=\\\"best\\\")\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"072799999cacae5916320e963a05181d\",\n \"score\": \"0.64643675\",\n \"text\": \"def update_training_plot(self):\\n self.axes1training.cla()\\n self.axes2training.cla()\\n self.axes3training.cla()\\n\\n self.axes3training.set_xlabel('Episode')\\n\\n reward, v, d = self.tr.read_averages()\\n\\n self.axes1training.plot(reward)\\n self.axes2training.plot(v)\\n self.axes3training.plot(d)\\n\\n self.canvas_training.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8f41fe53f2b966ef2e9e72c2f18c99b\",\n \"score\": \"0.6459311\",\n \"text\": \"def show_learning_curve_v2(file_paths: List[str], model_names: List[str], **kwargs) -> tuple:\\n import matplotlib.pyplot as plt\\n from matplotlib.ticker import MaxNLocator\\n from matplotlib.lines import Line2D\\n from matplotlib import colors as mcolors\\n\\n available_markers = list(Line2D.markers.keys())[2:]\\n available_colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS).keys())\\n\\n total_files = len(file_paths)\\n # print(available_markers)\\n # markers = available_markers[:total_files]\\n # markers_grouped = [markers[n:n + 2] for n in range(0, len(markers), 2)]\\n\\n markers = kwargs.get(\\\"markers\\\", [\\\"o\\\", \\\"v\\\", \\\"d\\\", \\\"x\\\", \\\"*\\\", \\\"+\\\", \\\"P\\\", \\\"s\\\"])\\n markers = markers[:total_files]\\n\\n if len(markers) != total_files:\\n raise ValueError(\\n f\\\"Not enough values for markers. markers contains {len(markers)} elements while it is supposed to\\\"\\n f\\\"contains at least {total_files} elements.\\\"\\n )\\n\\n colors = available_colors[:total_files]\\n\\n fig, axes = plt.subplots(1, 1, figsize=kwargs.get(\\\"figsize\\\", (8, 6)))\\n\\n for idx, (file_path, model_name, marker, color) in enumerate(zip(file_paths, model_names, markers, colors)):\\n with open(file_path, \\\"r\\\") as log_file:\\n lines: List[str] = log_file.readlines()\\n\\n for line_idx, line in enumerate(lines):\\n if line.__contains__(\\\"epoch\\\"):\\n first_index: int = line_idx - 1\\n break\\n\\n epochs: np.ndarray = np.loadtxt(file_path, usecols=1, skiprows=first_index)\\n loss: np.ndarray = np.loadtxt(file_path, usecols=3, skiprows=first_index)\\n val_loss: np.ndarray = np.loadtxt(file_path, usecols=5, skiprows=first_index)\\n\\n axes.plot(\\n epochs,\\n loss,\\n # marker=marker,\\n # markersize=kwargs.get(\\\"markersize\\\", 4),\\n linestyle='-',\\n lw=1.8,\\n label=f'{model_name}',\\n color=color\\n )\\n\\n axes.plot(\\n epochs,\\n val_loss,\\n # marker=marker,\\n # markersize=kwargs.get(\\\"markersize\\\", 4),\\n linestyle='--',\\n lw=1.8,\\n color=color\\n )\\n\\n fontsize = kwargs.get(\\\"font_size\\\", 14)\\n axes.set_ylabel('Loss RMSE [-]', fontsize=fontsize)\\n axes.set_xlabel('Epochs [-]', fontsize=fontsize)\\n axes.xaxis.set_major_locator(MaxNLocator(nbins=10, integer=True))\\n axes.set_xscale(kwargs.get(\\\"scale\\\", \\\"linear\\\"))\\n axes.set_yscale(kwargs.get(\\\"scale\\\", \\\"linear\\\"))\\n axes.tick_params(axis=\\\"both\\\", which=\\\"major\\\", labelsize=fontsize)\\n axes.legend(fontsize=fontsize)\\n axes.grid()\\n\\n axes.axhline(y=0.00022, xmin=0.0001, xmax=1, lw=2, color=\\\"k\\\")\\n axes.annotate(s=r\\\"deepx score ($2.2 \\\\times 10^{-4}$)\\\", xy=(1, 0.00023), fontsize=16)\\n\\n plt.tight_layout()\\n\\n if kwargs.get(\\\"save\\\", True):\\n os.makedirs(\\\"figures/\\\", exist_ok=True)\\n plt.savefig(kwargs.get(\\\"save_name\\\", f\\\"figures/model.png\\\"), dpi=300)\\n if kwargs.get(\\\"show\\\", True):\\n plt.show()\\n\\n return fig, axes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10845d45e8e17f5885f239b3e6352d1c\",\n \"score\": \"0.64541495\",\n \"text\": \"def plot_graphs(history, component):\\n plt.title('Train ' + ' and Validation ' + component)\\n plt.plot(history.history[component])\\n plt.plot(history.history['val_'+component])\\n plt.xlabel('Epochs')\\n plt.ylabel(component)\\n plt.legend([component, 'val_'+component])\\n plt.grid()\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a739d7b119eb64978aea124e0ed068bb\",\n \"score\": \"0.6453572\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\r\\n n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\\r\\n plt.figure(1)\\r\\n plt.title(title)\\r\\n if ylim is not None:\\r\\n plt.ylim(*ylim)\\r\\n plt.xlabel(\\\"Training examples\\\",fontsize = fontsize)\\r\\n plt.ylabel(\\\"Score\\\",fontsize = fontsize)\\r\\n train_sizes, train_scores, test_scores = learning_curve(\\r\\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\r\\n train_scores_mean = np.mean(train_scores, axis=1)\\r\\n train_scores_std = np.std(train_scores, axis=1)\\r\\n test_scores_mean = np.mean(test_scores, axis=1)\\r\\n test_scores_std = np.std(test_scores, axis=1)\\r\\n plt.grid()\\r\\n\\r\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\r\\n train_scores_mean + train_scores_std, alpha=0.1,\\r\\n color=\\\"r\\\")\\r\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\r\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\r\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\r\\n label=\\\"Training score\\\")\\r\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\r\\n label=\\\"Cross-validation score\\\")\\r\\n plt.xticks(size = fontsize)\\r\\n plt.yticks(size = fontsize)\\r\\n plt.legend(loc=\\\"best\\\")\\r\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4553e7cbf098de1565f12cdd3ffa3bf3\",\n \"score\": \"0.64534795\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\\n plt.figure()\\n plt.title(title)\\n if ylim is not None:\\n plt.ylim(*ylim)\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Score\\\")\\n train_sizes, train_scores, test_scores = learning_curve(\\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n plt.grid()\\n\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\", label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\", label=\\\"Cross-validation score\\\")\\n plt.legend(loc=\\\"best\\\")\\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37852c16230e65af81b1bc776da51a6f\",\n \"score\": \"0.6443604\",\n \"text\": \"def plot_loss(train_loss, val_loss, epochs):\\n epochs_label = np.arange(0, epochs+1, 1)\\n\\n fig, ax = plt.subplots()\\n\\n ax.plot(epochs_label, train_loss, label=\\\"Training Data\\\")\\n ax.plot(epochs_label, val_loss, label=\\\"Validation Data\\\")\\n ax.legend()\\n ax.set(xlabel='Epochs', ylabel='Loss', ylim=(0, 3), xlim=(0, 22))\\n ax.grid()\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7d340bd84d65d1779c15c4254770da7\",\n \"score\": \"0.6431484\",\n \"text\": \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n n_jobs=1, train_sizes=np.linspace(0.1, 1.0, 5), heldout_score=None):\\n plt.figure()\\n plt.title(title)\\n if ylim is not None:\\n plt.ylim(*ylim)\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Score\\\")\\n train_sizes, train_scores, test_scores = learning_curve(\\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n plt.grid()\\n\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label=\\\"Cross-validation score\\\")\\n\\n plt.legend(loc=\\\"best\\\")\\n\\n print \\\"train scores\\\\n\\\", train_scores\\n\\n print \\\"test_scores \\\\n\\\", test_scores\\n \\n return plt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b630a0d5430ecf79a4340cfc0d173906\",\n \"score\": \"0.642106\",\n \"text\": \"def mat_plot():\\n line_x = np.linspace(- 5, 5, 100)\\n line_y = square_func(line_x)\\n x_start = - 5\\n iterations = 50\\n learning_rate = 0.7\\n x = gradient_descent(x_start, derivative_func, iterations, learning_rate)\\n\\n # 绘制二阶曲线\\n plt.plot(line_x, line_y, c='b')\\n\\n # 绘制梯度下降过程的点\\n plt.scatter(x, square_func(x), c='r', )\\n plt.plot(x, square_func(x), c='r',\\n label='learning rate={}'.format(learning_rate))\\n\\n # legend函数显示图例\\n plt.legend()\\n # show函数显示\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe72fc138ed41fcdacd6efda4246bd65\",\n \"score\": \"0.64100766\",\n \"text\": \"def show_learning_rate(file_paths: List[str], model_names: List[str], **kwargs) -> tuple:\\n import matplotlib.pyplot as plt\\n from matplotlib.ticker import MaxNLocator\\n from matplotlib.lines import Line2D\\n from matplotlib import colors as mcolors\\n\\n available_markers = list(Line2D.markers.keys())[2:]\\n available_colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS).keys())\\n\\n total_files = len(file_paths)\\n # print(available_markers)\\n # markers = available_markers[:total_files]\\n # markers_grouped = [markers[n:n + 2] for n in range(0, len(markers), 2)]\\n\\n markers = kwargs.get(\\\"markers\\\", [\\\"o\\\", \\\"v\\\", \\\"d\\\", \\\"x\\\", \\\"*\\\", \\\"+\\\", \\\"P\\\", \\\"s\\\"])\\n markers = markers[:total_files]\\n\\n if len(markers) != total_files:\\n raise ValueError(\\n f\\\"Not enough values for markers. markers contains {len(markers)} elements while it is supposed to\\\"\\n f\\\"contains at least {total_files} elements.\\\"\\n )\\n\\n colors = available_colors[:total_files]\\n\\n fig, axes = plt.subplots(1, 1, figsize=kwargs.get(\\\"figsize\\\", (8, 6)))\\n\\n for idx, (file_path, model_name, marker, color) in enumerate(zip(file_paths, model_names, markers, colors)):\\n with open(file_path, \\\"r\\\") as log_file:\\n lines: List[str] = log_file.readlines()\\n\\n for line_idx, line in enumerate(lines):\\n if line.__contains__(\\\"epoch\\\"):\\n first_index: int = line_idx - 1\\n break\\n\\n epochs: np.ndarray = np.loadtxt(file_path, usecols=1, skiprows=first_index)\\n lr: np.ndarray = np.loadtxt(file_path, usecols=7, skiprows=first_index)\\n\\n axes.plot(\\n epochs,\\n lr,\\n # marker=marker,\\n # markersize=kwargs.get(\\\"markersize\\\", 4),\\n linestyle='-',\\n lw=1.8,\\n label=f'{model_name}',\\n color=color\\n )\\n\\n fontsize = kwargs.get(\\\"font_size\\\", 14)\\n axes.set_ylabel('Learning rate [-]', fontsize=fontsize)\\n axes.set_xlabel('Epochs [-]', fontsize=fontsize)\\n axes.xaxis.set_major_locator(MaxNLocator(nbins=10, integer=True))\\n axes.set_yscale(kwargs.get(\\\"scale\\\", \\\"linear\\\"))\\n axes.tick_params(axis=\\\"both\\\", which=\\\"major\\\", labelsize=fontsize)\\n axes.legend(fontsize=fontsize)\\n axes.grid()\\n plt.tight_layout()\\n\\n if kwargs.get(\\\"save\\\", True):\\n os.makedirs(\\\"figures/\\\", exist_ok=True)\\n plt.savefig(kwargs.get(\\\"save_name\\\", f\\\"figures/model.png\\\"), dpi=300)\\n if kwargs.get(\\\"show\\\", True):\\n plt.show()\\n\\n return fig, axes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"688640d2e8c0fe5fd2cee47fb25a91c2\",\n \"score\": \"0.64025366\",\n \"text\": \"def dual_plot_accuracy(trainingAccuracy, testingAccuracy, lr):\\n plt.xlabel('Number of Epochs')\\n plt.plot(trainingAccuracy, color = \\\"blue\\\", label = 'training accuracy')\\n plt.plot(testingAccuracy, color = \\\"green\\\", label = 'testing accuracy')\\n plt.title('Training vs Testing Accuracy for Learning Rate : ' + str(lr))\\n lgd = plt.legend(loc=9, bbox_to_anchor=(0.5, -0.1),ncol=2)\\n plt.savefig('training_vs_testing_'+str(lr)+'.png',bbox_extra_artists=(lgd,), bbox_inches='tight')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97641d969d7c320f1325b0aae10ed825\",\n \"score\": \"0.63984543\",\n \"text\": \"def plot_acc(acc_v, acc_t, save_plots_path):\\n\\n plt.figure()\\n plt.plot(acc_v, label='Validation acc')\\n plt.plot(acc_t, label='Training acc')\\n plt.legend()\\n title = 'Accuracy per epoch'\\n plt.title(title)\\n plt.xlabel(\\\"Epoch\\\")\\n plt.ylabel(\\\"Accuracy\\\")\\n plt.savefig(save_plots_path + \\\"sgd_accuracy_plot.png\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7361f09473514bd94291907f1dbd4e1\",\n \"score\": \"0.6388655\",\n \"text\": \"def plot_xgboost_learning_curve(iterations, scorestrain, scorescv) -> None:\\n plt.figure(figsize=(12, 7))\\n plt.plot(iterations,scorestrain, 'r')\\n plt.plot(iterations,scorescv, 'b')\\n plt.xlabel('# training examples')\\n plt.ylabel('MAE')\\n plt.legend(['Training Set', 'CV set'], loc='lower right')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"628e486b012325d2958b9a0a66a600ad\",\n \"score\": \"0.63880914\",\n \"text\": \"def plotting_graphs(y, tx, k_fold, initial_w, max_iters, gammas, lambdas, optimal_lambda_, optimal_gamma, i, seed=1, model=\\\"LOG_REG_GD\\\"):\\n training_losses, testing_losses, training_accuracy, testing_accuracy = [], [], [], []\\n \\n for gamma in range(len(gammas)):\\n loss_tr, loss_te, ca_tr, ca_te = total_cross_validation(y, tx, k_fold, initial_w, max_iters, gammas[gamma], optimal_lambda_, seed=1, batch_size=1, model=\\\"LOG_REG_GD\\\")\\n training_losses.append(loss_tr)\\n testing_losses.append(loss_te)\\n training_accuracy.append(ca_tr)\\n testing_accuracy.append(ca_te)\\n cross_validation_visualization(gammas, training_losses, testing_losses, i, parameter = \\\"gamma_\\\") # doing only for losses\\n\\n training_losses, testing_losses, training_accuracy, testing_accuracy = [], [], [], []\\n for lambda_ in range(len(lambdas)):\\n loss_tr, loss_te, ca_tr, ca_te = total_cross_validation(y, tx, k_fold, initial_w, max_iters, lambdas[lambda_], optimal_gamma, seed=1, batch_size=1, model=\\\"LOG_REG_GD\\\")\\n training_losses.append(loss_tr)\\n testing_losses.append(loss_te)\\n training_accuracy.append(ca_tr)\\n testing_accuracy.append(ca_te)\\n cross_validation_visualization(lambdas, training_losses, testing_losses, i, parameter = \\\"lambda_\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc049ccea867dacc2fc651de7b8e1382\",\n \"score\": \"0.6367363\",\n \"text\": \"def plot_lr_func(lr_func: Callable, total_steps: int):\\n ys = simulate_lr_func(lr_func, total_steps)\\n plot_scatter(ys, ytitle='Learning rate', xtitle='Training step')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26abfb62c6f3793bd5569d596683fcd8\",\n \"score\": \"0.6360308\",\n \"text\": \"def plot_cost(c_v, c_t, save_plots_path):\\n\\n plt.figure()\\n plt.plot(c_v, label='Validation loss')\\n plt.plot(c_t, label='Training loss')\\n plt.legend()\\n title = 'Loss per epoch'\\n plt.title(title)\\n plt.xlabel(\\\"Epoch\\\")\\n plt.ylabel(\\\"Loss\\\")\\n plt.savefig(save_plots_path + \\\"sgd_loss_plot.png\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09723e80b7c6529f0b74df7c37864f8b\",\n \"score\": \"0.635458\",\n \"text\": \"def plot_training_summary(filePath='',savePath='.s/imgs'):\\n assert os.path.isfile(filePath) , f'{filePath} does not exist'\\n try:\\n with open(filePath, 'rb') as f:\\n x = pickle.load(f)\\n except :\\n print(f'could not open {filePath}')\\n exit(1)\\n\\n d_l = np.array(x['discriminator_loss']).ravel()\\n g_l = np.array(x['Generator_loss']).ravel()\\n acc_history = np.array(x['acc_history'])\\n acc = acc_history.sum(axis=1) * 0.5\\n acc_real = acc_history[:,1]\\n acc_gen = acc_history[:,0]\\n kl = np.array(x[\\\"kl_divergence\\\"]).ravel()\\n\\n n = np.arange(len(d_l))\\n figname = os.path.split(filePath)[1].replace('.pickle','')\\n title = 'Loss and Accuracy plot'+'\\\\n'+ figname\\n\\n fig = plt.figure(figsize=(19.20,10.80))\\n fig.suptitle(title, fontsize=15,fontweight=\\\"bold\\\")\\n\\n axs1 = plt.subplot(222)\\n # axs1.set_title(title,fontsize=5.0,fontweight=\\\"bold\\\")\\n axs1.plot(n, g_l,label='Generator loss',linewidth=3)\\n axs1.plot(n, d_l,label='Discriminator loss',linewidth=3)\\n axs1.legend(loc=0, prop={'size': 13})\\n axs1.set_ylabel('Loss',fontsize=15.0,fontweight=\\\"bold\\\")\\n axs1.tick_params(labelsize=10)\\n # axs1.tick_params(axis='x',which='both',bottom=False,top=False,labelbottom=False,labelsize=20)\\n\\n # axs2.plot(n, acc,'r',label='Discriminator accuracy',linewidth=4)\\n axs2 = plt.subplot(221)\\n axs2.plot(n, acc_gen,label='Accuracy on Generated',linewidth=3)\\n axs2.plot(n, acc_real,label='Accuracy on Real',linewidth=3)\\n axs2.legend(loc=0,prop={'size': 13})\\n axs2.set_ylabel('Accuracy',fontsize=15.0,fontweight=\\\"bold\\\")\\n # axs2.set_xlabel('Epoch',fontsize=15.0,fontweight=\\\"bold\\\")\\n axs2.tick_params(labelsize=10)\\n\\n axs3 = plt.subplot(212)\\n n = np.arange(0,(len(kl)*10),10)\\n\\n axs3.plot(n, kl,label='KL',linewidth=3)\\n axs3.legend(loc=0,prop={'size': 13})\\n axs3.set_ylabel('KL-Divergence',fontsize=15.0,fontweight=\\\"bold\\\")\\n axs3.set_xlabel('Epoch',fontsize=15.0,fontweight=\\\"bold\\\")\\n axs3.tick_params(labelsize=10)\\n\\n # plt.tight_layout()\\n fig.tight_layout(rect=[0, 0.03, 1, 0.95])\\n if not os.path.exists(savePath):\\n os.makedirs(savePath)\\n\\n plt.savefig(os.path.join(savePath,figname+'.png'),dpi = 300)\\n plt.close('all') #plt.close(fig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"083d9a6126bd5a94725480fc7cc1e06b\",\n \"score\": \"0.6354436\",\n \"text\": \"def epoch_vizual(self, train_logs, path):\\n fig = plt.figure(figsize=(20, 20))\\n plt.rc(\\\"text\\\", usetex=True)\\n ax1 = fig.add_subplot(2, 1, 1)\\n ax2 = fig.add_subplot(2, 1, 2)\\n\\n ax1.semilogy(train_logs[\\\"g_grad\\\"], linewidth=7.0)\\n ax1.set_title(\\\"Norm of Generator gradient\\\", fontsize=45)\\n ax1.set_xlabel(\\\"Training Iteration\\\", fontsize=42)\\n ax1.set_ylabel(\\\"Norm of gradient\\\", fontsize=42)\\n ax1.tick_params(axis=\\\"x\\\", labelsize=40)\\n ax1.tick_params(axis=\\\"y\\\", labelsize=40)\\n ax1.grid()\\n\\n ax2.semilogy(train_logs[\\\"c_grad\\\"], linewidth=7.0)\\n ax2.set_title(\\\"Norm of Discriminator gradient\\\", fontsize=45)\\n ax2.set_xlabel(\\\"Training Iteration\\\", fontsize=42)\\n ax2.set_ylabel(\\\"Norm of gradient\\\", fontsize=42)\\n ax2.tick_params(axis=\\\"x\\\", labelsize=40)\\n ax2.tick_params(axis=\\\"y\\\", labelsize=40)\\n ax2.grid()\\n\\n plt.savefig(path)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b65ffa06b3056b5bc3f64eaf29cd038\",\n \"score\": \"0.635388\",\n \"text\": \"def visualize_epoch(history):\\n # Summarize history for accuracy\\n plt.plot(history['acc']) # Training accuracy\\n plt.plot(history['val_acc']) # Validation accuracy\\n plt.title('Epoch Iteration vs. Accuracy')\\n plt.xlabel('Epoch Number')\\n plt.ylabel('Accuracy')\\n plt.legend(['train', 'val'], loc='upper left')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"733dc15b79e629a3ffa4cf15031feb6f\",\n \"score\": \"0.63411385\",\n \"text\": \"def plot_learning_curve(self, resolution=100):\\n self.calculate_tour_distance()\\n # Get the divisor to limit the number of samples.\\n step = len(self.samples) // resolution\\n if step == 0:\\n step = 1\\n # Get samples.\\n x = list(range(0, len(self.samples) + 1, step))\\n y = [(self.samples + [self.chromosomes[0].tour_distance])[i] for i in x]\\n # Setup plot.\\n plt.grid(True)\\n plt.title('TSP Learning Curve: {0}'.format(str(self.chromosomes[0])))\\n plt.xlabel('Samples (every {0} generations)'.format(step))\\n plt.ylabel('Lowest Distance')\\n plt.plot(x, y)\\n # Save plot.\\n timestamp = datetime.now().strftime('%m-%d-%Y_%I-%M-%S-%p')\\n name = 'learning_curve_{0}.png'.format(timestamp)\\n save_dir = os.path.join(os.path.abspath(PLOT_DIR), name)\\n plt.savefig(save_dir, dpi=200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce59f4b1b4c650026be0209c9773dc5d\",\n \"score\": \"0.63339585\",\n \"text\": \"def plot_learning_curve(model, X_train, y_train, scoring='recall'):\\n\\n cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=42)\\n\\n train_sizes, train_scores, test_scores = learning_curve(model, \\n X_train, \\n y_train, \\n train_sizes=np.linspace(0.05, 1, 20),\\n cv=cv,\\n scoring=scoring,\\n n_jobs=-1\\n )\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n\\n plt.plot(train_sizes, train_scores_mean, label = 'Train')\\n plt.fill_between(train_sizes, \\n train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, \\n alpha=0.1)\\n\\n plt.plot(train_sizes, test_scores_mean, label = 'Val')\\n plt.fill_between(train_sizes, \\n test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, \\n alpha=0.1)\\n plt.legend()\\n plt.ylabel('score')\\n plt.xlabel('train sizes')\\n if scoring=='recall':\\n plt.ylim(0.6, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c249dd68937df33a9202e5c3cded280\",\n \"score\": \"0.63328385\",\n \"text\": \"def _draw(train_accuracy_list, valid_accuracy_list):\\n fig, ax = plt.subplots(1, 1)\\n x_axis = np.arange(1, 21)\\n ax.plot(x_axis, train_accuracy_list, label='Training')\\n ax.plot(x_axis, valid_accuracy_list, label='Validation')\\n ax.set_xlabel('K value')\\n ax.set_ylabel('Accuracy')\\n plt.legend(loc='best')\\n plt.show()\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b853c8a7b0b345faed311cf62880933d\",\n \"score\": \"0.633202\",\n \"text\": \"def plot_history(history=None):\\r\\n acc = history.history['acc']\\r\\n val_acc = history.history['val_acc']\\r\\n loss = history.history['loss']\\r\\n val_loss = history.history['val_loss']\\r\\n epochs = range(1, len(acc) + 1)\\r\\n plt.plot(epochs, acc, 'bo', label='Training acc')\\r\\n plt.plot(epochs, val_acc, 'r', label='Validation acc')\\r\\n plt.title('Training and Validation accuracy')\\r\\n plt.legend()\\r\\n\\r\\n plt.figure()\\r\\n plt.plot(epochs, loss, 'bo', label='Training loss')\\r\\n plt.plot(epochs, val_loss, 'r', label='Validation loss')\\r\\n plt.title('Training and Validation loss')\\r\\n plt.legend()\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4919aa76c64c12df39f0a238207283a\",\n \"score\": \"0.63313687\",\n \"text\": \"def plot_losses(self):\\n\\n plot_x = range(len(self.losses_log['train']))\\n\\n for type_data, type_plot, color, marker in [\\n ('train', plt.plot, None, None),\\n ('valid', plt.plot, None, None),\\n ('saved', plt.scatter, 'g', 'x'),\\n ]:\\n if type_data in self.losses_log.keys():\\n plot_y = self.losses_log[type_data]\\n if len(plot_y) > 0:\\n type_plot(plot_x, plot_y, label=type_data, c=color, marker=marker)\\n\\n\\n plt.legend()\\n plt.ylabel('loss')\\n plt.xlabel('epoch')\\n plt.grid()\\n plt.gcf().patch.set_facecolor('white')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cc10a02de0562854d72dc0e342be7f5\",\n \"score\": \"0.6329032\",\n \"text\": \"def plot_learning_curve(classifier, X, y, measurements=[0.1, 0.325, 0.55, 0.775, 1.], metric=None, n_jobs=-1,\\n save_to_folder=os.path.join(os.path.dirname(__file__), \\\"ExperimentResults\\\")):\\n sns.set_style(\\\"whitegrid\\\")\\n\\n plt.figure()\\n plt.title(\\\"Learning curves\\\")\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Score\\\")\\n train_sizes, train_scores, test_scores = skms.learning_curve(classifier, X, y, n_jobs=n_jobs,\\n train_sizes=measurements, scoring=metric)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n plt.grid()\\n\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label=\\\"Cross-validation score\\\")\\n\\n plt.legend(loc=\\\"best\\\")\\n\\n file_name = _get_file_name(\\\"\\\", classifier, \\\"learning_curve\\\", \\\"png\\\")\\n if not os.path.exists(save_to_folder):\\n os.mkdir(save_to_folder)\\n plt.savefig(os.path.join(save_to_folder, file_name))\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93c89bc0818f3dd895b6dce09e41d5e7\",\n \"score\": \"0.63228965\",\n \"text\": \"def plot_acc_and_loss():\\n # Accuracy results per epoch for training and validation data sets\\n acc = history.history['accuracy']\\n val_acc = history.history['val_accuracy']\\n\\n # Loss results per epoch for training and validation data sets\\n loss = history.history['loss']\\n val_loss = history.history['val_loss']\\n\\n # Plot training and validation accuracy\\n plt.figure(figsize=(8, 8))\\n plt.subplot(1, 2, 1)\\n plt.plot(range(len(acc)), acc, color='#9ACD32',\\n lw=3, label='Training Accuracy')\\n plt.plot(range(len(acc)), val_acc, color='#1E90FF',\\n lw=3, label='Validation Accuracy')\\n plt.xlabel(\\\"Epoch\\\")\\n plt.legend(loc='lower right')\\n plt.title('Training and Validation Accuracy')\\n\\n # Plot training and validation loss\\n plt.subplot(1, 2, 2)\\n plt.plot(range(len(loss)), loss, color='#9ACD32',\\n lw=3, label='Training Loss')\\n plt.plot(range(len(loss)), val_loss, color='#1E90FF',\\n lw=3, label='Validation Loss')\\n plt.xlabel(\\\"Epoch\\\", labelpad=8)\\n plt.legend(loc='lower left')\\n plt.title('Training and Validation Loss')\\n\\n plt.savefig('acc_and_loss.png')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7128363289948844668684bb1c175725\",\n \"score\": \"0.63189256\",\n \"text\": \"def Plot_Lift_Curve(X, y, Estimators, Target_Class = None, Number_of_Splits = 50, Train_Test_Split = True, Test_Size =0.3, Random_State = None, Stratify = None):\\n #Split the Data,if wanted, so that results aren't subject to overfitting\\n #Set up figure for axes\\n \\n if Train_Test_Split:\\n X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = Test_Size, random_state = Random_State, stratify = Stratify)\\n else:\\n X_train, X_test, y_train, y_test = X, X, y, y\\n\\n \\n \\n \\n if isinstance(Estimators, (list, type(np.array))):\\n number_of_axes = len(Estimators)\\n \\n if number_of_axes%2 ==0:\\n length_axis_0 = number_of_axes/2\\n else:\\n length_axis_0 = (number_of_axes//2)+1\\n\\n fig, axes = plt.subplots(nrows = int(length_axis_0), ncols= 2, sharex= False, sharey= False, figsize = (12,int(length_axis_0)*4)) \\n\\n\\n else:\\n Estimators = np.array([Estimators])\\n fig, axes = plt.subplots(1,1, figsize = (12,8))\\n axes = np.array([axes])\\n \\n for ax, Estimator in zip(axes.ravel(), Estimators):\\n\\n #Fit the model\\n model = Estimator.fit(X_train,y_train)\\n\\n #Get predicted classifications\\n predictions = model.predict(X_test)\\n #Predict scores so that samples can be ranked\\n if hasattr(model, \\\"predict_proba\\\"):\\n prediction_certainties_NDA = model.predict_proba(X_test)\\n\\n elif hasattr(model, \\\"decision_function\\\"):\\n prediction_certainties_NDA = model.decision_function(X_test)\\n\\n else:\\n print(\\\"Model doesn't have attribute: decision_function or predict_proba\\\")\\n\\n #Find which column in predictions to look at for ranking\\n class_index = np.where(model.classes_ == Target_Class)[0][0]\\n\\n #Get the target column to rank by certainty\\n if prediction_certainties_NDA.ndim >1:\\n prediction_certainty_array = prediction_certainties_NDA[:, class_index]\\n else:\\n prediction_certainty_array = prediction_certainties_NDA\\n\\n\\n #Get indicies of predictions sorted by certainty of correct classification\\n sorted_certainty_indicies = np.argsort(prediction_certainty_array)\\n \\n #Rank the actual labels by certainty they are of class: \\\"Target_Class\\\"\\n sorted_actual_labels = y_test[sorted_certainty_indicies]\\n \\n\\n class_prior = sum(y_test == Target_Class)/len(y_test)\\n \\n\\n def Lift(Sorted_Labels_, Portion_Targeted_, Class_Prior_, Target_Class_):\\n\\n #Work out how many samples we want to target\\n number_to_target = np.floor(Portion_Targeted_ * len(Sorted_Labels_))\\n number_to_target = int(number_to_target)\\n\\n #Create boolean for who we correctly targeted\\n correctly_targeted_bool = (Sorted_Labels_[:number_to_target] == Target_Class_)\\n \\n #How many did we get correct\\n number_correctly_classified = np.sum(correctly_targeted_bool)\\n \\n\\n #Calculate lift\\n lift_score = (number_correctly_classified)/(number_to_target*Class_Prior_) #Denominator is how many we would have got correct if random\\n\\n return lift_score\\n\\n #initialise list where lift scores go \\n lift_scores_list = [] \\n \\n X_percent_targeted = np.arange(0,100, 100/Number_of_Splits)\\n\\n for i in X_percent_targeted:\\n portion = (i+1)/(100)\\n lift = Lift(Sorted_Labels_ = sorted_actual_labels, Portion_Targeted_ = portion, Class_Prior_ = class_prior, Target_Class_= Target_Class)\\n\\n lift_scores_list.append(lift)\\n \\n \\n ax.plot(X_percent_targeted, lift_scores_list, \\\"r-\\\")\\n \\n ax.set_xlabel(\\\"Percentage of people Targeted\\\")\\n ax.set_title(\\\"Lift Curve for: {}\\\".format(Estimator))\\n ax.set_ylabel(\\\"Lift\\\")\\n ax.set_yticks(np.arange(max(lift_scores_list)+2)) \\n plt.tight_layout()\\n\\n\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"119d8040d186ceac358330dcb003c955\",\n \"score\": \"0.63074523\",\n \"text\": \"def plot_model_training_history(history_object):\\n plt.title('model accuracy')\\n plt.ylabel('accuracy')\\n plt.xlabel('epoch')\\n plt.plot(history_object.history['loss'])\\n plt.plot(history_object.history['val_loss'])\\n plt.legend(['train', 'test'], loc='upper left')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce99cb5b11ba6587b49c039efbe3daaa\",\n \"score\": \"0.63073224\",\n \"text\": \"def plot_accuracy(self):\\n plt.clf() # clear figure\\n history_dict = self.history.history\\n history_dict.keys()\\n acc = history_dict['acc']\\n val_acc = history_dict['val_acc']\\n epochs = range(1, len(acc) + 1)\\n plt.plot(epochs, acc, 'bo', label='Training acc')\\n plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n plt.title('Training and validation accuracy')\\n plt.xlabel('Epochs')\\n plt.ylabel('Accuracy')\\n plt.legend()\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77cae9211f0e7d8112b5a2eeeec7b228\",\n \"score\": \"0.63038343\",\n \"text\": \"def plot_learning_curve(model, X_train, y_train, name='test', scoring='recall'):\\n\\n \\n cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=42)\\n\\n train_sizes, train_scores, test_scores = learning_curve(model.best_estimator_, \\n X_train, \\n y_train, \\n train_sizes=np.linspace(0.05, 1, 20),\\n cv=cv,\\n scoring=scoring,\\n n_jobs=-1\\n )\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n\\n plt.plot(train_sizes, train_scores_mean, label = 'Train')\\n plt.fill_between(train_sizes, \\n train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, \\n alpha=0.1)\\n\\n plt.plot(train_sizes, test_scores_mean, label = 'Val')\\n plt.fill_between(train_sizes, \\n test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, \\n alpha=0.1)\\n plt.legend()\\n plt.ylabel('score')\\n plt.xlabel('train sizes')\\n if scoring=='recall':\\n plt.ylim(0.6, 1)\\n plt.savefig('../tree_figs/' + name + '.png', bbox_inches='tight')\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e6d91618a6b15768221aadda489d80c\",\n \"score\": \"0.6299842\",\n \"text\": \"def learning_curve(ww_training, rw_training, ww_test, rw_test):\\n np.random.shuffle(ww_training)\\n np.random.shuffle(rw_training)\\n dif_int = len(ww_training) - len(rw_training)\\n if dif_int < 0:\\n dif_int = 0\\n lower_size_int = len(ww_training) - dif_int\\n accuracy_array = np.empty((lower_size_int, 2))\\n \\n for n_trial in range(1, lower_size_int + 1):\\n accuracy_array[n_trial - 1] = [n_trial, 100 * experiment(ww_training[:n_trial], rw_training[:n_trial], ww_test, rw_test)]\\n \\n plt.title(\\\"Learning Curve\\\")\\n plt.xlabel(\\\"Number of Training Items\\\")\\n plt.ylabel(\\\"Accuracy (%)\\\")\\n plt.plot(accuracy_array[:, 0], accuracy_array[:, 1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7530cb297483c53f83dc100d73434ae\",\n \"score\": \"0.6299744\",\n \"text\": \"def train(self):\\n\\n steps = []\\n print(\\\"Training the agent...