None:\n await self.entity_description.set_command(self, True)","async def toggle(self, ctx):\n guild = ctx.message.guild\n\n enabled = await self.config.guild(guild).enabled()\n\n enabled = not enabled\n await self.config.guild(guild).enabled.set(enabled)\n\n if enabled is True:\n await ctx.send(\"AntiSpam has been enabled\")\n else:\n await ctx.send(\"AntiSpam has been disabled\")","def enable(self, message):\n self.Enable()","async def admin_disable(self, ctx: commands.Context):\n if ctx.guild.id not in self.guilds:\n await ctx.send('Team management is already disabled in this guild.')\n return\n await self._disable_guild(guild=ctx.guild)\n await ctx.send('Team management disabled.')","async def bingsetadult(self, ctx, setting):\n\n channel = ctx.message.channel\n server = ctx.message.server\n message = ctx.message\n if setting == 'channel':\n await self.bot.say(\"Do you want to enable %bingadult for this \" +\n \"channel? This will enable this \" +\n \"channel to use the %bingadult command, \" +\n \"which image searches Bing with Safe Search \" +\n \"turned off. Do note that this setting will \" +\n \"override the global server setting and thus \" +\n \"will allow %bingadult in this channel even \" +\n \"if the global server setting is off.\\n**ARE \" +\n \"YOU SURE YOU WANT TO TOGGLE %bingadult?**\" +\n \"\\n(y/n)\")\n elif setting == 'server':\n await self.bot.say(\"Do you want to enable %bingadult for this \" +\n \"server? This will enable your server to use \" +\n \"the %bingadult command, which image \" +\n \"searches Bing with Safe Search turned off. \" +\n \"Do note that this setting will be overriden \" +\n \"per channel if a channel is set to accept \" +\n \"usage of %bingadult. **ARE YOU SURE YOU \" +\n \"WANT TO TOGGLE %bingadult?**\\n(y/n)\")\n else:\n return await self.bot.say(\"This command accepts either server \" +\n \"or channel. Please use it again.\")\n response = await self.bot.wait_for_message(author=message.author)\n if response.content.lower().strip() == \"y\":\n if setting == 'channel':\n self.setadultchannel(channel, 'True')\n return await self.bot.say(\"Enabled %bingadult settings for \" +\n \"this channel.\")\n elif setting == 'server':\n self.setadultserver(server, 'True')\n return await self.bot.say(\"Enabled %bingadult settings for \" +\n \"this server.\")\n else:\n if setting == 'channel':\n self.setadultchannel(channel, 'False')\n return await self.bot.say(\"Disabled %bingadult settings for \" +\n \"this channel.\")\n elif setting == 'server':\n self.setadultserver(server, 'False')\n return await self.bot.say(\"Disabled %bingadult settings for \" +\n \"this server.\")","def enable(self, *args, **kwargs):\n pass","def setactive(self, irc, msg, args, channel):\n res = self._checkDBhasChannel(channel)\n if res is True:\n SQL = 'UPDATE registry SET isActive = ? WHERE channel = ?'\n SQLargs = (1, channel)\n self._SQLexec(SQL, SQLargs)\n self.lock.acquire()\n for x in range(0, len(self.channelscontrol)):\n v0 = str(self.channelscontrol[x][0])\n if v0 == channel:\n self.channelscontrol[x][1] = 1\n self.channelscontrol[x][4] = 0\n self.lock.release()\n irc.reply(\"Channel set active.\", prefixNick=True)\n g = threading.Thread(target=self._getLastBug, args=(irc, channel,))\n g.setDaemon(True)\n g.start()\n else:\n irc.reply(\"Channel does not exist in DB.\", prefixNick=True)","async def __add_commands(self):\r\n commands_to_add: List[ClientCommandStructure] = [\r\n cmd for cmd in ChatCommandHandler.register.values()\r\n if cmd.app not in self._api_commands\r\n ]\r\n\r\n if commands_to_add:\r\n for cmd in commands_to_add:\r\n endpoint = f\"applications/{self.client.bot.id}\"\r\n\r\n if cmd.app.guild_id is not MISSING:\r\n endpoint += f\"/guilds/{cmd.app.guild_id}\"\r\n\r\n await self.client.http.post(\r\n endpoint + \"/commands\",\r\n cmd.app.to_dict()\r\n )","async def enable(\n self,\n ctx,\n channel: typing.Union[discord.TextChannel],\n emoji: typing.Optional[str] = None\n ):\n\n # Skipping non-eventer users\n if not self._have_permission(ctx.author, ctx.guild):\n await ctx.send(embed=decoration.embeds.ERRORS[\"NO_PERM\"])\n return\n\n # Getting events\n event_types = connector.getAllEventTypes(guild_id=ctx.guild.id, enabled=False)\n\n # Send message with select dropdown for selecting event to activate\n dropdown = utils.createEventsDropdown(event_types, model=models.EventType)\n if dropdown is None:\n await ctx.send(embed=decoration.embeds.ERRORS[\"NO_ITEMS_IN_DROPDOWN\"])\n return\n\n await ctx.send(\"Выбери тип ивента для активации:\", components=[dropdown])\n # Wait for selection\n select_ctx = await wait_for_component(self.bot, components=dropdown)\n selected_event_type_id = int(select_ctx.selected_options[0])\n event_type = connector.getEventTypeByID(guild_id=ctx.guild.id, type_id=selected_event_type_id)\n\n # Send notification message\n started_at = datetime.now()\n NEW_EVENT = Embed(\n title=event_type.title,\n description=event_type.description,\n color=0x7EBC89\n )\n\n \n message = await channel.send(content=\"@everyone 🟢 Запущен новый тип ивентов!\\nДля подписки жми на реакцию\", embed=NEW_EVENT)\n await message.add_reaction(emoji)\n \n # Update event type data\n connector.enableEventType(ctx.guild.id, selected_event_type_id, started_at, message.id, channel.id, emoji)\n\n # Send back info message\n message_payload = [\n {\"ID Вида ивента\": selected_event_type_id, \"Название вида ивента\": event_type.title},\n started_at,\n ctx.author.name,\n ctx.author.avatar_url\n ]\n await select_ctx.edit_origin(content=\"\", embed=decoration.embeds.INFO[\"EVENT_TYPE_ENABLED\"](*message_payload), components=None)","async def lock(ctx):\n member = ctx.message.author\n channel = ctx.message.channel\n\n if (channel.category.name in [\"beta\", \"staff\", \"Pi-Bot\"]):\n return await ctx.send(\"This command is not suitable for this channel because of its category.\")\n\n member_role = discord.utils.get(member.guild.roles, name=ROLE_MR)\n if (channel.category.name == CATEGORY_STATES):\n await ctx.channel.set_permissions(member_role, add_reactions=False, send_messages=False)\n else:\n await ctx.channel.set_permissions(member_role, add_reactions=False, send_messages=False, read_messages=True)\n\n wiki_role = discord.utils.get(member.guild.roles, name=ROLE_WM)\n gm_role = discord.utils.get(member.guild.roles, name=ROLE_GM)\n admin_role = discord.utils.get(member.guild.roles, name=ROLE_AD)\n bot_role = discord.utils.get(member.guild.roles, name=ROLE_BT)\n await ctx.channel.set_permissions(wiki_role, add_reactions=True, send_messages=True, read_messages=True)\n await ctx.channel.set_permissions(gm_role, add_reactions=True, send_messages=True, read_messages=True)\n await ctx.channel.set_permissions(admin_role, add_reactions=True, send_messages=True, read_messages=True)\n await ctx.channel.set_permissions(bot_role, add_reactions=True, send_messages=True, read_messages=True)\n await ctx.send(\"Locked the channel to Member access.\")","async def on_message(message):\n #Before doing anything\n #Check to see if the message started with the command character\n if not message.content.startswith(commandCharacter):\n #If it didn't, return\n return\n \n #Ensure the bot wasn't the one who sent the message\n if message.author == client.user:\n #If it was, return\n return\n \n #Kill is checked by default (cannot be disabled)\n if message.content.startswith(commandCharacter+'kill'):\n await client.send_message(message.channel, 'Goodbye Forever...')\n await client.logout()\n os.system('stty sane')\n exit(0)\n \n #Parse through the list of all enabled commands\n for command in enabledCommands:\n #We want to ignore case when comparing the message content\n messageContent = message.content.lower()\n #If the message matches one of our commands, we will handle it\n #Requires whitespace after command name\n if messageContent.startswith(commandCharacter+command):\n await handleCommand(client, message, voicePlayerList)","async def add(self, ctx, *, channel: discord.VoiceChannel):\n auto_channels = await self.config.guild(ctx.guild).auto_channels()\n if auto_channels is None:\n auto_channels = []\n auto_channels.append(channel.id)\n await self.config.guild(ctx.guild).auto_channels.set(auto_channels)\n await ctx.send(\n _(\"Startchannel used for automatic voicechannels added: {channel}\").format(\n channel=channel.name\n )\n )","async def botlink(self, ctx):\n async with self.bot.pool.acquire() as conn:\n async with conn.cursor() as cur:\n await cur.execute('SELECT * FROM BLChannels WHERE \"id\"=%s;', (ctx.message.channel.id,))\n isregistered = await cur.fetchone()\n if isregistered: \n return\n else: \n embed = discord.Embed(title=\"Mafu, the multi use bot\", colour=discord.Colour(0x6c56b0),url=\"https://discordapp.com/oauth2/authorize?client_id=432292171371118592&scope=bot\") \n await ctx.send(embed=embed)","async def react(self, cmd, args, author, roles, dialogs, session, disable_checks=False):\n\n if not disable_checks:\n if not (author.alive or cmd in ALLTIMES_CMDS):\n await self.error(to=author, msg=msgs.DEAD_USER_INVOKES_CMD)\n return\n\n if not (self.is_current_role(author) or cmd in ALLTIMES_CMDS):\n await author.send(msgs.WRONG_ROLE)\n return\n\n try:\n if hasattr(self, cmd + '_cmd'):\n await getattr(self, cmd + '_cmd')(args, author, roles, dialogs)\n elif hasattr(self, 'external_' + cmd + '_cmd'):\n await getattr(self, 'external_' + cmd + '_cmd')(args, author, roles, dialogs, session)\n else:\n await self.command_not_found(cmd, author)\n\n except Exception as e:\n fmt = \"'%s %s' command invocation raised a(n) %s :\\n\\n%s\\n%s\\n\" % (\n cmd,\n \" \".join(args),\n e.__class__.__name__,\n \"\".join(traceback.format_tb(e.__traceback__)),\n str(e) or \"[no further info]\",\n )\n\n logger.error(fmt)\n await self.error(to=author, msg=msgs.COMMAND_HAS_RAISED % cmd)\n\n else:\n logger.debug(\"La commande de jeu '%s' vient d'être invoquée avec succès par %s\" % (cmd, author.user.name))","async def _all(self, ctx):\n guild_emoji = await self.config.guild(ctx.guild).guild()\n unicode_emoji = await self.config.guild(ctx.guild).unicode()\n if guild_emoji or unicode_emoji:\n await self.config.guild(ctx.guild).guild.set(False)\n await self.config.guild(ctx.guild).unicode.set(False)\n msg = _(\"Okay, I will not react to messages \" \"containing all emojis!\")\n await ctx.send(msg)\n else:\n await self.config.guild(ctx.guild).guild.set(True)\n await self.config.guild(ctx.guild).unicode.set(True)\n msg = _(\"Okay, I will react to messages \" \"containing all emojis!\")\n await ctx.send(msg)","async def çıkış(con):\r\n check=str(con.message.channel)\r\n if check == 'Direct Message with {}'.format(con.message.author.name):#COMMAND USED IN DM\r\n await bot.send_message(con.message.channel,\"**You must be in a `server voice channel` to use this command**\")\r\n\r\n if check != 'Direct Message with {}'.format(con.message.author.name):#COMMAND NOT IN DM\r\n \r\n # IF VOICE IS NOT CONNECTED\r\n if bot.is_voice_connected(con.message.server) == False:\r\n await bot.send_message(con.message.channel,\"**Bot kanala bağlanmamış !**\")\r\n\r\n # VOICE ALREADY CONNECTED\r\n if bot.is_voice_connected(con.message.server) == True:\r\n bot.loop.create_task(queue_songs(con,True))","def handle_autovoiceoff(bot, ievent):\n try:\n ievent.chan.data.autovoice = 0\n ievent.reply('autovoice disabled on %s' % ievent.channel)\n except TypeError: ievent.reply('no %s channel in database' % ievent.channel)","def enable_motor(self, enabled):\r\n self.enabled = enabled\r\n\r\n # Set motors in neutral if disabling.\r\n if not self.enabled:\r\n self.set_neutral()","async def _toggle(self, ctx, id: int = None):\n if id is None:\n id = ctx.channel.id\n if id in self.etrigs['channels']:\n self.etrigs['channels'].remove(id)\n else:\n self.etrigs['channels'].append(id)\n self.write_config()\n await ctx.message.add_reaction('\\u2705')","def checkIfEnabled(self):\n\n # Reload the command file to check for new commands\n importlib.reload(BotSettings)\n matches = BotSettings.config['commands']\n\n # Check for the match and if it is there return the value that goes with the command\n for key in matches:\n key.strip(\"!\")\n if key == self.command:\n return matches.get(key)\n\n # If reached the command does not exist\n return False","def settings_push_callback(channel):\n \n global settingsMode, settingsCount\n\n settingsMode = True\n settingsCount = 0\n sleep(.5) # A little time for debouncing\n \n with canvas(device) as draw:\n settingsMenu( draw, settingsCount)","def setup_commands(bot):\n # Reset the bot's command setup\n bot.reset_commands()\n # Load enabled mods\n for mod in bot.enabled_mods:\n try:\n full = 'mod_%s' % mod\n m = getattr(__import__('mods.%s' % full), full)\n except Exception:\n bot.log(ERROR, 'Importing the %s mod failed!' % mod)\n sys.excepthook(*sys.exc_info())\n continue\n\n try:\n bot.installed_mods[mod] = m\n # Check for a 404 handler, and replace the current one if there is\n p404 = getattr(m, 'handle_404', None)\n if p404:\n bot.cb_404 = p404\n\n # Check for a setup function, and run it if there is\n setup = getattr(m, 'setup', None)\n if setup:\n setup(bot)\n\n # Required command bank\n for cmd in m.command_bank:\n # Get the actual function\n func = getattr(m, cmd)\n # Get the args for the command\n data = m.command_bank[cmd]\n # If data[0] is true, mod_help will recognize this command\n if data[0]:\n bot.help_db[data[1]] = parse_help(func)\n # Get the main name and aliases inserted\n for alias in data[1:]:\n bot.command_db[alias] = func\n\n # Helper function for optional nameless multiples\n def add_optional(olist, name):\n olist.extend(getattr(m, f) for f in getattr(m, name, ()))\n\n # Optional filters are loaded and added to the list\n add_optional(bot.filters, 'filters')\n\n # Ditto for time-cycle callbacks\n add_optional(bot.periodic_cbs, 'periodic')\n\n # Handlers are the same, but structured as a dict with\n # \"type\": \"single function-name\" items\n handlers = getattr(m, 'handlers', None)\n if handlers:\n for cbtype in handlers:\n bot.handlers[cbtype].append(getattr(m, handlers[cbtype]))\n\n # Register any requirements\n # NOTE: By putting this at the end, we avoid the possibility of\n # getting fake requires.\n reqs = getattr(m, 'requires', None)\n if reqs:\n bot.required_mods.update(reqs)\n except Exception:\n bot.log(ERROR, 'Unable to install the %s mod!' % mod)\n del bot.installed_mods[mod]\n sys.excepthook(*sys.exc_info())\n\n missing = bot.required_mods - set(bot.installed_mods)\n if missing:\n raise MissingRequirementsError(missing)\n\n # And now for the post-install triggers.\n for mod, m in bot.installed_mods.items():\n post = getattr(m, 'post_prepare', None)\n if post:\n try:\n post(bot)\n except Exception:\n bot.log(ERROR, 'Unable to post-prepare the %s mod!' % mod)\n sys.excepthook(*sys.exc_info())","async def join(self, msg, *, channel: discord.VoiceChannel = None):\n if msg.voice_client is not None:\n return await msg.send(f\"Bot is already in a voice channel\\nDid you mean to use {msg.prefix}moveTo\")\n\n if msg.voice_client is None:\n if channel is None:\n return await msg.author.voice.channel.connect(), await msg.message.add_reaction(emoji='✅')\n\n return await channel.connect(), await msg.message.add_reaction(emoji='✅')\n\n else:\n if msg.voice_client.is_playing() is False and not self.player[msg.guild.id]['queue']:\n return await msg.author.voice.channel.connect(), await msg.message.add_reaction(emoji='✅')","def handle_autovoiceon(bot, ievent):\n try: ievent.chan.data.autovoice = 1\n except TypeError: ievent.reply('no %s in channel database' % ievent.channel) ; return\n ievent.reply('autovoice enabled on %s' % ievent.channel)","async def tc_disable(self, ctx):\n await self.config.guild(ctx.guild).private_textchannels_enabled.set(False)\n await ctx.send(_(\"Private text channels disabled.\"))","async def change(self, ctx: Context):\n\t\tawait self.send(f\"If you wish to see your settings, go on our site: https://asxlvm.github.io/#/settings • If you already saw your settings and wish to change them. What do you want to change?\", whisper=[ctx.author.id])\n\t\tawait asyncio.sleep(2)\n\t\tawait self.send(f\"Options: allowMentions [bool] • autoRejectFights [bool] • passiveMode [bool] • whisperEconomy [bool]• onJoinMsg [bool] • allowUserInteraction [bool] | [bool] = True / False\", whisper=[ctx.author.id])\n\t\twaitforevent = await self.wait_for('message', check=lambda message: ctx.author.id == message.author.id)\n\t\twfcl = waitforevent.content.lower()\n\t\tusers = await self.get_settings_data()\n\t\tuserid = ctx.author.id\n\t\tif wfcl == \"allowmentions true\":\n\t\t\tawait self.open_settings(userid, ctx.author.username)\n\t\t\tusers[str(userid)][\"allowMentions\"] = True\n\t\t\twith open(\"settings.json\", \"w\") as f:\n\t\t\t\tjson.dump(users, f)\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} I have changed allowMentions to True for you.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"allowmentions false\":\n\t\t\tawait self.open_settings(userid, ctx.author.username)\n\t\t\tusers[str(userid)][\"allowMentions\"] = False\n\t\t\twith open(\"settings.json\", \"w\") as f:\n\t\t\t\tjson.dump(users, f)\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} I have changed allowMentions to False for you.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"autorejectfights true\":\n\t\t\tawait self.open_settings(userid, ctx.author.username)\n\t\t\tusers[str(userid)][\"autoRejectFights\"] = True\n\t\t\twith open(\"settings.json\", \"w\") as f:\n\t\t\t\tjson.dump(users, f)\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} I have changed autoRejectFights to True for you.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"autorejectfights false\":\n\t\t\tawait self.open_settings(userid, ctx.author.username)\n\t\t\tusers[str(userid)][\"autoRejectFights\"] = False\n\t\t\twith open(\"settings.json\", \"w\") as f:\n\t\t\t\tjson.dump(users, f)\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} I have changed autoRejectFights to False for you.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"passivemode true\":\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} as there isn't economy right now, you may not change this setting.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"passivemode false\":\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} as there isn't economy right now, you may not change this setting.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"whispereconomy true\":\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} as there isn't economy right now, you may not change this setting.\")\n\t\telif wfcl == \"whispereconomy false\":\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} as there isn't economy right now, you may not change this setting.\")\n\t\telif wfcl == \"onjoinmsg true\":\n\t\t\tawait self.open_settings(userid, ctx.author.username)\n\t\t\tusers[str(userid)][\"onJoinMsg\"] = True\n\t\t\twith open(\"settings.json\", \"w\") as f:\n\t\t\t\tjson.dump(users, f)\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} I have changed onJoinMsg to True for you.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"onjoinmsg false\":\n\t\t\tawait self.open_settings(userid, ctx.author.username)\n\t\t\tusers[str(userid)][\"onJoinMsg\"] = False\n\t\t\twith open(\"settings.json\", \"w\") as f:\n\t\t\t\tjson.dump(users, f)\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} I have changed onJoinMsg to False for you.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"allowuserinteraction true\":\n\t\t\tawait self.open_settings(userid, ctx.author.username)\n\t\t\tusers[str(userid)][\"allowUserInteraction\"] = True\n\t\t\twith open(\"settings.json\", \"w\") as f:\n\t\t\t\tjson.dump(users, f)\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} I have changed allowUserInteraction to True for you.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"allowuserinteraction false\":\n\t\t\tawait self.open_settings(userid, ctx.author.username)\n\t\t\tusers[str(userid)][\"allowUserInteraction\"] = True\n\t\t\twith open(\"settings.json\", \"w\") as f:\n\t\t\t\tjson.dump(users, f)\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} I have changed allowUserInteraction to True for you.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"allowmentions\":\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} you didn't supply a boolean, run the command again.\", whisper=[ctx.author.id])\n\n\t\telif wfcl == \"autorejectfights\":\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} you didn't supply a boolean, run the command again.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"passivemode\":\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} you didn't supply a boolean, run the command again.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"whispereconomy\":\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} you didn't supply a boolean, run the command again.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"onjoinmsg\":\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} you didn't supply a boolean, run the command again.\", whisper=[ctx.author.id])\n\t\telif wfcl == \"allowuserinteraction\":\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} you didn't supply a boolean, run the command again.\", whisper=[ctx.author.id])\n\t\telse:\n\t\t\tawait asyncio.sleep(2)\n\t\t\treturn await self.send(f\"{ctx.author.mention} I believe that is an incorrect argument, try running the command again.\", whisper=[ctx.author.id])","def disable(self, name, channel):\n if channel not in self.disabled_extensions:\n self.disabled_extensions[channel] = set()\n\n if name not in self.extension_names:\n return False\n\n logger.info('Disabling %s on %s' % (name, channel))\n self.disabled_extensions[channel].add(name)\n\n return True","async def meow_disable(self, ctx: vbu.Context):\n\n try:\n self.meow_chats.remove(ctx.channel)\n except KeyError:\n return await ctx.send(\"Meow chat is already disabled in this channel.\")\n await ctx.send(f\"Meow chat has been disabled in {ctx.channel.mention} :<\")\n\n # See if there's a running task to keep it alive\n _, current_task = self.meow_disable_tasks.pop(ctx.channel.id, (None, None))\n if current_task:\n current_task.cancel()","async def plugin(self,ctx):\n special_case = {\"Anime\":\"myanimelist\",\"Anti Raid\":\"antiraid\"}\n plugin_setting = await self.redis.hgetall(\"{}:Config:Cogs\".format(ctx.message.guild.id))\n embed = discord.Embed()\n cogs = self.bot.cogs.keys()\n for x in cogs:\n setting = \tu\"\\U0001F534\" #red\n if x in (\"Core\", \"Remindme\", \"Tools\", \"REPL\",\"Events\"): # A Owner's thing only.\n if ctx.message.author.id != self.bot.owner.id:\n continue\n setting = u\"\\U0001F535\" #blue\n if x.lower() in plugin_setting or special_case.get(x) in plugin_setting:\n setting = \tu\"\\U0001F535\" #blue\n embed.add_field(name = x,value = setting)\n if ctx.message.guild.me.colour.value:\n embed.colour = ctx.message.guild.me.colour\n\n embed.set_footer(text = \"{} = Disable | {} = Enable\".format(u\"\\U0001F534\",u\"\\U0001F535\"))\n await ctx.send(embed=embed)","async def disable(self, ctx: Context, guild: int = None):\n\n if guild is None:\n guild = ctx.guild\n else:\n guild = self.bot.get_guild(guild)\n\n if not guild:\n return await ctx.message.add_reaction(\"⚠\")\n\n if guild.id not in self.active_guilds:\n return await ctx.message.add_reaction(\"⚠\")\n\n self._config_cache.pop(guild.id)\n self.config.delete(f\"guilds:{guild.id}\")\n\n await ctx.message.add_reaction(\"✅\")","async def musicbot(self, ctx, the_state):\r\n is_mod = False\r\n for role in ctx.message.author.roles:\r\n if role.name == \"Moderators\":\r\n is_mod = True\r\n if is_mod:\r\n if the_state == \"1\":\r\n self.music_off = False\r\n await ctx.send(\"Music Bot features now on\")\r\n else:\r\n self.music_off = True\r\n await ctx.send(\"Music Bot features now off\")\r\n else:\r\n await ctx.send(\"**Error:** You are not allowed to use this command!\")","def func(self):\n\n if not settings.IRC_ENABLED:\n string = \"IRC is not enabled. Activate it in game/settings.py.\"\n self.msg(string)\n return\n\n # If no args: list bots.\n if not self.args:\n # show all connections\n ircbots = [bot for bot in\n AccountDB.objects.filter(db_is_bot=True,\n username__startswith=\"ircbot-\")]\n if ircbots:\n from evennia.utils.evtable import EvTable\n table = EvTable(\"|w#dbref|n\", \"|wbotname|n\",\n \"|wev-channel/location|n\",\n \"|wirc-channel|n\", \"|wSSL|n\",\n maxwidth=_DEFAULT_WIDTH)\n for ircbot in ircbots:\n ircinfo = \"%s (%s:%s)\" % (\n ircbot.db.irc_channel, ircbot.db.irc_network,\n ircbot.db.irc_port)\n table.add_row(\"#%i\" % ircbot.id, ircbot.db.irc_botname,\n ircbot.attributes.get(\"ev_channel\", ircbot.db.ev_location.key),\n ircinfo, ircbot.db.irc_ssl)\n self.msg(table)\n self.msg(\"Use 'help @puppetbot' for more infomation.\")\n else:\n self.msg(\"No irc bots found.\")\n return\n\n # Switch options available only if valid bot is given.\n if self.switches:\n botname = \"ircbot-%s\" % self.lhs\n matches = AccountDB.objects.filter(db_is_bot=True, username=botname)\n dbref = utils.dbref(self.lhs)\n if not matches and dbref:\n # try dbref match\n matches = AccountDB.objects.filter(db_is_bot=True, id=dbref)\n if not matches:\n self.msg(\"No valid bot given. Consult 'help @puppetbot'\")\n return\n\n # Puppetbot/delete <bot> - Delete bot.\n if any(i in ['disconnect', 'remove', 'delete'] for i in self.switches):\n matches[0].delete()\n self.msg(\"IRC link/bot destroyed.\")\n return\n\n # Puppetbot/ping <bot> - ping bot.\n if \"ping\" in self.switches:\n matches[0].ping(self.caller)\n self.msg(\"Pinging \" + self.lhs)\n return\n\n # Puppetbot/about <bot> = msg - Set bot about message.\n if \"about\" in self.switches:\n if self.rhs:\n matches[0].db.botdesc = self.rhs\n self.msg(\"Bot about message changed to: \" + self.rhs)\n else:\n self.msg(\"No message given. 'About' desc change aborted.\")\n return\n\n # Puppetbot/who <bot> - Get IRC user list..\n if \"who\" in self.switches:\n # retrieve user list. The bot must handles the echo since it's\n # an asynchronous call.\n self.caller.msg(\"Requesting nicklist from %s (%s:%s).\" % (\n matches[0].db.irc_channel,\n matches[0].db.irc_network,\n matches[0].db.irc_port))\n matches[0].get_nicklist(self.caller)\n return\n\n # Puppetbot/reconnect <bot> - reconnect bot.\n if \"reconnect\" in self.switches:\n matches[0].reconnect()\n self.msg(\"Reconnecting \" + self.lhs)\n return\n\n # Puppetbot/reload <bot> - Delete all bots, recreates bots from new user list.\n if \"reload\" in self.switches:\n matches[0].db.ev_location.msg_contents(\"Puppet reload in progress.\")\n puppetlist = [puppet for puppet in search.search_tag(matches[0].key + \"-puppet\")]\n for puppet in puppetlist:\n puppet.delete()\n matches[0].get_nicklist()\n return\n\n # Puppetbot/ignore <bot> = puppet - Toggle ignore IRC user.\n if \"ignore\" in self.switches:\n if self.rhs:\n user = self.rhs.strip()\n # If already ignored, toggle off.\n if user in matches[0].db.userignorelist:\n matches[0].db.userignorelist.remove(user)\n matches[0].get_nicklist()\n return\n\n # Else ignore user.\n else:\n matches[0].db.userignorelist.append(user)\n if user in matches[0].db.puppetdict:\n matches[0].db.puppetdict[user].delete()\n del matches[0].db.puppetdict[user]\n return\n else:\n self.msg(\"Usage: Puppetbot/ignore <bot> = <puppet>\")\n return\n\n # Puppetbot/entrymsg <bot> = msg - Set default puppet creation message.\n if \"entrymsg\" in self.switches:\n if self.rhs:\n matches[0].db.puppetentrymsg = \" \" + self.rhs\n self.msg(\"Bot entry message changed to: \" + \" \" + self.rhs)\n else:\n self.msg(\"No message given. Message change aborted.\")\n return\n\n # Puppetbot/exitmsg <bot> = msg - Set default puppet deletion message.\n if \"exitmsg\" in self.switches:\n if self.rhs:\n matches[0].db.puppetexitmsg = \" \" + self.rhs\n self.msg(\"Bot exit message changed to: \" + \" \" + self.rhs)\n else:\n self.msg(\"No message given. Message change aborted.\")\n return\n\n # Puppetbot/prefix <bot> = msg - Set string put before username in puppet.key\n if \"prefix\" in self.switches:\n if self.rhs:\n matches[0].db.puppetprefix = self.rhs\n self.msg(\"Puppet prefix changed to: \" + self.rhs)\n self.msg(\"Use: '@puppetbot/reload <bot>' to implement changes.\")\n else:\n self.msg(\"No message given. Prefix change aborted.\")\n return\n\n # Puppetbot/suffix <bot> = msg - Set string put after username in puppet.key\n if \"suffix\" in self.switches:\n if self.rhs:\n matches[0].db.puppetsuffix = self.rhs\n self.msg(\"Puppet suffix changed to: \" + self.rhs)\n self.msg(\"Use: '@puppetbot/reload <bot>' to implement changes.\")\n else:\n self.msg(\"No message given. Suffix change aborted.\")\n return\n\n # Puppetbot/defaultdesc <bot> = msg - Set default puppet desc message.\n if \"defaultdesc\" in self.switches:\n if self.rhs:\n matches[0].db.puppetlastdesc = matches[0].db.puppetdefaultdesc\n matches[0].db.puppetdefaultdesc = self.rhs\n self.msg(\"Default puppet description changed to: \" + self.rhs)\n else:\n self.msg(\"No message given. Message change aborted.\")\n return\n\n # Puppetbot/softdesc <bot> = msg - Only changes non custom puppet descriptions to new default.\n if \"softdesc\" in self.switches:\n puppetlist = [puppet for puppet in\n search.search_tag(matches[0].key + \"-puppet\")]\n for puppet in puppetlist:\n if puppet.db.desc == matches[0].db.puppetlastdesc:\n puppet.db.desc = matches[0].db.puppetdefaultdesc\n self.msg(\"Puppets description changed to: \" + matches[0].db.puppetdefaultdesc)\n return\n\n # Puppetbot/forcedesc <bot> = msg - Changes all puppet descriptions to new default.\n if \"forcedesc\" in self.switches:\n puppetlist = [puppet for puppet in\n search.search_tag(matches[0].key + \"-puppet\")]\n for puppet in puppetlist:\n puppet.db.desc = matches[0].db.puppetdefaultdesc\n self.msg(\"Puppets description changed to: \" + matches[0].db.puppetdefaultdesc)\n return\n\n # Create Bot.\n location = self.caller.location\n self.args = self.args.replace('#', ' ') # Avoid Python comment issues\n try:\n irc_network, irc_port, irc_channel, irc_botname = \\\n [part.strip() for part in self.args.split(None, 4)]\n irc_channel = \"#%s\" % irc_channel\n except Exception:\n string = \"IRC bot definition '%s' is not valid.\" % self.args\n self.msg(string)\n return\n\n botname = \"ircbot-%s\" % irc_botname\n # create a new bot\n bot = AccountDB.objects.filter(username__iexact=botname)\n if bot:\n # re-use an existing bot\n bot = bot[0]\n if not bot.is_bot:\n self.msg(\"'%s' already exists and is not a bot.\" % botname)\n return\n else:\n try:\n bot = create.create_account(botname, None, None,\n typeclass=ServerBot)\n except Exception as err:\n self.msg(\"|rError, could not create the bot:|n '%s'.\" % err)\n return\n bot.start(ev_location=location, irc_botname=irc_botname,\n irc_channel=irc_channel, irc_network=irc_network,\n irc_port=irc_port)\n self.msg(\"Connection created. Starting IRC bot.\")","async def joinchannel(self, ctx: commands.Context, *channels: str):\n for channel in channels:\n channel_query = self._channel_query(channel)\n\n if channel_query == None:\n await ctx.send(f\"Unable to join {channel}.\")\n continue\n\n channel = self.bot.get_channel(channel_query.id)\n guild = self.bot.get_guild(SERVER_ID)\n member = guild.get_member(ctx.author.id)\n\n if channel == None:\n await ctx.send(f\"Unable to join {channel}.\")\n continue\n\n # Don't let a user join the channel again if they are already in it.\n if channel.permissions_for(member).is_superset(JOINED_PERMISSIONS):\n await ctx.send(f\"You're already a member of {channel}.\")\n continue\n\n await channel.set_permissions(member, read_messages=True, reason=\"UQCSbot added.\")\n join_message = await channel.send(f\"{member.display_name} joined {channel.mention}\")\n await join_message.add_reaction(\"👋\")\n await ctx.send(f\"You've joined {channel.mention}.\")","def vc_only():\n\n async def check(ctx):\n if ctx.guild and ctx.author.voice:\n if not ctx.guild.me.voice or ctx.author.voice.channel == ctx.guild.me.voice.channel:\n return True\n await ctx.reply(\"I'm already in another voice channel!\")\n return False\n await ctx.reply('You must join a server voice channel first!')\n return False\n\n return commands.check(check)","def test_switch_channels(self):\n\t\t# not available yet, experimental\n\t\tpass","def _fixupChannels(self):\n\n # Add extra disabled channels as needed\n for index, ch in enumerate(self.channels):\n ch.index = index # fixup indexes\n\n self._fillChannels()","async def notify_channels(message: discord.Message, *channels: discord.Channel):\n if message.server.id not in twitch_config.data[\"servers\"]:\n twitch_config.data[\"servers\"][message.server.id] = {}\n\n twitch_config.data[\"servers\"][message.server.id][\"notify_channels\"] = [c.id for c in channels]\n twitch_config.save()\n\n # Tell the user if notifications were disabled\n assert channels, \"**Disabled stream notifications in this server.**\"\n\n await client.say(message, \"**Notifying streams in:** {}\".format(utils.format_objects(*channels, sep=\" \")))","def on_enable(self) -> None:\n if self._send_notification_func:\n self._send_notification_func()\n self._send_notification_func = None","def setup(self, channels):\n self.channels = channels[:]","async def send_commands(ans: Message):\n await ans.answer(all_commands)","def on_enable(self) -> None:\n if self.app.state == self.app.States.clean:\n self._start_notification_cycle()","async def _guild(self, ctx):\n if await self.config.guild(ctx.guild).guild():\n await self.config.guild(ctx.guild).guild.set(False)\n msg = _(\"Okay, I will not react to messages \" \"containing server emojis!\")\n await ctx.send(msg)\n else:\n await self.config.guild(ctx.guild).guild.set(True)\n msg = _(\"Okay, I will react to messages \" \"containing server emojis!\")\n await ctx.send(msg)","async def set_channel(self, ctx, *, channel: discord.Channel=None):\n\n server = ctx.message.server\n\n temp = self.bot.dota_ticker_settings.get(server.id)\n\n if temp is None or not temp['enabled']:\n await self.bot.say('The match ticker has not been enabled on this server.')\n return\n\n if channel is None:\n await self.bot.say('No channel name or mention received.')\n return\n\n settings = {'enabled': True, 'channel_id': channel.id}\n\n await self.bot.dota_ticker_settings.put(server.id, settings)\n await self.bot.say('The match ticker has been enabled on {0.mention}.'.format(channel))","async def test_force_voice_sync_no_channel(self):\n await self.cog.cog_load()\n\n channel = MockVoiceChannel(guild=MockGuild(afk_channel=None))\n new_channel = MockVoiceChannel(delete=AsyncMock())\n channel.guild.create_voice_channel.return_value = new_channel\n\n await self.cog._force_voice_sync(channel)\n\n # Check channel creation\n overwrites = {\n channel.guild.default_role: PermissionOverwrite(speak=False, connect=False, view_channel=False)\n }\n channel.guild.create_voice_channel.assert_awaited_once_with(\"mute-temp\", overwrites=overwrites)\n\n # Check bot deleted channel\n new_channel.delete.assert_awaited_once()","async def guild(ctx):\n print(ctx.channel)\n if ctx.channel.name.lower() in channels:\n await ctx.send(f\"\"\"guild: {ctx.guild.name}\"\"\")","def setinactive(self, irc, msg, args, channel):\n res = self._checkDBhasChannel(channel)\n if res is True:\n SQL = 'UPDATE registry SET isActive = ? WHERE channel = ?'\n SQLargs = (0, channel)\n self._SQLexec(SQL, SQLargs)\n self.lock.acquire()\n for x in range(0, len(self.channelscontrol)):\n v0 = str(self.channelscontrol[x][0])\n if v0 == channel:\n self.channelscontrol[x][1] = 0\n self.lock.release()\n irc.reply(\"Channel set inactive.\", prefixNick=True)\n else:\n irc.reply(\"Channel does not exist in DB.\", prefixNick=True)","async def katıl(con,channel=None):\r\n check = str(con.message.channel)\r\n\r\n if check == 'Direct Message with {}'.format(con.message.author.name):#COMMAND IS IN DM\r\n await bot.send_message(con.message.channel, \"**You must be in a `server voice channel` to use this command**\")\r\n\r\n if check != 'Direct Message with {}'.format(con.message.author.name):#COMMAND NOT IN DM\r\n voice_status = bot.is_voice_connected(con.message.server)\r\n\r\n if voice_status == False:#VOICE NOT CONNECTED\r\n await bot.join_voice_channel(con.message.author.voice.voice_channel)\r\n\r\n if voice_status == True:#VOICE ALREADY CONNECTED\r\n await bot.send_message(con.message.channel, \"**Bot zaten bir kanala bağlı !**\")","def enable(self):\n if not self.labExperiment:\n super().enable()\n else:\n self.connection.command('open_dm', self.DMserial)\n status = self.connection.query('get_status')\n assert status == 0, 'Error connecting to DM. Error: ' + str(status)\n numActProfile = self.connection.query('num_actuators')\n assert numActProfile == self.numActProfile, 'Wrong number of profile actuators entered'\n print(\"'BM1k' is now enabled\")","async def async_turn_on(self) -> None:\n if CONF_POWER_COMMAND_TOPIC in self._config:\n mqtt_payload = self._command_templates[CONF_POWER_COMMAND_TEMPLATE](\n self._config[CONF_PAYLOAD_ON]\n )\n await self._publish(CONF_POWER_COMMAND_TOPIC, mqtt_payload)\n return\n # Fall back to default behavior without power command topic\n await super().async_turn_on()","async def update(self, ctx, channel):\n m = ctx.message\n g = await self.helpers.get_record('server', m.guild.id)\n a = m.author\n if not a.bot:\n if channel.startswith('#'):\n channel = channel[1:]\n elif channel.startswith('<#') and len(m.channel_mentions)>0:\n channel = m.channel_mentions[0].name\n channel = await self.helpers.choose_channel(ctx, m.guild, channel)\n if not channel:\n asyncio.ensure_future(ctx.send('Sorry, I could not find a channel with that name.'))\n return\n role = next((r for r in a.guild.roles if r.id == g['roles'].get('updates')), None)\n was_false = False\n try:\n if role.mentionable == False:\n was_false = True\n await role.edit(mentionable=True)\n pfx = await self.bot.get_prefix(m)\n mc = m.content\n passed = 0\n while not passed:\n for p in pfx:\n if mc.startswith(p):\n mc = mc[len(p):]\n passed = 1\n mc = re.sub(r'^([^\\s]+)','',mc).strip()\n mc = re.sub(r'^([^\\s]+)','',mc.strip()).strip()\n mc = f'{mc}\\n\\n{role.mention}'\n send_to = await channel.send(mc)\n elif role.mentionable == True:\n await ctx.send('Oops, looks like the role can be abused.'\n '\\nSet mentionable to off before doing an update command.')\n if was_false:\n await role.edit(mentionable=False)\n asyncio.ensure_future(ctx.send('Successfully posted the update!'))\n except discord.Forbidden:\n asyncio.ensure_future(ctx.send(f'I cannot toggle the updates role to be mentioned.'\n '\\nI would suggest placing my role above `{role.name}`'))","async def before_resend_post(self):\n await self.bot.wait_until_ready()\n self.aviso_channels = [self.bot.get_channel(\n int(channel_id)) for channel_id in self.bot.CONF[\"channels_id\"]]","async def hockey_commands(self, ctx: commands.Context) -> None:\n pass","def enable(self):\n pass","async def active(self, ctx: commands.Context, toggle: bool = None):\n guild = ctx.guild\n tog = self.config.guild(guild)\n role_config = [\n await tog.temprole(),\n await tog.autoroles(),\n ]\n if not role_config[1]:\n role_config[1] = None\n if toggle is None:\n message = \"The Verification settings is set to {}.\".format(await tog.toggle())\n if role_config.count(None) == 2 and await tog.toggle():\n await tog.toggle.set(False)\n message = (\n \"I have disabled verification since roles instructions has been \"\n \"removed. Check settings for more informations\"\n )\n return await ctx.send(message)\n\n if role_config.count(None) == 2:\n return await ctx.send(\n \"I am missing informations; I don't know if I should either give a temprorary \"\n \"role while verifying or give a role after the verification.\"\n )\n await tog.toggle.set(toggle)\n await ctx.send(\"Verification settings is now set to {choice}.\".format(choice=toggle))","async def unlock(ctx):\n member = ctx.message.author\n channel = ctx.message.channel\n\n if (channel.category.name in [\"beta\", \"staff\", \"Pi-Bot\"]):\n return await ctx.send(\"This command is not suitable for this channel because of its category.\")\n\n if (channel.category.name == CATEGORY_SO or channel.category.name == CATEGORY_GENERAL):\n await ctx.send(\"Synced permissions with channel category.\")\n return await channel.edit(sync_permissions=True)\n\n member_role = discord.utils.get(member.guild.roles, name=ROLE_MR)\n if (channel.category.name != CATEGORY_STATES):\n await ctx.channel.set_permissions(member_role, add_reactions=True, send_messages=True, read_messages=True)\n else:\n await ctx.channel.set_permissions(member_role, add_reactions=True, send_messages=True)\n\n wiki_role = discord.utils.get(member.guild.roles, name=ROLE_WM)\n gm_role = discord.utils.get(member.guild.roles, name=ROLE_GM)\n aRole = discord.utils.get(member.guild.roles, name=ROLE_AD)\n bRole = discord.utils.get(member.guild.roles, name=ROLE_BT)\n await ctx.channel.set_permissions(wiki_role, add_reactions=True, send_messages=True, read_messages=True)\n await ctx.channel.set_permissions(gm_role, add_reactions=True, send_messages=True, read_messages=True)\n await ctx.channel.set_permissions(aRole, add_reactions=True, send_messages=True, read_messages=True)\n await ctx.channel.set_permissions(bRole, add_reactions=True, send_messages=True, read_messages=True)\n await ctx.send(\"Unlocked the channel to Member access. Please check if permissions need to be synced.\")","async def _antiadv(self, ctx):\r\n serverid = ctx.message.server.id\r\n if ctx.invoked_subcommand is None:\r\n await send_cmd_help(ctx)\r\n if serverid not in self.adkillr:\r\n self.adkillr[serverid] = {'toggle': False, 'message': '{0.mention} don\\'t send links!', 'filters': []}\r\n dataIO.save_json(\"data/adkillr/adkillr.json\", self.adkillr)","async def _list(self, ctx):\n config = await self.config.guild(ctx.guild).channels()\n data = [self.bot.get_channel(x).mention for x in config]\n if ctx.channel.id in config:\n destination = ctx.author\n else:\n destination = ctx\n if not data:\n return await destination.send(\"There are no channels.\")\n await destination.send(\", \".join(data))","async def togglechannel(self, ctx, channel):\r\n\r\n user = ctx.message.author\r\n channel = await commands.clean_content().convert(ctx, channel)\r\n await ctx.message.delete()\r\n\r\n if channel == \"nsfw\":\r\n\r\n if self.bot.nsfw_role in user.roles:\r\n await user.remove_roles(self.bot.nsfw_role)\r\n await user.send(\"Access to NSFW channels revoked.\")\r\n else:\r\n await user.add_roles(self.bot.nsfw_role)\r\n await user.send(\"Access to NSFW channels granted.\")\r\n else:\r\n await user.send(\"{} is not a togglable channel.\".format(channel))","def slackbot(self, *args, **kwargs):\n while True:\n self.slack.api_connect()","async def async_turn_on_when_active(self, **kwargs: Any) -> None:\n raise NotImplementedError","def joinedChannel(self, channel, users):\n pass","async def kickoldusers(self, ctx):\n s = db.session()\n\n for member in self.bot.get_guild(CONFIG.server).members:\n\n message = s.query(db.Message).filter(db.Message.author == member.id).first()\n if not message:\n if not member.bot:\n server = self.bot.get_guild(CONFIG.server)\n\n print(server.system_channel)\n inviteLink = await self.bot.get_guild(CONFIG.server).system_channel.create_invite(xkcd=True,\n max_uses=1)\n\n await ctx.send(\"No messages from \" + member.name)\n await member.send(\n \"You have been kicked from Inferno games because you have not posted anything for a while\")\n await member.send(\"If you feel this was a mistake, or if you wish to become active again you can rejoin by clicking \" + str(inviteLink))\n await member.kick()\n s.close()","async def ccallow(self, ctx, channel: discord.TextChannel):\n channel_list = await self.config.guild(ctx.guild).channel_deny()\n if channel.id in channel_list:\n channel_list.remove(channel.id)\n else:\n return await ctx.send(\"Channel is not on the deny list.\")\n await self.config.guild(ctx.guild).channel_deny.set(channel_list)\n await ctx.send(f\"{channel.mention} will be allowed for chatchart use.\")","async def aoc_subscribe(self, ctx: commands.Context) -> None:\n if ctx.channel.id != settings.aoc.channel_id:\n await ctx.send(f\"Please use the <#{settings.aoc.channel_id}> channel\")\n return\n\n role = ctx.guild.get_role(settings.aoc.role_id)\n unsubscribe_command = f\"{ctx.prefix}{ctx.command.root_parent} unsubscribe\"\n\n if role not in ctx.author.roles:\n await ctx.author.add_roles(role)\n await ctx.send(\n \"Okay! You have been __subscribed__ to notifications about new Advent of Code tasks. \"\n f\"You can run `{unsubscribe_command}` to disable them again for you.\"\n )\n else:\n await ctx.send(\n \"Hey, you already are receiving notifications about new Advent of Code tasks. \"\n f\"If you don't want them any more, run `{unsubscribe_command}` instead.\"\n )","async def async_turn_on(self, **kwargs: Any) -> None:\n await self.async_turn_on_off(True)","def enable_freeplay_cube_lights(self, enable=True):\n if enable:\n self._set_cube_light_state(True)\n self._enable_cube_sleep(False, False)\n else:\n self._enable_cube_sleep(True, True)\n self._set_cube_light_state(False)","def enable(self) -> None:","def cmd_enable(self, app_name=None):\n rc = self.socket_command_with_project('enable', app_name)\n return rc","async def on_ready():\n await bot.change_presence(status=discord.Status.online, activity=discord.Game(activity))"],"string":"[\n \"def func(self):\\n from evennia.comms.models import ChannelDB\\n\\n caller = self.caller\\n if self.args not in (\\\"on\\\", \\\"off\\\"):\\n return super(CmdArxAllCom, self).func()\\n if self.args == \\\"on\\\":\\n # get names of all channels available to listen to\\n # and activate them all\\n channels = [\\n chan\\n for chan in ChannelDB.objects.get_all_channels()\\n if chan.access(caller, \\\"listen\\\")\\n ]\\n for channel in channels:\\n unmuted = channel.unmute(caller)\\n if unmuted:\\n self.msg(\\\"You unmute channel %s.\\\" % channel)\\n else:\\n caller.execute_cmd(\\\"addcom %s\\\" % channel.key)\\n return\\n channels = ChannelDB.objects.get_subscriptions(caller)\\n for channel in channels:\\n if channel.mute(caller):\\n self.msg(\\\"You mute channel %s.\\\" % channel)\",\n \"async def enable(self, ctx, function: typing.Union[CommandConverter, PluginConverter, GalaxyConverter],\\n *channels: discord.TextChannel):\\n channels = channels or (ctx.channel, )\\n await ctx.guild_profile.permissions.enable_function(function, channels)\\n # noinspection PyUnresolvedReferences\\n await ctx.send_line(f\\\"{ctx.emotes.web_emotion.galka} {function.name} has been enabled back in specified channels.\\\")\",\n \"async def enable(self, ctx):\\n await self.config.guild(ctx.guild).auto.set(True)\\n await ctx.send(_(\\\"Automatic voicechannel creation enabled.\\\"))\",\n \"async def disable_channel(self, ctx, *channels: discord.TextChannel):\\n channels = channels or (ctx.channel, )\\n await ctx.guild_profile.permissions.disable_channels(channels)\\n await ctx.send_line(f\\\"{ctx.emotes.web_emotion.galka} Bot commands and messages has been disabled in specified channels.\\\")\",\n \"async def managechannels(self, ctx:commands.Context):\",\n \"async def disable(self, ctx):\\n await self.config.guild(ctx.guild).auto.set(True)\\n await ctx.send(_(\\\"Automatic voicechannel creation disabled.\\\"))\",\n \"async def _msgvote_on(self, ctx):\\n\\n channel = ctx.message.channel\\n if channel.id in self.settings[\\\"channels_enabled\\\"]:\\n await self.bot.say(\\\"Msgvote mode is already on in this channel.\\\")\\n else:\\n self.settings[\\\"channels_enabled\\\"].append(channel.id)\\n dataIO.save_json(self.settings_path, self.settings)\\n await self.bot.say(\\\"Msgvote mode is now on in this channel.\\\")\",\n \"async def admin_enable(self, ctx: commands.Context):\\n if ctx.guild.id in self.guilds:\\n await ctx.send('Team management is already enabled in this guild.')\\n return\\n await self._enable_guild(guild=ctx.guild)\\n await ctx.send('Team management enabled.')\",\n \"async def _cmdf_chenable(self, substr, msg, privilege_level):\\n enabled_str = None\\n if utils.str_says_true(substr) or (len(substr) == 0):\\n self._ch_msg_isenabled = True\\n enabled_str = \\\"enabled.\\\"\\n else:\\n self._ch_msg_isenabled = False\\n enabled_str = \\\"disabled.\\\"\\n self._save_settings()\\n\\n buf = \\\"In-channel greetings is now \\\" + enabled_str\\n await self._client.send_msg(msg, buf)\\n return\",\n \"async def tc_enable(self, ctx):\\n await self.config.guild(ctx.guild).private_textchannels_enabled.set(True)\\n await ctx.send(_(\\\"Private text channels enabled.\\\"))\",\n \"async def enable(self, ctx: Context, *, guild: int = None):\\n\\n if guild is None:\\n guild = ctx.guild\\n else:\\n guild = self.bot.get_guild(guild)\\n\\n if not guild:\\n return await ctx.message.add_reaction(\\\"⚠\\\")\\n\\n self._create_guild_config(guild)\\n\\n await ctx.message.add_reaction(\\\"✅\\\")\",\n \"async def enable(self, ctx, *, channel: discord.Channel=None):\\n\\n server = ctx.message.server\\n\\n temp = self.bot.dota_ticker_settings.get(server.id)\\n\\n if temp is not None and temp['enabled']:\\n await self.bot.say('The match ticker has already been enabled on this server.')\\n return\\n\\n if channel is None:\\n channel = server.default_channel\\n\\n settings = {'enabled': True, 'channel_id': channel.id}\\n\\n await self.bot.dota_ticker_settings.put(server.id, settings)\\n await self.bot.say('The match ticker has been enabled on {0.mention}.'.format(channel))\",\n \"async def togglegames(self, ctx, *, channel: discord.TextChannel = None):\\n channel = channel or ctx.channel\\n user = await self.ex.get_user(ctx.author.id)\\n if not channel:\\n log.console(f\\\"Could not find text channel. -> User: {user.id} - Moderator.togglegames\\\")\\n msg = await self.ex.get_msg(user, \\\"moderator\\\", \\\"channel_not_found\\\")\\n return await ctx.send(msg)\\n\\n enabled_msg = \\\"enabled\\\" if await self.ex.u_moderator.toggle_games(channel.id) else \\\"disabled\\\"\\n\\n msg = await self.ex.get_msg(user, \\\"moderator\\\", \\\"channel_toggled\\\", [\\n [\\\"name\\\", ctx.author.display_name], [\\\"text_channel\\\", channel.name], [\\\"result\\\", enabled_msg]\\n ])\\n\\n return await ctx.send(msg)\",\n \"async def enable(self, ctx):\\n self.bot.db.execute(\\\"UPDATE starboards SET enabled = 1 WHERE channel_id = ?\\\", (ctx.channel.id,))\\n await ctx.say(\\\"star.enabled\\\")\",\n \"async def disable(self, ctx, function: typing.Union[CommandConverter, PluginConverter, GalaxyConverter],\\n *channels: discord.TextChannel):\\n channels = channels or (ctx.channel, )\\n await ctx.guild_profile.permissions.disable_function(function, channels)\\n # noinspection PyUnresolvedReferences\\n await ctx.send_line(f\\\"{ctx.emotes.web_emotion.galka} {function.name} has been disabled in specified channels.\\\")\",\n \"def bot_commands_only(cmd):\\n @functools.wraps(cmd)\\n async def bc_cmd(self, ctx, *args, **kwargs):\\n if ctx.guild:\\n settings = self.bot.settings[ctx.guild]\\n if settings.bot_commands_channels\\\\\\n and ctx.channel.id not in settings.bot_commands_channels\\\\\\n and ctx.author.id not in settings.admin_ids:\\n\\n for channel_id in settings.bot_commands_channels:\\n bc_ch = discord.utils.get(ctx.guild.channels, id=channel_id)\\n if bc_ch:\\n await ctx.send(f\\\"Please use {bc_ch.mention} for that command\\\")\\n return\\n return await cmd(self, ctx, *args, **kwargs)\\n return bc_cmd\",\n \"def do_setup(bot, msg, **kwargs):\\n #channel = kwargs.get('event').get('channel')\\n\\n bot.refresh()\\n\\n return True\",\n \"def enable(self, name, channel):\\n if channel not in self.disabled_extensions:\\n self.disabled_extensions[channel] = set()\\n\\n if name not in self.extension_names:\\n return False\\n\\n logger.info('Enabling %s on %s' % (name, channel))\\n self.disabled_extensions[channel].discard(name)\\n\\n return True\",\n \"async def admin_msg(self, message):\\n for channel in self.admin_channels.values():\\n if channel:\\n await channel.send(message)\",\n \"async def _msgvote_off(self, ctx):\\n\\n channel = ctx.message.channel\\n if channel.id not in self.settings[\\\"channels_enabled\\\"]:\\n await self.bot.say(\\\"Msgvote mode is already off in this channel.\\\")\\n else:\\n self.settings[\\\"channels_enabled\\\"].remove(channel.id)\\n dataIO.save_json(self.settings_path, self.settings)\\n await self.bot.say(\\\"Msgvote mode is now off in this channel.\\\")\",\n \"def _fillChannels(self):\\n\\n # Add extra disabled channels as needed\\n index = len(self.channels)\\n while index < self.iface.myInfo.max_channels:\\n ch = channel_pb2.Channel()\\n ch.role = channel_pb2.Channel.Role.DISABLED\\n ch.index = index\\n self.channels.append(ch)\\n index += 1\",\n \"async def meow_enable(self, ctx: vbu.Context, duration: vbu.TimeValue = None):\\n\\n self.meow_chats.add(ctx.channel)\\n if duration:\\n await ctx.send(f\\\"Meow chat has been enabled in {ctx.channel.mention} for {duration.clean_full} owo\\\")\\n else:\\n await ctx.send(f\\\"Meow chat has been enabled in {ctx.channel.mention} owo\\\")\\n\\n # See if we want to disable meow chat after a while\\n if duration:\\n async def waiter():\\n await asyncio.sleep(duration.delta.total_seconds())\\n try:\\n self.meow_chats.remove(ctx.channel)\\n await ctx.send(\\\"Turned off meow chat as scheduled :<\\\")\\n except KeyError:\\n pass\\n _, current_task = self.meow_disable_tasks.get(ctx.channel.id, (None, None))\\n if current_task:\\n current_task.cancel()\\n self.meow_disable_tasks[ctx.channel.id] = (discord.utils.utcnow() + duration.delta, self.bot.loop.create_task(waiter()))\",\n \"def enable_all(self, enable):\\n logging.debug(\\\"Called enable_all with value {}\\\".format(enable))\\n\\n if enable:\\n # Loop over all quads and channels in system, adding enable command to deferred\\n # executor queue\\n for quad_idx in range(len(self.quad)):\\n for channel in range(Quad.NUM_CHANNELS):\\n self.deferred_executor.enqueue(\\n self.quad_enable_channel, self.quad_enable_interval, quad_idx, channel\\n )\\n self.__all_enabled = True\\n else:\\n # Clear any pending turn-on command from the queue first, then turn off all channels\\n # immediately.\\n num_enables_pending = self.deferred_executor.pending()\\n if num_enables_pending > 0:\\n logging.debug(\\\"Clearing {} pending quad enable commands from queue\\\".format(\\n num_enables_pending\\n ))\\n self.deferred_executor.clear()\\n for quad_idx in range(len(self.quad)):\\n for channel in range(Quad.NUM_CHANNELS):\\n self.quad[quad_idx].set_enable(channel, False)\\n self.__all_enabled = False\",\n \"async def setwelcomechannel(self, ctx, *, channel : discord.TextChannel = None):\\n\\n isAdmin = ctx.message.author.permissions_in(ctx.message.channel).administrator\\n if not isAdmin:\\n checkAdmin = self.settings.getServerStat(ctx.message.guild, \\\"AdminArray\\\")\\n for role in ctx.message.author.roles:\\n for aRole in checkAdmin:\\n # Get the role that corresponds to the id\\n if str(aRole['ID']) == str(role.id):\\n isAdmin = True\\n\\n # Only allow admins to change server stats\\n if not isAdmin:\\n await ctx.channel.send('You do not have sufficient privileges to access this command.')\\n return\\n\\n if channel == None:\\n self.settings.setServerStat(ctx.message.guild, \\\"WelcomeChannel\\\", \\\"\\\")\\n if self._getDefault(ctx.guild):\\n msg = 'Welcome and goodbye messages will be displayed in the default channel (**{}**).'.format(self._getDefault(ctx.guild).mention)\\n else:\\n msg = \\\"Welcome and goodbye messages will **not** be displayed.\\\"\\n await ctx.channel.send(msg)\\n return\\n\\n # If we made it this far - then we can add it\\n self.settings.setServerStat(ctx.message.guild, \\\"WelcomeChannel\\\", channel.id)\\n\\n msg = 'Welcome and goodbye messages will be displayed in **{}**.'.format(channel.mention)\\n await ctx.channel.send(msg)\",\n \"async def cmd_galenable(self, ctx):\\n\\n # ===== SET LOCAL COG VARIABLE\\n self.cogset['enable']= True\\n\\n # ===== ADD THE FUNCTION TO THE SCHEDULER\\n self.scheduler.add_job(call_schedule,\\n 'date',\\n id=\\\"_delete_gallery_messages\\\",\\n run_date=get_next(hours=self.cogset['text_expirein']),\\n kwargs={\\\"func\\\": \\\"_delete_gallery_messages\\\"}\\n )\\n\\n # ===== SAVE SETTINGS \\n await cogset.SAVE(self.cogset, cogname=self.qualified_name)\\n\\n await ctx.channel.send(content=\\\"Galleries are **enabled**.\\\")\\n\\n return\",\n \"async def rebind(self, ctx, channel: discord.TextChannel=None):\\n player = self.bot.lavalink.players.get(ctx.guild.id)\\n if not player.is_connected:\\n return await ctx.send(\\\"I'm not connected to a voice channel :no_entry:\\\")\\n if not channel:\\n channel = ctx.channel\\n player.store('channel', channel.id)\\n await ctx.send(\\\"Messages will now be sent in {} <:done:403285928233402378>\\\".format(channel.mention))\",\n \"async def botmsg(self, ctx, type: bool):\\n async with self.config.toggles() as toggles:\\n if type:\\n toggles[\\\"botmessages\\\"] = True\\n await ctx.send(\\\"Bot message notifications have been enabled.\\\")\\n else:\\n toggles[\\\"botmessages\\\"] = False\\n await ctx.send(\\\"Bot message notifications have been disabled.\\\")\",\n \"async def applysetup(self, ctx: commands.Context):\\n pred = MessagePredicate.yes_or_no(ctx)\\n role = MessagePredicate.valid_role(ctx)\\n\\n applicant = get(ctx.guild.roles, name=\\\"Staff Applicant\\\")\\n channel = get(ctx.guild.text_channels, name=\\\"applications\\\")\\n\\n await ctx.send(\\n \\\"This will create required channel and role. Do you wish to continue? (yes/no)\\\"\\n )\\n try:\\n await self.bot.wait_for(\\\"message\\\", timeout=30, check=pred)\\n except asyncio.TimeoutError:\\n return await ctx.send(\\\"You took too long. Try again, please.\\\")\\n if not pred.result:\\n return await ctx.send(\\\"Setup cancelled.\\\")\\n if not applicant:\\n try:\\n applicant = await ctx.guild.create_role(\\n name=\\\"Staff Applicant\\\", reason=\\\"Application cog setup\\\"\\n )\\n except discord.Forbidden:\\n return await ctx.send(\\n \\\"Uh oh. Looks like I don't have permissions to manage roles.\\\"\\n )\\n if not channel:\\n await ctx.send(\\n \\\"Do you want everyone to see the applications channel? (yes/no)\\\"\\n )\\n try:\\n await self.bot.wait_for(\\\"message\\\", timeout=30, check=pred)\\n except asyncio.TimeoutError:\\n return await ctx.send(\\\"You took too long. Try again, please.\\\")\\n if pred.result:\\n overwrites = {\\n ctx.guild.default_role: discord.PermissionOverwrite(\\n send_messages=False\\n ),\\n ctx.guild.me: discord.PermissionOverwrite(send_messages=True),\\n }\\n else:\\n overwrites = {\\n ctx.guild.default_role: discord.PermissionOverwrite(\\n read_messages=False\\n ),\\n ctx.guild.me: discord.PermissionOverwrite(read_messages=True),\\n }\\n try:\\n channel = await ctx.guild.create_text_channel(\\n \\\"applications\\\",\\n overwrites=overwrites,\\n reason=\\\"Application cog setup\\\",\\n )\\n except discord.Forbidden:\\n return await ctx.send(\\n \\\"Uh oh. Looks like I don't have permissions to manage channels.\\\"\\n )\\n await ctx.send(f\\\"What role can accept or reject applicants?\\\")\\n try:\\n await self.bot.wait_for(\\\"message\\\", timeout=30, check=role)\\n except asyncio.TimeoutError:\\n return await ctx.send(\\\"You took too long. Try again, please.\\\")\\n accepter = role.result\\n await self.config.guild(ctx.guild).applicant_id.set(applicant.id)\\n await self.config.guild(ctx.guild).channel_id.set(channel.id)\\n await self.config.guild(ctx.guild).accepter_id.set(accepter.id)\\n await ctx.send(\\n \\\"You have finished the setup! Please, move your new channel to the category you want it in.\\\"\\n )\",\n \"async def applysetup(self, ctx: commands.Context):\\n pred = MessagePredicate.yes_or_no(ctx)\\n role = MessagePredicate.valid_role(ctx)\\n\\n applicant = get(ctx.guild.roles, name=\\\"Staff Applicant\\\")\\n channel = get(ctx.guild.text_channels, name=\\\"staff-applications\\\")\\n\\n await ctx.send(\\n \\\"This will create required channel and role. Do you wish to continue? (yes/no)\\\"\\n )\\n try:\\n await self.bot.wait_for(\\\"message\\\", timeout=30, check=pred)\\n except asyncio.TimeoutError:\\n return await ctx.send(\\\"You took too long. Try again, please.\\\")\\n if not pred.result:\\n return await ctx.send(\\\"Setup cancelled.\\\")\\n if not applicant:\\n try:\\n applicant = await ctx.guild.create_role(\\n name=\\\"Staff Applicant\\\", reason=\\\"Application cog setup\\\"\\n )\\n except discord.Forbidden:\\n return await ctx.send(\\n \\\"Uh oh. Looks like I don't have permissions to manage roles.\\\"\\n )\\n if not channel:\\n await ctx.send(\\\"Do you want everyone to see the applications channel? (yes/no)\\\")\\n try:\\n await self.bot.wait_for(\\\"message\\\", timeout=30, check=pred)\\n except asyncio.TimeoutError:\\n return await ctx.send(\\\"You took too long. Try again, please.\\\")\\n if pred.result:\\n overwrites = {\\n ctx.guild.default_role: discord.PermissionOverwrite(send_messages=False),\\n ctx.guild.me: discord.PermissionOverwrite(send_messages=True),\\n }\\n else:\\n overwrites = {\\n ctx.guild.default_role: discord.PermissionOverwrite(read_messages=False),\\n ctx.guild.me: discord.PermissionOverwrite(read_messages=True),\\n }\\n try:\\n channel = await ctx.guild.create_text_channel(\\n \\\"staff-applications\\\",\\n overwrites=overwrites,\\n reason=\\\"Application cog setup\\\",\\n )\\n except discord.Forbidden:\\n return await ctx.send(\\n \\\"Uh oh. Looks like I don't have permissions to manage channels.