\\\")\\n for i in tqdm(range(self.n_episodes)):\\n # let agent play\\n episode_stats = self.play()\\n steps.append(episode_stats[\\\"steps\\\"])\\n\\n if i % 10 == 0:\\n self.eps = self.eps - i * self.reduction\\n\\n # learn from experience\\n self.learn()\\n\\n plt.plot(steps)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51d14e0aebc445409e2ce9b5ec0dc91a\",\n \"score\": \"0.6295639\",\n \"text\": \"def training_curve(train_loss, perc_iter, val_loss, psnr, ssim, x, lr, epoch, iters_per_epoch, \\n fig: matplotlib.figure.Figure, axis: matplotlib.axes.Axes, linewidth=0.25):\\n # Linear scale of loss curve\\n ax = axis[0]\\n ax.clear()\\n line1, = ax.plot(x, val_loss, label=\\\"Validation Loss\\\", color='red', linewidth=linewidth)\\n line2, = ax.plot(x, train_loss, label=\\\"Train Loss\\\", color='blue', linewidth=linewidth)\\n ax.plot(x, np.repeat(np.amin(val_loss), len(x)), linestyle=':', linewidth=linewidth)\\n ax.set_xlabel(\\\"Epoch(s) / Iteration: {}\\\".format(iters_per_epoch))\\n ax.set_ylabel(\\\"Image Loss\\\")\\n ax.set_title(\\\"Loss\\\")\\n\\n if not np.isnan(perc_iter).all():\\n ax = axis[4]\\n ax.clear()\\n line4, = ax.plot(x, perc_iter, label=\\\"Perceptual Loss\\\", color='green', linewidth=linewidth)\\n ax.set_ylabel(\\\"Perceptual Loss\\\")\\n\\n ax.legend(handles=(line1, line2, line4, )) if not np.isnan(perc_iter).all() else ax.legend(handles=(line1, line2, ))\\n\\n # Log scale of loss curve\\n ax = axis[1]\\n ax.clear()\\n line1, = ax.plot(x, val_loss, label=\\\"Validation Loss\\\", color='red', linewidth=linewidth)\\n line2, = ax.plot(x, train_loss, label=\\\"Train Loss\\\", color='blue', linewidth=linewidth)\\n ax.plot(x, np.repeat(np.amin(val_loss), len(x)), linestyle=':', linewidth=linewidth)\\n ax.set_xlabel(\\\"Epoch(s) / Iteration: {}\\\".format(iters_per_epoch))\\n ax.set_yscale('log')\\n ax.set_title(\\\"Loss(Log scale)\\\")\\n\\n if not np.isnan(perc_iter).all():\\n ax = axis[5]\\n ax.clear()\\n line4, = ax.plot(x, perc_iter, label=\\\"Perceptual Loss\\\", color='green', linewidth=linewidth)\\n ax.set_ylabel(\\\"Perceptual Loss\\\")\\n\\n ax.legend(handles=(line1, line2, line4, )) if not np.isnan(perc_iter).all() else ax.legend(handles=(line1, line2, ))\\n\\n # Linear scale of PSNR, SSIM\\n ax = axis[2]\\n ax.clear()\\n line1, = ax.plot(x, psnr, label=\\\"PSNR\\\", color='blue', linewidth=linewidth)\\n ax.plot(x, np.repeat(np.amax(psnr), len(x)), linestyle=':', linewidth=linewidth)\\n ax.set_xlabel(\\\"Epochs(s) / Iteration: {}\\\".format(iters_per_epoch))\\n ax.set_ylabel(\\\"Average PSNR\\\")\\n ax.set_title(\\\"Validation Performance\\\")\\n\\n ax.legend(handles=(line1, ))\\n\\n # Learning Rate Curve\\n ax = axis[3]\\n ax.clear()\\n line1, = ax.plot(x, lr, label=\\\"Learning Rate\\\", color='cyan', linewidth=linewidth)\\n ax.set_xlabel(\\\"Epochs(s) / Iteration: {}\\\".format(iters_per_epoch))\\n ax.set_title(\\\"Learning Rate\\\")\\n ax.set_yscale('log')\\n\\n ax.legend(handles=(line1, ))\\n \\n return fig, axis\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f041d83c1a08843dffef745d5adbcd2\",\n \"score\": \"0.62874764\",\n \"text\": \"def training(override=None):\\n name = BEST if override is None else override\\n data = read(name)\\n\\n plt.figure(1)\\n\\n size = 5\\n chunks = list(chunk_list(data[:, 1], size))\\n averages = [sum(chunk) / len(chunk) for chunk in chunks]\\n\\n plt.plot(range(0, len(data), size), averages)\\n plt.axhline(y=200, color='g', linestyle='--')\\n\\n try:\\n success = [i[0] for i in data if i[2] >= 200][0] - 200\\n plt.axvline(x=success, color='r')\\n except IndexError:\\n print('[WARN] Learner did not succeed')\\n\\n plt.title('Learning Performance')\\n plt.xlabel('Episode')\\n plt.ylabel('Episode Total Reward (Averaged Per {})'.format(size))\\n\\n plt.savefig(PLOT_DIR + 'training.{}.png'.format(name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9e766f64fb7f208f87f0d8d13661b12\",\n \"score\": \"0.6285915\",\n \"text\": \"def plot(self, **kwargs):\\n plt.plot(self.epochs, self.kl_weights, **kwargs)\\n plt.xlabel(\\\"Epoch\\\")\\n plt.ylabel(\\\"KL Loss Weight\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e8b3436ae7471fa1b6c26da3f9311fc\",\n \"score\": \"0.6276074\",\n \"text\": \"def plot_loss(train_log,valid_log):\\n \\n num_log = len(train_log) # number of element in input array\\n \\n fig = plt.figure()\\n fig.suptitle(\\\"Loss vs. Epoch\\\")\\n ax = fig.add_subplot(111)\\n ax.set_xlabel(\\\"Epoch\\\")\\n ax.set_ylabel(\\\"Loss\\\")\\n ax.plot(np.arange(0,num_log),train_log,'r-',label='train_loss')\\n ax.plot(np.arange(0,num_log),valid_log,'b-',label='validation_loss')\\n ax.legend(loc=4,prop={'size':15})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e17734e3a4bdc51a40a4b05d75d42eb6\",\n \"score\": \"0.6271056\",\n \"text\": \"def part_b(t_lst, loss_test_lst, loss_train_lst):\\n\\n plt.figure()\\n plot_graph(loss_test_lst, t_lst, \\\"q5_part_b_test\\\", \\\"\\\", \\\"Iteration vs Loss Test\\\", \\\"Loss\\\", \\\"Iteration\\\")\\n plt.figure()\\n plot_graph(loss_train_lst, t_lst, \\\"q5_part_b_train\\\", \\\"\\\", \\\"Iteration vs Loss Train\\\", \\\"Loss\\\", \\\"Iteration\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca9b7d62ac761328a408f091d495eed2\",\n \"score\": \"0.62491584\",\n \"text\": \"def populate_graph():\\n \\\"\\\"\\\"\\n plt.figure()\\n x = [2,3,4,5,6,7,8,9,10,11,12]\\n #train = [0.989662, 0.997180, 0.999060, 0.999060, 0.999060, 1.0, 1.0]\\n #test = [0.939850, 0.954887, 0.939850, 0.947368, 0.947368, 0.932331, 0.939850]\\n train = [0.986842,0.996241,0.997180,0.998120,0.999060,0.999060,0.999060,0.999060,0.999060,0.999060,0.999060]\\n test = [0.932331,0.932331,0.947368,0.947368,0.947368,0.947368,0.947368,0.954887,0.954887,0.954887,0.954887]\\n \\n\\n plt.plot(x,train,'-r', label=\\\"Train Accuracy\\\")\\n plt.plot(x,test,'-b', label=\\\"Test Accuracy\\\")\\n plt.title(\\\"TF-IDF Passes vs Accuracy\\\")\\n plt.xlabel(\\\"Passes\\\")\\n plt.ylabel(\\\"Accuracy\\\")\\n plt.axis([2,12,0.93,1.02])\\n plt.legend(loc=\\\"center right\\\")\\n plt.show()\\n plt.close()\\n \\\"\\\"\\\"\\n plt.figure()\\n x=[1,2,3,4,5,6,7,8]\\n train =[0.965226,0.985902,0.989662,0.990602,0.995301,0.997241,1.0,1.0]\\n test = [0.932331,0.954887,0.954887,0.954887,0.962406,0.962406,0.962406,0.962406]\\n plt.plot(x,train,'-r', label=\\\"Train Accuracy\\\")\\n plt.plot(x,test,'-b', label=\\\"Test Accuracy\\\")\\n plt.title(\\\"Passes vs Accuracy\\\")\\n plt.xlabel(\\\"Passes\\\")\\n plt.ylabel(\\\"Accuracy\\\")\\n plt.axis([1,8,0.93,1.02])\\n plt.legend()\\n plt.show()\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d35ac155396f7209e187ac4f38b8de15\",\n \"score\": \"0.6243018\",\n \"text\": \"def plot_graphs(self):\\n\\n plt.plot(self.history.history[self.metric])\\n plt.plot(self.history.history['val_' + self.metric], '')\\n plt.xlabel(\\\"Epochs\\\")\\n plt.ylabel(self.metric)\\n plt.legend([self.metric, 'val_'+self.metric])\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33e2f6a994e0ade5ccad224d56f48b35\",\n \"score\": \"0.6230619\",\n \"text\": \"def neural_net_implementation(x, y, tag=\\\"\\\"):\\n from keras.models import Sequential\\n from keras.layers import Dense\\n from keras.callbacks import EarlyStopping\\n from sklearn.model_selection import train_test_split\\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\\n\\n model = Sequential()\\n model.add(Dense(50, input_dim=x.shape[1], activation='relu'))\\n model.add(Dense(150, activation='relu'))\\n model.add(Dense(1))\\n model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])\\n monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, verbose=1, mode='auto')\\n history = model.fit(x, y, validation_data=(x_test, y_test), callbacks=[monitor], verbose=0, epochs=5000)\\n\\n score, acc = model.evaluate(x_test, y_test)\\n print(\\\"Score (RMSE) : {}\\\".format(score))\\n print(\\\"accuracy is :{}\\\".format(acc))\\n\\n df = pd.DataFrame(history.history)\\n df.plot(subplots=True, grid=True, figsize=(10, 15))\\n plt.savefig(tag)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b108737f98f9f52d38eb216dd1cb473d\",\n \"score\": \"0.6223654\",\n \"text\": \"def plot_learning_curve(estimator,fitted_dt, title, X, y, ylim=None, cv=None,\\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 10)):\\n\\n train_sizes, train_scores, test_scores = learning_curve(\\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n\\n return train_sizes, train_scores_mean, test_scores_mean\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":98,"cells":{"query_id":{"kind":"string","value":"b0235b98074062dc84c332d6568a79c7"},"query":{"kind":"string","value":"Flip a bit and win!!!!!!"},"positive_passages":{"kind":"list like","value":[{"docid":"c4a9a8e4c27026a69c7db13d18888223","score":"0.0","text":"def flip_bit_to_win(num):\n \"\"\" Convert to binary \"\"\"\n binary = int(num, 2)\n current = 0\n last = 0\n longest = 0\n for i in range(sequence_length):\n bit = bit_manipulation.getbit(binary, i)\n if bit:\n current += 1\n longest = max(longest, current + last + 1)\n else:\n last = current\n current = 0\n return longest","title":""}],"string":"[\n {\n \"docid\": \"c4a9a8e4c27026a69c7db13d18888223\",\n \"score\": \"0.0\",\n \"text\": \"def flip_bit_to_win(num):\\n \\\"\\\"\\\" Convert to binary \\\"\\\"\\\"\\n binary = int(num, 2)\\n current = 0\\n last = 0\\n longest = 0\\n for i in range(sequence_length):\\n bit = bit_manipulation.getbit(binary, i)\\n if bit:\\n current += 1\\n longest = max(longest, current + last + 1)\\n else:\\n last = current\\n current = 0\\n return longest\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ce646fdea7446fd490869ab2c3aa4a03","score":"0.7966279","text":"def flip(self):\n self._repeat(table=bit_flipped)","title":""},{"docid":"a5472cfb27a488c804cc7408fd66cca3","score":"0.7797152","text":"def test_bit_flip(self):\n\n self.run_steane_test(\"bit flip\", True, False)","title":""},{"docid":"07a66b6896f903a57e010ffbd046655a","score":"0.74029297","text":"def _flip_bit(self, seed):\n flipper = 1 << random.randint(0, _n_bits - 2)\n return seed ^ flipper","title":""},{"docid":"a8cb1559daf488ffdc1b51e13254ea87","score":"0.7287755","text":"def flipbits(byte):\n\n bytecopy = 0\n for i in range(8):\n if (byte >> i) & 1 == 1:\n bytecopy += 1\n bytecopy = bytecopy << 1\n\n return bytecopy","title":""},{"docid":"3ce4c60af288319dfd9ba332248b0df5","score":"0.7200212","text":"def flip(num):\n return num * -1","title":""},{"docid":"e56b4301fa05185d589d0bff376157e5","score":"0.7164742","text":"def flip(array, i):\n array[i] ^= 1","title":""},{"docid":"d57685a31359612e4048fa9902584ffd","score":"0.7127928","text":"def flip(self):\n self.flipped = not self.flipped","title":""},{"docid":"333d362f2fed38df49481300e5fa5bab","score":"0.70924306","text":"def flip(self, im):\n return im","title":""},{"docid":"fff3997b1079c60f81d9954b32e4124b","score":"0.7054641","text":"def __perform_flip(self, image):\n image = np.fliplr(image)\n return image","title":""},{"docid":"fc11d1d9a704f40cf0f81fa4d7ac8078","score":"0.7006961","text":"def __flip(self):\n self.board_arr = np.rot90(self.board_arr, 2)\n self.board_arr *= -1 # converts all black to white + v.v.","title":""},{"docid":"2f3140257ad983c84703193a05f55d6c","score":"0.69987965","text":"def flip(self,c):\n l=[0, 8, 4, 12, 2, 10, 6, 14, 1, 9, 5, 13, 3, 11, 7, 15];\n return ((l[c&0x0F]) << 4) + l[(c & 0xF0) >> 4];","title":""},{"docid":"c7d2cd26b369ee4e392734be011fd491","score":"0.6993518","text":"def setFlip(self, flip):\n flipParity = 0\n i = Edge.BR - 1\n while i >= Edge.UR:\n self.eo[i] = int((flip % 2))\n flipParity = flipParity + self.eo[i]\n flip = flip / 2\n i = i - 1\n self.eo[Edge.BR] = int(((2 - flipParity % 2) % 2))\n print \"Done with setFlip()\"\n print self.eo","title":""},{"docid":"fa97627a80ad00b896ff4af8b0b4e919","score":"0.693533","text":"def flip(self, flip): \n self.data.d = flip_array(self.im, flip)","title":""},{"docid":"cd807fc28ad553acae84b719687679ba","score":"0.6930386","text":"def flip(S): #Used to help convert an 8 bit number to a TC number\r\n if S == '':\r\n return ''\r\n if S[0] == '0':\r\n return '1' + flip(S[1:])\r\n else:\r\n return '0' + flip(S[1:])","title":""},{"docid":"438cbd5100d376b7761514e24d88e499","score":"0.69281137","text":"def flip(self):\n self._flip = not self._flip","title":""},{"docid":"07de94ffb3fb776101f6437e54687333","score":"0.6923269","text":"def flip_color(self):\r\n\r\n pass","title":""},{"docid":"eac0e47ce7cb781a84104e98ae0c98be","score":"0.6909622","text":"def flip_bit(bit):\n\n\tif type(bit) != str:\n\t\traise TypeError(\"The input parameter should be of string type.\")\n\n\tif bit != \"0\" and bit != \"1\":\n\t\traise ValueError(\"The parameter should be a 0 or 1, encoded as a string\")\n\n\tif bit == \"0\":\n\t\treturn \"1\"\n\telse:\n\t\treturn \"0\"","title":""},{"docid":"1430a62a423d2e5e1e82c89a0324b81d","score":"0.6888029","text":"def flip_one_bit(x: float):\n bit_array = bitstring.BitArray(float=x, length=32)\n original_bits = np.array(bit_array).astype(bool)\n\n bit_to_flip = int(np.random.uniform(0, 1) * 32)\n original_bits[bit_to_flip] = np.invert(original_bits[bit_to_flip])\n\n bit_array.bin = ''.join(['0' if not bit else '1' for bit in original_bits])\n\n return bit_array.float","title":""},{"docid":"649a3fbf5131903919e8edc28a906c9b","score":"0.68879527","text":"def invert_bit(bit):\n if bit:\n bit = 0\n else:\n bit = 1\n return bit","title":""},{"docid":"f4da891c7d019848286e0b02ccd31065","score":"0.68744814","text":"def flip_tile(tile):\n return np.fliplr(tile)","title":""},{"docid":"c78684111ff0b27fe6103ef5e5cbb92b","score":"0.6811382","text":"def flip_int(num, b):\n o = 0\n for i in range(b/2):\n o |= ((num & (0xff << i*8)) << (b-(2*i+1))*8)\n o |= ((num & (0xff << (b-(i+1))*8)) >> (b-(2*i+1)) * 8)\n if b % 2 == 1:\n o |= (num & (0xff << (b/2)*8))\n return o","title":""},{"docid":"0f733e72894a2d1e261718d3994a150a","score":"0.6782888","text":"def flip(self, bev_direction='horizontal'):\n pass","title":""},{"docid":"e78e7d059ad1c5105a6fec56c870ba2d","score":"0.6776725","text":"def bit_flip(x: torch.Tensor, p: float) -> torch.Tensor:\n # language=rst\n x = x.float()\n i = torch.bernoulli(p * torch.ones_like(x)).byte()\n x = x.byte()\n x[i] = ~x[i]\n return x.float()","title":""},{"docid":"7df152a542293294f60edb176925bfcc","score":"0.67738813","text":"def Flip(src, dst=None, flip_mode=None): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"b336fe80ea67d9581cbd2ff1c389c93a","score":"0.6757504","text":"def flip(self, x):\n\t\tif x: pass #pygame.transform.flip(tmpimage,1,0)\n\t\telse: pass #pygame.transform.flip(tmpimage,0,1)\n\t\tpass","title":""},{"docid":"549c89c24bbf98736755b7ec7c6fcb0a","score":"0.67574906","text":"def __invert__(self):\n if self.flip:\n return Isometry(self.shift, self.flip)\n else:\n return Isometry(-self.shift, self.flip)","title":""},{"docid":"268c2bd5f5a797c2377dbd6ba39c4d38","score":"0.6709418","text":"def _fliplr(im):\n return np.fliplr(im)","title":""},{"docid":"9341ff03e373414899b4e8ef5a9f6c55","score":"0.66236055","text":"def flip_a_coin(self):\n if random.randint(0, 1) == 0:\n self.__mode = \"ECB\"\n else:\n self.__mode = \"CBC\"","title":""},{"docid":"1dad58e9836ff2c2c3bda470ca9a46bd","score":"0.6607879","text":"def getFlip(self):\n ret = 0\n i = Edge.UR\n while i < Edge.BR:\n ret = int((2 * ret + self.eo[i]))\n i += 1\n return ret","title":""},{"docid":"d40cf07cebef22ca469d5aa82b4c8ae3","score":"0.6575785","text":"def bswap1(i):\n\n return i & 0xff","title":""},{"docid":"33cb744026f5441f198e633b6a38f72d","score":"0.65521175","text":"def turn(n):\r\n return (((n & -n) << 1) & n) != 0","title":""},{"docid":"93e211d5397c98af8fa509855f8e7e2e","score":"0.65465194","text":"def Invert(self):","title":""},{"docid":"93e211d5397c98af8fa509855f8e7e2e","score":"0.65465194","text":"def Invert(self):","title":""},{"docid":"93e211d5397c98af8fa509855f8e7e2e","score":"0.65465194","text":"def Invert(self):","title":""},{"docid":"e3cd4164d3bfd6193145ee7fba15c300","score":"0.65242046","text":"def flip_ver(w):\n return [s[::-1] for s in w]","title":""},{"docid":"fe502485059d6f83734fcd50d82cbc06","score":"0.65087336","text":"def flip_array_state(array_state):\n if array_state == 1:\n array_state = -1\n else:\n array_state = 1\n return array_state","title":""},{"docid":"4cb682b90805e30a41247d07175dbce7","score":"0.6489366","text":"def __flip(self, img):\n return [img.flip(2), img.flip(3)]","title":""},{"docid":"291d0bff41113b7ce4e94d90520c74d9","score":"0.6459794","text":"def flip(self):\n try:\n S=copy.deepcopy(self.S)\n self.S.S11=S.S22\n self.S.S22=S.S11\n self.S.S12=S.S21\n self.S.S21=S.S12\n except AttributeError:\n raise RuntimeError('structure not solved yet')\n self.layers = self.layers[::-1]\n self.d = self.d[::-1]","title":""},{"docid":"3eda56c84b2f1c68efce53f33fb54d07","score":"0.6453623","text":"def double_flip(image_array):\n return vertical_flip(horizontal_flip(image_array))","title":""},{"docid":"ebaaff58c0b6227a198ae0df5e7867b8","score":"0.6423945","text":"def vflip(self) -> None:\n lib.TCOD_image_vflip(self.image_c)","title":""},{"docid":"e495067156897ea67ac12b29171f99a3","score":"0.642288","text":"def flip(x, y):\n return y, x","title":""},{"docid":"08f160ffe1610cb5c1171a6174ad3b48","score":"0.6421534","text":"def flip_image(image):\n flipped_image = np.fliplr(image)\n return flipped_image","title":""},{"docid":"dab8c9f10b6cd5ae0bdfbf6f44216bd6","score":"0.641418","text":"def flip(self):\r\n self.hidden = not self.hidden","title":""},{"docid":"e32fdc60ce16fe211c005ccb82a6c93d","score":"0.64125526","text":"def reverseBits1(self, n):\n # basically, swapping the first halp and the second half\n n = (n >> 16) | (n << 16)\n n = ((n & 0xff00ff00) >> 8) | ((n & 0x00ff00ff) << 8)\n n = ((n & 0xf0f0f0f0) >> 4) | ((n & 0x0f0f0f0f) << 4)\n n = ((n & 0xcccccccc) >> 2) | ((n & 0x33333333) << 2)\n n = ((n & 0xaaaaaaaa) >> 1) | ((n & 0x55555555) << 1)\n return n","title":""},{"docid":"58d6cf3a7a2ee8fd98d795069d02c4b9","score":"0.6402065","text":"def torch_fliplr(x: Tensor):\n return x.flip(3)","title":""},{"docid":"9a2291fa9dfd5328ed7799c782a51c02","score":"0.6359118","text":"def flip_bits(seq, width):\n out = []\n h = pow(2, width//2)\n for x in seq:\n high_bits = x//h\n low_bits = x % h\n out.append(high_bits + low_bits * h)\n return out","title":""},{"docid":"417b59144fbc91e5bec5d48e84a056ee","score":"0.6343225","text":"def flip_player(self):\n if self.player == 1:\n self.player = -1\n else:\n self.player = 1","title":""},{"docid":"9328a0951e7d7f8f061f32e1ec35cfe2","score":"0.63378704","text":"def _flip(self, direction):\n if self.direction != direction:\n self.image = pygame.transform.flip(self.image, True, False)\n self.direction = direction","title":""},{"docid":"5158e176444433bd018efda493e087c0","score":"0.6337502","text":"def flip(self):\n if self.filter_mask is None:\n return self.raw_flip\n else:\n return self.raw_flip[self.filter_mask]","title":""},{"docid":"ccd37b5a2d5d44d26ce9232c728105a9","score":"0.6332241","text":"def flip(self, a, axis=None):\n raise NotImplementedError()","title":""},{"docid":"1eb0950b3004d106b5e2a491563a8115","score":"0.63109","text":"def flip_right():\n # TODO: Assert battery is high enough to perform flip before attempting\n send_and_wait(\"flip r\")","title":""},{"docid":"a14c86188e2b886acfa969dec867bfec","score":"0.6303685","text":"def flip(self, axis=\"y\"):\n return self.mirror(axis=axis)","title":""},{"docid":"4620a9591fe83ea47782397cba8ad8e0","score":"0.62975","text":"def flip(self, bot, event):\n if not event.text:\n bot.message(\"(╯°□°）╯︵ ┻━┻\", target=bot.channel)\n else:\n normal = \"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz1234567890.,!?）)(_><\"\n upsidedown = \"∀ᗺƆpƎℲפHIſʞ˥WNOԀQɹS┴∩ΛMX⅄ZɐqɔpǝɟƃɥᴉɾʞlɯuodbɹsʇnʌʍxʎzƖᄅƐㄣϛ9ㄥ860˙'¡¿(()¯<>\"\n mapping = {a: b for a, b in zip(normal, upsidedown)}\n flipped = \"\".join(mapping.get(c, c) for c in reversed(event.text))\n bot.message(\"(╯°□°）╯︵ \" + flipped)","title":""},{"docid":"b251e9ff7d187fbc691bd4d5d2bf4cb4","score":"0.6292544","text":"def flip(self, mode='h'):\n # TODO: Implement the flip function. Remember to record the boolean values is_horizontal_flip and\n # is_vertical_flip.\n \n #######################################################################\n # #\n # #\n # TODO: YOUR CODE HERE #\n # #\n # #\n #######################################################################\n x = self.x\n params = self.params\n \n if mode=='h':\n params['is_horizontal_flip'] = not params['is_horizontal_flip']\n x = np.flip(x, axis=2)\n if mode=='v':\n params['is_vertical_flip'] = not params['is_vertical_flip']\n x = np.flip(x, axis=1)\n if mode=='hv':\n params['is_horizontal_flip'] = not params['is_horizontal_flip']\n x = np.flip(x, axis=2)\n params['is_vertical_flip'] = not params['is_vertical_flip']\n x = np.flip(x, axis=1)\n \n self.x = x","title":""},{"docid":"72806623c263b028502b5ce5c37ab63a","score":"0.62729913","text":"def flip_image(image):\n\n return pygame.transform.flip(image, True, False)","title":""},{"docid":"683cd9e93bb70d688ccaa351c9bf832d","score":"0.62667066","text":"def flipImage(image):\n return cv2.flip(image, 1)","title":""},{"docid":"9a9990487129e329051a960a968185e7","score":"0.6265247","text":"def restore_2complement(msb, lsb):\n ...","title":""},{"docid":"2a2bde6e369d5dda8b13398ea63e57c7","score":"0.62650806","text":"def flip_image(self, img, mode='h'):\n\n if mode == 'h':\n aug = iaa.Fliplr()\n else:\n aug = iaa.Flipud()\n \n flipped_img = aug(image=img)\n return flipped_img","title":""},{"docid":"75380059610786d2dbc3b3ed56c45d46","score":"0.6259178","text":"def flip_ud(self,data):\n\n pass","title":""},{"docid":"91a5f969e41341e2c19bc7c36e248bdb","score":"0.62308806","text":"def flip80(data: bytes) -> bytes:\n if len(data) % 4 != 0:\n raise ValueError('data must have a size multiple of 4')\n return b''.join(x[::-1] for x in ichunks(data, 4))","title":""},{"docid":"1e4dd6276985a0398f8988350e985c2c","score":"0.62290126","text":"def flip_hor(w):\n return w[::-1]","title":""},{"docid":"b627bfb49948b4c4f66ab165e1012526","score":"0.6221524","text":"def flip_backward():\n # TODO: Assert battery is high enough to perform flip before attempting\n send_and_wait(\"flip b\")","title":""},{"docid":"8215f0821080aebaeab86ed679799b0f","score":"0.621459","text":"def byteswap( ):","title":""},{"docid":"628a4b38b881ec61c1ad6c34ff777316","score":"0.6213563","text":"def flip(self, x=False, y=False):\n self._flipx = x\n self._flipy = y","title":""},{"docid":"f445ef18329e25c50675b29a56616f5d","score":"0.6210618","text":"def swap16(x):\n return ((x & 0xff00) >> 8) | \\\n ((x & 0x00ff) << 8)","title":""},{"docid":"ac5013259009fbb7a38644fe0f44ca8a","score":"0.6208504","text":"def flipVert():\n\n Im_pix = getRGB('in.png') # read in the in.png image\n print(\"The first two pixels of the first row are\", Im_pix[0][0:2])\n\n New_pix = Im_pix[::-1]\n\n\n saveRGB(New_pix, 'out.png')","title":""},{"docid":"d80dabb5203c857685c9f565a8081536","score":"0.6208115","text":"def vflip(val=None):\r\n\t\tCamera.modified[0] = True\r\n\t\tif Camera.opened:\r\n\t\t\t# print(\"Vflip %d\"%val)\r\n\t\t\treturn camera.vflip(val)\r\n\t\treturn None","title":""},{"docid":"ab82fc69023971940328eddf5892517e","score":"0.61914253","text":"def flip(self):\r\n # Flip the card and update the image\r\n self.set[self.index].flip()\r\n self.update_image()","title":""},{"docid":"9d1a5d2324c49310a562d0658e0d016e","score":"0.6190427","text":"def flip(x):\n return np.sign(np.random.rand(1)-0.5) * x","title":""},{"docid":"16ba589744430eba8ebb3a7cde1b2fb8","score":"0.6181124","text":"def flip_x(self):\n return self._flip_x","title":""},{"docid":"7c33441ed607357f8ef47bce59d07a6b","score":"0.6179949","text":"def test_roundtrip_flip(self):\n # Compress and decompressed a flipped LDR image\n decompFile = self.get_tmp_image_path(\"LDR\", \"decomp\")\n\n command = [\n self.binary, \"-tl\",\n \"./Test/Data/Tiles/ldr.png\",\n decompFile, \"4x4\", \"-fast\", \"-yflip\"]\n\n self.exec(command)\n\n # Compare TL (0, 0) with TL - should match - i.e. no flip\n colorRef = self.get_color_refs(\"LDR\", \"TL\")\n\n img = tli.Image(decompFile)\n colorVal = img.get_colors([(0, 0)])\n\n self.assertColorSame(colorRef[0], colorVal[0])","title":""},{"docid":"b5bc0969eadf8ac4b83d2d5001aa30cd","score":"0.61564654","text":"def flip_v(self):\n lst_t = self.m\n self.m = lst_t[::-1]\n self.clean_all_but_m()","title":""},{"docid":"49aefdb7fd9e3b08d38c75210e61335a","score":"0.6145693","text":"def flip_effect(image: Image.Image) -> Image.Image:\n image = ImageOps.mirror(image)\n\n return image","title":""},{"docid":"848d56ce66575af50e2356731a6c33cb","score":"0.6134909","text":"def flip_lr(img):\n return np.flip(img.copy(), 4)","title":""},{"docid":"6158a59e135e2b7e70a4cb59b398c617","score":"0.6128792","text":"def vertical_flip(self, picture, output_encoding):\n img = images.Image(picture.data)\n img.vertical_flip()\n transformed = img.execute_transforms(output_encoding)\n return transformed","title":""},{"docid":"dc9e623de2ff7cce69d1ca07d1394e27","score":"0.6115801","text":"def flip_alleles(self):\n assert(len(self.alt_alleles) == 1)\n self.ref_allele, self.alt_alleles[0] = self.alt_alleles[0], self.ref_allele\n self.alt_allele = self.alt_alleles[0]\n self.flipped = True","title":""},{"docid":"237f2cd4a1b3ee96e0b983e660266821","score":"0.6113052","text":"def bswap2(i):\n\n return (i >> 8) & 0xff, i & 0xff","title":""},{"docid":"324d3665cac1291f7d1126015a566ef5","score":"0.6107709","text":"def two_bit_flip(node):\r\n\r\n node_list = list(node)\r\n out = set()\r\n bit_length = len(node_list)\r\n for i in range(bit_length):\r\n for j in range(bit_length):\r\n new_node = node_list[:]\r\n if i != j:\r\n new_node[i] = ('1' if node[i] == '0' else '0')\r\n new_node[j] = ('1' if node[j] == '0' else '0')\r\n else:\r\n new_node[i] = ('1' if node[i] == '0' else '0')\r\n out.add(''.join(new_node))\r\n return out","title":""},{"docid":"5ebd1f8b92354eb971e6205d8df5149a","score":"0.6102803","text":"def pairwiseSwap(n):\n #put odd at even places then even at odd places\n return ((n & 0xAAAAAAAA) >> 1) | ((n & 0x55555555) << 1)","title":""},{"docid":"ee0e00c5aa68fed60bda000a08cd99cd","score":"0.6102466","text":"def __flip__(x):\n return np.flip(x, axis = 1)","title":""},{"docid":"e4855c6ca70e4fe028908b595a26e6ca","score":"0.60916454","text":"def invert_bitstring(string):\n return string.replace(\"1\", \"2\").replace(\"0\", \"1\").replace(\"2\", \"0\")","title":""},{"docid":"2aac649f5f256757daa47d1d938a3ca4","score":"0.6083563","text":"def flip ( self, direction, position, color ):\n \n if direction == 1:\n # north\n row_inc = -1\n col_inc = 0\n elif direction == 2:\n # northeast\n row_inc = -1\n col_inc = 1\n elif direction == 3:\n # east\n row_inc = 0\n col_inc = 1\n elif direction == 4:\n # southeast\n row_inc = 1\n col_inc = 1\n elif direction == 5:\n # south\n row_inc = 1\n col_inc = 0\n elif direction == 6:\n # southwest\n row_inc = 1\n col_inc = -1\n elif direction == 7:\n # west\n row_inc = 0\n col_inc = -1\n elif direction == 8:\n # northwest\n row_inc = -1\n col_inc = -1\n \n places = [] # pieces to flip\n i = position[0] + row_inc\n j = position[1] + col_inc \n\n if color == WHITE:\n other = BLACK\n else:\n other = WHITE\n \n if i in range( 8 ) and j in range( 8 ) and self.board[i][j] == other:\n # assures there is at least one piece to flip\n places = places + [(i,j)]\n i = i + row_inc\n j = j + col_inc\n while i in range( 8 ) and j in range( 8 ) and self.board[i][j] == other:\n # search for more pieces to flip\n places = places + [(i,j)]\n i = i + row_inc\n j = j + col_inc\n if i in range( 8 ) and j in range( 8 ) and self.board[i][j] == color:\n # found a piece of the right color to flip the pieces between\n for pos in places:\n # flips\n self.board[pos[0]][pos[1]] = color","title":""},{"docid":"a6e38d49dca2e80287ba7c1506df3419","score":"0.608276","text":"def flip_forward():\n # TODO: Assert battery is high enough to perform flip before attempting\n send_and_wait(\"flip f\")","title":""},{"docid":"572c35cc26040ffe2cd10d5f29384115","score":"0.60784805","text":"def bswap3(i):\n\n return (i >> 16) & 0xff, (i >> 8) & 0xff, i & 0xff","title":""},{"docid":"1585ff409341421811fd930a873ba95d","score":"0.606961","text":"def _flip(shp):\n for ring in workflow.utils.generate_rings(shp):\n for i,c in enumerate(ring):\n ring[i] = c[1],c[0]\n return shp","title":""},{"docid":"7671783ff28736f076a276364df8bae7","score":"0.6061207","text":"def reverse_bit(num):\n result = 0\n for _ in range(8):\n result <<= 1\n result += (num & 1)\n num >>= 1\n return result","title":""},{"docid":"b3bc6bff7f45526183b2a3d8428875fc","score":"0.6051063","text":"def _flip(self):\n time = self._get_ticks()\n self._ticks = time - self._time\n self._elapsed += self._ticks\n self._time = time\n # Once per second...\n if self._elapsed < 1:\n return\n elif self._elapsed >= self._reset_threshold:\n self.reset()\n else:\n self._elapsed %= 1\n # Save stats and clear counters.\n self._tps = 0.0\n self._fps = self._frame_count\n self._ups = self._update_count\n self._frame_count = self._update_count = 0","title":""},{"docid":"c8ffb1679636e4236000a7f54b8da12c","score":"0.6049809","text":"def doflip(dec,inc):\n if inc <0:\n inc=-inc\n dec=dec+180.\n if dec > 360: dec=dec-360.\n return dec,inc","title":""},{"docid":"7c479c196d50309c47a8f24bfec5ea6c","score":"0.6039424","text":"def prob_flip(self):\n random_num = random.randint(1, 10)\n\n if random_num == 1:\n self.flip = True","title":""},{"docid":"60141950f85200d6b49139f7cf3a72ee","score":"0.60368615","text":"def auto_flip_normal(self):\n return False","title":""},{"docid":"b680b75fdb31343f3c23a15665d31117","score":"0.60357386","text":"def flip(stack, n):\n return inverse(stack[:n][::-1]) + stack[n:]","title":""},{"docid":"c62b005fcf730347399136d7478e1430","score":"0.6029708","text":"def random_flip(image, steering):\n if np.random.randint(2) == 0:\n image = np.fliplr(image)\n steering = -steering\n\n return image, steering","title":""},{"docid":"b751ba9a19b9c482e71da95b4ceeed5b","score":"0.6020218","text":"def flip_board(self):\n\n self.board = self.get_flipped_board()","title":""},{"docid":"beca608e9a88236973aca21ba7824d9a","score":"0.6015986","text":"def flip_bytes(self, bytes):\n byte_list = list(bytes)\n if binascii.hexlify(self.magic_number) in [MAGIC_SWAP, MAGIC_SWAP_NANO]:\n byte_list.reverse()\n return ''.join(byte_list)","title":""},{"docid":"866d2e67526175406fad301f86587134","score":"0.60141283","text":"def doFlip(board, color, pos, direction):\n\tcurrX = pos[0] + direction[0]\n\tcurrY = pos[1] + direction[1]\n\twhile board[currX][currY] == opponent(color):\n\t\tboard[currX][currY] = color\n\t\t(currX, currY) = (currX + direction[0], currY + direction[1])","title":""},{"docid":"7e381c139205b97cb1d82fd901888094","score":"0.60135734","text":"def flip (self):\r\n return Link(self[1], self[0])","title":""},{"docid":"9c6d159fb28e2bcedf2ada70fa296e38","score":"0.60082066","text":"def __invert__(self):\n product = Bitmap(self.cardinality)\n for i in range(0, len(self.bit_array)):\n product.bit_array[i] = ~self.bit_array[i]\n return product","title":""},{"docid":"3fff317a8f3556cda00b2306676b04f9","score":"0.60057265","text":"def flip(point):\n \n x, y = point\n return (x, -y)","title":""},{"docid":"cab5ff7e17e6df6147a6f8dbeb5fba01","score":"0.6002178","text":"def _invert_sub_bytes(state: List[int]) -> List[int]:\n return SimpleAES._substitute(state, SimpleAES.INVERSE_SBOX)","title":""},{"docid":"e001c860e560f1e9698e6906da81f4eb","score":"0.6000485","text":"def mirror(array):\n return np.rot90(np.flipud(array), 3)","title":""},{"docid":"fb8b6635cb630c222711252a987441e3","score":"0.59873736","text":"def reverse_bits(x: int) -> int:\n i = 0\n j = 63\n while i < j:\n x = swap_bits(x, i, j)\n i += 1\n j -= 1\n\n return x","title":""}],"string":"[\n {\n \"docid\": \"ce646fdea7446fd490869ab2c3aa4a03\",\n \"score\": \"0.7966279\",\n \"text\": \"def flip(self):\\n self._repeat(table=bit_flipped)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5472cfb27a488c804cc7408fd66cca3\",\n \"score\": \"0.7797152\",\n \"text\": \"def test_bit_flip(self):\\n\\n self.run_steane_test(\\\"bit flip\\\", True, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07a66b6896f903a57e010ffbd046655a\",\n \"score\": \"0.74029297\",\n \"text\": \"def _flip_bit(self, seed):\\n flipper = 1 << random.randint(0, _n_bits - 2)\\n return seed ^ flipper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8cb1559daf488ffdc1b51e13254ea87\",\n \"score\": \"0.7287755\",\n \"text\": \"def flipbits(byte):\\n\\n bytecopy = 0\\n for i in range(8):\\n if (byte >> i) & 1 == 1:\\n bytecopy += 1\\n bytecopy = bytecopy << 1\\n\\n return bytecopy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ce4c60af288319dfd9ba332248b0df5\",\n \"score\": \"0.7200212\",\n \"text\": \"def flip(num):\\n return num * -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e56b4301fa05185d589d0bff376157e5\",\n \"score\": \"0.7164742\",\n \"text\": \"def flip(array, i):\\n array[i] ^= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d57685a31359612e4048fa9902584ffd\",\n \"score\": \"0.7127928\",\n \"text\": \"def flip(self):\\n self.flipped = not self.flipped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"333d362f2fed38df49481300e5fa5bab\",\n \"score\": \"0.70924306\",\n \"text\": \"def flip(self, im):\\n return im\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fff3997b1079c60f81d9954b32e4124b\",\n \"score\": \"0.7054641\",\n \"text\": \"def __perform_flip(self, image):\\n image = np.fliplr(image)\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc11d1d9a704f40cf0f81fa4d7ac8078\",\n \"score\": \"0.7006961\",\n \"text\": \"def __flip(self):\\n self.board_arr = np.rot90(self.board_arr, 2)\\n self.board_arr *= -1 # converts all black to white + v.v.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f3140257ad983c84703193a05f55d6c\",\n \"score\": \"0.69987965\",\n \"text\": \"def flip(self,c):\\n l=[0, 8, 4, 12, 2, 10, 6, 14, 1, 9, 5, 13, 3, 11, 7, 15];\\n return ((l[c&0x0F]) << 4) + l[(c & 0xF0) >> 4];\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7d2cd26b369ee4e392734be011fd491\",\n \"score\": \"0.6993518\",\n \"text\": \"def setFlip(self, flip):\\n flipParity = 0\\n i = Edge.BR - 1\\n while i >= Edge.UR:\\n self.eo[i] = int((flip % 2))\\n flipParity = flipParity + self.eo[i]\\n flip = flip / 2\\n i = i - 1\\n self.eo[Edge.BR] = int(((2 - flipParity % 2) % 2))\\n print \\\"Done with setFlip()\\\"\\n print self.eo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa97627a80ad00b896ff4af8b0b4e919\",\n \"score\": \"0.693533\",\n \"text\": \"def flip(self, flip): \\n self.data.d = flip_array(self.im, flip)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd807fc28ad553acae84b719687679ba\",\n \"score\": \"0.6930386\",\n \"text\": \"def flip(S): #Used to help convert an 8 bit number to a TC number\\r\\n if S == '':\\r\\n return ''\\r\\n if S[0] == '0':\\r\\n return '1' + flip(S[1:])\\r\\n else:\\r\\n return '0' + flip(S[1:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"438cbd5100d376b7761514e24d88e499\",\n \"score\": \"0.69281137\",\n \"text\": \"def flip(self):\\n self._flip = not self._flip\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07de94ffb3fb776101f6437e54687333\",\n \"score\": \"0.6923269\",\n \"text\": \"def flip_color(self):\\r\\n\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eac0e47ce7cb781a84104e98ae0c98be\",\n \"score\": \"0.6909622\",\n \"text\": \"def flip_bit(bit):\\n\\n\\tif type(bit) != str:\\n\\t\\traise TypeError(\\\"The input parameter should be of string type.\\\")\\n\\n\\tif bit != \\\"0\\\" and bit != \\\"1\\\":\\n\\t\\traise ValueError(\\\"The parameter should be a 0 or 1, encoded as a string\\\")\\n\\n\\tif bit == \\\"0\\\":\\n\\t\\treturn \\\"1\\\"\\n\\telse:\\n\\t\\treturn \\\"0\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1430a62a423d2e5e1e82c89a0324b81d\",\n \"score\": \"0.6888029\",\n \"text\": \"def flip_one_bit(x: float):\\n bit_array = bitstring.BitArray(float=x, length=32)\\n original_bits = np.array(bit_array).astype(bool)\\n\\n bit_to_flip = int(np.random.uniform(0, 1) * 32)\\n original_bits[bit_to_flip] = np.invert(original_bits[bit_to_flip])\\n\\n bit_array.bin = ''.join(['0' if not bit else '1' for bit in original_bits])\\n\\n return bit_array.float\",\n \"title\": \"\"\n },\n {\n \"docid\": \"649a3fbf5131903919e8edc28a906c9b\",\n \"score\": \"0.68879527\",\n \"text\": \"def invert_bit(bit):\\n if bit:\\n bit = 0\\n else:\\n bit = 1\\n return bit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4da891c7d019848286e0b02ccd31065\",\n \"score\": \"0.68744814\",\n \"text\": \"def flip_tile(tile):\\n return np.fliplr(tile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c78684111ff0b27fe6103ef5e5cbb92b\",\n \"score\": \"0.6811382\",\n \"text\": \"def flip_int(num, b):\\n o = 0\\n for i in range(b/2):\\n o |= ((num & (0xff << i*8)) << (b-(2*i+1))*8)\\n o |= ((num & (0xff << (b-(i+1))*8)) >> (b-(2*i+1)) * 8)\\n if b % 2 == 1:\\n o |= (num & (0xff << (b/2)*8))\\n return o\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f733e72894a2d1e261718d3994a150a\",\n \"score\": \"0.6782888\",\n \"text\": \"def flip(self, bev_direction='horizontal'):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e78e7d059ad1c5105a6fec56c870ba2d\",\n \"score\": \"0.6776725\",\n \"text\": \"def bit_flip(x: torch.Tensor, p: float) -> torch.Tensor:\\n # language=rst\\n x = x.float()\\n i = torch.bernoulli(p * torch.ones_like(x)).byte()\\n x = x.byte()\\n x[i] = ~x[i]\\n return x.float()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7df152a542293294f60edb176925bfcc\",\n \"score\": \"0.67738813\",\n \"text\": \"def Flip(src, dst=None, flip_mode=None): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b336fe80ea67d9581cbd2ff1c389c93a\",\n \"score\": \"0.6757504\",\n \"text\": \"def flip(self, x):\\n\\t\\tif x: pass #pygame.transform.flip(tmpimage,1,0)\\n\\t\\telse: pass #pygame.transform.flip(tmpimage,0,1)\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"549c89c24bbf98736755b7ec7c6fcb0a\",\n \"score\": \"0.67574906\",\n \"text\": \"def __invert__(self):\\n if self.flip:\\n return Isometry(self.shift, self.flip)\\n else:\\n return Isometry(-self.shift, self.flip)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"268c2bd5f5a797c2377dbd6ba39c4d38\",\n \"score\": \"0.6709418\",\n \"text\": \"def _fliplr(im):\\n return np.fliplr(im)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9341ff03e373414899b4e8ef5a9f6c55\",\n \"score\": \"0.66236055\",\n \"text\": \"def flip_a_coin(self):\\n if random.randint(0, 1) == 0:\\n self.__mode = \\\"ECB\\\"\\n else:\\n self.__mode = \\\"CBC\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dad58e9836ff2c2c3bda470ca9a46bd\",\n \"score\": \"0.6607879\",\n \"text\": \"def getFlip(self):\\n ret = 0\\n i = Edge.UR\\n while i < Edge.BR:\\n ret = int((2 * ret + self.eo[i]))\\n i += 1\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d40cf07cebef22ca469d5aa82b4c8ae3\",\n \"score\": \"0.6575785\",\n \"text\": \"def bswap1(i):\\n\\n return i & 0xff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33cb744026f5441f198e633b6a38f72d\",\n \"score\": \"0.65521175\",\n \"text\": \"def turn(n):\\r\\n return (((n & -n) << 1) & n) != 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93e211d5397c98af8fa509855f8e7e2e\",\n \"score\": \"0.65465194\",\n \"text\": \"def Invert(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93e211d5397c98af8fa509855f8e7e2e\",\n \"score\": \"0.65465194\",\n \"text\": \"def Invert(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93e211d5397c98af8fa509855f8e7e2e\",\n \"score\": \"0.65465194\",\n \"text\": \"def Invert(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3cd4164d3bfd6193145ee7fba15c300\",\n \"score\": \"0.65242046\",\n \"text\": \"def flip_ver(w):\\n return [s[::-1] for s in w]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe502485059d6f83734fcd50d82cbc06\",\n \"score\": \"0.65087336\",\n \"text\": \"def flip_array_state(array_state):\\n if array_state == 1:\\n array_state = -1\\n else:\\n array_state = 1\\n return array_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cb682b90805e30a41247d07175dbce7\",\n \"score\": \"0.6489366\",\n \"text\": \"def __flip(self, img):\\n return [img.flip(2), img.flip(3)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"291d0bff41113b7ce4e94d90520c74d9\",\n \"score\": \"0.6459794\",\n \"text\": \"def flip(self):\\n try:\\n S=copy.deepcopy(self.S)\\n self.S.S11=S.S22\\n self.S.S22=S.S11\\n self.S.S12=S.S21\\n self.S.S21=S.S12\\n except AttributeError:\\n raise RuntimeError('structure not solved yet')\\n self.layers = self.layers[::-1]\\n self.d = self.d[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eda56c84b2f1c68efce53f33fb54d07\",\n \"score\": \"0.6453623\",\n \"text\": \"def double_flip(image_array):\\n return vertical_flip(horizontal_flip(image_array))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebaaff58c0b6227a198ae0df5e7867b8\",\n \"score\": \"0.6423945\",\n \"text\": \"def vflip(self) -> None:\\n lib.TCOD_image_vflip(self.image_c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e495067156897ea67ac12b29171f99a3\",\n \"score\": \"0.642288\",\n \"text\": \"def flip(x, y):\\n return y, x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08f160ffe1610cb5c1171a6174ad3b48\",\n \"score\": \"0.6421534\",\n \"text\": \"def flip_image(image):\\n flipped_image = np.fliplr(image)\\n return flipped_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dab8c9f10b6cd5ae0bdfbf6f44216bd6\",\n \"score\": \"0.641418\",\n \"text\": \"def flip(self):\\r\\n self.hidden = not self.hidden\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e32fdc60ce16fe211c005ccb82a6c93d\",\n \"score\": \"0.64125526\",\n \"text\": \"def reverseBits1(self, n):\\n # basically, swapping the first halp and the second half\\n n = (n >> 16) | (n << 16)\\n n = ((n & 0xff00ff00) >> 8) | ((n & 0x00ff00ff) << 8)\\n n = ((n & 0xf0f0f0f0) >> 4) | ((n & 0x0f0f0f0f) << 4)\\n n = ((n & 0xcccccccc) >> 2) | ((n & 0x33333333) << 2)\\n n = ((n & 0xaaaaaaaa) >> 1) | ((n & 0x55555555) << 1)\\n return n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58d6cf3a7a2ee8fd98d795069d02c4b9\",\n \"score\": \"0.6402065\",\n \"text\": \"def torch_fliplr(x: Tensor):\\n return x.flip(3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a2291fa9dfd5328ed7799c782a51c02\",\n \"score\": \"0.6359118\",\n \"text\": \"def flip_bits(seq, width):\\n out = []\\n h = pow(2, width//2)\\n for x in seq:\\n high_bits = x//h\\n low_bits = x % h\\n out.append(high_bits + low_bits * h)\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"417b59144fbc91e5bec5d48e84a056ee\",\n \"score\": \"0.6343225\",\n \"text\": \"def flip_player(self):\\n if self.player == 1:\\n self.player = -1\\n else:\\n self.player = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9328a0951e7d7f8f061f32e1ec35cfe2\",\n \"score\": \"0.63378704\",\n \"text\": \"def _flip(self, direction):\\n if self.direction != direction:\\n self.image = pygame.transform.flip(self.image, True, False)\\n self.direction = direction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5158e176444433bd018efda493e087c0\",\n \"score\": \"0.6337502\",\n \"text\": \"def flip(self):\\n if self.filter_mask is None:\\n return self.raw_flip\\n else:\\n return self.raw_flip[self.filter_mask]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccd37b5a2d5d44d26ce9232c728105a9\",\n \"score\": \"0.6332241\",\n \"text\": \"def flip(self, a, axis=None):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eb0950b3004d106b5e2a491563a8115\",\n \"score\": \"0.63109\",\n \"text\": \"def flip_right():\\n # TODO: Assert battery is high enough to perform flip before attempting\\n send_and_wait(\\\"flip r\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a14c86188e2b886acfa969dec867bfec\",\n \"score\": \"0.6303685\",\n \"text\": \"def flip(self, axis=\\\"y\\\"):\\n return self.mirror(axis=axis)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4620a9591fe83ea47782397cba8ad8e0\",\n \"score\": \"0.62975\",\n \"text\": \"def flip(self, bot, event):\\n if not event.text:\\n bot.message(\\\"(╯°□°）╯︵ ┻━┻\\\", target=bot.channel)\\n else:\\n normal = \\\"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz1234567890.,!?）)(_><\\\"\\n upsidedown = \\\"∀ᗺƆpƎℲפHIſʞ˥WNOԀQɹS┴∩ΛMX⅄ZɐqɔpǝɟƃɥᴉɾʞlɯuodbɹsʇnʌʍxʎzƖᄅƐㄣϛ9ㄥ860˙'¡¿(()¯<>\\\"\\n mapping = {a: b for a, b in zip(normal, upsidedown)}\\n flipped = \\\"\\\".join(mapping.get(c, c) for c in reversed(event.text))\\n bot.message(\\\"(╯°□°）╯︵ \\\" + flipped)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b251e9ff7d187fbc691bd4d5d2bf4cb4\",\n \"score\": \"0.6292544\",\n \"text\": \"def flip(self, mode='h'):\\n # TODO: Implement the flip function. Remember to record the boolean values is_horizontal_flip and\\n # is_vertical_flip.\\n \\n #######################################################################\\n # #\\n # #\\n # TODO: YOUR CODE HERE #\\n # #\\n # #\\n #######################################################################\\n x = self.x\\n params = self.params\\n \\n if mode=='h':\\n params['is_horizontal_flip'] = not params['is_horizontal_flip']\\n x = np.flip(x, axis=2)\\n if mode=='v':\\n params['is_vertical_flip'] = not params['is_vertical_flip']\\n x = np.flip(x, axis=1)\\n if mode=='hv':\\n params['is_horizontal_flip'] = not params['is_horizontal_flip']\\n x = np.flip(x, axis=2)\\n params['is_vertical_flip'] = not params['is_vertical_flip']\\n x = np.flip(x, axis=1)\\n \\n self.x = x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72806623c263b028502b5ce5c37ab63a\",\n \"score\": \"0.62729913\",\n \"text\": \"def flip_image(image):\\n\\n return pygame.transform.flip(image, True, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"683cd9e93bb70d688ccaa351c9bf832d\",\n \"score\": \"0.62667066\",\n \"text\": \"def flipImage(image):\\n return cv2.flip(image, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a9990487129e329051a960a968185e7\",\n \"score\": \"0.6265247\",\n \"text\": \"def restore_2complement(msb, lsb):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a2bde6e369d5dda8b13398ea63e57c7\",\n \"score\": \"0.62650806\",\n \"text\": \"def flip_image(self, img, mode='h'):\\n\\n if mode == 'h':\\n aug = iaa.Fliplr()\\n else:\\n aug = iaa.Flipud()\\n \\n flipped_img = aug(image=img)\\n return flipped_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75380059610786d2dbc3b3ed56c45d46\",\n \"score\": \"0.6259178\",\n \"text\": \"def flip_ud(self,data):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91a5f969e41341e2c19bc7c36e248bdb\",\n \"score\": \"0.62308806\",\n \"text\": \"def flip80(data: bytes) -> bytes:\\n if len(data) % 4 != 0:\\n raise ValueError('data must have a size multiple of 4')\\n return b''.join(x[::-1] for x in ichunks(data, 4))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4dd6276985a0398f8988350e985c2c\",\n \"score\": \"0.62290126\",\n \"text\": \"def flip_hor(w):\\n return w[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b627bfb49948b4c4f66ab165e1012526\",\n \"score\": \"0.6221524\",\n \"text\": \"def flip_backward():\\n # TODO: Assert battery is high enough to perform flip before attempting\\n send_and_wait(\\\"flip b\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8215f0821080aebaeab86ed679799b0f\",\n \"score\": \"0.621459\",\n \"text\": \"def byteswap( ):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"628a4b38b881ec61c1ad6c34ff777316\",\n \"score\": \"0.6213563\",\n \"text\": \"def flip(self, x=False, y=False):\\n self._flipx = x\\n self._flipy = y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f445ef18329e25c50675b29a56616f5d\",\n \"score\": \"0.6210618\",\n \"text\": \"def swap16(x):\\n return ((x & 0xff00) >> 8) | \\\\\\n ((x & 0x00ff) << 8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac5013259009fbb7a38644fe0f44ca8a\",\n \"score\": \"0.6208504\",\n \"text\": \"def flipVert():\\n\\n Im_pix = getRGB('in.png') # read in the in.png image\\n print(\\\"The first two pixels of the first row are\\\", Im_pix[0][0:2])\\n\\n New_pix = Im_pix[::-1]\\n\\n\\n saveRGB(New_pix, 'out.png')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d80dabb5203c857685c9f565a8081536\",\n \"score\": \"0.6208115\",\n \"text\": \"def vflip(val=None):\\r\\n\\t\\tCamera.modified[0] = True\\r\\n\\t\\tif Camera.opened:\\r\\n\\t\\t\\t# print(\\\"Vflip %d\\\"%val)\\r\\n\\t\\t\\treturn camera.vflip(val)\\r\\n\\t\\treturn None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab82fc69023971940328eddf5892517e\",\n \"score\": \"0.61914253\",\n \"text\": \"def flip(self):\\r\\n # Flip the card and update the image\\r\\n self.set[self.index].flip()\\r\\n self.update_image()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d1a5d2324c49310a562d0658e0d016e\",\n \"score\": \"0.6190427\",\n \"text\": \"def flip(x):\\n return np.sign(np.random.rand(1)-0.5) * x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ba589744430eba8ebb3a7cde1b2fb8\",\n \"score\": \"0.6181124\",\n \"text\": \"def flip_x(self):\\n return self._flip_x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c33441ed607357f8ef47bce59d07a6b\",\n \"score\": \"0.6179949\",\n \"text\": \"def test_roundtrip_flip(self):\\n # Compress and decompressed a flipped LDR image\\n decompFile = self.get_tmp_image_path(\\\"LDR\\\", \\\"decomp\\\")\\n\\n command = [\\n self.binary, \\\"-tl\\\",\\n \\\"./Test/Data/Tiles/ldr.png\\\",\\n decompFile, \\\"4x4\\\", \\\"-fast\\\", \\\"-yflip\\\"]\\n\\n self.exec(command)\\n\\n # Compare TL (0, 0) with TL - should match - i.e. no flip\\n colorRef = self.get_color_refs(\\\"LDR\\\", \\\"TL\\\")\\n\\n img = tli.Image(decompFile)\\n colorVal = img.get_colors([(0, 0)])\\n\\n self.assertColorSame(colorRef[0], colorVal[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5bc0969eadf8ac4b83d2d5001aa30cd\",\n \"score\": \"0.61564654\",\n \"text\": \"def flip_v(self):\\n lst_t = self.m\\n self.m = lst_t[::-1]\\n self.clean_all_but_m()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49aefdb7fd9e3b08d38c75210e61335a\",\n \"score\": \"0.6145693\",\n \"text\": \"def flip_effect(image: Image.Image) -> Image.Image:\\n image = ImageOps.mirror(image)\\n\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"848d56ce66575af50e2356731a6c33cb\",\n \"score\": \"0.6134909\",\n \"text\": \"def flip_lr(img):\\n return np.flip(img.copy(), 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6158a59e135e2b7e70a4cb59b398c617\",\n \"score\": \"0.6128792\",\n \"text\": \"def vertical_flip(self, picture, output_encoding):\\n img = images.Image(picture.data)\\n img.vertical_flip()\\n transformed = img.execute_transforms(output_encoding)\\n return transformed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc9e623de2ff7cce69d1ca07d1394e27\",\n \"score\": \"0.6115801\",\n \"text\": \"def flip_alleles(self):\\n assert(len(self.alt_alleles) == 1)\\n self.ref_allele, self.alt_alleles[0] = self.alt_alleles[0], self.ref_allele\\n self.alt_allele = self.alt_alleles[0]\\n self.flipped = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"237f2cd4a1b3ee96e0b983e660266821\",\n \"score\": \"0.6113052\",\n \"text\": \"def bswap2(i):\\n\\n return (i >> 8) & 0xff, i & 0xff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"324d3665cac1291f7d1126015a566ef5\",\n \"score\": \"0.6107709\",\n \"text\": \"def two_bit_flip(node):\\r\\n\\r\\n node_list = list(node)\\r\\n out = set()\\r\\n bit_length = len(node_list)\\r\\n for i in range(bit_length):\\r\\n for j in range(bit_length):\\r\\n new_node = node_list[:]\\r\\n if i != j:\\r\\n new_node[i] = ('1' if node[i] == '0' else '0')\\r\\n new_node[j] = ('1' if node[j] == '0' else '0')\\r\\n else:\\r\\n new_node[i] = ('1' if node[i] == '0' else '0')\\r\\n out.add(''.join(new_node))\\r\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ebd1f8b92354eb971e6205d8df5149a\",\n \"score\": \"0.6102803\",\n \"text\": \"def pairwiseSwap(n):\\n #put odd at even places then even at odd places\\n return ((n & 0xAAAAAAAA) >> 1) | ((n & 0x55555555) << 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee0e00c5aa68fed60bda000a08cd99cd\",\n \"score\": \"0.6102466\",\n \"text\": \"def __flip__(x):\\n return np.flip(x, axis = 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4855c6ca70e4fe028908b595a26e6ca\",\n \"score\": \"0.60916454\",\n \"text\": \"def invert_bitstring(string):\\n return string.replace(\\\"1\\\", \\\"2\\\").replace(\\\"0\\\", \\\"1\\\").replace(\\\"2\\\", \\\"0\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aac649f5f256757daa47d1d938a3ca4\",\n \"score\": \"0.6083563\",\n \"text\": \"def flip ( self, direction, position, color ):\\n \\n if direction == 1:\\n # north\\n row_inc = -1\\n col_inc = 0\\n elif direction == 2:\\n # northeast\\n row_inc = -1\\n col_inc = 1\\n elif direction == 3:\\n # east\\n row_inc = 0\\n col_inc = 1\\n elif direction == 4:\\n # southeast\\n row_inc = 1\\n col_inc = 1\\n elif direction == 5:\\n # south\\n row_inc = 1\\n col_inc = 0\\n elif direction == 6:\\n # southwest\\n row_inc = 1\\n col_inc = -1\\n elif direction == 7:\\n # west\\n row_inc = 0\\n col_inc = -1\\n elif direction == 8:\\n # northwest\\n row_inc = -1\\n col_inc = -1\\n \\n places = [] # pieces to flip\\n i = position[0] + row_inc\\n j = position[1] + col_inc \\n\\n if color == WHITE:\\n other = BLACK\\n else:\\n other = WHITE\\n \\n if i in range( 8 ) and j in range( 8 ) and self.board[i][j] == other:\\n # assures there is at least one piece to flip\\n places = places + [(i,j)]\\n i = i + row_inc\\n j = j + col_inc\\n while i in range( 8 ) and j in range( 8 ) and self.board[i][j] == other:\\n # search for more pieces to flip\\n places = places + [(i,j)]\\n i = i + row_inc\\n j = j + col_inc\\n if i in range( 8 ) and j in range( 8 ) and self.board[i][j] == color:\\n # found a piece of the right color to flip the pieces between\\n for pos in places:\\n # flips\\n self.board[pos[0]][pos[1]] = color\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6e38d49dca2e80287ba7c1506df3419\",\n \"score\": \"0.608276\",\n \"text\": \"def flip_forward():\\n # TODO: Assert battery is high enough to perform flip before attempting\\n send_and_wait(\\\"flip f\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"572c35cc26040ffe2cd10d5f29384115\",\n \"score\": \"0.60784805\",\n \"text\": \"def bswap3(i):\\n\\n return (i >> 16) & 0xff, (i >> 8) & 0xff, i & 0xff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1585ff409341421811fd930a873ba95d\",\n \"score\": \"0.606961\",\n \"text\": \"def _flip(shp):\\n for ring in workflow.utils.generate_rings(shp):\\n for i,c in enumerate(ring):\\n ring[i] = c[1],c[0]\\n return shp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7671783ff28736f076a276364df8bae7\",\n \"score\": \"0.6061207\",\n \"text\": \"def reverse_bit(num):\\n result = 0\\n for _ in range(8):\\n result <<= 1\\n result += (num & 1)\\n num >>= 1\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3bc6bff7f45526183b2a3d8428875fc\",\n \"score\": \"0.6051063\",\n \"text\": \"def _flip(self):\\n time = self._get_ticks()\\n self._ticks = time - self._time\\n self._elapsed += self._ticks\\n self._time = time\\n # Once per second...\\n if self._elapsed < 1:\\n return\\n elif self._elapsed >= self._reset_threshold:\\n self.reset()\\n else:\\n self._elapsed %= 1\\n # Save stats and clear counters.\\n self._tps = 0.0\\n self._fps = self._frame_count\\n self._ups = self._update_count\\n self._frame_count = self._update_count = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8ffb1679636e4236000a7f54b8da12c\",\n \"score\": \"0.6049809\",\n \"text\": \"def doflip(dec,inc):\\n if inc <0:\\n inc=-inc\\n dec=dec+180.\\n if dec > 360: dec=dec-360.\\n return dec,inc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c479c196d50309c47a8f24bfec5ea6c\",\n \"score\": \"0.6039424\",\n \"text\": \"def prob_flip(self):\\n random_num = random.randint(1, 10)\\n\\n if random_num == 1:\\n self.flip = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60141950f85200d6b49139f7cf3a72ee\",\n \"score\": \"0.60368615\",\n \"text\": \"def auto_flip_normal(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b680b75fdb31343f3c23a15665d31117\",\n \"score\": \"0.60357386\",\n \"text\": \"def flip(stack, n):\\n return inverse(stack[:n][::-1]) + stack[n:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c62b005fcf730347399136d7478e1430\",\n \"score\": \"0.6029708\",\n \"text\": \"def random_flip(image, steering):\\n if np.random.randint(2) == 0:\\n image = np.fliplr(image)\\n steering = -steering\\n\\n return image, steering\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b751ba9a19b9c482e71da95b4ceeed5b\",\n \"score\": \"0.6020218\",\n \"text\": \"def flip_board(self):\\n\\n self.board = self.get_flipped_board()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beca608e9a88236973aca21ba7824d9a\",\n \"score\": \"0.6015986\",\n \"text\": \"def flip_bytes(self, bytes):\\n byte_list = list(bytes)\\n if binascii.hexlify(self.magic_number) in [MAGIC_SWAP, MAGIC_SWAP_NANO]:\\n byte_list.reverse()\\n return ''.join(byte_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"866d2e67526175406fad301f86587134\",\n \"score\": \"0.60141283\",\n \"text\": \"def doFlip(board, color, pos, direction):\\n\\tcurrX = pos[0] + direction[0]\\n\\tcurrY = pos[1] + direction[1]\\n\\twhile board[currX][currY] == opponent(color):\\n\\t\\tboard[currX][currY] = color\\n\\t\\t(currX, currY) = (currX + direction[0], currY + direction[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e381c139205b97cb1d82fd901888094\",\n \"score\": \"0.60135734\",\n \"text\": \"def flip (self):\\r\\n return Link(self[1], self[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c6d159fb28e2bcedf2ada70fa296e38\",\n \"score\": \"0.60082066\",\n \"text\": \"def __invert__(self):\\n product = Bitmap(self.cardinality)\\n for i in range(0, len(self.bit_array)):\\n product.bit_array[i] = ~self.bit_array[i]\\n return product\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fff317a8f3556cda00b2306676b04f9\",\n \"score\": \"0.60057265\",\n \"text\": \"def flip(point):\\n \\n x, y = point\\n return (x, -y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cab5ff7e17e6df6147a6f8dbeb5fba01\",\n \"score\": \"0.6002178\",\n \"text\": \"def _invert_sub_bytes(state: List[int]) -> List[int]:\\n return SimpleAES._substitute(state, SimpleAES.INVERSE_SBOX)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e001c860e560f1e9698e6906da81f4eb\",\n \"score\": \"0.6000485\",\n \"text\": \"def mirror(array):\\n return np.rot90(np.flipud(array), 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb8b6635cb630c222711252a987441e3\",\n \"score\": \"0.59873736\",\n \"text\": \"def reverse_bits(x: int) -> int:\\n i = 0\\n j = 63\\n while i < j:\\n x = swap_bits(x, i, j)\\n i += 1\\n j -= 1\\n\\n return x\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":99,"cells":{"query_id":{"kind":"string","value":"a596b7b2e171e5f8cb5c520718640e74"},"query":{"kind":"string","value":"Takes in a save directory and outputs a list of dictionaries with the"},"positive_passages":{"kind":"list like","value":[{"docid":"1d4acaacaf0b082acc2d5fa21ec896ba","score":"0.0","text":"def get_escapes(save_dir):\n\n escapes = []\n with open(os.path.join(save_dir, \"esc.11\"), 'r') as f:\n try:\n # Skip the header\n next(f)\n except StopIteration: # Empty file\n return []\n \n for line in f:\n data_line = line.split()\n escapes.append({\n \"id\": int(data_line[5]), # id is the 6th item in row\n \"time\": float(data_line[0]), # Physical time is 1st item in row\n \"type\": int(data_line[4]) # Star type is the 5th item in row\n })\n\n print 'parsed escapes from ' + save_dir\n\n return escapes","title":""}],"string":"[\n {\n \"docid\": \"1d4acaacaf0b082acc2d5fa21ec896ba\",\n \"score\": \"0.0\",\n \"text\": \"def get_escapes(save_dir):\\n\\n escapes = []\\n with open(os.path.join(save_dir, \\\"esc.11\\\"), 'r') as f:\\n try:\\n # Skip the header\\n next(f)\\n except StopIteration: # Empty file\\n return []\\n \\n for line in f:\\n data_line = line.split()\\n escapes.append({\\n \\\"id\\\": int(data_line[5]), # id is the 6th item in row\\n \\\"time\\\": float(data_line[0]), # Physical time is 1st item in row\\n \\\"type\\\": int(data_line[4]) # Star type is the 5th item in row\\n })\\n\\n print 'parsed escapes from ' + save_dir\\n\\n return escapes\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b3150e0f984564995f68996ee9055be9","score":"0.66876465","text":"def parsed_files():\n files = []\n for filename in os.listdir(SAVE_DIRECTORY):\n path = os.path.join(SAVE_DIRECTORY, filename)\n if os.path.isfile(path):\n files.append(filename)\n return files","title":""},{"docid":"ebd7bb813b4191ed668c986047fb6b3e","score":"0.6362293","text":"def load_json(filename, savepath='/'):\n if filename in os.listdir(savepath):\n return json.load(open(savepath+filename, 'r'))\n else:\n print(\"{} not in directory\".format(filename))\n return {}","title":""},{"docid":"1f098d7dddfa8e6cc051797b01c6a235","score":"0.62440974","text":"def get_final_dicts(folder_path: str,\n without: List[str] = (\"learn-embeddings\",)) \\\n -> List[Dict[str, Any]]:\n run_folder_paths = [os.path.abspath(os.path.join(folder_path, x))\n for x in sorted(os.listdir(folder_path))\n if x not in without]\n\n return [get_entry_dict_from_json(os.path.join(path, \"results_val.json\"))\n for path in run_folder_paths]","title":""},{"docid":"d9ab37fbd222c0decc3fe133bc73abbd","score":"0.61829424","text":"def list_dic(path):\n return [ dic for dic in listdir(path) if not isfile(join(path,dic))]","title":""},{"docid":"fb61303537c9e0537b8cd5cdd6c6107d","score":"0.6160078","text":"def get_saved_model_files(model_save_dir: str, model_name=\"model\") -> List:\n return sorted(\n [\n # Split off generation_ prefix to return just number, which is enough to sort all\n # of the tuples lexicographically (there is at most one model for each generation)\n (int(os.path.dirname(model_path).split(\"generation_\", 1)[1]), model_path)\n for model_path in glob.glob(\n os.path.join(\n get_model_save_path(model_save_dir, \"*\"), f\"{model_name}_best.npz\"\n )\n )\n ]\n )","title":""},{"docid":"c995fb1f4dc5366d6e6cdab48da7ac36","score":"0.6137265","text":"def save_file(save_dict,filename):\n\n #Finding repository home directory\n data_dir = os.path.dirname(os.path.abspath(__file__))\n data_dir = os.path.dirname(data_dir)\n \n #Saving trapping data to JSON file\n data_dir = os.path.dirname(data_dir) + \"/trapping_data\"\n data_file = data_dir + \"/{}.json\".format(filename)\n \n with open(data_file,\"w\") as write_file:\n json.dump(save_dict,write_file)\n \n return True","title":""},{"docid":"39c572ed6e89d01d75e5ac3ab91af6fd","score":"0.603015","text":"def export_songs(self):\n exportdict={}\n consongs=self.csongs\n for song in consongs:\n currsong=consongs[song]\n currsong.check_streak()\n currsong.check_level()\n exportdict[currsong.song_name]=currsong.get_song()\n with open('songs2.json', 'w') as json_file:\n json.dump(exportdict, json_file)","title":""},{"docid":"e2d17f87bd91c638e97c91700ae2067e","score":"0.60284495","text":"def get_save_files():\n dir_path = get_dir()\n files = os.listdir(dir_path)\n save_files = []\n for file in files:\n if file.endswith(\".txt\"):\n save_files.append(file)\n return save_files","title":""},{"docid":"f74b297f2317c1c3322c1294df097b83","score":"0.59729433","text":"def get_dict(path):\n field = {}\n contents = os.listdir(path)\n for item in contents:\n key = item.split('.')