\\\"\\n )\\n await ctx.send(f\\\"What role can accept or reject applicants?\\\")\\n try:\\n await self.bot.wait_for(\\\"message\\\", timeout=30, check=role)\\n except asyncio.TimeoutError:\\n return await ctx.send(\\\"You took too long. Try again, please.\\\")\\n accepter = role.result\\n await self.config.guild(ctx.guild).applicant_id.set(applicant.id)\\n await self.config.guild(ctx.guild).channel_id.set(channel.id)\\n await self.config.guild(ctx.guild).accepter_id.set(accepter.id)\\n await ctx.send(\\n \\\"You have finished the setup! Please, move your new channel to the category you want it in.\\\"\\n )\",\n \"async def toggle(self, ctx: BBContext):\\n\\n self.code_enabled = not self.code_enabled\\n e = 'enabled.' if self.code_enabled else 'disabled.'\\n await ctx.send(f\\\"Bunker code auto reaction has been : **{e}**\\\")\\n self.bot.logger.info('Bunker code listener %s by %s', e, str(ctx.author))\",\n \"async def _set_channels(self, ctx: Context):\\n\\n guild: discord.Guild = ctx.guild\\n\\n signup = await guild.create_text_channel(\\\"sign-ups\\\")\\n await self.config.guild(guild).signup_channel.set(signup.id)\\n\\n host_role = await self.role_from_config(guild, \\\"host_id\\\")\\n\\n na_overwrites = {\\n guild.default_role: discord.PermissionOverwrite(\\n read_messages=False\\n ),\\n host_role: discord.PermissionOverwrite(\\n read_messages=True,\\n send_messages=True\\n ),\\n guild.me: discord.PermissionOverwrite(\\n read_messages=True,\\n send_messages=True\\n )\\n }\\n\\n nightaction = await guild.create_text_channel(\\n \\\"night-action\\\", overwrites=na_overwrites\\n )\\n await self.config.guild(guild).na_channel_id.set(nightaction.id)\\n\\n txt = _(\\n \\\"Sign-ups: {}\\\\nNight Actions: {}\\\"\\n ).format(\\n signup.mention, nightaction.mention,\\n )\\n\\n embed = discord.Embed(\\n color=0x37BFFF, title=\\\"Created Channels!\\\", description=txt\\n )\\n\\n try:\\n await ctx.send(embed=embed)\\n except discord.Forbidden:\\n await ctx.send(\\\"Created required channels!\\\")\\n await ctx.send(txt)\",\n \"async def async_turn_on(self, **kwargs: Any) -> None:\\n await self.entity_description.set_command(self, True)\",\n \"async def toggle(self, ctx):\\n guild = ctx.message.guild\\n\\n enabled = await self.config.guild(guild).enabled()\\n\\n enabled = not enabled\\n await self.config.guild(guild).enabled.set(enabled)\\n\\n if enabled is True:\\n await ctx.send(\\\"AntiSpam has been enabled\\\")\\n else:\\n await ctx.send(\\\"AntiSpam has been disabled\\\")\",\n \"def enable(self, message):\\n self.Enable()\",\n \"async def admin_disable(self, ctx: commands.Context):\\n if ctx.guild.id not in self.guilds:\\n await ctx.send('Team management is already disabled in this guild.')\\n return\\n await self._disable_guild(guild=ctx.guild)\\n await ctx.send('Team management disabled.')\",\n \"async def bingsetadult(self, ctx, setting):\\n\\n channel = ctx.message.channel\\n server = ctx.message.server\\n message = ctx.message\\n if setting == 'channel':\\n await self.bot.say(\\\"Do you want to enable %bingadult for this \\\" +\\n \\\"channel? This will enable this \\\" +\\n \\\"channel to use the %bingadult command, \\\" +\\n \\\"which image searches Bing with Safe Search \\\" +\\n \\\"turned off. Do note that this setting will \\\" +\\n \\\"override the global server setting and thus \\\" +\\n \\\"will allow %bingadult in this channel even \\\" +\\n \\\"if the global server setting is off.\\\\n**ARE \\\" +\\n \\\"YOU SURE YOU WANT TO TOGGLE %bingadult?**\\\" +\\n \\\"\\\\n(y/n)\\\")\\n elif setting == 'server':\\n await self.bot.say(\\\"Do you want to enable %bingadult for this \\\" +\\n \\\"server? This will enable your server to use \\\" +\\n \\\"the %bingadult command, which image \\\" +\\n \\\"searches Bing with Safe Search turned off. \\\" +\\n \\\"Do note that this setting will be overriden \\\" +\\n \\\"per channel if a channel is set to accept \\\" +\\n \\\"usage of %bingadult. **ARE YOU SURE YOU \\\" +\\n \\\"WANT TO TOGGLE %bingadult?**\\\\n(y/n)\\\")\\n else:\\n return await self.bot.say(\\\"This command accepts either server \\\" +\\n \\\"or channel. Please use it again.\\\")\\n response = await self.bot.wait_for_message(author=message.author)\\n if response.content.lower().strip() == \\\"y\\\":\\n if setting == 'channel':\\n self.setadultchannel(channel, 'True')\\n return await self.bot.say(\\\"Enabled %bingadult settings for \\\" +\\n \\\"this channel.\\\")\\n elif setting == 'server':\\n self.setadultserver(server, 'True')\\n return await self.bot.say(\\\"Enabled %bingadult settings for \\\" +\\n \\\"this server.\\\")\\n else:\\n if setting == 'channel':\\n self.setadultchannel(channel, 'False')\\n return await self.bot.say(\\\"Disabled %bingadult settings for \\\" +\\n \\\"this channel.\\\")\\n elif setting == 'server':\\n self.setadultserver(server, 'False')\\n return await self.bot.say(\\\"Disabled %bingadult settings for \\\" +\\n \\\"this server.\\\")\",\n \"def enable(self, *args, **kwargs):\\n pass\",\n \"def setactive(self, irc, msg, args, channel):\\n res = self._checkDBhasChannel(channel)\\n if res is True:\\n SQL = 'UPDATE registry SET isActive = ? WHERE channel = ?'\\n SQLargs = (1, channel)\\n self._SQLexec(SQL, SQLargs)\\n self.lock.acquire()\\n for x in range(0, len(self.channelscontrol)):\\n v0 = str(self.channelscontrol[x][0])\\n if v0 == channel:\\n self.channelscontrol[x][1] = 1\\n self.channelscontrol[x][4] = 0\\n self.lock.release()\\n irc.reply(\\\"Channel set active.\\\", prefixNick=True)\\n g = threading.Thread(target=self._getLastBug, args=(irc, channel,))\\n g.setDaemon(True)\\n g.start()\\n else:\\n irc.reply(\\\"Channel does not exist in DB.\\\", prefixNick=True)\",\n \"async def __add_commands(self):\\r\\n commands_to_add: List[ClientCommandStructure] = [\\r\\n cmd for cmd in ChatCommandHandler.register.values()\\r\\n if cmd.app not in self._api_commands\\r\\n ]\\r\\n\\r\\n if commands_to_add:\\r\\n for cmd in commands_to_add:\\r\\n endpoint = f\\\"applications/{self.client.bot.id}\\\"\\r\\n\\r\\n if cmd.app.guild_id is not MISSING:\\r\\n endpoint += f\\\"/guilds/{cmd.app.guild_id}\\\"\\r\\n\\r\\n await self.client.http.post(\\r\\n endpoint + \\\"/commands\\\",\\r\\n cmd.app.to_dict()\\r\\n )\",\n \"async def enable(\\n self,\\n ctx,\\n channel: typing.Union[discord.TextChannel],\\n emoji: typing.Optional[str] = None\\n ):\\n\\n # Skipping non-eventer users\\n if not self._have_permission(ctx.author, ctx.guild):\\n await ctx.send(embed=decoration.embeds.ERRORS[\\\"NO_PERM\\\"])\\n return\\n\\n # Getting events\\n event_types = connector.getAllEventTypes(guild_id=ctx.guild.id, enabled=False)\\n\\n # Send message with select dropdown for selecting event to activate\\n dropdown = utils.createEventsDropdown(event_types, model=models.EventType)\\n if dropdown is None:\\n await ctx.send(embed=decoration.embeds.ERRORS[\\\"NO_ITEMS_IN_DROPDOWN\\\"])\\n return\\n\\n await ctx.send(\\\"Выбери тип ивента для активации:\\\", components=[dropdown])\\n # Wait for selection\\n select_ctx = await wait_for_component(self.bot, components=dropdown)\\n selected_event_type_id = int(select_ctx.selected_options[0])\\n event_type = connector.getEventTypeByID(guild_id=ctx.guild.id, type_id=selected_event_type_id)\\n\\n # Send notification message\\n started_at = datetime.now()\\n NEW_EVENT = Embed(\\n title=event_type.title,\\n description=event_type.description,\\n color=0x7EBC89\\n )\\n\\n \\n message = await channel.send(content=\\\"@everyone 🟢 Запущен новый тип ивентов!\\\\nДля подписки жми на реакцию\\\", embed=NEW_EVENT)\\n await message.add_reaction(emoji)\\n \\n # Update event type data\\n connector.enableEventType(ctx.guild.id, selected_event_type_id, started_at, message.id, channel.id, emoji)\\n\\n # Send back info message\\n message_payload = [\\n {\\\"ID Вида ивента\\\": selected_event_type_id, \\\"Название вида ивента\\\": event_type.title},\\n started_at,\\n ctx.author.name,\\n ctx.author.avatar_url\\n ]\\n await select_ctx.edit_origin(content=\\\"\\\", embed=decoration.embeds.INFO[\\\"EVENT_TYPE_ENABLED\\\"](*message_payload), components=None)\",\n \"async def lock(ctx):\\n member = ctx.message.author\\n channel = ctx.message.channel\\n\\n if (channel.category.name in [\\\"beta\\\", \\\"staff\\\", \\\"Pi-Bot\\\"]):\\n return await ctx.send(\\\"This command is not suitable for this channel because of its category.\\\")\\n\\n member_role = discord.utils.get(member.guild.roles, name=ROLE_MR)\\n if (channel.category.name == CATEGORY_STATES):\\n await ctx.channel.set_permissions(member_role, add_reactions=False, send_messages=False)\\n else:\\n await ctx.channel.set_permissions(member_role, add_reactions=False, send_messages=False, read_messages=True)\\n\\n wiki_role = discord.utils.get(member.guild.roles, name=ROLE_WM)\\n gm_role = discord.utils.get(member.guild.roles, name=ROLE_GM)\\n admin_role = discord.utils.get(member.guild.roles, name=ROLE_AD)\\n bot_role = discord.utils.get(member.guild.roles, name=ROLE_BT)\\n await ctx.channel.set_permissions(wiki_role, add_reactions=True, send_messages=True, read_messages=True)\\n await ctx.channel.set_permissions(gm_role, add_reactions=True, send_messages=True, read_messages=True)\\n await ctx.channel.set_permissions(admin_role, add_reactions=True, send_messages=True, read_messages=True)\\n await ctx.channel.set_permissions(bot_role, add_reactions=True, send_messages=True, read_messages=True)\\n await ctx.send(\\\"Locked the channel to Member access.\\\")\",\n \"async def on_message(message):\\n #Before doing anything\\n #Check to see if the message started with the command character\\n if not message.content.startswith(commandCharacter):\\n #If it didn't, return\\n return\\n \\n #Ensure the bot wasn't the one who sent the message\\n if message.author == client.user:\\n #If it was, return\\n return\\n \\n #Kill is checked by default (cannot be disabled)\\n if message.content.startswith(commandCharacter+'kill'):\\n await client.send_message(message.channel, 'Goodbye Forever...')\\n await client.logout()\\n os.system('stty sane')\\n exit(0)\\n \\n #Parse through the list of all enabled commands\\n for command in enabledCommands:\\n #We want to ignore case when comparing the message content\\n messageContent = message.content.lower()\\n #If the message matches one of our commands, we will handle it\\n #Requires whitespace after command name\\n if messageContent.startswith(commandCharacter+command):\\n await handleCommand(client, message, voicePlayerList)\",\n \"async def add(self, ctx, *, channel: discord.VoiceChannel):\\n auto_channels = await self.config.guild(ctx.guild).auto_channels()\\n if auto_channels is None:\\n auto_channels = []\\n auto_channels.append(channel.id)\\n await self.config.guild(ctx.guild).auto_channels.set(auto_channels)\\n await ctx.send(\\n _(\\\"Startchannel used for automatic voicechannels added: {channel}\\\").format(\\n channel=channel.name\\n )\\n )\",\n \"async def botlink(self, ctx):\\n async with self.bot.pool.acquire() as conn:\\n async with conn.cursor() as cur:\\n await cur.execute('SELECT * FROM BLChannels WHERE \\\"id\\\"=%s;', (ctx.message.channel.id,))\\n isregistered = await cur.fetchone()\\n if isregistered: \\n return\\n else: \\n embed = discord.Embed(title=\\\"Mafu, the multi use bot\\\", colour=discord.Colour(0x6c56b0),url=\\\"https://discordapp.com/oauth2/authorize?client_id=432292171371118592&scope=bot\\\") \\n await ctx.send(embed=embed)\",\n \"async def react(self, cmd, args, author, roles, dialogs, session, disable_checks=False):\\n\\n if not disable_checks:\\n if not (author.alive or cmd in ALLTIMES_CMDS):\\n await self.error(to=author, msg=msgs.DEAD_USER_INVOKES_CMD)\\n return\\n\\n if not (self.is_current_role(author) or cmd in ALLTIMES_CMDS):\\n await author.send(msgs.WRONG_ROLE)\\n return\\n\\n try:\\n if hasattr(self, cmd + '_cmd'):\\n await getattr(self, cmd + '_cmd')(args, author, roles, dialogs)\\n elif hasattr(self, 'external_' + cmd + '_cmd'):\\n await getattr(self, 'external_' + cmd + '_cmd')(args, author, roles, dialogs, session)\\n else:\\n await self.command_not_found(cmd, author)\\n\\n except Exception as e:\\n fmt = \\\"'%s %s' command invocation raised a(n) %s :\\\\n\\\\n%s\\\\n%s\\\\n\\\" % (\\n cmd,\\n \\\" \\\".join(args),\\n e.__class__.__name__,\\n \\\"\\\".join(traceback.format_tb(e.__traceback__)),\\n str(e) or \\\"[no further info]\\\",\\n )\\n\\n logger.error(fmt)\\n await self.error(to=author, msg=msgs.COMMAND_HAS_RAISED % cmd)\\n\\n else:\\n logger.debug(\\\"La commande de jeu '%s' vient d'être invoquée avec succès par %s\\\" % (cmd, author.user.name))\",\n \"async def _all(self, ctx):\\n guild_emoji = await self.config.guild(ctx.guild).guild()\\n unicode_emoji = await self.config.guild(ctx.guild).unicode()\\n if guild_emoji or unicode_emoji:\\n await self.config.guild(ctx.guild).guild.set(False)\\n await self.config.guild(ctx.guild).unicode.set(False)\\n msg = _(\\\"Okay, I will not react to messages \\\" \\\"containing all emojis!\\\")\\n await ctx.send(msg)\\n else:\\n await self.config.guild(ctx.guild).guild.set(True)\\n await self.config.guild(ctx.guild).unicode.set(True)\\n msg = _(\\\"Okay, I will react to messages \\\" \\\"containing all emojis!\\\")\\n await ctx.send(msg)\",\n \"async def çıkış(con):\\r\\n check=str(con.message.channel)\\r\\n if check == 'Direct Message with {}'.format(con.message.author.name):#COMMAND USED IN DM\\r\\n await bot.send_message(con.message.channel,\\\"**You must be in a `server voice channel` to use this command**\\\")\\r\\n\\r\\n if check != 'Direct Message with {}'.format(con.message.author.name):#COMMAND NOT IN DM\\r\\n \\r\\n # IF VOICE IS NOT CONNECTED\\r\\n if bot.is_voice_connected(con.message.server) == False:\\r\\n await bot.send_message(con.message.channel,\\\"**Bot kanala bağlanmamış !**\\\")\\r\\n\\r\\n # VOICE ALREADY CONNECTED\\r\\n if bot.is_voice_connected(con.message.server) == True:\\r\\n bot.loop.create_task(queue_songs(con,True))\",\n \"def handle_autovoiceoff(bot, ievent):\\n try:\\n ievent.chan.data.autovoice = 0\\n ievent.reply('autovoice disabled on %s' % ievent.channel)\\n except TypeError: ievent.reply('no %s channel in database' % ievent.channel)\",\n \"def enable_motor(self, enabled):\\r\\n self.enabled = enabled\\r\\n\\r\\n # Set motors in neutral if disabling.\\r\\n if not self.enabled:\\r\\n self.set_neutral()\",\n \"async def _toggle(self, ctx, id: int = None):\\n if id is None:\\n id = ctx.channel.id\\n if id in self.etrigs['channels']:\\n self.etrigs['channels'].remove(id)\\n else:\\n self.etrigs['channels'].append(id)\\n self.write_config()\\n await ctx.message.add_reaction('\\\\u2705')\",\n \"def checkIfEnabled(self):\\n\\n # Reload the command file to check for new commands\\n importlib.reload(BotSettings)\\n matches = BotSettings.config['commands']\\n\\n # Check for the match and if it is there return the value that goes with the command\\n for key in matches:\\n key.strip(\\\"!\\\")\\n if key == self.command:\\n return matches.get(key)\\n\\n # If reached the command does not exist\\n return False\",\n \"def settings_push_callback(channel):\\n \\n global settingsMode, settingsCount\\n\\n settingsMode = True\\n settingsCount = 0\\n sleep(.5) # A little time for debouncing\\n \\n with canvas(device) as draw:\\n settingsMenu( draw, settingsCount)\",\n \"def setup_commands(bot):\\n # Reset the bot's command setup\\n bot.reset_commands()\\n # Load enabled mods\\n for mod in bot.enabled_mods:\\n try:\\n full = 'mod_%s' % mod\\n m = getattr(__import__('mods.%s' % full), full)\\n except Exception:\\n bot.log(ERROR, 'Importing the %s mod failed!' % mod)\\n sys.excepthook(*sys.exc_info())\\n continue\\n\\n try:\\n bot.installed_mods[mod] = m\\n # Check for a 404 handler, and replace the current one if there is\\n p404 = getattr(m, 'handle_404', None)\\n if p404:\\n bot.cb_404 = p404\\n\\n # Check for a setup function, and run it if there is\\n setup = getattr(m, 'setup', None)\\n if setup:\\n setup(bot)\\n\\n # Required command bank\\n for cmd in m.command_bank:\\n # Get the actual function\\n func = getattr(m, cmd)\\n # Get the args for the command\\n data = m.command_bank[cmd]\\n # If data[0] is true, mod_help will recognize this command\\n if data[0]:\\n bot.help_db[data[1]] = parse_help(func)\\n # Get the main name and aliases inserted\\n for alias in data[1:]:\\n bot.command_db[alias] = func\\n\\n # Helper function for optional nameless multiples\\n def add_optional(olist, name):\\n olist.extend(getattr(m, f) for f in getattr(m, name, ()))\\n\\n # Optional filters are loaded and added to the list\\n add_optional(bot.filters, 'filters')\\n\\n # Ditto for time-cycle callbacks\\n add_optional(bot.periodic_cbs, 'periodic')\\n\\n # Handlers are the same, but structured as a dict with\\n # \\\"type\\\": \\\"single function-name\\\" items\\n handlers = getattr(m, 'handlers', None)\\n if handlers:\\n for cbtype in handlers:\\n bot.handlers[cbtype].append(getattr(m, handlers[cbtype]))\\n\\n # Register any requirements\\n # NOTE: By putting this at the end, we avoid the possibility of\\n # getting fake requires.\\n reqs = getattr(m, 'requires', None)\\n if reqs:\\n bot.required_mods.update(reqs)\\n except Exception:\\n bot.log(ERROR, 'Unable to install the %s mod!' % mod)\\n del bot.installed_mods[mod]\\n sys.excepthook(*sys.exc_info())\\n\\n missing = bot.required_mods - set(bot.installed_mods)\\n if missing:\\n raise MissingRequirementsError(missing)\\n\\n # And now for the post-install triggers.\\n for mod, m in bot.installed_mods.items():\\n post = getattr(m, 'post_prepare', None)\\n if post:\\n try:\\n post(bot)\\n except Exception:\\n bot.log(ERROR, 'Unable to post-prepare the %s mod!' % mod)\\n sys.excepthook(*sys.exc_info())\",\n \"async def join(self, msg, *, channel: discord.VoiceChannel = None):\\n if msg.voice_client is not None:\\n return await msg.send(f\\\"Bot is already in a voice channel\\\\nDid you mean to use {msg.prefix}moveTo\\\")\\n\\n if msg.voice_client is None:\\n if channel is None:\\n return await msg.author.voice.channel.connect(), await msg.message.add_reaction(emoji='✅')\\n\\n return await channel.connect(), await msg.message.add_reaction(emoji='✅')\\n\\n else:\\n if msg.voice_client.is_playing() is False and not self.player[msg.guild.id]['queue']:\\n return await msg.author.voice.channel.connect(), await msg.message.add_reaction(emoji='✅')\",\n \"def handle_autovoiceon(bot, ievent):\\n try: ievent.chan.data.autovoice = 1\\n except TypeError: ievent.reply('no %s in channel database' % ievent.channel) ; return\\n ievent.reply('autovoice enabled on %s' % ievent.channel)\",\n \"async def tc_disable(self, ctx):\\n await self.config.guild(ctx.guild).private_textchannels_enabled.set(False)\\n await ctx.send(_(\\\"Private text channels disabled.\\\"))\",\n \"async def change(self, ctx: Context):\\n\\t\\tawait self.send(f\\\"If you wish to see your settings, go on our site: https://asxlvm.github.io/#/settings • If you already saw your settings and wish to change them. What do you want to change?\\\", whisper=[ctx.author.id])\\n\\t\\tawait asyncio.sleep(2)\\n\\t\\tawait self.send(f\\\"Options: allowMentions [bool] • autoRejectFights [bool] • passiveMode [bool] • whisperEconomy [bool]• onJoinMsg [bool] • allowUserInteraction [bool] | [bool] = True / False\\\", whisper=[ctx.author.id])\\n\\t\\twaitforevent = await self.wait_for('message', check=lambda message: ctx.author.id == message.author.id)\\n\\t\\twfcl = waitforevent.content.lower()\\n\\t\\tusers = await self.get_settings_data()\\n\\t\\tuserid = ctx.author.id\\n\\t\\tif wfcl == \\\"allowmentions true\\\":\\n\\t\\t\\tawait self.open_settings(userid, ctx.author.username)\\n\\t\\t\\tusers[str(userid)][\\\"allowMentions\\\"] = True\\n\\t\\t\\twith open(\\\"settings.json\\\", \\\"w\\\") as f:\\n\\t\\t\\t\\tjson.dump(users, f)\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} I have changed allowMentions to True for you.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"allowmentions false\\\":\\n\\t\\t\\tawait self.open_settings(userid, ctx.author.username)\\n\\t\\t\\tusers[str(userid)][\\\"allowMentions\\\"] = False\\n\\t\\t\\twith open(\\\"settings.json\\\", \\\"w\\\") as f:\\n\\t\\t\\t\\tjson.dump(users, f)\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} I have changed allowMentions to False for you.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"autorejectfights true\\\":\\n\\t\\t\\tawait self.open_settings(userid, ctx.author.username)\\n\\t\\t\\tusers[str(userid)][\\\"autoRejectFights\\\"] = True\\n\\t\\t\\twith open(\\\"settings.json\\\", \\\"w\\\") as f:\\n\\t\\t\\t\\tjson.dump(users, f)\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} I have changed autoRejectFights to True for you.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"autorejectfights false\\\":\\n\\t\\t\\tawait self.open_settings(userid, ctx.author.username)\\n\\t\\t\\tusers[str(userid)][\\\"autoRejectFights\\\"] = False\\n\\t\\t\\twith open(\\\"settings.json\\\", \\\"w\\\") as f:\\n\\t\\t\\t\\tjson.dump(users, f)\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} I have changed autoRejectFights to False for you.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"passivemode true\\\":\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} as there isn't economy right now, you may not change this setting.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"passivemode false\\\":\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} as there isn't economy right now, you may not change this setting.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"whispereconomy true\\\":\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} as there isn't economy right now, you may not change this setting.\\\")\\n\\t\\telif wfcl == \\\"whispereconomy false\\\":\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} as there isn't economy right now, you may not change this setting.\\\")\\n\\t\\telif wfcl == \\\"onjoinmsg true\\\":\\n\\t\\t\\tawait self.open_settings(userid, ctx.author.username)\\n\\t\\t\\tusers[str(userid)][\\\"onJoinMsg\\\"] = True\\n\\t\\t\\twith open(\\\"settings.json\\\", \\\"w\\\") as f:\\n\\t\\t\\t\\tjson.dump(users, f)\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} I have changed onJoinMsg to True for you.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"onjoinmsg false\\\":\\n\\t\\t\\tawait self.open_settings(userid, ctx.author.username)\\n\\t\\t\\tusers[str(userid)][\\\"onJoinMsg\\\"] = False\\n\\t\\t\\twith open(\\\"settings.json\\\", \\\"w\\\") as f:\\n\\t\\t\\t\\tjson.dump(users, f)\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} I have changed onJoinMsg to False for you.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"allowuserinteraction true\\\":\\n\\t\\t\\tawait self.open_settings(userid, ctx.author.username)\\n\\t\\t\\tusers[str(userid)][\\\"allowUserInteraction\\\"] = True\\n\\t\\t\\twith open(\\\"settings.json\\\", \\\"w\\\") as f:\\n\\t\\t\\t\\tjson.dump(users, f)\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} I have changed allowUserInteraction to True for you.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"allowuserinteraction false\\\":\\n\\t\\t\\tawait self.open_settings(userid, ctx.author.username)\\n\\t\\t\\tusers[str(userid)][\\\"allowUserInteraction\\\"] = True\\n\\t\\t\\twith open(\\\"settings.json\\\", \\\"w\\\") as f:\\n\\t\\t\\t\\tjson.dump(users, f)\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} I have changed allowUserInteraction to True for you.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"allowmentions\\\":\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} you didn't supply a boolean, run the command again.\\\", whisper=[ctx.author.id])\\n\\n\\t\\telif wfcl == \\\"autorejectfights\\\":\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} you didn't supply a boolean, run the command again.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"passivemode\\\":\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} you didn't supply a boolean, run the command again.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"whispereconomy\\\":\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} you didn't supply a boolean, run the command again.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"onjoinmsg\\\":\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} you didn't supply a boolean, run the command again.\\\", whisper=[ctx.author.id])\\n\\t\\telif wfcl == \\\"allowuserinteraction\\\":\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} you didn't supply a boolean, run the command again.\\\", whisper=[ctx.author.id])\\n\\t\\telse:\\n\\t\\t\\tawait asyncio.sleep(2)\\n\\t\\t\\treturn await self.send(f\\\"{ctx.author.mention} I believe that is an incorrect argument, try running the command again.\\\", whisper=[ctx.author.id])\",\n \"def disable(self, name, channel):\\n if channel not in self.disabled_extensions:\\n self.disabled_extensions[channel] = set()\\n\\n if name not in self.extension_names:\\n return False\\n\\n logger.info('Disabling %s on %s' % (name, channel))\\n self.disabled_extensions[channel].add(name)\\n\\n return True\",\n \"async def meow_disable(self, ctx: vbu.Context):\\n\\n try:\\n self.meow_chats.remove(ctx.channel)\\n except KeyError:\\n return await ctx.send(\\\"Meow chat is already disabled in this channel.\\\")\\n await ctx.send(f\\\"Meow chat has been disabled in {ctx.channel.mention} :<\\\")\\n\\n # See if there's a running task to keep it alive\\n _, current_task = self.meow_disable_tasks.pop(ctx.channel.id, (None, None))\\n if current_task:\\n current_task.cancel()\",\n \"async def plugin(self,ctx):\\n special_case = {\\\"Anime\\\":\\\"myanimelist\\\",\\\"Anti Raid\\\":\\\"antiraid\\\"}\\n plugin_setting = await self.redis.hgetall(\\\"{}:Config:Cogs\\\".format(ctx.message.guild.id))\\n embed = discord.Embed()\\n cogs = self.bot.cogs.keys()\\n for x in cogs:\\n setting = \\tu\\\"\\\\U0001F534\\\" #red\\n if x in (\\\"Core\\\", \\\"Remindme\\\", \\\"Tools\\\", \\\"REPL\\\",\\\"Events\\\"): # A Owner's thing only.\\n if ctx.message.author.id != self.bot.owner.id:\\n continue\\n setting = u\\\"\\\\U0001F535\\\" #blue\\n if x.lower() in plugin_setting or special_case.get(x) in plugin_setting:\\n setting = \\tu\\\"\\\\U0001F535\\\" #blue\\n embed.add_field(name = x,value = setting)\\n if ctx.message.guild.me.colour.value:\\n embed.colour = ctx.message.guild.me.colour\\n\\n embed.set_footer(text = \\\"{} = Disable | {} = Enable\\\".format(u\\\"\\\\U0001F534\\\",u\\\"\\\\U0001F535\\\"))\\n await ctx.send(embed=embed)\",\n \"async def disable(self, ctx: Context, guild: int = None):\\n\\n if guild is None:\\n guild = ctx.guild\\n else:\\n guild = self.bot.get_guild(guild)\\n\\n if not guild:\\n return await ctx.message.add_reaction(\\\"⚠\\\")\\n\\n if guild.id not in self.active_guilds:\\n return await ctx.message.add_reaction(\\\"⚠\\\")\\n\\n self._config_cache.pop(guild.id)\\n self.config.delete(f\\\"guilds:{guild.id}\\\")\\n\\n await ctx.message.add_reaction(\\\"✅\\\")\",\n \"async def musicbot(self, ctx, the_state):\\r\\n is_mod = False\\r\\n for role in ctx.message.author.roles:\\r\\n if role.name == \\\"Moderators\\\":\\r\\n is_mod = True\\r\\n if is_mod:\\r\\n if the_state == \\\"1\\\":\\r\\n self.music_off = False\\r\\n await ctx.send(\\\"Music Bot features now on\\\")\\r\\n else:\\r\\n self.music_off = True\\r\\n await ctx.send(\\\"Music Bot features now off\\\")\\r\\n else:\\r\\n await ctx.send(\\\"**Error:** You are not allowed to use this command!\\\")\",\n \"def func(self):\\n\\n if not settings.IRC_ENABLED:\\n string = \\\"IRC is not enabled. Activate it in game/settings.py.\\\"\\n self.msg(string)\\n return\\n\\n # If no args: list bots.\\n if not self.args:\\n # show all connections\\n ircbots = [bot for bot in\\n AccountDB.objects.filter(db_is_bot=True,\\n username__startswith=\\\"ircbot-\\\")]\\n if ircbots:\\n from evennia.utils.evtable import EvTable\\n table = EvTable(\\\"|w#dbref|n\\\", \\\"|wbotname|n\\\",\\n \\\"|wev-channel/location|n\\\",\\n \\\"|wirc-channel|n\\\", \\\"|wSSL|n\\\",\\n maxwidth=_DEFAULT_WIDTH)\\n for ircbot in ircbots:\\n ircinfo = \\\"%s (%s:%s)\\\" % (\\n ircbot.db.irc_channel, ircbot.db.irc_network,\\n ircbot.db.irc_port)\\n table.add_row(\\\"#%i\\\" % ircbot.id, ircbot.db.irc_botname,\\n ircbot.attributes.get(\\\"ev_channel\\\", ircbot.db.ev_location.key),\\n ircinfo, ircbot.db.irc_ssl)\\n self.msg(table)\\n self.msg(\\\"Use 'help @puppetbot' for more infomation.\\\")\\n else:\\n self.msg(\\\"No irc bots found.\\\")\\n return\\n\\n # Switch options available only if valid bot is given.\\n if self.switches:\\n botname = \\\"ircbot-%s\\\" % self.lhs\\n matches = AccountDB.objects.filter(db_is_bot=True, username=botname)\\n dbref = utils.dbref(self.lhs)\\n if not matches and dbref:\\n # try dbref match\\n matches = AccountDB.objects.filter(db_is_bot=True, id=dbref)\\n if not matches:\\n self.msg(\\\"No valid bot given. Consult 'help @puppetbot'\\\")\\n return\\n\\n # Puppetbot/delete <bot> - Delete bot.\\n if any(i in ['disconnect', 'remove', 'delete'] for i in self.switches):\\n matches[0].delete()\\n self.msg(\\\"IRC link/bot destroyed.\\\")\\n return\\n\\n # Puppetbot/ping <bot> - ping bot.\\n if \\\"ping\\\" in self.switches:\\n matches[0].ping(self.caller)\\n self.msg(\\\"Pinging \\\" + self.lhs)\\n return\\n\\n # Puppetbot/about <bot> = msg - Set bot about message.\\n if \\\"about\\\" in self.switches:\\n if self.rhs:\\n matches[0].db.botdesc = self.rhs\\n self.msg(\\\"Bot about message changed to: \\\" + self.rhs)\\n else:\\n self.msg(\\\"No message given. 'About' desc change aborted.\\\")\\n return\\n\\n # Puppetbot/who <bot> - Get IRC user list..\\n if \\\"who\\\" in self.switches:\\n # retrieve user list. The bot must handles the echo since it's\\n # an asynchronous call.\\n self.caller.msg(\\\"Requesting nicklist from %s (%s:%s).\\\" % (\\n matches[0].db.irc_channel,\\n matches[0].db.irc_network,\\n matches[0].db.irc_port))\\n matches[0].get_nicklist(self.caller)\\n return\\n\\n # Puppetbot/reconnect <bot> - reconnect bot.\\n if \\\"reconnect\\\" in self.switches:\\n matches[0].reconnect()\\n self.msg(\\\"Reconnecting \\\" + self.lhs)\\n return\\n\\n # Puppetbot/reload <bot> - Delete all bots, recreates bots from new user list.\\n if \\\"reload\\\" in self.switches:\\n matches[0].db.ev_location.msg_contents(\\\"Puppet reload in progress.\\\")\\n puppetlist = [puppet for puppet in search.search_tag(matches[0].key + \\\"-puppet\\\")]\\n for puppet in puppetlist:\\n puppet.delete()\\n matches[0].get_nicklist()\\n return\\n\\n # Puppetbot/ignore <bot> = puppet - Toggle ignore IRC user.\\n if \\\"ignore\\\" in self.switches:\\n if self.rhs:\\n user = self.rhs.strip()\\n # If already ignored, toggle off.\\n if user in matches[0].db.userignorelist:\\n matches[0].db.userignorelist.remove(user)\\n matches[0].get_nicklist()\\n return\\n\\n # Else ignore user.\\n else:\\n matches[0].db.userignorelist.append(user)\\n if user in matches[0].db.puppetdict:\\n matches[0].db.puppetdict[user].delete()\\n del matches[0].db.puppetdict[user]\\n return\\n else:\\n self.msg(\\\"Usage: Puppetbot/ignore <bot> = <puppet>\\\")\\n return\\n\\n # Puppetbot/entrymsg <bot> = msg - Set default puppet creation message.\\n if \\\"entrymsg\\\" in self.switches:\\n if self.rhs:\\n matches[0].db.puppetentrymsg = \\\" \\\" + self.rhs\\n self.msg(\\\"Bot entry message changed to: \\\" + \\\" \\\" + self.rhs)\\n else:\\n self.msg(\\\"No message given. Message change aborted.\\\")\\n return\\n\\n # Puppetbot/exitmsg <bot> = msg - Set default puppet deletion message.\\n if \\\"exitmsg\\\" in self.switches:\\n if self.rhs:\\n matches[0].db.puppetexitmsg = \\\" \\\" + self.rhs\\n self.msg(\\\"Bot exit message changed to: \\\" + \\\" \\\" + self.rhs)\\n else:\\n self.msg(\\\"No message given. Message change aborted.\\\")\\n return\\n\\n # Puppetbot/prefix <bot> = msg - Set string put before username in puppet.key\\n if \\\"prefix\\\" in self.switches:\\n if self.rhs:\\n matches[0].db.puppetprefix = self.rhs\\n self.msg(\\\"Puppet prefix changed to: \\\" + self.rhs)\\n self.msg(\\\"Use: '@puppetbot/reload <bot>' to implement changes.\\\")\\n else:\\n self.msg(\\\"No message given. Prefix change aborted.\\\")\\n return\\n\\n # Puppetbot/suffix <bot> = msg - Set string put after username in puppet.key\\n if \\\"suffix\\\" in self.switches:\\n if self.rhs:\\n matches[0].db.puppetsuffix = self.rhs\\n self.msg(\\\"Puppet suffix changed to: \\\" + self.rhs)\\n self.msg(\\\"Use: '@puppetbot/reload <bot>' to implement changes.\\\")\\n else:\\n self.msg(\\\"No message given. Suffix change aborted.\\\")\\n return\\n\\n # Puppetbot/defaultdesc <bot> = msg - Set default puppet desc message.\\n if \\\"defaultdesc\\\" in self.switches:\\n if self.rhs:\\n matches[0].db.puppetlastdesc = matches[0].db.puppetdefaultdesc\\n matches[0].db.puppetdefaultdesc = self.rhs\\n self.msg(\\\"Default puppet description changed to: \\\" + self.rhs)\\n else:\\n self.msg(\\\"No message given. Message change aborted.\\\")\\n return\\n\\n # Puppetbot/softdesc <bot> = msg - Only changes non custom puppet descriptions to new default.\\n if \\\"softdesc\\\" in self.switches:\\n puppetlist = [puppet for puppet in\\n search.search_tag(matches[0].key + \\\"-puppet\\\")]\\n for puppet in puppetlist:\\n if puppet.db.desc == matches[0].db.puppetlastdesc:\\n puppet.db.desc = matches[0].db.puppetdefaultdesc\\n self.msg(\\\"Puppets description changed to: \\\" + matches[0].db.puppetdefaultdesc)\\n return\\n\\n # Puppetbot/forcedesc <bot> = msg - Changes all puppet descriptions to new default.\\n if \\\"forcedesc\\\" in self.switches:\\n puppetlist = [puppet for puppet in\\n search.search_tag(matches[0].key + \\\"-puppet\\\")]\\n for puppet in puppetlist:\\n puppet.db.desc = matches[0].db.puppetdefaultdesc\\n self.msg(\\\"Puppets description changed to: \\\" + matches[0].db.puppetdefaultdesc)\\n return\\n\\n # Create Bot.\\n location = self.caller.location\\n self.args = self.args.replace('#', ' ') # Avoid Python comment issues\\n try:\\n irc_network, irc_port, irc_channel, irc_botname = \\\\\\n [part.strip() for part in self.args.split(None, 4)]\\n irc_channel = \\\"#%s\\\" % irc_channel\\n except Exception:\\n string = \\\"IRC bot definition '%s' is not valid.\\\" % self.args\\n self.msg(string)\\n return\\n\\n botname = \\\"ircbot-%s\\\" % irc_botname\\n # create a new bot\\n bot = AccountDB.objects.filter(username__iexact=botname)\\n if bot:\\n # re-use an existing bot\\n bot = bot[0]\\n if not bot.is_bot:\\n self.msg(\\\"'%s' already exists and is not a bot.\\\" % botname)\\n return\\n else:\\n try:\\n bot = create.create_account(botname, None, None,\\n typeclass=ServerBot)\\n except Exception as err:\\n self.msg(\\\"|rError, could not create the bot:|n '%s'.\\\" % err)\\n return\\n bot.start(ev_location=location, irc_botname=irc_botname,\\n irc_channel=irc_channel, irc_network=irc_network,\\n irc_port=irc_port)\\n self.msg(\\\"Connection created. Starting IRC bot.\\\")\",\n \"async def joinchannel(self, ctx: commands.Context, *channels: str):\\n for channel in channels:\\n channel_query = self._channel_query(channel)\\n\\n if channel_query == None:\\n await ctx.send(f\\\"Unable to join {channel}.\\\")\\n continue\\n\\n channel = self.bot.get_channel(channel_query.id)\\n guild = self.bot.get_guild(SERVER_ID)\\n member = guild.get_member(ctx.author.id)\\n\\n if channel == None:\\n await ctx.send(f\\\"Unable to join {channel}.\\\")\\n continue\\n\\n # Don't let a user join the channel again if they are already in it.\\n if channel.permissions_for(member).is_superset(JOINED_PERMISSIONS):\\n await ctx.send(f\\\"You're already a member of {channel}.\\\")\\n continue\\n\\n await channel.set_permissions(member, read_messages=True, reason=\\\"UQCSbot added.\\\")\\n join_message = await channel.send(f\\\"{member.display_name} joined {channel.mention}\\\")\\n await join_message.add_reaction(\\\"👋\\\")\\n await ctx.send(f\\\"You've joined {channel.mention}.\\\")\",\n \"def vc_only():\\n\\n async def check(ctx):\\n if ctx.guild and ctx.author.voice:\\n if not ctx.guild.me.voice or ctx.author.voice.channel == ctx.guild.me.voice.channel:\\n return True\\n await ctx.reply(\\\"I'm already in another voice channel!\\\")\\n return False\\n await ctx.reply('You must join a server voice channel first!')\\n return False\\n\\n return commands.check(check)\",\n \"def test_switch_channels(self):\\n\\t\\t# not available yet, experimental\\n\\t\\tpass\",\n \"def _fixupChannels(self):\\n\\n # Add extra disabled channels as needed\\n for index, ch in enumerate(self.channels):\\n ch.index = index # fixup indexes\\n\\n self._fillChannels()\",\n \"async def notify_channels(message: discord.Message, *channels: discord.Channel):\\n if message.server.id not in twitch_config.data[\\\"servers\\\"]:\\n twitch_config.data[\\\"servers\\\"][message.server.id] = {}\\n\\n twitch_config.data[\\\"servers\\\"][message.server.id][\\\"notify_channels\\\"] = [c.id for c in channels]\\n twitch_config.save()\\n\\n # Tell the user if notifications were disabled\\n assert channels, \\\"**Disabled stream notifications in this server.**\\\"\\n\\n await client.say(message, \\\"**Notifying streams in:** {}\\\".format(utils.format_objects(*channels, sep=\\\" \\\")))\",\n \"def on_enable(self) -> None:\\n if self._send_notification_func:\\n self._send_notification_func()\\n self._send_notification_func = None\",\n \"def setup(self, channels):\\n self.channels = channels[:]\",\n \"async def send_commands(ans: Message):\\n await ans.answer(all_commands)\",\n \"def on_enable(self) -> None:\\n if self.app.state == self.app.States.clean:\\n self._start_notification_cycle()\",\n \"async def _guild(self, ctx):\\n if await self.config.guild(ctx.guild).guild():\\n await self.config.guild(ctx.guild).guild.set(False)\\n msg = _(\\\"Okay, I will not react to messages \\\" \\\"containing server emojis!\\\")\\n await ctx.send(msg)\\n else:\\n await self.config.guild(ctx.guild).guild.set(True)\\n msg = _(\\\"Okay, I will react to messages \\\" \\\"containing server emojis!\\\")\\n await ctx.send(msg)\",\n \"async def set_channel(self, ctx, *, channel: discord.Channel=None):\\n\\n server = ctx.message.server\\n\\n temp = self.bot.dota_ticker_settings.get(server.id)\\n\\n if temp is None or not temp['enabled']:\\n await self.bot.say('The match ticker has not been enabled on this server.')\\n return\\n\\n if channel is None:\\n await self.bot.say('No channel name or mention received.')\\n return\\n\\n settings = {'enabled': True, 'channel_id': channel.id}\\n\\n await self.bot.dota_ticker_settings.put(server.id, settings)\\n await self.bot.say('The match ticker has been enabled on {0.mention}.'.format(channel))\",\n \"async def test_force_voice_sync_no_channel(self):\\n await self.cog.cog_load()\\n\\n channel = MockVoiceChannel(guild=MockGuild(afk_channel=None))\\n new_channel = MockVoiceChannel(delete=AsyncMock())\\n channel.guild.create_voice_channel.return_value = new_channel\\n\\n await self.cog._force_voice_sync(channel)\\n\\n # Check channel creation\\n overwrites = {\\n channel.guild.default_role: PermissionOverwrite(speak=False, connect=False, view_channel=False)\\n }\\n channel.guild.create_voice_channel.assert_awaited_once_with(\\\"mute-temp\\\", overwrites=overwrites)\\n\\n # Check bot deleted channel\\n new_channel.delete.assert_awaited_once()\",\n \"async def guild(ctx):\\n print(ctx.channel)\\n if ctx.channel.name.lower() in channels:\\n await ctx.send(f\\\"\\\"\\\"guild: {ctx.guild.name}\\\"\\\"\\\")\",\n \"def setinactive(self, irc, msg, args, channel):\\n res = self._checkDBhasChannel(channel)\\n if res is True:\\n SQL = 'UPDATE registry SET isActive = ? WHERE channel = ?'\\n SQLargs = (0, channel)\\n self._SQLexec(SQL, SQLargs)\\n self.lock.acquire()\\n for x in range(0, len(self.channelscontrol)):\\n v0 = str(self.channelscontrol[x][0])\\n if v0 == channel:\\n self.channelscontrol[x][1] = 0\\n self.lock.release()\\n irc.reply(\\\"Channel set inactive.\\\", prefixNick=True)\\n else:\\n irc.reply(\\\"Channel does not exist in DB.\\\", prefixNick=True)\",\n \"async def katıl(con,channel=None):\\r\\n check = str(con.message.channel)\\r\\n\\r\\n if check == 'Direct Message with {}'.format(con.message.author.name):#COMMAND IS IN DM\\r\\n await bot.send_message(con.message.channel, \\\"**You must be in a `server voice channel` to use this command**\\\")\\r\\n\\r\\n if check != 'Direct Message with {}'.format(con.message.author.name):#COMMAND NOT IN DM\\r\\n voice_status = bot.is_voice_connected(con.message.server)\\r\\n\\r\\n if voice_status == False:#VOICE NOT CONNECTED\\r\\n await bot.join_voice_channel(con.message.author.voice.voice_channel)\\r\\n\\r\\n if voice_status == True:#VOICE ALREADY CONNECTED\\r\\n await bot.send_message(con.message.channel, \\\"**Bot zaten bir kanala bağlı !**\\\")\",\n \"def enable(self):\\n if not self.labExperiment:\\n super().enable()\\n else:\\n self.connection.command('open_dm', self.DMserial)\\n status = self.connection.query('get_status')\\n assert status == 0, 'Error connecting to DM. Error: ' + str(status)\\n numActProfile = self.connection.query('num_actuators')\\n assert numActProfile == self.numActProfile, 'Wrong number of profile actuators entered'\\n print(\\\"'BM1k' is now enabled\\\")\",\n \"async def async_turn_on(self) -> None:\\n if CONF_POWER_COMMAND_TOPIC in self._config:\\n mqtt_payload = self._command_templates[CONF_POWER_COMMAND_TEMPLATE](\\n self._config[CONF_PAYLOAD_ON]\\n )\\n await self._publish(CONF_POWER_COMMAND_TOPIC, mqtt_payload)\\n return\\n # Fall back to default behavior without power command topic\\n await super().async_turn_on()\",\n \"async def update(self, ctx, channel):\\n m = ctx.message\\n g = await self.helpers.get_record('server', m.guild.id)\\n a = m.author\\n if not a.bot:\\n if channel.startswith('#'):\\n channel = channel[1:]\\n elif channel.startswith('<#') and len(m.channel_mentions)>0:\\n channel = m.channel_mentions[0].name\\n channel = await self.helpers.choose_channel(ctx, m.guild, channel)\\n if not channel:\\n asyncio.ensure_future(ctx.send('Sorry, I could not find a channel with that name.'))\\n return\\n role = next((r for r in a.guild.roles if r.id == g['roles'].get('updates')), None)\\n was_false = False\\n try:\\n if role.mentionable == False:\\n was_false = True\\n await role.edit(mentionable=True)\\n pfx = await self.bot.get_prefix(m)\\n mc = m.content\\n passed = 0\\n while not passed:\\n for p in pfx:\\n if mc.startswith(p):\\n mc = mc[len(p):]\\n passed = 1\\n mc = re.sub(r'^([^\\\\s]+)','',mc).strip()\\n mc = re.sub(r'^([^\\\\s]+)','',mc.strip()).strip()\\n mc = f'{mc}\\\\n\\\\n{role.mention}'\\n send_to = await channel.send(mc)\\n elif role.mentionable == True:\\n await ctx.send('Oops, looks like the role can be abused.'\\n '\\\\nSet mentionable to off before doing an update command.')\\n if was_false:\\n await role.edit(mentionable=False)\\n asyncio.ensure_future(ctx.send('Successfully posted the update!'))\\n except discord.Forbidden:\\n asyncio.ensure_future(ctx.send(f'I cannot toggle the updates role to be mentioned.'\\n '\\\\nI would suggest placing my role above `{role.name}`'))\",\n \"async def before_resend_post(self):\\n await self.bot.wait_until_ready()\\n self.aviso_channels = [self.bot.get_channel(\\n int(channel_id)) for channel_id in self.bot.CONF[\\\"channels_id\\\"]]\",\n \"async def hockey_commands(self, ctx: commands.Context) -> None:\\n pass\",\n \"def enable(self):\\n pass\",\n \"async def active(self, ctx: commands.Context, toggle: bool = None):\\n guild = ctx.guild\\n tog = self.config.guild(guild)\\n role_config = [\\n await tog.temprole(),\\n await tog.autoroles(),\\n ]\\n if not role_config[1]:\\n role_config[1] = None\\n if toggle is None:\\n message = \\\"The Verification settings is set to {}.\\\".format(await tog.toggle())\\n if role_config.count(None) == 2 and await tog.toggle():\\n await tog.toggle.set(False)\\n message = (\\n \\\"I have disabled verification since roles instructions has been \\\"\\n \\\"removed. Check settings for more informations\\\"\\n )\\n return await ctx.send(message)\\n\\n if role_config.count(None) == 2:\\n return await ctx.send(\\n \\\"I am missing informations; I don't know if I should either give a temprorary \\\"\\n \\\"role while verifying or give a role after the verification.\\\"\\n )\\n await tog.toggle.set(toggle)\\n await ctx.send(\\\"Verification settings is now set to {choice}.\\\".format(choice=toggle))\",\n \"async def unlock(ctx):\\n member = ctx.message.author\\n channel = ctx.message.channel\\n\\n if (channel.category.name in [\\\"beta\\\", \\\"staff\\\", \\\"Pi-Bot\\\"]):\\n return await ctx.send(\\\"This command is not suitable for this channel because of its category.\\\")\\n\\n if (channel.category.name == CATEGORY_SO or channel.category.name == CATEGORY_GENERAL):\\n await ctx.send(\\\"Synced permissions with channel category.\\\")\\n return await channel.edit(sync_permissions=True)\\n\\n member_role = discord.utils.get(member.guild.roles, name=ROLE_MR)\\n if (channel.category.name != CATEGORY_STATES):\\n await ctx.channel.set_permissions(member_role, add_reactions=True, send_messages=True, read_messages=True)\\n else:\\n await ctx.channel.set_permissions(member_role, add_reactions=True, send_messages=True)\\n\\n wiki_role = discord.utils.get(member.guild.roles, name=ROLE_WM)\\n gm_role = discord.utils.get(member.guild.roles, name=ROLE_GM)\\n aRole = discord.utils.get(member.guild.roles, name=ROLE_AD)\\n bRole = discord.utils.get(member.guild.roles, name=ROLE_BT)\\n await ctx.channel.set_permissions(wiki_role, add_reactions=True, send_messages=True, read_messages=True)\\n await ctx.channel.set_permissions(gm_role, add_reactions=True, send_messages=True, read_messages=True)\\n await ctx.channel.set_permissions(aRole, add_reactions=True, send_messages=True, read_messages=True)\\n await ctx.channel.set_permissions(bRole, add_reactions=True, send_messages=True, read_messages=True)\\n await ctx.send(\\\"Unlocked the channel to Member access. Please check if permissions need to be synced.\\\")\",\n \"async def _antiadv(self, ctx):\\r\\n serverid = ctx.message.server.id\\r\\n if ctx.invoked_subcommand is None:\\r\\n await send_cmd_help(ctx)\\r\\n if serverid not in self.adkillr:\\r\\n self.adkillr[serverid] = {'toggle': False, 'message': '{0.mention} don\\\\'t send links!', 'filters': []}\\r\\n dataIO.save_json(\\\"data/adkillr/adkillr.json\\\", self.adkillr)\",\n \"async def _list(self, ctx):\\n config = await self.config.guild(ctx.guild).channels()\\n data = [self.bot.get_channel(x).mention for x in config]\\n if ctx.channel.id in config:\\n destination = ctx.author\\n else:\\n destination = ctx\\n if not data:\\n return await destination.send(\\\"There are no channels.\\\")\\n await destination.send(\\\", \\\".join(data))\",\n \"async def togglechannel(self, ctx, channel):\\r\\n\\r\\n user = ctx.message.author\\r\\n channel = await commands.clean_content().convert(ctx, channel)\\r\\n await ctx.message.delete()\\r\\n\\r\\n if channel == \\\"nsfw\\\":\\r\\n\\r\\n if self.bot.nsfw_role in user.roles:\\r\\n await user.remove_roles(self.bot.nsfw_role)\\r\\n await user.send(\\\"Access to NSFW channels revoked.\\\")\\r\\n else:\\r\\n await user.add_roles(self.bot.nsfw_role)\\r\\n await user.send(\\\"Access to NSFW channels granted.\\\")\\r\\n else:\\r\\n await user.send(\\\"{} is not a togglable channel.\\\".format(channel))\",\n \"def slackbot(self, *args, **kwargs):\\n while True:\\n self.slack.api_connect()\",\n \"async def async_turn_on_when_active(self, **kwargs: Any) -> None:\\n raise NotImplementedError\",\n \"def joinedChannel(self, channel, users):\\n pass\",\n \"async def kickoldusers(self, ctx):\\n s = db.session()\\n\\n for member in self.bot.get_guild(CONFIG.server).members:\\n\\n message = s.query(db.Message).filter(db.Message.author == member.id).first()\\n if not message:\\n if not member.bot:\\n server = self.bot.get_guild(CONFIG.server)\\n\\n print(server.system_channel)\\n inviteLink = await self.bot.get_guild(CONFIG.server).system_channel.create_invite(xkcd=True,\\n max_uses=1)\\n\\n await ctx.send(\\\"No messages from \\\" + member.name)\\n await member.send(\\n \\\"You have been kicked from Inferno games because you have not posted anything for a while\\\")\\n await member.send(\\\"If you feel this was a mistake, or if you wish to become active again you can rejoin by clicking \\\" + str(inviteLink))\\n await member.kick()\\n s.close()\",\n \"async def ccallow(self, ctx, channel: discord.TextChannel):\\n channel_list = await self.config.guild(ctx.guild).channel_deny()\\n if channel.id in channel_list:\\n channel_list.remove(channel.id)\\n else:\\n return await ctx.send(\\\"Channel is not on the deny list.\\\")\\n await self.config.guild(ctx.guild).channel_deny.set(channel_list)\\n await ctx.send(f\\\"{channel.mention} will be allowed for chatchart use.\\\")\",\n \"async def aoc_subscribe(self, ctx: commands.Context) -> None:\\n if ctx.channel.id != settings.aoc.channel_id:\\n await ctx.send(f\\\"Please use the <#{settings.aoc.channel_id}> channel\\\")\\n return\\n\\n role = ctx.guild.get_role(settings.aoc.role_id)\\n unsubscribe_command = f\\\"{ctx.prefix}{ctx.command.root_parent} unsubscribe\\\"\\n\\n if role not in ctx.author.roles:\\n await ctx.author.add_roles(role)\\n await ctx.send(\\n \\\"Okay! You have been __subscribed__ to notifications about new Advent of Code tasks. \\\"\\n f\\\"You can run `{unsubscribe_command}` to disable them again for you.\\\"\\n )\\n else:\\n await ctx.send(\\n \\\"Hey, you already are receiving notifications about new Advent of Code tasks. \\\"\\n f\\\"If you don't want them any more, run `{unsubscribe_command}` instead.\\\"\\n )\",\n \"async def async_turn_on(self, **kwargs: Any) -> None:\\n await self.async_turn_on_off(True)\",\n \"def enable_freeplay_cube_lights(self, enable=True):\\n if enable:\\n self._set_cube_light_state(True)\\n self._enable_cube_sleep(False, False)\\n else:\\n self._enable_cube_sleep(True, True)\\n self._set_cube_light_state(False)\",\n \"def enable(self) -> None:\",\n \"def cmd_enable(self, app_name=None):\\n rc = self.socket_command_with_project('enable', app_name)\\n return rc\",\n \"async def on_ready():\\n await bot.change_presence(status=discord.Status.online, activity=discord.Game(activity))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67751026","0.6625209","0.65478224","0.6417928","0.6308489","0.610151","0.60823476","0.5960483","0.5899421","0.5865763","0.5858996","0.58107245","0.580726","0.5788629","0.56712127","0.56574357","0.5640026","0.5632828","0.56324667","0.5491965","0.5487777","0.5460501","0.53856504","0.5375579","0.5361948","0.53510016","0.53294945","0.5323976","0.5307654","0.53053236","0.5294086","0.52887547","0.5273635","0.5272416","0.5270729","0.5263645","0.5249834","0.52458054","0.5238229","0.5229098","0.521966","0.52119553","0.51837945","0.5174677","0.5168641","0.5167749","0.5147641","0.5135227","0.5119572","0.5109995","0.5100038","0.5098255","0.50931036","0.50876915","0.5083672","0.5080926","0.5077113","0.5066866","0.5064708","0.5057918","0.5056153","0.5052001","0.5048911","0.5046145","0.50350416","0.50280565","0.50211674","0.501853","0.5013926","0.50119674","0.5009944","0.50099367","0.50088274","0.49876854","0.49866146","0.49859917","0.49843055","0.49792323","0.4971737","0.49698457","0.49552295","0.49511176","0.49478614","0.49443612","0.4939758","0.49368444","0.49288353","0.49226052","0.4922523","0.49110878","0.4909087","0.49012452","0.49006915","0.48929065","0.4892776","0.4890192","0.4886106","0.48819223","0.48800886","0.48792046"],"string":"[\n \"0.67751026\",\n \"0.6625209\",\n \"0.65478224\",\n \"0.6417928\",\n \"0.6308489\",\n \"0.610151\",\n \"0.60823476\",\n \"0.5960483\",\n \"0.5899421\",\n \"0.5865763\",\n \"0.5858996\",\n \"0.58107245\",\n \"0.580726\",\n \"0.5788629\",\n \"0.56712127\",\n \"0.56574357\",\n \"0.5640026\",\n \"0.5632828\",\n \"0.56324667\",\n \"0.5491965\",\n \"0.5487777\",\n \"0.5460501\",\n \"0.53856504\",\n \"0.5375579\",\n \"0.5361948\",\n \"0.53510016\",\n \"0.53294945\",\n \"0.5323976\",\n \"0.5307654\",\n \"0.53053236\",\n \"0.5294086\",\n \"0.52887547\",\n \"0.5273635\",\n \"0.5272416\",\n \"0.5270729\",\n \"0.5263645\",\n \"0.5249834\",\n \"0.52458054\",\n \"0.5238229\",\n \"0.5229098\",\n \"0.521966\",\n \"0.52119553\",\n \"0.51837945\",\n \"0.5174677\",\n \"0.5168641\",\n \"0.5167749\",\n \"0.5147641\",\n \"0.5135227\",\n \"0.5119572\",\n \"0.5109995\",\n \"0.5100038\",\n \"0.5098255\",\n \"0.50931036\",\n \"0.50876915\",\n \"0.5083672\",\n \"0.5080926\",\n \"0.5077113\",\n \"0.5066866\",\n \"0.5064708\",\n \"0.5057918\",\n \"0.5056153\",\n \"0.5052001\",\n \"0.5048911\",\n \"0.5046145\",\n \"0.50350416\",\n \"0.50280565\",\n \"0.50211674\",\n \"0.501853\",\n \"0.5013926\",\n \"0.50119674\",\n \"0.5009944\",\n \"0.50099367\",\n \"0.50088274\",\n \"0.49876854\",\n \"0.49866146\",\n \"0.49859917\",\n \"0.49843055\",\n \"0.49792323\",\n \"0.4971737\",\n \"0.49698457\",\n \"0.49552295\",\n \"0.49511176\",\n \"0.49478614\",\n \"0.49443612\",\n \"0.4939758\",\n \"0.49368444\",\n \"0.49288353\",\n \"0.49226052\",\n \"0.4922523\",\n \"0.49110878\",\n \"0.4909087\",\n \"0.49012452\",\n \"0.49006915\",\n \"0.48929065\",\n \"0.4892776\",\n \"0.4890192\",\n \"0.4886106\",\n \"0.48819223\",\n \"0.48800886\",\n \"0.48792046\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73707277"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18095,"cells":{"query":{"kind":"string","value":"JSON resolver plugin that loads the schema endpoint. Injected into InvenioRecords JSON resolver."},"document":{"kind":"string","value":"def jsonresolver_loader(url_map):\n url_map.add(Rule(\n \"/api/taxonomies/<string:code>/<path:slug>\",\n endpoint=get_taxonomy_term,\n host=current_app.config.get('SERVER_NAME')\n ))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _load_schema(self, json_schema):\n # use jsonrefs to resolve all $refs in json\n data = jsonref.loads(json.dumps(json_schema))\n return self.__initialise_template(data)","def resolver():\n if RESOLVER:\n return RESOLVER\n path = str(pathlib.Path(__file__).parents[1].joinpath(\"schema\", \"app.json\"))\n with open(path) as stream:\n schema = json.load(stream)\n globals()[\"RESOLVER\"] = RefResolver(\n \"https://schema.timeflux.io/app.json\", None\n ).from_schema(schema)\n return RESOLVER","def __init__(self, schema ):\n self.schema = schema","def schema_handler(self, schema):\n dict_for_render = schema.get('properties', {}).items()\n if schema.get('$ref', None):\n def_name = schema.get('$ref').split('/')[-1]\n dict_for_render = self.definitions[def_name].get('properties', {}).items()\n elif schema.get('properties', None) is None:\n return ''\n\n answer_dict = {}\n json_dict = {}\n for opt_name, opt_value in dict_for_render:\n var_type = opt_value.get('format', None) or opt_value.get('type', None) or 'object'\n json_name = self.indent + f':jsonparameter {var_type} {opt_name}:'\n json_dict[json_name] = self.get_json_props_for_response(var_type, opt_value)\n\n answer_dict[opt_name] = self.get_response_example(opt_name, var_type, opt_value)\n if var_type == 'string':\n answer_dict[opt_name] = answer_dict[opt_name].format(opt_name)\n\n self.write('')\n for line in json.dumps(answer_dict, indent=4).split('\\n'):\n self.write(line, self.indent_depth)\n\n self.write('')\n for json_param_name, json_param_value in json_dict.items():\n desc = f'{json_param_value[\"title\"]}{json_param_value[\"props_str\"]}' or 'None'\n self.write(json_param_name + ' ' + desc)","def load_data(schema):\n\n def wrapper(func):\n @functools.wraps(func)\n def second_wrapper(*args, **kwargs):\n data = schema().load(request.get_json()).data\n return func(data=data, *args, **kwargs)\n\n return second_wrapper\n\n return wrapper","def schema(self):\n # NOTE This is exactly the same as the other thing.\n return {\n \"$id\": f\"{self.request.resource_url(self)}#schema\",\n \"type\": \"object\",\n \"properties\": {\n \"foo\": {\"type\": \"string\"},\n # generated fields shouldn't be submitted or in forms\n \"url\": {\"type\": \"string\", \"generated\": True},\n }\n }","def _load_json_schema(filename):\n\n relative_path = join(\"schemas\", filename)\n absolute_path = join(dirname(__file__), relative_path)\n\n base_path = dirname(absolute_path)\n base_uri = 'file://{}/'.format(base_path)\n\n print(f\"base uri {base_uri}\")\n print(f\"base path {base_path}\")\n print(f\"relative_path {relative_path}\")\n print(f\"absolute_path {absolute_path}\")\n\n with open(absolute_path) as schema_file:\n return jsonref.loads(schema_file.read(), base_uri=base_uri, jsonschema=True)","def __init__(self, schema=None):\n self.schema = schema or {}","def load_request_data(schema):\n\n def wrapper(func):\n @wraps(func)\n def decorated_func(*args, **kwargs):\n data = schema().load(request.get_json())\n return func(data=data, *args, **kwargs)\n\n return decorated_func\n\n return wrapper","def resolve_schema(self, data):\n if not isinstance(data, dict):\n return\n\n # OAS 2 component or OAS 3 header\n if \"schema\" in data:\n data[\"schema\"] = self.openapi.resolve_schema_dict(data[\"schema\"])\n # OAS 3 component except header\n if self.openapi_version.major >= 3:\n if \"content\" in data:\n for content_type in data[\"content\"]:\n schema = data[\"content\"][content_type][\"schema\"]\n data[\"content\"][content_type][\n \"schema\"\n ] = self.openapi.resolve_schema_dict(schema)","def load_schema(self):\n\n schema = {\n \"type\": \"object\",\n \"properties\": {}\n }\n\n msd = self.parse_xml(self.schema_path)\n for concept in msd.findall('.//Concept'):\n concept_id = self.alter_key(concept.attrib['id'])\n self.add_item_to_field_order(concept_id)\n concept_name = concept.find('./Name').text\n concept_description = concept.find('./Description').text\n parent = concept.find('./Parent/Ref')\n key_parts = [concept_id, concept_id] if parent is None else [parent.attrib['id'], concept_id]\n translation_key = '.'.join(key_parts)\n jsonschema_field = {\n 'type': ['string', 'null'],\n 'title': concept_name,\n 'description': concept_description,\n 'translation_key': translation_key,\n }\n if self.scope is not None:\n jsonschema_field['scope'] = self.scope\n schema['properties'][concept_id] = jsonschema_field\n\n self.schema = schema","def load_schema(path, collection, readonly):\n return JSONStorage(path, collection, readonly)","def fetch_schema(self) -> None:\n if self.schema_file:\n logger.info(\"Loaded schema from file '%s'\", self.schema_file)\n self._schema = load_schema_file(self.schema_file)\n else:\n url = self.schema_url or urljoin(self.base_url, \"schema/openapi.yaml\")\n logger.info(\"Fetching schema at '%s'\", url)\n self._schema = schema_fetcher.fetch(url, {\"v\": \"3\"})","def schema_helper(self, name, _, schema=None, **kwargs):\n if schema is None:\n return None\n\n schema_instance = resolve_schema_instance(schema)\n\n schema_key = make_schema_key(schema_instance)\n self.warn_if_schema_already_in_spec(schema_key)\n self.openapi.refs[schema_key] = name\n\n json_schema = self.openapi.schema2jsonschema(schema_instance)\n\n return json_schema","def load_json_schema(filename):\n relative_path = join('../schema', filename)\n absolute_path = join(dirname(__file__), relative_path)\n\n base_path = dirname(absolute_path)\n base_uri = 'file://{}/'.format(base_path)\n\n with open(absolute_path) as schema_file:\n return jsonref.loads(\n schema_file.read(), base_uri=base_uri, jsonschema=True)","def json_mapper(self, sid_ref_id, json_schema_url, **kwargs):\n expected_json_values = self.create_value.get_db_values(sid_ref_id, **kwargs)\n json_data_template = self.get_data_template(json_schema_url)\n return result_formatter(expected_json_values,reference=cilKeyReference.sid_soi_reference)","def endpoint_schema(endpoint, extra_definitions={}):\n # load common schema template and update metadata\n schema = common.load_json(\"./templates/provider/endpoint.json\")\n schema[\"$id\"] = schema[\"$id\"].replace(\"endpoint.json\", f\"{endpoint}.json\")\n schema[\"title\"] = schema[\"title\"].replace(\"endpoint\", endpoint)\n\n # merge custom definitions with relevant common definitions\n definitions = common.load_definitions(\n \"string\",\n \"timestamp\",\n \"uuid\",\n \"version\",\n common.MDS_FEATURE_POINT\n )\n definitions.update(common.point_definition())\n definitions.update(extra_definitions)\n\n endpoint_schema = common.load_json(f\"./templates/provider/{endpoint}.json\")\n\n # for all but stops, merge standard vehicle info with items schema\n if endpoint not in [\"stops\"]:\n items = endpoint_schema[endpoint][\"items\"]\n vehicle = common.vehicle_definition()\n items[\"required\"] = vehicle[\"required\"] + items[\"required\"]\n items[\"properties\"] = { **vehicle[\"properties\"], **items[\"properties\"] }\n definitions.update(common.load_definitions(\"propulsion_type\", \"propulsion_types\", \"vehicle_type\"))\n\n # merge endpoint schema into the endpoint template\n data_schema = schema[\"properties\"][\"data\"]\n data_schema[\"required\"] = [endpoint]\n data_schema[\"properties\"] = endpoint_schema\n\n # insert definitions\n schema[\"definitions\"].update(definitions)\n\n return schema","def load_resolved_schema(spec_path, file_name=None, schema_obj=None, path_prefix=True):\r\n\r\n # Only one of file_name or schema_obj must be set\r\n assert bool(file_name) != bool(schema_obj)\r\n\r\n if path_prefix:\r\n spec_path = os.path.join(spec_path, \"APIs/schemas/\")\r\n base_path = os.path.abspath(spec_path)\r\n if not base_path.endswith(\"/\"):\r\n base_path = base_path + \"/\"\r\n if os.name == \"nt\":\r\n base_uri_path = \"file:///\" + base_path.replace('\\\\', '/')\r\n else:\r\n base_uri_path = \"file://\" + base_path\r\n\r\n loader = jsonref.JsonLoader(cache_results=False)\r\n\r\n if file_name:\r\n json_file = str(Path(base_path) / file_name)\r\n with open(json_file, \"r\") as f:\r\n schema = jsonref.load(f, base_uri=base_uri_path, loader=loader, jsonschema=True)\r\n elif schema_obj:\r\n # Work around an exception when there's nothing to resolve using an object\r\n if \"$ref\" in schema_obj:\r\n schema = jsonref.JsonRef.replace_refs(schema_obj, base_uri=base_uri_path, loader=loader, jsonschema=True)\r\n else:\r\n schema = schema_obj\r\n\r\n return schema","def resolve_nested_schema(self, schema):\n try:\n schema_instance = resolve_schema_instance(schema)\n # If schema is a string and is not found in registry,\n # assume it is a schema reference\n except marshmallow.exceptions.RegistryError:\n return schema\n schema_key = make_schema_key(schema_instance)\n if schema_key not in self.refs:\n name = self.schema_name_resolver(schema)\n if not name:\n try:\n json_schema = self.schema2jsonschema(schema_instance)\n except RuntimeError:\n raise APISpecError(\n \"Name resolver returned None for schema {schema} which is \"\n \"part of a chain of circular referencing schemas. Please\"\n \" ensure that the schema_name_resolver passed to\"\n \" MarshmallowPlugin returns a string for all circular\"\n \" referencing schemas.\".format(schema=schema)\n )\n if getattr(schema, \"many\", False):\n return {\"type\": \"array\", \"items\": json_schema}\n return json_schema\n name = get_unique_schema_name(self.spec.components, name)\n self.spec.components.schema(name, schema=schema)\n return self.get_ref_dict(schema_instance)","def _patch_schema(self):\n fields = get_json()['data']['attributes'].keys()\n return make_entity_schema(\n self.SCHEMA, self.RESOURCE_NAME,\n make_data_schema(\n self.SCHEMA, id_required=True,\n only=fields, partial=True\n )\n )","def _load_json_schema(filename):\n\n relative_path = join('schemas', filename)\n absolute_path = join(dirname(__file__), relative_path)\n\n with open(absolute_path) as schema_file:\n return json.loads(schema_file.read())","def schema(self):\n # TODO The schema of a container resource...\n # This is the same as the leaf.\n # However, this isn't actually the schema of the response\n return {\n \"$id\": f\"{self.request.resource_url(self)}#schema\",\n \"type\": \"object\",\n \"properties\": {\n \"foo\": {\"type\": \"string\"},\n # generated fields shouldn't be submitted or in forms\n \"url\": {\"type\": \"string\", \"generated\": True},\n }\n }","def decide_schema(self, json_data):\n pass","def decide_schema(self, json_data):\n pass","def testSchemaLoadingAsString(self):\n api = self.ApiFromDiscoveryDoc('latitude.v1.json')\n self.assertEquals(4, len(api._schemas))","def pyang_plugin_init():\n plugin.register_plugin(SwaggerPlugin())","def schema(self):","def get_parser_context(self, http_request):\n res = super().get_parser_context(http_request)\n res['json_schema'] = self.create_payload_schema\n return res","def response(schema):\n def _response(function):\n @wraps(function)\n def wrapper(*args, **kwargs):\n if issubclass(schema, BaseModel):\n has_root = True if '__root__' in schema.__fields__ else False\n function_res = function(*args, **kwargs)\n\n if not function_res:\n if has_root is True:\n return jsonify([])\n return jsonify({})\n\n if type(function_res) == list:\n res = schema.parse_obj(function_res)\n else:\n res = schema.from_orm(function_res)\n\n res = res.dict()\n\n if has_root is True:\n return jsonify(res['__root__'])\n\n return jsonify(res)\n elif isinstance(schema, dict):\n return jsonify(schema)\n else:\n raise CustomException('invalid response type', code=400)\n\n return wrapper\n return _response","def parse(self, stream, media_type=None, parser_context=None):\n\n result = super(parsers.JSONParser, self).parse(\n stream, media_type=media_type, parser_context=parser_context\n )\n if not isinstance(result, dict) or \"data\" not in result:\n raise ParseError(\"Received document does not contain primary data\")\n\n data = result.get(\"data\")\n view = parser_context[\"view\"]\n\n from rest_framework_json_api.views import RelationshipView\n\n if isinstance(view, RelationshipView):\n # We skip parsing the object as JSONAPI Resource Identifier Object and not a regular\n # Resource Object\n if isinstance(data, list):\n for resource_identifier_object in data:\n if not (\n resource_identifier_object.get(\"id\")\n and resource_identifier_object.get(\"type\")\n ):\n raise ParseError(\n \"Received data contains one or more malformed JSONAPI \"\n \"Resource Identifier Object(s)\"\n )\n elif not (data.get(\"id\") and data.get(\"type\")):\n raise ParseError(\n \"Received data is not a valid JSONAPI Resource Identifier Object\"\n )\n\n return data\n\n request = parser_context.get(\"request\")\n\n # Sanity check\n if not isinstance(data, dict):\n raise ParseError(\n \"Received data is not a valid JSONAPI Resource Identifier Object\"\n )\n\n # Check for inconsistencies\n if request.method in (\"PUT\", \"POST\", \"PATCH\", \"DELETE\"):\n resource_name = utils.get_resource_name(\n parser_context, expand_polymorphic_types=True\n )\n if isinstance(resource_name, str):\n if data.get(\"type\") != resource_name:\n raise exceptions.Conflict(\n \"The resource object's type ({data_type}) is not the type that \"\n \"constitute the collection represented by the endpoint \"\n \"({resource_type}).\".format(\n data_type=data.get(\"type\"), resource_type=resource_name\n )\n )\n else:\n if data.get(\"type\") not in resource_name:\n raise exceptions.Conflict(\n \"The resource object's type ({data_type}) is not the type that \"\n \"constitute the collection represented by the endpoint \"\n \"(one of [{resource_types}]).\".format(\n data_type=data.get(\"type\"),\n resource_types=\", \".join(resource_name),\n )\n )\n if not data.get(\"id\") and request.method in (\"PATCH\", \"PUT\", \"DELETE\"):\n raise ParseError(\n \"The resource identifier object must contain an 'id' member\"\n )\n\n if request.method in (\"PATCH\", \"PUT\", \"DELETE\"):\n lookup_url_kwarg = getattr(view, \"lookup_url_kwarg\", None) or getattr(\n view, \"lookup_field\", None\n )\n if lookup_url_kwarg and str(data.get(\"id\")) != str(\n view.kwargs[lookup_url_kwarg]\n ):\n raise exceptions.Conflict(\n \"The resource object's id ({data_id}) does not match url's \"\n \"lookup id ({url_id})\".format(\n data_id=data.get(\"id\"), url_id=view.kwargs[lookup_url_kwarg]\n )\n )\n\n # Construct the return data\n serializer_class = getattr(view, \"serializer_class\", None)\n parsed_data = {\"id\": data.get(\"id\")} if \"id\" in data else {}\n # `type` field needs to be allowed in none polymorphic serializers\n if serializer_class is not None:\n if issubclass(serializer_class, serializers.PolymorphicModelSerializer):\n parsed_data[\"type\"] = data.get(\"type\")\n parsed_data.update(self.parse_attributes(data))\n parsed_data.update(self.parse_relationships(data))\n parsed_data.update(self.parse_metadata(result))\n return parsed_data","def _load_schemas(self) -> None:\n schema_paths = self._root.rglob(\"*.json\")\n for schema_path in schema_paths:\n schema = json.loads(schema_path.read_text())\n\n if self._suffix:\n schema[\"name\"] = f'{schema[\"name\"]}{self._suffix}'\n\n fqn = get_avro_fqn(schema)\n self.schemas[fqn] = schema","def _json_import(self, imppath):\n # TODO: Settle on JSON format for colortable\n pass","def _get_schema_from_object(self, data):\n if \"items\" in data:\n return self._get_schema_from_object(data[\"items\"])\n\n url_key = None\n\n if '$id' in data:\n url_key = '$id'\n\n if 'id' in data:\n url_key = 'id'\n\n if url_key:\n url = data[url_key]\n schema = Schema().build()\n schema.domain_entity = self.get_domain_entity_from_url(url)\n schema.high_level_entity = self.get_high_level_entity_from_url(url)\n schema.module = self.get_module_from_url(url)\n schema.url = url\n return schema\n\n return None","def _handle_with_schema_validation(success_handler, schema_dict):\n try:\n schema = Schema(schema_dict)\n args = schema(request.args.to_dict())\n except MultipleInvalid as e:\n return abort(400, 'value at %s failed validation: %s' % (e.path, e.msg))\n result = success_handler(args)\n return jsonify(result)","def __init__(self, schema=None):\n self._dict = {}\n self.schema = schema","def get_schema(self) -> dict:","def schema2jsonschema(self, schema):\n fields = get_fields(schema)\n Meta = getattr(schema, \"Meta\", None)\n partial = getattr(schema, \"partial\", None)\n ordered = getattr(schema, \"ordered\", False)\n\n jsonschema = self.fields2jsonschema(fields, partial=partial, ordered=ordered)\n\n if hasattr(Meta, \"title\"):\n jsonschema[\"title\"] = Meta.title\n if hasattr(Meta, \"description\"):\n jsonschema[\"description\"] = Meta.description\n if hasattr(Meta, \"unknown\") and Meta.unknown != marshmallow.EXCLUDE:\n jsonschema[\"additionalProperties\"] = Meta.unknown == marshmallow.INCLUDE\n\n return jsonschema","def load_schema(name):\r\n\r\n data = pkgutil.get_data(__package__, \"schemas/{0}.json\".format(name))\r\n return json.loads(data.decode(\"utf-8\"))","def generate_url_schema():\n json_str = json.dumps({'fields': [\n {'name': 'url', 'type': 'STRING', 'mode': 'NULLABLE'},\n {'name': 'count', 'type': 'INTEGER', 'mode': 'NULLABLE'},\n {'name': 'ts', 'type': 'TIMESTAMP', 'mode': 'NULLABLE'}]})\n return parse_table_schema_from_json(json_str)","def parse_schema(schemaurl, schema_dir=None):\n if schema_dir:\n try:\n # attempts to open .schema file in directory schema_dir\n local_schema_path = schema_dir + \"/\" + \\\n schemaurl[schemaurl.rfind('/') + 1:-1] + \".schema\"\n print \"Looking for schema in file %s\" % (local_schema_path)\n schema = json.load(open(local_schema_path))\n except Exception as e:\n print \"Couldn't load schema %s from file %s\\n%s\" % (\n schemaurl, schema_dir, str(e))\n return None\n else:\n # load the schema directly from schemaurl, i.e., from the web\n try:\n schema = json.load(urllib2.urlopen(schemaurl))\n except Exception as e:\n print \"Couldn't load schema %s\\n%s\" % (schemaurl, str(e))\n return None\n\n if 'extends' in schema and '$ref' in schema['extends']:\n\n parent_schema = json.load(urllib2.urlopen(schema['extends']['$ref']))\n while (True): # exits loop when no additional extensions (break below)\n for key in sorted(parent_schema.keys()):\n if key not in schema:\n schema[key] = parent_schema[key]\n # need to merge these keys individually\n if key == 'properties':\n for key in sorted(parent_schema['properties'].keys()):\n if key not in schema['properties']:\n schema['properties'][key] = parent_schema[\n 'properties'][key]\n if 'extends' in parent_schema:\n parent_schema = json.load(\n urllib2.urlopen(parent_schema['extends']['$ref']))\n else:\n break\n # essentially a do while loop (exit condition)\n\n return schema","def test_read_json_schema():\n json_schema = os.path.join(TEST_DATA_PATH, 'example_schema.json')\n schema_tree = schema.load_schema(json_schema, resolve_references=True)\n schema.check_schema(schema_tree)","def introspect_schema(cls, endpoint: str, transport: Transporter) -> Dict:\n return request_schema(endpoint, transport.session)","def to_json_schema(cls):\n return parsers.to_json_schema(cls)","def define_idaapi_resolver():\n dn = epydoc.apidoc.DottedName.__init__\n def resolver(piece):\n if piece is not None and isinstance(piece, basestring) and piece.startswith(\"idaapi_\"):\n res = \"idaapi\"\n else:\n res = piece\n log(\"Resolver '%s' => '%s'\" % (piece, res))\n return res\n def wrapper(self, *pieces, **options):\n return dn(self, *map(resolver, pieces), **options);\n epydoc.apidoc.DottedName.__init__ = wrapper","def json_schema(schema_file=None, output=\"-\"):\n schemas = read_yaml(schema_file)\n dump_yaml(output, JsonSchemaConverterFromAccessSchema.convert_schemas(schemas))","def JSONSchema(self, default=None):\n return self.data.get('metadata', {}).get('$schema', default)","def _load(self, list_of_schema_urls):\n for uri in list_of_schema_urls:\n with urllib.request.urlopen(uri) as url:\n data = {}\n try:\n data = json.loads(url.read().decode())\n except:\n print(\"Failed to read schema from \" + uri)\n self._parser._load_schema(data)\n return self","def lookup_table():\n datapackage = {\n 'resources': [\n {\n 'schema': {\n 'fields': [\n {\n 'name': 'foo',\n 'maps_to': 'project_id'\n },\n {\n 'name': 'bar',\n 'maps_to': 'invalid_fiscal_field'\n },\n {\n 'name': 'spam',\n 'maps_to': None\n },\n {\n 'name': 'eggs',\n },\n\n ]\n\n }\n }\n ]\n }\n return build_lookup_table(datapackage)","def _load_json_schema(filename: str):\n relative_path = path.join('schemas', filename)\n absolute_path = path.join(path.dirname(__file__), relative_path)\n\n with open(absolute_path, 'r', encoding='utf-8') as schema_file:\n schema = json.loads(schema_file.read())\n\n return schema","def get_schema():\n if not os.path.isfile(_schema_file):\n create_schema()\n with open(_schema_file, 'r') as fd:\n out = decode_json(fd)\n return out","def validate(schema):\n def decorator(func):\n def wrapper(self, req, resp, *args, **kwargs):\n try:\n raw_json = req.stream.read()\n obj = json.loads(raw_json.decode('utf-8'))\n obj['req_id'] = req.context.get('request_id')\n except Exception:\n raise falcon.HTTPBadRequest(\n title='Invalid data',\n description='Could not properly parse the provided data as JSON',\n code='001'\n )\n\n try:\n jsonschema.validate(obj, schema)\n except jsonschema.ValidationError as e:\n raise falcon.HTTPBadRequest(\n title='Failed data validation',\n description=e.message,\n code='002'\n )\n\n return func(self, req, resp, *args, parsed=obj, **kwargs)\n return wrapper\n return decorator","def opt_engine_rest_api():\n request_json = request.get_json()\n return process_request(request_json)","def load_dde_schemas(self, schema):\n url = DDE_SCHEMA_BASE_URL + schema\n if self.verbose:\n print(f'Loading registered DDE schema from \"{url}\"')\n return load_json_or_yaml(url)[\"source\"]","def schema(self):\n return _parse_schema_resource(self._properties.get(\"schema\", {}))","def load_default_schema(self):\n self.schema = preprocess_schema(load_schemaorg())\n self.schemaorg_schema = self.schema\n if \"@context\" in self.schema:\n self.context.update(self.schema[\"@context\"])\n self.schema_extension_only = self.schema\n self.schemaorg_nx = load_schema_into_networkx(self.schema)\n self.schema_extension_nx = self.schemaorg_nx\n self.schema_nx = self.schemaorg_nx\n isolates = list(nx.isolates(self.schema_nx))\n self.extended_class_only_graph = self.schema_extension_nx.subgraph([node for node, attrdict in self.schema_extension_nx.node.items() if attrdict['type'] == 'Class' and node not in isolates])\n self.full_class_only_graph = self.schema_nx.subgraph([node for node, attrdict in self.schema_nx.node.items() if attrdict['type'] == 'Class'])\n self.property_only_graph = self.schema_nx.subgraph([node for node, attrdict in self.schema_nx.node.items() if attrdict['type'] == 'Property'])\n # instantiate converters for classes and properties\n self._all_class_uris = [node for node,attrdict in self.schema_nx.node.items() if attrdict['type'] in ['Class', 'DataType']]\n self.cls_converter = CurieUriConverter(self.context,\n self._all_class_uris)\n self._all_prop_uris = list(self.property_only_graph.nodes())\n self.prop_converter = CurieUriConverter(self.context,\n self._all_prop_uris)\n self.default_schema_loaded = True","def use_json(request):\r\n request.uri = '{0}.json'.format(request.uri)","def handler(type=None, resource=None, id=None, json_pointer=None,\n app=None, content=None, root=None, content_pointer=None):\n languages = list(app.config.get(\"MULTILINGUAL_SUPPORTED_LANGUAGES\", []))\n\n default_template = app.config.get(\"ELASTICSEARCH_DEFAULT_LANGUAGE_TEMPLATE\", {})\n template = app.config.get(\"ELASTICSEARCH_LANGUAGE_TEMPLATES\", {})\n\n data_dict= dict()\n for language in languages:\n if id is not None:\n language_with_context = language + '#' + id\n if language_with_context in template.keys():\n data_dict[language] = template[language_with_context]\n continue\n data_dict[language] = template.get(language, default_template)\n\n\n return {\n \"type\": \"object\",\n \"properties\": data_dict\n }","def load_dde_schemas(schema, verbose=False):\n url = DDE_SCHEMA_BASE_URL + schema\n if verbose:\n print(f'Loading registered DDE schema from \"{url}\"')\n return load_json_or_yaml(url)[\"source\"]","def validate_against_schema(self, json_doc):\n if self.uri not in self.se.validation:\n raise RuntimeError(\"$validation is not defined for {} field; thus the json document could not be validated\".format(self.name))\n else:\n validate(json_doc, self.se.validation[self.uri])\n print('The JSON document is valid')","def json_friendly(self):","def __init__(self, endpoint: str, transporter: Transporter, settings: Settings, cache: Optional[Cache]):\n self.endpoint = endpoint\n self.transport = transporter\n self.settings = settings\n self.cache = cache\n\n if self.cache is not None:\n schema_introspection = self.cache.retrieve(self.endpoint, SCHEMA_KEY)\n if schema_introspection is None:\n schema_introspection = self.introspect_schema(endpoint, transporter)\n self.cache.store(self.endpoint, SCHEMA_KEY, schema_introspection)\n else:\n schema_introspection = self.introspect_schema(endpoint, transporter)\n\n # graphql schema properties\n self.raw_schema = schema_introspection.get(self.settings.default_response_key, {}).get(\"__schema\", {})\n self.query_type: str = self.parse_query_type(self.raw_schema)\n self.mutation_type: str = self.parse_mutation_type(self.raw_schema)\n self.subscription_type: str = self.parse_subscription_type(self.raw_schema)\n\n self.types: Dict[str, SchemaType] = self.parse_types(self.raw_schema.get(\"types\", []))\n self.directives: Dict[str, Directive] = self.parse_directives(self.raw_schema.get(\"directives\", []))\n\n # custom schema properties\n self.queries: Tuple[Operation] = self.parse_operations(self.query_type)\n self.mutations: Tuple[Operation] = self.parse_operations(self.mutation_type)\n self.subscriptions: Tuple[Operation] = self.parse_operations(self.subscription_type)","def parse_with(schema, arg_name='entity', many=False, **kwargs):\n\n def decorator(f):\n @functools.wraps(f)\n def inner(*fargs, **fkwargs):\n json = request.get_json() or {}\n try:\n entity = schema.load(json,many=many, **kwargs)\n fkwargs.update({arg_name: entity})\n\n except (ValidationError, ValueError, Exception) as e:\n logger.error(\"parse_with: {}\".format(str(e)))\n abort(400, message=\"An error occurred. \\n {}\".format((str(e))))\n\n return f(*fargs, **fkwargs)\n\n return inner\n\n return decorator","def getResolver(self):\n pass;","def load_validation_schema(self) -> t.Dict[str, t.Any]:\n if self._schema is None:\n try:\n self._schema = json.loads(self.schema())\n except KeyError:\n device_type_striped = self._device_type.lower().rstrip(string.digits)\n with open(_CT_FILES[device_type_striped], encoding=\"utf-8\") as file_:\n self._schema = json.load(file_)\n return self._schema # type: ignore","def _convert(self, fn, suffix='json', path='jsonschema', name=None,\n root_class_name=None, data_files=[], target_class=None):\n ie = JsonSchemaImportEngine()\n d = os.path.join(INPUT_DIR, path)\n schema = ie.load(os.path.join(d, f'{fn}.{suffix}'), name=name, format=suffix, root_class_name=root_class_name)\n model_path = os.path.join(OUTPUT_DIR, f'{fn}.yaml')\n write_schema(schema, model_path)\n roundtrip_path = os.path.join(OUTPUT_DIR, f'{fn}.roundtrip.json')\n with open(roundtrip_path, 'w') as stream:\n stream.write(JsonSchemaGenerator(model_path).serialize())\n python_path = os.path.join(OUTPUT_DIR, f'{fn}.py')\n with open(python_path, 'w') as stream:\n stream.write(PythonGenerator(model_path).serialize())\n compile_python(python_path)\n # TODO: test data_files\n return schema","def getSchema(cls):\n pass","def load_schema(self, schema):\n if not self.default_schema_loaded:\n self.load_default_schema()\n # load JSON-LD file of user defined schema\n self.schema_extension_only = preprocess_schema(load_json_or_yaml(schema))\n if \"@context\" in self.schema_extension_only:\n self.context.update(self.schema_extension_only[\"@context\"])\n # convert user defined schema into a networkx DiGraph\n self.schema_extension_nx = load_schema_into_networkx(self.schema_extension_only)\n # update undefined classes/properties\n undefined_nodes = [node for node, attrdict in self.schema_extension_nx.node.items() if not attrdict]\n attr_dict = {}\n \n for _node in undefined_nodes:\n if _node in self.schemaorg_nx.nodes():\n attr_dict[_node] = self.schemaorg_nx.nodes[_node]\n nx.set_node_attributes(self.schema_extension_nx, attr_dict)\n # merge networkx graph of user-defined schema with networkx graph of schema defined by Schema.org\n #self.schema_nx = merge_schema_networkx(self.schemaorg_nx, self.schema_extension_nx)\n self.schema_nx = self.schema_extension_nx\t\n SchemaValidator(self.schema_extension_only, self.schema_nx).validate_full_schema()\n # merge together the given schema and the schema defined by schemaorg\n #self.schema = merge_schema(self.schema_extension_only, self.schemaorg_schema)\n self.schema = self.schemaorg_schema\n # split the schema networkx into individual ones\n isolates = list(nx.isolates(self.schema_nx))\n \n for node, attrdict in self.schema_extension_nx.node.items():\n if not 'type' in attrdict:\n self.schema_extension_nx.nodes[node][\"type\"] = \"Class\" \n for node, attrdict in self.schema_nx.node.items():\n if not 'type' in attrdict:\n self.schema_nx.nodes[node][\"type\"] = \"Class\" \n \n self.extended_class_only_graph = self.schema_extension_nx.subgraph([node for node, attrdict in self.schema_extension_nx.node.items() if attrdict['type'] == 'Class' and node not in isolates])\n self.full_class_only_graph = self.schema_nx.subgraph([node for node, attrdict in self.schema_nx.node.items() if attrdict['type'] == 'Class'])\n self.property_only_graph = self.schema_nx.subgraph([node for node, attrdict in self.schema_nx.node.items() if attrdict['type'] == 'Property'])\n # instantiate converters for classes and properties\n self._all_class_uris = [node for node,attrdict in self.schema_nx.node.items() if attrdict['type'] in ['Class', 'DataType']]\n self.cls_converter = CurieUriConverter(self.context,\n self._all_class_uris)\n self._all_prop_uris = list(self.property_only_graph.nodes())\n self.prop_converter = CurieUriConverter(self.context,\n self._all_prop_uris)","def resolver(schema):\n name = schema.__name__\n if name.endswith(\"Schema\"):\n return name[:-6] or name\n return name","def get_json(schema):\n\n data = request.get_json(force=True, silent=True, cache=False)\n\n message = schema.validate(data)\n\n if message:\n raise BadRequest(message)\n\n return data","def _Dynamic_GetSchema(self, req, schema, request_id=None):\n # This is not used, but it is required for the method signature.\n del request_id\n\n app_str = req.app()\n self.__ValidateAppId(app_str)\n schema.set_more_results(False)","def test_uri_field_deserialization(value):\n\n class Entity:\n def __init__(self, field):\n self.field = field\n\n schema = fields.Namespace(\"http://schema.org/\")\n\n class OldEntitySchema(JsonLDSchema):\n field = Uri(schema.field, allow_none=True)\n\n class Meta:\n rdf_type = schema.Entity\n model = Entity\n\n data = {\"@type\": [\"http://schema.org/Entity\"], \"http://schema.org/field\": value}\n\n entity = OldEntitySchema().load(data)\n\n assert entity.field == value","def __init__(self, collection_id):\n BaseResourceHandler.__init__(self, collection_id)","def resolve_schema_in_request_body(self, request_body):\n content = request_body[\"content\"]\n for content_type in content:\n schema = content[content_type][\"schema\"]\n content[content_type][\"schema\"] = self.openapi.resolve_schema_dict(schema)","def apicall():\r\n# try:\r\n print request.get_json()\r\n test_json = request.get_json()\r\n logger.info(\"input json object loaded\")\r\n logger.info(test_json)\r\n k=MetaData(test_json)\r\n int_res=k.getData()\r\n print '------------------------------'\r\n print int_res\r\n return jsonify(int_res)","def __json_schema_generator(file):\n try:\n data = json.load(file)\n metadata_set = set()\n try:\n for datum in data['meta']['view']['columns']:\n metadata_set.add(datum['name'])\n except Exception as e:\n metadata_set.clear()\n for datum in data:\n if isinstance(datum, str):\n metadata_set.add(datum)\n else:\n for datum_property in datum:\n metadata_set.add(str(datum_property))\n\n metadata_list = list(metadata_set)\n # assumes list of objects with sparsse data\n # OR\n # for data_property in data[0]:\n # metadata_list.append(data_property)\n # assumes list of objects and that first entry has full list of properties\n\n return SchemaGenerator.__build_schema(metadata_list)\n except Exception as e:\n logging.error('Failed to parse json file into schema: ' + str(e))\n raise FailedCreatingSchemaException(\"Failed to create schema from json file.\")","def __init__(__self__,\n resource_name: str,\n args: AppUserBaseSchemaPropertyArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def schema(self):\n pass","def load(self, request, resource=None, **kwargs):\n schema = self.get_schema(request, resource=resource, **kwargs)\n data = yield from self.parse(request)\n resource, errors = schema.load(data, partial=resource is not None)\n if errors:\n raise RESTBadRequest(reason='Bad request', json={'errors': errors})\n return resource","def test_uses_json_decoder(schema_builder, json_spec):\n builder = ExtractorBuilder(schema_builder)\n extractor = builder.build_param_extractor(json_spec)\n assert isinstance(extractor.decoder, JSONDecoder)\n extractor = builder.build_body_extractor({'content': json_spec['content']})\n assert isinstance(extractor.decoder, JSONDecoder)","def test_successful_parse_undocumented_endpoints(monkeypatch) -> None:\n monkeypatch.setattr(django_settings, 'SWAGGER_TESTER', {'PATH': yml_path})\n monkeypatch.setattr('django_swagger_tester.static_schema.loader.LoadStaticSchema.get_schema', ret_schema)\n for url in ['/api/v1/cars/incorrect/', '/api/v1/trucks/incorrect/']:\n base = LoadStaticSchema(url, 'get', status_code=200)\n base.get_response_schema()","def do_post_parse_json(self, *args, **kwargs): # real signature unknown\n pass","def _install_hypothesis_jsonschema_compatibility_shim() -> None:\n hypothesis_jsonschema._from_schema._get_format_filter = _get_format_filter","def _resolver_middleware(next_middleware, root, info, **args):\n pin = Pin.get_from(graphql)\n if not pin or not pin.enabled():\n return next_middleware(root, info, **args)\n\n with pin.tracer.trace(\n name=\"graphql.resolve\",\n resource=info.field_name,\n span_type=SpanTypes.GRAPHQL,\n ) as span:\n span.set_tag_str(COMPONENT, config.graphql.integration_name)\n\n return next_middleware(root, info, **args)","def get_schema(path):\n with open(path, 'r') as f:\n return json.load(f)","def load_json(request, json):\n return json","def __init__(self, schema_row):\n self.schema = []\n for field in schema_row['fields']:\n self.schema.append(field['type'])","def resolve(self, spec: \"ModelSpec\"):","def get_schema() -> dict:\n raise NotImplementedError()","def import_and_index_resolutions():\n sys.path.append(os.path.abspath('import_scripts'))\n import import_resolutioninstance\n import_resolutioninstance.ResolutionImporter().load_items()","def get_schema(schema): # noqa: E501\n return 'do some magic!'","def get(self, entity):\n return jsonify({p.schema: p.to_json() for p in entity.profiles})","def resolve_schema_references(self, definition):\n # type: (Generator, Dict) -> None\n if \"$ref\" in definition:\n schema_reference = definition.pop(\"$ref\")\n section, name = schema_reference.split(\"/\")[-2:]\n referenced_definition = self.parser.specification[section][name]\n definition.update(referenced_definition)\n\n for value in definition.values():\n if isinstance(value, dict):\n self.resolve_schema_references(value)","def schema_mappings(self):\n pass","def __init__(self):\r\n self.apiroot = 'http://ws.spotify.com/'\r\n self.add_filter(self.use_json)","def load_schema(filename):\n with open(filename) as f:\n schema = json.load(f)\n\n return schema","def check_valid_schema(context):\n data = context.response.json()\n validate_schema(data)","def __init__(self, model, options):\n self.model = model\n\n self.index_name = getattr(options, \"index_name\",\n self.model._meta.app_label.lower())\n self.doctype_name = getattr(options, \"doctype_name\",\n self.model._meta.object_name.lower())\n\n # initialize mapping for instance\n self._mapping = {\"properties\": {}}\n\n self.id_field = getattr(options, \"id_field\", \"pk\")\n self.excluded_fields = getattr(options, \"excluded_fields\", tuple())\n # mapping of custom fields used in elasticsearch but not in the django db and\n # their related django style fields. See `mapping_utils.FIELD_MAPPING`\n self.custom_fields = getattr(options, \"custom_fields\", dict())\n\n # mapping of additional options such as \"index\" \"analyze\" \"store\" etc...\n self.additional_options = getattr(options, \"additional_options\", dict())\n\n _fields = getattr(options, \"fields\", [])\n _fields.extend(self.custom_fields.keys())\n self.fields = self._get_fields(_fields, self.excluded_fields)\n\n self.id_field_type = getattr(options, \"id_field_type\", \"long\")\n self.fields.append(mapping.SearchField(\"id\", self.id_field_type))\n\n # get the serializer class\n self.serializer_class = getattr(options, \"serializer_class\",\n serializers.ModelJSONSerializer)","def read(self):\n schema_object = self.get_schema_object()\n if self.id is not None:\n self.response = self.request('GET', self.read_endpoint + \"/\"\n + str(self.id), \"\")\n\n else:\n self.response = self.request('GET', self.read_endpoint, \"\")\n self.log.debug(self.response.status)\n payload_schema = self.response.read()\n if payload_schema != None and payload_schema != \"\":\n schema_object.set_data(payload_schema, self.accept_type)\n else:\n return None\n return schema_object","def validate_json_schema(self, json_schema):\n cls = validators.validator_for(json_schema)\n cls.check_schema(json_schema)","def __init__(self, filepath):\n BaseHandler.__init__(self)\n\n # open the YAML file and parse configuration\n try:\n with open(filepath, \"r\") as fp:\n fp = open(filepath, \"r\")\n config = yaml.safe_load(fp)\n except Exception as exc:\n raise OpenFileError(f\"Unable to open file {filepath}: {exc}\")\n\n # add last-modified header from config, if available\n try:\n last_modified = config[\"dataset\"][\"last_modified\"]\n if isinstance(last_modified, tuple):\n last_modified = last_modified[0]\n if isinstance(last_modified, basestring):\n last_modified = datetime.strptime(last_modified, \"%Y-%m-%d %H:%M:%S\")\n self.additional_headers.append(\n (\"Last-modified\", formatdate(time.mktime(last_modified.timetuple())))\n )\n except KeyError:\n pass\n\n # Peek types. I'm trying to avoid having the user know about Opendap types,\n # so they are not specified in the config file. Instead, we request a single\n # row of data to inspect the data types.\n # FIXME: type peeking does not work if there are NA values in the sequence!!!\n cols = tuple(key for key in config if \"col\" in config[key])\n with session_scope(config[\"database\"][\"dsn\"]) as conn:\n query = \"SELECT {cols} FROM {table} LIMIT 1\".format(\n cols=\", \".join(config[key][\"col\"] for key in cols),\n table=config[\"database\"][\"table\"],\n )\n results = conn.execute(query)\n first_row = results.fetchone()\n\n dtypes = {}\n for col, value, description in zip(\n cols, first_row, results.cursor.description\n ):\n # FIXME: This is fraaaagile, and depends on internal, undocumented behaviour from SQLAlchemy\n if value is None and description[1]:\n # the value is NULL... try to use the typecode\n dtypes[col] = {\n 700: np.dtype(\"float64\"),\n 701: np.dtype(\"float64\"),\n 1114: np.dtype(\"datetime64\"),\n }[description[1]]\n elif type(value) == datetime:\n dtypes[col] = np.dtype(\"datetime64\")\n else:\n dtypes[col] = np.array(value).dtype\n\n # create the dataset, adding attributes from the config file\n attrs = config.get(\"dataset\", {}).copy()\n name = attrs.pop(\"name\", os.path.split(filepath)[1])\n self.dataset = DatasetType(name, attrs)\n\n # and now create the sequence\n attrs = config.get(\"sequence\", {}).copy()\n name = attrs.pop(\"name\", \"sequence\")\n seq = self.dataset[quote(name)] = SequenceType(name, config, attrs)\n for var in cols:\n attrs = {k: v for k, v in config[var].items() if k != \"col\"}\n seq[var] = BaseType(var, attributes=attrs)\n\n # set the data\n seq.data = SQLData(config, seq.id, tuple(cols), dtypes, copy.copy(seq))"],"string":"[\n \"def _load_schema(self, json_schema):\\n # use jsonrefs to resolve all $refs in json\\n data = jsonref.loads(json.dumps(json_schema))\\n return self.__initialise_template(data)\",\n \"def resolver():\\n if RESOLVER:\\n return RESOLVER\\n path = str(pathlib.Path(__file__).parents[1].joinpath(\\\"schema\\\", \\\"app.json\\\"))\\n with open(path) as stream:\\n schema = json.load(stream)\\n globals()[\\\"RESOLVER\\\"] = RefResolver(\\n \\\"https://schema.timeflux.io/app.json\\\", None\\n ).from_schema(schema)\\n return RESOLVER\",\n \"def __init__(self, schema ):\\n self.schema = schema\",\n \"def schema_handler(self, schema):\\n dict_for_render = schema.get('properties', {}).items()\\n if schema.get('$ref', None):\\n def_name = schema.get('$ref').split('/')[-1]\\n dict_for_render = self.definitions[def_name].get('properties', {}).items()\\n elif schema.get('properties', None) is None:\\n return ''\\n\\n answer_dict = {}\\n json_dict = {}\\n for opt_name, opt_value in dict_for_render:\\n var_type = opt_value.get('format', None) or opt_value.get('type', None) or 'object'\\n json_name = self.indent + f':jsonparameter {var_type} {opt_name}:'\\n json_dict[json_name] = self.get_json_props_for_response(var_type, opt_value)\\n\\n answer_dict[opt_name] = self.get_response_example(opt_name, var_type, opt_value)\\n if var_type == 'string':\\n answer_dict[opt_name] = answer_dict[opt_name].format(opt_name)\\n\\n self.write('')\\n for line in json.dumps(answer_dict, indent=4).split('\\\\n'):\\n self.write(line, self.indent_depth)\\n\\n self.write('')\\n for json_param_name, json_param_value in json_dict.items():\\n desc = f'{json_param_value[\\\"title\\\"]}{json_param_value[\\\"props_str\\\"]}' or 'None'\\n self.write(json_param_name + ' ' + desc)\",\n \"def load_data(schema):\\n\\n def wrapper(func):\\n @functools.wraps(func)\\n def second_wrapper(*args, **kwargs):\\n data = schema().load(request.get_json()).data\\n return func(data=data, *args, **kwargs)\\n\\n return second_wrapper\\n\\n return wrapper\",\n \"def schema(self):\\n # NOTE This is exactly the same as the other thing.\\n return {\\n \\\"$id\\\": f\\\"{self.request.resource_url(self)}#schema\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"foo\\\": {\\\"type\\\": \\\"string\\\"},\\n # generated fields shouldn't be submitted or in forms\\n \\\"url\\\": {\\\"type\\\": \\\"string\\\", \\\"generated\\\": True},\\n }\\n }\",\n \"def _load_json_schema(filename):\\n\\n relative_path = join(\\\"schemas\\\", filename)\\n absolute_path = join(dirname(__file__), relative_path)\\n\\n base_path = dirname(absolute_path)\\n base_uri = 'file://{}/'.format(base_path)\\n\\n print(f\\\"base uri {base_uri}\\\")\\n print(f\\\"base path {base_path}\\\")\\n print(f\\\"relative_path {relative_path}\\\")\\n print(f\\\"absolute_path {absolute_path}\\\")\\n\\n with open(absolute_path) as schema_file:\\n return jsonref.loads(schema_file.read(), base_uri=base_uri, jsonschema=True)\",\n \"def __init__(self, schema=None):\\n self.schema = schema or {}\",\n \"def load_request_data(schema):\\n\\n def wrapper(func):\\n @wraps(func)\\n def decorated_func(*args, **kwargs):\\n data = schema().load(request.get_json())\\n return func(data=data, *args, **kwargs)\\n\\n return decorated_func\\n\\n return wrapper\",\n \"def resolve_schema(self, data):\\n if not isinstance(data, dict):\\n return\\n\\n # OAS 2 component or OAS 3 header\\n if \\\"schema\\\" in data:\\n data[\\\"schema\\\"] = self.openapi.resolve_schema_dict(data[\\\"schema\\\"])\\n # OAS 3 component except header\\n if self.openapi_version.major >= 3:\\n if \\\"content\\\" in data:\\n for content_type in data[\\\"content\\\"]:\\n schema = data[\\\"content\\\"][content_type][\\\"schema\\\"]\\n data[\\\"content\\\"][content_type][\\n \\\"schema\\\"\\n ] = self.openapi.resolve_schema_dict(schema)\",\n \"def load_schema(self):\\n\\n schema = {\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {}\\n }\\n\\n msd = self.parse_xml(self.schema_path)\\n for concept in msd.findall('.//Concept'):\\n concept_id = self.alter_key(concept.attrib['id'])\\n self.add_item_to_field_order(concept_id)\\n concept_name = concept.find('./Name').text\\n concept_description = concept.find('./Description').text\\n parent = concept.find('./Parent/Ref')\\n key_parts = [concept_id, concept_id] if parent is None else [parent.attrib['id'], concept_id]\\n translation_key = '.'.join(key_parts)\\n jsonschema_field = {\\n 'type': ['string', 'null'],\\n 'title': concept_name,\\n 'description': concept_description,\\n 'translation_key': translation_key,\\n }\\n if self.scope is not None:\\n jsonschema_field['scope'] = self.scope\\n schema['properties'][concept_id] = jsonschema_field\\n\\n self.schema = schema\",\n \"def load_schema(path, collection, readonly):\\n return JSONStorage(path, collection, readonly)\",\n \"def fetch_schema(self) -> None:\\n if self.schema_file:\\n logger.info(\\\"Loaded schema from file '%s'\\\", self.schema_file)\\n self._schema = load_schema_file(self.schema_file)\\n else:\\n url = self.schema_url or urljoin(self.base_url, \\\"schema/openapi.yaml\\\")\\n logger.info(\\\"Fetching schema at '%s'\\\", url)\\n self._schema = schema_fetcher.fetch(url, {\\\"v\\\": \\\"3\\\"})\",\n \"def schema_helper(self, name, _, schema=None, **kwargs):\\n if schema is None:\\n return None\\n\\n schema_instance = resolve_schema_instance(schema)\\n\\n schema_key = make_schema_key(schema_instance)\\n self.warn_if_schema_already_in_spec(schema_key)\\n self.openapi.refs[schema_key] = name\\n\\n json_schema = self.openapi.schema2jsonschema(schema_instance)\\n\\n return json_schema\",\n \"def load_json_schema(filename):\\n relative_path = join('../schema', filename)\\n absolute_path = join(dirname(__file__), relative_path)\\n\\n base_path = dirname(absolute_path)\\n base_uri = 'file://{}/'.format(base_path)\\n\\n with open(absolute_path) as schema_file:\\n return jsonref.loads(\\n schema_file.read(), base_uri=base_uri, jsonschema=True)\",\n \"def json_mapper(self, sid_ref_id, json_schema_url, **kwargs):\\n expected_json_values = self.create_value.get_db_values(sid_ref_id, **kwargs)\\n json_data_template = self.get_data_template(json_schema_url)\\n return result_formatter(expected_json_values,reference=cilKeyReference.sid_soi_reference)\",\n \"def endpoint_schema(endpoint, extra_definitions={}):\\n # load common schema template and update metadata\\n schema = common.load_json(\\\"./templates/provider/endpoint.json\\\")\\n schema[\\\"$id\\\"] = schema[\\\"$id\\\"].replace(\\\"endpoint.json\\\", f\\\"{endpoint}.json\\\")\\n schema[\\\"title\\\"] = schema[\\\"title\\\"].replace(\\\"endpoint\\\", endpoint)\\n\\n # merge custom definitions with relevant common definitions\\n definitions = common.load_definitions(\\n \\\"string\\\",\\n \\\"timestamp\\\",\\n \\\"uuid\\\",\\n \\\"version\\\",\\n common.MDS_FEATURE_POINT\\n )\\n definitions.update(common.point_definition())\\n definitions.update(extra_definitions)\\n\\n endpoint_schema = common.load_json(f\\\"./templates/provider/{endpoint}.json\\\")\\n\\n # for all but stops, merge standard vehicle info with items schema\\n if endpoint not in [\\\"stops\\\"]:\\n items = endpoint_schema[endpoint][\\\"items\\\"]\\n vehicle = common.vehicle_definition()\\n items[\\\"required\\\"] = vehicle[\\\"required\\\"] + items[\\\"required\\\"]\\n items[\\\"properties\\\"] = { **vehicle[\\\"properties\\\"], **items[\\\"properties\\\"] }\\n definitions.update(common.load_definitions(\\\"propulsion_type\\\", \\\"propulsion_types\\\", \\\"vehicle_type\\\"))\\n\\n # merge endpoint schema into the endpoint template\\n data_schema = schema[\\\"properties\\\"][\\\"data\\\"]\\n data_schema[\\\"required\\\"] = [endpoint]\\n data_schema[\\\"properties\\\"] = endpoint_schema\\n\\n # insert definitions\\n schema[\\\"definitions\\\"].update(definitions)\\n\\n return schema\",\n \"def load_resolved_schema(spec_path, file_name=None, schema_obj=None, path_prefix=True):\\r\\n\\r\\n # Only one of file_name or schema_obj must be set\\r\\n assert bool(file_name) != bool(schema_obj)\\r\\n\\r\\n if path_prefix:\\r\\n spec_path = os.path.join(spec_path, \\\"APIs/schemas/\\\")\\r\\n base_path = os.path.abspath(spec_path)\\r\\n if not base_path.endswith(\\\"/\\\"):\\r\\n base_path = base_path + \\\"/\\\"\\r\\n if os.name == \\\"nt\\\":\\r\\n base_uri_path = \\\"file:///\\\" + base_path.replace('\\\\\\\\', '/')\\r\\n else:\\r\\n base_uri_path = \\\"file://\\\" + base_path\\r\\n\\r\\n loader = jsonref.JsonLoader(cache_results=False)\\r\\n\\r\\n if file_name:\\r\\n json_file = str(Path(base_path) / file_name)\\r\\n with open(json_file, \\\"r\\\") as f:\\r\\n schema = jsonref.load(f, base_uri=base_uri_path, loader=loader, jsonschema=True)\\r\\n elif schema_obj:\\r\\n # Work around an exception when there's nothing to resolve using an object\\r\\n if \\\"$ref\\\" in schema_obj:\\r\\n schema = jsonref.JsonRef.replace_refs(schema_obj, base_uri=base_uri_path, loader=loader, jsonschema=True)\\r\\n else:\\r\\n schema = schema_obj\\r\\n\\r\\n return schema\",\n \"def resolve_nested_schema(self, schema):\\n try:\\n schema_instance = resolve_schema_instance(schema)\\n # If schema is a string and is not found in registry,\\n # assume it is a schema reference\\n except marshmallow.exceptions.RegistryError:\\n return schema\\n schema_key = make_schema_key(schema_instance)\\n if schema_key not in self.refs:\\n name = self.schema_name_resolver(schema)\\n if not name:\\n try:\\n json_schema = self.schema2jsonschema(schema_instance)\\n except RuntimeError:\\n raise APISpecError(\\n \\\"Name resolver returned None for schema {schema} which is \\\"\\n \\\"part of a chain of circular referencing schemas. Please\\\"\\n \\\" ensure that the schema_name_resolver passed to\\\"\\n \\\" MarshmallowPlugin returns a string for all circular\\\"\\n \\\" referencing schemas.\\\".format(schema=schema)\\n )\\n if getattr(schema, \\\"many\\\", False):\\n return {\\\"type\\\": \\\"array\\\", \\\"items\\\": json_schema}\\n return json_schema\\n name = get_unique_schema_name(self.spec.components, name)\\n self.spec.components.schema(name, schema=schema)\\n return self.get_ref_dict(schema_instance)\",\n \"def _patch_schema(self):\\n fields = get_json()['data']['attributes'].keys()\\n return make_entity_schema(\\n self.SCHEMA, self.RESOURCE_NAME,\\n make_data_schema(\\n self.SCHEMA, id_required=True,\\n only=fields, partial=True\\n )\\n )\",\n \"def _load_json_schema(filename):\\n\\n relative_path = join('schemas', filename)\\n absolute_path = join(dirname(__file__), relative_path)\\n\\n with open(absolute_path) as schema_file:\\n return json.loads(schema_file.read())\",\n \"def schema(self):\\n # TODO The schema of a container resource...\\n # This is the same as the leaf.\\n # However, this isn't actually the schema of the response\\n return {\\n \\\"$id\\\": f\\\"{self.request.resource_url(self)}#schema\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"foo\\\": {\\\"type\\\": \\\"string\\\"},\\n # generated fields shouldn't be submitted or in forms\\n \\\"url\\\": {\\\"type\\\": \\\"string\\\", \\\"generated\\\": True},\\n }\\n }\",\n \"def decide_schema(self, json_data):\\n pass\",\n \"def decide_schema(self, json_data):\\n pass\",\n \"def testSchemaLoadingAsString(self):\\n api = self.ApiFromDiscoveryDoc('latitude.v1.json')\\n self.assertEquals(4, len(api._schemas))\",\n \"def pyang_plugin_init():\\n plugin.register_plugin(SwaggerPlugin())\",\n \"def schema(self):\",\n \"def get_parser_context(self, http_request):\\n res = super().get_parser_context(http_request)\\n res['json_schema'] = self.create_payload_schema\\n return res\",\n \"def response(schema):\\n def _response(function):\\n @wraps(function)\\n def wrapper(*args, **kwargs):\\n if issubclass(schema, BaseModel):\\n has_root = True if '__root__' in schema.__fields__ else False\\n function_res = function(*args, **kwargs)\\n\\n if not function_res:\\n if has_root is True:\\n return jsonify([])\\n return jsonify({})\\n\\n if type(function_res) == list:\\n res = schema.parse_obj(function_res)\\n else:\\n res = schema.from_orm(function_res)\\n\\n res = res.dict()\\n\\n if has_root is True:\\n return jsonify(res['__root__'])\\n\\n return jsonify(res)\\n elif isinstance(schema, dict):\\n return jsonify(schema)\\n else:\\n raise CustomException('invalid response type', code=400)\\n\\n return wrapper\\n return _response\",\n \"def parse(self, stream, media_type=None, parser_context=None):\\n\\n result = super(parsers.JSONParser, self).parse(\\n stream, media_type=media_type, parser_context=parser_context\\n )\\n if not isinstance(result, dict) or \\\"data\\\" not in result:\\n raise ParseError(\\\"Received document does not contain primary data\\\")\\n\\n data = result.get(\\\"data\\\")\\n view = parser_context[\\\"view\\\"]\\n\\n from rest_framework_json_api.views import RelationshipView\\n\\n if isinstance(view, RelationshipView):\\n # We skip parsing the object as JSONAPI Resource Identifier Object and not a regular\\n # Resource Object\\n if isinstance(data, list):\\n for resource_identifier_object in data:\\n if not (\\n resource_identifier_object.get(\\\"id\\\")\\n and resource_identifier_object.get(\\\"type\\\")\\n ):\\n raise ParseError(\\n \\\"Received data contains one or more malformed JSONAPI \\\"\\n \\\"Resource Identifier Object(s)\\\"\\n )\\n elif not (data.get(\\\"id\\\") and data.get(\\\"type\\\")):\\n raise ParseError(\\n \\\"Received data is not a valid JSONAPI Resource Identifier Object\\\"\\n )\\n\\n return data\\n\\n request = parser_context.get(\\\"request\\\")\\n\\n # Sanity check\\n if not isinstance(data, dict):\\n raise ParseError(\\n \\\"Received data is not a valid JSONAPI Resource Identifier Object\\\"\\n )\\n\\n # Check for inconsistencies\\n if request.method in (\\\"PUT\\\", \\\"POST\\\", \\\"PATCH\\\", \\\"DELETE\\\"):\\n resource_name = utils.get_resource_name(\\n parser_context, expand_polymorphic_types=True\\n )\\n if isinstance(resource_name, str):\\n if data.get(\\\"type\\\") != resource_name:\\n raise exceptions.Conflict(\\n \\\"The resource object's type ({data_type}) is not the type that \\\"\\n \\\"constitute the collection represented by the endpoint \\\"\\n \\\"({resource_type}).\\\".format(\\n data_type=data.get(\\\"type\\\"), resource_type=resource_name\\n )\\n )\\n else:\\n if data.get(\\\"type\\\") not in resource_name:\\n raise exceptions.Conflict(\\n \\\"The resource object's type ({data_type}) is not the type that \\\"\\n \\\"constitute the collection represented by the endpoint \\\"\\n \\\"(one of [{resource_types}]).\\\".format(\\n data_type=data.get(\\\"type\\\"),\\n resource_types=\\\", \\\".join(resource_name),\\n )\\n )\\n if not data.get(\\\"id\\\") and request.method in (\\\"PATCH\\\", \\\"PUT\\\", \\\"DELETE\\\"):\\n raise ParseError(\\n \\\"The resource identifier object must contain an 'id' member\\\"\\n )\\n\\n if request.method in (\\\"PATCH\\\", \\\"PUT\\\", \\\"DELETE\\\"):\\n lookup_url_kwarg = getattr(view, \\\"lookup_url_kwarg\\\", None) or getattr(\\n view, \\\"lookup_field\\\", None\\n )\\n if lookup_url_kwarg and str(data.get(\\\"id\\\")) != str(\\n view.kwargs[lookup_url_kwarg]\\n ):\\n raise exceptions.Conflict(\\n \\\"The resource object's id ({data_id}) does not match url's \\\"\\n \\\"lookup id ({url_id})\\\".format(\\n data_id=data.get(\\\"id\\\"), url_id=view.kwargs[lookup_url_kwarg]\\n )\\n )\\n\\n # Construct the return data\\n serializer_class = getattr(view, \\\"serializer_class\\\", None)\\n parsed_data = {\\\"id\\\": data.get(\\\"id\\\")} if \\\"id\\\" in data else {}\\n # `type` field needs to be allowed in none polymorphic serializers\\n if serializer_class is not None:\\n if issubclass(serializer_class, serializers.PolymorphicModelSerializer):\\n parsed_data[\\\"type\\\"] = data.get(\\\"type\\\")\\n parsed_data.update(self.parse_attributes(data))\\n parsed_data.update(self.parse_relationships(data))\\n parsed_data.update(self.parse_metadata(result))\\n return parsed_data\",\n \"def _load_schemas(self) -> None:\\n schema_paths = self._root.rglob(\\\"*.json\\\")\\n for schema_path in schema_paths:\\n schema = json.loads(schema_path.read_text())\\n\\n if self._suffix:\\n schema[\\\"name\\\"] = f'{schema[\\\"name\\\"]}{self._suffix}'\\n\\n fqn = get_avro_fqn(schema)\\n self.schemas[fqn] = schema\",\n \"def _json_import(self, imppath):\\n # TODO: Settle on JSON format for colortable\\n pass\",\n \"def _get_schema_from_object(self, data):\\n if \\\"items\\\" in data:\\n return self._get_schema_from_object(data[\\\"items\\\"])\\n\\n url_key = None\\n\\n if '$id' in data:\\n url_key = '$id'\\n\\n if 'id' in data:\\n url_key = 'id'\\n\\n if url_key:\\n url = data[url_key]\\n schema = Schema().build()\\n schema.domain_entity = self.get_domain_entity_from_url(url)\\n schema.high_level_entity = self.get_high_level_entity_from_url(url)\\n schema.module = self.get_module_from_url(url)\\n schema.url = url\\n return schema\\n\\n return None\",\n \"def _handle_with_schema_validation(success_handler, schema_dict):\\n try:\\n schema = Schema(schema_dict)\\n args = schema(request.args.to_dict())\\n except MultipleInvalid as e:\\n return abort(400, 'value at %s failed validation: %s' % (e.path, e.msg))\\n result = success_handler(args)\\n return jsonify(result)\",\n \"def __init__(self, schema=None):\\n self._dict = {}\\n self.schema = schema\",\n \"def get_schema(self) -> dict:\",\n \"def schema2jsonschema(self, schema):\\n fields = get_fields(schema)\\n Meta = getattr(schema, \\\"Meta\\\", None)\\n partial = getattr(schema, \\\"partial\\\", None)\\n ordered = getattr(schema, \\\"ordered\\\", False)\\n\\n jsonschema = self.fields2jsonschema(fields, partial=partial, ordered=ordered)\\n\\n if hasattr(Meta, \\\"title\\\"):\\n jsonschema[\\\"title\\\"] = Meta.title\\n if hasattr(Meta, \\\"description\\\"):\\n jsonschema[\\\"description\\\"] = Meta.description\\n if hasattr(Meta, \\\"unknown\\\") and Meta.unknown != marshmallow.EXCLUDE:\\n jsonschema[\\\"additionalProperties\\\"] = Meta.unknown == marshmallow.INCLUDE\\n\\n return jsonschema\",\n \"def load_schema(name):\\r\\n\\r\\n data = pkgutil.get_data(__package__, \\\"schemas/{0}.json\\\".format(name))\\r\\n return json.loads(data.decode(\\\"utf-8\\\"))\",\n \"def generate_url_schema():\\n json_str = json.dumps({'fields': [\\n {'name': 'url', 'type': 'STRING', 'mode': 'NULLABLE'},\\n {'name': 'count', 'type': 'INTEGER', 'mode': 'NULLABLE'},\\n {'name': 'ts', 'type': 'TIMESTAMP', 'mode': 'NULLABLE'}]})\\n return parse_table_schema_from_json(json_str)\",\n \"def parse_schema(schemaurl, schema_dir=None):\\n if schema_dir:\\n try:\\n # attempts to open .schema file in directory schema_dir\\n local_schema_path = schema_dir + \\\"/\\\" + \\\\\\n schemaurl[schemaurl.rfind('/') + 1:-1] + \\\".schema\\\"\\n print \\\"Looking for schema in file %s\\\" % (local_schema_path)\\n schema = json.load(open(local_schema_path))\\n except Exception as e:\\n print \\\"Couldn't load schema %s from file %s\\\\n%s\\\" % (\\n schemaurl, schema_dir, str(e))\\n return None\\n else:\\n # load the schema directly from schemaurl, i.e., from the web\\n try:\\n schema = json.load(urllib2.urlopen(schemaurl))\\n except Exception as e:\\n print \\\"Couldn't load schema %s\\\\n%s\\\" % (schemaurl, str(e))\\n return None\\n\\n if 'extends' in schema and '$ref' in schema['extends']:\\n\\n parent_schema = json.load(urllib2.urlopen(schema['extends']['$ref']))\\n while (True): # exits loop when no additional extensions (break below)\\n for key in sorted(parent_schema.keys()):\\n if key not in schema:\\n schema[key] = parent_schema[key]\\n # need to merge these keys individually\\n if key == 'properties':\\n for key in sorted(parent_schema['properties'].keys()):\\n if key not in schema['properties']:\\n schema['properties'][key] = parent_schema[\\n 'properties'][key]\\n if 'extends' in parent_schema:\\n parent_schema = json.load(\\n urllib2.urlopen(parent_schema['extends']['$ref']))\\n else:\\n break\\n # essentially a do while loop (exit condition)\\n\\n return schema\",\n \"def test_read_json_schema():\\n json_schema = os.path.join(TEST_DATA_PATH, 'example_schema.json')\\n schema_tree = schema.load_schema(json_schema, resolve_references=True)\\n schema.check_schema(schema_tree)\",\n \"def introspect_schema(cls, endpoint: str, transport: Transporter) -> Dict:\\n return request_schema(endpoint, transport.session)\",\n \"def to_json_schema(cls):\\n return parsers.to_json_schema(cls)\",\n \"def define_idaapi_resolver():\\n dn = epydoc.apidoc.DottedName.__init__\\n def resolver(piece):\\n if piece is not None and isinstance(piece, basestring) and piece.startswith(\\\"idaapi_\\\"):\\n res = \\\"idaapi\\\"\\n else:\\n res = piece\\n log(\\\"Resolver '%s' => '%s'\\\" % (piece, res))\\n return res\\n def wrapper(self, *pieces, **options):\\n return dn(self, *map(resolver, pieces), **options);\\n epydoc.apidoc.DottedName.__init__ = wrapper\",\n \"def json_schema(schema_file=None, output=\\\"-\\\"):\\n schemas = read_yaml(schema_file)\\n dump_yaml(output, JsonSchemaConverterFromAccessSchema.convert_schemas(schemas))\",\n \"def JSONSchema(self, default=None):\\n return self.data.get('metadata', {}).get('$schema', default)\",\n \"def _load(self, list_of_schema_urls):\\n for uri in list_of_schema_urls:\\n with urllib.request.urlopen(uri) as url:\\n data = {}\\n try:\\n data = json.loads(url.read().decode())\\n except:\\n print(\\\"Failed to read schema from \\\" + uri)\\n self._parser._load_schema(data)\\n return self\",\n \"def lookup_table():\\n datapackage = {\\n 'resources': [\\n {\\n 'schema': {\\n 'fields': [\\n {\\n 'name': 'foo',\\n 'maps_to': 'project_id'\\n },\\n {\\n 'name': 'bar',\\n 'maps_to': 'invalid_fiscal_field'\\n },\\n {\\n 'name': 'spam',\\n 'maps_to': None\\n },\\n {\\n 'name': 'eggs',\\n },\\n\\n ]\\n\\n }\\n }\\n ]\\n }\\n return build_lookup_table(datapackage)\",\n \"def _load_json_schema(filename: str):\\n relative_path = path.join('schemas', filename)\\n absolute_path = path.join(path.dirname(__file__), relative_path)\\n\\n with open(absolute_path, 'r', encoding='utf-8') as schema_file:\\n schema = json.loads(schema_file.read())\\n\\n return schema\",\n \"def get_schema():\\n if not os.path.isfile(_schema_file):\\n create_schema()\\n with open(_schema_file, 'r') as fd:\\n out = decode_json(fd)\\n return out\",\n \"def validate(schema):\\n def decorator(func):\\n def wrapper(self, req, resp, *args, **kwargs):\\n try:\\n raw_json = req.stream.read()\\n obj = json.loads(raw_json.decode('utf-8'))\\n obj['req_id'] = req.context.get('request_id')\\n except Exception:\\n raise falcon.HTTPBadRequest(\\n title='Invalid data',\\n description='Could not properly parse the provided data as JSON',\\n code='001'\\n )\\n\\n try:\\n jsonschema.validate(obj, schema)\\n except jsonschema.ValidationError as e:\\n raise falcon.HTTPBadRequest(\\n title='Failed data validation',\\n description=e.message,\\n code='002'\\n )\\n\\n return func(self, req, resp, *args, parsed=obj, **kwargs)\\n return wrapper\\n return decorator\",\n \"def opt_engine_rest_api():\\n request_json = request.get_json()\\n return process_request(request_json)\",\n \"def load_dde_schemas(self, schema):\\n url = DDE_SCHEMA_BASE_URL + schema\\n if self.verbose:\\n print(f'Loading registered DDE schema from \\\"{url}\\\"')\\n return load_json_or_yaml(url)[\\\"source\\\"]\",\n \"def schema(self):\\n return _parse_schema_resource(self._properties.get(\\\"schema\\\", {}))\",\n \"def load_default_schema(self):\\n self.schema = preprocess_schema(load_schemaorg())\\n self.schemaorg_schema = self.schema\\n if \\\"@context\\\" in self.schema:\\n self.context.update(self.schema[\\\"@context\\\"])\\n self.schema_extension_only = self.schema\\n self.schemaorg_nx = load_schema_into_networkx(self.schema)\\n self.schema_extension_nx = self.schemaorg_nx\\n self.schema_nx = self.schemaorg_nx\\n isolates = list(nx.isolates(self.schema_nx))\\n self.extended_class_only_graph = self.schema_extension_nx.subgraph([node for node, attrdict in self.schema_extension_nx.node.items() if attrdict['type'] == 'Class' and node not in isolates])\\n self.full_class_only_graph = self.schema_nx.subgraph([node for node, attrdict in self.schema_nx.node.items() if attrdict['type'] == 'Class'])\\n self.property_only_graph = self.schema_nx.subgraph([node for node, attrdict in self.schema_nx.node.items() if attrdict['type'] == 'Property'])\\n # instantiate converters for classes and properties\\n self._all_class_uris = [node for node,attrdict in self.schema_nx.node.items() if attrdict['type'] in ['Class', 'DataType']]\\n self.cls_converter = CurieUriConverter(self.context,\\n self._all_class_uris)\\n self._all_prop_uris = list(self.property_only_graph.nodes())\\n self.prop_converter = CurieUriConverter(self.context,\\n self._all_prop_uris)\\n self.default_schema_loaded = True\",\n \"def use_json(request):\\r\\n request.uri = '{0}.json'.format(request.uri)\",\n \"def handler(type=None, resource=None, id=None, json_pointer=None,\\n app=None, content=None, root=None, content_pointer=None):\\n languages = list(app.config.get(\\\"MULTILINGUAL_SUPPORTED_LANGUAGES\\\", []))\\n\\n default_template = app.config.get(\\\"ELASTICSEARCH_DEFAULT_LANGUAGE_TEMPLATE\\\", {})\\n template = app.config.get(\\\"ELASTICSEARCH_LANGUAGE_TEMPLATES\\\", {})\\n\\n data_dict= dict()\\n for language in languages:\\n if id is not None:\\n language_with_context = language + '#' + id\\n if language_with_context in template.keys():\\n data_dict[language] = template[language_with_context]\\n continue\\n data_dict[language] = template.get(language, default_template)\\n\\n\\n return {\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": data_dict\\n }\",\n \"def load_dde_schemas(schema, verbose=False):\\n url = DDE_SCHEMA_BASE_URL + schema\\n if verbose:\\n print(f'Loading registered DDE schema from \\\"{url}\\\"')\\n return load_json_or_yaml(url)[\\\"source\\\"]\",\n \"def validate_against_schema(self, json_doc):\\n if self.uri not in self.se.validation:\\n raise RuntimeError(\\\"$validation is not defined for {} field; thus the json document could not be validated\\\".format(self.name))\\n else:\\n validate(json_doc, self.se.validation[self.uri])\\n print('The JSON document is valid')\",\n \"def json_friendly(self):\",\n \"def __init__(self, endpoint: str, transporter: Transporter, settings: Settings, cache: Optional[Cache]):\\n self.endpoint = endpoint\\n self.transport = transporter\\n self.settings = settings\\n self.cache = cache\\n\\n if self.cache is not None:\\n schema_introspection = self.cache.retrieve(self.endpoint, SCHEMA_KEY)\\n if schema_introspection is None:\\n schema_introspection = self.introspect_schema(endpoint, transporter)\\n self.cache.store(self.endpoint, SCHEMA_KEY, schema_introspection)\\n else:\\n schema_introspection = self.introspect_schema(endpoint, transporter)\\n\\n # graphql schema properties\\n self.raw_schema = schema_introspection.get(self.settings.default_response_key, {}).get(\\\"__schema\\\", {})\\n self.query_type: str = self.parse_query_type(self.raw_schema)\\n self.mutation_type: str = self.parse_mutation_type(self.raw_schema)\\n self.subscription_type: str = self.parse_subscription_type(self.raw_schema)\\n\\n self.types: Dict[str, SchemaType] = self.parse_types(self.raw_schema.get(\\\"types\\\", []))\\n self.directives: Dict[str, Directive] = self.parse_directives(self.raw_schema.get(\\\"directives\\\", []))\\n\\n # custom schema properties\\n self.queries: Tuple[Operation] = self.parse_operations(self.query_type)\\n self.mutations: Tuple[Operation] = self.parse_operations(self.mutation_type)\\n self.subscriptions: Tuple[Operation] = self.parse_operations(self.subscription_type)\",\n \"def parse_with(schema, arg_name='entity', many=False, **kwargs):\\n\\n def decorator(f):\\n @functools.wraps(f)\\n def inner(*fargs, **fkwargs):\\n json = request.get_json() or {}\\n try:\\n entity = schema.load(json,many=many, **kwargs)\\n fkwargs.update({arg_name: entity})\\n\\n except (ValidationError, ValueError, Exception) as e:\\n logger.error(\\\"parse_with: {}\\\".format(str(e)))\\n abort(400, message=\\\"An error occurred. \\\\n {}\\\".format((str(e))))\\n\\n return f(*fargs, **fkwargs)\\n\\n return inner\\n\\n return decorator\",\n \"def getResolver(self):\\n pass;\",\n \"def load_validation_schema(self) -> t.Dict[str, t.Any]:\\n if self._schema is None:\\n try:\\n self._schema = json.loads(self.schema())\\n except KeyError:\\n device_type_striped = self._device_type.lower().rstrip(string.digits)\\n with open(_CT_FILES[device_type_striped], encoding=\\\"utf-8\\\") as file_:\\n self._schema = json.load(file_)\\n return self._schema # type: ignore\",\n \"def _convert(self, fn, suffix='json', path='jsonschema', name=None,\\n root_class_name=None, data_files=[], target_class=None):\\n ie = JsonSchemaImportEngine()\\n d = os.path.join(INPUT_DIR, path)\\n schema = ie.load(os.path.join(d, f'{fn}.