[0]\n field[key] = get_data(item, path)\n\n return field","title":""},{"docid":"987670a454b236137692116d750161ca","score":"0.59442055","text":"def make_dict(experiment_dir, basedir):\n results = {}\n for filename in os.listdir(os.path.join(basedir, experiment_dir)):\n if filename[-3:] == 'npy':\n path = os.path.join(basedir, experiment_dir, filename)\n tesults[filename[:-4]] = np.load(path)\n \n return results","title":""},{"docid":"1a877d1da2db40b9a3cce02da6093003","score":"0.59196275","text":"def getSetsFromDirDict(path):\n import os\n import h5py\n # if the path is not to a dictionary, return empty directory\n if not os.path.isdir(path):\n return {}\n # form empty directory\n data = {}\n # convert path to directory object\n dir = os.fsencode(path)\n # iterate through files in directory\n for fn in os.listdir(dir):\n # decode the filename to string\n filename = os.fsdecode(fn)\n # if the file name ends with .hdf5\n if filename.endswith(\".hdf5\"):\n # attempt to open HDf5 in read mode\n h5_file = h5py.File(filename,'r')\n # call function to get all datasets in file\n datasets = getAllDatasets(h5_file)\n # update dictionary with found datasets\n data.update(datasets)\n h5_file.close()\n return data","title":""},{"docid":"8f327994d8aa45ec29b206f511fab95e","score":"0.5914333","text":"def list_filenames():\n _, filenames = default_storage.listdir('saved_data')\n return [filename for filename in filenames]","title":""},{"docid":"89e2a5d6ca5948fd8d07fd2f0ac5ec3e","score":"0.5868946","text":"def toJSON(directory):\n for filename in os.listdir(directory):\n input_file = directory + filename\n output_file = directory + \"json/\" + filename[:-4] + \".json\"\n if filename.endswith(\".txt\"):\n writeDist(input_file, output_file)","title":""},{"docid":"a5f01c688a893c018755693b63d6d7bc","score":"0.5847419","text":"def save_parses(corpus, lexicon, savepath):\n parse_dict = dict() \n for i, (ch_sent, en_sent) in enumerate(corpus):\n parses = parse_forests(ch_sent, en_sent, lexicon) \n parse_dict[i] = parses \n f = open(savepath + 'parse-dict.pkl', 'wb')\n pickle.dump(parse_dict, f, protocol=4)\n f.close()","title":""},{"docid":"111b3fd9e09120a8a35d51e695dbaca7","score":"0.5819619","text":"def read(self) -> List[Dict]:\n file_list = []\n # generate a list of files in subdirectories\n for root, _dirs, files in os.walk(self.path):\n # for pattern in self.file_filters:\n for match in filter(self.file_filters.match, files):\n # for match in fnmatch.filter(files, pattern):\n path = Path(os.path.join(root, match))\n # ignore the .json file created by the Store\n if path.is_file() and path.name != self.json_name:\n # filter based on depth\n depth = len(path.relative_to(self.path).parts) - 1\n if self.max_depth is None or depth <= self.max_depth:\n file_list.append(self._create_record_from_file(path))\n\n return file_list","title":""},{"docid":"b9dd8efc80d6a84236b6dd7a4a4f524e","score":"0.58180135","text":"def create_dictionary(self):\n for filename in self.path:\n with open(filename, 'rt', encoding='utf-8') as f:\n contents = f.read()\n if filename == 'data/disambiguations_en.tql':\n print('processing disambigution file')\n contents = (contents.replace(\"<http://dbpedia.org/ontology/wikiPageDisambiguates>\", ''))\n save_file = \"output/disambiguate_offline_dict.p\"\n\n elif filename == 'data/redirects_en.tql':\n print('processing redirects file')\n contents = (contents.replace(\"<http://dbpedia.org/ontology/wikiPageRedirects>\", ''))\n save_file = 'output/redirect_offline_dict.p'\n\n else:\n print('it skips the file ' + filename)\n continue\n\n contents = (contents.replace(\"<http://en.wikipedia.org/wiki/\", ''))\n contents = contents.split('\\n')\n\n for content in contents:\n array = []\n array.append(content)\n self.big_list.append(array[0].split(' '))\n\n for list in self.big_list:\n key = (list[0].replace('<http://dbpedia.org/resource/', '').replace('>', '').replace(\n '_(disambiguation)', '').replace('_', ' '))\n # print(key)\n if len(list) > 2:\n self.list_key.append(key)\n value = (list[2].replace('<', '').replace('>', ''))\n self.list_value.append(value)\n\n for key in self.list_key:\n self.dict[key] = []\n\n for i, key in enumerate(self.list_key):\n self.dict[key].append(self.list_value[i])\n\n if not os.path.isdir('output/'):\n os.makedirs('output')\n with open(save_file, \"wb\") as f:\n pickle.dump(self.dict, f)","title":""},{"docid":"c4c8569939213cdeb7cc6d5d229e3e32","score":"0.58179826","text":"def generate_data_dict(root_path):\n\n file_dict = {}\n for (dirpath, dirnames, filenames) in os.walk(root_path):\n for file in filenames:\n file_dict[file] = os.path.join(dirpath, file)\n\n return file_dict","title":""},{"docid":"1a82b066aed929f99801918b55bdea97","score":"0.57961017","text":"def get_eurostats_file_list(s): \n rdf_path_prefix = \"data/rdf/eurostats/\"\n observation_list = []\n for file in os.listdir(rdf_path_prefix):\n observation = {}\n observation_name = str(os.path.basename(file).split('.')[0])\n observation['id'] = observation_name\n observation_list.append(observation)\n return observation_list","title":""},{"docid":"9fc14d67d65dbd57bad428f1196a290c","score":"0.5788976","text":"def list_files_info():\n _, filenames = default_storage.listdir('saved_data')\n files = [{'name': filename,\n 'created': default_storage.get_created_time(f'saved_data/{filename}'),\n 'modified': default_storage.get_modified_time(f'saved_data/{filename}')\n } for filename in filenames if re.search('^[a-z0-9]', filename)]\n\n return files","title":""},{"docid":"5096a3ec26994f88ec3ba6c712bb9fda","score":"0.5774086","text":"def _load_data():\n data = {}\n for file_name in os.listdir(dir_data):\n name, extension = os.path.splitext(file_name)\n if extension != '.json':\n continue\n file_path = os.path.join(dir_data, file_name)\n with open(file_path, 'r') as file_obj:\n data[name] = json.load(file_obj)\n\n data['model_paths'] = [os.path.join(dir_models, f)\n for f in os.listdir(dir_models)]\n data['2D_files'] = [os.path.join(dir_2D, f) for f in os.listdir(dir_2D)]\n return data","title":""},{"docid":"522067b406854e6b31bcd6f042a892a5","score":"0.5773998","text":"def get_archive_details(self):\n\t\tpath = os.path.join(self.paths[\"raw\"])\n\t\titems = os.listdir(path)\n\t\textensions = self.get_supported_archive_extensions()\n\t\tarchives = {}\n\n\t\t# iterate through directory contents\n\t\tfor item in items:\n\t\t\tfor extension in extensions:\n\t\t\t\tif item.endswith(extension):\n\t\t\t\t\tname = item[:-len(extension)]\n\t\t\t\t\tarchives[name] = {\n\t\t\t\t\t\t\"name\": item,\n\t\t\t\t\t\t\"extension\": extension,\n\t\t\t\t\t\t\"path\": os.path.join(path, item)\n\t\t\t\t\t}\n\t\t\t\t\tbreak\n\n\t\treturn archives","title":""},{"docid":"5c41295ca0bcd03447586b93ed6ff172","score":"0.5767471","text":"def story_dict(self):\n global SCREENPLAY_DIR\n\n screenplay_dir = SCREENPLAY_DIR + self.currentGame.get()[:-4]\n\n path_dict = {}\n for splay_name in os.listdir(screenplay_dir):\n splay_path = os.path.join(screenplay_dir, splay_name)\n\n if os.path.isfile(splay_path) and splay_name.endswith(\".txt\"):\n path_dict[splay_name[:-4]] = splay_path\n\n return path_dict","title":""},{"docid":"9b9ae490567e845d768f4c41dd9002e6","score":"0.5732806","text":"def save(self):\n my_obj_dict = {}\n for key in FileStorage.__objects:\n my_obj_dict[key] = FileStorage.__objects[key].to_dict()\n with open(FileStorage.__file_path, 'w') as file_path:\n json.dump(my_obj_dict, file_path)","title":""},{"docid":"a6faeb562d8ba73b6c9f0cde1ab99a73","score":"0.57288295","text":"def retrieve_imgs(dir_path, filename):\n # List of folders contained in dir_path folder\n Dir = [f'{dir_path}{dir}' for dir in next(os.walk(dir_path))[1]]\n try:\n # On some OS the root dict is also in the list, must be removed\n Dir.remove(dir_path)\n except ValueError:\n pass\n\n path_dict = dict()\n for dir in Dir:\n name = os.path.basename(os.path.normpath(dir)) # Extract name of study\n path = os.path.abspath(dir) # Turn into absolute paths\n path_dict[name] = f'{path}/{filename}'\n\n return path_dict","title":""},{"docid":"83adf1e83824c6f9c179cf5f05abfab6","score":"0.57074124","text":"def get_mediums(folder, prefix=DATA_PATH):\r\n directory = prefix + folder\r\n mediums = dict()\r\n for filename in os.listdir(directory):\r\n with open(directory + SEPERATOR + filename, \"r\") as f:\r\n mediums[filename] = json.load(f)\r\n return mediums","title":""},{"docid":"c6e8fb5e37594db3bee6625d9a7c6678","score":"0.5695201","text":"def make_records(self,directory):\n npz_files = glob.glob(os.path.join(directory, '*.npz'))\n meta_files = glob.glob(os.path.join(directory,'*.geojson'))\n\n records = []\n for npz in npz_files:\n ii = npz.split('/')[-1].split('.')[0]\n meta = [m for m in meta_files if m.split('/')[-1].split('.')[0]==ii][0]\n records.append({'data':npz, 'meta':meta})\n\n shuffle(records)\n pickle.dump(records, open(os.path.join(directory,'records.pickle'),'wb'))","title":""},{"docid":"8327c94df7df0e2f919538c8ea965036","score":"0.56923634","text":"def get_avail_worlds(self):\n worlds = {}\n savesdir = mclevelbase.saveFileDir\n if os.path.exists(savesdir):\n for dirent in os.listdir(savesdir):\n dirent_path = os.path.join(savesdir, dirent)\n if os.path.isdir(dirent_path):\n test_path = os.path.join(dirent_path, 'level.dat')\n if os.path.exists(test_path):\n worlds[dirent] = test_path\n return worlds","title":""},{"docid":"716f5f2d385239579fb82cda2b821cfd","score":"0.5688074","text":"def json_files():\n for (i, file_) in enumerate(os.listdir(DATA_DIR)):\n if file_.endswith('.json'):\n yield os.path.join(DATA_DIR, file_)","title":""},{"docid":"a6d35504a25ef3d654f87b69d4d62619","score":"0.56841433","text":"def test_wg_save_to_file(self):\n r1 = Rectangle(1, 1, 1, 1, 1)\n r2 = Rectangle(2, 2, 2, 2, 2)\n l = [r1, r2]\n Rectangle.save_to_file(l)\n with open(\"Rectangle.json\", \"r\") as f:\n ls = [r1.to_dictionary(), r2.to_dictionary()]\n self.assertEqual(json.dumps(ls), f.read())","title":""},{"docid":"d37c7aaeb2dfd345677d76294faa7140","score":"0.56829023","text":"def FSaveOutput(cls, path, fileName):\n print(cls.archive)\n print(cls.objects)\n data = dict()\n for n in cls.archive['objects'].keys():\n data[str(n)] = {\n \"ID\" : cls.archive['objects'][n].id,\n \"locations\" : tuple(map(tuple, cls.archive['objects'][n].locations)),\n \"frames\" : cls.archive['objects'][n].frames,\n \"left\": list(map(int, cls.archive['objects'][n].left)),\n \"top\": list(map(int, cls.archive['objects'][n].top)),\n \"width\": list(map(int, cls.archive['objects'][n].width)),\n \"height\" : list(map(int, cls.archive['objects'][n].height)),\n \"area\": list(map(int, cls.archive['objects'][n].area))\n\n }\n for n in cls.objects['objects'].keys():\n if str(n) not in data:\n data[str(n)] = {\n \"ID\" : cls.objects['objects'][n].id,\n \"locations\" : tuple(map(tuple, cls.objects['objects'][n].locations)),\n \"frames\" : cls.objects['objects'][n].frames,\n \"left\": list(map(int, cls.objects['objects'][n].left)),\n \"top\": list(map(int, cls.objects['objects'][n].top)),\n \"width\": list(map(int, cls.objects['objects'][n].width)),\n \"height\" : list(map(int, cls.objects['objects'][n].height)),\n \"area\": list(map(int, cls.objects['objects'][n].area))\n\n }\n path = os.path.join(path, fileName) \n with open(path, 'w') as outFile:\n json.dump(data, outFile)\n return data","title":""},{"docid":"35b952eed32c715126a006bcb64763e8","score":"0.5679103","text":"def get_files_list(self):\n list_of_files = os.listdir(os.getcwd() + \"/dictionary\")\n return list_of_files","title":""},{"docid":"23796e940fc0b1f9c07e688bb36a5f31","score":"0.5651889","text":"def get_all_final_dicts(folder_path: str) -> List[List[Dict[str, Any]]]:\n all_final_tuples = list()\n\n for folder_path in [os.path.abspath(os.path.join(folder_path, x))\n for x in sorted(os.listdir(folder_path))]:\n all_final_tuples.append(get_final_dicts(folder_path))\n\n return all_final_tuples","title":""},{"docid":"9d6a22dc854b005f247954e7776b320a","score":"0.5649538","text":"def get_file_list_from_dict_file(filename):\n previousSearchDictionary = {}\n try:\n with open(filename,'rb') as fp:\n previousSearchDictionary = pickle.load(fp)\n except IOError, e:\n print \"Failed to find previous file search backup. %s\" % e\n return previousSearchDictionary","title":""},{"docid":"4cad9745ece421d33715d96776506855","score":"0.56466347","text":"def books_dict():\n\n import os\n \n books_dir = \"./Data/Books\"\n \n book_titles = {}\n for language in os.listdir(books_dir):\n \n authors = {}\n \n for author in os.listdir(books_dir + \"/\" + language):\n titles = []\n for title in os.listdir(books_dir + \"/\" + language + \"/\" + author):\n titles.append(title.replace(\".txt\", \"\"))\n \n authors[author] = titles\n \n book_titles[language] = authors\n\n return book_titles","title":""},{"docid":"9a6ab32a2dbfcbf14239cad08581d23d","score":"0.5646264","text":"def save(self):\n this_dict = {}\n for key in FileStorage.__objects:\n this_dict[key] = FileStorage.__objects[key].to_dict()\n with open(FileStorage.__file_path, 'wt') as json_file:\n json.dump(this_dict, json_file)","title":""},{"docid":"11033d062ece2cb814772e1cf74d7c88","score":"0.5642413","text":"def save(self):\n sdict = {}\n for k, v in FileStorage.__objects.items():\n sdict[k] = v.to_dict()\n with open(self.__file_path, mode=\"w\") as f:\n json.dump(sdict, f)","title":""},{"docid":"dd6c9dce54b2de496af733a161f1e478","score":"0.5638316","text":"def load_saves():\n if os.path.exists(\"mydict.dict\"):\n my_dict = corpora.Dictionary.load(\"mydict.dict\")\n if os.path.exists(\"mycorpus.corp\"):\n my_corpus = corpora.MmCorpus(\"mycorpus.corp\")\n if os.path.exists(\"mydocs.json\"):\n with open(\"tales.json\", \"r\") as afile:\n doc_map = json.load(afile)\n else:\n return None\n return (doc_map, my_dict, my_corpus)","title":""},{"docid":"9661eb83680eca2df8e86ba7ecc337aa","score":"0.5635972","text":"def save_json_profile(self, dic):\n\n for k in self.sound_buttons.keys():\n files = str(Path(\"/\".join([str(self.parent_path), self.sound_buttons[k].text()])))\n if files not in dic['Files']:\n dic['Files'].append(files)\n os.chdir(self.config_path)\n with open(f'{dic[\"Name\"]}.json', 'w') as json_file:\n json.dump(dic, json_file)","title":""},{"docid":"28c80da4ba35519ab7d3b737548d9593","score":"0.56190675","text":"def scan_data() -> dict:\n geo_files = {}\n for file in os.listdir(__data_folder__):\n if file.endswith(\".csv\"):\n geo_files[file] = os.path.join(__data_folder__, file)\n return geo_files","title":""},{"docid":"ed1888d9277d2589b12a5efa3edaabdc","score":"0.55890954","text":"def save(self):\n dic = {}\n for k, v in FileStorage.__objects.items():\n dic[k] = v.to_dict()\n with open(FileStorage.__file_path, \"w\") as f:\n json.dump(dic, f)","title":""},{"docid":"e0d1c00cf3f82413e3c22483649504b5","score":"0.55861855","text":"def dir2_dict(path: Path) -> dict:\n return {parse_datafile_name(file.name): file for file in path.iterdir() if is_datafile_path(file)}","title":""},{"docid":"010541d738438ca340e7cd849f3d6fe2","score":"0.5581375","text":"def save(self):\r\n dic_json = {}\r\n for key, value in self.__objects.items():\r\n dic_json[key] = value.to_dict()\r\n with open(self.__file_path, 'w') as f:\r\n json.dump(dic_json, f)","title":""},{"docid":"c29ab1e78de6a2303d100539aa19eb66","score":"0.5576172","text":"def js_list_dicts(url):\n\n\ttry:\n\t\tld=os.listdir(url)\n\texcept: return []\n\n\tld=[i for i in ld if i[-5:]==\".json\" and os.access(\"{}/{}\".format(url,i),os.R_OK)]\n\n\treturn ld","title":""},{"docid":"5c8ef57dd7576fec984c9d996229cf64","score":"0.5569854","text":"def get_paper(paths):\n data = []\n for path in paths:\n with open(path) as json_file:\n data.append(json.load(json_file))\n return data","title":""},{"docid":"42d8c1d77ec4c8c53a3a734ea92c3bc9","score":"0.556464","text":"def _save_data(self, data_dict: dict) -> None: \r\n \r\n sub_directories = ['train', 'val', 'test']\r\n \r\n file_counter = 0\r\n\r\n for sub in sub_directories:\r\n for dat in data_dict[sub].items():\r\n file_path = self.output_dir / sub / dat[0]\r\n for fp in dat[1]:\r\n output_name = file_path / \"{:05d}.npy\".format(file_counter)\r\n np.save(output_name, fp)\r\n file_counter += 1","title":""},{"docid":"0023a6c46583861dea1ddc0d6a207262","score":"0.5560187","text":"def save_args_as_json(path_save_dir: str, args: argparse.Namespace) -> None:\n path_json = os.path.join(path_save_dir, 'predict_options.json')\n while os.path.exists(path_json):\n number = path_json.split('.')[-2]\n if not number.isdigit():\n number = '-1'\n number = str(int(number) + 1)\n path_json = os.path.join(\n path_save_dir, '.'.join(['predict_options', number, 'json'])\n )\n with open(path_json, 'w') as fo:\n json.dump(vars(args), fo, indent=4, sort_keys=True)\n print('Saved:', path_json)","title":""},{"docid":"3f3ae3c79ebc463169f02cfa77300469","score":"0.55545866","text":"def save_list(outlist,fname):\r\n with open(fname,'wb') as f: \r\n pickle.dump(outlist, f)","title":""},{"docid":"fcf6446b2ce4363ad04df096337be293","score":"0.5552494","text":"def save_json(ls, file_path):\n with open(file_path, 'w') as fp:\n fp.write('[\\n ' + ',\\n '.join(json.dumps(i) for i in ls) + '\\n]')","title":""},{"docid":"40e9469712103675af5b20d4c0d20eea","score":"0.5536901","text":"def _load_data(self) -> dict: \r\n\r\n print(\"sort data into new directories\")\r\n data = {x : [] for x in self.class_names}\r\n\r\n for i in self.input_dir.rglob('*.npy'):\r\n obs = np.load(i, allow_pickle=True).item()\r\n\r\n file_class = self.class_names[obs['action']-1]\r\n\r\n if file_class not in self.exclusion_list:\r\n\r\n new_file = {\r\n \"obs\": obs['observation'],\r\n \"condition\": obs['condition']\r\n }\r\n\r\n data[file_class].append(new_file)\r\n \r\n return data","title":""},{"docid":"d2abbac9db3052f826e856b8782fd154","score":"0.553421","text":"def model(self):\n if not self.trained:\n raise ValueError, \"Not trained\"\n paths=[\n \"model-final.others\",\n \"model-final.phi\",\n \"model-final.theta\",\n \"model-final.tassign\",\n \"model-final.twords\",\n \"wordmap.txt\",\n ]\n retval = {}\n for path in paths:\n full_path = os.path.join(self.workdir, path)\n if os.path.exists(full_path):\n with open(full_path) as f:\n retval[path] = f.read()\n return retval","title":""},{"docid":"c641ee8b217c32e7562c06d4f3c3ee77","score":"0.55215305","text":"def dir_to_dict(rootdir):\n try:\n subdirs = next(os.walk(rootdir))[1]\n except StopIteration:\n raise FileNotFoundError(\n 'The path provided \\'{}\\' '\n 'does not exist or '\n 'does not match the desired file structure.'.format(rootdir)\n )\n \n if not subdirs:\n return [os.path.join(rootdir, file)\n for file in os.listdir(rootdir)]\n d = dict()\n for s_dir in subdirs:\n d[s_dir] = dir_to_dict(os.path.join(rootdir, s_dir))\n return d","title":""},{"docid":"7f28b99ced001edbb00d648d7e89ee00","score":"0.55203843","text":"def save(self):\n dict_obj = {}\n for key, value in FileStorage.__objects.items():\n dict_obj[key] = value.to_dict()\n with open(FileStorage.__file_path, \"w\") as f:\n json.dump(dict_obj, f)","title":""},{"docid":"ecf694101d979196781b9d1a222b30c9","score":"0.5520274","text":"def directories_data_to_metax(files):\n metax_directories = []\n for file in files:\n metax_directory_object = {}\n metax_directory_object[\"identifier\"] = file[\"identifier\"]\n metax_directory_object[\"title\"] = file[\"title\"]\n metax_directory_object[\"description\"] = file[\"description\"] if \"description\" in file else \"\"\n metax_directory_object[\"use_category\"] = file[\"useCategory\"]\n metax_directories.append(metax_directory_object)\n return metax_directories","title":""},{"docid":"7f3e7b20604e742292b5fe3448c8691b","score":"0.5519393","text":"def extract_files(directory):\n dic = {}\n folders = []\n for folder in os.listdir(directory):\n folder = os.path.join(directory, folder)\n if os.path.isdir(folder):\n folders.append(folder)\n for folder in folders:\n archives_folder = os.path.join(folder, \"archives\")\n files = os.listdir(archives_folder)\n iterate_files(files, archives_folder, os.path.join(folder, \"wav\"))\n return dic","title":""},{"docid":"a18301182cfb89e429d7c4ce703031ed","score":"0.55163425","text":"def load_files(directory):\n result = {}\n for filename in os.listdir(directory):\n with open(os.path.join(directory, filename)) as f:\n result[filename] = f.read()\n\n return result","title":""},{"docid":"5f8bdb1aec975144b6ca89305e4be8c1","score":"0.551416","text":"def _save_file(self):\n file = open(self.file_name, \"w\")\n json.dump([x.to_dict() for x in self._grade_list], file, indent=4)\n file.close()","title":""},{"docid":"094c0123689a48ad570f536dd78d0576","score":"0.55115896","text":"def dump_dicts(self):\n if not os.path.exists(self.pickle_dir):\n os.makedirs(self.pickle_dir)\n data = {\n 'item2num': self.encoder.item2num,\n 'num2item': self.encoder.num2item\n }\n with open(self.pickle_path, 'wb') as file:\n pickle.dump(data, file, pickle.HIGHEST_PROTOCOL)","title":""},{"docid":"943a6b1c094ba099ccc509ff47b8dced","score":"0.55070764","text":"def get_env():\r\n\r\n dirs_files = {}\r\n if 'ToSort' in os.listdir():\r\n for item in os.walk(os.path.join(os.getcwd(), 'ToSort')):\r\n file_list = []\r\n directory = str(item[0])\r\n for i in item[2]:\r\n file_list.append(i)\r\n dirs_files[directory] = file_list\r\n return dirs_files\r\n else:\r\n print('There is no ToSort directory present here!')","title":""},{"docid":"6b4f16ed9345eb8c510061cb114a3831","score":"0.5496495","text":"def __restore(self,d, basepath=\"/\", filterDirs=None):\n try:\n for key in d.GetListOfKeys():\n kname = key.GetName()\n if key.IsFolder():\n if filterDirs and kname not in filterDirs: \n continue\n for i in self.__restore(d.Get(kname), basepath+kname+\"/\"):\n yield i\n else:\n yield basepath+kname, d.Get(kname)\n except AttributeError, e:\n self._logger.debug(\"Ignore reading object of type %s.\",type(d))","title":""},{"docid":"0c146ac8d97af076180f9fdb2903ee11","score":"0.5489158","text":"def list_files():\n entries = [meta.__json__() for meta in FILES.values()]\n return json.dumps({\"files\": entries})","title":""},{"docid":"2388620505254402580e19332958d144","score":"0.5474785","text":"def load_files(directory): # successfully implemented\n files = os.listdir(directory)\n file_data_list = list()\n\n for file in files:\n with open(os.path.join(directory, file), encoding='utf-8') as f:\n text = f.read()\n file_data_list.append((file, text))\n print(f\"loaded {file}\")\n\n file_data = dict(file_data_list)\n \n return file_data","title":""},{"docid":"fa2a04f36473ca7e6258acb718143704","score":"0.5472414","text":"def save(data, directory):\n\n with open(directory, \"w\") as f:\n f.write(json.dumps(data))\n f.close()\n print(\"Saved to \" + directory)","title":""},{"docid":"28eecd9f04e699f7f016b01d42ec0871","score":"0.54713994","text":"def save_file_as(self):\n filepath = str(QtGui.QFileDialog.getSaveFileName(self, 'Save File', self.filepath, \"JSON (*.json)\"))\n filename = os.path.basename(filepath)\n if filename and filepath:\n self.filename = filename\n self.filepath = filepath\n # -- Generate a dictionary of all items in the launch list, each item being a dictionary itself with that items all robot launch data --\n savedata_launch_tree_dict = {}\n # Iterate over all existing (top level) items (a.k.a. robots) in the robotTree and add their column values to the launch_tree_dict.\n root = self.robotTree.invisibleRootItem()\n child_count = root.childCount()\n for i in range(child_count):\n item = root.child(i)\n savedata_launch_tree_dict[unicode(item.text(0))] = {\n \"robot_ID\": unicode(item.text(0)),\n \"navigation\": unicode(item.text(1)),\n \"robot_type\": unicode(item.text(2)),\n \"ds\": unicode(item.text(3))\n }\n\n # -- Generate a dictionary of all docking station objects, each docking station being a dictionary itself with all ds' data. --\n savedata_docking_station_dict = {}\n for ds in self.docking_station_dict.values():\n ds_id, ds_origin_x, ds_origin_y, ds_origin_theta_rad, ds_rows, ds_columns, ds_cell_offset_x, ds_cell_offset_y, ds_cell_theta_rad = ds.get_attributes()\n savedata_docking_station_dict[ds_id] = {\n \"id\": ds_id,\n \"origin_x\": ds_origin_x,\n \"origin_y\": ds_origin_y,\n \"origin_theta_rad\": ds_origin_theta_rad,\n \"rows\": ds_rows,\n \"columns\": ds_columns,\n \"cell_offset_x\": ds_cell_offset_x,\n \"cell_offset_y\": ds_cell_offset_y,\n \"cell_offset_theta_rad\": ds_cell_theta_rad\n }\n \n # -- Generate top level dictionary which contains all launch input fields, a dict of the launch list and a dict of the docking station settings. --\n savedata_dict = {\n # Launch tab\n unicode(self.comboBoxRobotType.objectName()): unicode(self.comboBoxRobotType.itemText(self.comboBoxRobotType.currentIndex())),\n unicode(self.comboBoxDockingStationLaunch.objectName()): unicode(self.comboBoxDockingStationLaunch.itemText(self.comboBoxDockingStationLaunch.currentIndex())),\n unicode(self.spinBoxRobotID.objectName()): self.spinBoxRobotID.value(),\n unicode(self.checkBoxSFMMPDM.objectName()): self.checkBoxSFMMPDM.isChecked(),\n unicode(self.robotTree.objectName()): savedata_launch_tree_dict,\n \n # Docking station tab\n unicode(self.comboBoxDockingStationID.objectName()): unicode(self.comboBoxDockingStationID.itemText(self.comboBoxDockingStationID.currentIndex())),\n \"docking_stations\": savedata_docking_station_dict\n }\n with open(filepath, 'w') as json_savefile:\n json.dump(savedata_dict, json_savefile, indent=4)\n \n self.unsaved_changes_inactive()","title":""},{"docid":"c2fdec46313dc78d4e40a013a7bf2d28","score":"0.54627717","text":"def get_all(folder = '/home/pandyr/Dropbox (MIT)/Research/Plasmids'):\n all_data = {} # initialize storage \n # Iterate through all files in fasta folder\n for file in os.listdir(folder):\n if file.endswith(\".dna\"):\n seq = get(folder + '/' + file)\n seqname = get_seqname(file)\n all_data[seqname] = Dseqrecord(seq,linear = False)\n return all_data","title":""},{"docid":"a1273862645cee6c40a8a235337e9403","score":"0.5461379","text":"def getFileDict(case):\n return files","title":""},{"docid":"127c356dd3a430fcfef215290d3315ac","score":"0.54609305","text":"def folderlist_gen(fname):\n folders = [] # list of folders\n with open(fname) as file:\n for line in file:\n line = line.strip() # get rid of \"\\n\"\n line = \"\\\\\".join([\".\", \"Modified Data\", line]) # get a full path\n folders.append(line)\n files = [] # list of files in all folders\n for folder in folders:\n files = files + dlist_gen(folder + \"\\\\\") # dlist_gen by folder\n return files","title":""},{"docid":"bb00423bb3af99f8c315017249501fe5","score":"0.54605085","text":"def loadwtfs(folder):\n wtfbyname = {}\n for wtfpath in wtffiles(folder):\n try:\n name = wtfname(wtfpath)\n wtfdata = Simulate.readwtf(wtfpath)\n wtfbyname[name] = {}\n wtfbyname[name]['wtfpath'] = wtfpath\n wtfbyname[name]['wtfdata'] = wtfdata\n except Exception as exc:\n print exc\n\n return wtfbyname","title":""},{"docid":"52ae031f80a4a38e47efde76ce659840","score":"0.54601353","text":"def get_dir_file(self, path):\n result = {}\n dir_list = bucket.list()\n for o in dir_list:\n info = self.get_info(o['name'])\n result[o['name']] = info\n return result","title":""},{"docid":"c3bf5366d6b10e1af5c32ecf75830811","score":"0.54583526","text":"def export_listDirectory( self, dir_path, mode ):\n conn , error, userDict = self.__irodsClient( )\n if not conn:\n return S_ERROR( error )\n\n file_path = self.__resolveFileID( dir_path, userDict )\n irodsHome = userDict.get( 'iRodsHome', IRODS_HOME ) \n irodsPath = irodsHome + file_path\n gLogger.debug( \"file_path to read: %s\" % irodsPath )\n\n is_file = False\n irodsFile = irodsOpen( conn , irodsPath , \"r\" )\n if not irodsFile:\n is_file = True\n else:\n irodsDir = os.path.dirname( irodsPath )\n coll = irodsCollection( conn, irodsDir )\n if not coll:\n return S_ERROR( 'Directory not found' )\n objects = coll.getObjects()\n fileList = [ x[0] for x in objects ]\n dirList = coll.getSubCollections() \n\n resultDict = {}\n if mode == 'l':\n if is_file:\n result = self.__getFileStat( dir_path )\n if result['OK']:\n resultDict[dir_path] = result['Value']\n return S_OK( resultDict )\n else:\n return S_ERROR( 'Failed to get the file stat info' )\n else:\n failed_list = []\n one_OK = False\n for fname in dirList+fileList:\n result = self.__getFileStat( dir_path + '/' + fname )\n if result['OK']:\n resultDict[fname] = result['Value']\n one_OK = True\n else:\n failed_list.append( fname )\n if failed_list:\n if one_OK:\n result = S_ERROR( 'Failed partially to get the file stat info' )\n else:\n result = S_ERROR( 'Failed to get the file stat info' )\n result['FailedList'] = failed_list\n result['Value'] = resultDict\n else:\n result = S_OK( resultDict )\n\n return result\n else:\n return S_OK( dirList )","title":""},{"docid":"ec378cc7a2b10329dd5b33d19ffe390b","score":"0.5448974","text":"def fetch_reads(self):\n reads = []\n # walk through the raw directory\n for root, dirs, files in os.walk(self.raw_dir):\n for fname in files:\n # skip files that are not one of the recognized extensions.\n extensions = Alaska.RAW_EXT + Alaska.ARCH_EXT\n if not fname.endswith(extensions):\n continue\n\n # otherwise, save info about the file\n path = '{}/{}/{}'.format(Alaska.ROOT_PATH, root, fname)\n folder = root.replace(self.raw_dir, '')\n filename = fname\n size = os.path.getsize(path)\n read = {\n 'folder': folder,\n 'filename': fname,\n 'size': '{} bytes'.format(size),\n 'path': path\n }\n reads.append(read)\n return reads","title":""},{"docid":"b686c243b030034e4ef40bb8b8974196","score":"0.54465556","text":"def load_parses(savepath):\n f = open(savepath + 'parse-dict.pkl', 'rb')\n parse_dict = pickle.load(f)\n f.close()\n return parse_dict","title":""},{"docid":"6235661e0f9994b4fe581b6ea774fa9d","score":"0.5446284","text":"def create_data_lists(path, output_folder):\n path = os.path.abspath(path)\n\n train_images = list()\n train_objects = list()\n n_objects = 0\n\n # Training data\n # Find IDs of images in training data\n with open(os.path.join(path, 'train.txt')) as f:\n ids = f.read().splitlines()\n\n for id in ids:\n # Parse annotation's XML file\n objects = parse_annotation(os.path.join(path, 'Annotations', id + '.xml'))\n if len(objects['labels']) == 0:\n continue\n n_objects += len(objects['labels'])\n train_objects.append(objects)\n train_images.append(os.path.join(path, id + '.ppm'))\n\n assert len(train_objects) == len(train_images)\n\n # Save to file\n with open(os.path.join(output_folder, 'TRAIN_images.json'), 'w') as j:\n json.dump(train_images, j)\n with open(os.path.join(output_folder, 'TRAIN_objects.json'), 'w') as j:\n json.dump(train_objects, j)\n with open(os.path.join(output_folder, 'label_map.json'), 'w') as j:\n json.dump(label_map, j) # save label map too\n\n print('\\nThere are %d training images containing a total of %d objects. Files have been saved to %s.' % (\n len(train_images), n_objects, os.path.abspath(output_folder)))\n\n # Test data\n test_images = list()\n test_objects = list()\n n_objects = 0\n\n # Find IDs of images in the test data\n with open(os.path.join(path, 'test.txt')) as f:\n ids = f.read().splitlines()\n\n for id in ids:\n # Parse annotation's XML file\n objects = parse_annotation(os.path.join(path, 'Annotations', id + '.xml'))\n if len(objects['labels']) == 0: # might change this later\n continue\n test_objects.append(objects)\n n_objects += len(objects['labels'])\n test_images.append(os.path.join(path, id + '.ppm'))\n\n assert len(test_objects) == len(test_images)\n\n # Save to file\n with open(os.path.join(output_folder, 'TEST_images.json'), 'w') as j:\n json.dump(test_images, j)\n with open(os.path.join(output_folder, 'TEST_objects.json'), 'w') as j:\n json.dump(test_objects, j)\n\n print('\\nThere are %d test images containing a total of %d objects. Files have been saved to %s.' % (\n len(test_images), n_objects, os.path.abspath(output_folder)))","title":""},{"docid":"ae84ed29a5c48bf21a92fcf3c173f12d","score":"0.5445115","text":"def save_to_file(cls, list_objs):\n lst = []\n if list_objs:\n for i in list_objs:\n lst.append(i.to_dictionary())\n with open(cls.__name__ + '.json', 'w') as f:\n f.write(Base.to_json_string(lst))","title":""},{"docid":"0415d69dd9b68c8f50d9dbdff3ccefae","score":"0.544258","text":"def collect_pickle_data():\n\tphrase_names = []\n\tphrase_labels = []\n\tphrases = []\n\tphrase_counter = 0\n\tfor phrase_name in os.listdir(ASL_FRAME_PATH): # for each phrase\n\t\tphrase_dir = \"\"\n\t\tphrase_dir = ASL_FRAME_PATH+\"\\\\\"+phrase_name\n\t\tpickle_dir = phrase_dir+\"\\\\pickle\"\n\t\tphrase_names.append(phrase_name)\n\t\tphrase_labels.append(phrase_counter)\n\t\tphrases[phrase_counter] = []\n\t\tphrase_counter += 1\n\t\tfor phrase_file in os.listdir(phrase_dir): # for each mp4 file of the phrase\n\t\t\tphrase_file_dir = phrase_dir+\"\\\\\"+phrase_file\n\t\t\thand_data_file_name = pickle_dir+\"\\\\\"+phrase_file+\".pickle\"\n\t\t\t# load array of hands for this phrase file\n\t\t\thand_data = pickle.load(open(hand_data_file_name, \"rb\"))\n\t\t\tphrases[phrase_counter].append(hand_data)","title":""},{"docid":"e119e5c0dc41f5ca50e4e47ad78cc032","score":"0.544183","text":"def persist(self, model_dir):\n regex_file = os.path.join(model_dir, DICT_NER_MODEL_FILE_NAME)\n utils.write_json_to_file(regex_file, self.known_patterns, indent=4)\n\n return {\"dict_ner_file\": DICT_NER_MODEL_FILE_NAME}","title":""},{"docid":"dbf2040ae4d6b1f0115443bb98da1a16","score":"0.5433404","text":"def read_decade(folder):\n\n path = \"/home/artoo/Workspace_local/Python/Final_Project_02806/data/wiki/\" + folder + \"/\"\n file_list = os.listdir(path)\n decade = {}\n\n for each_file in file_list:\n\n each_file = codecs.open(path + each_file, 'r', 'utf-8')\n try:\n source = json.loads(each_file.read())\n except ValueError as ve:\n print \"\\nError while reading series...\"\n print ve.message + \"\\n\"\n\n title = source['query']['pages'].itervalues().next()['title']\n content = source['query']['pages'].itervalues().next()['revisions'][0]['*']\n\n\n decade[title] = content\n\n return decade","title":""},{"docid":"3a54ea74aa985188fc2e73283dc49914","score":"0.54311633","text":"def read(self):\n\n valid = self.find_valid_directories()\n\n mapping = dict()\n for directory in valid:\n documents = JaguarCalcDrone.get_documents_calc_dir(directory)\n mapping[directory] = compute_state_hash(documents)\n\n return mapping","title":""},{"docid":"3a54ea74aa985188fc2e73283dc49914","score":"0.54311633","text":"def read(self):\n\n valid = self.find_valid_directories()\n\n mapping = dict()\n for directory in valid:\n documents = JaguarCalcDrone.get_documents_calc_dir(directory)\n mapping[directory] = compute_state_hash(documents)\n\n return mapping","title":""},{"docid":"d1f7f80c621ea768e3365c48c8e5931d","score":"0.54308647","text":"def path_list2dict(file_list: list, suffix: str) -> dict:\n file_dict = {}\n for path in file_list:\n key = path[path.rfind('/') + 1:len(path) - len(suffix)]\n file_dict[key] = path\n return file_dict","title":""},{"docid":"37770f31c2a904df8720da6a33e45c02","score":"0.543051","text":"def read(self):\n\n valid = self.find_valid_directories()\n\n mapping = dict()\n for directory in valid:\n documents = AutoTSCalcDrone.get_documents_calc_dir(directory)\n mapping[directory] = compute_state_hash(documents)\n\n return mapping","title":""},{"docid":"703e4a634ac7bd1098e8c6a7afc0b131","score":"0.5427156","text":"def available_saved(self):\n return [*os.listdir(self._mem_file)]","title":""},{"docid":"ac0244ca685e1d1652a4b534d9f1bb99","score":"0.54217535","text":"def get_dicts():\n dicts_list = ['abbreviations', 'us_uk']\n all_dics = [ampersand, clean_symbols, convert_sgml, single_space]\n\n some_dics = {}\n \n for dictionary in dics_list:\n with open(dictionary, 'r') as f:\n some_dics[dictionary] = pickle.load(f)\n\n all_dics.extend(some_dics.values())\n return all_dics","title":""},{"docid":"f6f220fb72e7bc81bb59caafbe3d6ba4","score":"0.5415177","text":"def dump(self, folder: str) -> None:","title":""},{"docid":"edbed9cfe25f31c741610d20130c8761","score":"0.5414246","text":"def save(self):\n json_objects = {}\n for key in self.__objects:\n json_objects[key] = self.__objects[key].to_dict()\n with open(self.__file_path, 'w') as f:\n json.dump(json_objects, f)","title":""},{"docid":"4f823e468138c3ab55a298897f786bd4","score":"0.54107964","text":"def save_to_file(cls, list_objs):\n listj = []\n f = cls.__name__ + '.json'\n with open(f, 'w', encoding=\"UTF-8\") as file:\n if list_objs:\n for k in list_objs:\n listj.append(k.to_dictionary())\n file.write(cls.to_json_string(listj))\n else:\n file.write(cls.to_json_string(listj))","title":""},{"docid":"fcd66bb3c9d5f69a9ccec6038d1ab292","score":"0.54071915","text":"def get_summaries_dict(summaries_dir):\n summaries_dict_data = defaultdict(dict)\n\n for d in os.scandir(summaries_dir):\n if d.is_dir():\n for f in os.listdir(d.path):\n absolute_path = d.path + '/' + f\n\n with open(absolute_path, encoding=\"utf8\", errors='ignore') as article_file:\n data = article_file.read()\n summaries_dict_data[d.name][f] = data\n\n return summaries_dict_data","title":""},{"docid":"6429bfe2d479b3b39eeac1ebd4d416a7","score":"0.5407166","text":"def all_files_data(dir_name):\n skip_list = ['.DS_Store']\n all_files_data_list = []\n all_files = os.listdir(dir_name)\n all_files.sort(key=natural_keys)\n for f in all_files:\n if f not in skip_list:\n with open(dir_name + f) as data_file:\n all_files_data_list.append(json.load(data_file))\n\n keys_sorted = []\n for d in all_files_data_list:\n sorted_keys = [x for x in d['blocks']]\n sorted_keys.sort(key=natural_keys)\n keys_sorted.append(sorted_keys)\n\n return all_files_data_list, keys_sorted","title":""},{"docid":"82b3b5e7180697298630a31e3105c608","score":"0.54049057","text":"def contents(self):\n rv = {}\n Dhosts = self.dir.dirs()\n for Dhost in Dhosts:\n host = str(Dhost.basename())\n if host not in rv:\n rv[host] = {}\n Dnames = Dhost.dirs()\n for Dname in Dnames:\n name = str(Dname.basename())\n if name not in rv[host]:\n rv[host][name] = {}\n Dids = self.non_dot(Dname)\n for Did in Dids:\n id = str(Did.basename())\n props = self.props(Did)\n props[\"active\"] = \"unknown\"\n rv[host][name][id] = props\n return rv","title":""},{"docid":"b6d89e81d0ed109b398a1ce21fc399b1","score":"0.5401883","text":"def savefiles(self):\n return self.options['savefiles']","title":""},{"docid":"25150655e5241147f8078536e2837ee9","score":"0.53987396","text":"def loadData(vocab, directory):\n top_level = os.listdir(directory)\n dataset = []\n for d in top_level:\n if d[-1] == '/':\n label = d[:-1]\n subdir = d\n else:\n label = d\n subdir = d+\"/\"\n files = os.listdir(directory+subdir)\n for f in files:\n bow = create_bow(vocab, directory+subdir+f)\n dataset.append({'label': label, 'bow': bow})\n return dataset","title":""},{"docid":"393cc4edda01e097e4c98a86c2ad8153","score":"0.53958565","text":"def save_list(path, lst):\n output = open(\"{}.pkl\".format(path), \"wb\")\n pickle.dump(avg, output)\n output.close()\n\n return True","title":""},{"docid":"826846232adecf0b78490048383cf189","score":"0.53956956","text":"def dump_obj(name, data, directory, it=-1):\n for d in data:\n # Save list\n if isinstance(data[d], list):\n print '\\titem: ', d, '[', len(data[d]), ']'\n # File name\n fstr = '/' + name + '_' + d + ('_' + str(it) if it != -1 else '') + '.txt'\n\n # Create file and dump all items\n with open(directory + fstr, 'w') as fd:\n \tfd.write('\\n'.join(map(lambda x: str(x), data[d])) + \"\\n\")\n\t\t\n # Save global\n else:\n print '\\titem: ', d\n fstr = '/' + name + '_global' + ('_' + str(it) if it != -1 else '') + '.txt'\n\n # Create file and dump all items\n with open(directory + fstr, 'a') as fd:\n \tfd.write(d + ':' + str(data[d]) + '\\n')","title":""},{"docid":"5932f38cf8c8b7c14dd8573ab10f9d00","score":"0.53907824","text":"def load_files(directory):\n dictionary={}\n for file in os.listdir(directory):\n dictionary[file]=open(directory+os.sep+file,encoding='utf-8').read()\n return dictionary","title":""},{"docid":"246e95c121f82a7f9c86d0ce3274e9c8","score":"0.5386953","text":"def ListDataFiles():\n print \"Listing Data Files\"\n htmlReturn = \"\"\n files = os.listdir(\"Data\")\n print \"Found files: \", files\n return json.dumps( files )","title":""},{"docid":"3372854f5f8a0eaf56d5064d2e2c9081","score":"0.5383845","text":"def file_list(file_name):\n global dict_artists\n output = []\n\n print('Creating file \"' + file_name + '\"...')\n output.append(\"MUSIC LIST\")\n output.append('----------\\n')\n for k_artist in sorted(dict_artists.keys()):\n for k_album in sorted(dict_artists[k_artist].keys()):\n output.append('ARTIST: ' + k_artist)\n output.append('ALBUM: ' + k_album)\n output.append('TRACKS:')\n for track in sorted(dict_artists[k_artist][k_album]):\n output.append(\" \" + track)\n output.append(\"\")\n\n write_file(file_name, output)\n print('File created')\n\n return","title":""},{"docid":"fa69c3e5f51b14f285c2544f593a90d2","score":"0.53769517","text":"def getFileNamesAndPaths():\r\n os.chdir(json_file_dir)\r\n files_in_JSON_dir = os.listdir(json_file_dir)\r\n for file in glob.glob(\"*.txt\"):\r\n files_to_zip.append(file)","title":""},{"docid":"9d19063a7726884c40bcf475bfafa9a5","score":"0.5375869","text":"def generate_folders_and_files():\r\n\r\n if params['EXPERIMENT_NAME']:\r\n params['FILE_PATH'] = getcwd() + \"/results/\" + params[\r\n 'EXPERIMENT_NAME'] + \"/\"\r\n else:\r\n params['FILE_PATH'] = getcwd() + \"/results/\"\r\n\r\n # Generate save folders\r\n if not path.isdir(params['FILE_PATH']):\r\n mkdir(params['FILE_PATH'])\r\n if not path.isdir(params['FILE_PATH'] + str(params['TIME_STAMP'])):\r\n mkdir(params['FILE_PATH'] + str(params['TIME_STAMP']))\r\n\r\n save_params()\r\n save_stats_headers()","title":""},{"docid":"feb9a4834357d3b75268d16b2bb58550","score":"0.5374786","text":"def make_atlas_list():\n jsondict = {}\n jsondict['Atlas'] = []\n jsondict['ShortDesc'] = []\n jsondict['Maps'] = []\n jsondict['MapDesc'] = []\n jsondict['Type'] = []\n jsondict[\"LongDesc\"]=[]\n jsondict[\"ReferencesAndLinks\"]=[]\n\n directories = ['Diedrichsen_2009','Buckner_2011','Xue_2021','Ji_2019','King_2019']\n for name in directories:\n with open(name +'/atlas_description.json') as jsonfile:\n file = json.load(jsonfile)\n jsondict[\"Atlas\"].append(name)\n jsondict[\"ShortDesc\"].append(file[\"ShortDesc\"])\n jsondict[\"Maps\"].append(file[\"Maps\"])\n jsondict[\"Type\"].append(file[\"Type\"])\n jsondict[\"MapDesc\"].append(file[\"MapDesc\"])\n jsondict[\"LongDesc\"].append(file[\"LongDesc\"])\n jsondict[\"ReferencesAndLinks\"].append(file[\"ReferencesAndLinks\"])\n with open('package_description.json','w') as outfile:\n json.dump(jsondict,outfile,indent = 5)","title":""},{"docid":"702e51beecf4279a6ad1993aff2f6fb3","score":"0.53741115","text":"def save(self):\n my_dict = {}\n for k, v in self.__objects.items():\n my_dict[k] = v.to_dict()\n with open(FileStorage.__file_path, mode='w', encoding='UTF-8') as f:\n json.dump(my_dict, f)","title":""},{"docid":"89135d4969110decd230e561a368cbf3","score":"0.5371747","text":"def copys():\n for fname in os.listdir(Result.froot):\n if fname.startswith('wiki_p'):\n result = Result('', '', fname)\n logging.info(\"copying %s\" % fname)\n with open(fname + '.s', 'w') as fout:\n cPickle.dump([None, result.s], fout, protocol=-1)","title":""},{"docid":"03414003afbc84e37c02bf847b9df0fc","score":"0.5371028","text":"def crawlFolder(path):\n # path =\"dataset/plot/\"\n files = [f for f in listdir(path) if isfile(join(path, f))]\n files.sort()\n dataset = []\n for item in files:\n file_name = join(path, item)\n file = open(file_name, \"r\")\n data = file.read()\n file.close()\n dataset.append([data, file_name])\n return dataset","title":""}],"string":"[\n {\n \"docid\": \"b3150e0f984564995f68996ee9055be9\",\n \"score\": \"0.66876465\",\n \"text\": \"def parsed_files():\\n files = []\\n for filename in os.listdir(SAVE_DIRECTORY):\\n path = os.path.join(SAVE_DIRECTORY, filename)\\n if os.path.isfile(path):\\n files.append(filename)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd7bb813b4191ed668c986047fb6b3e\",\n \"score\": \"0.6362293\",\n \"text\": \"def load_json(filename, savepath='/'):\\n if filename in os.listdir(savepath):\\n return json.load(open(savepath+filename, 'r'))\\n else:\\n print(\\\"{} not in directory\\\".format(filename))\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f098d7dddfa8e6cc051797b01c6a235\",\n \"score\": \"0.62440974\",\n \"text\": \"def get_final_dicts(folder_path: str,\\n without: List[str] = (\\\"learn-embeddings\\\",)) \\\\\\n -> List[Dict[str, Any]]:\\n run_folder_paths = [os.path.abspath(os.path.join(folder_path, x))\\n for x in sorted(os.listdir(folder_path))\\n if x not in without]\\n\\n return [get_entry_dict_from_json(os.path.join(path, \\\"results_val.json\\\"))\\n for path in run_folder_paths]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9ab37fbd222c0decc3fe133bc73abbd\",\n \"score\": \"0.61829424\",\n \"text\": \"def list_dic(path):\\n return [ dic for dic in listdir(path) if not isfile(join(path,dic))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb61303537c9e0537b8cd5cdd6c6107d\",\n \"score\": \"0.6160078\",\n \"text\": \"def get_saved_model_files(model_save_dir: str, model_name=\\\"model\\\") -> List:\\n return sorted(\\n [\\n # Split off generation_ prefix to return just number, which is enough to sort all\\n # of the tuples lexicographically (there is at most one model for each generation)\\n (int(os.path.dirname(model_path).split(\\\"generation_\\\", 1)[1]), model_path)\\n for model_path in glob.glob(\\n os.path.join(\\n get_model_save_path(model_save_dir, \\\"*\\\"), f\\\"{model_name}_best.npz\\\"\\n )\\n )\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c995fb1f4dc5366d6e6cdab48da7ac36\",\n \"score\": \"0.6137265\",\n \"text\": \"def save_file(save_dict,filename):\\n\\n #Finding repository home directory\\n data_dir = os.path.dirname(os.path.abspath(__file__))\\n data_dir = os.path.dirname(data_dir)\\n \\n #Saving trapping data to JSON file\\n data_dir = os.path.dirname(data_dir) + \\\"/trapping_data\\\"\\n data_file = data_dir + \\\"/{}.json\\\".format(filename)\\n \\n with open(data_file,\\\"w\\\") as write_file:\\n json.dump(save_dict,write_file)\\n \\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39c572ed6e89d01d75e5ac3ab91af6fd\",\n \"score\": \"0.603015\",\n \"text\": \"def export_songs(self):\\n exportdict={}\\n consongs=self.csongs\\n for song in consongs:\\n currsong=consongs[song]\\n currsong.check_streak()\\n currsong.check_level()\\n exportdict[currsong.song_name]=currsong.get_song()\\n with open('songs2.json', 'w') as json_file:\\n json.dump(exportdict, json_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2d17f87bd91c638e97c91700ae2067e\",\n \"score\": \"0.60284495\",\n \"text\": \"def get_save_files():\\n dir_path = get_dir()\\n files = os.listdir(dir_path)\\n save_files = []\\n for file in files:\\n if file.endswith(\\\".txt\\\"):\\n save_files.append(file)\\n return save_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f74b297f2317c1c3322c1294df097b83\",\n \"score\": \"0.59729433\",\n \"text\": \"def get_dict(path):\\n field = {}\\n contents = os.listdir(path)\\n for item in contents:\\n key = item.split('.')[0]\\n field[key] = get_data(item, path)\\n\\n return field\",\n \"title\": \"\"\n },\n {\n \"docid\": \"987670a454b236137692116d750161ca\",\n \"score\": \"0.59442055\",\n \"text\": \"def make_dict(experiment_dir, basedir):\\n results = {}\\n for filename in os.listdir(os.path.join(basedir, experiment_dir)):\\n if filename[-3:] == 'npy':\\n path = os.path.join(basedir, experiment_dir, filename)\\n tesults[filename[:-4]] = np.load(path)\\n \\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a877d1da2db40b9a3cce02da6093003\",\n \"score\": \"0.59196275\",\n \"text\": \"def getSetsFromDirDict(path):\\n import os\\n import h5py\\n # if the path is not to a dictionary, return empty directory\\n if not os.path.isdir(path):\\n return {}\\n # form empty directory\\n data = {}\\n # convert path to directory object\\n dir = os.fsencode(path)\\n # iterate through files in directory\\n for fn in os.listdir(dir):\\n # decode the filename to string\\n filename = os.fsdecode(fn)\\n # if the file name ends with .hdf5\\n if filename.endswith(\\\".hdf5\\\"):\\n # attempt to open HDf5 in read mode\\n h5_file = h5py.File(filename,'r')\\n # call function to get all datasets in file\\n datasets = getAllDatasets(h5_file)\\n # update dictionary with found datasets\\n data.update(datasets)\\n h5_file.close()\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f327994d8aa45ec29b206f511fab95e\",\n \"score\": \"0.5914333\",\n \"text\": \"def list_filenames():\\n _, filenames = default_storage.listdir('saved_data')\\n return [filename for filename in filenames]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89e2a5d6ca5948fd8d07fd2f0ac5ec3e\",\n \"score\": \"0.5868946\",\n \"text\": \"def toJSON(directory):\\n for filename in os.listdir(directory):\\n input_file = directory + filename\\n output_file = directory + \\\"json/\\\" + filename[:-4] + \\\".json\\\"\\n if filename.endswith(\\\".txt\\\"):\\n writeDist(input_file, output_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f01c688a893c018755693b63d6d7bc\",\n \"score\": \"0.5847419\",\n \"text\": \"def save_parses(corpus, lexicon, savepath):\\n parse_dict = dict() \\n for i, (ch_sent, en_sent) in enumerate(corpus):\\n parses = parse_forests(ch_sent, en_sent, lexicon) \\n parse_dict[i] = parses \\n f = open(savepath + 'parse-dict.pkl', 'wb')\\n pickle.dump(parse_dict, f, protocol=4)\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"111b3fd9e09120a8a35d51e695dbaca7\",\n \"score\": \"0.5819619\",\n \"text\": \"def read(self) -> List[Dict]:\\n file_list = []\\n # generate a list of files in subdirectories\\n for root, _dirs, files in os.walk(self.path):\\n # for pattern in self.file_filters:\\n for match in filter(self.file_filters.match, files):\\n # for match in fnmatch.filter(files, pattern):\\n path = Path(os.path.join(root, match))\\n # ignore the .json file created by the Store\\n if path.is_file() and path.name != self.json_name:\\n # filter based on depth\\n depth = len(path.relative_to(self.path).parts) - 1\\n if self.max_depth is None or depth <= self.max_depth:\\n file_list.append(self._create_record_from_file(path))\\n\\n return file_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9dd8efc80d6a84236b6dd7a4a4f524e\",\n \"score\": \"0.58180135\",\n \"text\": \"def create_dictionary(self):\\n for filename in self.path:\\n with open(filename, 'rt', encoding='utf-8') as f:\\n contents = f.read()\\n if filename == 'data/disambiguations_en.tql':\\n print('processing disambigution file')\\n contents = (contents.replace(\\\"<http://dbpedia.org/ontology/wikiPageDisambiguates>\\\", ''))\\n save_file = \\\"output/disambiguate_offline_dict.p\\\"\\n\\n elif filename == 'data/redirects_en.tql':\\n print('processing redirects file')\\n contents = (contents.replace(\\\"<http://dbpedia.org/ontology/wikiPageRedirects>\\\", ''))\\n save_file = 'output/redirect_offline_dict.p'\\n\\n else:\\n print('it skips the file ' + filename)\\n continue\\n\\n contents = (contents.replace(\\\"<http://en.wikipedia.org/wiki/\\\", ''))\\n contents = contents.split('\\\\n')\\n\\n for content in contents:\\n array = []\\n array.append(content)\\n self.big_list.append(array[0].split(' '))\\n\\n for list in self.big_list:\\n key = (list[0].replace('<http://dbpedia.org/resource/', '').replace('>', '').replace(\\n '_(disambiguation)', '').replace('_', ' '))\\n # print(key)\\n if len(list) > 2:\\n self.list_key.append(key)\\n value = (list[2].replace('<', '').replace('>', ''))\\n self.list_value.append(value)\\n\\n for key in self.list_key:\\n self.dict[key] = []\\n\\n for i, key in enumerate(self.list_key):\\n self.dict[key].append(self.list_value[i])\\n\\n if not os.path.isdir('output/'):\\n os.makedirs('output')\\n with open(save_file, \\\"wb\\\") as f:\\n pickle.dump(self.dict, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4c8569939213cdeb7cc6d5d229e3e32\",\n \"score\": \"0.58179826\",\n \"text\": \"def generate_data_dict(root_path):\\n\\n file_dict = {}\\n for (dirpath, dirnames, filenames) in os.walk(root_path):\\n for file in filenames:\\n file_dict[file] = os.path.join(dirpath, file)\\n\\n return file_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a82b066aed929f99801918b55bdea97\",\n \"score\": \"0.57961017\",\n \"text\": \"def get_eurostats_file_list(s): \\n rdf_path_prefix = \\\"data/rdf/eurostats/\\\"\\n observation_list = []\\n for file in os.listdir(rdf_path_prefix):\\n observation = {}\\n observation_name = str(os.path.basename(file).split('.')[0])\\n observation['id'] = observation_name\\n observation_list.append(observation)\\n return observation_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fc14d67d65dbd57bad428f1196a290c\",\n \"score\": \"0.5788976\",\n \"text\": \"def list_files_info():\\n _, filenames = default_storage.listdir('saved_data')\\n files = [{'name': filename,\\n 'created': default_storage.get_created_time(f'saved_data/{filename}'),\\n 'modified': default_storage.get_modified_time(f'saved_data/{filename}')\\n } for filename in filenames if re.search('^[a-z0-9]', filename)]\\n\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5096a3ec26994f88ec3ba6c712bb9fda\",\n \"score\": \"0.5774086\",\n \"text\": \"def _load_data():\\n data = {}\\n for file_name in os.listdir(dir_data):\\n name, extension = os.path.splitext(file_name)\\n if extension != '.json':\\n continue\\n file_path = os.path.join(dir_data, file_name)\\n with open(file_path, 'r') as file_obj:\\n data[name] = json.load(file_obj)\\n\\n data['model_paths'] = [os.path.join(dir_models, f)\\n for f in os.listdir(dir_models)]\\n data['2D_files'] = [os.path.join(dir_2D, f) for f in os.listdir(dir_2D)]\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"522067b406854e6b31bcd6f042a892a5\",\n \"score\": \"0.5773998\",\n \"text\": \"def get_archive_details(self):\\n\\t\\tpath = os.path.join(self.paths[\\\"raw\\\"])\\n\\t\\titems = os.listdir(path)\\n\\t\\textensions = self.get_supported_archive_extensions()\\n\\t\\tarchives = {}\\n\\n\\t\\t# iterate through directory contents\\n\\t\\tfor item in items:\\n\\t\\t\\tfor extension in extensions:\\n\\t\\t\\t\\tif item.endswith(extension):\\n\\t\\t\\t\\t\\tname = item[:-len(extension)]\\n\\t\\t\\t\\t\\tarchives[name] = {\\n\\t\\t\\t\\t\\t\\t\\\"name\\\": item,\\n\\t\\t\\t\\t\\t\\t\\\"extension\\\": extension,\\n\\t\\t\\t\\t\\t\\t\\\"path\\\": os.path.join(path, item)\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\tbreak\\n\\n\\t\\treturn archives\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c41295ca0bcd03447586b93ed6ff172\",\n \"score\": \"0.5767471\",\n \"text\": \"def story_dict(self):\\n global SCREENPLAY_DIR\\n\\n screenplay_dir = SCREENPLAY_DIR + self.currentGame.get()[:-4]\\n\\n path_dict = {}\\n for splay_name in os.listdir(screenplay_dir):\\n splay_path = os.path.join(screenplay_dir, splay_name)\\n\\n if os.path.isfile(splay_path) and splay_name.endswith(\\\".txt\\\"):\\n path_dict[splay_name[:-4]] = splay_path\\n\\n return path_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b9ae490567e845d768f4c41dd9002e6\",\n \"score\": \"0.5732806\",\n \"text\": \"def save(self):\\n my_obj_dict = {}\\n for key in FileStorage.__objects:\\n my_obj_dict[key] = FileStorage.__objects[key].to_dict()\\n with open(FileStorage.__file_path, 'w') as file_path:\\n json.dump(my_obj_dict, file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6faeb562d8ba73b6c9f0cde1ab99a73\",\n \"score\": \"0.57288295\",\n \"text\": \"def retrieve_imgs(dir_path, filename):\\n # List of folders contained in dir_path folder\\n Dir = [f'{dir_path}{dir}' for dir in next(os.walk(dir_path))[1]]\\n try:\\n # On some OS the root dict is also in the list, must be removed\\n Dir.remove(dir_path)\\n except ValueError:\\n pass\\n\\n path_dict = dict()\\n for dir in Dir:\\n name = os.path.basename(os.path.normpath(dir)) # Extract name of study\\n path = os.path.abspath(dir) # Turn into absolute paths\\n path_dict[name] = f'{path}/{filename}'\\n\\n return path_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83adf1e83824c6f9c179cf5f05abfab6\",\n \"score\": \"0.57074124\",\n \"text\": \"def get_mediums(folder, prefix=DATA_PATH):\\r\\n directory = prefix + folder\\r\\n mediums = dict()\\r\\n for filename in os.listdir(directory):\\r\\n with open(directory + SEPERATOR + filename, \\\"r\\\") as f:\\r\\n mediums[filename] = json.load(f)\\r\\n return mediums\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6e8fb5e37594db3bee6625d9a7c6678\",\n \"score\": \"0.5695201\",\n \"text\": \"def make_records(self,directory):\\n npz_files = glob.glob(os.path.join(directory, '*.npz'))\\n meta_files = glob.glob(os.path.join(directory,'*.geojson'))\\n\\n records = []\\n for npz in npz_files:\\n ii = npz.split('/')[-1].split('.')[0]\\n meta = [m for m in meta_files if m.split('/')[-1].split('.')[0]==ii][0]\\n records.append({'data':npz, 'meta':meta})\\n\\n shuffle(records)\\n pickle.dump(records, open(os.path.join(directory,'records.pickle'),'wb'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8327c94df7df0e2f919538c8ea965036\",\n \"score\": \"0.56923634\",\n \"text\": \"def get_avail_worlds(self):\\n worlds = {}\\n savesdir = mclevelbase.saveFileDir\\n if os.path.exists(savesdir):\\n for dirent in os.listdir(savesdir):\\n dirent_path = os.path.join(savesdir, dirent)\\n if os.path.isdir(dirent_path):\\n test_path = os.path.join(dirent_path, 'level.dat')\\n if os.path.exists(test_path):\\n worlds[dirent] = test_path\\n return worlds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"716f5f2d385239579fb82cda2b821cfd\",\n \"score\": \"0.5688074\",\n \"text\": \"def json_files():\\n for (i, file_) in enumerate(os.listdir(DATA_DIR)):\\n if file_.endswith('.json'):\\n yield os.path.join(DATA_DIR, file_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6d35504a25ef3d654f87b69d4d62619\",\n \"score\": \"0.56841433\",\n \"text\": \"def test_wg_save_to_file(self):\\n r1 = Rectangle(1, 1, 1, 1, 1)\\n r2 = Rectangle(2, 2, 2, 2, 2)\\n l = [r1, r2]\\n Rectangle.save_to_file(l)\\n with open(\\\"Rectangle.json\\\", \\\"r\\\") as f:\\n ls = [r1.to_dictionary(), r2.to_dictionary()]\\n self.assertEqual(json.dumps(ls), f.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d37c7aaeb2dfd345677d76294faa7140\",\n \"score\": \"0.56829023\",\n \"text\": \"def FSaveOutput(cls, path, fileName):\\n print(cls.archive)\\n print(cls.objects)\\n data = dict()\\n for n in cls.archive['objects'].keys():\\n data[str(n)] = {\\n \\\"ID\\\" : cls.archive['objects'][n].id,\\n \\\"locations\\\" : tuple(map(tuple, cls.archive['objects'][n].locations)),\\n \\\"frames\\\" : cls.archive['objects'][n].frames,\\n \\\"left\\\": list(map(int, cls.archive['objects'][n].left)),\\n \\\"top\\\": list(map(int, cls.archive['objects'][n].top)),\\n \\\"width\\\": list(map(int, cls.archive['objects'][n].width)),\\n \\\"height\\\" : list(map(int, cls.archive['objects'][n].height)),\\n \\\"area\\\": list(map(int, cls.archive['objects'][n].area))\\n\\n }\\n for n in cls.objects['objects'].keys():\\n if str(n) not in data:\\n data[str(n)] = {\\n \\\"ID\\\" : cls.objects['objects'][n].id,\\n \\\"locations\\\" : tuple(map(tuple, cls.objects['objects'][n].locations)),\\n \\\"frames\\\" : cls.objects['objects'][n].frames,\\n \\\"left\\\": list(map(int, cls.objects['objects'][n].left)),\\n \\\"top\\\": list(map(int, cls.objects['objects'][n].top)),\\n \\\"width\\\": list(map(int, cls.objects['objects'][n].width)),\\n \\\"height\\\" : list(map(int, cls.objects['objects'][n].height)),\\n \\\"area\\\": list(map(int, cls.objects['objects'][n].area))\\n\\n }\\n path = os.path.join(path, fileName) \\n with open(path, 'w') as outFile:\\n json.dump(data, outFile)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35b952eed32c715126a006bcb64763e8\",\n \"score\": \"0.5679103\",\n \"text\": \"def get_files_list(self):\\n list_of_files = os.listdir(os.getcwd() + \\\"/dictionary\\\")\\n return list_of_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23796e940fc0b1f9c07e688bb36a5f31\",\n \"score\": \"0.5651889\",\n \"text\": \"def get_all_final_dicts(folder_path: str) -> List[List[Dict[str, Any]]]:\\n all_final_tuples = list()\\n\\n for folder_path in [os.path.abspath(os.path.join(folder_path, x))\\n for x in sorted(os.listdir(folder_path))]:\\n all_final_tuples.append(get_final_dicts(folder_path))\\n\\n return all_final_tuples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d6a22dc854b005f247954e7776b320a\",\n \"score\": \"0.5649538\",\n \"text\": \"def get_file_list_from_dict_file(filename):\\n previousSearchDictionary = {}\\n try:\\n with open(filename,'rb') as fp:\\n previousSearchDictionary = pickle.load(fp)\\n except IOError, e:\\n print \\\"Failed to find previous file search backup. %s\\\" % e\\n return previousSearchDictionary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cad9745ece421d33715d96776506855\",\n \"score\": \"0.56466347\",\n \"text\": \"def books_dict():\\n\\n import os\\n \\n books_dir = \\\"./Data/Books\\\"\\n \\n book_titles = {}\\n for language in os.listdir(books_dir):\\n \\n authors = {}\\n \\n for author in os.listdir(books_dir + \\\"/\\\" + language):\\n titles = []\\n for title in os.listdir(books_dir + \\\"/\\\" + language + \\\"/\\\" + author):\\n titles.append(title.replace(\\\".txt\\\", \\\"\\\"))\\n \\n authors[author] = titles\\n \\n book_titles[language] = authors\\n\\n return book_titles\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a6ab32a2dbfcbf14239cad08581d23d\",\n \"score\": \"0.5646264\",\n \"text\": \"def save(self):\\n this_dict = {}\\n for key in FileStorage.__objects:\\n this_dict[key] = FileStorage.__objects[key].to_dict()\\n with open(FileStorage.__file_path, 'wt') as json_file:\\n json.dump(this_dict, json_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11033d062ece2cb814772e1cf74d7c88\",\n \"score\": \"0.5642413\",\n \"text\": \"def save(self):\\n sdict = {}\\n for k, v in FileStorage.__objects.items():\\n sdict[k] = v.to_dict()\\n with open(self.__file_path, mode=\\\"w\\\") as f:\\n json.dump(sdict, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd6c9dce54b2de496af733a161f1e478\",\n \"score\": \"0.5638316\",\n \"text\": \"def load_saves():\\n if os.path.exists(\\\"mydict.dict\\\"):\\n my_dict = corpora.Dictionary.load(\\\"mydict.dict\\\")\\n if os.path.exists(\\\"mycorpus.corp\\\"):\\n my_corpus = corpora.MmCorpus(\\\"mycorpus.corp\\\")\\n if os.path.exists(\\\"mydocs.json\\\"):\\n with open(\\\"tales.json\\\", \\\"r\\\") as afile:\\n doc_map = json.load(afile)\\n else:\\n return None\\n return (doc_map, my_dict, my_corpus)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9661eb83680eca2df8e86ba7ecc337aa\",\n \"score\": \"0.5635972\",\n \"text\": \"def save_json_profile(self, dic):\\n\\n for k in self.sound_buttons.keys():\\n files = str(Path(\\\"/\\\".join([str(self.parent_path), self.sound_buttons[k].text()])))\\n if files not in dic['Files']:\\n dic['Files'].append(files)\\n os.chdir(self.config_path)\\n with open(f'{dic[\\\"Name\\\"]}.json', 'w') as json_file:\\n json.dump(dic, json_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28c80da4ba35519ab7d3b737548d9593\",\n \"score\": \"0.56190675\",\n \"text\": \"def scan_data() -> dict:\\n geo_files = {}\\n for file in os.listdir(__data_folder__):\\n if file.endswith(\\\".csv\\\"):\\n geo_files[file] = os.path.join(__data_folder__, file)\\n return geo_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed1888d9277d2589b12a5efa3edaabdc\",\n \"score\": \"0.55890954\",\n \"text\": \"def save(self):\\n dic = {}\\n for k, v in FileStorage.__objects.items():\\n dic[k] = v.to_dict()\\n with open(FileStorage.__file_path, \\\"w\\\") as f:\\n json.dump(dic, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0d1c00cf3f82413e3c22483649504b5\",\n \"score\": \"0.55861855\",\n \"text\": \"def dir2_dict(path: Path) -> dict:\\n return {parse_datafile_name(file.name): file for file in path.iterdir() if is_datafile_path(file)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"010541d738438ca340e7cd849f3d6fe2\",\n \"score\": \"0.5581375\",\n \"text\": \"def save(self):\\r\\n dic_json = {}\\r\\n for key, value in self.__objects.items():\\r\\n dic_json[key] = value.to_dict()\\r\\n with open(self.__file_path, 'w') as f:\\r\\n json.dump(dic_json, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29ab1e78de6a2303d100539aa19eb66\",\n \"score\": \"0.5576172\",\n \"text\": \"def js_list_dicts(url):\\n\\n\\ttry:\\n\\t\\tld=os.listdir(url)\\n\\texcept: return []\\n\\n\\tld=[i for i in ld if i[-5:]==\\\".json\\\" and os.access(\\\"{}/{}\\\".format(url,i),os.R_OK)]\\n\\n\\treturn ld\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c8ef57dd7576fec984c9d996229cf64\",\n \"score\": \"0.5569854\",\n \"text\": \"def get_paper(paths):\\n data = []\\n for path in paths:\\n with open(path) as json_file:\\n data.append(json.load(json_file))\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42d8c1d77ec4c8c53a3a734ea92c3bc9\",\n \"score\": \"0.556464\",\n \"text\": \"def _save_data(self, data_dict: dict) -> None: \\r\\n \\r\\n sub_directories = ['train', 'val', 'test']\\r\\n \\r\\n file_counter = 0\\r\\n\\r\\n for sub in sub_directories:\\r\\n for dat in data_dict[sub].items():\\r\\n file_path = self.output_dir / sub / dat[0]\\r\\n for fp in dat[1]:\\r\\n output_name = file_path / \\\"{:05d}.npy\\\".format(file_counter)\\r\\n np.save(output_name, fp)\\r\\n file_counter += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0023a6c46583861dea1ddc0d6a207262\",\n \"score\": \"0.5560187\",\n \"text\": \"def save_args_as_json(path_save_dir: str, args: argparse.Namespace) -> None:\\n path_json = os.path.join(path_save_dir, 'predict_options.json')\\n while os.path.exists(path_json):\\n number = path_json.split('.')