{suffix}'), name=name, format=suffix, root_class_name=root_class_name)\\n model_path = os.path.join(OUTPUT_DIR, f'{fn}.yaml')\\n write_schema(schema, model_path)\\n roundtrip_path = os.path.join(OUTPUT_DIR, f'{fn}.roundtrip.json')\\n with open(roundtrip_path, 'w') as stream:\\n stream.write(JsonSchemaGenerator(model_path).serialize())\\n python_path = os.path.join(OUTPUT_DIR, f'{fn}.py')\\n with open(python_path, 'w') as stream:\\n stream.write(PythonGenerator(model_path).serialize())\\n compile_python(python_path)\\n # TODO: test data_files\\n return schema\",\n \"def getSchema(cls):\\n pass\",\n \"def load_schema(self, schema):\\n if not self.default_schema_loaded:\\n self.load_default_schema()\\n # load JSON-LD file of user defined schema\\n self.schema_extension_only = preprocess_schema(load_json_or_yaml(schema))\\n if \\\"@context\\\" in self.schema_extension_only:\\n self.context.update(self.schema_extension_only[\\\"@context\\\"])\\n # convert user defined schema into a networkx DiGraph\\n self.schema_extension_nx = load_schema_into_networkx(self.schema_extension_only)\\n # update undefined classes/properties\\n undefined_nodes = [node for node, attrdict in self.schema_extension_nx.node.items() if not attrdict]\\n attr_dict = {}\\n \\n for _node in undefined_nodes:\\n if _node in self.schemaorg_nx.nodes():\\n attr_dict[_node] = self.schemaorg_nx.nodes[_node]\\n nx.set_node_attributes(self.schema_extension_nx, attr_dict)\\n # merge networkx graph of user-defined schema with networkx graph of schema defined by Schema.org\\n #self.schema_nx = merge_schema_networkx(self.schemaorg_nx, self.schema_extension_nx)\\n self.schema_nx = self.schema_extension_nx\\t\\n SchemaValidator(self.schema_extension_only, self.schema_nx).validate_full_schema()\\n # merge together the given schema and the schema defined by schemaorg\\n #self.schema = merge_schema(self.schema_extension_only, self.schemaorg_schema)\\n self.schema = self.schemaorg_schema\\n # split the schema networkx into individual ones\\n isolates = list(nx.isolates(self.schema_nx))\\n \\n for node, attrdict in self.schema_extension_nx.node.items():\\n if not 'type' in attrdict:\\n self.schema_extension_nx.nodes[node][\\\"type\\\"] = \\\"Class\\\" \\n for node, attrdict in self.schema_nx.node.items():\\n if not 'type' in attrdict:\\n self.schema_nx.nodes[node][\\\"type\\\"] = \\\"Class\\\" \\n \\n self.extended_class_only_graph = self.schema_extension_nx.subgraph([node for node, attrdict in self.schema_extension_nx.node.items() if attrdict['type'] == 'Class' and node not in isolates])\\n self.full_class_only_graph = self.schema_nx.subgraph([node for node, attrdict in self.schema_nx.node.items() if attrdict['type'] == 'Class'])\\n self.property_only_graph = self.schema_nx.subgraph([node for node, attrdict in self.schema_nx.node.items() if attrdict['type'] == 'Property'])\\n # instantiate converters for classes and properties\\n self._all_class_uris = [node for node,attrdict in self.schema_nx.node.items() if attrdict['type'] in ['Class', 'DataType']]\\n self.cls_converter = CurieUriConverter(self.context,\\n self._all_class_uris)\\n self._all_prop_uris = list(self.property_only_graph.nodes())\\n self.prop_converter = CurieUriConverter(self.context,\\n self._all_prop_uris)\",\n \"def resolver(schema):\\n name = schema.__name__\\n if name.endswith(\\\"Schema\\\"):\\n return name[:-6] or name\\n return name\",\n \"def get_json(schema):\\n\\n data = request.get_json(force=True, silent=True, cache=False)\\n\\n message = schema.validate(data)\\n\\n if message:\\n raise BadRequest(message)\\n\\n return data\",\n \"def _Dynamic_GetSchema(self, req, schema, request_id=None):\\n # This is not used, but it is required for the method signature.\\n del request_id\\n\\n app_str = req.app()\\n self.__ValidateAppId(app_str)\\n schema.set_more_results(False)\",\n \"def test_uri_field_deserialization(value):\\n\\n class Entity:\\n def __init__(self, field):\\n self.field = field\\n\\n schema = fields.Namespace(\\\"http://schema.org/\\\")\\n\\n class OldEntitySchema(JsonLDSchema):\\n field = Uri(schema.field, allow_none=True)\\n\\n class Meta:\\n rdf_type = schema.Entity\\n model = Entity\\n\\n data = {\\\"@type\\\": [\\\"http://schema.org/Entity\\\"], \\\"http://schema.org/field\\\": value}\\n\\n entity = OldEntitySchema().load(data)\\n\\n assert entity.field == value\",\n \"def __init__(self, collection_id):\\n BaseResourceHandler.__init__(self, collection_id)\",\n \"def resolve_schema_in_request_body(self, request_body):\\n content = request_body[\\\"content\\\"]\\n for content_type in content:\\n schema = content[content_type][\\\"schema\\\"]\\n content[content_type][\\\"schema\\\"] = self.openapi.resolve_schema_dict(schema)\",\n \"def apicall():\\r\\n# try:\\r\\n print request.get_json()\\r\\n test_json = request.get_json()\\r\\n logger.info(\\\"input json object loaded\\\")\\r\\n logger.info(test_json)\\r\\n k=MetaData(test_json)\\r\\n int_res=k.getData()\\r\\n print '------------------------------'\\r\\n print int_res\\r\\n return jsonify(int_res)\",\n \"def __json_schema_generator(file):\\n try:\\n data = json.load(file)\\n metadata_set = set()\\n try:\\n for datum in data['meta']['view']['columns']:\\n metadata_set.add(datum['name'])\\n except Exception as e:\\n metadata_set.clear()\\n for datum in data:\\n if isinstance(datum, str):\\n metadata_set.add(datum)\\n else:\\n for datum_property in datum:\\n metadata_set.add(str(datum_property))\\n\\n metadata_list = list(metadata_set)\\n # assumes list of objects with sparsse data\\n # OR\\n # for data_property in data[0]:\\n # metadata_list.append(data_property)\\n # assumes list of objects and that first entry has full list of properties\\n\\n return SchemaGenerator.__build_schema(metadata_list)\\n except Exception as e:\\n logging.error('Failed to parse json file into schema: ' + str(e))\\n raise FailedCreatingSchemaException(\\\"Failed to create schema from json file.\\\")\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: AppUserBaseSchemaPropertyArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def schema(self):\\n pass\",\n \"def load(self, request, resource=None, **kwargs):\\n schema = self.get_schema(request, resource=resource, **kwargs)\\n data = yield from self.parse(request)\\n resource, errors = schema.load(data, partial=resource is not None)\\n if errors:\\n raise RESTBadRequest(reason='Bad request', json={'errors': errors})\\n return resource\",\n \"def test_uses_json_decoder(schema_builder, json_spec):\\n builder = ExtractorBuilder(schema_builder)\\n extractor = builder.build_param_extractor(json_spec)\\n assert isinstance(extractor.decoder, JSONDecoder)\\n extractor = builder.build_body_extractor({'content': json_spec['content']})\\n assert isinstance(extractor.decoder, JSONDecoder)\",\n \"def test_successful_parse_undocumented_endpoints(monkeypatch) -> None:\\n monkeypatch.setattr(django_settings, 'SWAGGER_TESTER', {'PATH': yml_path})\\n monkeypatch.setattr('django_swagger_tester.static_schema.loader.LoadStaticSchema.get_schema', ret_schema)\\n for url in ['/api/v1/cars/incorrect/', '/api/v1/trucks/incorrect/']:\\n base = LoadStaticSchema(url, 'get', status_code=200)\\n base.get_response_schema()\",\n \"def do_post_parse_json(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def _install_hypothesis_jsonschema_compatibility_shim() -> None:\\n hypothesis_jsonschema._from_schema._get_format_filter = _get_format_filter\",\n \"def _resolver_middleware(next_middleware, root, info, **args):\\n pin = Pin.get_from(graphql)\\n if not pin or not pin.enabled():\\n return next_middleware(root, info, **args)\\n\\n with pin.tracer.trace(\\n name=\\\"graphql.resolve\\\",\\n resource=info.field_name,\\n span_type=SpanTypes.GRAPHQL,\\n ) as span:\\n span.set_tag_str(COMPONENT, config.graphql.integration_name)\\n\\n return next_middleware(root, info, **args)\",\n \"def get_schema(path):\\n with open(path, 'r') as f:\\n return json.load(f)\",\n \"def load_json(request, json):\\n return json\",\n \"def __init__(self, schema_row):\\n self.schema = []\\n for field in schema_row['fields']:\\n self.schema.append(field['type'])\",\n \"def resolve(self, spec: \\\"ModelSpec\\\"):\",\n \"def get_schema() -> dict:\\n raise NotImplementedError()\",\n \"def import_and_index_resolutions():\\n sys.path.append(os.path.abspath('import_scripts'))\\n import import_resolutioninstance\\n import_resolutioninstance.ResolutionImporter().load_items()\",\n \"def get_schema(schema): # noqa: E501\\n return 'do some magic!'\",\n \"def get(self, entity):\\n return jsonify({p.schema: p.to_json() for p in entity.profiles})\",\n \"def resolve_schema_references(self, definition):\\n # type: (Generator, Dict) -> None\\n if \\\"$ref\\\" in definition:\\n schema_reference = definition.pop(\\\"$ref\\\")\\n section, name = schema_reference.split(\\\"/\\\")[-2:]\\n referenced_definition = self.parser.specification[section][name]\\n definition.update(referenced_definition)\\n\\n for value in definition.values():\\n if isinstance(value, dict):\\n self.resolve_schema_references(value)\",\n \"def schema_mappings(self):\\n pass\",\n \"def __init__(self):\\r\\n self.apiroot = 'http://ws.spotify.com/'\\r\\n self.add_filter(self.use_json)\",\n \"def load_schema(filename):\\n with open(filename) as f:\\n schema = json.load(f)\\n\\n return schema\",\n \"def check_valid_schema(context):\\n data = context.response.json()\\n validate_schema(data)\",\n \"def __init__(self, model, options):\\n self.model = model\\n\\n self.index_name = getattr(options, \\\"index_name\\\",\\n self.model._meta.app_label.lower())\\n self.doctype_name = getattr(options, \\\"doctype_name\\\",\\n self.model._meta.object_name.lower())\\n\\n # initialize mapping for instance\\n self._mapping = {\\\"properties\\\": {}}\\n\\n self.id_field = getattr(options, \\\"id_field\\\", \\\"pk\\\")\\n self.excluded_fields = getattr(options, \\\"excluded_fields\\\", tuple())\\n # mapping of custom fields used in elasticsearch but not in the django db and\\n # their related django style fields. See `mapping_utils.FIELD_MAPPING`\\n self.custom_fields = getattr(options, \\\"custom_fields\\\", dict())\\n\\n # mapping of additional options such as \\\"index\\\" \\\"analyze\\\" \\\"store\\\" etc...\\n self.additional_options = getattr(options, \\\"additional_options\\\", dict())\\n\\n _fields = getattr(options, \\\"fields\\\", [])\\n _fields.extend(self.custom_fields.keys())\\n self.fields = self._get_fields(_fields, self.excluded_fields)\\n\\n self.id_field_type = getattr(options, \\\"id_field_type\\\", \\\"long\\\")\\n self.fields.append(mapping.SearchField(\\\"id\\\", self.id_field_type))\\n\\n # get the serializer class\\n self.serializer_class = getattr(options, \\\"serializer_class\\\",\\n serializers.ModelJSONSerializer)\",\n \"def read(self):\\n schema_object = self.get_schema_object()\\n if self.id is not None:\\n self.response = self.request('GET', self.read_endpoint + \\\"/\\\"\\n + str(self.id), \\\"\\\")\\n\\n else:\\n self.response = self.request('GET', self.read_endpoint, \\\"\\\")\\n self.log.debug(self.response.status)\\n payload_schema = self.response.read()\\n if payload_schema != None and payload_schema != \\\"\\\":\\n schema_object.set_data(payload_schema, self.accept_type)\\n else:\\n return None\\n return schema_object\",\n \"def validate_json_schema(self, json_schema):\\n cls = validators.validator_for(json_schema)\\n cls.check_schema(json_schema)\",\n \"def __init__(self, filepath):\\n BaseHandler.__init__(self)\\n\\n # open the YAML file and parse configuration\\n try:\\n with open(filepath, \\\"r\\\") as fp:\\n fp = open(filepath, \\\"r\\\")\\n config = yaml.safe_load(fp)\\n except Exception as exc:\\n raise OpenFileError(f\\\"Unable to open file {filepath}: {exc}\\\")\\n\\n # add last-modified header from config, if available\\n try:\\n last_modified = config[\\\"dataset\\\"][\\\"last_modified\\\"]\\n if isinstance(last_modified, tuple):\\n last_modified = last_modified[0]\\n if isinstance(last_modified, basestring):\\n last_modified = datetime.strptime(last_modified, \\\"%Y-%m-%d %H:%M:%S\\\")\\n self.additional_headers.append(\\n (\\\"Last-modified\\\", formatdate(time.mktime(last_modified.timetuple())))\\n )\\n except KeyError:\\n pass\\n\\n # Peek types. I'm trying to avoid having the user know about Opendap types,\\n # so they are not specified in the config file. Instead, we request a single\\n # row of data to inspect the data types.\\n # FIXME: type peeking does not work if there are NA values in the sequence!!!\\n cols = tuple(key for key in config if \\\"col\\\" in config[key])\\n with session_scope(config[\\\"database\\\"][\\\"dsn\\\"]) as conn:\\n query = \\\"SELECT {cols} FROM {table} LIMIT 1\\\".format(\\n cols=\\\", \\\".join(config[key][\\\"col\\\"] for key in cols),\\n table=config[\\\"database\\\"][\\\"table\\\"],\\n )\\n results = conn.execute(query)\\n first_row = results.fetchone()\\n\\n dtypes = {}\\n for col, value, description in zip(\\n cols, first_row, results.cursor.description\\n ):\\n # FIXME: This is fraaaagile, and depends on internal, undocumented behaviour from SQLAlchemy\\n if value is None and description[1]:\\n # the value is NULL... try to use the typecode\\n dtypes[col] = {\\n 700: np.dtype(\\\"float64\\\"),\\n 701: np.dtype(\\\"float64\\\"),\\n 1114: np.dtype(\\\"datetime64\\\"),\\n }[description[1]]\\n elif type(value) == datetime:\\n dtypes[col] = np.dtype(\\\"datetime64\\\")\\n else:\\n dtypes[col] = np.array(value).dtype\\n\\n # create the dataset, adding attributes from the config file\\n attrs = config.get(\\\"dataset\\\", {}).copy()\\n name = attrs.pop(\\\"name\\\", os.path.split(filepath)[1])\\n self.dataset = DatasetType(name, attrs)\\n\\n # and now create the sequence\\n attrs = config.get(\\\"sequence\\\", {}).copy()\\n name = attrs.pop(\\\"name\\\", \\\"sequence\\\")\\n seq = self.dataset[quote(name)] = SequenceType(name, config, attrs)\\n for var in cols:\\n attrs = {k: v for k, v in config[var].items() if k != \\\"col\\\"}\\n seq[var] = BaseType(var, attributes=attrs)\\n\\n # set the data\\n seq.data = SQLData(config, seq.id, tuple(cols), dtypes, copy.copy(seq))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63883114","0.58803993","0.5456595","0.54404473","0.5353926","0.53508955","0.5329297","0.53031135","0.5302368","0.52422416","0.5230475","0.51963526","0.51711226","0.51576716","0.51557875","0.5149049","0.5118869","0.51106626","0.5100498","0.508437","0.5075743","0.5065301","0.50647026","0.50647026","0.50591904","0.5046476","0.503554","0.50218886","0.5007239","0.4995478","0.4991476","0.49783063","0.49761328","0.49739146","0.49560034","0.49538866","0.49510893","0.4935797","0.49331942","0.49328345","0.4919937","0.49187502","0.48842296","0.48505154","0.483905","0.48364073","0.48360285","0.48210913","0.48162642","0.4811525","0.48110196","0.4799082","0.47982508","0.47544405","0.47518295","0.4750936","0.47500676","0.47460708","0.47427404","0.47295412","0.4729403","0.4726546","0.47243026","0.4719694","0.47194728","0.4715292","0.47036594","0.4698739","0.46881512","0.4687433","0.46838564","0.46792114","0.4676856","0.46758473","0.46684232","0.46666375","0.46566072","0.46483132","0.46415257","0.46319464","0.46026042","0.46019974","0.4571029","0.4566347","0.45648482","0.4561817","0.45586234","0.45566392","0.45560044","0.4553438","0.4553165","0.4549958","0.45492205","0.45456994","0.45450857","0.45433038","0.45429206","0.45331827","0.4532131","0.45225593"],"string":"[\n \"0.63883114\",\n \"0.58803993\",\n \"0.5456595\",\n \"0.54404473\",\n \"0.5353926\",\n \"0.53508955\",\n \"0.5329297\",\n \"0.53031135\",\n \"0.5302368\",\n \"0.52422416\",\n \"0.5230475\",\n \"0.51963526\",\n \"0.51711226\",\n \"0.51576716\",\n \"0.51557875\",\n \"0.5149049\",\n \"0.5118869\",\n \"0.51106626\",\n \"0.5100498\",\n \"0.508437\",\n \"0.5075743\",\n \"0.5065301\",\n \"0.50647026\",\n \"0.50647026\",\n \"0.50591904\",\n \"0.5046476\",\n \"0.503554\",\n \"0.50218886\",\n \"0.5007239\",\n \"0.4995478\",\n \"0.4991476\",\n \"0.49783063\",\n \"0.49761328\",\n \"0.49739146\",\n \"0.49560034\",\n \"0.49538866\",\n \"0.49510893\",\n \"0.4935797\",\n \"0.49331942\",\n \"0.49328345\",\n \"0.4919937\",\n \"0.49187502\",\n \"0.48842296\",\n \"0.48505154\",\n \"0.483905\",\n \"0.48364073\",\n \"0.48360285\",\n \"0.48210913\",\n \"0.48162642\",\n \"0.4811525\",\n \"0.48110196\",\n \"0.4799082\",\n \"0.47982508\",\n \"0.47544405\",\n \"0.47518295\",\n \"0.4750936\",\n \"0.47500676\",\n \"0.47460708\",\n \"0.47427404\",\n \"0.47295412\",\n \"0.4729403\",\n \"0.4726546\",\n \"0.47243026\",\n \"0.4719694\",\n \"0.47194728\",\n \"0.4715292\",\n \"0.47036594\",\n \"0.4698739\",\n \"0.46881512\",\n \"0.4687433\",\n \"0.46838564\",\n \"0.46792114\",\n \"0.4676856\",\n \"0.46758473\",\n \"0.46684232\",\n \"0.46666375\",\n \"0.46566072\",\n \"0.46483132\",\n \"0.46415257\",\n \"0.46319464\",\n \"0.46026042\",\n \"0.46019974\",\n \"0.4571029\",\n \"0.4566347\",\n \"0.45648482\",\n \"0.4561817\",\n \"0.45586234\",\n \"0.45566392\",\n \"0.45560044\",\n \"0.4553438\",\n \"0.4553165\",\n \"0.4549958\",\n \"0.45492205\",\n \"0.45456994\",\n \"0.45450857\",\n \"0.45433038\",\n \"0.45429206\",\n \"0.45331827\",\n \"0.4532131\",\n \"0.45225593\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5573852"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":18096,"cells":{"query":{"kind":"string","value":"Name scope. Must be defined by implementations."},"document":{"kind":"string","value":"def name_scope(self):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def scope(self, name):\r\n raise NotImplementedError","def _set_name_scope(self):\n if self.name is None:\n self._name_scope = self.__class__.__name__\n elif self.name == '<lambda>':\n self._name_scope = 'lambda'\n else:\n # E.g. 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Can be overridden by implementations that would like to disable this behavior."},"document":{"kind":"string","value":"def check_num_anchors(self):\n return True"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def num_anchors_per_location(self):\n pass","def adjust_anchors(self):\n pass","def is_anchor_valid(self):\n return self.properties.get('IsAnchorValid', None)","def _assert_correct_number_of_anchors(self, anchors_list,\n feature_map_shape_list):\n expected_num_anchors = 0\n actual_num_anchors = 0\n for num_anchors_per_location, feature_map_shape, anchors in zip(\n self.num_anchors_per_location(), feature_map_shape_list, anchors_list):\n expected_num_anchors += (num_anchors_per_location\n * feature_map_shape[0]\n * feature_map_shape[1])\n actual_num_anchors += anchors.num_boxes()\n return tf.assert_equal(expected_num_anchors, actual_num_anchors)","def check_linked(self):\n\n count = 0\n for house in self.houses.values():\n if house.link:\n count += 1\n if count is 150:\n return True\n else:\n return False","def use_proxy(self, request):\n if self.adsl:\n return True\n if \"depth\" in request.meta and int(request.meta['depth']) <= 2:\n return False\n i = random.randint(1, 10)\n return i <= 2","def generate_anchors(self):\n self.anchors = np.zeros((self.anchor_num, 4), dtype=np.float32)\n size = self.stride * self.stride\n count = 0\n for r in self.ratios:\n ws = int(math.sqrt(size * 1. / r))\n hs = int(ws * r)\n\n for s in self.scales:\n w = ws * s\n h = hs * s\n self.anchors[count][:] = [-w * 0.5, -h * 0.5, w * 0.5, h * 0.5][:]\n count += 1","def dynamic_links(self) -> bool:\n return pulumi.get(self, \"dynamic_links\")","def check_hits(self):\n return 3 <= len(self.seqdata.hits) <= 10","def get_anchor_points(self):\n rows, cols = np.where(self.overlap_mask)\n self.anchor_points = tuple(zip(rows, cols))[:: self.sampling_int]\n print(\"# of anchors: {}\".format(len(self.anchor_points)))","def provides_defaults_for(self, rule):\n return not self.is_build_only and \\\n self.endpoint == rule.endpoint and self != rule and \\\n self.arguments == rule.arguments","def enable_all_link_aliasing(self):\n return self._enable_all_link_aliasing","def ExclusiveAddressUse(self) -> bool:","def ExclusiveAddressUse(self) -> bool:","def ExclusiveAddressUse(self) -> bool:","def ExclusiveAddressUse(self) -> bool:","def __len__(self) -> int:\n return len(self.links)","def isAnchor(node):\n # TODO What is considered an anchor needs to be subject to an option\n return bool((isinstance(node, nodes.target)\n or isinstance(node, nodes.Structural))\n and node[DuAttrIds]\n and not node.get(DuAttrRefuri, None))","def use_proxy(self, request):\n # if \"depth\" in request.meta and int(request.meta['depth']) <= 2:\n # return False\n # i = random.randint(1, 10)\n # return i <= 2\n return True","def num_adversaries(self) -> int:\n pass","def use_proxy(self, request):\n #if \"depth\" in request.meta and int(request.meta['depth']) <= 2:\n # return False\n #i = random.randint(1, 10)\n #return i <= 2\n return True","def _should_restore_default_page_size(self, params):\n params = True if params else False\n return params and not self.request_record_limit_is_reduced and self.last_api_call_is_successful","def num_anchors_per_localization(self):\n num_rot = len(self._rotations)\n num_size = np.array(self._sizes).reshape([-1, 3]).shape[0]\n return num_rot * num_size","def sectional_overwrite_check(self):\n\n for rule in self.options['sectional_overwrite']:\n if self.lineage_test(rule):\n return True\n return False","def onGoal(self):\n return self.index == len(self.path)","def isAutomaticLink(self):\n return _osgAnimation.AnimationManagerBase_isAutomaticLink(self)","def sectional_overwrite_no_negate_check(self):\n\n for rule in self.options[\n 'sectional_overwrite_no_negate']:\n if self.lineage_test(rule):\n return True\n return False","def anchors(self):\n dims = self.dims\n anchors = []\n for peak in self:\n possible_anchors = []\n for combination in combinations(range(dims), 2):\n spins = [peak[i] for i in combination]\n if any(s.res_num is None or s.atom is None for s in spins):\n continue\n res_nums = [spin.res_num for spin in spins]\n atoms = [spin.atom for spin in spins]\n elements = [atom[0] for atom in atoms]\n positions = [atom[1:] for atom in atoms]\n same_res_num = res_nums[0] == res_nums[1]\n valid_pairs = [set(('H', 'N')), set(('H', 'C'))]\n is_proton_heavy_pair = set(elements) in valid_pairs\n same_position = all(c[0] == c[1] for c in zip(*positions))\n if same_res_num and is_proton_heavy_pair and same_position:\n if '' in positions and set(elements) != set(('H', 'N')):\n # One of the atom names must have been 'H', 'N' or 'C'\n # Of these, only the amide proton anchor is valid\n continue\n if elements[0] == 'H':\n possible_anchors.append(combination)\n else:\n possible_anchors.append(combination[::-1])\n if len(possible_anchors) > 1:\n pa_sets = [set(pa) for pa in possible_anchors]\n overlap = set.intersection(*pa_sets)\n if overlap:\n # Ambiguous, overlapping anchors\n continue\n for poss_anc in possible_anchors:\n if poss_anc not in anchors:\n anchors.append(poss_anc)\n anchors = tuple(anchors)\n return anchors","def have_to_paginate(self):\n if self.get_max_per_page() and self.get_nb_results() > self.get_max_per_page():\n return True\n return False","def is_anchored(self):\n return self.anchor is not None","def _filter_anchors(anchors):\n #TODO: Implement this function\n return anchors","def HasMultiplePages(self):\r\n\r\n return True","def with_manual_check_never(self):\n self.__manual_check = constants.NEVER\n return self","def running(self):\r\n return self.__maxlen__ > 0","def still_has_questions(self):\n return self.question_number < len(self.question_list)","def needToLink(self):\n return _osgAnimation.AnimationManagerBase_needToLink(self)","def can_go_forward(self):\n return self._pointer + 1 < len(self._items)","def clicks_ignored(self):\n result = self._ignore_count > 0\n self._ignore_count = max(0, self._ignore_count - 1)\n return result","def num_links(self, num_links):\n self._num_links = num_links","def with_manual_check_always(self):\n self.__manual_check = constants.ALWAYS\n return self","def hintsLeft(self):\n return self.hints > 0","def _is_new_(self):\n\n #we can't call get_href in here, otherwise we'll have an infinite loop:\n return False if self._read_property_(self.HREF_PROP_NAME) else True","def noyable(self):\n return False","def allocate(self) -> bool:\n if hasattr(self.at_options, 'allocate'):\n return self.at_options.allocate == 1\n return False","def can_recycle(self):\n return self.max_cycles < 0 or self.cycles < self.max_cycles - 1","def has_next(self):\n return self.count < len(self)","def inc_para_gateways(self):\r\n self.num_para_gateways += 1","def delay_checks(self):\n return False","def pageable(self):\n return maxSRAM(self.mem) <= self.dev.SRAM_PAGE_LEN","def max_num_links(self):\n return self._max_num_links","def dupable_matches_required(self):\n return 2","def shuffle_if_needed(self):\n if self.index >= len(self.wallpapers):\n self.shuffle()\n return True\n else:\n return False","def KeepAdvancingSolutionLoop(self):\n return self.step < self.nsteps","def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):\n split_inside_flags = torch.split(inside_flags, num_level_anchors)\n num_level_anchors_inside = [\n int(flags.sum()) for flags in split_inside_flags\n ]\n return num_level_anchors_inside","def generate_all_anchors(self):\n self.feature_sizes = [int(np.round(self.resolution/stride)) for stride in self.strides]\n \n #generate all anchors for each level of the FPN\n all_anchors = [self.generate_feature_level_base_anchors(size=size) for size in self.sizes]\n all_anchors = [self.shift_and_duplicate(layer_anchors, feature_size, stride) for layer_anchors, feature_size, stride in zip(all_anchors, self.feature_sizes, self.strides)]\n all_anchors = tf.concat(all_anchors, axis=0)\n\n return all_anchors","def valid_anchor_boxes(anchor_boxes, image_size):\n img_height, img_width = image_size\n y, x, height, width = np.transpose(anchor_boxes)\n\n # TODO(Mocurin) Optimize?\n # Indicator matrix\n indicators = np.array([y - height // 2 >= 0,\n x - width // 2 >= 0,\n y + height // 2 <= img_height,\n x + width // 2 <= img_width]).transpose()\n\n # Get indices of anchor boxes inside image\n return np.flatnonzero(np.all(indicators, axis=1, keepdims=False))","def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):\n\n split_inside_flags = torch.split(inside_flags, num_level_anchors)\n num_level_anchors_inside = [\n int(flags.sum()) for flags in split_inside_flags\n ]\n return num_level_anchors_inside","def num_links(self):\n return self._num_links","def fixed_order(self) -> bool:\n if hasattr(self.at_options, 'fixed_order'):\n return self.at_options.fixed_order == 1\n return False","def isHighLinkDensity(self, e):\n links = Parser.getElementsByTag(e, tag='a')\n if links is None or len(links) == 0:\n return False\n \n text = Parser.getText(e)\n words = text.split(' ')\n numberOfWords = float(len(words))\n sb = []\n for link in links:\n sb.append(Parser.getText(link))\n \n linkText = ''.join(sb)\n linkWords = linkText.split(' ')\n numberOfLinkWords = float(len(linkWords))\n numberOfLinks = float(len(links))\n linkDivisor = float(numberOfLinkWords / numberOfWords)\n score = float(linkDivisor * numberOfLinks)\n if score >= 1.0:\n return True\n return False\n # return True if score > 1.0 else False","def is_last_allowable_page(self):\n if self.countable:\n return False\n if self.is_last_page:\n return False\n\n # If we have 10-item pages, the max limit is 40, and we've skipped 38,\n # it's still okay to see the next (integral) page\n if int(self.current_page + 1) * self.page_size > self.maximum_skip:\n return True\n\n return False","def max_num_links(self, max_num_links):\n self._max_num_links = max_num_links","def break_count(self):\n return len(self.link_ids) + len(self.crossring_cleavages)","def get_anchors(self, featmap_sizes, img_metas, device='cuda'):\n num_imgs = len(img_metas)\n\n # since feature map sizes of all images are the same, we only compute\n # anchors for one time\n multi_level_anchors = self.anchor_generator.grid_anchors(\n featmap_sizes, device)\n anchor_list = [multi_level_anchors for _ in range(num_imgs)]\n\n # for each image, we compute valid flags of multi level anchors\n valid_flag_list = []\n for img_id, img_meta in enumerate(img_metas):\n multi_level_flags = self.anchor_generator.valid_flags(\n featmap_sizes, img_meta['pad_shape'], device)\n valid_flag_list.append(multi_level_flags)\n\n return anchor_list, valid_flag_list","def has_rule(self):\n # Someday I'll have a real implementation, but for now I just:\n return False","def testAnchor(self):\n self.assertEqual(['http://foo.com/page.html#anchor'], grab('http://foo.com/page.html#anchor', self.needScheme))","def condition_absence_ancestors(self, position):\n return 2 * position + 1 >= self.size_heap and 2 * position + 2 >= self.size_heap","def test_store_elements_count_exceed_default_limit(self, mocker):\n proxy = mocker.patch('saana_lib.ranking.RankingToDatabase.proxy')\n _compute = mocker.patch('saana_lib.ranking.Ranking.compute')\n\n _compute.return_value = dict((i, list(range(5))) for i in range(10))\n self.klass.store()\n assert proxy.call_count == 20","def test_recommended_num_references(self):\n\n batch = Batch(Mock())\n self.check_instance(batch)\n\n self.assertIsNone(batch.recommended_num_references)\n self.check_instance(batch)\n\n batch._recommended_num_references = 10\n self.assertEqual(batch.recommended_num_references, 10)\n self.check_instance(batch, recom_num_ref=10)\n\n batch._recommended_num_references = 20\n self.assertEqual(batch.recommended_num_references, 20)\n self.check_instance(batch, recom_num_ref=20)","def get_all_anchors(stride=None, sizes=None):\n if stride is None:\n stride = cfg.ANCHOR.ANCHOR_STRIDE\n if sizes is None:\n sizes = cfg.ANCHOR.ANCHOR_SIZES\n # Generates a NAx4 matrix of anchor boxes in (x1, y1, x2, y2) format. Anchors\n # are centered on stride / 2, have (approximate) sqrt areas of the specified\n # sizes, and aspect ratios as given.\n cell_anchors = generate_anchors(\n stride,\n scales=np.array(sizes, dtype=np.float) / stride,\n ratios=np.array(cfg.ANCHOR.ANCHOR_RATIOS, dtype=np.float))\n # anchors are intbox here.\n # anchors at featuremap [0,0] are centered at fpcoor (8,8) (half of stride)\n\n max_size = cfg.DATA.MAX_SIZE\n field_size = int(np.ceil(max_size / stride))\n shifts = np.arange(0, field_size) * stride\n shift_x, shift_y = np.meshgrid(shifts, shifts)\n shift_x = shift_x.flatten()\n shift_y = shift_y.flatten()\n shifts = np.vstack((shift_x, shift_y, shift_x, shift_y)).transpose()\n # Kx4, K = field_size * field_size\n K = shifts.shape[0]\n\n A = cell_anchors.shape[0]\n field_of_anchors = (\n cell_anchors.reshape((1, A, 4)) +\n shifts.reshape((1, K, 4)).transpose((1, 0, 2)))\n field_of_anchors = field_of_anchors.reshape((field_size, field_size, A, 4))\n # FSxFSxAx4\n # Many rounding happens inside the anchor code anyway\n # assert np.all(field_of_anchors == field_of_anchors.astype('int32'))\n field_of_anchors = field_of_anchors.astype('float32')\n field_of_anchors[:, :, :, [2, 3]] += 1\n return field_of_anchors","def test_target_number_less_than_alp(self):\n alp = list(range(10))\n targets = generate_targets(alp, 5)\n self.assertEqual(len(targets), 5)\n self.assertEqual(len(targets), len(set(targets)))","def _optimized(self):\n return False","def set_internal(self):\n self.internal_bisect = True # pragma: no cover","def _arguments_valid(self) -> bool:\n return self.find and self.near and self.max_results >= 1","def manual_check(self):\n return self.__manual_check","def has_next(self):\n return self.page < self.pages","def has_next(self):\n return self.page < self.pages","def has_next(self):\n return self.page < self.pages","def should_pay_attention(self):\n return random.randint(1,100) > self.wander","def everytime(self):\n return True","def optimized_for_frequent_attach(self) -> Optional[bool]:\n return pulumi.get(self, \"optimized_for_frequent_attach\")","def reordered(self):\n return bool(self.reordered_rules)","def still_has_questions(self):\n return self.question_number < len(self.question_list) #returns boolean value","def is_highlink_density(self, e):\r\n links = self.parser.getElementsByTag(e, tag='a')\r\n if links is None or len(links) == 0:\r\n return False\r\n\r\n text = self.parser.getText(e)\r\n words = text.split(' ')\r\n words_number = float(len(words))\r\n sb = []\r\n for link in links:\r\n sb.append(self.parser.getText(link))\r\n\r\n linkText = ''.join(sb)\r\n linkWords = linkText.split(' ')\r\n numberOfLinkWords = float(len(linkWords))\r\n numberOfLinks = float(len(links))\r\n linkDivisor = float(numberOfLinkWords / words_number)\r\n score = float(linkDivisor * numberOfLinks)\r\n if score >= 1.0:\r\n return True\r\n return False\r\n # return True if score > 1.0 else False\r","def is_highlink_density(self, e):\n links = self.parser.getElementsByTag(e, tag='a')\n if links is None or len(links) == 0:\n return False\n\n text = self.parser.getText(e)\n words = text.split(' ')\n words_number = float(len(words))\n sb = []\n for link in links:\n sb.append(self.parser.getText(link))\n\n linkText = ''.join(sb)\n linkWords = linkText.split(' ')\n numberOfLinkWords = float(len(linkWords))\n numberOfLinks = float(len(links))\n linkDivisor = float(numberOfLinkWords / words_number)\n score = float(linkDivisor * numberOfLinks)\n if score >= 1.0:\n return True\n return False\n # return True if score > 1.0 else False","def _generate_anchors(point, sizes, aspect_ratios, layout, beta, include_depth):\n\n distance = point[2]\n base_size = sizes[0]\n scales = sizes[1:] / base_size\n # beta = 8\n scales = (beta/distance)*scales\n\n center = (point[0], point[1])\n anchor = np.array([center[0] - base_size/2.0, center[1] - base_size/2.0,\n center[0] + base_size/2.0, center[1] + base_size/2.0],\n dtype=np.float)\n\n anchors = _ratio_enum(anchor, aspect_ratios)\n anchors = np.vstack(\n [_scale_enum(anchors[i, :], scales) for i in range(anchors.shape[0])]\n )\n\n all_anchors = np.empty((0,4))\n for l in layout:\n new_anchors = _shift_anchors(anchors, l)\n all_anchors = np.vstack((all_anchors, new_anchors))\n\n if int(include_depth)==1:\n # Add the distance as the 5th element to all anchors\n new_shape = (all_anchors.shape[0], all_anchors.shape[1]+1)\n new_anchors = np.ones(new_shape) * distance\n new_anchors[:,:-1] = all_anchors\n all_anchors = new_anchors\n\n return all_anchors","def should_start_analysis(self):\n return len(self.task_queue) >= self.bulk_size","def is_doc_length_exceeded(self):\n return self._tag == 'doc_length_exceeded'","def is_doc_length_exceeded(self):\n return self._tag == 'doc_length_exceeded'","def isLinkADRAns(self):\n return self.cid == LINKADRANS","def embed_links(_) -> int:\n return 1 << 14","def embed_links(_) -> int:\n return 1 << 14","def __bool__(self):\n return bool(self.count())","def _check_unaligned_alns(self, aln_parse_function, *extra_args):\n fake_aln_unaligned_1 = Fake_HTSeq_aln('AAA', 'name', unaligned=True, optional_field_data={'XM':1})\n fake_aln_unaligned_2 = Fake_HTSeq_aln('AAA', 'name', unaligned=True, optional_field_data={})\n fake_aln_multi_aligned_1 = Fake_HTSeq_aln('AAA', 'name', unaligned=True, optional_field_data={'XM':2})\n fake_aln_multi_aligned_2 = Fake_HTSeq_aln('AAA', 'name', unaligned=True, optional_field_data={'XM':20})\n assert aln_parse_function(fake_aln_unaligned_1, *extra_args) == SPECIAL_POSITIONS.unaligned\n assert aln_parse_function(fake_aln_unaligned_2, *extra_args) == SPECIAL_POSITIONS.unaligned\n assert aln_parse_function(fake_aln_multi_aligned_1, *extra_args) == SPECIAL_POSITIONS.multi_aligned\n assert aln_parse_function(fake_aln_multi_aligned_2, *extra_args) == SPECIAL_POSITIONS.multi_aligned","def __create_anchors(self, sizes, aspects):\n k = len(sizes) * len(aspects)\n img_anchors = []\n for i in sizes:\n for j in aspects:\n img_anchors.append(\n [0, 0, 2 * i * j[0] / (j[0] + j[1]), 2 * i * j[1] / (j[0] + j[1])])\n\n self.anchors = np.asarray(img_anchors)","def should_check_refcount(self):\n raise NotImplementedError()","def __len__(self) -> int:\n\t\treturn len(self._articles)","def is_inequality(self): \n return False","def has_aids(self):\n\n return self.exclude(aids=None)","def check_manipulation(self):\n if len(self.used_algo) > self.THRESHOLD:\n self.logger.log(\n \"Possible system manipulation detected as deviating hashing algorithm used.\",\n logtype=\"warning\"\n )"],"string":"[\n \"def num_anchors_per_location(self):\\n pass\",\n \"def adjust_anchors(self):\\n pass\",\n \"def is_anchor_valid(self):\\n return self.properties.get('IsAnchorValid', None)\",\n \"def _assert_correct_number_of_anchors(self, anchors_list,\\n feature_map_shape_list):\\n expected_num_anchors = 0\\n actual_num_anchors = 0\\n for num_anchors_per_location, feature_map_shape, anchors in zip(\\n self.num_anchors_per_location(), feature_map_shape_list, anchors_list):\\n expected_num_anchors += (num_anchors_per_location\\n * feature_map_shape[0]\\n * feature_map_shape[1])\\n actual_num_anchors += anchors.num_boxes()\\n return tf.assert_equal(expected_num_anchors, actual_num_anchors)\",\n \"def check_linked(self):\\n\\n count = 0\\n for house in self.houses.values():\\n if house.link:\\n count += 1\\n if count is 150:\\n return True\\n else:\\n return False\",\n \"def use_proxy(self, request):\\n if self.adsl:\\n return True\\n if \\\"depth\\\" in request.meta and int(request.meta['depth']) <= 2:\\n return False\\n i = random.randint(1, 10)\\n return i <= 2\",\n \"def generate_anchors(self):\\n self.anchors = np.zeros((self.anchor_num, 4), dtype=np.float32)\\n size = self.stride * self.stride\\n count = 0\\n for r in self.ratios:\\n ws = int(math.sqrt(size * 1. / r))\\n hs = int(ws * r)\\n\\n for s in self.scales:\\n w = ws * s\\n h = hs * s\\n self.anchors[count][:] = [-w * 0.5, -h * 0.5, w * 0.5, h * 0.5][:]\\n count += 1\",\n \"def dynamic_links(self) -> bool:\\n return pulumi.get(self, \\\"dynamic_links\\\")\",\n \"def check_hits(self):\\n return 3 <= len(self.seqdata.hits) <= 10\",\n \"def get_anchor_points(self):\\n rows, cols = np.where(self.overlap_mask)\\n self.anchor_points = tuple(zip(rows, cols))[:: self.sampling_int]\\n print(\\\"# of anchors: {}\\\".format(len(self.anchor_points)))\",\n \"def provides_defaults_for(self, rule):\\n return not self.is_build_only and \\\\\\n self.endpoint == rule.endpoint and self != rule and \\\\\\n self.arguments == rule.arguments\",\n \"def enable_all_link_aliasing(self):\\n return self._enable_all_link_aliasing\",\n \"def ExclusiveAddressUse(self) -> bool:\",\n \"def ExclusiveAddressUse(self) -> bool:\",\n \"def ExclusiveAddressUse(self) -> bool:\",\n \"def ExclusiveAddressUse(self) -> bool:\",\n \"def __len__(self) -> int:\\n return len(self.links)\",\n \"def isAnchor(node):\\n # TODO What is considered an anchor needs to be subject to an option\\n return bool((isinstance(node, nodes.target)\\n or isinstance(node, nodes.Structural))\\n and node[DuAttrIds]\\n and not node.get(DuAttrRefuri, None))\",\n \"def use_proxy(self, request):\\n # if \\\"depth\\\" in request.meta and int(request.meta['depth']) <= 2:\\n # return False\\n # i = random.randint(1, 10)\\n # return i <= 2\\n return True\",\n \"def num_adversaries(self) -> int:\\n pass\",\n \"def use_proxy(self, request):\\n #if \\\"depth\\\" in request.meta and int(request.meta['depth']) <= 2:\\n # return False\\n #i = random.randint(1, 10)\\n #return i <= 2\\n return True\",\n \"def _should_restore_default_page_size(self, params):\\n params = True if params else False\\n return params and not self.request_record_limit_is_reduced and self.last_api_call_is_successful\",\n \"def num_anchors_per_localization(self):\\n num_rot = len(self._rotations)\\n num_size = np.array(self._sizes).reshape([-1, 3]).shape[0]\\n return num_rot * num_size\",\n \"def sectional_overwrite_check(self):\\n\\n for rule in self.options['sectional_overwrite']:\\n if self.lineage_test(rule):\\n return True\\n return False\",\n \"def onGoal(self):\\n return self.index == len(self.path)\",\n \"def isAutomaticLink(self):\\n return _osgAnimation.AnimationManagerBase_isAutomaticLink(self)\",\n \"def sectional_overwrite_no_negate_check(self):\\n\\n for rule in self.options[\\n 'sectional_overwrite_no_negate']:\\n if self.lineage_test(rule):\\n return True\\n return False\",\n \"def anchors(self):\\n dims = self.dims\\n anchors = []\\n for peak in self:\\n possible_anchors = []\\n for combination in combinations(range(dims), 2):\\n spins = [peak[i] for i in combination]\\n if any(s.res_num is None or s.atom is None for s in spins):\\n continue\\n res_nums = [spin.res_num for spin in spins]\\n atoms = [spin.atom for spin in spins]\\n elements = [atom[0] for atom in atoms]\\n positions = [atom[1:] for atom in atoms]\\n same_res_num = res_nums[0] == res_nums[1]\\n valid_pairs = [set(('H', 'N')), set(('H', 'C'))]\\n is_proton_heavy_pair = set(elements) in valid_pairs\\n same_position = all(c[0] == c[1] for c in zip(*positions))\\n if same_res_num and is_proton_heavy_pair and same_position:\\n if '' in positions and set(elements) != set(('H', 'N')):\\n # One of the atom names must have been 'H', 'N' or 'C'\\n # Of these, only the amide proton anchor is valid\\n continue\\n if elements[0] == 'H':\\n possible_anchors.append(combination)\\n else:\\n possible_anchors.append(combination[::-1])\\n if len(possible_anchors) > 1:\\n pa_sets = [set(pa) for pa in possible_anchors]\\n overlap = set.intersection(*pa_sets)\\n if overlap:\\n # Ambiguous, overlapping anchors\\n continue\\n for poss_anc in possible_anchors:\\n if poss_anc not in anchors:\\n anchors.append(poss_anc)\\n anchors = tuple(anchors)\\n return anchors\",\n \"def have_to_paginate(self):\\n if self.get_max_per_page() and self.get_nb_results() > self.get_max_per_page():\\n return True\\n return False\",\n \"def is_anchored(self):\\n return self.anchor is not None\",\n \"def _filter_anchors(anchors):\\n #TODO: Implement this function\\n return anchors\",\n \"def HasMultiplePages(self):\\r\\n\\r\\n return True\",\n \"def with_manual_check_never(self):\\n self.__manual_check = constants.NEVER\\n return self\",\n \"def running(self):\\r\\n return self.__maxlen__ > 0\",\n \"def still_has_questions(self):\\n return self.question_number < len(self.question_list)\",\n \"def needToLink(self):\\n return _osgAnimation.AnimationManagerBase_needToLink(self)\",\n \"def can_go_forward(self):\\n return self._pointer + 1 < len(self._items)\",\n \"def clicks_ignored(self):\\n result = self._ignore_count > 0\\n self._ignore_count = max(0, self._ignore_count - 1)\\n return result\",\n \"def num_links(self, num_links):\\n self._num_links = num_links\",\n \"def with_manual_check_always(self):\\n self.__manual_check = constants.ALWAYS\\n return self\",\n \"def hintsLeft(self):\\n return self.hints > 0\",\n \"def _is_new_(self):\\n\\n #we can't call get_href in here, otherwise we'll have an infinite loop:\\n return False if self._read_property_(self.HREF_PROP_NAME) else True\",\n \"def noyable(self):\\n return False\",\n \"def allocate(self) -> bool:\\n if hasattr(self.at_options, 'allocate'):\\n return self.at_options.allocate == 1\\n return False\",\n \"def can_recycle(self):\\n return self.max_cycles < 0 or self.cycles < self.max_cycles - 1\",\n \"def has_next(self):\\n return self.count < len(self)\",\n \"def inc_para_gateways(self):\\r\\n self.num_para_gateways += 1\",\n \"def delay_checks(self):\\n return False\",\n \"def pageable(self):\\n return maxSRAM(self.mem) <= self.dev.SRAM_PAGE_LEN\",\n \"def max_num_links(self):\\n return self._max_num_links\",\n \"def dupable_matches_required(self):\\n return 2\",\n \"def shuffle_if_needed(self):\\n if self.index >= len(self.wallpapers):\\n self.shuffle()\\n return True\\n else:\\n return False\",\n \"def KeepAdvancingSolutionLoop(self):\\n return self.step < self.nsteps\",\n \"def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):\\n split_inside_flags = torch.split(inside_flags, num_level_anchors)\\n num_level_anchors_inside = [\\n int(flags.sum()) for flags in split_inside_flags\\n ]\\n return num_level_anchors_inside\",\n \"def generate_all_anchors(self):\\n self.feature_sizes = [int(np.round(self.resolution/stride)) for stride in self.strides]\\n \\n #generate all anchors for each level of the FPN\\n all_anchors = [self.generate_feature_level_base_anchors(size=size) for size in self.sizes]\\n all_anchors = [self.shift_and_duplicate(layer_anchors, feature_size, stride) for layer_anchors, feature_size, stride in zip(all_anchors, self.feature_sizes, self.strides)]\\n all_anchors = tf.concat(all_anchors, axis=0)\\n\\n return all_anchors\",\n \"def valid_anchor_boxes(anchor_boxes, image_size):\\n img_height, img_width = image_size\\n y, x, height, width = np.transpose(anchor_boxes)\\n\\n # TODO(Mocurin) Optimize?\\n # Indicator matrix\\n indicators = np.array([y - height // 2 >= 0,\\n x - width // 2 >= 0,\\n y + height // 2 <= img_height,\\n x + width // 2 <= img_width]).transpose()\\n\\n # Get indices of anchor boxes inside image\\n return np.flatnonzero(np.all(indicators, axis=1, keepdims=False))\",\n \"def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):\\n\\n split_inside_flags = torch.split(inside_flags, num_level_anchors)\\n num_level_anchors_inside = [\\n int(flags.sum()) for flags in split_inside_flags\\n ]\\n return num_level_anchors_inside\",\n \"def num_links(self):\\n return self._num_links\",\n \"def fixed_order(self) -> bool:\\n if hasattr(self.at_options, 'fixed_order'):\\n return self.at_options.fixed_order == 1\\n return False\",\n \"def isHighLinkDensity(self, e):\\n links = Parser.getElementsByTag(e, tag='a')\\n if links is None or len(links) == 0:\\n return False\\n \\n text = Parser.getText(e)\\n words = text.split(' ')\\n numberOfWords = float(len(words))\\n sb = []\\n for link in links:\\n sb.append(Parser.getText(link))\\n \\n linkText = ''.join(sb)\\n linkWords = linkText.split(' ')\\n numberOfLinkWords = float(len(linkWords))\\n numberOfLinks = float(len(links))\\n linkDivisor = float(numberOfLinkWords / numberOfWords)\\n score = float(linkDivisor * numberOfLinks)\\n if score >= 1.0:\\n return True\\n return False\\n # return True if score > 1.0 else False\",\n \"def is_last_allowable_page(self):\\n if self.countable:\\n return False\\n if self.is_last_page:\\n return False\\n\\n # If we have 10-item pages, the max limit is 40, and we've skipped 38,\\n # it's still okay to see the next (integral) page\\n if int(self.current_page + 1) * self.page_size > self.maximum_skip:\\n return True\\n\\n return False\",\n \"def max_num_links(self, max_num_links):\\n self._max_num_links = max_num_links\",\n \"def break_count(self):\\n return len(self.link_ids) + len(self.crossring_cleavages)\",\n \"def get_anchors(self, featmap_sizes, img_metas, device='cuda'):\\n num_imgs = len(img_metas)\\n\\n # since feature map sizes of all images are the same, we only compute\\n # anchors for one time\\n multi_level_anchors = self.anchor_generator.grid_anchors(\\n featmap_sizes, device)\\n anchor_list = [multi_level_anchors for _ in range(num_imgs)]\\n\\n # for each image, we compute valid flags of multi level anchors\\n valid_flag_list = []\\n for img_id, img_meta in enumerate(img_metas):\\n multi_level_flags = self.anchor_generator.valid_flags(\\n featmap_sizes, img_meta['pad_shape'], device)\\n valid_flag_list.append(multi_level_flags)\\n\\n return anchor_list, valid_flag_list\",\n \"def has_rule(self):\\n # Someday I'll have a real implementation, but for now I just:\\n return False\",\n \"def testAnchor(self):\\n self.assertEqual(['http://foo.com/page.html#anchor'], grab('http://foo.com/page.html#anchor', self.needScheme))\",\n \"def condition_absence_ancestors(self, position):\\n return 2 * position + 1 >= self.size_heap and 2 * position + 2 >= self.size_heap\",\n \"def test_store_elements_count_exceed_default_limit(self, mocker):\\n proxy = mocker.patch('saana_lib.ranking.RankingToDatabase.proxy')\\n _compute = mocker.patch('saana_lib.ranking.Ranking.compute')\\n\\n _compute.return_value = dict((i, list(range(5))) for i in range(10))\\n self.klass.store()\\n assert proxy.call_count == 20\",\n \"def test_recommended_num_references(self):\\n\\n batch = Batch(Mock())\\n self.check_instance(batch)\\n\\n self.assertIsNone(batch.recommended_num_references)\\n self.check_instance(batch)\\n\\n batch._recommended_num_references = 10\\n self.assertEqual(batch.recommended_num_references, 10)\\n self.check_instance(batch, recom_num_ref=10)\\n\\n batch._recommended_num_references = 20\\n self.assertEqual(batch.recommended_num_references, 20)\\n self.check_instance(batch, recom_num_ref=20)\",\n \"def get_all_anchors(stride=None, sizes=None):\\n if stride is None:\\n stride = cfg.ANCHOR.ANCHOR_STRIDE\\n if sizes is None:\\n sizes = cfg.ANCHOR.ANCHOR_SIZES\\n # Generates a NAx4 matrix of anchor boxes in (x1, y1, x2, y2) format. Anchors\\n # are centered on stride / 2, have (approximate) sqrt areas of the specified\\n # sizes, and aspect ratios as given.\\n cell_anchors = generate_anchors(\\n stride,\\n scales=np.array(sizes, dtype=np.float) / stride,\\n ratios=np.array(cfg.ANCHOR.ANCHOR_RATIOS, dtype=np.float))\\n # anchors are intbox here.\\n # anchors at featuremap [0,0] are centered at fpcoor (8,8) (half of stride)\\n\\n max_size = cfg.DATA.MAX_SIZE\\n field_size = int(np.ceil(max_size / stride))\\n shifts = np.arange(0, field_size) * stride\\n shift_x, shift_y = np.meshgrid(shifts, shifts)\\n shift_x = shift_x.flatten()\\n shift_y = shift_y.flatten()\\n shifts = np.vstack((shift_x, shift_y, shift_x, shift_y)).transpose()\\n # Kx4, K = field_size * field_size\\n K = shifts.shape[0]\\n\\n A = cell_anchors.shape[0]\\n field_of_anchors = (\\n cell_anchors.reshape((1, A, 4)) +\\n shifts.reshape((1, K, 4)).transpose((1, 0, 2)))\\n field_of_anchors = field_of_anchors.reshape((field_size, field_size, A, 4))\\n # FSxFSxAx4\\n # Many rounding happens inside the anchor code anyway\\n # assert np.all(field_of_anchors == field_of_anchors.astype('int32'))\\n field_of_anchors = field_of_anchors.astype('float32')\\n field_of_anchors[:, :, :, [2, 3]] += 1\\n return field_of_anchors\",\n \"def test_target_number_less_than_alp(self):\\n alp = list(range(10))\\n targets = generate_targets(alp, 5)\\n self.assertEqual(len(targets), 5)\\n self.assertEqual(len(targets), len(set(targets)))\",\n \"def _optimized(self):\\n return False\",\n \"def set_internal(self):\\n self.internal_bisect = True # pragma: no cover\",\n \"def _arguments_valid(self) -> bool:\\n return self.find and self.near and self.max_results >= 1\",\n \"def manual_check(self):\\n return self.__manual_check\",\n \"def has_next(self):\\n return self.page < self.pages\",\n \"def has_next(self):\\n return self.page < self.pages\",\n \"def has_next(self):\\n return self.page < self.pages\",\n \"def should_pay_attention(self):\\n return random.randint(1,100) > self.wander\",\n \"def everytime(self):\\n return True\",\n \"def optimized_for_frequent_attach(self) -> Optional[bool]:\\n return pulumi.get(self, \\\"optimized_for_frequent_attach\\\")\",\n \"def reordered(self):\\n return bool(self.reordered_rules)\",\n \"def still_has_questions(self):\\n return self.question_number < len(self.question_list) #returns boolean value\",\n \"def is_highlink_density(self, e):\\r\\n links = self.parser.getElementsByTag(e, tag='a')\\r\\n if links is None or len(links) == 0:\\r\\n return False\\r\\n\\r\\n text = self.parser.getText(e)\\r\\n words = text.split(' ')\\r\\n words_number = float(len(words))\\r\\n sb = []\\r\\n for link in links:\\r\\n sb.append(self.parser.getText(link))\\r\\n\\r\\n linkText = ''.join(sb)\\r\\n linkWords = linkText.split(' ')\\r\\n numberOfLinkWords = float(len(linkWords))\\r\\n numberOfLinks = float(len(links))\\r\\n linkDivisor = float(numberOfLinkWords / words_number)\\r\\n score = float(linkDivisor * numberOfLinks)\\r\\n if score >= 1.0:\\r\\n return True\\r\\n return False\\r\\n # return True if score > 1.0 else False\\r\",\n \"def is_highlink_density(self, e):\\n links = self.parser.getElementsByTag(e, tag='a')\\n if links is None or len(links) == 0:\\n return False\\n\\n text = self.parser.getText(e)\\n words = text.split(' ')\\n words_number = float(len(words))\\n sb = []\\n for link in links:\\n sb.append(self.parser.getText(link))\\n\\n linkText = ''.join(sb)\\n linkWords = linkText.split(' ')\\n numberOfLinkWords = float(len(linkWords))\\n numberOfLinks = float(len(links))\\n linkDivisor = float(numberOfLinkWords / words_number)\\n score = float(linkDivisor * numberOfLinks)\\n if score >= 1.0:\\n return True\\n return False\\n # return True if score > 1.0 else False\",\n \"def _generate_anchors(point, sizes, aspect_ratios, layout, beta, include_depth):\\n\\n distance = point[2]\\n base_size = sizes[0]\\n scales = sizes[1:] / base_size\\n # beta = 8\\n scales = (beta/distance)*scales\\n\\n center = (point[0], point[1])\\n anchor = np.array([center[0] - base_size/2.0, center[1] - base_size/2.0,\\n center[0] + base_size/2.0, center[1] + base_size/2.0],\\n dtype=np.float)\\n\\n anchors = _ratio_enum(anchor, aspect_ratios)\\n anchors = np.vstack(\\n [_scale_enum(anchors[i, :], scales) for i in range(anchors.shape[0])]\\n )\\n\\n all_anchors = np.empty((0,4))\\n for l in layout:\\n new_anchors = _shift_anchors(anchors, l)\\n all_anchors = np.vstack((all_anchors, new_anchors))\\n\\n if int(include_depth)==1:\\n # Add the distance as the 5th element to all anchors\\n new_shape = (all_anchors.shape[0], all_anchors.shape[1]+1)\\n new_anchors = np.ones(new_shape) * distance\\n new_anchors[:,:-1] = all_anchors\\n all_anchors = new_anchors\\n\\n return all_anchors\",\n \"def should_start_analysis(self):\\n return len(self.task_queue) >= self.bulk_size\",\n \"def is_doc_length_exceeded(self):\\n return self._tag == 'doc_length_exceeded'\",\n \"def is_doc_length_exceeded(self):\\n return self._tag == 'doc_length_exceeded'\",\n \"def isLinkADRAns(self):\\n return self.cid == LINKADRANS\",\n \"def embed_links(_) -> int:\\n return 1 << 14\",\n \"def embed_links(_) -> int:\\n return 1 << 14\",\n \"def __bool__(self):\\n return bool(self.count())\",\n \"def _check_unaligned_alns(self, aln_parse_function, *extra_args):\\n fake_aln_unaligned_1 = Fake_HTSeq_aln('AAA', 'name', unaligned=True, optional_field_data={'XM':1})\\n fake_aln_unaligned_2 = Fake_HTSeq_aln('AAA', 'name', unaligned=True, optional_field_data={})\\n fake_aln_multi_aligned_1 = Fake_HTSeq_aln('AAA', 'name', unaligned=True, optional_field_data={'XM':2})\\n fake_aln_multi_aligned_2 = Fake_HTSeq_aln('AAA', 'name', unaligned=True, optional_field_data={'XM':20})\\n assert aln_parse_function(fake_aln_unaligned_1, *extra_args) == SPECIAL_POSITIONS.unaligned\\n assert aln_parse_function(fake_aln_unaligned_2, *extra_args) == SPECIAL_POSITIONS.unaligned\\n assert aln_parse_function(fake_aln_multi_aligned_1, *extra_args) == SPECIAL_POSITIONS.multi_aligned\\n assert aln_parse_function(fake_aln_multi_aligned_2, *extra_args) == SPECIAL_POSITIONS.multi_aligned\",\n \"def __create_anchors(self, sizes, aspects):\\n k = len(sizes) * len(aspects)\\n img_anchors = []\\n for i in sizes:\\n for j in aspects:\\n img_anchors.append(\\n [0, 0, 2 * i * j[0] / (j[0] + j[1]), 2 * i * j[1] / (j[0] + j[1])])\\n\\n self.anchors = np.asarray(img_anchors)\",\n \"def should_check_refcount(self):\\n raise NotImplementedError()\",\n \"def __len__(self) -> int:\\n\\t\\treturn len(self._articles)\",\n \"def is_inequality(self): \\n return False\",\n \"def has_aids(self):\\n\\n return self.exclude(aids=None)\",\n \"def check_manipulation(self):\\n if len(self.used_algo) > self.THRESHOLD:\\n self.logger.log(\\n \\\"Possible system manipulation detected as deviating hashing algorithm used.\\\",\\n logtype=\\\"warning\\\"\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68195313","0.6357228","0.6135154","0.5779674","0.5483983","0.54224265","0.5373215","0.5297422","0.5296277","0.5272198","0.52359825","0.5194844","0.51695627","0.51695627","0.51695627","0.51695627","0.5161574","0.51068735","0.51042","0.5082342","0.507974","0.50734025","0.50408137","0.50343597","0.5019171","0.50056624","0.49957573","0.49922353","0.49905455","0.49815124","0.49711856","0.4965019","0.49613315","0.49250796","0.49225068","0.49149248","0.49100593","0.4898258","0.48975632","0.4895321","0.486583","0.48583898","0.48564988","0.48560464","0.48542538","0.4852834","0.485129","0.48365748","0.48171207","0.4801873","0.47779664","0.47624663","0.4759587","0.4758842","0.47562525","0.47435862","0.47240305","0.47212908","0.471694","0.47158992","0.47140563","0.47126472","0.4712578","0.47124138","0.47021553","0.46983537","0.4679449","0.46778542","0.46586296","0.4640804","0.46386614","0.46261293","0.46168908","0.4611264","0.46056685","0.4602377","0.4602377","0.4602377","0.4600015","0.45993376","0.4598585","0.4593165","0.45896253","0.45854956","0.4581427","0.4579137","0.45782122","0.4574439","0.4574439","0.45737916","0.45688954","0.45688954","0.45657358","0.45585012","0.4558306","0.4557945","0.45562893","0.45557386","0.4545181","0.45428726"],"string":"[\n \"0.68195313\",\n \"0.6357228\",\n \"0.6135154\",\n \"0.5779674\",\n \"0.5483983\",\n \"0.54224265\",\n \"0.5373215\",\n \"0.5297422\",\n \"0.5296277\",\n \"0.5272198\",\n \"0.52359825\",\n \"0.5194844\",\n \"0.51695627\",\n \"0.51695627\",\n \"0.51695627\",\n \"0.51695627\",\n \"0.5161574\",\n \"0.51068735\",\n \"0.51042\",\n \"0.5082342\",\n \"0.507974\",\n \"0.50734025\",\n \"0.50408137\",\n \"0.50343597\",\n \"0.5019171\",\n \"0.50056624\",\n \"0.49957573\",\n \"0.49922353\",\n \"0.49905455\",\n \"0.49815124\",\n \"0.49711856\",\n \"0.4965019\",\n \"0.49613315\",\n \"0.49250796\",\n \"0.49225068\",\n \"0.49149248\",\n \"0.49100593\",\n \"0.4898258\",\n \"0.48975632\",\n \"0.4895321\",\n \"0.486583\",\n \"0.48583898\",\n \"0.48564988\",\n \"0.48560464\",\n \"0.48542538\",\n \"0.4852834\",\n \"0.485129\",\n \"0.48365748\",\n \"0.48171207\",\n \"0.4801873\",\n \"0.47779664\",\n \"0.47624663\",\n \"0.4759587\",\n \"0.4758842\",\n \"0.47562525\",\n \"0.47435862\",\n \"0.47240305\",\n \"0.47212908\",\n \"0.471694\",\n \"0.47158992\",\n \"0.47140563\",\n \"0.47126472\",\n \"0.4712578\",\n \"0.47124138\",\n \"0.47021553\",\n \"0.46983537\",\n \"0.4679449\",\n \"0.46778542\",\n \"0.46586296\",\n \"0.4640804\",\n \"0.46386614\",\n \"0.46261293\",\n \"0.46168908\",\n \"0.4611264\",\n \"0.46056685\",\n \"0.4602377\",\n \"0.4602377\",\n \"0.4602377\",\n \"0.4600015\",\n \"0.45993376\",\n \"0.4598585\",\n \"0.4593165\",\n \"0.45896253\",\n \"0.45854956\",\n \"0.4581427\",\n \"0.4579137\",\n \"0.45782122\",\n \"0.4574439\",\n \"0.4574439\",\n \"0.45737916\",\n \"0.45688954\",\n \"0.45688954\",\n \"0.45657358\",\n \"0.45585012\",\n \"0.4558306\",\n \"0.4557945\",\n \"0.45562893\",\n \"0.45557386\",\n \"0.4545181\",\n \"0.45428726\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77952874"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18098,"cells":{"query":{"kind":"string","value":"Returns the number of anchors per spatial location."},"document":{"kind":"string","value":"def num_anchors_per_location(self):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def num_anchors_per_localization(self):\n num_rot = len(self._rotations)\n num_size = np.array(self._sizes).reshape([-1, 3]).shape[0]\n return num_rot * num_size","def num_locations(self):\n return len(self.locations)","def get_location_count(self):\n return len(self.matrix)","def _assert_correct_number_of_anchors(self, anchors_list,\n feature_map_shape_list):\n expected_num_anchors = 0\n actual_num_anchors = 0\n for num_anchors_per_location, feature_map_shape, anchors in zip(\n self.num_anchors_per_location(), feature_map_shape_list, anchors_list):\n expected_num_anchors += (num_anchors_per_location\n * feature_map_shape[0]\n * feature_map_shape[1])\n actual_num_anchors += anchors.num_boxes()\n return tf.assert_equal(expected_num_anchors, actual_num_anchors)","def location_length(self, location):\n if location == \"parent_blocks\":\n return np.sum(self.cbc.array.astype(np.bool))\n return self.num_cells","def location_length(self, location):\n if location == \"parent_blocks\":\n return np.sum(self.cbc.array.astype(np.bool))\n return self.num_cells","def location_length(self, location):\n if location == \"parent_blocks\":\n return np.sum(self.cbc.array.astype(np.bool))\n return self.num_cells","def location_length(self, location):\n if location == \"vertices\":\n return self.num_nodes\n return self.num_cells","def get_num_landmarks(self):\n return len(self.landmarks_info)","def location_length(self, location):\n if location == 'cells':\n return self.num_cells\n return self.num_nodes","def num_links(self):\n count=0.0\n for cluster in self.clusters:\n if self.clusters[cluster] == self.clusters[cluster].antecessor:\n numberofmembers=self.clusters[cluster].number_of_members\n count+=numberofmembers\n return count","def get_anchor_points(self):\n rows, cols = np.where(self.overlap_mask)\n self.anchor_points = tuple(zip(rows, cols))[:: self.sampling_int]\n print(\"# of anchors: {}\".format(len(self.anchor_points)))","def length(self):\n total_length = 0\n for location_a, location_b in zip(\n self.locations[:-1], self.locations[1:]):\n total_length += Line(location_a, location_b).length\n return total_length","def numCoords(self):\n return self.nCoords","def location_length(self, location):\n return self.num_cells","def check_num_anchors(self):\n return True","def locations_n(self):\n return self.locations[1]","def nspatials(self):\n return int(len(self)/2)","def get_position_count(self):\n return self.positions.count()","def Points_Counting(self):\n return len(self.