[-2]\\n if not number.isdigit():\\n number = '-1'\\n number = str(int(number) + 1)\\n path_json = os.path.join(\\n path_save_dir, '.'.join(['predict_options', number, 'json'])\\n )\\n with open(path_json, 'w') as fo:\\n json.dump(vars(args), fo, indent=4, sort_keys=True)\\n print('Saved:', path_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f3ae3c79ebc463169f02cfa77300469\",\n \"score\": \"0.55545866\",\n \"text\": \"def save_list(outlist,fname):\\r\\n with open(fname,'wb') as f: \\r\\n pickle.dump(outlist, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcf6446b2ce4363ad04df096337be293\",\n \"score\": \"0.5552494\",\n \"text\": \"def save_json(ls, file_path):\\n with open(file_path, 'w') as fp:\\n fp.write('[\\\\n ' + ',\\\\n '.join(json.dumps(i) for i in ls) + '\\\\n]')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40e9469712103675af5b20d4c0d20eea\",\n \"score\": \"0.5536901\",\n \"text\": \"def _load_data(self) -> dict: \\r\\n\\r\\n print(\\\"sort data into new directories\\\")\\r\\n data = {x : [] for x in self.class_names}\\r\\n\\r\\n for i in self.input_dir.rglob('*.npy'):\\r\\n obs = np.load(i, allow_pickle=True).item()\\r\\n\\r\\n file_class = self.class_names[obs['action']-1]\\r\\n\\r\\n if file_class not in self.exclusion_list:\\r\\n\\r\\n new_file = {\\r\\n \\\"obs\\\": obs['observation'],\\r\\n \\\"condition\\\": obs['condition']\\r\\n }\\r\\n\\r\\n data[file_class].append(new_file)\\r\\n \\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2abbac9db3052f826e856b8782fd154\",\n \"score\": \"0.553421\",\n \"text\": \"def model(self):\\n if not self.trained:\\n raise ValueError, \\\"Not trained\\\"\\n paths=[\\n \\\"model-final.others\\\",\\n \\\"model-final.phi\\\",\\n \\\"model-final.theta\\\",\\n \\\"model-final.tassign\\\",\\n \\\"model-final.twords\\\",\\n \\\"wordmap.txt\\\",\\n ]\\n retval = {}\\n for path in paths:\\n full_path = os.path.join(self.workdir, path)\\n if os.path.exists(full_path):\\n with open(full_path) as f:\\n retval[path] = f.read()\\n return retval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c641ee8b217c32e7562c06d4f3c3ee77\",\n \"score\": \"0.55215305\",\n \"text\": \"def dir_to_dict(rootdir):\\n try:\\n subdirs = next(os.walk(rootdir))[1]\\n except StopIteration:\\n raise FileNotFoundError(\\n 'The path provided \\\\'{}\\\\' '\\n 'does not exist or '\\n 'does not match the desired file structure.'.format(rootdir)\\n )\\n \\n if not subdirs:\\n return [os.path.join(rootdir, file)\\n for file in os.listdir(rootdir)]\\n d = dict()\\n for s_dir in subdirs:\\n d[s_dir] = dir_to_dict(os.path.join(rootdir, s_dir))\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f28b99ced001edbb00d648d7e89ee00\",\n \"score\": \"0.55203843\",\n \"text\": \"def save(self):\\n dict_obj = {}\\n for key, value in FileStorage.__objects.items():\\n dict_obj[key] = value.to_dict()\\n with open(FileStorage.__file_path, \\\"w\\\") as f:\\n json.dump(dict_obj, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecf694101d979196781b9d1a222b30c9\",\n \"score\": \"0.5520274\",\n \"text\": \"def directories_data_to_metax(files):\\n metax_directories = []\\n for file in files:\\n metax_directory_object = {}\\n metax_directory_object[\\\"identifier\\\"] = file[\\\"identifier\\\"]\\n metax_directory_object[\\\"title\\\"] = file[\\\"title\\\"]\\n metax_directory_object[\\\"description\\\"] = file[\\\"description\\\"] if \\\"description\\\" in file else \\\"\\\"\\n metax_directory_object[\\\"use_category\\\"] = file[\\\"useCategory\\\"]\\n metax_directories.append(metax_directory_object)\\n return metax_directories\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f3e7b20604e742292b5fe3448c8691b\",\n \"score\": \"0.5519393\",\n \"text\": \"def extract_files(directory):\\n dic = {}\\n folders = []\\n for folder in os.listdir(directory):\\n folder = os.path.join(directory, folder)\\n if os.path.isdir(folder):\\n folders.append(folder)\\n for folder in folders:\\n archives_folder = os.path.join(folder, \\\"archives\\\")\\n files = os.listdir(archives_folder)\\n iterate_files(files, archives_folder, os.path.join(folder, \\\"wav\\\"))\\n return dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a18301182cfb89e429d7c4ce703031ed\",\n \"score\": \"0.55163425\",\n \"text\": \"def load_files(directory):\\n result = {}\\n for filename in os.listdir(directory):\\n with open(os.path.join(directory, filename)) as f:\\n result[filename] = f.read()\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f8bdb1aec975144b6ca89305e4be8c1\",\n \"score\": \"0.551416\",\n \"text\": \"def _save_file(self):\\n file = open(self.file_name, \\\"w\\\")\\n json.dump([x.to_dict() for x in self._grade_list], file, indent=4)\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"094c0123689a48ad570f536dd78d0576\",\n \"score\": \"0.55115896\",\n \"text\": \"def dump_dicts(self):\\n if not os.path.exists(self.pickle_dir):\\n os.makedirs(self.pickle_dir)\\n data = {\\n 'item2num': self.encoder.item2num,\\n 'num2item': self.encoder.num2item\\n }\\n with open(self.pickle_path, 'wb') as file:\\n pickle.dump(data, file, pickle.HIGHEST_PROTOCOL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"943a6b1c094ba099ccc509ff47b8dced\",\n \"score\": \"0.55070764\",\n \"text\": \"def get_env():\\r\\n\\r\\n dirs_files = {}\\r\\n if 'ToSort' in os.listdir():\\r\\n for item in os.walk(os.path.join(os.getcwd(), 'ToSort')):\\r\\n file_list = []\\r\\n directory = str(item[0])\\r\\n for i in item[2]:\\r\\n file_list.append(i)\\r\\n dirs_files[directory] = file_list\\r\\n return dirs_files\\r\\n else:\\r\\n print('There is no ToSort directory present here!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b4f16ed9345eb8c510061cb114a3831\",\n \"score\": \"0.5496495\",\n \"text\": \"def __restore(self,d, basepath=\\\"/\\\", filterDirs=None):\\n try:\\n for key in d.GetListOfKeys():\\n kname = key.GetName()\\n if key.IsFolder():\\n if filterDirs and kname not in filterDirs: \\n continue\\n for i in self.__restore(d.Get(kname), basepath+kname+\\\"/\\\"):\\n yield i\\n else:\\n yield basepath+kname, d.Get(kname)\\n except AttributeError, e:\\n self._logger.debug(\\\"Ignore reading object of type %s.\\\",type(d))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c146ac8d97af076180f9fdb2903ee11\",\n \"score\": \"0.5489158\",\n \"text\": \"def list_files():\\n entries = [meta.__json__() for meta in FILES.values()]\\n return json.dumps({\\\"files\\\": entries})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2388620505254402580e19332958d144\",\n \"score\": \"0.5474785\",\n \"text\": \"def load_files(directory): # successfully implemented\\n files = os.listdir(directory)\\n file_data_list = list()\\n\\n for file in files:\\n with open(os.path.join(directory, file), encoding='utf-8') as f:\\n text = f.read()\\n file_data_list.append((file, text))\\n print(f\\\"loaded {file}\\\")\\n\\n file_data = dict(file_data_list)\\n \\n return file_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa2a04f36473ca7e6258acb718143704\",\n \"score\": \"0.5472414\",\n \"text\": \"def save(data, directory):\\n\\n with open(directory, \\\"w\\\") as f:\\n f.write(json.dumps(data))\\n f.close()\\n print(\\\"Saved to \\\" + directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28eecd9f04e699f7f016b01d42ec0871\",\n \"score\": \"0.54713994\",\n \"text\": \"def save_file_as(self):\\n filepath = str(QtGui.QFileDialog.getSaveFileName(self, 'Save File', self.filepath, \\\"JSON (*.json)\\\"))\\n filename = os.path.basename(filepath)\\n if filename and filepath:\\n self.filename = filename\\n self.filepath = filepath\\n # -- Generate a dictionary of all items in the launch list, each item being a dictionary itself with that items all robot launch data --\\n savedata_launch_tree_dict = {}\\n # Iterate over all existing (top level) items (a.k.a. robots) in the robotTree and add their column values to the launch_tree_dict.\\n root = self.robotTree.invisibleRootItem()\\n child_count = root.childCount()\\n for i in range(child_count):\\n item = root.child(i)\\n savedata_launch_tree_dict[unicode(item.text(0))] = {\\n \\\"robot_ID\\\": unicode(item.text(0)),\\n \\\"navigation\\\": unicode(item.text(1)),\\n \\\"robot_type\\\": unicode(item.text(2)),\\n \\\"ds\\\": unicode(item.text(3))\\n }\\n\\n # -- Generate a dictionary of all docking station objects, each docking station being a dictionary itself with all ds' data. --\\n savedata_docking_station_dict = {}\\n for ds in self.docking_station_dict.values():\\n ds_id, ds_origin_x, ds_origin_y, ds_origin_theta_rad, ds_rows, ds_columns, ds_cell_offset_x, ds_cell_offset_y, ds_cell_theta_rad = ds.get_attributes()\\n savedata_docking_station_dict[ds_id] = {\\n \\\"id\\\": ds_id,\\n \\\"origin_x\\\": ds_origin_x,\\n \\\"origin_y\\\": ds_origin_y,\\n \\\"origin_theta_rad\\\": ds_origin_theta_rad,\\n \\\"rows\\\": ds_rows,\\n \\\"columns\\\": ds_columns,\\n \\\"cell_offset_x\\\": ds_cell_offset_x,\\n \\\"cell_offset_y\\\": ds_cell_offset_y,\\n \\\"cell_offset_theta_rad\\\": ds_cell_theta_rad\\n }\\n \\n # -- Generate top level dictionary which contains all launch input fields, a dict of the launch list and a dict of the docking station settings. --\\n savedata_dict = {\\n # Launch tab\\n unicode(self.comboBoxRobotType.objectName()): unicode(self.comboBoxRobotType.itemText(self.comboBoxRobotType.currentIndex())),\\n unicode(self.comboBoxDockingStationLaunch.objectName()): unicode(self.comboBoxDockingStationLaunch.itemText(self.comboBoxDockingStationLaunch.currentIndex())),\\n unicode(self.spinBoxRobotID.objectName()): self.spinBoxRobotID.value(),\\n unicode(self.checkBoxSFMMPDM.objectName()): self.checkBoxSFMMPDM.isChecked(),\\n unicode(self.robotTree.objectName()): savedata_launch_tree_dict,\\n \\n # Docking station tab\\n unicode(self.comboBoxDockingStationID.objectName()): unicode(self.comboBoxDockingStationID.itemText(self.comboBoxDockingStationID.currentIndex())),\\n \\\"docking_stations\\\": savedata_docking_station_dict\\n }\\n with open(filepath, 'w') as json_savefile:\\n json.dump(savedata_dict, json_savefile, indent=4)\\n \\n self.unsaved_changes_inactive()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2fdec46313dc78d4e40a013a7bf2d28\",\n \"score\": \"0.54627717\",\n \"text\": \"def get_all(folder = '/home/pandyr/Dropbox (MIT)/Research/Plasmids'):\\n all_data = {} # initialize storage \\n # Iterate through all files in fasta folder\\n for file in os.listdir(folder):\\n if file.endswith(\\\".dna\\\"):\\n seq = get(folder + '/' + file)\\n seqname = get_seqname(file)\\n all_data[seqname] = Dseqrecord(seq,linear = False)\\n return all_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1273862645cee6c40a8a235337e9403\",\n \"score\": \"0.5461379\",\n \"text\": \"def getFileDict(case):\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"127c356dd3a430fcfef215290d3315ac\",\n \"score\": \"0.54609305\",\n \"text\": \"def folderlist_gen(fname):\\n folders = [] # list of folders\\n with open(fname) as file:\\n for line in file:\\n line = line.strip() # get rid of \\\"\\\\n\\\"\\n line = \\\"\\\\\\\\\\\".join([\\\".\\\", \\\"Modified Data\\\", line]) # get a full path\\n folders.append(line)\\n files = [] # list of files in all folders\\n for folder in folders:\\n files = files + dlist_gen(folder + \\\"\\\\\\\\\\\") # dlist_gen by folder\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb00423bb3af99f8c315017249501fe5\",\n \"score\": \"0.54605085\",\n \"text\": \"def loadwtfs(folder):\\n wtfbyname = {}\\n for wtfpath in wtffiles(folder):\\n try:\\n name = wtfname(wtfpath)\\n wtfdata = Simulate.readwtf(wtfpath)\\n wtfbyname[name] = {}\\n wtfbyname[name]['wtfpath'] = wtfpath\\n wtfbyname[name]['wtfdata'] = wtfdata\\n except Exception as exc:\\n print exc\\n\\n return wtfbyname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52ae031f80a4a38e47efde76ce659840\",\n \"score\": \"0.54601353\",\n \"text\": \"def get_dir_file(self, path):\\n result = {}\\n dir_list = bucket.list()\\n for o in dir_list:\\n info = self.get_info(o['name'])\\n result[o['name']] = info\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3bf5366d6b10e1af5c32ecf75830811\",\n \"score\": \"0.54583526\",\n \"text\": \"def export_listDirectory( self, dir_path, mode ):\\n conn , error, userDict = self.__irodsClient( )\\n if not conn:\\n return S_ERROR( error )\\n\\n file_path = self.__resolveFileID( dir_path, userDict )\\n irodsHome = userDict.get( 'iRodsHome', IRODS_HOME ) \\n irodsPath = irodsHome + file_path\\n gLogger.debug( \\\"file_path to read: %s\\\" % irodsPath )\\n\\n is_file = False\\n irodsFile = irodsOpen( conn , irodsPath , \\\"r\\\" )\\n if not irodsFile:\\n is_file = True\\n else:\\n irodsDir = os.path.dirname( irodsPath )\\n coll = irodsCollection( conn, irodsDir )\\n if not coll:\\n return S_ERROR( 'Directory not found' )\\n objects = coll.getObjects()\\n fileList = [ x[0] for x in objects ]\\n dirList = coll.getSubCollections() \\n\\n resultDict = {}\\n if mode == 'l':\\n if is_file:\\n result = self.__getFileStat( dir_path )\\n if result['OK']:\\n resultDict[dir_path] = result['Value']\\n return S_OK( resultDict )\\n else:\\n return S_ERROR( 'Failed to get the file stat info' )\\n else:\\n failed_list = []\\n one_OK = False\\n for fname in dirList+fileList:\\n result = self.__getFileStat( dir_path + '/' + fname )\\n if result['OK']:\\n resultDict[fname] = result['Value']\\n one_OK = True\\n else:\\n failed_list.append( fname )\\n if failed_list:\\n if one_OK:\\n result = S_ERROR( 'Failed partially to get the file stat info' )\\n else:\\n result = S_ERROR( 'Failed to get the file stat info' )\\n result['FailedList'] = failed_list\\n result['Value'] = resultDict\\n else:\\n result = S_OK( resultDict )\\n\\n return result\\n else:\\n return S_OK( dirList )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec378cc7a2b10329dd5b33d19ffe390b\",\n \"score\": \"0.5448974\",\n \"text\": \"def fetch_reads(self):\\n reads = []\\n # walk through the raw directory\\n for root, dirs, files in os.walk(self.raw_dir):\\n for fname in files:\\n # skip files that are not one of the recognized extensions.\\n extensions = Alaska.RAW_EXT + Alaska.ARCH_EXT\\n if not fname.endswith(extensions):\\n continue\\n\\n # otherwise, save info about the file\\n path = '{}/{}/{}'.format(Alaska.ROOT_PATH, root, fname)\\n folder = root.replace(self.raw_dir, '')\\n filename = fname\\n size = os.path.getsize(path)\\n read = {\\n 'folder': folder,\\n 'filename': fname,\\n 'size': '{} bytes'.format(size),\\n 'path': path\\n }\\n reads.append(read)\\n return reads\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b686c243b030034e4ef40bb8b8974196\",\n \"score\": \"0.54465556\",\n \"text\": \"def load_parses(savepath):\\n f = open(savepath + 'parse-dict.pkl', 'rb')\\n parse_dict = pickle.load(f)\\n f.close()\\n return parse_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6235661e0f9994b4fe581b6ea774fa9d\",\n \"score\": \"0.5446284\",\n \"text\": \"def create_data_lists(path, output_folder):\\n path = os.path.abspath(path)\\n\\n train_images = list()\\n train_objects = list()\\n n_objects = 0\\n\\n # Training data\\n # Find IDs of images in training data\\n with open(os.path.join(path, 'train.txt')) as f:\\n ids = f.read().splitlines()\\n\\n for id in ids:\\n # Parse annotation's XML file\\n objects = parse_annotation(os.path.join(path, 'Annotations', id + '.xml'))\\n if len(objects['labels']) == 0:\\n continue\\n n_objects += len(objects['labels'])\\n train_objects.append(objects)\\n train_images.append(os.path.join(path, id + '.ppm'))\\n\\n assert len(train_objects) == len(train_images)\\n\\n # Save to file\\n with open(os.path.join(output_folder, 'TRAIN_images.json'), 'w') as j:\\n json.dump(train_images, j)\\n with open(os.path.join(output_folder, 'TRAIN_objects.json'), 'w') as j:\\n json.dump(train_objects, j)\\n with open(os.path.join(output_folder, 'label_map.json'), 'w') as j:\\n json.dump(label_map, j) # save label map too\\n\\n print('\\\\nThere are %d training images containing a total of %d objects. Files have been saved to %s.' % (\\n len(train_images), n_objects, os.path.abspath(output_folder)))\\n\\n # Test data\\n test_images = list()\\n test_objects = list()\\n n_objects = 0\\n\\n # Find IDs of images in the test data\\n with open(os.path.join(path, 'test.txt')) as f:\\n ids = f.read().splitlines()\\n\\n for id in ids:\\n # Parse annotation's XML file\\n objects = parse_annotation(os.path.join(path, 'Annotations', id + '.xml'))\\n if len(objects['labels']) == 0: # might change this later\\n continue\\n test_objects.append(objects)\\n n_objects += len(objects['labels'])\\n test_images.append(os.path.join(path, id + '.ppm'))\\n\\n assert len(test_objects) == len(test_images)\\n\\n # Save to file\\n with open(os.path.join(output_folder, 'TEST_images.json'), 'w') as j:\\n json.dump(test_images, j)\\n with open(os.path.join(output_folder, 'TEST_objects.json'), 'w') as j:\\n json.dump(test_objects, j)\\n\\n print('\\\\nThere are %d test images containing a total of %d objects. Files have been saved to %s.' % (\\n len(test_images), n_objects, os.path.abspath(output_folder)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae84ed29a5c48bf21a92fcf3c173f12d\",\n \"score\": \"0.5445115\",\n \"text\": \"def save_to_file(cls, list_objs):\\n lst = []\\n if list_objs:\\n for i in list_objs:\\n lst.append(i.to_dictionary())\\n with open(cls.__name__ + '.json', 'w') as f:\\n f.write(Base.to_json_string(lst))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0415d69dd9b68c8f50d9dbdff3ccefae\",\n \"score\": \"0.544258\",\n \"text\": \"def collect_pickle_data():\\n\\tphrase_names = []\\n\\tphrase_labels = []\\n\\tphrases = []\\n\\tphrase_counter = 0\\n\\tfor phrase_name in os.listdir(ASL_FRAME_PATH): # for each phrase\\n\\t\\tphrase_dir = \\\"\\\"\\n\\t\\tphrase_dir = ASL_FRAME_PATH+\\\"\\\\\\\\\\\"+phrase_name\\n\\t\\tpickle_dir = phrase_dir+\\\"\\\\\\\\pickle\\\"\\n\\t\\tphrase_names.append(phrase_name)\\n\\t\\tphrase_labels.append(phrase_counter)\\n\\t\\tphrases[phrase_counter] = []\\n\\t\\tphrase_counter += 1\\n\\t\\tfor phrase_file in os.listdir(phrase_dir): # for each mp4 file of the phrase\\n\\t\\t\\tphrase_file_dir = phrase_dir+\\\"\\\\\\\\\\\"+phrase_file\\n\\t\\t\\thand_data_file_name = pickle_dir+\\\"\\\\\\\\\\\"+phrase_file+\\\".pickle\\\"\\n\\t\\t\\t# load array of hands for this phrase file\\n\\t\\t\\thand_data = pickle.load(open(hand_data_file_name, \\\"rb\\\"))\\n\\t\\t\\tphrases[phrase_counter].append(hand_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e119e5c0dc41f5ca50e4e47ad78cc032\",\n \"score\": \"0.544183\",\n \"text\": \"def persist(self, model_dir):\\n regex_file = os.path.join(model_dir, DICT_NER_MODEL_FILE_NAME)\\n utils.write_json_to_file(regex_file, self.known_patterns, indent=4)\\n\\n return {\\\"dict_ner_file\\\": DICT_NER_MODEL_FILE_NAME}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbf2040ae4d6b1f0115443bb98da1a16\",\n \"score\": \"0.5433404\",\n \"text\": \"def read_decade(folder):\\n\\n path = \\\"/home/artoo/Workspace_local/Python/Final_Project_02806/data/wiki/\\\" + folder + \\\"/\\\"\\n file_list = os.listdir(path)\\n decade = {}\\n\\n for each_file in file_list:\\n\\n each_file = codecs.open(path + each_file, 'r', 'utf-8')\\n try:\\n source = json.loads(each_file.read())\\n except ValueError as ve:\\n print \\\"\\\\nError while reading series...\\\"\\n print ve.message + \\\"\\\\n\\\"\\n\\n title = source['query']['pages'].itervalues().next()['title']\\n content = source['query']['pages'].itervalues().next()['revisions'][0]['*']\\n\\n\\n decade[title] = content\\n\\n return decade\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a54ea74aa985188fc2e73283dc49914\",\n \"score\": \"0.54311633\",\n \"text\": \"def read(self):\\n\\n valid = self.find_valid_directories()\\n\\n mapping = dict()\\n for directory in valid:\\n documents = JaguarCalcDrone.get_documents_calc_dir(directory)\\n mapping[directory] = compute_state_hash(documents)\\n\\n return mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a54ea74aa985188fc2e73283dc49914\",\n \"score\": \"0.54311633\",\n \"text\": \"def read(self):\\n\\n valid = self.find_valid_directories()\\n\\n mapping = dict()\\n for directory in valid:\\n documents = JaguarCalcDrone.get_documents_calc_dir(directory)\\n mapping[directory] = compute_state_hash(documents)\\n\\n return mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1f7f80c621ea768e3365c48c8e5931d\",\n \"score\": \"0.54308647\",\n \"text\": \"def path_list2dict(file_list: list, suffix: str) -> dict:\\n file_dict = {}\\n for path in file_list:\\n key = path[path.rfind('/') + 1:len(path) - len(suffix)]\\n file_dict[key] = path\\n return file_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37770f31c2a904df8720da6a33e45c02\",\n \"score\": \"0.543051\",\n \"text\": \"def read(self):\\n\\n valid = self.find_valid_directories()\\n\\n mapping = dict()\\n for directory in valid:\\n documents = AutoTSCalcDrone.get_documents_calc_dir(directory)\\n mapping[directory] = compute_state_hash(documents)\\n\\n return mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"703e4a634ac7bd1098e8c6a7afc0b131\",\n \"score\": \"0.5427156\",\n \"text\": \"def available_saved(self):\\n return [*os.listdir(self._mem_file)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac0244ca685e1d1652a4b534d9f1bb99\",\n \"score\": \"0.54217535\",\n \"text\": \"def get_dicts():\\n dicts_list = ['abbreviations', 'us_uk']\\n all_dics = [ampersand, clean_symbols, convert_sgml, single_space]\\n\\n some_dics = {}\\n \\n for dictionary in dics_list:\\n with open(dictionary, 'r') as f:\\n some_dics[dictionary] = pickle.load(f)\\n\\n all_dics.extend(some_dics.values())\\n return all_dics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6f220fb72e7bc81bb59caafbe3d6ba4\",\n \"score\": \"0.5415177\",\n \"text\": \"def dump(self, folder: str) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edbed9cfe25f31c741610d20130c8761\",\n \"score\": \"0.5414246\",\n \"text\": \"def save(self):\\n json_objects = {}\\n for key in self.__objects:\\n json_objects[key] = self.__objects[key].to_dict()\\n with open(self.__file_path, 'w') as f:\\n json.dump(json_objects, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f823e468138c3ab55a298897f786bd4\",\n \"score\": \"0.54107964\",\n \"text\": \"def save_to_file(cls, list_objs):\\n listj = []\\n f = cls.__name__ + '.json'\\n with open(f, 'w', encoding=\\\"UTF-8\\\") as file:\\n if list_objs:\\n for k in list_objs:\\n listj.append(k.to_dictionary())\\n file.write(cls.to_json_string(listj))\\n else:\\n file.write(cls.to_json_string(listj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcd66bb3c9d5f69a9ccec6038d1ab292\",\n \"score\": \"0.54071915\",\n \"text\": \"def get_summaries_dict(summaries_dir):\\n summaries_dict_data = defaultdict(dict)\\n\\n for d in os.scandir(summaries_dir):\\n if d.is_dir():\\n for f in os.listdir(d.path):\\n absolute_path = d.path + '/' + f\\n\\n with open(absolute_path, encoding=\\\"utf8\\\", errors='ignore') as article_file:\\n data = article_file.read()\\n summaries_dict_data[d.name][f] = data\\n\\n return summaries_dict_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6429bfe2d479b3b39eeac1ebd4d416a7\",\n \"score\": \"0.5407166\",\n \"text\": \"def all_files_data(dir_name):\\n skip_list = ['.DS_Store']\\n all_files_data_list = []\\n all_files = os.listdir(dir_name)\\n all_files.sort(key=natural_keys)\\n for f in all_files:\\n if f not in skip_list:\\n with open(dir_name + f) as data_file:\\n all_files_data_list.append(json.load(data_file))\\n\\n keys_sorted = []\\n for d in all_files_data_list:\\n sorted_keys = [x for x in d['blocks']]\\n sorted_keys.sort(key=natural_keys)\\n keys_sorted.append(sorted_keys)\\n\\n return all_files_data_list, keys_sorted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82b3b5e7180697298630a31e3105c608\",\n \"score\": \"0.54049057\",\n \"text\": \"def contents(self):\\n rv = {}\\n Dhosts = self.dir.dirs()\\n for Dhost in Dhosts:\\n host = str(Dhost.basename())\\n if host not in rv:\\n rv[host] = {}\\n Dnames = Dhost.dirs()\\n for Dname in Dnames:\\n name = str(Dname.basename())\\n if name not in rv[host]:\\n rv[host][name] = {}\\n Dids = self.non_dot(Dname)\\n for Did in Dids:\\n id = str(Did.basename())\\n props = self.props(Did)\\n props[\\\"active\\\"] = \\\"unknown\\\"\\n rv[host][name][id] = props\\n return rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6d89e81d0ed109b398a1ce21fc399b1\",\n \"score\": \"0.5401883\",\n \"text\": \"def savefiles(self):\\n return self.options['savefiles']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25150655e5241147f8078536e2837ee9\",\n \"score\": \"0.53987396\",\n \"text\": \"def loadData(vocab, directory):\\n top_level = os.listdir(directory)\\n dataset = []\\n for d in top_level:\\n if d[-1] == '/':\\n label = d[:-1]\\n subdir = d\\n else:\\n label = d\\n subdir = d+\\\"/\\\"\\n files = os.listdir(directory+subdir)\\n for f in files:\\n bow = create_bow(vocab, directory+subdir+f)\\n dataset.append({'label': label, 'bow': bow})\\n return dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"393cc4edda01e097e4c98a86c2ad8153\",\n \"score\": \"0.53958565\",\n \"text\": \"def save_list(path, lst):\\n output = open(\\\"{}.pkl\\\".format(path), \\\"wb\\\")\\n pickle.dump(avg, output)\\n output.close()\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"826846232adecf0b78490048383cf189\",\n \"score\": \"0.53956956\",\n \"text\": \"def dump_obj(name, data, directory, it=-1):\\n for d in data:\\n # Save list\\n if isinstance(data[d], list):\\n print '\\\\titem: ', d, '[', len(data[d]), ']'\\n # File name\\n fstr = '/' + name + '_' + d + ('_' + str(it) if it != -1 else '') + '.txt'\\n\\n # Create file and dump all items\\n with open(directory + fstr, 'w') as fd:\\n \\tfd.write('\\\\n'.join(map(lambda x: str(x), data[d])) + \\\"\\\\n\\\")\\n\\t\\t\\n # Save global\\n else:\\n print '\\\\titem: ', d\\n fstr = '/' + name + '_global' + ('_' + str(it) if it != -1 else '') + '.txt'\\n\\n # Create file and dump all items\\n with open(directory + fstr, 'a') as fd:\\n \\tfd.write(d + ':' + str(data[d]) + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5932f38cf8c8b7c14dd8573ab10f9d00\",\n \"score\": \"0.53907824\",\n \"text\": \"def load_files(directory):\\n dictionary={}\\n for file in os.listdir(directory):\\n dictionary[file]=open(directory+os.sep+file,encoding='utf-8').read()\\n return dictionary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"246e95c121f82a7f9c86d0ce3274e9c8\",\n \"score\": \"0.5386953\",\n \"text\": \"def ListDataFiles():\\n print \\\"Listing Data Files\\\"\\n htmlReturn = \\\"\\\"\\n files = os.listdir(\\\"Data\\\")\\n print \\\"Found files: \\\", files\\n return json.dumps( files )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3372854f5f8a0eaf56d5064d2e2c9081\",\n \"score\": \"0.5383845\",\n \"text\": \"def file_list(file_name):\\n global dict_artists\\n output = []\\n\\n print('Creating file \\\"' + file_name + '\\\"...')\\n output.append(\\\"MUSIC LIST\\\")\\n output.append('----------\\\\n')\\n for k_artist in sorted(dict_artists.keys()):\\n for k_album in sorted(dict_artists[k_artist].keys()):\\n output.append('ARTIST: ' + k_artist)\\n output.append('ALBUM: ' + k_album)\\n output.append('TRACKS:')\\n for track in sorted(dict_artists[k_artist][k_album]):\\n output.append(\\\" \\\" + track)\\n output.append(\\\"\\\")\\n\\n write_file(file_name, output)\\n print('File created')\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa69c3e5f51b14f285c2544f593a90d2\",\n \"score\": \"0.53769517\",\n \"text\": \"def getFileNamesAndPaths():\\r\\n os.chdir(json_file_dir)\\r\\n files_in_JSON_dir = os.listdir(json_file_dir)\\r\\n for file in glob.glob(\\\"*.txt\\\"):\\r\\n files_to_zip.append(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d19063a7726884c40bcf475bfafa9a5\",\n \"score\": \"0.5375869\",\n \"text\": \"def generate_folders_and_files():\\r\\n\\r\\n if params['EXPERIMENT_NAME']:\\r\\n params['FILE_PATH'] = getcwd() + \\\"/results/\\\" + params[\\r\\n 'EXPERIMENT_NAME'] + \\\"/\\\"\\r\\n else:\\r\\n params['FILE_PATH'] = getcwd() + \\\"/results/\\\"\\r\\n\\r\\n # Generate save folders\\r\\n if not path.isdir(params['FILE_PATH']):\\r\\n mkdir(params['FILE_PATH'])\\r\\n if not path.isdir(params['FILE_PATH'] + str(params['TIME_STAMP'])):\\r\\n mkdir(params['FILE_PATH'] + str(params['TIME_STAMP']))\\r\\n\\r\\n save_params()\\r\\n save_stats_headers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feb9a4834357d3b75268d16b2bb58550\",\n \"score\": \"0.5374786\",\n \"text\": \"def make_atlas_list():\\n jsondict = {}\\n jsondict['Atlas'] = []\\n jsondict['ShortDesc'] = []\\n jsondict['Maps'] = []\\n jsondict['MapDesc'] = []\\n jsondict['Type'] = []\\n jsondict[\\\"LongDesc\\\"]=[]\\n jsondict[\\\"ReferencesAndLinks\\\"]=[]\\n\\n directories = ['Diedrichsen_2009','Buckner_2011','Xue_2021','Ji_2019','King_2019']\\n for name in directories:\\n with open(name +'/atlas_description.json') as jsonfile:\\n file = json.load(jsonfile)\\n jsondict[\\\"Atlas\\\"].append(name)\\n jsondict[\\\"ShortDesc\\\"].append(file[\\\"ShortDesc\\\"])\\n jsondict[\\\"Maps\\\"].append(file[\\\"Maps\\\"])\\n jsondict[\\\"Type\\\"].append(file[\\\"Type\\\"])\\n jsondict[\\\"MapDesc\\\"].append(file[\\\"MapDesc\\\"])\\n jsondict[\\\"LongDesc\\\"].append(file[\\\"LongDesc\\\"])\\n jsondict[\\\"ReferencesAndLinks\\\"].append(file[\\\"ReferencesAndLinks\\\"])\\n with open('package_description.json','w') as outfile:\\n json.dump(jsondict,outfile,indent = 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702e51beecf4279a6ad1993aff2f6fb3\",\n \"score\": \"0.53741115\",\n \"text\": \"def save(self):\\n my_dict = {}\\n for k, v in self.__objects.items():\\n my_dict[k] = v.to_dict()\\n with open(FileStorage.__file_path, mode='w', encoding='UTF-8') as f:\\n json.dump(my_dict, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89135d4969110decd230e561a368cbf3\",\n \"score\": \"0.5371747\",\n \"text\": \"def copys():\\n for fname in os.listdir(Result.froot):\\n if fname.startswith('wiki_p'):\\n result = Result('', '', fname)\\n logging.info(\\\"copying %s\\\" % fname)\\n with open(fname + '.s', 'w') as fout:\\n cPickle.dump([None, result.s], fout, protocol=-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03414003afbc84e37c02bf847b9df0fc\",\n \"score\": \"0.5371028\",\n \"text\": \"def crawlFolder(path):\\n # path =\\\"dataset/plot/\\\"\\n files = [f for f in listdir(path) if isfile(join(path, f))]\\n files.sort()\\n dataset = []\\n for item in files:\\n file_name = join(path, item)\\n file = open(file_name, \\\"r\\\")\\n data = file.read()\\n file.close()\\n dataset.append([data, file_name])\\n return dataset\",\n \"title\": \"\"\n 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query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
1ad8b2bda71c27dd30959fac0f57aaa8 | Finds the shortest path through a weighted graph using a heuristic. Used for planning & pathfinding in low dimension spaces. | [
{
"docid": "337086241106fd93d954e33e0b7111a4",
"score": "0.6438782",
"text": "def a_star(start_vertex, adjacent, cost, heuristic, goal, max_depth=None):\n if max_depth is not None and max_depth <= 0:\n raise ValueError(\"Argument max_depth must be greater than zero\")\n\n class InternalNode... | [
{
"docid": "5478766196043feca0d87a016cf5f766",
"score": "0.7408174",
"text": "def shortest_path(graph, distances, visited_set, use_heuristic=False):\n n_list = []\n h = 0\n for name, dist in distances.items():\n if name not in visited_set and dist != float('inf'):\n if use_heu... |
0de9fca772bfb65735c5d9615ad30b7f | store decoded instruction, prepare for next one | [
{
"docid": "32f9f6ffb70ceefd08ea42257e2ae3c0",
"score": "0.57765085",
"text": "def _save_instruction(self, insn):\n self.instructions.append(insn)\n self.cycles = 0\n self.used_words = []\n self.first_address = None",
"title": ""
}
] | [
{
"docid": "251672139dd55cedcb073d1046e8b9c9",
"score": "0.66286063",
"text": "def _decode(self):\n try:\n ip = self.getReg(self.REG_IP)\n return Instruction.decode(self)\n except InstructionError, e:\n raise ProcessorError(ip, \"error decoding instruction: %s\" % e)",
"title": ... |
45a4339872939e50f1b9f5e7efc859c3 | Return the metadata of the requested table | [
{
"docid": "587eaf079abab28e435c2b2b70d0eb05",
"score": "0.67164326",
"text": "def table_info(request, name=None, get_table_size=False):\n\n try:\n if name is None:\n return JsonResponse({\n 'success': False,\n 'err': \"Invalid table name\"\n ... | [
{
"docid": "3fb576fc72d1a4763e97ad9a7ca78b31",
"score": "0.7701821",
"text": "def _metadata_table(self):\n return _make_metadata_table(self)",
"title": ""
},
{
"docid": "609a06854c7b48a6deecaa33488245e4",
"score": "0.76902467",
"text": "def get_table_metadata(self):\n r... |
4a18047a82e15e9da2c85135e680c633 | Get the best ARIMA model. | [
{
"docid": "0282a9f33bdc11ac5c8b5843ba0d9963",
"score": "0.7164151",
"text": "def get_best_model(self):\n return self.auto_est.get_best_model()",
"title": ""
}
] | [
{
"docid": "d8842cd0d2022b7bbda5c575f8abd437",
"score": "0.6984959",
"text": "def ARIMA_model(training_data,exogenous_value,order):\n\n arima_model = sm.tsa.statespace.SARIMAX(endog=training_data.values, exog=exogenous_value, order=order).fit()\n \n return arima_model",
"title": ""
},
{... |
ac4a70a179548769a54b4e8bcda2a33e | max Informationbased Nonparametric Exploration. | [
{
"docid": "e66a409de6378bd34be549f1e3ec9e89",
"score": "0.0",
"text": "def mic_test(self, inputs):\n # init\n mic_matrix = np.full(\n (self.num_features, self.num_features), np.nan)\n sign_matrix = np.zeros(\n (self.num_features, self.num_features))\n\n ... | [
{
"docid": "b1786dfbdc9d9be4ed12f80d58b1acee",
"score": "0.6003945",
"text": "def _argmax(self, params, **kwargs):\n raise NotImplementedError",
"title": ""
},
{
"docid": "ceef1e74a0096d55b64ef192415892ef",
"score": "0.598692",
"text": "def max(input, dim):\n pass",
"ti... |
e27c418436ca52894e43d72ed2eed4f1 | Computes the accuracy over the k top predictions for the specified values of k | [
{
"docid": "1fb344a00114e8ceae403851687e0b81",
"score": "0.7847017",
"text": "def accuracy(output, target, topk=(1,)):\n with torch.no_grad():\n maxk = max(topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct... | [
{
"docid": "092b204d6f194e96b85acc310eb7d8eb",
"score": "0.8249487",
"text": "def accuracy(output, target, topk=(1,)):\n maxk = max(topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\... |
264d245dad9dd026a02a3524d8c95b3e | Kill the previously started Quixote server. | [
{
"docid": "ae941954f9eabbecaef43c4c42270d0b",
"score": "0.0",
"text": "def kill_server():\n global _server_url\n if _server_url != None:\n try:\n fp = urllib.urlopen('%sexit' % (_server_url,))\n except:\n pass\n\n _server_url = None",
"title": ""
}
] | [
{
"docid": "271d9573d0475ee33c60157648dadab6",
"score": "0.7437033",
"text": "def kill():\n return Server.kill()",
"title": ""
},
{
"docid": "1857b1c801e91961f9d85a091702b042",
"score": "0.7063344",
"text": "def killServer():\n if TikaServerProcess:\n try:\n o... |
870b64780dfa182edf12e4df39653c90 | Smooth the data with von Mises functions | [
{
"docid": "fdf5009c7e87693ef95b73de8a36878e",
"score": "0.632402",
"text": "def vonmises_smoothing(self, **kwargs):\n return self.component_fitting(mode='vonmises', **kwargs)",
"title": ""
}
] | [
{
"docid": "5babe5be0badb185409d0cd539883ea3",
"score": "0.5696802",
"text": "def smooth(values, dt, tau):\n result = np.empty(len(values))\n result[0] = values[0]\n weights = np.exp(-dt / tau)\n for i in range(1, len(values)):\n result[i] = weights[i] * result[i - 1] + (1 - weights[i... |
e7b7cfb0d91468ec87cc607502d3d264 | Parse specification in the specification YAML file. | [
{
"docid": "1ddd55b5916bf21eab1a67ee8e2b71de",
"score": "0.7328811",
"text": "def read_specification(self):\n try:\n self._cfg_yaml = load(self._cfg_file)\n except YAMLError as err:\n raise PresentationError(msg=\"An error occurred while parsing the \"\n ... | [
{
"docid": "427c032eef2b4c90af30cbfcb501e46f",
"score": "0.65131885",
"text": "def fileToSpec(file):\n\n stream = open(file, 'r') if file is not None else sys.stdin\n return Specification(yaml.load(stream))",
"title": ""
},
{
"docid": "77bdebc986d9400600856c6fb64388c2",
"score": "0... |
cb0ddeca3dc7638e06e9b469917ee008 | Returns the string representation of the model | [
{
"docid": "0f283634439620e9ecd215d87804ec00",
"score": "0.0",
"text": "def to_str(self):\n return pprint.pformat(self.to_dict())",
"title": ""
}
] | [
{
"docid": "aa510b1d67cd504f00d31d7600881756",
"score": "0.8602374",
"text": "def _repr_model_(self):\n return str(self)",
"title": ""
},
{
"docid": "0fbfd148e937420716433d412dc3b6bf",
"score": "0.85093755",
"text": "def __str__(self):\n\n return str(self.model)",
"... |
8cf8965b43b8078af4ecb4b9011ddce9 | Sets the com_adobe_granite_dropwizard_metrics of this ComAdobeGraniteApicontrollerFilterResolverHookFactoryProperties. | [
{
"docid": "b9ae26c2fabe116fd5b071ee4d5f46af",
"score": "0.6842898",
"text": "def com_adobe_granite_dropwizard_metrics(self, com_adobe_granite_dropwizard_metrics: ConfigNodePropertyString):\n\n self._com_adobe_granite_dropwizard_metrics = com_adobe_granite_dropwizard_metrics",
"title": ""
}... | [
{
"docid": "5237d670163db01b323ffb3fb5e1c006",
"score": "0.64473355",
"text": "def set_metrics(self):\n pass",
"title": ""
},
{
"docid": "e0ce85d34ef93a98035a9f9937453ade",
"score": "0.60261935",
"text": "def com_adobe_granite_dropwizard_metrics(self) -> ConfigNodePropertyStri... |
9f51ea2ac5634ec1411934d9594f7912 | Get counts of how many images are of each category for singlelabel classification, and (optionally) plot a histogram of counts. csv_file must be in form below. If csv does not have a header row with column names, then set skip_first=False. | [
{
"docid": "4df68d23c91c6350ab90cc0a2db2ec12",
"score": "0.83755827",
"text": "def get_cat_counts(csv_file,skip_first=True,plot_hist=True):\n \n if skip_first == False: \n df = pd.read_csv(csv_file, names = ['img_name','category'])\n if skip_first == True: \n df = pd.read_csv(csv_... | [
{
"docid": "4622c558732975aebb15df0e63ccfd57",
"score": "0.59433186",
"text": "def generate_category_labels_from_csv(csv_dir):\n categories = {}\n df = pd.read_csv(csv_dir)\n\n for row in df.itertuples():\n categories[row[1]] = row[2]\n return categories",
"title": ""
},
{
... |
c5a655b906dda30ad96710b528390928 | project Vector onto TransCom regions | [
{
"docid": "e227b2e3424294691783bd382df97a96",
"score": "0.0",
"text": "def vector2tc(self, vectordata, cov=False):\n\n M = self.tcmatrix\n if cov:\n return np.dot(np.transpose(M), np.dot(vectordata, M))\n else:\n return np.dot(vectordata.squeeze(), M)",
"t... | [
{
"docid": "8ad5bad2918b718686e9f9f86502b8d0",
"score": "0.66207576",
"text": "def translate(self, vec):\n vec = toKiCADPoint(vec)\n for drawing in self.board.GetDrawings():\n drawing.Move(vec)\n for footprint in self.board.GetFootprints():\n footprint.Move(vec... |
3b234e0e1778749fcf491ff4b7f7ca78 | Test fma as str_floatnum_floatnum_str for invalid integer array Array code d. | [
{
"docid": "3bf97e6bdacdd1cfd5a82f80fb09bc30",
"score": "0.0",
"text": "def test_fma_invalid_param_str_floatnum_floatnum_str_1306(self):\n\t\t# This version is expected to pass.\n\t\tarrayfunc.fma(self.floatarrayx, self.floatnumy, self.floatnumz, self.floatarrayout)\n\n\t\t# This is the actual test.\n\t... | [
{
"docid": "23478df374aff5d6fc56718d28b5f6d7",
"score": "0.813768",
"text": "def test_fma_invalid_param_intarray_intarray_str_floatnum_616(self):\n\t\t# This version is expected to pass.\n\t\tarrayfunc.fma(self.floatarrayx, self.floatarrayy, self.floatarrayz, self.floatarrayout)\n\n\t\t# This is the act... |
c08c0ec5a4b64a882c6fc119f5bdfc3d | Request a token from service | [
{
"docid": "f9d8e22cb53b8dc916d04b680d731cf2",
"score": "0.70175856",
"text": "def request_token(self):\n token_url = 'https://uaa.%s/oauth/token' % self.url\n headers = {\n 'accept': 'application/json',\n 'authorization': 'Basic Y2Y6'\n }\n params = {\n... | [
{
"docid": "d80d4585dd37923bedbd7e387d3b3c0f",
"score": "0.71444774",
"text": "def _token_request(self, request):\n\n if not self._client.token_endpoint:\n return None\n\n logger.debug('making token request: %s', request)\n client_auth_method = self._client.registration_r... |
1d7b961c4d756a1399b3a684b2970aa4 | Vypise intervalove a bodove odhady a vrati je jako seznam. | [
{
"docid": "44e2ae3e49eff67b9fb8472bb5a35fc1",
"score": "0.0",
"text": "def report(lkhd):\r\n\r\n ind = min_coord(lkhd)\r\n maxmin = bounds_coord(lkhd)\r\n print('Odhad alfa je {:.1f} a lezi v intervalu {:.1f} az {:.1f}'.format(\r\n alfa[ind[1]], np.amin(alfa[maxmin[1, :]]), np.amax(alfa... | [
{
"docid": "4dba9c6fe59a97d18ff90d5380eee0ed",
"score": "0.6994862",
"text": "def interval(self):\n return self.__interval",
"title": ""
},
{
"docid": "abf75eb67d30f8eb092840890ba092db",
"score": "0.69546163",
"text": "def get_interval(self):\n raise NotImplementedError('Im... |
4c3ea5f57e7fba027a51a350dc39a2c3 | Initialize the record, along with a new key. | [
{
"docid": "cf8eccd5cbe67c3396ccd2fc2c6a72eb",
"score": "0.7322634",
"text": "def __init__(self, *args, **kwargs):\r\n record.Record.__init__(self, *args, **kwargs)\r\n self.key = UserKey()",
"title": ""
}
] | [
{
"docid": "9f83103fd350a79e82cf1005d868d1e8",
"score": "0.7128397",
"text": "def test_init(self):\r\n fake_key = partial(Key, \"Eggs\", \"Bacon\")\r\n keys = [fake_key(str(uuid.uuid1())) for x in range(10)]\r\n rs = sets.KeyRecordSet(keys, Record)",
"title": ""
},
{
"do... |
17ab4bad13651f35c6e6f6dd301a6c2e | Function used the evaluation values of a ndeval result file | [
{
"docid": "5a2a617339fd18521cf95d513b36c691",
"score": "0.0",
"text": "def extract_ndeval(fields_order, evaluation_file):\n\t# Reading the evaluation line\n\tline = evaluation_file.next()\n\n\t# Creating a dictionnary to the evaluation values from the score fields and the current line scores\n\tquery_e... | [
{
"docid": "fbe1c46ad4fcda03d97ace45f90a2225",
"score": "0.67118865",
"text": "def evaluate(self, include_fileset=False):",
"title": ""
},
{
"docid": "4d55883f629ee2dbfe0f7971e66f7607",
"score": "0.64297915",
"text": "def compile_results(self):\n\n\t\t# highly performant code\n\t\tma... |
85832575005847dd2e7a417403fc5077 | initialize some example perfdata values | [
{
"docid": "2c13da305dee4caf21e277503dbac08c",
"score": "0.759437",
"text": "def setUp(self):\n\n self.perfdata = []\n self.perfdata.append(PERF1)\n self.perfdata.append(PERF2)\n self.perfdata.append(PERF3)\n self.perfdata.append(PERF4)\n\n self.valid_timestamps... | [
{
"docid": "99aad547ec6852fbb8815e8d21a811d8",
"score": "0.70477575",
"text": "def bench_initialize_data():\n return loaddata(ns, name, data, (0,0,0))",
"title": ""
},
{
"docid": "102869a1a4cb6e2df119fa3c6ca121d8",
"score": "0.6749036",
"text": "def setUp(self): \n self... |
60307ba7af660df799ca16892d1c3d96 | Force a dependency texture to render immediately | [
{
"docid": "c989829526008705e68dc27b33ec1a70",
"score": "0.6691529",
"text": "def renderDependency(self, dep):\n if not dep.hasRenderState():\n dep.attachRenderState(self.rstate)\n dep.viewport.configureOpenGL()\n dep.drawFrame()\n assert(dep.rendered)",
"title... | [
{
"docid": "4755d787a7f1c6803e2f74285aef7817",
"score": "0.61700636",
"text": "def preRenderUpdate(self):\n self._neighborBuffers[self.currentIndex[0]].setActive(False)\n self._resolveBuffer.setShaderInput(\"lastTex\",\n self._neighborBuffers[self.... |
ec81f3492a646512203d20127819cc35 | while a GUEST user is connected GC will not remove none of its projects nor the user itself | [
{
"docid": "e05592dcc37128643debb195e9757a51",
"score": "0.5476483",
"text": "async def test_t1_while_guest_is_connected_no_resources_are_removed(\n disable_garbage_collector_task: None,\n client: TestClient,\n socketio_client_factory: Callable,\n aiopg_engine: aiopg.sa.engine.Engine,\n t... | [
{
"docid": "664815eaee10249845f72ab0dcc66027",
"score": "0.6786619",
"text": "def cleanup(self):\n\n LOG.info('Cleaning up user accounts')\n for u in self.user_manager.iterator():\n LOG.info('User: %s' % u.username)\n\n u_projects = u.api.projects.list()\n ... |
aca8b0b191781ab46894f7e7536a5683 | Function called by referee to inform internal players that they have won a single game of Fish. Since this is not an interaction specified by the remote proxy pattern, this function simply returns True without communicating with the client | [
{
"docid": "c08c04bcb029308b7827d8977f1561e9",
"score": "0.59076196",
"text": "def inform_of_winners(self, winners: List[PlayerColor]) -> bool:\n self.winners = winners\n return True",
"title": ""
}
] | [
{
"docid": "515c2ff2fb6c1d143e40310233fce082",
"score": "0.7147915",
"text": "def is_won(game):\n return game.is_over()",
"title": ""
},
{
"docid": "4e510514175d15cc771d36a131b4a970",
"score": "0.7048353",
"text": "def aPlayerHasWon(game):\n game.playerAMoveCount = len(ai.getAl... |
d448ec83f80bfab685df5598fb3a40a0 | Unit test for macro including check in rst files test_rst_orphan uses python unittest to check the return value of the f_guidelines rst_check_orphan test function. rst files which are | [
{
"docid": "3478261bb81e70199354fe89cdb843a7",
"score": "0.65763324",
"text": "def test_rst_orphan(self):\n self.assertEqual(\n GuidelineViolations.NO_VIOLATION,\n f_guidelines.rst_check_orphan.test(\":orphan:\"),\n )\n self.assertEqual(\n GuidelineV... | [
{
"docid": "127a25217fc9d8778ac27ae4fd9954b6",
"score": "0.74015856",
"text": "def test_rst_macro(self):\n\n regex_string = rules[\"languages\"][\"reStructuredText\"][\"include\"][\n \"include_files\"\n ][0][\"macros.txt\"][\"regex\"]\n macro_regex = re.compile(regex_stri... |
d0d73c71f77331a5d87c6cba7055c1aa | Builds graph from this network and returns it. | [
{
"docid": "a7656b063a75969adf00c965d0bda3d4",
"score": "0.7587391",
"text": "def buildGraph(self):\n self.normalize()\n \n g = nx.DiGraph()\n \n for k, n in self.nodes.items():\n g.add_node(n.node_id, color=n.color(), n_type=n.neuron_type)\n \n ... | [
{
"docid": "49435ede5f626dcb445420c22594340d",
"score": "0.72999275",
"text": "def build_graph(self):\n raise NotImplementedError(\"Subclass must override build_graph()!\")",
"title": ""
},
{
"docid": "a8b3c9bb9f9d47bd9ab83839a14249f9",
"score": "0.7252171",
"text": "def _buil... |
176b2178ec759add208e5f2e37c56bcc | draw the cursor to the screen, if in the correct frame. | [
{
"docid": "9914733cd4a98c403be05fbbb2bc738c",
"score": "0.7841671",
"text": "def draw_cursor(self):\n self.frames += 1\n if self.frames > CURSORFRAMES:\n # now, draw the cursor.\n self.cursor = self.rel_cursor\n else:\n self.cursor = \"\"\n\n ... | [
{
"docid": "9dee6bb52a2c987bc487ef2555435009",
"score": "0.7041617",
"text": "def _draw_frame(self):\n\n self.game._draw_frame(self.display_screen)",
"title": ""
},
{
"docid": "0edf21cb213c09a4787aab2c799c1603",
"score": "0.70361966",
"text": "def draw(self, screenin):",
"... |
41ddf87b8aee80fd7cf604541f22294a | Helper to create a Variable stored on CPU memory. | [
{
"docid": "ac97d0bd00f3797ebc78ca2edbc129b9",
"score": "0.66969126",
"text": "def _variable_on_cpu(name, shape, initializer):\n with tf.device('/cpu:0'):\n dtype = tf.float32\n var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype)\n return var",
"title": ""
}
] | [
{
"docid": "be3d6a4f1ce7cad92376233912acba95",
"score": "0.70131457",
"text": "def create_variable(self, name, alloc, type_, max_stack_depth, batch_size):\n del name\n if alloc is instructions.VariableAllocation.NULL:\n return instructions.NullVariable()\n elif alloc is instructions.Variab... |
f94c54ea292a2de26628b579b34fc3eb | Return a new scenario tree with some scenarios replaced by their average value. | [
{
"docid": "5cda6255fc665cb4292de1dd123a9e91",
"score": "0.6675452",
"text": "def average(scenario_tree: ScenarioTree, \n map_stage_to_rvar_names: Optional[Dict[int, List[str]]] = None) -> ScenarioTree:\n scen_tree = copy.deepcopy(scenario_tree)\n scen_tree.average(map_stage_to_rvar_nam... | [
{
"docid": "05091a307b7093f5a2a0aaf3c23828af",
"score": "0.60327834",
"text": "def _average_across_tree(self, map_stage_to_rvar_names: Optional[Dict[int, List[str]]] = None):\n if map_stage_to_rvar_names is None:\n map_stage_to_rvar_names = self.map_stage_to_rvar_names\n for sta... |
f15e5784845dfd893a9a0e9205b03fac | Hdf5 export method for the block. | [
{
"docid": "38ffe91035bc6cfbc02ef4285e6fecb3",
"score": "0.6027526",
"text": "def export_hdf5(self, handle):\n handle.create_dataset(\n 'type', (1, ), 'S10', ['flatten'.encode('ascii', 'ignore')]\n )\n handle.create_dataset('shape', data=np.array(self.shape))",
"title... | [
{
"docid": "97eea5b6ccf830da77780d2f683a11af",
"score": "0.696666",
"text": "def export_hdf5(self, handle):\n pass",
"title": ""
},
{
"docid": "97eea5b6ccf830da77780d2f683a11af",
"score": "0.696666",
"text": "def export_hdf5(self, handle):\n pass",
"title": ""
},
... |
81045398f6bf9043f0bdfafd927600a6 | Validates a token with a given secret. | [
{
"docid": "64b94e0d0462089561d740ee3f3aa6f1",
"score": "0.75841236",
"text": "def is_token_valid(token: str, secret: str) -> bool:\n try:\n jwt.decode(\n str.encode(token),\n secret,\n algorithms=[config.JWT_ALG],\n )\n except jwt.exceptions.PyJWTErr... | [
{
"docid": "e495750d8ead23368478d2c478c57f9a",
"score": "0.66990536",
"text": "def valid_token(token, config):\n try:\n decode(token, config)\n except Exception:\n warning_log(\"Invalid token\")\n return True",
"title": ""
},
{
"docid": "2104e0e41d18f133dcc033abd75... |
78810dbe1d7a972670d163c504d04198 | Returns wether the protocol of the url is http or https. Returns none if another protocol or no protocol is present in the url. | [
{
"docid": "9e0eb7302df8c884ce41107550c7b028",
"score": "0.6897188",
"text": "def get_protocol(url):\n result = re.search(r\"^https?://\", url)\n return result.group(0) if result else None",
"title": ""
}
] | [
{
"docid": "fb4ea7a5904658bcad37a1c1ff29503a",
"score": "0.78023887",
"text": "def https(url):\n if url[:8] == 'https://':\n return url\n if url[:7] != 'http://':\n return False\n return 'https://' + url[7:]",
"title": ""
},
{
"docid": "7d9f7a68784ca4580941add2adbfa1d5... |
5a142a1cebc1ed0adef91f854e0c3ce4 | the actual registering of an endpoint is done here. | [
{
"docid": "f9048ac479685c39a2c61886bed642a8",
"score": "0.65512407",
"text": "def _register_endpoint(self, name, **config):\n\n if self.type == self.REDIRECT_ENDPOINT:\n raise ImproperlyConfigured, 'redirect Endpoint \"%s\" can not register endpoints' % self.name\n\n if name in... | [
{
"docid": "11ed93be0cd90e54073d90d0a452e2c0",
"score": "0.7848222",
"text": "def register(endpoint, procedure = None, options = None):",
"title": ""
},
{
"docid": "d57ff7b5b76c5233c71b72af4561a897",
"score": "0.717257",
"text": "def register(self, endpoint, **config):\n name ... |
e9ef466346bc02a0f0c94016b424fbbd | user_enable() Allow all mechanisms that are ON before `PK11Slot.user_disable()` was called to be available again. Sets disable reason to PK11_DIS_NONE. | [
{
"docid": "fe3d49d2d76a409740151d11d523087a",
"score": "0.6470902",
"text": "def user_enable(self): # real signature unknown; restored from __doc__\n pass",
"title": ""
}
] | [
{
"docid": "e0fe69d17b21cbb553f9c9ea3ca6fabf",
"score": "0.66574407",
"text": "def user_disable(self): # real signature unknown; restored from __doc__\n pass",
"title": ""
},
{
"docid": "e06e1cc2b999c023e3fc44bcf4ccfaac",
"score": "0.631137",
"text": "def disable_user(self, ma... |
a50c599aad9213465e9e1baf10f6f5ac | Column vector of requirement weightings. Each requirement contributions to the overall model according to its weight. | [
{
"docid": "89df50998c31df86946bc1ba30e971fb",
"score": "0.8679849",
"text": "def weight(self):\n vec = np.array([[reqt.weight for reqt in self.requirements]])\n return vec.T # Return as column vector",
"title": ""
}
] | [
{
"docid": "8ef7c41c0a6c88cc1bdd1fb886ab57b7",
"score": "0.688155",
"text": "def weights(self):\n pass",
"title": ""
},
{
"docid": "3edddcd5c994c6d7de7defb22a244e47",
"score": "0.65906084",
"text": "def get_weights(self) -> List[float]:\n return self.weight",
"title... |
f270e502ea2e3a9b37363537d159d82c | Return True if cls_name is a message object based on info in unified | [
{
"docid": "6bbf76d43d154cf986de127c27fdb77c",
"score": "0.62210727",
"text": "def class_is_stats_message(cls):\n\n return \"stats_type\" in of_g.unified[cls][\"union\"]",
"title": ""
}
] | [
{
"docid": "289d5549feaa3be809320a36ef7358a7",
"score": "0.75476646",
"text": "def class_is_message(cls):\n return \"xid\" in of_g.unified[cls][\"union\"] and cls != \"of_header\"",
"title": ""
},
{
"docid": "a21cb78dfc53864e804c668d44d734ad",
"score": "0.6970383",
"text": "def is... |
cc2bd32ddcea8a8d7dbd16a413b490a0 | Execute a shell command and capture its output (expected to be a single line). This is a thin wrapper around system_to_string(). | [
{
"docid": "a036753f66663e66c6f0767e0436ad49",
"score": "0.6720015",
"text": "def system_to_one_line(cmd: str, *args: Any, **kwargs: Any) -> Tuple[int, str]:\n rc, output = system_to_string(cmd, *args, **kwargs)\n output = get_first_line(output)\n return rc, output",
"title": ""
}
] | [
{
"docid": "3426284eb7b0b3ad1a0d0cf6a75517ad",
"score": "0.7754001",
"text": "def shell_output(command: str):\n process = subprocess.run(\n args=[command],\n capture_output=True,\n shell=True,\n text=True # capture STDOUT as text\n )\n if process.returncode == 0:\n ... |
278e7ae4349320b7f95151fb8856a03b | Contains(self, long pos) > bool Returns true if the given position is within this range. Allow for the possibility of an empty range assume the position is within this empty range. | [
{
"docid": "c0e75285b3986e56a0efafa16a793303",
"score": "0.58059067",
"text": "def Contains(*args, **kwargs):\n return _richtext.RichTextRange_Contains(*args, **kwargs)",
"title": ""
}
] | [
{
"docid": "95343c548c87a7abe9748f2a672de283",
"score": "0.8172459",
"text": "def contains(self, pos: tuple[int, int]) -> bool:\n\n start = self._start\n end = self._end\n\n return start[0] <= pos[0] <= end[0] and start[1] <= pos[1] <= end[1]",
"title": ""
},
{
"docid": ... |
a654cc135a8083e07cfc3aa0eafe3c1c | Defines a convolution transposed convolution net | [
{
"docid": "e2e1af57c24c4e88823752d69fb91bad",
"score": "0.6090154",
"text": "def conv_pool_transconv_one_hot(img_bands = 4, img_rows = 64, img_cols = 64,nb_blocks=4, in_blocks=[3,4,6,3], filter_depth=[64,128,256,512], dense_layers=1 ,categorical=False,nb_classes=-1, droprate=0.9):\n assert len(filter_... | [
{
"docid": "39880de3ececd47957d4c19d44597456",
"score": "0.6619897",
"text": "def Conv_Layer(self,inputs,filter_num=64,ks1=3,ks2=3,s1=1,s2=1,\r\n Transpose=False,use_bias=False,only_conv=False):\r\n initializer = tf.random_normal_initializer(0., 0.02)\r\n if Transpose:\r\... |
74bd46482e33b59d3bfb90ab88f35618 | Returns available information on a given phylome | [
{
"docid": "fa7b59e0fe3fe2e63eab76834cf0f64b",
"score": "0.6757125",
"text": "def get_phylome_info(self, phylome_id):\n\n # Check if the phylome id code is well-constructed\n if not self.__check_input_parameter__(str_number = phylome_id):\n raise NameError(\"get_phylome_info: Check your input... | [
{
"docid": "fa5a9555f5750d8e42f613d44ab8f13e",
"score": "0.7154412",
"text": "def phylum_info(self):\n pass",
"title": ""
},
{
"docid": "a2a4e825c3e16dc56fbf36b7cf0d2419",
"score": "0.63739514",
"text": "def subphylum_info(self):\n pass",
"title": ""
},
{
"d... |
b95fb77af48d39a5a1e3ddde474dcc60 | Declare victory to peers | [
{
"docid": "26d3ea3b6b7348df1fe87db9bbe9cb85",
"score": "0.68546593",
"text": "def declare_victory(self, reason):\n if(self.bully == self.pid):\n print(\"The Leader is: {} as {}\".format(self.pr_leader(),reason))\n for member in self.members.items():\n pid , a... | [
{
"docid": "37c5d4f5f7cde9c85b7343d837efc0b6",
"score": "0.5788176",
"text": "def vending_machines(self):",
"title": ""
},
{
"docid": "eaddc986737d9d4ec7daccf067c3d1ea",
"score": "0.544734",
"text": "def __init__(self, vending_machine):\n self.vending_machine = vending_machine... |
8c48f6972f194d991d3d4fd7a6dd0b07 | generate string containing random combinations of % and ASCII symbols | [
{
"docid": "bbc619b59abd48787cbab876d1c7b0ba",
"score": "0.6609214",
"text": "def get_exotic_str(size=10):\n name = u\"\"\n while len(name) < size:\n num = random.randint(1, 3)\n name = name + u\"%\" * num + get_random_str(2)\n return name",
"title": ""
}
] | [
{
"docid": "78ebee311b61a78eeb0842c08318db9d",
"score": "0.64790833",
"text": "def generate_alphanumerics():\n spaces_before = random.randint(0, 10)\n spaces_after = random.randint(0,10)\n core_length = random.randint(2,20)\n core_string = ''.join(random.choices(string.ascii_... |
08e2b273bd45cef527c56322381ab929 | x.__eq__(y) x==yx.__eq__(y) x==yx.__eq__(y) x==y | [
{
"docid": "f6cf9ec283538288c58388f140ed6876",
"score": "0.0",
"text": "def __eq__(self, *args): #cannot find CLR method\r\n pass",
"title": ""
}
] | [
{
"docid": "28f9b1bc378d0090c502f0b953f818c0",
"score": "0.8320325",
"text": "def eq(x, y):\n return x == y",
"title": ""
},
{
"docid": "8560fc02cea59b845bfbedfe81c8eb4e",
"score": "0.7849895",
"text": "def __eq__(self, other):\n\t\treturn self.x == other.x and self.y == other.y",... |
89af3611257577a703bd2e6424bdc71d | Encode bounding boxes (that are in centersize form) w.r.t. the corresponding prior boxes (that are in centersize form). For the center coordinates, find the offset with respect to the prior box, and scale by the size of the prior box. For the size coordinates, scale by the size of the prior box, and convert to the logs... | [
{
"docid": "72eb7bc0a8d80969db4fb1de69dca5e6",
"score": "0.0",
"text": "def cxcy_to_gcxgcy(cxcy, priors_cxcy):\n\n # The 10 and 5 below are referred to as 'variances' in the original Caffe repo, completely empirical\n # They are for some sort of numerical conditioning, for 'scaling the localizatio... | [
{
"docid": "1c8b3617401588754bfc0bc6ba4f101c",
"score": "0.6071095",
"text": "def encode(self, boxes, labels, img):\r\n boxes = boxes.box\r\n #print(img)\r\n _, h, w = img\r\n boxes /= torch.Tensor([[w, h, w, h]]).expand_as(boxes) # normalize (x1, y1, x2, y2) w.r.t. image ... |
f5f1a12e423f64898c87722da6b0c697 | Don't have extraction on event class. | [
{
"docid": "79d08c89e43444b6e17aa3003457ef12",
"score": "0.5585676",
"text": "def applyExtraction(self, evt):\n return evt",
"title": ""
}
] | [
{
"docid": "7d776ad9ddd3ecf0dfcfdd983b9d9ea6",
"score": "0.63723147",
"text": "def processEvent(self, event):\n pass",
"title": ""
},
{
"docid": "fad067ebf8bcbda50b08c793865316b0",
"score": "0.6328626",
"text": "def __init__(self):\n _LOGGER.error(\"Event class is not m... |
8a1fab3c54bbd5998de5c3cb6c71b86b | Fetch account transactions pending | [
{
"docid": "49c3248688c051b4d4a533f3c9b576f7",
"score": "0.72934383",
"text": "def get_account_transactions_pending(self, account_id=None, query_params=None):\n\n return self.fetch('get',\n '%s/%s/transactions/pending' % (ACCOUNTS, account_id),\n ... | [
{
"docid": "7cb379d40366857996bc7be31b7c555a",
"score": "0.6982897",
"text": "def get_latest_transactions(self):",
"title": ""
},
{
"docid": "732597fde4bf630d817f5643921c959f",
"score": "0.67092973",
"text": "def transaction_request(self, acct, start, end, status):\n if status... |
f968f34a257cf2d6f3d05f77ac35c02c | Registers for notification of updated assessments. ``AssessmentReceiver.changedAssessments()`` is invoked when an assessment in this assessment bank is changed. | [
{
"docid": "bd42644844bcf3be048f5518d5509323",
"score": "0.7559024",
"text": "def register_for_changed_assessments(self):\n pass",
"title": ""
}
] | [
{
"docid": "03c592e690d6c4a948d9b29d61ce96c3",
"score": "0.7275361",
"text": "def register_for_changed_assessments_offered(self):\n pass",
"title": ""
},
{
"docid": "d8638ba531e80f7d89b21a423be92304",
"score": "0.70348734",
"text": "def register_for_changed_assessments_taken(s... |
de6db4ef752c53d8676de255dba988df | take a description as formatted by _transform_block, search for and return the first preceeding header (string defined by cd) | [
{
"docid": "361975eaf1cdd14e939f8b5f5ecae174",
"score": "0.0",
"text": "def _get_keybind_category(self, key_desc):\n query = \"(?<=\\\\n)({}.*?)\\\\n(?=.*{})\".format(re.escape(self.category_descr), re.escape(key_desc))\n category_search = re.findall(query, self._get_raw_config(), flags=re... | [
{
"docid": "e13ea169042ee5445f2f49734d97a207",
"score": "0.65945625",
"text": "def get_header(block):\n # Standard header\n if block.type is BlockType.HEADER:\n return block.header\n # Text followed by :\n elif is_valid_header(block.content.get_partial_clean()):\n return 7\n ... |
45a270b227dd168f8c816fa65256ce17 | A dict of this user's manga stats, with keys as strings, and values as numerics. | [
{
"docid": "e41855e15012b2c4c9f88eb45ae6c253",
"score": "0.69878834",
"text": "def manga_stats(self):\n return self._manga_stats",
"title": ""
}
] | [
{
"docid": "6e5d1b8f1ebbc44afbe20dd37487c1fa",
"score": "0.71393543",
"text": "def _make_stats(self):\n return {\n \"load_avg\": self.load_avg.to_dict(),\n \"service_time\": self.service_time.to_dict(),\n }",
"title": ""
},
{
"docid": "13dcc88a79d8dadee074... |
928fc070f71adccefb437f5252756b01 | This decorator checks for a token, verifies if it is valid and redirects to the login page if needed | [
{
"docid": "074034c9511ffb2c577fbf453134a2c0",
"score": "0.74882853",
"text": "def token_required(func):\n @wraps(func)\n def decorated(*args, **kwargs):\n global login_token\n if not app.config[\"DEBUG\"]:\n url_token = request.args.get('token')\n if url_token ... | [
{
"docid": "6254f1b48b0bbeec6eb32b333e524b1a",
"score": "0.83707714",
"text": "def login_required(f):\n @wraps(f)\n def decorator(*args, **kwargs):\n if not valid_token():\n return redirect(url_for('views.login', **{\"continue\":request.url}))\n return f(*args, **kwargs)\n... |
b854cc0f94f5ca5dbf66e9174e0af376 | Write a JSON object to cloud storage. | [
{
"docid": "afce2bb582e97c7fadb81c2706acd731",
"score": "0.7463363",
"text": "def json_write(bucket, path, data):\n blob = _make_blob(bucket, path)\n return blob.upload_from_string(json.dumps(data))",
"title": ""
}
] | [
{
"docid": "87c2e2abe4ae47b3be26825827779192",
"score": "0.7004237",
"text": "def store_json(bucket, dataset, key, to_json):\n json_file = NamedTemporaryFile(mode=\"w+b\")\n json_file.write(json.dumps(to_json).encode())\n # Reset to the beginning:\n json_file.seek(0)\n store_file(bucket, ... |
5a5ae203ea2c110f171c5dc86de46448 | concatenate files and print on the standard output | [
{
"docid": "ca75e9e0396c765c4871f1ef36b0373c",
"score": "0.6244764",
"text": "def cat(args):\r\n # for every file, attempt to open, read every line, and print\r\n for argument in args[2:]:\r\n file = open(argument)\r\n lines = file.readlines()\r\n for line in lines:\r\n ... | [
{
"docid": "23e5d8240e71cd89332d10b1504d6198",
"score": "0.7242691",