__traectory_list)","def get_num_link_geometries(self, body, link_idx=0):\n with self.lock:\n return self.send_command('get_num_link_geometries ' + body.GetName()\n + ' ' + str(link_idx))","def num_neighbors(self):\n return self._num_neighbors","def num_arcs(self):\n return sum(len(state) for state in self)","def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):\n split_inside_flags = torch.split(inside_flags, num_level_anchors)\n num_level_anchors_inside = [\n int(flags.sum()) for flags in split_inside_flags\n ]\n return num_level_anchors_inside","def size(self):\n try:\n return len(self._adjacency_list)\n except Exception as error:\n print(f'An error occurred: {error}')","def number_of_open_sites(self):\n return sum(sum(line) for line in self._grid)","def size(self):\n return len(self._adjacency_list.keys())","def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):\n\n split_inside_flags = torch.split(inside_flags, num_level_anchors)\n num_level_anchors_inside = [\n int(flags.sum()) for flags in split_inside_flags\n ]\n return num_level_anchors_inside","def size(self):\n return len(self._adjacency_list)","def number_of_locations_by_area(area_id):\n\n if area_id == '':\n raise Exception(\"area ID could not be ''\")\n\n locations = db_access.get_locations_for_area(area_id)\n\n return len(locations)","def count_amino_acids(self):\n n = 0\n for chain in self.iter_chains():\n n += chain.count_amino_acids()\n return n","def GetCount(self):\r\n\r\n if not self._anchor:\r\n # the tree is empty\r\n return 0\r\n\r\n count = self._anchor.GetChildrenCount()\r\n \r\n if not self.HasAGWFlag(TR_HIDE_ROOT):\r\n # take the root itself into account\r\n count = count + 1\r\n \r\n return count","def size(self):\n\n return len(self._adjacency_list)","def count_amenity(src_points, candidates, rad):\n # Create tree from the candidate points\n tree = BallTree(candidates, leaf_size=15, metric='haversine')\n\n # Get distance and index of nearest amenity\n dist, nearest_ind = tree.query(src_points, k=1)\n\n dist = dist * 6371000\n # Count number of amenity within radius\n count = tree.query_radius(src_points, r=rad, count_only=True)\n # Get indexes of all the amenity within radius\n all_ind = tree.query_radius(src_points, r=rad)\n\n return count, dist.ravel(), nearest_ind, all_ind\n\n # Return the number of schools within the distance for each apartment wrt sale date","def houses(self):\n num = 0\n points = 0\n # TODO: add pattern matching\n if \"s\" in self.__as_str:\n num += 1\n if \"f\" in self.__as_str:\n num += 1\n if \"1\" in self.__as_str or \"2\" in self.__as_str or \"3\" in self.__as_str or \"4\" in self.__as_str:\n num += 1\n if \"o\" in self.__as_str:\n num += 1\n if \"p\" in self.__as_str:\n num += 1\n for i in range(4):\n for j in range(4):\n if self.as_list[i][j] == 'h':\n if 'f' in self.neighbours(i, j):\n points += 1\n else:\n points += num\n return points","def number_internal_links(self, node_list):\n n_links = self.internal_adjacency(node_list).sum()\n if self.directed:\n return n_links\n else:\n return n_links // 2","def n_points(self) -> ir.IntegerValue:\n return ops.GeoNPoints(self).to_expr()","def _num_nodes(self):\n return int(self._node_map[-1])","def vertex_count(self) -> int:\n return len(self._vertex_map)","def get_num_plants(self) -> int:\r\n\r\n return len(self.plants)","def count_paper_streets(x_intercepts: list, y_intercepts: list, homes: list) -> int:\n homes = __get_homes_not_on_intersections(x_intercepts, y_intercepts, homes)\n paper_streets = [street for street in __generate_streets(x_intercepts, y_intercepts) if street.isdisjoint(homes)]\n paper_groups = __group_streets(paper_streets)\n return len(paper_groups)","def get_num_arcs(self):\n num_arcs = 0\n for node in self._nodes.values(): num_arcs += node._deg\n return (num_arcs / 2) + 1","def get_num_arcs(self):\n num_arcs = 0\n for node in self._nodes.values(): num_arcs += node._deg\n return (num_arcs / 2) + 1","def num_links(self):\n return self._num_links","def n(self):\n return len(self.marks)","def get_num_pos(self, positive: torch.Tensor) -> int:\n # positive anchor sampling\n num_pos = int(self.batch_size_per_image * self.positive_fraction)\n # protect against not enough positive examples\n num_pos = min(positive.numel(), num_pos)\n return num_pos","def num_arcs(self):\n return len(self.arcs)","def nr_points(self):\n return len(self.x)","def nsites(self) -> int:\n return len(self.A)","def count_sizes(fastq_file, amplicon, guide_rna, window=20, anchor_length=10):\n editing_position = amplicon.index(guide_rna)\n\n guide_plus_window = (editing_position - window, editing_position + len(guide_rna) + window)\n\n left_anchor_start = guide_plus_window[0] - anchor_length\n right_anchor_end = guide_plus_window[1] + anchor_length\n\n left_anchor_end = left_anchor_start + anchor_length\n right_anchor_start = right_anchor_end - anchor_length\n\n a = amplicon[left_anchor_start: left_anchor_end]\n b = amplicon[right_anchor_start: right_anchor_end]\n\n pattern = a + \".*\" + b\n\n window_size = (right_anchor_end - left_anchor_start) + 1\n\n os.system(\"zcat {} | grep -o {} > counts\".format(fastq_file, pattern))\n\n with open(\"counts\", 'r') as handle:\n lines = handle.readlines()\n\n # return difference between window size and read length\n return Counter([len(x) - window_size for x in lines])","def __len__(self):\n\t\treturn len(self._coords)","def size(self):\n num_vert = 0\n num_edg = 0\n for vertex in self.vertices():\n num_vert += 1\n num_edg += len(self.neighbors(vertex))\n return (num_vert, num_edg)","def count(self):\n return len(self.__links)","def count_balls(self, **kwargs):\n return 0","def get_annotation_count(self):\n return self._num_annos","def length(self):\n points = [Point(v, crs=self.crs) for v in self.vertices]\n distances = [a.distance(b) for a, b in zip(points[:-1], points[1:])]\n return sum(distances)","def count(self):\n\t\treturn len(list(self.nodes))","def num_rules(self) -> int:\n return self._rule_count","def grid_point_count(self):\n return pytools.product(self.grid_point_counts())","def numAtoms(self):\n return self.nAtoms","def get_number_of_locations():\n count = 0\n tree = ET.parse('./devset_topics.xml')\n root = tree.getroot()\n for item in root.findall('./topic'):\n count = count + 1\n return count","def num_addresses(self):\n if hasattr(self, '_m_num_addresses'):\n return self._m_num_addresses if hasattr(self, '_m_num_addresses') else None\n\n self._m_num_addresses = self.num_addresses_raw.value\n return self._m_num_addresses if hasattr(self, '_m_num_addresses') else None","def __len__(self):\n return len(self._coords)","def return_num_edges(self):\n return sum(map(lambda x: len(x),self.__adj))","def num_links(self):\n return len(self._link_reg)","def __len__(self):\r\n return int(np.ceil(len(self.pathways) / float(self.batch_size)))","def approximate_neighbors_count(self) -> Optional[int]:\n return pulumi.get(self, \"approximate_neighbors_count\")","def aln_length(self) -> int:\n return sum([l for l, _ in self])","def get_num_streets(market):\r\n return len(market)","def get_num_goats(self) -> int:\n return len(self.get_all_goat_positions())","def number_of_nodes(self):\n return int(self._data['number_of_nodes'])","def getNNodesTot(self):\n nNodesTot = 0\n for iElt in Elements._all:\n nNodesTot += len(iElt.coord)\n return nNodesTot","def count(seats: List[str]) -> int:\n # Map dimensions\n m = len(seats)\n n = len(seats[0]) if m else 0\n \n count = 0\n \n # Count locations filled with \"#\"\n for i in range(m):\n for j in range(n):\n if seats[i][j] == \"#\":\n count += 1\n\n return count","def count(self):\n return len(self.wallpapers)","def n_tracers(self):\n # Extract parameters\n pzs = self.params[0]\n return len(pzs)","def n_tracers(self):\n # Extract parameters\n pzs = self.params[0]\n return len(pzs)","def _num_edges(self):\n return int(self._edge_map[-1])","def EdgesCount(self):\n return len(self.edges)","def GetNumberOfPoints(self):\n return self.GetNumberOfElements(ArrayAssociation.POINT)","def gal_count(clusters):\n sum = 0\n for x in clusters:\n sum += x.ngal\n return sum","def num_atoms(self):\n return self.h5['{}/{}'.format(SETTINGS, N_ATOMS)][()]","def n(self):\n return len(self.data.sites)","def getNbStations(self) :\n return len(self._stations)","def get_number_of_atoms_to_optimize(self):\n v = self.c.get(simulation_cell=True)\n return len(v.data.stoichiometry)","def number_of_atoms_within_radius(self, distance_cutoff):\n n_atoms = 0\n atom_ids = []\n for contact in self.nearby_atoms:\n other_id = contact.atom_id_no_altloc()\n if (not other_id in atom_ids):\n if (contact.distance() < distance_cutoff):\n n_atoms += 1\n atom_ids.append(other_id) # check for alt confs.\n return n_atoms","def get_num_nodes(self):\n\n return sum(self.topology)","def npoints(self):\n return _property_op(arctern.ST_NPoints, self)","def number_of_loc_changes(self) -> int:\n raise NotImplementedError('not implemented')","def total_num_atoms(self):\n return self.GetNumberOfAtoms()","def len(self):\n start = self.head\n count = 0\n while start:\n count+=1\n start = start.getLink()\n return count","def n_points(self):\n return self.points.shape[0]","def nb_triples(self) -> int:\n return 0","def lena(self) -> int:\n return self._core.lena()","def count_total_num_hits(outlist_hits):\n\n total_num_hits = 0\n\n for accession in outlist_hits.keys():\n total_num_hits += len(outlist_hits[accession])\n\n return total_num_hits","def get_neighbors_count(self, atom):\n return self._graph.get_connected_vertices_count(atom)","def number_of_nodes(self) -> int:\n return self.graph.number_of_nodes()","def max_num_neighbors(self):\n return self._max_num_neighbors","def get_num_locations(self, project):\n locations = Location.objects.filter(\n Q(private=False) |\n Q(private_for_project=project)).count()\n return locations","def __len__(self):\n if self.altloc:\n return len(self.altloc)\n return 0","def get_cluster_count(self) -> int:\n return len(self.get_all_cluster_ids())"],"string":"[\n \"def num_anchors_per_localization(self):\\n num_rot = len(self._rotations)\\n num_size = np.array(self._sizes).reshape([-1, 3]).shape[0]\\n return num_rot * num_size\",\n \"def num_locations(self):\\n return len(self.locations)\",\n \"def get_location_count(self):\\n return len(self.matrix)\",\n \"def _assert_correct_number_of_anchors(self, anchors_list,\\n feature_map_shape_list):\\n expected_num_anchors = 0\\n actual_num_anchors = 0\\n for num_anchors_per_location, feature_map_shape, anchors in zip(\\n self.num_anchors_per_location(), feature_map_shape_list, anchors_list):\\n expected_num_anchors += (num_anchors_per_location\\n * feature_map_shape[0]\\n * feature_map_shape[1])\\n actual_num_anchors += anchors.num_boxes()\\n return tf.assert_equal(expected_num_anchors, actual_num_anchors)\",\n \"def location_length(self, location):\\n if location == \\\"parent_blocks\\\":\\n return np.sum(self.cbc.array.astype(np.bool))\\n return self.num_cells\",\n \"def location_length(self, location):\\n if location == \\\"parent_blocks\\\":\\n return np.sum(self.cbc.array.astype(np.bool))\\n return self.num_cells\",\n \"def location_length(self, location):\\n if location == \\\"parent_blocks\\\":\\n return np.sum(self.cbc.array.astype(np.bool))\\n return self.num_cells\",\n \"def location_length(self, location):\\n if location == \\\"vertices\\\":\\n return self.num_nodes\\n return self.num_cells\",\n \"def get_num_landmarks(self):\\n return len(self.landmarks_info)\",\n \"def location_length(self, location):\\n if location == 'cells':\\n return self.num_cells\\n return self.num_nodes\",\n \"def num_links(self):\\n count=0.0\\n for cluster in self.clusters:\\n if self.clusters[cluster] == self.clusters[cluster].antecessor:\\n numberofmembers=self.clusters[cluster].number_of_members\\n count+=numberofmembers\\n return count\",\n \"def get_anchor_points(self):\\n rows, cols = np.where(self.overlap_mask)\\n self.anchor_points = tuple(zip(rows, cols))[:: self.sampling_int]\\n print(\\\"# of anchors: {}\\\".format(len(self.anchor_points)))\",\n \"def length(self):\\n total_length = 0\\n for location_a, location_b in zip(\\n self.locations[:-1], self.locations[1:]):\\n total_length += Line(location_a, location_b).length\\n return total_length\",\n \"def numCoords(self):\\n return self.nCoords\",\n \"def location_length(self, location):\\n return self.num_cells\",\n \"def check_num_anchors(self):\\n return True\",\n \"def locations_n(self):\\n return self.locations[1]\",\n \"def nspatials(self):\\n return int(len(self)/2)\",\n \"def get_position_count(self):\\n return self.positions.count()\",\n \"def Points_Counting(self):\\n return len(self.__traectory_list)\",\n \"def get_num_link_geometries(self, body, link_idx=0):\\n with self.lock:\\n return self.send_command('get_num_link_geometries ' + body.GetName()\\n + ' ' + str(link_idx))\",\n \"def num_neighbors(self):\\n return self._num_neighbors\",\n \"def num_arcs(self):\\n return sum(len(state) for state in self)\",\n \"def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):\\n split_inside_flags = torch.split(inside_flags, num_level_anchors)\\n num_level_anchors_inside = [\\n int(flags.sum()) for flags in split_inside_flags\\n ]\\n return num_level_anchors_inside\",\n \"def size(self):\\n try:\\n return len(self._adjacency_list)\\n except Exception as error:\\n print(f'An error occurred: {error}')\",\n \"def number_of_open_sites(self):\\n return sum(sum(line) for line in self._grid)\",\n \"def size(self):\\n return len(self._adjacency_list.keys())\",\n \"def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):\\n\\n split_inside_flags = torch.split(inside_flags, num_level_anchors)\\n num_level_anchors_inside = [\\n int(flags.sum()) for flags in split_inside_flags\\n ]\\n return num_level_anchors_inside\",\n \"def size(self):\\n return len(self._adjacency_list)\",\n \"def number_of_locations_by_area(area_id):\\n\\n if area_id == '':\\n raise Exception(\\\"area ID could not be ''\\\")\\n\\n locations = db_access.get_locations_for_area(area_id)\\n\\n return len(locations)\",\n \"def count_amino_acids(self):\\n n = 0\\n for chain in self.iter_chains():\\n n += chain.count_amino_acids()\\n return n\",\n \"def GetCount(self):\\r\\n\\r\\n if not self._anchor:\\r\\n # the tree is empty\\r\\n return 0\\r\\n\\r\\n count = self._anchor.GetChildrenCount()\\r\\n \\r\\n if not self.HasAGWFlag(TR_HIDE_ROOT):\\r\\n # take the root itself into account\\r\\n count = count + 1\\r\\n \\r\\n return count\",\n \"def size(self):\\n\\n return len(self._adjacency_list)\",\n \"def count_amenity(src_points, candidates, rad):\\n # Create tree from the candidate points\\n tree = BallTree(candidates, leaf_size=15, metric='haversine')\\n\\n # Get distance and index of nearest amenity\\n dist, nearest_ind = tree.query(src_points, k=1)\\n\\n dist = dist * 6371000\\n # Count number of amenity within radius\\n count = tree.query_radius(src_points, r=rad, count_only=True)\\n # Get indexes of all the amenity within radius\\n all_ind = tree.query_radius(src_points, r=rad)\\n\\n return count, dist.ravel(), nearest_ind, all_ind\\n\\n # Return the number of schools within the distance for each apartment wrt sale date\",\n \"def houses(self):\\n num = 0\\n points = 0\\n # TODO: add pattern matching\\n if \\\"s\\\" in self.__as_str:\\n num += 1\\n if \\\"f\\\" in self.__as_str:\\n num += 1\\n if \\\"1\\\" in self.__as_str or \\\"2\\\" in self.__as_str or \\\"3\\\" in self.__as_str or \\\"4\\\" in self.__as_str:\\n num += 1\\n if \\\"o\\\" in self.__as_str:\\n num += 1\\n if \\\"p\\\" in self.__as_str:\\n num += 1\\n for i in range(4):\\n for j in range(4):\\n if self.as_list[i][j] == 'h':\\n if 'f' in self.neighbours(i, j):\\n points += 1\\n else:\\n points += num\\n return points\",\n \"def number_internal_links(self, node_list):\\n n_links = self.internal_adjacency(node_list).sum()\\n if self.directed:\\n return n_links\\n else:\\n return n_links // 2\",\n \"def n_points(self) -> ir.IntegerValue:\\n return ops.GeoNPoints(self).to_expr()\",\n \"def _num_nodes(self):\\n return int(self._node_map[-1])\",\n \"def vertex_count(self) -> int:\\n return len(self._vertex_map)\",\n \"def get_num_plants(self) -> int:\\r\\n\\r\\n return len(self.plants)\",\n \"def count_paper_streets(x_intercepts: list, y_intercepts: list, homes: list) -> int:\\n homes = __get_homes_not_on_intersections(x_intercepts, y_intercepts, homes)\\n paper_streets = [street for street in __generate_streets(x_intercepts, y_intercepts) if street.isdisjoint(homes)]\\n paper_groups = __group_streets(paper_streets)\\n return len(paper_groups)\",\n \"def get_num_arcs(self):\\n num_arcs = 0\\n for node in self._nodes.values(): num_arcs += node._deg\\n return (num_arcs / 2) + 1\",\n \"def get_num_arcs(self):\\n num_arcs = 0\\n for node in self._nodes.values(): num_arcs += node._deg\\n return (num_arcs / 2) + 1\",\n \"def num_links(self):\\n return self._num_links\",\n \"def n(self):\\n return len(self.marks)\",\n \"def get_num_pos(self, positive: torch.Tensor) -> int:\\n # positive anchor sampling\\n num_pos = int(self.batch_size_per_image * self.positive_fraction)\\n # protect against not enough positive examples\\n num_pos = min(positive.numel(), num_pos)\\n return num_pos\",\n \"def num_arcs(self):\\n return len(self.arcs)\",\n \"def nr_points(self):\\n return len(self.x)\",\n \"def nsites(self) -> int:\\n return len(self.A)\",\n \"def count_sizes(fastq_file, amplicon, guide_rna, window=20, anchor_length=10):\\n editing_position = amplicon.index(guide_rna)\\n\\n guide_plus_window = (editing_position - window, editing_position + len(guide_rna) + window)\\n\\n left_anchor_start = guide_plus_window[0] - anchor_length\\n right_anchor_end = guide_plus_window[1] + anchor_length\\n\\n left_anchor_end = left_anchor_start + anchor_length\\n right_anchor_start = right_anchor_end - anchor_length\\n\\n a = amplicon[left_anchor_start: left_anchor_end]\\n b = amplicon[right_anchor_start: right_anchor_end]\\n\\n pattern = a + \\\".*\\\" + b\\n\\n window_size = (right_anchor_end - left_anchor_start) + 1\\n\\n os.system(\\\"zcat {} | grep -o {} > counts\\\".format(fastq_file, pattern))\\n\\n with open(\\\"counts\\\", 'r') as handle:\\n lines = handle.readlines()\\n\\n # return difference between window size and read length\\n return Counter([len(x) - window_size for x in lines])\",\n \"def __len__(self):\\n\\t\\treturn len(self._coords)\",\n \"def size(self):\\n num_vert = 0\\n num_edg = 0\\n for vertex in self.vertices():\\n num_vert += 1\\n num_edg += len(self.neighbors(vertex))\\n return (num_vert, num_edg)\",\n \"def count(self):\\n return len(self.__links)\",\n \"def count_balls(self, **kwargs):\\n return 0\",\n \"def get_annotation_count(self):\\n return self._num_annos\",\n \"def length(self):\\n points = [Point(v, crs=self.crs) for v in self.vertices]\\n distances = [a.distance(b) for a, b in zip(points[:-1], points[1:])]\\n return sum(distances)\",\n \"def count(self):\\n\\t\\treturn len(list(self.nodes))\",\n \"def num_rules(self) -> int:\\n return self._rule_count\",\n \"def grid_point_count(self):\\n return pytools.product(self.grid_point_counts())\",\n \"def numAtoms(self):\\n return self.nAtoms\",\n \"def get_number_of_locations():\\n count = 0\\n tree = ET.parse('./devset_topics.xml')\\n root = tree.getroot()\\n for item in root.findall('./topic'):\\n count = count + 1\\n return count\",\n \"def num_addresses(self):\\n if hasattr(self, '_m_num_addresses'):\\n return self._m_num_addresses if hasattr(self, '_m_num_addresses') else None\\n\\n self._m_num_addresses = self.num_addresses_raw.value\\n return self._m_num_addresses if hasattr(self, '_m_num_addresses') else None\",\n \"def __len__(self):\\n return len(self._coords)\",\n \"def return_num_edges(self):\\n return sum(map(lambda x: len(x),self.__adj))\",\n \"def num_links(self):\\n return len(self._link_reg)\",\n \"def __len__(self):\\r\\n return int(np.ceil(len(self.pathways) / float(self.batch_size)))\",\n \"def approximate_neighbors_count(self) -> Optional[int]:\\n return pulumi.get(self, \\\"approximate_neighbors_count\\\")\",\n \"def aln_length(self) -> int:\\n return sum([l for l, _ in self])\",\n \"def get_num_streets(market):\\r\\n return len(market)\",\n \"def get_num_goats(self) -> int:\\n return len(self.get_all_goat_positions())\",\n \"def number_of_nodes(self):\\n return int(self._data['number_of_nodes'])\",\n \"def getNNodesTot(self):\\n nNodesTot = 0\\n for iElt in Elements._all:\\n nNodesTot += len(iElt.coord)\\n return nNodesTot\",\n \"def count(seats: List[str]) -> int:\\n # Map dimensions\\n m = len(seats)\\n n = len(seats[0]) if m else 0\\n \\n count = 0\\n \\n # Count locations filled with \\\"#\\\"\\n for i in range(m):\\n for j in range(n):\\n if seats[i][j] == \\\"#\\\":\\n count += 1\\n\\n return count\",\n \"def count(self):\\n return len(self.wallpapers)\",\n \"def n_tracers(self):\\n # Extract parameters\\n pzs = self.params[0]\\n return len(pzs)\",\n \"def n_tracers(self):\\n # Extract parameters\\n pzs = self.params[0]\\n return len(pzs)\",\n \"def _num_edges(self):\\n return int(self._edge_map[-1])\",\n \"def EdgesCount(self):\\n return len(self.edges)\",\n \"def GetNumberOfPoints(self):\\n return self.GetNumberOfElements(ArrayAssociation.POINT)\",\n \"def gal_count(clusters):\\n sum = 0\\n for x in clusters:\\n sum += x.ngal\\n return sum\",\n \"def num_atoms(self):\\n return self.h5['{}/{}'.format(SETTINGS, N_ATOMS)][()]\",\n \"def n(self):\\n return len(self.data.sites)\",\n \"def getNbStations(self) :\\n return len(self._stations)\",\n \"def get_number_of_atoms_to_optimize(self):\\n v = self.c.get(simulation_cell=True)\\n return len(v.data.stoichiometry)\",\n \"def number_of_atoms_within_radius(self, distance_cutoff):\\n n_atoms = 0\\n atom_ids = []\\n for contact in self.nearby_atoms:\\n other_id = contact.atom_id_no_altloc()\\n if (not other_id in atom_ids):\\n if (contact.distance() < distance_cutoff):\\n n_atoms += 1\\n atom_ids.append(other_id) # check for alt confs.\\n return n_atoms\",\n \"def get_num_nodes(self):\\n\\n return sum(self.topology)\",\n \"def npoints(self):\\n return _property_op(arctern.ST_NPoints, self)\",\n \"def number_of_loc_changes(self) -> int:\\n raise NotImplementedError('not implemented')\",\n \"def total_num_atoms(self):\\n return self.GetNumberOfAtoms()\",\n \"def len(self):\\n start = self.head\\n count = 0\\n while start:\\n count+=1\\n start = start.getLink()\\n return count\",\n \"def n_points(self):\\n return self.points.shape[0]\",\n \"def nb_triples(self) -> int:\\n return 0\",\n \"def lena(self) -> int:\\n return self._core.lena()\",\n \"def count_total_num_hits(outlist_hits):\\n\\n total_num_hits = 0\\n\\n for accession in outlist_hits.keys():\\n total_num_hits += len(outlist_hits[accession])\\n\\n return total_num_hits\",\n \"def get_neighbors_count(self, atom):\\n return self._graph.get_connected_vertices_count(atom)\",\n \"def number_of_nodes(self) -> int:\\n return self.graph.number_of_nodes()\",\n \"def max_num_neighbors(self):\\n return self._max_num_neighbors\",\n \"def get_num_locations(self, project):\\n locations = Location.objects.filter(\\n Q(private=False) |\\n Q(private_for_project=project)).count()\\n return locations\",\n \"def __len__(self):\\n if self.altloc:\\n return len(self.altloc)\\n return 0\",\n \"def get_cluster_count(self) -> int:\\n return len(self.get_all_cluster_ids())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.749816","0.6937425","0.66813695","0.6656154","0.6582445","0.6582445","0.6582445","0.6580193","0.65188473","0.65087336","0.6447474","0.63943326","0.6365181","0.6354651","0.6345681","0.6322735","0.6313625","0.6276137","0.62072563","0.6178259","0.61153775","0.604023","0.59424984","0.59311706","0.5919097","0.5913937","0.5910243","0.5892373","0.58897513","0.58889234","0.5877631","0.585736","0.5841872","0.5829146","0.5803828","0.57949555","0.5792567","0.5787075","0.57867444","0.57853436","0.5780554","0.5761903","0.5761903","0.5750357","0.5748322","0.5746312","0.57407564","0.5739738","0.573933","0.57255524","0.5723828","0.5720566","0.5720464","0.5712977","0.57075584","0.5706935","0.57039493","0.5700627","0.5692829","0.56927747","0.56896096","0.568521","0.5684101","0.56833","0.5681893","0.567745","0.5667067","0.56564504","0.5652004","0.563449","0.56191534","0.5612316","0.5605051","0.5603563","0.55948156","0.55948156","0.559433","0.55939996","0.5583945","0.5578761","0.5571258","0.557022","0.55700266","0.5569517","0.5562555","0.5562505","0.5553796","0.55530953","0.55494034","0.5544476","0.55388874","0.55335724","0.552293","0.55217487","0.55184543","0.55180633","0.55158997","0.5513141","0.55065507","0.5506412"],"string":"[\n \"0.749816\",\n \"0.6937425\",\n \"0.66813695\",\n \"0.6656154\",\n \"0.6582445\",\n \"0.6582445\",\n \"0.6582445\",\n \"0.6580193\",\n \"0.65188473\",\n \"0.65087336\",\n \"0.6447474\",\n \"0.63943326\",\n \"0.6365181\",\n \"0.6354651\",\n \"0.6345681\",\n \"0.6322735\",\n \"0.6313625\",\n \"0.6276137\",\n \"0.62072563\",\n \"0.6178259\",\n \"0.61153775\",\n \"0.604023\",\n \"0.59424984\",\n \"0.59311706\",\n \"0.5919097\",\n \"0.5913937\",\n \"0.5910243\",\n \"0.5892373\",\n \"0.58897513\",\n \"0.58889234\",\n \"0.5877631\",\n \"0.585736\",\n \"0.5841872\",\n \"0.5829146\",\n \"0.5803828\",\n \"0.57949555\",\n \"0.5792567\",\n \"0.5787075\",\n \"0.57867444\",\n \"0.57853436\",\n \"0.5780554\",\n \"0.5761903\",\n \"0.5761903\",\n \"0.5750357\",\n \"0.5748322\",\n \"0.5746312\",\n \"0.57407564\",\n \"0.5739738\",\n \"0.573933\",\n \"0.57255524\",\n \"0.5723828\",\n \"0.5720566\",\n \"0.5720464\",\n \"0.5712977\",\n \"0.57075584\",\n \"0.5706935\",\n \"0.57039493\",\n \"0.5700627\",\n \"0.5692829\",\n \"0.56927747\",\n \"0.56896096\",\n \"0.568521\",\n \"0.5684101\",\n \"0.56833\",\n \"0.5681893\",\n \"0.567745\",\n \"0.5667067\",\n \"0.56564504\",\n \"0.5652004\",\n \"0.563449\",\n \"0.56191534\",\n \"0.5612316\",\n \"0.5605051\",\n \"0.5603563\",\n \"0.55948156\",\n \"0.55948156\",\n \"0.559433\",\n \"0.55939996\",\n \"0.5583945\",\n \"0.5578761\",\n \"0.5571258\",\n \"0.557022\",\n \"0.55700266\",\n \"0.5569517\",\n \"0.5562555\",\n \"0.5562505\",\n \"0.5553796\",\n \"0.55530953\",\n \"0.55494034\",\n \"0.5544476\",\n \"0.55388874\",\n \"0.55335724\",\n \"0.552293\",\n \"0.55217487\",\n \"0.55184543\",\n \"0.55180633\",\n \"0.55158997\",\n \"0.5513141\",\n \"0.55065507\",\n \"0.5506412\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8555305"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":18099,"cells":{"query":{"kind":"string","value":"Generates a collection of bounding boxes to be used as anchors."},"document":{"kind":"string","value":"def generate(self, feature_map_shape_list, **params):\n if self.check_num_anchors and (\n len(feature_map_shape_list) != len(self.num_anchors_per_location())):\n raise ValueError('Number of feature maps is expected to equal the length '\n 'of `num_anchors_per_location`.')\n with tf.name_scope(self.name_scope()):\n anchors_list = self._generate(feature_map_shape_list, **params)\n if self.check_num_anchors:\n with tf.control_dependencies([\n self._assert_correct_number_of_anchors(\n anchors_list, feature_map_shape_list)]):\n for item in anchors_list:\n item.set(tf.identity(item.get()))\n return anchors_list"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def decode_boxes(raw_boxes, anchors):\n boxes = np.zeros_like(raw_boxes)\n\n x_center = raw_boxes[..., 0] / x_scale * anchors[:, 2] + anchors[:, 0]\n y_center = raw_boxes[..., 1] / y_scale * anchors[:, 3] + anchors[:, 1]\n\n w = raw_boxes[..., 2] / w_scale * anchors[:, 2]\n h = raw_boxes[..., 3] / h_scale * anchors[:, 3]\n\n boxes[..., 0] = y_center - h / 2. # ymin\n boxes[..., 1] = x_center - w / 2. # xmin\n boxes[..., 2] = y_center + h / 2. # ymax\n boxes[..., 3] = x_center + w / 2. # xmax\n\n for k in range(num_keypoints):\n offset = 4 + k*2\n keypoint_x = raw_boxes[..., offset] / x_scale * anchors[:, 2] + anchors[:, 0]\n keypoint_y = raw_boxes[..., offset + 1] / y_scale * anchors[:, 3] + anchors[:, 1]\n boxes[..., offset] = keypoint_x\n boxes[..., offset + 1] = keypoint_y\n\n return boxes","def _decode_boxes(raw_boxes, anchors):\n boxes = np.zeros(raw_boxes.shape, np.float32)\n SCALE = 128\n x_center = raw_boxes[..., 0] / SCALE * anchors[:, 2] + anchors[:, 0]\n y_center = raw_boxes[..., 1] / SCALE * anchors[:, 3] + anchors[:, 1]\n\n w = raw_boxes[..., 2] / SCALE * anchors[:, 2]\n h = raw_boxes[..., 3] / SCALE * anchors[:, 3]\n\n boxes[..., 0] = y_center - h / 2. # ymin\n boxes[..., 1] = x_center - w / 2. # xmin\n boxes[..., 2] = y_center + h / 2. # ymax\n boxes[..., 3] = x_center + w / 2. # xmax\n\n for k in range(6):\n offset = 4 + k * 2\n keypoint_x = raw_boxes[..., offset] / SCALE * anchors[:, 2] + anchors[:, 0]\n keypoint_y = raw_boxes[..., offset + 1] / SCALE * anchors[:, 3] + anchors[:, 1]\n boxes[..., offset] = keypoint_x\n boxes[..., offset + 1] = keypoint_y\n\n return boxes","def generate_anchor_boxes(image_size, feature_map_size, sizes, scales):\n image_height, image_width = image_size\n fm_height, fm_width = feature_map_size\n height_stride = int(image_height / fm_height)\n width_stride = int(image_width / fm_width)\n\n # Compose horizontal and vertical positions into grid and reshape result into (-1, 2)\n y_centers = np.arange(0, image_height, height_stride)\n x_centers = np.arange(0, image_width, width_stride)\n centers = np.dstack(np.meshgrid(y_centers, x_centers)).reshape((-1, 2))\n\n # Creates anchor boxes pyramid. Somewhat vectorized version of itertools.product\n r_scales = np.repeat([scales], len(sizes), axis=0).ravel()\n r_sides = np.repeat([sizes], len(scales), axis=1).ravel()\n ab_pyramid = np.transpose([r_sides / (r_scales ** .5),\n r_sides * (r_scales ** .5)]).astype(int)\n\n # Creates combinations of all anchor boxes centers and sides\n r_centers = np.repeat(centers, len(ab_pyramid), axis=0)\n r_ab_pyramid = np.repeat([ab_pyramid], len(centers), axis=0).reshape((-1, 2))\n return np.hstack((r_centers, r_ab_pyramid))","def generate_anchors(self):\n self.anchors = np.zeros((self.anchor_num, 4), dtype=np.float32)\n size = self.stride * self.stride\n count = 0\n for r in self.ratios:\n ws = int(math.sqrt(size * 1. / r))\n hs = int(ws * r)\n\n for s in self.scales:\n w = ws * s\n h = hs * s\n self.anchors[count][:] = [-w * 0.5, -h * 0.5, w * 0.5, h * 0.5][:]\n count += 1","def __call__(self, loc, scores, anchors, img_size):\n anchors = bbox.loc2bbox(anchor)","def BoundingBoxes(self, show_plot=False, figure=None):\n\n self.__do_essential_memebers_exist__()\n\n ndim = self.InferSpatialDimension()\n all_elem_coords = self.points[self.elements]\n mins = all_elem_coords.min(axis=1)\n maxs = all_elem_coords.max(axis=1)\n bboxes = np.zeros((2*self.nelem,self.points.shape[1]))\n bboxes[::2] = mins\n bboxes[1::2] = maxs\n bboxes = bboxes.reshape(self.nelem,2,self.points.shape[1])\n\n if show_plot:\n if ndim == 3:\n point_generator = lambda bbox: np.array([\n [ bbox[0,0], bbox[0,1], bbox[0,2] ],\n [ bbox[1,0], bbox[0,1], bbox[0,2] ],\n [ bbox[1,0], bbox[1,1], bbox[0,2] ],\n [ bbox[0,0], bbox[1,1], bbox[0,2] ],\n [ bbox[0,0], bbox[0,1], bbox[1,2] ],\n [ bbox[1,0], bbox[0,1], bbox[1,2] ],\n [ bbox[1,0], bbox[1,1], bbox[1,2] ],\n [ bbox[0,0], bbox[1,1], bbox[1,2] ]\n ])\n elif ndim == 2:\n point_generator = lambda bbox: np.array([\n [ bbox[0,0], bbox[0,1] ],\n [ bbox[1,0], bbox[0,1] ],\n [ bbox[1,0], bbox[1,1] ],\n [ bbox[0,0], bbox[1,1] ]\n ])\n\n nsize = 4 if ndim ==2 else 8\n ranger = np.arange(nsize)\n bmesh = Mesh()\n bmesh.element_type = \"quad\" if ndim ==2 else \"hex\"\n bmesh.elements = np.arange(self.nelem*nsize).reshape(self.nelem,nsize)\n bmesh.points = np.zeros((self.nelem*nsize,ndim))\n bmesh.nelem = self.nelem\n bmesh.nnode = bmesh.points.shape[0]\n for i in range(0,self.nelem):\n bmesh.points[i*nsize:(i+1)*nsize,:] = point_generator(bboxes[i])\n\n if ndim == 2:\n import matplotlib.pyplot as plt\n if figure is None:\n figure = plt.figure()\n self.SimplePlot(figure=figure, show_plot=False)\n bmesh.SimplePlot(figure=figure, show_plot=False, edge_color='r')\n plt.show()\n else:\n import os\n os.environ['ETS_TOOLKIT'] = 'qt4'\n from mayavi import mlab\n if figure is None:\n figure = mlab.figure(bgcolor=(1,1,1),fgcolor=(1,1,1),size=(1000,800))\n self.SimplePlot(figure=figure, show_plot=False)\n bmesh.SimplePlot(figure=figure, show_plot=False, plot_faces=False, edge_color='r')\n mlab.show()\n\n return bboxes","def get_bound(box_list):\n box_xyxy_list = []\n for box in box_list:\n box_xyxy = xywh2xyxy(box)\n box_xyxy_list.append(box_xyxy)\n\n box_xyxy_list = np.array(box_xyxy_list)\n x1max, y1max, x2max, y2max = np.amax(box_xyxy_list, axis=0)\n x1min, y1min, x2min, y2min = np.amin(box_xyxy_list, axis=0)\n\n boundbox = xyxy2xywh([x1min, y1min, x2max, y2max])\n return boundbox","def _generate_boxes(self):\n boxes_all = []\n for level in range(self.min_level, self.max_level + 1):\n boxes_l = []\n for scale in range(self.num_scales):\n for aspect_ratio in self.aspect_ratios:\n stride = 2 ** level\n intermidate_scale = 2 ** (scale / float(self.num_scales))\n base_anchor_size = self.anchor_size * stride * intermidate_scale\n aspect_x = aspect_ratio ** 0.5\n aspect_y = aspect_ratio ** -0.5\n half_anchor_size_x = base_anchor_size * aspect_x / 2.0\n half_anchor_size_y = base_anchor_size * aspect_y / 2.0\n x = tf.range(stride / 2, self.image_size[1], stride)\n y = tf.range(stride / 2, self.image_size[0], stride)\n xv, yv = tf.meshgrid(x, y)\n xv = tf.cast(tf.reshape(xv, [-1]), dtype=tf.float32)\n yv = tf.cast(tf.reshape(yv, [-1]), dtype=tf.float32)\n # Tensor shape Nx4.\n boxes = tf.stack([yv - half_anchor_size_y, xv - half_anchor_size_x,\n yv + half_anchor_size_y, xv + half_anchor_size_x],\n axis=1)\n boxes_l.append(boxes)\n # Concat anchors on the same level to tensor shape NxAx4.\n boxes_l = tf.stack(boxes_l, axis=1)\n boxes_l = tf.reshape(boxes_l, [-1, 4])\n boxes_all.append(boxes_l)\n return tf.concat(boxes_all, axis=0)","def calc_bounding_box(self):\n self.BB = self.geos.abs_el(0).BB\n for geo in self.geos.abs_iter():\n self.BB = self.BB.joinBB(geo.BB)","def get_all_anchors(stride=None, sizes=None):\n if stride is None:\n stride = cfg.ANCHOR.ANCHOR_STRIDE\n if sizes is None:\n sizes = cfg.ANCHOR.ANCHOR_SIZES\n # Generates a NAx4 matrix of anchor boxes in (x1, y1, x2, y2) format. Anchors\n # are centered on stride / 2, have (approximate) sqrt areas of the specified\n # sizes, and aspect ratios as given.\n cell_anchors = generate_anchors(\n stride,\n scales=np.array(sizes, dtype=np.float) / stride,\n ratios=np.array(cfg.ANCHOR.ANCHOR_RATIOS, dtype=np.float))\n # anchors are intbox here.\n # anchors at featuremap [0,0] are centered at fpcoor (8,8) (half of stride)\n\n max_size = cfg.DATA.MAX_SIZE\n field_size = int(np.ceil(max_size / stride))\n shifts = np.arange(0, field_size) * stride\n shift_x, shift_y = np.meshgrid(shifts, shifts)\n shift_x = shift_x.flatten()\n shift_y = shift_y.flatten()\n shifts = np.vstack((shift_x, shift_y, shift_x, shift_y)).transpose()\n # Kx4, K = field_size * field_size\n K = shifts.shape[0]\n\n A = cell_anchors.shape[0]\n field_of_anchors = (\n cell_anchors.reshape((1, A, 4)) +\n shifts.reshape((1, K, 4)).transpose((1, 0, 2)))\n field_of_anchors = field_of_anchors.reshape((field_size, field_size, A, 4))\n # FSxFSxAx4\n # Many rounding happens inside the anchor code anyway\n # assert np.all(field_of_anchors == field_of_anchors.astype('int32'))\n field_of_anchors = field_of_anchors.astype('float32')\n field_of_anchors[:, :, :, [2, 3]] += 1\n return field_of_anchors","def draw_bounding_boxes(display, bounding_boxes):\n\n bb_surface = pygame.Surface((VIEW_WIDTH, VIEW_HEIGHT))\n bb_surface.set_colorkey((0, 0, 0))\n for bbox in bounding_boxes:\n points = [(int(bbox[i, 0]), int(bbox[i, 1])) for i in range(8)]\n # draw lines\n # base\n pygame.draw.line(bb_surface, BB_COLOR, points[0], points[1])\n pygame.draw.line(bb_surface, BB_COLOR, points[1], points[2])\n pygame.draw.line(bb_surface, BB_COLOR, points[2], points[3])\n pygame.draw.line(bb_surface, BB_COLOR, points[3], points[0])\n # top\n pygame.draw.line(bb_surface, BB_COLOR, points[4], points[5])\n pygame.draw.line(bb_surface, BB_COLOR, points[5], points[6])\n pygame.draw.line(bb_surface, BB_COLOR, points[6], points[7])\n pygame.draw.line(bb_surface, BB_COLOR, points[7], points[4])\n # base-top\n pygame.draw.line(bb_surface, BB_COLOR, points[0], points[4])\n pygame.draw.line(bb_surface, BB_COLOR, points[1], points[5])\n pygame.draw.line(bb_surface, BB_COLOR, points[2], points[6])\n pygame.draw.line(bb_surface, BB_COLOR, points[3], points[7])\n display.blit(bb_surface, (0, 0))","def bounding_boxes(self, detections):\n bboxes = []\n while len(detections) > 0:\n det = detections.pop(0)\n merging = True\n while merging:\n merging = False\n pointer = 0\n while pointer < len(detections):\n if self.get_distance(det, detections[pointer]) <= self.max_distance:\n det = self.merge_boxes(det, detections[pointer])\n merging = True\n detections.pop(pointer)\n else:\n pointer += 1\n if det[4] >= self.min_area:\n bboxes.append(det)\n return bboxes","def _get_anchor_grid(self, width, height, batch_size):\n anchors = tf.cast(self._anchors, dtype = self.dtype)\n anchors = tf.reshape(anchors, [1, -1])\n anchors = tf.repeat(anchors, width*height, axis = 0)\n anchors = tf.reshape(anchors, [1, width, height, self._num, -1])\n anchors = tf.repeat(anchors, batch_size, axis = 0)\n return anchors","def adjust_regular_roi_anchors(bounds: QRectF, anchors: list):\n for point in anchors:\n off = point.boundingRect().width() / 2\n if point.position == AnchorPosition.LEFT:\n point.setPos(bounds.left() - off, bounds.top() - off + bounds.height() / 2)\n elif point.position == AnchorPosition.RIGHT:\n point.setPos(bounds.right() - off, bounds.top() - off + bounds.height() / 2)\n elif point.position == AnchorPosition.TOP:\n point.setPos(bounds.left() - off + bounds.width() / 2, bounds.top() - off)\n elif point.position == AnchorPosition.TOP_LEFT:\n point.setPos(bounds.left() - off, bounds.top() - off)\n elif point.position == AnchorPosition.TOP_RIGHT:\n point.setPos(bounds.right() - off, bounds.top() - off)\n elif point.position == AnchorPosition.BOTTOM:\n point.setPos(bounds.left() - off + bounds.width() / 2, bounds.bottom() - off)\n elif point.position == AnchorPosition.BOTTOM_LEFT:\n point.setPos(bounds.left() - off, bounds.bottom() - off)\n elif point.position == AnchorPosition.BOTTOM_RIGHT:\n point.setPos(bounds.right() - off, bounds.bottom() - off)","def get_boxes_v2(output, img_size, anchors):\n bias_w, bias_h = anchors\n \n w_img, h_img = img_size[1], img_size[0]\n grid_w, grid_h, num_boxes = output.shape[:3]\n\n offset_x = \\\n np.tile(np.arange(grid_w)[:, np.newaxis], (grid_h, 1, num_boxes))\n offset_y = np.transpose(offset_x, (1, 0, 2))\n boxes = output.copy()\n boxes[:, :, :, 0] = (offset_x + logistic(boxes[:, :, :, 0])) / grid_w\n boxes[:, :, :, 1] = (offset_y + logistic(boxes[:, :, :, 1])) / grid_h\n boxes[:, :, :, 2] = np.exp(boxes[:, :, :, 2]) * bias_w / grid_w\n boxes[:, :, :, 3] = np.exp(boxes[:, :, :, 3]) * bias_h / grid_h\n\n boxes[:, :, :, [0, 2]] *= w_img\n boxes[:, :, :, [1, 3]] *= h_img\n\n return boxes","def bounding_box(points):\n x, y, z = zip(*points)\n min_x = min(x)\n max_x = max(x)\n min_y = min(y)\n max_y = max(y)\n min_z = min(z)\n max_z = max(z)\n return [\n [min_x, min_y, min_z],\n [max_x, min_y, min_z],\n [max_x, max_y, min_z],\n [min_x, max_y, min_z],\n [min_x, min_y, max_z],\n [max_x, min_y, max_z],\n [max_x, max_y, max_z],\n [min_x, max_y, max_z],\n ]","def create_overlap_metric(anchor_boxes):\n y, x, h, w = np.transpose(anchor_boxes)\n ab_area = w * h\n y0 = y - h // 2\n x0 = x - w // 2\n y1 = y + h // 2\n x1 = x + w // 2\n\n def overlap(gt_boxes):\n overlaps = []\n for gt_box in gt_boxes:\n gt_y0, gt_x0, gt_y1, gt_x1 = gt_box\n int_y0 = np.maximum(gt_y0, y0)\n int_x0 = np.maximum(gt_x0, x0)\n int_y1 = np.minimum(gt_y1, y1)\n int_x1 = np.minimum(gt_x1, x1)\n int_area = np.maximum(0, int_x1 - int_x0) * np.maximum(0, int_y1 - int_y0)\n overlaps.append(int_area / ab_area)\n overlaps = np.transpose(overlaps)\n gt_indices = np.argmax(overlaps, axis=1)\n overlaps = np.squeeze(np.take_along_axis(overlaps, gt_indices[:, np.newaxis], axis=1))\n gt_boxes = np.take(gt_boxes, gt_indices, axis=0)\n return overlaps, gt_boxes\n return overlap","def generate_boxes(self, img):\r\n return [Box(left, top, img) for (left, top) in self.coords]","def containing(*boxes):\n if not boxes:\n raise ValueError('At least one bounding box must be specified')\n boxes_objs = map(BoundingBox, boxes)\n start = boxes_objs[0].start\n end = boxes_objs[0].end\n for box in boxes_objs[1:]:\n start = np.minimum(start, box.start)\n end = np.maximum(end, box.end)\n return BoundingBox(start=start, end=end)","def rpn_anchor_boxes(image_size, *args, **kwargs):\n anchor_boxes = generate_anchor_boxes(image_size, *args, **kwargs)\n valid_ab_indices = valid_anchor_boxes(anchor_boxes, image_size)\n return anchor_boxes, valid_ab_indices","def bounding_box(self):\n box_min = []\n box_max = []\n if self.is_empty():\n raise ValueError('empty polytope is not allowed')\n for i in range(0, self.space_dimension()):\n x = Variable(i)\n coords = [ v.coefficient(x) for v in self.generators() ]\n max_coord = max(coords)\n min_coord = min(coords)\n box_max.append(max_coord)\n box_min.append(min_coord)\n return (tuple(box_min), tuple(box_max))","def bounding_box(self):\n latlon00 = self.ij_to_latlon(-1,-1)\n latlon01 = self.ij_to_latlon(-1,self.domain_size[1]+1)\n latlon11 = self.ij_to_latlon(self.domain_size[0]+1,self.domain_size[1]+1)\n latlon10 = self.ij_to_latlon(self.domain_size[0]+1,-1)\n return (latlon00,latlon01,latlon11,latlon10)","def boundingBox(self):\n y_max = np.max(self.points[:,0])\n x_max = np.max(self.points[:,1])\n y_min = np.min(self.points[:,0])\n x_min = np.min(self.points[:,1])\n \n return ((x_max, y_max), (x_min, y_min))","def _generate_bboxes(self, probs, offsets, scale, threshold):\n # applying P-Net is equivalent, in some sense, to\n # moving 12x12 window with stride 2\n stride = 2\n cell_size = 12\n\n # extract positive probability and resize it as [n, m] dim tensor.\n probs = probs[:, 1, :, :]\n\n # indices of boxes where there is probably a face\n mask = probs > threshold\n inds = mask.nonzero()\n\n if inds.shape[0] == 0:\n return torch.empty(0, dtype=torch.int32, device=self.device), \\\n torch.empty(0, dtype=torch.float32, device=self.device), \\\n torch.empty(0, dtype=torch.float32, device=self.device), \\\n torch.empty(0, dtype=torch.int32, device=self.device)\n\n # transformations of bounding boxes\n tx1, ty1, tx2, ty2 = [offsets[inds[:, 0], i, inds[:, 1], inds[:, 2]]\n for i in range(4)]\n\n offsets = torch.stack([tx1, ty1, tx2, ty2], 1)\n score = probs[inds[:, 0], inds[:, 1], inds[:, 2]]\n\n # P-Net is applied to scaled images\n # so we need to rescale bounding boxes back\n bounding_boxes = torch.stack([\n stride*inds[:, -1] + 1.0,\n stride*inds[:, -2] + 1.0,\n stride*inds[:, -1] + 1.0 + cell_size,\n (stride*inds[:, -2] + 1.0 + cell_size),\n ], 0).transpose(0, 1).float()\n\n bounding_boxes = torch.round(bounding_boxes / scale).int()\n return bounding_boxes, score, offsets, inds[:, 0].int()","def get_bounding_boxes(dets):\n bounding_boxes = []\n for box in dets:\n bounding_box = {'top_left_x': box.left(),\n 'top_left_y': box.top(),\n 'bottom_right_x': box.right(),\n 'bottom_right_y': box.bottom()}\n bounding_boxes.append(bounding_box)\n return bounding_boxes","def bounding_box(points):\n x, y, z = zip(*points)\n min_x = min(x)\n max_x = max(x)\n min_y = min(y)\n max_y = max(y)\n min_z = min(z)\n max_z = max(z)\n return [(min_x, min_y, min_z),\n (max_x, min_y, min_z),\n (max_x, max_y, min_z),\n (min_x, max_y, min_z),\n (min_x, min_y, max_z),\n (max_x, min_y, max_z),\n (max_x, max_y, max_z),\n (min_x, max_y, max_z)]","def gen_random_bboxes(self, n, meta):\n def get_mins_and_maxs(d):\n a = int(d / (4 * grid_dim + 1))\n b = 3 * a\n mins = []\n maxs = []\n for i in range(grid_dim):\n mn = (1 + i) * a + i * b\n mx = mn + b\n mins.append(mn)\n maxs.append(mx)\n return mins, maxs\n\n grid_dim = int(np.ceil(np.sqrt(n)))\n xmins, xmaxs = get_mins_and_maxs(meta['width'])\n ymins, ymaxs = get_mins_and_maxs(meta['height'])\n\n bboxes = []\n for ymin, ymax in zip(ymins, ymaxs):\n for xmin, xmax in zip(xmins, xmaxs):\n bboxes.append({'xmin': xmin, 'xmax': xmax, 'ymin': ymin, 'ymax': ymax})\n\n return bboxes","def draw_bounding_boxes(self, bounding_boxes, output):\n for i, bb in enumerate(bounding_boxes):\n\n if bb[2] > output.shape[1] or bb[3] > output.shape[0] or bb[0] < 0 or bb[1] < 0:\n continue\n cv2.rectangle(\n np.asarray(output), (bb[0], bb[1]), (bb[2], bb[3]), self.colors[i % len(self.colors)],\n thickness=2\n )","def generate_anchors(base_size, ratios, scales, rotations):\n num_anchors = len(ratios) * len(scales) * len(rotations)\n # initialize output anchors\n anchors = np.zeros((num_anchors, 5))\n # scale base_size\n anchors[:, 2:4] = base_size * np.tile(scales, (2, len(ratios) * len(rotations))).T\n # compute areas of anchors\n areas = anchors[:, 2] * anchors[:, 3]\n # correct for ratios\n anchors[:, 2] = np.sqrt(areas / np.repeat(ratios, len(scales) * len(rotations)))\n anchors[:, 3] = anchors[:, 2] * np.repeat(ratios, len(scales) * len(rotations))\n # add rotations\n anchors[:, 4] = np.tile(np.repeat(rotations, len(scales)), (1, len(ratios))).T[:, 0]\n # transform from (x_ctr, y_ctr, w, h) -> (x1, y1, x2, y2)\n anchors[:, 0:3:2] -= np.tile(anchors[:, 2] * 0.5, (2, 1)).T\n anchors[:, 1:4:2] -= np.tile(anchors[:, 3] * 0.5, (2, 1)).T\n return anchors","def compute_bb(self):\n all_shapes = list(self.parts.values()) + list(self.edges.values())\n bbox_vertices = cascaded_union(all_shapes).envelope.exterior.coords.xy\n min_x = min(bbox_vertices[0])\n max_x = max(bbox_vertices[0])\n min_y = min(bbox_vertices[1])\n max_y = max(bbox_vertices[1])\n return [min_x, max_x,min_y, max_y]","def get_boxes(self):\r\n\r\n boxes = [(\" \", self.worldbox.tl, self.worldbox.br)]\r\n# boxes = []\r\n boxes += [(\".\", b.tl, b.br) for b in self.wallboxes]\r\n boxes += [(\"x\", b.tl, b.br) for b in self.targetboxes]\r\n agentscale = 100\r\n boxes += [(\"a\", (self.i_state[0] - self.dx * agentscale, self.i_state[1] - self.dx * agentscale),\r\n (self.i_state[0] + self.dx * agentscale, self.i_state[1] + self.dx * agentscale))]\r\n return boxes","def _polygons_to_bboxes(polygons):\n# Build bounding boxes\n bboxes = np.empty([len(polygons), 4])\n for n, p in enumerate(polygons):\n try:\n left, bottom = np.min(p, axis = 0)\n except:\n import pdb\n pdb.set_trace()\n right, top = np.max(p, axis = 0)\n bboxes[n] = [left, bottom, right, top]\n return bboxes","def get_bboxes_from_deltas(anchors, deltas):\n all_anc_width = anchors[:, :, 3] - anchors[:, :, 1]\n all_anc_height = anchors[:, :, 2] - anchors[:, :, 0]\n all_anc_ctr_x = anchors[:, :, 1] + 0.5 * all_anc_width\n all_anc_ctr_y = anchors[:, :, 0] + 0.5 * all_anc_height\n #\n all_bbox_width = tf.exp(deltas[:, :, 3]) * all_anc_width\n all_bbox_height = tf.exp(deltas[:, :, 2]) * all_anc_height\n all_bbox_ctr_x = (deltas[:, :, 1] * all_anc_width) + all_anc_ctr_x\n all_bbox_ctr_y = (deltas[:, :, 0] * all_anc_height) + all_anc_ctr_y\n #\n y1 = all_bbox_ctr_y - (0.5 * all_bbox_height)\n x1 = all_bbox_ctr_x - (0.5 * all_bbox_width)\n y2 = all_bbox_height + y1\n x2 = all_bbox_width + x1\n #\n return tf.stack([y1, x1, y2, x2], axis=2)","def __create_anchors(self, sizes, aspects):\n k = len(sizes) * len(aspects)\n img_anchors = []\n for i in sizes:\n for j in aspects:\n img_anchors.append(\n [0, 0, 2 * i * j[0] / (j[0] + j[1]), 2 * i * j[1] / (j[0] + j[1])])\n\n self.anchors = np.asarray(img_anchors)","def bbox(self):\n return [self._x0, self._y0, self._x1, self._y1]","def activations_to_bboxes(actn, anchors, grid_sizes):\n #taken from fastai\n anchors = anchors.type(torch.float64)\n actn_offsets = torch.tanh(actn)\n\n actn_centers = actn_offsets[:, :2]/2 * grid_sizes + anchors[:, :2]\n actn_hw = (actn_offsets[:, 2:]/2+1) * anchors[:, 2:]\n\n return hw2corners(actn_centers, actn_hw)","def boundingBox(points):\n min_x, min_y = float('inf'), float('inf')\n max_x, max_y = float('-inf'), float('-inf')\n for x, _, y in points:\n min_x = min(min_x, x)\n min_y = min(min_y, y)\n max_x = max(max_x, x)\n max_y = max(max_y, y)\n\n return (min_x, min_y), (max_x, min_y), (max_x, max_y), (min_x, max_y)","def create_bbox(nodes, per_selection=False):\n\n if per_selection:\n for node in nodes:\n return cube_from_bbox(node.boundingBox())\n else:\n bbox = pm.dt.BoundingBox()\n for node in nodes:\n bbox.expand(node.boundingBox().min())\n bbox.expand(node.boundingBox().max())\n return cube_from_bbox(bbox)","def draw_boxes(indexes, frame, all_boxes):\n bbox = []\n mid_points = []\n\n for i in indexes:\n x = i[0]\n box = all_boxes[x]\n bbox.append(box)\n mid_points.append(mid_point(frame, box))\n x1, y1, w, h = box[0], box[1], box[2], box[3]\n x2, y2 = x1+w, y1+h\n\n cv2.rectangle(frame, (x1,y1),(x2,y2),(255,0,0),2) \n\n return mid_points, bbox","def gen_valid_bboxes(self, records, names):\n labels = []\n boxes = []\n scores = []\n for rec in records:\n scores.append(rec['score'])\n labels.append(names[rec['category_id'] - 1])\n bx, by, bw, bh = rec['bbox']\n boxes.append({'xmin': bx, 'ymin': by, 'xmax': bx+bw, 'ymax': by+bh})\n\n return labels, boxes, scores","def bounding_box_xy(points):\n x, y = islice(zip(*points), 2)\n min_x = min(x)\n max_x = max(x)\n min_y = min(y)\n max_y = max(y)\n return [\n [min_x, min_y, 0.0],\n [max_x, min_y, 0.0],\n [max_x, max_y, 0.0],\n [min_x, max_y, 0.0],\n ]","def abox2bbox(aboxes):\n\n c = aboxes[..., :2, 2]\n e = np.linalg.norm(aboxes[..., :2, :2], ord=2, axis=-1)\n bboxes = np.concatenate([c - e, c + e], axis=-1)\n return bboxes","def set_anchors(mc):\n H, W, C = _get_output_shape(mc)\n B = mc.ANCHOR_PER_GRID\n X = np.array(mc.INITIAL_ANCHOR_SHAPES)\n X[:,0] *= mc.IMAGE_WIDTH\n X[:,1] *= mc.IMAGE_HEIGHT\n anchor_shapes = np.reshape( # it refers to the anchor width and height\n [X] * H * W,\n (H, W, B, 2)\n )\n center_x = np.reshape(\n np.transpose(\n np.reshape(\n np.array([np.arange(1, W+1)*float(mc.IMAGE_WIDTH)/(W+1)]*H*B), \n (B, H, W)\n ),\n (1, 2, 0)\n ),\n (H, W, B, 1)\n )\n center_y = np.reshape(\n np.transpose(\n np.reshape(\n np.array([np.arange(1, H+1)*float(mc.IMAGE_HEIGHT)/(H+1)]*W*B),\n (B, W, H)\n ),\n (2, 1, 0)\n ),\n (H, W, B, 1)\n )\n anchors = np.reshape(\n np.concatenate((center_x, center_y, anchor_shapes), axis=3),\n (-1, 4)\n )\n\n return anchors","def bounding_box(points):\n x, y, w, h = cv2.boundingRect(np.array([p for p in points]))\n bounding = Box(x, y, w, h)\n return bounding","def generate_all_anchors(self):\n self.feature_sizes = [int(np.round(self.resolution/stride)) for stride in self.strides]\n \n #generate all anchors for each level of the FPN\n all_anchors = [self.generate_feature_level_base_anchors(size=size) for size in self.sizes]\n all_anchors = [self.shift_and_duplicate(layer_anchors, feature_size, stride) for layer_anchors, feature_size, stride in zip(all_anchors, self.feature_sizes, self.strides)]\n all_anchors = tf.concat(all_anchors, axis=0)\n\n return all_anchors","def create_pos_overlap_metric(anchor_boxes):\n y, x, h, w = np.transpose(anchor_boxes)\n y0 = y - h // 2\n x0 = x - w // 2\n y1 = y + h // 2\n x1 = x + w // 2\n\n def pos_overlap(gt_boxes):\n pos_overlaps = []\n for gt_box in gt_boxes:\n gt_y0, gt_x0, gt_y1, gt_x1 = gt_box\n gt_area = (gt_x1 - gt_x0) * (gt_y1 - gt_y0)\n int_y0 = np.maximum(gt_y0, y0)\n int_x0 = np.maximum(gt_x0, x0)\n int_y1 = np.minimum(gt_y1, y1)\n int_x1 = np.minimum(gt_x1, x1)\n int_area = np.maximum(0, int_x1 - int_x0) * np.maximum(0, int_y1 - int_y0)\n pos_overlaps.append(int_area / gt_area)\n # Group by anchor boxes\n pos_overlaps = np.transpose(pos_overlaps)\n # Get max metric index\n gt_indices = np.argmax(pos_overlaps, axis=1)\n # Choose max metric\n pos_overlaps = np.squeeze(np.take_along_axis(pos_overlaps, gt_indices[:, np.newaxis], axis=1))\n # Take respective ground-truth boxes. No reason to return indices, at least in RPN\n gt_boxes = np.take(gt_boxes, gt_indices, axis=0)\n return pos_overlaps, gt_boxes\n return pos_overlap","def _generate_anchors(point, sizes, aspect_ratios, layout, beta, include_depth):\n\n distance = point[2]\n base_size = sizes[0]\n scales = sizes[1:] / base_size\n # beta = 8\n scales = (beta/distance)*scales\n\n center = (point[0], point[1])\n anchor = np.array([center[0] - base_size/2.0, center[1] - base_size/2.0,\n center[0] + base_size/2.0, center[1] + base_size/2.0],\n dtype=np.float)\n\n anchors = _ratio_enum(anchor, aspect_ratios)\n anchors = np.vstack(\n [_scale_enum(anchors[i, :], scales) for i in range(anchors.shape[0])]\n )\n\n all_anchors = np.empty((0,4))\n for l in layout:\n new_anchors = _shift_anchors(anchors, l)\n all_anchors = np.vstack((all_anchors, new_anchors))\n\n if int(include_depth)==1:\n # Add the distance as the 5th element to all anchors\n new_shape = (all_anchors.shape[0], all_anchors.shape[1]+1)\n new_anchors = np.ones(new_shape) * distance\n new_anchors[:,:-1] = all_anchors\n all_anchors = new_anchors\n\n return all_anchors","def _generate_anchors(self, sizes, ratios, step, alloc_size, offsets):\n assert len(sizes) == 2, \"SSD requires sizes to be (size_min, size_max)\"\n anchors = []\n for i in range(alloc_size[0]):\n for j in range(alloc_size[1]):\n cy = (i + offsets[0]) * step\n cx = (j + offsets[1]) * step\n # ratio = ratios[0], size = size_min or sqrt(size_min * size_max)\n r = ratios[0]\n anchors.append([cx, cy, sizes[0] / 2, sizes[0] / 2])\n # size = sizes[0], ratio = ...\n for r in ratios[1:]:\n sr = np.sqrt(r)\n w = sizes[0] * sr\n h = sizes[0] / sr\n anchors.append([cx, cy, w, h])\n if self._index > 0:\n anchors.append([cx, cy, sizes[1], sizes[1]])\n return np.array(anchors).reshape(1, 1, alloc_size[0], alloc_size[1], -1)","def create_prior_boxes(self):\n # value of k for each feature map to create k^2 boxes for each feature map\n feature_map_dims = {'conv4_3': 38, 'conv7': 19, 'conv8_2': 10, 'conv9_2': 5}\n\n # scale for boxes across different feature maps. boxes for inner feature maps\n # are scaled much lower to detect small objects\n obj_scales = {'conv4_3': 0.1, 'conv7': 0.21, 'conv8_2': 0.255, 'conv9_2': 0.30}\n\n # Defined aspect ratio calculated from mean of (w/h) across all bounding boxes\n # from the dataset. The mean is 0.66 with deviation of 0.07. So aspect ratio is kept\n # at 0.66 for all feature maps\n aspect_ratios = {'conv4_3': [0.5], 'conv7': [0.55], 'conv8_2': [0.6], 'conv9_2': [.66]}\n\n fmaps = list(feature_map_dims.keys())\n prior_boxes = []\n for k, fmap in enumerate(fmaps):\n # for each feature map, create k*k boxes\n for i in range(feature_map_dims[fmap]):\n for j in range(feature_map_dims[fmap]):\n # calculate center coordinates of boxes\n cx = (j + 0.5) / feature_map_dims[fmap]\n cy = (i + 0.5) / feature_map_dims[fmap]\n\n # For each\n for ratio in aspect_ratios[fmap]:\n prior_boxes.append([cx, cy, obj_scales[fmap] * sqrt(ratio), obj_scales[fmap] / sqrt(ratio)])\n\n prior_boxes = torch.FloatTensor(prior_boxes).to(device) # (1930, 4)\n prior_boxes.clamp_(0, 1) # (1930, 4)\n\n return prior_boxes","def extract_bounding_boxes(self, scene):\n objs = scene[\"objects\"]\n rotation = scene[\"directions\"][\"right\"]\n\n xmin = []\n ymin = []\n xmax = []\n ymax = []\n\n for i, obj in enumerate(objs):\n [x, y, z] = obj[\"pixel_coords\"]\n\n [x1, y1, z1] = obj[\"3d_coords\"]\n\n cos_theta, sin_theta, _ = rotation\n\n x1 = x1 * cos_theta + y1 * sin_theta\n y1 = x1 * -sin_theta + y1 * cos_theta\n\n height_d = 6.9 * z1 * (15 - y1) / 2.0\n height_u = height_d\n width_l = height_d\n width_r = height_d\n\n if obj[\"shape\"] == \"cylinder\":\n d = 9.4 + y1\n h = 6.4\n s = z1\n\n height_u *= (s * (h / d + 1)) \\\n / ((s * (h / d + 1)) - (s * (h - s) / d))\n height_d = height_u * (h - s + d) / (h + s + d)\n\n width_l *= 11 / (10 + y1)\n width_r = width_l\n\n if obj[\"shape\"] == \"cube\":\n height_u *= 1.3 * 10 / (10 + y1)\n height_d = height_u\n width_l = height_u\n width_r = height_u\n\n ymin.append((y - height_d) / 320.0)\n ymax.append((y + height_u) / 320.0)\n xmin.append((x - width_l) / 480.0)\n xmax.append((x + width_r) / 480.0)\n\n return xmin, ymin, xmax, ymax","def get_final_bounding_box(boxes, nms_idx, width: int, height: int):\n x1 = np.inf\n y1 = np.inf\n x2 = -np.inf\n y2 = -np.inf\n\n bx = [boxes[i] for i in nms_idx]\n for box in bx:\n xmin = np.min(box[[0, 2]])\n xmax = np.max(box[[0, 2]])\n ymin = np.min(box[[1, 3]])\n ymax = np.max(box[[1, 3]])\n\n x1 = np.min([xmin, x1])\n y1 = np.min([ymin, y1])\n x2 = np.max([xmax, x2])\n y2 = np.max([ymax, y2])\n return x1, y1, x2, y2","def xywh_xyxy(boxes):\n bbox = np.zeros(boxes.shape)\n bbox[:, 0] = boxes[:, 0] \n bbox[:, 1] = boxes[:, 1] \n bbox[:, 2] = boxes[:, 0] + 1 * boxes[:, 2]\n bbox[:, 3] = boxes[:, 1] + 1 * boxes[:, 3]\n return bbox","def _generate_anchors(self, sizes, ratios, step, alloc_size, offsets):\n assert len(sizes) == 2, \"SSD requires sizes to be (size_min, size_max)\"\n anchors = []\n for i in range(alloc_size[0]):\n for j in range(alloc_size[1]):\n cy = (i + offsets[0]) * step\n cx = (j + offsets[1]) * step\n # ratio = ratios[0], size = size_min or sqrt(size_min * size_max)\n r = ratios[0]\n anchors.append([cx, cy, sizes[0], sizes[0]])\n anchors.append([cx, cy, sizes[1], sizes[1]])\n # size = sizes[0], ratio = ...\n for r in ratios[1:]:\n sr = np.sqrt(r)\n w = sizes[0] * sr\n h = sizes[0] / sr\n anchors.append([cx, cy, w, h])\n return np.array(anchors).reshape(1, 1, alloc_size[0], alloc_size[1], -1)","def generate_anchors(base_size=16, ratios=None, scales=None):\n\n if ratios is None:\n ratios = np.array([0.5, 1, 2])\n\n if scales is None:\n scales = np.array([2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)])\n\n num_anchors = len(ratios) * len(scales)\n\n # initialize output anchors\n anchors = np.zeros((num_anchors, 4))\n\n # scale base_size\n anchors[:, 2:] = base_size * np.tile(scales, (2, len(ratios))).T\n\n # compute areas of anchors\n areas = anchors[:, 2] * anchors[:, 3]\n\n # correct for ratios\n anchors[:, 2] = np.sqrt(areas / np.repeat(ratios, len(scales)))\n anchors[:, 3] = anchors[:, 2] * np.repeat(ratios, len(scales))\n\n # transform from (x_ctr, y_ctr, w, h) -> (x1, y1, x2, y2)\n anchors[:, 0::2] -= np.tile(anchors[:, 2] * 0.5, (2, 1)).T\n anchors[:, 1::2] -= np.tile(anchors[:, 3] * 0.5, (2, 1)).T\n\n return anchors","def generate_anchors(base_size=16, ratios=None, scales=None):\n\n if ratios is None:\n ratios = np.array([0.5, 1, 2])\n\n if scales is None:\n scales = np.array([2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)])\n\n num_anchors = len(ratios) * len(scales)\n\n # initialize output anchors\n anchors = np.zeros((num_anchors, 4))\n\n # scale base_size\n anchors[:, 2:] = base_size * np.tile(scales, (2, len(ratios))).T\n\n # compute areas of anchors\n areas = anchors[:, 2] * anchors[:, 3]\n\n # correct for ratios\n anchors[:, 2] = np.sqrt(areas / np.repeat(ratios, len(scales)))\n anchors[:, 3] = anchors[:, 2] * np.repeat(ratios, len(scales))\n\n # transform from (x_ctr, y_ctr, w, h) -> (x1, y1, x2, y2)\n anchors[:, 0::2] -= np.tile(anchors[:, 2] * 0.5, (2, 1)).T\n anchors[:, 1::2] -= np.tile(anchors[:, 3] * 0.5, (2, 1)).T\n\n return anchors","def boundingBox(self):\n minx, miny, maxx, maxy = self.substrates.bounds\n return pcbnew.BOX2I(\n pcbnew.VECTOR2I(int(minx), int(miny)),\n pcbnew.VECTOR2I(int(maxx - minx), int(maxy - miny)))","def process_bounding_boxes(ground_truth_file: Path) -> List[Dict]:\n index_shifts = defaultdict(lambda: 0)\n index_shifts[\"cubicle\"] = 1108\n folder_name = ground_truth_file.parent.name\n \n annotations = []\n ground_truth_dataframe = pd.read_csv(str(ground_truth_file),\n header=None,\n sep=\" \",\n names=[\"frameID\", \"0\", \"x\", \"y\", \"width\", \"height\"])\n for index, row in ground_truth_dataframe.iterrows():\n image_id = int(row[\"frameID\"]) + index_shifts[folder_name]\n image_id = f\"{folder_name}_{str(image_id)}\"\n x = int(row[\"x\"])\n y = int(row[\"y\"])\n width = int(row[\"width\"])\n height = int(row[\"height\"])\n\n area = width * height\n bbox = [x, y, width, height]\n\n label_data = {\"id\": index,\n \"image_id\": image_id,\n \"category_id\": 1,\n \"bbox\": bbox,\n \"area\": area,\n \"iscrowd\": False}\n annotations.append(label_data)\n\n return annotations","def layout_bounding_boxes(canvas_x, canvas_y, canvas_width, line_height,\n space_widths, y_space, sizes):\n\n cur_x, cur_y = canvas_x, canvas_y\n cur_size = 0\n cur_line = 0\n boxes = []\n line_breaks = []\n line_poss = []\n line_poss.append((cur_x, cur_y))\n while cur_size < len(sizes):\n sz = sizes[cur_size]\n if cur_x + sz[0] > canvas_width + canvas_x:\n cur_line += 1\n cur_y = canvas_y - cur_line * (y_space + line_height)\n cur_x = canvas_x\n line_poss.append((cur_x, cur_y))\n line_breaks.append(cur_size)\n else:\n boxes.append((cur_x, cur_y, sz[0], sz[1]))\n cur_x += sz[0]\n if cur_size < len(space_widths):\n cur_x += space_widths[cur_size]\n cur_size += 1\n return boxes, line_breaks, line_poss","def set_grid_anchors(grid_sizes, strides, cell_anchors):\n anchors = []\n assert cell_anchors is not None\n\n for size, stride, base_anchors in zip(grid_sizes, strides, cell_anchors):\n grid_height, grid_width = size\n stride_height, stride_width = stride\n\n # For output anchor, compute [x_center, y_center, x_center, y_center]\n shifts_x = np.arange(0, grid_width) * stride_width\n shifts_y = np.arange(0, grid_height) * stride_height\n shift_x, shift_y = np.meshgrid(shifts_y, shifts_x)\n shift_x = shift_x.reshape(-1)\n shift_y = shift_y.reshape(-1)\n shifts = np.stack((shift_x, shift_y, shift_x, shift_y), axis=1)\n\n # For every (base anchor, output anchor) pair,\n # offset each zero-centered base anchor by the center of the output anchor.\n anchors.append(\n (shifts.reshape((-1, 1, 4)) + base_anchors.reshape((1, -1, 4))).reshape(-1, 4)\n )\n\n return anchors","def load_anchors_blocks(path):\t\t\n\t#TODO : automatization\n\tleft = Building_Block(abbrev=\"l\", num_atoms=2,origin=0, para_pos=0, para_angle=0, meta_pos=0 , meta_angle = 0., ortho_pos=0, ortho_angle=-2.*np.pi/3, fixed_left = 0,complexity=1, path=path+\"/anchor_small_left.xyz\")\n\tright = Building_Block(abbrev=\"r\", num_atoms=2,origin=0, para_pos=0, para_angle=0., meta_pos=0 , meta_angle = 0., ortho_pos=0, ortho_angle=-2.*np.pi/3, fixed_left = -1,complexity=1, path=path+\"/anchor_small_right.xyz\")\n\t\n\tanchors = [left,right]\n\n\treturn anchors","def generate_random_bbox(n, img_size, min_length, max_length):\n H, W = img_size\n y_min = np.random.uniform(0, H - max_length, size=(n,))\n x_min = np.random.uniform(0, W - max_length, size=(n,))\n y_max = y_min + np.random.uniform(min_length, max_length, size=(n,))\n x_max = x_min + np.random.uniform(min_length, max_length, size=(n,))\n bbox = np.stack((y_min, x_min, y_max, x_max), axis=1).astype(np.float32)\n return bbox","def fix_faceboxes_bb(\n max_y: int,\n max_x: int,\n bounding_box: List[int]) -> List[int]:\n y1, x1, y2, x2 = bounding_box[:4]\n x1 = max(min(x1, max_x), 0)\n x2 = max(min(x2, max_x), 0)\n y1 = max(min(y1, max_y), 0)\n y2 = max(min(y2, max_y), 0)\n return [x1, y1, x2, y2]","def _compute_bounding_box(self, points_2d):\n max_x = max(map(lambda point: int(point[0]), points_2d))\n min_x = min(map(lambda point: int(point[0]), points_2d))\n max_y = max(map(lambda point: int(point[1]), points_2d))\n min_y = min(map(lambda point: int(point[1]), points_2d))\n\n width = max_x - min_x + 1\n height = max_y - min_y + 1\n\n return [min_x, min_y, width, height]","def center_to_AABB_box3d(boxes):\n AABBs =[] \n num_det = len(boxes)\n for i in range(num_det):\n box = boxes[i:i+1]\n corner = np.array(center_to_corner_box3d(box[:, :3], box[:, 3:6], box[:, -1])[0].tolist())\n AABBs.append(o3d.geometry.AxisAlignedBoundingBox(corner.min(axis=0), corner.max(axis=0)))\n return AABBs","def bounding_box(vertices, (height, width), extend=5):\n x_min = min(x for x, y in vertices) - extend\n x_max = max(x for x, y in vertices) + extend\n y_min = min(y for x, y in vertices) - extend\n y_max = max(y for x, y in vertices) + extend\n \n return max(x_min, 0), min(x_max, width), max(y_min, 0), min(y_max, height)","def get_bounding_box(self):\n lon, lat = self.coordinates\n\n ll = (np.min(lon),np.min(lat))\n ul = (np.min(lon),np.max(lat))\n ur = (np.max(lon),np.max(lat))\n lr = (np.max(lon),np.min(lat))\n\n return (ll, ul, ur, lr)","def make_wander_box(self):\n x = int(self.location[0])\n y = int(self.location[1])\n box_list = []\n box_rects = []\n\n for i in range(x-3, x+4):\n box_list.append([i, y-3])\n box_list.append([i, y+3])\n\n for i in range(y-2, y+3):\n box_list.append([x-3, i])\n box_list.append([x+3, i])\n\n for box in box_list:\n left = box[0]*32\n top = box[1]*32\n box_rects.append(pg.Rect(left, top, 32, 32))\n\n return box_rects","def unparam_bbox(t, anchors, max_shape=None):\n t = np.array(t, np.float32)\n anchors = np.array(anchors, np.float32)\n\n yx = t[:, :2] * anchors[:, 2:] + anchors[:, :2]\n hw = np.exp(t[:, 2:]) * anchors[:, 2:]\n\n bboxes = np.concatenate((yx, hw), axis=1)\n\n if max_shape != None:\n bboxes = rectify_bbox(bboxes, max_shape)\n\n return bboxes","def center(self, anchors):\n # self.anchors_ = boxes[np.random.choice(n, self.k, replace=True)]\n if isinstance(anchors, list):\n anchors = np.array(anchors)\n self.anchors_ = anchors","def anchor_target_layer(rpn_cls_score, gt_boxes, im_info, _feat_stride, all_anchors, num_anchors):\n A = num_anchors\n total_anchors = all_anchors.shape[0]\n K = total_anchors / num_anchors\n\n # allow boxes to sit over the edge by a small amount\n _allowed_border = 0\n\n # map of shape (..., H, W)\n height, width = rpn_cls_score.shape[1:3]\n\n # only keep anchors inside the image\n inds_inside = np.where(\n (all_anchors[:, 0] >= -_allowed_border) &\n (all_anchors[:, 1] >= -_allowed_border) &\n (all_anchors[:, 2] < im_info[1] + _allowed_border) & # width\n (all_anchors[:, 3] < im_info[0] + _allowed_border) # height\n )[0]\n\n # keep only inside anchors\n anchors = all_anchors[inds_inside, :]\n\n # label: 1 is positive, 0 is negative, -1 is dont care\n labels = np.empty((len(inds_inside),), dtype=np.float32)\n labels.fill(-1)\n\n # overlaps between the anchors and the gt boxes\n # overlaps (ex, gt)\n overlaps = bbox_overlaps(\n np.ascontiguousarray(anchors, dtype=np.float),\n np.ascontiguousarray(gt_boxes, dtype=np.float))\n argmax_overlaps = overlaps.argmax(axis=1)\n max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]\n gt_argmax_overlaps = overlaps.argmax(axis=0)\n gt_max_overlaps = overlaps[gt_argmax_overlaps,\n np.arange(overlaps.shape[1])]\n gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]\n\n if not cfg.TRAIN.RPN_CLOBBER_POSITIVES:\n # assign bg labels first so that positive labels can clobber them\n # first set the negatives\n labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0\n\n # fg label: for each gt, anchor with highest overlap\n labels[gt_argmax_overlaps] = 1\n\n # fg label: above threshold IOU\n labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1\n\n if cfg.TRAIN.RPN_CLOBBER_POSITIVES:\n # assign bg labels last so that negative labels can clobber positives\n labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0\n\n # subsample positive labels if we have too many\n num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)\n fg_inds = np.where(labels == 1)[0]\n if len(fg_inds) > num_fg:\n disable_inds = npr.choice(\n fg_inds, size=(len(fg_inds) - num_fg), replace=False)\n labels[disable_inds] = -1\n\n # subsample negative labels if we have too many\n num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1)\n bg_inds = np.where(labels == 0)[0]\n if len(bg_inds) > num_bg:\n disable_inds = npr.choice(\n bg_inds, size=(len(bg_inds) - num_bg), replace=False)\n labels[disable_inds] = -1\n\n bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)\n bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])\n\n bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)\n # only the positive ones have regression targets\n bbox_inside_weights[labels == 1, :] = np.array(cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS)\n\n bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)\n if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0:\n # uniform weighting of examples (given non-uniform sampling)\n num_examples = np.sum(labels >= 0)\n positive_weights = np.ones((1, 4)) * 1.0 / num_examples\n negative_weights = np.ones((1, 4)) * 1.0 / num_examples\n else:\n assert ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) &\n (cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1))\n positive_weights = (cfg.TRAIN.RPN_POSITIVE_WEIGHT /\n np.sum(labels == 1))\n negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) /\n np.sum(labels == 0))\n bbox_outside_weights[labels == 1, :] = positive_weights\n bbox_outside_weights[labels == 0, :] = negative_weights\n\n # map up to original set of anchors\n labels = _unmap(labels, total_anchors, inds_inside, fill=-1)\n bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)\n bbox_inside_weights = _unmap(bbox_inside_weights, total_anchors, inds_inside, fill=0)\n bbox_outside_weights = _unmap(bbox_outside_weights, total_anchors, inds_inside, fill=0)\n\n # labels\n labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2)\n labels = labels.reshape((1, 1, A * height, width))\n rpn_labels = labels\n\n # bbox_targets\n bbox_targets = bbox_targets \\\n .reshape((1, height, width, A * 4))\n\n rpn_bbox_targets = bbox_targets\n # bbox_inside_weights\n bbox_inside_weights = bbox_inside_weights \\\n .reshape((1, height, width, A * 4))\n\n rpn_bbox_inside_weights = bbox_inside_weights\n\n # bbox_outside_weights\n bbox_outside_weights = bbox_outside_weights \\\n .reshape((1, height, width, A * 4))\n\n rpn_bbox_outside_weights = bbox_outside_weights\n return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights","def bboxes_encode(self, labels, bboxes, anchors,\n scope=None):\n return encode_decode.tf_ssd_bboxes_encode(\n labels, bboxes, anchors,\n self.params.num_classes,\n self.params.no_annotation_label,\n ignore_threshold=0.5,\n prior_scaling=self.params.prior_scaling,\n scope=scope)","def _initial_normal_bbox(self):\n cx = cy = .5\n width = FLAGS.bbox_grid / float(FLAGS.cropbox_grid)\n x1 = cx - width / 2\n x2 = cx + width / 2\n y1 = cy - width / 2\n y2 = cy + width / 2\n return [y1,x1,y2,x2]","def create_cell_anchors():\n k_max, k_min = cfg.FPN.RPN_MAX_LEVEL, cfg.FPN.RPN_MIN_LEVEL\n scales_per_octave = cfg.RETINANET.SCALES_PER_OCTAVE\n aspect_ratios = cfg.RETINANET.ASPECT_RATIOS\n anchor_scale = cfg.RETINANET.ANCHOR_SCALE\n A = scales_per_octave * len(aspect_ratios)\n anchors = {}\n for lvl in range(k_min, k_max + 1):\n # create cell anchors array\n stride = 2. ** lvl\n cell_anchors = np.zeros((A, 4))\n a = 0\n for octave in range(scales_per_octave):\n octave_scale = 2 ** (octave / float(scales_per_octave))\n for aspect in aspect_ratios:\n anchor_sizes = (stride * octave_scale * anchor_scale, )\n anchor_aspect_ratios = (aspect, )\n cell_anchors[a, :] = generate_anchors(\n stride=stride, sizes=anchor_sizes,\n aspect_ratios=anchor_aspect_ratios)\n a += 1\n anchors[lvl] = cell_anchors\n return anchors","def show_boxes(img, boundary_boxes, gt_boxes=None):\n\n for (x_tl, y_tl, x_br, y_br) in boundary_boxes:\n cv2.rectangle(img, (x_tl, y_tl),\n (x_br, y_br),\n (0, 0, 255), 2)\n\n if gt_boxes is not None:\n for (x_tl, y_tl, x_br, y_br) in gt_boxes:\n cv2.rectangle(img, (x_tl, y_tl),\n (x_br, y_br),\n (0, 255, 0), 2)\n\n cv2.imshow(\"img\", img)\n cv2.waitKey(0)\n cv2.destroyAllWindows()","def extract_bboxes(mask):\r\n boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)\r\n for i in range(mask.shape[-1]):\r\n m = mask[:, :, i]\r\n # Bounding box.\r\n horizontal_indicies = np.where(np.any(m, axis=0))[0]\r\n vertical_indicies = np.where(np.any(m, axis=1))[0]\r\n if horizontal_indicies.shape[0]:\r\n x1, x2 = horizontal_indicies[[0, -1]]\r\n y1, y2 = vertical_indicies[[0, -1]]\r\n # x2 and y2 should not be part of the box. Increment by 1.\r\n x2 += 1\r\n y2 += 1\r\n else:\r\n # No mask for this instance. Might happen due to\r\n # resizing or cropping. Set bbox to zeros\r\n x1, x2, y1, y2 = 0, 0, 0, 0\r\n boxes[i] = np.array([y1, x1, y2, x2])\r\n return boxes.astype(np.int32)","def valid_anchor_boxes(anchor_boxes, image_size):\n img_height, img_width = image_size\n y, x, height, width = np.transpose(anchor_boxes)\n\n # TODO(Mocurin) Optimize?\n # Indicator matrix\n indicators = np.array([y - height // 2 >= 0,\n x - width // 2 >= 0,\n y + height // 2 <= img_height,\n x + width // 2 <= img_width]).transpose()\n\n # Get indices of anchor boxes inside image\n return np.flatnonzero(np.all(indicators, axis=1, keepdims=False))","def _boxes_coordinates(self,\n image,\n boxes,\n classes,\n scores,\n max_boxes_to_draw=20,\n min_score_thresh=.5):\n\n if not max_boxes_to_draw:\n max_boxes_to_draw = boxes.shape[0]\n number_boxes = min(max_boxes_to_draw, boxes.shape[0])\n final_boxes = []\n final_scores = []\n for i in range(number_boxes):\n if self.category_index[classes[i]]['name'] not in \\\n self.classes_to_detect:\n continue\n if scores is None or scores[i] > min_score_thresh:\n box = tuple(boxes[i].tolist())\n ymin, xmin, ymax, xmax = box\n\n im_height, im_width, _ = image.shape\n left, right, top, bottom = [int(z) for z in\n (xmin * im_width, xmax * im_width,\n ymin * im_height,\n ymax * im_height)]\n\n final_boxes.append([top, left, bottom, right])\n final_scores.append(scores[i])\n return final_boxes, final_scores","def generate_anchors(base_size = 16, ratios = [0.5, 1, 2.0], \n scales = 2**np.arange(4,7)):\n n_anchors = len(ratios)*len(scales)\n\n base_anchor = np.array([int(base_size*0.5), \n int(base_size*0.5), \n base_size, \n base_size], np.float32) \n # First, generate a list of anchors of the appropriate area:\n scaled_anchors = np.tile(base_anchor,(len(scales), 1))\n final_anchors = np.zeros((len(scales),len(ratios), 4), np.float32)\n for s, i in zip(scales, xrange(len(scales))):\n scaled_anchors[i,2:] *= s\n for r, j in zip(ratios, xrange(len(ratios))):\n t = np.copy(scaled_anchors[i])\n t[2] *= np.sqrt(r)\n t[3] *= 1./np.sqrt(r)\n final_anchors[i,j] = np.round(t)\n return np.reshape(final_anchors, (n_anchors, 4))","def draw_bounding_boxes(self, image_path):\n img = cv.imread(image_path, cv.IMREAD_ANYDEPTH)\n bboxes = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\n unique, counts = np.unique(img, return_counts=True)\n for uni in unique:\n if uni == 0:\n continue\n self.get_instance_bounding_box(img, bboxes, uni)\n\n cv.namedWindow('building bounding boxes', cv.WINDOW_NORMAL)\n cv.imshow('building bounding boxes', bboxes)\n cv.waitKey(0)\n cv.destroyAllWindows()","def get_bboxes(self,\n cls_scores,\n bbox_preds,\n centernesses,\n img_metas,\n cfg=None,\n rescale=False,\n with_nms=True):\n cfg = self.test_cfg if cfg is None else cfg\n assert len(cls_scores) == len(bbox_preds)\n num_levels = len(cls_scores)\n device = cls_scores[0].device\n featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]\n mlvl_anchors = self.anchor_generator.grid_anchors(\n featmap_sizes, device=device)\n\n result_list = []\n for img_id in range(len(img_metas)):\n cls_score_list = [\n cls_scores[i][img_id].detach() for i in range(num_levels)\n ]\n bbox_pred_list = [\n bbox_preds[i][img_id].detach() for i in range(num_levels)\n ]\n centerness_pred_list = [\n centernesses[i][img_id].detach() for i in range(num_levels)\n ]\n img_shape = img_metas[img_id]['img_shape']\n scale_factor = img_metas[img_id]['scale_factor']\n proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list,\n centerness_pred_list,\n mlvl_anchors, img_shape,\n scale_factor, cfg, rescale,\n with_nms)\n result_list.append(proposals)\n return result_list","def extract_bboxes(mask):\n boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)\n for i in range(mask.shape[-1]):\n m = mask[:, :, i]\n # Bounding box.\n horizontal_indicies = np.where(np.any(m, axis=0))[0]\n vertical_indicies = np.where(np.any(m, axis=1))[0]\n if horizontal_indicies.shape[0]:\n x1, x2 = horizontal_indicies[[0, -1]]\n y1, y2 = vertical_indicies[[0, -1]]\n # x2 and y2 should not be part of the box. Increment by 1.\n x2 += 1\n y2 += 1\n else:\n # No mask for this instance. Might happen due to\n # resizing or cropping. Set bbox to zeros\n x1, x2, y1, y2 = 0, 0, 0, 0\n boxes[i] = np.array([x1, y1, x2, y2])\n return boxes.astype(np.int32)","def extract_bboxes(mask):\n boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)\n for i in range(mask.shape[-1]):\n m = mask[:, :, i]\n # Bounding box.\n horizontal_indicies = np.where(np.any(m, axis=0))[0]\n vertical_indicies = np.where(np.any(m, axis=1))[0]\n if horizontal_indicies.shape[0]:\n x1, x2 = horizontal_indicies[[0, -1]]\n y1, y2 = vertical_indicies[[0, -1]]\n # x2 and y2 should not be part of the box. Increment by 1.\n x2 += 1\n y2 += 1\n else:\n # No mask for this instance. Might happen due to\n # resizing or cropping. Set bbox to zeros\n x1, x2, y1, y2 = 0, 0, 0, 0\n boxes[i] = np.array([y1, x1, y2, x2])\n return boxes.astype(np.int32)","def compute_bb(self) -> List[float]:\n all_shapes = list(self.parts.values())\n bbox_vertices = unary_union(all_shapes).envelope.exterior.coords.xy\n min_x = min(bbox_vertices[0])\n max_x = max(bbox_vertices[0])\n min_y = min(bbox_vertices[1])\n max_y = max(bbox_vertices[1])\n return [min_x, max_x, min_y, max_y]","def build_boxes(self):\n for index in self.box_space.points:\n if self.rank_of_box[index] == self.my_rank:\n self.my_boxes.append(Box(self, index))","def get_bounding_box_lonlats(self,npts=100):\n\n # Top of bounding box\n tplons = np.linspace(self.corners[0].lon,self.corners[1].lon,npts)\n tplats = np.linspace(self.corners[0].lat,self.corners[1].lat,npts)\n # Right side of bounding box\n rtlons = np.linspace(self.corners[1].lon,self.corners[2].lon,npts)\n rtlats = np.linspace(self.corners[1].lat,self.corners[2].lat,npts)\n # Bottom of bounding box\n btlons = np.linspace(self.corners[2].lon,self.corners[3].lon,npts)\n btlats = np.linspace(self.corners[2].lat,self.corners[3].lat,npts)\n # Left side of bounding box\n ltlons = np.linspace(self.corners[3].lon,self.corners[0].lon,npts)\n ltlats = np.linspace(self.corners[3].lat,self.corners[0].lat,npts)\n\n retval = [(list(tplons),list(tplats)),\n (list(rtlons),list(rtlats)),\n (list(btlons),list(btlats)),\n (list(ltlons),list(ltlats))]\n return retval","def show_bboxes(img, bounding_boxes=None, facial_landmarks=[]):\n\n img_copy = img.copy()\n draw = ImageDraw.Draw(img_copy)\n# for b in bounding_boxes:\n# draw.rectangle([\n# (b[0], b[1]), (b[2], b[3])\n# ], outline='white')\n\n for p in facial_landmarks:\n for i in range(106):\n draw.ellipse([\n (p[i*2] - 1.0, p[2*i + 1] - 1.0),\n (p[i*2] + 1.0, p[2*i+1] + 1.0)\n ], outline='blue')\n font = ImageFont.truetype(\"arial.ttf\", 10)\n draw.text([p[2*i], p[2*i+1]], str(i), font=font)\n\n return img_copy","def get_bounding_box(self):\n if len(self.elements) == 0:\n return None\n if not (self._bb_valid and\n all(ref._bb_valid for ref in self.get_dependencies(True))):\n bb = numpy.array(((1e300, 1e300), (-1e300, -1e300)))\n all_polygons = []\n for element in self.elements:\n if isinstance(element, PolygonSet):\n all_polygons.extend(element.polygons)\n elif isinstance(element, CellReference) or isinstance(\n element, CellArray):\n element_bb = element.get_bounding_box()\n if element_bb is not None:\n bb[0, 0] = min(bb[0, 0], element_bb[0, 0])\n bb[0, 1] = min(bb[0, 1], element_bb[0, 1])\n bb[1, 0] = max(bb[1, 0], element_bb[1, 0])\n bb[1, 1] = max(bb[1, 1], element_bb[1, 1])\n if len(all_polygons) > 0:\n all_points = numpy.concatenate(all_polygons).transpose()\n bb[0, 0] = min(bb[0, 0], all_points[0].min())\n bb[0, 1] = min(bb[0, 1], all_points[1].min())\n bb[1, 0] = max(bb[1, 0], all_points[0].max())\n bb[1, 1] = max(bb[1, 1], all_points[1].max())\n self._bb_valid = True\n _bounding_boxes[self] = bb\n return _bounding_boxes[self]","def __build_anchors(anchor_parameters, features):\n anchors = [\n layers.Anchors(\n size=anchor_parameters.sizes[i],\n stride=anchor_parameters.strides[i],\n ratios=anchor_parameters.ratios,\n scales=anchor_parameters.scales,\n name='anchors_{}'.format(i)\n )(f) for i, f in enumerate(features)\n ]\n\n return keras.layers.Concatenate(axis=1, name='anchors')(anchors)","def generate_bboxes_with_scores(cls_map, scale, threshold=0.5, size=12, stride=2):\n assert len(cls_map.shape) == 2\n\n indices = np.where(cls_map >= threshold)\n bboxes = np.concatenate((\n ((indices[1] * stride) / scale).reshape(-1, 1),\n ((indices[0] * stride) / scale).reshape(-1, 1),\n ((indices[1] * stride + size) / scale).reshape(-1, 1),\n ((indices[0] * stride + size) / scale).reshape(-1, 1),\n cls_map[indices].reshape(-1, 1)\n ), axis=1)\n return bboxes, indices","def _boundRect(self):\n self.upperleft = list(map(min, zip(*self.addresstamp)))\n self.bottomright = list(map(max, zip(*self.addresstamp)))\n self.upperright = [self.bottomright[0], self.upperleft[1]]\n self.bottomleft = [self.upperleft[0], self.bottomright[1]]\n (self.width, self.height) = (self.upperright[0] - self.bottomleft[0], self.bottomleft[1] - self.upperright[1])\n assert self.width >= 0\n assert self.height >= 0\n self.center = [self.upperleft[0] + self.width / float(2), self.upperleft[1] + self.height / float(2)]\n self.corners = [self.upperright, self.bottomleft, self.upperleft, self.bottomright]","def create_bounds(self):\n # Bounds should be created for\n x0, y0 = (0, 0)\n x1 = self.game_area_size[0]\n y1 = self.game_area_size[1]\n space = self.space\n\n borders = [\n phy.Segment(space.static_body, phy.Vec2d(x0, y0), phy.Vec2d(x1, y0), 0),\n phy.Segment(space.static_body, phy.Vec2d(x0, y0), phy.Vec2d(x0, y1), 0),\n phy.Segment(space.static_body, phy.Vec2d(x1, y0), phy.Vec2d(x1, y1), 0),\n phy.Segment(space.static_body, phy.Vec2d(x0, y1), phy.Vec2d(x1, y1), 0),\n ]\n for b in borders:\n b.elasticity = 0.5\n self.space.add(borders)","def test_geotransform2bbox(self):\n\n M = 5\n N = 10\n for gt in GEOTRANSFORMS:\n bbox = geotransform2bbox(gt, M, N)\n\n # FIXME: Need better tests here, but this is better than nothing\n\n # Lower bounds\n assert bbox[0] == gt[0]\n\n # Upper bounds\n assert bbox[3] == gt[3]","def build(self, box, size):\n centroid = box.centroid\n edge1_midpoint = get_midpoint_of_edge(\n box.vertices[0], box.vertices[1])\n distance_to_edge1 = centroid.distance_to_xyz(edge1_midpoint)\n quadrat_indexes1 = range(int(distance_to_edge1 / size) + 1)\n\n edge2_midpoint = get_midpoint_of_edge(\n box.vertices[0], box.vertices[3])\n distance_to_edge2 = centroid.distance_to_xyz(edge2_midpoint)\n quadrat_indexes2 = range(int(distance_to_edge2 / size) + 1)\n\n quadrats = list()\n for i in quadrat_indexes2:\n for j in quadrat_indexes1:\n if i == 0 and j == 0:\n quadrats.append(Quadrat((0, 0), size, centroid))\n else:\n top_right = Vertex(centroid.x + i * size,\n centroid.y + (j * size), centroid.z)\n top_left = Vertex(centroid.x + i * size,\n centroid.y - (j * size), centroid.z)\n bottom_right = Vertex(\n centroid.x - i * size, centroid.y + (j * size), centroid.z)\n bottom_left = Vertex(\n centroid.x - i * size, centroid.y - (j * size), centroid.z)\n four_quadrats = [Quadrat((i, j), size, top_right),\n Quadrat((i, -1 * j), size, top_left),\n Quadrat((-1 * i, j), size, bottom_right),\n Quadrat((-1 * i, -1 * j), size, bottom_left)]\n quadrats_inside = list(\n filter(lambda x: box.contains(x.midpoint), four_quadrats))\n quadrats += quadrats_inside\n # print(\"There are this many quadrats: \" + str(len(quadrats)))\n return tuple(set(quadrats))","def _boundRect(self):\n addresstamp = reduce(lambda x, y: x + y, [v.addresstamp for v in self.footprints])\n self.upperleft = list(map(min, zip(*addresstamp)))\n self.bottomright = list(map(max, zip(*addresstamp)))\n self.upperright = [self.bottomright[0], self.upperleft[1]]\n self.bottomleft = [self.upperleft[0], self.bottomright[1]]\n (self.width, self.height) = (self.upperright[0] - self.bottomleft[0], self.bottomleft[1] - self.upperright[1])\n assert self.width >= 0\n assert self.height >= 0\n self.center = [self.upperleft[0] + self.width / float(2), self.upperleft[1] + self.height / float(2)]\n self.corners = [self.upperright, self.bottomleft, self.upperleft, self.bottomright]","def bbox(self):\n lower = (self.x.min(), self.y.min())\n upper = (self.x.max(), self.y.max())\n return (lower, upper)","def test_get_all_boxes(self, postfix_directory):\n print(\"Test_All_Boxes\")\n protein_file = os.path.join(postfix_directory, \"PfATP4.pdb\")\n ligand_file = os.path.join(postfix_directory, \"SJ733.pdb\")\n coords = rdkit_util.load_molecule(protein_file)[0]\n\n boxes = dc.dock.binding_pocket.get_all_boxes(coords)\n assert isinstance(boxes, list)\n # Pocket is of form ((x_min, x_max), (y_min, y_max), (z_min, z_max))\n for pocket in boxes:\n assert len(pocket) == 3\n assert len(pocket[0]) == 2\n assert len(pocket[1]) == 2\n assert len(pocket[2]) == 2\n (x_min, x_max), (y_min, y_max), (z_min, z_max) = pocket\n assert x_min < x_max\n assert y_min < y_max\n assert z_min < z_max","def get_bounding_box_lonlats(self,npts=100):\n g = Geod(ellps='WGS84')\n\n # Top of bounding box\n # g.npts returns a list of tuples of lon/lat pairs\n # [(lon0,lat0),(lon1,lat1),(lon2,lat2)]\n # zip reformats that into 2 tuples of lons and lats\n # [(lon0,lon1,lon2),(lat0,lat1,lat2)]\n # list(tplons) returns list of lons\n # [lon0,lon1,lon2]\n # list(tplats) returns list of lats\n # [lat0,lat1,lat2]\n tplons,tplats = zip(*g.npts(self.corners[0].lon, self.corners[0].lat, \n self.corners[1].lon, self.corners[1].lat,\n npts,radians=True))\n # Right side of bounding box\n rtlons,rtlats = zip(*g.npts(self.corners[1].lon, self.corners[1].lat, \n self.corners[2].lon, self.corners[2].lat,\n npts,radians=True))\n # Bottom of bounding box\n btlons,btlats = zip(*g.npts(self.corners[2].lon, self.corners[2].lat, \n self.corners[3].lon, self.corners[3].lat,\n npts,radians=True))\n # Left side of bounding box\n ltlons,ltlats = zip(*g.npts(self.corners[3].lon, self.corners[3].lat, \n self.corners[0].lon, self.corners[0].lat,\n npts,radians=True))\n\n retval = [(list(tplons),list(tplats)),\n (list(rtlons),list(rtlats)),\n (list(btlons),list(btlats)),\n (list(ltlons),list(ltlats))]\n return retval","def covering_box(boxes):\n x_min = np.amin([b.x for b in boxes])\n x_max = np.amax([b.x + b.width for b in boxes])\n y_min = np.amin([b.y for b in boxes])\n y_max = np.amax([b.y + b.height for b in boxes])\n cover = Box(x_min, y_min, x_max - x_min, y_max - y_min)\n return cover","def intersection_with_anchors(bbox):\n int_ymin = tf.maximum(ymin, bbox[0])\n int_xmin = tf.maximum(xmin, bbox[1])\n int_ymax = tf.minimum(ymax, bbox[2])\n int_xmax = tf.minimum(xmax, bbox[3])\n h = tf.maximum(int_ymax - int_ymin, 0.)\n w = tf.maximum(int_xmax - int_xmin, 0.)\n inter_vol = h * w\n scores = tf.div(inter_vol, vol_anchors)\n return scores","def bounding_box(primitive):\n\n if primitive[\"shape\"] == \"circle\":\n bbox = [[primitive[\"center\"][0] - primitive[\"radius\"],\n primitive[\"center\"][1] - primitive[\"radius\"]],\n [primitive[\"center\"][0] + primitive[\"radius\"],\n primitive[\"center\"][1] + primitive[\"radius\"]]]\n else:\n x_coords, y_coords = zip(*primitive[\"vertices\"])\n bbox = [[min(x_coords), min(y_coords)],\n [max(x_coords), max(y_coords)]]\n\n primitive[\"bounding_box\"] = bbox\n return primitive","def draw_bbox(n):\n return drawBbox(named(n))"],"string":"[\n \"def decode_boxes(raw_boxes, anchors):\\n boxes = np.zeros_like(raw_boxes)\\n\\n x_center = raw_boxes[..., 0] / x_scale * anchors[:, 2] + anchors[:, 0]\\n y_center = raw_boxes[..., 1] / y_scale * anchors[:, 3] + anchors[:, 1]\\n\\n w = raw_boxes[..., 2] / w_scale * anchors[:, 2]\\n h = raw_boxes[..., 3] / h_scale * anchors[:, 3]\\n\\n boxes[..., 0] = y_center - h / 2. # ymin\\n boxes[..., 1] = x_center - w / 2. # xmin\\n boxes[..., 2] = y_center + h / 2. # ymax\\n boxes[..., 3] = x_center + w / 2. # xmax\\n\\n for k in range(num_keypoints):\\n offset = 4 + k*2\\n keypoint_x = raw_boxes[..., offset] / x_scale * anchors[:, 2] + anchors[:, 0]\\n keypoint_y = raw_boxes[..., offset + 1] / y_scale * anchors[:, 3] + anchors[:, 1]\\n boxes[..., offset] = keypoint_x\\n boxes[..., offset + 1] = keypoint_y\\n\\n return boxes\",\n \"def _decode_boxes(raw_boxes, anchors):\\n boxes = np.zeros(raw_boxes.shape, np.float32)\\n SCALE = 128\\n x_center = raw_boxes[..., 0] / SCALE * anchors[:, 2] + anchors[:, 0]\\n y_center = raw_boxes[..., 1] / SCALE * anchors[:, 3] + anchors[:, 1]\\n\\n w = raw_boxes[..., 2] / SCALE * anchors[:, 2]\\n h = raw_boxes[..., 3] / SCALE * anchors[:, 3]\\n\\n boxes[..., 0] = y_center - h / 2. # ymin\\n boxes[..., 1] = x_center - w / 2. # xmin\\n boxes[..., 2] = y_center + h / 2. # ymax\\n boxes[..., 3] = x_center + w / 2. # xmax\\n\\n for k in range(6):\\n offset = 4 + k * 2\\n keypoint_x = raw_boxes[..., offset] / SCALE * anchors[:, 2] + anchors[:, 0]\\n keypoint_y = raw_boxes[..., offset + 1] / SCALE * anchors[:, 3] + anchors[:, 1]\\n boxes[..., offset] = keypoint_x\\n boxes[..., offset + 1] = keypoint_y\\n\\n return boxes\",\n \"def generate_anchor_boxes(image_size, feature_map_size, sizes, scales):\\n image_height, image_width = image_size\\n fm_height, fm_width = feature_map_size\\n height_stride = int(image_height / fm_height)\\n width_stride = int(image_width / fm_width)\\n\\n # Compose horizontal and vertical positions into grid and reshape result into (-1, 2)\\n y_centers = np.arange(0, image_height, height_stride)\\n x_centers = np.arange(0, image_width, width_stride)\\n centers = np.dstack(np.meshgrid(y_centers, x_centers)).reshape((-1, 2))\\n\\n # Creates anchor boxes pyramid. Somewhat vectorized version of itertools.product\\n r_scales = np.repeat([scales], len(sizes), axis=0).ravel()\\n r_sides = np.repeat([sizes], len(scales), axis=1).ravel()\\n ab_pyramid = np.transpose([r_sides / (r_scales ** .5),\\n r_sides * (r_scales ** .5)]).astype(int)\\n\\n # Creates combinations of all anchor boxes centers and sides\\n r_centers = np.repeat(centers, len(ab_pyramid), axis=0)\\n r_ab_pyramid = np.repeat([ab_pyramid], len(centers), axis=0).reshape((-1, 2))\\n return np.hstack((r_centers, r_ab_pyramid))\",\n \"def generate_anchors(self):\\n self.anchors = np.zeros((self.anchor_num, 4), dtype=np.float32)\\n size = self.stride * self.stride\\n count = 0\\n for r in self.ratios:\\n ws = int(math.sqrt(size * 1. / r))\\n hs = int(ws * r)\\n\\n for s in self.scales:\\n w = ws * s\\n h = hs * s\\n self.anchors[count][:] = [-w * 0.5, -h * 0.5, w * 0.5, h * 0.5][:]\\n count += 1\",\n \"def __call__(self, loc, scores, anchors, img_size):\\n anchors = bbox.loc2bbox(anchor)\",\n \"def BoundingBoxes(self, show_plot=False, figure=None):\\n\\n self.__do_essential_memebers_exist__()\\n\\n ndim = self.InferSpatialDimension()\\n all_elem_coords = self.points[self.elements]\\n mins = all_elem_coords.min(axis=1)\\n maxs = all_elem_coords.max(axis=1)\\n bboxes = np.zeros((2*self.nelem,self.points.shape[1]))\\n bboxes[::2] = mins\\n bboxes[1::2] = maxs\\n bboxes = bboxes.reshape(self.nelem,2,self.points.shape[1])\\n\\n if show_plot:\\n if ndim == 3:\\n point_generator = lambda bbox: np.array([\\n [ bbox[0,0], bbox[0,1], bbox[0,2] ],\\n [ bbox[1,0], bbox[0,1], bbox[0,2] ],\\n [ bbox[1,0], bbox[1,1], bbox[0,2] ],\\n [ bbox[0,0], bbox[1,1], bbox[0,2] ],\\n [ bbox[0,0], bbox[0,1], bbox[1,2] ],\\n [ bbox[1,0], bbox[0,1], bbox[1,2] ],\\n [ bbox[1,0], bbox[1,1], bbox[1,2] ],\\n [ bbox[0,0], bbox[1,1], bbox[1,2] ]\\n ])\\n elif ndim == 2:\\n point_generator = lambda bbox: np.array([\\n [ bbox[0,0], bbox[0,1] ],\\n [ bbox[1,0], bbox[0,1] ],\\n [ bbox[1,0], bbox[1,1] ],\\n [ bbox[0,0], bbox[1,1] ]\\n ])\\n\\n nsize = 4 if ndim ==2 else 8\\n ranger = np.arange(nsize)\\n bmesh = Mesh()\\n bmesh.element_type = \\\"quad\\\" if ndim ==2 else \\\"hex\\\"\\n bmesh.elements = np.arange(self.nelem*nsize).reshape(self.nelem,nsize)\\n bmesh.points = np.zeros((self.nelem*nsize,ndim))\\n bmesh.nelem = self.nelem\\n bmesh.nnode = bmesh.points.shape[0]\\n for i in range(0,self.nelem):\\n bmesh.points[i*nsize:(i+1)*nsize,:] = point_generator(bboxes[i])\\n\\n if ndim == 2:\\n import matplotlib.pyplot as plt\\n if figure is None:\\n figure = plt.figure()\\n self.SimplePlot(figure=figure, show_plot=False)\\n bmesh.SimplePlot(figure=figure, show_plot=False, edge_color='r')\\n plt.show()\\n else:\\n import os\\n os.environ['ETS_TOOLKIT'] = 'qt4'\\n from mayavi import mlab\\n if figure is None:\\n figure = mlab.figure(bgcolor=(1,1,1),fgcolor=(1,1,1),size=(1000,800))\\n self.SimplePlot(figure=figure, show_plot=False)\\n bmesh.SimplePlot(figure=figure, show_plot=False, plot_faces=False, edge_color='r')\\n mlab.show()\\n\\n return bboxes\",\n \"def get_bound(box_list):\\n box_xyxy_list = []\\n for box in box_list:\\n box_xyxy = xywh2xyxy(box)\\n box_xyxy_list.append(box_xyxy)\\n\\n box_xyxy_list = np.array(box_xyxy_list)\\n x1max, y1max, x2max, y2max = np.amax(box_xyxy_list, axis=0)\\n x1min, y1min, x2min, y2min = np.amin(box_xyxy_list, axis=0)\\n\\n boundbox = xyxy2xywh([x1min, y1min, x2max, y2max])\\n return boundbox\",\n \"def _generate_boxes(self):\\n boxes_all = []\\n for level in range(self.min_level, self.max_level + 1):\\n boxes_l = []\\n for scale in range(self.num_scales):\\n for aspect_ratio in self.aspect_ratios:\\n stride = 2 ** level\\n intermidate_scale = 2 ** (scale / float(self.num_scales))\\n base_anchor_size = self.anchor_size * stride * intermidate_scale\\n aspect_x = aspect_ratio ** 0.5\\n aspect_y = aspect_ratio ** -0.5\\n half_anchor_size_x = base_anchor_size * aspect_x / 2.0\\n half_anchor_size_y = base_anchor_size * aspect_y / 2.0\\n x = tf.range(stride / 2, self.image_size[1], stride)\\n y = tf.range(stride / 2, self.image_size[0], stride)\\n xv, yv = tf.meshgrid(x, y)\\n xv = tf.cast(tf.reshape(xv, [-1]), dtype=tf.float32)\\n yv = tf.cast(tf.reshape(yv, [-1]), dtype=tf.float32)\\n # Tensor shape Nx4.\\n boxes = tf.stack([yv - half_anchor_size_y, xv - half_anchor_size_x,\\n yv + half_anchor_size_y, xv + half_anchor_size_x],\\n axis=1)\\n boxes_l.append(boxes)\\n # Concat anchors on the same level to tensor shape NxAx4.\\n boxes_l = tf.stack(boxes_l, axis=1)\\n boxes_l = tf.reshape(boxes_l, [-1, 4])\\n boxes_all.append(boxes_l)\\n return tf.concat(boxes_all, axis=0)\",\n \"def calc_bounding_box(self):\\n self.BB = self.geos.abs_el(0).BB\\n for geo in self.geos.abs_iter():\\n self.BB = self.BB.joinBB(geo.BB)\",\n \"def get_all_anchors(stride=None, sizes=None):\\n if stride is None:\\n stride = cfg.ANCHOR.ANCHOR_STRIDE\\n if sizes is None:\\n sizes = cfg.ANCHOR.ANCHOR_SIZES\\n # Generates a NAx4 matrix of anchor boxes in (x1, y1, x2, y2) format. Anchors\\n # are centered on stride / 2, have (approximate) sqrt areas of the specified\\n # sizes, and aspect ratios as given.\\n cell_anchors = generate_anchors(\\n stride,\\n scales=np.array(sizes, dtype=np.float) / stride,\\n ratios=np.array(cfg.ANCHOR.ANCHOR_RATIOS, dtype=np.float))\\n # anchors are intbox here.\\n # anchors at featuremap [0,0] are centered at fpcoor (8,8) (half of stride)\\n\\n max_size = cfg.DATA.MAX_SIZE\\n field_size = int(np.ceil(max_size / stride))\\n shifts = np.arange(0, field_size) * stride\\n shift_x, shift_y = np.meshgrid(shifts, shifts)\\n shift_x = shift_x.flatten()\\n shift_y = shift_y.flatten()\\n shifts = np.vstack((shift_x, shift_y, shift_x, shift_y)).transpose()\\n # Kx4, K = field_size * field_size\\n K = shifts.shape[0]\\n\\n A = cell_anchors.shape[0]\\n field_of_anchors = (\\n cell_anchors.reshape((1, A, 4)) +\\n shifts.reshape((1, K, 4)).transpose((1, 0, 2)))\\n field_of_anchors = field_of_anchors.reshape((field_size, field_size, A, 4))\\n # FSxFSxAx4\\n # Many rounding happens inside the anchor code anyway\\n # assert np.all(field_of_anchors == field_of_anchors.astype('int32'))\\n field_of_anchors = field_of_anchors.astype('float32')\\n field_of_anchors[:, :, :, [2, 3]] += 1\\n return field_of_anchors\",\n \"def draw_bounding_boxes(display, bounding_boxes):\\n\\n bb_surface = pygame.Surface((VIEW_WIDTH, VIEW_HEIGHT))\\n bb_surface.set_colorkey((0, 0, 0))\\n for bbox in bounding_boxes:\\n points = [(int(bbox[i, 0]), int(bbox[i, 1])) for i in range(8)]\\n # draw lines\\n # base\\n pygame.draw.line(bb_surface, BB_COLOR, points[0], points[1])\\n pygame.draw.line(bb_surface, BB_COLOR, points[1], points[2])\\n pygame.draw.line(bb_surface, BB_COLOR, points[2], points[3])\\n pygame.draw.line(bb_surface, BB_COLOR, points[3], points[0])\\n # top\\n pygame.draw.line(bb_surface, BB_COLOR, points[4], points[5])\\n pygame.draw.line(bb_surface, BB_COLOR, points[5], points[6])\\n pygame.draw.line(bb_surface, BB_COLOR, points[6], points[7])\\n pygame.draw.line(bb_surface, BB_COLOR, points[7], points[4])\\n # base-top\\n pygame.draw.line(bb_surface, BB_COLOR, points[0], points[4])\\n pygame.draw.line(bb_surface, BB_COLOR, points[1], points[5])\\n pygame.draw.line(bb_surface, BB_COLOR, points[2], points[6])\\n pygame.draw.line(bb_surface, BB_COLOR, points[3], points[7])\\n display.blit(bb_surface, (0, 0))\",\n \"def bounding_boxes(self, detections):\\n bboxes = []\\n while len(detections) > 0:\\n det = detections.pop(0)\\n merging = True\\n while merging:\\n merging = False\\n pointer = 0\\n while pointer < len(detections):\\n if self.get_distance(det, detections[pointer]) <= self.max_distance:\\n det = self.merge_boxes(det, detections[pointer])\\n merging = True\\n detections.pop(pointer)\\n else:\\n pointer += 1\\n if det[4] >= self.min_area:\\n bboxes.append(det)\\n return bboxes\",\n \"def _get_anchor_grid(self, width, height, batch_size):\\n anchors = tf.cast(self._anchors, dtype = self.dtype)\\n anchors = tf.reshape(anchors, [1, -1])\\n anchors = tf.repeat(anchors, width*height, axis = 0)\\n anchors = tf.reshape(anchors, [1, width, height, self._num, -1])\\n anchors = tf.repeat(anchors, batch_size, axis = 0)\\n return anchors\",\n \"def adjust_regular_roi_anchors(bounds: QRectF, anchors: list):\\n for point in anchors:\\n off = point.boundingRect().width() / 2\\n if point.position == AnchorPosition.LEFT:\\n point.setPos(bounds.left() - off, bounds.top() - off + bounds.height() / 2)\\n elif point.position == AnchorPosition.RIGHT:\\n point.setPos(bounds.right() - off, bounds.top() - off + bounds.height() / 2)\\n elif point.position == AnchorPosition.TOP:\\n point.setPos(bounds.left() - off + bounds.width() / 2, bounds.top() - off)\\n elif point.position == AnchorPosition.TOP_LEFT:\\n point.setPos(bounds.left() - off, bounds.top() - off)\\n elif point.position == AnchorPosition.TOP_RIGHT:\\n point.setPos(bounds.right() - off, bounds.top() - off)\\n elif point.position == AnchorPosition.BOTTOM:\\n point.setPos(bounds.left() - off + bounds.width() / 2, bounds.bottom() - off)\\n elif point.position == AnchorPosition.BOTTOM_LEFT:\\n point.setPos(bounds.left() - off, bounds.bottom() - off)\\n elif point.position == AnchorPosition.BOTTOM_RIGHT:\\n point.setPos(bounds.right() - off, bounds.bottom() - off)\",\n \"def get_boxes_v2(output, img_size, anchors):\\n bias_w, bias_h = anchors\\n \\n w_img, h_img = img_size[1], img_size[0]\\n grid_w, grid_h, num_boxes = output.shape[:3]\\n\\n offset_x = \\\\\\n np.tile(np.arange(grid_w)[:, np.newaxis], (grid_h, 1, num_boxes))\\n offset_y = np.transpose(offset_x, (1, 0, 2))\\n boxes = output.copy()\\n boxes[:, :, :, 0] = (offset_x + logistic(boxes[:, :, :, 0])) / grid_w\\n boxes[:, :, :, 1] = (offset_y + logistic(boxes[:, :, :, 1])) / grid_h\\n boxes[:, :, :, 2] = np.exp(boxes[:, :, :, 2]) * bias_w / grid_w\\n boxes[:, :, :, 3] = np.exp(boxes[:, :, :, 3]) * bias_h / grid_h\\n\\n boxes[:, :, :, [0, 2]] *= w_img\\n boxes[:, :, :, [1, 3]] *= h_img\\n\\n return boxes\",\n \"def bounding_box(points):\\n x, y, z = zip(*points)\\n min_x = min(x)\\n max_x = max(x)\\n min_y = min(y)\\n max_y = max(y)\\n min_z = min(z)\\n max_z = max(z)\\n return [\\n [min_x, min_y, min_z],\\n [max_x, min_y, min_z],\\n [max_x, max_y, min_z],\\n [min_x, max_y, min_z],\\n [min_x, min_y, max_z],\\n [max_x, min_y, max_z],\\n [max_x, max_y, max_z],\\n [min_x, max_y, max_z],\\n ]\",\n \"def create_overlap_metric(anchor_boxes):\\n y, x, h, w = np.transpose(anchor_boxes)\\n ab_area = w * h\\n y0 = y - h // 2\\n x0 = x - w // 2\\n y1 = y + h // 2\\n x1 = x + w // 2\\n\\n def overlap(gt_boxes):\\n overlaps = []\\n for gt_box in gt_boxes:\\n gt_y0, gt_x0, gt_y1, gt_x1 = gt_box\\n int_y0 = np.maximum(gt_y0, y0)\\n int_x0 = np.maximum(gt_x0, x0)\\n int_y1 = np.minimum(gt_y1, y1)\\n int_x1 = np.minimum(gt_x1, x1)\\n int_area = np.maximum(0, int_x1 - int_x0) * np.maximum(0, int_y1 - int_y0)\\n overlaps.append(int_area / ab_area)\\n overlaps = np.transpose(overlaps)\\n gt_indices = np.argmax(overlaps, axis=1)\\n overlaps = np.squeeze(np.take_along_axis(overlaps, gt_indices[:, np.newaxis], axis=1))\\n gt_boxes = np.take(gt_boxes, gt_indices, axis=0)\\n return overlaps, gt_boxes\\n return overlap\",\n \"def generate_boxes(self, img):\\r\\n return [Box(left, top, img) for (left, top) in self.coords]\",\n \"def containing(*boxes):\\n if not boxes:\\n raise ValueError('At least one bounding box must be specified')\\n boxes_objs = map(BoundingBox, boxes)\\n start = boxes_objs[0].start\\n end = boxes_objs[0].end\\n for box in boxes_objs[1:]:\\n start = np.minimum(start, box.start)\\n end = np.maximum(end, box.end)\\n return BoundingBox(start=start, end=end)\",\n \"def rpn_anchor_boxes(image_size, *args, **kwargs):\\n anchor_boxes = generate_anchor_boxes(image_size, *args, **kwargs)\\n valid_ab_indices = valid_anchor_boxes(anchor_boxes, image_size)\\n return anchor_boxes, valid_ab_indices\",\n \"def bounding_box(self):\\n box_min = []\\n box_max = []\\n if self.is_empty():\\n raise ValueError('empty polytope is not allowed')\\n for i in range(0, self.space_dimension()):\\n x = Variable(i)\\n coords = [ v.coefficient(x) for v in self.generators() ]\\n max_coord = max(coords)\\n min_coord = min(coords)\\n box_max.append(max_coord)\\n box_min.append(min_coord)\\n return (tuple(box_min), tuple(box_max))\",\n \"def bounding_box(self):\\n latlon00 = self.ij_to_latlon(-1,-1)\\n latlon01 = self.ij_to_latlon(-1,self.domain_size[1]+1)\\n latlon11 = self.ij_to_latlon(self.domain_size[0]+1,self.domain_size[1]+1)\\n latlon10 = self.ij_to_latlon(self.domain_size[0]+1,-1)\\n return (latlon00,latlon01,latlon11,latlon10)\",\n \"def boundingBox(self):\\n y_max = np.max(self.points[:,0])\\n x_max = np.max(self.points[:,1])\\n y_min = np.min(self.points[:,0])\\n x_min = np.min(self.points[:,1])\\n \\n return ((x_max, y_max), (x_min, y_min))\",\n \"def _generate_bboxes(self, probs, offsets, scale, threshold):\\n # applying P-Net is equivalent, in some sense, to\\n # moving 12x12 window with stride 2\\n stride = 2\\n cell_size = 12\\n\\n # extract positive probability and resize it as [n, m] dim tensor.\\n probs = probs[:, 1, :, :]\\n\\n # indices of boxes where there is probably a face\\n mask = probs > threshold\\n inds = mask.nonzero()\\n\\n if inds.shape[0] == 0:\\n return torch.empty(0, dtype=torch.int32, device=self.device), \\\\\\n torch.empty(0, dtype=torch.float32, device=self.device), \\\\\\n torch.empty(0, dtype=torch.float32, device=self.device), \\\\\\n torch.empty(0, dtype=torch.int32, device=self.device)\\n\\n # transformations of bounding boxes\\n tx1, ty1, tx2, ty2 = [offsets[inds[:, 0], i, inds[:, 1], inds[:, 2]]\\n for i in range(4)]\\n\\n offsets = torch.stack([tx1, ty1, tx2, ty2], 1)\\n score = probs[inds[:, 0], inds[:, 1], inds[:, 2]]\\n\\n # P-Net is applied to scaled images\\n # so we need to rescale bounding boxes back\\n bounding_boxes = torch.stack([\\n stride*inds[:, -1] + 1.0,\\n stride*inds[:, -2] + 1.0,\\n stride*inds[:, -1] + 1.0 + cell_size,\\n (stride*inds[:, -2] + 1.0 + cell_size),\\n ], 0).transpose(0, 1).float()\\n\\n bounding_boxes = torch.round(bounding_boxes / scale).int()\\n return bounding_boxes, score, offsets, inds[:, 0].int()\",\n \"def get_bounding_boxes(dets):\\n bounding_boxes = []\\n for box in dets:\\n bounding_box = {'top_left_x': box.left(),\\n 'top_left_y': box.top(),\\n 'bottom_right_x': box.right(),\\n 'bottom_right_y': box.bottom()}\\n bounding_boxes.append(bounding_box)\\n return bounding_boxes\",\n \"def bounding_box(points):\\n x, y, z = zip(*points)\\n min_x = min(x)\\n max_x = max(x)\\n min_y = min(y)\\n max_y = max(y)\\n min_z = min(z)\\n max_z = max(z)\\n return [(min_x, min_y, min_z),\\n (max_x, min_y, min_z),\\n (max_x, max_y, min_z),\\n (min_x, max_y, min_z),\\n (min_x, min_y, max_z),\\n (max_x, min_y, max_z),\\n (max_x, max_y, max_z),\\n (min_x, max_y, max_z)]\",\n \"def gen_random_bboxes(self, n, meta):\\n def get_mins_and_maxs(d):\\n a = int(d / (4 * grid_dim + 1))\\n b = 3 * a\\n mins = []\\n maxs = []\\n for i in range(grid_dim):\\n mn = (1 + i) * a + i * b\\n mx = mn + b\\n mins.append(mn)\\n maxs.append(mx)\\n return mins, maxs\\n\\n grid_dim = int(np.ceil(np.sqrt(n)))\\n xmins, xmaxs = get_mins_and_maxs(meta['width'])\\n ymins, ymaxs = get_mins_and_maxs(meta['height'])\\n\\n bboxes = []\\n for ymin, ymax in zip(ymins, ymaxs):\\n for xmin, xmax in zip(xmins, xmaxs):\\n bboxes.append({'xmin': xmin, 'xmax': xmax, 'ymin': ymin, 'ymax': ymax})\\n\\n return bboxes\",\n \"def draw_bounding_boxes(self, bounding_boxes, output):\\n for i, bb in enumerate(bounding_boxes):\\n\\n if bb[2] > output.shape[1] or bb[3] > output.shape[0] or bb[0] < 0 or bb[1] < 0:\\n continue\\n cv2.rectangle(\\n np.asarray(output), (bb[0], bb[1]), (bb[2], bb[3]), self.colors[i % len(self.colors)],\\n thickness=2\\n )\",\n \"def generate_anchors(base_size, ratios, scales, rotations):\\n num_anchors = len(ratios) * len(scales) * len(rotations)\\n # initialize output anchors\\n anchors = np.zeros((num_anchors, 5))\\n # scale base_size\\n anchors[:, 2:4] = base_size * np.tile(scales, (2, len(ratios) * len(rotations))).T\\n # compute areas of anchors\\n areas = anchors[:, 2] * anchors[:, 3]\\n # correct for ratios\\n anchors[:, 2] = np.sqrt(areas / np.repeat(ratios, len(scales) * len(rotations)))\\n anchors[:, 3] = anchors[:, 2] * np.repeat(ratios, len(scales) * len(rotations))\\n # add rotations\\n anchors[:, 4] = np.tile(np.repeat(rotations, len(scales)), (1, len(ratios))).T[:, 0]\\n # transform from (x_ctr, y_ctr, w, h) -> (x1, y1, x2, y2)\\n anchors[:, 0:3:2] -= np.tile(anchors[:, 2] * 0.5, (2, 1)).T\\n anchors[:, 1:4:2] -= np.tile(anchors[:, 3] * 0.5, (2, 1)).T\\n return anchors\",\n \"def compute_bb(self):\\n all_shapes = list(self.parts.values()) + list(self.edges.values())\\n bbox_vertices = cascaded_union(all_shapes).envelope.exterior.coords.xy\\n min_x = min(bbox_vertices[0])\\n max_x = max(bbox_vertices[0])\\n min_y = min(bbox_vertices[1])\\n max_y = max(bbox_vertices[1])\\n return [min_x, max_x,min_y, max_y]\",\n \"def get_boxes(self):\\r\\n\\r\\n boxes = [(\\\" \\\", self.worldbox.tl, self.worldbox.br)]\\r\\n# boxes = []\\r\\n boxes += [(\\\".\\\", b.tl, b.br) for b in self.wallboxes]\\r\\n boxes += [(\\\"x\\\", b.tl, b.br) for b in self.targetboxes]\\r\\n agentscale = 100\\r\\n boxes += [(\\\"a\\\", (self.i_state[0] - self.dx * agentscale, self.i_state[1] - self.dx * agentscale),\\r\\n (self.i_state[0] + self.dx * agentscale, self.i_state[1] + self.dx * agentscale))]\\r\\n return boxes\",\n \"def _polygons_to_bboxes(polygons):\\n# Build bounding boxes\\n bboxes = np.empty([len(polygons), 4])\\n for n, p in enumerate(polygons):\\n try:\\n left, bottom = np.min(p, axis = 0)\\n except:\\n import pdb\\n pdb.set_trace()\\n right, top = np.max(p, axis = 0)\\n bboxes[n] = [left, bottom, right, top]\\n return bboxes\",\n \"def get_bboxes_from_deltas(anchors, deltas):\\n all_anc_width = anchors[:, :, 3] - anchors[:, :, 1]\\n all_anc_height = anchors[:, :, 2] - anchors[:, :, 0]\\n all_anc_ctr_x = anchors[:, :, 1] + 0.5 * all_anc_width\\n all_anc_ctr_y = anchors[:, :, 0] + 0.5 * all_anc_height\\n #\\n all_bbox_width = tf.exp(deltas[:, :, 3]) * all_anc_width\\n all_bbox_height = tf.exp(deltas[:, :, 2]) * all_anc_height\\n all_bbox_ctr_x = (deltas[:, :, 1] * all_anc_width) + all_anc_ctr_x\\n all_bbox_ctr_y = (deltas[:, :, 0] * all_anc_height) + all_anc_ctr_y\\n #\\n y1 = all_bbox_ctr_y - (0.5 * all_bbox_height)\\n x1 = all_bbox_ctr_x - (0.5 * all_bbox_width)\\n y2 = all_bbox_height + y1\\n x2 = all_bbox_width + x1\\n #\\n return tf.stack([y1, x1, y2, x2], axis=2)\",\n \"def __create_anchors(self, sizes, aspects):\\n k = len(sizes) * len(aspects)\\n img_anchors = []\\n for i in sizes:\\n for j in aspects:\\n img_anchors.append(\\n [0, 0, 2 * i * j[0] / (j[0] + j[1]), 2 * i * j[1] / (j[0] + j[1])])\\n\\n self.anchors = np.asarray(img_anchors)\",\n \"def bbox(self):\\n return [self._x0, self._y0, self._x1, self._y1]\",\n \"def activations_to_bboxes(actn, anchors, grid_sizes):\\n #taken from fastai\\n anchors = anchors.type(torch.float64)\\n actn_offsets = torch.tanh(actn)\\n\\n actn_centers = actn_offsets[:, :2]/2 * grid_sizes + anchors[:, :2]\\n actn_hw = (actn_offsets[:, 2:]/2+1) * anchors[:, 2:]\\n\\n return hw2corners(actn_centers, actn_hw)\",\n \"def boundingBox(points):\\n min_x, min_y = float('inf'), float('inf')\\n max_x, max_y = float('-inf'), float('-inf')\\n for x, _, y in points:\\n min_x = min(min_x, x)\\n min_y = min(min_y, y)\\n max_x = max(max_x, x)\\n max_y = max(max_y, y)\\n\\n return (min_x, min_y), (max_x, min_y), (max_x, max_y), (min_x, max_y)\",\n \"def create_bbox(nodes, per_selection=False):\\n\\n if per_selection:\\n for node in nodes:\\n return cube_from_bbox(node.boundingBox())\\n else:\\n bbox = pm.dt.BoundingBox()\\n for node in nodes:\\n bbox.expand(node.boundingBox().min())\\n bbox.expand(node.boundingBox().max())\\n return cube_from_bbox(bbox)\",\n \"def draw_boxes(indexes, frame, all_boxes):\\n bbox = []\\n mid_points = []\\n\\n for i in indexes:\\n x = i[0]\\n box = all_boxes[x]\\n bbox.append(box)\\n mid_points.append(mid_point(frame, box))\\n x1, y1, w, h = box[0], box[1], box[2], box[3]\\n x2, y2 = x1+w, y1+h\\n\\n cv2.rectangle(frame, (x1,y1),(x2,y2),(255,0,0),2) \\n\\n return mid_points, bbox\",\n \"def gen_valid_bboxes(self, records, names):\\n labels = []\\n boxes = []\\n scores = []\\n for rec in records:\\n scores.append(rec['score'])\\n labels.append(names[rec['category_id'] - 1])\\n bx, by, bw, bh = rec['bbox']\\n boxes.append({'xmin': bx, 'ymin': by, 'xmax': bx+bw, 'ymax': by+bh})\\n\\n return labels, boxes, scores\",\n \"def bounding_box_xy(points):\\n x, y = islice(zip(*points), 2)\\n min_x = min(x)\\n max_x = max(x)\\n min_y = min(y)\\n max_y = max(y)\\n return [\\n [min_x, min_y, 0.0],\\n [max_x, min_y, 0.0],\\n [max_x, max_y, 0.0],\\n [min_x, max_y, 0.0],\\n ]\",\n \"def abox2bbox(aboxes):\\n\\n c = aboxes[..., :2, 2]\\n e = np.linalg.norm(aboxes[..., :2, :2], ord=2, axis=-1)\\n bboxes = np.concatenate([c - e, c + e], axis=-1)\\n return bboxes\",\n \"def set_anchors(mc):\\n H, W, C = _get_output_shape(mc)\\n B = mc.ANCHOR_PER_GRID\\n X = np.array(mc.INITIAL_ANCHOR_SHAPES)\\n X[:,0] *= mc.IMAGE_WIDTH\\n X[:,1] *= mc.IMAGE_HEIGHT\\n anchor_shapes = np.reshape( # it refers to the anchor width and height\\n [X] * H * W,\\n (H, W, B, 2)\\n )\\n center_x = np.reshape(\\n np.transpose(\\n np.reshape(\\n np.array([np.arange(1, W+1)*float(mc.IMAGE_WIDTH)/(W+1)]*H*B), \\n (B, H, W)\\n ),\\n (1, 2, 0)\\n ),\\n (H, W, B, 1)\\n )\\n center_y = np.reshape(\\n np.transpose(\\n np.reshape(\\n np.array([np.arange(1, H+1)*float(mc.IMAGE_HEIGHT)/(H+1)]*W*B),\\n (B, W, H)\\n ),\\n (2, 1, 0)\\n ),\\n (H, W, B, 1)\\n )\\n anchors = np.reshape(\\n np.concatenate((center_x, center_y, anchor_shapes), axis=3),\\n (-1, 4)\\n )\\n\\n return anchors\",\n \"def bounding_box(points):\\n x, y, w, h = cv2.boundingRect(np.array([p for p in points]))\\n bounding = Box(x, y, w, h)\\n return bounding\",\n \"def generate_all_anchors(self):\\n self.feature_sizes = [int(np.round(self.resolution/stride)) for stride in self.strides]\\n \\n #generate all anchors for each level of the FPN\\n all_anchors = [self.generate_feature_level_base_anchors(size=size) for size in self.sizes]\\n all_anchors = [self.shift_and_duplicate(layer_anchors, feature_size, stride) for layer_anchors, feature_size, stride in zip(all_anchors, self.feature_sizes, self.strides)]\\n all_anchors = tf.concat(all_anchors, axis=0)\\n\\n return all_anchors\",\n \"def create_pos_overlap_metric(anchor_boxes):\\n y, x, h, w = np.transpose(anchor_boxes)\\n y0 = y - h // 2\\n x0 = x - w // 2\\n y1 = y + h // 2\\n x1 = x + w // 2\\n\\n def pos_overlap(gt_boxes):\\n pos_overlaps = []\\n for gt_box in gt_boxes:\\n gt_y0, gt_x0, gt_y1, gt_x1 = gt_box\\n gt_area = (gt_x1 - gt_x0) * (gt_y1 - gt_y0)\\n int_y0 = np.maximum(gt_y0, y0)\\n int_x0 = np.maximum(gt_x0, x0)\\n int_y1 = np.minimum(gt_y1, y1)\\n int_x1 = np.minimum(gt_x1, x1)\\n int_area = np.maximum(0, int_x1 - int_x0) * np.maximum(0, int_y1 - int_y0)\\n pos_overlaps.append(int_area / gt_area)\\n # Group by anchor boxes\\n pos_overlaps = np.transpose(pos_overlaps)\\n # Get max metric index\\n gt_indices = np.argmax(pos_overlaps, axis=1)\\n # Choose max metric\\n pos_overlaps = np.squeeze(np.take_along_axis(pos_overlaps, gt_indices[:, np.newaxis], axis=1))\\n # Take respective ground-truth boxes. No reason to return indices, at least in RPN\\n gt_boxes = np.take(gt_boxes, gt_indices, axis=0)\\n return pos_overlaps, gt_boxes\\n return pos_overlap\",\n \"def _generate_anchors(point, sizes, aspect_ratios, layout, beta, include_depth):\\n\\n distance = point[2]\\n base_size = sizes[0]\\n scales = sizes[1:] / base_size\\n # beta = 8\\n scales = (beta/distance)*scales\\n\\n center = (point[0], point[1])\\n anchor = np.array([center[0] - base_size/2.0, center[1] - base_size/2.0,\\n center[0] + base_size/2.0, center[1] + base_size/2.0],\\n dtype=np.float)\\n\\n anchors = _ratio_enum(anchor, aspect_ratios)\\n anchors = np.vstack(\\n [_scale_enum(anchors[i, :], scales) for i in range(anchors.shape[0])]\\n )\\n\\n all_anchors = np.empty((0,4))\\n for l in layout:\\n new_anchors = _shift_anchors(anchors, l)\\n all_anchors = np.vstack((all_anchors, new_anchors))\\n\\n if int(include_depth)==1:\\n # Add the distance as the 5th element to all anchors\\n new_shape = (all_anchors.shape[0], all_anchors.shape[1]+1)\\n new_anchors = np.ones(new_shape) * distance\\n new_anchors[:,:-1] = all_anchors\\n all_anchors = new_anchors\\n\\n return all_anchors\",\n \"def _generate_anchors(self, sizes, ratios, step, alloc_size, offsets):\\n assert len(sizes) == 2, \\\"SSD requires sizes to be (size_min, size_max)\\\"\\n anchors = []\\n for i in range(alloc_size[0]):\\n for j in range(alloc_size[1]):\\n cy = (i + offsets[0]) * step\\n cx = (j + offsets[1]) * step\\n # ratio = ratios[0], size = size_min or sqrt(size_min * size_max)\\n r = ratios[0]\\n anchors.append([cx, cy, sizes[0] / 2, sizes[0] / 2])\\n # size = sizes[0], ratio = ...\\n for r in ratios[1:]:\\n sr = np.sqrt(r)\\n w = sizes[0] * sr\\n h = sizes[0] / sr\\n anchors.append([cx, cy, w, h])\\n if self._index > 0:\\n anchors.append([cx, cy, sizes[1], sizes[1]])\\n return np.array(anchors).reshape(1, 1, alloc_size[0], alloc_size[1], -1)\",\n \"def create_prior_boxes(self):\\n # value of k for each feature map to create k^2 boxes for each feature map\\n feature_map_dims = {'conv4_3': 38, 'conv7': 19, 'conv8_2': 10, 'conv9_2': 5}\\n\\n # scale for boxes across different feature maps. boxes for inner feature maps\\n # are scaled much lower to detect small objects\\n obj_scales = {'conv4_3': 0.1, 'conv7': 0.21, 'conv8_2': 0.255, 'conv9_2': 0.30}\\n\\n # Defined aspect ratio calculated from mean of (w/h) across all bounding boxes\\n # from the dataset. The mean is 0.66 with deviation of 0.07. So aspect ratio is kept\\n # at 0.66 for all feature maps\\n aspect_ratios = {'conv4_3': [0.5], 'conv7': [0.55], 'conv8_2': [0.6], 'conv9_2': [.66]}\\n\\n fmaps = list(feature_map_dims.keys())\\n prior_boxes = []\\n for k, fmap in enumerate(fmaps):\\n # for each feature map, create k*k boxes\\n for i in range(feature_map_dims[fmap]):\\n for j in range(feature_map_dims[fmap]):\\n # calculate center coordinates of boxes\\n cx = (j + 0.5) / feature_map_dims[fmap]\\n cy = (i + 0.5) / feature_map_dims[fmap]\\n\\n # For each\\n for ratio in aspect_ratios[fmap]:\\n prior_boxes.append([cx, cy, obj_scales[fmap] * sqrt(ratio), obj_scales[fmap] / sqrt(ratio)])\\n\\n prior_boxes = torch.FloatTensor(prior_boxes).to(device) # (1930, 4)\\n prior_boxes.clamp_(0, 1) # (1930, 4)\\n\\n return prior_boxes\",\n \"def extract_bounding_boxes(self, scene):\\n objs = scene[\\\"objects\\\"]\\n rotation = scene[\\\"directions\\\"][\\\"right\\\"]\\n\\n xmin = []\\n ymin = []\\n xmax = []\\n ymax = []\\n\\n for i, obj in enumerate(objs):\\n [x, y, z] = obj[\\\"pixel_coords\\\"]\\n\\n [x1, y1, z1] = obj[\\\"3d_coords\\\"]\\n\\n cos_theta, sin_theta, _ = rotation\\n\\n x1 = x1 * cos_theta + y1 * sin_theta\\n y1 = x1 * -sin_theta + y1 * cos_theta\\n\\n height_d = 6.9 * z1 * (15 - y1) / 2.0\\n height_u = height_d\\n width_l = height_d\\n width_r = height_d\\n\\n if obj[\\\"shape\\\"] == \\\"cylinder\\\":\\n d = 9.4 + y1\\n h = 6.4\\n s = z1\\n\\n height_u *= (s * (h / d + 1)) \\\\\\n / ((s * (h / d + 1)) - (s * (h - s) / d))\\n height_d = height_u * (h - s + d) / (h + s + d)\\n\\n width_l *= 11 / (10 + y1)\\n width_r = width_l\\n\\n if obj[\\\"shape\\\"] == \\\"cube\\\":\\n height_u *= 1.3 * 10 / (10 + y1)\\n height_d = height_u\\n width_l = height_u\\n width_r = height_u\\n\\n ymin.append((y - height_d) / 320.0)\\n ymax.append((y + height_u) / 320.0)\\n xmin.append((x - width_l) / 480.0)\\n xmax.append((x + width_r) / 480.0)\\n\\n return xmin, ymin, xmax, ymax\",\n \"def get_final_bounding_box(boxes, nms_idx, width: int, height: int):\\n x1 = np.inf\\n y1 = np.inf\\n x2 = -np.inf\\n y2 = -np.inf\\n\\n bx = [boxes[i] for i in nms_idx]\\n for box in bx:\\n xmin = np.min(box[[0, 2]])\\n xmax = np.max(box[[0, 2]])\\n ymin = np.min(box[[1, 3]])\\n ymax = np.max(box[[1, 3]])\\n\\n x1 = np.min([xmin, x1])\\n y1 = np.min([ymin, y1])\\n x2 = np.max([xmax, x2])\\n y2 = np.max([ymax, y2])\\n return x1, y1, x2, y2\",\n \"def xywh_xyxy(boxes):\\n bbox = np.zeros(boxes.shape)\\n bbox[:, 0] = boxes[:, 0] \\n bbox[:, 1] = boxes[:, 1] \\n bbox[:, 2] = boxes[:, 0] + 1 * boxes[:, 2]\\n bbox[:, 3] = boxes[:, 1] + 1 * boxes[:, 3]\\n return bbox\",\n \"def _generate_anchors(self, sizes, ratios, step, alloc_size, offsets):\\n assert len(sizes) == 2, \\\"SSD requires sizes to be (size_min, size_max)\\\"\\n anchors = []\\n for i in range(alloc_size[0]):\\n for j in range(alloc_size[1]):\\n cy = (i + offsets[0]) * step\\n cx = (j + offsets[1]) * step\\n # ratio = ratios[0], size = size_min or sqrt(size_min * size_max)\\n r = ratios[0]\\n anchors.append([cx, cy, sizes[0], sizes[0]])\\n anchors.append([cx, cy, sizes[1], sizes[1]])\\n # size = sizes[0], ratio = ...\\n for r in ratios[1:]:\\n sr = np.sqrt(r)\\n w = sizes[0] * sr\\n h = sizes[0] / sr\\n anchors.append([cx, cy, w, h])\\n return np.array(anchors).reshape(1, 1, alloc_size[0], alloc_size[1], -1)\",\n \"def generate_anchors(base_size=16, ratios=None, scales=None):\\n\\n if ratios is None:\\n ratios = np.array([0.5, 1, 2])\\n\\n if scales is None:\\n scales = np.array([2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)])\\n\\n num_anchors = len(ratios) * len(scales)\\n\\n # initialize output anchors\\n anchors = np.zeros((num_anchors, 4))\\n\\n # scale base_size\\n anchors[:, 2:] = base_size * np.tile(scales, (2, len(ratios))).T\\n\\n # compute areas of anchors\\n areas = anchors[:, 2] * anchors[:, 3]\\n\\n # correct for ratios\\n anchors[:, 2] = np.sqrt(areas / np.repeat(ratios, len(scales)))\\n anchors[:, 3] = anchors[:, 2] * np.repeat(ratios, len(scales))\\n\\n # transform from (x_ctr, y_ctr, w, h) -> (x1, y1, x2, y2)\\n anchors[:, 0::2] -= np.tile(anchors[:, 2] * 0.5, (2, 1)).T\\n anchors[:, 1::2] -= np.tile(anchors[:, 3] * 0.5, (2, 1)).T\\n\\n return anchors\",\n \"def generate_anchors(base_size=16, ratios=None, scales=None):\\n\\n if ratios is None:\\n ratios = np.array([0.5, 1, 2])\\n\\n if scales is None:\\n scales = np.array([2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)])\\n\\n num_anchors = len(ratios) * len(scales)\\n\\n # initialize output anchors\\n anchors = np.zeros((num_anchors, 4))\\n\\n # scale base_size\\n anchors[:, 2:] = base_size * np.tile(scales, (2, len(ratios))).T\\n\\n # compute areas of anchors\\n areas = anchors[:, 2] * anchors[:, 3]\\n\\n # correct for ratios\\n anchors[:, 2] = np.sqrt(areas / np.repeat(ratios, len(scales)))\\n anchors[:, 3] = anchors[:, 2] * np.repeat(ratios, len(scales))\\n\\n # transform from (x_ctr, y_ctr, w, h) -> (x1, y1, x2, y2)\\n anchors[:, 0::2] -= np.tile(anchors[:, 2] * 0.5, (2, 1)).T\\n anchors[:, 1::2] -= np.tile(anchors[:, 3] * 0.5, (2, 1)).T\\n\\n return anchors\",\n \"def boundingBox(self):\\n minx, miny, maxx, maxy = self.substrates.bounds\\n return pcbnew.BOX2I(\\n pcbnew.VECTOR2I(int(minx), int(miny)),\\n pcbnew.VECTOR2I(int(maxx - minx), int(maxy - miny)))\",\n \"def process_bounding_boxes(ground_truth_file: Path) -> List[Dict]:\\n index_shifts = defaultdict(lambda: 0)\\n index_shifts[\\\"cubicle\\\"] = 1108\\n folder_name = ground_truth_file.parent.name\\n \\n annotations = []\\n ground_truth_dataframe = pd.read_csv(str(ground_truth_file),\\n header=None,\\n sep=\\\" \\\",\\n names=[\\\"frameID\\\", \\\"0\\\", \\\"x\\\", \\\"y\\\", \\\"width\\\", \\\"height\\\"])\\n for index, row in ground_truth_dataframe.iterrows():\\n image_id = int(row[\\\"frameID\\\"]) + index_shifts[folder_name]\\n image_id = f\\\"{folder_name}_{str(image_id)}\\\"\\n x = int(row[\\\"x\\\"])\\n y = int(row[\\\"y\\\"])\\n width = int(row[\\\"width\\\"])\\n height = int(row[\\\"height\\\"])\\n\\n area = width * height\\n bbox = [x, y, width, height]\\n\\n label_data = {\\\"id\\\": index,\\n \\\"image_id\\\": image_id,\\n \\\"category_id\\\": 1,\\n \\\"bbox\\\": bbox,\\n \\\"area\\\": area,\\n \\\"iscrowd\\\": False}\\n annotations.append(label_data)\\n\\n return annotations\",\n \"def layout_bounding_boxes(canvas_x, canvas_y, canvas_width, line_height,\\n space_widths, y_space, sizes):\\n\\n cur_x, cur_y = canvas_x, canvas_y\\n cur_size = 0\\n cur_line = 0\\n boxes = []\\n line_breaks = []\\n line_poss = []\\n line_poss.append((cur_x, cur_y))\\n while cur_size < len(sizes):\\n sz = sizes[cur_size]\\n if cur_x + sz[0] > canvas_width + canvas_x:\\n cur_line += 1\\n cur_y = canvas_y - cur_line * (y_space + line_height)\\n cur_x = canvas_x\\n line_poss.append((cur_x, cur_y))\\n line_breaks.append(cur_size)\\n else:\\n boxes.append((cur_x, cur_y, sz[0], sz[1]))\\n cur_x += sz[0]\\n if cur_size < len(space_widths):\\n cur_x += space_widths[cur_size]\\n cur_size += 1\\n return boxes, line_breaks, line_poss\",\n \"def set_grid_anchors(grid_sizes, strides, cell_anchors):\\n anchors = []\\n assert cell_anchors is not None\\n\\n for size, stride, base_anchors in zip(grid_sizes, strides, cell_anchors):\\n grid_height, grid_width = size\\n stride_height, stride_width = stride\\n\\n # For output anchor, compute [x_center, y_center, x_center, y_center]\\n shifts_x = np.arange(0, grid_width) * stride_width\\n shifts_y = np.arange(0, grid_height) * stride_height\\n shift_x, shift_y = np.meshgrid(shifts_y, shifts_x)\\n shift_x = shift_x.reshape(-1)\\n shift_y = shift_y.reshape(-1)\\n shifts = np.stack((shift_x, shift_y, shift_x, shift_y), axis=1)\\n\\n # For every (base anchor, output anchor) pair,\\n # offset each zero-centered base anchor by the center of the output anchor.