"text": "def concat_files(self, in_files, out_file, concat_opts=None):\n concat_file = f'{out_file}.concat'\n self.write_debug(f'Writing concat spec to {concat_file}')\n with open(concat_file, 'w', encoding='utf-8'... |
1cff34ef942b9ac38d0ec62678e277de | Return the directory name with give __file__. | [
{
"docid": "111ea80cd841421e7f9fc192363d582d",
"score": "0.69222474",
"text": "def getDirName(magicFile):\n dirPath = os.path.dirname(os.path.abspath(magicFile))\n parts = dirPath.split(os.path.sep)\n return parts[-1]",
"title": ""
}
] | [
{
"docid": "50bfc1f95a5c3b0fd2195fa403f8eabf",
"score": "0.78758234",
"text": "def dir_name(self):\n return os.path.split(self.path)[0]",
"title": ""
},
{
"docid": "709bd8acf9d73662e780a61c5798a2cd",
"score": "0.7685119",
"text": "def directory(self):\n return \"%s.d\" ... |
1c8c674bc4911d3e990ecfa6a4ed6b08 | Return a tuple of availability zones that have the instance_type. This function builds on get_supported_az_for_instance_types, but simplifies the input to 1 instance type and returns a tuple | [
{
"docid": "61d8f6b23b3a6fc37b9fcaa914e21ad3",
"score": "0.7708298",
"text": "def get_supported_az_for_instance_type(self, instance_type: str):\n return self.get_supported_az_for_instance_types([instance_type])[instance_type]",
"title": ""
}
] | [
{
"docid": "03a5145e5f4e16bab8475e545fe9c442",
"score": "0.7645274",
"text": "def get_supported_az_for_instance_types(self, instance_types: List[str]):\n # first looks for info in cache, then using only one API call for all infos that is not inside the cache\n result = {}\n offering... |
4166f43da3751428ef835a8d56b0b615 | Create a quadtree of 'n' points. | [
{
"docid": "7679c017510138d7ac6a406eafbc687e",
"score": "0.0",
"text": "def random(cls,n=50,**kwargs):\n motions=[Motion.random(n=2) for i in range(n)]\n return cls(motions,**kwargs)",
"title": ""
}
] | [
{
"docid": "1ad95cfc250155d495868d7d4a817b52",
"score": "0.6579787",
"text": "def random(cls,n=50):\n return cls(QuadTree.random(n=n))",
"title": ""
},
{
"docid": "0240cf3dd9442d3bd81eef06fb677339",
"score": "0.5730425",
"text": "def quad_children(x, y):\n for xo in ran... |
6693dd0d3545fa42d0a2ba4f46360eaf | Makes featured profiles for IdeaLab galleries. | [
{
"docid": "9895d20dfb920c5da005bd25ddbc885b",
"score": "0.7489455",
"text": "def makeGallery():\n if params['subtype'] in ['intro', 'new_idea', 'ieg_draft', 'participants_wanted']:\n featured_list = getFeaturedProfiles()\n else:\n sys.exit(\"unrecognized featured content type \" + p... | [
{
"docid": "d1d605377361822796ab2f870c16fe02",
"score": "0.6286335",
"text": "def getFeaturedProfiles():\n featured_list = []\n profile_page = profiles.Profiles(params[params['subtype']]['input page path'], params[params['subtype']]['input page id'], params)\n profile_list = profile_page.getPag... |
5f5a8118ee90ba9e1701b257a7d1a143 | Base method to send SMS. This method can be extended by child classes to implement SMS sending. | [
{
"docid": "1690f7412ec9e9cfd82fcd436276c908",
"score": "0.6439799",
"text": "def send_sms(self, from_, to, message, callback_url=None):\n\n url = self.base_url + self._PATHS['smsoutbound']\n\n # If 'to' contains only digits, it's an MSISDN, else it's an obfuscated identity\n data =... | [
{
"docid": "6509fd35a9af8eb08b911ce9c72047bc",
"score": "0.8653039",
"text": "def sendSMS(self):\n pass",
"title": ""
},
{
"docid": "43250301a65dfa6a517da438b5c8fba7",
"score": "0.7880894",
"text": "def send_sms(self, dest, text, sender=''):\n raise NotImplementedError"... |
681fc39a4870de1c1a271867c75b5d32 | Although this is a stateless net, we use the "state" parameter to pass in the previous labels, unlike LSTMs where state would represent hidden activations of the network. | [
{
"docid": "094a33d07d0505c0ce9c03f5b164856b",
"score": "0.0",
"text": "def forward(\n self, y: Optional[torch.Tensor] = None, state: Optional[List[torch.Tensor]] = None,\n ):\n outs = []\n\n [B, U] = y.shape\n appended_y = y\n if state != None:\n appende... | [
{
"docid": "61360cec1be5e56379cec7fe0daf3464",
"score": "0.6701732",
"text": "def transfer_states_to_neuron(self):",
"title": ""
},
{
"docid": "4c02c69e0c8275194edcd4056c1c0d8f",
"score": "0.6692958",
"text": "def __setstate__(self, state):\n self._weights, self._biases, self._lay... |
f52497ff6f61c9d916ba7093bec81fb8 | Draws the current screen into the delegate surface. | [
{
"docid": "a9e4a2c22ec5aa8ab39948f0ee55e842",
"score": "0.84791386",
"text": "def draw(self):\n self.get_current_screen().draw(self._surface)",
"title": ""
}
] | [
{
"docid": "c5d38c3a6f645ce37cd070c245ef3740",
"score": "0.80792165",
"text": "def draw(self, screen):\r\n pass",
"title": ""
},
{
"docid": "b8114732a0c0f6957a74b9dfc4ecb49c",
"score": "0.7948619",
"text": "def draw_to_screen(self):\n self.screen.blit(self.image, self.r... |
9cf5b7f802daf350491f7a8ef3479226 | Sets the low_power_usb of this VmediaPolicyAllOf. | [
{
"docid": "0e0153707a1160a732bbf16c6c24f3da",
"score": "0.8350906",
"text": "def low_power_usb(self, low_power_usb):\n\n self._low_power_usb = low_power_usb",
"title": ""
}
] | [
{
"docid": "8b3ea43d75887285e4acb801785985e4",
"score": "0.7069202",
"text": "def set_low_power(self):\n logger.debug('Enable low power mode by setting mods=00.')\n self.write_byte_data(MMA8451_REG_CTRL_REG2, MODS_LOW_POWER)",
"title": ""
},
{
"docid": "25fe1506f473f48c2a5ed745... |
7487a15acbb8dfd8d4a407ea4c31057a | Get Holy Day by a calendar date. | [
{
"docid": "672203003ea0314e126c6176bb7f0299",
"score": "0.7131204",
"text": "def getHolyDayByDate(self, requestedDate):\n msg = \"Attempting to retrieve holy days from db\"\n LOGGER.info(msg)\n\n nowUtc = datetime.datetime.now(tz=pytz.utc)\n timezone = pytz.timezone(\"Americ... | [
{
"docid": "b79f4db962ca72f0fc671163a0e769ec",
"score": "0.61988795",
"text": "def get_day(self, year, month, day):\n calendar = self.get_shift(year=year, month=month)\n worked_hours = []\n # return next(day_it for day_it in calendar if day_it['day'] == day)\n for day_it in c... |
0e1f6e0748b12918c1e4009c77bcba45 | empty() Returns True if the number of elements is zero right now. Note that in theory, another thread might add an element right after this function returns. | [
{
"docid": "01e9f670209cb7c4b87b0e2d2f7b7cf3",
"score": "0.7976789",
"text": "def empty(self):\n return len(self) == 0",
"title": ""
}
] | [
{
"docid": "bf783b751c53b1ba6330e4774a061220",
"score": "0.86242735",
"text": "def empty(self):\n size = len(self.elements)\n return size == 0",
"title": ""
},
{
"docid": "8923db9a35badf5089ab54692bedb339",
"score": "0.8620145",
"text": "def is_empty(self):\n ret... |
1a7c1e11ab620d2d2f722df6363b4910 | this function iteratively fits data x into kmeans model. The result of the iteration is the cluster centers. | [
{
"docid": "ad98ebbf93f2f20d7067e3531f00c41a",
"score": "0.77107215",
"text": "def fit(self, x):\n print(\"Fitting to the dataset\")\n # intialize self.centers\n self.init_center(x)\n\n sse_vs_iter = []\n for iter in range(self.max_iter):\n # finds the clust... | [
{
"docid": "bdf5b75e75a58acd0d2c646dd0b33d12",
"score": "0.75578046",
"text": "def fit(self, X):\n centroids = self.initialize_centroid(X, self.k)\n for _ in range(self.max_iterations):\n clusters = self.create_clusters(centroids, X)\n previous_centroids = centroids\n... |
5634b262d0535a092a55ffe19925bc68 | Game state equality, should ignore time etc | [
{
"docid": "74b30c6c9e02724f00211ec16ee93ae0",
"score": "0.0",
"text": "def __eq__(self, other):\n # return self.freeze() == other.freeze()\n return self.hash() == other.hash()",
"title": ""
}
] | [
{
"docid": "2b03fb0045e81b569ea12501b54e388f",
"score": "0.7577427",
"text": "def same_game_state(self, other):\n return self._grid == other._grid",
"title": ""
},
{
"docid": "03495e28a8f46ac341da85e94c6ed1d4",
"score": "0.75027466",
"text": "def test_equal(self):\n # A... |
1d12fdf6c5c44b325b63ded3f3b4277a | View for making comments disabled | [
{
"docid": "015b6ffa6f8930b9340a381c1851d352",
"score": "0.66315573",
"text": "def moderate_disable(id):\n comment = Comment.query.get_or_404(id)\n comment.disabled = True\n db.session.add(comment)\n db.session.commit()\n return redirect(url_for('.moderate', page=request.args.get('page', ... | [
{
"docid": "d1edd7aa6f5051596a7b655d90922d2e",
"score": "0.6276129",
"text": "def _commentingAllowed(self):\n return not self.data.timeline.allWorkStopped() or (\n not self.data.timeline.allReviewsStopped() and\n self.data.mentorFor(self.data.task.org))",
"title": ""
},
{
"d... |
3c6f5cdd46e7eaa0cc49ddef5eb8891f | Load the model from a file. | [
{
"docid": "e5cc9afc8f4498687ccd2a8e123d696d",
"score": "0.78549993",
"text": "def load_model(self):\n file = open(self.config.MODEL_PATH, \"rb\")\n self.model = pickle.load(file, encoding=\"ASCII\")",
"title": ""
}
] | [
{
"docid": "18ad33d60e09bfae3e842f06f0a67318",
"score": "0.8907899",
"text": "def load_model(self, model_file=None):",
"title": ""
},
{
"docid": "2cab4c4c48e3d4248586f2e5f9e4b748",
"score": "0.8749351",
"text": "def load_model(from_file):\n\n raise NotImplementedError",
"title... |
3b7c4cb44eafac4ca3a16bc12bd88e8c | Constructor of a E3DC object (does not connect) | [
{
"docid": "e96047e58faa3af1385bd64817240138",
"score": "0.0",
"text": "def __init__(self, connectType, **kwargs):\n \n self.connectType = connectType\n self.username = kwargs['username']\n if connectType == self.CONNECT_LOCAL:\n self.ip = kwargs['ipAddress']\n ... | [
{
"docid": "96ef8aefbc4172ef186932c042c41f2c",
"score": "0.70455325",
"text": "def __init__(self, dmc, dc):\n self.dmc = dmc\n self.dc = dc",
"title": ""
},
{
"docid": "c17b28b949b3c432f89cf53a2bd9acb5",
"score": "0.65099466",
"text": "def __init__(self):\n\t\tself.c = ... |
73254f537c8813c1e5a36d369989817a | Return the key containing the name. | [
{
"docid": "f1629cfe983f8b594a7731d02471a2fc",
"score": "0.0",
"text": "def get_name(self, key):\n for key in (\"name\", \"title\"):\n if self.has_key(key) and self.key_type(key) != self.NULL:\n return self.get_as_string(key)\n\n return \"\"",
"title": ""
}
... | [
{
"docid": "fcbc38fcf59b807073d7b2b11a46a90b",
"score": "0.7750027",
"text": "def key_name(self) -> str:\n return pulumi.get(self, \"key_name\")",
"title": ""
},
{
"docid": "b9e7d32ea82387c66113d51c9652e778",
"score": "0.76042306",
"text": "def get_key() -> str:\n pass"... |
676b480059773d0858c5368503ccb578 | Method to authenticate a gateway user with keycloak | [
{
"docid": "e20774330e11fc2e7c2d3f316b80732e",
"score": "0.0",
"text": "def authenticate_using_user_details(self, user_credentials):\n try:\n token, user_info = self._get_token_and_user_info_password_flow(user_credentials)\n return token, user_info\n except Exception ... | [
{
"docid": "c10f37577cc7938d2d6345b30b60916a",
"score": "0.717711",
"text": "def __authenticate(self) -> None:\n print(\"Autenticando em \"+self.keycloak_url)\n url = self.keycloak_url+'auth/realms/master/protocol/openid-connect/token'\n authorization_redirect_url = {\n '... |
008c164f15999607aa3ddeb5d7c9d707 | The manifest is checked first for the version. | [
{
"docid": "b20276b1c591ed526c8d07c5e39e1cfa",
"score": "0.66188157",
"text": "def test_version_from_manifest(self):\n bundle = self.mkbundle('in', output='out-%(version)s')\n self.env.manifest.version = 'manifest'\n self.env.versions.version = 'versions'\n assert bundle.get_... | [
{
"docid": "be2504fb1e6538c36389418206ea740c",
"score": "0.7081394",
"text": "def getManifest():",
"title": ""
},
{
"docid": "3a57e0dad785654907e4bb733f8d9293",
"score": "0.7056929",
"text": "def manifest(self):",
"title": ""
},
{
"docid": "71afad78257634cc5cd7ea6bcb4b41e... |
6c531d94ee0e432b369d07ab4a9f34f9 | From the given sentence object each token is transformed to string and appended to the list of tokens | [
{
"docid": "2143e175fb070bba04a2974497a4d6a4",
"score": "0.0",
"text": "def list_of_tokens(doc: object) -> list:\n token_list = [str(each_token.text) for each_token in doc]\n\n return token_list",
"title": ""
}
] | [
{
"docid": "ffac20314eb07ec3f9f0ee6e88325681",
"score": "0.70261323",
"text": "def sentences_from_tokens(self, tokens):\n ...",
"title": ""
},
{
"docid": "69db74418c3f4c52186cec53ef074834",
"score": "0.66899365",
"text": "def _bert_tokenize_sentence(self, tokenizer, sentence):... |
153cc40ad1617f690e4092e1f97d0c19 | Check if the union tag is ``team_merge_request_sent_shown_to_secondary_team``. | [
{
"docid": "188728c806e278886e546c75e4a0b053",
"score": "0.8531224",
"text": "def is_team_merge_request_sent_shown_to_secondary_team(self):\n return self._tag == 'team_merge_request_sent_shown_to_secondary_team'",
"title": ""
}
] | [
{
"docid": "08f029aa16ff41c12bcd2548e8f33fe5",
"score": "0.8294527",
"text": "def is_team_merge_request_accepted_shown_to_secondary_team(self):\n return self._tag == 'team_merge_request_accepted_shown_to_secondary_team'",
"title": ""
},
{
"docid": "08f029aa16ff41c12bcd2548e8f33fe5",
... |
6d95649681b571edb5fbe7eca9d04f63 | The Downloader class is responsible for downloading packages and it's dependencies. | [
{
"docid": "fb9c173552dd7a86c779c9213f9eb5d5",
"score": "0.0",
"text": "def __init__(self, packages, out_dir=\"./out\"):\n self.packages = packages\n self.out_dir = out_dir",
"title": ""
}
] | [
{
"docid": "3182200d6d9fd7aa36eccd72373d2d6a",
"score": "0.7099037",
"text": "def downloader(self):",
"title": ""
},
{
"docid": "eeabcea7046da8a48556000edd2aaf7e",
"score": "0.6681995",
"text": "def download_packages(\n self, package_names, dependencies_to_exclude=[], version=... |
fd005ae43ef0b77114e68de5875f0a27 | cards = [Card(), Card()] | [
{
"docid": "34b9b0d2a71573cd0482676a13efab1d",
"score": "0.0",
"text": "def bulk_append_card(self, cards: 'list[Card, ...]'):\n assert type(cards)==list\n for each in cards:\n self.append(each)",
"title": ""
}
] | [
{
"docid": "213766e651ded64ad47c308fc1952569",
"score": "0.8249101",
"text": "def __init__(self, cards = None):\n if cards:\n self.cards = cards\n else:\n self.cards = []",
"title": ""
},
{
"docid": "4b2167436ba39c61662dae8e230b7a27",
"score": "0.79806... |
036ea3cba0d5886a92390ca5f00e53ac | Get triplets for training model. A triplet contains an anchor, a positive, and a negative. Select cowatch pair as anchor and positive, randomly sample a negative. | [
{
"docid": "e15247c245c934970e0faaefc5f13293",
"score": "0.5595899",
"text": "def mine_triplets(all_cowatch, features):\n if not isinstance(all_cowatch,list) and not isinstance(features,np.ndarray):\n logging.error(\"Invalid arguments. Type should be list, dict instead of\"+\n str(type(all_cowa... | [
{
"docid": "308c55f9a7cea6a9d8a73c76adfcb079",
"score": "0.6431219",
"text": "def generate_triplets(test=False):\r\n while True:\r\n list_a = []\r\n list_p = []\r\n list_n = []\r\n\r\n for i in range(batch_size):\r\n #print(i)\r\n a, p, n = get_triple... |
663abec506a80adc4aae7d86de59149f | Can't invite user to a team that's not mine | [
{
"docid": "ed6d47cefd3415d42dea1050f5b96dbf",
"score": "0.7434971",
"text": "def test_cannot_invite_to_another_team(self):\n with self.loggedInAs(\"alice\", \"123\"):\n resp = self.client.rpost('invitation_create',\n follow=True,\n ... | [
{
"docid": "76547565f0096434d7b0733d36b2d200",
"score": "0.7399999",
"text": "def test_cannot_invite_team_members(self):\n self.alice_team.add_team_member(self.carl)\n with self.loggedInAs(\"alice\", \"123\"):\n resp = self.client.rpost('invitation_create',\n ... |
f6460b17f437800c1dc656d38a0e0277 | Compute the hold that maximizes the expected value when the discarded dice are rolled. | [
{
"docid": "8545d861eb4b47194135a89caaf51845",
"score": "0.73834246",
"text": "def strategy(hand, num_die_sides):\r\n max_value = 0.0\r\n max_hold = ()\r\n for hold in gen_all_holds(hand):\r\n expect = expected_value(hold, num_die_sides, len(hand) - len(hold))\r\n if expect > max_... | [
{
"docid": "934bd0f032a9cd531682d68eed0c0178",
"score": "0.74292517",
"text": "def strategy(hand, num_die_sides):\r\n \r\n max_expected_value = 0\r\n optimal_hold = []\r\n #For a given hand, generate all possible holds\r\n possible_holds = gen_all_holds(hand)\r\n #For a given hold, gen... |
dc1d4fe4f1d93ed1ddab5f76cd7ad3aa | Initialize SELF to the position depicted in the string STARTING_BOARD. STARTING_BOARD has the form "xxxxx\nxxxxx\nxxxxx..." where each x is either '.' or '', and the newlines separate rows. The number of x's in each row must be equal. Leading and trailing whitespace is ignored. | [
{
"docid": "5174b15c020eecd0fb50dfaec66f2d75",
"score": "0.72269595",
"text": "def __init__(self, starting_board):\n\n # Represent a board as a list of lists of single characters, one list\n # per row, and within each row, one character per column. B0[r][c]\n # represents the curre... | [
{
"docid": "4410bf08e2a6e5a89d54d361679396f8",
"score": "0.6600261",
"text": "def __init__(self):\r\n self.board = [\" \", \" \", \" \", \r\n \" \", \" \", \" \", \r\n \" \", \" \", \" \"]",
"title": ""
},
{
"docid": "ddec5ebac7407ba018aef650f... |
1ad7eec27d6dcf86ec7bfe3e8c287c14 | recv_directions should return None on fail | [
{
"docid": "9e9e5b3274c5bd1868f8bedfaf2f99dc",
"score": "0.76010746",
"text": "def test_recv_directions_fail():\n string = \"CHAMPLFLFLFL\"\n result = io.recv_directions(string)\n assert_equals(result, None)",
"title": ""
}
] | [
{
"docid": "83eebef10ad9f2d31ef09830ac9b3935",
"score": "0.6898982",
"text": "def test_recv_robot_info_fail():\n string = \"CHAMP 32 21 W\"\n result = io.recv_directions(string)\n assert_equals(result, None)",
"title": ""
},
{
"docid": "8bb3f0d680ee7375c96d32094a0b36ce",
"score"... |
d9417c0ed441a06210fbba27dce0044f | Normalize a diff/patch file before it's applied. This can be used to take an uploaded diff file and modify it so that it can be properly applied. This may, for instance, uncollapse | [
{
"docid": "82a3c2d96d17af243a4641b25270daa8",
"score": "0.6414623",
"text": "def normalize_patch(\n self,\n patch: bytes,\n filename: str,\n revision: str,\n ) -> bytes:\n return patch",
"title": ""
}
] | [
{
"docid": "4660344ce841b80aca6c91afd8767e2d",
"score": "0.60515046",
"text": "def _normalize(self):\n # Ingest the data\n data = self._read_raw()\n\n # Clean data using `_normalize`\n df = self.normalize(data)\n success = self._store_clean(df)\n\n return succes... |
57e037236dea359dab847af220308afd | Create a Segment which is generated with autodeploy. | [
{
"docid": "b858a4d4afbf131479cd11a1bed0d561",
"score": "0.59141743",
"text": "def create(self, semantic_model, mode, config_path=None, extra_args=None):\n if semantic_model is SegmentationModel.DEEPLABV3_MOBILENETV2_TF:\n from ..algo_cv.seg.semantic_segmentation_deeplabv3_mobilenetv2_tf \\\n ... | [
{
"docid": "58f3a284f2f5c2d3c04123c457864dea",
"score": "0.6247422",
"text": "def create_segment(self, segment_name: str = \"\") -> Segment:\n segment = Segment(segment_name)\n self._segments.add(segment)\n return segment",
"title": ""
},
{
"docid": "0be78daf1ae027f5b4d5... |
c3768705e91bc725c5e38587c0f8dbad | The vendorassigned unique ID for this range.This ID is incremented automaticaly for each DHCP client. | [
{
"docid": "de7633b6ddf44a0a599ddea910e9b394",
"score": "0.6511509",
"text": "def Dhcp6DuidVendorId(self):\n return self._get_attribute('dhcp6DuidVendorId')",
"title": ""
}
] | [
{
"docid": "377d93c9e4a72cbec71921e0ba1e58df",
"score": "0.70701176",
"text": "def unique_id(self) -> str:\n return f\"{self.dev.system.serial}_{self.dev.name}\"",
"title": ""
},
{
"docid": "c0706950846448c4986cd03a0ade5b30",
"score": "0.70366126",
"text": "def unique_id(self)... |
77a12a55419b78ec5094f90baa955a1c | Entry action for state 'brakelightsOn'.. | [
{
"docid": "0ae417aa5b0342c341ca4d9999c060a3",
"score": "0.7652496",
"text": "def __entry_action_main_region__car_off_r2__shuting_down_r2_1_brakelights_on(self):\n\t\tself.timer_service.set_timer(self, 2, 300, False)\n\t\tself.operation_callback.brakelights(\"on\")",
"title": ""
}
] | [
{
"docid": "ce20a3588caa97f044bab2c4dfca4013",
"score": "0.7132828",
"text": "def __enter_sequence_main_region__car_off_r2__shuting_down_r2_1_brakelights_on_default(self):\n\t\tself.__entry_action_main_region__car_off_r2__shuting_down_r2_1_brakelights_on()\n\t\tself.__state_vector[0] = self.State.main_r... |
dada524dfeb353a742892134297adb29 | Handles the result of the opponent's move. Transforms the captured_piece bool and value into just an optional value in order to build the request. | [
{
"docid": "b7b1c037d7c5438bb04c97623bdc34f9",
"score": "0.75338936",
"text": "def handle_opponent_move_result(self, captured_piece, captured_square):\n if self.out_of_time:\n return None\n\n try:\n request = make_handle_opponent_move_request(captured_square if captur... | [
{
"docid": "ba095000e813e088faa4d1f0cf47c18a",
"score": "0.7908477",
"text": "def handle_opponent_move_result(self, captured_my_piece: bool, capture_square: Optional[Square]):\n pass",
"title": ""
},
{
"docid": "b57d357d58c7ecf57ed7abac53f608b1",
"score": "0.7078982",
"text": ... |
11a8c4e157a89ec7f31cfed011cf3406 | Format the record with colors. | [
{
"docid": "3d34b659680ba38fc1fc925980d13126",
"score": "0.75765485",
"text": "def format(self, record):\r\n color = self.color_seq % (30 + self.colors[record.levelname])\r\n message = logging.Formatter.format(self, record)\r\n message = message.replace('$RESET', self.reset_seq)\\\r... | [
{
"docid": "dd614764164891b95a84fe3ad9e2b61d",
"score": "0.8127916",
"text": "def color_format(self, record):\n message = super().format(record)\n parts = message.split('\\n', 1)\n if '<color>' in parts[0] and '</color>' in parts[0]:\n bef, dur, aft = self.SPLIT_COLOR(par... |
b998911a668d0cd73cf46f4ea01aa101 | Returns true if both objects are equal | [
{
"docid": "f89ca6807840c0f7c2fecc9b650a234a",
"score": "0.0",
"text": "def __eq__(self, other):\n if not isinstance(other, Episode):\n return False\n\n return self.__dict__ == other.__dict__",
"title": ""
}
] | [
{
"docid": "35781205f766178c26df8024228c9541",
"score": "0.8067084",
"text": "def __eq__(self, other: object) -> bool:\n return self.__class__ == other.__class__ and self.points == other.points",
"title": ""
},
{
"docid": "df1ca4bbc0e302958ca9db15d603d19b",
"score": "0.7999691",
... |
aed1e738084454900d605240cb481d43 | Sync a room for a client which is starting without any state | [
{
"docid": "3cbcddfc577383c7fb2465514a7dfefb",
"score": "0.0",
"text": "def full_state_sync_for_archived_room(self, room_id, sync_config,\n leave_event_id, leave_token,\n timeline_since_token, tags_by_room):\n\n bat... | [
{
"docid": "9b07bce640e05bbb4999c959ad52528c",
"score": "0.61042523",
"text": "def test_incremental_sync(self) -> None:\n channel = self.make_request(\"GET\", \"/sync\", access_token=self.tok)\n self.assertEqual(channel.code, 200, channel.result)\n next_batch = channel.json_body[\"n... |
0a48dcc0e5d8acc80bdcf94d7eb2230f | Return only compounds with no RO5 violations | [
{
"docid": "3e3ac8481a00d1140725d7ed8624fbd8",
"score": "0.71112686",
"text": "def filter_ro5(mols):\n return mols.filter(molecule_properties__num_ro5_violations=0)",
"title": ""
}
] | [
{
"docid": "ab98fc705bde3128e358773730bc475c",
"score": "0.56570566",
"text": "def find_redundant_compounds(self): \n\n stoich = self.stoichiometry\n\n # Transposing, to use the qr algorithm\n stoich_t = stoich.T\n n_rows = stoich_t.shape[0]\n n_cols = stoich_t.shape... |
e9edfb819f9cb3682910bd9b2ef8e39d | Make directory and subdirectories. | [
{
"docid": "efb6daf34dbf00c329c8f286cb5fcfdc",
"score": "0.64890075",
"text": "def MakeDirs( self, path ) :\n \n # modules:\n import os\n \n # split in domain and remaining path:\n if ':' in path :\n domain,dpath = path.split(':',1)\n else :\n ... | [
{
"docid": "ce93fd6c8b470bab7977b1f05bf747f8",
"score": "0.7336835",
"text": "def mkdirs():\n dirs = [\n PYTHON_DIR,\n BUILDS_DIR,\n SHARED_DIR,\n RUN_DIR,\n STATIC_DIR,\n LOG_DIR,\n MEDIA_DIR,\n ]\n for dir in dirs:\n _run_web(\"mkdir -p ... |
689ef4dc44033992faded2f451aa3274 | Sets the diameter of the | [
{
"docid": "f8714e9170fbddd16ab67ffce4d4ee82",
"score": "0.6536274",
"text": "def set_diameter(self, value):\n if self._device.successfully_set_diameter(value):\n return \"{}\".format(self.Stopped)\n else:\n run_status = self.get_run_status()\n return \"{}N... | [
{
"docid": "2e8922b60569a2d01d86bc413100f22d",
"score": "0.85909635",
"text": "def diameter(self, diameter):\n\n self._diameter = diameter",
"title": ""
},
{
"docid": "7353f3cffe18dd73e85d4804f159418a",
"score": "0.701887",
"text": "def diameter(self):\r\n return self.r... |
8c9d60bda2e7faa6539e0ead3b569e28 | Initialize the emitter with an event loop. | [
{
"docid": "3d57c407a3975b589594f2da47098ef7",
"score": "0.673187",
"text": "def __init__(self, loop=None):\n self._loop = loop or asyncio.get_event_loop()\n self._listeners = collections.defaultdict(list)\n self._once = collections.defaultdict(list)\n self._max_listeners = s... | [
{
"docid": "e0c3369da3f4c1abd76581fe10e69f2f",
"score": "0.69916075",
"text": "def __init__(self, loop=None) -> None:\n\n self.loop = loop if loop else asyncio.get_event_loop()",
"title": ""
},
{
"docid": "1fb97ccd138ea175404892d6753ba676",
"score": "0.6815965",
"text": "def s... |
7937c999a5afa4e76d49a4c8dc45d0e3 | Returns the point at time t (0>1) along the curve. | [
{
"docid": "4d96d37707793141520f29d32bd33f79",
"score": "0.72235733",
"text": "def pointAtTime(self,t):\n x = (1 - t) * (1 - t) * self[0].x + 2 * (1 - t) * t * self[1].x + t * t * self[2].x;\n y = (1 - t) * (1 - t) * self[0].y + 2 * (1 - t) * t * self[1].y + t * t * self[2].y;\n return Point(x,... | [
{
"docid": "df6681abd3f94fe59f3e39c4d68cc2c1",
"score": "0.6694643",
"text": "def get(self, t):\n # return self.value[np.where((self.time - t) <= 0)[0][-1]] # last one that is <= 0\n return self._value[\n (self._time.searchsorted(t + 1) - 1).clip(0, len(self._value) - 1)\n ... |
70ee95c12ccaa52305a62aa13c0596da | List the keys under path, if any, of the original store. | [
{
"docid": "67ed1062aee6a8d18ebc3daeff4a9172",
"score": "0.6513379",
"text": "def listdir(self, path: str = None) -> List[str]:\n return zarr.storage.listdir(self._store, path)",
"title": ""
}
] | [
{
"docid": "fedbcb88517228f6e49224e6c7ab64ae",
"score": "0.7502651",
"text": "def traversed_keys(self, root_path: str) -> List[str]:\n return self.redis_client_traversed.hkeys(root_path)",
"title": ""
},
{
"docid": "37907698267ce4b2bc7e19d9e795d099",
"score": "0.7457224",
"tex... |
444eac86840ee38810c270d702dc34e7 | Return True if the node is significant at the 5% level according to the chisquared distrubtion with 1 df. | [
{
"docid": "8a857b67f48c7662b3e4bb4e57d94841",
"score": "0.76875913",
"text": "def _significant(self, node):\n\t\tx_square_cutoff = 3.841 #1df @ 5% level\n\t\tx_square_node = float(\"inf\") \n\n\t\treturn x_square_node > x_square_cutoff",
"title": ""
}
] | [
{
"docid": "556cfbe6e05fad6380053e80fac12608",
"score": "0.5665601",
"text": "def techChange_sunny(p):\n return p[:, 0] > 0.325",
"title": ""
},
{
"docid": "fc21c6804aef56340de033ee2840d9c3",
"score": "0.5657123",
"text": "def __feat_num_significant(self, col, df_uni_var_norm, df_... |
0e10ce96f1b52e7bb623b034794ee907 | Get the primary database driver. | [
{
"docid": "287097578a00ab75ea8ce1b57de8262b",
"score": "0.8098757",
"text": "def db_driver(self) -> DbDriver:\n\n primary_db_driver = None\n\n # noinspection PyUnusedLocal\n def filter_db_drivers(service_id, service, config):\n nonlocal primary_db_driver\n if ... | [
{
"docid": "ea9c2f86bbbbb9465beea3207fedac07",
"score": "0.7569865",
"text": "def driver(self):\n return self._c.driver",
"title": ""
},
{
"docid": "00e26464acdf461fbf5fae34c90c48af",
"score": "0.7451214",
"text": "def _get_driver(self):\n if self._config and \"driver\"... |
c3b2cf2d9c71baccb00c198de43cf5af | Iterates columnwise over the marker alignment. Excludes samples. | [
{
"docid": "300674607fabeb0d2f4d672f073bedbc",
"score": "0.5351515",
"text": "def iter_marker_sites(self, start=0, stop=None, size=1):\n if self.markers is None or self.markers.nrows == 0:\n return\n if stop is None:\n stop = self.nsites\n if (stop - start) % s... | [
{
"docid": "a0d44a244dde0b542e3a237cd14452b2",
"score": "0.55822045",
"text": "def others_in_column(self, row_, col_):\n for r in range(9):\n if r != row_:\n yield self[r, col_]",
"title": ""
},
{
"docid": "5988acb9ea0c626347aba9bba02a519c",
"score": "0.5... |
db08e1177f874ec5a2073ffa3d3b937c | Parse swiftrecon output into list of lists grouped by the content of the delimited blocks. | [
{
"docid": "c075bcdf0c76921e47a2d0a4551ff995",
"score": "0.71614885",
"text": "def parse_swift_recon(recon_out):\n\n lines = recon_out.splitlines()\n delimiter_regex = re.compile(r'^={79}')\n collection = []\n\n delimiter_positions = [ind for ind, x in enumerate(lines)\n ... | [
{
"docid": "2d0bf5d3c0c116e3052fefa8030ca8ba",
"score": "0.65458053",
"text": "def processBlock(block):\n out = []\n a = \"\"\n for i in block:\n for j in i:\n if a == \"\":\n a = j\n else:\n a = a + \"|\" + j\n\n out = a.split(\"|\"... |
f21ec1a454b7684d26c31c403a205e32 | A callback called when device setup failed to connect after repeated tries | [
{
"docid": "3d4150e47b5c449f1fe6ef81e9f5672e",
"score": "0.6424305",
"text": "def devices_scan_failed(self):\n pass",
"title": ""
}
] | [
{
"docid": "6ef6703ff92b4c3939b494a6ae277183",
"score": "0.6651631",
"text": "def test_on_connect_error(self):\n # this assumed the ssdb server being tested against doesn't use 1023\n # port. An error should be raised on connect \n bad_connection = Connection(port=1023)\n ... |
63e2a9a9fcbb147fd55d6f76d423eb0b | Cache potential symbols in the symbol table. | [
{
"docid": "f242891fa3149bdb7fe2262b204e019f",
"score": "0.5748251",
"text": "def _enumerateSymbols(self, machoCtx) -> None:\n\n\t\tsymtab: symtab_command = machoCtx.getLoadCommand(\n\t\t\t(LoadCommands.LC_SYMTAB,)\n\t\t)\n\t\tif not symtab:\n\t\t\tself._logger.warning(\"Unable to find LC_SYMTAB.\")\n\t... | [
{
"docid": "5d0760fc5481f25d465307101e3bd6f3",
"score": "0.66358835",
"text": "def _load_symbols_cache(self, cache):\n\n #\n # Symbols\n #\n\n for sym, address in cache['symbols'].items():\n if self._process.file_type == \"PE\" or \\\n (self.elf ... |
dd99e7bd4990e39891a390e9335bdb98 | Plot the learning curve of the different networks trained | [
{
"docid": "3ffe038286369685daca2d21ff9f7913",
"score": "0.6725776",
"text": "def plot_learning(steps, stats_mean_list=None, stats_std_list=None,\n labels=[], title=\"Learning Curve\", ylabel=\"Loss\",\n y_lim=[0.0, 1.7], share_x=False, share_y=False,\n ... | [
{
"docid": "c021a9f6d6801419b68365d93bbefc7a",
"score": "0.7735428",
"text": "def plot_learning_curve(training_losses, validation_losses): \n plt.ylabel('Loss')\n plt.xlabel('Training Steps')\n plt.plot(training_losses, label=\"training\")\n plt.plot(validation_losses, label=\"validation\")\n... |
b0235b98074062dc84c332d6568a79c7 | Flip a bit and win!!!!!! | [
{
"docid": "c4a9a8e4c27026a69c7db13d18888223",
"score": "0.0",
"text": "def flip_bit_to_win(num):\n \"\"\" Convert to binary \"\"\"\n binary = int(num, 2)\n current = 0\n last = 0\n longest = 0\n for i in range(sequence_length):\n bit = bit_manipulation.getbit(binary, i)\n ... | [
{
"docid": "ce646fdea7446fd490869ab2c3aa4a03",
"score": "0.7966279",
"text": "def flip(self):\n self._repeat(table=bit_flipped)",
"title": ""
},
{
"docid": "a5472cfb27a488c804cc7408fd66cca3",
"score": "0.7797152",
"text": "def test_bit_flip(self):\n\n self.run_steane_te... |
a596b7b2e171e5f8cb5c520718640e74 | Takes in a save directory and outputs a list of dictionaries with the | [
{
"docid": "1d4acaacaf0b082acc2d5fa21ec896ba",
"score": "0.0",
"text": "def get_escapes(save_dir):\n\n escapes = []\n with open(os.path.join(save_dir, \"esc.11\"), 'r') as f:\n try:\n # Skip the header\n next(f)\n except StopIteration: # Empty file\n ... | [
{
"docid": "b3150e0f984564995f68996ee9055be9",
"score": "0.66876465",
"text": "def parsed_files():\n files = []\n for filename in os.listdir(SAVE_DIRECTORY):\n path = os.path.join(SAVE_DIRECTORY, filename)\n if os.path.isfile(path):\n files.append(filename)\n return fil... |