\\n anchors.append(\\n (shifts.reshape((-1, 1, 4)) + base_anchors.reshape((1, -1, 4))).reshape(-1, 4)\\n )\\n\\n return anchors\",\n \"def load_anchors_blocks(path):\\t\\t\\n\\t#TODO : automatization\\n\\tleft = Building_Block(abbrev=\\\"l\\\", num_atoms=2,origin=0, para_pos=0, para_angle=0, meta_pos=0 , meta_angle = 0., ortho_pos=0, ortho_angle=-2.*np.pi/3, fixed_left = 0,complexity=1, path=path+\\\"/anchor_small_left.xyz\\\")\\n\\tright = Building_Block(abbrev=\\\"r\\\", num_atoms=2,origin=0, para_pos=0, para_angle=0., meta_pos=0 , meta_angle = 0., ortho_pos=0, ortho_angle=-2.*np.pi/3, fixed_left = -1,complexity=1, path=path+\\\"/anchor_small_right.xyz\\\")\\n\\t\\n\\tanchors = [left,right]\\n\\n\\treturn anchors\",\n \"def generate_random_bbox(n, img_size, min_length, max_length):\\n H, W = img_size\\n y_min = np.random.uniform(0, H - max_length, size=(n,))\\n x_min = np.random.uniform(0, W - max_length, size=(n,))\\n y_max = y_min + np.random.uniform(min_length, max_length, size=(n,))\\n x_max = x_min + np.random.uniform(min_length, max_length, size=(n,))\\n bbox = np.stack((y_min, x_min, y_max, x_max), axis=1).astype(np.float32)\\n return bbox\",\n \"def fix_faceboxes_bb(\\n max_y: int,\\n max_x: int,\\n bounding_box: List[int]) -> List[int]:\\n y1, x1, y2, x2 = bounding_box[:4]\\n x1 = max(min(x1, max_x), 0)\\n x2 = max(min(x2, max_x), 0)\\n y1 = max(min(y1, max_y), 0)\\n y2 = max(min(y2, max_y), 0)\\n return [x1, y1, x2, y2]\",\n \"def _compute_bounding_box(self, points_2d):\\n max_x = max(map(lambda point: int(point[0]), points_2d))\\n min_x = min(map(lambda point: int(point[0]), points_2d))\\n max_y = max(map(lambda point: int(point[1]), points_2d))\\n min_y = min(map(lambda point: int(point[1]), points_2d))\\n\\n width = max_x - min_x + 1\\n height = max_y - min_y + 1\\n\\n return [min_x, min_y, width, height]\",\n \"def center_to_AABB_box3d(boxes):\\n AABBs =[] \\n num_det = len(boxes)\\n for i in range(num_det):\\n box = boxes[i:i+1]\\n corner = np.array(center_to_corner_box3d(box[:, :3], box[:, 3:6], box[:, -1])[0].tolist())\\n AABBs.append(o3d.geometry.AxisAlignedBoundingBox(corner.min(axis=0), corner.max(axis=0)))\\n return AABBs\",\n \"def bounding_box(vertices, (height, width), extend=5):\\n x_min = min(x for x, y in vertices) - extend\\n x_max = max(x for x, y in vertices) + extend\\n y_min = min(y for x, y in vertices) - extend\\n y_max = max(y for x, y in vertices) + extend\\n \\n return max(x_min, 0), min(x_max, width), max(y_min, 0), min(y_max, height)\",\n \"def get_bounding_box(self):\\n lon, lat = self.coordinates\\n\\n ll = (np.min(lon),np.min(lat))\\n ul = (np.min(lon),np.max(lat))\\n ur = (np.max(lon),np.max(lat))\\n lr = (np.max(lon),np.min(lat))\\n\\n return (ll, ul, ur, lr)\",\n \"def make_wander_box(self):\\n x = int(self.location[0])\\n y = int(self.location[1])\\n box_list = []\\n box_rects = []\\n\\n for i in range(x-3, x+4):\\n box_list.append([i, y-3])\\n box_list.append([i, y+3])\\n\\n for i in range(y-2, y+3):\\n box_list.append([x-3, i])\\n box_list.append([x+3, i])\\n\\n for box in box_list:\\n left = box[0]*32\\n top = box[1]*32\\n box_rects.append(pg.Rect(left, top, 32, 32))\\n\\n return box_rects\",\n \"def unparam_bbox(t, anchors, max_shape=None):\\n t = np.array(t, np.float32)\\n anchors = np.array(anchors, np.float32)\\n\\n yx = t[:, :2] * anchors[:, 2:] + anchors[:, :2]\\n hw = np.exp(t[:, 2:]) * anchors[:, 2:]\\n\\n bboxes = np.concatenate((yx, hw), axis=1)\\n\\n if max_shape != None:\\n bboxes = rectify_bbox(bboxes, max_shape)\\n\\n return bboxes\",\n \"def center(self, anchors):\\n # self.anchors_ = boxes[np.random.choice(n, self.k, replace=True)]\\n if isinstance(anchors, list):\\n anchors = np.array(anchors)\\n self.anchors_ = anchors\",\n \"def anchor_target_layer(rpn_cls_score, gt_boxes, im_info, _feat_stride, all_anchors, num_anchors):\\n A = num_anchors\\n total_anchors = all_anchors.shape[0]\\n K = total_anchors / num_anchors\\n\\n # allow boxes to sit over the edge by a small amount\\n _allowed_border = 0\\n\\n # map of shape (..., H, W)\\n height, width = rpn_cls_score.shape[1:3]\\n\\n # only keep anchors inside the image\\n inds_inside = np.where(\\n (all_anchors[:, 0] >= -_allowed_border) &\\n (all_anchors[:, 1] >= -_allowed_border) &\\n (all_anchors[:, 2] < im_info[1] + _allowed_border) & # width\\n (all_anchors[:, 3] < im_info[0] + _allowed_border) # height\\n )[0]\\n\\n # keep only inside anchors\\n anchors = all_anchors[inds_inside, :]\\n\\n # label: 1 is positive, 0 is negative, -1 is dont care\\n labels = np.empty((len(inds_inside),), dtype=np.float32)\\n labels.fill(-1)\\n\\n # overlaps between the anchors and the gt boxes\\n # overlaps (ex, gt)\\n overlaps = bbox_overlaps(\\n np.ascontiguousarray(anchors, dtype=np.float),\\n np.ascontiguousarray(gt_boxes, dtype=np.float))\\n argmax_overlaps = overlaps.argmax(axis=1)\\n max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]\\n gt_argmax_overlaps = overlaps.argmax(axis=0)\\n gt_max_overlaps = overlaps[gt_argmax_overlaps,\\n np.arange(overlaps.shape[1])]\\n gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]\\n\\n if not cfg.TRAIN.RPN_CLOBBER_POSITIVES:\\n # assign bg labels first so that positive labels can clobber them\\n # first set the negatives\\n labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0\\n\\n # fg label: for each gt, anchor with highest overlap\\n labels[gt_argmax_overlaps] = 1\\n\\n # fg label: above threshold IOU\\n labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1\\n\\n if cfg.TRAIN.RPN_CLOBBER_POSITIVES:\\n # assign bg labels last so that negative labels can clobber positives\\n labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0\\n\\n # subsample positive labels if we have too many\\n num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)\\n fg_inds = np.where(labels == 1)[0]\\n if len(fg_inds) > num_fg:\\n disable_inds = npr.choice(\\n fg_inds, size=(len(fg_inds) - num_fg), replace=False)\\n labels[disable_inds] = -1\\n\\n # subsample negative labels if we have too many\\n num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1)\\n bg_inds = np.where(labels == 0)[0]\\n if len(bg_inds) > num_bg:\\n disable_inds = npr.choice(\\n bg_inds, size=(len(bg_inds) - num_bg), replace=False)\\n labels[disable_inds] = -1\\n\\n bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)\\n bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])\\n\\n bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)\\n # only the positive ones have regression targets\\n bbox_inside_weights[labels == 1, :] = np.array(cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS)\\n\\n bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)\\n if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0:\\n # uniform weighting of examples (given non-uniform sampling)\\n num_examples = np.sum(labels >= 0)\\n positive_weights = np.ones((1, 4)) * 1.0 / num_examples\\n negative_weights = np.ones((1, 4)) * 1.0 / num_examples\\n else:\\n assert ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) &\\n (cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1))\\n positive_weights = (cfg.TRAIN.RPN_POSITIVE_WEIGHT /\\n np.sum(labels == 1))\\n negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) /\\n np.sum(labels == 0))\\n bbox_outside_weights[labels == 1, :] = positive_weights\\n bbox_outside_weights[labels == 0, :] = negative_weights\\n\\n # map up to original set of anchors\\n labels = _unmap(labels, total_anchors, inds_inside, fill=-1)\\n bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)\\n bbox_inside_weights = _unmap(bbox_inside_weights, total_anchors, inds_inside, fill=0)\\n bbox_outside_weights = _unmap(bbox_outside_weights, total_anchors, inds_inside, fill=0)\\n\\n # labels\\n labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2)\\n labels = labels.reshape((1, 1, A * height, width))\\n rpn_labels = labels\\n\\n # bbox_targets\\n bbox_targets = bbox_targets \\\\\\n .reshape((1, height, width, A * 4))\\n\\n rpn_bbox_targets = bbox_targets\\n # bbox_inside_weights\\n bbox_inside_weights = bbox_inside_weights \\\\\\n .reshape((1, height, width, A * 4))\\n\\n rpn_bbox_inside_weights = bbox_inside_weights\\n\\n # bbox_outside_weights\\n bbox_outside_weights = bbox_outside_weights \\\\\\n .reshape((1, height, width, A * 4))\\n\\n rpn_bbox_outside_weights = bbox_outside_weights\\n return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights\",\n \"def bboxes_encode(self, labels, bboxes, anchors,\\n scope=None):\\n return encode_decode.tf_ssd_bboxes_encode(\\n labels, bboxes, anchors,\\n self.params.num_classes,\\n self.params.no_annotation_label,\\n ignore_threshold=0.5,\\n prior_scaling=self.params.prior_scaling,\\n scope=scope)\",\n \"def _initial_normal_bbox(self):\\n cx = cy = .5\\n width = FLAGS.bbox_grid / float(FLAGS.cropbox_grid)\\n x1 = cx - width / 2\\n x2 = cx + width / 2\\n y1 = cy - width / 2\\n y2 = cy + width / 2\\n return [y1,x1,y2,x2]\",\n \"def create_cell_anchors():\\n k_max, k_min = cfg.FPN.RPN_MAX_LEVEL, cfg.FPN.RPN_MIN_LEVEL\\n scales_per_octave = cfg.RETINANET.SCALES_PER_OCTAVE\\n aspect_ratios = cfg.RETINANET.ASPECT_RATIOS\\n anchor_scale = cfg.RETINANET.ANCHOR_SCALE\\n A = scales_per_octave * len(aspect_ratios)\\n anchors = {}\\n for lvl in range(k_min, k_max + 1):\\n # create cell anchors array\\n stride = 2. ** lvl\\n cell_anchors = np.zeros((A, 4))\\n a = 0\\n for octave in range(scales_per_octave):\\n octave_scale = 2 ** (octave / float(scales_per_octave))\\n for aspect in aspect_ratios:\\n anchor_sizes = (stride * octave_scale * anchor_scale, )\\n anchor_aspect_ratios = (aspect, )\\n cell_anchors[a, :] = generate_anchors(\\n stride=stride, sizes=anchor_sizes,\\n aspect_ratios=anchor_aspect_ratios)\\n a += 1\\n anchors[lvl] = cell_anchors\\n return anchors\",\n \"def show_boxes(img, boundary_boxes, gt_boxes=None):\\n\\n for (x_tl, y_tl, x_br, y_br) in boundary_boxes:\\n cv2.rectangle(img, (x_tl, y_tl),\\n (x_br, y_br),\\n (0, 0, 255), 2)\\n\\n if gt_boxes is not None:\\n for (x_tl, y_tl, x_br, y_br) in gt_boxes:\\n cv2.rectangle(img, (x_tl, y_tl),\\n (x_br, y_br),\\n (0, 255, 0), 2)\\n\\n cv2.imshow(\\\"img\\\", img)\\n cv2.waitKey(0)\\n cv2.destroyAllWindows()\",\n \"def extract_bboxes(mask):\\r\\n boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)\\r\\n for i in range(mask.shape[-1]):\\r\\n m = mask[:, :, i]\\r\\n # Bounding box.\\r\\n horizontal_indicies = np.where(np.any(m, axis=0))[0]\\r\\n vertical_indicies = np.where(np.any(m, axis=1))[0]\\r\\n if horizontal_indicies.shape[0]:\\r\\n x1, x2 = horizontal_indicies[[0, -1]]\\r\\n y1, y2 = vertical_indicies[[0, -1]]\\r\\n # x2 and y2 should not be part of the box. Increment by 1.\\r\\n x2 += 1\\r\\n y2 += 1\\r\\n else:\\r\\n # No mask for this instance. Might happen due to\\r\\n # resizing or cropping. Set bbox to zeros\\r\\n x1, x2, y1, y2 = 0, 0, 0, 0\\r\\n boxes[i] = np.array([y1, x1, y2, x2])\\r\\n return boxes.astype(np.int32)\",\n \"def valid_anchor_boxes(anchor_boxes, image_size):\\n img_height, img_width = image_size\\n y, x, height, width = np.transpose(anchor_boxes)\\n\\n # TODO(Mocurin) Optimize?\\n # Indicator matrix\\n indicators = np.array([y - height // 2 >= 0,\\n x - width // 2 >= 0,\\n y + height // 2 <= img_height,\\n x + width // 2 <= img_width]).transpose()\\n\\n # Get indices of anchor boxes inside image\\n return np.flatnonzero(np.all(indicators, axis=1, keepdims=False))\",\n \"def _boxes_coordinates(self,\\n image,\\n boxes,\\n classes,\\n scores,\\n max_boxes_to_draw=20,\\n min_score_thresh=.5):\\n\\n if not max_boxes_to_draw:\\n max_boxes_to_draw = boxes.shape[0]\\n number_boxes = min(max_boxes_to_draw, boxes.shape[0])\\n final_boxes = []\\n final_scores = []\\n for i in range(number_boxes):\\n if self.category_index[classes[i]]['name'] not in \\\\\\n self.classes_to_detect:\\n continue\\n if scores is None or scores[i] > min_score_thresh:\\n box = tuple(boxes[i].tolist())\\n ymin, xmin, ymax, xmax = box\\n\\n im_height, im_width, _ = image.shape\\n left, right, top, bottom = [int(z) for z in\\n (xmin * im_width, xmax * im_width,\\n ymin * im_height,\\n ymax * im_height)]\\n\\n final_boxes.append([top, left, bottom, right])\\n final_scores.append(scores[i])\\n return final_boxes, final_scores\",\n \"def generate_anchors(base_size = 16, ratios = [0.5, 1, 2.0], \\n scales = 2**np.arange(4,7)):\\n n_anchors = len(ratios)*len(scales)\\n\\n base_anchor = np.array([int(base_size*0.5), \\n int(base_size*0.5), \\n base_size, \\n base_size], np.float32) \\n # First, generate a list of anchors of the appropriate area:\\n scaled_anchors = np.tile(base_anchor,(len(scales), 1))\\n final_anchors = np.zeros((len(scales),len(ratios), 4), np.float32)\\n for s, i in zip(scales, xrange(len(scales))):\\n scaled_anchors[i,2:] *= s\\n for r, j in zip(ratios, xrange(len(ratios))):\\n t = np.copy(scaled_anchors[i])\\n t[2] *= np.sqrt(r)\\n t[3] *= 1./np.sqrt(r)\\n final_anchors[i,j] = np.round(t)\\n return np.reshape(final_anchors, (n_anchors, 4))\",\n \"def draw_bounding_boxes(self, image_path):\\n img = cv.imread(image_path, cv.IMREAD_ANYDEPTH)\\n bboxes = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\\n unique, counts = np.unique(img, return_counts=True)\\n for uni in unique:\\n if uni == 0:\\n continue\\n self.get_instance_bounding_box(img, bboxes, uni)\\n\\n cv.namedWindow('building bounding boxes', cv.WINDOW_NORMAL)\\n cv.imshow('building bounding boxes', bboxes)\\n cv.waitKey(0)\\n cv.destroyAllWindows()\",\n \"def get_bboxes(self,\\n cls_scores,\\n bbox_preds,\\n centernesses,\\n img_metas,\\n cfg=None,\\n rescale=False,\\n with_nms=True):\\n cfg = self.test_cfg if cfg is None else cfg\\n assert len(cls_scores) == len(bbox_preds)\\n num_levels = len(cls_scores)\\n device = cls_scores[0].device\\n featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]\\n mlvl_anchors = self.anchor_generator.grid_anchors(\\n featmap_sizes, device=device)\\n\\n result_list = []\\n for img_id in range(len(img_metas)):\\n cls_score_list = [\\n cls_scores[i][img_id].detach() for i in range(num_levels)\\n ]\\n bbox_pred_list = [\\n bbox_preds[i][img_id].detach() for i in range(num_levels)\\n ]\\n centerness_pred_list = [\\n centernesses[i][img_id].detach() for i in range(num_levels)\\n ]\\n img_shape = img_metas[img_id]['img_shape']\\n scale_factor = img_metas[img_id]['scale_factor']\\n proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list,\\n centerness_pred_list,\\n mlvl_anchors, img_shape,\\n scale_factor, cfg, rescale,\\n with_nms)\\n result_list.append(proposals)\\n return result_list\",\n \"def extract_bboxes(mask):\\n boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)\\n for i in range(mask.shape[-1]):\\n m = mask[:, :, i]\\n # Bounding box.\\n horizontal_indicies = np.where(np.any(m, axis=0))[0]\\n vertical_indicies = np.where(np.any(m, axis=1))[0]\\n if horizontal_indicies.shape[0]:\\n x1, x2 = horizontal_indicies[[0, -1]]\\n y1, y2 = vertical_indicies[[0, -1]]\\n # x2 and y2 should not be part of the box. Increment by 1.\\n x2 += 1\\n y2 += 1\\n else:\\n # No mask for this instance. Might happen due to\\n # resizing or cropping. Set bbox to zeros\\n x1, x2, y1, y2 = 0, 0, 0, 0\\n boxes[i] = np.array([x1, y1, x2, y2])\\n return boxes.astype(np.int32)\",\n \"def extract_bboxes(mask):\\n boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)\\n for i in range(mask.shape[-1]):\\n m = mask[:, :, i]\\n # Bounding box.\\n horizontal_indicies = np.where(np.any(m, axis=0))[0]\\n vertical_indicies = np.where(np.any(m, axis=1))[0]\\n if horizontal_indicies.shape[0]:\\n x1, x2 = horizontal_indicies[[0, -1]]\\n y1, y2 = vertical_indicies[[0, -1]]\\n # x2 and y2 should not be part of the box. Increment by 1.\\n x2 += 1\\n y2 += 1\\n else:\\n # No mask for this instance. Might happen due to\\n # resizing or cropping. Set bbox to zeros\\n x1, x2, y1, y2 = 0, 0, 0, 0\\n boxes[i] = np.array([y1, x1, y2, x2])\\n return boxes.astype(np.int32)\",\n \"def compute_bb(self) -> List[float]:\\n all_shapes = list(self.parts.values())\\n bbox_vertices = unary_union(all_shapes).envelope.exterior.coords.xy\\n min_x = min(bbox_vertices[0])\\n max_x = max(bbox_vertices[0])\\n min_y = min(bbox_vertices[1])\\n max_y = max(bbox_vertices[1])\\n return [min_x, max_x, min_y, max_y]\",\n \"def build_boxes(self):\\n for index in self.box_space.points:\\n if self.rank_of_box[index] == self.my_rank:\\n self.my_boxes.append(Box(self, index))\",\n \"def get_bounding_box_lonlats(self,npts=100):\\n\\n # Top of bounding box\\n tplons = np.linspace(self.corners[0].lon,self.corners[1].lon,npts)\\n tplats = np.linspace(self.corners[0].lat,self.corners[1].lat,npts)\\n # Right side of bounding box\\n rtlons = np.linspace(self.corners[1].lon,self.corners[2].lon,npts)\\n rtlats = np.linspace(self.corners[1].lat,self.corners[2].lat,npts)\\n # Bottom of bounding box\\n btlons = np.linspace(self.corners[2].lon,self.corners[3].lon,npts)\\n btlats = np.linspace(self.corners[2].lat,self.corners[3].lat,npts)\\n # Left side of bounding box\\n ltlons = np.linspace(self.corners[3].lon,self.corners[0].lon,npts)\\n ltlats = np.linspace(self.corners[3].lat,self.corners[0].lat,npts)\\n\\n retval = [(list(tplons),list(tplats)),\\n (list(rtlons),list(rtlats)),\\n (list(btlons),list(btlats)),\\n (list(ltlons),list(ltlats))]\\n return retval\",\n \"def show_bboxes(img, bounding_boxes=None, facial_landmarks=[]):\\n\\n img_copy = img.copy()\\n draw = ImageDraw.Draw(img_copy)\\n# for b in bounding_boxes:\\n# draw.rectangle([\\n# (b[0], b[1]), (b[2], b[3])\\n# ], outline='white')\\n\\n for p in facial_landmarks:\\n for i in range(106):\\n draw.ellipse([\\n (p[i*2] - 1.0, p[2*i + 1] - 1.0),\\n (p[i*2] + 1.0, p[2*i+1] + 1.0)\\n ], outline='blue')\\n font = ImageFont.truetype(\\\"arial.ttf\\\", 10)\\n draw.text([p[2*i], p[2*i+1]], str(i), font=font)\\n\\n return img_copy\",\n \"def get_bounding_box(self):\\n if len(self.elements) == 0:\\n return None\\n if not (self._bb_valid and\\n all(ref._bb_valid for ref in self.get_dependencies(True))):\\n bb = numpy.array(((1e300, 1e300), (-1e300, -1e300)))\\n all_polygons = []\\n for element in self.elements:\\n if isinstance(element, PolygonSet):\\n all_polygons.extend(element.polygons)\\n elif isinstance(element, CellReference) or isinstance(\\n element, CellArray):\\n element_bb = element.get_bounding_box()\\n if element_bb is not None:\\n bb[0, 0] = min(bb[0, 0], element_bb[0, 0])\\n bb[0, 1] = min(bb[0, 1], element_bb[0, 1])\\n bb[1, 0] = max(bb[1, 0], element_bb[1, 0])\\n bb[1, 1] = max(bb[1, 1], element_bb[1, 1])\\n if len(all_polygons) > 0:\\n all_points = numpy.concatenate(all_polygons).transpose()\\n bb[0, 0] = min(bb[0, 0], all_points[0].min())\\n bb[0, 1] = min(bb[0, 1], all_points[1].min())\\n bb[1, 0] = max(bb[1, 0], all_points[0].max())\\n bb[1, 1] = max(bb[1, 1], all_points[1].max())\\n self._bb_valid = True\\n _bounding_boxes[self] = bb\\n return _bounding_boxes[self]\",\n \"def __build_anchors(anchor_parameters, features):\\n anchors = [\\n layers.Anchors(\\n size=anchor_parameters.sizes[i],\\n stride=anchor_parameters.strides[i],\\n ratios=anchor_parameters.ratios,\\n scales=anchor_parameters.scales,\\n name='anchors_{}'.format(i)\\n )(f) for i, f in enumerate(features)\\n ]\\n\\n return keras.layers.Concatenate(axis=1, name='anchors')(anchors)\",\n \"def generate_bboxes_with_scores(cls_map, scale, threshold=0.5, size=12, stride=2):\\n assert len(cls_map.shape) == 2\\n\\n indices = np.where(cls_map >= threshold)\\n bboxes = np.concatenate((\\n ((indices[1] * stride) / scale).reshape(-1, 1),\\n ((indices[0] * stride) / scale).reshape(-1, 1),\\n ((indices[1] * stride + size) / scale).reshape(-1, 1),\\n ((indices[0] * stride + size) / scale).reshape(-1, 1),\\n cls_map[indices].reshape(-1, 1)\\n ), axis=1)\\n return bboxes, indices\",\n \"def _boundRect(self):\\n self.upperleft = list(map(min, zip(*self.addresstamp)))\\n self.bottomright = list(map(max, zip(*self.addresstamp)))\\n self.upperright = [self.bottomright[0], self.upperleft[1]]\\n self.bottomleft = [self.upperleft[0], self.bottomright[1]]\\n (self.width, self.height) = (self.upperright[0] - self.bottomleft[0], self.bottomleft[1] - self.upperright[1])\\n assert self.width >= 0\\n assert self.height >= 0\\n self.center = [self.upperleft[0] + self.width / float(2), self.upperleft[1] + self.height / float(2)]\\n self.corners = [self.upperright, self.bottomleft, self.upperleft, self.bottomright]\",\n \"def create_bounds(self):\\n # Bounds should be created for\\n x0, y0 = (0, 0)\\n x1 = self.game_area_size[0]\\n y1 = self.game_area_size[1]\\n space = self.space\\n\\n borders = [\\n phy.Segment(space.static_body, phy.Vec2d(x0, y0), phy.Vec2d(x1, y0), 0),\\n phy.Segment(space.static_body, phy.Vec2d(x0, y0), phy.Vec2d(x0, y1), 0),\\n phy.Segment(space.static_body, phy.Vec2d(x1, y0), phy.Vec2d(x1, y1), 0),\\n phy.Segment(space.static_body, phy.Vec2d(x0, y1), phy.Vec2d(x1, y1), 0),\\n ]\\n for b in borders:\\n b.elasticity = 0.5\\n self.space.add(borders)\",\n \"def test_geotransform2bbox(self):\\n\\n M = 5\\n N = 10\\n for gt in GEOTRANSFORMS:\\n bbox = geotransform2bbox(gt, M, N)\\n\\n # FIXME: Need better tests here, but this is better than nothing\\n\\n # Lower bounds\\n assert bbox[0] == gt[0]\\n\\n # Upper bounds\\n assert bbox[3] == gt[3]\",\n \"def build(self, box, size):\\n centroid = box.centroid\\n edge1_midpoint = get_midpoint_of_edge(\\n box.vertices[0], box.vertices[1])\\n distance_to_edge1 = centroid.distance_to_xyz(edge1_midpoint)\\n quadrat_indexes1 = range(int(distance_to_edge1 / size) + 1)\\n\\n edge2_midpoint = get_midpoint_of_edge(\\n box.vertices[0], box.vertices[3])\\n distance_to_edge2 = centroid.distance_to_xyz(edge2_midpoint)\\n quadrat_indexes2 = range(int(distance_to_edge2 / size) + 1)\\n\\n quadrats = list()\\n for i in quadrat_indexes2:\\n for j in quadrat_indexes1:\\n if i == 0 and j == 0:\\n quadrats.append(Quadrat((0, 0), size, centroid))\\n else:\\n top_right = Vertex(centroid.x + i * size,\\n centroid.y + (j * size), centroid.z)\\n top_left = Vertex(centroid.x + i * size,\\n centroid.y - (j * size), centroid.z)\\n bottom_right = Vertex(\\n centroid.x - i * size, centroid.y + (j * size), centroid.z)\\n bottom_left = Vertex(\\n centroid.x - i * size, centroid.y - (j * size), centroid.z)\\n four_quadrats = [Quadrat((i, j), size, top_right),\\n Quadrat((i, -1 * j), size, top_left),\\n Quadrat((-1 * i, j), size, bottom_right),\\n Quadrat((-1 * i, -1 * j), size, bottom_left)]\\n quadrats_inside = list(\\n filter(lambda x: box.contains(x.midpoint), four_quadrats))\\n quadrats += quadrats_inside\\n # print(\\\"There are this many quadrats: \\\" + str(len(quadrats)))\\n return tuple(set(quadrats))\",\n \"def _boundRect(self):\\n addresstamp = reduce(lambda x, y: x + y, [v.addresstamp for v in self.footprints])\\n self.upperleft = list(map(min, zip(*addresstamp)))\\n self.bottomright = list(map(max, zip(*addresstamp)))\\n self.upperright = [self.bottomright[0], self.upperleft[1]]\\n self.bottomleft = [self.upperleft[0], self.bottomright[1]]\\n (self.width, self.height) = (self.upperright[0] - self.bottomleft[0], self.bottomleft[1] - self.upperright[1])\\n assert self.width >= 0\\n assert self.height >= 0\\n self.center = [self.upperleft[0] + self.width / float(2), self.upperleft[1] + self.height / float(2)]\\n self.corners = [self.upperright, self.bottomleft, self.upperleft, self.bottomright]\",\n \"def bbox(self):\\n lower = (self.x.min(), self.y.min())\\n upper = (self.x.max(), self.y.max())\\n return (lower, upper)\",\n \"def test_get_all_boxes(self, postfix_directory):\\n print(\\\"Test_All_Boxes\\\")\\n protein_file = os.path.join(postfix_directory, \\\"PfATP4.pdb\\\")\\n ligand_file = os.path.join(postfix_directory, \\\"SJ733.pdb\\\")\\n coords = rdkit_util.load_molecule(protein_file)[0]\\n\\n boxes = dc.dock.binding_pocket.get_all_boxes(coords)\\n assert isinstance(boxes, list)\\n # Pocket is of form ((x_min, x_max), (y_min, y_max), (z_min, z_max))\\n for pocket in boxes:\\n assert len(pocket) == 3\\n assert len(pocket[0]) == 2\\n assert len(pocket[1]) == 2\\n assert len(pocket[2]) == 2\\n (x_min, x_max), (y_min, y_max), (z_min, z_max) = pocket\\n assert x_min < x_max\\n assert y_min < y_max\\n assert z_min < z_max\",\n \"def get_bounding_box_lonlats(self,npts=100):\\n g = Geod(ellps='WGS84')\\n\\n # Top of bounding box\\n # g.npts returns a list of tuples of lon/lat pairs\\n # [(lon0,lat0),(lon1,lat1),(lon2,lat2)]\\n # zip reformats that into 2 tuples of lons and lats\\n # [(lon0,lon1,lon2),(lat0,lat1,lat2)]\\n # list(tplons) returns list of lons\\n # [lon0,lon1,lon2]\\n # list(tplats) returns list of lats\\n # [lat0,lat1,lat2]\\n tplons,tplats = zip(*g.npts(self.corners[0].lon, self.corners[0].lat, \\n self.corners[1].lon, self.corners[1].lat,\\n npts,radians=True))\\n # Right side of bounding box\\n rtlons,rtlats = zip(*g.npts(self.corners[1].lon, self.corners[1].lat, \\n self.corners[2].lon, self.corners[2].lat,\\n npts,radians=True))\\n # Bottom of bounding box\\n btlons,btlats = zip(*g.npts(self.corners[2].lon, self.corners[2].lat, \\n self.corners[3].lon, self.corners[3].lat,\\n npts,radians=True))\\n # Left side of bounding box\\n ltlons,ltlats = zip(*g.npts(self.corners[3].lon, self.corners[3].lat, \\n self.corners[0].lon, self.corners[0].lat,\\n npts,radians=True))\\n\\n retval = [(list(tplons),list(tplats)),\\n (list(rtlons),list(rtlats)),\\n (list(btlons),list(btlats)),\\n (list(ltlons),list(ltlats))]\\n return retval\",\n \"def covering_box(boxes):\\n x_min = np.amin([b.x for b in boxes])\\n x_max = np.amax([b.x + b.width for b in boxes])\\n y_min = np.amin([b.y for b in boxes])\\n y_max = np.amax([b.y + b.height for b in boxes])\\n cover = Box(x_min, y_min, x_max - x_min, y_max - y_min)\\n return cover\",\n \"def intersection_with_anchors(bbox):\\n int_ymin = tf.maximum(ymin, bbox[0])\\n int_xmin = tf.maximum(xmin, bbox[1])\\n int_ymax = tf.minimum(ymax, bbox[2])\\n int_xmax = tf.minimum(xmax, bbox[3])\\n h = tf.maximum(int_ymax - int_ymin, 0.)\\n w = tf.maximum(int_xmax - int_xmin, 0.)\\n inter_vol = h * w\\n scores = tf.div(inter_vol, vol_anchors)\\n return scores\",\n \"def bounding_box(primitive):\\n\\n if primitive[\\\"shape\\\"] == \\\"circle\\\":\\n bbox = [[primitive[\\\"center\\\"][0] - primitive[\\\"radius\\\"],\\n primitive[\\\"center\\\"][1] - primitive[\\\"radius\\\"]],\\n [primitive[\\\"center\\\"][0] + primitive[\\\"radius\\\"],\\n primitive[\\\"center\\\"][1] + primitive[\\\"radius\\\"]]]\\n else:\\n x_coords, y_coords = zip(*primitive[\\\"vertices\\\"])\\n bbox = [[min(x_coords), min(y_coords)],\\n [max(x_coords), max(y_coords)]]\\n\\n primitive[\\\"bounding_box\\\"] = bbox\\n return primitive\",\n \"def draw_bbox(n):\\n return drawBbox(named(n))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6876775","0.6868104","0.6695623","0.6521236","0.64991575","0.6486309","0.6483232","0.6424955","0.6403451","0.6394041","0.63770133","0.6352162","0.63495255","0.63414896","0.6316883","0.63068944","0.62946934","0.62793225","0.62689435","0.62679905","0.62486845","0.62380606","0.6225511","0.62195885","0.62191516","0.6208311","0.6138171","0.6131372","0.61301357","0.6128687","0.61281043","0.61176336","0.6103638","0.60881287","0.6073954","0.6069494","0.60607404","0.6054591","0.6049669","0.6034285","0.6033371","0.60054356","0.6003282","0.6000986","0.5981946","0.5975944","0.5970708","0.5949939","0.59479105","0.5946527","0.594396","0.5943639","0.5936878","0.59242857","0.59242857","0.5921794","0.59152806","0.5908783","0.5900313","0.58994156","0.5896867","0.58967644","0.58903825","0.58778757","0.58767366","0.5863213","0.58575094","0.585427","0.5842903","0.5825898","0.5814396","0.58052444","0.57995236","0.579939","0.5796837","0.5792759","0.5787457","0.5786399","0.5778132","0.5774051","0.5764886","0.5760413","0.5754397","0.5751309","0.57398343","0.57360256","0.5731696","0.57308435","0.5729542","0.57213074","0.5716613","0.57090634","0.5706625","0.569801","0.5696795","0.5696311","0.56956726","0.5694234","0.569296","0.56927204","0.56918997"],"string":"[\n \"0.6876775\",\n \"0.6868104\",\n \"0.6695623\",\n \"0.6521236\",\n \"0.64991575\",\n \"0.6486309\",\n \"0.6483232\",\n \"0.6424955\",\n \"0.6403451\",\n \"0.6394041\",\n \"0.63770133\",\n \"0.6352162\",\n \"0.63495255\",\n \"0.63414896\",\n \"0.6316883\",\n \"0.63068944\",\n \"0.62946934\",\n \"0.62793225\",\n \"0.62689435\",\n \"0.62679905\",\n \"0.62486845\",\n \"0.62380606\",\n \"0.6225511\",\n \"0.62195885\",\n \"0.62191516\",\n \"0.6208311\",\n \"0.6138171\",\n \"0.6131372\",\n \"0.61301357\",\n \"0.6128687\",\n \"0.61281043\",\n \"0.61176336\",\n \"0.6103638\",\n \"0.60881287\",\n \"0.6073954\",\n \"0.6069494\",\n \"0.60607404\",\n \"0.6054591\",\n \"0.6049669\",\n \"0.6034285\",\n \"0.6033371\",\n \"0.60054356\",\n \"0.6003282\",\n \"0.6000986\",\n \"0.5981946\",\n \"0.5975944\",\n \"0.5970708\",\n \"0.5949939\",\n \"0.59479105\",\n \"0.5946527\",\n \"0.594396\",\n \"0.5943639\",\n \"0.5936878\",\n \"0.59242857\",\n \"0.59242857\",\n \"0.5921794\",\n \"0.59152806\",\n \"0.5908783\",\n \"0.5900313\",\n \"0.58994156\",\n \"0.5896867\",\n \"0.58967644\",\n \"0.58903825\",\n \"0.58778757\",\n \"0.58767366\",\n \"0.5863213\",\n \"0.58575094\",\n \"0.585427\",\n \"0.5842903\",\n \"0.5825898\",\n \"0.5814396\",\n \"0.58052444\",\n \"0.57995236\",\n \"0.579939\",\n \"0.5796837\",\n \"0.5792759\",\n \"0.5787457\",\n \"0.5786399\",\n \"0.5778132\",\n \"0.5774051\",\n \"0.5764886\",\n \"0.5760413\",\n \"0.5754397\",\n \"0.5751309\",\n \"0.57398343\",\n \"0.57360256\",\n \"0.5731696\",\n \"0.57308435\",\n \"0.5729542\",\n \"0.57213074\",\n \"0.5716613\",\n \"0.57090634\",\n \"0.5706625\",\n \"0.569801\",\n \"0.5696795\",\n \"0.5696311\",\n \"0.56956726\",\n \"0.5694234\",\n \"0.569296\",\n \"0.56927204\",\n \"0.56918997\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":180,"numItemsPerPage":100,"numTotalItems":95772,"offset":18000,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjI0MDgyOCwianRpIjoiYzliOTM3ZWMtNGNmZi00Nzk2LTk5MmQtNDM1YzNmZWI4NTIxIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYyNDQ0MjgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.PIky39hl2CK0Cmu_M1Qn5DVFs2BWYj-L4J5_J4wN-HygjIY94D_JpcwX8tuFZL5vcMWwRc6pT2r89HVLBeqGCg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
build a virtualenv to run python in (do not use system python) | def build_virtualenv(
venv_name,
which_python='python3',
_atexit_register=True,
logger=p_logging.DEFAULT_LOGGER,
):
logger.info('--removing existing venv %s', path.abspath(venv_name))
rm_log = local['rm']('-rf', venv_name)
logger.debug(rm_log)
logger.info('--creating fre... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_virtualenv():\n\n puts(yellow(\"Install dependencies from requirements.txt\"))\n with cd(env.source_dir):\n with prefix('source %s' % in_rwd('bin/activate')):\n sudo('pip install -r %s' % env.requirements_file,\n user=env.app_user)\n sudo('python setup.p... | [
"0.80692744",
"0.7908599",
"0.774222",
"0.7519093",
"0.74175507",
"0.7393784",
"0.7345013",
"0.7202901",
"0.7137042",
"0.6831256",
"0.67260164",
"0.6682618",
"0.66242117",
"0.6602086",
"0.659668",
"0.6590112",
"0.6586367",
"0.65805423",
"0.65607154",
"0.6559693",
"0.6536713",... | 0.68260074 | 10 |
generate plumbum command given an incoming string | def parse_command(self, command):
split_list = command.split()
command = local[split_list[0]]
if 'python' in split_list[0]:
self.logger.debug(self.venv_pip)
command = self.venv_python
if split_list[0] == 'pip' or split_list[0] == 'pip3':
self.logger.d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _build_command(self, cmd, unit):\n return '#' + unit + cmd + NEWLINE",
"def label(cmd):\r\n cmd = cmd.replace('make][.DP', 'make1][.NP')\r\n cmd = cmd.replace('make][.SC', 'make2][.SC')\r\n cmd = re.sub('(draw.*)one','\\\\1one1',cmd)\r\n cmd = re.sub('(make1.*)one','\\\\1one1',cmd)\r\n ... | [
"0.610332",
"0.592388",
"0.5890794",
"0.5611963",
"0.55802214",
"0.5506339",
"0.5454742",
"0.5393428",
"0.53576297",
"0.5340454",
"0.53389555",
"0.52919143",
"0.5271593",
"0.5251179",
"0.52395785",
"0.5224731",
"0.52002454",
"0.5191659",
"0.5186986",
"0.5183798",
"0.5174906",... | 0.0 | -1 |
entry point for launching app | def run_main(): # pragma: no cover
RunTestsCLI.run() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def startapp():",
"def main():\n LOGGER.info('Loading Application')\n main_app = Application()\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-c\", \"--console\", help=\"Command Line Mode\", action=\"store_true\")\n args = parser.parse_args()\n if args.console:\n LOGGER.inf... | [
"0.82018286",
"0.7624813",
"0.7525161",
"0.7458898",
"0.73670477",
"0.73670477",
"0.73670477",
"0.73534673",
"0.72916853",
"0.72408944",
"0.7198092",
"0.7178572",
"0.71465766",
"0.71312237",
"0.7105006",
"0.709632",
"0.70753425",
"0.7052808",
"0.70301676",
"0.69803256",
"0.69... | 0.0 | -1 |
Automatically make targets file in the requested format | def make_targets_file(
data_path: str, split: Union[str, dict], output_filename: str
) -> None:
# Logger
fuse_logger_start(console_verbose_level=logging.INFO)
lgr = logging.getLogger("Fuse")
lgr.info("KNIGHT: make targets file", {"attrs": ["bold", "underline"]})
lgr.info(
f"targets_filen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def formatTargets(self, **kwargs):\n for key, val in kwargs.items():\n self.formatTarget(key, val)",
"def formatAllTargets(self):\n self.formatTargets(**self.targets)",
"def targets(path, args):\n if args:\n return \" \".join([\"{0}{1}\".format(path, target) for target in args])",
"def make_ta... | [
"0.6089231",
"0.60107285",
"0.59871346",
"0.5941533",
"0.5851913",
"0.57587314",
"0.57550573",
"0.57475406",
"0.566028",
"0.56577504",
"0.55643654",
"0.55010134",
"0.54606646",
"0.5458965",
"0.5450588",
"0.544378",
"0.5424429",
"0.54133",
"0.54092586",
"0.5373693",
"0.5369220... | 0.5215766 | 31 |
This function observes a given process through an event and when it fires an action is performed. | def observe_process(action: Callable, mode, *events: Event) -> None:
fts = set()
results = []
with futures.ThreadPoolExecutor(max_workers=os.cpu_count()) as executor:
try:
logger.log('[*] Press CTRL+C to cancel this operation')
for event in events:
ft = execu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process(self, event):\n pass",
"def process_event(self, event):\r\n pass",
"def process_event(self, event):\n\t\tself.current_screen.control_manager.process_event(event)",
"def event_queue_proc(self,event):\r\n event()",
"def process(self, user_event: UserEvent) -> None:\n p... | [
"0.7059733",
"0.6694472",
"0.64632255",
"0.6247462",
"0.60620666",
"0.60335714",
"0.60333025",
"0.60097194",
"0.58359826",
"0.5816169",
"0.5780159",
"0.57303345",
"0.5709481",
"0.56664777",
"0.56647664",
"0.5661354",
"0.56491786",
"0.5625206",
"0.561545",
"0.55729115",
"0.554... | 0.57487816 | 11 |
Attach a text label above each bar in rects, displaying its height. | def autolabel(rects):
for rect in rects:
height = rect.get_height()
ax.annotate(
"{}".format(height),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha="center",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def autolabel(rects, ax):\n global BAR_NUMBER_SIZE\n for rect in rects:\n height = rect.get_height()\n ax.text(rect.get_x() + rect.get_width()/2.,\n height,\n str(round(height, 1)),\n ha ='center',\n va ='bottom',\n size... | [
"0.807113",
"0.79336554",
"0.7861467",
"0.7835557",
"0.7822906",
"0.77769476",
"0.7776375",
"0.7745001",
"0.7730597",
"0.7730573",
"0.7708679",
"0.77040344",
"0.7690377",
"0.7677892",
"0.76754576",
"0.76730984",
"0.76562864",
"0.7648832",
"0.7645011",
"0.7640306",
"0.76307166... | 0.73544943 | 49 |
Test that if empty file is given, None is returned. | def test_empty_file(self):
field = TypedFileField(required=False)
for v in EMPTY_VALUES:
assert field.clean(v) is None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_empty_file(fpath):\n return \\\n fpath is not None and \\\n os.path.isfile(fpath) and \\\n os.path.getsize(fpath) == 0",
"def is_empty(file):\n with open(file, 'rb') as f:\n return not f.read(1)",
"def input_file_test(input):\n try:\n if os.stat(input).st_size... | [
"0.76489717",
"0.75652725",
"0.74976176",
"0.7404431",
"0.73362535",
"0.715226",
"0.7049083",
"0.69203573",
"0.6779363",
"0.6743024",
"0.66844594",
"0.6634716",
"0.66292167",
"0.6496561",
"0.64861906",
"0.64728576",
"0.64662516",
"0.640066",
"0.63500714",
"0.6261185",
"0.6241... | 0.6451045 | 17 |
Test that the extension validation is working properly | def test_extensions(self):
field = TypedFileField(required=False, ext_whitelist=self.good_extensions)
for ext in self.good_extensions:
name = 'somefooname.%s' % ext
file = UploadedFile(name=name, size=1)
assert field.clean(file) is file
for ext in self.bad_e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_validate(self):\n pass",
"def test_validators():",
"def validate_extension(extension):\n\n error_flag = 0\n error_string = ''\n\n if isinstance(extension, dict):\n try:\n schema = jsonref.load_uri(extension['extension_schema'])\n try:\n print... | [
"0.7298474",
"0.69515324",
"0.6950843",
"0.68759197",
"0.68593514",
"0.68426394",
"0.6806111",
"0.6804598",
"0.6562722",
"0.65436846",
"0.65347743",
"0.65035516",
"0.6489072",
"0.6402878",
"0.6388997",
"0.6346691",
"0.63387173",
"0.6312984",
"0.62796175",
"0.62742496",
"0.625... | 0.75316143 | 0 |
Test that the mimetypes are validate correctly | def test_mimetypes(self):
field = TypedFileField(required=False, type_whitelist=self.good_types, use_magic=False)
for t in self.good_types:
name = 'somefooname'
file = UploadedFile(name=name, size=1, content_type=t)
assert field.clean(file) is file
for t in ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_mimetypes_magic(self, mock_get_content_type):\n\n def get_content_type(value):\n return value.content_type\n\n mock_get_content_type.side_effect = get_content_type\n\n field = TypedFileField(required=False, type_whitelist=self.good_types, use_magic=True)\n\n for t in... | [
"0.7667273",
"0.763851",
"0.75550187",
"0.75023365",
"0.7006191",
"0.69769895",
"0.69073343",
"0.69062054",
"0.6781376",
"0.67640036",
"0.67325264",
"0.6723504",
"0.6719304",
"0.6687197",
"0.6671605",
"0.66456544",
"0.66422045",
"0.66077816",
"0.65924525",
"0.65606976",
"0.65... | 0.82419276 | 0 |
Test that the mimetypes are validate correctly | def test_mimetypes_magic(self, mock_get_content_type):
def get_content_type(value):
return value.content_type
mock_get_content_type.side_effect = get_content_type
field = TypedFileField(required=False, type_whitelist=self.good_types, use_magic=True)
for t in self.good_typ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_mimetypes(self):\n field = TypedFileField(required=False, type_whitelist=self.good_types, use_magic=False)\n\n for t in self.good_types:\n name = 'somefooname'\n file = UploadedFile(name=name, size=1, content_type=t)\n assert field.clean(file) is file\n\n ... | [
"0.82419276",
"0.763851",
"0.75550187",
"0.75023365",
"0.7006191",
"0.69769895",
"0.69073343",
"0.69062054",
"0.6781376",
"0.67640036",
"0.67325264",
"0.6723504",
"0.6719304",
"0.6687197",
"0.6671605",
"0.66456544",
"0.66422045",
"0.66077816",
"0.65924525",
"0.65606976",
"0.6... | 0.7667273 | 1 |
Make sure ``ValidationError`` is raised if uploaded file has no mimetype | def test_no_mimetype(self):
field = TypedFileField(required=False, type_whitelist=self.good_types, use_magic=False)
for t in self.good_types:
name = 'somefooname'
file = UploadedFile(name=name, size=1, content_type=t)
del file.content_type
with pytest.rai... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_no_mimetype_magic(self, mock_get_content_type):\n mock_get_content_type.side_effect = ValueError\n\n field = TypedFileField(required=False, type_whitelist=self.good_types)\n\n for t in self.good_types:\n name = 'somefooname'\n file = UploadedFile(name=name, size=... | [
"0.7776475",
"0.72709423",
"0.7118607",
"0.7027781",
"0.7002521",
"0.69914484",
"0.694859",
"0.68884635",
"0.686069",
"0.6736035",
"0.6707866",
"0.66616106",
"0.66365695",
"0.66283804",
"0.6582579",
"0.6581422",
"0.6486669",
"0.64784694",
"0.6478206",
"0.6458501",
"0.632179",... | 0.79070336 | 0 |
Make sure ``ValidationError`` is raised if uploaded file has no mimetype | def test_no_mimetype_magic(self, mock_get_content_type):
mock_get_content_type.side_effect = ValueError
field = TypedFileField(required=False, type_whitelist=self.good_types)
for t in self.good_types:
name = 'somefooname'
file = UploadedFile(name=name, size=1, content_t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_no_mimetype(self):\n field = TypedFileField(required=False, type_whitelist=self.good_types, use_magic=False)\n\n for t in self.good_types:\n name = 'somefooname'\n file = UploadedFile(name=name, size=1, content_type=t)\n del file.content_type\n wit... | [
"0.7906906",
"0.7269755",
"0.7116972",
"0.7030246",
"0.7002452",
"0.69941306",
"0.6949124",
"0.6888954",
"0.6862505",
"0.67371505",
"0.6708364",
"0.6663474",
"0.66362625",
"0.66306883",
"0.658149",
"0.65809655",
"0.64851123",
"0.647826",
"0.64769727",
"0.64585227",
"0.6320358... | 0.7776174 | 1 |
Test that both extensions and mimetypes are validated correctly both at the same time | def test_both(self):
field = TypedFileField(required=False,
ext_whitelist=self.good_extensions,
type_whitelist=self.good_types,
use_magic=False)
for ext in self.good_extensions:
name = 'somefooname.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_mimetypes(self):\n field = TypedFileField(required=False, type_whitelist=self.good_types, use_magic=False)\n\n for t in self.good_types:\n name = 'somefooname'\n file = UploadedFile(name=name, size=1, content_type=t)\n assert field.clean(file) is file\n\n ... | [
"0.7914891",
"0.7877988",
"0.77082807",
"0.74534076",
"0.7424329",
"0.7364778",
"0.730363",
"0.72946537",
"0.72751206",
"0.7241609",
"0.7228069",
"0.68160814",
"0.6785955",
"0.6745132",
"0.6717975",
"0.6690857",
"0.66653776",
"0.6623098",
"0.66182786",
"0.65562433",
"0.650258... | 0.72777843 | 8 |
Initialize class with lfp data | def __init__(self, lfp_data):
self.lfp_data = lfp_data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n \n self.load_PSF_data()",
"def __init__(self, *args, **kwargs):\n super(AbsLoopinData, self).__init__(\n # All set outside\n ('linl_lis', LinlLis()),\n ('linh', Byte()),\n *args, **kwargs\n )",
"def __init__(self, fea... | [
"0.75963",
"0.67351204",
"0.6706532",
"0.66661716",
"0.6611448",
"0.6609953",
"0.66079646",
"0.66079646",
"0.66079646",
"0.66079646",
"0.6534196",
"0.6520125",
"0.65192205",
"0.650805",
"0.6506647",
"0.6488057",
"0.6477326",
"0.6463792",
"0.64620787",
"0.64137286",
"0.6397195... | 0.88286996 | 0 |
Remove temporal mean from each trial | def remove_temporal_mean(self):
if not hasattr(self, 'detrended_data'):
self.detrend_data()
self.mean_removed_data = self.detrended_data - \
np.mean(self.detrended_data, axis=-1, keepdims=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subtract_mean_across_trials(self):\n if not hasattr(self, 'std_divided_data'):\n self.divide_by_temporal_std()\n self.mean_across_trials_subtracted_data = \\\n self.std_divided_data - \\\n np.mean(self.std_divided_data, axis=1, keepdims=True)",
"def divide_by_te... | [
"0.7029652",
"0.62682873",
"0.598299",
"0.5920819",
"0.5916785",
"0.58507323",
"0.5822636",
"0.5814729",
"0.5688639",
"0.5680869",
"0.5662162",
"0.5650724",
"0.5647669",
"0.55737066",
"0.5537115",
"0.55138284",
"0.55138284",
"0.5509475",
"0.5509475",
"0.54969525",
"0.54960907... | 0.7598146 | 0 |
Divide by temporal standard deviation | def divide_by_temporal_std(self):
if not hasattr(self, 'mean_removed_data'):
self.remove_temporal_mean()
self.std_divided_data = self.mean_removed_data / \
np.std(self.mean_removed_data, axis=-1, keepdims=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stdev(values):\n mean = avg(values)\n diffs = [(value - mean) ** 2 for value in values]\n return avg(diffs) ** 0.5",
"def stdev(items):\n return Series.std(Series(items))",
"def stdev_from_mean(x):\r\n x = array(x)\r\n return (x - mean(x)) / std(x)",
"def _std(self, data):\n var ... | [
"0.70303154",
"0.70081013",
"0.6894062",
"0.68192405",
"0.680471",
"0.67917585",
"0.67652786",
"0.6757293",
"0.6757293",
"0.6733499",
"0.671972",
"0.6718157",
"0.67139",
"0.6705973",
"0.6677462",
"0.6677462",
"0.66751814",
"0.6662546",
"0.6652855",
"0.66227466",
"0.66132027",... | 0.8091293 | 0 |
Subtract mean across trials from each trial (for each timepoint) | def subtract_mean_across_trials(self):
if not hasattr(self, 'std_divided_data'):
self.divide_by_temporal_std()
self.mean_across_trials_subtracted_data = \
self.std_divided_data - \
np.mean(self.std_divided_data, axis=1, keepdims=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def avgtr(self):\n return np.diff(self.trtimes).mean()",
"def trial_atr(trial, omit_missing_frames=True):\n frames = trial.HMM_MLE\n if omit_missing_frames:\n frames = frames[frames >= 0]\n\n runs = calc_run_lengths(trial.HMM_MLE)\n return_times = []\n current_return_time = 0\n for run in runs:\n... | [
"0.6891229",
"0.6561155",
"0.6152613",
"0.61516047",
"0.6104623",
"0.60979486",
"0.60725135",
"0.6048731",
"0.598965",
"0.5979206",
"0.59755933",
"0.5951833",
"0.5951288",
"0.5923516",
"0.58769214",
"0.58767086",
"0.5859882",
"0.5859882",
"0.5859882",
"0.584206",
"0.5766064",... | 0.759768 | 0 |
Divide by standard deviation across trials (for each timepoint) | def divide_by_std_across_trials(self):
if not hasattr(self, 'mean_across_trials_subtracted_data'):
self.subtract_mean_across_trials()
self.std_across_trials_divided_data = \
self.mean_across_trials_subtracted_data / \
np.std(self.mean_across_trials_subtracted_data,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stdev(items):\n return Series.std(Series(items))",
"def calc_standard_deviation(data: list) -> float:\n mean = calc_mean(data)\n acc = 0.0\n for n in data:\n acc += (n - mean) ** 2\n acc /= len(data) - 1\n return math.sqrt(acc)",
"def stdDev(data):\r\n sum = 0\r\n ave = avera... | [
"0.7041086",
"0.6919613",
"0.6913145",
"0.6878369",
"0.68775237",
"0.6814619",
"0.67827666",
"0.67530537",
"0.67530537",
"0.67328584",
"0.6639971",
"0.6564795",
"0.6559348",
"0.65451086",
"0.65384686",
"0.65182203",
"0.64876235",
"0.64876235",
"0.64549816",
"0.6444664",
"0.64... | 0.7416995 | 0 |
alpha = threshold for single test (will be Bonferroni corrected internally) wanted_fraction = minimum fraction of tests that should be significant (stationary) | def run_adfuller_test(preprocessed_data, alpha=0.05, wanted_fraction=0.95):
inds = list(np.ndindex(preprocessed_data.shape[:-1]))
def return_adfuller_pval(this_ind): return adfuller(
preprocessed_data[this_ind])[1]
pval_list = np.array(parallelize(return_adfuller_pval, inds, n_jobs=30))
alpha =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pvalue_test(self, alpha=0.01):\n CL = int((1-alpha)*100) # confidence level\n \n if self.p_value < alpha:\n print(\"Null hypothesis rejected at {:d}%CL => distributions are different\".format(CL))\n else:\n print(\"Null hypothesis NOT rejected => distr... | [
"0.59391797",
"0.58778065",
"0.5851814",
"0.58380985",
"0.57947487",
"0.579216",
"0.57526",
"0.5734187",
"0.56922245",
"0.56904244",
"0.56162506",
"0.5595958",
"0.5584986",
"0.5574947",
"0.5539639",
"0.5525329",
"0.5498995",
"0.5490676",
"0.5487545",
"0.54838616",
"0.5480658"... | 0.6295856 | 0 |
Calculate granger causality time_series = time x trials x channels | def calc_granger(time_series,
time_halfbandwidth_product=1,
sampling_frequency=1000,
time_window_duration=0.3,
time_window_step=0.05,
):
m = Multitaper(
time_series,
sampling_frequency=sampling_frequency, # in Hz
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def graphite_cracking_rate_Ai2020(T_dim):\n k_cr = 3.9e-20\n Eac_cr = 0 # to be implemented\n arrhenius = np.exp(Eac_cr / pybamm.constants.R * (1 / T_dim - 1 / 298.15))\n return k_cr * arrhenius",
"def calc_granger_actual(self):\n if not hasattr(self, 'input_data'):\n self.preproce... | [
"0.5973435",
"0.5885312",
"0.5802749",
"0.574831",
"0.56886405",
"0.55259013",
"0.54229647",
"0.5363545",
"0.53522897",
"0.5351203",
"0.53407997",
"0.53008807",
"0.5212511",
"0.51963246",
"0.51937586",
"0.51852894",
"0.5148304",
"0.5119841",
"0.51136154",
"0.5099041",
"0.5090... | 0.61610305 | 0 |
preprocessed_data = (n_channels, n_trials, n_timepoints) sampling_frequency = in Hz n_shuffles = number of shuffles to perform wanted_window = window to calculate granger causality in alpha = significance level multitaper_time_window_duration = duration of time window for multitaper multitaper_time_window_step = step o... | def __init__(self,
good_lfp_data,
# preprocessed_data,
sampling_frequency=1000,
n_shuffles=500,
wanted_window=[1500, 4000],
alpha=0.05,
multitaper_time_halfbandwidth_product=1,
multita... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cubetest_per_topic(topic_truth, topic_result, gamma, max_height, cutoff):\n subtopic_num = topic_truth[1]\n topic_truth = topic_truth[0]\n\n subtopic_height = Counter() # current height of every subtopic\n subtopic_count = Counter() # #docs found relevant to every subtopic (nrels)\n\n weight_p... | [
"0.57132494",
"0.5595162",
"0.5551956",
"0.5525474",
"0.5525352",
"0.55149806",
"0.5493697",
"0.54556054",
"0.5432164",
"0.5368782",
"0.53298104",
"0.53258",
"0.53194267",
"0.53181934",
"0.5303471",
"0.5290147",
"0.52515495",
"0.5232192",
"0.52276266",
"0.52183676",
"0.520918... | 0.68344384 | 0 |
Calculate bootstrapped actual granger causality to allow for estimation of error | def calc_granger_actual(self):
if not hasattr(self, 'input_data'):
self.preprocess_and_check_stationarity()
# input_data shape = (n_timepoints, n_trials, n_channels)
# Calculate as many bootstrapped samples as n_shuffles
trial_inds = np.random.randint(
0, self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def binom_bca_bootstrap_err(k, n, B=10000, CL=[0.025, 0.975], acceleration=True, return_full=False):\n theta_MLE = k/n\n k_i = bootstrap_sample_binomial(k, n, B)\n\n # Bootstrap estimates of the parameter\n theta_i = k_i / n\n theta0_star = np.sum(theta_i) / B\n print(f'theta_MLE = {theta_MLE}, t... | [
"0.62454176",
"0.6181481",
"0.5973252",
"0.58579695",
"0.5794351",
"0.57939696",
"0.57455033",
"0.57455033",
"0.5720694",
"0.57071906",
"0.5694225",
"0.5676966",
"0.56485546",
"0.56252354",
"0.56042624",
"0.55519134",
"0.55401033",
"0.55351764",
"0.5533983",
"0.55071896",
"0.... | 0.63861793 | 0 |
Calculate shuffled granger causality | def calc_granger_shuffle(self):
if not hasattr(self, 'input_data'):
self.preprocess_and_check_stationarity()
temp_series = [np.stack([np.random.permutation(x)
for x in self.input_data.T]).T
for i in trange(self.n_shuffles)]
outs... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calc_granger_actual(self):\n if not hasattr(self, 'input_data'):\n self.preprocess_and_check_stationarity()\n # input_data shape = (n_timepoints, n_trials, n_channels)\n # Calculate as many bootstrapped samples as n_shuffles\n trial_inds = np.random.randint(\n ... | [
"0.62754345",
"0.62195766",
"0.60361123",
"0.5824733",
"0.58219045",
"0.563918",
"0.5584704",
"0.5575912",
"0.5573002",
"0.55611855",
"0.54603237",
"0.54584056",
"0.5441802",
"0.5441032",
"0.5434684",
"0.5428206",
"0.5410833",
"0.54057086",
"0.54057086",
"0.53993386",
"0.5378... | 0.6487357 | 0 |
Mask is True when granger causality is NOT SIGNIFICANT | def get_granger_sig_mask(self):
if not hasattr(self, 'percentile_granger'):
self.calc_shuffle_threshold()
if not hasattr(self, 'granger_actual'):
self.calc_granger_actual()
mean_granger_actual = np.mean(self.granger_actual, axis=0)
self.masked_granger = np.ma.mask... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mask(self):",
"def cmask(self):\n mask = np.zeros(18)\n if 'full' in self.CONS: mask[:] = 1\n if 'f0' in self.CONS: mask[0] = 1\n if 'f1' in self.CONS: mask[1:4] = 1\n if 'f2' in self.CONS: mask[4:10] = 1\n if 'vx' in self.CONS: mask[10] = 1\n if 'vy' in self.... | [
"0.6703793",
"0.63538605",
"0.63142383",
"0.6256875",
"0.61741894",
"0.6091233",
"0.60839707",
"0.6051521",
"0.5936672",
"0.59186983",
"0.5887224",
"0.58444947",
"0.57600707",
"0.5741366",
"0.57228017",
"0.5708867",
"0.5700054",
"0.5693821",
"0.56917423",
"0.56917423",
"0.568... | 0.66818357 | 1 |
Load a .ini file in a ConfigObject. Dont raise if the file does not exist | def ini(filename, **defaults):
filename = sh.path(filename)
defaults.update(home=sh.path('~'))
return ConfigObject(filename=filename, defaults=defaults) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_config():\n config_path = Path(\"config.ini\")\n if config_path.exists():\n config.read(config_path)\n else:\n config[\"database\"] = {\"location\": \"image-database.db\"}\n config[\"images\"] = {\"extensions\": \".jpeg,.jpg,.png,.gif,.tiff\"}\n with open(config_path,... | [
"0.7024221",
"0.7011484",
"0.698936",
"0.69812244",
"0.6818969",
"0.67843896",
"0.6672474",
"0.6648733",
"0.66448754",
"0.66153926",
"0.6569366",
"0.653835",
"0.65142953",
"0.6496884",
"0.6484515",
"0.6464613",
"0.64596313",
"0.6449164",
"0.64488745",
"0.6447785",
"0.6437697"... | 0.6802061 | 5 |
A list of return codes of all processes launched by the pipe | def returncodes(self):
for p in self.processes:
p.wait()
codes = [p.poll() for p in self.processes]
if set(codes) == set([0]):
return []
return codes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ListProcesses(self):\n stdout, stderr = self.RunCmdOnDevice(\n ['/bin/ps', '--no-headers', '-A', '-o', 'pid,ppid,args:4096,state'],\n quiet=True)\n assert stderr == '', stderr\n procs = []\n for l in stdout.split('\\n'):\n if l == '':\n continue\n m = re.match(r'^\\s*... | [
"0.6246769",
"0.6245669",
"0.6231879",
"0.6155173",
"0.61222064",
"0.6060783",
"0.5963847",
"0.5958712",
"0.59503996",
"0.5923416",
"0.59230506",
"0.5919986",
"0.59160084",
"0.58853406",
"0.58609474",
"0.58411664",
"0.5837716",
"0.5834148",
"0.5834146",
"0.58337736",
"0.58334... | 0.7935474 | 0 |
True if one or more process failed | def failed(self):
output = self.__call__()
return output.failed | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_processes(process_list):\n running = 1 # 0 when the subprocesses are all done\n while running:\n for proc in process_list:\n proc.poll()\n if proc.returncode == 1:\n raise RuntimeError(\"Program \" +\n \"number \" +\n ... | [
"0.7545346",
"0.72061265",
"0.69463867",
"0.68636256",
"0.64848185",
"0.6456872",
"0.6443327",
"0.6412077",
"0.6346322",
"0.63018525",
"0.62907344",
"0.6174255",
"0.6166908",
"0.6157817",
"0.61569697",
"0.6152704",
"0.6116674",
"0.61150193",
"0.60692024",
"0.6061821",
"0.6015... | 0.5971587 | 22 |
True if all processes succeeded | def succeeded(self):
output = self.__call__()
if output.succeeded:
return output or True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_processes(process_list):\n running = 1 # 0 when the subprocesses are all done\n while running:\n for proc in process_list:\n proc.poll()\n if proc.returncode == 1:\n raise RuntimeError(\"Program \" +\n \"number \" +\n ... | [
"0.7873151",
"0.65517205",
"0.65272486",
"0.6456628",
"0.6421741",
"0.635912",
"0.63176507",
"0.62866455",
"0.62855405",
"0.62604",
"0.62322694",
"0.6218841",
"0.6209144",
"0.62087065",
"0.6196792",
"0.61760706",
"0.6167658",
"0.61458474",
"0.61261225",
"0.6108483",
"0.608764... | 0.0 | -1 |
combined stderr of all processes | def stderr(self):
if self._stderr is None:
stderr = [p.stderr.read() for p in self.processes if p.stderr]
output = b'\n'.join(stderr).strip()
if not isinstance(output, str):
output = output.decode(self.encoding, 'ignore')
self._stderr = output
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nostderr():\n save_stderr = sys.stderr\n sys.stderr = cStringIO.StringIO()\n yield\n sys.stderr = save_stderr",
"def stderr(self, stderr: str) -> Tuple[List[Message], List[AnnotateCode], str]:\n return [], [], stderr",
"def result_stderr(result):\n return result[1][1]",
"def get_std... | [
"0.6715245",
"0.66839",
"0.6574798",
"0.65431535",
"0.6496524",
"0.64380735",
"0.63869214",
"0.6328884",
"0.62826824",
"0.6277269",
"0.6198289",
"0.6133846",
"0.60173345",
"0.5962739",
"0.5962001",
"0.5930159",
"0.59266466",
"0.5872949",
"0.58673817",
"0.5847088",
"0.5786829"... | 0.68787926 | 0 |
Run processes in background. Return the last piped Popen object | def bg(self):
p = None
self.processes = []
self._stderr = None
stdin = sys.stdin
cmds = self.commands
if [c for c in cmds if c._cmd_args[:1] == ['sudo']]:
check_sudo()
for cmd in cmds:
if isinstance(cmd, Stdin):
stdin = cm... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def call(*args, **kwargs):\n return Popen(*args, **kwargs).wait()",
"def start(self):\n last_stdout = None\n self.processes = []\n for cmd in self.cmds:\n # TODO: handle exceptions raised by Popen\n p = subprocess.Popen(cmd, stdin=last_stdout, stdout=subprocess.PIPE,... | [
"0.6664132",
"0.64688516",
"0.64178354",
"0.6342972",
"0.6325031",
"0.62582344",
"0.62191474",
"0.60025233",
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"0.5794157",
"0.5777509",
"0.5762344",
"0.5744797",
"0.572683",
"0.56833637",
"0.5670176"... | 0.7648508 | 0 |
standard output of the pipe. A file descriptor or an iteraror | def stdout(self):
p = self.bg()
if isinstance(p, PyPipe):
return p.iter_stdout
else:
return p.stdout | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def output_to_pipe(pipe_in):\n os.dup2(pipe_in, 1) # stdout\n # os.dup2(pipe_in, 2) # stderr",
"def standard_output(self) -> global___Statement.StandardOutput:",
"def echo_pipe(name: str, pipe: IO[AnyStr], dest_file: TextIO):\r\n assert pipe\r\n with pipe:\r\n for line in iter(pipe.readlin... | [
"0.72244734",
"0.70827913",
"0.65350837",
"0.6514286",
"0.6406539",
"0.6382835",
"0.63103026",
"0.6296839",
"0.62585413",
"0.62052107",
"0.6111356",
"0.5982335",
"0.59437376",
"0.59391224",
"0.5939096",
"0.5938583",
"0.5837392",
"0.58273935",
"0.58165693",
"0.57854456",
"0.57... | 0.6322155 | 6 |
Run a batch of the same command and manage a pool of processes for you | def map(cls, args,
pool_size=None, stop_on_failure=False, **kwargs):
kw = dict(
stdin=sys.stdin, stderr=PIPE,
stdout=PIPE
)
kw.update(kwargs)
if pool_size is None:
import multiprocessing
pool_size = multiprocessing.cpu_count()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_commands(self):\n processes = []\n\n i = 0\n ## get list of commands\n commands = self.get_commands()\n cnum = multiprocessing.cpu_count()\n\n while len(commands)>0:\n while len(processes)<cnum-1:\n c = commands.pop()\n i+=1... | [
"0.70449847",
"0.6387804",
"0.63765144",
"0.6361185",
"0.635158",
"0.6335699",
"0.62907577",
"0.6248162",
"0.61241436",
"0.60282695",
"0.5980476",
"0.59682316",
"0.59614676",
"0.594237",
"0.5920128",
"0.5910792",
"0.5903998",
"0.58932984",
"0.58870876",
"0.58841103",
"0.58738... | 0.61576796 | 8 |
Change the current directory | def cd(self, directory):
return ChangeDir(directory) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change_dir(path): \r\n os.chdir(path)",
"def change_directory(path):\n os.chdir(path)",
"def change_to_current_path(to_change_path):\n os.chdir(to_change_path)",
"def changeDirectory( self, directory ):\n if directory[0] == '/':\n directory = directory.lstrip( '/' )\n self.cwd = '%s... | [
"0.8097169",
"0.80569446",
"0.8038111",
"0.7640401",
"0.7405466",
"0.73729455",
"0.73583424",
"0.7357387",
"0.7277819",
"0.7239829",
"0.723306",
"0.71557087",
"0.71235436",
"0.7116997",
"0.70742506",
"0.69925034",
"0.696881",
"0.6960464",
"0.6955261",
"0.69533086",
"0.6878237... | 0.61863726 | 74 |
Add extra dependencies in a virtualenv | def requires(*requirements, **kwargs):
if '/.tox/' in sys.executable:
venv = os.path.dirname(os.path.dirname(sys.executable))
elif env.virtual_env: # pragma: no cover
venv = env.chut_virtualenv = env.virtual_env
else: # pragma: no cover
venv = os.path.expanduser(kwargs.get('venv', ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sub_install_python_requirements():\n # Activate the virtualenv\n activate = 'source {0}/{1}/bin/activate'.format(\n env.virtualenv['dir'], env.virtualenv['name'])\n run(activate)\n\n # Install Python requirements\n install = 'pip install -r /vagrant/Flask_app/requirements.txt'\n\n # Jo... | [
"0.74376476",
"0.7316353",
"0.7110245",
"0.70823187",
"0.7031737",
"0.7028379",
"0.70093244",
"0.6918647",
"0.68816835",
"0.68357944",
"0.6831954",
"0.6818253",
"0.67393124",
"0.67264587",
"0.67101854",
"0.6637899",
"0.6598489",
"0.65739876",
"0.6546227",
"0.6543257",
"0.6529... | 0.7067952 | 4 |
Generate chut scripts contained in location | def chutifab(self, *args):
ll = logging.getLogger(posixpath.basename(sys.argv[0]))
level = ll.level
ll.setLevel(logging.WARN)
if not args:
args = ['.']
for location in args:
Generator(destination='.chutifab')(location)
ll.setLevel(level)
se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _add_scripts(prefix):\n mapping = {\"MAST_HOME\": prefix}\n if \"Windows\" in platform.system():\n script_dir = os.path.join(INSTALL_DIR, \"files\", \"windows\")\n files = [\n \"mast.bat\",\n \"mast-system.bat\",\n \"mast-accounts.bat\",\n \"mast-... | [
"0.6096537",
"0.6009406",
"0.59709835",
"0.5967113",
"0.5920322",
"0.59109473",
"0.58882314",
"0.5841919",
"0.57877",
"0.5781333",
"0.5743029",
"0.57190037",
"0.5708141",
"0.5695298",
"0.5688432",
"0.56231797",
"0.5622278",
"0.55802655",
"0.5565092",
"0.55514556",
"0.55338746... | 0.67919713 | 0 |
Upload a script and run it. ``args`` are used as command line arguments. ``kwargs`` are passed to `fabric`'s `run` | def run(self, script, *args, **kwargs):
return self._run('run', script, *args, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def script_run(ctx: click.Context, name, script_arguments):\n subcommand_script.cmd_run(ctx.obj, name, script_arguments)",
"def run(args):\n\n drive_uid = str(args[\"drive_uid\"])\n file_uid = str(args[\"file_uid\"])\n chunk_idx = int(args[\"chunk_index\"])\n secret = str(args[\"secret\"])\n da... | [
"0.6333176",
"0.6069562",
"0.60669595",
"0.5997453",
"0.5979731",
"0.59490883",
"0.5912231",
"0.59050536",
"0.59018415",
"0.5843688",
"0.5803377",
"0.57960784",
"0.5723168",
"0.56878614",
"0.5683986",
"0.5648805",
"0.5643183",
"0.5575065",
"0.5519657",
"0.5519369",
"0.5487893... | 0.68447685 | 0 |
Upload a script and run it using sudo. ``args`` are used as command line arguments. ``kwargs`` are passed to `fabric`'s `sudo` | def sudo(self, script, *args, **kwargs):
return self._run('sudo', script, *args, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _upload_template(filename, destination, **kwargs):\n user = kwargs.pop('user')\n kwargs['use_sudo'] = True\n upload_template(filename, destination, **kwargs)\n sudo('chown %(user)s:%(user)s %(dest)s' % {'user': user, 'dest': destination})",
"def run_remote_script(self, script_file, args=None, log... | [
"0.6268761",
"0.589509",
"0.58233005",
"0.58182013",
"0.5760758",
"0.57154745",
"0.5654159",
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"0.5543384",
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"0.5439649",
"0.5400255",
"0.5376021",
"0.5350383",
"0.5334175",
"0.5298767",
"0.5295162",
... | 0.6812293 | 0 |
Return a random ktuple of unique elements selected from population. | def rand_tuple(population, k, required_inds=None):
if isinstance(population, int):
population = xrange(population)
if required_inds is None:
required_inds = []
if not isinstance(required_inds, collections.Iterable):
required_inds = [required_inds]
t = set(random.sample(populati... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def random_sample(population, k):\r\n \r\n newpopulation = population[:]\r\n if len(population) < k:\r\n raise ValueError, \"sample larger than population\"\r\n\r\n retlist = []\r\n populationsize = len(population)-1\r\n\r\n for num in range(k):\r\n pos = random_randint(0,populationsize-num)\r\n ret... | [
"0.7027959",
"0.6738937",
"0.67178",
"0.6524128",
"0.649793",
"0.6422931",
"0.6414868",
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"0.61237365",
"0.610168",
"0.60970324",
"0.60753673",
"0.6051624",
"0.6015738",
... | 0.78772885 | 0 |
The purpose of this is to help the API decide which fields to expose to the user when they are creating and updateing an Object. This function should be implemented in inheriting models and overriden to provide additional fields. Tastypie ignores any relational fields on the model. See the ModelResource definitions for... | def get_api_fields(cls):
return ['fqdn', 'ttl', 'description', 'views'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def objectFields(self):\n raise NotImplementedError",
"def get_fields(self, request, obj=None):\n if obj:\n return self.fields\n return self.add_fields",
"def clean_fields(self, exclude=None):\n obj = self._obj\n if obj is None:\n return None\n\n self... | [
"0.64550966",
"0.61312807",
"0.60282755",
"0.5996905",
"0.5970836",
"0.59571695",
"0.5911654",
"0.5871284",
"0.58693796",
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"0.5681614",
"0.56656194",
"0.5655514",
"0.56459785",
"0.5596161",
"0.55887556",
"0.55... | 0.59325325 | 6 |
If a CNAME RR is present at a node, no other data should be present; this ensures that the data for a canonical name and its aliases cannot be different." | def check_for_cname(record):
CNAME = cydns.cname.models.CNAME
if hasattr(record, 'label'):
if CNAME.objects.filter(domain=record.domain,
label=record.label).exists():
raise ValidationError("A CNAME with this name already exists.")
else:
if CNAME.ob... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_cname_response(self):\n fqdn = \"cname.github.com\"\n answer = self.resolver.query(fqdn, \"CNAME\")\n for rr in answer:\n if rr.target.to_text() != \"github.map.fastly.net.\":\n raise TestException(\"Unexpected target for {0}: {1}\"\n ... | [
"0.66299236",
"0.660377",
"0.6489522",
"0.6237036",
"0.5940322",
"0.5899176",
"0.5880195",
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"0.507616",
"0.507153",
"0.50488186",
"0.50211823",
"0.49776897",
"0.4961214",
"0.49337912",... | 0.6656125 | 0 |
If an object's domain is delegated it should not be able to be changed. Delegated domains cannot have objects created in them. | def check_for_delegation(record):
try:
if not record.domain.delegated:
return
except ObjectDoesNotExist:
return
if not record.pk: # We don't exist yet.
raise ValidationError("No objects can be created in the {0}"
"domain. It is delegated."
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _adddomain(self, domain: Domain):\n\n domain = copy.deepcopy(domain)\n if self.model is not None:\n # Check that model and domain are compatible\n self._validate_model_domain(self.model, domain)\n\n # Add in domain\n self.domain = domain\n\n ... | [
"0.58941036",
"0.5639527",
"0.5625555",
"0.5514755",
"0.54754555",
"0.54411316",
"0.53744304",
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"0.5263368",
"0.5252164",
"0.5234074",
"0.5162429",
"0.51530653",
"0.5148863",
"0.51440006",
"0.5141241",
"0.512088... | 0.6326696 | 0 |
Represent an intersection using the Cantor pairing function. | def intersection(st, ave):
return (st+ave)*(st+ave+1)//2 + ave | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_union_intersection():\n X = np.random.randn(d, 100)\n assert np.array_equal(lincon.indicator_intersection(X), 1-lincon.indicator_union(X))",
"def intersection(x, y, f, p):",
"def intersection(self, other):\n return self._geomgen(capi.geom_intersection, other)",
"def intersect(self, *arg... | [
"0.6529781",
"0.64454335",
"0.643646",
"0.6377812",
"0.6290983",
"0.6158032",
"0.61379325",
"0.6099429",
"0.60879844",
"0.60427094",
"0.6039375",
"0.6011817",
"0.5966033",
"0.5949825",
"0.5942497",
"0.5928957",
"0.59143835",
"0.59129083",
"0.5910618",
"0.5907354",
"0.5888551"... | 0.5358017 | 95 |
Return the taxicab distance between two intersections. >>> times_square = intersection(46, 7) >>> ess_a_bagel = intersection(51, 3) >>> taxicab(times_square, ess_a_bagel) 9 >>> taxicab(ess_a_bagel, times_square) 9 | def taxicab(a, b):
"*** YOUR CODE HERE ***"
return abs(street(a)-street(b)) + abs(avenue(a)-avenue(b)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def taxicab(a, b):\n street_1, street_2 = street(a), street(b)\n avenue_1, avenue_2 = avenue(a), avenue(b)\n return abs(street_1 - street_2) + abs(avenue_1 - avenue_2)",
"def taxicab(a, b):\n \"*** YOUR CODE HERE ***\"\n return abs(street(a) - street(b)) + abs(avenue(a) - avenue(b))",
"def taxic... | [
"0.7120618",
"0.67128575",
"0.67128575",
"0.65796095",
"0.65704274",
"0.5223073",
"0.50749713",
"0.5042515",
"0.5000128",
"0.48629433",
"0.48522303",
"0.4831717",
"0.47960907",
"0.4795959",
"0.478697",
"0.4773556",
"0.4723682",
"0.4699743",
"0.46614695",
"0.46481127",
"0.4647... | 0.68399566 | 1 |
Returns a new list containing square roots of the elements of the original list that are perfect squares. >>> seq = [8, 49, 8, 9, 2, 1, 100, 102] >>> squares(seq) [7, 3, 1, 10] >>> seq = [500, 30] >>> squares(seq) [] | def squares(s):
"*** YOUR CODE HERE ***"
return [int(i**0.5) for i in s if round(i ** 0.5) ** 2 == i ] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_squares(self):\n\t\tself.squares = [x for x in range(self.low, self.high + 1) if sqrt(x) == int(sqrt(x))]",
"def squares(s):\n \"*** YOUR CODE HERE ***\"\n result = []\n for num in s:\n sr = round(math.sqrt(num))\n if sr * sr == num:\n result.append(sr)\n return resu... | [
"0.7534428",
"0.7139836",
"0.7011327",
"0.69586444",
"0.6890902",
"0.6872329",
"0.68216246",
"0.66573334",
"0.66424423",
"0.66406393",
"0.66404545",
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"0.65243477",
"0.65034103",
"0.6489072",
"0.64806134",
"0.64675397",
"0.6315774",
"0.6... | 0.6735762 | 7 |
Return the value of G(n), computed recursively. >>> g(1) 1 >>> g(2) 2 >>> g(3) 3 >>> g(4) 10 >>> g(5) 22 >>> from construct_check import check >>> check(HW_SOURCE_FILE, 'g', ['While', 'For']) True | def g(n):
"*** YOUR CODE HERE ***"
if n <= 3:
return n
else:
return g(n-1) + 2*g(n-2) + 3*g(n-3) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def g(n):\n \"*** YOUR CODE HERE ***\"\n if n < 4:\n return n\n else:\n return g(n-1) + 2*g(n-2) + 3*g(n-3)",
"def g(n):\n \"*** YOUR CODE HERE ***\"\n if n <=3:\n return n\n else:\n return g(n-1)+2*g(n-2)+3*g(n-3)",
"def g(n):\n \"*** YOUR CODE HERE ***\"\n ... | [
"0.6509589",
"0.6436936",
"0.63279766",
"0.63279766",
"0.615563",
"0.6023759",
"0.5937535",
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"0.5292581",
"0.5288211",
"0.52790266",
"0.5253362",
"0.5252296",
"0.51862425... | 0.65330666 | 0 |
Return the value of G(n), computed iteratively. >>> g_iter(1) 1 >>> g_iter(2) 2 >>> g_iter(3) 3 >>> g_iter(4) 10 >>> g_iter(5) 22 >>> from construct_check import check >>> check(HW_SOURCE_FILE, 'g_iter', ['Recursion']) True | def g_iter(n):
if n <= 3:
return n
else:
g_n_1, g_n_2, g_n_3 = 3, 2, 1
# always update the g_i until reach the final n
for i in range(4,n+1):
g_i = g_n_1 + 2*g_n_2 + 3*g_n_3
# update the g(n-1), g(n-2), g(n-3)
g_n_1, g_n_2, g_n_3 = g_i, g_n_1, g_n_2
return g_i
"*** YOUR CODE HERE ***" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def g_iter(n):\n \"*** YOUR CODE HERE ***\"\n if n < 4:\n return n\n else:\n g1 = 1\n g2 = 2\n g3 = 3\n i = 3\n while(i < n):\n i += 1\n t = g3 + 2*g2 + 3*g1\n g1 = g2\n g2 = g3\n g3 = t\n return g3",
"de... | [
"0.71208894",
"0.68869877",
"0.6281551",
"0.622201",
"0.6130775",
"0.6093902",
"0.60279924",
"0.5978761",
"0.59504557",
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"0.583596",
"0.57956004",
"0.5788619",
"0.570271",
"0.56376046",
"0.5634359... | 0.73005706 | 0 |
Return the nth element of the pingpong sequence. >>> pingpong(7) 7 >>> pingpong(8) 6 >>> pingpong(15) 1 >>> pingpong(21) 1 >>> pingpong(22) 0 >>> pingpong(30) 6 >>> pingpong(68) 2 >>> pingpong(69) 1 >>> pingpong(70) 0 >>> pingpong(71) 1 >>> pingpong(72) 0 >>> pingpong(100) 2 >>> from construct_check import check >>> ch... | def pingpong(n):
# define two helper function, the recursion start from the bottom
# from 1 to n
"*** YOUR CODE HERE ***"
def positive(n, k, turn):
if turn == n:
return k
else:
if (k % 7 == 0) or has_seven(k):
return negative(n, k-1, turn + 1)
else:
return positive(n, k+1, turn + 1)
def negat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pingpong(n):\n def helper(k, direction, ret):\n if k == n:\n return ret +direction\n elif k%7 == 0 or num_sevens(k):\n return helper(k+1, -direction, ret + direction)\n else:\n return helper(k+1, direction, ret + direction)\n return helper(1, 1, 0)",
... | [
"0.5579512",
"0.55709857",
"0.5553509",
"0.554925",
"0.55054665",
"0.5184937",
"0.5149973",
"0.51207805",
"0.51080966",
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"0.49914345",
"0.49744314",
"0.4963783",
"0.49512187",
"0.49058053",
"0.4... | 0.48134068 | 26 |
Returns True if at least one of the digits of k is a 7, False otherwise. >>> has_seven(3) False >>> has_seven(7) True >>> has_seven(2734) True >>> has_seven(2634) False >>> has_seven(734) True >>> has_seven(7777) True | def has_seven(k):
if k % 10 == 7:
return True
elif k < 10:
return False
else:
return has_seven(k // 10) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_seven(k):\n if k == 0:\n return False\n else:\n if k%10 == 7:\n return True\n return has_seven(k//10)",
"def has_seven(k):\n \n if k % 10 == 7:\n return True\n else:\n if k<10:\n return False\n return has_seven(k//10)",
"def... | [
"0.8716564",
"0.8706798",
"0.8690393",
"0.8690393",
"0.8690393",
"0.61994755",
"0.58175665",
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"0.5434485",
"0.5352723",
"0.53297573",
"0.5328972",
"0.5325144",
"0.531923... | 0.8931878 | 0 |
Return the number of ways to make change for amount. >>> count_change(7) 6 >>> count_change(10) 14 >>> count_change(20) 60 >>> count_change(100) 9828 | def count_change(amount):
options = [2**i for i in range(amount+1) if 2**i <= amount]
options = sorted(options, reverse = True)
length = len(options)
# print(length)
def helper(remains, i, options, length):
# loop until reaching the smallest coin
if i >= length :
return 0
# check the remains
if remains... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_change(amount):\n def change_with_maxcoin(total, maxcoin):\n if total == 0:\n return 1\n if maxcoin == 0:\n return 0\n cnt = 0\n num_change = 0\n while cnt * maxcoin <= total:\n num_change += change_with_maxcoin(total - cnt * maxcoin,... | [
"0.7939031",
"0.76921844",
"0.7645819",
"0.7632281",
"0.751326",
"0.735496",
"0.7268972",
"0.69675857",
"0.6723433",
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"0.56916153",
"0.5571448",
"0.5469678",
"0.54315627",
"0.5374645",
"0.53648406... | 0.79869974 | 0 |
Return the value of an expression that computes factorial. >>> make_anonymous_factorial()(5) 120 >>> from construct_check import check >>> check(HW_SOURCE_FILE, 'make_anonymous_factorial', ['Assign', 'AugAssign', 'FunctionDef', 'Recursion']) True | def make_anonymous_factorial():
# u need to use a helper function if your lambda statement does
# not have a name
# fact = lambda n: 1 if n == 1 else mul(n, fact(sub(n, 1)))
def recursive2(f, n):
return f(f, n)
def recursive(n):
return recursive2((lambda rec, n: 1 if n == 1 else mul(n, rec(sub(n, 1)))), n)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_anonymous_factorial():\n return 'YOUR_EXPRESSION_HERE'",
"def make_anonymous_factorial():\n return 'YOUR_EXPRESSION_HERE'",
"def make_anonymous_factorial():\n return 'YOUR_EXPRESSION_HERE'",
"def make_anonymous_factorial():\n return 'YOUR_EXPRESSION_HERE'",
"def make_anonymous_factoria... | [
"0.84055257",
"0.84055257",
"0.84055257",
"0.84055257",
"0.7953051",
"0.7495947",
"0.6420814",
"0.6418577",
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"0.6187498",
"0.61707485",
"0.61447144",
"0.6141441",
"0.6130753",
"0.6130... | 0.7648021 | 5 |
Implementation of Focal Loss from the paper in multiclass classification | def categorical_focal_loss(gamma=2.0, alpha=0.25):
def focal_loss(y_true, y_pred):
# Define epsilon so that the backpropagation will not result in NaN for 0 divisor case
epsilon = backend.epsilon()
# Add the epsilon to prediction value
#y_pred = y_pred + epsilon
# Clip the pr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def focal_loss(self,labels, logits, gamma=2):\n y_pred = tf.nn.softmax(logits, dim=-1) # [batch_size,num_classes]\n # labels = tf.one_hot(labels, depth=y_pred.shape[1])\n L = -labels * ((1 - y_pred) ** gamma) * tf.log(y_pred)\n L = tf.reduce_sum(L, axis=1)\n return L",
"def fo... | [
"0.6775613",
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"0.6491725",
"0.64844924",
"0.6475553",
"0.64732516",
"0.64350957",
"0.6420379",
"0.63795304",
"0.6373628",
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"0.62419695",
"0.62229663",
"0.6222136",
"0.6222136",
"0.62179106",
"0.62... | 0.7187947 | 0 |
Look at each neighbor of a point and, if healthy, have chance to infect | def infect(r, c):
subset = grid[r-1:r+2, c-1:c+2]
print(f"Looking at ({r-1}, {c-1}) through ({r+1}, {c+1})")
# np.where(subset == 0)
# subset[subset == 0] = np.fromfunction(calc_infect, shape=())
#v_calc_infect(subset[subset == 0])
#for i in np.nditer(subset):
# if subset[i] == 0:
# ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def investigate(self, nearest_neighbors):\n pass",
"def _check_neighbors(self):\n for direction, dir_info in self.DIRECTIONS.items():\n pos = Point(\n self.position.x + dir_info[\"mask\"][0],\n self.position.y + dir_info[\"mask\"][1]\n )\n ... | [
"0.6369916",
"0.6350412",
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"0.6063208",
"0.6038064",
"0.602581",
"0.60251755",
"0.60057694",
"0.6004... | 0.0 | -1 |
Perform actions on all infected members of the population in a random order | def turn(grid):
# Select infected people
rows, cols = np.where(grid == 1)
#print(f"Infected at {rows}, {cols}")
# In random order, go through each infected
idx = np.arange(len(rows))
np.random.shuffle(idx)
for i in idx:
# Chance to heal
if np.random.binomial(1, heal_rate):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _mutate(self, individuals):\n for cur in individuals:\n if random.random() < self.mutation_probability:\n self.op.mutate(cur['individual'])\n cur['fitness'] = None",
"def hesitant_action(self):\n if not self.agent.done:\n if not self.opponenet... | [
"0.6152134",
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"0.61022955",
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"0.5767429",
"0.57490784",
"0.5739122",
"0.572956",
"0.57269686",
"0.57267815",... | 0.6190832 | 0 |
Count up the number of | def add_tally(grid):
# Count number of each patient type in the grid
tally['healthy'].append(len(grid[grid == 0]))
tally['sickos'].append(len(grid[grid == 1]))
tally['immune'].append(len(grid[grid == -1]))
tally['dead'].append(len(grid[grid == 2]))
tally['time'].append(tally['time'][-1]+1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count():",
"def Count(self) -> int:",
"def Count(self) -> int:",
"def Count(self) -> int:",
"def Count(self) -> int:",
"def counter(self) -> int:",
"def counter(self) -> int:",
"def count() -> int:\n pass",
"def count(self):\n # TODO not implemented yet\n return 0",
"def coun... | [
"0.8492241",
"0.78293914",
"0.78293914",
"0.78293914",
"0.78293914",
"0.7768418",
"0.7768418",
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"0.7112772",
"0.70056784",
"0.6985037",
"0.69749457",
"0.69513613",
"0.68868035",
"0.6... | 0.0 | -1 |
Function that's called automatically by the animation loop | def updatefig(*args):
p1.set_array(turn(grid))
p2.set_data(tally['time'], tally['sickos'])
p3.set_data(tally['time'], tally['immune'])
p4.set_data(tally['time'], tally['dead'])
ax2.set_xlim(0, max(tally['time']))
# ax2.set_ylim(0, max(max(sickos), max(immune)))
# End sim if the disease is go... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_animations(self):\n self.animate_bloop(700, 160, 50)",
"def animate(self, u):\n pass",
"def animate(self, u):\n pass",
"def start_animation(self):\n\t\ttime.sleep(1)\n\t\tself.fishbowl.animate_balls()",
"def Gameloop():",
"def _init_anim(self):\n pass",
"def event1151... | [
"0.7553557",
"0.7044329",
"0.7044329",
"0.69232357",
"0.6861924",
"0.6777186",
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"0.6617808",
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"0.6549287",
"0.6549287",
"0.6549287",
"0.6549287",
"0.6549287",
... | 0.0 | -1 |
CLI frontend function. It takes command line options e.g. host, port and provides `help` message. | def main():
docopt = docoptinit(__doc__)
logging.basicConfig(level=logging.INFO,
format='[%(asctime)s] [%(levelname)s] [ %(filename)s:%(lineno)s - %(name)s ] %(message)s ')
logging.info('basic config')
# qb.set_logger(__file__, debug=docopt['--debug'])
host = docopt['--host']... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cli_help(self):\n self._generate_cli_version()\n self._generate_cli_help()\n sys.exit(0)",
"def test_cli_help(self):\n output = self.update_command('-h')",
"def cli():\n return",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
... | [
"0.7485873",
"0.7443428",
"0.7438209",
"0.7409361",
"0.7409361",
"0.7409361",
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"0.7409361",
"0.7409361",
"0.7409361",
"0.7409361",
"0.7409361",
"0.7409361",
"... | 0.0 | -1 |
removes the old repo in server and clones a new one. the configures the host. | def flush_repo():
server = get_server()
run("rm -rf %(project_name)s" % env)
git.clone()
server.setup() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_source(self):\n cwd = None\n if os.path.exists(self.path):\n cwd = self.path\n cmd = 'git fetch && git reset --hard origin/master'\n else:\n cmd = 'git clone %s %s' % (self.repo_url, self.path)\n Command(cmd, cwd=cwd)",
"def deploy_pull_mast... | [
"0.61914355",
"0.61613387",
"0.61292326",
"0.610431",
"0.6018636",
"0.600334",
"0.5974268",
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"0.5908282",
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"0.57253504",
"0.56959504",
"0.5688279",
"0.5687978",
"0.5686983",
"0.56722623",
"0.5639738... | 0.7116184 | 0 |
pull, commit, push and test in the app. | def prepare_deploy():
from fabdeploy.django import test as django_test
django_test()
git.add_commit_pull()
git.push() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pull():\n _with_deploy_env(['git pull'])",
"def d_test():\n\tlocal(\"git push origin --all\")\n\twith cd(TEST_ROOT):\n\t\trun(\"git pull\")",
"def d_ploy():\n\tlocal(\"git push origin --all\")\n\twith cd(LIVE_ROOT):\n\t\trun(\"git pull\")",
"def call_git_pull():\n print(\"This will pull the rem... | [
"0.73450637",
"0.71330017",
"0.6839212",
"0.67483294",
"0.6688655",
"0.6645566",
"0.66057",
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"0.62662596",
"0.6240535",
"0.6227741",
"0.6202468",
"0.61747855",
"0.61554646",
"0.61433... | 0.64939606 | 9 |
empty static_root and collects the static files | def deploy_static():
from fabdeploy.django import collectstatic as django_collectstatic
# run("rm -rf %(root_path)s%(project_name)s/static/*" % env) # call again git_add_commit_pull
django_collectstatic() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def copy_static_resources(self):\n if not hasattr(settings, 'STATIC_ROOT'):\n raise MissingStaticRoot()\n destination = os.path.join(STORAGE_PATH, 'static')\n if os.path.exists(destination):\n shutil.rmtree(destination)\n shutil.copytree(settings.STATIC_ROOT, desti... | [
"0.7267547",
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"0.70244145",
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"0.670332",
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"0.6453902",
"0.63838065",
"0.6336708",
"0.6258352",
"0.61938727",
"0.6126046",
"0.6057303",
"0.60528827",
"0.60449046",
"0.601442",
"0.60042685",... | 0.6938294 | 4 |
deploy the application to the server | def run(syncdb=False):
from fabdeploy.django import migrate as django_migrate, syncdb as django_syncdb
import time
env.release = time.strftime('%Y%m%d%H%M%S')
prepare_deploy() # pull, test, push
git.remote_pull()
app.install_requirements()
django_migrate(syncdb) # syncdb in case is first tim... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deploy():",
"def deploy():\n upload_static()\n compile_code()\n upload_code()\n upload_supervisor()\n start_server()",
"def deploy():\n require('hosts', provided_by=[prod])\n require('whole_path', provided_by=[prod])\n require('code_root')\n upload_tar_from_git(env.whole_path)\n ... | [
"0.8416337",
"0.7875095",
"0.784592",
"0.78096145",
"0.7726005",
"0.76824063",
"0.7591086",
"0.75828916",
"0.7564499",
"0.7564499",
"0.7564499",
"0.7369355",
"0.7331053",
"0.7323487",
"0.73168474",
"0.7304545",
"0.7243055",
"0.72401834",
"0.7240129",
"0.7224798",
"0.7142134",... | 0.6559371 | 65 |
fetch the data and labels | def __init__(self, path_image, path_imagefile, path_bndboxfile, transform):
# -------------------- DATA ARGUMENT
self.shape = 446
self.hue = 0.1
self.saturation = 1.5
self.exposure = 1.5
self.imagelist = []
self.labellist = []
self.transform = tran... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fetch_data(self):",
"def get_labels():\n return if_found(dao.get_labels())",
"def _fetch_data(self):\n pass",
"def _fetch_labels(self, list_fams, no_imgs, num_samples) -> Tuple[np.ndarray, List]:\n y_train = np.zeros(num_samples)\n pos = 0\n label = 0\n indexes = []\... | [
"0.7082837",
"0.68395245",
"0.6802044",
"0.6746683",
"0.67372286",
"0.67104995",
"0.6593471",
"0.6433335",
"0.63847095",
"0.63727075",
"0.6362536",
"0.6323776",
"0.629321",
"0.6279828",
"0.6273046",
"0.6208926",
"0.6184165",
"0.6184165",
"0.6184165",
"0.6152687",
"0.61433417"... | 0.0 | -1 |
Create a custom function that maps to one of the CUDA Math library functions. The function name must match one of the already existing names in the CUDA | def function(name: str, expr: vecpy.base.Expr, *args) -> vecpy.base.Function:
return vecpy.base.Function(name, expr, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def map_string2func(funcname, clss, compute_capability):\n if \"_get_\" + funcname not in globals():\n raise AttributeError(\"kernel type '\" + funcname + \"' not understood\")\n return globals()[\"_get_\" + funcname](clss, compute_capability)",
"def function_application(func):\n if func not in N... | [
"0.68791264",
"0.63282084",
"0.6155065",
"0.6080748",
"0.6060965",
"0.60382944",
"0.59281456",
"0.58298725",
"0.58285344",
"0.57828945",
"0.57409996",
"0.57352984",
"0.57184166",
"0.56775355",
"0.5672887",
"0.5661893",
"0.55811965",
"0.5579806",
"0.557754",
"0.55657697",
"0.5... | 0.5445002 | 29 |
Returns a list of restaurants whose name, description and/or tags match the query_string and are closer than 3km from the paramter latitude and longitude coordinates. | def restaurant_search():
# Get query string parameters
query_string = request.args.get('q')
latitude = request.args.get('lat')
longitude = request.args.get('lon')
# Check that request parameters are valid, if not send 400 response
if query_string == None or len(query_string) < 1:
respo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def restaurants_search() -> str:\n args = request.args\n if 'q' in args and 'lat' in args and 'lon' in args: # check if correct params are given\n if len(args['q']) > 0 and len(args['lat']) > 0 and len(args['lon']) > 0: # check that\n # params satisfy length requirements\n try:... | [
"0.6773024",
"0.66979843",
"0.6554955",
"0.65408885",
"0.6487632",
"0.64766216",
"0.6475124",
"0.6210143",
"0.6178921",
"0.60852146",
"0.608248",
"0.60315293",
"0.6000485",
"0.5993607",
"0.58657825",
"0.5844165",
"0.58390087",
"0.5809332",
"0.5793267",
"0.57707137",
"0.576198... | 0.6517051 | 4 |
Returns True if value can be converted to float, otherwise returns False.\n | def isfloat(value):
try:
float(value)
return True
except ValueError:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_floatable(value):\n\n try:\n float(value)\n return True\n except:\n return False",
"def isFloat(value): \n try:\n float(value)\n return True\n except ValueError:\n return False",
"def is_float(self, value):\n try:\n float(val... | [
"0.8476934",
"0.83771354",
"0.8343819",
"0.8308899",
"0.8175327",
"0.81586117",
"0.8139705",
"0.81321037",
"0.8056236",
"0.8011302",
"0.7905029",
"0.7842661",
"0.76165664",
"0.7579738",
"0.7570646",
"0.7516587",
"0.7417768",
"0.7385605",
"0.7295912",
"0.72901154",
"0.72365445... | 0.82445174 | 4 |
return nuber of items in repo | def size(self):
return len(self._items) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unmerged_total(self):\n return int(self.git.rev_list('--count', '{}..{}'.format(self.base_branch, self.topic_branch)))",
"def test_repositories(self):\n\t\ttot_repos = total_repos(self)\n\t\t#self.assertEqual(tot_repos, \"6052353)",
"def n_public_repos(gh, user):\n return getuser(gh, user).public... | [
"0.63817924",
"0.6111955",
"0.6042396",
"0.60301703",
"0.59792984",
"0.59792984",
"0.5954415",
"0.58658177",
"0.5719205",
"0.56176406",
"0.5599673",
"0.5552937",
"0.5523746",
"0.5484504",
"0.5479842",
"0.5449454",
"0.5437292",
"0.5430239",
"0.5408196",
"0.5402071",
"0.5392688... | 0.0 | -1 |
add in repo an object | def adauga(self, item):
if item in self._items:
raise RepoError("item deja existent!\n")
self._items.append(item) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add(obj):",
"def add_object(self, obj):\n\t\tself.objects.append(obj)",
"def add_object(self, object):\n object.save()",
"def add_content_object_to_repo(self, name, co, wired=True):\n name = Name(name).toUri()\n if not wired:\n data = co.wireEncode().toRawStr()\n el... | [
"0.67793566",
"0.67431223",
"0.67289394",
"0.6702126",
"0.6696394",
"0.66702986",
"0.664344",
"0.64573383",
"0.64354074",
"0.6379205",
"0.63360125",
"0.6306308",
"0.63011926",
"0.62364924",
"0.6183897",
"0.6140982",
"0.6124869",
"0.61231977",
"0.6122632",
"0.6120433",
"0.6049... | 0.5787345 | 47 |
search in repo an item | def cauta(self, item):
if item not in self._items:
raise RepoError("item inexistent")
for x in self._items:
if x == item:
return x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _search_repository(self, query):\n \n # raise repository search dialog\n dlg = RepositoryView(self, self._library, query=query)\n response = dlg.ShowModal()\n articles = dlg.GetArticles()\n dlg.Destroy()\n \n # check response\n if response != wx.ID... | [
"0.6507103",
"0.6487686",
"0.63771313",
"0.635545",
"0.63503957",
"0.62970537",
"0.62911946",
"0.6150294",
"0.6143739",
"0.6120025",
"0.6084103",
"0.6054245",
"0.603377",
"0.6031059",
"0.602512",
"0.601455",
"0.6002547",
"0.59994215",
"0.59819794",
"0.59696996",
"0.5959124",
... | 0.6053503 | 12 |
check if object exists in repo | def exista(self, item):
if item not in self._items:
return False
for x in self._items:
if x == item:
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exists(self):\n return self._repository is not None",
"def exists(self, obj):\n return False",
"def objExists(*args, **kwargs)->bool:\n pass",
"def git_has_object(project: Project, name: str) -> bool:\n ret = project.git(\"rev-parse\", \"--verify\", name, _ok_code=[0, 128])\n retur... | [
"0.7361505",
"0.727437",
"0.7086809",
"0.705437",
"0.6815172",
"0.67559314",
"0.6645725",
"0.6632751",
"0.6463651",
"0.64450514",
"0.64416546",
"0.64301205",
"0.64293253",
"0.6377242",
"0.6319144",
"0.6310503",
"0.6303328",
"0.6303328",
"0.6284294",
"0.627665",
"0.6234188",
... | 0.0 | -1 |
modify the name of the object with the string | def modifica(self, item, change):
item = self.cauta(item)
self.delete(item)
item.set_nume(change)
self.adauga(item) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_object_name(remote, object_id, new_name):\n cmd = mmapi.StoredCommands()\n cmd.AppendSceneCommand_SetObjectName(object_id, new_name)\n remote.runCommand(cmd)",
"def new_name(self,new_name):\n self.name = new_name",
"def replace(name, newobject):",
"def __set_name__(self, cls, name):\n... | [
"0.7426106",
"0.7336926",
"0.72198415",
"0.7127601",
"0.7119643",
"0.70936084",
"0.70936084",
"0.7019569",
"0.69729173",
"0.694107",
"0.69359905",
"0.68720067",
"0.6853922",
"0.6847054",
"0.68378264",
"0.6826936",
"0.68108994",
"0.67999107",
"0.6772332",
"0.6772332",
"0.67723... | 0.0 | -1 |
modify the name of a discipline with the string | def modifica_materie(self, item, change):
item = self.cauta(item)
self.delete(item)
item.set_materie(change)
self.adauga(item) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def new_name(self,new_name):\n self.name = new_name",
"def get_discipline_name(self):\n return self.__discipline_name",
"def MotherNaming(s, pref=None):\n l=s.split(\"_\")\n l=[\"%s%s\" % (i[0].upper(),i[1:]) for i in l]\n return (pref or \"\") + \"\".join(l)",
"def set_name(self, newn... | [
"0.6241928",
"0.6217428",
"0.59670246",
"0.58978",
"0.5894144",
"0.58591086",
"0.58377343",
"0.57294095",
"0.57294095",
"0.56621903",
"0.56435573",
"0.5610684",
"0.55671847",
"0.5564039",
"0.55466604",
"0.55393904",
"0.553432",
"0.55148774",
"0.5498157",
"0.54763716",
"0.5444... | 0.0 | -1 |
return a list with all the objects | def get_all(self):
return self._items[:] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetObjects(self): \r\n return self.model.GetObjects()",
"def all(self):\n return (self.__objects)",
"def all(self):\n return self.__objects",
"def all(self):\n return self.__objects",
"def all(self):\n return self.__objects",
"def all(self):\n return s... | [
"0.79968905",
"0.7928298",
"0.7899466",
"0.7899466",
"0.7899466",
"0.7899466",
"0.7899466",
"0.7899466",
"0.78799754",
"0.78036886",
"0.77928257",
"0.7701835",
"0.765167",
"0.76110023",
"0.74869895",
"0.7473495",
"0.7435969",
"0.7390489",
"0.738795",
"0.73557454",
"0.73383856... | 0.6596494 | 74 |
return a list with items with the same student | def get_all_by_student(self, stud_id):
l = []
for item in self._items:
if item.get_student() == stud_id:
l.append(item)
return l[:] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def students(self):\n\t\treturn self.grade_set.all().distinct()",
"def find_duplicate(student_list):\r\n place_holder = student_info('null', 'null', '0', '0')\r\n current = place_holder\r\n dupe = []\r\n final = []\r\n for student in student_list:\r\n previous = current\r\n current =... | [
"0.6781505",
"0.65362155",
"0.6199378",
"0.60326993",
"0.601739",
"0.5969962",
"0.5895063",
"0.583807",
"0.5774767",
"0.57600665",
"0.5728087",
"0.57097805",
"0.5650832",
"0.56497097",
"0.5617477",
"0.560614",
"0.55885255",
"0.55699545",
"0.55072105",
"0.5501444",
"0.5463087"... | 0.6604642 | 1 |
return a list with items with the same discipline | def get_all_by_discipline(self, disc_id):
l = []
for i in self._items:
if i.get_id_disciplina() == disc_id:
l.append(i)
return l[:] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deduped(items):\n \n return list(set(items))",
"def Collection_select_cheap(C:list, n: float)->list:\r\n result = []\r\n for rest in C:\r\n if Restaurant_is_cheap(rest, n) == True:\r\n result.append(rest)\r\n return result",
"def duplicates(items):\n duplicate_items = se... | [
"0.57249516",
"0.559361",
"0.54721725",
"0.5366635",
"0.5349736",
"0.53270197",
"0.532001",
"0.53142184",
"0.5301576",
"0.5295073",
"0.52504534",
"0.5219532",
"0.5206376",
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"0.51740515",
"0.5167879",
"0.5167069",
"0.514371",
"0.51328325",
"0.51251054",
"0.51210004... | 0.6530238 | 0 |
Master segment function Based on SETTINGS, run corresponding function | def segment(data): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(filepath, maskpath):\n analytics.result = {}\n img_mask = nib.load(maskpath).get_fdata()\n print(\"loading\\n\", flush=True)\n # segmentation\n print(\"loading segmentation...\\n\", flush=True)\n seg = nib.load(filepath).get_fdata()\n # post processing\n print(\"applying some post ... | [
"0.570246",
"0.56575155",
"0.56105673",
"0.5582549",
"0.5550274",
"0.5524165",
"0.5475396",
"0.54618686",
"0.54181546",
"0.5416725",
"0.54031515",
"0.53997654",
"0.5375668",
"0.5363712",
"0.5326585",
"0.53262764",
"0.5325988",
"0.5306261",
"0.52622974",
"0.52571845",
"0.52416... | 0.5211432 | 27 |
Sliding window algorithm realization Output 'segments' contains start and end indexes for each step Assumption data is contiguous data | def segment_sliding_window(data, winSizeMillisecond=1000, stepSizeMillisecond=100):
logger.info("Sliding window with win size %.2f second and step size %.2f second",
winSizeMillisecond, stepSizeMillisecond)
if stepSizeMillisecond <= 0:
raise ValueError("Step size must be larger t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _segment(data, segment_length=200,\n seq_length=None,\n stride=None,\n input_type='trials'):\n x_out = []\n if input_type == 'trials':\n seq_length = 1\n\n if not stride:\n stride = segment_length\n\n for jj, xx in enumerate(data):\n\n n_ch, ... | [
"0.67323786",
"0.66023433",
"0.6593062",
"0.65148705",
"0.6402961",
"0.6359427",
"0.6332763",
"0.6283209",
"0.6280443",
"0.62024176",
"0.61759365",
"0.6168342",
"0.6120425",
"0.6099909",
"0.6057716",
"0.6040668",
"0.5987201",
"0.5953549",
"0.5936035",
"0.5916517",
"0.5867777"... | 0.7024497 | 0 |
Create a (spatial) probability distribution with four active cells, such that the expected value is centered on `(x, y)`. | def cross_entropy_four_hot(x: float, y: float, width: int, height: int) -> torch.Tensor:
t = torch.zeros(height, width)
from_left = (x + 1.0) / 2.0 * (width - 1)
from_top = (y + 1.0) / 2.0 * (height - 1)
width_idx = math.floor(from_left)
height_idx = math.floor(from_top)
left_frac = from_left... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prob4():\n#raise NotImplementedError(\"Problem 4 Incomplete\")\n h = lambda x : x[0] < -1 and x[1] > 1\n f = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([0,0]),cov=np.eye(2))\n g = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([-1,1]),cov=np.eye(2))\n X = np.random.multivari... | [
"0.62995535",
"0.6053863",
"0.6011577",
"0.5927498",
"0.5884353",
"0.578833",
"0.5772819",
"0.5764375",
"0.57246447",
"0.571302",
"0.56987005",
"0.56945825",
"0.56622314",
"0.5659095",
"0.5632271",
"0.5623151",
"0.5592304",
"0.5591458",
"0.5576871",
"0.5529894",
"0.55254793",... | 0.0 | -1 |
Convert django model to geojson | def to_geojson(model, contrib_id):
feature_collection = []
for record in model.objects.filter(contributer_id=contrib_id):
try:
properies = {
"name": record.name,
"address": record.address,
"email": record.email,
"website": recor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def default(self, o): \n if isinstance(o, GEOSGeometry):\n dictval = json.loads(o.geojson)\n #raise Exception(o.ewkt)\n dictval['__GEOSGeometry__'] = ['__init__', [o.ewkt]] #json class hint; see http://json-rpc.org/wiki/specification\n return dictval\n else... | [
"0.6924277",
"0.6681148",
"0.6647204",
"0.6602624",
"0.6577234",
"0.6551587",
"0.6533658",
"0.6529714",
"0.64571106",
"0.645591",
"0.6319082",
"0.6317624",
"0.62623715",
"0.6253182",
"0.62173903",
"0.6201155",
"0.6189135",
"0.61708796",
"0.6170053",
"0.61695904",
"0.6141281",... | 0.6926797 | 0 |
Run tests for apply. | def test_apply(buffer, window, out):
buffer = np.array(buffer, dtype=np.float32)
windows.apply(buffer, window)
assert_almost_equal(buffer, np.array(out)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runTests(self):\n \n pass",
"def _run_ci_test():\n _run_install(False)\n _run_coverage_html(False)\n _run_typecheck_xml(False)\n _run_lint(True)",
"def run_tests(self):\n raise NotImplementedError",
"def run_tests(self):\n\n self.manifest_path = os.path.join('tests',\n... | [
"0.72071713",
"0.6894174",
"0.6725332",
"0.6708562",
"0.67010826",
"0.6690568",
"0.66734636",
"0.6645388",
"0.66027665",
"0.6576913",
"0.64471364",
"0.6384366",
"0.63726693",
"0.6348226",
"0.6334608",
"0.63228375",
"0.6313171",
"0.630247",
"0.6302341",
"0.62591064",
"0.625864... | 0.0 | -1 |
Run tests for hanning. | def test_hanning(length, window):
assert_almost_equal(windows.hanning(length), np.array(window)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runTests(self):\n \n pass",
"def main():\n run_test_all()",
"def tests():",
"def run_tests(self):\n raise NotImplementedError",
"def runtest(self):",
"def __main() :\n launchTests()",
"def do_test(self):\n\t\tshutit_global.shutit_global_object.yield_to_draw()\n\t\tif not self... | [
"0.69998217",
"0.67782",
"0.6729991",
"0.67242986",
"0.6668787",
"0.65461665",
"0.65364975",
"0.6497846",
"0.64213234",
"0.64198595",
"0.6398026",
"0.63839835",
"0.6382242",
"0.63557005",
"0.63405514",
"0.63068205",
"0.6247848",
"0.6240262",
"0.622832",
"0.62153053",
"0.61878... | 0.0 | -1 |
Run tests for product. | def test_product(window1, window2, out):
if window1 is not None:
window1 = np.array(window1)
if window2 is not None:
window2 = np.array(window2)
if out is None:
assert windows.product(window1, window2) is None
else:
assert_almost_equal(windows.product(window1, window2), ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_product(self):\n self.assertEqual(self.test_product.name, self.test_product_name)\n self.assertEqual(self.test_product.price, self.test_product_price)",
"def runtests(projectfolder, product, app_dir):\n\n featuremodel_path = path.join(projectfolder, \"productline\", \"model.xml\")\n ... | [
"0.7426649",
"0.7392313",
"0.7278057",
"0.7218119",
"0.71037585",
"0.6960336",
"0.69112176",
"0.6868908",
"0.68441224",
"0.66730344",
"0.6667417",
"0.6661973",
"0.6645852",
"0.663013",
"0.66245073",
"0.65999675",
"0.65693885",
"0.6536961",
"0.6534648",
"0.6513289",
"0.6512881... | 0.0 | -1 |
Call the defined shared _lstm_model_fn. | def _model_fn(features, labels, mode, config):
return _transformer_model_fn(
features=features,
labels=labels,
mode=mode,
head=head_lib._regression_head_with_mean_squared_error_loss(
label_dimension=label_dimension,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def model_fn(model_dir):\n print(\"Loading model.\")\n\n # First, load the parameters used to create the model.\n model_info = {}\n model_info_path = os.path.join(model_dir, 'model_info.pth')\n with open(model_info_path, 'rb') as f:\n model_info = torch.load(f)\n\n print(\"model_info: {}\"... | [
"0.6602407",
"0.65134597",
"0.64115566",
"0.6322604",
"0.6283986",
"0.6278085",
"0.62462604",
"0.61601216",
"0.61334085",
"0.6110869",
"0.61028546",
"0.6077902",
"0.6000676",
"0.5919205",
"0.5910395",
"0.5875338",
"0.5792687",
"0.57925415",
"0.5742876",
"0.570209",
"0.5696264... | 0.5779005 | 18 |
This constructor takes care of the fact that we want different | def dropout_constr(options, use_noise, trng, sampling):
# if dropout is off, or we don't need it because we're sampling, multiply by 1
# this is also why we make all arguments optional
def get_layer(shape=None, dropout_probability=0, num=1):
if num > 1:
return theano.shared(numpy.array(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n raise NotImplementedError",
"def __init__(self):\n raise NotImplementedError",
"def __init__(self):\n raise NotImplementedError",
"def __init__(self):\n raise NotImplementedError",
"def __init__(self) -> None:\n # Values are already set on __new__.\n... | [
"0.6959799",
"0.6959799",
"0.6959799",
"0.6959799",
"0.6956357",
"0.6907315",
"0.68319875",
"0.6811867",
"0.6758628",
"0.6758628",
"0.66888803",
"0.6647492",
"0.66381866",
"0.6590327",
"0.6586049",
"0.6569259",
"0.65685713",
"0.65660447",
"0.65438515",
"0.65360767",
"0.652982... | 0.0 | -1 |
Normalize the columns of a matrix | def weight_norm(W, s):
_eps = numpy_floatX(1e-5)
W_norms = tensor.sqrt((W * W).sum(axis=0, keepdims=True) + _eps)
W_norms_s = W_norms * s # do this first to ensure proper broadcasting
return W / W_norms_s | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalize_columns(mat):\n norm = np.sqrt((mat**2).sum(0))\n return mat / norm",
"def normalize_col(input_matrix):\n\n col_sums = np.nan_to_num(input_matrix).sum(axis=0, keepdims=True)\n\n #new_matrix = input_matrix / col_sums if np.isscalar(col_sums) else input_matrix / col_sums[np.newax... | [
"0.8688684",
"0.8052972",
"0.7980672",
"0.7900643",
"0.78383934",
"0.75666314",
"0.73540807",
"0.731092",
"0.72516435",
"0.7244838",
"0.722297",
"0.71880865",
"0.71880865",
"0.7166997",
"0.71601856",
"0.71208394",
"0.70959723",
"0.70877707",
"0.7086986",
"0.70312995",
"0.7003... | 0.0 | -1 |
sudoku is a pointer to the parent sudoku object | def __init__(self, idx, sudoku, is_given=False, cands=None, notify=False):
self.sudoku = sudoku
self.idx = idx
self.is_given = is_given
self.row = idx // 9 + 1
self.col = idx % 9 + 1
self.box = ((idx % 9) // 3) + 3 * (idx // 9 // 3) + 1
#used for debugging purpo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve(sudoku):\n\n # Go through all numbers in the Sudoku.\n for row in range(9):\n for column in range(9):\n # Try all possible combinations of numbers recursively and look for\n # one that is a correct solution.\n if sudoku[row][column] is None:\n ... | [
"0.6209334",
"0.6017485",
"0.58750165",
"0.5834461",
"0.57241505",
"0.5716464",
"0.57155645",
"0.5701739",
"0.5681319",
"0.56612116",
"0.5656719",
"0.562426",
"0.5623384",
"0.5614225",
"0.55433786",
"0.55363554",
"0.5503499",
"0.55015236",
"0.54771733",
"0.5473852",
"0.542645... | 0.5963765 | 2 |
returns the candidates as a simple string | def cand_str(self):
return "".join([str(x) for x in self.cands]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_candidates(self):\n return u', '.join([c.identifier for c in self.candidates.all()])",
"def get_all_candidates(self) -> list:",
"def view_candidates(self):\n items = ['id', self.filter, 'half_light', 'separation', 'P_c']\n for add_on in ['P_O', 'P_Ox']:\n if add_on in se... | [
"0.7313972",
"0.634011",
"0.6055387",
"0.59636027",
"0.5877696",
"0.585959",
"0.5841727",
"0.5791971",
"0.5778096",
"0.5705545",
"0.5683101",
"0.56824327",
"0.5674035",
"0.5649382",
"0.56227845",
"0.5609205",
"0.5592801",
"0.5583754",
"0.5580004",
"0.55474",
"0.55360365",
"... | 0.6516208 | 1 |
returns all the cells in the same row, without the current cell inclusive determines if the cell itself is included | def iter_row(self, inclusive=False, **kwargs):
ret = self.sudoku.row(self.row, **kwargs)
if not inclusive:
ret -= self
return ret | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cells(self, row, col):\r\n surrounding_cells = self.get_surrounding_cells(row, col)\r\n closed_cells = self.filter_cells(surrounding_cells, '?')\r\n mine_cells = self.filter_cells(surrounding_cells, 'x')\r\n numbered_cells = list(set(surrounding_cells).difference(closed_cells))\... | [
"0.71352303",
"0.6480721",
"0.6268291",
"0.6262645",
"0.62549186",
"0.6136679",
"0.61350024",
"0.61134684",
"0.6101507",
"0.60813344",
"0.6079841",
"0.6076919",
"0.6074675",
"0.60700923",
"0.60681236",
"0.60458994",
"0.6036992",
"0.60198283",
"0.5929248",
"0.5918401",
"0.5892... | 0.589297 | 20 |
returns all the cells in the same col, without the current cell inclusive determines if the cell itself is included | def iter_col(self, inclusive=False, **kwargs):
ret = self.sudoku.col(self.col, **kwargs)
if not inclusive:
ret -= self
return ret | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cells(self, row, col):\r\n surrounding_cells = self.get_surrounding_cells(row, col)\r\n closed_cells = self.filter_cells(surrounding_cells, '?')\r\n mine_cells = self.filter_cells(surrounding_cells, 'x')\r\n numbered_cells = list(set(surrounding_cells).difference(closed_cells))\... | [
"0.7064214",
"0.6296526",
"0.6143211",
"0.61400205",
"0.5967883",
"0.5850532",
"0.58066463",
"0.5805396",
"0.57250965",
"0.5717699",
"0.57054645",
"0.5698774",
"0.568885",
"0.5681665",
"0.56682837",
"0.5637537",
"0.5636615",
"0.5634904",
"0.5617153",
"0.56157374",
"0.55999815... | 0.58380413 | 6 |
returns all the cells in the same box, without the current cell inclusive determines if the cell itself is included | def iter_box(self, inclusive=False, **kwargs):
ret = self.sudoku.box(self.box, **kwargs)
if not inclusive:
ret -= self
return ret | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cells(self, row, col):\r\n surrounding_cells = self.get_surrounding_cells(row, col)\r\n closed_cells = self.filter_cells(surrounding_cells, '?')\r\n mine_cells = self.filter_cells(surrounding_cells, 'x')\r\n numbered_cells = list(set(surrounding_cells).difference(closed_cells))\... | [
"0.6804994",
"0.6405771",
"0.6399067",
"0.63050866",
"0.6237328",
"0.6216472",
"0.6136879",
"0.61026025",
"0.60741955",
"0.601029",
"0.59894836",
"0.5928568",
"0.5905775",
"0.5898801",
"0.5895932",
"0.58847314",
"0.5870224",
"0.5865845",
"0.58648765",
"0.584756",
"0.5796867",... | 0.6200451 | 6 |
returns all the cells in the same row or col, without the current cell inclusive determines if the cell itself is included | def iter_rowcol(self, inclusive=False, **kwargs):
ret = self.sudoku.row(self.row, **kwargs) + \
self.sudoku.col(self.col, **kwargs)
if not inclusive:
ret -= self
return ret | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cells(self, row, col):\r\n surrounding_cells = self.get_surrounding_cells(row, col)\r\n closed_cells = self.filter_cells(surrounding_cells, '?')\r\n mine_cells = self.filter_cells(surrounding_cells, 'x')\r\n numbered_cells = list(set(surrounding_cells).difference(closed_cells))\... | [
"0.7665889",
"0.6758721",
"0.6708378",
"0.6522236",
"0.6457412",
"0.63777393",
"0.6367174",
"0.635705",
"0.63372207",
"0.6306044",
"0.62925977",
"0.6252321",
"0.62497276",
"0.6222248",
"0.6141052",
"0.61358106",
"0.6101616",
"0.6090473",
"0.6044252",
"0.6036179",
"0.6006793",... | 0.5831525 | 37 |
returns all the cells in the same row, col or box, without the current cell inclusive determines if the cell itself is included | def iter_related(self, inclusive=False, **kwargs):
ret = self.sudoku.row(self.row, **kwargs) + \
self.sudoku.col(self.col, **kwargs) + \
self.sudoku.box(self.box, **kwargs)
if not inclusive:
ret -= self
return ret | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cells(self, row, col):\r\n surrounding_cells = self.get_surrounding_cells(row, col)\r\n closed_cells = self.filter_cells(surrounding_cells, '?')\r\n mine_cells = self.filter_cells(surrounding_cells, 'x')\r\n numbered_cells = list(set(surrounding_cells).difference(closed_cells))\... | [
"0.7512581",
"0.67403513",
"0.67195696",
"0.65978575",
"0.6567471",
"0.6452255",
"0.6296703",
"0.628843",
"0.6267234",
"0.6255064",
"0.622117",
"0.62134117",
"0.620172",
"0.6194931",
"0.6168033",
"0.6118354",
"0.6111063",
"0.6110185",
"0.60929686",
"0.6083315",
"0.60692847",
... | 0.63505846 | 6 |
Creates the panel and passes it as a return value | def UOWrapperPanel(parent, interpParent, uOpName, pfd):
#Empty panel... Overload this function for each specific UO
return FlashMainPanel(parent, interpParent, uOpName, pfd) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_panel(self):\n return\n # return Panel(self)",
"def create(self):\n self.panel = pg.rect.Rect(self.position, self.dimensions)",
"def create_main_panel(self):\n self.panel = wx.Panel(self)\n\n self.init_plot()\n self.canvas = FigCanvas(self.panel, -1, self.fi... | [
"0.8496374",
"0.74238116",
"0.7307615",
"0.7078812",
"0.69499403",
"0.68423665",
"0.68091047",
"0.66479605",
"0.663662",
"0.6614185",
"0.65787846",
"0.6572077",
"0.65553594",
"0.65231746",
"0.6521527",
"0.6508235",
"0.64754564",
"0.6388567",
"0.6313709",
"0.6289764",
"0.62435... | 0.57041156 | 69 |
Disables provided function from one or multiple channels which are specified. A function can be any of the commands, plugins or galaxies which are allowed to be disabled. | async def disable(self, ctx, function: typing.Union[CommandConverter, PluginConverter, GalaxyConverter],
*channels: discord.TextChannel):
channels = channels or (ctx.channel, )
await ctx.guild_profile.permissions.disable_function(function, channels)
# noinspection PyUnresol... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def disable(func):\n return func",
"async def disable_channel(self, ctx, *channels: discord.TextChannel):\n channels = channels or (ctx.channel, )\n await ctx.guild_profile.permissions.disable_channels(channels)\n await ctx.send_line(f\"{ctx.emotes.web_emotion.galka} Bot commands and m... | [
"0.7478053",
"0.6590839",
"0.63677406",
"0.61984825",
"0.5935865",
"0.5847337",
"0.57535195",
"0.5705342",
"0.57022613",
"0.5698396",
"0.56209135",
"0.5606587",
"0.5573692",
"0.55229694",
"0.55216694",
"0.5494032",
"0.54801023",
"0.5455171",
"0.5423171",
"0.5408966",
"0.53884... | 0.8369485 | 0 |
Enables provided function in all of the specified channels. A function can be any of the commands, plugins or galaxies. | async def enable(self, ctx, function: typing.Union[CommandConverter, PluginConverter, GalaxyConverter],
*channels: discord.TextChannel):
channels = channels or (ctx.channel, )
await ctx.guild_profile.permissions.enable_function(function, channels)
# noinspection PyUnresolved... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def enable_channel(self, ctx, *channels: discord.TextChannel):\n channels = channels or (ctx.channel, )\n await ctx.guild_profile.permissions.enable_channels(channels)\n await ctx.send_line(f\"{ctx.emotes.web_emotion.galka} Bot commands and messages has been enabled in specified chann... | [
"0.6668181",
"0.6548446",
"0.6338657",
"0.5989139",
"0.574073",
"0.56826746",
"0.56814885",
"0.55161613",
"0.54051787",
"0.5351172",
"0.5333117",
"0.5286296",
"0.52859515",
"0.52859515",
"0.52859515",
"0.52859515",
"0.52859515",
"0.52859515",
"0.52859515",
"0.52859515",
"0.52... | 0.78735584 | 0 |
Disables bot commands and most of its automatic messages in current or provided channels. | async def disable_channel(self, ctx, *channels: discord.TextChannel):
channels = channels or (ctx.channel, )
await ctx.guild_profile.permissions.disable_channels(channels)
await ctx.send_line(f"{ctx.emotes.web_emotion.galka} Bot commands and messages has been disabled in specified channels.") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def blacklist_commands(self, ctx):\r\n await self.amethyst.send_command_help(ctx)",
"async def disable(self, ctx, function: typing.Union[CommandConverter, PluginConverter, GalaxyConverter],\n *channels: discord.TextChannel):\n channels = channels or (ctx.channel, )\n ... | [
"0.7108723",
"0.7096003",
"0.69496554",
"0.67883027",
"0.6707562",
"0.6565387",
"0.6368427",
"0.63573396",
"0.6339441",
"0.6334304",
"0.63192993",
"0.62177265",
"0.61687315",
"0.61560297",
"0.6059689",
"0.6038491",
"0.5994542",
"0.59823614",
"0.5947161",
"0.59250474",
"0.5920... | 0.78277546 | 0 |
Enables back bot commands and its automatic messages in current or provided channels if it was disabled previously. | async def enable_channel(self, ctx, *channels: discord.TextChannel):
channels = channels or (ctx.channel, )
await ctx.guild_profile.permissions.enable_channels(channels)
await ctx.send_line(f"{ctx.emotes.web_emotion.galka} Bot commands and messages has been enabled in specified channels.") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def func(self):\n from evennia.comms.models import ChannelDB\n\n caller = self.caller\n if self.args not in (\"on\", \"off\"):\n return super(CmdArxAllCom, self).func()\n if self.args == \"on\":\n # get names of all channels available to listen to\n # an... | [
"0.67751026",
"0.6625209",
"0.65478224",
"0.6417928",
"0.6308489",
"0.610151",
"0.60823476",
"0.5960483",
"0.5899421",
"0.5865763",
"0.5858996",
"0.58107245",
"0.580726",
"0.5788629",
"0.56712127",
"0.56574357",
"0.5640026",
"0.5632828",
"0.56324667",
"0.5491965",
"0.5487777"... | 0.73707277 | 0 |
JSON resolver plugin that loads the schema endpoint. Injected into InvenioRecords JSON resolver. | def jsonresolver_loader(url_map):
url_map.add(Rule(
"/api/taxonomies/<string:code>/<path:slug>",
endpoint=get_taxonomy_term,
host=current_app.config.get('SERVER_NAME')
)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_schema(self, json_schema):\n # use jsonrefs to resolve all $refs in json\n data = jsonref.loads(json.dumps(json_schema))\n return self.__initialise_template(data)",
"def resolver():\n if RESOLVER:\n return RESOLVER\n path = str(pathlib.Path(__file__).parents[1].joinpat... | [
"0.63883114",
"0.58803993",
"0.5456595",
"0.54404473",
"0.5353926",
"0.53508955",
"0.5329297",
"0.53031135",
"0.5302368",
"0.52422416",
"0.5230475",
"0.51963526",
"0.51711226",
"0.51576716",
"0.51557875",
"0.5149049",
"0.5118869",
"0.51106626",
"0.5100498",
"0.508437",
"0.507... | 0.5573852 | 2 |
Name scope. Must be defined by implementations. | def name_scope(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scope(self, name):\r\n raise NotImplementedError",
"def _set_name_scope(self):\n if self.name is None:\n self._name_scope = self.__class__.__name__\n elif self.name == '<lambda>':\n self._name_scope = 'lambda'\n else:\n # E.g. '_my_loss' => 'my_loss'\n self._name_scope = self.... | [
"0.83334064",
"0.78577036",
"0.7441628",
"0.7270988",
"0.72071725",
"0.71555185",
"0.7115489",
"0.7110687",
"0.71026707",
"0.7065051",
"0.6968165",
"0.6964567",
"0.69457537",
"0.69046456",
"0.6899518",
"0.6899518",
"0.6892065",
"0.6835145",
"0.6835145",
"0.6819266",
"0.681926... | 0.8675015 | 0 |
Whether to dynamically check the number of anchors generated. Can be overridden by implementations that would like to disable this behavior. | def check_num_anchors(self):
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def num_anchors_per_location(self):\n pass",
"def adjust_anchors(self):\n pass",
"def is_anchor_valid(self):\n return self.properties.get('IsAnchorValid', None)",
"def _assert_correct_number_of_anchors(self, anchors_list,\n feature_map_shape_list):\n ... | [
"0.68195313",
"0.6357228",
"0.6135154",
"0.5779674",
"0.5483983",
"0.54224265",
"0.5373215",
"0.5297422",
"0.5296277",
"0.5272198",
"0.52359825",
"0.5194844",
"0.51695627",
"0.51695627",
"0.51695627",
"0.51695627",
"0.5161574",
"0.51068735",
"0.51042",
"0.5082342",
"0.507974"... | 0.77952874 | 0 |
Returns the number of anchors per spatial location. | def num_anchors_per_location(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def num_anchors_per_localization(self):\n num_rot = len(self._rotations)\n num_size = np.array(self._sizes).reshape([-1, 3]).shape[0]\n return num_rot * num_size",
"def num_locations(self):\n return len(self.locations)",
"def get_location_count(self):\n return len(self.matrix... | [
"0.749816",
"0.6937425",
"0.66813695",
"0.6656154",
"0.6582445",
"0.6582445",
"0.6582445",
"0.6580193",
"0.65188473",
"0.65087336",
"0.6447474",
"0.63943326",
"0.6365181",
"0.6354651",
"0.6345681",
"0.6322735",
"0.6313625",
"0.6276137",
"0.62072563",
"0.6178259",
"0.61153775"... | 0.8555305 | 0 |
Generates a collection of bounding boxes to be used as anchors. | def generate(self, feature_map_shape_list, **params):
if self.check_num_anchors and (
len(feature_map_shape_list) != len(self.num_anchors_per_location())):
raise ValueError('Number of feature maps is expected to equal the length '
'of `num_anchors_per_location`.')
with tf.na... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decode_boxes(raw_boxes, anchors):\n boxes = np.zeros_like(raw_boxes)\n\n x_center = raw_boxes[..., 0] / x_scale * anchors[:, 2] + anchors[:, 0]\n y_center = raw_boxes[..., 1] / y_scale * anchors[:, 3] + anchors[:, 1]\n\n w = raw_boxes[..., 2] / w_scale * anchors[:, 2]\n h = raw_boxes[..., 3] / h... | [
"0.6876775",
"0.6868104",
"0.6695623",
"0.6521236",
"0.64991575",
"0.6486309",
"0.6483232",
"0.6424955",
"0.6403451",
"0.6394041",
"0.63770133",
"0.6352162",
"0.63495255",
"0.63414896",
"0.6316883",
"0.63068944",
"0.62946934",
"0.62793225",
"0.62689435",
"0.62679905",
"0.6248... | 0.0 | -1 |
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